From ae8b633ba354eaad163e8decf0e4752b5ce58ac2 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Fri, 18 Oct 2024 12:59:19 -0400 Subject: [PATCH 001/222] [Bugfix] Fix offline_inference_with_prefix.py (#9505) --- examples/offline_inference_with_prefix.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/offline_inference_with_prefix.py b/examples/offline_inference_with_prefix.py index 3b3e0ae64a037..f8a9727ea192f 100644 --- a/examples/offline_inference_with_prefix.py +++ b/examples/offline_inference_with_prefix.py @@ -29,11 +29,13 @@ sampling_params = SamplingParams(temperature=0.0) # Create an LLM. -regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.4) +regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.3) +# The second LLM needs to request a higher gpu_memory_utilization because +# the first LLM has already allocated a full 30% of the gpu memory. prefix_cached_llm = LLM(model="facebook/opt-125m", enable_prefix_caching=True, - gpu_memory_utilization=0.4) + gpu_memory_utilization=0.6) print("Results without `enable_prefix_caching`") # Generate texts from the prompts. The output is a list of RequestOutput objects From 7dbe738d653b563c646883c1ae6f6df927436d01 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Fri, 18 Oct 2024 14:15:28 -0400 Subject: [PATCH 002/222] [Misc] benchmark: Add option to set max concurrency (#9390) Signed-off-by: Russell Bryant --- benchmarks/benchmark_serving.py | 40 ++++++++++++++++++++++++++++++--- 1 file changed, 37 insertions(+), 3 deletions(-) diff --git a/benchmarks/benchmark_serving.py b/benchmarks/benchmark_serving.py index 1381004c9f02b..68f1e221c4bfb 100644 --- a/benchmarks/benchmark_serving.py +++ b/benchmarks/benchmark_serving.py @@ -398,6 +398,7 @@ async def benchmark( selected_percentile_metrics: List[str], selected_percentiles: List[str], ignore_eos: bool, + max_concurrency: Optional[int], ): if backend in ASYNC_REQUEST_FUNCS: request_func = ASYNC_REQUEST_FUNCS[backend] @@ -446,9 +447,25 @@ async def benchmark( print("Profiler started") print(f"Traffic request rate: {request_rate}") + print(f"Maximum request concurrency: {max_concurrency}") pbar = None if disable_tqdm else tqdm(total=len(input_requests)) + # This can be used once the minimum Python version is 3.10 or higher, + # and it will simplify the code in limited_request_func. + # semaphore = (asyncio.Semaphore(max_concurrency) + # if max_concurrency else contextlib.nullcontext()) + semaphore = (asyncio.Semaphore(max_concurrency) + if max_concurrency else None) + + async def limited_request_func(request_func_input, pbar): + if semaphore is None: + return await request_func(request_func_input=request_func_input, + pbar=pbar) + async with semaphore: + return await request_func(request_func_input=request_func_input, + pbar=pbar) + benchmark_start_time = time.perf_counter() tasks: List[asyncio.Task] = [] async for request in get_request(input_requests, request_rate): @@ -464,8 +481,8 @@ async def benchmark( ignore_eos=ignore_eos) tasks.append( asyncio.create_task( - request_func(request_func_input=request_func_input, - pbar=pbar))) + limited_request_func(request_func_input=request_func_input, + pbar=pbar))) outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks) if profile: @@ -682,6 +699,7 @@ def main(args: argparse.Namespace): float(p) for p in args.metric_percentiles.split(",") ], ignore_eos=args.ignore_eos, + max_concurrency=args.max_concurrency, )) # Save config and results to json @@ -711,13 +729,16 @@ def main(args: argparse.Namespace): # Traffic result_json["request_rate"] = ( args.request_rate if args.request_rate < float("inf") else "inf") + result_json["max_concurrency"] = args.max_concurrency # Merge with benchmark result result_json = {**result_json, **benchmark_result} # Save to file base_model_id = model_id.split("/")[-1] - file_name = f"{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json" #noqa + max_concurrency_str = (f"-concurrency{args.max_concurrency}" + if args.max_concurrency is not None else "") + file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json" #noqa if args.result_filename: file_name = args.result_filename if args.result_dir: @@ -768,6 +789,19 @@ def main(args: argparse.Namespace): default=None, help="Path to the sharegpt/sonnet dataset. " "Or the huggingface dataset ID if using HF dataset.") + parser.add_argument( + "--max-concurrency", + type=int, + default=None, + help="Maximum number of concurrent requests. This can be used " + "to help simulate an environment where a higher level component " + "is enforcing a maximum number of concurrent requests. While the " + "--request-rate argument controls the rate at which requests are " + "initiated, this argument will control how many are actually allowed " + "to execute at a time. This means that when used in combination, the " + "actual request rate may be lower than specified with --request-rate, " + "if the server is not processing requests fast enough to keep up.") + parser.add_argument( "--model", type=str, From 051eaf6db3d8feeb0779a4e942aadc85eda2f8b2 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Sat, 19 Oct 2024 02:31:58 +0800 Subject: [PATCH 003/222] [Model] Add user-configurable task for models that support both generation and embedding (#9424) --- docs/source/models/supported_models.rst | 8 ++ docs/source/models/vlm.rst | 4 +- ...ine_inference_vision_language_embedding.py | 1 + examples/openai_api_client_for_multimodal.py | 4 +- tests/conftest.py | 4 +- tests/core/test_chunked_prefill_scheduler.py | 15 ++- tests/core/test_scheduler.py | 56 ++++++----- tests/core/test_scheduler_encoder_decoder.py | 7 +- tests/distributed/test_pipeline_parallel.py | 23 ++++- tests/entrypoints/llm/test_chat.py | 92 +++++++++++++++++++ tests/entrypoints/llm/test_generate.py | 88 ------------------ tests/entrypoints/llm/test_init.py | 22 +++++ tests/entrypoints/openai/test_serving_chat.py | 2 +- tests/entrypoints/openai/test_vision.py | 2 + tests/entrypoints/test_chat_utils.py | 3 +- tests/lora/test_worker.py | 5 +- .../vision_language/test_phi3v.py | 1 + .../embedding/vision_language/test_phi3v.py | 1 + tests/models/utils.py | 6 +- tests/multimodal/test_mapper.py | 4 + tests/multimodal/test_processor_kwargs.py | 7 +- tests/quantization/test_configs.py | 3 +- tests/test_config.py | 57 ++++++++++-- tests/test_utils.py | 12 +-- tests/utils.py | 8 +- vllm/config.py | 77 +++++++++++----- vllm/core/scheduler.py | 2 +- vllm/engine/arg_utils.py | 17 +++- vllm/engine/llm_engine.py | 7 +- vllm/entrypoints/llm.py | 56 ++++++++--- vllm/entrypoints/openai/serving_embedding.py | 3 +- vllm/utils.py | 50 +++++++++- vllm/worker/worker.py | 5 +- 33 files changed, 451 insertions(+), 201 deletions(-) create mode 100644 tests/entrypoints/llm/test_chat.py create mode 100644 tests/entrypoints/llm/test_init.py diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index b5fa83b437ac4..ee2844c8b27a0 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -294,6 +294,10 @@ Text Embedding - - ✅︎ +.. important:: + Some model architectures support both generation and embedding tasks. + In this case, you have to pass :code:`--task embedding` to run the model in embedding mode. + Reward Modeling --------------- @@ -482,6 +486,10 @@ Multimodal Embedding - 🚧 - ✅︎ +.. important:: + Some model architectures support both generation and embedding tasks. + In this case, you have to pass :code:`--task embedding` to run the model in embedding mode. + ---- If your model uses one of the above model architectures, you can seamlessly run your model with vLLM. diff --git a/docs/source/models/vlm.rst b/docs/source/models/vlm.rst index 7dd42ec1bb9c9..a7b55d1c0c1ff 100644 --- a/docs/source/models/vlm.rst +++ b/docs/source/models/vlm.rst @@ -181,8 +181,8 @@ Below is an example on how to launch the same ``microsoft/Phi-3.5-vision-instruc .. code-block:: bash - vllm serve microsoft/Phi-3.5-vision-instruct --max-model-len 4096 \ - --trust-remote-code --limit-mm-per-prompt image=2 + vllm serve microsoft/Phi-3.5-vision-instruct --task generate \ + --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2 .. important:: Since OpenAI Vision API is based on `Chat Completions `_ API, diff --git a/examples/offline_inference_vision_language_embedding.py b/examples/offline_inference_vision_language_embedding.py index 8e62199e1db7b..cfedd145a015d 100644 --- a/examples/offline_inference_vision_language_embedding.py +++ b/examples/offline_inference_vision_language_embedding.py @@ -7,6 +7,7 @@ # Create an LLM. llm = LLM( model="TIGER-Lab/VLM2Vec-Full", + task="embedding", trust_remote_code=True, max_model_len=4096, max_num_seqs=2, diff --git a/examples/openai_api_client_for_multimodal.py b/examples/openai_api_client_for_multimodal.py index 704236be72d03..beb83e494ed0b 100644 --- a/examples/openai_api_client_for_multimodal.py +++ b/examples/openai_api_client_for_multimodal.py @@ -7,8 +7,8 @@ vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja (multi-image inference with Phi-3.5-vision-instruct) -vllm serve microsoft/Phi-3.5-vision-instruct --max-model-len 4096 \ - --trust-remote-code --limit-mm-per-prompt image=2 +vllm serve microsoft/Phi-3.5-vision-instruct --task generate \ + --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt image=2 (audio inference with Ultravox) vllm serve fixie-ai/ultravox-v0_3 --max-model-len 4096 diff --git a/tests/conftest.py b/tests/conftest.py index 5df7da9ee64e2..ea7156c60e334 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -25,7 +25,7 @@ from vllm import LLM, SamplingParams from vllm.assets.image import ImageAsset from vllm.assets.video import VideoAsset -from vllm.config import TokenizerPoolConfig +from vllm.config import TaskOption, TokenizerPoolConfig from vllm.connections import global_http_connection from vllm.distributed import (destroy_distributed_environment, destroy_model_parallel, @@ -619,6 +619,7 @@ class VllmRunner: def __init__( self, model_name: str, + task: TaskOption = "auto", tokenizer_name: Optional[str] = None, # Use smaller max model length, otherwise bigger model cannot run due # to kv cache size limit. @@ -634,6 +635,7 @@ def __init__( ) -> None: self.model = LLM( model=model_name, + task=task, tokenizer=tokenizer_name, trust_remote_code=True, dtype=dtype, diff --git a/tests/core/test_chunked_prefill_scheduler.py b/tests/core/test_chunked_prefill_scheduler.py index f97caa06ff02d..308dad1850c9a 100644 --- a/tests/core/test_chunked_prefill_scheduler.py +++ b/tests/core/test_chunked_prefill_scheduler.py @@ -33,7 +33,8 @@ def test_simple(): num_seq_group = 4 max_model_len = 16 max_num_batched_tokens = 64 - scheduler_config = SchedulerConfig(max_num_batched_tokens, + scheduler_config = SchedulerConfig("generate", + max_num_batched_tokens, num_seq_group, max_model_len, enable_chunked_prefill=True) @@ -78,6 +79,7 @@ def test_chunk(): max_model_len = 80 max_num_batched_tokens = 64 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -126,6 +128,7 @@ def test_complex(): max_model_len = 80 max_num_batched_tokens = 64 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -196,6 +199,7 @@ def test_maximal_decoding(): max_model_len = 8 max_num_batched_tokens = 2 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -289,6 +293,7 @@ def test_prompt_limit(): max_model_len = 64 max_num_batched_tokens = 32 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -321,7 +326,8 @@ def test_prompt_limit_exceed(): max_seqs = 64 max_model_len = 32 max_num_batched_tokens = 64 - scheduler_config = SchedulerConfig(max_num_batched_tokens, + scheduler_config = SchedulerConfig("generate", + max_num_batched_tokens, max_seqs, max_model_len, enable_chunked_prefill=True) @@ -348,6 +354,7 @@ def test_swap(): max_model_len = 200 max_num_batched_tokens = 30 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -404,6 +411,7 @@ def test_running_prefill_prioritized_over_swap(): max_model_len = 200 max_num_batched_tokens = 30 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -498,6 +506,7 @@ def test_chunked_prefill_preempt(): max_model_len = 200 max_num_batched_tokens = 30 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -563,6 +572,7 @@ def test_chunked_prefill_max_seqs(): max_model_len = 80 max_num_batched_tokens = 64 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, @@ -617,6 +627,7 @@ def test_perfix_caching(): max_model_len = 80 max_num_batched_tokens = 64 scheduler_config = SchedulerConfig( + "generate", max_num_batched_tokens, max_seqs, max_model_len, diff --git a/tests/core/test_scheduler.py b/tests/core/test_scheduler.py index defa6c1bdaf78..00b6349b9f8c5 100644 --- a/tests/core/test_scheduler.py +++ b/tests/core/test_scheduler.py @@ -20,9 +20,10 @@ def test_scheduler_add_seq_group(): block_size = 4 scheduler_config = SchedulerConfig( - 100, - 64, - 1, + "generate", + max_num_batched_tokens=100, + max_num_seqs=64, + max_model_len=1, ) cache_config = CacheConfig(block_size, 1.0, 1, cache_dtype="auto") cache_config.num_cpu_blocks = 4 @@ -42,9 +43,10 @@ def test_scheduler_add_seq_group(): def test_scheduler_abort_seq_group(): block_size = 4 scheduler_config = SchedulerConfig( - 100, - 64, - 1, + "generate", + max_num_batched_tokens=100, + max_num_seqs=64, + max_model_len=1, ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") cache_config.num_cpu_blocks = 4 @@ -70,9 +72,10 @@ def test_scheduler_schedule_simple(): num_seq_group = 4 max_model_len = 16 scheduler_config = SchedulerConfig( - 64, - num_seq_group, - max_model_len, + "generate", + max_num_batched_tokens=64, + max_num_seqs=num_seq_group, + max_model_len=max_model_len, ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") cache_config.num_cpu_blocks = 8 @@ -114,9 +117,10 @@ def test_scheduler_prefill_prioritized(): max_model_len = 30 max_batched_num_tokens = 30 scheduler_config = SchedulerConfig( - max_batched_num_tokens, - 2, - max_model_len, + "generate", + max_num_batched_tokens=max_batched_num_tokens, + max_num_seqs=2, + max_model_len=max_model_len, ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") cache_config.num_cpu_blocks = 16 @@ -145,9 +149,10 @@ def test_scheduler_schedule_preempt_abort(): block_size = 4 max_model_len = 16 scheduler_config = SchedulerConfig( - 64, - 2, - max_model_len, + "generate", + max_num_batched_tokens=64, + max_num_seqs=2, + max_model_len=max_model_len, ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") cache_config.num_cpu_blocks = 2 @@ -204,9 +209,10 @@ def test_scheduler_max_seqs(): max_seq_group = 2 max_model_len = 16 scheduler_config = SchedulerConfig( - 64, - max_seq_group, - max_model_len, + "generate", + max_num_batched_tokens=64, + max_num_seqs=max_seq_group, + max_model_len=max_model_len, ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") cache_config.num_cpu_blocks = 8 @@ -248,9 +254,10 @@ def test_scheduler_max_seqs(): def test_scheduler_delay_factor(): block_size = 4 scheduler_config = SchedulerConfig( - 100, - 64, - 16, + "generate", + max_num_batched_tokens=100, + max_num_seqs=64, + max_model_len=16, delay_factor=0.5, ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") @@ -350,9 +357,10 @@ def initialize_scheduler( ): block_size = block_size scheduler_config = SchedulerConfig( - max_token_budget, - max_num_seqs, - max_model_len, + "generate", + max_num_batched_tokens=max_token_budget, + max_num_seqs=max_num_seqs, + max_model_len=max_model_len, ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") cache_config.num_cpu_blocks = num_cpu_blocks diff --git a/tests/core/test_scheduler_encoder_decoder.py b/tests/core/test_scheduler_encoder_decoder.py index 50c047f30b80d..7cd0416d321ef 100644 --- a/tests/core/test_scheduler_encoder_decoder.py +++ b/tests/core/test_scheduler_encoder_decoder.py @@ -36,7 +36,12 @@ def test_scheduler_schedule_simple_encoder_decoder(): block_size = 4 num_seq_group = 4 max_model_len = 16 - scheduler_config = SchedulerConfig(64, num_seq_group, max_model_len) + scheduler_config = SchedulerConfig( + task="generate", + max_num_batched_tokens=64, + max_num_seqs=num_seq_group, + max_model_len=max_model_len, + ) cache_config = CacheConfig(block_size, 1.0, 1, "auto") cache_config.num_cpu_blocks = 16 # enc and dec prompts per seq_group cache_config.num_gpu_blocks = 16 # enc and dec prompts per seq_group diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index 88d0a4ba7f57b..fee201850f203 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -11,6 +11,7 @@ import pytest +from vllm.config import TaskOption from vllm.logger import init_logger from ..utils import compare_two_settings, fork_new_process_for_each_test @@ -31,6 +32,7 @@ class ParallelSetup(NamedTuple): class PPTestSettings: parallel_setups: List[ParallelSetup] distributed_backends: List[str] + task: TaskOption trust_remote_code: bool tokenizer_mode: Optional[str] @@ -39,6 +41,7 @@ def detailed( *, tp_base: int = 1, pp_base: int = 2, + task: TaskOption = "auto", trust_remote_code: bool = False, tokenizer_mode: Optional[str] = None, ): @@ -66,6 +69,7 @@ def detailed( chunked_prefill=False), ], distributed_backends=["mp", "ray"], + task=task, trust_remote_code=trust_remote_code, tokenizer_mode=tokenizer_mode, ) @@ -75,6 +79,7 @@ def fast( *, tp_base: int = 1, pp_base: int = 2, + task: TaskOption = "auto", trust_remote_code: bool = False, tokenizer_mode: Optional[str] = None, ): @@ -86,6 +91,7 @@ def fast( chunked_prefill=False), ], distributed_backends=["mp"], + task=task, trust_remote_code=trust_remote_code, tokenizer_mode=tokenizer_mode, ) @@ -94,7 +100,7 @@ def iter_params(self, model_name: str): for parallel_setup in self.parallel_setups: for distributed_backend in self.distributed_backends: yield (model_name, parallel_setup, distributed_backend, - self.trust_remote_code, self.tokenizer_mode) + self.task, self.trust_remote_code, self.tokenizer_mode) # NOTE: You can adjust tp_base and/or pp_base locally to fit the model in GPU @@ -213,6 +219,7 @@ def _compare_tp( model_name: str, parallel_setup: ParallelSetup, distributed_backend: str, + task: TaskOption, trust_remote_code: bool, tokenizer_mode: Optional[str], num_gpus_available: int, @@ -240,6 +247,8 @@ def _compare_tp( common_args.append("--enable-chunked-prefill") if eager_mode: common_args.append("--enforce-eager") + if task != "auto": + common_args.extend(["--task", task]) if trust_remote_code: common_args.append("--trust-remote-code") if tokenizer_mode: @@ -297,7 +306,7 @@ def _compare_tp( @pytest.mark.parametrize( - ("model_name", "parallel_setup", "distributed_backend", + ("model_name", "parallel_setup", "distributed_backend", "task", "trust_remote_code", "tokenizer_mode"), [ params for model_name, settings in GENERATION_MODEL_SETTINGS.items() @@ -310,6 +319,7 @@ def test_tp_language_generation( model_name: str, parallel_setup: ParallelSetup, distributed_backend: str, + task: TaskOption, trust_remote_code: bool, tokenizer_mode: Optional[str], num_gpus_available, @@ -317,6 +327,7 @@ def test_tp_language_generation( _compare_tp(model_name, parallel_setup, distributed_backend, + task, trust_remote_code, tokenizer_mode, num_gpus_available, @@ -324,7 +335,7 @@ def test_tp_language_generation( @pytest.mark.parametrize( - ("model_name", "parallel_setup", "distributed_backend", + ("model_name", "parallel_setup", "distributed_backend", "task", "trust_remote_code", "tokenizer_mode"), [ params for model_name, settings in EMBEDDING_MODEL_SETTINGS.items() @@ -337,6 +348,7 @@ def test_tp_language_embedding( model_name: str, parallel_setup: ParallelSetup, distributed_backend: str, + task: TaskOption, trust_remote_code: bool, tokenizer_mode: Optional[str], num_gpus_available, @@ -344,6 +356,7 @@ def test_tp_language_embedding( _compare_tp(model_name, parallel_setup, distributed_backend, + task, trust_remote_code, tokenizer_mode, num_gpus_available, @@ -351,7 +364,7 @@ def test_tp_language_embedding( @pytest.mark.parametrize( - ("model_name", "parallel_setup", "distributed_backend", + ("model_name", "parallel_setup", "distributed_backend", "task", "trust_remote_code", "tokenizer_mode"), [ params for model_name, settings in MULTIMODAL_MODEL_SETTINGS.items() @@ -364,6 +377,7 @@ def test_tp_multimodal_generation( model_name: str, parallel_setup: ParallelSetup, distributed_backend: str, + task: TaskOption, trust_remote_code: bool, tokenizer_mode: Optional[str], num_gpus_available, @@ -371,6 +385,7 @@ def test_tp_multimodal_generation( _compare_tp(model_name, parallel_setup, distributed_backend, + task, trust_remote_code, tokenizer_mode, num_gpus_available, diff --git a/tests/entrypoints/llm/test_chat.py b/tests/entrypoints/llm/test_chat.py new file mode 100644 index 0000000000000..b57348a4d9a58 --- /dev/null +++ b/tests/entrypoints/llm/test_chat.py @@ -0,0 +1,92 @@ +from typing import List + +import pytest + +from vllm import LLM + +from ..openai.test_vision import TEST_IMAGE_URLS + + +def test_chat(): + llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") + + prompt1 = "Explain the concept of entropy." + messages = [ + { + "role": "system", + "content": "You are a helpful assistant" + }, + { + "role": "user", + "content": prompt1 + }, + ] + outputs = llm.chat(messages) + assert len(outputs) == 1 + + +def test_multi_chat(): + llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") + + prompt1 = "Explain the concept of entropy." + prompt2 = "Explain what among us is." + + conversation1 = [ + { + "role": "system", + "content": "You are a helpful assistant" + }, + { + "role": "user", + "content": prompt1 + }, + ] + + conversation2 = [ + { + "role": "system", + "content": "You are a helpful assistant" + }, + { + "role": "user", + "content": prompt2 + }, + ] + + messages = [conversation1, conversation2] + + outputs = llm.chat(messages) + assert len(outputs) == 2 + + +@pytest.mark.parametrize("image_urls", + [[TEST_IMAGE_URLS[0], TEST_IMAGE_URLS[1]]]) +def test_chat_multi_image(image_urls: List[str]): + llm = LLM( + model="microsoft/Phi-3.5-vision-instruct", + dtype="bfloat16", + max_model_len=4096, + max_num_seqs=5, + enforce_eager=True, + trust_remote_code=True, + limit_mm_per_prompt={"image": 2}, + ) + + messages = [{ + "role": + "user", + "content": [ + *({ + "type": "image_url", + "image_url": { + "url": image_url + } + } for image_url in image_urls), + { + "type": "text", + "text": "What's in this image?" + }, + ], + }] + outputs = llm.chat(messages) + assert len(outputs) >= 0 diff --git a/tests/entrypoints/llm/test_generate.py b/tests/entrypoints/llm/test_generate.py index 6543c4bb1b58e..5e32d7baabe4b 100644 --- a/tests/entrypoints/llm/test_generate.py +++ b/tests/entrypoints/llm/test_generate.py @@ -6,7 +6,6 @@ from vllm import LLM, RequestOutput, SamplingParams from ...conftest import cleanup -from ..openai.test_vision import TEST_IMAGE_URLS MODEL_NAME = "facebook/opt-125m" @@ -104,90 +103,3 @@ def test_multiple_sampling_params(llm: LLM): # sampling_params is None, default params should be applied outputs = llm.generate(PROMPTS, sampling_params=None) assert len(PROMPTS) == len(outputs) - - -def test_chat(): - - llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") - - prompt1 = "Explain the concept of entropy." - messages = [ - { - "role": "system", - "content": "You are a helpful assistant" - }, - { - "role": "user", - "content": prompt1 - }, - ] - outputs = llm.chat(messages) - assert len(outputs) == 1 - - -def test_multi_chat(): - - llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") - - prompt1 = "Explain the concept of entropy." - prompt2 = "Explain what among us is." - - conversation1 = [ - { - "role": "system", - "content": "You are a helpful assistant" - }, - { - "role": "user", - "content": prompt1 - }, - ] - - conversation2 = [ - { - "role": "system", - "content": "You are a helpful assistant" - }, - { - "role": "user", - "content": prompt2 - }, - ] - - messages = [conversation1, conversation2] - - outputs = llm.chat(messages) - assert len(outputs) == 2 - - -@pytest.mark.parametrize("image_urls", - [[TEST_IMAGE_URLS[0], TEST_IMAGE_URLS[1]]]) -def test_chat_multi_image(image_urls: List[str]): - llm = LLM( - model="microsoft/Phi-3.5-vision-instruct", - dtype="bfloat16", - max_model_len=4096, - max_num_seqs=5, - enforce_eager=True, - trust_remote_code=True, - limit_mm_per_prompt={"image": 2}, - ) - - messages = [{ - "role": - "user", - "content": [ - *({ - "type": "image_url", - "image_url": { - "url": image_url - } - } for image_url in image_urls), - { - "type": "text", - "text": "What's in this image?" - }, - ], - }] - outputs = llm.chat(messages) - assert len(outputs) >= 0 diff --git a/tests/entrypoints/llm/test_init.py b/tests/entrypoints/llm/test_init.py new file mode 100644 index 0000000000000..c9a4ad44fea30 --- /dev/null +++ b/tests/entrypoints/llm/test_init.py @@ -0,0 +1,22 @@ +import pytest + +from vllm import LLM + +from ...utils import error_on_warning + +MODEL_NAME = "facebook/opt-125m" + + +def test_pos_args_deprecated(): + with error_on_warning(DeprecationWarning): + LLM(model=MODEL_NAME, tokenizer=MODEL_NAME) + + with error_on_warning(DeprecationWarning): + LLM(MODEL_NAME, tokenizer=MODEL_NAME) + + with pytest.warns(DeprecationWarning, match="'tokenizer'"): + LLM(MODEL_NAME, MODEL_NAME) + + with pytest.warns(DeprecationWarning, + match="'tokenizer', 'tokenizer_mode'"): + LLM(MODEL_NAME, MODEL_NAME, "auto") diff --git a/tests/entrypoints/openai/test_serving_chat.py b/tests/entrypoints/openai/test_serving_chat.py index ec550fe82c70f..d9342fad9f018 100644 --- a/tests/entrypoints/openai/test_serving_chat.py +++ b/tests/entrypoints/openai/test_serving_chat.py @@ -22,12 +22,12 @@ class MockHFConfig: @dataclass class MockModelConfig: + task = "generate" tokenizer = MODEL_NAME trust_remote_code = False tokenizer_mode = "auto" max_model_len = 100 tokenizer_revision = None - embedding_mode = False multimodal_config = MultiModalConfig() hf_config = MockHFConfig() diff --git a/tests/entrypoints/openai/test_vision.py b/tests/entrypoints/openai/test_vision.py index 81d79601124a7..8311a5cb3c2d4 100644 --- a/tests/entrypoints/openai/test_vision.py +++ b/tests/entrypoints/openai/test_vision.py @@ -23,6 +23,8 @@ @pytest.fixture(scope="module") def server(): args = [ + "--task", + "generate", "--dtype", "bfloat16", "--max-model-len", diff --git a/tests/entrypoints/test_chat_utils.py b/tests/entrypoints/test_chat_utils.py index 6ded5102c9314..9165a1d397137 100644 --- a/tests/entrypoints/test_chat_utils.py +++ b/tests/entrypoints/test_chat_utils.py @@ -18,7 +18,8 @@ @pytest.fixture(scope="module") def phi3v_model_config(): return ModelConfig(PHI3V_MODEL_ID, - PHI3V_MODEL_ID, + task="generate", + tokenizer=PHI3V_MODEL_ID, tokenizer_mode="auto", trust_remote_code=True, dtype="bfloat16", diff --git a/tests/lora/test_worker.py b/tests/lora/test_worker.py index 732e91a52c0a9..2f7ac85507425 100644 --- a/tests/lora/test_worker.py +++ b/tests/lora/test_worker.py @@ -15,7 +15,8 @@ def test_worker_apply_lora(sql_lora_files): worker = Worker( model_config=ModelConfig( "meta-llama/Llama-2-7b-hf", - "meta-llama/Llama-2-7b-hf", + task="auto", + tokenizer="meta-llama/Llama-2-7b-hf", tokenizer_mode="auto", trust_remote_code=False, seed=0, @@ -27,7 +28,7 @@ def test_worker_apply_lora(sql_lora_files): load_format="dummy", ), parallel_config=ParallelConfig(1, 1, False), - scheduler_config=SchedulerConfig(32, 32, 32), + scheduler_config=SchedulerConfig("generate", 32, 32, 32), device_config=DeviceConfig("cuda"), cache_config=CacheConfig(block_size=16, gpu_memory_utilization=1., diff --git a/tests/models/decoder_only/vision_language/test_phi3v.py b/tests/models/decoder_only/vision_language/test_phi3v.py index 12e8a961877cd..808421abd9103 100644 --- a/tests/models/decoder_only/vision_language/test_phi3v.py +++ b/tests/models/decoder_only/vision_language/test_phi3v.py @@ -89,6 +89,7 @@ def run_test( # max_model_len should be greater than image_feature_size with vllm_runner(model, + task="generate", max_model_len=4096, max_num_seqs=2, dtype=dtype, diff --git a/tests/models/embedding/vision_language/test_phi3v.py b/tests/models/embedding/vision_language/test_phi3v.py index ea6b56cd02625..0ca90e6bfa52e 100644 --- a/tests/models/embedding/vision_language/test_phi3v.py +++ b/tests/models/embedding/vision_language/test_phi3v.py @@ -28,6 +28,7 @@ def test_models( # if we run HF first, the cuda initialization will be done and it # will hurt multiprocessing backend with fork method (the default method). with vllm_runner(model, + task="embedding", max_model_len=4096, max_num_seqs=2, dtype=dtype, diff --git a/tests/models/utils.py b/tests/models/utils.py index 86a624483c58a..2ea233a9a599c 100644 --- a/tests/models/utils.py +++ b/tests/models/utils.py @@ -3,7 +3,7 @@ import torch -from vllm.config import ModelConfig +from vllm.config import ModelConfig, TaskOption from vllm.inputs import InputContext from vllm.sequence import Logprob, PromptLogprobs, SampleLogprobs from vllm.utils import is_cpu @@ -248,6 +248,7 @@ def check_logprobs_close( def build_model_context(model_name: str, + task: TaskOption = "auto", tokenizer_name: Optional[str] = None, trust_remote_code: bool = False, dtype: Optional[Union[str, torch.dtype]] = None, @@ -273,7 +274,8 @@ def build_model_context(model_name: str, model_config = ModelConfig( model_name, - tokenizer_name, + task=task, + tokenizer=tokenizer_name, tokenizer_mode="auto", trust_remote_code=trust_remote_code, dtype=dtype, diff --git a/tests/multimodal/test_mapper.py b/tests/multimodal/test_mapper.py index 7d09b81060efd..13ad4a7966b9d 100644 --- a/tests/multimodal/test_mapper.py +++ b/tests/multimodal/test_mapper.py @@ -24,6 +24,7 @@ def test_clip_image_processor(image_assets, mm_registry, dtype, size_factor): model_config = ModelConfig( model=MODEL_NAME, + task="auto", tokenizer=MODEL_NAME, tokenizer_mode="auto", trust_remote_code=False, @@ -67,6 +68,7 @@ def test_llava_next_image_processor(image_assets, mm_registry, dtype, model_config = ModelConfig( model=MODEL_NAME, + task="auto", tokenizer=MODEL_NAME, tokenizer_mode="auto", trust_remote_code=False, @@ -109,6 +111,7 @@ def test_mm_limits(image_assets, mm_registry, num_images, limit, is_valid): model_config = ModelConfig( model=MODEL_NAME, + task="auto", tokenizer=MODEL_NAME, tokenizer_mode="auto", trust_remote_code=False, @@ -139,6 +142,7 @@ def test_image_mapper_multi(image_assets, mm_registry, num_images): model_config = ModelConfig( model=MODEL_NAME, + task="auto", tokenizer=MODEL_NAME, tokenizer_mode="auto", trust_remote_code=False, diff --git a/tests/multimodal/test_processor_kwargs.py b/tests/multimodal/test_processor_kwargs.py index 7b9e0b6e5234b..5044740c3e734 100644 --- a/tests/multimodal/test_processor_kwargs.py +++ b/tests/multimodal/test_processor_kwargs.py @@ -221,6 +221,7 @@ def test_max_tokens_kwarg_overrides(num_crops): expected_seq_count = DEFAULT_NUM_CROPS if num_crops is None else num_crops ctx = build_model_context(MULTIMODAL_MODEL_ID, + task="generate", trust_remote_code=True, mm_processor_kwargs=mm_processor_kwargs, limit_mm_per_prompt={"image": 1}) @@ -256,6 +257,7 @@ def test_max_tokens_kwarg_overrides(num_crops): def test_max_tokens_with_sad_kwarg_overrides(mm_processor_kwargs): """Ensure that max token calcs filters out invalid mm_processor_kwargs""" ctx = build_model_context(MULTIMODAL_MODEL_ID, + task="generate", trust_remote_code=True, mm_processor_kwargs=mm_processor_kwargs, limit_mm_per_prompt={"image": 1}) @@ -278,12 +280,13 @@ def test_max_tokens_with_sad_kwarg_overrides(mm_processor_kwargs): ### Test overrides for the mapper @pytest.mark.parametrize("num_crops", [DEFAULT_NUM_CROPS, NUM_CROPS_OVERRIDE]) -def test_default_mapper_with_processer_kwargs(image_assets, num_crops): +def test_default_mapper_with_processor_kwargs(image_assets, num_crops): """Ensure that the mapper processor kwargs can fall back to HF models.""" # NOTE - we don't validate bad inputs for the default mapper, because it's # through the automodel interface in transformers, so we can't easily # inspect what kwargs are or are not allowed. ctx = build_model_context(MULTIMODAL_MODEL_ID, + task="generate", trust_remote_code=True, mm_processor_kwargs={"num_crops": num_crops}, limit_mm_per_prompt={"image": 1}) @@ -311,6 +314,7 @@ def test_custom_mapper_kwarg_overrides(image_assets, init_num_crops, init_num_crops, inference_num_crops) ctx = build_model_context(MULTIMODAL_MODEL_ID, + task="generate", trust_remote_code=True, mm_processor_kwargs=init_kwargs, limit_mm_per_prompt={"image": 1}) @@ -348,6 +352,7 @@ def test_custom_mapper_with_sad_kwarg_overrides(image_assets, """Ensure that custom mappers filters out invalid mm_processor_kwargs""" # Should filter out the init time kwargs ctx = build_model_context(MULTIMODAL_MODEL_ID, + task="generate", trust_remote_code=True, mm_processor_kwargs=mm_processor_kwargs, limit_mm_per_prompt={"image": 1}) diff --git a/tests/quantization/test_configs.py b/tests/quantization/test_configs.py index d18233fe1aeae..cf77ccec7a191 100644 --- a/tests/quantization/test_configs.py +++ b/tests/quantization/test_configs.py @@ -57,7 +57,8 @@ def test_auto_gptq(model_arg_exptype: Tuple[str, None, str]) -> None: try: model_config = ModelConfig(model_path, - model_path, + task="auto", + tokenizer=model_path, tokenizer_mode="auto", trust_remote_code=False, seed=0, diff --git a/tests/test_config.py b/tests/test_config.py index b89429005e1d0..69918b67607d9 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -2,6 +2,42 @@ from vllm.config import ModelConfig + +@pytest.mark.parametrize(("model_id", "expected_task"), [ + ("facebook/opt-125m", "generate"), + ("intfloat/e5-mistral-7b-instruct", "embedding"), +]) +def test_auto_task(model_id, expected_task): + config = ModelConfig( + model_id, + task="auto", + tokenizer=model_id, + tokenizer_mode="auto", + trust_remote_code=False, + seed=0, + dtype="float16", + ) + + assert config.task == expected_task + + +@pytest.mark.parametrize(("model_id", "bad_task"), [ + ("facebook/opt-125m", "embedding"), + ("intfloat/e5-mistral-7b-instruct", "generate"), +]) +def test_incorrect_task(model_id, bad_task): + with pytest.raises(ValueError, match=r"does not support the .* task"): + ModelConfig( + model_id, + task=bad_task, + tokenizer=model_id, + tokenizer_mode="auto", + trust_remote_code=False, + seed=0, + dtype="float16", + ) + + MODEL_IDS_EXPECTED = [ ("Qwen/Qwen1.5-7B", 32768), ("mistralai/Mistral-7B-v0.1", 4096), @@ -14,7 +50,8 @@ def test_disable_sliding_window(model_id_expected): model_id, expected = model_id_expected model_config = ModelConfig( model_id, - model_id, + task="auto", + tokenizer=model_id, tokenizer_mode="auto", trust_remote_code=False, seed=0, @@ -32,7 +69,8 @@ def test_get_sliding_window(): # when use_sliding_window is False. qwen2_model_config = ModelConfig( "Qwen/Qwen1.5-7B", - "Qwen/Qwen1.5-7B", + task="auto", + tokenizer="Qwen/Qwen1.5-7B", tokenizer_mode="auto", trust_remote_code=False, seed=0, @@ -49,7 +87,8 @@ def test_get_sliding_window(): mistral_model_config = ModelConfig( "mistralai/Mistral-7B-v0.1", - "mistralai/Mistral-7B-v0.1", + task="auto", + tokenizer="mistralai/Mistral-7B-v0.1", tokenizer_mode="auto", trust_remote_code=False, seed=0, @@ -70,7 +109,8 @@ def test_rope_customization(): llama_model_config = ModelConfig( "meta-llama/Meta-Llama-3-8B-Instruct", - "meta-llama/Meta-Llama-3-8B-Instruct", + task="auto", + tokenizer="meta-llama/Meta-Llama-3-8B-Instruct", tokenizer_mode="auto", trust_remote_code=False, dtype="float16", @@ -82,7 +122,8 @@ def test_rope_customization(): llama_model_config = ModelConfig( "meta-llama/Meta-Llama-3-8B-Instruct", - "meta-llama/Meta-Llama-3-8B-Instruct", + task="auto", + tokenizer="meta-llama/Meta-Llama-3-8B-Instruct", tokenizer_mode="auto", trust_remote_code=False, dtype="float16", @@ -98,7 +139,8 @@ def test_rope_customization(): longchat_model_config = ModelConfig( "lmsys/longchat-13b-16k", - "lmsys/longchat-13b-16k", + task="auto", + tokenizer="lmsys/longchat-13b-16k", tokenizer_mode="auto", trust_remote_code=False, dtype="float16", @@ -112,7 +154,8 @@ def test_rope_customization(): longchat_model_config = ModelConfig( "lmsys/longchat-13b-16k", - "lmsys/longchat-13b-16k", + task="auto", + tokenizer="lmsys/longchat-13b-16k", tokenizer_mode="auto", trust_remote_code=False, dtype="float16", diff --git a/tests/test_utils.py b/tests/test_utils.py index 268e6f8194abb..0fed8e678fc76 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -59,7 +59,7 @@ def dummy(*, old_arg: object = None, new_arg: object = None): with pytest.warns(DeprecationWarning, match="'old_arg'"): dummy(old_arg=1) - with error_on_warning(): + with error_on_warning(DeprecationWarning): dummy(new_arg=1) @@ -69,10 +69,10 @@ def test_deprecate_kwargs_never(): def dummy(*, old_arg: object = None, new_arg: object = None): pass - with error_on_warning(): + with error_on_warning(DeprecationWarning): dummy(old_arg=1) - with error_on_warning(): + with error_on_warning(DeprecationWarning): dummy(new_arg=1) @@ -86,15 +86,15 @@ def dummy(*, old_arg: object = None, new_arg: object = None): with pytest.warns(DeprecationWarning, match="'old_arg'"): dummy(old_arg=1) - with error_on_warning(): + with error_on_warning(DeprecationWarning): dummy(new_arg=1) is_deprecated = False - with error_on_warning(): + with error_on_warning(DeprecationWarning): dummy(old_arg=1) - with error_on_warning(): + with error_on_warning(DeprecationWarning): dummy(new_arg=1) diff --git a/tests/utils.py b/tests/utils.py index 115cab80691f0..2ab7329485dfc 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -8,7 +8,7 @@ import warnings from contextlib import contextmanager from pathlib import Path -from typing import Any, Callable, Dict, List, Literal, Optional, Union +from typing import Any, Callable, Dict, List, Literal, Optional, Type, Union import openai import pytest @@ -454,13 +454,13 @@ def multi_process_parallel( @contextmanager -def error_on_warning(): +def error_on_warning(category: Type[Warning] = Warning): """ Within the scope of this context manager, tests will fail if any warning - is emitted. + of the given category is emitted. """ with warnings.catch_warnings(): - warnings.simplefilter("error") + warnings.filterwarnings("error", category=category) yield diff --git a/vllm/config.py b/vllm/config.py index 4533fb017188c..7f8f936428543 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -1,8 +1,8 @@ import enum import json from dataclasses import dataclass, field, fields -from typing import (TYPE_CHECKING, Any, ClassVar, Dict, List, Mapping, - Optional, Tuple, Type, Union) +from typing import (TYPE_CHECKING, Any, ClassVar, Dict, Final, List, Literal, + Mapping, Optional, Set, Tuple, Type, Union) import torch from transformers import PretrainedConfig @@ -33,6 +33,9 @@ _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120 +Task = Literal["generate", "embedding"] +TaskOption = Literal["auto", Task] + class ModelConfig: """Configuration for the model. @@ -40,7 +43,11 @@ class ModelConfig: Args: model: Name or path of the huggingface model to use. It is also used as the content for `model_name` tag in metrics - output when `served_model_name` is not specified. + output when `served_model_name` is not specified. + task: The task to use the model for. Each vLLM instance only supports + one task, even if the same model can be used for multiple tasks. + When the model only supports one task, "auto" can be used to select + it; otherwise, you must specify explicitly which task to use. tokenizer: Name or path of the huggingface tokenizer to use. tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if available, "slow" will always use the slow tokenizer, and @@ -108,6 +115,7 @@ class ModelConfig: def __init__(self, model: str, + task: TaskOption, tokenizer: str, tokenizer_mode: str, trust_remote_code: bool, @@ -207,7 +215,11 @@ def __init__(self, self.override_neuron_config = override_neuron_config if is_neuron( ) else None - self._verify_embedding_mode() + + supported_tasks, task = self._resolve_task(task, self.hf_config) + self.supported_tasks = supported_tasks + self.task: Final = task + self._verify_quantization() self._verify_cuda_graph() self._verify_bnb_config() @@ -241,18 +253,41 @@ def _verify_tokenizer_mode(self) -> None: "either 'auto', 'slow' or 'mistral'.") self.tokenizer_mode = tokenizer_mode - def _verify_embedding_mode(self) -> None: - architectures = getattr(self.hf_config, "architectures", []) + def _resolve_task( + self, + task_option: TaskOption, + hf_config: PretrainedConfig, + ) -> Tuple[Set[Task], Task]: + architectures = getattr(hf_config, "architectures", []) + + task_support: Dict[Task, bool] = { + # NOTE: Listed from highest to lowest priority, + # in case the model supports multiple of them + "generate": ModelRegistry.is_text_generation_model(architectures), + "embedding": ModelRegistry.is_embedding_model(architectures), + } + supported_tasks_lst: List[Task] = [ + task for task, is_supported in task_support.items() if is_supported + ] + supported_tasks = set(supported_tasks_lst) + + if task_option == "auto": + selected_task = next(iter(supported_tasks_lst)) - # TODO: Allow the same model architecture to be specified as either - # generation or embedding model - if "Phi3VForCausalLM" in architectures: - # Match both remote and local names - embedding_mode = "/VLM2Vec" in self.model + if len(supported_tasks) > 1: + logger.info( + "This model supports multiple tasks: %s. " + "Defaulting to '%s'.", supported_tasks, selected_task) else: - embedding_mode = ModelRegistry.is_embedding_model(architectures) + if task_option not in supported_tasks: + msg = ( + f"This model does not support the '{task_option}' task. " + f"Supported tasks: {supported_tasks}") + raise ValueError(msg) + + selected_task = task_option - self.embedding_mode = embedding_mode + return supported_tasks, selected_task def _parse_quant_hf_config(self): quant_cfg = getattr(self.hf_config, "quantization_config", None) @@ -401,7 +436,7 @@ def verify_async_output_proc(self, parallel_config, speculative_config, # Async postprocessor is not necessary with embedding mode # since there is no token generation - if self.embedding_mode: + if self.task == "embedding": self.use_async_output_proc = False # Reminder: Please update docs/source/serving/compatibility_matrix.rst @@ -582,11 +617,6 @@ def is_encoder_decoder_model(self) -> bool: (hasattr(self.hf_config, "text_config") and getattr( self.hf_config.text_config, "is_encoder_decoder", False))) - @property - def is_embedding_model(self) -> bool: - """Extract the embedding model flag.""" - return self.embedding_mode - @property def is_multimodal_model(self) -> bool: return self.multimodal_config is not None @@ -943,6 +973,7 @@ class SchedulerConfig: """Scheduler configuration. Args: + task: The task to use the model for. max_num_batched_tokens: Maximum number of tokens to be processed in a single iteration. max_num_seqs: Maximum number of sequences to be processed in a single @@ -957,7 +988,6 @@ class SchedulerConfig: prompt latency) before scheduling next prompt. enable_chunked_prefill: If True, prefill requests can be chunked based on the remaining max_num_batched_tokens. - embedding_mode: Whether the running model is for embedding. preemption_mode: Whether to perform preemption by swapping or recomputation. If not specified, we determine the mode as follows: We use recomputation by default since it incurs lower overhead than @@ -972,13 +1002,13 @@ class SchedulerConfig: """ def __init__(self, + task: Task, max_num_batched_tokens: Optional[int], max_num_seqs: int, max_model_len: int, num_lookahead_slots: int = 0, delay_factor: float = 0.0, enable_chunked_prefill: bool = False, - embedding_mode: bool = False, is_multimodal_model: bool = False, preemption_mode: Optional[str] = None, num_scheduler_steps: int = 1, @@ -1002,7 +1032,7 @@ def __init__(self, # for higher throughput. max_num_batched_tokens = max(max_model_len, 2048) - if embedding_mode: + if task == "embedding": # For embedding, choose specific value for higher throughput max_num_batched_tokens = max( max_num_batched_tokens, @@ -1022,12 +1052,12 @@ def __init__(self, "Chunked prefill is enabled with max_num_batched_tokens=%d.", self.max_num_batched_tokens) + self.task: Final = task self.max_num_seqs = max_num_seqs self.max_model_len = max_model_len self.num_lookahead_slots = num_lookahead_slots self.delay_factor = delay_factor self.chunked_prefill_enabled = enable_chunked_prefill - self.embedding_mode = embedding_mode self.preemption_mode = preemption_mode self.num_scheduler_steps = num_scheduler_steps self.multi_step_stream_outputs = multi_step_stream_outputs @@ -1239,6 +1269,7 @@ def maybe_create_spec_config( ngram_prompt_lookup_min = 0 draft_model_config = ModelConfig( model=speculative_model, + task=target_model_config.task, tokenizer=target_model_config.tokenizer, tokenizer_mode=target_model_config.tokenizer_mode, trust_remote_code=target_model_config.trust_remote_code, diff --git a/vllm/core/scheduler.py b/vllm/core/scheduler.py index f0c8e6bab4862..8d3fce106dd2c 100644 --- a/vllm/core/scheduler.py +++ b/vllm/core/scheduler.py @@ -313,7 +313,7 @@ def __init__( self.lora_config = lora_config version = "selfattn" - if (self.scheduler_config.embedding_mode + if (self.scheduler_config.task == "embedding" or self.cache_config.is_attention_free): version = "placeholder" diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 41963dcb16922..480d3709224ba 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -3,7 +3,7 @@ import json from dataclasses import dataclass from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional, - Tuple, Type, Union, cast) + Tuple, Type, Union, cast, get_args) import torch @@ -12,7 +12,7 @@ DeviceConfig, EngineConfig, LoadConfig, LoadFormat, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, - SpeculativeConfig, TokenizerPoolConfig) + SpeculativeConfig, TaskOption, TokenizerPoolConfig) from vllm.executor.executor_base import ExecutorBase from vllm.logger import init_logger from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS @@ -84,6 +84,7 @@ class EngineArgs: model: str = 'facebook/opt-125m' served_model_name: Optional[Union[str, List[str]]] = None tokenizer: Optional[str] = None + task: TaskOption = "auto" skip_tokenizer_init: bool = False tokenizer_mode: str = 'auto' trust_remote_code: bool = False @@ -198,6 +199,15 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: type=str, default=EngineArgs.model, help='Name or path of the huggingface model to use.') + parser.add_argument( + '--task', + default=EngineArgs.task, + choices=get_args(TaskOption), + help='The task to use the model for. Each vLLM instance only ' + 'supports one task, even if the same model can be used for ' + 'multiple tasks. When the model only supports one task, "auto" ' + 'can be used to select it; otherwise, you must specify explicitly ' + 'which task to use.') parser.add_argument( '--tokenizer', type=nullable_str, @@ -838,6 +848,7 @@ def from_cli_args(cls, args: argparse.Namespace): def create_model_config(self) -> ModelConfig: return ModelConfig( model=self.model, + task=self.task, # We know this is not None because we set it in __post_init__ tokenizer=cast(str, self.tokenizer), tokenizer_mode=self.tokenizer_mode, @@ -1026,13 +1037,13 @@ def create_engine_config(self) -> EngineConfig: " please file an issue with detailed information.") scheduler_config = SchedulerConfig( + task=model_config.task, max_num_batched_tokens=self.max_num_batched_tokens, max_num_seqs=self.max_num_seqs, max_model_len=model_config.max_model_len, num_lookahead_slots=num_lookahead_slots, delay_factor=self.scheduler_delay_factor, enable_chunked_prefill=self.enable_chunked_prefill, - embedding_mode=model_config.embedding_mode, is_multimodal_model=model_config.is_multimodal_model, preemption_mode=self.preemption_mode, num_scheduler_steps=self.num_scheduler_steps, diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 61c21887e6816..eede3486e5e8f 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -344,7 +344,7 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: observability_config=self.observability_config, ) - if not self.model_config.embedding_mode: + if self.model_config.task != "embedding": self._initialize_kv_caches() # If usage stat is enabled, collect relevant info. @@ -1116,7 +1116,7 @@ def _process_model_outputs(self, seq_group.metrics.model_execute_time = ( o.model_execute_time) - if self.model_config.embedding_mode: + if self.model_config.task == "embedding": self._process_sequence_group_outputs(seq_group, output) else: self.output_processor.process_prompt_logprob(seq_group, output) @@ -1855,9 +1855,6 @@ def create_trace_span(self, seq_group: SequenceGroup) -> None: def is_encoder_decoder_model(self): return self.input_preprocessor.is_encoder_decoder_model() - def is_embedding_model(self): - return self.model_config.is_embedding_model - def _validate_model_inputs(self, inputs: Union[DecoderOnlyInputs, EncoderDecoderInputs]): if self.model_config.is_multimodal_model: diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index 088ec35798de8..1f7893d54de68 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -8,7 +8,7 @@ from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput, BeamSearchSequence, get_beam_search_score) -from vllm.engine.arg_utils import EngineArgs +from vllm.engine.arg_utils import EngineArgs, TaskOption from vllm.engine.llm_engine import LLMEngine from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam, apply_hf_chat_template, @@ -29,7 +29,7 @@ get_cached_tokenizer) from vllm.transformers_utils.tokenizer_group import TokenizerGroup from vllm.usage.usage_lib import UsageContext -from vllm.utils import Counter, deprecate_kwargs, is_list_of +from vllm.utils import Counter, deprecate_args, deprecate_kwargs, is_list_of logger = init_logger(__name__) @@ -108,6 +108,12 @@ class LLM: DEPRECATE_LEGACY: ClassVar[bool] = False """A flag to toggle whether to deprecate the legacy generate/encode API.""" + DEPRECATE_INIT_POSARGS: ClassVar[bool] = True + """ + A flag to toggle whether to deprecate positional arguments in + :meth:`LLM.__init__`. + """ + @classmethod @contextmanager def deprecate_legacy_api(cls): @@ -117,6 +123,13 @@ def deprecate_legacy_api(cls): cls.DEPRECATE_LEGACY = False + @deprecate_args( + start_index=2, # Ignore self and model + is_deprecated=lambda: LLM.DEPRECATE_INIT_POSARGS, + additional_message=( + "All positional arguments other than `model` will be " + "replaced with keyword arguments in an upcoming version."), + ) def __init__( self, model: str, @@ -139,6 +152,8 @@ def __init__( disable_custom_all_reduce: bool = False, disable_async_output_proc: bool = False, mm_processor_kwargs: Optional[Dict[str, Any]] = None, + # After positional args are removed, move this right below `model` + task: TaskOption = "auto", **kwargs, ) -> None: ''' @@ -153,6 +168,7 @@ def __init__( engine_args = EngineArgs( model=model, + task=task, tokenizer=tokenizer, tokenizer_mode=tokenizer_mode, skip_tokenizer_init=skip_tokenizer_init, @@ -316,10 +332,21 @@ def generate( considered legacy and may be deprecated in the future. You should instead pass them via the ``inputs`` parameter. """ - if self.llm_engine.model_config.embedding_mode: - raise ValueError( + task = self.llm_engine.model_config.task + if task != "generate": + messages = [ "LLM.generate() is only supported for (conditional) generation " - "models (XForCausalLM, XForConditionalGeneration).") + "models (XForCausalLM, XForConditionalGeneration).", + ] + + supported_tasks = self.llm_engine.model_config.supported_tasks + if "generate" in supported_tasks: + messages.append( + "Your model supports the 'generate' task, but is " + f"currently initialized for the '{task}' task. Please " + "initialize the model using `--task generate`.") + + raise ValueError(" ".join(messages)) if prompt_token_ids is not None: parsed_prompts = self._convert_v1_inputs( @@ -692,10 +719,18 @@ def encode( considered legacy and may be deprecated in the future. You should instead pass them via the ``inputs`` parameter. """ - if not self.llm_engine.model_config.embedding_mode: - raise ValueError( - "LLM.encode() is only supported for embedding models (XModel)." - ) + task = self.llm_engine.model_config.task + if task != "embedding": + messages = ["LLM.encode() is only supported for embedding models."] + + supported_tasks = self.llm_engine.model_config.supported_tasks + if "embedding" in supported_tasks: + messages.append( + "Your model supports the 'embedding' task, but is " + f"currently initialized for the '{task}' task. Please " + "initialize the model using `--task embedding`.") + + raise ValueError(" ".join(messages)) if prompt_token_ids is not None: parsed_prompts = self._convert_v1_inputs( @@ -905,6 +940,3 @@ def _run_engine( def _is_encoder_decoder_model(self): return self.llm_engine.is_encoder_decoder_model() - - def _is_embedding_model(self): - return self.llm_engine.is_embedding_model() diff --git a/vllm/entrypoints/openai/serving_embedding.py b/vllm/entrypoints/openai/serving_embedding.py index e9504cfa64b65..6c46aae2838f6 100644 --- a/vllm/entrypoints/openai/serving_embedding.py +++ b/vllm/entrypoints/openai/serving_embedding.py @@ -83,7 +83,8 @@ def __init__( lora_modules=None, prompt_adapters=None, request_logger=request_logger) - self._enabled = self._check_embedding_mode(model_config.embedding_mode) + self._enabled = self._check_embedding_mode( + model_config.task == "embedding") async def create_embedding( self, diff --git a/vllm/utils.py b/vllm/utils.py index 07769da3c86d4..0147d595fec70 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -1034,10 +1034,54 @@ def identity(value: T) -> T: F = TypeVar('F', bound=Callable[..., Any]) +def deprecate_args( + start_index: int, + is_deprecated: Union[bool, Callable[[], bool]] = True, + additional_message: Optional[str] = None, +) -> Callable[[F], F]: + + if not callable(is_deprecated): + is_deprecated = partial(identity, is_deprecated) + + def wrapper(fn: F) -> F: + + params = inspect.signature(fn).parameters + pos_types = ( + inspect.Parameter.POSITIONAL_ONLY, + inspect.Parameter.POSITIONAL_OR_KEYWORD, + ) + pos_kws = [ + kw for kw, param in params.items() if param.kind in pos_types + ] + + @wraps(fn) + def inner(*args, **kwargs): + if is_deprecated(): + deprecated_args = pos_kws[start_index:len(args)] + if deprecated_args: + msg = ( + f"The positional arguments {deprecated_args} are " + "deprecated and will be removed in a future update.") + if additional_message is not None: + msg += f" {additional_message}" + + warnings.warn( + DeprecationWarning(msg), + stacklevel=3, # The inner function takes up one level + ) + + return fn(*args, **kwargs) + + return inner # type: ignore + + return wrapper + + def deprecate_kwargs( - *kws: str, - is_deprecated: Union[bool, Callable[[], bool]] = True, - additional_message: Optional[str] = None) -> Callable[[F], F]: + *kws: str, + is_deprecated: Union[bool, Callable[[], bool]] = True, + additional_message: Optional[str] = None, +) -> Callable[[F], F]: deprecated_kws = set(kws) if not callable(is_deprecated): diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index 9c46bb4258609..018ab5b828786 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -92,7 +92,7 @@ def __init__( ModelRunnerClass: Type[GPUModelRunnerBase] = ModelRunner if model_runner_cls is not None: ModelRunnerClass = model_runner_cls - elif self._is_embedding_model(): + elif model_config.task == "embedding": ModelRunnerClass = EmbeddingModelRunner elif self._is_encoder_decoder_model(): ModelRunnerClass = EncoderDecoderModelRunner @@ -147,9 +147,6 @@ def stop_profile(self): def _is_encoder_decoder_model(self): return self.model_config.is_encoder_decoder_model - def _is_embedding_model(self): - return self.model_config.is_embedding_model - def init_device(self) -> None: if self.device_config.device.type == "cuda": # torch.distributed.all_reduce does not free the input tensor until From 67a7e5ef384206f20294ce9bed2fa8953c83058a Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Fri, 18 Oct 2024 15:17:53 -0400 Subject: [PATCH 004/222] [CI/Build] Add error matching config for mypy (#9512) --- .github/workflows/matchers/mypy.json | 16 ++++++++++++++++ .github/workflows/mypy.yaml | 3 ++- tools/mypy.sh | 4 ++++ 3 files changed, 22 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/matchers/mypy.json diff --git a/.github/workflows/matchers/mypy.json b/.github/workflows/matchers/mypy.json new file mode 100644 index 0000000000000..f048fce528941 --- /dev/null +++ b/.github/workflows/matchers/mypy.json @@ -0,0 +1,16 @@ +{ + "problemMatcher": [ + { + "owner": "mypy", + "pattern": [ + { + "regexp": "^(.+):(\\d+):\\s(error|warning):\\s(.+)$", + "file": 1, + "line": 2, + "severity": 3, + "message": 4 + } + ] + } + ] +} diff --git a/.github/workflows/mypy.yaml b/.github/workflows/mypy.yaml index 4b98324e3a812..5f1e5f8eeaf7d 100644 --- a/.github/workflows/mypy.yaml +++ b/.github/workflows/mypy.yaml @@ -32,4 +32,5 @@ jobs: pip install types-setuptools - name: Mypy run: | - tools/mypy.sh + echo "::add-matcher::.github/workflows/matchers/mypy.json" + tools/mypy.sh 1 diff --git a/tools/mypy.sh b/tools/mypy.sh index d69b61c7f34fc..14b0976a27da5 100755 --- a/tools/mypy.sh +++ b/tools/mypy.sh @@ -2,6 +2,10 @@ CI=${1:-0} +if [ $CI -eq 1 ]; then + set -e +fi + run_mypy() { echo "Running mypy on $1" if [ $CI -eq 1 ] && [ -z "$1" ]; then From 3921a2f29e30df293459d824e20d2e546e4af0c7 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Fri, 18 Oct 2024 15:29:56 -0400 Subject: [PATCH 005/222] [Model] Support Pixtral models in the HF Transformers format (#9036) --- docs/source/models/supported_models.rst | 2 +- examples/offline_inference_vision_language.py | 17 + vllm/model_executor/layers/activation.py | 2 + vllm/model_executor/models/llava.py | 74 +++- vllm/model_executor/models/pixtral.py | 410 +++++++++++++++++- vllm/model_executor/models/qwen2_vl.py | 6 +- vllm/transformers_utils/processor.py | 4 + 7 files changed, 503 insertions(+), 12 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index ee2844c8b27a0..318139a749d88 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -437,7 +437,7 @@ Text Generation * - :code:`PixtralForConditionalGeneration` - Pixtral - T + I\ :sup:`+` - - :code:`mistralai/Pixtral-12B-2409` + - :code:`mistralai/Pixtral-12B-2409`, :code:`mistral-community/pixtral-12b` etc. - - ✅︎ * - :code:`QWenLMHeadModel` diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index 4c88dcc2f087b..06b424abd50b5 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -277,6 +277,22 @@ def run_qwen2_vl(question: str, modality: str): return llm, prompt, stop_token_ids +# Pixtral HF-format +def run_pixtral_hf(question: str, modality: str): + assert modality == "image" + + model_name = "mistral-community/pixtral-12b" + + llm = LLM( + model=model_name, + max_model_len=8192, + ) + + prompt = f"[INST]{question}\n[IMG][/INST]" + stop_token_ids = None + return llm, prompt, stop_token_ids + + # LLama 3.2 def run_mllama(question: str, modality: str): assert modality == "image" @@ -347,6 +363,7 @@ def run_glm4v(question: str, modality: str): "NVLM_D": run_nvlm_d, "qwen_vl": run_qwen_vl, "qwen2_vl": run_qwen2_vl, + "pixtral_hf": run_pixtral_hf, "mllama": run_mllama, "molmo": run_molmo, "glm4v": run_glm4v, diff --git a/vllm/model_executor/layers/activation.py b/vllm/model_executor/layers/activation.py index cf99306c9caef..8de3385a257f8 100644 --- a/vllm/model_executor/layers/activation.py +++ b/vllm/model_executor/layers/activation.py @@ -264,6 +264,8 @@ def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor): lambda: nn.ReLU(), "relu2": lambda: ReLUSquaredActivation(), + "silu": + lambda: nn.SiLU(), "quick_gelu": lambda: QuickGELU(), }) diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index fd2827c0eff09..a83b7d05df7aa 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -5,7 +5,8 @@ import torch import torch.nn as nn from PIL import Image -from transformers import CLIPVisionConfig, LlavaConfig, SiglipVisionConfig +from transformers import (CLIPVisionConfig, LlavaConfig, PixtralVisionConfig, + SiglipVisionConfig) from vllm.attention import AttentionMetadata from vllm.config import CacheConfig, MultiModalConfig @@ -22,6 +23,10 @@ dummy_seq_data_for_clip, get_max_clip_image_tokens, input_processor_for_clip) from .interfaces import SupportsMultiModal, SupportsPP +from .pixtral import (PixtralHFVisionModel, dummy_image_for_pixtral_hf, + dummy_seq_data_for_pixtral_hf, + get_max_pixtral_hf_image_tokens, + input_processor_for_pixtral_hf) from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip, get_max_siglip_image_tokens, input_processor_for_siglip) @@ -31,8 +36,13 @@ class LlavaImagePixelInputs(TypedDict): type: Literal["pixel_values"] - data: torch.Tensor - """Shape: `(batch_size * num_images, num_channels, height, width)`""" + data: Union[torch.Tensor, List[torch.Tensor]] + """ + Shape: `(batch_size * num_images, num_channels, height, width)` + + Note that `height` or `width` may be different per batch and image, + in which case the data is passed as a list instead of a batched tensor. + """ class LlavaImageEmbeddingInputs(TypedDict): @@ -77,6 +87,8 @@ def get_max_llava_image_tokens(ctx: InputContext): num_image_tokens = get_max_clip_image_tokens(vision_config) elif isinstance(vision_config, SiglipVisionConfig): num_image_tokens = get_max_siglip_image_tokens(vision_config) + elif isinstance(vision_config, PixtralVisionConfig): + num_image_tokens = get_max_pixtral_hf_image_tokens(vision_config) else: msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) @@ -120,6 +132,17 @@ def dummy_data_for_llava(ctx: InputContext, seq_len: int, mm_data = dummy_image_for_siglip(vision_config, num_images) return seq_data, mm_data + elif isinstance(vision_config, PixtralVisionConfig): + seq_data = dummy_seq_data_for_pixtral_hf( + vision_config, + seq_len, + num_images, + image_token_id=hf_config.image_token_index, + image_feature_size_override=image_feature_size, + ) + + mm_data = dummy_image_for_pixtral_hf(vision_config, num_images) + return seq_data, mm_data msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) @@ -163,6 +186,15 @@ def input_processor_for_llava(ctx: InputContext, inputs: DecoderOnlyInputs): image_token_id=hf_config.image_token_index, image_feature_size_override=image_feature_size, ) + elif isinstance(vision_config, PixtralVisionConfig): + # We ignore image_feature_size_override since we have non-uniform + # image sizes for Pixtral + return input_processor_for_pixtral_hf( + model_config, + vision_config, + inputs, + image_token_id=hf_config.image_token_index, + ) msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) @@ -189,6 +221,9 @@ def _init_vision_tower(hf_config: LlavaConfig): vision_config, num_hidden_layers_override=num_hidden_layers, ) + elif isinstance(vision_config, PixtralVisionConfig): + # TODO: allow layer override? + return PixtralHFVisionModel(vision_config) msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) @@ -210,6 +245,15 @@ def __init__(self, self.config = config self.multimodal_config = multimodal_config + # NOTE: These are special cases for Pixtral-12B in the HF-format + # https://huggingface.co/mistral-community/pixtral-12b/blob/main/config.json # noqa + if (config.text_config.architectures is None + and config.text_config.model_type == "mistral"): + config.text_config.architectures = ["MistralForCausalLM"] + if (config.projector_hidden_act is None + and config.vision_config.hidden_act == "gelu"): + config.projector_hidden_act = "gelu" + # TODO: Optionally initializes this for supporting embeddings. self.vision_tower = _init_vision_tower(config) self.multi_modal_projector = LlavaMultiModalProjector( @@ -246,6 +290,7 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[LlavaImageInputs]: pixel_values = kwargs.pop("pixel_values", None) + image_sizes = kwargs.pop("image_sizes", None) image_embeds = kwargs.pop("image_embeds", None) if pixel_values is None and image_embeds is None: @@ -256,6 +301,26 @@ def _parse_and_validate_image_input( raise ValueError("Incorrect type of pixel values. " f"Got type: {type(pixel_values)}") + # Case for models like PixtralHF that have dynamic image sizes + # so we need to produce a list of tensors + if image_sizes is not None: + images = pixel_values + if isinstance(images, torch.Tensor): + # if passed as batch take all images + NN, N, B, C, W, H = images.shape + images = images.reshape(NN * N * B, C, W, H) + images = [images[i] for i in range(images.size(0))] + elif isinstance(images, list): + # if passed as list flatten lists of tensors + while isinstance(images, list) and len(images) == 1: + images = images[0] + + # TODO: Add validation based on image_sizes + return LlavaImagePixelInputs( + type="pixel_values", + data=images, + ) + return LlavaImagePixelInputs( type="pixel_values", data=self._validate_pixel_values( @@ -286,7 +351,8 @@ def _select_image_features(self, image_features: torch.Tensor, *, def _image_pixels_to_features( self, - vision_tower: Union[CLIPVisionModel, SiglipVisionModel], + vision_tower: Union[CLIPVisionModel, SiglipVisionModel, + PixtralHFVisionModel], pixel_values: torch.Tensor, ) -> torch.Tensor: diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index f34d21fdef56f..d09cbe5ca02e9 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -3,18 +3,26 @@ from itertools import tee from typing import Iterable, List, Mapping, Optional, Tuple, Union +import numpy import torch import torch.nn as nn import torch.nn.functional as F from mistral_common.protocol.instruct.messages import ImageChunk from PIL import Image -from transformers import PretrainedConfig +from transformers import PixtralVisionConfig, PretrainedConfig +from transformers.models.pixtral.image_processing_pixtral import ( + _num_image_tokens) +from transformers.models.pixtral.modeling_pixtral import ( + PixtralRotaryEmbedding, apply_rotary_pos_emb, + generate_block_attention_mask, position_ids_in_meshgrid) from xformers.ops.fmha import memory_efficient_attention from xformers.ops.fmha.attn_bias import BlockDiagonalMask from vllm.attention import AttentionMetadata -from vllm.config import CacheConfig, MultiModalConfig -from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext +from vllm.config import CacheConfig, ModelConfig, MultiModalConfig +from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext, + token_inputs) +from vllm.model_executor.layers.activation import get_act_fn from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.sampler import Sampler, SamplerOutput @@ -25,6 +33,8 @@ from vllm.multimodal.base import MultiModalInputs from vllm.multimodal.utils import cached_get_tokenizer from vllm.sequence import IntermediateTensors, SequenceData +from vllm.transformers_utils.processor import cached_get_processor +from vllm.utils import is_list_of from .interfaces import SupportsMultiModal, SupportsPP from .utils import init_vllm_registered_model @@ -576,3 +586,397 @@ def __init__(self, args: VisionEncoderArgs, dim: int): def forward(self, x: torch.Tensor) -> torch.Tensor: return self.w_out(self.gelu(self.w_in(x))) + + +#### HF Transformers version of Pixtral #### +# Based off https://github.com/huggingface/transformers/blob/d7950bff82b18c823193d17d72188c5e46d06c83/src/transformers/models/pixtral/modeling_pixtral.py +# This model follows the Llava family, meaning image embeddings are placed +# instead of the `[IMG]` token placeholders. +# The model uses [`PixtralVisionModel`] for its vision encoder, +# and [`MistralForCausalLM`] for its language decoder. + + +def get_pixtral_hf_patch_grid_length(*, image_size: int, + patch_size: int) -> int: + # Since interpolation is applied, the image size need not be divisible + # assert image_size % patch_size == 0 + return image_size // patch_size + + +def get_pixtral_hf_num_patches(*, image_size: int, patch_size: int) -> int: + grid_length = get_pixtral_hf_patch_grid_length(image_size=image_size, + patch_size=patch_size) + return grid_length * grid_length + + +def get_max_pixtral_hf_image_feature_size( + hf_config: PixtralVisionConfig) -> int: + return get_pixtral_hf_num_patches(image_size=hf_config.image_size, + patch_size=hf_config.patch_size) + + +def get_max_pixtral_hf_image_tokens(hf_config: PixtralVisionConfig) -> int: + return get_max_pixtral_hf_image_feature_size(hf_config) + + +def dummy_seq_data_for_pixtral_hf( + hf_config: PixtralVisionConfig, + seq_len: int, + num_images: int, + *, + image_token_id: int, + image_feature_size_override: Optional[int] = None, +): + if image_feature_size_override is None: + image_feature_size = get_max_pixtral_hf_image_feature_size(hf_config) + else: + image_feature_size = image_feature_size_override + + return SequenceData.from_prompt_token_counts( + (image_token_id, image_feature_size * num_images), + (0, seq_len - image_feature_size * num_images), + ) + + +def dummy_image_for_pixtral_hf( + hf_config: PixtralVisionConfig, + num_images: int, + *, + image_width_override: Optional[int] = None, + image_height_override: Optional[int] = None, +): + width = height = hf_config.image_size + if image_width_override is not None: + width = image_width_override + if image_height_override is not None: + height = image_height_override + + image = Image.new("RGB", (width, height), color=0) + return {"image": image if num_images == 1 else [image] * num_images} + + +def get_pixtral_hf_image_feature_size(hf_config: PixtralVisionConfig, + image_width: int, + image_height: int) -> Tuple[int, int]: + # Adapted from transformers.models.pixtral.image_processing_pixtral.get_resize_output_image_size # noqa: E501 + # https://github.com/huggingface/transformers/blob/2bd4d5897dc73e8b172832070a6f9e567a0df017/src/transformers/models/pixtral/image_processing_pixtral.py#L180 # noqa: E501 + max_width, max_height = hf_config.image_size, hf_config.image_size + patch_width, patch_height = hf_config.patch_size, hf_config.patch_size + + ratio = max(image_width / max_width, image_height / max_height) + + if ratio > 1: + image_width = int(numpy.ceil(image_width / ratio)) + image_height = int(numpy.ceil(image_height / ratio)) + + num_height_tokens, num_width_tokens = _num_image_tokens( + (image_height, image_width), (patch_height, patch_width)) + + return num_width_tokens, num_height_tokens + + +def input_processor_for_pixtral_hf( + model_config: ModelConfig, + hf_config: PixtralVisionConfig, + inputs: DecoderOnlyInputs, + *, + image_token_id: int, + image_feature_size_override: Optional[Union[int, List[int]]] = None, +) -> DecoderOnlyInputs: + assert image_feature_size_override is None, ( + "image_feature_size_override is not supported for Pixtral") + + multi_modal_data = inputs.get("multi_modal_data") + if multi_modal_data is None or "image" not in multi_modal_data: + return inputs + + processor = cached_get_processor(model_config.model) + + image_data = multi_modal_data["image"] + if isinstance(image_data, Image.Image): + image_data = [image_data] + elif not is_list_of(image_data, Image.Image): + raise TypeError(f"Invalid image type: {type(image_data)}") + + new_prompt = inputs.get("prompt") + new_token_ids = inputs["prompt_token_ids"] + + # Update new_prompt if present + if new_prompt: + replace_strings = [] + for image in image_data: + w, h = image.size + + (num_width_tokens, + num_height_tokens) = get_pixtral_hf_image_feature_size( + hf_config, image_width=w, image_height=h) + + replace_tokens = [[processor.image_token] * num_width_tokens + + [processor.image_break_token] + ] * num_height_tokens + # Flatten list + replace_tokens = [ + item for sublist in replace_tokens for item in sublist + ] + replace_tokens[-1] = processor.image_end_token + replace_str = "".join(replace_tokens) + replace_strings.append(replace_str) + new_prompt = new_prompt.replace(processor.image_token, + "", 1) + + while "" in new_prompt: + replace_str = replace_strings.pop(0) + new_prompt = new_prompt.replace("", replace_str, 1) + + # Update new_token_ids + image_token_id = 10 + image_break_id = 12 + image_end_id = 13 + placeholder_token_id = -999 + replace_tokens_list = [] + for image in image_data: + w, h = image.size + + num_width_tokens, num_height_tokens = get_pixtral_hf_image_feature_size( + hf_config, image_width=w, image_height=h) + + replace_tokens = [[image_token_id] * num_width_tokens + + [image_break_id]] * num_height_tokens + # Flatten list + replace_tokens = [ + item for sublist in replace_tokens for item in sublist + ] + replace_tokens[-1] = image_end_id + replace_tokens_list.append(replace_tokens) + # Replace image id with placeholder id + next_image_index = new_token_ids.index(image_token_id) + new_token_ids[next_image_index] = placeholder_token_id + + while placeholder_token_id in new_token_ids: + replace_tokens = replace_tokens_list.pop(0) + next_image_index = new_token_ids.index(placeholder_token_id) + prefix = new_token_ids[:next_image_index] + postfix = new_token_ids[next_image_index + 1:] + new_token_ids = prefix + replace_tokens + postfix + + # NOTE: Create a defensive copy of the original inputs + return token_inputs(prompt_token_ids=new_token_ids, + prompt=new_prompt, + multi_modal_data=multi_modal_data) + + +class PixtralHFMLP(nn.Module): + + def __init__(self, config: PixtralVisionConfig): + super().__init__() + assert config.intermediate_size is not None + self.gate_proj = nn.Linear(config.hidden_size, + config.intermediate_size, + bias=False) + self.up_proj = nn.Linear(config.hidden_size, + config.intermediate_size, + bias=False) + self.down_proj = nn.Linear(config.intermediate_size, + config.hidden_size, + bias=False) + self.act = get_act_fn(config.hidden_act) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.down_proj(self.act(self.gate_proj(x)) * self.up_proj(x)) + + +class PixtralHFAttention(nn.Module): + + def __init__(self, config: PixtralVisionConfig): + super().__init__() + self.config = config + assert not config.hidden_size % config.num_attention_heads + self.n_heads = config.num_attention_heads + self.head_dim = config.hidden_size // config.num_attention_heads + + self.scale = self.head_dim**-0.5 + + self.q_proj = nn.Linear(config.hidden_size, + config.hidden_size, + bias=False) + self.k_proj = nn.Linear(config.hidden_size, + config.hidden_size, + bias=False) + self.v_proj = nn.Linear(config.hidden_size, + config.hidden_size, + bias=False) + self.o_proj = nn.Linear(config.hidden_size, + config.hidden_size, + bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + position_embeddings: torch.Tensor, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Input shape: Batch x Time x Channel""" + + batch_size, patches, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(batch_size, patches, self.n_heads, + self.head_dim).transpose(1, 2) + key_states = key_states.view(batch_size, patches, self.n_heads, + self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, patches, self.n_heads, + self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, + key_states, + cos, + sin, + unsqueeze_dim=0) + + attn_weights = torch.matmul(query_states, key_states.transpose( + 2, 3)) * self.scale + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, + dim=-1, + dtype=torch.float32).to( + query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(batch_size, patches, -1) + + return self.o_proj(attn_output) + + +class PixtralHFTransformerBlock(nn.Module): + + def __init__(self, config: PixtralVisionConfig): + super().__init__() + self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5) + self.attention = PixtralHFAttention(config) + self.feed_forward = PixtralHFMLP(config) + self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + position_embeddings: torch.Tensor, + ) -> torch.Tensor: + r = self.attention.forward(self.attention_norm(hidden_states), + attention_mask=attention_mask, + position_embeddings=position_embeddings) + h = hidden_states + r + r = self.feed_forward.forward(self.ffn_norm(h)) + out = h + r + return out + + +class PixtralHFTransformer(nn.Module): + + def __init__(self, config: PixtralVisionConfig): + super().__init__() + self.layers = torch.nn.ModuleList() + for _ in range(config.num_hidden_layers): + self.layers.append(PixtralHFTransformerBlock(config)) + + def forward( + self, + x: torch.Tensor, + attention_mask: torch.Tensor, + position_embeddings: torch.Tensor, + ) -> torch.Tensor: + for layer in self.layers: + x = layer(x, attention_mask, position_embeddings) + return x + + +class PixtralHFVisionModel(nn.Module): + + def __init__(self, config: PixtralVisionConfig): + super().__init__() + + self.config = config + self.patch_conv = nn.Conv2d( + in_channels=config.num_channels, + out_channels=config.hidden_size, + kernel_size=config.patch_size, + stride=config.patch_size, + bias=False, + ) + self.ln_pre = RMSNorm(config.hidden_size, eps=1e-5) + self.transformer = PixtralHFTransformer(config) + self.dtype = next(self.parameters()).dtype + self.device = next(self.parameters()).device + self.patch_positional_embedding = PixtralRotaryEmbedding( + config, self.device) + + def forward( + self, + pixel_values: List[torch.Tensor], + ) -> torch.Tensor: + """ + Args: + pixel_values: tensor of token features for + all tokens of all images of shape (N_toks, D) + Returns: + image_features: tensor of token features for + all tokens of all images of shape (N_toks, D) + """ + # pass images through initial convolution independently + patch_embeds_list = [ + self.patch_conv( + img.reshape(-1, img.shape[-3], img.shape[-2], + img.shape[-1]).to(self.dtype)) + for img in pixel_values + ] + + # flatten to a single sequence + patch_embeds = torch.cat( + [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list], dim=1) + patch_embeds = self.ln_pre(patch_embeds) + + # positional embeddings + position_ids = position_ids_in_meshgrid( + patch_embeds_list, + max_width=self.config.image_size // self.config.patch_size).to( + self.device) + + position_embedding = self.patch_positional_embedding( + patch_embeds, position_ids) + attention_mask = generate_block_attention_mask( + [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], + patch_embeds) + out = self.transformer(patch_embeds, attention_mask, + position_embedding) + + return out + + # (TODO) Add prefix argument for filtering out weights to be loaded + # ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986 + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + stacked_params_mapping = [] + params_dict = dict(self.named_parameters()) + + for name, loaded_weight in weights: + for (param_name, weight_name, shard_id) in stacked_params_mapping: + if weight_name not in name: + continue + + param = params_dict[name.replace(weight_name, param_name)] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index f7d632a83cc33..a3540abdc23d3 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -22,7 +22,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """Inference-only Qwen2-VL model compatible with HuggingFace weights.""" -from functools import lru_cache, partial +from functools import partial from typing import (Any, Callable, Iterable, List, Literal, Mapping, Optional, Tuple, Type, TypedDict, Union) @@ -63,7 +63,7 @@ from vllm.multimodal.image import cached_get_image_processor from vllm.sequence import IntermediateTensors, SequenceData from vllm.transformers_utils.config import uses_mrope -from vllm.transformers_utils.processor import get_processor +from vllm.transformers_utils.processor import cached_get_processor from .interfaces import SupportsMultiModal, SupportsPP from .utils import (PPMissingLayer, get_vit_attn_backend, @@ -544,8 +544,6 @@ def forward( # === Vision input helpers === # -cached_get_processor = lru_cache(get_processor) - def mm_input_mapper_for_qwen2_vl( ctx: InputContext, diff --git a/vllm/transformers_utils/processor.py b/vllm/transformers_utils/processor.py index 98663f7f0bd07..f1523667b0466 100644 --- a/vllm/transformers_utils/processor.py +++ b/vllm/transformers_utils/processor.py @@ -1,3 +1,4 @@ +from functools import lru_cache from typing import Any, cast @@ -37,6 +38,9 @@ def get_processor( return cast(ProcessorMixin, processor) +cached_get_processor = lru_cache(get_processor) + + def get_image_processor( processor_name: str, *args: Any, From 9bb10a7d276e085c72f2545cea1a3565937e7b22 Mon Sep 17 00:00:00 2001 From: Kunjan Date: Fri, 18 Oct 2024 13:50:18 -0700 Subject: [PATCH 006/222] [MISC] Add lora requests to metrics (#9477) Co-authored-by: Kunjan Patel --- vllm/engine/llm_engine.py | 24 +++++++++++++++++++++++- vllm/engine/metrics.py | 29 ++++++++++++++++++++++++++++- vllm/engine/metrics_types.py | 3 +++ 3 files changed, 54 insertions(+), 2 deletions(-) diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index eede3486e5e8f..a90bfce8491fb 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -1,4 +1,5 @@ import time +from collections import Counter as collectionsCounter from collections import deque from contextlib import contextmanager from dataclasses import dataclass @@ -1617,6 +1618,25 @@ def _get_stats(self, n_requests: List[int] = [] finished_reason_requests: List[str] = [] + # Lora requests + running_lora_adapters = dict( + collectionsCounter([ + running_request.lora_request.lora_name + for scheduler in self.scheduler + for running_request in scheduler.running + if running_request.lora_request + ])) + waiting_lora_adapters = dict( + collectionsCounter([ + waiting_request.lora_request.lora_name + for scheduler in self.scheduler + for waiting_request in scheduler.waiting + if waiting_request.lora_request + ])) + max_lora_stat = "0" + if self.lora_config: + max_lora_stat = str(self.lora_config.max_loras) + # NOTE: This loop assumes prefill seq_groups are before # decode seq_groups in scheduled_seq_groups. if scheduler_outputs is not None: @@ -1738,7 +1758,9 @@ def _get_stats(self, num_generation_tokens_requests=num_generation_tokens_requests, n_requests=n_requests, finished_reason_requests=finished_reason_requests, - ) + max_lora=str(max_lora_stat), + waiting_lora_adapters=list(waiting_lora_adapters.keys()), + running_lora_adapters=list(running_lora_adapters.keys())) def add_lora(self, lora_request: LoRARequest) -> bool: return self.model_executor.add_lora(lora_request) diff --git a/vllm/engine/metrics.py b/vllm/engine/metrics.py index 98bf59be3469d..a46625eff1e4a 100644 --- a/vllm/engine/metrics.py +++ b/vllm/engine/metrics.py @@ -34,7 +34,11 @@ class Metrics: See https://prometheus.github.io/client_python/multiprocess/ for more details on limitations. """ + labelname_finish_reason = "finished_reason" + labelname_waiting_lora_adapters = "waiting_lora_adapters" + labelname_running_lora_adapters = "running_lora_adapters" + labelname_max_lora = "max_lora" _gauge_cls = prometheus_client.Gauge _counter_cls = prometheus_client.Counter _histogram_cls = prometheus_client.Histogram @@ -55,6 +59,16 @@ def __init__(self, labelnames: List[str], max_model_len: int): documentation="Number of requests waiting to be processed.", labelnames=labelnames, multiprocess_mode="sum") + self.gauge_lora_info = self._gauge_cls( + name="vllm:lora_requests_info", + documentation="Running stats on lora requests.", + labelnames=[ + self.labelname_running_lora_adapters, + self.labelname_max_lora, + self.labelname_waiting_lora_adapters, + ], + multiprocess_mode="livemostrecent", + ) self.gauge_scheduler_swapped = self._gauge_cls( name="vllm:num_requests_swapped", documentation="Number of requests swapped to CPU.", @@ -426,6 +440,9 @@ def _log_histogram(self, histogram, data: Union[List[int], for datum in data: histogram.labels(**self.labels).observe(datum) + def _log_gauge_string(self, gauge, data: Dict[str, str]) -> None: + gauge.labels(**data).set(1) + def _log_prometheus(self, stats: Stats) -> None: # System state data self._log_gauge(self.metrics.gauge_scheduler_running, @@ -442,7 +459,17 @@ def _log_prometheus(self, stats: Stats) -> None: stats.cpu_prefix_cache_hit_rate) self._log_gauge(self.metrics.gauge_gpu_prefix_cache_hit_rate, stats.gpu_prefix_cache_hit_rate) - + # Including max-lora in metric, in future this property of lora + # config maybe extended to be dynamic. + lora_info = { + self.metrics.labelname_running_lora_adapters: + ",".join(stats.running_lora_adapters), + self.metrics.labelname_waiting_lora_adapters: + ",".join(stats.waiting_lora_adapters), + self.metrics.labelname_max_lora: + stats.max_lora, + } + self._log_gauge_string(self.metrics.gauge_lora_info, lora_info) # Iteration level data self._log_counter(self.metrics.counter_num_preemption, stats.num_preemption_iter) diff --git a/vllm/engine/metrics_types.py b/vllm/engine/metrics_types.py index bafd5fa1a8a82..e9a5bd3b586be 100644 --- a/vllm/engine/metrics_types.py +++ b/vllm/engine/metrics_types.py @@ -51,6 +51,9 @@ class Stats: num_generation_tokens_requests: List[int] n_requests: List[int] finished_reason_requests: List[str] + waiting_lora_adapters: List[str] + running_lora_adapters: List[str] + max_lora: str spec_decode_metrics: Optional["SpecDecodeWorkerMetrics"] = None From d11bf435a0bfdefece204aa6a725e849dc00d8cb Mon Sep 17 00:00:00 2001 From: Cody Yu Date: Fri, 18 Oct 2024 14:30:55 -0700 Subject: [PATCH 007/222] [MISC] Consolidate cleanup() and refactor offline_inference_with_prefix.py (#9510) --- examples/offline_inference_with_prefix.py | 19 +++++++++----- tests/async_engine/test_async_llm_engine.py | 4 +-- tests/conftest.py | 23 ++++------------ tests/core/block/e2e/conftest.py | 5 ++-- tests/entrypoints/llm/test_encode.py | 5 ++-- tests/entrypoints/llm/test_generate.py | 5 ++-- .../llm/test_generate_multiple_loras.py | 5 ++-- tests/entrypoints/llm/test_guided_generate.py | 5 ++-- tests/entrypoints/llm/test_lazy_outlines.py | 9 +++++-- .../offline_mode/test_offline_mode.py | 5 ++-- tests/lora/conftest.py | 26 +++++-------------- tests/lora/test_baichuan.py | 9 +++---- tests/lora/test_llama.py | 9 +++---- tests/lora/test_quant_model.py | 9 +++---- tests/metrics/test_metrics.py | 5 ++-- .../vision_language/test_intern_vit.py | 7 ++--- .../test_disable_sliding_window.py | 6 ++--- tests/spec_decode/e2e/conftest.py | 4 +-- tests/tensorizer_loader/conftest.py | 13 ++-------- vllm/distributed/parallel_state.py | 16 +++++++++++- 20 files changed, 84 insertions(+), 105 deletions(-) diff --git a/examples/offline_inference_with_prefix.py b/examples/offline_inference_with_prefix.py index f8a9727ea192f..67b755a155966 100644 --- a/examples/offline_inference_with_prefix.py +++ b/examples/offline_inference_with_prefix.py @@ -1,4 +1,5 @@ from vllm import LLM, SamplingParams +from vllm.distributed import cleanup_dist_env_and_memory # NOTE: This is just a running example. For benchmarking purpose, # please see benchmarks/benchmark_prefix_caching.py @@ -28,14 +29,9 @@ # Create a sampling params object. sampling_params = SamplingParams(temperature=0.0) -# Create an LLM. -regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.3) +# Create an LLM without prefix caching as a baseline. +regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.4) -# The second LLM needs to request a higher gpu_memory_utilization because -# the first LLM has already allocated a full 30% of the gpu memory. -prefix_cached_llm = LLM(model="facebook/opt-125m", - enable_prefix_caching=True, - gpu_memory_utilization=0.6) print("Results without `enable_prefix_caching`") # Generate texts from the prompts. The output is a list of RequestOutput objects @@ -52,6 +48,15 @@ print("-" * 80) +# Destroy the LLM object and free up the GPU memory. +del regular_llm +cleanup_dist_env_and_memory() + +# Create an LLM with prefix caching enabled. +prefix_cached_llm = LLM(model="facebook/opt-125m", + enable_prefix_caching=True, + gpu_memory_utilization=0.4) + # Warmup so that the shared prompt's KV cache is computed. prefix_cached_llm.generate(generating_prompts[0], sampling_params) diff --git a/tests/async_engine/test_async_llm_engine.py b/tests/async_engine/test_async_llm_engine.py index 1903a7582dc89..8a04693ba676d 100644 --- a/tests/async_engine/test_async_llm_engine.py +++ b/tests/async_engine/test_async_llm_engine.py @@ -12,11 +12,11 @@ from vllm import SamplingParams from vllm.config import ParallelConfig +from vllm.distributed import cleanup_dist_env_and_memory from vllm.engine.async_llm_engine import AsyncEngineArgs, AsyncLLMEngine from vllm.outputs import RequestOutput as RealRequestOutput from vllm.sampling_params import RequestOutputKind -from ..conftest import cleanup from ..utils import wait_for_gpu_memory_to_clear @@ -157,7 +157,7 @@ async def async_engine(): engine.shutdown_background_loop() del engine await asyncio.sleep(0.1) - cleanup() + cleanup_dist_env_and_memory() @pytest.fixture() diff --git a/tests/conftest.py b/tests/conftest.py index ea7156c60e334..4c9180415da32 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,3 @@ -import contextlib -import gc import json import os import sys @@ -27,8 +25,7 @@ from vllm.assets.video import VideoAsset from vllm.config import TaskOption, TokenizerPoolConfig from vllm.connections import global_http_connection -from vllm.distributed import (destroy_distributed_environment, - destroy_model_parallel, +from vllm.distributed import (cleanup_dist_env_and_memory, init_distributed_environment, initialize_model_parallel) from vllm.inputs import (ExplicitEncoderDecoderPrompt, TextPrompt, @@ -140,17 +137,7 @@ def dist_init(): ) initialize_model_parallel(1, 1) yield - cleanup() - - -def cleanup(): - destroy_model_parallel() - destroy_distributed_environment() - with contextlib.suppress(AssertionError): - torch.distributed.destroy_process_group() - gc.collect() - if not is_cpu(): - torch.cuda.empty_cache() + cleanup_dist_env_and_memory() @pytest.fixture() @@ -167,7 +154,7 @@ def should_do_global_cleanup_after_test(request) -> bool: def cleanup_fixture(should_do_global_cleanup_after_test: bool): yield if should_do_global_cleanup_after_test: - cleanup() + cleanup_dist_env_and_memory() @pytest.fixture(autouse=True) @@ -606,7 +593,7 @@ def __enter__(self): def __exit__(self, exc_type, exc_value, traceback): del self.model - cleanup() + cleanup_dist_env_and_memory() @pytest.fixture(scope="session") @@ -861,7 +848,7 @@ def __enter__(self): def __exit__(self, exc_type, exc_value, traceback): del self.model - cleanup() + cleanup_dist_env_and_memory() @pytest.fixture(scope="session") diff --git a/tests/core/block/e2e/conftest.py b/tests/core/block/e2e/conftest.py index e870597b7a011..70577ec052a2c 100644 --- a/tests/core/block/e2e/conftest.py +++ b/tests/core/block/e2e/conftest.py @@ -3,10 +3,9 @@ import pytest from vllm import LLM +from vllm.distributed import cleanup_dist_env_and_memory from vllm.model_executor.utils import set_random_seed -from ....conftest import cleanup - @pytest.fixture def baseline_llm_generator(common_llm_kwargs, per_test_common_llm_kwargs, @@ -37,7 +36,7 @@ def generator_inner(): yield llm del llm - cleanup() + cleanup_dist_env_and_memory() for llm in generator_inner(): yield llm diff --git a/tests/entrypoints/llm/test_encode.py b/tests/entrypoints/llm/test_encode.py index 1885f2e168d80..4c9f796e5ed71 100644 --- a/tests/entrypoints/llm/test_encode.py +++ b/tests/entrypoints/llm/test_encode.py @@ -4,8 +4,7 @@ import pytest from vllm import LLM, EmbeddingRequestOutput, PoolingParams - -from ...conftest import cleanup +from vllm.distributed import cleanup_dist_env_and_memory MODEL_NAME = "intfloat/e5-mistral-7b-instruct" @@ -41,7 +40,7 @@ def llm(): del llm - cleanup() + cleanup_dist_env_and_memory() def assert_outputs_equal(o1: List[EmbeddingRequestOutput], diff --git a/tests/entrypoints/llm/test_generate.py b/tests/entrypoints/llm/test_generate.py index 5e32d7baabe4b..7d2b377752725 100644 --- a/tests/entrypoints/llm/test_generate.py +++ b/tests/entrypoints/llm/test_generate.py @@ -4,8 +4,7 @@ import pytest from vllm import LLM, RequestOutput, SamplingParams - -from ...conftest import cleanup +from vllm.distributed import cleanup_dist_env_and_memory MODEL_NAME = "facebook/opt-125m" @@ -39,7 +38,7 @@ def llm(): del llm - cleanup() + cleanup_dist_env_and_memory() def assert_outputs_equal(o1: List[RequestOutput], o2: List[RequestOutput]): diff --git a/tests/entrypoints/llm/test_generate_multiple_loras.py b/tests/entrypoints/llm/test_generate_multiple_loras.py index 9f5727ecd0406..eb2113692e7b4 100644 --- a/tests/entrypoints/llm/test_generate_multiple_loras.py +++ b/tests/entrypoints/llm/test_generate_multiple_loras.py @@ -5,10 +5,9 @@ from huggingface_hub import snapshot_download from vllm import LLM +from vllm.distributed import cleanup_dist_env_and_memory from vllm.lora.request import LoRARequest -from ...conftest import cleanup - MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta" PROMPTS = [ @@ -39,7 +38,7 @@ def llm(): del llm - cleanup() + cleanup_dist_env_and_memory() @pytest.fixture(scope="module") diff --git a/tests/entrypoints/llm/test_guided_generate.py b/tests/entrypoints/llm/test_guided_generate.py index 2841dfc6bd9c2..67c79415f322a 100644 --- a/tests/entrypoints/llm/test_guided_generate.py +++ b/tests/entrypoints/llm/test_guided_generate.py @@ -5,12 +5,11 @@ import jsonschema import pytest +from vllm.distributed import cleanup_dist_env_and_memory from vllm.entrypoints.llm import LLM from vllm.outputs import RequestOutput from vllm.sampling_params import GuidedDecodingParams, SamplingParams -from ...conftest import cleanup - MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta" @@ -23,7 +22,7 @@ def llm(): with llm.deprecate_legacy_api(): yield weakref.proxy(llm) del llm - cleanup() + cleanup_dist_env_and_memory() @pytest.mark.skip_global_cleanup diff --git a/tests/entrypoints/llm/test_lazy_outlines.py b/tests/entrypoints/llm/test_lazy_outlines.py index 010969ad4750d..cbfb0cc32c1ce 100644 --- a/tests/entrypoints/llm/test_lazy_outlines.py +++ b/tests/entrypoints/llm/test_lazy_outlines.py @@ -1,6 +1,7 @@ import sys from vllm import LLM, SamplingParams +from vllm.distributed import cleanup_dist_env_and_memory def test_lazy_outlines(sample_regex): @@ -14,6 +15,7 @@ def test_lazy_outlines(sample_regex): ] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) + # Create an LLM without guided decoding as a baseline. llm = LLM(model="facebook/opt-125m", enforce_eager=True, gpu_memory_utilization=0.3) @@ -26,8 +28,11 @@ def test_lazy_outlines(sample_regex): # make sure outlines is not imported assert 'outlines' not in sys.modules - # The second LLM needs to request a higher gpu_memory_utilization because - # the first LLM has already allocated a full 30% of the gpu memory. + # Destroy the LLM object and free up the GPU memory. + del llm + cleanup_dist_env_and_memory() + + # Create an LLM with guided decoding enabled. llm = LLM(model="facebook/opt-125m", enforce_eager=True, guided_decoding_backend="lm-format-enforcer", diff --git a/tests/entrypoints/offline_mode/test_offline_mode.py b/tests/entrypoints/offline_mode/test_offline_mode.py index fe40af271c1cd..c89d315b664af 100644 --- a/tests/entrypoints/offline_mode/test_offline_mode.py +++ b/tests/entrypoints/offline_mode/test_offline_mode.py @@ -6,8 +6,7 @@ import pytest from vllm import LLM - -from ...conftest import cleanup +from vllm.distributed import cleanup_dist_env_and_memory MODEL_NAME = "facebook/opt-125m" @@ -27,7 +26,7 @@ def llm(): del llm - cleanup() + cleanup_dist_env_and_memory() @pytest.mark.skip_global_cleanup diff --git a/tests/lora/conftest.py b/tests/lora/conftest.py index 405c0d0efad65..e40f0dd74602e 100644 --- a/tests/lora/conftest.py +++ b/tests/lora/conftest.py @@ -1,20 +1,16 @@ -import contextlib -import gc import tempfile from collections import OrderedDict from typing import Dict, List, TypedDict from unittest.mock import MagicMock, patch import pytest -import ray import torch import torch.nn as nn from huggingface_hub import snapshot_download import vllm from vllm.config import LoRAConfig -from vllm.distributed import (destroy_distributed_environment, - destroy_model_parallel, +from vllm.distributed import (cleanup_dist_env_and_memory, init_distributed_environment, initialize_model_parallel) from vllm.model_executor.layers.linear import (ColumnParallelLinear, @@ -48,16 +44,6 @@ class ContextInfo(TypedDict): }] -def cleanup(): - destroy_model_parallel() - destroy_distributed_environment() - with contextlib.suppress(AssertionError): - torch.distributed.destroy_process_group() - gc.collect() - torch.cuda.empty_cache() - ray.shutdown() - - @pytest.fixture() def should_do_global_cleanup_after_test(request) -> bool: """Allow subdirectories to skip global cleanup by overriding this fixture. @@ -72,7 +58,7 @@ def should_do_global_cleanup_after_test(request) -> bool: def cleanup_fixture(should_do_global_cleanup_after_test: bool): yield if should_do_global_cleanup_after_test: - cleanup() + cleanup_dist_env_and_memory(shutdown_ray=True) @pytest.fixture @@ -87,7 +73,7 @@ def dist_init(): ) initialize_model_parallel(1, 1) yield - cleanup() + cleanup_dist_env_and_memory(shutdown_ray=True) @pytest.fixture @@ -238,7 +224,7 @@ def long_context_lora_files_32k(): def long_context_infos(long_context_lora_files_16k_1, long_context_lora_files_16k_2, long_context_lora_files_32k): - cleanup() + cleanup_dist_env_and_memory(shutdown_ray=True) infos: Dict[int, ContextInfo] = {} for lora_checkpoint_info in LONG_LORA_INFOS: lora_id = lora_checkpoint_info["lora_id"] @@ -259,7 +245,7 @@ def long_context_infos(long_context_lora_files_16k_1, @pytest.fixture def llama_2_7b_engine_extra_embeddings(): - cleanup() + cleanup_dist_env_and_memory(shutdown_ray=True) get_model_old = get_model def get_model_patched(*, model_config, device_config, **kwargs): @@ -272,7 +258,7 @@ def get_model_patched(*, model_config, device_config, **kwargs): engine = vllm.LLM("meta-llama/Llama-2-7b-hf", enable_lora=False) yield engine.llm_engine del engine - cleanup() + cleanup_dist_env_and_memory(shutdown_ray=True) @pytest.fixture diff --git a/tests/lora/test_baichuan.py b/tests/lora/test_baichuan.py index cbc3668997817..0ba2ce3617b67 100644 --- a/tests/lora/test_baichuan.py +++ b/tests/lora/test_baichuan.py @@ -3,10 +3,9 @@ import pytest import vllm +from vllm.distributed import cleanup_dist_env_and_memory from vllm.lora.request import LoRARequest -from .conftest import cleanup - MODEL_PATH = "baichuan-inc/Baichuan-7B" PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:""" # noqa: E501 @@ -80,7 +79,7 @@ def test_baichuan_tensor_parallel_equality(baichuan_lora_files, output_tp1 = do_sample(llm_tp1, baichuan_lora_files, lora_id=1) del llm_tp1 - cleanup() + cleanup_dist_env_and_memory() llm_tp2 = vllm.LLM(MODEL_PATH, enable_lora=True, @@ -93,7 +92,7 @@ def test_baichuan_tensor_parallel_equality(baichuan_lora_files, output_tp2 = do_sample(llm_tp2, baichuan_lora_files, lora_id=2) del llm_tp2 - cleanup() + cleanup_dist_env_and_memory() assert output_tp1 == output_tp2 @@ -108,6 +107,6 @@ def test_baichuan_tensor_parallel_equality(baichuan_lora_files, output_tp4 = do_sample(llm_tp4, baichuan_lora_files, lora_id=2) del llm_tp4 - cleanup() + cleanup_dist_env_and_memory() assert output_tp1 == output_tp4 diff --git a/tests/lora/test_llama.py b/tests/lora/test_llama.py index ad8490353998f..e2a4f1ed0496a 100644 --- a/tests/lora/test_llama.py +++ b/tests/lora/test_llama.py @@ -4,10 +4,9 @@ import ray import vllm +from vllm.distributed import cleanup_dist_env_and_memory from vllm.lora.request import LoRARequest -from .conftest import cleanup - MODEL_PATH = "meta-llama/Llama-2-7b-hf" @@ -93,7 +92,7 @@ def test_llama_tensor_parallel_equality(sql_lora_files, num_gpus_available): output_tp1 = do_sample(llm_tp1, sql_lora_files, lora_id=1) del llm_tp1 - cleanup() + cleanup_dist_env_and_memory() llm_tp2 = vllm.LLM(MODEL_PATH, enable_lora=True, @@ -103,7 +102,7 @@ def test_llama_tensor_parallel_equality(sql_lora_files, num_gpus_available): output_tp2 = do_sample(llm_tp2, sql_lora_files, lora_id=1) del llm_tp2 - cleanup() + cleanup_dist_env_and_memory() assert output_tp1 == output_tp2 @@ -115,7 +114,7 @@ def test_llama_tensor_parallel_equality(sql_lora_files, num_gpus_available): output_tp4 = do_sample(llm_tp4, sql_lora_files, lora_id=1) del llm_tp4 - cleanup() + cleanup_dist_env_and_memory() assert output_tp1 == output_tp4 diff --git a/tests/lora/test_quant_model.py b/tests/lora/test_quant_model.py index 5636c96435024..d004c65929418 100644 --- a/tests/lora/test_quant_model.py +++ b/tests/lora/test_quant_model.py @@ -6,11 +6,10 @@ import pytest import vllm +from vllm.distributed import cleanup_dist_env_and_memory from vllm.lora.request import LoRARequest from vllm.utils import is_hip -from .conftest import cleanup - @dataclass class ModelWithQuantization: @@ -160,7 +159,7 @@ def expect_match(output, expected_output): print("removing lora") del llm - cleanup() + cleanup_dist_env_and_memory() @pytest.mark.parametrize("model", MODELS) @@ -181,7 +180,7 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available, output_tp1 = do_sample(llm_tp1, tinyllama_lora_files, lora_id=1) del llm_tp1 - cleanup() + cleanup_dist_env_and_memory() llm_tp2 = vllm.LLM( model=model.model_path, @@ -194,6 +193,6 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available, output_tp2 = do_sample(llm_tp2, tinyllama_lora_files, lora_id=1) del llm_tp2 - cleanup() + cleanup_dist_env_and_memory() assert output_tp1 == output_tp2 diff --git a/tests/metrics/test_metrics.py b/tests/metrics/test_metrics.py index 8798ff078843a..92e6086e312f7 100644 --- a/tests/metrics/test_metrics.py +++ b/tests/metrics/test_metrics.py @@ -6,13 +6,12 @@ from prometheus_client import REGISTRY from vllm import EngineArgs, LLMEngine +from vllm.distributed import cleanup_dist_env_and_memory from vllm.engine.arg_utils import AsyncEngineArgs from vllm.engine.async_llm_engine import AsyncLLMEngine from vllm.engine.metrics import RayPrometheusStatLogger from vllm.sampling_params import SamplingParams -from ..conftest import cleanup - MODELS = [ "facebook/opt-125m", ] @@ -307,7 +306,7 @@ def test_metric_spec_decode_interval( finally: del engine - cleanup() + cleanup_dist_env_and_memory() def assert_metrics(engine: LLMEngine, disable_log_stats: bool, diff --git a/tests/models/decoder_only/vision_language/test_intern_vit.py b/tests/models/decoder_only/vision_language/test_intern_vit.py index 3c3b95b38baac..98f313eb9b9af 100644 --- a/tests/models/decoder_only/vision_language/test_intern_vit.py +++ b/tests/models/decoder_only/vision_language/test_intern_vit.py @@ -6,7 +6,7 @@ from huggingface_hub import snapshot_download from transformers import AutoConfig, AutoModel, CLIPImageProcessor -from ....conftest import _ImageAssets, cleanup +from ....conftest import _ImageAssets # we use snapshot_download to prevent conflicts between # dynamic_module and trust_remote_code for hf_runner @@ -45,12 +45,13 @@ def run_intern_vit_test( for pixel_value in pixel_values ] + from vllm.distributed import cleanup_dist_env_and_memory from vllm.model_executor.models.intern_vit import InternVisionModel vllm_model = InternVisionModel(config) vllm_model.load_weights(hf_model.state_dict().items()) del hf_model - cleanup() + cleanup_dist_env_and_memory() vllm_model = vllm_model.to("cuda", dtype) vllm_outputs_per_image = [ @@ -58,7 +59,7 @@ def run_intern_vit_test( for pixel_value in pixel_values ] del vllm_model - cleanup() + cleanup_dist_env_and_memory() cos_similar = nn.CosineSimilarity(dim=-1) for vllm_output, hf_output in zip(vllm_outputs_per_image, diff --git a/tests/prefix_caching/test_disable_sliding_window.py b/tests/prefix_caching/test_disable_sliding_window.py index eeac6ab43c05f..5a28943b7ecbc 100644 --- a/tests/prefix_caching/test_disable_sliding_window.py +++ b/tests/prefix_caching/test_disable_sliding_window.py @@ -4,8 +4,8 @@ """ import pytest -from tests.conftest import cleanup from vllm import LLM +from vllm.distributed import cleanup_dist_env_and_memory MODEL_LEN_LEN = [ # Example models with sliding window. @@ -31,7 +31,7 @@ def test_disable_sliding_window(model_len_len, ): model_config.max_model_len) del vllm_disabled_model - cleanup() + cleanup_dist_env_and_memory() vllm_enabled_model = LLM(model, disable_sliding_window=False) vllm_enabled_model.generate("Hi my name is") @@ -41,4 +41,4 @@ def test_disable_sliding_window(model_len_len, ): model_config.max_model_len) del vllm_enabled_model - cleanup() + cleanup_dist_env_and_memory() diff --git a/tests/spec_decode/e2e/conftest.py b/tests/spec_decode/e2e/conftest.py index b450ef97c89d4..b9cb3858c0068 100644 --- a/tests/spec_decode/e2e/conftest.py +++ b/tests/spec_decode/e2e/conftest.py @@ -4,10 +4,10 @@ import pytest from vllm import LLM, SamplingParams +from vllm.distributed import cleanup_dist_env_and_memory from vllm.model_executor.utils import set_random_seed from vllm.sequence import PromptLogprobs, SampleLogprobs -from ...conftest import cleanup from ...models.utils import (TokensTextLogprobs, TokensTextLogprobsPromptLogprobs, check_logprobs_close, check_outputs_equal) @@ -44,7 +44,7 @@ def generate(): yield llm del llm - cleanup() + cleanup_dist_env_and_memory() return generate diff --git a/tests/tensorizer_loader/conftest.py b/tests/tensorizer_loader/conftest.py index 07b9c6b3c6be6..2a45653622448 100644 --- a/tests/tensorizer_loader/conftest.py +++ b/tests/tensorizer_loader/conftest.py @@ -1,27 +1,18 @@ -import contextlib import functools import gc from typing import Callable, TypeVar import pytest -import ray import torch from typing_extensions import ParamSpec -from vllm.distributed import (destroy_distributed_environment, - destroy_model_parallel) +from vllm.distributed import cleanup_dist_env_and_memory from vllm.model_executor.model_loader.tensorizer import TensorizerConfig @pytest.fixture(autouse=True) def cleanup(): - destroy_model_parallel() - destroy_distributed_environment() - with contextlib.suppress(AssertionError): - torch.distributed.destroy_process_group() - ray.shutdown() - gc.collect() - torch.cuda.empty_cache() + cleanup_dist_env_and_memory(shutdown_ray=True) _P = ParamSpec("_P") diff --git a/vllm/distributed/parallel_state.py b/vllm/distributed/parallel_state.py index 6e1970bfed98a..8d4b673d2e6e4 100644 --- a/vllm/distributed/parallel_state.py +++ b/vllm/distributed/parallel_state.py @@ -20,6 +20,7 @@ steps. """ import contextlib +import gc import pickle import weakref from collections import namedtuple @@ -36,7 +37,7 @@ import vllm.envs as envs from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import supports_custom_op +from vllm.utils import is_cpu, supports_custom_op @dataclass @@ -1129,6 +1130,19 @@ def destroy_distributed_environment(): torch.distributed.destroy_process_group() +def cleanup_dist_env_and_memory(shutdown_ray: bool = False): + destroy_model_parallel() + destroy_distributed_environment() + with contextlib.suppress(AssertionError): + torch.distributed.destroy_process_group() + if shutdown_ray: + import ray # Lazy import Ray + ray.shutdown() + gc.collect() + if not is_cpu(): + torch.cuda.empty_cache() + + def in_the_same_node_as(pg: ProcessGroup, source_rank: int = 0) -> List[bool]: """ This is a collective operation that returns if each rank is in the same node From 0c9a5258f905ff3b03019f9134914ab90dbdac01 Mon Sep 17 00:00:00 2001 From: Thomas Parnell Date: Sat, 19 Oct 2024 02:55:48 +0200 Subject: [PATCH 008/222] [Kernel] Add env variable to force flashinfer backend to enable tensor cores (#9497) Signed-off-by: Thomas Parnell Co-authored-by: Chih-Chieh Yang Co-authored-by: Cody Yu --- vllm/attention/backends/flashinfer.py | 7 +++++-- vllm/envs.py | 6 ++++++ 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/vllm/attention/backends/flashinfer.py b/vllm/attention/backends/flashinfer.py index dd9a0fb9d94df..1dd2a21fdb51a 100644 --- a/vllm/attention/backends/flashinfer.py +++ b/vllm/attention/backends/flashinfer.py @@ -17,6 +17,7 @@ import torch +import vllm.envs as envs from vllm import _custom_ops as ops from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl, AttentionMetadata, @@ -124,7 +125,8 @@ def _get_decode_wrapper(self): self.runner.parallel_config)) num_kv_heads = self.runner.model_config.get_num_kv_heads( self.runner.parallel_config) - use_tensor_cores = num_qo_heads // num_kv_heads > 4 + use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or ( + num_qo_heads // num_kv_heads > 4) self._decode_wrapper = BatchDecodeWithPagedKVCacheWrapper( self._get_workspace_buffer(), "NHD", @@ -183,7 +185,8 @@ def graph_capture_get_metadata_for_batch( self.runner.parallel_config)) num_kv_heads = self.runner.model_config.get_num_kv_heads( self.runner.parallel_config) - use_tensor_cores = num_qo_heads // num_kv_heads > 4 + use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or ( + num_qo_heads // num_kv_heads > 4) self._graph_decode_wrapper = \ CUDAGraphBatchDecodeWithPagedKVCacheWrapper( self._graph_decode_workspace_buffer, _indptr_buffer, diff --git a/vllm/envs.py b/vllm/envs.py index 2396e87e20c39..385db82d89249 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -32,6 +32,7 @@ VLLM_ATTENTION_BACKEND: Optional[str] = None VLLM_USE_FLASHINFER_SAMPLER: bool = False VLLM_USE_FLASHINFER_REJECTION_SAMPLER: bool = False + VLLM_FLASHINFER_FORCE_TENSOR_CORES: bool = False VLLM_PP_LAYER_PARTITION: Optional[str] = None VLLM_CPU_KVCACHE_SPACE: int = 0 VLLM_CPU_OMP_THREADS_BIND: str = "" @@ -286,6 +287,11 @@ def get_default_config_root(): "VLLM_USE_FLASHINFER_SAMPLER": lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_SAMPLER", "0"))), + # If set, vllm will force flashinfer to use tensor cores; + # otherwise will use heuristic based on model architecture. + "VLLM_FLASHINFER_FORCE_TENSOR_CORES": + lambda: bool(int(os.getenv("VLLM_FLASHINFER_FORCE_TENSOR_CORES", "0"))), + # Pipeline stage partition strategy "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None), From 337ed76671812c4599560f73b8fa511927814e37 Mon Sep 17 00:00:00 2001 From: sasha0552 Date: Sat, 19 Oct 2024 01:12:32 +0000 Subject: [PATCH 009/222] [Bugfix] Fix offline mode when using `mistral_common` (#9457) --- .../offline_mode/test_offline_mode.py | 56 ++++++++++--------- vllm/transformers_utils/tokenizers/mistral.py | 34 ++++++++++- 2 files changed, 62 insertions(+), 28 deletions(-) diff --git a/tests/entrypoints/offline_mode/test_offline_mode.py b/tests/entrypoints/offline_mode/test_offline_mode.py index c89d315b664af..65699e609e4a8 100644 --- a/tests/entrypoints/offline_mode/test_offline_mode.py +++ b/tests/entrypoints/offline_mode/test_offline_mode.py @@ -1,50 +1,56 @@ """Tests for HF_HUB_OFFLINE mode""" import importlib import sys -import weakref import pytest from vllm import LLM from vllm.distributed import cleanup_dist_env_and_memory -MODEL_NAME = "facebook/opt-125m" +MODEL_CONFIGS = [ + { + "model": "facebook/opt-125m", + "enforce_eager": True, + "gpu_memory_utilization": 0.20, + "max_model_len": 64, + "max_num_batched_tokens": 64, + "max_num_seqs": 64, + "tensor_parallel_size": 1, + }, + { + "model": "mistralai/Mistral-7B-Instruct-v0.1", + "enforce_eager": True, + "gpu_memory_utilization": 0.95, + "max_model_len": 64, + "max_num_batched_tokens": 64, + "max_num_seqs": 64, + "tensor_parallel_size": 1, + "tokenizer_mode": "mistral", + }, +] @pytest.fixture(scope="module") -def llm(): - # pytest caches the fixture so we use weakref.proxy to - # enable garbage collection - llm = LLM(model=MODEL_NAME, - max_num_batched_tokens=4096, - tensor_parallel_size=1, - gpu_memory_utilization=0.10, - enforce_eager=True) +def cache_models(): + # Cache model files first + for model_config in MODEL_CONFIGS: + LLM(**model_config) + cleanup_dist_env_and_memory() - with llm.deprecate_legacy_api(): - yield weakref.proxy(llm) - - del llm - - cleanup_dist_env_and_memory() + yield @pytest.mark.skip_global_cleanup -def test_offline_mode(llm: LLM, monkeypatch): - # we use the llm fixture to ensure the model files are in-cache - del llm - +@pytest.mark.usefixtures("cache_models") +def test_offline_mode(monkeypatch): # Set HF to offline mode and ensure we can still construct an LLM try: monkeypatch.setenv("HF_HUB_OFFLINE", "1") # Need to re-import huggingface_hub and friends to setup offline mode _re_import_modules() # Cached model files should be used in offline mode - LLM(model=MODEL_NAME, - max_num_batched_tokens=4096, - tensor_parallel_size=1, - gpu_memory_utilization=0.20, - enforce_eager=True) + for model_config in MODEL_CONFIGS: + LLM(**model_config) finally: # Reset the environment after the test # NB: Assuming tests are run in online mode diff --git a/vllm/transformers_utils/tokenizers/mistral.py b/vllm/transformers_utils/tokenizers/mistral.py index 86e226ff9973a..23ea657ffb0a9 100644 --- a/vllm/transformers_utils/tokenizers/mistral.py +++ b/vllm/transformers_utils/tokenizers/mistral.py @@ -4,6 +4,7 @@ from pathlib import Path from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast +import huggingface_hub from huggingface_hub import HfApi, hf_hub_download from mistral_common.protocol.instruct.request import ChatCompletionRequest # yapf: disable @@ -24,6 +25,26 @@ class Encoding: input_ids: List[int] +def list_local_repo_files(repo_id: str, revision: Optional[str]) -> List[str]: + repo_cache = os.path.join( + huggingface_hub.constants.HF_HUB_CACHE, + huggingface_hub.constants.REPO_ID_SEPARATOR.join( + ["models", *repo_id.split("/")])) + + if revision is None: + revision_file = os.path.join(repo_cache, "refs", "main") + if os.path.isfile(revision_file): + with open(revision_file) as file: + revision = file.read() + + if revision: + revision_dir = os.path.join(repo_cache, "snapshots", revision) + if os.path.isdir(revision_dir): + return os.listdir(revision_dir) + + return [] + + def find_tokenizer_file(files: List[str]): file_pattern = re.compile(r"^tokenizer\.model\.v.*$|^tekken\.json$") @@ -90,9 +111,16 @@ def from_pretrained(cls, @staticmethod def _download_mistral_tokenizer_from_hf(tokenizer_name: str, revision: Optional[str]) -> str: - api = HfApi() - repo_info = api.model_info(tokenizer_name) - files = [s.rfilename for s in repo_info.siblings] + try: + hf_api = HfApi() + files = hf_api.list_repo_files(repo_id=tokenizer_name, + revision=revision) + except ConnectionError as exc: + files = list_local_repo_files(repo_id=tokenizer_name, + revision=revision) + + if len(files) == 0: + raise exc filename = find_tokenizer_file(files) From 380e18639f315a696bd5dcc93a24f250573b95a9 Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Fri, 18 Oct 2024 20:25:19 -0500 Subject: [PATCH 010/222] :bug: fix torch memory profiling (#9516) Signed-off-by: Joe Runde --- tests/quantization/test_bitsandbytes.py | 3 +-- tests/worker/test_profile.py | 11 ++++++----- vllm/worker/worker.py | 11 +++++++---- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/tests/quantization/test_bitsandbytes.py b/tests/quantization/test_bitsandbytes.py index f2acf0d70afef..0f01f5f819ea4 100644 --- a/tests/quantization/test_bitsandbytes.py +++ b/tests/quantization/test_bitsandbytes.py @@ -107,8 +107,7 @@ def validate_generated_texts(hf_runner, quantization='bitsandbytes', load_format='bitsandbytes', tensor_parallel_size=vllm_tp_size, - enforce_eager=False, - gpu_memory_utilization=0.8) as llm: + enforce_eager=False) as llm: vllm_outputs = llm.generate_greedy(prompts, 8) vllm_logs = log_generated_texts(prompts, vllm_outputs, "VllmRunner") diff --git a/tests/worker/test_profile.py b/tests/worker/test_profile.py index 7e9138dc8d779..acd2ed6836365 100644 --- a/tests/worker/test_profile.py +++ b/tests/worker/test_profile.py @@ -54,16 +54,17 @@ def mock_mem_info(): gpu_blocks, _ = worker.determine_num_available_blocks() # Peak vram usage by torch should be 0.7077 GiB - # Non-torch allocations should be 0.0079 GiB + # No memory should be allocated outside of torch # 9.0 GiB should be the utilization target - # 8.2843 GiB should be available for the KV cache + # 8.2923 GiB should be available for the KV cache block_size = CacheEngine.get_cache_block_size( engine_config.cache_config, engine_config.model_config, engine_config.parallel_config) - expected_blocks = (8.2843 * 1024**3) // block_size + expected_blocks = (8.2923 * 1024**3) // block_size # Check within a small tolerance for portability # Hardware, kernel, or dependency changes could all affect memory - # utilization - assert abs(gpu_blocks - expected_blocks) < 5 + # utilization. + # A 10 block tolerance here should be about 6MB of wiggle room. + assert abs(gpu_blocks - expected_blocks) < 10 diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index 018ab5b828786..fd30962e5d6bb 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -232,10 +232,11 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: # gpu outside of `torch`. NCCL operations, for example, can use a few # GB during a forward pass torch.cuda.empty_cache() - # After emptying the torch cache, any other increase in gpu ram should - # be from non-torch allocations. - non_torch_allocations = free_memory_pre_profile - \ - torch.cuda.mem_get_info()[0] + torch_allocated_bytes = torch.cuda.memory_stats( + )["allocated_bytes.all.current"] + total_allocated_bytes = torch.cuda.mem_get_info( + )[1] - torch.cuda.mem_get_info()[0] + non_torch_allocations = total_allocated_bytes - torch_allocated_bytes if non_torch_allocations > 0: peak_memory += non_torch_allocations @@ -259,10 +260,12 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: logger.info( "Memory profiling results: total_gpu_memory=%.2fGiB" " initial_memory_usage=%.2fGiB peak_torch_memory=%.2fGiB" + " memory_usage_post_profile=%.2fGib" " non_torch_memory=%.2fGiB kv_cache_size=%.2fGiB" " gpu_memory_utilization=%.2f", total_gpu_memory / (1024**3), (total_gpu_memory - free_memory_pre_profile) / (1024**3), (peak_memory - non_torch_allocations) / (1024**3), + total_allocated_bytes / (1024**3), non_torch_allocations / (1024**3), available_kv_cache_memory / (1024**3), self.cache_config.gpu_memory_utilization) From 1325872ec8c97d797c18f490bdb6be7f4def5aa8 Mon Sep 17 00:00:00 2001 From: Nick Hill Date: Sat, 19 Oct 2024 04:21:01 +0100 Subject: [PATCH 011/222] [Frontend] Avoid creating guided decoding LogitsProcessor unnecessarily (#9521) --- vllm/sampling_params.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/vllm/sampling_params.py b/vllm/sampling_params.py index 4f2ae75e65f3a..9993cec13d649 100644 --- a/vllm/sampling_params.py +++ b/vllm/sampling_params.py @@ -49,14 +49,17 @@ class GuidedDecodingParams: @staticmethod def from_optional( - json: Optional[Union[Dict, BaseModel, str]], + json: Optional[Union[Dict, BaseModel, str]] = None, regex: Optional[str] = None, choice: Optional[List[str]] = None, grammar: Optional[str] = None, json_object: Optional[bool] = None, backend: Optional[str] = None, whitespace_pattern: Optional[str] = None, - ) -> "GuidedDecodingParams": + ) -> Optional["GuidedDecodingParams"]: + if all(arg is None + for arg in (json, regex, choice, grammar, json_object)): + return None # Extract json schemas from pydantic models if isinstance(json, (BaseModel, type(BaseModel))): json = json.model_json_schema() From 82c25151ec54f723de8589ccc3ad24d4a1817e90 Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Fri, 18 Oct 2024 22:26:36 -0500 Subject: [PATCH 012/222] [Doc] update gpu-memory-utilization flag docs (#9507) Signed-off-by: Joe Runde --- vllm/engine/arg_utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 480d3709224ba..56582ab618797 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -428,7 +428,11 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: help='The fraction of GPU memory to be used for the model ' 'executor, which can range from 0 to 1. For example, a value of ' '0.5 would imply 50%% GPU memory utilization. If unspecified, ' - 'will use the default value of 0.9.') + 'will use the default value of 0.9. This is a global gpu memory ' + 'utilization limit, for example if 50%% of the gpu memory is ' + 'already used before vLLM starts and --gpu-memory-utilization is ' + 'set to 0.9, then only 40%% of the gpu memory will be allocated ' + 'to the model executor.') parser.add_argument( '--num-gpu-blocks-override', type=int, From dfd951ed9b9eb4af2452764edd808599b5e8901e Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Sat, 19 Oct 2024 01:42:20 -0400 Subject: [PATCH 013/222] [CI/Build] Add error matching for ruff output (#9513) Signed-off-by: Russell Bryant --- .github/workflows/matchers/ruff.json | 17 +++++++++++++++++ .github/workflows/ruff.yml | 3 ++- 2 files changed, 19 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/matchers/ruff.json diff --git a/.github/workflows/matchers/ruff.json b/.github/workflows/matchers/ruff.json new file mode 100644 index 0000000000000..f6d4479ee1996 --- /dev/null +++ b/.github/workflows/matchers/ruff.json @@ -0,0 +1,17 @@ +{ + "problemMatcher": [ + { + "owner": "ruff", + "pattern": [ + { + "regexp": "^(.+?):(\\d+):(\\d+): (\\w+): (.+)$", + "file": 1, + "line": 2, + "column": 3, + "code": 4, + "message": 5 + } + ] + } + ] + } diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml index b88907e4ab45b..9cc8a9e914474 100644 --- a/.github/workflows/ruff.yml +++ b/.github/workflows/ruff.yml @@ -28,7 +28,8 @@ jobs: pip install -r requirements-lint.txt - name: Analysing the code with ruff run: | - ruff check . + echo "::add-matcher::.github/workflows/matchers/ruff.json" + ruff check --output-format github . - name: Spelling check with codespell run: | codespell --toml pyproject.toml From 85dc92fc98298b83e735752d8dbfc856f28c6e1c Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Sat, 19 Oct 2024 02:04:18 -0400 Subject: [PATCH 014/222] [CI/Build] Configure matcher for actionlint workflow (#9511) Signed-off-by: Russell Bryant --- .github/workflows/actionlint.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/actionlint.yml b/.github/workflows/actionlint.yml index 2a0e3239f58da..b80749aaa8fec 100644 --- a/.github/workflows/actionlint.yml +++ b/.github/workflows/actionlint.yml @@ -34,4 +34,5 @@ jobs: - name: "Run actionlint" run: | + echo "::add-matcher::.github/workflows/matchers/actionlint.json" tools/actionlint.sh -color From c5eea3c8ba7586e54f87b53a104cf2ac0f75069c Mon Sep 17 00:00:00 2001 From: Yue Zhang <130511128+yue-anyscale@users.noreply.github.com> Date: Fri, 18 Oct 2024 23:17:07 -0700 Subject: [PATCH 015/222] [Frontend] Support simpler image input format (#9478) --- tests/entrypoints/test_chat_utils.py | 26 +++++ vllm/entrypoints/chat_utils.py | 139 ++++++++++++++++++++++----- 2 files changed, 140 insertions(+), 25 deletions(-) diff --git a/tests/entrypoints/test_chat_utils.py b/tests/entrypoints/test_chat_utils.py index 9165a1d397137..1d8c328b73259 100644 --- a/tests/entrypoints/test_chat_utils.py +++ b/tests/entrypoints/test_chat_utils.py @@ -388,3 +388,29 @@ def test_parse_chat_messages_rejects_too_many_images_across_messages( "text": "What about these two?" }] }], phi3v_model_config, phi3v_tokenizer) + + +def test_parse_chat_messages_multiple_images_uncommon_input( + phi3v_model_config, + phi3v_tokenizer, + image_url, +): + conversation, mm_data = parse_chat_messages([{ + "role": + "user", + "content": [ + "What's in these images?", { + "image_url": image_url + }, { + "image_url": image_url + } + ] + }], phi3v_model_config, phi3v_tokenizer) + + assert conversation == [{ + "role": + "user", + "content": + "<|image_1|>\n<|image_2|>\nWhat's in these images?" + }] + _assert_mm_data_is_image_input(mm_data, 2) diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index 4b79fdacc827f..f64af27a957be 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -5,8 +5,8 @@ from collections import defaultdict from functools import lru_cache, partial from pathlib import Path -from typing import (Any, Awaitable, Dict, Generic, Iterable, List, Literal, - Mapping, Optional, Tuple, TypeVar, Union, cast) +from typing import (Any, Awaitable, Callable, Dict, Generic, Iterable, List, + Literal, Mapping, Optional, Tuple, TypeVar, Union, cast) # yapf conflicts with isort for this block # yapf: disable @@ -59,10 +59,35 @@ class CustomChatCompletionContentPartParam(TypedDict, total=False): """The type of the content part.""" +class CustomChatCompletionContentSimpleImageParam(TypedDict, total=False): + """A simpler version of the param that only accepts a plain image_url. + This is supported by OpenAI API, although it is not documented. + + Example: + { + "image_url": "https://example.com/image.jpg" + } + """ + image_url: Required[str] + + +class CustomChatCompletionContentSimpleAudioParam(TypedDict, total=False): + """A simpler version of the param that only accepts a plain audio_url. + + Example: + { + "audio_url": "https://example.com/audio.mp3" + } + """ + audio_url: Required[str] + + ChatCompletionContentPartParam: TypeAlias = Union[ OpenAIChatCompletionContentPartParam, ChatCompletionContentPartAudioParam, ChatCompletionContentPartRefusalParam, - CustomChatCompletionContentPartParam] + CustomChatCompletionContentPartParam, + CustomChatCompletionContentSimpleImageParam, + CustomChatCompletionContentSimpleAudioParam, str] class CustomChatCompletionMessageParam(TypedDict, total=False): @@ -387,6 +412,71 @@ def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int], _RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam) MODEL_KEEP_MULTI_MODAL_CONTENT = {'mllama'} +# Define a mapping from part types to their corresponding parsing functions. +MM_PARSER_MAP: Dict[str, Callable[[ChatCompletionContentPartParam], str]] = { + "text": + lambda part: _TextParser(part).get("text", ""), + "image_url": + lambda part: _ImageParser(part).get("image_url", {}).get("url", ""), + "audio_url": + lambda part: _AudioParser(part).get("audio_url", {}).get("url", ""), + "refusal": + lambda part: _RefusalParser(part).get("refusal", ""), +} + + +def _parse_chat_message_content_mm_part( + part: ChatCompletionContentPartParam) -> Tuple[str, str]: + """ + Parses a given multi modal content part based on its type. + + Args: + part: A dict containing the content part, with a potential 'type' field. + + Returns: + A tuple (part_type, content) where: + - part_type: Type of the part (e.g., 'text', 'image_url'). + - content: Parsed content (e.g., text, image URL). + + Raises: + ValueError: If the 'type' field is missing and no direct URL is found. + """ + assert isinstance( + part, dict) # This is needed to avoid mypy errors: part.get() from str + part_type = part.get("type", None) + + if isinstance(part_type, str) and part_type in MM_PARSER_MAP: + content = MM_PARSER_MAP[part_type](part) + + # Special case for 'image_url.detail' + if part_type == "image_url" and part.get("detail") != "auto": + logger.warning("'image_url.detail' is currently not supported " + "and will be ignored.") + + return part_type, content + + # Handle missing 'type' but provided direct URL fields. + if part_type is None: + if part.get("image_url") is not None: + image_params = cast(CustomChatCompletionContentSimpleImageParam, + part) + return "image_url", image_params.get("image_url", "") + if part.get("audio_url") is not None: + audio_params = cast(CustomChatCompletionContentSimpleAudioParam, + part) + return "audio_url", audio_params.get("audio_url", "") + + # Raise an error if no 'type' or direct URL is found. + raise ValueError("Missing 'type' field in multimodal part.") + + if not isinstance(part_type, str): + raise ValueError("Invalid 'type' field in multimodal part.") + return part_type, "unknown part_type content" + + +VALID_MESSAGE_CONTENT_MM_PART_TYPES = ("text", "refusal", "image_url", + "audio_url") + def _parse_chat_message_content_parts( role: str, @@ -402,29 +492,28 @@ def _parse_chat_message_content_parts( has_image = False for part in parts: - part_type = part["type"] - if part_type == "text": - text = _TextParser(part)["text"] + if isinstance(part, str): # Handle plain text parts + text = _TextParser(part) texts.append(text) - elif part_type == "image_url": - image_url = _ImageParser(part)["image_url"] - - if image_url.get("detail", "auto") != "auto": - logger.warning( - "'image_url.detail' is currently not supported and " - "will be ignored.") - - mm_parser.parse_image(image_url["url"]) - has_image = True - elif part_type == "audio_url": - audio_url = _AudioParser(part)["audio_url"] - - mm_parser.parse_audio(audio_url["url"]) - elif part_type == "refusal": - text = _RefusalParser(part)["refusal"] - texts.append(text) - else: - raise NotImplementedError(f"Unknown part type: {part_type}") + else: # Handle structured dictionary parts + part_type, content = _parse_chat_message_content_mm_part(part) + + # if part_type is text/refusal/image_url/audio_url but + # content is empty, logg a warning and skip + if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content: + logger.warning("Skipping multimodal part " + "with empty / unparsable content.") + continue + + if part_type in ("text", "refusal"): + texts.append(content) + elif part_type == "image_url": + mm_parser.parse_image(content) + has_image = True + elif part_type == "audio_url": + mm_parser.parse_audio(content) + else: + raise NotImplementedError(f"Unknown part type: {part_type}") text_prompt = "\n".join(texts) if keep_multimodal_content: From 263d8ee150a737ddb8b2d49254bf712d8bb08a0b Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Sat, 19 Oct 2024 14:49:40 +0800 Subject: [PATCH 016/222] [Bugfix] Fix missing task for speculative decoding (#9524) --- vllm/config.py | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/vllm/config.py b/vllm/config.py index 7f8f936428543..f57aa4048ae9b 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -33,8 +33,10 @@ _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120 -Task = Literal["generate", "embedding"] -TaskOption = Literal["auto", Task] +TaskOption = Literal["auto", "generate", "embedding"] + +# "draft" is only used internally for speculative decoding +_Task = Literal["generate", "embedding", "draft"] class ModelConfig: @@ -115,7 +117,7 @@ class ModelConfig: def __init__(self, model: str, - task: TaskOption, + task: Union[TaskOption, _Task], tokenizer: str, tokenizer_mode: str, trust_remote_code: bool, @@ -255,18 +257,21 @@ def _verify_tokenizer_mode(self) -> None: def _resolve_task( self, - task_option: TaskOption, + task_option: Union[TaskOption, _Task], hf_config: PretrainedConfig, - ) -> Tuple[Set[Task], Task]: + ) -> Tuple[Set[_Task], _Task]: + if task_option == "draft": + return {"draft"}, "draft" + architectures = getattr(hf_config, "architectures", []) - task_support: Dict[Task, bool] = { + task_support: Dict[_Task, bool] = { # NOTE: Listed from highest to lowest priority, # in case the model supports multiple of them "generate": ModelRegistry.is_text_generation_model(architectures), "embedding": ModelRegistry.is_embedding_model(architectures), } - supported_tasks_lst: List[Task] = [ + supported_tasks_lst: List[_Task] = [ task for task, is_supported in task_support.items() if is_supported ] supported_tasks = set(supported_tasks_lst) @@ -1002,7 +1007,7 @@ class SchedulerConfig: """ def __init__(self, - task: Task, + task: _Task, max_num_batched_tokens: Optional[int], max_num_seqs: int, max_model_len: int, @@ -1269,7 +1274,7 @@ def maybe_create_spec_config( ngram_prompt_lookup_min = 0 draft_model_config = ModelConfig( model=speculative_model, - task=target_model_config.task, + task="draft", tokenizer=target_model_config.tokenizer, tokenizer_mode=target_model_config.tokenizer_mode, trust_remote_code=target_model_config.trust_remote_code, From 8e3e7f271326e8cdb32c8f9581b2f98013a567c7 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Sat, 19 Oct 2024 10:44:29 -0400 Subject: [PATCH 017/222] [Model][Pixtral] Optimizations for input_processor_for_pixtral_hf (#9514) --- vllm/model_executor/models/pixtral.py | 81 ++++++++++++++------------- 1 file changed, 41 insertions(+), 40 deletions(-) diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index d09cbe5ca02e9..b07ac5baecda9 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -701,63 +701,64 @@ def input_processor_for_pixtral_hf( new_prompt = inputs.get("prompt") new_token_ids = inputs["prompt_token_ids"] + image_token = processor.image_token + image_break_token = processor.image_break_token + image_end_token = processor.image_end_token + # Update new_prompt if present if new_prompt: - replace_strings = [] - for image in image_data: - w, h = image.size + parts = new_prompt.split(image_token) + assert len(parts) - 1 == len(image_data) + new_parts = [parts[0]] # Start with the part before any image tokens + for image, next_part in zip(image_data, parts[1:]): + w, h = image.size (num_width_tokens, num_height_tokens) = get_pixtral_hf_image_feature_size( hf_config, image_width=w, image_height=h) - replace_tokens = [[processor.image_token] * num_width_tokens + - [processor.image_break_token] - ] * num_height_tokens - # Flatten list - replace_tokens = [ - item for sublist in replace_tokens for item in sublist + replace_tokens = [image_token] * num_width_tokens + [ + image_break_token ] - replace_tokens[-1] = processor.image_end_token - replace_str = "".join(replace_tokens) - replace_strings.append(replace_str) - new_prompt = new_prompt.replace(processor.image_token, - "", 1) + replace_tokens = replace_tokens * num_height_tokens + replace_tokens[-1] = image_end_token - while "" in new_prompt: - replace_str = replace_strings.pop(0) - new_prompt = new_prompt.replace("", replace_str, 1) + new_parts.append("".join(replace_tokens)) + new_parts.append(next_part) + + new_prompt = "".join(new_parts) # Update new_token_ids - image_token_id = 10 - image_break_id = 12 - image_end_id = 13 + convert_tokens_to_ids = processor.tokenizer.convert_tokens_to_ids + image_token_id = convert_tokens_to_ids(image_token) + image_break_id = convert_tokens_to_ids(image_break_token) + image_end_id = convert_tokens_to_ids(image_end_token) placeholder_token_id = -999 + # Find all image token indices at once + placeholder_indices = [ + idx for idx, token_id in enumerate(new_token_ids) + if token_id == image_token_id + ] + assert len(placeholder_indices) == len(image_data) replace_tokens_list = [] - for image in image_data: - w, h = image.size + for placeholder_idx, image in zip(placeholder_indices, image_data): + new_token_ids[placeholder_idx] = placeholder_token_id - num_width_tokens, num_height_tokens = get_pixtral_hf_image_feature_size( - hf_config, image_width=w, image_height=h) + w, h = image.size + (num_width_tokens, + num_height_tokens) = get_pixtral_hf_image_feature_size(hf_config, + image_width=w, + image_height=h) - replace_tokens = [[image_token_id] * num_width_tokens + - [image_break_id]] * num_height_tokens - # Flatten list - replace_tokens = [ - item for sublist in replace_tokens for item in sublist - ] + replace_tokens = [image_token_id] * num_width_tokens + [image_break_id] + replace_tokens = replace_tokens * num_height_tokens replace_tokens[-1] = image_end_id replace_tokens_list.append(replace_tokens) - # Replace image id with placeholder id - next_image_index = new_token_ids.index(image_token_id) - new_token_ids[next_image_index] = placeholder_token_id - - while placeholder_token_id in new_token_ids: - replace_tokens = replace_tokens_list.pop(0) - next_image_index = new_token_ids.index(placeholder_token_id) - prefix = new_token_ids[:next_image_index] - postfix = new_token_ids[next_image_index + 1:] - new_token_ids = prefix + replace_tokens + postfix + + # Backward iteration for replacement without affecting known indices + for placeholder_idx, replace_tokens in zip(reversed(placeholder_indices), + reversed(replace_tokens_list)): + new_token_ids[placeholder_idx:placeholder_idx + 1] = replace_tokens # NOTE: Create a defensive copy of the original inputs return token_inputs(prompt_token_ids=new_token_ids, From 5b59fe0f08c16e56813f2dad442d44cab222668b Mon Sep 17 00:00:00 2001 From: Chen Zhang Date: Sat, 19 Oct 2024 17:05:02 -0700 Subject: [PATCH 018/222] [Bugfix] Pass json-schema to GuidedDecodingParams and make test stronger (#9530) --- tests/entrypoints/openai/test_chat.py | 22 ++++++++++++++++++---- vllm/entrypoints/openai/protocol.py | 16 +++++++++++----- 2 files changed, 29 insertions(+), 9 deletions(-) diff --git a/tests/entrypoints/openai/test_chat.py b/tests/entrypoints/openai/test_chat.py index 3af0032fd2fb0..a29747603622b 100644 --- a/tests/entrypoints/openai/test_chat.py +++ b/tests/entrypoints/openai/test_chat.py @@ -851,14 +851,28 @@ async def test_response_format_json_object(client: openai.AsyncOpenAI): @pytest.mark.asyncio async def test_response_format_json_schema(client: openai.AsyncOpenAI): + prompt = 'what is 1+1? The format is "result": 2' + # Check that this prompt cannot lead to a valid JSON without json_schema for _ in range(2): resp = await client.chat.completions.create( model=MODEL_NAME, messages=[{ - "role": - "user", - "content": ('what is 1+1? please respond with a JSON object, ' - 'the format is {"result": 2}') + "role": "user", + "content": prompt + }], + ) + content = resp.choices[0].message.content + assert content is not None + with pytest.raises((json.JSONDecodeError, AssertionError)): + loaded = json.loads(content) + assert loaded == {"result": 2}, loaded + + for _ in range(2): + resp = await client.chat.completions.create( + model=MODEL_NAME, + messages=[{ + "role": "user", + "content": prompt }], response_format={ "type": "json_schema", diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 6f1135f8093ba..06114339b7c69 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -314,9 +314,15 @@ def to_sampling_params(self, default_max_tokens: int) -> SamplingParams: prompt_logprobs = self.top_logprobs guided_json_object = None - if (self.response_format is not None - and self.response_format.type == "json_object"): - guided_json_object = True + if self.response_format is not None: + if self.response_format.type == "json_object": + guided_json_object = True + elif self.response_format.type == "json_schema": + json_schema = self.response_format.json_schema + assert json_schema is not None + self.guided_json = json_schema.json_schema + if self.guided_decoding_backend is None: + self.guided_decoding_backend = "lm-format-enforcer" guided_decoding = GuidedDecodingParams.from_optional( json=self._get_guided_json_from_tool() or self.guided_json, @@ -537,8 +543,8 @@ class CompletionRequest(OpenAIBaseModel): default=None, description= ("Similar to chat completion, this parameter specifies the format of " - "output. Only {'type': 'json_object'} or {'type': 'text' } is " - "supported."), + "output. Only {'type': 'json_object'}, {'type': 'json_schema'} or " + "{'type': 'text' } is supported."), ) guided_json: Optional[Union[str, dict, BaseModel]] = Field( default=None, From 962d2c63495e930cdd3b59479dce1de48be57ecd Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Sun, 20 Oct 2024 01:29:14 -0400 Subject: [PATCH 019/222] [Model][Pixtral] Use memory_efficient_attention for PixtralHFVision (#9520) --- vllm/model_executor/models/pixtral.py | 62 +++++++++------------------ 1 file changed, 21 insertions(+), 41 deletions(-) diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index b07ac5baecda9..13c5149a63919 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -13,8 +13,7 @@ from transformers.models.pixtral.image_processing_pixtral import ( _num_image_tokens) from transformers.models.pixtral.modeling_pixtral import ( - PixtralRotaryEmbedding, apply_rotary_pos_emb, - generate_block_attention_mask, position_ids_in_meshgrid) + PixtralRotaryEmbedding, apply_rotary_pos_emb, position_ids_in_meshgrid) from xformers.ops.fmha import memory_efficient_attention from xformers.ops.fmha.attn_bias import BlockDiagonalMask @@ -813,48 +812,30 @@ def __init__(self, config: PixtralVisionConfig): def forward( self, hidden_states: torch.Tensor, - attention_mask: torch.Tensor, + attention_mask: BlockDiagonalMask, position_embeddings: torch.Tensor, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - """Input shape: Batch x Time x Channel""" + batch, patches, _ = hidden_states.size() - batch_size, patches, _ = hidden_states.size() - - query_states = self.q_proj(hidden_states) - key_states = self.k_proj(hidden_states) - value_states = self.v_proj(hidden_states) - - query_states = query_states.view(batch_size, patches, self.n_heads, - self.head_dim).transpose(1, 2) - key_states = key_states.view(batch_size, patches, self.n_heads, - self.head_dim).transpose(1, 2) - value_states = value_states.view(batch_size, patches, self.n_heads, - self.head_dim).transpose(1, 2) + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + # Transpose q and k to apply HF's Rotary Position Embedding + q = q.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2) + k = k.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2) cos, sin = position_embeddings - query_states, key_states = apply_rotary_pos_emb(query_states, - key_states, - cos, - sin, - unsqueeze_dim=0) - - attn_weights = torch.matmul(query_states, key_states.transpose( - 2, 3)) * self.scale - - if attention_mask is not None: - attn_weights = attn_weights + attention_mask + q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0) - # upcast attention to fp32 - attn_weights = nn.functional.softmax(attn_weights, - dim=-1, - dtype=torch.float32).to( - query_states.dtype) - attn_output = torch.matmul(attn_weights, value_states) + # Transpose q and k back for attention + q = q.transpose(1, 2).contiguous() + k = k.transpose(1, 2).contiguous() + v = v.reshape(batch, patches, self.n_heads, self.head_dim) - attn_output = attn_output.transpose(1, 2).contiguous() - attn_output = attn_output.reshape(batch_size, patches, -1) + out = memory_efficient_attention(q, k, v, attn_bias=attention_mask) + out = out.reshape(batch, patches, self.n_heads * self.head_dim) - return self.o_proj(attn_output) + return self.o_proj(out) class PixtralHFTransformerBlock(nn.Module): @@ -869,7 +850,7 @@ def __init__(self, config: PixtralVisionConfig): def forward( self, hidden_states: torch.Tensor, - attention_mask: torch.Tensor, + attention_mask: BlockDiagonalMask, position_embeddings: torch.Tensor, ) -> torch.Tensor: r = self.attention.forward(self.attention_norm(hidden_states), @@ -892,7 +873,7 @@ def __init__(self, config: PixtralVisionConfig): def forward( self, x: torch.Tensor, - attention_mask: torch.Tensor, + attention_mask: BlockDiagonalMask, position_embeddings: torch.Tensor, ) -> torch.Tensor: for layer in self.layers: @@ -953,9 +934,8 @@ def forward( position_embedding = self.patch_positional_embedding( patch_embeds, position_ids) - attention_mask = generate_block_attention_mask( - [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], - patch_embeds) + attention_mask = BlockDiagonalMask.from_seqlens( + [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], ) out = self.transformer(patch_embeds, attention_mask, position_embedding) From 4fa3e3334978dce74eba296ee8cc2e970ed20e5e Mon Sep 17 00:00:00 2001 From: Chen Zhang Date: Sun, 20 Oct 2024 10:57:52 -0700 Subject: [PATCH 020/222] [Kernel] Support sliding window in flash attention backend (#9403) --- tests/kernels/test_attention_selector.py | 35 ++++++++++-------------- tests/kernels/test_flash_attn.py | 29 +++++++++++--------- vllm/attention/backends/flash_attn.py | 13 ++++----- vllm/attention/layer.py | 7 ++--- vllm/attention/selector.py | 10 ++----- vllm/worker/cache_engine.py | 1 - vllm/worker/cpu_model_runner.py | 1 - vllm/worker/cpu_worker.py | 1 - vllm/worker/model_runner.py | 1 - vllm/worker/openvino_model_runner.py | 1 - vllm/worker/openvino_worker.py | 1 - vllm/worker/tpu_model_runner.py | 1 - vllm/worker/xpu_model_runner.py | 1 - 13 files changed, 41 insertions(+), 61 deletions(-) diff --git a/tests/kernels/test_attention_selector.py b/tests/kernels/test_attention_selector.py index f471dcee938be..5671207ac847e 100644 --- a/tests/kernels/test_attention_selector.py +++ b/tests/kernels/test_attention_selector.py @@ -20,21 +20,21 @@ def test_env(name: str, device: str, monkeypatch): if device == "cpu": with patch("vllm.attention.selector.is_cpu", return_value=True): - backend = which_attn_to_use(16, None, torch.float16, torch.float16, - 16, False) + backend = which_attn_to_use(16, torch.float16, torch.float16, 16, + False) assert backend.name == "TORCH_SDPA" elif device == "hip": with patch("vllm.attention.selector.is_hip", return_value=True): - backend = which_attn_to_use(16, None, torch.float16, torch.float16, - 16, False) + backend = which_attn_to_use(16, torch.float16, torch.float16, 16, + False) assert backend.name == "ROCM_FLASH" elif device == "openvino": with patch("vllm.attention.selector.is_openvino", return_value=True): - backend = which_attn_to_use(16, None, torch.float16, torch.float16, - 16, False) + backend = which_attn_to_use(16, torch.float16, torch.float16, 16, + False) assert backend.name == "OPENVINO" else: - backend = which_attn_to_use(16, None, torch.float16, torch.float16, 16, + backend = which_attn_to_use(16, torch.float16, torch.float16, 16, False) assert backend.name == name @@ -46,37 +46,32 @@ def test_flash_attn(monkeypatch): # Unsupported CUDA arch with patch("torch.cuda.get_device_capability", return_value=(7, 5)): - backend = which_attn_to_use(16, None, torch.float16, None, 16, False) + backend = which_attn_to_use(16, torch.float16, None, 16, False) assert backend.name != STR_FLASH_ATTN_VAL # Unsupported data type - backend = which_attn_to_use(16, None, torch.float8_e4m3fn, None, 16, False) + backend = which_attn_to_use(16, torch.float8_e4m3fn, None, 16, False) assert backend.name != STR_FLASH_ATTN_VAL # Unsupported kv cache data type - backend = which_attn_to_use(16, None, torch.float16, "fp8", 16, False) + backend = which_attn_to_use(16, torch.float16, "fp8", 16, False) assert backend.name != STR_FLASH_ATTN_VAL # Unsupported block size - backend = which_attn_to_use(16, None, torch.float16, None, 8, False) - assert backend.name != STR_FLASH_ATTN_VAL - - # Unsupported sliding window - backend = which_attn_to_use(16, 1, torch.float16, None, 16, False) + backend = which_attn_to_use(16, torch.float16, None, 8, False) assert backend.name != STR_FLASH_ATTN_VAL # flash-attn is not installed with patch.dict('sys.modules', {'vllm_flash_attn': None}): - backend = which_attn_to_use(16, None, torch.float16, None, 16, False) + backend = which_attn_to_use(16, torch.float16, None, 16, False) assert backend.name != STR_FLASH_ATTN_VAL # Unsupported head size - backend = which_attn_to_use(17, None, torch.float16, None, 16, False) + backend = which_attn_to_use(17, torch.float16, None, 16, False) assert backend.name != STR_FLASH_ATTN_VAL # Attention-free models should bypass env and use PlaceholderAttention - backend = which_attn_to_use(16, None, torch.float16, torch.float16, 16, - True) + backend = which_attn_to_use(16, torch.float16, torch.float16, 16, True) assert backend.name != STR_FLASH_ATTN_VAL @@ -84,4 +79,4 @@ def test_invalid_env(monkeypatch): """Throw an exception if the backend name is invalid.""" override_backend_env_variable(monkeypatch, STR_INVALID_VAL) with pytest.raises(ValueError): - which_attn_to_use(16, None, torch.float16, None, 16, False) + which_attn_to_use(16, torch.float16, None, 16, False) diff --git a/tests/kernels/test_flash_attn.py b/tests/kernels/test_flash_attn.py index 3e9b4d9a4f8a0..35c29c5bd1028 100644 --- a/tests/kernels/test_flash_attn.py +++ b/tests/kernels/test_flash_attn.py @@ -78,6 +78,7 @@ def ref_paged_attn( @pytest.mark.parametrize("dtype", DTYPES) @pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0]) @pytest.mark.parametrize("num_blocks", NUM_BLOCKS) +@pytest.mark.parametrize("sliding_window", [None, 256]) @torch.inference_mode() def test_flash_attn_with_paged_kv( kv_lens: List[int], @@ -87,6 +88,7 @@ def test_flash_attn_with_paged_kv( block_size: int, soft_cap: Optional[float], num_blocks: int, + sliding_window: Optional[int], ) -> None: torch.set_default_device("cuda") seed_everything(0) @@ -96,6 +98,8 @@ def test_flash_attn_with_paged_kv( assert num_query_heads % num_kv_heads == 0 max_kv_len = max(kv_lens) scale = head_size**-0.5 + window_size = ((sliding_window - 1, 0) if sliding_window is not None else + (-1, -1)) query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype) key_cache = torch.randn(num_blocks, @@ -121,18 +125,18 @@ def test_flash_attn_with_paged_kv( block_table=block_tables, cache_seqlens=kv_lens_tensor, softcap=soft_cap if soft_cap is not None else 0, + window_size=window_size, ).squeeze(1) - ref_output = ref_paged_attn( - query=query, - key_cache=key_cache, - value_cache=value_cache, - query_lens=[1] * num_seqs, - kv_lens=kv_lens, - block_tables=block_tables, - scale=scale, - soft_cap=soft_cap, - ) + ref_output = ref_paged_attn(query=query, + key_cache=key_cache, + value_cache=value_cache, + query_lens=[1] * num_seqs, + kv_lens=kv_lens, + block_tables=block_tables, + scale=scale, + soft_cap=soft_cap, + sliding_window=sliding_window) torch.testing.assert_close(output, ref_output, atol=2e-2, rtol=1e-2), \ f"{torch.max(torch.abs(output - ref_output))}" @@ -141,7 +145,7 @@ def test_flash_attn_with_paged_kv( @pytest.mark.parametrize("num_heads", NUM_HEADS) @pytest.mark.parametrize("head_size", HEAD_SIZES) @pytest.mark.parametrize("block_size", BLOCK_SIZES) -@pytest.mark.parametrize("sliding_window", [None]) +@pytest.mark.parametrize("sliding_window", [None, 256]) @pytest.mark.parametrize("dtype", DTYPES) @pytest.mark.parametrize("soft_cap", [None, 10.0, 50.0]) @pytest.mark.parametrize("num_blocks", NUM_BLOCKS) @@ -166,8 +170,7 @@ def test_varlen_with_paged_kv( assert num_query_heads % num_kv_heads == 0 max_query_len = max(query_lens) max_kv_len = max(kv_lens) - window_size = ((sliding_window, - sliding_window) if sliding_window is not None else + window_size = ((sliding_window - 1, 0) if sliding_window is not None else (-1, -1)) scale = head_size**-0.5 diff --git a/vllm/attention/backends/flash_attn.py b/vllm/attention/backends/flash_attn.py index d54dbdcb19495..d538286a0dddd 100644 --- a/vllm/attention/backends/flash_attn.py +++ b/vllm/attention/backends/flash_attn.py @@ -524,8 +524,8 @@ def __init__( if alibi_slopes is not None: alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32) self.alibi_slopes = alibi_slopes - self.sliding_window = ((sliding_window, sliding_window) - if sliding_window is not None else (-1, -1)) + self.sliding_window = ((sliding_window - 1, + 0) if sliding_window is not None else (-1, -1)) self.kv_cache_dtype = kv_cache_dtype if logits_soft_cap is None: # In flash-attn, setting logits_soft_cap as 0 means no soft cap. @@ -535,12 +535,6 @@ def __init__( assert self.num_heads % self.num_kv_heads == 0 self.num_queries_per_kv = self.num_heads // self.num_kv_heads - if sliding_window is not None: - # NOTE(woosuk): flash-attn's sliding window does not work with - # paged KV cache. - raise ValueError( - "Sliding window is not supported in FlashAttention.") - support_head_sizes = FlashAttentionBackend.get_supported_head_sizes() if head_size not in support_head_sizes: raise ValueError( @@ -704,6 +698,7 @@ def unified_flash_attention( max_seqlen_k=max_seq_len, softmax_scale=softmax_scale, causal=True, + window_size=window_size, alibi_slopes=alibi_slopes, block_table=prefill_meta.block_tables, softcap=logits_soft_cap, @@ -725,6 +720,7 @@ def unified_flash_attention( max_seqlen_k=decode_meta.max_decode_seq_len, softmax_scale=softmax_scale, causal=True, + window_size=window_size, alibi_slopes=alibi_slopes, softcap=logits_soft_cap, block_table=decode_meta.block_tables, @@ -739,6 +735,7 @@ def unified_flash_attention( cache_seqlens=decode_meta.seq_lens_tensor, softmax_scale=softmax_scale, causal=True, + window_size=window_size, alibi_slopes=alibi_slopes, softcap=logits_soft_cap, ).squeeze(1) diff --git a/vllm/attention/layer.py b/vllm/attention/layer.py index b46f0721d0caf..33d05cbd3fe01 100644 --- a/vllm/attention/layer.py +++ b/vllm/attention/layer.py @@ -78,10 +78,9 @@ def __init__( # During model initialization, the default dtype is set as the model # weight and activation dtype. dtype = torch.get_default_dtype() - attn_backend = get_attn_backend(head_size, sliding_window, dtype, - kv_cache_dtype, block_size, - is_attention_free, blocksparse_params - is not None) + attn_backend = get_attn_backend(head_size, dtype, kv_cache_dtype, + block_size, is_attention_free, + blocksparse_params is not None) impl_cls = attn_backend.get_impl_cls() self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads, alibi_slopes, sliding_window, kv_cache_dtype, diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index 7edb7676ea2cd..4ff86573e664d 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -90,7 +90,6 @@ def get_global_forced_attn_backend() -> Optional[_Backend]: @lru_cache(maxsize=None) def get_attn_backend( head_size: int, - sliding_window: Optional[int], dtype: torch.dtype, kv_cache_dtype: Optional[str], block_size: int, @@ -105,8 +104,8 @@ def get_attn_backend( BlocksparseFlashAttentionBackend) return BlocksparseFlashAttentionBackend - backend = which_attn_to_use(head_size, sliding_window, dtype, - kv_cache_dtype, block_size, is_attention_free) + backend = which_attn_to_use(head_size, dtype, kv_cache_dtype, block_size, + is_attention_free) if backend == _Backend.FLASH_ATTN: from vllm.attention.backends.flash_attn import ( # noqa: F401 FlashAttentionBackend) @@ -155,7 +154,6 @@ def get_attn_backend( def which_attn_to_use( head_size: int, - sliding_window: Optional[int], dtype: torch.dtype, kv_cache_dtype: Optional[str], block_size: int, @@ -243,10 +241,6 @@ def which_attn_to_use( "Cannot use FlashAttention-2 backend for block size not " "divisible by 16.") selected_backend = _Backend.XFORMERS - elif sliding_window is not None: - logger.info( - "Cannot use FlashAttention-2 backend due to sliding window.") - selected_backend = _Backend.XFORMERS # FlashAttn is valid for the model, checking if the package is installed. if selected_backend == _Backend.FLASH_ATTN: diff --git a/vllm/worker/cache_engine.py b/vllm/worker/cache_engine.py index 090f95e6e892c..ac3270d1c9909 100644 --- a/vllm/worker/cache_engine.py +++ b/vllm/worker/cache_engine.py @@ -53,7 +53,6 @@ def __init__( # Get attention backend. self.attn_backend = get_attn_backend(self.head_size, - model_config.get_sliding_window(), model_config.dtype, cache_config.cache_dtype, self.block_size, diff --git a/vllm/worker/cpu_model_runner.py b/vllm/worker/cpu_model_runner.py index dd38b550eb011..5032896600b3b 100644 --- a/vllm/worker/cpu_model_runner.py +++ b/vllm/worker/cpu_model_runner.py @@ -420,7 +420,6 @@ def __init__( self.block_size = cache_config.block_size self.attn_backend = get_attn_backend( self.model_config.get_head_size(), - self.model_config.get_sliding_window(), self.model_config.dtype, self.kv_cache_dtype, self.block_size, diff --git a/vllm/worker/cpu_worker.py b/vllm/worker/cpu_worker.py index b84562851f0f8..ab93471b5af74 100644 --- a/vllm/worker/cpu_worker.py +++ b/vllm/worker/cpu_worker.py @@ -57,7 +57,6 @@ def __init__(self, cache_config: CacheConfig, model_config: ModelConfig, # Get attention backend. self.attn_backend = get_attn_backend( self.model_config.get_head_size(), - self.model_config.get_sliding_window(), self.model_config.dtype, cache_config.cache_dtype, self.block_size, diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index a82956985af55..dc1674cd1ea20 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -1011,7 +1011,6 @@ def __init__( self.attn_backend = get_attn_backend( self.model_config.get_head_size(), - self.model_config.get_sliding_window(), self.model_config.dtype, self.kv_cache_dtype, self.block_size, diff --git a/vllm/worker/openvino_model_runner.py b/vllm/worker/openvino_model_runner.py index 760b18427e22b..a164fbe3393c4 100644 --- a/vllm/worker/openvino_model_runner.py +++ b/vllm/worker/openvino_model_runner.py @@ -75,7 +75,6 @@ def __init__( self.attn_backend = get_attn_backend( self.model_config.get_head_size(), - self.model_config.get_sliding_window(), self.model_config.dtype, self.kv_cache_dtype, self.block_size, diff --git a/vllm/worker/openvino_worker.py b/vllm/worker/openvino_worker.py index 24425fece850f..bc245d19663d6 100644 --- a/vllm/worker/openvino_worker.py +++ b/vllm/worker/openvino_worker.py @@ -71,7 +71,6 @@ def __init__( # Get attention backend. self.attn_backend = get_attn_backend( self.head_size, - self.model_config.get_sliding_window(), self.model_config.dtype, self.cache_config.cache_dtype, self.block_size, diff --git a/vllm/worker/tpu_model_runner.py b/vllm/worker/tpu_model_runner.py index f7e5f660c0249..87ced7818a676 100644 --- a/vllm/worker/tpu_model_runner.py +++ b/vllm/worker/tpu_model_runner.py @@ -114,7 +114,6 @@ def __init__( dtype=np.int32) self.attn_backend = get_attn_backend( self.model_config.get_head_size(), - self.model_config.get_sliding_window(), self.model_config.dtype, self.cache_config.cache_dtype, self.block_size, diff --git a/vllm/worker/xpu_model_runner.py b/vllm/worker/xpu_model_runner.py index 5ff4626c060b3..75a6de3b24ba4 100644 --- a/vllm/worker/xpu_model_runner.py +++ b/vllm/worker/xpu_model_runner.py @@ -374,7 +374,6 @@ def __init__( self.attn_backend = get_attn_backend( self.model_config.get_head_size(), - self.model_config.get_sliding_window(), self.model_config.dtype, self.kv_cache_dtype, self.block_size, From 855e0e6f97e5ddd5addf042f25c1f11522214569 Mon Sep 17 00:00:00 2001 From: Andy Dai <76841985+Imss27@users.noreply.github.com> Date: Sun, 20 Oct 2024 11:39:32 -0700 Subject: [PATCH 021/222] [Frontend][Misc] Goodput metric support (#9338) --- benchmarks/benchmark_serving.py | 93 ++++++++++++++++++++++++++++++++- 1 file changed, 91 insertions(+), 2 deletions(-) diff --git a/benchmarks/benchmark_serving.py b/benchmarks/benchmark_serving.py index 68f1e221c4bfb..0d205014b15bf 100644 --- a/benchmarks/benchmark_serving.py +++ b/benchmarks/benchmark_serving.py @@ -53,6 +53,8 @@ except ImportError: from argparse import ArgumentParser as FlexibleArgumentParser +MILLISECONDS_TO_SECONDS_CONVERSION = 1000 + @dataclass class BenchmarkMetrics: @@ -60,6 +62,7 @@ class BenchmarkMetrics: total_input: int total_output: int request_throughput: float + request_goodput: float output_throughput: float total_token_throughput: float mean_ttft_ms: float @@ -316,12 +319,15 @@ def calculate_metrics( tokenizer: PreTrainedTokenizerBase, selected_percentile_metrics: List[str], selected_percentiles: List[float], + gootput_config_dict: Dict[str, float], ) -> Tuple[BenchmarkMetrics, List[int]]: actual_output_lens: List[int] = [] total_input = 0 completed = 0 + good_completed = 0 itls: List[float] = [] tpots: List[float] = [] + all_tpots: List[float] = [] ttfts: List[float] = [] e2els: List[float] = [] for i in range(len(outputs)): @@ -335,9 +341,13 @@ def calculate_metrics( add_special_tokens=False).input_ids) actual_output_lens.append(output_len) total_input += input_requests[i][1] + tpot = 0 if output_len > 1: - tpots.append( - (outputs[i].latency - outputs[i].ttft) / (output_len - 1)) + tpot = (outputs[i].latency - outputs[i].ttft) / (output_len - + 1) + tpots.append(tpot) + # Note: if output_len <= 1, we regard tpot as 0 for goodput + all_tpots.append(tpot) itls += outputs[i].itl ttfts.append(outputs[i].ttft) e2els.append(outputs[i].latency) @@ -345,6 +355,28 @@ def calculate_metrics( else: actual_output_lens.append(0) + if gootput_config_dict: + valid_metrics = [] + slo_values = [] + + if "ttft" in gootput_config_dict: + valid_metrics.append(ttfts) + slo_values.append(gootput_config_dict["ttft"] / + MILLISECONDS_TO_SECONDS_CONVERSION) + if "tpot" in gootput_config_dict: + valid_metrics.append(all_tpots) + slo_values.append(gootput_config_dict["tpot"] / + MILLISECONDS_TO_SECONDS_CONVERSION) + if "e2el" in gootput_config_dict: + valid_metrics.append(e2els) + slo_values.append(gootput_config_dict["e2el"] / + MILLISECONDS_TO_SECONDS_CONVERSION) + + for req_metric in zip(*valid_metrics): + is_good_req = all([s >= r for s, r in zip(slo_values, req_metric)]) + if is_good_req: + good_completed += 1 + if completed == 0: warnings.warn( "All requests failed. This is likely due to a misconfiguration " @@ -355,6 +387,7 @@ def calculate_metrics( total_input=total_input, total_output=sum(actual_output_lens), request_throughput=completed / dur_s, + request_goodput=good_completed / dur_s, output_throughput=sum(actual_output_lens) / dur_s, total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s, mean_ttft_ms=np.mean(ttfts or 0) * @@ -398,6 +431,7 @@ async def benchmark( selected_percentile_metrics: List[str], selected_percentiles: List[str], ignore_eos: bool, + gootput_config_dict: Dict[str, float], max_concurrency: Optional[int], ): if backend in ASYNC_REQUEST_FUNCS: @@ -512,6 +546,7 @@ async def limited_request_func(request_func_input, pbar): tokenizer=tokenizer, selected_percentile_metrics=selected_percentile_metrics, selected_percentiles=selected_percentiles, + gootput_config_dict=gootput_config_dict, ) print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, c='=')) @@ -523,6 +558,9 @@ async def limited_request_func(request_func_input, pbar): metrics.total_output)) print("{:<40} {:<10.2f}".format("Request throughput (req/s):", metrics.request_throughput)) + if gootput_config_dict: + print("{:<40} {:<10.2f}".format("Request goodput (req/s):", + metrics.request_goodput)) print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):", metrics.output_throughput)) print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):", @@ -534,6 +572,8 @@ async def limited_request_func(request_func_input, pbar): "total_input_tokens": metrics.total_input, "total_output_tokens": metrics.total_output, "request_throughput": metrics.request_throughput, + "request_goodput:": + metrics.request_goodput if gootput_config_dict else None, "output_throughput": metrics.output_throughput, "total_token_throughput": metrics.total_token_throughput, "input_lens": [output.prompt_len for output in outputs], @@ -587,6 +627,41 @@ def process_one_metric( return result +def check_goodput_args(args): + # Check and parse goodput arguments + gootput_config_dict = {} + VALID_NAMES = ["ttft", "tpot", "e2el"] + if args.goodput: + gootput_config_dict = parse_goodput(args.goodput) + for slo_name, slo_val in gootput_config_dict.items(): + if slo_name not in VALID_NAMES: + raise ValueError( + f"Invalid metric name found, {slo_name}: {slo_val}. " + "The service level objective name should be one of " + f"{str(VALID_NAMES)}. ") + if slo_val < 0: + raise ValueError( + f"Invalid value found, {slo_name}: {slo_val}. " + "The service level objective value should be " + "non-negative.") + return gootput_config_dict + + +def parse_goodput(slo_pairs): + gootput_config_dict = {} + try: + for slo_pair in slo_pairs: + slo_name, slo_val = slo_pair.split(":") + gootput_config_dict[slo_name] = float(slo_val) + except ValueError as err: + raise argparse.ArgumentTypeError( + "Invalid format found for service level objectives. " + "Specify service level objectives for goodput as \"KEY:VALUE\" " + "pairs, where the key is a metric name, and the value is a " + "number in milliseconds.") from err + return gootput_config_dict + + def main(args: argparse.Namespace): print(args) random.seed(args.seed) @@ -681,6 +756,8 @@ def main(args: argparse.Namespace): else: raise ValueError(f"Unknown dataset: {args.dataset_name}") + gootput_config_dict = check_goodput_args(args) + benchmark_result = asyncio.run( benchmark( backend=backend, @@ -699,6 +776,7 @@ def main(args: argparse.Namespace): float(p) for p in args.metric_percentiles.split(",") ], ignore_eos=args.ignore_eos, + gootput_config_dict=gootput_config_dict, max_concurrency=args.max_concurrency, )) @@ -915,6 +993,17 @@ def main(args: argparse.Namespace): "Default value is \"99\". " "Use \"--percentile-metrics\" to select metrics.", ) + parser.add_argument( + "--goodput", + nargs="+", + required=False, + help="Specify service level objectives for goodput as \"KEY:VALUE\" " + "pairs, where the key is a metric name, and the value is in " + "milliseconds. Multiple \"KEY:VALUE\" pairs can be provided, " + "separated by spaces. Allowed request level metric names are " + "\"ttft\", \"tpot\", \"e2el\". For more context on the definition of " + "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 " + "and the blog: https://hao-ai-lab.github.io/blogs/distserve") # group for dataset specific arguments sonnet_group = parser.add_argument_group("sonnet dataset options") From 696b01af8fac1819b2409cc0f205c73ef553558c Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Mon, 21 Oct 2024 12:27:50 +0800 Subject: [PATCH 022/222] [CI/Build] Split up decoder-only LM tests (#9488) Co-authored-by: Nick Hill --- .buildkite/test-pipeline.yaml | 13 ++++- .../decoder_only/language/test_big_models.py | 10 ++-- .../decoder_only/language/test_danube3_4b.py | 52 ------------------- 3 files changed, 18 insertions(+), 57 deletions(-) delete mode 100644 tests/models/decoder_only/language/test_danube3_4b.py diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index c4fc43dc0abb8..8c98aa36ac0ff 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -310,13 +310,22 @@ steps: - pytest -v -s models/test_oot_registration.py # it needs a clean process - pytest -v -s models/*.py --ignore=models/test_oot_registration.py -- label: Decoder-only Language Models Test # 1h36min +- label: Decoder-only Language Models Test (Standard) # 35min #mirror_hardwares: [amd] source_file_dependencies: - vllm/ - tests/models/decoder_only/language commands: - - pytest -v -s models/decoder_only/language + - pytest -v -s models/decoder_only/language/test_models.py + - pytest -v -s models/decoder_only/language/test_big_models.py + +- label: Decoder-only Language Models Test (Extended) # 1h20min + nightly: true + source_file_dependencies: + - vllm/ + - tests/models/decoder_only/language + commands: + - pytest -v -s models/decoder_only/language --ignore=models/decoder_only/language/test_models.py --ignore=models/decoder_only/language/test_big_models.py - label: Decoder-only Multi-Modal Models Test # 1h31min #mirror_hardwares: [amd] diff --git a/tests/models/decoder_only/language/test_big_models.py b/tests/models/decoder_only/language/test_big_models.py index fcc158639748d..75625b35209ce 100644 --- a/tests/models/decoder_only/language/test_big_models.py +++ b/tests/models/decoder_only/language/test_big_models.py @@ -21,10 +21,14 @@ ] if not current_platform.is_cpu(): - # MiniCPM requires fused_moe which is not supported by CPU - MODELS.append("openbmb/MiniCPM3-4B") + MODELS += [ + # fused_moe which not supported on CPU + "openbmb/MiniCPM3-4B", + # Head size isn't supported on CPU + "h2oai/h2o-danube3-4b-base", + ] -#TODO: remove this after CPU float16 support ready +# TODO: remove this after CPU float16 support ready target_dtype = "float" if current_platform.is_cpu() else "half" diff --git a/tests/models/decoder_only/language/test_danube3_4b.py b/tests/models/decoder_only/language/test_danube3_4b.py deleted file mode 100644 index bdd498edc293d..0000000000000 --- a/tests/models/decoder_only/language/test_danube3_4b.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Compare the outputs of HF and vLLM when using greedy sampling. - -This tests danube3 separately because its head size isn't supported on CPU yet. - -Run `pytest tests/models/test_danube3_4b.py`. -""" -import pytest - -from ...utils import check_outputs_equal - -MODELS = ["h2oai/h2o-danube3-4b-base"] - -target_dtype = "half" - - -@pytest.mark.parametrize("model", MODELS) -@pytest.mark.parametrize("dtype", [target_dtype]) -@pytest.mark.parametrize("max_tokens", [32]) -def test_models( - hf_runner, - vllm_runner, - example_prompts, - model: str, - dtype: str, - max_tokens: int, -) -> None: - with hf_runner(model, dtype=dtype) as hf_model: - hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) - - with vllm_runner(model, dtype=dtype) as vllm_model: - vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) - - check_outputs_equal( - outputs_0_lst=hf_outputs, - outputs_1_lst=vllm_outputs, - name_0="hf", - name_1="vllm", - ) - - -@pytest.mark.parametrize("model", MODELS) -@pytest.mark.parametrize("dtype", [target_dtype]) -def test_model_print( - vllm_runner, - model: str, - dtype: str, -) -> None: - with vllm_runner(model, dtype=dtype) as vllm_model: - # This test is for verifying whether the model's extra_repr - # can be printed correctly. - print(vllm_model.model.llm_engine.model_executor.driver_worker. - model_runner.model) From 496e991da82467874092e0be589071b971a63ab7 Mon Sep 17 00:00:00 2001 From: Thomas Parnell Date: Mon, 21 Oct 2024 16:29:57 +0200 Subject: [PATCH 023/222] [Doc] Consistent naming of attention backends (#9498) Signed-off-by: Thomas Parnell --- vllm/attention/backends/flash_attn.py | 2 +- vllm/attention/backends/flashinfer.py | 2 +- vllm/attention/backends/ipex_attn.py | 2 +- vllm/attention/backends/openvino.py | 2 +- vllm/attention/backends/pallas.py | 4 ++++ vllm/attention/backends/placeholder_attn.py | 2 +- vllm/attention/backends/rocm_flash_attn.py | 2 +- vllm/attention/backends/torch_sdpa.py | 2 +- vllm/attention/backends/utils.py | 12 ++++++------ vllm/attention/backends/xformers.py | 2 +- vllm/spec_decode/draft_model_runner.py | 2 +- vllm/spec_decode/spec_decode_worker.py | 2 +- vllm/worker/model_runner.py | 2 +- vllm/worker/multi_step_model_runner.py | 4 ++-- 14 files changed, 23 insertions(+), 19 deletions(-) diff --git a/vllm/attention/backends/flash_attn.py b/vllm/attention/backends/flash_attn.py index d538286a0dddd..ffa05e80623ac 100644 --- a/vllm/attention/backends/flash_attn.py +++ b/vllm/attention/backends/flash_attn.py @@ -32,7 +32,7 @@ def get_supported_head_sizes() -> List[int]: @staticmethod def get_name() -> str: - return "flash-attn" + return "FLASH_ATTN" @staticmethod def get_impl_cls() -> Type["FlashAttentionImpl"]: diff --git a/vllm/attention/backends/flashinfer.py b/vllm/attention/backends/flashinfer.py index 1dd2a21fdb51a..e43fb134a6a5a 100644 --- a/vllm/attention/backends/flashinfer.py +++ b/vllm/attention/backends/flashinfer.py @@ -40,7 +40,7 @@ class FlashInferBackend(AttentionBackend): @staticmethod def get_name() -> str: - return "flashinfer" + return "FLASHINFER" @staticmethod def get_impl_cls() -> Type["FlashInferImpl"]: diff --git a/vllm/attention/backends/ipex_attn.py b/vllm/attention/backends/ipex_attn.py index 7398732ddfc92..1eb5fe10d76db 100644 --- a/vllm/attention/backends/ipex_attn.py +++ b/vllm/attention/backends/ipex_attn.py @@ -19,7 +19,7 @@ class IpexAttnBackend(AttentionBackend): @staticmethod def get_name() -> str: - return "ipex-attn" + return "IPEX" @staticmethod def get_impl_cls() -> Type["IpexAttnBackendImpl"]: diff --git a/vllm/attention/backends/openvino.py b/vllm/attention/backends/openvino.py index 8b36230730380..6fddfc2002120 100644 --- a/vllm/attention/backends/openvino.py +++ b/vllm/attention/backends/openvino.py @@ -38,7 +38,7 @@ class OpenVINOAttentionBackend(AttentionBackend): @staticmethod def get_name() -> str: - return "openvino" + return "OPENVINO" @staticmethod def get_impl_cls(): diff --git a/vllm/attention/backends/pallas.py b/vllm/attention/backends/pallas.py index 56d3d3b482e58..6fee81de14420 100644 --- a/vllm/attention/backends/pallas.py +++ b/vllm/attention/backends/pallas.py @@ -11,6 +11,10 @@ class PallasAttentionBackend(AttentionBackend): + @staticmethod + def get_name() -> str: + return "PALLAS" + @staticmethod def get_impl_cls() -> Type["PallasAttentionBackendImpl"]: return PallasAttentionBackendImpl diff --git a/vllm/attention/backends/placeholder_attn.py b/vllm/attention/backends/placeholder_attn.py index 3987986f1786b..4116fbf00020c 100644 --- a/vllm/attention/backends/placeholder_attn.py +++ b/vllm/attention/backends/placeholder_attn.py @@ -20,7 +20,7 @@ class PlaceholderAttentionBackend(AttentionBackend): @staticmethod def get_name() -> str: - return "placeholder-attn" + return "NO_ATTENTION" @staticmethod def get_impl_cls() -> Type["PlaceholderAttentionImpl"]: diff --git a/vllm/attention/backends/rocm_flash_attn.py b/vllm/attention/backends/rocm_flash_attn.py index 682eac50126ad..c2aec4aaa74e7 100644 --- a/vllm/attention/backends/rocm_flash_attn.py +++ b/vllm/attention/backends/rocm_flash_attn.py @@ -28,7 +28,7 @@ class ROCmFlashAttentionBackend(AttentionBackend): @staticmethod def get_name() -> str: - return "rocm-flash-attn" + return "ROCM_FLASH" @staticmethod def get_impl_cls() -> Type["ROCmFlashAttentionImpl"]: diff --git a/vllm/attention/backends/torch_sdpa.py b/vllm/attention/backends/torch_sdpa.py index ef8d576616838..1fb7c37578f20 100644 --- a/vllm/attention/backends/torch_sdpa.py +++ b/vllm/attention/backends/torch_sdpa.py @@ -25,7 +25,7 @@ class TorchSDPABackend(AttentionBackend): @staticmethod def get_name() -> str: - return "torch-sdpa" + return "TORCH_SDPA" @staticmethod def get_impl_cls() -> Type["TorchSDPABackendImpl"]: diff --git a/vllm/attention/backends/utils.py b/vllm/attention/backends/utils.py index 358a223e7ed0e..d1a44f3e8bfa6 100644 --- a/vllm/attention/backends/utils.py +++ b/vllm/attention/backends/utils.py @@ -317,8 +317,8 @@ def graph_capture_get_metadata_for_batch( if is_encoder_decoder_model: # The encoder decoder model works only with XFormers backend. # Assert the same. - assert self.runner.attn_backend.get_name() == "xformers", \ - f"Expected attn_backend name to be 'xformers', but "\ + assert self.runner.attn_backend.get_name() == "XFORMERS", \ + f"Expected attn_backend name to be 'XFORMERS', but "\ f" got '{self.runner.attn_backend.get_name()}'" self._update_captured_metadata_for_enc_dec_model( batch_size=batch_size, attn_metadata=attn_metadata) @@ -337,8 +337,8 @@ def get_graph_input_buffers( if is_encoder_decoder_model: # The encoder decoder model works only with XFormers backend. # Assert the same. - assert self.runner.attn_backend.get_name() == "xformers", \ - f"Expected attn_backend name to be 'xformers', but "\ + assert self.runner.attn_backend.get_name() == "XFORMERS", \ + f"Expected attn_backend name to be 'XFORMERS', but "\ f" got '{self.runner.attn_backend.get_name()}'" self._add_additonal_input_buffers_for_enc_dec_model( attn_metadata=attn_metadata, input_buffers=input_buffers) @@ -356,8 +356,8 @@ def prepare_graph_input_buffers( if is_encoder_decoder_model: # The encoder decoder model works only with XFormers backend. # Assert the same. - assert self.runner.attn_backend.get_name() == "xformers", \ - f"Expected attn_backend name to be 'xformers', but "\ + assert self.runner.attn_backend.get_name() == "XFORMERS", \ + f"Expected attn_backend name to be 'XFORMERS', but "\ f" got '{self.runner.attn_backend.get_name()}'" self._prepare_input_buffers_for_enc_dec_model( attn_metadata, input_buffers) diff --git a/vllm/attention/backends/xformers.py b/vllm/attention/backends/xformers.py index 650bc6ec7750a..5aaf13d8ea744 100644 --- a/vllm/attention/backends/xformers.py +++ b/vllm/attention/backends/xformers.py @@ -24,7 +24,7 @@ class XFormersBackend(AttentionBackend): @staticmethod def get_name() -> str: - return "xformers" + return "XFORMERS" @staticmethod def get_impl_cls() -> Type["XFormersImpl"]: diff --git a/vllm/spec_decode/draft_model_runner.py b/vllm/spec_decode/draft_model_runner.py index aaf6ec5f508c8..3aa999fcb9ebb 100644 --- a/vllm/spec_decode/draft_model_runner.py +++ b/vllm/spec_decode/draft_model_runner.py @@ -179,7 +179,7 @@ def supports_gpu_multi_step(self, execute_model_req: ExecuteModelRequest): return False # TODO: Add support for other attn backends - if self.attn_backend.get_name() != "flash-attn": + if self.attn_backend.get_name() != "FLASH_ATTN": return False # TODO: Add support for LORA diff --git a/vllm/spec_decode/spec_decode_worker.py b/vllm/spec_decode/spec_decode_worker.py index 50d2767a03752..316db43502d3b 100644 --- a/vllm/spec_decode/spec_decode_worker.py +++ b/vllm/spec_decode/spec_decode_worker.py @@ -184,7 +184,7 @@ def create_worker( if not disable_mqa_scorer: if scorer_worker.model_runner.attn_backend.get_name( - ) != "flash-attn": + ) != "FLASH_ATTN": disable_mqa_scorer = True logger.info( "[Speculative Decoding] Disabling MQA scorer as the " diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index dc1674cd1ea20..f98fb7e4f01df 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -1855,7 +1855,7 @@ def forward( self.input_buffers["input_ids"].copy_(input_ids, non_blocking=True) self.input_buffers["positions"].copy_(positions, non_blocking=True) - if self.backend_name != "placeholder-attn": + if self.backend_name != "NO_ATTENTION": self.input_buffers["slot_mapping"].copy_( attn_metadata.slot_mapping, non_blocking=True) diff --git a/vllm/worker/multi_step_model_runner.py b/vllm/worker/multi_step_model_runner.py index 0cd0047bebf2d..be2f0d79154d6 100644 --- a/vllm/worker/multi_step_model_runner.py +++ b/vllm/worker/multi_step_model_runner.py @@ -29,8 +29,8 @@ logger = init_logger(__name__) -MULTI_STEP_ATTENTION_BACKENDS = ["flash-attn", "rocm-flash-attn", "flashinfer"] -MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS = ["flash-attn"] +MULTI_STEP_ATTENTION_BACKENDS = ["FLASH_ATTN", "ROCM_FLASH", "FLASHINFER"] +MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS = ["FLASH_ATTN"] def _get_supported_attention_backends(chunked_prefill_enabled: bool) \ -> List[str]: From f6b97293aa7d52e52e9c5144cc98330733a8cf0d Mon Sep 17 00:00:00 2001 From: Dhia Eddine Rhaiem <163106757+dhiaEddineRhaiem@users.noreply.github.com> Date: Mon, 21 Oct 2024 20:50:16 +0400 Subject: [PATCH 024/222] [Model] FalconMamba Support (#9325) --- docs/source/models/supported_models.rst | 5 +++ .../decoder_only/language/test_mamba.py | 2 +- vllm/model_executor/layers/layernorm.py | 1 - vllm/model_executor/models/mamba.py | 38 ++++++++++++++----- vllm/model_executor/models/registry.py | 1 + 5 files changed, 35 insertions(+), 12 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 318139a749d88..62ab8c067f5d0 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -87,6 +87,11 @@ Text Generation - :code:`tiiuae/falcon-7b`, :code:`tiiuae/falcon-40b`, :code:`tiiuae/falcon-rw-7b`, etc. - - ✅︎ + * - :code:`FalconMambaForCausalLM` + - FalconMamba + - :code:`tiiuae/falcon-mamba-7b`, :code:`tiiuae/falcon-mamba-7b-instruct`, etc. + - ✅︎ + - * - :code:`GemmaForCausalLM` - Gemma - :code:`google/gemma-2b`, :code:`google/gemma-7b`, etc. diff --git a/tests/models/decoder_only/language/test_mamba.py b/tests/models/decoder_only/language/test_mamba.py index c27bf6a60a4f4..2dc231c595ffa 100644 --- a/tests/models/decoder_only/language/test_mamba.py +++ b/tests/models/decoder_only/language/test_mamba.py @@ -10,7 +10,7 @@ from ...utils import check_outputs_equal -MODELS = ["state-spaces/mamba-130m-hf"] +MODELS = ["state-spaces/mamba-130m-hf", "tiiuae/falcon-mamba-tiny-dev"] # Use lower-level interfaces to create this greedy generator, as mamba will diff --git a/vllm/model_executor/layers/layernorm.py b/vllm/model_executor/layers/layernorm.py index 10fae84dab723..30b43f375dd5c 100644 --- a/vllm/model_executor/layers/layernorm.py +++ b/vllm/model_executor/layers/layernorm.py @@ -27,7 +27,6 @@ def __init__( self.variance_epsilon = eps self.variance_size_override = (None if var_hidden_size == hidden_size else var_hidden_size) - self.weight = nn.Parameter(torch.ones(hidden_size)) def forward_native( diff --git a/vllm/model_executor/models/mamba.py b/vllm/model_executor/models/mamba.py index 7f2efb9895f25..9f4f391a6682e 100644 --- a/vllm/model_executor/models/mamba.py +++ b/vllm/model_executor/models/mamba.py @@ -22,7 +22,7 @@ QuantizationConfig) from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.layers.vocab_parallel_embedding import ( - VocabParallelEmbedding) + DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( composed_weight_loader, default_weight_loader, sharded_weight_loader) from vllm.model_executor.models.interfaces import (HasInnerState, @@ -59,7 +59,7 @@ def __init__(self, config: MambaConfig, layer_idx): self.conv_kernel_size = config.conv_kernel self.intermediate_size = config.intermediate_size self.time_step_rank = int(config.time_step_rank) - + self.is_falcon_mamba = config.model_type == "falcon_mamba" self.conv1d = ColumnParallelLinear( input_size=self.conv_kernel_size, output_size=self.intermediate_size, @@ -109,6 +109,13 @@ def __init__(self, config: MambaConfig, layer_idx): input_is_parallel=True, ) self.activation = config.hidden_act + if self.is_falcon_mamba: + self.dt_layernorm = RMSNorm(self.time_step_rank, + eps=config.mixer_rms_eps) + self.b_layernorm = RMSNorm(self.ssm_state_size, + eps=config.mixer_rms_eps) + self.c_layernorm = RMSNorm(self.ssm_state_size, + eps=config.mixer_rms_eps) def forward(self, hidden_states: torch.Tensor, attn_metadata: AttentionMetadata, @@ -158,8 +165,12 @@ def forward(self, hidden_states: torch.Tensor, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1, ) - - # Note that Jamba normalizes B, C, and time_step here but Mamba doesn't. + # Note that Jamba and FalconMamba normalizes B, C, and time_step here + # but Mamba doesn't. + if self.is_falcon_mamba: + time_step = self.dt_layernorm(time_step.contiguous()) + B = self.b_layernorm(B.contiguous()) + C = self.c_layernorm(C.contiguous()) discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1) # 3.c perform the recurrence y ← SSM(A, B, C)(x) @@ -213,11 +224,9 @@ def __init__(self, super().__init__() self.layer_idx = layer_idx self.config = config + self.is_falcon_mamba = config.model_type == "falcon_mamba" self.mixer = MambaMixer(config, layer_idx) - self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) - self.pre_ff_layernorm = RMSNorm(config.hidden_size, - eps=config.layer_norm_epsilon) def forward( self, @@ -319,8 +328,18 @@ def __init__( self.unpadded_vocab_size = config.vocab_size if lora_config: self.unpadded_vocab_size += lora_config.lora_extra_vocab_size - - self.lm_head = self.backbone.embeddings + if config.tie_word_embeddings: + self.lm_head = self.backbone.embeddings + else: + self.lm_head = ParallelLMHead( + self.unpadded_vocab_size, + config.hidden_size, + org_num_embeddings=config.vocab_size, + padding_size=DEFAULT_VOCAB_PADDING_SIZE + # We need bigger padding if using lora for kernel + # compatibility + if not lora_config else lora_config.lora_vocab_padding_size, + ) # Used to track and store by the Mamba cache between steps. self.mamba_cache: Optional[MambaCacheManager] = None @@ -398,7 +417,6 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): for name, loaded_weight in weights: if "A_log" in name: name = name.replace("A_log", "A") - # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index f442ce0f63e3e..2a04ece24c8bd 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -53,6 +53,7 @@ # For decapoda-research/llama-* "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"), "MambaForCausalLM": ("mamba", "MambaForCausalLM"), + "FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"), "MistralForCausalLM": ("llama", "LlamaForCausalLM"), "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"), "QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"), From 8ca895484117e55c66c8b5643929866e634e5ce3 Mon Sep 17 00:00:00 2001 From: yudian0504 <138860534+yudian0504@users.noreply.github.com> Date: Tue, 22 Oct 2024 01:33:30 +0800 Subject: [PATCH 025/222] [Bugfix][Misc]: fix graph capture for decoder (#9549) --- vllm/worker/model_runner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index f98fb7e4f01df..8b74f06e77be0 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -828,7 +828,7 @@ def build(self) -> ModelInputForGPU: cuda_graph_pad_size = self._get_cuda_graph_pad_size( num_seqs=len(seq_lens), - max_decode_seq_len=max_encoder_seq_len, + max_decode_seq_len=max_decode_seq_len, max_encoder_seq_len=max_encoder_seq_len) batch_size = len(input_tokens) From ec6bd6c4c6a62f6a6d53d092ba44cc2e82cdf324 Mon Sep 17 00:00:00 2001 From: Varad Ahirwadkar <86718090+varad-ahirwadkar@users.noreply.github.com> Date: Mon, 21 Oct 2024 23:13:02 +0530 Subject: [PATCH 026/222] [BugFix] Use correct python3 binary in Docker.ppc64le entrypoint (#9492) Signed-off-by: Varad Ahirwadkar --- Dockerfile.ppc64le | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile.ppc64le b/Dockerfile.ppc64le index a84e00fd5677f..cd5fcf481f07c 100644 --- a/Dockerfile.ppc64le +++ b/Dockerfile.ppc64le @@ -33,4 +33,4 @@ WORKDIR /workspace/ RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks -ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"] +ENTRYPOINT ["/opt/conda/bin/python3", "-m", "vllm.entrypoints.openai.api_server"] From 5241aa1494a7410f7e89eb341700821e30d04199 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Mon, 21 Oct 2024 14:20:07 -0400 Subject: [PATCH 027/222] [Model][Bugfix] Fix batching with multi-image in PixtralHF (#9518) --- vllm/model_executor/models/llava.py | 60 +++++++++++++++++++++------ vllm/model_executor/models/pixtral.py | 11 ++--- 2 files changed, 54 insertions(+), 17 deletions(-) diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index a83b7d05df7aa..a666dcba290f2 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -287,6 +287,34 @@ def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: return data + def _validate_image_sizes(self, images: List[torch.Tensor], + sizes: List[torch.Tensor]) -> List[torch.Tensor]: + if not isinstance(sizes, list): + sizes = [sizes] + + total_images = sum(size.numel() // 2 for size in sizes) + if total_images != len(images): + raise ValueError("Mismatch in number of images. " + f"Expected {total_images}, got {len(images)}") + img_idx = 0 + for size in sizes: + # Flatten the size tensor to a list of (height, width) pairs + size = size.view(-1, 2).tolist() + for expected_h, expected_w in size: + if img_idx >= len(images): + raise ValueError("Ran out of images before sizes. " + f"{img_idx} >= {len(images)}") + img = images[img_idx] + if img.shape[-2:] != (expected_h, expected_w): + raise ValueError( + "Image size mismatch. Expected " + f"{(expected_h, expected_w)}, got {img.shape[-2:]}") + if img.shape[-3] != 3: + raise ValueError("Image channel mismatch. Expected 3, " + f"got {img.shape[-3]}") + img_idx += 1 + return images + def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[LlavaImageInputs]: pixel_values = kwargs.pop("pixel_values", None) @@ -305,20 +333,28 @@ def _parse_and_validate_image_input( # so we need to produce a list of tensors if image_sizes is not None: images = pixel_values - if isinstance(images, torch.Tensor): - # if passed as batch take all images - NN, N, B, C, W, H = images.shape - images = images.reshape(NN * N * B, C, W, H) - images = [images[i] for i in range(images.size(0))] - elif isinstance(images, list): - # if passed as list flatten lists of tensors - while isinstance(images, list) and len(images) == 1: - images = images[0] - - # TODO: Add validation based on image_sizes + + def flatten_to_3d_tensors(item): + if isinstance(item, torch.Tensor): + if item.dim() >= 3: + return [t for t in item.view(-1, *item.shape[-3:])] + else: + raise ValueError( + f"Unexpected tensor dimension: {item.dim()}") + elif isinstance(item, list): + return [ + t for subitem in item + for t in flatten_to_3d_tensors(subitem) + ] + else: + raise ValueError(f"Unexpected type: {type(item)}") + + # Restructure the batched images into a list of lists of images + images = flatten_to_3d_tensors(pixel_values) + return LlavaImagePixelInputs( type="pixel_values", - data=images, + data=self._validate_image_sizes(images, image_sizes), ) return LlavaImagePixelInputs( diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index 13c5149a63919..f33871c0d5acc 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -907,17 +907,18 @@ def forward( ) -> torch.Tensor: """ Args: - pixel_values: tensor of token features for - all tokens of all images of shape (N_toks, D) + pixel_values: Each image to be processed will be a separate tensor + in pixel_values. This means it will be a list of tensors + because multiple requests batched can have multiple images, + each with their own shape potentially + Returns: image_features: tensor of token features for all tokens of all images of shape (N_toks, D) """ # pass images through initial convolution independently patch_embeds_list = [ - self.patch_conv( - img.reshape(-1, img.shape[-3], img.shape[-2], - img.shape[-1]).to(self.dtype)) + self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in pixel_values ] From 9d9186be971f0553cea771177db43edafb005b72 Mon Sep 17 00:00:00 2001 From: Nick Hill Date: Mon, 21 Oct 2024 21:28:10 +0100 Subject: [PATCH 028/222] [Frontend] Reduce frequency of client cancellation checking (#7959) --- vllm/utils.py | 57 ++++++++++++++++++++++++++++++++++----------------- 1 file changed, 38 insertions(+), 19 deletions(-) diff --git a/vllm/utils.py b/vllm/utils.py index 0147d595fec70..695764dadc123 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -13,10 +13,11 @@ import sys import tempfile import threading +import time import uuid import warnings import weakref -from asyncio import FIRST_COMPLETED, ensure_future +from asyncio import FIRST_COMPLETED, AbstractEventLoop, Future, Task from collections.abc import Mapping from functools import lru_cache, partial, wraps from platform import uname @@ -437,6 +438,12 @@ def _async_wrapper(*args: P.args, **kwargs: P.kwargs) -> asyncio.Future: return _async_wrapper +def _next_task(iterator: AsyncGenerator[T, None], + loop: AbstractEventLoop) -> Task: + # Can use anext() in python >= 3.10 + return loop.create_task(iterator.__anext__()) # type: ignore[arg-type] + + async def iterate_with_cancellation( iterator: AsyncGenerator[T, None], is_cancelled: Callable[[], Awaitable[bool]], @@ -445,19 +452,27 @@ async def iterate_with_cancellation( at least once per second to check for client cancellation. """ - # Can use anext() in python >= 3.10 - awaits = [ensure_future(iterator.__anext__())] + loop = asyncio.get_running_loop() + + awaits: List[Future[T]] = [_next_task(iterator, loop)] + next_cancel_check: float = 0 while True: - done, pending = await asyncio.wait(awaits, timeout=1) - if await is_cancelled(): - with contextlib.suppress(BaseException): - awaits[0].cancel() - await iterator.aclose() - raise asyncio.CancelledError("client cancelled") + done, pending = await asyncio.wait(awaits, timeout=1.5) + + # Check for cancellation at most once per second + time_now = time.time() + if time_now >= next_cancel_check: + if await is_cancelled(): + with contextlib.suppress(BaseException): + awaits[0].cancel() + await iterator.aclose() + raise asyncio.CancelledError("client cancelled") + next_cancel_check = time_now + 1 + if done: try: item = await awaits[0] - awaits[0] = ensure_future(iterator.__anext__()) + awaits[0] = _next_task(iterator, loop) yield item except StopAsyncIteration: # we are done @@ -478,25 +493,29 @@ async def merge_async_iterators( to check for client cancellation. """ - # Can use anext() in python >= 3.10 - awaits = { - ensure_future(pair[1].__anext__()): pair - for pair in enumerate(iterators) - } - timeout = None if is_cancelled is None else 1 + loop = asyncio.get_running_loop() + + awaits = {_next_task(pair[1], loop): pair for pair in enumerate(iterators)} + timeout = None if is_cancelled is None else 1.5 + next_cancel_check: float = 0 try: while awaits: done, pending = await asyncio.wait(awaits.keys(), return_when=FIRST_COMPLETED, timeout=timeout) - if is_cancelled is not None and await is_cancelled(): - raise asyncio.CancelledError("client cancelled") + if is_cancelled is not None: + # Check for cancellation at most once per second + time_now = time.time() + if time_now >= next_cancel_check: + if await is_cancelled(): + raise asyncio.CancelledError("client cancelled") + next_cancel_check = time_now + 1 for d in done: pair = awaits.pop(d) try: item = await d i, it = pair - awaits[ensure_future(it.__anext__())] = pair + awaits[_next_task(it, loop)] = pair yield i, item except StopAsyncIteration: pass From d621c43df72e118d9cbfb4ca408b84bdeefa4a94 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 21 Oct 2024 13:54:57 -0700 Subject: [PATCH 029/222] [doc] fix format (#9562) --- docs/source/getting_started/installation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/getting_started/installation.rst b/docs/source/getting_started/installation.rst index 99c695ac4ddb1..5c19f3cf7f1a0 100644 --- a/docs/source/getting_started/installation.rst +++ b/docs/source/getting_started/installation.rst @@ -116,7 +116,7 @@ The script will: Now, you can edit the Python code in the current directory, and the changes will be reflected when you run vLLM. -Once you have finished editing or want to install another vLLM wheel, you should exit the development environment using `the same script `_ with the ``--quit-dev``(or ``-q`` for short) flag: +Once you have finished editing or want to install another vLLM wheel, you should exit the development environment using `the same script `_ with the ``--quit-dev`` (or ``-q`` for short) flag: .. code-block:: console From 15713e3b7579d56758fab1150c99dd49633b5669 Mon Sep 17 00:00:00 2001 From: Nick Hill Date: Mon, 21 Oct 2024 22:14:29 +0100 Subject: [PATCH 030/222] [BugFix] Update draft model TP size check to allow matching target TP size (#9394) Co-authored-by: Baoyuan Qi --- vllm/config.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vllm/config.py b/vllm/config.py index f57aa4048ae9b..00dd047e6d058 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -1408,11 +1408,11 @@ def create_draft_parallel_config( else: speculative_draft_tensor_parallel_size = \ target_parallel_config.tensor_parallel_size - elif speculative_draft_tensor_parallel_size != 1: - # TODO(wooyeon): allow tp values larger than 1 + elif speculative_draft_tensor_parallel_size not in ( + 1, target_parallel_config.tensor_parallel_size): raise ValueError( f"{speculative_draft_tensor_parallel_size=} cannot be " - f"other value than 1") + f"other value than 1 or target model tensor_parallel_size") draft_parallel_config = ParallelConfig( pipeline_parallel_size=target_parallel_config. From 711f3a7806de8729e8e9cedf04e056c374d8e626 Mon Sep 17 00:00:00 2001 From: Wallas Henrique Date: Mon, 21 Oct 2024 18:49:41 -0300 Subject: [PATCH 031/222] [Frontend] Don't log duplicate error stacktrace for every request in the batch (#9023) Signed-off-by: Wallas Santos --- tests/mq_llm_engine/test_error_handling.py | 51 +++++++++++++++++----- vllm/engine/multiprocessing/client.py | 12 +++++ 2 files changed, 53 insertions(+), 10 deletions(-) diff --git a/tests/mq_llm_engine/test_error_handling.py b/tests/mq_llm_engine/test_error_handling.py index 616a15a1328de..205ab00aa6b17 100644 --- a/tests/mq_llm_engine/test_error_handling.py +++ b/tests/mq_llm_engine/test_error_handling.py @@ -59,15 +59,7 @@ async def test_evil_forward(tmp_socket): await asyncio.sleep(2.0) await client.check_health() - # Throws an error in first forward pass. - with pytest.raises(RAISED_ERROR): - async for _ in client.generate(prompt="Hello my name is", - sampling_params=SamplingParams(), - request_id=uuid.uuid4()): - pass - assert client.errored - - # Engine is errored, should get ENGINE_DEAD_ERROR. + # Throws an error that should get ENGINE_DEAD_ERROR. with pytest.raises(MQEngineDeadError): async for _ in client.generate(prompt="Hello my name is", sampling_params=SamplingParams(), @@ -149,7 +141,7 @@ async def test_failed_abort(tmp_socket): client = await engine.make_client() assert client.is_running - # Firsh check health should work. + # First check health should work. await client.check_health() # Trigger an abort on the client side. @@ -174,6 +166,45 @@ async def test_failed_abort(tmp_socket): client.close() +@pytest.mark.asyncio +async def test_batch_error(tmp_socket): + with RemoteMQLLMEngine(engine_args=ENGINE_ARGS, + ipc_path=tmp_socket, + run_fn=run_with_evil_abort) as engine: + + client = await engine.make_client() + assert client.is_running + + # First check health should work. + await client.check_health() + + # Batch of requests + async def do_generate(client): + # min_tokens=2048 to keep busy the engine busy + # to get enough time to get process a request + # that will crash the engine + params = SamplingParams(min_tokens=2048, max_tokens=2048) + async for _ in client.generate(prompt="Hello my name is", + sampling_params=params, + request_id=uuid.uuid4()): + pass + + tasks = [asyncio.create_task(do_generate(client)) for _ in range(10)] + + # This request will force a processing batch to raise + # an exception and next the engine get errored + await client.abort(request_id="foo") + + # The batch of those request failed, then they + # should get the same exception as a MQEngineDeadError. + errors = await asyncio.gather(*tasks, return_exceptions=True) + for e in errors: + assert isinstance(e, MQEngineDeadError) + assert "KeyError" in repr(e) + + client.close() + + @pytest.mark.asyncio async def test_bad_request(tmp_socket): with RemoteMQLLMEngine(engine_args=ENGINE_ARGS, diff --git a/vllm/engine/multiprocessing/client.py b/vllm/engine/multiprocessing/client.py index 9732c7098e160..9e5a6b21f4c18 100644 --- a/vllm/engine/multiprocessing/client.py +++ b/vllm/engine/multiprocessing/client.py @@ -204,8 +204,20 @@ async def run_output_handler_loop(self): # (and record only the first one) if is_engine_errored and not self._errored_with: self._errored_with = exception + # If engine is errored, no matter the type of exception + # it will no longer be able to receive new requests, + # therefore we have to inform that the current + # processed requests failed as well. Send back a dead + # engine error give this feedback and also give a + # 'hint' to the server to shutdown next. + exception = self.dead_error if request_id is None: + # If request_id is None, then the engine raised an + # exception for a batch, and we may not know the + # request that caused it, neither if it was actually + # caused by any of them (e.g. CUDA OOM). Therefore we + # broadcast the same exception for all requests. for queue_i in tuple(self.output_queues.values()): queue_i.put_nowait(exception) else: From 575dcebe9adc587b26feba02e4c1d13cb69c0305 Mon Sep 17 00:00:00 2001 From: Kuntai Du Date: Mon, 21 Oct 2024 18:45:15 -0500 Subject: [PATCH 032/222] [CI] Make format checker error message more user-friendly by using emoji (#9564) This PR makes format checker error message more user-friendly by adding emojis. --- format.sh | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/format.sh b/format.sh index 1ac028d00e3a4..be6ee0ce46dcb 100755 --- a/format.sh +++ b/format.sh @@ -21,6 +21,20 @@ builtin cd "$(dirname "${BASH_SOURCE:-$0}")" ROOT="$(git rev-parse --show-toplevel)" builtin cd "$ROOT" || exit 1 +check_command() { + if ! command -v "$1" &> /dev/null; then + echo "❓❓$1 is not installed, please run \`pip install -r requirements-lint.txt\`" + exit 1 + fi +} + +check_command yapf +check_command ruff +check_command mypy +check_command codespell +check_command isort +check_command clang-format + YAPF_VERSION=$(yapf --version | awk '{print $2}') RUFF_VERSION=$(ruff --version | awk '{print $2}') MYPY_VERSION=$(mypy --version | awk '{print $2}') @@ -31,7 +45,7 @@ CLANGFORMAT_VERSION=$(clang-format --version | awk '{print $3}') # # params: tool name, tool version, required version tool_version_check() { if [[ $2 != $3 ]]; then - echo "Wrong $1 version installed: $3 is required, not $2." + echo "❓❓Wrong $1 version installed: $3 is required, not $2." exit 1 fi } @@ -281,10 +295,12 @@ tools/actionlint.sh -color echo 'vLLM actionlint: Done' if ! git diff --quiet &>/dev/null; then - echo 'Reformatted files. Please review and stage the changes.' - echo 'Changes not staged for commit:' - echo + echo + echo "🔍🔍There are files changed by the format checker or by you that are not added and committed:" git --no-pager diff --name-only + echo "🔍🔍Format checker passed, but please add, commit and push all the files above to include changes made by the format checker." exit 1 +else + echo "✨🎉 Format check passed! Congratulations! 🎉✨" fi From ef7faad1b8e6473556b732a7e8d5bc9be5df556f Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Mon, 21 Oct 2024 19:10:56 -0500 Subject: [PATCH 033/222] :bug: Fixup more test failures from memory profiling (#9563) Signed-off-by: Joe Runde --- ...Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml | 11 +++++++++++ .buildkite/lm-eval-harness/configs/models-small.txt | 2 +- tests/lora/test_minicpmv.py | 1 + 3 files changed, 13 insertions(+), 1 deletion(-) create mode 100644 .buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml diff --git a/.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml b/.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml new file mode 100644 index 0000000000000..78347f63fa793 --- /dev/null +++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml @@ -0,0 +1,11 @@ +# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1 +model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8" +tasks: +- name: "gsm8k" + metrics: + - name: "exact_match,strict-match" + value: 0.356 + - name: "exact_match,flexible-extract" + value: 0.358 +limit: 1000 +num_fewshot: 5 diff --git a/.buildkite/lm-eval-harness/configs/models-small.txt b/.buildkite/lm-eval-harness/configs/models-small.txt index 64a0f428587af..6057229ac50f3 100644 --- a/.buildkite/lm-eval-harness/configs/models-small.txt +++ b/.buildkite/lm-eval-harness/configs/models-small.txt @@ -1,6 +1,6 @@ Meta-Llama-3-8B-Instruct.yaml Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml -Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml +Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml diff --git a/tests/lora/test_minicpmv.py b/tests/lora/test_minicpmv.py index 81b8188e638c9..be040060d02b2 100644 --- a/tests/lora/test_minicpmv.py +++ b/tests/lora/test_minicpmv.py @@ -61,6 +61,7 @@ def test_minicpmv_lora(minicpmv_lora_files): max_loras=4, max_lora_rank=64, trust_remote_code=True, + gpu_memory_utilization=0.97 # This model is pretty big for CI gpus ) output1 = do_sample(llm, minicpmv_lora_files, lora_id=1) From 76a5e13270f32216bb28cfe185bada5e88e407d7 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 21 Oct 2024 17:31:44 -0700 Subject: [PATCH 034/222] [core] move parallel sampling out from vllm core (#9302) --- tests/entrypoints/openai/test_completion.py | 34 ++++++ vllm/engine/llm_engine.py | 52 +++++++-- vllm/outputs.py | 43 ++++--- vllm/sequence.py | 122 +++++++++++++++++++- 4 files changed, 222 insertions(+), 29 deletions(-) diff --git a/tests/entrypoints/openai/test_completion.py b/tests/entrypoints/openai/test_completion.py index cc72a49ebbbda..f03bdb045f640 100644 --- a/tests/entrypoints/openai/test_completion.py +++ b/tests/entrypoints/openai/test_completion.py @@ -340,6 +340,40 @@ async def test_completion_streaming(client: openai.AsyncOpenAI, assert "".join(chunks) == single_output +@pytest.mark.asyncio +@pytest.mark.parametrize( + "model_name", + [MODEL_NAME, "zephyr-lora", "zephyr-pa"], +) +async def test_parallel_streaming(client: openai.AsyncOpenAI, model_name: str): + """Streaming for parallel sampling. + The tokens from multiple samples, are flattened into a single stream, + with an index to indicate which sample the token belongs to. + """ + + prompt = "What is an LLM?" + n = 3 + max_tokens = 5 + + stream = await client.completions.create(model=model_name, + prompt=prompt, + max_tokens=max_tokens, + n=n, + stream=True) + chunks: List[List[str]] = [[] for i in range(n)] + finish_reason_count = 0 + async for chunk in stream: + index = chunk.choices[0].index + text = chunk.choices[0].text + chunks[index].append(text) + if chunk.choices[0].finish_reason is not None: + finish_reason_count += 1 + assert finish_reason_count == n + for chunk in chunks: + assert len(chunk) == max_tokens + print("".join(chunk)) + + @pytest.mark.asyncio @pytest.mark.parametrize( "model_name", diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index a90bfce8491fb..25c4e76d9b159 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -44,8 +44,10 @@ from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.sampling_params import RequestOutputKind, SamplingParams from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest, - Sequence, SequenceGroup, SequenceGroupMetadata, - SequenceGroupOutput, SequenceStatus) + ParallelSampleSequenceGroup, Sequence, + SequenceGroup, SequenceGroupBase, + SequenceGroupMetadata, SequenceGroupOutput, + SequenceStatus) from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context, init_tracer) from vllm.transformers_utils.config import try_get_generation_config @@ -474,6 +476,8 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: ), )) + self.seq_id_to_seq_group: Dict[str, SequenceGroupBase] = {} + def _initialize_kv_caches(self) -> None: """Initialize the KV cache in the worker(s). @@ -642,7 +646,10 @@ def _add_processed_request( prompt_adapter_request: Optional[PromptAdapterRequest], trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, - ) -> None: + ) -> SequenceGroup: + """Add a processed request to the engine's request pool. + return the created sequence group. + """ self._validate_model_inputs(processed_inputs) # Create the sequences. block_size = self.cache_config.block_size @@ -696,6 +703,8 @@ def _add_processed_request( min_cost_scheduler = self.scheduler[costs.index(min(costs))] min_cost_scheduler.add_seq_group(seq_group) + return seq_group + def stop_remote_worker_execution_loop(self) -> None: self.model_executor.stop_remote_worker_execution_loop() @@ -711,7 +720,7 @@ def add_request( trace_headers: Optional[Mapping[str, str]] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, priority: int = 0, - ) -> None: + ) -> Optional[SequenceGroup]: ... @overload @@ -725,7 +734,7 @@ def add_request( trace_headers: Optional[Mapping[str, str]] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, priority: int = 0, - ) -> None: + ) -> Optional[SequenceGroup]: ... @deprecate_kwargs( @@ -744,7 +753,7 @@ def add_request( priority: int = 0, *, inputs: Optional[PromptType] = None, # DEPRECATED - ) -> None: + ) -> Optional[SequenceGroup]: """Add a request to the engine's request pool. The request is added to the request pool and will be processed by the @@ -788,6 +797,22 @@ def add_request( >>> # continue the request processing >>> ... """ + + if isinstance(params, SamplingParams) and params.n > 1: + ParallelSampleSequenceGroup.add_request( + request_id, + self, + params, + prompt=prompt, + arrival_time=arrival_time, + lora_request=lora_request, + trace_headers=trace_headers, + prompt_adapter_request=prompt_adapter_request, + priority=priority, + inputs=inputs, + ) + return None + if inputs is not None: prompt = inputs assert prompt is not None and params is not None @@ -818,7 +843,7 @@ def add_request( processed_inputs["mm_processor_kwargs"] = preprocessed_inputs.get( "mm_processor_kwargs") - self._add_processed_request( + return self._add_processed_request( request_id=request_id, processed_inputs=processed_inputs, params=params, @@ -1135,7 +1160,9 @@ def _process_model_outputs(self, seq_group = scheduled_seq_group.seq_group seq_group.maybe_set_first_token_time(now) request_output = RequestOutputFactory.create( - seq_group, use_cache=self.use_cached_outputs) + seq_group, + self.seq_id_to_seq_group, + use_cache=self.use_cached_outputs) if request_output: ctx.request_outputs.append(request_output) @@ -1175,7 +1202,9 @@ def _process_model_outputs(self, seq_group = scheduled_seq_group.seq_group seq_group.maybe_set_first_token_time(now) request_output = RequestOutputFactory.create( - seq_group, use_cache=self.use_cached_outputs) + seq_group, + self.seq_id_to_seq_group, + use_cache=self.use_cached_outputs) if request_output: ctx.request_outputs.append(request_output) @@ -1194,7 +1223,10 @@ def _process_model_outputs(self, continue request_output = RequestOutputFactory.create( - seq_group, use_cache=self.use_cached_outputs) + seq_group, + self.seq_id_to_seq_group, + use_cache=self.use_cached_outputs, + ) if request_output: ctx.request_outputs.append(request_output) diff --git a/vllm/outputs.py b/vllm/outputs.py index 07650241cb638..951976310e7ae 100644 --- a/vllm/outputs.py +++ b/vllm/outputs.py @@ -1,13 +1,13 @@ import time from dataclasses import dataclass -from typing import List, Optional +from typing import Dict, List, Optional from typing import Sequence as GenericSequence from typing import Union from vllm.lora.request import LoRARequest from vllm.sampling_params import RequestOutputKind from vllm.sequence import (PromptLogprobs, RequestMetrics, SampleLogprobs, - SequenceGroup, SequenceStatus) + SequenceGroup, SequenceGroupBase, SequenceStatus) @dataclass @@ -114,14 +114,28 @@ def __init__( self.encoder_prompt_token_ids = encoder_prompt_token_ids @classmethod - def from_seq_group(cls, seq_group: SequenceGroup, - use_cache: bool) -> Optional["RequestOutput"]: + def from_seq_group( + cls, seq_group: SequenceGroup, use_cache: bool, + seq_id_to_seq_group: Dict[str, SequenceGroupBase] + ) -> Optional["RequestOutput"]: + finished = seq_group.is_finished() + + if seq_group.request_id in seq_id_to_seq_group: + group: SequenceGroupBase = seq_id_to_seq_group[ + seq_group.request_id] + if finished: + group.finish_seq(seq_group) + assembled_seq_group = group.maybe_assemble_group(seq_group) + if assembled_seq_group is None: + return None + return cls.from_seq_group(assembled_seq_group, use_cache, + seq_id_to_seq_group) + sampling_params = seq_group.sampling_params if sampling_params is None: raise ValueError( "Sampling parameters are missing for a CompletionRequest.") - finished = seq_group.is_finished() if sampling_params.output_kind == RequestOutputKind.FINAL_ONLY and ( not finished): return None @@ -136,15 +150,7 @@ def from_seq_group(cls, seq_group: SequenceGroup, outputs=[], finished=False) - seqs = seq_group.get_seqs() - if len(seqs) == 1: - top_n_seqs = seqs - else: - # Get the top-n sequences. - n = sampling_params._real_n or sampling_params.n - sorting_key = lambda seq: seq.get_cumulative_logprob() - sorted_seqs = sorted(seqs, key=sorting_key, reverse=True) - top_n_seqs = sorted_seqs[:n] + top_n_seqs = seq_group.get_seqs() # Create the outputs. # NOTE: We need omit logprobs here explicitly because the sequence @@ -208,7 +214,7 @@ def from_seq_group(cls, seq_group: SequenceGroup, else: output = CompletionOutput( - seqs.index(seq), output_text, [output_token_ids] + top_n_seqs.index(seq), output_text, [output_token_ids] if isinstance(output_token_ids, int) else output_token_ids, seq.get_cumulative_logprob() if include_logprobs else None, output_logprobs, @@ -309,10 +315,13 @@ def __repr__(self): class RequestOutputFactory: @staticmethod - def create(seq_group: SequenceGroup, use_cache: bool = False): + def create(seq_group: SequenceGroup, + seq_id_to_seq_group: Dict[str, SequenceGroupBase], + use_cache: bool = False): # Determine the type based on a condition, for example: if hasattr(seq_group, 'embeddings') and seq_group.embeddings is not None: return EmbeddingRequestOutput.from_seq_group(seq_group) else: - return RequestOutput.from_seq_group(seq_group, use_cache) + return RequestOutput.from_seq_group(seq_group, use_cache, + seq_id_to_seq_group) diff --git a/vllm/sequence.py b/vllm/sequence.py index e580d69ec5afb..93f58f00ef77b 100644 --- a/vllm/sequence.py +++ b/vllm/sequence.py @@ -4,7 +4,7 @@ from abc import ABC, abstractmethod from array import array from collections import defaultdict -from dataclasses import dataclass +from dataclasses import dataclass, field from functools import cached_property, reduce from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional from typing import Sequence as GenericSequence @@ -17,7 +17,7 @@ from vllm.lora.request import LoRARequest from vllm.pooling_params import PoolingParams from vllm.prompt_adapter.request import PromptAdapterRequest -from vllm.sampling_params import SamplingParams +from vllm.sampling_params import RequestOutputKind, SamplingParams from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics if TYPE_CHECKING: @@ -1401,3 +1401,121 @@ def clone( last_sampled_token_ids=self.last_sampled_token_ids.clone() if self.last_sampled_token_ids is not None else None, async_callback=self.async_callback) + + +@dataclass +class SequenceGroupBase: + group_id: str # the original request id before splitting + + assembled_seq_group: Optional[SequenceGroup] = None + + # seq id to a unique index inside this group + seq_id_to_index: Dict[str, int] = field(default_factory=dict) + + # seq ids to be finished + to_be_finished: Dict[str, SequenceGroup] = field(default_factory=dict) + + # seq id to finished sequences + finished_reqs: Dict[str, SequenceGroup] = field(default_factory=dict) + + streaming: bool = False + + output_produced: bool = False + + @staticmethod + def add_request(request_id: str, engine, params, *args, **kwargs): + """When we are ready to add a request with request_id and params + into the engine, we can split the request into multiple requests. + """ + raise NotImplementedError + + def finish_seq(self, seq: SequenceGroup): + """The sequence `seq` finishes, we should record the information. + """ + del self.to_be_finished[seq.request_id] + self.finished_reqs[seq.request_id] = seq + + def maybe_assemble_group( + self, seq_group: SequenceGroup) -> Optional[SequenceGroup]: + """Assemble the sequence group, for producing the final + output, or adding request in the engine again. + """ + raise NotImplementedError + + +class ParallelSampleSequenceGroup(SequenceGroupBase): + + @staticmethod + def add_request(request_id: str, engine, params, **kwargs): + original_params = params + params = copy.deepcopy(original_params) + params.n = 1 + group = ParallelSampleSequenceGroup(request_id) + seqs = [] + for i in range(original_params.n): + request_id_i = f"{request_id}_parallel_sample_{i}" + group.seq_id_to_index[request_id_i] = i + seq_group = engine.add_request( + request_id_i, + params=params, + **kwargs, + ) # type: ignore + assert seq_group is not None + engine.seq_id_to_seq_group[request_id_i] = group + group.to_be_finished[request_id_i] = seq_group + seqs.append(seq_group.seqs[0]) + + # for parallel sampling, the `assembled_seq_group` is always + # available, since we have all the sequences ready, and they + # will not change. + group.assembled_seq_group = SequenceGroup( + request_id=request_id, + seqs=seqs, + arrival_time=seq_group.arrival_time, + sampling_params=original_params, + lora_request=seq_group.lora_request, + embeddings=seq_group.embeddings, + pooling_params=seq_group.pooling_params, + encoder_seq=seq_group.encoder_seq, + trace_headers=seq_group.trace_headers, + prompt_adapter_request=seq_group.prompt_adapter_request, + priority=seq_group.priority, + ) + + group.streaming = params.output_kind == RequestOutputKind.DELTA + group.output_produced = False + + def maybe_assemble_group( + self, seq_group: SequenceGroup) -> Optional[SequenceGroup]: + + # in the streaming mode, we will return the assembled sequence + # for the first sequence, and then return None for the rest of + # sequences + if self.streaming: + if self.seq_id_to_index[seq_group.request_id] == 0: + return self.assembled_seq_group + return None + + # in the non-streaming mode, we will return the assembled sequence + # once after all sequences finish, and then return None for the + # rest of the time + + if len(self.to_be_finished) > 0: + return None + + assert self.assembled_seq_group is not None + params = self.assembled_seq_group.sampling_params + assert isinstance(params, SamplingParams) + if not self.output_produced: + self.output_produced = True + if params._real_n is not None: + # Get the top-n sequences. + n = params._real_n or params.n + seqs = self.assembled_seq_group.seqs + sorting_key = lambda seq: seq.get_cumulative_logprob() + sorted_seqs = sorted(seqs, key=sorting_key, reverse=True) + top_n_seqs = sorted_seqs[:n] + self.assembled_seq_group.seqs = top_n_seqs + return self.assembled_seq_group + if self.output_produced: + return None From b729901139c93edd9ef8d48a16d269f070d8ba42 Mon Sep 17 00:00:00 2001 From: Travis Johnson Date: Mon, 21 Oct 2024 20:46:24 -0600 Subject: [PATCH 035/222] [Bugfix]: serialize config by value for --trust-remote-code (#6751) Signed-off-by: Travis Johnson Co-authored-by: Cyrus Leung --- tests/distributed/test_pipeline_parallel.py | 63 ++++++++++++--------- vllm/engine/arg_utils.py | 4 ++ vllm/transformers_utils/config.py | 62 ++++++++++++++++++++ vllm/utils.py | 2 + 4 files changed, 103 insertions(+), 28 deletions(-) diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index fee201850f203..49c80bd640423 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -28,19 +28,25 @@ class ParallelSetup(NamedTuple): chunked_prefill: bool +class PPTestOptions(NamedTuple): + multi_node_only: bool + trust_remote_code: bool + tokenizer_mode: Optional[str] + + @dataclass class PPTestSettings: parallel_setups: List[ParallelSetup] distributed_backends: List[str] task: TaskOption - trust_remote_code: bool - tokenizer_mode: Optional[str] + test_options: PPTestOptions @staticmethod def detailed( *, tp_base: int = 1, pp_base: int = 2, + multi_node_only: bool = False, task: TaskOption = "auto", trust_remote_code: bool = False, tokenizer_mode: Optional[str] = None, @@ -70,8 +76,9 @@ def detailed( ], distributed_backends=["mp", "ray"], task=task, - trust_remote_code=trust_remote_code, - tokenizer_mode=tokenizer_mode, + test_options=PPTestOptions(multi_node_only=multi_node_only, + trust_remote_code=trust_remote_code, + tokenizer_mode=tokenizer_mode), ) @staticmethod @@ -80,6 +87,7 @@ def fast( tp_base: int = 1, pp_base: int = 2, task: TaskOption = "auto", + multi_node_only: bool = False, trust_remote_code: bool = False, tokenizer_mode: Optional[str] = None, ): @@ -92,15 +100,18 @@ def fast( ], distributed_backends=["mp"], task=task, - trust_remote_code=trust_remote_code, - tokenizer_mode=tokenizer_mode, + test_options=PPTestOptions(multi_node_only=multi_node_only, + trust_remote_code=trust_remote_code, + tokenizer_mode=tokenizer_mode), ) def iter_params(self, model_name: str): + opts = self.test_options + for parallel_setup in self.parallel_setups: for distributed_backend in self.distributed_backends: yield (model_name, parallel_setup, distributed_backend, - self.task, self.trust_remote_code, self.tokenizer_mode) + self.task, opts) # NOTE: You can adjust tp_base and/or pp_base locally to fit the model in GPU @@ -110,6 +121,7 @@ def iter_params(self, model_name: str): GENERATION_MODEL_SETTINGS = { # [DETAILED TESTS] "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(), + "microsoft/Phi-3-mini-4k-instruct": PPTestSettings.detailed(trust_remote_code=True, multi_node_only=True), # noqa: E501 # [FAST TESTS] # Uses Llama # "BAAI/AquilaChat-7B": PPTestSettings.fast(), @@ -151,10 +163,8 @@ def iter_params(self, model_name: str): "facebook/opt-iml-max-1.3b": PPTestSettings.fast(), "OrionStarAI/Orion-14B-Chat": PPTestSettings.fast(trust_remote_code=True), "microsoft/phi-2": PPTestSettings.fast(), - "microsoft/Phi-3-mini-4k-instruct": PPTestSettings.fast(), "microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(trust_remote_code=True), # noqa: E501 - # FIXME: https://github.com/vllm-project/vllm/issues/8553 - # "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.fast(trust_remote_code=True), # noqa: E501 + "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.fast(trust_remote_code=True), # noqa: E501 "adept/persimmon-8b-chat": PPTestSettings.fast(), "Qwen/Qwen-7B-Chat": PPTestSettings.fast(trust_remote_code=True), "Qwen/Qwen2-beta-7B-Chat": PPTestSettings.fast(), @@ -205,6 +215,7 @@ def iter_params(self, model_name: str): # [LANGUAGE GENERATION] "meta-llama/Meta-Llama-3-8B", "ibm/PowerLM-3b", + "microsoft/Phi-3-mini-4k-instruct", # [LANGUAGE EMBEDDING] "intfloat/e5-mistral-7b-instruct", "BAAI/bge-multilingual-gemma2", @@ -220,19 +231,21 @@ def _compare_tp( parallel_setup: ParallelSetup, distributed_backend: str, task: TaskOption, - trust_remote_code: bool, - tokenizer_mode: Optional[str], + test_options: PPTestOptions, num_gpus_available: int, *, - method: Literal["generate", "encode"] = "encode", + method: Literal["generate", "encode"], ): tp_size, pp_size, eager_mode, chunked_prefill = parallel_setup + multi_node_only, trust_remote_code, tokenizer_mode = test_options if num_gpus_available < tp_size * pp_size: pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs") if VLLM_MULTI_NODE and distributed_backend == "mp": pytest.skip("Skipping multi-node pipeline parallel test for " "multiprocessing distributed backend") + if multi_node_only and not VLLM_MULTI_NODE: + pytest.skip("Not in multi-node setting") common_args = [ # use half precision for speed and memory savings in CI environment @@ -307,7 +320,7 @@ def _compare_tp( @pytest.mark.parametrize( ("model_name", "parallel_setup", "distributed_backend", "task", - "trust_remote_code", "tokenizer_mode"), + "test_options"), [ params for model_name, settings in GENERATION_MODEL_SETTINGS.items() for params in settings.iter_params(model_name) @@ -320,23 +333,21 @@ def test_tp_language_generation( parallel_setup: ParallelSetup, distributed_backend: str, task: TaskOption, - trust_remote_code: bool, - tokenizer_mode: Optional[str], + test_options: PPTestOptions, num_gpus_available, ): _compare_tp(model_name, parallel_setup, distributed_backend, task, - trust_remote_code, - tokenizer_mode, + test_options, num_gpus_available, method="generate") @pytest.mark.parametrize( ("model_name", "parallel_setup", "distributed_backend", "task", - "trust_remote_code", "tokenizer_mode"), + "test_options"), [ params for model_name, settings in EMBEDDING_MODEL_SETTINGS.items() for params in settings.iter_params(model_name) @@ -349,23 +360,21 @@ def test_tp_language_embedding( parallel_setup: ParallelSetup, distributed_backend: str, task: TaskOption, - trust_remote_code: bool, - tokenizer_mode: Optional[str], + test_options: PPTestOptions, num_gpus_available, ): _compare_tp(model_name, parallel_setup, distributed_backend, task, - trust_remote_code, - tokenizer_mode, + test_options, num_gpus_available, method="encode") @pytest.mark.parametrize( ("model_name", "parallel_setup", "distributed_backend", "task", - "trust_remote_code", "tokenizer_mode"), + "test_options"), [ params for model_name, settings in MULTIMODAL_MODEL_SETTINGS.items() for params in settings.iter_params(model_name) @@ -378,15 +387,13 @@ def test_tp_multimodal_generation( parallel_setup: ParallelSetup, distributed_backend: str, task: TaskOption, - trust_remote_code: bool, - tokenizer_mode: Optional[str], + test_options: PPTestOptions, num_gpus_available, ): _compare_tp(model_name, parallel_setup, distributed_backend, task, - trust_remote_code, - tokenizer_mode, + test_options, num_gpus_available, method="generate") diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 56582ab618797..a5cfaf3977a4f 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -16,6 +16,8 @@ from vllm.executor.executor_base import ExecutorBase from vllm.logger import init_logger from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS +from vllm.transformers_utils.config import ( + maybe_register_config_serialize_by_value) from vllm.transformers_utils.utils import check_gguf_file from vllm.utils import FlexibleArgumentParser @@ -924,6 +926,8 @@ def create_engine_config(self) -> EngineConfig: "supported for multimodal models and has been disabled.") self.enable_prefix_caching = False + maybe_register_config_serialize_by_value(self.trust_remote_code) + cache_config = CacheConfig( # neuron needs block_size = max_model_len block_size=self.block_size if self.device != "neuron" else diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 46405f3529215..9bd2531d7a15c 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -232,6 +232,68 @@ def get_config( return config +def maybe_register_config_serialize_by_value(trust_remote_code: bool) -> None: + """Try to register HF model configuration class to serialize by value + + With trust_remote_code, the config class is typically an instance of a + custom class imported from the HF modules cache. The class will not be + importable in spawned workers by default (and won't exist at all on + other nodes), which breaks serialization of the config. + + In this function we tell the cloudpickle serialization library to pass + instances of these generated classes by value instead of by reference, + i.e. the class definition is serialized along with its data so that the + class module does not need to be importable on the receiving end. This + registration only works if the modules cache has already been + initialized. + + + See: https://github.com/cloudpipe/cloudpickle?tab=readme-ov-file#overriding-pickles-serialization-mechanism-for-importable-constructs + """ + if not trust_remote_code: + return + + try: + import transformers_modules + except ImportError: + logger.debug("Could not import transformers_modules used for remote" + " code. If remote code is not needed remove" + " `--trust-remote-code`.") + return + + try: + import cloudpickle + cloudpickle.register_pickle_by_value(transformers_modules) + + # ray vendors its own version of cloudpickle + from vllm.executor.ray_utils import ray + if ray: + ray.cloudpickle.register_pickle_by_value(transformers_modules) + + # multiprocessing uses pickle to serialize arguments when using spawn + # Here we get pickle to use cloudpickle to serialize ModelConfig objects + # that contain instances of the custom config class to avoid + # serialization problems if the generated module (and model) has a `.` + # in its name + import multiprocessing + import pickle + + from vllm.config import ModelConfig + + def _reduce_modelconfig(mc: ModelConfig): + return (pickle.loads, (cloudpickle.dumps(mc), )) + + multiprocessing.reducer.register(ModelConfig, _reduce_modelconfig) + + except Exception as e: + logger.warning( + "Unable to register remote classes used by" + " trust_remote_code with by-value serialization. This may" + " lead to a later error. If remote code is not needed" + " remove `--trust-remote-code`", + exc_info=e) + + def load_params_config(model, revision) -> PretrainedConfig: # This function loads a params.json config which # should be used when loading models in mistral format diff --git a/vllm/utils.py b/vllm/utils.py index 695764dadc123..d1a995a3ac8c5 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -968,6 +968,8 @@ def flatten_2d_lists(lists: List[List[T]]) -> List[T]: return [item for sublist in lists for item in sublist] +# TODO: This function can be removed if transformer_modules classes are +# serialized by value when communicating between processes def init_cached_hf_modules() -> None: """ Lazy initialization of the Hugging Face modules. From f085995a7b073f0f4a330f469d9f489160e5b7a1 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Tue, 22 Oct 2024 10:47:29 +0800 Subject: [PATCH 036/222] [CI/Build] Remove unnecessary `fork_new_process` (#9484) --- tests/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/utils.py b/tests/utils.py index 2ab7329485dfc..e983104e3cb0c 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -587,7 +587,7 @@ def large_gpu_test(*, min_gb: int): ) def wrapper(f: Callable[_P, None]) -> Callable[_P, None]: - return test_skipif(fork_new_process_for_each_test(f)) + return test_skipif(f) return wrapper From 29acd2c34cc542c96dbb584ea089f4b5404e54ef Mon Sep 17 00:00:00 2001 From: ngrozae <104074686+ngrozae@users.noreply.github.com> Date: Tue, 22 Oct 2024 04:47:52 +0200 Subject: [PATCH 037/222] [Bugfix][OpenVINO] fix_dockerfile_openvino (#9552) --- Dockerfile.openvino | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Dockerfile.openvino b/Dockerfile.openvino index c89864da91180..a05ff452cd36e 100644 --- a/Dockerfile.openvino +++ b/Dockerfile.openvino @@ -15,11 +15,11 @@ RUN --mount=type=bind,source=.git,target=.git \ if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi # install build requirements -RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/vllm/requirements-build.txt +RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/requirements-build.txt # build vLLM with OpenVINO backend -RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install /workspace/vllm/ +RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install /workspace -COPY examples/ /workspace/vllm/examples -COPY benchmarks/ /workspace/vllm/benchmarks +COPY examples/ /workspace/examples +COPY benchmarks/ /workspace/benchmarks CMD ["/bin/bash"] From 74692421f7d5013c313790559f7fc2a338ae5272 Mon Sep 17 00:00:00 2001 From: Falko1 <61779598+Falko1@users.noreply.github.com> Date: Tue, 22 Oct 2024 04:53:36 +0200 Subject: [PATCH 038/222] [Bugfix]: phi.py get rope_theta from config file (#9503) Co-authored-by: Isotr0py <2037008807@qq.com> --- vllm/model_executor/models/phi.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/vllm/model_executor/models/phi.py b/vllm/model_executor/models/phi.py index 0918f21a40e27..ec20cb249ba9b 100644 --- a/vllm/model_executor/models/phi.py +++ b/vllm/model_executor/models/phi.py @@ -102,8 +102,9 @@ def __init__(self, # pylint: disable=C0301 # Refer to: # https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518 - rope_theta = 10000 - max_position_embeddings = getattr(config, "n_positions", 2048) + rope_theta = getattr(config, "rope_theta", 10000.0) + max_position_embeddings = getattr(config, "max_position_embeddings", + 2048) self.rotary_emb = get_rope( self.head_size, rotary_dim=rotary_dim, From c0292211cea53dc5a761b3e51ce37a6c6aecd593 Mon Sep 17 00:00:00 2001 From: Wallas Henrique Date: Tue, 22 Oct 2024 01:52:14 -0300 Subject: [PATCH 039/222] [CI/Build] Replaced some models on tests for smaller ones (#9570) Signed-off-by: Wallas Santos --- tests/basic_correctness/test_basic_correctness.py | 2 +- tests/basic_correctness/test_chunked_prefill.py | 2 +- tests/basic_correctness/test_cpu_offload.py | 4 ++-- tests/compile/test_basic_correctness.py | 3 +-- tests/entrypoints/llm/test_chat.py | 4 ++-- tests/entrypoints/openai/test_chat.py | 3 --- tests/entrypoints/openai/test_shutdown.py | 2 +- tests/test_sharded_state_loader.py | 10 +++++++--- 8 files changed, 15 insertions(+), 15 deletions(-) diff --git a/tests/basic_correctness/test_basic_correctness.py b/tests/basic_correctness/test_basic_correctness.py index 0fe88e792520a..3c2ca1bddd906 100644 --- a/tests/basic_correctness/test_basic_correctness.py +++ b/tests/basic_correctness/test_basic_correctness.py @@ -19,7 +19,7 @@ MODELS = [ "facebook/opt-125m", - "meta-llama/Llama-2-7b-hf", + "meta-llama/Llama-3.2-1B", ] TARGET_TEST_SUITE = os.environ.get("TARGET_TEST_SUITE", "L4") diff --git a/tests/basic_correctness/test_chunked_prefill.py b/tests/basic_correctness/test_chunked_prefill.py index c3e3835aff0af..51aec8c873d12 100644 --- a/tests/basic_correctness/test_chunked_prefill.py +++ b/tests/basic_correctness/test_chunked_prefill.py @@ -16,7 +16,7 @@ MODELS = [ "facebook/opt-125m", - "meta-llama/Llama-2-7b-hf", + "meta-llama/Llama-3.2-1B", ] diff --git a/tests/basic_correctness/test_cpu_offload.py b/tests/basic_correctness/test_cpu_offload.py index a5df5639cf948..d7f36a7812802 100644 --- a/tests/basic_correctness/test_cpu_offload.py +++ b/tests/basic_correctness/test_cpu_offload.py @@ -2,5 +2,5 @@ def test_cpu_offload(): - compare_two_settings("meta-llama/Llama-2-7b-hf", [], - ["--cpu-offload-gb", "4"]) + compare_two_settings("meta-llama/Llama-3.2-1B", [], + ["--cpu-offload-gb", "1"]) diff --git a/tests/compile/test_basic_correctness.py b/tests/compile/test_basic_correctness.py index b6ec7413978f4..77c56d91d0a8b 100644 --- a/tests/compile/test_basic_correctness.py +++ b/tests/compile/test_basic_correctness.py @@ -13,8 +13,7 @@ @pytest.mark.parametrize( "model, model_args, pp_size, tp_size, attn_backend, method, fullgraph", [ - ("meta-llama/Meta-Llama-3-8B", [], 2, 2, "FLASH_ATTN", "generate", - True), + ("meta-llama/Llama-3.2-1B", [], 2, 2, "FLASH_ATTN", "generate", True), ("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", ["--quantization", "compressed-tensors" ], 1, 1, "FLASH_ATTN", "generate", True), diff --git a/tests/entrypoints/llm/test_chat.py b/tests/entrypoints/llm/test_chat.py index b57348a4d9a58..fc66386fd2d2a 100644 --- a/tests/entrypoints/llm/test_chat.py +++ b/tests/entrypoints/llm/test_chat.py @@ -8,7 +8,7 @@ def test_chat(): - llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") + llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct") prompt1 = "Explain the concept of entropy." messages = [ @@ -26,7 +26,7 @@ def test_chat(): def test_multi_chat(): - llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") + llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct") prompt1 = "Explain the concept of entropy." prompt2 = "Explain what among us is." diff --git a/tests/entrypoints/openai/test_chat.py b/tests/entrypoints/openai/test_chat.py index a29747603622b..d1aebbd70d256 100644 --- a/tests/entrypoints/openai/test_chat.py +++ b/tests/entrypoints/openai/test_chat.py @@ -16,9 +16,6 @@ # any model with a chat template should work here MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta" -# technically this needs Mistral-7B-v0.1 as base, but we're not testing -# generation quality here -LORA_NAME = "typeof/zephyr-7b-beta-lora" @pytest.fixture(scope="module") diff --git a/tests/entrypoints/openai/test_shutdown.py b/tests/entrypoints/openai/test_shutdown.py index 25ab91ef69333..6fcc92022855b 100644 --- a/tests/entrypoints/openai/test_shutdown.py +++ b/tests/entrypoints/openai/test_shutdown.py @@ -6,7 +6,7 @@ from ...utils import RemoteOpenAIServer -MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta" +MODEL_NAME = "meta-llama/Llama-3.2-1B" @pytest.mark.asyncio diff --git a/tests/test_sharded_state_loader.py b/tests/test_sharded_state_loader.py index f5d9569046a63..2412da5037ece 100644 --- a/tests/test_sharded_state_loader.py +++ b/tests/test_sharded_state_loader.py @@ -46,9 +46,10 @@ def test_filter_subtensors(): @pytest.fixture(scope="module") def llama_2_7b_files(): with TemporaryDirectory() as cache_dir: - input_dir = snapshot_download("meta-llama/Llama-2-7b-hf", + input_dir = snapshot_download("meta-llama/Llama-3.2-1B", cache_dir=cache_dir, - ignore_patterns="*.bin*") + ignore_patterns=["*.bin*", "original/*"]) + yield input_dir @@ -58,9 +59,12 @@ def _run_writer(input_dir, output_dir, weights_patterns, **kwargs): # Dump worker states to output directory llm_sharded_writer.llm_engine.model_executor.save_sharded_state( path=output_dir) + # Copy metadata files to output directory for file in os.listdir(input_dir): - if not any(file.endswith(ext) for ext in weights_patterns): + if not any( + file.endswith(ext) and not os.path.isdir(file) + for ext in weights_patterns): shutil.copy(f"{input_dir}/{file}", output_dir) From ca30c3c84b1c1a89b7083524854d81440e80c5bd Mon Sep 17 00:00:00 2001 From: Kuntai Du Date: Mon, 21 Oct 2024 23:55:49 -0500 Subject: [PATCH 040/222] [Core] Remove evictor_v1 (#9572) --- vllm/core/block/prefix_caching_block.py | 2 +- vllm/core/{evictor_v2.py => evictor.py} | 0 vllm/core/evictor_v1.py | 106 ------------------------ 3 files changed, 1 insertion(+), 107 deletions(-) rename vllm/core/{evictor_v2.py => evictor.py} (100%) delete mode 100644 vllm/core/evictor_v1.py diff --git a/vllm/core/block/prefix_caching_block.py b/vllm/core/block/prefix_caching_block.py index 7c8a2bc493513..57527e39b9bdd 100644 --- a/vllm/core/block/prefix_caching_block.py +++ b/vllm/core/block/prefix_caching_block.py @@ -7,7 +7,7 @@ from vllm.core.block.interfaces import Block, BlockAllocator, BlockId, Device from vllm.core.block.naive_block import (BlockPool, NaiveBlock, NaiveBlockAllocator) -from vllm.core.evictor_v2 import EvictionPolicy, Evictor, make_evictor +from vllm.core.evictor import EvictionPolicy, Evictor, make_evictor PrefixHash = int diff --git a/vllm/core/evictor_v2.py b/vllm/core/evictor.py similarity index 100% rename from vllm/core/evictor_v2.py rename to vllm/core/evictor.py diff --git a/vllm/core/evictor_v1.py b/vllm/core/evictor_v1.py deleted file mode 100644 index 5db5a08a5bb67..0000000000000 --- a/vllm/core/evictor_v1.py +++ /dev/null @@ -1,106 +0,0 @@ -import enum -from abc import ABC, abstractmethod -from typing import OrderedDict - -from vllm.block import PhysicalTokenBlock - - -class EvictionPolicy(enum.Enum): - """Enum for eviction policy used by make_evictor to instantiate the correct - Evictor subclass. - """ - LRU = enum.auto() - - -class Evictor(ABC): - """The Evictor subclasses should be used by the BlockAllocator class to - handle eviction of freed PhysicalTokenBlocks. - """ - - @abstractmethod - def __init__(self): - pass - - @abstractmethod - def __contains__(self, block_hash: int) -> bool: - pass - - @abstractmethod - def evict(self) -> PhysicalTokenBlock: - """Runs the eviction algorithm and returns the evicted block""" - pass - - @abstractmethod - def add(self, block: PhysicalTokenBlock): - """Adds block to the evictor, making it a candidate for eviction""" - pass - - @abstractmethod - def remove(self, block_hash: int) -> PhysicalTokenBlock: - """Simply removes the block with the hash value block_hash from the - evictor. Caller is responsible for making sure that block_hash is - contained in the evictor before calling remove. Should be used to - "bring back" blocks that have been freed but not evicted yet. - """ - pass - - @property - @abstractmethod - def num_blocks(self) -> int: - pass - - -class LRUEvictor(Evictor): - """Evicts in a least-recently-used order using the last_accessed timestamp - that's recorded in the PhysicalTokenBlock. If there are multiple blocks with - the same last_accessed time, then the one with the largest num_hashed_tokens - will be evicted. If two blocks each have the lowest last_accessed time and - highest num_hashed_tokens value, then one will be chose arbitrarily - """ - - def __init__(self): - self.free_table: OrderedDict[int, PhysicalTokenBlock] = OrderedDict() - - def __contains__(self, block_hash: int) -> bool: - return block_hash in self.free_table - - def evict(self) -> PhysicalTokenBlock: - if len(self.free_table) == 0: - raise ValueError("No usable cache memory left") - - evicted_block = next(iter(self.free_table.values())) - # The blocks with the lowest timestamps should be placed consecutively - # at the start of OrderedDict. Loop through all these blocks to - # find the one with maximum number of hashed tokens. - for _, block in self.free_table.items(): - if evicted_block.last_accessed < block.last_accessed: - break - if evicted_block.num_hashed_tokens < block.num_hashed_tokens: - evicted_block = block - - self.free_table.pop(evicted_block.block_hash) - - evicted_block.computed = False - return evicted_block - - def add(self, block: PhysicalTokenBlock): - self.free_table[block.block_hash] = block - - def remove(self, block_hash: int) -> PhysicalTokenBlock: - if block_hash not in self.free_table: - raise ValueError( - "Attempting to remove block that's not in the evictor") - block: PhysicalTokenBlock = self.free_table[block_hash] - self.free_table.pop(block_hash) - return block - - @property - def num_blocks(self) -> int: - return len(self.free_table) - - -def make_evictor(eviction_policy: EvictionPolicy) -> Evictor: - if eviction_policy == EvictionPolicy.LRU: - return LRUEvictor() - else: - raise ValueError(f"Unknown cache eviction policy: {eviction_policy}") From f7db5f0fa9db2ea5680e373fcb1b21fb0c32797e Mon Sep 17 00:00:00 2001 From: Rafael Vasquez Date: Tue, 22 Oct 2024 02:43:24 -0400 Subject: [PATCH 041/222] [Doc] Use shell code-blocks and fix section headers (#9508) Signed-off-by: Rafael Vasquez --- docs/source/getting_started/debugging.rst | 8 ++--- docs/source/getting_started/installation.rst | 34 ++++++++++---------- docs/source/models/vlm.rst | 4 +-- 3 files changed, 23 insertions(+), 23 deletions(-) diff --git a/docs/source/getting_started/debugging.rst b/docs/source/getting_started/debugging.rst index cfd2dcb3bd5d3..91978065faf42 100644 --- a/docs/source/getting_started/debugging.rst +++ b/docs/source/getting_started/debugging.rst @@ -107,15 +107,15 @@ If GPU/CPU communication cannot be established, you can use the following Python If you are testing with a single node, adjust ``--nproc-per-node`` to the number of GPUs you want to use: -.. code-block:: shell +.. code-block:: console - NCCL_DEBUG=TRACE torchrun --nproc-per-node= test.py + $ NCCL_DEBUG=TRACE torchrun --nproc-per-node= test.py If you are testing with multi-nodes, adjust ``--nproc-per-node`` and ``--nnodes`` according to your setup and set ``MASTER_ADDR`` to the correct IP address of the master node, reachable from all nodes. Then, run: -.. code-block:: shell +.. code-block:: console - NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR test.py + $ NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR test.py If the script runs successfully, you should see the message ``sanity check is successful!``. diff --git a/docs/source/getting_started/installation.rst b/docs/source/getting_started/installation.rst index 5c19f3cf7f1a0..a706b285edede 100644 --- a/docs/source/getting_started/installation.rst +++ b/docs/source/getting_started/installation.rst @@ -7,14 +7,14 @@ Installation vLLM is a Python library that also contains pre-compiled C++ and CUDA (12.1) binaries. Requirements -=========================== +============ * OS: Linux -* Python: 3.8 -- 3.12 +* Python: 3.8 - 3.12 * GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.) Install released versions -=========================== +========================= You can install vLLM using pip: @@ -51,9 +51,9 @@ You can install vLLM using pip: .. _install-the-latest-code: Install the latest code -========================= +======================= -LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on x86 platform with cuda 12 for every commit since v0.5.3. You can download and install the latest one with the following command: +LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on a x86 platform with CUDA 12 for every commit since ``v0.5.3``. You can download and install it with the following command: .. code-block:: console @@ -66,7 +66,7 @@ If you want to access the wheels for previous commits, you can specify the commi $ export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch $ pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl -Note that the wheels are built with Python 3.8 abi (see `PEP 425 `_ for more details about abi), so **they are compatible with Python 3.8 and later**. The version string in the wheel file name (``1.0.0.dev``) is just a placeholder to have a unified URL for the wheels. The actual versions of wheels are contained in the wheel metadata. +Note that the wheels are built with Python 3.8 ABI (see `PEP 425 `_ for more details about ABI), so **they are compatible with Python 3.8 and later**. The version string in the wheel file name (``1.0.0.dev``) is just a placeholder to have a unified URL for the wheels. The actual versions of wheels are contained in the wheel metadata. Another way to access the latest code is to use the docker images: @@ -77,17 +77,17 @@ Another way to access the latest code is to use the docker images: These docker images are used for CI and testing only, and they are not intended for production use. They will be expired after several days. -Latest code can contain bugs and may not be stable. Please use it with caution. +The latest code can contain bugs and may not be stable. Please use it with caution. .. _build_from_source: Build from source -================== +================= .. _python-only-build: Python-only build (without compilation) ----------------------------------------- +--------------------------------------- If you only need to change Python code, you can simply build vLLM without compilation. @@ -122,22 +122,22 @@ Once you have finished editing or want to install another vLLM wheel, you should $ python python_only_dev.py --quit-dev -The script with ``--quit-dev`` flag will: +The ``--quit-dev`` flag will: * Remove the symbolic link from the current directory to the vLLM package. * Restore the original vLLM package from the backup. -If you update the vLLM wheel and want to rebuild from the source and make further edits, you will need to start `all above <#python-only-build>`_ over again. +If you update the vLLM wheel and rebuild from the source to make further edits, you will need to repeat the `Python-only build <#python-only-build>`_ steps again. .. note:: There is a possibility that your source code may have a different commit ID compared to the latest vLLM wheel, which could potentially lead to unknown errors. - It is recommended to use the same commit ID for the source code as the vLLM wheel you have installed. Please refer to `the above section <#install-the-latest-code>`_ for instructions on how to install a specified wheel. + It is recommended to use the same commit ID for the source code as the vLLM wheel you have installed. Please refer to `the section above <#install-the-latest-code>`_ for instructions on how to install a specified wheel. Full build (with compilation) ---------------------------------- +----------------------------- -If you want to modify C++ or CUDA code, you'll need to build vLLM from source. This can take several minutes: +If you want to modify C++ or CUDA code, you'll need to build vLLM from source. This can take several minutes: .. code-block:: console @@ -153,7 +153,7 @@ If you want to modify C++ or CUDA code, you'll need to build vLLM from source. T Use an existing PyTorch installation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ There are scenarios where the PyTorch dependency cannot be easily installed via pip, e.g.: * Building vLLM with PyTorch nightly or a custom PyTorch build. @@ -171,7 +171,7 @@ To build vLLM using an existing PyTorch installation: Troubleshooting -~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~ To avoid your system being overloaded, you can limit the number of compilation jobs to be run simultaneously, via the environment variable ``MAX_JOBS``. For example: @@ -207,7 +207,7 @@ Here is a sanity check to verify that the CUDA Toolkit is correctly installed: Unsupported OS build ----------------------- +-------------------- vLLM can fully run only on Linux but for development purposes, you can still build it on other systems (for example, macOS), allowing for imports and a more convenient development environment. The binaries will not be compiled and won't work on non-Linux systems. diff --git a/docs/source/models/vlm.rst b/docs/source/models/vlm.rst index a7b55d1c0c1ff..a47902ab4fc9d 100644 --- a/docs/source/models/vlm.rst +++ b/docs/source/models/vlm.rst @@ -247,9 +247,9 @@ A full code example can be found in `examples/openai_api_client_for_multimodal.p By default, the timeout for fetching images through http url is ``5`` seconds. You can override this by setting the environment variable: - .. code-block:: shell + .. code-block:: console - export VLLM_IMAGE_FETCH_TIMEOUT= + $ export VLLM_IMAGE_FETCH_TIMEOUT= .. note:: There is no need to format the prompt in the API request since it will be handled by the server. From 0d02747f2ed5f65bd7100b6dcf1805cefb458f5d Mon Sep 17 00:00:00 2001 From: chenqianfzh <51831990+chenqianfzh@users.noreply.github.com> Date: Tue, 22 Oct 2024 00:13:23 -0700 Subject: [PATCH 042/222] support TP in qwen2 bnb (#9574) --- vllm/model_executor/models/qwen2.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/vllm/model_executor/models/qwen2.py b/vllm/model_executor/models/qwen2.py index cb04cc4850951..23eb1482ffef1 100644 --- a/vllm/model_executor/models/qwen2.py +++ b/vllm/model_executor/models/qwen2.py @@ -364,6 +364,20 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP): ] embedding_modules = {} embedding_padding_modules = [] + + # BitandBytes specific attributes + default_bitsandbytes_target_modules = [ + ".gate_proj.", + ".down_proj.", + ".up_proj.", + ".q_proj.", + ".k_proj.", + ".v_proj.", + ".o_proj.", + ] + + # in TP, these weights are partitioned along the column dimension (dim=-1) + column_parallel_weights_modules = [".down_proj.", ".o_proj."] bitsandbytes_stacked_params_mapping = { # shard_name, weight_name, index "q_proj": ("qkv_proj", 0), From 3ddbe25502fb8c49e67096ba6e641ecdc3519757 Mon Sep 17 00:00:00 2001 From: wangshuai09 <391746016@qq.com> Date: Tue, 22 Oct 2024 15:50:43 +0800 Subject: [PATCH 043/222] [Hardware][CPU] using current_platform.is_cpu (#9536) --- tests/conftest.py | 6 ++++-- tests/encoder_decoder/test_e2e_correctness.py | 6 +++--- tests/kernels/test_attention_selector.py | 3 ++- .../decoder_only/language/test_phimoe.py | 4 ++-- .../decoder_only/vision_language/test_fuyu.py | 6 +++--- .../vision_language/test_internvl.py | 6 +++--- .../vision_language/test_phi3v.py | 5 +++-- tests/models/utils.py | 8 ++++---- .../test_encoder_decoder_model_runner.py | 11 +++++----- vllm/attention/backends/torch_sdpa.py | 8 ++++---- .../ops/blocksparse_attention/interface.py | 20 +++++++++---------- vllm/attention/selector.py | 6 +++--- vllm/distributed/parallel_state.py | 6 +++--- vllm/model_executor/custom_op.py | 4 ++-- vllm/model_executor/models/qwen2_vl.py | 8 ++++---- vllm/model_executor/models/utils.py | 6 +++--- vllm/utils.py | 11 +--------- 17 files changed, 60 insertions(+), 64 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 4c9180415da32..fc8bd1a473476 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -32,9 +32,10 @@ to_enc_dec_tuple_list, zip_enc_dec_prompts) from vllm.logger import init_logger from vllm.outputs import RequestOutput +from vllm.platforms import current_platform from vllm.sampling_params import BeamSearchParams from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, cuda_device_count_stateless, - identity, is_cpu) + identity) logger = init_logger(__name__) @@ -236,7 +237,8 @@ class HfRunner: def wrap_device(self, input: _T, device: Optional[str] = None) -> _T: if device is None: - return self.wrap_device(input, "cpu" if is_cpu() else "cuda") + return self.wrap_device( + input, "cpu" if current_platform.is_cpu() else "cuda") if hasattr(input, "device") and input.device.type == device: return input diff --git a/tests/encoder_decoder/test_e2e_correctness.py b/tests/encoder_decoder/test_e2e_correctness.py index 9324a737a779c..bef0c515b9073 100644 --- a/tests/encoder_decoder/test_e2e_correctness.py +++ b/tests/encoder_decoder/test_e2e_correctness.py @@ -7,8 +7,8 @@ import pytest from transformers import AutoModelForSeq2SeqLM +from vllm.platforms import current_platform from vllm.sequence import SampleLogprobs -from vllm.utils import is_cpu from ..conftest import DecoderPromptType from ..models.utils import check_logprobs_close @@ -35,7 +35,7 @@ def vllm_to_hf_output( @pytest.mark.parametrize("decoder_prompt_type", list(DecoderPromptType)) @pytest.mark.parametrize("enforce_eager", [True, False]) @pytest.mark.skipif( - is_cpu(), + current_platform.is_cpu(), reason="CPU backend is not currently supported with encoder/decoder models" ) def test_encoder_decoder_e2e( @@ -50,7 +50,7 @@ def test_encoder_decoder_e2e( enforce_eager: bool, ) -> None: ''' - End-to-End (E2E) test for the encoder-decoder framework. + End-to-End (E2E) test for the encoder-decoder framework. This test evaluates the encoder-decoder functionality using the BART model. We compare the outputs of the Hugging Face and vLLM implementations to ensure that both implementations produce consistent diff --git a/tests/kernels/test_attention_selector.py b/tests/kernels/test_attention_selector.py index 5671207ac847e..8bcee98403775 100644 --- a/tests/kernels/test_attention_selector.py +++ b/tests/kernels/test_attention_selector.py @@ -19,7 +19,8 @@ def test_env(name: str, device: str, monkeypatch): override_backend_env_variable(monkeypatch, name) if device == "cpu": - with patch("vllm.attention.selector.is_cpu", return_value=True): + with patch("vllm.attention.selector.current_platform.is_cpu", + return_value=True): backend = which_attn_to_use(16, torch.float16, torch.float16, 16, False) assert backend.name == "TORCH_SDPA" diff --git a/tests/models/decoder_only/language/test_phimoe.py b/tests/models/decoder_only/language/test_phimoe.py index 89afbcf1c03ac..c997359a2781e 100644 --- a/tests/models/decoder_only/language/test_phimoe.py +++ b/tests/models/decoder_only/language/test_phimoe.py @@ -5,7 +5,7 @@ import pytest import torch -from vllm.utils import is_cpu +from vllm.platforms import current_platform from ....utils import large_gpu_test from ...utils import check_logprobs_close @@ -70,7 +70,7 @@ def test_phimoe_routing_function(): assert torch.equal(topk_ids, ground_truth[test_id]["topk_ids"]) -@pytest.mark.skipif(condition=is_cpu(), +@pytest.mark.skipif(condition=current_platform.is_cpu(), reason="This test takes a lot time to run on CPU, " "and vllm CI's disk space is not enough for this model.") @large_gpu_test(min_gb=80) diff --git a/tests/models/decoder_only/vision_language/test_fuyu.py b/tests/models/decoder_only/vision_language/test_fuyu.py index 7827ecb19a744..1affcd10ee72d 100644 --- a/tests/models/decoder_only/vision_language/test_fuyu.py +++ b/tests/models/decoder_only/vision_language/test_fuyu.py @@ -3,8 +3,8 @@ import pytest from vllm.multimodal.utils import rescale_image_size +from vllm.platforms import current_platform from vllm.sequence import SampleLogprobs -from vllm.utils import is_cpu from ....conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets from ...utils import check_logprobs_close @@ -46,7 +46,7 @@ def run_test( All the image fixtures for the test are from IMAGE_ASSETS. For huggingface runner, we provide the PIL images as input. - For vllm runner, we provide MultiModalDataDict objects + For vllm runner, we provide MultiModalDataDict objects and corresponding MultiModalConfig as input. Note, the text input is also adjusted to abide by vllm contract. The text output is sanitized to be able to compare with hf. @@ -103,7 +103,7 @@ def run_test( target_dtype = "half" -if is_cpu(): +if current_platform.is_cpu(): target_dtype = "bfloat16" diff --git a/tests/models/decoder_only/vision_language/test_internvl.py b/tests/models/decoder_only/vision_language/test_internvl.py index 49cab75d8ea53..58d88f0a28829 100644 --- a/tests/models/decoder_only/vision_language/test_internvl.py +++ b/tests/models/decoder_only/vision_language/test_internvl.py @@ -7,7 +7,7 @@ from transformers import AutoConfig from vllm.multimodal.utils import rescale_image_size -from vllm.utils import is_cpu +from vllm.platforms import current_platform from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner, _ImageAssets) @@ -78,7 +78,7 @@ def run_test( All the image fixtures for the test are from IMAGE_ASSETS. For huggingface runner, we provide the PIL images as input. - For vllm runner, we provide MultiModalDataDict objects + For vllm runner, we provide MultiModalDataDict objects and corresponding MultiModalConfig as input. Note, the text input is also adjusted to abide by vllm contract. The text output is sanitized to be able to compare with hf. @@ -244,7 +244,7 @@ def run_awq_test( target_dtype = "half" -if is_cpu(): +if current_platform.is_cpu(): target_dtype = "bfloat16" diff --git a/tests/models/decoder_only/vision_language/test_phi3v.py b/tests/models/decoder_only/vision_language/test_phi3v.py index 808421abd9103..dfe10629f1c66 100644 --- a/tests/models/decoder_only/vision_language/test_phi3v.py +++ b/tests/models/decoder_only/vision_language/test_phi3v.py @@ -10,8 +10,9 @@ from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID from vllm.multimodal import MultiModalRegistry from vllm.multimodal.utils import rescale_image_size +from vllm.platforms import current_platform from vllm.sequence import SampleLogprobs -from vllm.utils import is_cpu, is_hip +from vllm.utils import is_hip from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner, _ImageAssets) @@ -49,7 +50,7 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str, target_dtype = "half" -if is_cpu(): +if current_platform.is_cpu(): target_dtype = "bfloat16" # ROCm Triton FA can run into shared memory issues with these models, diff --git a/tests/models/utils.py b/tests/models/utils.py index 2ea233a9a599c..f7802d98ad678 100644 --- a/tests/models/utils.py +++ b/tests/models/utils.py @@ -5,8 +5,8 @@ from vllm.config import ModelConfig, TaskOption from vllm.inputs import InputContext +from vllm.platforms import current_platform from vllm.sequence import Logprob, PromptLogprobs, SampleLogprobs -from vllm.utils import is_cpu TokensText = Tuple[List[int], str] @@ -19,7 +19,7 @@ def check_outputs_equal( name_1: str, ): """ - Compare the two sequences generated by different models, + Compare the two sequences generated by different models, which should be equal. """ assert len(outputs_0_lst) == len(outputs_1_lst) @@ -255,7 +255,7 @@ def build_model_context(model_name: str, mm_processor_kwargs: Optional[Dict] = None, limit_mm_per_prompt: Optional[Dict] = None): """Creates an InputContext for a given model. - + Args: model_name: Name of the model being considered. tokenizer_name: Name of the tokenizer being considered. @@ -270,7 +270,7 @@ def build_model_context(model_name: str, if tokenizer_name is None: tokenizer_name = model_name if dtype is None: - dtype = "bfloat16" if is_cpu() else "half" + dtype = "bfloat16" if current_platform.is_cpu() else "half" model_config = ModelConfig( model_name, diff --git a/tests/worker/test_encoder_decoder_model_runner.py b/tests/worker/test_encoder_decoder_model_runner.py index 3dccc1b325d95..e75884a7395e2 100644 --- a/tests/worker/test_encoder_decoder_model_runner.py +++ b/tests/worker/test_encoder_decoder_model_runner.py @@ -5,8 +5,9 @@ import torch from vllm.engine.arg_utils import EngineArgs +from vllm.platforms import current_platform from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata -from vllm.utils import is_cpu, make_tensor_with_pad +from vllm.utils import make_tensor_with_pad from vllm.worker.enc_dec_model_runner import EncoderDecoderModelRunner from vllm.worker.model_runner import _get_graph_batch_size @@ -31,7 +32,7 @@ def _create_model_runner(model: str, *args, return model_runner -@pytest.mark.skipif(condition=is_cpu(), +@pytest.mark.skipif(condition=current_platform.is_cpu(), reason="CPU backend is currently " "unsupported for encoder/ " "decoder models") @@ -74,7 +75,7 @@ def test_empty_seq_group(): assert return_seq_lens is None -@pytest.mark.skipif(condition=is_cpu(), +@pytest.mark.skipif(condition=current_platform.is_cpu(), reason="CPU backend is currently " "unsupported for encoder/ " "decoder models") @@ -264,7 +265,7 @@ def test_prepare_prompt(batch_size): assert torch.equal(actual, expected) -@pytest.mark.skipif(condition=is_cpu(), +@pytest.mark.skipif(condition=current_platform.is_cpu(), reason="CPU backend is currently " "unsupported for encoder/ " "decoder models") @@ -490,7 +491,7 @@ def test_prepare_decode(batch_size, multiple_seqs_per_seq_group): def test_prepare_decode_cuda_graph(batch_size, multiple_seqs_per_seq_group): """ Tests that for encoder-decoder models with CUDA Graph capture and replay - enabled, the tensors used during the decode phase are correctly padded + enabled, the tensors used during the decode phase are correctly padded for varying input batch sizes. """ model_runner = _create_model_runner( diff --git a/vllm/attention/backends/torch_sdpa.py b/vllm/attention/backends/torch_sdpa.py index 1fb7c37578f20..f985f70728a60 100644 --- a/vllm/attention/backends/torch_sdpa.py +++ b/vllm/attention/backends/torch_sdpa.py @@ -10,9 +10,9 @@ AttentionMetadata, AttentionType) from vllm.attention.backends.utils import CommonAttentionState from vllm.attention.ops.paged_attn import PagedAttentionMetadata -from vllm.utils import is_cpu +from vllm.platforms import current_platform -if is_cpu(): +if current_platform.is_cpu(): try: from vllm.attention.ops.ipex_attn import PagedAttention except ImportError: @@ -234,10 +234,10 @@ def get_seq_len_block_table_args( on the type of attention operation. Decoder attn -> select entirely decoder self-attention-related fields - Encoder/decoder cross-attn -> select encoder sequence lengths & + Encoder/decoder cross-attn -> select encoder sequence lengths & cross-attn block-tables fields Encoder attn -> select encoder sequence lengths fields & no block tables - + Arguments: * attn_metadata: Attention metadata structure associated with attention diff --git a/vllm/attention/ops/blocksparse_attention/interface.py b/vllm/attention/ops/blocksparse_attention/interface.py index 1ead541f391b5..e4dc576d27932 100644 --- a/vllm/attention/ops/blocksparse_attention/interface.py +++ b/vllm/attention/ops/blocksparse_attention/interface.py @@ -3,7 +3,7 @@ import torch from vllm.platforms import current_platform -from vllm.utils import is_cpu, is_hip +from vllm.utils import is_hip from .utils import (dense_to_crow_col, get_head_sliding_step, get_sparse_attn_mask) @@ -32,7 +32,7 @@ def __init__( ): super().__init__() if use_spda is None: - use_spda = is_hip() or is_cpu() or not \ + use_spda = is_hip() or current_platform.is_cpu() or not \ IS_COMPUTE_8_OR_ABOVE device = device or (torch.cuda.current_device() if current_platform.is_cuda_alike() else "cpu") @@ -109,13 +109,13 @@ def varlen_attn(self, q, k, v: shape = (num_tokens, num_heads_q/kv, head_size). Support grouped attention, with `q[:, i*r:(i*r + r)]` is correspondent to `k[:, i]`, where `r` is the q/k ratio. - cu_seqlens_k: shape=(batch_size + 1,), - indicating segment of samples, + cu_seqlens_k: shape=(batch_size + 1,), + indicating segment of samples, e.g., `k[cu_seqlen[i]:cu_seqlne[i+1]]` is q of sample i cu_seqlens_q: shape=(batch_size + 1, ). Default None: same as cu_seqlens_k for prefilling or [0, 1, .., batch_size] for decoding. - The only case you need to specify is when q is a mix of + The only case you need to specify is when q is a mix of prefilling and decoding. sm_scale: softmax scale, default to 1/sqrt(head_size). @@ -171,7 +171,7 @@ def transpose_and_unpad(x_padded, cu_seqlens): def spda(self, q, k, v, cu_seqlens_k, cu_seqlens_q=None, sm_scale=None): """For CPU, V100 or other older GPUs. - NOTE: torch SPDA supports nested tensor, + NOTE: torch SPDA supports nested tensor, but seems extremely slow. Choose to pad instead. """ assert (cu_seqlens_q is None or @@ -201,8 +201,8 @@ def spda(self, q, k, v, cu_seqlens_k, cu_seqlens_q=None, sm_scale=None): return self.transpose_and_unpad(spda_output, cu_seqlens) def forward(self, q, k, v, cu_seqlens_k, cu_seqlens_q=None, sm_scale=None): - """Dispatch to `varlen_attn` (Ampere or newer) or - `self.spda`(cpu, Volta, Turing or older)based on + """Dispatch to `varlen_attn` (Ampere or newer) or + `self.spda`(cpu, Volta, Turing or older)based on the type of device used and cuda compute capability. q, k, v: shape = (num_tokens, num_heads_q/kv, head_size). @@ -213,8 +213,8 @@ def forward(self, q, k, v, cu_seqlens_k, cu_seqlens_q=None, sm_scale=None): cu_seqlens_q: shape=(batch_size + 1, ). Default None: same as cu_seqlens_k for prefilling or [0, 1, .., batch_size] for decoding. - The only case you need to specify - is when q is a mix of prefilling + The only case you need to specify + is when q is a mix of prefilling and decoding. sm_scale: softmax scale, default to 1/sqrt(head_size). diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index 4ff86573e664d..c4d02187e1658 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -10,7 +10,7 @@ from vllm.attention.backends.abstract import AttentionBackend from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import STR_BACKEND_ENV_VAR, is_cpu, is_hip, is_openvino, is_xpu +from vllm.utils import STR_BACKEND_ENV_VAR, is_hip, is_openvino, is_xpu logger = init_logger(__name__) @@ -121,7 +121,7 @@ def get_attn_backend( ROCmFlashAttentionBackend) return ROCmFlashAttentionBackend elif backend == _Backend.TORCH_SDPA: - assert is_cpu(), RuntimeError( + assert current_platform.is_cpu(), RuntimeError( "Torch SDPA backend is only used for the CPU device.") logger.info("Using Torch SDPA backend.") from vllm.attention.backends.torch_sdpa import TorchSDPABackend @@ -183,7 +183,7 @@ def which_attn_to_use( if backend_by_env_var is not None: selected_backend = backend_name_to_enum(backend_by_env_var) - if is_cpu(): + if current_platform.is_cpu(): if selected_backend != _Backend.TORCH_SDPA: logger.info("Cannot use %s backend on CPU.", selected_backend) return _Backend.TORCH_SDPA diff --git a/vllm/distributed/parallel_state.py b/vllm/distributed/parallel_state.py index 8d4b673d2e6e4..ab47d62921d2c 100644 --- a/vllm/distributed/parallel_state.py +++ b/vllm/distributed/parallel_state.py @@ -7,7 +7,7 @@ The typical workflow is: - call `init_distributed_environment` to initialize the distributed environment. -- call `initialize_model_parallel` or `ensure_model_parallel_initialized` to +- call `initialize_model_parallel` or `ensure_model_parallel_initialized` to initialize the model parallel groups. - any code dealing with the distributed stuff @@ -37,7 +37,7 @@ import vllm.envs as envs from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import is_cpu, supports_custom_op +from vllm.utils import supports_custom_op @dataclass @@ -1139,7 +1139,7 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False): import ray # Lazy import Ray ray.shutdown() gc.collect() - if not is_cpu(): + if not current_platform.is_cpu(): torch.cuda.empty_cache() diff --git a/vllm/model_executor/custom_op.py b/vllm/model_executor/custom_op.py index 549be116772c9..d7506d268e73b 100644 --- a/vllm/model_executor/custom_op.py +++ b/vllm/model_executor/custom_op.py @@ -7,7 +7,7 @@ from vllm.compilation.levels import CompilationLevel from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import is_cpu, is_hip, is_xpu, print_warning_once +from vllm.utils import is_hip, is_xpu, print_warning_once logger = init_logger(__name__) @@ -74,7 +74,7 @@ def dispatch_forward(self): if is_hip(): return self.forward_hip - elif is_cpu(): + elif current_platform.is_cpu(): return self.forward_cpu elif current_platform.is_tpu(): return self.forward_tpu diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index a3540abdc23d3..9cca6b65e3277 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -78,7 +78,7 @@ class Qwen2VLImagePixelInputs(TypedDict): type: Literal["pixel_values"] data: torch.Tensor - """Shape: + """Shape: `(num_patches, num_channels * patch_size * patch_size)` """ @@ -102,14 +102,14 @@ class Qwen2VLImageEmbeddingInputs(TypedDict): class Qwen2VLVideoInputs(TypedDict): pixel_values_videos: torch.Tensor - """Shape: - `(num_patches, + """Shape: + `(num_patches, num_channels * temporal_patch_size * patch_size * patch_size)` """ video_grid_thw: torch.Tensor """Shape: `(num_videos, 3)` - + This should be in `(grid_t, grid_h, grid_w)` format. """ diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index 9e2f5476f3aff..ec1d76d2117f3 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -21,7 +21,7 @@ from vllm.multimodal.base import NestedTensors from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors -from vllm.utils import is_cpu, is_pin_memory_available +from vllm.utils import is_pin_memory_available logger = init_logger(__name__) @@ -474,7 +474,7 @@ def make_empty_intermediate_tensors( class LLMWrapper(nn.Module): """ - To align with the key names of LoRA trained with PEFT, we need to add an + To align with the key names of LoRA trained with PEFT, we need to add an additional layer to the llm's implementation. """ @@ -515,7 +515,7 @@ def get_vit_attn_backend() -> _Backend: "so we use xformers backend instead. You can run " "`pip install flash-attn` to use flash-attention backend.") selected_backend = _Backend.XFORMERS - elif is_cpu(): + elif current_platform.is_cpu(): selected_backend = _Backend.TORCH_SDPA else: selected_backend = _Backend.XFORMERS diff --git a/vllm/utils.py b/vllm/utils.py index d1a995a3ac8c5..428c2095dcd5d 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -318,15 +318,6 @@ def is_hip() -> bool: return torch.version.hip is not None -@lru_cache(maxsize=None) -def is_cpu() -> bool: - from importlib.metadata import PackageNotFoundError, version - try: - return "cpu" in version("vllm") - except PackageNotFoundError: - return False - - @lru_cache(maxsize=None) def is_openvino() -> bool: from importlib.metadata import PackageNotFoundError, version @@ -798,7 +789,7 @@ def is_pin_memory_available() -> bool: elif is_neuron(): print_warning_once("Pin memory is not supported on Neuron.") return False - elif is_cpu() or is_openvino(): + elif current_platform.is_cpu() or is_openvino(): return False return True From 6c5af09b3969721da2e3a32d612a0fdd5cb077d6 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Tue, 22 Oct 2024 01:24:07 -0700 Subject: [PATCH 044/222] [V1] Implement vLLM V1 [1/N] (#9289) --- vllm/attention/selector.py | 8 + vllm/engine/multiprocessing/engine.py | 27 +- vllm/entrypoints/llm.py | 7 +- vllm/envs.py | 5 + .../model_executor/layers/logits_processor.py | 10 +- vllm/transformers_utils/detokenizer.py | 168 +---- vllm/transformers_utils/detokenizer_utils.py | 167 +++++ vllm/v1/attention/__init__.py | 0 vllm/v1/attention/backends/__init__.py | 0 vllm/v1/attention/backends/flash_attn.py | 241 ++++++ vllm/v1/core/__init__.py | 0 vllm/v1/core/kv_cache_manager.py | 108 +++ vllm/v1/core/scheduler.py | 412 +++++++++++ vllm/v1/engine/__init__.py | 0 vllm/v1/engine/llm_engine.py | 523 +++++++++++++ vllm/v1/executor/__init__.py | 0 vllm/v1/executor/gpu_executor.py | 100 +++ vllm/v1/outputs.py | 37 + vllm/v1/request.py | 92 +++ vllm/v1/sample/__init__.py | 0 vllm/v1/sample/metadata.py | 22 + vllm/v1/sample/sampler.py | 161 ++++ vllm/v1/tokenizer/__init__.py | 0 vllm/v1/tokenizer/detokenizer.py | 215 ++++++ vllm/v1/worker/__init__.py | 0 vllm/v1/worker/gpu_model_runner.py | 690 ++++++++++++++++++ vllm/v1/worker/gpu_worker.py | 245 +++++++ 27 files changed, 3058 insertions(+), 180 deletions(-) create mode 100644 vllm/transformers_utils/detokenizer_utils.py create mode 100644 vllm/v1/attention/__init__.py create mode 100644 vllm/v1/attention/backends/__init__.py create mode 100644 vllm/v1/attention/backends/flash_attn.py create mode 100644 vllm/v1/core/__init__.py create mode 100644 vllm/v1/core/kv_cache_manager.py create mode 100644 vllm/v1/core/scheduler.py create mode 100644 vllm/v1/engine/__init__.py create mode 100644 vllm/v1/engine/llm_engine.py create mode 100644 vllm/v1/executor/__init__.py create mode 100644 vllm/v1/executor/gpu_executor.py create mode 100644 vllm/v1/outputs.py create mode 100644 vllm/v1/request.py create mode 100644 vllm/v1/sample/__init__.py create mode 100644 vllm/v1/sample/metadata.py create mode 100644 vllm/v1/sample/sampler.py create mode 100644 vllm/v1/tokenizer/__init__.py create mode 100644 vllm/v1/tokenizer/detokenizer.py create mode 100644 vllm/v1/worker/__init__.py create mode 100644 vllm/v1/worker/gpu_model_runner.py create mode 100644 vllm/v1/worker/gpu_worker.py diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index c4d02187e1658..714c4f7fdb4e5 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -17,6 +17,7 @@ class _Backend(enum.Enum): FLASH_ATTN = enum.auto() + FLASH_ATTN_VLLM_V1 = enum.auto() XFORMERS = enum.auto() ROCM_FLASH = enum.auto() TORCH_SDPA = enum.auto() @@ -110,6 +111,10 @@ def get_attn_backend( from vllm.attention.backends.flash_attn import ( # noqa: F401 FlashAttentionBackend) return FlashAttentionBackend + if backend == _Backend.FLASH_ATTN_VLLM_V1: + from vllm.v1.attention.backends.flash_attn import ( # noqa: F401 + FlashAttentionBackend as FlashAttentionBackendV1) + return FlashAttentionBackendV1 if backend == _Backend.XFORMERS: logger.info("Using XFormers backend.") from vllm.attention.backends.xformers import ( # noqa: F401 @@ -215,6 +220,9 @@ def which_attn_to_use( logger.info("%s is not supported in AMD GPUs.", selected_backend) return _Backend.ROCM_FLASH + if envs.VLLM_USE_V1: + return _Backend.FLASH_ATTN_VLLM_V1 + # FlashAttn in NVIDIA GPUs. if selected_backend == _Backend.FLASH_ATTN: if not current_platform.has_device_capability(80): diff --git a/vllm/engine/multiprocessing/engine.py b/vllm/engine/multiprocessing/engine.py index ad0e970f36ff5..f67acdf660759 100644 --- a/vllm/engine/multiprocessing/engine.py +++ b/vllm/engine/multiprocessing/engine.py @@ -8,7 +8,7 @@ import cloudpickle import zmq -from vllm import AsyncEngineArgs, LLMEngine, SamplingParams +from vllm import AsyncEngineArgs, SamplingParams from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig, ParallelConfig, SchedulerConfig) # yapf conflicts with isort for this block @@ -21,12 +21,17 @@ RPCStartupRequest, RPCStartupResponse, RPCUProfileRequest) # yapf: enable -from vllm.envs import VLLM_RPC_TIMEOUT +from vllm.envs import VLLM_RPC_TIMEOUT, VLLM_USE_V1 from vllm.executor.gpu_executor import GPUExecutor from vllm.logger import init_logger from vllm.outputs import RequestOutput from vllm.usage.usage_lib import UsageContext +if VLLM_USE_V1: + from vllm.v1.engine.llm_engine import LLMEngine +else: + from vllm.engine.llm_engine import LLMEngine + CONFIG_TYPE = Union[ModelConfig, DecodingConfig, ParallelConfig, SchedulerConfig, LoRAConfig] @@ -136,14 +141,16 @@ def from_engine_args(cls, engine_args: AsyncEngineArgs, executor_class = LLMEngine._get_executor_cls(engine_config) - return cls( - ipc_path=ipc_path, - use_async_sockets=engine_config.model_config.use_async_output_proc, - **engine_config.to_dict(), - executor_class=executor_class, - log_requests=not engine_args.disable_log_requests, - log_stats=not engine_args.disable_log_stats, - usage_context=usage_context) + use_async_sockets = (engine_config.model_config.use_async_output_proc + and not VLLM_USE_V1) + + return cls(ipc_path=ipc_path, + use_async_sockets=use_async_sockets, + **engine_config.to_dict(), + executor_class=executor_class, + log_requests=not engine_args.disable_log_requests, + log_stats=not engine_args.disable_log_stats, + usage_context=usage_context) def start(self): try: diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index 1f7893d54de68..db97fe0a0285b 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -6,10 +6,10 @@ from tqdm import tqdm +from vllm import envs from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput, BeamSearchSequence, get_beam_search_score) from vllm.engine.arg_utils import EngineArgs, TaskOption -from vllm.engine.llm_engine import LLMEngine from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam, apply_hf_chat_template, apply_mistral_chat_template, @@ -31,6 +31,11 @@ from vllm.usage.usage_lib import UsageContext from vllm.utils import Counter, deprecate_args, deprecate_kwargs, is_list_of +if envs.VLLM_USE_V1: + from vllm.v1.engine.llm_engine import LLMEngine # type: ignore +else: + from vllm.engine.llm_engine import LLMEngine # type: ignore + logger = init_logger(__name__) diff --git a/vllm/envs.py b/vllm/envs.py index 385db82d89249..a20271229c567 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -68,6 +68,7 @@ VLLM_TORCH_COMPILE_LEVEL: int = 0 VLLM_CUSTOM_OPS: List[str] = [] VLLM_DISABLED_KERNELS: List[str] = [] + VLLM_USE_V1: bool = False def get_default_cache_root(): @@ -450,6 +451,10 @@ def get_default_config_root(): "VLLM_DISABLED_KERNELS": lambda: [] if "VLLM_DISABLED_KERNELS" not in os.environ else os.environ[ "VLLM_DISABLED_KERNELS"].split(","), + + # If set, use the V1 code path. + "VLLM_USE_V1": + lambda: bool(int(os.getenv("VLLM_USE_V1", "0"))), } # end-env-vars-definition diff --git a/vllm/model_executor/layers/logits_processor.py b/vllm/model_executor/layers/logits_processor.py index 1d5b6fad2e160..288f5a1134b6b 100644 --- a/vllm/model_executor/layers/logits_processor.py +++ b/vllm/model_executor/layers/logits_processor.py @@ -48,14 +48,15 @@ def forward( self, lm_head: VocabParallelEmbedding, hidden_states: torch.Tensor, - sampling_metadata: SamplingMetadata, + sampling_metadata: Optional[SamplingMetadata] = None, embedding_bias: Optional[torch.Tensor] = None, ) -> Optional[torch.Tensor]: if self.logits_as_input: logits = hidden_states else: - hidden_states = _prune_hidden_states(hidden_states, - sampling_metadata) + if sampling_metadata is not None: + hidden_states = _prune_hidden_states(hidden_states, + sampling_metadata) # Get the logits for the next tokens. logits = self._get_logits(hidden_states, lm_head, embedding_bias) @@ -69,7 +70,8 @@ def forward( logits *= self.scale # Apply logits processors (if any). - logits = _apply_logits_processors(logits, sampling_metadata) + if sampling_metadata is not None: + logits = _apply_logits_processors(logits, sampling_metadata) return logits diff --git a/vllm/transformers_utils/detokenizer.py b/vllm/transformers_utils/detokenizer.py index 2b418f3603a0b..345ea14f9f273 100644 --- a/vllm/transformers_utils/detokenizer.py +++ b/vllm/transformers_utils/detokenizer.py @@ -1,8 +1,10 @@ -from typing import Dict, List, Optional, Tuple +from typing import Dict, List, Optional from vllm.sequence import (VLLM_INVALID_TOKEN_ID, Logprob, SamplingParams, Sequence, SequenceGroup) +from .detokenizer_utils import (convert_prompt_ids_to_tokens, + detokenize_incrementally) from .tokenizer import AnyTokenizer from .tokenizer_group import BaseTokenizerGroup @@ -161,167 +163,3 @@ def decode_sequence_inplace(self, seq: Sequence, seq.output_text += new_decoded_token_text return len(new_decoded_token_text) - - -def _replace_none_with_empty(tokens: List[Optional[str]]): - for i, token in enumerate(tokens): - if token is None: - tokens[i] = "" - - -def _convert_tokens_to_string_with_added_encoders( - tokenizer: AnyTokenizer, - output_tokens: List[str], - skip_special_tokens: bool, - spaces_between_special_tokens: bool, -) -> str: - # Adapted from - # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/tokenization_utils.py#L921 - # NOTE(woosuk): The following code is slow because it runs a for loop over - # the output_tokens. In Python, running a for loop over a list can be slow - # even when the loop body is very simple. - sub_texts: List[str] = [] - current_sub_text: List[str] = [] - all_special_tokens = set(tokenizer.all_special_tokens) - for token in output_tokens: - if skip_special_tokens and token in all_special_tokens: - continue - if token in tokenizer.get_added_vocab(): - if current_sub_text: - sub_text = tokenizer.convert_tokens_to_string(current_sub_text) - sub_texts.append(sub_text) - current_sub_text = [] - sub_texts.append(token) - else: - current_sub_text.append(token) - if current_sub_text: - sub_text = tokenizer.convert_tokens_to_string(current_sub_text) - sub_texts.append(sub_text) - if spaces_between_special_tokens: - return " ".join(sub_texts) - else: - return "".join(sub_texts) - - -# 5 is an arbitrary value that should work for all -# tokenizers (bigger = more conservative). -INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET = 5 - - -def convert_prompt_ids_to_tokens( - tokenizer: AnyTokenizer, - prompt_ids: List[int], - skip_special_tokens: bool = False, -) -> Tuple[List[str], int, int]: - """Converts the prompt ids to tokens and returns the tokens and offsets - for incremental detokenization. - - Note that not all tokens are converted to strings. Only the tokens that - are necessary for incremental detokenization are converted to strings. - """ - # We do not need to convert the whole prompt to tokens. - # Offset a little more in case we have special tokens. - new_tokens = tokenizer.convert_ids_to_tokens( - prompt_ids[-INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET - 2:], - skip_special_tokens=skip_special_tokens) - read_offset = len(new_tokens) - prefix_offset = max( - read_offset - INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET, 0) - # This is required to guard against out-of-vocab prompt token ids - _replace_none_with_empty(new_tokens) # type: ignore[arg-type] - return new_tokens, prefix_offset, read_offset - - -# Based on -# https://github.com/huggingface/text-generation-inference/blob/v0.9.4/server/text_generation_server/models/model.py#L62C9-L62C15 -# under Apache 2.0 license -def detokenize_incrementally( - tokenizer: AnyTokenizer, - all_input_ids: List[int], - prev_tokens: Optional[List[str]], - prefix_offset: int, - read_offset: int, - skip_special_tokens: bool = False, - spaces_between_special_tokens: bool = True, -) -> Tuple[List[str], str, int, int]: - """Detokenizes the input ids incrementally and returns the new tokens - and the new text. - - If `prev_tokens` is None, this function will convert the input ids to - tokens and return the tokens and the new text. Otherwise, it will return the - new tokens and the new text. - - This function will also return the new prefix offset and the new read - offset to be used in the next iteration. - - The offsets are necessary to defeat cleanup algorithms in the decode which - decide to add a space or not depending on the surrounding ids. - - Args: - tokenizer: The tokenizer to use. - all_input_ids: The input ids. The last id is the new token id. - prev_tokens: The previous tokens. If None, this function will convert - the input ids to tokens and return the tokens and the new text. - prefix_offset: The prefix offset. - read_offset: The read offset. - skip_special_tokens: Whether to skip special tokens. - spaces_between_special_tokens: Whether to add spaces between special - tokens. - """ - new_token_id = all_input_ids[-1] - # This is the first iteration for this sequence - is_first_iter = prev_tokens is None - if is_first_iter: - (prev_tokens, prefix_offset, - read_offset) = convert_prompt_ids_to_tokens( - tokenizer, - all_input_ids[:-1], - skip_special_tokens=skip_special_tokens) - assert prev_tokens is not None - - # If the new token id is out of bounds, return an empty string. - if 0 <= new_token_id < len(tokenizer): - # Put new_token_id in a list so skip_special_tokens is respected - new_tokens = tokenizer.convert_ids_to_tokens( - [new_token_id], skip_special_tokens=skip_special_tokens) - if isinstance(new_tokens, str): - new_tokens = [new_tokens] - else: - new_tokens = [""] - output_tokens = prev_tokens + new_tokens - - # If this is the first iteration, return all tokens. - if is_first_iter: - new_tokens = output_tokens - - # The prefix text is necessary only to defeat cleanup algorithms in - # the decode which decide to add a space or not depending on the - # surrounding ids. - if tokenizer.is_fast or not tokenizer.get_added_vocab(): - prefix_text = tokenizer.convert_tokens_to_string( - output_tokens[prefix_offset:read_offset]) - new_text = tokenizer.convert_tokens_to_string( - output_tokens[prefix_offset:]) - else: - prefix_text = _convert_tokens_to_string_with_added_encoders( - tokenizer, - output_tokens[prefix_offset:read_offset], - skip_special_tokens=skip_special_tokens, - spaces_between_special_tokens=spaces_between_special_tokens, - ) - new_text = _convert_tokens_to_string_with_added_encoders( - tokenizer, - output_tokens[prefix_offset:], - skip_special_tokens=skip_special_tokens, - spaces_between_special_tokens=spaces_between_special_tokens, - ) - - if len(new_text) <= len(prefix_text) or new_text.endswith("�"): - # utf-8 char at the end means it's a potential unfinished byte sequence - # from byte fallback tokenization. - # If it's in the middle, it's probably a real invalid id generated - # by the model - return new_tokens, "", prefix_offset, read_offset - - new_text = new_text[len(prefix_text):] - return new_tokens, new_text, read_offset, len(output_tokens) diff --git a/vllm/transformers_utils/detokenizer_utils.py b/vllm/transformers_utils/detokenizer_utils.py new file mode 100644 index 0000000000000..37ff8a236e791 --- /dev/null +++ b/vllm/transformers_utils/detokenizer_utils.py @@ -0,0 +1,167 @@ +from typing import List, Optional, Tuple + +from .tokenizer import AnyTokenizer + + +def _replace_none_with_empty(tokens: List[Optional[str]]): + for i, token in enumerate(tokens): + if token is None: + tokens[i] = "" + + +def _convert_tokens_to_string_with_added_encoders( + tokenizer: AnyTokenizer, + output_tokens: List[str], + skip_special_tokens: bool, + spaces_between_special_tokens: bool, +) -> str: + # Adapted from + # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/tokenization_utils.py#L921 + # NOTE(woosuk): The following code is slow because it runs a for loop over + # the output_tokens. In Python, running a for loop over a list can be slow + # even when the loop body is very simple. + sub_texts: List[str] = [] + current_sub_text: List[str] = [] + all_special_tokens = set(tokenizer.all_special_tokens) + for token in output_tokens: + if skip_special_tokens and token in all_special_tokens: + continue + if token in tokenizer.get_added_vocab(): + if current_sub_text: + sub_text = tokenizer.convert_tokens_to_string(current_sub_text) + sub_texts.append(sub_text) + current_sub_text = [] + sub_texts.append(token) + else: + current_sub_text.append(token) + if current_sub_text: + sub_text = tokenizer.convert_tokens_to_string(current_sub_text) + sub_texts.append(sub_text) + if spaces_between_special_tokens: + return " ".join(sub_texts) + else: + return "".join(sub_texts) + + +# 5 is an arbitrary value that should work for all +# tokenizers (bigger = more conservative). +INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET = 5 + + +def convert_prompt_ids_to_tokens( + tokenizer: AnyTokenizer, + prompt_ids: List[int], + skip_special_tokens: bool = False, +) -> Tuple[List[str], int, int]: + """Converts the prompt ids to tokens and returns the tokens and offsets + for incremental detokenization. + + Note that not all tokens are converted to strings. Only the tokens that + are necessary for incremental detokenization are converted to strings. + """ + # We do not need to convert the whole prompt to tokens. + # Offset a little more in case we have special tokens. + new_tokens = tokenizer.convert_ids_to_tokens( + prompt_ids[-INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET - 2:], + skip_special_tokens=skip_special_tokens) + read_offset = len(new_tokens) + prefix_offset = max( + read_offset - INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET, 0) + # This is required to guard against out-of-vocab prompt token ids + _replace_none_with_empty(new_tokens) # type: ignore[arg-type] + return new_tokens, prefix_offset, read_offset + + +# Based on +# https://github.com/huggingface/text-generation-inference/blob/v0.9.4/server/text_generation_server/models/model.py#L62C9-L62C15 +# under Apache 2.0 license +def detokenize_incrementally( + tokenizer: AnyTokenizer, + all_input_ids: List[int], + prev_tokens: Optional[List[str]], + prefix_offset: int, + read_offset: int, + skip_special_tokens: bool = False, + spaces_between_special_tokens: bool = True, +) -> Tuple[List[str], str, int, int]: + """Detokenizes the input ids incrementally and returns the new tokens + and the new text. + + If `prev_tokens` is None, this function will convert the input ids to + tokens and return the tokens and the new text. Otherwise, it will return the + new tokens and the new text. + + This function will also return the new prefix offset and the new read + offset to be used in the next iteration. + + The offsets are necessary to defeat cleanup algorithms in the decode which + decide to add a space or not depending on the surrounding ids. + + Args: + tokenizer: The tokenizer to use. + all_input_ids: The input ids. The last id is the new token id. + prev_tokens: The previous tokens. If None, this function will convert + the input ids to tokens and return the tokens and the new text. + prefix_offset: The prefix offset. + read_offset: The read offset. + skip_special_tokens: Whether to skip special tokens. + spaces_between_special_tokens: Whether to add spaces between special + tokens. + """ + new_token_id = all_input_ids[-1] + # This is the first iteration for this sequence + is_first_iter = prev_tokens is None + if is_first_iter: + (prev_tokens, prefix_offset, + read_offset) = convert_prompt_ids_to_tokens( + tokenizer, + all_input_ids[:-1], + skip_special_tokens=skip_special_tokens) + assert prev_tokens is not None + + # If the new token id is out of bounds, return an empty string. + if 0 <= new_token_id < len(tokenizer): + # Put new_token_id in a list so skip_special_tokens is respected + new_tokens = tokenizer.convert_ids_to_tokens( + [new_token_id], skip_special_tokens=skip_special_tokens) + if isinstance(new_tokens, str): + new_tokens = [new_tokens] + else: + new_tokens = [""] + output_tokens = prev_tokens + new_tokens + + # If this is the first iteration, return all tokens. + if is_first_iter: + new_tokens = output_tokens + + # The prefix text is necessary only to defeat cleanup algorithms in + # the decode which decide to add a space or not depending on the + # surrounding ids. + if tokenizer.is_fast or not tokenizer.get_added_vocab(): + prefix_text = tokenizer.convert_tokens_to_string( + output_tokens[prefix_offset:read_offset]) + new_text = tokenizer.convert_tokens_to_string( + output_tokens[prefix_offset:]) + else: + prefix_text = _convert_tokens_to_string_with_added_encoders( + tokenizer, + output_tokens[prefix_offset:read_offset], + skip_special_tokens=skip_special_tokens, + spaces_between_special_tokens=spaces_between_special_tokens, + ) + new_text = _convert_tokens_to_string_with_added_encoders( + tokenizer, + output_tokens[prefix_offset:], + skip_special_tokens=skip_special_tokens, + spaces_between_special_tokens=spaces_between_special_tokens, + ) + + if len(new_text) <= len(prefix_text) or new_text.endswith("�"): + # utf-8 char at the end means it's a potential unfinished byte sequence + # from byte fallback tokenization. + # If it's in the middle, it's probably a real invalid id generated + # by the model + return new_tokens, "", prefix_offset, read_offset + + new_text = new_text[len(prefix_text):] + return new_tokens, new_text, read_offset, len(output_tokens) diff --git a/vllm/v1/attention/__init__.py b/vllm/v1/attention/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/attention/backends/__init__.py b/vllm/v1/attention/backends/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py new file mode 100644 index 0000000000000..0530b1a6762ce --- /dev/null +++ b/vllm/v1/attention/backends/flash_attn.py @@ -0,0 +1,241 @@ +"""Attention layer with FlashAttention.""" +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Type + +import torch + +from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl, + AttentionMetadata, AttentionType) +from vllm.forward_context import get_forward_context +from vllm.vllm_flash_attn import flash_attn_varlen_func + + +class FlashAttentionBackend(AttentionBackend): + + @staticmethod + def get_supported_head_sizes() -> List[int]: + return [32, 64, 96, 128, 160, 192, 224, 256] + + @staticmethod + def get_name() -> str: + return "flash-attn-vllm-v1" + + @staticmethod + def get_impl_cls() -> Type["FlashAttentionImpl"]: + return FlashAttentionImpl + + @staticmethod + def get_metadata_cls() -> Type["AttentionMetadata"]: + return FlashAttentionMetadata + + @staticmethod + def get_kv_cache_shape( + num_blocks: int, + block_size: int, + num_kv_heads: int, + head_size: int, + ) -> Tuple[int, ...]: + if block_size % 16 != 0: + raise ValueError("Block size must be a multiple of 16.") + return (2, num_blocks, block_size, num_kv_heads, head_size) + + +@dataclass +class FlashAttentionMetadata: + # NOTE(sang): Definition of context_len, query_len, and seq_len. + # |---------- N-1 iteration --------| + # |---------------- N iteration ---------------------| + # |- tokenA -|......................|-- newTokens ---| + # |---------- context_len ----------| + # |-------------------- seq_len ---------------------| + # |-- query_len ---| + + max_query_len: int + query_start_loc: torch.Tensor + max_seq_len: int + seq_start_loc: torch.Tensor + block_table: torch.Tensor + slot_mapping: torch.Tensor + + +class FlashAttentionImpl(AttentionImpl): + + def __init__( + self, + num_heads: int, + head_size: int, + scale: float, + num_kv_heads: int, + alibi_slopes: Optional[List[float]], + sliding_window: Optional[int], + kv_cache_dtype: str, + blocksparse_params: Optional[Dict[str, Any]] = None, + logits_soft_cap: Optional[float] = None, + ) -> None: + if blocksparse_params is not None: + raise ValueError( + "FlashAttention does not support block-sparse attention.") + self.num_heads = num_heads + self.head_size = head_size + self.scale = float(scale) + self.num_kv_heads = num_kv_heads + if alibi_slopes is not None: + alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32) + self.alibi_slopes = alibi_slopes + self.sliding_window = ((sliding_window, sliding_window) + if sliding_window is not None else (-1, -1)) + self.kv_cache_dtype = kv_cache_dtype + if logits_soft_cap is None: + # In flash-attn, setting logits_soft_cap as 0 means no soft cap. + logits_soft_cap = 0 + self.logits_soft_cap = logits_soft_cap + + assert self.num_heads % self.num_kv_heads == 0 + self.num_queries_per_kv = self.num_heads // self.num_kv_heads + + if sliding_window is not None: + # NOTE(woosuk): flash-attn's sliding window does not work with + # paged KV cache. + raise ValueError( + "Sliding window is not supported in FlashAttention.") + + support_head_sizes = FlashAttentionBackend.get_supported_head_sizes() + if head_size not in support_head_sizes: + raise ValueError( + f"Head size {head_size} is not supported by FlashAttention. " + f"Supported head sizes are: {support_head_sizes}.") + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + kv_cache: torch.Tensor, + attn_metadata: FlashAttentionMetadata, + k_scale: float = 1.0, + v_scale: float = 1.0, + attn_type: AttentionType = AttentionType.DECODER, + ) -> torch.Tensor: + """Forward pass with FlashAttention. + + Args: + query: shape = [num_tokens, num_heads * head_size] + key: shape = [num_tokens, num_kv_heads * head_size] + value: shape = [num_tokens, num_kv_heads * head_size] + kv_cache = [2, num_blocks, block_size, num_kv_heads, head_size] + attn_metadata: Metadata for attention. + Returns: + shape = [num_tokens, num_heads * head_size] + """ + if attn_type != AttentionType.DECODER: + raise NotImplementedError("Encoder self-attention and " + "encoder/decoder cross-attention " + "are not implemented for " + "FlashAttentionImpl") + + # NOTE(woosuk): FlashAttention does not support FP8 KV cache. + assert k_scale == 1.0 and v_scale == 1.0, ( + "key/v_scale is not supported in FlashAttention.") + + output = torch.ops.vllm.unified_flash_attention( + query, + key, + value, + self.num_heads, + self.head_size, + self.num_kv_heads, + kv_cache, + self.kv_cache_dtype, + k_scale, + v_scale, + self.scale, + self.sliding_window, + self.alibi_slopes, + self.logits_soft_cap, + ) + return output + + +@torch.library.custom_op("vllm::unified_flash_attention", + mutates_args=["kv_cache"]) +def unified_flash_attention( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + num_heads: int, + head_size: int, + num_kv_heads: int, + kv_cache: torch.Tensor, + kv_cache_dtype: str, + k_scale: float, + v_scale: float, + softmax_scale: float, + window_size: Optional[List[int]] = None, + alibi_slopes: Optional[torch.Tensor] = None, + logits_soft_cap: Optional[float] = None, +) -> torch.Tensor: + current_metadata = get_forward_context() + if current_metadata is None: + # Profiling run. + return torch.empty_like(query) + + assert current_metadata is not None + assert isinstance(current_metadata, FlashAttentionMetadata) + attn_metadata: FlashAttentionMetadata = current_metadata + + num_tokens, hidden_size = query.shape + # Reshape the query, key, and value tensors. + query = query.view(-1, num_heads, head_size) + key = key.view(-1, num_kv_heads, head_size) + value = value.view(-1, num_kv_heads, head_size) + + # Reshape the input keys and values and store them in the cache. + key_cache = kv_cache[0] + value_cache = kv_cache[1] + torch.ops._C_cache_ops.reshape_and_cache_flash( + key, + value, + kv_cache[0], + kv_cache[1], + attn_metadata.slot_mapping, + kv_cache_dtype, + k_scale, + v_scale, + ) + + output = flash_attn_varlen_func( + q=query, + k=key_cache, + v=value_cache, + cu_seqlens_q=attn_metadata.query_start_loc, + max_seqlen_q=attn_metadata.max_query_len, + cu_seqlens_k=attn_metadata.seq_start_loc, + max_seqlen_k=attn_metadata.max_seq_len, + softmax_scale=softmax_scale, + causal=True, + alibi_slopes=alibi_slopes, + window_size=window_size, + block_table=attn_metadata.block_table, + softcap=logits_soft_cap, + ) + return output.view(num_tokens, hidden_size) + + +@unified_flash_attention.register_fake +def _( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + num_heads: int, + head_size: int, + num_kv_heads: int, + kv_cache: torch.Tensor, + kv_cache_dtype: str, + k_scale: float, + v_scale: float, + softmax_scale: float, + window_size: Optional[List[int]] = None, + alibi_slopes: Optional[torch.Tensor] = None, + logits_soft_cap: Optional[float] = None, +) -> torch.Tensor: + return torch.empty_like(query) diff --git a/vllm/v1/core/__init__.py b/vllm/v1/core/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/core/kv_cache_manager.py b/vllm/v1/core/kv_cache_manager.py new file mode 100644 index 0000000000000..9b735a8be10d7 --- /dev/null +++ b/vllm/v1/core/kv_cache_manager.py @@ -0,0 +1,108 @@ +from typing import Dict, List, Optional + +import numpy as np + +from vllm.logger import init_logger +from vllm.utils import cdiv +from vllm.v1.request import Request + +logger = init_logger(__name__) + + +class KVCacheManager: + + def __init__( + self, + block_size: int, + num_gpu_blocks: int, + sliding_window: Optional[int] = None, + enable_caching: bool = True, + num_preallocate_tokens: int = 64, + ) -> None: + self.block_size = block_size + self.num_gpu_blocks = num_gpu_blocks + self.sliding_window = sliding_window + self.enable_caching = enable_caching + # NOTE(woosuk): To avoid frequent block allocation, we preallocate some + # blocks for each request. For example, when a request reaches the end + # of its block table, we preallocate N blocks in advance. This way, we + # reduce the overhead of updating free_block_ids and ref_cnts for each + # request every step (at the cost of some memory waste). + # NOTE(woosuk): This is different from the "lookahead" slots since this + # does not guarantee that the request always has N empty blocks. After + # the request gets N empty blocks, it starts to use the blocks without + # further allocation. When it uses up all the N empty blocks, it gets + # N new empty blocks. + self.num_preallocate_tokens = num_preallocate_tokens + self.num_preallocate_blocks = cdiv(num_preallocate_tokens, block_size) + + self.free_block_ids = list(range(num_gpu_blocks)) + self.req_to_block_ids: Dict[str, List[int]] = {} + self.ref_cnts = np.zeros(num_gpu_blocks, dtype=np.int32) + + def get_computed_blocks(self, request: Request) -> List[int]: + if not self.enable_caching: + # No prefix caching. + return [] + # TODO(woosuk): Implement hash-based caching. + return [] + + def append_slots( + self, + request: Request, + num_tokens: int, + ) -> Optional[List[int]]: + num_required_blocks = cdiv(request.num_computed_tokens + num_tokens, + self.block_size) + req_block_ids = self.req_to_block_ids[request.request_id] + if num_required_blocks <= len(req_block_ids): + # No new block is needed. + return [] + + num_new_blocks = num_required_blocks - len(req_block_ids) + num_free_blocks = len(self.free_block_ids) + if num_new_blocks > num_free_blocks: + # Cannot allocate new blocks. + return None + + # Allocate new blocks. + num_new_blocks = min(num_new_blocks + self.num_preallocate_blocks, + num_free_blocks) + new_block_ids = self._get_new_blocks(num_new_blocks) + req_block_ids.extend(new_block_ids) + self.ref_cnts[new_block_ids] += 1 + return new_block_ids + + def allocate_slots( + self, + request: Request, + num_tokens: int, + computed_block_ids: List[int], + ) -> Optional[List[int]]: + num_required_blocks = cdiv(num_tokens, self.block_size) + num_free_blocks = len(self.free_block_ids) + if num_required_blocks > num_free_blocks: + # Cannot allocate new blocks. + return None + + num_new_blocks = min(num_required_blocks + self.num_preallocate_blocks, + num_free_blocks) + new_block_ids = self._get_new_blocks(num_new_blocks) + block_ids = computed_block_ids + new_block_ids + self.req_to_block_ids[request.request_id] = block_ids + self.ref_cnts[block_ids] += 1 + return new_block_ids + + def free(self, request: Request) -> None: + block_ids = self.req_to_block_ids.pop(request.request_id) + self.ref_cnts[block_ids] -= 1 + for block_id in block_ids: + ref_cnt = self.ref_cnts[block_id] + if ref_cnt == 0: + self.free_block_ids.append(block_id) + + def _get_new_blocks(self, num_blocks: int) -> List[int]: + assert num_blocks <= len(self.free_block_ids) + new_block_ids = self.free_block_ids[-num_blocks:] + self.free_block_ids = self.free_block_ids[:-num_blocks] + return new_block_ids diff --git a/vllm/v1/core/scheduler.py b/vllm/v1/core/scheduler.py new file mode 100644 index 0000000000000..41659ff62747d --- /dev/null +++ b/vllm/v1/core/scheduler.py @@ -0,0 +1,412 @@ +from collections import deque +from dataclasses import dataclass +from typing import Deque, Dict, Iterable, List, Optional, Set, Tuple, Union + +from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig +from vllm.logger import init_logger +from vllm.multimodal import MultiModalDataDict +from vllm.sampling_params import SamplingParams +from vllm.v1.core.kv_cache_manager import KVCacheManager +from vllm.v1.outputs import ModelRunnerOutput +from vllm.v1.request import Request, RequestStatus + +logger = init_logger(__name__) + + +class Scheduler: + + def __init__( + self, + scheduler_config: SchedulerConfig, + cache_config: CacheConfig, + lora_config: Optional[LoRAConfig], + ) -> None: + self.scheduler_config = scheduler_config + self.cache_config = cache_config + self.lora_config = lora_config + # TODO: Support LoRA. + assert lora_config is None, "V1 does not support LoRA yet." + + num_gpu_blocks = cache_config.num_gpu_blocks + assert isinstance(num_gpu_blocks, int) and num_gpu_blocks > 0 + # Create the block space manager. + self.kv_cache_manager = KVCacheManager( + block_size=self.cache_config.block_size, + num_gpu_blocks=num_gpu_blocks, + sliding_window=self.cache_config.sliding_window, + enable_caching=True) + self.block_size = self.cache_config.block_size + + # Scheduling constraints. + self.max_num_running_reqs = self.scheduler_config.max_num_seqs + self.max_num_scheduled_tokens = \ + self.scheduler_config.max_num_batched_tokens + self.max_model_len = self.scheduler_config.max_model_len + + # req_id -> Request + self.requests: Dict[str, Request] = {} + # Priority queues for requests. + self.waiting: Deque[Request] = deque() + self.running: List[Request] = [] + + # The request IDs that are finished in between the previous and the + # current steps. This is used to notify the workers about the finished + # requests so that they can free the cached states for those requests. + # This is flushed at the end of each scheduling step. + self.finished_req_ids: Set[str] = set() + + # OPTIMIZATION: Cache the RunningRequestData objects to avoid creating + # them at each scheduling step. + # Request id -> RunningRequestData + self.running_reqs_data: Dict[str, RunningRequestData] = {} + + def schedule(self) -> "SchedulerOutput": + scheduled_new_reqs: List[Request] = [] + scheduled_resumed_reqs: List[Request] = [] + scheduled_running_reqs: List[Request] = [] + preempted_reqs: List[Request] = [] + + # NOTE(woosuk) on the scheduling algorithm: + # There's no "decoding phase" nor "prefill phase" in the scheduler. + # Each request just has the num_computed_tokens and num_tokens, + # which is equal to len(prompt_token_ids) + len(output_token_ids). + # At each step, the scheduler tries to assign tokens to the requests + # so that each request's num_computed_tokens can catch up its + # num_tokens. This is general enough to cover chunked prefills, + # prefix caching, and the "jump forward" optimization in the future. + + req_to_new_block_ids: Dict[str, List[int]] = {} + num_scheduled_tokens: Dict[str, int] = {} + token_budget = self.max_num_scheduled_tokens + + # First, schedule the RUNNING requests. + req_index = 0 + while req_index < len(self.running): + if token_budget == 0: + break + + request = self.running[req_index] + num_new_tokens = request.num_tokens - request.num_computed_tokens + num_new_tokens = min(num_new_tokens, token_budget) + assert num_new_tokens > 0 + + while True: + new_block_ids = self.kv_cache_manager.append_slots( + request, num_new_tokens) + if new_block_ids is None: + # The request cannot be scheduled. + # Preempt the lowest-priority request. + preempted_req = self.running.pop() + self.kv_cache_manager.free(preempted_req) + preempted_req.status = RequestStatus.PREEMPTED + preempted_req.num_computed_tokens = 0 + + self.waiting.appendleft(preempted_req) + preempted_reqs.append(preempted_req) + if preempted_req == request: + # No more request to preempt. + break + else: + # The request can be scheduled. + scheduled_running_reqs.append(request) + + req_to_new_block_ids[request.request_id] = new_block_ids + num_scheduled_tokens[request.request_id] = num_new_tokens + token_budget -= num_new_tokens + req_index += 1 + break + + # Next, schedule the WAITING requests. + if not preempted_reqs: + while self.waiting: + if len(self.running) == self.max_num_running_reqs: + break + if token_budget == 0: + break + + request = self.waiting[0] + # Get already-cached tokens. + computed_block_ids = self.kv_cache_manager.get_computed_blocks( + request) + # NOTE(woosuk): Since incomplete blocks are not eligible for + # sharing, `num_computed_tokens` is always a multiple of + # `block_size`. + num_computed_tokens = len(computed_block_ids) * self.block_size + # Number of tokens to be scheduled. + # We use `request.num_tokens` instead of + # `request.num_prompt_tokens` to consider the resumed requests, + # which have output tokens. + num_new_tokens = request.num_tokens - num_computed_tokens + num_new_tokens = min(num_new_tokens, token_budget) + assert num_new_tokens > 0 + new_block_ids = self.kv_cache_manager.allocate_slots( + request, num_new_tokens, computed_block_ids) + if new_block_ids is None: + # The request cannot be scheduled. + break + request.num_computed_tokens = num_computed_tokens + + self.waiting.popleft() + self.running.append(request) + if request.status == RequestStatus.WAITING: + scheduled_new_reqs.append(request) + elif request.status == RequestStatus.PREEMPTED: + scheduled_resumed_reqs.append(request) + else: + raise RuntimeError( + f"Invalid request status: {request.status}") + + req_to_new_block_ids[request.request_id] = ( + computed_block_ids + new_block_ids) + num_scheduled_tokens[request.request_id] = num_new_tokens + token_budget -= num_new_tokens + request.status = RequestStatus.RUNNING + + # Check if the scheduling constraints are satisfied. + total_num_scheduled_tokens = sum(num_scheduled_tokens.values()) + assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens + assert token_budget >= 0 + assert len(self.running) <= self.max_num_running_reqs + assert (len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + + len(scheduled_running_reqs) == len(self.running)) + + # Construct the scheduler output. + new_reqs_data = [ + NewRequestData.from_request(req, + req_to_new_block_ids[req.request_id], + req.num_computed_tokens) + for req in scheduled_new_reqs + ] + resumed_reqs_data = [ + ResumedRequestData.from_request( + req, req_to_new_block_ids[req.request_id], + req.num_computed_tokens) for req in scheduled_resumed_reqs + ] + running_reqs_data = [ + self._make_running_request_data( + req, req_to_new_block_ids[req.request_id], + req.num_computed_tokens) for req in scheduled_running_reqs + ] + preempted_req_ids = {req.request_id for req in preempted_reqs} + scheduler_output = SchedulerOutput( + scheduled_new_reqs=new_reqs_data, + scheduled_resumed_reqs=resumed_reqs_data, + scheduled_running_reqs=running_reqs_data, + num_scheduled_tokens=num_scheduled_tokens, + total_num_scheduled_tokens=total_num_scheduled_tokens, + preempted_req_ids=preempted_req_ids, + # finished_req_ids is an existing state in the scheduler, + # instead of being newly scheduled in this step. + # It contains the request IDs that are finished in between + # the previous and the current steps. + finished_req_ids=self.finished_req_ids, + ) + + self.finished_req_ids = set() + return scheduler_output + + def _make_running_request_data( + self, + request: Request, + new_block_ids: List[int], + num_computed_tokens: int, + ) -> "RunningRequestData": + # OPTIMIZATION: Cache the RunningRequestData objects to avoid creating + # them at each scheduling step. + if request.request_id in self.running_reqs_data: + req_data = self.running_reqs_data[request.request_id] + req_data.new_block_ids = new_block_ids + req_data.num_computed_tokens = num_computed_tokens + else: + req_data = RunningRequestData.from_request(request, new_block_ids, + num_computed_tokens) + self.running_reqs_data[request.request_id] = req_data + return req_data + + def update_from_output( + self, + scheduler_output: "SchedulerOutput", + model_runner_output: "ModelRunnerOutput", + ) -> List[Tuple[Request, int]]: + # NOTE(woosuk): This method doesn't consider speculative decoding. + sampled_token_ids = model_runner_output.sampled_token_ids_cpu.tolist() + num_scheduled_tokens = scheduler_output.num_scheduled_tokens + new_running: List[Request] = [] + # (request, num_sampled_tokens) + sampled: List[Tuple[Request, int]] = [] + for request in self.running: + req_id = request.request_id + request.num_computed_tokens += num_scheduled_tokens[req_id] + # When the request's num_computed_tokens catches up its num_tokens, + # the request generates output tokens. Otherwise, we ignore the + # sampler output for the request. + assert request.num_computed_tokens <= request.num_tokens + if request.num_computed_tokens == request.num_tokens: + req_index = model_runner_output.req_id_to_index[req_id] + # NOTE(woosuk): Currently, we assume that each request + # generates at most one token at each step. + token_id = sampled_token_ids[req_index] + request.output_token_ids.append(token_id) + sampled.append((request, 1)) + # TODO: Update the KV cache manager for prefix caching. + + # Check if the request is finished. + stopped = self._check_stop(request) + if stopped: + continue + + new_running.append(request) + self.running = new_running + return sampled + + def _check_stop(self, request: Request) -> bool: + if (request.num_tokens >= self.max_model_len + or request.num_output_tokens >= request.max_tokens): + request.status = RequestStatus.FINISHED_LENGTH_CAPPED + self._free_request(request) + return True + + sampling_params = request.sampling_params + last_token_id = request.output_token_ids[-1] + if (not sampling_params.ignore_eos + and last_token_id == request.eos_token_id): + request.status = RequestStatus.FINISHED_STOPPED + self._free_request(request) + return True + + if last_token_id in (sampling_params.stop_token_ids or ()): + request.status = RequestStatus.FINISHED_STOPPED + request.stop_reason = last_token_id + self._free_request(request) + return True + return False + + def add_request(self, request: Request) -> None: + self.waiting.append(request) + self.requests[request.request_id] = request + + def finish_requests( + self, + request_ids: Union[str, Iterable[str]], + finished_status: RequestStatus, + ) -> None: + """Handles the finish signal from outside the scheduler. + + For example, the API server can abort a request when the client + disconnects. + """ + assert RequestStatus.is_finished(finished_status) + if isinstance(request_ids, str): + request_ids = (request_ids, ) + request_ids = set(request_ids) + + for req_id in request_ids: + request = self.requests.get(req_id) + if request is None: + # Invalid request ID. + continue + + if request.status == RequestStatus.RUNNING: + self.running.remove(request) + else: + self.waiting.remove(request) + request.status = finished_status + self._free_request(request) + + def _free_request(self, request: Request) -> None: + assert request.is_finished() + self.kv_cache_manager.free(request) + self.running_reqs_data.pop(request.request_id, None) + del self.requests[request.request_id] + self.finished_req_ids.add(request.request_id) + + def get_num_unfinished_requests(self) -> int: + return len(self.waiting) + len(self.running) + + def has_unfinished_requests(self) -> bool: + return self.get_num_unfinished_requests() > 0 + + +@dataclass +class NewRequestData: + + req_id: str + prompt_token_ids: List[int] + prompt: Optional[str] + multi_modal_data: Optional[MultiModalDataDict] + sampling_params: SamplingParams + block_ids: List[int] + num_computed_tokens: int + + @classmethod + def from_request( + cls, + request: Request, + block_ids: List[int], + num_computed_tokens: int, + ) -> "NewRequestData": + return cls( + req_id=request.request_id, + prompt_token_ids=request.inputs["prompt_token_ids"], + prompt=request.inputs.get("prompt"), + multi_modal_data=request.inputs.get("multi_modal_data"), + sampling_params=request.sampling_params, + block_ids=block_ids, + num_computed_tokens=num_computed_tokens, + ) + + +@dataclass +class ResumedRequestData: + + req_id: str + block_ids: List[int] + num_computed_tokens: int + + @classmethod + def from_request( + cls, + request: Request, + block_ids: List[int], + num_computed_tokens: int, + ) -> "ResumedRequestData": + return cls( + req_id=request.request_id, + block_ids=block_ids, + num_computed_tokens=num_computed_tokens, + ) + + +@dataclass +class RunningRequestData: + + req_id: str + new_block_ids: List[int] + num_computed_tokens: int + + @classmethod + def from_request( + cls, + request: Request, + new_block_ids: List[int], + num_computed_tokens: int, + ) -> "RunningRequestData": + return cls( + req_id=request.request_id, + new_block_ids=new_block_ids, + num_computed_tokens=num_computed_tokens, + ) + + +@dataclass +class SchedulerOutput: + + scheduled_new_reqs: List[NewRequestData] + scheduled_resumed_reqs: List[ResumedRequestData] + scheduled_running_reqs: List[RunningRequestData] + + num_scheduled_tokens: Dict[str, int] + total_num_scheduled_tokens: int + + preempted_req_ids: Set[str] + finished_req_ids: Set[str] diff --git a/vllm/v1/engine/__init__.py b/vllm/v1/engine/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/engine/llm_engine.py b/vllm/v1/engine/llm_engine.py new file mode 100644 index 0000000000000..511b417086c63 --- /dev/null +++ b/vllm/v1/engine/llm_engine.py @@ -0,0 +1,523 @@ +import time +from typing import (Any, Dict, Iterable, List, Mapping, Optional, Tuple, Type, + Union) + +from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, + EngineConfig, LoadConfig, LoRAConfig, ModelConfig, + ObservabilityConfig, ParallelConfig, + PromptAdapterConfig, SchedulerConfig, + SpeculativeConfig) +from vllm.engine.arg_utils import EngineArgs +from vllm.engine.metrics_types import StatLoggerBase +from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, + EncoderDecoderLLMInputs, InputRegistry, PromptType) +from vllm.inputs.preprocess import InputPreprocessor +from vllm.logger import init_logger +from vllm.lora.request import LoRARequest +from vllm.outputs import CompletionOutput, RequestOutput +from vllm.pooling_params import PoolingParams +from vllm.prompt_adapter.request import PromptAdapterRequest +from vllm.sampling_params import RequestOutputKind, SamplingParams +from vllm.transformers_utils.config import try_get_generation_config +from vllm.transformers_utils.tokenizer_group import ( + BaseTokenizerGroup, init_tokenizer_from_configs) +from vllm.usage.usage_lib import UsageContext +from vllm.v1.core.scheduler import Scheduler +from vllm.v1.executor.gpu_executor import GPUExecutor +from vllm.v1.request import Request, RequestStatus +from vllm.v1.tokenizer.detokenizer import Detokenizer, DetokenizerInputs +from vllm.version import __version__ as VLLM_VERSION + +logger = init_logger(__name__) + + +class LLMEngine: + + def __init__( + self, + model_config: ModelConfig, + cache_config: CacheConfig, + parallel_config: ParallelConfig, + scheduler_config: SchedulerConfig, + device_config: DeviceConfig, + load_config: LoadConfig, + lora_config: Optional[LoRAConfig], + speculative_config: Optional[SpeculativeConfig], + decoding_config: Optional[DecodingConfig], + observability_config: Optional[ObservabilityConfig], + prompt_adapter_config: Optional[PromptAdapterConfig], + executor_class: Type[GPUExecutor], + log_stats: bool, + usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, + stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, + input_registry: InputRegistry = INPUT_REGISTRY, + use_cached_outputs: bool = False, + ) -> None: + # Override the configs for V1. + # FIXME + if usage_context == UsageContext.LLM_CLASS: + scheduler_config.max_num_seqs = 1024 + scheduler_config.max_num_batched_tokens = 8192 + elif usage_context == UsageContext.OPENAI_API_SERVER: + scheduler_config.max_num_seqs = 1024 + scheduler_config.max_num_batched_tokens = 2048 + + logger.info( + "Initializing an LLM engine (v%s) with config: " + "model=%r, speculative_config=%r, tokenizer=%r, " + "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, " + "override_neuron_config=%s, " + "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, " + "trust_remote_code=%s, dtype=%s, max_seq_len=%d, " + "download_dir=%r, load_format=%s, tensor_parallel_size=%d, " + "pipeline_parallel_size=%d, " + "disable_custom_all_reduce=%s, quantization=%s, " + "enforce_eager=%s, kv_cache_dtype=%s, " + "quantization_param_path=%s, device_config=%s, " + "decoding_config=%r, observability_config=%r, " + "seed=%d, served_model_name=%s, " + "num_scheduler_steps=%d, enable_prefix_caching=%s, " + "use_async_output_proc=%s, mm_processor_kwargs=%s)", + VLLM_VERSION, + model_config.model, + speculative_config, + model_config.tokenizer, + model_config.skip_tokenizer_init, + model_config.tokenizer_mode, + model_config.revision, + model_config.override_neuron_config, + model_config.rope_scaling, + model_config.rope_theta, + model_config.tokenizer_revision, + model_config.trust_remote_code, + model_config.dtype, + model_config.max_model_len, + load_config.download_dir, + load_config.load_format, + parallel_config.tensor_parallel_size, + parallel_config.pipeline_parallel_size, + parallel_config.disable_custom_all_reduce, + model_config.quantization, + model_config.enforce_eager, + cache_config.cache_dtype, + model_config.quantization_param_path, + device_config.device, + decoding_config, + observability_config, + model_config.seed, + model_config.served_model_name, + scheduler_config.num_scheduler_steps, + cache_config.enable_prefix_caching, + model_config.use_async_output_proc, + model_config.mm_processor_kwargs, + ) + + self.model_config = model_config + self.cache_config = cache_config + self.lora_config = lora_config + self.parallel_config = parallel_config + self.scheduler_config = scheduler_config + self.device_config = device_config + self.speculative_config = speculative_config + self.load_config = load_config + self.decoding_config = decoding_config or DecodingConfig() + self.prompt_adapter_config = prompt_adapter_config + self.observability_config = observability_config or ObservabilityConfig( + ) + self.log_stats = log_stats + + assert not self.model_config.skip_tokenizer_init + self.tokenizer = self._init_tokenizer() + if self.tokenizer: + # Ping the tokenizer to ensure liveness if it runs in a + # different process. + self.tokenizer.ping() + self.detokenizer = Detokenizer(self.model_config.tokenizer) + + self.generation_config_fields = _load_generation_config_dict( + model_config) + self.input_preprocessor = InputPreprocessor(model_config, + self.tokenizer) + self.input_registry = input_registry + self.input_processor = input_registry.create_input_processor( + model_config) + + # Request id -> Request + self.requests: Dict[str, Request] = {} + # NOTE(woosuk): Now that the detokenizer works asynchronously, we need + # to keep track of how many steps each request has been lagged behind + # in terms of detokenization. + # Request id -> how many detokenizer steps the request should wait for. + self.num_lagged_steps: Dict[str, int] = {} + # OPTIMIZATION: Cache the request output and update it incrementally. + # This is used to avoid creating a new RequestOutput object every step. + # Request id -> RequestOutput + self.request_outputs: Dict[str, RequestOutput] = {} + + self.model_executor = executor_class( + model_config=model_config, + cache_config=cache_config, + parallel_config=parallel_config, + scheduler_config=scheduler_config, + device_config=device_config, + lora_config=lora_config, + speculative_config=speculative_config, + load_config=load_config, + prompt_adapter_config=prompt_adapter_config, + observability_config=self.observability_config, + ) + assert self.model_config.task != "embedding" + self._initialize_kv_caches() + + # Create the scheduler. + # NOTE: the cache_config here have been updated with the numbers of + # GPU and CPU blocks, which are profiled in the distributed executor. + self.scheduler = Scheduler(scheduler_config, cache_config, lora_config) + + def _initialize_kv_caches(self) -> None: + num_gpu_blocks, _ = self.model_executor.determine_num_available_blocks( + ) + + if self.cache_config.num_gpu_blocks_override is not None: + num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override + logger.info( + "Overriding num_gpu_blocks=%d with " + "num_gpu_blocks_override=%d", num_gpu_blocks, + num_gpu_blocks_override) + num_gpu_blocks = num_gpu_blocks_override + + self.cache_config.num_gpu_blocks = num_gpu_blocks + self.cache_config.num_cpu_blocks = 0 + self.model_executor.initialize_cache(num_gpu_blocks) + + @classmethod + def from_engine_args( + cls, + engine_args: EngineArgs, + usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, + stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, + ) -> "LLMEngine": + """Creates an LLM engine from the engine arguments.""" + # Create the engine configs. + engine_config = engine_args.create_engine_config() + executor_class = cls._get_executor_cls(engine_config) + # Create the LLM engine. + engine = cls( + **engine_config.to_dict(), + executor_class=executor_class, + log_stats=not engine_args.disable_log_stats, + usage_context=usage_context, + stat_loggers=stat_loggers, + ) + return engine + + def _init_tokenizer(self) -> BaseTokenizerGroup: + return init_tokenizer_from_configs( + model_config=self.model_config, + scheduler_config=self.scheduler_config, + parallel_config=self.parallel_config, + enable_lora=bool(self.lora_config)) + + def _verify_args(self) -> None: + self.model_config.verify_with_parallel_config(self.parallel_config) + self.cache_config.verify_with_parallel_config(self.parallel_config) + if self.lora_config: + self.lora_config.verify_with_model_config(self.model_config) + self.lora_config.verify_with_scheduler_config( + self.scheduler_config) + if self.prompt_adapter_config: + self.prompt_adapter_config.verify_with_model_config( + self.model_config) + + def _add_processed_request( + self, + request_id: str, + processed_inputs: Union[DecoderOnlyInputs, EncoderDecoderLLMInputs], + params: Union[SamplingParams, PoolingParams], + arrival_time: float, + lora_request: Optional[LoRARequest], + prompt_adapter_request: Optional[PromptAdapterRequest], + trace_headers: Optional[Mapping[str, str]] = None, + ) -> None: + assert prompt_adapter_request is None + assert trace_headers is None + self._validate_model_inputs(processed_inputs) + eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request) + + # TODO(woosuk): Support embedding mode. + assert isinstance(params, SamplingParams) + sampling_params = params.clone() + sampling_params.update_from_generation_config( + self.generation_config_fields, eos_token_id) + + # TODO(woosuk): Check max_logprobs + # TODO(woosuk): Support encoder-decoder models. + req = Request(request_id, processed_inputs, params, eos_token_id, + arrival_time) + self.requests[request_id] = req + self.num_lagged_steps[request_id] = 0 + self.scheduler.add_request(req) + + def stop_remote_worker_execution_loop(self) -> None: + raise NotImplementedError("TP not implemented yet.") + + def add_request( + self, + request_id: str, + prompt: PromptType, + params: Union[SamplingParams, PoolingParams], + arrival_time: Optional[float] = None, + lora_request: Optional[LoRARequest] = None, + trace_headers: Optional[Mapping[str, str]] = None, + prompt_adapter_request: Optional[PromptAdapterRequest] = None, + priority: int = 0, + ) -> None: + if lora_request is not None and not self.lora_config: + raise ValueError(f"Got lora_request {lora_request} but LoRA is " + "not enabled!") + if arrival_time is None: + arrival_time = time.time() + assert priority == 0, "vLLM V1 does not support priority at the moment." + + preprocessed_inputs = self.input_preprocessor.preprocess( + prompt, + request_id=request_id, + lora_request=lora_request, + prompt_adapter_request=prompt_adapter_request, + ) + processed_inputs = self.input_processor(preprocessed_inputs) + + self._add_processed_request( + request_id=request_id, + processed_inputs=processed_inputs, + params=params, + arrival_time=arrival_time, + lora_request=lora_request, + prompt_adapter_request=prompt_adapter_request, + trace_headers=trace_headers, + ) + + def abort_request(self, request_id: Union[str, Iterable[str]]) -> None: + self.scheduler.finish_requests(request_id, + RequestStatus.FINISHED_ABORTED) + + def get_num_unfinished_requests(self) -> int: + """Gets the number of unfinished requests.""" + return len(self.requests) + + def has_unfinished_requests(self) -> bool: + """Returns True if there are unfinished requests.""" + return len(self.requests) > 0 + + def step(self) -> List[RequestOutput]: + # NOTE(woosuk): This method may return an empty list when the + # detokenizer is still processing the outputs. This should not be + # considered as the end of the generation process. + # FIXME(woosuk): Currently, the step method is inefficient because it + # creates RequestOutput objects for all running requests, while they + # may not be needed unless the output is streamed to the client. + if self.scheduler.has_unfinished_requests(): + scheduler_output = self.scheduler.schedule() + output = self.model_executor.execute_model(scheduler_output) + sampled = self.scheduler.update_from_output( + scheduler_output, output) + self.send_to_detokenizer(sampled) + req_outputs = self.recv_from_detokenizer() + return req_outputs + + def send_to_detokenizer(self, sampled: List[Tuple[Request, int]]) -> None: + inputs = DetokenizerInputs( + req_ids=[], + prompt_token_ids=[], + new_token_ids=[], + skip_special_tokens=[], + spaces_between_special_tokens=[], + free_req_ids=[], # TODO(woosuk): Implement freeing. + ) + for req, num_tokens in sampled: + inputs.req_ids.append(req.request_id) + if len(req.output_token_ids) == num_tokens: + # The request is first detokenized. + inputs.prompt_token_ids.append(req.prompt_token_ids) + else: + # The prompt token ids are already cached in the detokenizer. + inputs.prompt_token_ids.append([]) + inputs.new_token_ids.append(req.output_token_ids[-num_tokens:]) + inputs.skip_special_tokens.append( + req.sampling_params.skip_special_tokens) + inputs.spaces_between_special_tokens.append( + req.sampling_params.spaces_between_special_tokens) + + # Update the number of lagged steps. + self.num_lagged_steps[req.request_id] += 1 + self.detokenizer.send(inputs) + + def recv_from_detokenizer(self) -> List[RequestOutput]: + detokenizer_output = self.detokenizer.recv() + if detokenizer_output is None: + return [] + + req_outputs: List[RequestOutput] = [] + num_reqs = len(detokenizer_output.req_ids) + for i in range(num_reqs): + req_id = detokenizer_output.req_ids[i] + req = self.requests[req_id] + req.output_text += detokenizer_output.detokenized_texts[i] + + self.num_lagged_steps[req_id] -= 1 + finished = (self.num_lagged_steps[req_id] == 0 + and req.is_finished()) + req_output = self._make_request_output( + req, detokenizer_output.num_output_token_ids[i], + detokenizer_output.detokenized_texts[i], finished) + req_outputs.append(req_output) + + if finished: + del self.requests[req_id] + del self.num_lagged_steps[req_id] + del self.request_outputs[req_id] + return req_outputs + + def terminate_detokenizer(self) -> None: + self.detokenizer.terminate() + + def _make_request_output( + self, + request: Request, + num_output_tokens: int, + new_output_text: str, + finished: bool, + ) -> RequestOutput: + req_output = self.request_outputs.get(request.request_id) + if req_output is None: + # TODO: Support `n` > 1. + completion_output = CompletionOutput( + index=0, + text="", + token_ids=[], + cumulative_logprob=None, + logprobs=None, # TODO + finish_reason=None, + stop_reason=None, + lora_request=None, + ) + req_output = RequestOutput( + request_id=request.request_id, + prompt=request.prompt, + prompt_token_ids=request.prompt_token_ids, + prompt_logprobs=None, # TODO + outputs=[completion_output], + finished=False, + metrics=None, + lora_request=None, + encoder_prompt=None, + encoder_prompt_token_ids=None, + ) + self.request_outputs[request.request_id] = req_output + + completion_output = req_output.outputs[0] + if request.sampling_params.output_kind == RequestOutputKind.CUMULATIVE: + completion_output.text += new_output_text + completion_output.token_ids = ( + request.output_token_ids[:num_output_tokens]) + elif request.sampling_params.output_kind == RequestOutputKind.DELTA: + completion_output.text = new_output_text + num_prev_tokens = len(completion_output.token_ids) + completion_output.token_ids = request.output_token_ids[ + num_prev_tokens:num_output_tokens] + elif (request.sampling_params.output_kind == + RequestOutputKind.FINAL_ONLY): + if finished: + completion_output.text = request.output_text + completion_output.token_ids = request.output_token_ids + else: + completion_output.text = "" + completion_output.token_ids = [] + + if finished: + completion_output.finish_reason = request.get_finished_reason() + completion_output.stop_reason = request.stop_reason + req_output.finished = finished + return req_output + + def check_health(self) -> None: + if self.tokenizer: + self.tokenizer.check_health() + self.model_executor.check_health() + + def _validate_model_inputs(self, inputs: Union[DecoderOnlyInputs, + EncoderDecoderLLMInputs]): + prompt_ids = inputs.get("prompt_token_ids") + if prompt_ids is None or len(prompt_ids) == 0: + raise ValueError("Prompt cannot be empty") + + if self.model_config.is_multimodal_model: + max_prompt_len = self.model_config.max_model_len + + if len(prompt_ids) > max_prompt_len: + raise ValueError( + f"The prompt (total length {len(prompt_ids)}) is too long " + f"to fit into the model (context length {max_prompt_len}). " + "Make sure that `max_model_len` is no smaller than the " + "number of text tokens plus multimodal tokens. For image " + "inputs, the number of image tokens depends on the number " + "of images, and possibly their aspect ratios as well.") + + @classmethod + def validate_outputs(cls, outputs, output_type): + return outputs + + def get_model_config(self) -> ModelConfig: + """Gets the model configuration.""" + return self.model_config + + def get_parallel_config(self) -> ParallelConfig: + """Gets the parallel configuration.""" + return self.parallel_config + + def get_decoding_config(self) -> DecodingConfig: + """Gets the decoding configuration.""" + return self.decoding_config + + def get_scheduler_config(self) -> SchedulerConfig: + """Gets the scheduler configuration.""" + return self.scheduler_config + + def get_lora_config(self) -> LoRAConfig: + """Gets the LoRA configuration.""" + return self.lora_config + + @classmethod + def _get_executor_cls(cls, engine_config: EngineConfig): + return GPUExecutor + + def is_tracing_enabled(self) -> bool: + return False + + def do_log_stats(self, *args, **kwargs) -> None: + pass + + def is_encoder_decoder_model(self) -> bool: + return False + + def start_profile(self) -> None: + pass + + def stop_profile(self) -> None: + pass + + def get_tokenizer_group(self, *args, **kwargs): + return self.tokenizer + + +def _load_generation_config_dict(model_config: ModelConfig) -> Dict[str, Any]: + config = try_get_generation_config( + model_config.model, + trust_remote_code=model_config.trust_remote_code, + revision=model_config.revision, + ) + + if config is None: + return {} + + return config.to_diff_dict() diff --git a/vllm/v1/executor/__init__.py b/vllm/v1/executor/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/executor/gpu_executor.py b/vllm/v1/executor/gpu_executor.py new file mode 100644 index 0000000000000..c780c7031c3d6 --- /dev/null +++ b/vllm/v1/executor/gpu_executor.py @@ -0,0 +1,100 @@ +import os +from typing import Optional, Tuple + +from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, + ModelConfig, ObservabilityConfig, ParallelConfig, + PromptAdapterConfig, SchedulerConfig, + SpeculativeConfig) +from vllm.logger import init_logger +from vllm.utils import get_distributed_init_method, get_ip, get_open_port +from vllm.v1.outputs import ModelRunnerOutput +from vllm.v1.worker.gpu_worker import Worker + +logger = init_logger(__name__) + + +class GPUExecutor: + + def __init__( + self, + model_config: ModelConfig, + cache_config: CacheConfig, + parallel_config: ParallelConfig, + scheduler_config: SchedulerConfig, + device_config: DeviceConfig, + load_config: LoadConfig, + lora_config: Optional[LoRAConfig], + speculative_config: Optional[SpeculativeConfig], + prompt_adapter_config: Optional[PromptAdapterConfig], + observability_config: Optional[ObservabilityConfig], + ) -> None: + self.model_config = model_config + self.cache_config = cache_config + self.lora_config = lora_config + self.load_config = load_config + self.parallel_config = parallel_config + self.scheduler_config = scheduler_config + self.device_config = device_config + self.speculative_config = speculative_config + self.prompt_adapter_config = prompt_adapter_config + self.observability_config = observability_config + + self.worker = self._create_worker() + self.worker.initialize() + self.worker.load_model() + + def _create_worker( + self, + local_rank: int = 0, + rank: int = 0, + distributed_init_method: Optional[str] = None) -> Worker: + """Return worker init args for a given rank.""" + # see https://github.com/NVIDIA/nccl/issues/1234 + os.environ['NCCL_CUMEM_ENABLE'] = '0' + + if distributed_init_method is None: + distributed_init_method = get_distributed_init_method( + get_ip(), get_open_port()) + return Worker( + model_config=self.model_config, + parallel_config=self.parallel_config, + scheduler_config=self.scheduler_config, + device_config=self.device_config, + cache_config=self.cache_config, + load_config=self.load_config, + local_rank=local_rank, + rank=rank, + distributed_init_method=distributed_init_method, + lora_config=self.lora_config, + speculative_config=self.speculative_config, + prompt_adapter_config=self.prompt_adapter_config, + observability_config=self.observability_config, + ) + + def determine_num_available_blocks(self) -> Tuple[int, int]: + """Determine the number of available KV blocks by invoking the + underlying worker. + """ + return self.worker.determine_num_available_blocks() + + def initialize_cache(self, num_gpu_blocks: int) -> None: + """Initialize the KV cache by invoking the underlying worker. + """ + # NOTE: This is logged in the executor because there can be >1 worker + # with other executors. We could log in the engine level, but work + # remains to abstract away the device for non-GPU configurations. + logger.info("# GPU blocks: %d", num_gpu_blocks) + self.worker.initialize_cache(num_gpu_blocks) + self.worker.compile_or_warm_up_model() + + def execute_model( + self, + scheduler_output, + ) -> ModelRunnerOutput: + output = self.worker.execute_model(scheduler_output) + return output + + def check_health(self) -> None: + # GPUExecutor will always be healthy as long as + # it's running. + return diff --git a/vllm/v1/outputs.py b/vllm/v1/outputs.py new file mode 100644 index 0000000000000..8574987728844 --- /dev/null +++ b/vllm/v1/outputs.py @@ -0,0 +1,37 @@ +from dataclasses import dataclass +from typing import Dict, List, Optional + +import torch + + +@dataclass +class SamplerOutput: + + # [num_reqs] + sampled_token_ids: torch.Tensor + + # [num_reqs, max_num_logprobs + 1] + logprob_token_ids: Optional[torch.Tensor] + # [num_reqs, max_num_logprobs + 1] + logprobs: Optional[torch.Tensor] + + # TODO: Support prompt logprobs. + prompt_logprob_token_ids: Optional[torch.Tensor] + prompt_logprobs: Optional[torch.Tensor] + + +@dataclass +class ModelRunnerOutput: + + # [num_reqs] + req_ids: List[str] + # req_id -> index + req_id_to_index: Dict[str, int] + + # [num_reqs] + sampled_token_ids_cpu: torch.Tensor + + # [num_reqs, max_num_logprobs + 1] + logprob_token_ids_cpu: Optional[torch.Tensor] + # [num_reqs, max_num_logprobs + 1] + logprobs_cpu: Optional[torch.Tensor] diff --git a/vllm/v1/request.py b/vllm/v1/request.py new file mode 100644 index 0000000000000..be7d4d165d280 --- /dev/null +++ b/vllm/v1/request.py @@ -0,0 +1,92 @@ +import enum +from typing import TYPE_CHECKING, List, Optional, Union + +from vllm.lora.request import LoRARequest +from vllm.sampling_params import SamplingParams +from vllm.sequence import RequestMetrics + +if TYPE_CHECKING: + from vllm.inputs import DecoderOnlyInputs + + +class Request: + + def __init__( + self, + request_id: str, + inputs: "DecoderOnlyInputs", + sampling_params: SamplingParams, + eos_token_id: Optional[int], + arrival_time: float, + lora_request: Optional[LoRARequest] = None, + ) -> None: + self.request_id = request_id + self.inputs = inputs + self.sampling_params = sampling_params + # Because of LoRA, the eos token id can be different for each request. + self.eos_token_id = eos_token_id + self.metrics = RequestMetrics(arrival_time=arrival_time, + last_token_time=arrival_time, + first_scheduled_time=None, + first_token_time=None, + time_in_queue=None) + self.lora_request = lora_request + + self.status = RequestStatus.WAITING + self.stop_reason: Union[int, str, None] = None + assert sampling_params.max_tokens is not None + self.max_tokens = sampling_params.max_tokens + + self.prompt = inputs.get("prompt") + self.prompt_token_ids = inputs["prompt_token_ids"] + self.num_prompt_tokens = len(self.prompt_token_ids) + self.output_token_ids: List[int] = [] + self.output_text = "" + self.num_computed_tokens = 0 + + @property + def num_tokens(self) -> int: + return self.num_prompt_tokens + len(self.output_token_ids) + + @property + def num_output_tokens(self) -> int: + return len(self.output_token_ids) + + def is_finished(self) -> bool: + return RequestStatus.is_finished(self.status) + + def get_finished_reason(self) -> Union[str, None]: + return RequestStatus.get_finished_reason(self.status) + + +class RequestStatus(enum.IntEnum): + """Status of a sequence.""" + WAITING = 0 + RUNNING = 1 + PREEMPTED = 2 + # Note: anything after PREEMPTED (2) will be considered + # as a finished status. + FINISHED_STOPPED = 3 + FINISHED_LENGTH_CAPPED = 4 + FINISHED_ABORTED = 5 + FINISHED_IGNORED = 6 + + @staticmethod + def is_finished(status: "RequestStatus") -> bool: + return status > RequestStatus.PREEMPTED + + @staticmethod + def get_finished_reason(status: "RequestStatus") -> Union[str, None]: + return _FINISHED_REASON_MAP.get(status) + + +# Mapping of finished statuses to their finish reasons. +# NOTE: The ignored sequences are the sequences whose prompt lengths +# are longer than the model's length cap. Therefore, the stop +# reason should also be "length" as in OpenAI API. +_FINISHED_REASON_MAP = { + RequestStatus.FINISHED_STOPPED: "stop", + RequestStatus.FINISHED_LENGTH_CAPPED: "length", + RequestStatus.FINISHED_ABORTED: "abort", + RequestStatus.FINISHED_IGNORED: "length", +} diff --git a/vllm/v1/sample/__init__.py b/vllm/v1/sample/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/sample/metadata.py b/vllm/v1/sample/metadata.py new file mode 100644 index 0000000000000..28614377b27b9 --- /dev/null +++ b/vllm/v1/sample/metadata.py @@ -0,0 +1,22 @@ +from dataclasses import dataclass +from typing import List, Optional + +import torch + + +@dataclass +class SamplingMetadata: + + temperature: torch.Tensor + all_greedy: bool + all_random: bool + + top_p: torch.Tensor + top_k: torch.Tensor + no_top_p: bool + no_top_k: bool + + generators: List[Optional[torch.Generator]] + no_generator: bool + + max_num_logprobs: int diff --git a/vllm/v1/sample/sampler.py b/vllm/v1/sample/sampler.py new file mode 100644 index 0000000000000..157c4dd6d771e --- /dev/null +++ b/vllm/v1/sample/sampler.py @@ -0,0 +1,161 @@ +"""A layer that samples the next tokens from the model's outputs.""" +from typing import List, Optional + +import torch +import torch.nn as nn + +from vllm.v1.outputs import SamplerOutput +from vllm.v1.sample.metadata import SamplingMetadata + +_SAMPLING_EPS = 1e-5 + + +class Sampler(nn.Module): + + def forward( + self, + logits: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> SamplerOutput: + logits = self.apply_temperature(logits, sampling_metadata.temperature) + logits = self.apply_top_k_top_p(logits, sampling_metadata) + + probs = self.get_probs(logits) + sampled = self.sample(probs, sampling_metadata) + # Use int32 to reduce the tensor size. + sampled = sampled.to(torch.int32) + + if sampling_metadata.max_num_logprobs > 0: + logprobs = self.get_logprobs(logits) + # FIXME: Mask the sampled token_id, get topk logprobs, + # and concatenate the topk with the sampled token_id. + topk_logprobs, topk_indices = torch.topk( + logprobs, sampling_metadata.max_num_logprobs, dim=-1) + # Use int32 to reduce the tensor size. + topk_indices = topk_indices.to(torch.int32) + else: + topk_logprobs = None + topk_indices = None + + sampler_output = SamplerOutput( + sampled_token_ids=sampled, + logprob_token_ids=topk_indices, + logprobs=topk_logprobs, + prompt_logprob_token_ids=None, + prompt_logprobs=None, + ) + return sampler_output + + def apply_temperature( + self, + logits: torch.Tensor, + temp: torch.Tensor, + ) -> torch.Tensor: + # Use float32 to apply temperature scaling. + logits = logits.to(torch.float32) + # Avoid division by zero. + temp = torch.where(temp < _SAMPLING_EPS, 1.0, temp) + # Use in-place division to avoid creating a new tensor. + logits.div_(temp.unsqueeze(dim=1)) + return logits + + def apply_top_k_top_p( + self, + logits: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> torch.Tensor: + return _apply_top_k_top_p( + logits, + sampling_metadata.no_top_k, + sampling_metadata.top_k, + sampling_metadata.no_top_p, + sampling_metadata.top_p, + ) + + def get_probs(self, logits: torch.Tensor) -> torch.Tensor: + return torch.softmax(logits, dim=-1, dtype=torch.float32) + + def get_logprobs(self, logits: torch.Tensor) -> torch.Tensor: + return torch.log_softmax(logits, dim=-1, dtype=torch.float32) + + def greedy_sample(self, probs: torch.Tensor) -> torch.Tensor: + return probs.argmax(dim=-1).view(-1) + + def random_sample( + self, + probs: torch.Tensor, + generators: List[Optional[torch.Generator]], + no_generator: bool, + ) -> torch.Tensor: + q = torch.empty_like(probs) + # NOTE(woosuk): To batch-process the requests without their own seeds, + # which is the common case, we first assume that every request does + # not have its own seed. Then, we overwrite the values for the requests + # that have their own seeds. + q.exponential_() + if not no_generator: + assert len(generators) == probs.shape[0] + # TODO(woosuk): This can be slow because we handle each request + # one by one. Optimize this. + for i, generator in enumerate(generators): + if generator is not None: + q[i].exponential_(generator=generator) + return probs.div_(q).argmax(dim=-1).view(-1) + + def sample( + self, + probs: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> torch.Tensor: + assert not (sampling_metadata.all_greedy + and sampling_metadata.all_random) + if sampling_metadata.all_greedy: + return self.greedy_sample(probs) + if sampling_metadata.all_random: + return self.random_sample(probs, sampling_metadata.generators, + sampling_metadata.no_generator) + + greedy_sampled = self.greedy_sample(probs) + random_sampled = self.random_sample(probs, + sampling_metadata.generators, + sampling_metadata.no_generator) + sampled = torch.where( + sampling_metadata.temperature < _SAMPLING_EPS, + greedy_sampled, + random_sampled, + ) + return sampled + + +# TODO(woosuk): Optimize this with a custom kernel. +def _apply_top_k_top_p( + logits: torch.Tensor, + no_top_k: bool, + k: torch.Tensor, + no_top_p: bool, + p: torch.Tensor, +) -> torch.Tensor: + if no_top_k and no_top_p: + return logits + logits_sort, logits_idx = logits.sort(dim=-1, descending=False) + + if not no_top_k: + # Apply top-k. + top_k_mask = logits_sort.size(1) - k.to(torch.long) + # Get all the top_k values. + top_k_mask = logits_sort.gather(1, top_k_mask.unsqueeze(dim=1)) + top_k_mask = logits_sort < top_k_mask + logits_sort.masked_fill_(top_k_mask, -float("inf")) + + if not no_top_p: + # Apply top-p. + probs_sort = logits_sort.softmax(dim=-1) + probs_sum = probs_sort.cumsum(dim=-1) + top_p_mask = probs_sum <= 1 - p.unsqueeze(dim=1) + # at least one + top_p_mask[:, -1] = False + logits_sort.masked_fill_(top_p_mask, -float("inf")) + + # Re-sort the probabilities. + logits = logits_sort.scatter(dim=-1, index=logits_idx, src=logits_sort) + return logits diff --git a/vllm/v1/tokenizer/__init__.py b/vllm/v1/tokenizer/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/tokenizer/detokenizer.py b/vllm/v1/tokenizer/detokenizer.py new file mode 100644 index 0000000000000..4bbcf4717981e --- /dev/null +++ b/vllm/v1/tokenizer/detokenizer.py @@ -0,0 +1,215 @@ +import multiprocessing +from dataclasses import dataclass +from typing import Dict, List, Optional + +import msgspec +import zmq +from msgspec import msgpack + +from vllm.transformers_utils.detokenizer_utils import ( + convert_prompt_ids_to_tokens, detokenize_incrementally) +from vllm.transformers_utils.tokenizer import get_tokenizer +from vllm.utils import get_open_port + + +class DetokenizerInputs(msgspec.Struct): + + # [num_reqs] + req_ids: List[str] + # A request's prompt token ids is sent to the detokenizer only when + # the request is first detokenized. Otherwise, an empty list is sent. + prompt_token_ids: List[List[int]] + new_token_ids: List[List[int]] + skip_special_tokens: List[bool] + spaces_between_special_tokens: List[bool] + + # [num_free_reqs] + free_req_ids: List[str] + + +class DetokenizerOutputs(msgspec.Struct): + + # [num_reqs] + req_ids: List[str] + detokenized_texts: List[str] + # NOTE(woosuk): The number of the output token ids of each request + # at the time of detokenization. The detokenizer returns this to the engine + # because the request state (including the output token ids) is + # asynchronously updated in the engine, while RequestOutput requires the + # output token ids to be consistent with the detokenized text. + num_output_token_ids: List[int] + + +class Detokenizer: + + def __init__(self, tokenizer_name: str): + # FIXME(woosuk): Currently, the detokenizer is just a hacky prototype. + # For example, it does not terminate properly. We need to improve this. + self.push_port = get_open_port() + self.pull_port = get_open_port() + self.detokenizer = DetokenizerProc(tokenizer_name, self.push_port, + self.pull_port) + self.detokenizer.start() + + self.zmq_context = zmq.Context() + self.push_socket = self.zmq_context.socket(zmq.PUSH) + self.push_socket.connect(f"tcp://localhost:{self.push_port}") + self.pull_socket = self.zmq_context.socket(zmq.PULL) + self.pull_socket.connect(f"tcp://localhost:{self.pull_port}") + self.poller = zmq.Poller() + self.poller.register(self.pull_socket, zmq.POLLIN) + self.msgpack_encoder = msgpack.Encoder() + self.msgpack_decoder = msgpack.Decoder(DetokenizerOutputs) + + def send(self, inputs: DetokenizerInputs) -> None: + self.push_socket.send(self.msgpack_encoder.encode(inputs), + flags=zmq.NOBLOCK) + + def recv(self) -> Optional[DetokenizerOutputs]: + socks = dict(self.poller.poll(timeout=0)) + if self.pull_socket in socks and socks[self.pull_socket] == zmq.POLLIN: + msg = self.pull_socket.recv() + return self.msgpack_decoder.decode(msg) + return None + + def terminate(self) -> None: + self.push_socket.send(b"", flags=zmq.NOBLOCK) + self.detokenizer.join() + + +class DetokenizerProc(multiprocessing.Process): + + def __init__( + self, + tokenizer_name: str, + pull_port: int, + push_port: int, + ): + super().__init__() + self.tokenizer_name = tokenizer_name + # NOTE: The pull_port of the detokenizer should be the same as the + # push_port of the engine. Vice versa. + self.pull_port = pull_port + self.push_port = push_port + + def run(self): + # Initialize these objects after the process is forked since they are + # not picklable. + self.msgpack_encoder = msgpack.Encoder() + self.msgpack_decoder = msgpack.Decoder(DetokenizerInputs) + self.tokenizer = get_tokenizer(self.tokenizer_name) + # req_id -> RequestState + self.request_states: Dict[str, RequestState] = {} + + self.zmq_context = zmq.Context() + self.pull_socket = self.zmq_context.socket(zmq.PULL) + self.pull_socket.bind(f"tcp://*:{self.pull_port}") + self.push_socket = self.zmq_context.socket(zmq.PUSH) + self.push_socket.bind(f"tcp://*:{self.push_port}") + + while True: + message = self.pull_socket.recv() + if message == b"": + # Terminate signal. + break + inputs = self.msgpack_decoder.decode(message) + + for req_id in inputs.free_req_ids: + self.free(req_id) + + detokenized_texts: List[str] = [] + num_output_token_ids: List[int] = [] + num_reqs = len(inputs.req_ids) + for i in range(num_reqs): + req_id = inputs.req_ids[i] + if req_id not in self.request_states: + self.add_request( + request_id=req_id, + prompt_token_ids=inputs.prompt_token_ids[i], + skip_special_tokens=inputs.skip_special_tokens[i], + spaces_between_special_tokens=inputs. + spaces_between_special_tokens[i], + ) + new_str = self.detokenize(req_id, inputs.new_token_ids[i]) + detokenized_texts.append(new_str) + req_state = self.request_states[req_id] + num_output_token_ids.append( + len(req_state.token_ids) - req_state.num_prompt_tokens) + + detokenized = DetokenizerOutputs( + req_ids=inputs.req_ids, + detokenized_texts=detokenized_texts, + num_output_token_ids=num_output_token_ids, + ) + self.push_socket.send(self.msgpack_encoder.encode(detokenized), + flags=zmq.NOBLOCK) + + def add_request( + self, + request_id: str, + prompt_token_ids: List[int], + skip_special_tokens: bool, + spaces_between_special_tokens: bool, + ) -> None: + tokens, prefix_offset, read_offset = convert_prompt_ids_to_tokens( + tokenizer=self.tokenizer, + prompt_ids=prompt_token_ids, + skip_special_tokens=skip_special_tokens, + ) + self.request_states[request_id] = RequestState( + req_id=request_id, + token_ids=prompt_token_ids, + tokens=tokens, + num_prompt_tokens=len(prompt_token_ids), + prefix_offset=prefix_offset, + read_offset=read_offset, + skip_special_tokens=skip_special_tokens, + spaces_between_special_tokens=spaces_between_special_tokens, + ) + + def free(self, request_id: str) -> None: + del self.request_states[request_id] + + def detokenize(self, request_id: str, new_token_ids: List[int]) -> str: + # TODO(woosuk): This method becomes very inefficient when the number of + # new_token_ids is more than 1. We need to optimize this. + req_state = self.request_states[request_id] + decoded_text = "" + for new_token_id in new_token_ids: + req_state.token_ids.append(new_token_id) + (new_tokens, new_decoded_token_text, prefix_offset, + read_offset) = detokenize_incrementally( + tokenizer=self.tokenizer, + all_input_ids=req_state.token_ids, + prev_tokens=req_state.tokens, + prefix_offset=req_state.prefix_offset, + read_offset=req_state.read_offset, + skip_special_tokens=req_state.skip_special_tokens, + spaces_between_special_tokens=req_state. + spaces_between_special_tokens, + ) + + req_state.tokens.extend(new_tokens) + req_state.prefix_offset = prefix_offset + req_state.read_offset = read_offset + req_state.output_text += new_decoded_token_text + decoded_text += new_decoded_token_text + return decoded_text + + +@dataclass +class RequestState: + + req_id: str + + token_ids: List[int] + tokens: List[str] + num_prompt_tokens: int + + prefix_offset: int + read_offset: int + + skip_special_tokens: bool + spaces_between_special_tokens: bool + + output_text: str = "" diff --git a/vllm/v1/worker/__init__.py b/vllm/v1/worker/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py new file mode 100644 index 0000000000000..e84645ac7a4ae --- /dev/null +++ b/vllm/v1/worker/gpu_model_runner.py @@ -0,0 +1,690 @@ +from dataclasses import dataclass +from typing import TYPE_CHECKING, Dict, List, Optional, Set +from unittest.mock import patch + +import numpy as np +import torch +import torch.distributed +import torch.nn as nn + +from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, + ModelConfig, ObservabilityConfig, ParallelConfig, + PromptAdapterConfig, SchedulerConfig) +from vllm.forward_context import set_forward_context +from vllm.logger import init_logger +from vllm.model_executor.model_loader import get_model +from vllm.multimodal import MultiModalDataDict +from vllm.sampling_params import SamplingParams, SamplingType +from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, cdiv, + is_pin_memory_available) +from vllm.v1.attention.backends.flash_attn import (FlashAttentionBackend, + FlashAttentionMetadata) +from vllm.v1.outputs import ModelRunnerOutput +from vllm.v1.sample.metadata import SamplingMetadata +from vllm.v1.sample.sampler import Sampler + +if TYPE_CHECKING: + from vllm.v1.core.scheduler import SchedulerOutput + +logger = init_logger(__name__) + + +class GPUModelRunner: + + def __init__( + self, + model_config: ModelConfig, + parallel_config: ParallelConfig, + scheduler_config: SchedulerConfig, + device_config: DeviceConfig, + cache_config: CacheConfig, + load_config: LoadConfig, + lora_config: Optional[LoRAConfig] = None, + prompt_adapter_config: Optional[PromptAdapterConfig] = None, + observability_config: Optional[ObservabilityConfig] = None, + ): + self.model_config = model_config + self.parallel_config = parallel_config + self.scheduler_config = scheduler_config + self.device_config = device_config + self.cache_config = cache_config + self.lora_config = lora_config + self.load_config = load_config + self.prompt_adapter_config = prompt_adapter_config + self.observability_config = observability_config + + self.device = self.device_config.device + self.pin_memory = is_pin_memory_available() + self.dtype = self.model_config.dtype + if cache_config.cache_dtype == "auto": + self.kv_cache_dtype = self.dtype + else: + self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[ + cache_config.cache_dtype] + + self.sliding_window = model_config.get_sliding_window() + self.block_size = cache_config.block_size + self.max_model_len = model_config.max_model_len + self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size) + self.max_num_tokens = scheduler_config.max_num_batched_tokens + + # Model-related. + self.num_attn_layers = model_config.get_num_attention_layers( + parallel_config) + self.num_kv_heads = model_config.get_num_kv_heads(parallel_config) + self.head_size = model_config.get_head_size() + + # Lazy initialization + # self.model: nn.Module # Set after load_model + self.kv_caches: List[torch.Tensor] = [] + + # Request states. + self.requests: Dict[str, CachedRequestState] = {} + # Persistent batch. + self.input_batch = InputBatch( + max_num_reqs=self.scheduler_config.max_num_seqs, + max_model_len=self.max_model_len, + max_num_blocks_per_req=self.max_num_blocks_per_req, + device=self.device, + pin_memory=self.pin_memory, + ) + + def _update_states(self, scheduler_output: "SchedulerOutput") -> None: + # Remove stopped requests from the cached states. + # Keep the states of the pre-empted requests. + for req_id in scheduler_output.finished_req_ids: + self.requests.pop(req_id, None) + + # Remove the requests from the persistent batch. + stopped_req_ids = set().union( + scheduler_output.preempted_req_ids, + scheduler_output.finished_req_ids, + ) + removed_req_indices: List[int] = [] + for req_id in stopped_req_ids: + req_index = self.input_batch.remove_request(req_id) + if req_index is not None: + removed_req_indices.append(req_index) + + # Update the states of the running requests. + for req_data in scheduler_output.scheduled_running_reqs: + req_id = req_data.req_id + req_state = self.requests[req_id] + req_index = self.input_batch.req_id_to_index[req_id] + + # Update the num_computed_tokens. + req_state.num_computed_tokens = req_data.num_computed_tokens + self.input_batch.num_computed_tokens_cpu[req_index] = ( + req_data.num_computed_tokens) + + # Update the block table. + num_new_blocks = len(req_data.new_block_ids) + if num_new_blocks == 0: + continue + start_index = len(req_state.block_ids) + end_index = start_index + num_new_blocks + req_state.block_ids.extend(req_data.new_block_ids) + self.input_batch.block_table_cpu[ + req_index, start_index:end_index] = req_data.new_block_ids + + req_ids_to_add: List[str] = [] + # Add new requests to the cached states. + for req_data in scheduler_output.scheduled_new_reqs: + req_id = req_data.req_id + self.requests[req_id] = CachedRequestState( + req_id=req_id, + prompt_token_ids=req_data.prompt_token_ids, + prompt=req_data.prompt, + multi_modal_data=req_data.multi_modal_data, + sampling_params=req_data.sampling_params, + generator=None, # TODO + block_ids=req_data.block_ids, + num_computed_tokens=req_data.num_computed_tokens, + output_token_ids=[], + ) + req_ids_to_add.append(req_id) + + # Update the cached states of the resumed requests. + for req_data in scheduler_output.scheduled_resumed_reqs: + req_id = req_data.req_id + req_state = self.requests[req_id] + + req_state.block_ids = req_data.block_ids + req_state.num_computed_tokens = req_data.num_computed_tokens + req_ids_to_add.append(req_id) + + # Add the new or resumed requests to the persistent batch. + # The smaller empty indices are filled first. + removed_req_indices = sorted(removed_req_indices, reverse=True) + for req_id in req_ids_to_add: + req_state = self.requests[req_id] + if removed_req_indices: + # Fill the empty index. + req_index = removed_req_indices.pop() + else: + # Append to the end. + req_index = None + self.input_batch.add_request(req_state, req_index) + + # Condense the batched states if there are empty indices. + if removed_req_indices: + self.input_batch.condense(removed_req_indices) + + def _prepare_inputs(self, scheduler_output: "SchedulerOutput"): + total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens + assert total_num_scheduled_tokens > 0 + num_reqs = self.input_batch.num_reqs + assert num_reqs > 0 + + # OPTIMIZATION: Start copying the block table first. + # This way, we can overlap the copy with the following CPU operations. + self.input_batch.block_table[:num_reqs].copy_( + self.input_batch.block_table_cpu_tensor[:num_reqs], + non_blocking=True) + + # Get the number of scheduled tokens for each request. + # TODO: The Python loop can be slow. Optimize. + num_scheduled_tokens = [] + max_num_scheduled_tokens = 0 + for req_id in self.input_batch.req_ids[:num_reqs]: + num_tokens = scheduler_output.num_scheduled_tokens[req_id] + num_scheduled_tokens.append(num_tokens) + max_num_scheduled_tokens = max(max_num_scheduled_tokens, + num_tokens) + num_scheduled_tokens = np.array(num_scheduled_tokens, dtype=np.int32) + assert max_num_scheduled_tokens > 0 + + # Get request indices. + # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2] + indices = np.arange(num_reqs) + req_indices = np.repeat(indices, num_scheduled_tokens) + + # Get batched arange. + # E.g., [2, 5, 3] -> [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] + arange_matrix = np.tile(np.arange(max_num_scheduled_tokens), + (num_reqs, 1)) + mask = arange_matrix < num_scheduled_tokens[:, np.newaxis] + arange = arange_matrix[mask] + + # Get positions. + positions = torch.empty((total_num_scheduled_tokens, ), + dtype=torch.int32, + device="cpu", + pin_memory=self.pin_memory) + positions_np = positions.numpy() + np.add(self.input_batch.num_computed_tokens_cpu[req_indices], + arange, + out=positions_np) + + # Get token indices. + # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] + # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2] + # where M is the max_model_len. + token_indices = positions_np + req_indices * self.max_model_len + token_indices = torch.from_numpy(token_indices) + input_ids = torch.empty((total_num_scheduled_tokens, ), + dtype=torch.int32, + device="cpu", + pin_memory=self.pin_memory) + torch.index_select(torch.from_numpy( + self.input_batch.token_ids_cpu).flatten(), + 0, + token_indices, + out=input_ids) + + # Calculate the slot mapping. + block_numbers = self.input_batch.block_table_cpu_tensor.flatten()[ + token_indices // self.block_size] + block_offsets = token_indices % self.block_size + slot_mapping = torch.empty((total_num_scheduled_tokens, ), + dtype=torch.int32, + device="cpu", + pin_memory=self.pin_memory) + torch.add(block_numbers * self.block_size, + block_offsets, + out=slot_mapping) + + # Prepare the attention metadata. + query_start_loc = torch.empty((num_reqs + 1, ), + dtype=torch.int32, + device="cpu", + pin_memory=self.pin_memory) + query_start_loc_np = query_start_loc.numpy() + query_start_loc_np[0] = 0 + np.cumsum(num_scheduled_tokens, out=query_start_loc_np[1:]) + + seq_lens = (self.input_batch.num_computed_tokens_cpu[:num_reqs] + + num_scheduled_tokens) + max_seq_len = seq_lens.max() + seq_start_loc = torch.empty((num_reqs + 1, ), + dtype=torch.int32, + device="cpu", + pin_memory=self.pin_memory) + seq_start_loc_np = seq_start_loc.numpy() + seq_start_loc_np[0] = 0 + np.cumsum(seq_lens, out=seq_start_loc_np[1:]) + + input_ids = input_ids.to(self.device, non_blocking=True) + positions = positions.to(self.device, non_blocking=True).long() + query_start_loc = query_start_loc.to(self.device, non_blocking=True) + seq_start_loc = seq_start_loc.to(self.device, non_blocking=True) + slot_mapping = slot_mapping.to(self.device, non_blocking=True).long() + attn_metadata = FlashAttentionMetadata( + max_query_len=max_num_scheduled_tokens, + query_start_loc=query_start_loc, + max_seq_len=max_seq_len, + seq_start_loc=seq_start_loc, + block_table=self.input_batch.block_table[:num_reqs], + slot_mapping=slot_mapping, + ) + # NOTE(woosuk): Due to chunked prefills, there can be at most 1 partial + # request in the batch. While we should not sample any token from this + # partial request, we do so for simplicity. We will ignore the sampled + # token from the partial request. + # TODO: Support prompt logprobs. + logits_indices = query_start_loc[1:] - 1 + return input_ids, positions, attn_metadata, logits_indices + + def _prepare_sampling( + self, + scheduler_output: "SchedulerOutput", + ) -> SamplingMetadata: + skip_copy = True + if (scheduler_output.finished_req_ids + or scheduler_output.preempted_req_ids): + skip_copy = False + if (scheduler_output.scheduled_new_reqs + or scheduler_output.scheduled_resumed_reqs): + skip_copy = False + # Create the sampling metadata. + sampling_metadata = self.input_batch.make_sampling_metadata(skip_copy) + return sampling_metadata + + @torch.inference_mode() + def execute_model( + self, + scheduler_output: "SchedulerOutput", + ) -> ModelRunnerOutput: + self._update_states(scheduler_output) + inputs = self._prepare_inputs(scheduler_output) + input_ids, positions, attn_metadata, logits_indices = inputs + + with set_forward_context(attn_metadata): + hidden_states = self.model( + input_ids=input_ids, + positions=positions, + kv_caches=self.kv_caches, + attn_metadata=attn_metadata, + ) + hidden_states = hidden_states[logits_indices] + logits = self.model.compute_logits(hidden_states, None) + + # Sample the next token and get logprobs if needed. + sampling_metadata = self._prepare_sampling(scheduler_output) + sampler_output = self.model.sample( + logits=logits, + sampling_metadata=sampling_metadata, + ) + + # NOTE: CPU-GPU synchronization happens here. + sampled_token_ids = sampler_output.sampled_token_ids.cpu() + sampled_token_ids_list = sampled_token_ids.tolist() + # TODO(woosuk): The following loop can be slow since it iterates over + # the requests one by one. Optimize. + num_reqs = self.input_batch.num_reqs + for i, req_id in enumerate(self.input_batch.req_ids[:num_reqs]): + req_state = self.requests[req_id] + seq_len = (req_state.num_computed_tokens + + scheduler_output.num_scheduled_tokens[req_id]) + assert seq_len <= req_state.num_tokens + if seq_len == req_state.num_tokens: + # Append the sampled token to the output token ids. + token_id = sampled_token_ids_list[i] + self.input_batch.token_ids_cpu[i, seq_len] = token_id + req_state.output_token_ids.append(token_id) + else: + # Ignore the sampled token from the partial request. + # Rewind the generator state as if the token was not sampled. + generator = self.input_batch.generators[i] + if generator is not None: + offset = generator.get_offset() + generator = generator.set_offset(offset - 1) + self.input_batch.generators[i] = generator + + if sampler_output.logprob_token_ids is None: + logprob_token_ids = None + else: + logprob_token_ids = sampler_output.logprob_token_ids.cpu() + if sampler_output.logprobs is None: + logprobs = None + else: + logprobs = sampler_output.logprobs.cpu() + model_runner_output = ModelRunnerOutput( + req_ids=self.input_batch.req_ids[:num_reqs], + req_id_to_index=self.input_batch.req_id_to_index, + sampled_token_ids_cpu=sampled_token_ids, + logprob_token_ids_cpu=logprob_token_ids, + logprobs_cpu=logprobs, + ) + return model_runner_output + + def load_model(self) -> None: + logger.info("Starting to load model %s...", self.model_config.model) + with DeviceMemoryProfiler() as m: # noqa: SIM117 + with patch("vllm.model_executor.layers.sampler.Sampler", Sampler): + self.model = get_model(model_config=self.model_config, + device_config=self.device_config, + load_config=self.load_config, + lora_config=self.lora_config, + parallel_config=self.parallel_config, + scheduler_config=self.scheduler_config, + cache_config=self.cache_config) + + self.model_memory_usage = m.consumed_memory + logger.info("Loading model weights took %.4f GB", + self.model_memory_usage / float(2**30)) + + def _dummy_run(self, model: nn.Module, num_tokens: int) -> None: + input_ids = torch.zeros(num_tokens, + dtype=torch.int32, + device=self.device) + positions = torch.zeros(num_tokens, + dtype=torch.long, + device=self.device) + kv_caches = [None for _ in range(self.num_attn_layers)] + model(input_ids, positions, kv_caches, attn_metadata=None) + return + + @torch.inference_mode() + def profile_run(self) -> None: + self._dummy_run(self.model, self.max_num_tokens) + torch.cuda.synchronize() + return + + @torch.inference_mode() + def capture_model(self) -> None: + # TODO: Implement CUDA graph support. + return + + def initialize_kv_cache(self, num_blocks: int) -> None: + assert len(self.kv_caches) == 0 + kv_cache_shape = FlashAttentionBackend.get_kv_cache_shape( + num_blocks, self.block_size, self.num_kv_heads, self.head_size) + for _ in range(self.num_attn_layers): + self.kv_caches.append( + torch.zeros(kv_cache_shape, + dtype=self.kv_cache_dtype, + device=self.device)) + + +@dataclass +class CachedRequestState: + + req_id: str + prompt_token_ids: List[int] + prompt: Optional[str] + multi_modal_data: Optional["MultiModalDataDict"] + sampling_params: SamplingParams + generator: Optional[torch.Generator] + + block_ids: List[int] + num_computed_tokens: int + output_token_ids: List[int] + + @property + def num_tokens(self) -> int: + return len(self.prompt_token_ids) + len(self.output_token_ids) + + +class InputBatch: + + def __init__( + self, + max_num_reqs: int, + max_model_len: int, + max_num_blocks_per_req: int, + device: torch.device, + pin_memory: bool, + ): + self.max_num_reqs = max_num_reqs + self.max_model_len = max_model_len + self.max_num_blocks_per_req = max_num_blocks_per_req + self.device = device + self.pin_memory = pin_memory + + self.req_ids: List[Optional[str]] = [None] * max_num_reqs + self.req_id_to_index: Dict[str, int] = {} + + self.token_ids_cpu = np.empty((max_num_reqs, max_model_len), + dtype=np.int32) + self.num_computed_tokens_cpu = np.empty(max_num_reqs, dtype=np.int32) + + # Attention-related. + self.block_table = torch.zeros((max_num_reqs, max_num_blocks_per_req), + device=self.device, + dtype=torch.int32) + self.block_table_cpu_tensor = torch.zeros( + (max_num_reqs, max_num_blocks_per_req), + device="cpu", + dtype=torch.int32, + pin_memory=pin_memory, + ) + self.block_table_cpu = self.block_table_cpu_tensor.numpy() + + # Sampling-related. + self.temperature = torch.empty((max_num_reqs, ), + dtype=torch.float32, + device=device) + self.temperature_cpu_tensor = torch.empty((max_num_reqs, ), + dtype=torch.float32, + device="cpu", + pin_memory=pin_memory) + self.temperature_cpu = self.temperature_cpu_tensor.numpy() + self.greedy_reqs: Set[str] = set() + self.random_reqs: Set[str] = set() + + self.top_p = torch.empty((max_num_reqs, ), + dtype=torch.float32, + device=device) + self.top_p_cpu_tensor = torch.empty((max_num_reqs, ), + dtype=torch.float32, + device="cpu", + pin_memory=pin_memory) + self.top_p_cpu = self.top_p_cpu_tensor.numpy() + self.top_p_reqs: Set[str] = set() + + self.top_k = torch.empty((max_num_reqs, ), + dtype=torch.int32, + device=device) + self.top_k_cpu_tensor = torch.empty((max_num_reqs, ), + dtype=torch.int32, + device="cpu", + pin_memory=pin_memory) + self.top_k_cpu = self.top_k_cpu_tensor.numpy() + self.top_k_reqs: Set[str] = set() + + self.generators: List[Optional[torch.Generator]] = [None + ] * max_num_reqs + + self.num_logprobs: Dict[str, int] = {} + self.prompt_logprob_reqs: Set[str] = set() + + def add_request( + self, + request: "CachedRequestState", + req_index: Optional[int] = None, + ) -> None: + if req_index is None: + req_index = self.num_reqs + assert req_index < self.max_num_reqs + + self.req_ids[req_index] = request.req_id + self.req_id_to_index[request.req_id] = req_index + + # Copy the prompt token ids and output token ids. + num_prompt_tokens = len(request.prompt_token_ids) + self.token_ids_cpu[ + req_index, :num_prompt_tokens] = request.prompt_token_ids + start_idx = num_prompt_tokens + end_idx = start_idx + len(request.output_token_ids) + self.token_ids_cpu[req_index, + start_idx:end_idx] = request.output_token_ids + + self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens + num_blocks = len(request.block_ids) + self.block_table_cpu[req_index, :num_blocks] = request.block_ids + + sampling_params = request.sampling_params + self.temperature_cpu[req_index] = sampling_params.temperature + if sampling_params.sampling_type == SamplingType.GREEDY: + self.greedy_reqs.add(req_index) + elif sampling_params.sampling_type == SamplingType.RANDOM: + self.random_reqs.add(req_index) + elif sampling_params.sampling_type == SamplingType.RANDOM_SEED: + # TODO(woosuk): Support per-request random seed. + raise NotImplementedError("Per-request seed is not supported yet.") + + self.top_p_cpu[req_index] = sampling_params.top_p + if sampling_params.top_p < 1: + self.top_p_reqs.add(req_index) + self.top_k_cpu[req_index] = sampling_params.top_k + if sampling_params.top_k > 0: + self.top_k_reqs.add(req_index) + + self.generators[req_index] = request.generator + + num_logprobs = sampling_params.logprobs + if num_logprobs is not None and num_logprobs > 0: + self.num_logprobs[request.req_id] = num_logprobs + if sampling_params.prompt_logprobs: + self.prompt_logprob_reqs.add(req_index) + + def remove_request(self, req_id: str) -> Optional[int]: + req_index = self.req_id_to_index.pop(req_id, None) + if req_index is None: + return None + self.req_ids[req_index] = None + + self.greedy_reqs.discard(req_id) + self.random_reqs.discard(req_id) + self.top_p_reqs.discard(req_id) + self.top_k_reqs.discard(req_id) + self.generators[req_index] = None + self.num_logprobs.pop(req_id, None) + self.prompt_logprob_reqs.discard(req_id) + return req_index + + def clear(self) -> None: + self.req_ids = [None] * self.max_num_reqs + self.req_id_to_index.clear() + self.greedy_reqs.clear() + self.random_reqs.clear() + self.top_p_reqs.clear() + self.top_k_reqs.clear() + self.generators.clear() + self.num_logprobs.clear() + self.prompt_logprob_reqs.clear() + + def condense(self, empty_req_indices: List[int]) -> None: + if self.num_reqs == 0: + # The batched states are empty. + return + + # NOTE(woosuk): This function assumes that the empty_req_indices + # is sorted in descending order. + last_req_index = self.num_reqs + len(empty_req_indices) - 1 + while empty_req_indices: + # Find the largest non-empty index. + while last_req_index in empty_req_indices: + last_req_index -= 1 + + # Find the smallest empty index. + empty_index = empty_req_indices.pop() + if empty_index >= last_req_index: + break + + # Swap the states. + req_id = self.req_ids[last_req_index] + self.req_ids[empty_index] = req_id + self.req_ids[last_req_index] = None + self.req_id_to_index[req_id] = empty_index + + # TODO(woosuk): Optimize the copy of token_ids_cpu and + # block_table_cpu. + self.token_ids_cpu[empty_index] = self.token_ids_cpu[ + last_req_index] + self.num_computed_tokens_cpu[ + empty_index] = self.num_computed_tokens_cpu[last_req_index] + self.block_table_cpu[empty_index] = self.block_table_cpu[ + last_req_index] + self.temperature_cpu[empty_index] = self.temperature_cpu[ + last_req_index] + self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index] + self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index] + self.generators[empty_index] = self.generators[last_req_index] + + # Decrement last_req_index since it is now empty. + last_req_index -= 1 + + def make_sampling_metadata( + self, + skip_copy: bool = False, + ) -> SamplingMetadata: + if not skip_copy: + self.temperature[:self.num_reqs].copy_( + self.temperature_cpu_tensor[:self.num_reqs], non_blocking=True) + self.top_p[:self.num_reqs].copy_( + self.top_p_cpu_tensor[:self.num_reqs], non_blocking=True) + self.top_k[:self.num_reqs].copy_( + self.top_k_cpu_tensor[:self.num_reqs], non_blocking=True) + return SamplingMetadata( + temperature=self.temperature[:self.num_reqs], + all_greedy=self.all_greedy, + all_random=self.all_random, + top_p=self.top_p[:self.num_reqs], + top_k=self.top_k[:self.num_reqs], + no_top_p=self.no_top_p, + no_top_k=self.no_top_k, + generators=self.generators[:self.num_reqs], + no_generator=self.no_generator, + max_num_logprobs=self.max_num_logprobs, + ) + + @property + def num_reqs(self) -> int: + return len(self.req_id_to_index) + + @property + def all_greedy(self) -> bool: + return len(self.random_reqs) == 0 + + @property + def all_random(self) -> bool: + return len(self.greedy_reqs) == 0 + + @property + def no_top_p(self) -> bool: + return len(self.top_p_reqs) == 0 + + @property + def no_top_k(self) -> bool: + return len(self.top_k_reqs) == 0 + + @property + def no_generator(self) -> bool: + return len(self.generators) == 0 + + @property + def max_num_logprobs(self) -> int: + if self.num_logprobs: + return max(self.num_logprobs.values()) + else: + return 0 + + @property + def no_logprob(self) -> bool: + return len(self.num_logprobs) == 0 + + @property + def no_prompt_logprob(self) -> bool: + return len(self.prompt_logprob_reqs) == 0 diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py new file mode 100644 index 0000000000000..8c5ca2ec35666 --- /dev/null +++ b/vllm/v1/worker/gpu_worker.py @@ -0,0 +1,245 @@ +"""A GPU worker class.""" +import gc +import os +from typing import TYPE_CHECKING, Optional, Tuple + +import torch +import torch.distributed + +from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, + ModelConfig, ObservabilityConfig, ParallelConfig, + PromptAdapterConfig, SchedulerConfig, + SpeculativeConfig) +from vllm.distributed import (ensure_model_parallel_initialized, + init_distributed_environment, + set_custom_all_reduce) +from vllm.logger import init_logger +from vllm.model_executor import set_random_seed +from vllm.platforms import current_platform +from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size +from vllm.v1.outputs import ModelRunnerOutput +from vllm.v1.worker.gpu_model_runner import GPUModelRunner + +logger = init_logger(__name__) + +if TYPE_CHECKING: + from vllm.v1.core.scheduler import SchedulerOutput + + +class Worker: + + def __init__( + self, + model_config: ModelConfig, + parallel_config: ParallelConfig, + scheduler_config: SchedulerConfig, + device_config: DeviceConfig, + cache_config: CacheConfig, + load_config: LoadConfig, + local_rank: int, + rank: int, + distributed_init_method: str, + speculative_config: Optional[SpeculativeConfig] = None, + lora_config: Optional[LoRAConfig] = None, + prompt_adapter_config: Optional[PromptAdapterConfig] = None, + observability_config: Optional[ObservabilityConfig] = None, + ): + self.model_config = model_config + self.parallel_config = parallel_config + self.scheduler_config = scheduler_config + self.device_config = device_config + self.cache_config = cache_config + self.load_config = load_config + self.local_rank = local_rank + self.rank = rank + self.distributed_init_method = distributed_init_method + self.lora_config = lora_config + self.speculative_config = speculative_config + self.prompt_adapter_config = prompt_adapter_config + self.observability_config = observability_config + + if self.model_config.trust_remote_code: + # note: lazy import to avoid importing torch before initializing + from vllm.utils import init_cached_hf_modules + init_cached_hf_modules() + + self.model_runner = GPUModelRunner( + model_config, + parallel_config, + scheduler_config, + device_config, + cache_config, + load_config, + lora_config=lora_config, + ) + + def initialize(self): + if self.device_config.device.type == "cuda": + # torch.distributed.all_reduce does not free the input tensor until + # the synchronization point. This causes the memory usage to grow + # as the number of all_reduce calls increases. This env var disables + # this behavior. + # Related issue: + # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 + os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" + + # This env var set by Ray causes exceptions with graph building. + os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None) + self.device = torch.device(f"cuda:{self.local_rank}") + torch.cuda.set_device(self.device) + + _check_if_gpu_supports_dtype(self.model_config.dtype) + gc.collect() + torch.cuda.empty_cache() + self.init_gpu_memory = torch.cuda.mem_get_info()[0] + else: + raise RuntimeError( + f"Not support device type: {self.device_config.device}") + # Initialize the distributed environment. + init_worker_distributed_environment(self.parallel_config, self.rank, + self.distributed_init_method, + self.local_rank) + # Set random seed. + set_random_seed(self.model_config.seed) + + def load_model(self) -> None: + self.model_runner.load_model() + + @torch.inference_mode() + def determine_num_available_blocks(self) -> Tuple[int, int]: + """Profiles the peak memory usage of the model to determine how many + KV blocks may be allocated without OOMs. + + The engine will first conduct a profiling of the existing memory usage. + Then, it calculate the maximum possible number of GPU and CPU blocks + that can be allocated with the remaining free memory. + + .. tip:: + You may limit the usage of GPU memory + by adjusting the `gpu_memory_utilization` parameter. + """ + # Profile the memory usage of the model and get the maximum number of + # cache blocks that can be allocated with the remaining free memory. + torch.cuda.empty_cache() + + # Execute a forward pass with dummy inputs to profile the memory usage + # of the model. + self.model_runner.profile_run() + + # Calculate the number of blocks that can be allocated with the + # profiled peak memory. + torch.cuda.synchronize() + free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info() + # NOTE(woosuk): Here we assume that the other processes using the same + # GPU did not change their memory usage during the profiling. + peak_memory = self.init_gpu_memory - free_gpu_memory + assert peak_memory > 0, ( + "Error in memory profiling. " + f"Initial free memory {self.init_gpu_memory}, current free memory" + f" {free_gpu_memory}. This happens when the GPU memory was " + "not properly cleaned up before initializing the vLLM instance.") + + cache_block_size = _get_cache_block_size(self.cache_config, + self.model_config, + self.parallel_config) + num_gpu_blocks = int( + (total_gpu_memory * self.cache_config.gpu_memory_utilization - + peak_memory) // cache_block_size) + num_gpu_blocks = max(num_gpu_blocks, 0) + # if self.model_runner.lora_manager: + # self.model_runner.remove_all_loras() + gc.collect() + torch.cuda.empty_cache() + return num_gpu_blocks, 0 + + def initialize_cache(self, num_gpu_blocks: int) -> None: + """Allocate GPU and CPU KV cache with the specified number of blocks.""" + if num_gpu_blocks <= 0: + raise ValueError("No available memory for the cache blocks. " + "Try increasing `gpu_memory_utilization` when " + "initializing the engine.") + + max_seq_len = self.cache_config.block_size * num_gpu_blocks + max_model_len = self.model_config.max_model_len + if max_model_len > max_seq_len: + raise ValueError( + f"The model's max seq len ({max_model_len}) " + "is larger than the maximum number of tokens that can be " + f"stored in KV cache ({max_seq_len}). Try increasing " + "`gpu_memory_utilization` or decreasing `max_model_len` when " + "initializing the engine.") + + self.model_runner.initialize_kv_cache(num_gpu_blocks) + + def compile_or_warm_up_model(self) -> None: + if not self.model_config.enforce_eager: + self.model_runner.capture_model() + # Reset the seed to ensure that the random state is not affected by + # the model initialization and profiling. + set_random_seed(self.model_config.seed) + + @torch.inference_mode() + def execute_model( + self, + scheduler_output: "SchedulerOutput", + ) -> ModelRunnerOutput: + output = self.model_runner.execute_model(scheduler_output) + # TODO(woosuk): Send the output to the engine process. + return output + + +def init_worker_distributed_environment( + parallel_config: ParallelConfig, + rank: int, + distributed_init_method: Optional[str] = None, + local_rank: int = -1, +) -> None: + """Initialize the distributed environment.""" + set_custom_all_reduce(not parallel_config.disable_custom_all_reduce) + + init_distributed_environment(parallel_config.world_size, rank, + distributed_init_method, local_rank) + + ensure_model_parallel_initialized(parallel_config.tensor_parallel_size, + parallel_config.pipeline_parallel_size) + + +def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype): + # Check if the GPU supports the dtype. + if torch_dtype == torch.bfloat16: # noqa: SIM102 + if not current_platform.has_device_capability(80): + capability = current_platform.get_device_capability() + gpu_name = current_platform.get_device_name() + + if capability is None: + compute_str = "does not have a compute capability" + else: + version_str = capability.as_version_str() + compute_str = f"has compute capability {version_str}" + + raise ValueError( + "Bfloat16 is only supported on GPUs with compute capability " + f"of at least 8.0. Your {gpu_name} GPU {compute_str}. " + "You can use float16 instead by explicitly setting the" + "`dtype` flag in CLI, for example: --dtype=half.") + + +def _get_cache_block_size( + cache_config: CacheConfig, + model_config: ModelConfig, + parallel_config: ParallelConfig, +) -> int: + head_size = model_config.get_head_size() + num_heads = model_config.get_num_kv_heads(parallel_config) + num_attention_layers = model_config.get_num_attention_layers( + parallel_config) + + key_cache_block = cache_config.block_size * num_heads * head_size + value_cache_block = key_cache_block + total = num_attention_layers * (key_cache_block + value_cache_block) + if cache_config.cache_dtype == "auto": + dtype = model_config.dtype + else: + dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype] + dtype_size = get_dtype_size(dtype) + return dtype_size * total From a48e3ec0523b4ac7230159bb38ae1dc4a2f0346a Mon Sep 17 00:00:00 2001 From: Jee Jee Li Date: Tue, 22 Oct 2024 19:32:51 +0800 Subject: [PATCH 045/222] [CI/Build][LoRA] Temporarily fix long context failure issue (#9579) --- tests/lora/test_long_context.py | 31 ++++++++++++++++++++----------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/tests/lora/test_long_context.py b/tests/lora/test_long_context.py index 389a3ccbc17ec..c8edb02a88d4b 100644 --- a/tests/lora/test_long_context.py +++ b/tests/lora/test_long_context.py @@ -28,9 +28,15 @@ def _create_lora_request(lora_id, long_context_infos): context_len = long_context_infos[lora_id]["context_length"] scaling_factor = context_len_to_scaling_factor[context_len] - return LoRARequest(context_len, lora_id, - long_context_infos[lora_id]["lora"], None, - 4096 * scaling_factor) + return LoRARequest( + # There are 2 LoRAs for 16K, we need to add lora_id to indicate + # they are different LoRAs. + context_len + str(lora_id), + lora_id, + long_context_infos[lora_id]["lora"], + None, + 4096 * scaling_factor, + ) def evaluate_json_response(model_response, golden_response): @@ -108,14 +114,17 @@ def lora_llm(long_context_infos): for info in long_context_infos.values() ] - llm = vllm.LLM("meta-llama/Llama-2-13b-chat-hf", - enable_lora=True, - max_num_seqs=16, - max_loras=2, - long_lora_scaling_factors=tuple(scaling_factors), - max_num_batched_tokens=4096 * 8, - tensor_parallel_size=4, - distributed_executor_backend="mp") + llm = vllm.LLM( + "meta-llama/Llama-2-13b-chat-hf", + enable_lora=True, + max_num_seqs=16, + max_loras=2, + long_lora_scaling_factors=tuple(scaling_factors), + max_num_batched_tokens=4096 * 8, + tensor_parallel_size=4, + # FIXME enable async output processor + disable_async_output_proc=True, + distributed_executor_backend="mp") yield llm del llm From 9dbcce84a73742805433414ff9000cfe7a5ef1c5 Mon Sep 17 00:00:00 2001 From: xendo Date: Tue, 22 Oct 2024 14:51:41 +0200 Subject: [PATCH 046/222] [Neuron] [Bugfix] Fix neuron startup (#9374) Co-authored-by: Jerzy Zagorski --- vllm/_custom_ops.py | 3 ++- vllm/config.py | 13 +++++++------ vllm/platforms/__init__.py | 10 ++++++++++ vllm/platforms/interface.py | 4 ++++ vllm/platforms/neuron.py | 9 +++++++++ vllm/triton_utils/importing.py | 5 ++++- vllm/utils.py | 11 +---------- 7 files changed, 37 insertions(+), 18 deletions(-) create mode 100644 vllm/platforms/neuron.py diff --git a/vllm/_custom_ops.py b/vllm/_custom_ops.py index b2952bbfa917c..a25f7abca5498 100644 --- a/vllm/_custom_ops.py +++ b/vllm/_custom_ops.py @@ -26,7 +26,8 @@ import vllm._moe_C # noqa: F401 supports_moe_ops = True -if TYPE_CHECKING: +# neuron has torch version that doesn't even have impl_abstract +if TYPE_CHECKING or current_platform.is_neuron(): def register_fake(fn): return lambda name: fn diff --git a/vllm/config.py b/vllm/config.py index 00dd047e6d058..12935e77c2aa7 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -17,8 +17,7 @@ get_hf_image_processor_config, get_hf_text_config) from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory, - is_hip, is_neuron, is_openvino, is_xpu, - print_warning_once) + is_hip, is_openvino, is_xpu, print_warning_once) if TYPE_CHECKING: from ray.util.placement_group import PlacementGroup @@ -215,8 +214,10 @@ def __init__(self, self.is_attention_free = self._init_attention_free() self.has_inner_state = self._init_has_inner_state() - self.override_neuron_config = override_neuron_config if is_neuron( - ) else None + if current_platform.is_neuron(): + self.override_neuron_config = override_neuron_config + else: + self.override_neuron_config = None supported_tasks, task = self._resolve_task(task, self.hf_config) self.supported_tasks = supported_tasks @@ -368,7 +369,7 @@ def _verify_quantization(self) -> None: "Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ" " is not set, enabling VLLM_USE_TRITON_AWQ.") envs.VLLM_USE_TRITON_AWQ = True - if is_neuron( + if current_platform.is_neuron( ) and self.quantization not in neuron_supported_quantization: raise ValueError( f"{self.quantization} quantization is currently not " @@ -1112,7 +1113,7 @@ def __init__(self, device: str = "auto") -> None: # Automated device type detection if current_platform.is_cuda_alike(): self.device_type = "cuda" - elif is_neuron(): + elif current_platform.is_neuron(): self.device_type = "neuron" elif is_openvino(): self.device_type = "openvino" diff --git a/vllm/platforms/__init__.py b/vllm/platforms/__init__.py index c648862b2d757..58912158139bd 100644 --- a/vllm/platforms/__init__.py +++ b/vllm/platforms/__init__.py @@ -58,6 +58,13 @@ except Exception: pass +is_neuron = False +try: + import transformers_neuronx # noqa: F401 + is_neuron = True +except ImportError: + pass + if is_tpu: # people might install pytorch built with cuda but run on tpu # so we need to check tpu first @@ -75,6 +82,9 @@ elif is_cpu: from .cpu import CpuPlatform current_platform = CpuPlatform() +elif is_neuron: + from .neuron import NeuronPlatform + current_platform = NeuronPlatform() else: current_platform = UnspecifiedPlatform() diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py index 00742a290e42a..d36367f2bc9c1 100644 --- a/vllm/platforms/interface.py +++ b/vllm/platforms/interface.py @@ -10,6 +10,7 @@ class PlatformEnum(enum.Enum): TPU = enum.auto() XPU = enum.auto() CPU = enum.auto() + NEURON = enum.auto() UNSPECIFIED = enum.auto() @@ -48,6 +49,9 @@ def is_xpu(self) -> bool: def is_cpu(self) -> bool: return self._enum == PlatformEnum.CPU + def is_neuron(self) -> bool: + return self._enum == PlatformEnum.NEURON + def is_cuda_alike(self) -> bool: """Stateless version of :func:`torch.cuda.is_available`.""" return self._enum in (PlatformEnum.CUDA, PlatformEnum.ROCM) diff --git a/vllm/platforms/neuron.py b/vllm/platforms/neuron.py new file mode 100644 index 0000000000000..07d8398eda525 --- /dev/null +++ b/vllm/platforms/neuron.py @@ -0,0 +1,9 @@ +from .interface import Platform, PlatformEnum + + +class NeuronPlatform(Platform): + _enum = PlatformEnum.NEURON + + @classmethod + def get_device_name(cls, device_id: int = 0) -> str: + return "neuron" diff --git a/vllm/triton_utils/importing.py b/vllm/triton_utils/importing.py index ce46082247639..ef7ca149266b6 100644 --- a/vllm/triton_utils/importing.py +++ b/vllm/triton_utils/importing.py @@ -1,10 +1,13 @@ from importlib.util import find_spec from vllm.logger import init_logger +from vllm.platforms import current_platform logger = init_logger(__name__) -HAS_TRITON = find_spec("triton") is not None +# neuron has too old torch +HAS_TRITON = find_spec( + "triton") is not None and not current_platform.is_neuron() if not HAS_TRITON: logger.info("Triton not installed; certain GPU-related functions" diff --git a/vllm/utils.py b/vllm/utils.py index 428c2095dcd5d..797c1bcfd5342 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -327,15 +327,6 @@ def is_openvino() -> bool: return False -@lru_cache(maxsize=None) -def is_neuron() -> bool: - try: - import transformers_neuronx - except ImportError: - transformers_neuronx = None - return transformers_neuronx is not None - - @lru_cache(maxsize=None) def is_xpu() -> bool: from importlib.metadata import PackageNotFoundError, version @@ -786,7 +777,7 @@ def is_pin_memory_available() -> bool: elif is_xpu(): print_warning_once("Pin memory is not supported on XPU.") return False - elif is_neuron(): + elif current_platform.is_neuron(): print_warning_once("Pin memory is not supported on Neuron.") return False elif current_platform.is_cpu() or is_openvino(): From bb392ea2d2bfde4ce101ff8c87774b85100469c9 Mon Sep 17 00:00:00 2001 From: Isotr0py <2037008807@qq.com> Date: Wed, 23 Oct 2024 00:01:46 +0800 Subject: [PATCH 047/222] [Model][VLM] Initialize support for Mono-InternVL model (#9528) --- docs/source/models/supported_models.rst | 2 +- .../vision_language/test_internvl.py | 21 ++- vllm/model_executor/models/intern_vit.py | 31 ++++ vllm/model_executor/models/internlm2_ve.py | 166 ++++++++++++++++++ vllm/model_executor/models/internvl.py | 61 +++++-- vllm/model_executor/models/registry.py | 1 + 6 files changed, 254 insertions(+), 28 deletions(-) create mode 100644 vllm/model_executor/models/internlm2_ve.py diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 62ab8c067f5d0..3d8df3c9f8c9f 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -376,7 +376,7 @@ Text Generation * - :code:`InternVLChatModel` - InternVL2 - T + I\ :sup:`E+` - - :code:`OpenGVLab/InternVL2-4B`, :code:`OpenGVLab/InternVL2-8B`, etc. + - :code:`OpenGVLab/Mono-InternVL-2B`, :code:`OpenGVLab/InternVL2-4B`, :code:`OpenGVLab/InternVL2-8B`, etc. - - ✅︎ * - :code:`LlavaForConditionalGeneration` diff --git a/tests/models/decoder_only/vision_language/test_internvl.py b/tests/models/decoder_only/vision_language/test_internvl.py index 58d88f0a28829..fc842ec4a6171 100644 --- a/tests/models/decoder_only/vision_language/test_internvl.py +++ b/tests/models/decoder_only/vision_language/test_internvl.py @@ -7,7 +7,6 @@ from transformers import AutoConfig from vllm.multimodal.utils import rescale_image_size -from vllm.platforms import current_platform from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner, _ImageAssets) @@ -19,15 +18,20 @@ "cherry_blossom": "<|im_start|>User\n\nWhat is the season?<|im_end|>\n<|im_start|>Assistant\n", # noqa: E501 }) -HF_MULTIIMAGE_IMAGE_PROMPT = "<|im_start|>User\nImage-1: \nImage-2: \nDescribe the two images in detail.<|im_end|>\n<|im_start|>Assistant\n" # noqa: E501 +HF_MULTIIMAGE_IMAGE_PROMPT = "<|im_start|>User\nImage-1: \nImage-2: \nDescribe the two images in short.<|im_end|>\n<|im_start|>Assistant\n" # noqa: E501 models = [ "OpenGVLab/InternVL2-1B", "OpenGVLab/InternVL2-2B", + # NOTE: Mono-InternVL-2B doesn't work with fp16, + # it will result NaN during inference. + # See: https://huggingface.co/OpenGVLab/Mono-InternVL-2B/discussions/9 + "OpenGVLab/Mono-InternVL-2B", # Broken due to outdated implementation of Phi-3 # See: https://huggingface.co/OpenGVLab/InternVL2-4B/discussions/3 # "OpenGVLab/InternVL2-4B", ] +target_dtype = "bfloat16" # adapted from https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py @@ -52,9 +56,15 @@ def generate( input_embeds = input_embeds.reshape(B, N, C) - outputs = self.language_model.generate( + forward_kwargs = dict( inputs_embeds=input_embeds, attention_mask=attention_mask, + ) + if getattr(self, "use_visual_token_mask", False): + visual_token_mask = selected.reshape(B, N, 1).to(input_embeds.dtype) + forward_kwargs["visual_token_mask"] = visual_token_mask + outputs = self.language_model.generate( + **forward_kwargs, **generate_kwargs, ) @@ -243,11 +253,6 @@ def run_awq_test( ) -target_dtype = "half" -if current_platform.is_cpu(): - target_dtype = "bfloat16" - - @pytest.mark.parametrize("model", models) @pytest.mark.parametrize( "size_factors", diff --git a/vllm/model_executor/models/intern_vit.py b/vllm/model_executor/models/intern_vit.py index 35be1cec3d434..b59671e914e7d 100644 --- a/vllm/model_executor/models/intern_vit.py +++ b/vllm/model_executor/models/intern_vit.py @@ -97,6 +97,37 @@ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: return embeddings +class InternVisionPatchModel(nn.Module): + + def __init__(self, config: PretrainedConfig): + super().__init__() + self.config = config + self.embeddings = InternVisionEmbeddings(config) + + def get_input_embeddings(self): + return self.embeddings + + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + pixel_embeds: Optional[torch.Tensor] = None, + ) -> torch.FloatTensor: + if pixel_values is None and pixel_embeds is None: + raise ValueError( + 'You have to specify pixel_values or pixel_embeds') + + if pixel_embeds is not None: + hidden_states = pixel_embeds + elif pixel_values is not None: + if pixel_values.ndim == 4: + hidden_states = self.embeddings(pixel_values) + else: + raise ValueError( + f'wrong pixel_values size: {pixel_values.shape}') + + return hidden_states + + class InternParallelAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" diff --git a/vllm/model_executor/models/internlm2_ve.py b/vllm/model_executor/models/internlm2_ve.py new file mode 100644 index 0000000000000..6effd70b75da3 --- /dev/null +++ b/vllm/model_executor/models/internlm2_ve.py @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +from typing import List, Optional, Tuple, Union + +import torch +from torch import nn +from transformers import PretrainedConfig + +from vllm.attention import AttentionMetadata +from vllm.config import CacheConfig +from vllm.distributed import get_pp_group +from vllm.model_executor.layers.layernorm import RMSNorm +from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.models.internlm2 import (InternLM2Attention, + InternLM2ForCausalLM, + InternLM2MLP, InternLM2Model) +from vllm.sequence import IntermediateTensors + +from .utils import make_layers + + +class InternLM2VEDecoderLayer(nn.Module): + + def __init__( + self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + rope_theta = getattr(config, "rope_theta", 10000) + rope_scaling = getattr(config, "rope_scaling", None) + max_position_embeddings = getattr(config, "max_position_embeddings", + 8192) + self.attention = InternLM2Attention( + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + num_kv_heads=config.num_key_value_heads, + rope_theta=rope_theta, + rope_scaling=rope_scaling, + max_position_embeddings=max_position_embeddings, + cache_config=cache_config, + quant_config=quant_config, + ) + self.feed_forward = InternLM2MLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + quant_config=quant_config, + ) + self.feed_forward_ve = InternLM2MLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + quant_config=quant_config, + ) + self.attention_norm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + kv_cache: torch.Tensor, + attn_metadata: AttentionMetadata, + residual: Optional[torch.Tensor], + visual_token_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Self Attention + if residual is None: + residual = hidden_states + hidden_states = self.attention_norm(hidden_states) + else: + hidden_states, residual = self.attention_norm( + hidden_states, residual) + hidden_states = self.attention( + positions=positions, + hidden_states=hidden_states, + kv_cache=kv_cache, + attn_metadata=attn_metadata, + ) + + # Fully Connected + hidden_states, residual = self.ffn_norm(hidden_states, residual) + if visual_token_mask is not None and visual_token_mask.any(): + visual_token_mask = visual_token_mask.repeat( + 1, self.hidden_size).bool() + text_token_mask = ~visual_token_mask + hidden_states[visual_token_mask] = self.feed_forward_ve( + hidden_states[visual_token_mask].reshape( + -1, self.hidden_size)).flatten() + if text_token_mask.any(): + hidden_states[text_token_mask] = self.feed_forward( + hidden_states[text_token_mask].reshape( + -1, self.hidden_size)).flatten() + else: + hidden_states = self.feed_forward(hidden_states) + return hidden_states, residual + + +class InternLM2VEModel(InternLM2Model): + + def __init__( + self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__(config, cache_config, quant_config) + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, + lambda prefix: InternLM2VEDecoderLayer(config, cache_config, + quant_config), + prefix=f"{prefix}.layers") + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + visual_token_mask: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.tok_embeddings(input_ids) + residual = None + else: + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] + for i in range(self.start_layer, self.end_layer): + layer = self.layers[i] + hidden_states, residual = layer( + positions, + hidden_states, + kv_caches[i - self.start_layer], + attn_metadata, + residual, + visual_token_mask=visual_token_mask, + ) + if not get_pp_group().is_last_rank: + return IntermediateTensors({ + "hidden_states": hidden_states, + "residual": residual + }) + hidden_states, _ = self.norm(hidden_states, residual) + return hidden_states + + +class InternLM2VEForCausalLM(InternLM2ForCausalLM): + + def __init__( + self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + ) -> None: + super().__init__(config, cache_config, quant_config) + self.model = InternLM2VEModel(config, cache_config, quant_config) diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index aada92cdf2456..a80e00e34957c 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -21,7 +21,8 @@ token_inputs) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.sampler import Sampler, SamplerOutput -from vllm.model_executor.models.intern_vit import InternVisionModel +from vllm.model_executor.models.intern_vit import (InternVisionModel, + InternVisionPatchModel) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.base import MultiModalInputs @@ -427,13 +428,9 @@ def __init__(self, self.downsample_ratio = config.downsample_ratio self.ps_version = config.ps_version - vision_feature_layer = self.select_layer - if vision_feature_layer < 0: - num_hidden_layers = config.vision_config.num_hidden_layers \ - + vision_feature_layer + 1 - else: - num_hidden_layers = vision_feature_layer + 1 - self.vision_model = self._init_vision_model(config, num_hidden_layers) + self.llm_arch_name = config.text_config.architectures[0] + self.is_mono = self.llm_arch_name == 'InternLM2VEForCausalLM' + self.vision_model = self._init_vision_model(config, self.is_mono) self.language_model = init_vllm_registered_model( config.text_config, cache_config, quant_config) @@ -451,10 +448,19 @@ def sampler(self): return Sampler() - def _init_vision_model(self, config: PretrainedConfig, - num_hidden_layers: int): - return InternVisionModel(config.vision_config, - num_hidden_layers_override=num_hidden_layers) + def _init_vision_model(self, config: PretrainedConfig, is_mono: bool): + if not is_mono: + vision_feature_layer = self.select_layer + if vision_feature_layer < 0: + num_hidden_layers = config.vision_config.num_hidden_layers \ + + vision_feature_layer + 1 + else: + num_hidden_layers = vision_feature_layer + 1 + return InternVisionModel( + config.vision_config, + num_hidden_layers_override=num_hidden_layers) + else: + return InternVisionPatchModel(config.vision_config) def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential: vit_hidden_size = config.vision_config.hidden_size @@ -562,6 +568,14 @@ def _process_image_input( return image_embeds + def _get_visual_token_mask(self, input_ids: torch.Tensor) -> torch.Tensor: + if self.is_mono: + visual_token_mask = ( + input_ids == self.img_context_token_id).reshape(-1, 1) + else: + visual_token_mask = None + return visual_token_mask + def forward( self, input_ids: torch.Tensor, @@ -574,6 +588,7 @@ def forward( if intermediate_tensors is not None: input_ids = None inputs_embeds = None + visual_token_mask = None else: image_input = self._parse_and_validate_image_input(**kwargs) if image_input is not None: @@ -583,16 +598,24 @@ def forward( inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, vision_embeddings, self.img_context_token_id) + visual_token_mask = self._get_visual_token_mask(input_ids) input_ids = None else: inputs_embeds = None - - hidden_states = self.language_model.model(input_ids, - positions, - kv_caches, - attn_metadata, - intermediate_tensors, - inputs_embeds=inputs_embeds) + visual_token_mask = None + + forward_kwargs = { + "input_ids": input_ids, + "positions": positions, + "kv_caches": kv_caches, + "attn_metadata": attn_metadata, + "intermediate_tensors": intermediate_tensors, + "inputs_embeds": inputs_embeds, + } + if self.is_mono: + forward_kwargs.update({"visual_token_mask": visual_token_mask}) + + hidden_states = self.language_model.model(**forward_kwargs) return hidden_states def compute_logits( diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 2a04ece24c8bd..8745e0cbd97b6 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -47,6 +47,7 @@ "GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM"), "InternLMForCausalLM": ("llama", "LlamaForCausalLM"), "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"), + "InternLM2VEForCausalLM": ("internlm2_ve", "InternLM2VEForCausalLM"), "JAISLMHeadModel": ("jais", "JAISLMHeadModel"), "JambaForCausalLM": ("jamba", "JambaForCausalLM"), "LlamaForCausalLM": ("llama", "LlamaForCausalLM"), From 08075c34483843c75b4420bac92377b59ff9a8ac Mon Sep 17 00:00:00 2001 From: gopalsarda Date: Tue, 22 Oct 2024 21:44:22 +0530 Subject: [PATCH 048/222] [Bugfix] Eagle: change config name for fc bias (#9580) --- vllm/model_executor/models/eagle.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/models/eagle.py b/vllm/model_executor/models/eagle.py index 13811d33768a6..a87e1c0228627 100644 --- a/vllm/model_executor/models/eagle.py +++ b/vllm/model_executor/models/eagle.py @@ -44,7 +44,7 @@ def __init__(self, config: EAGLEConfig, *args, **kwargs) -> None: self.model = model_cls(self.config.model, *args, **kwargs) self.fc = nn.Linear(config.model.hidden_size * 2, config.model.hidden_size, - bias=getattr(self.config, "bias", False)) + bias=getattr(self.config, "eagle_fc_bias", False)) self.orig_vocab_size = config.vocab_size self.truncated_vocab_size = config.truncated_vocab_size From 32a1ee74a0838e37e3b9dea2312ada925011c5ba Mon Sep 17 00:00:00 2001 From: Yuan Date: Tue, 22 Oct 2024 10:38:04 -0700 Subject: [PATCH 049/222] [Hardware][Intel CPU][DOC] Update docs for CPU backend (#6212) Signed-off-by: Yuan Zhou Co-authored-by: Rafael Vasquez Co-authored-by: Gubrud, Aaron D Co-authored-by: adgubrud <96072084+adgubrud@users.noreply.github.com> --- .../getting_started/cpu-installation.rst | 23 ++- docs/source/index.rst | 1 + docs/source/serving/deploying_with_nginx.rst | 142 ++++++++++++++++++ 3 files changed, 165 insertions(+), 1 deletion(-) create mode 100644 docs/source/serving/deploying_with_nginx.rst diff --git a/docs/source/getting_started/cpu-installation.rst b/docs/source/getting_started/cpu-installation.rst index f544325a0776c..d12aeebbbc184 100644 --- a/docs/source/getting_started/cpu-installation.rst +++ b/docs/source/getting_started/cpu-installation.rst @@ -3,7 +3,13 @@ Installation with CPU ======================== -vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32 and BF16. +vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32 and BF16. vLLM CPU backend supports the following vLLM features: + +- Tensor Parallel (``-tp = N``) +- Quantization (``INT8 W8A8, AWQ``) + +.. note:: + FP16 data type and more advanced features on `chunked-prefill`, `prefix-caching` and `FP8 KV cache` are under development and will be available soon. Table of contents: @@ -141,5 +147,20 @@ Performance tips - If using vLLM CPU backend on a multi-socket machine with NUMA, be aware to set CPU cores using ``VLLM_CPU_OMP_THREADS_BIND`` to avoid cross NUMA node memory access. +CPU Backend Considerations +-------------------------- + +- The CPU backend significantly differs from the GPU backend since the vLLM architecture was originally optimized for GPU use. A number of optimizations are needed to enhance its performance. + +- Decouple the HTTP serving components from the inference components. In a GPU backend configuration, the HTTP serving and tokenization tasks operate on the CPU, while inference runs on the GPU, which typically does not pose a problem. However, in a CPU-based setup, the HTTP serving and tokenization can cause significant context switching and reduced cache efficiency. Therefore, it is strongly recommended to segregate these two components for improved performance. + +- On CPU based setup with NUMA enabled, the memory access performance may be largely impacted by the `topology `_. For NUMA architecture, two optimizations are to recommended: Tensor Parallel or Data Parallel. + + * Using Tensor Parallel for a latency constraints deployment: following GPU backend design, a Megatron-LM's parallel algorithm will be used to shard the model, based on the number of NUMA nodes (e.g. TP = 2 for a two NUMA node system). With `TP feature on CPU `_ merged, Tensor Parallel is supported for serving and offline inferencing. In general each NUMA node is treated as one GPU card. Below is the example script to enable Tensor Parallel = 2 for serving: + + .. code-block:: console + + $ VLLM_CPU_KVCACHE_SPACE=40 VLLM_CPU_OMP_THREADS_BIND="0-31|32-63" vllm serve meta-llama/Llama-2-7b-chat-hf -tp=2 --distributed-executor-backend mp + * Using Data Parallel for maximum throughput: to launch an LLM serving endpoint on each NUMA node along with one additional load balancer to dispatch the requests to those endpoints. Common solutions like `Nginx <../serving/deploying_with_nginx.html>`_ or HAProxy are recommended. Anyscale Ray project provides the feature on LLM `serving `_. Here is the example to setup a scalable LLM serving with `Ray Serve `_. \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index d20e46b4a3656..c328c049b430c 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -80,6 +80,7 @@ Documentation serving/openai_compatible_server serving/deploying_with_docker serving/deploying_with_k8s + serving/deploying_with_nginx serving/distributed_serving serving/metrics serving/env_vars diff --git a/docs/source/serving/deploying_with_nginx.rst b/docs/source/serving/deploying_with_nginx.rst new file mode 100644 index 0000000000000..b5dff02b6bae6 --- /dev/null +++ b/docs/source/serving/deploying_with_nginx.rst @@ -0,0 +1,142 @@ +.. _nginxloadbalancer: + +Deploying with Nginx Loadbalancer +================================= + +This document shows how to launch multiple vLLM serving containers and use Nginx to act as a load balancer between the servers. + +Table of contents: + +#. :ref:`Build Nginx Container ` +#. :ref:`Create Simple Nginx Config file ` +#. :ref:`Build vLLM Container ` +#. :ref:`Create Docker Network ` +#. :ref:`Launch vLLM Containers ` +#. :ref:`Launch Nginx ` +#. :ref:`Verify That vLLM Servers Are Ready ` + +.. _nginxloadbalancer_nginx_build: + +Build Nginx Container +--------------------- + +This guide assumes that you have just cloned the vLLM project and you're currently in the vllm root directory. + +.. code-block:: console + + export vllm_root=`pwd` + +Create a file named ``Dockerfile.nginx``: + +.. code-block:: console + + FROM nginx:latest + RUN rm /etc/nginx/conf.d/default.conf + EXPOSE 80 + CMD ["nginx", "-g", "daemon off;"] + +Build the container: + +.. code-block:: console + + docker build . -f Dockerfile.nginx --tag nginx-lb + +.. _nginxloadbalancer_nginx_conf: + +Create Simple Nginx Config file +------------------------------- + +Create a file named ``nginx_conf/nginx.conf``. Note that you can add as many servers as you'd like. In the below example we'll start with two. To add more, add another ``server vllmN:8000 max_fails=3 fail_timeout=10000s;`` entry to ``upstream backend``. + +.. code-block:: console + + upstream backend { + least_conn; + server vllm0:8000 max_fails=3 fail_timeout=10000s; + server vllm1:8000 max_fails=3 fail_timeout=10000s; + } + server { + listen 80; + location / { + proxy_pass http://backend; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + } + } + +.. _nginxloadbalancer_nginx_vllm_container: + +Build vLLM Container +-------------------- + +.. code-block:: console + + cd $vllm_root + docker build -f Dockerfile . --tag vllm + + +If you are behind proxy, you can pass the proxy settings to the docker build command as shown below: + +.. code-block:: console + + cd $vllm_root + docker build -f Dockerfile . --tag vllm --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy + +.. _nginxloadbalancer_nginx_docker_network: + +Create Docker Network +--------------------- + +.. code-block:: console + + docker network create vllm_nginx + + +.. _nginxloadbalancer_nginx_launch_container: + +Launch vLLM Containers +---------------------- + +Notes: + +* If you have your HuggingFace models cached somewhere else, update ``hf_cache_dir`` below. +* If you don't have an existing HuggingFace cache you will want to start ``vllm0`` and wait for the model to complete downloading and the server to be ready. This will ensure that ``vllm1`` can leverage the model you just downloaded and it won't have to be downloaded again. +* The below example assumes GPU backend used. If you are using CPU backend, remove ``--gpus all``, add ``VLLM_CPU_KVCACHE_SPACE`` and ``VLLM_CPU_OMP_THREADS_BIND`` environment variables to the docker run command. +* Adjust the model name that you want to use in your vLLM servers if you don't want to use ``Llama-2-7b-chat-hf``. + +.. code-block:: console + + mkdir -p ~/.cache/huggingface/hub/ + hf_cache_dir=~/.cache/huggingface/ + docker run -itd --ipc host --privileged --network vllm_nginx --gpus all --shm-size=10.24gb -v $hf_cache_dir:/root/.cache/huggingface/ -p 8081:8000 --name vllm0 vllm --model meta-llama/Llama-2-7b-chat-hf + docker run -itd --ipc host --privileged --network vllm_nginx --gpus all --shm-size=10.24gb -v $hf_cache_dir:/root/.cache/huggingface/ -p 8082:8000 --name vllm1 vllm --model meta-llama/Llama-2-7b-chat-hf + +.. note:: + If you are behind proxy, you can pass the proxy settings to the docker run command via ``-e http_proxy=$http_proxy -e https_proxy=$https_proxy``. + +.. _nginxloadbalancer_nginx_launch_nginx: + +Launch Nginx +------------ + +.. code-block:: console + + docker run -itd -p 8000:80 --network vllm_nginx -v ./nginx_conf/:/etc/nginx/conf.d/ --name nginx-lb nginx-lb:latest + +.. _nginxloadbalancer_nginx_verify_nginx: + +Verify That vLLM Servers Are Ready +---------------------------------- + +.. code-block:: console + + docker logs vllm0 | grep Uvicorn + docker logs vllm1 | grep Uvicorn + +Both outputs should look like this: + +.. code-block:: console + + INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit) From 434984e665fe4134ec749de5f1c412b7a1e647a1 Mon Sep 17 00:00:00 2001 From: Yuhong Guo Date: Wed, 23 Oct 2024 02:07:30 +0800 Subject: [PATCH 050/222] [Frontend] Support custom request_id from request (#9550) Co-authored-by: Yuhong Guo --- vllm/entrypoints/openai/protocol.py | 6 ++++++ vllm/entrypoints/openai/serving_chat.py | 4 ++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 06114339b7c69..733decf80a711 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -284,6 +284,12 @@ class ChatCompletionRequest(OpenAIBaseModel): "The priority of the request (lower means earlier handling; " "default: 0). Any priority other than 0 will raise an error " "if the served model does not use priority scheduling.")) + request_id: str = Field( + default_factory=lambda: f"{random_uuid()}", + description=( + "The request_id related to this request. If the caller does " + "not set it, a random_uuid will be generated. This id is used " + "through out the inference process and return in response.")) # doc: end-chat-completion-extra-params diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index c3fa0e44e5e8d..b9b240b64850e 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -38,7 +38,7 @@ from vllm.tracing import (contains_trace_headers, extract_trace_headers, log_tracing_disabled_warning) from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer -from vllm.utils import iterate_with_cancellation, random_uuid +from vllm.utils import iterate_with_cancellation logger = init_logger(__name__) @@ -176,7 +176,7 @@ async def create_chat_completion( "\"auto\" tool choice requires " "--enable-auto-tool-choice and --tool-call-parser to be set") - request_id = f"chat-{random_uuid()}" + request_id = f"chat-{request.request_id}" request_metadata = RequestResponseMetadata(request_id=request_id) if raw_request: From cd5601ac37baadb6a6efa3450f1546ddab84c973 Mon Sep 17 00:00:00 2001 From: Ronen Schaffer Date: Tue, 22 Oct 2024 21:11:53 +0300 Subject: [PATCH 051/222] [BugFix] Prevent exporting duplicate OpenTelemetry spans (#9017) --- tests/tracing/test_tracing.py | 30 ++++++++++++++++++++++++++---- vllm/engine/llm_engine.py | 13 ++++++++++--- 2 files changed, 36 insertions(+), 7 deletions(-) diff --git a/tests/tracing/test_tracing.py b/tests/tracing/test_tracing.py index 64ed8e26f38ed..fe5fc979c66a3 100644 --- a/tests/tracing/test_tracing.py +++ b/tests/tracing/test_tracing.py @@ -87,8 +87,19 @@ def test_traces(trace_service): f"The fake trace service didn't receive a trace within " f"the {timeout} seconds timeout") - attributes = decode_attributes(trace_service.request.resource_spans[0]. - scope_spans[0].spans[0].attributes) + request = trace_service.request + assert len(request.resource_spans) == 1, ( + f"Expected 1 resource span, " + f"but got {len(request.resource_spans)}") + assert len(request.resource_spans[0].scope_spans) == 1, ( + f"Expected 1 scope span, " + f"but got {len(request.resource_spans[0].scope_spans)}") + assert len(request.resource_spans[0].scope_spans[0].spans) == 1, ( + f"Expected 1 span, " + f"but got {len(request.resource_spans[0].scope_spans[0].spans)}") + + attributes = decode_attributes( + request.resource_spans[0].scope_spans[0].spans[0].attributes) assert attributes.get(SpanAttributes.LLM_RESPONSE_MODEL) == model assert attributes.get( SpanAttributes.LLM_REQUEST_ID) == outputs[0].request_id @@ -142,8 +153,19 @@ def test_traces_with_detailed_steps(trace_service): f"The fake trace service didn't receive a trace within " f"the {timeout} seconds timeout") - attributes = decode_attributes(trace_service.request.resource_spans[0]. - scope_spans[0].spans[0].attributes) + request = trace_service.request + assert len(request.resource_spans) == 1, ( + f"Expected 1 resource span, " + f"but got {len(request.resource_spans)}") + assert len(request.resource_spans[0].scope_spans) == 1, ( + f"Expected 1 scope span, " + f"but got {len(request.resource_spans[0].scope_spans)}") + assert len(request.resource_spans[0].scope_spans[0].spans) == 1, ( + f"Expected 1 span, " + f"but got {len(request.resource_spans[0].scope_spans[0].spans)}") + + attributes = decode_attributes( + request.resource_spans[0].scope_spans[0].spans[0].attributes) assert attributes.get(SpanAttributes.LLM_RESPONSE_MODEL) == model assert attributes.get( SpanAttributes.LLM_REQUEST_ID) == outputs[0].request_id diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 25c4e76d9b159..3a29e6a9ae094 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -1245,7 +1245,7 @@ def _process_model_outputs(self, skip) # Tracing - self.do_tracing(scheduler_outputs) + self.do_tracing(scheduler_outputs, finished_before) return None @@ -1840,11 +1840,18 @@ def stop_profile(self) -> None: def is_tracing_enabled(self) -> bool: return self.tracer is not None - def do_tracing(self, scheduler_outputs: SchedulerOutputs) -> None: + def do_tracing(self, + scheduler_outputs: SchedulerOutputs, + finished_before: Optional[List[int]] = None) -> None: if self.tracer is None: return - for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups: + for idx, scheduled_seq_group in enumerate( + scheduler_outputs.scheduled_seq_groups): + # Skip double tracing when using async output proc + if finished_before and idx in finished_before: + continue + seq_group = scheduled_seq_group.seq_group if seq_group.is_finished(): self.create_trace_span(seq_group) From 17c79f3c364be166b68923bced94f902c00bd8bb Mon Sep 17 00:00:00 2001 From: youkaichao Date: Tue, 22 Oct 2024 13:43:37 -0700 Subject: [PATCH 052/222] [torch.compile] auto infer dynamic_arg_dims from type annotation (#9589) --- vllm/compilation/decorators.py | 68 ++++++++++++++++++++++++++-- vllm/model_executor/models/gemma2.py | 8 +--- vllm/model_executor/models/llama.py | 8 +--- 3 files changed, 65 insertions(+), 19 deletions(-) diff --git a/vllm/compilation/decorators.py b/vllm/compilation/decorators.py index 3ae74cc5cb7dd..0449f9354d0a2 100644 --- a/vllm/compilation/decorators.py +++ b/vllm/compilation/decorators.py @@ -1,24 +1,58 @@ import inspect -from typing import Dict, List, Union +from typing import Dict, List, Optional, Union import torch import vllm.envs as envs from vllm.compilation.levels import CompilationLevel from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher +from vllm.logger import init_logger from vllm.sequence import IntermediateTensors from vllm.utils import supports_dynamo +logger = init_logger(__name__) -def support_torch_compile(dynamic_arg_dims: Dict[str, Union[int, List[int]]]): + +def support_torch_compile( + cls: Optional[type] = None, + dynamic_arg_dims: Optional[Dict[str, Union[int, List[int]]]] = None): """ A decorator to add support for compiling the forward method of a class. + Usage 1: use directly as a decorator without arguments: + + ```python + @support_torch_compile + class MyModel(nn.Module): + def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]): + ... + ``` + + Usage 2: use as a decorator with arguments: + + ```python + @support_torch_compile(dynamic_arg_dims={"x": 0, "y": 0}) + class MyModel(nn.Module): + def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]): + ... + ``` + `dynamic_arg_dims` is a dictionary that maps argument names to the dynamic dimensions of the argument. The dynamic dimensions can be either a single integer or a list of integers. - Depending on the value of arguments: + if `dynamic_arg_dims` is `None`, it is inferred from the type annotation + of the `forward` method, based on the following default rules: + + - if the argument is annotated as `torch.Tensor` or + `Optional[torch.Tensor]`, the first dimension will be + marked as dynamic. + - if the argument is annotated as `IntermediateTensors`, the first + dimension of all the tensors in the intermediate tensors + will be marked as dynamic. + + During runtime, when we actually mark dimensions of tensors, + it depends on the value of arguments: - if it is a single integer, the corresponding dimension of the argument will be marked as dynamic. @@ -38,11 +72,35 @@ def cls_decorator_helper(cls: type): if not hasattr(cls, 'forward'): raise TypeError("decorated class should have a forward method.") sig = inspect.signature(cls.forward) - for k in dynamic_arg_dims: + inferred_dynamic_arg_dims = dynamic_arg_dims + if inferred_dynamic_arg_dims is None: + inferred_dynamic_arg_dims = {} + for k, v in sig.parameters.items(): + if v.annotation in [ + torch.Tensor, Optional[torch.Tensor], + IntermediateTensors, Optional[IntermediateTensors] + ]: + inferred_dynamic_arg_dims[k] = 0 + + logger.debug(("Inferred dynamic dimensions for " + "forward method of %s: %s"), cls, + list(inferred_dynamic_arg_dims.keys())) + + if len(inferred_dynamic_arg_dims) == 0: + raise ValueError( + "No dynamic dimensions found in the forward method of " + f"{cls}. Please provide dynamic_arg_dims explicitly.") + + for k in inferred_dynamic_arg_dims: if k not in sig.parameters: raise ValueError( f"Argument {k} not found in the forward method of {cls}") - return _support_torch_compile(cls, dynamic_arg_dims) + return _support_torch_compile(cls, inferred_dynamic_arg_dims) + + if cls is not None: + # use `support_torch_compile` as a decorator without arguments + assert isinstance(cls, type) + return cls_decorator_helper(cls) return cls_decorator_helper diff --git a/vllm/model_executor/models/gemma2.py b/vllm/model_executor/models/gemma2.py index f958268741cd5..d79248f93f5ae 100644 --- a/vllm/model_executor/models/gemma2.py +++ b/vllm/model_executor/models/gemma2.py @@ -241,13 +241,7 @@ def forward( return hidden_states, residual -@support_torch_compile( - dynamic_arg_dims={ - "input_ids": 0, - "positions": 0, - "inputs_embeds": 0, - "intermediate_tensors": 0, - }) +@support_torch_compile class Gemma2Model(nn.Module): def __init__( diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py index fd88ae8b50402..c346e3e808e3f 100644 --- a/vllm/model_executor/models/llama.py +++ b/vllm/model_executor/models/llama.py @@ -268,13 +268,7 @@ def forward( return hidden_states, residual -@support_torch_compile( - dynamic_arg_dims={ - "input_ids": 0, - "positions": 0, - "inputs_embeds": 0, - "intermediate_tensors": 0, - }) +@support_torch_compile class LlamaModel(nn.Module): def __init__( From 23b899a8e62c7ea07981bf8487b0dc2cb17847b8 Mon Sep 17 00:00:00 2001 From: Aurick Qiao Date: Tue, 22 Oct 2024 18:38:12 -0400 Subject: [PATCH 053/222] [Bugfix] fix detokenizer shallow copy (#5919) --- vllm/transformers_utils/detokenizer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/transformers_utils/detokenizer.py b/vllm/transformers_utils/detokenizer.py index 345ea14f9f273..7c8423d2b0a34 100644 --- a/vllm/transformers_utils/detokenizer.py +++ b/vllm/transformers_utils/detokenizer.py @@ -90,7 +90,7 @@ def decode_prompt_logprobs_inplace(self, seq_group: SequenceGroup, prefix_offset = next_iter_prefix_offset read_offset = next_iter_read_offset if prev_tokens is None: - prev_tokens = next_iter_tokens + prev_tokens = next_iter_tokens.copy() else: prev_tokens.extend(next_iter_tokens) From cb6fdaa0a0b31985df4fa3ddf069c022c1faacb9 Mon Sep 17 00:00:00 2001 From: Jeremy Arnold <103538711+JArnoldAMD@users.noreply.github.com> Date: Tue, 22 Oct 2024 17:40:38 -0500 Subject: [PATCH 054/222] [Misc] Make benchmarks use EngineArgs (#9529) --- benchmarks/benchmark_latency.py | 155 +--------------- benchmarks/benchmark_prefix_caching.py | 24 +-- benchmarks/benchmark_prioritization.py | 134 +------------- benchmarks/benchmark_throughput.py | 237 ++----------------------- 4 files changed, 38 insertions(+), 512 deletions(-) diff --git a/benchmarks/benchmark_latency.py b/benchmarks/benchmark_latency.py index ea1a7788f621d..0a14aedd5feba 100644 --- a/benchmarks/benchmark_latency.py +++ b/benchmarks/benchmark_latency.py @@ -1,5 +1,6 @@ """Benchmark the latency of processing a single batch of requests.""" import argparse +import dataclasses import json import time from pathlib import Path @@ -10,43 +11,19 @@ from tqdm import tqdm from vllm import LLM, SamplingParams -from vllm.engine.arg_utils import DEVICE_OPTIONS, EngineArgs +from vllm.engine.arg_utils import EngineArgs from vllm.inputs import PromptType -from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS from vllm.utils import FlexibleArgumentParser def main(args: argparse.Namespace): print(args) + engine_args = EngineArgs.from_cli_args(args) + # NOTE(woosuk): If the request cannot be processed in a single batch, # the engine will automatically process the request in multiple batches. - llm = LLM( - model=args.model, - speculative_model=args.speculative_model, - num_speculative_tokens=args.num_speculative_tokens, - speculative_draft_tensor_parallel_size=\ - args.speculative_draft_tensor_parallel_size, - tokenizer=args.tokenizer, - quantization=args.quantization, - tensor_parallel_size=args.tensor_parallel_size, - trust_remote_code=args.trust_remote_code, - dtype=args.dtype, - max_model_len=args.max_model_len, - enforce_eager=args.enforce_eager, - kv_cache_dtype=args.kv_cache_dtype, - quantization_param_path=args.quantization_param_path, - device=args.device, - ray_workers_use_nsight=args.ray_workers_use_nsight, - enable_chunked_prefill=args.enable_chunked_prefill, - download_dir=args.download_dir, - block_size=args.block_size, - gpu_memory_utilization=args.gpu_memory_utilization, - load_format=args.load_format, - distributed_executor_backend=args.distributed_executor_backend, - otlp_traces_endpoint=args.otlp_traces_endpoint, - enable_prefix_caching=args.enable_prefix_caching, - ) + llm = LLM(**dataclasses.asdict(engine_args)) sampling_params = SamplingParams( n=args.n, @@ -125,19 +102,6 @@ def run_to_completion(profile_dir: Optional[str] = None): parser = FlexibleArgumentParser( description='Benchmark the latency of processing a single batch of ' 'requests till completion.') - parser.add_argument('--model', type=str, default='facebook/opt-125m') - parser.add_argument('--speculative-model', type=str, default=None) - parser.add_argument('--num-speculative-tokens', type=int, default=None) - parser.add_argument('--speculative-draft-tensor-parallel-size', - '-spec-draft-tp', - type=int, - default=None) - parser.add_argument('--tokenizer', type=str, default=None) - parser.add_argument('--quantization', - '-q', - choices=[*QUANTIZATION_METHODS, None], - default=None) - parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1) parser.add_argument('--input-len', type=int, default=32) parser.add_argument('--output-len', type=int, default=128) parser.add_argument('--batch-size', type=int, default=8) @@ -154,45 +118,6 @@ def run_to_completion(profile_dir: Optional[str] = None): type=int, default=30, help='Number of iterations to run.') - parser.add_argument('--trust-remote-code', - action='store_true', - help='trust remote code from huggingface') - parser.add_argument( - '--max-model-len', - type=int, - default=None, - help='Maximum length of a sequence (including prompt and output). ' - 'If None, will be derived from the model.') - parser.add_argument( - '--dtype', - type=str, - default='auto', - choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'], - help='data type for model weights and activations. ' - 'The "auto" option will use FP16 precision ' - 'for FP32 and FP16 models, and BF16 precision ' - 'for BF16 models.') - parser.add_argument('--enforce-eager', - action='store_true', - help='enforce eager mode and disable CUDA graph') - parser.add_argument( - '--kv-cache-dtype', - type=str, - choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'], - default="auto", - help='Data type for kv cache storage. If "auto", will use model ' - 'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ' - 'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)') - parser.add_argument( - '--quantization-param-path', - type=str, - default=None, - help='Path to the JSON file containing the KV cache scaling factors. ' - 'This should generally be supplied, when KV cache dtype is FP8. ' - 'Otherwise, KV cache scaling factors default to 1.0, which may cause ' - 'accuracy issues. FP8_E5M2 (without scaling) is only supported on ' - 'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is ' - 'instead supported for common inference criteria.') parser.add_argument( '--profile', action='store_true', @@ -203,78 +128,12 @@ def run_to_completion(profile_dir: Optional[str] = None): default=None, help=('path to save the pytorch profiler output. Can be visualized ' 'with ui.perfetto.dev or Tensorboard.')) - parser.add_argument("--device", - type=str, - default="auto", - choices=DEVICE_OPTIONS, - help='device type for vLLM execution') - parser.add_argument('--block-size', - type=int, - default=16, - help='block size of key/value cache') - parser.add_argument( - '--enable-chunked-prefill', - action='store_true', - help='If True, the prefill requests can be chunked based on the ' - 'max_num_batched_tokens') - parser.add_argument("--enable-prefix-caching", - action='store_true', - help="Enable automatic prefix caching") - parser.add_argument( - "--ray-workers-use-nsight", - action='store_true', - help="If specified, use nsight to profile ray workers", - ) - parser.add_argument('--download-dir', - type=str, - default=None, - help='directory to download and load the weights, ' - 'default to the default cache dir of huggingface') parser.add_argument( '--output-json', type=str, default=None, help='Path to save the latency results in JSON format.') - parser.add_argument('--gpu-memory-utilization', - type=float, - default=0.9, - help='the fraction of GPU memory to be used for ' - 'the model executor, which can range from 0 to 1.' - 'If unspecified, will use the default value of 0.9.') - parser.add_argument( - '--load-format', - type=str, - default=EngineArgs.load_format, - choices=[ - 'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer', - 'bitsandbytes' - ], - help='The format of the model weights to load.\n\n' - '* "auto" will try to load the weights in the safetensors format ' - 'and fall back to the pytorch bin format if safetensors format ' - 'is not available.\n' - '* "pt" will load the weights in the pytorch bin format.\n' - '* "safetensors" will load the weights in the safetensors format.\n' - '* "npcache" will load the weights in pytorch format and store ' - 'a numpy cache to speed up the loading.\n' - '* "dummy" will initialize the weights with random values, ' - 'which is mainly for profiling.\n' - '* "tensorizer" will load the weights using tensorizer from ' - 'CoreWeave. See the Tensorize vLLM Model script in the Examples' - 'section for more information.\n' - '* "bitsandbytes" will load the weights using bitsandbytes ' - 'quantization.\n') - parser.add_argument( - '--distributed-executor-backend', - choices=['ray', 'mp'], - default=None, - help='Backend to use for distributed serving. When more than 1 GPU ' - 'is used, will be automatically set to "ray" if installed ' - 'or "mp" (multiprocessing) otherwise.') - parser.add_argument( - '--otlp-traces-endpoint', - type=str, - default=None, - help='Target URL to which OpenTelemetry traces will be sent.') + + parser = EngineArgs.add_cli_args(parser) args = parser.parse_args() main(args) diff --git a/benchmarks/benchmark_prefix_caching.py b/benchmarks/benchmark_prefix_caching.py index a354358e43aa3..1aac029992dbf 100644 --- a/benchmarks/benchmark_prefix_caching.py +++ b/benchmarks/benchmark_prefix_caching.py @@ -25,6 +25,7 @@ --input-length-range 128:256 """ +import dataclasses import json import random import time @@ -33,6 +34,7 @@ from transformers import PreTrainedTokenizerBase from vllm import LLM, SamplingParams +from vllm.engine.arg_utils import EngineArgs from vllm.utils import FlexibleArgumentParser try: @@ -129,12 +131,9 @@ def main(args): filtered_datasets = [(PROMPT, prompt_len, args.output_len) ] * args.num_prompts - llm = LLM(model=args.model, - tokenizer_mode='auto', - trust_remote_code=True, - enforce_eager=True, - tensor_parallel_size=args.tensor_parallel_size, - enable_prefix_caching=args.enable_prefix_caching) + engine_args = EngineArgs.from_cli_args(args) + + llm = LLM(**dataclasses.asdict(engine_args)) sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len) @@ -162,18 +161,11 @@ def main(args): parser = FlexibleArgumentParser( description= 'Benchmark the performance with or without automatic prefix caching.') - parser.add_argument('--model', - type=str, - default='baichuan-inc/Baichuan2-13B-Chat') parser.add_argument("--dataset-path", type=str, default=None, help="Path to the dataset.") - parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1) parser.add_argument('--output-len', type=int, default=10) - parser.add_argument('--enable-prefix-caching', - action='store_true', - help='enable prefix caching') parser.add_argument('--num-prompts', type=int, default=1, @@ -190,9 +182,7 @@ def main(args): default='128:256', help='Range of input lengths for sampling prompts,' 'specified as "min:max" (e.g., "128:256").') - parser.add_argument("--seed", - type=int, - default=0, - help='Random seed for reproducibility') + + parser = EngineArgs.add_cli_args(parser) args = parser.parse_args() main(args) diff --git a/benchmarks/benchmark_prioritization.py b/benchmarks/benchmark_prioritization.py index 8843e3a927a01..e0c9e6a6db502 100644 --- a/benchmarks/benchmark_prioritization.py +++ b/benchmarks/benchmark_prioritization.py @@ -1,5 +1,6 @@ """Benchmark offline prioritization.""" import argparse +import dataclasses import json import random import time @@ -7,7 +8,8 @@ from transformers import AutoTokenizer, PreTrainedTokenizerBase -from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS +from vllm.engine.arg_utils import EngineArgs +from vllm.utils import FlexibleArgumentParser def sample_requests( @@ -62,46 +64,11 @@ def sample_requests( def run_vllm( requests: List[Tuple[str, int, int]], - model: str, - tokenizer: str, - quantization: Optional[str], - tensor_parallel_size: int, - seed: int, n: int, - trust_remote_code: bool, - dtype: str, - max_model_len: Optional[int], - enforce_eager: bool, - kv_cache_dtype: str, - quantization_param_path: Optional[str], - device: str, - enable_prefix_caching: bool, - enable_chunked_prefill: bool, - max_num_batched_tokens: int, - gpu_memory_utilization: float = 0.9, - download_dir: Optional[str] = None, + engine_args: EngineArgs, ) -> float: from vllm import LLM, SamplingParams - llm = LLM( - model=model, - tokenizer=tokenizer, - quantization=quantization, - tensor_parallel_size=tensor_parallel_size, - seed=seed, - trust_remote_code=trust_remote_code, - dtype=dtype, - max_model_len=max_model_len, - gpu_memory_utilization=gpu_memory_utilization, - enforce_eager=enforce_eager, - kv_cache_dtype=kv_cache_dtype, - quantization_param_path=quantization_param_path, - device=device, - enable_prefix_caching=enable_prefix_caching, - download_dir=download_dir, - enable_chunked_prefill=enable_chunked_prefill, - max_num_batched_tokens=max_num_batched_tokens, - disable_log_stats=False, - ) + llm = LLM(**dataclasses.asdict(engine_args)) # Add the requests to the engine. prompts = [] @@ -142,16 +109,8 @@ def main(args: argparse.Namespace): args.output_len) if args.backend == "vllm": - elapsed_time = run_vllm(requests, args.model, args.tokenizer, - args.quantization, args.tensor_parallel_size, - args.seed, args.n, args.trust_remote_code, - args.dtype, args.max_model_len, - args.enforce_eager, args.kv_cache_dtype, - args.quantization_param_path, args.device, - args.enable_prefix_caching, - args.enable_chunked_prefill, - args.max_num_batched_tokens, - args.gpu_memory_utilization, args.download_dir) + elapsed_time = run_vllm(requests, args.n, + EngineArgs.from_cli_args(args)) else: raise ValueError(f"Unknown backend: {args.backend}") total_num_tokens = sum(prompt_len + output_len @@ -173,7 +132,7 @@ def main(args: argparse.Namespace): if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Benchmark the throughput.") + parser = FlexibleArgumentParser(description="Benchmark the throughput.") parser.add_argument("--backend", type=str, choices=["vllm", "hf", "mii"], @@ -191,13 +150,6 @@ def main(args: argparse.Namespace): default=None, help="Output length for each request. Overrides the " "output length from the dataset.") - parser.add_argument("--model", type=str, default="facebook/opt-125m") - parser.add_argument("--tokenizer", type=str, default=None) - parser.add_argument('--quantization', - '-q', - choices=[*QUANTIZATION_METHODS, None], - default=None) - parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1) parser.add_argument("--n", type=int, default=1, @@ -206,81 +158,13 @@ def main(args: argparse.Namespace): type=int, default=200, help="Number of prompts to process.") - parser.add_argument("--seed", type=int, default=0) - parser.add_argument('--trust-remote-code', - action='store_true', - help='trust remote code from huggingface') - parser.add_argument( - '--max-model-len', - type=int, - default=None, - help='Maximum length of a sequence (including prompt and output). ' - 'If None, will be derived from the model.') - parser.add_argument( - '--dtype', - type=str, - default='auto', - choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'], - help='data type for model weights and activations. ' - 'The "auto" option will use FP16 precision ' - 'for FP32 and FP16 models, and BF16 precision ' - 'for BF16 models.') - parser.add_argument('--gpu-memory-utilization', - type=float, - default=0.9, - help='the fraction of GPU memory to be used for ' - 'the model executor, which can range from 0 to 1.' - 'If unspecified, will use the default value of 0.9.') - parser.add_argument("--enforce-eager", - action="store_true", - help="enforce eager execution") - parser.add_argument( - '--kv-cache-dtype', - type=str, - choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'], - default="auto", - help='Data type for kv cache storage. If "auto", will use model ' - 'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ' - 'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)') - parser.add_argument( - '--quantization-param-path', - type=str, - default=None, - help='Path to the JSON file containing the KV cache scaling factors. ' - 'This should generally be supplied, when KV cache dtype is FP8. ' - 'Otherwise, KV cache scaling factors default to 1.0, which may cause ' - 'accuracy issues. FP8_E5M2 (without scaling) is only supported on ' - 'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is ' - 'instead supported for common inference criteria.') - parser.add_argument( - "--device", - type=str, - default="cuda", - choices=["cuda", "cpu"], - help='device type for vLLM execution, supporting CUDA and CPU.') - parser.add_argument( - "--enable-prefix-caching", - action='store_true', - help="enable automatic prefix caching for vLLM backend.") - parser.add_argument("--enable-chunked-prefill", - action='store_true', - help="enable chunked prefill for vLLM backend.") - parser.add_argument('--max-num-batched-tokens', - type=int, - default=None, - help='maximum number of batched tokens per ' - 'iteration') - parser.add_argument('--download-dir', - type=str, - default=None, - help='directory to download and load the weights, ' - 'default to the default cache dir of huggingface') parser.add_argument( '--output-json', type=str, default=None, help='Path to save the throughput results in JSON format.') + parser = EngineArgs.add_cli_args(parser) args = parser.parse_args() if args.tokenizer is None: args.tokenizer = args.model diff --git a/benchmarks/benchmark_throughput.py b/benchmarks/benchmark_throughput.py index e26706af606b0..5cca92edb251b 100644 --- a/benchmarks/benchmark_throughput.py +++ b/benchmarks/benchmark_throughput.py @@ -1,5 +1,6 @@ """Benchmark offline inference throughput.""" import argparse +import dataclasses import json import random import time @@ -11,10 +12,9 @@ from transformers import (AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase) -from vllm.engine.arg_utils import DEVICE_OPTIONS, AsyncEngineArgs, EngineArgs +from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs from vllm.entrypoints.openai.api_server import ( build_async_engine_client_from_engine_args) -from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS from vllm.sampling_params import BeamSearchParams from vllm.utils import FlexibleArgumentParser, merge_async_iterators @@ -67,53 +67,11 @@ def sample_requests( def run_vllm( requests: List[Tuple[str, int, int]], - model: str, - tokenizer: str, - quantization: Optional[str], - tensor_parallel_size: int, - seed: int, n: int, - trust_remote_code: bool, - dtype: str, - max_model_len: Optional[int], - enforce_eager: bool, - kv_cache_dtype: str, - quantization_param_path: Optional[str], - device: str, - enable_prefix_caching: bool, - enable_chunked_prefill: bool, - max_num_batched_tokens: int, - distributed_executor_backend: Optional[str], - gpu_memory_utilization: float = 0.9, - num_scheduler_steps: int = 1, - download_dir: Optional[str] = None, - load_format: str = EngineArgs.load_format, - disable_async_output_proc: bool = False, + engine_args: EngineArgs, ) -> float: from vllm import LLM, SamplingParams - llm = LLM( - model=model, - tokenizer=tokenizer, - quantization=quantization, - tensor_parallel_size=tensor_parallel_size, - seed=seed, - trust_remote_code=trust_remote_code, - dtype=dtype, - max_model_len=max_model_len, - gpu_memory_utilization=gpu_memory_utilization, - enforce_eager=enforce_eager, - kv_cache_dtype=kv_cache_dtype, - quantization_param_path=quantization_param_path, - device=device, - enable_prefix_caching=enable_prefix_caching, - download_dir=download_dir, - enable_chunked_prefill=enable_chunked_prefill, - max_num_batched_tokens=max_num_batched_tokens, - distributed_executor_backend=distributed_executor_backend, - load_format=load_format, - num_scheduler_steps=num_scheduler_steps, - disable_async_output_proc=disable_async_output_proc, - ) + llm = LLM(**dataclasses.asdict(engine_args)) # Add the requests to the engine. prompts: List[str] = [] @@ -155,56 +113,11 @@ def run_vllm( async def run_vllm_async( requests: List[Tuple[str, int, int]], - model: str, - tokenizer: str, - quantization: Optional[str], - tensor_parallel_size: int, - seed: int, n: int, - trust_remote_code: bool, - dtype: str, - max_model_len: Optional[int], - enforce_eager: bool, - kv_cache_dtype: str, - quantization_param_path: Optional[str], - device: str, - enable_prefix_caching: bool, - enable_chunked_prefill: bool, - max_num_batched_tokens: int, - distributed_executor_backend: Optional[str], - gpu_memory_utilization: float = 0.9, - num_scheduler_steps: int = 1, - download_dir: Optional[str] = None, - load_format: str = EngineArgs.load_format, - disable_async_output_proc: bool = False, + engine_args: AsyncEngineArgs, disable_frontend_multiprocessing: bool = False, ) -> float: from vllm import SamplingParams - engine_args = AsyncEngineArgs( - model=model, - tokenizer=tokenizer, - quantization=quantization, - tensor_parallel_size=tensor_parallel_size, - seed=seed, - trust_remote_code=trust_remote_code, - dtype=dtype, - max_model_len=max_model_len, - gpu_memory_utilization=gpu_memory_utilization, - enforce_eager=enforce_eager, - kv_cache_dtype=kv_cache_dtype, - quantization_param_path=quantization_param_path, - device=device, - enable_prefix_caching=enable_prefix_caching, - download_dir=download_dir, - enable_chunked_prefill=enable_chunked_prefill, - max_num_batched_tokens=max_num_batched_tokens, - distributed_executor_backend=distributed_executor_backend, - load_format=load_format, - num_scheduler_steps=num_scheduler_steps, - disable_async_output_proc=disable_async_output_proc, - worker_use_ray=False, - disable_log_requests=True, - ) async with build_async_engine_client_from_engine_args( engine_args, disable_frontend_multiprocessing) as llm: @@ -328,23 +241,17 @@ def main(args: argparse.Namespace): args.output_len) if args.backend == "vllm": - run_args = [ - requests, args.model, args.tokenizer, args.quantization, - args.tensor_parallel_size, args.seed, args.n, - args.trust_remote_code, args.dtype, args.max_model_len, - args.enforce_eager, args.kv_cache_dtype, - args.quantization_param_path, args.device, - args.enable_prefix_caching, args.enable_chunked_prefill, - args.max_num_batched_tokens, args.distributed_executor_backend, - args.gpu_memory_utilization, args.num_scheduler_steps, - args.download_dir, args.load_format, args.disable_async_output_proc - ] - if args.async_engine: - run_args.append(args.disable_frontend_multiprocessing) - elapsed_time = uvloop.run(run_vllm_async(*run_args)) + elapsed_time = uvloop.run( + run_vllm_async( + requests, + args.n, + AsyncEngineArgs.from_cli_args(args), + args.disable_frontend_multiprocessing, + )) else: - elapsed_time = run_vllm(*run_args) + elapsed_time = run_vllm(requests, args.n, + EngineArgs.from_cli_args(args)) elif args.backend == "hf": assert args.tensor_parallel_size == 1 elapsed_time = run_hf(requests, args.model, tokenizer, args.n, @@ -391,13 +298,6 @@ def main(args: argparse.Namespace): default=None, help="Output length for each request. Overrides the " "output length from the dataset.") - parser.add_argument("--model", type=str, default="facebook/opt-125m") - parser.add_argument("--tokenizer", type=str, default=None) - parser.add_argument('--quantization', - '-q', - choices=[*QUANTIZATION_METHODS, None], - default=None) - parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1) parser.add_argument("--n", type=int, default=1, @@ -406,123 +306,15 @@ def main(args: argparse.Namespace): type=int, default=1000, help="Number of prompts to process.") - parser.add_argument("--seed", type=int, default=0) parser.add_argument("--hf-max-batch-size", type=int, default=None, help="Maximum batch size for HF backend.") - parser.add_argument('--trust-remote-code', - action='store_true', - help='trust remote code from huggingface') - parser.add_argument( - '--max-model-len', - type=int, - default=None, - help='Maximum length of a sequence (including prompt and output). ' - 'If None, will be derived from the model.') - parser.add_argument( - '--dtype', - type=str, - default='auto', - choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'], - help='data type for model weights and activations. ' - 'The "auto" option will use FP16 precision ' - 'for FP32 and FP16 models, and BF16 precision ' - 'for BF16 models.') - parser.add_argument('--gpu-memory-utilization', - type=float, - default=0.9, - help='the fraction of GPU memory to be used for ' - 'the model executor, which can range from 0 to 1.' - 'If unspecified, will use the default value of 0.9.') - parser.add_argument("--enforce-eager", - action="store_true", - help="enforce eager execution") - parser.add_argument( - '--kv-cache-dtype', - type=str, - choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'], - default="auto", - help='Data type for kv cache storage. If "auto", will use model ' - 'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ' - 'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)') - parser.add_argument( - '--quantization-param-path', - type=str, - default=None, - help='Path to the JSON file containing the KV cache scaling factors. ' - 'This should generally be supplied, when KV cache dtype is FP8. ' - 'Otherwise, KV cache scaling factors default to 1.0, which may cause ' - 'accuracy issues. FP8_E5M2 (without scaling) is only supported on ' - 'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is ' - 'instead supported for common inference criteria.') - parser.add_argument("--device", - type=str, - default="auto", - choices=DEVICE_OPTIONS, - help='device type for vLLM execution') - parser.add_argument( - "--num-scheduler-steps", - type=int, - default=1, - help="Maximum number of forward steps per scheduler call.") - parser.add_argument( - "--enable-prefix-caching", - action='store_true', - help="Enable automatic prefix caching for vLLM backend.") - parser.add_argument("--enable-chunked-prefill", - action='store_true', - help="enable chunked prefill for vLLM backend.") - parser.add_argument('--max-num-batched-tokens', - type=int, - default=None, - help='maximum number of batched tokens per ' - 'iteration') - parser.add_argument('--download-dir', - type=str, - default=None, - help='directory to download and load the weights, ' - 'default to the default cache dir of huggingface') parser.add_argument( '--output-json', type=str, default=None, help='Path to save the throughput results in JSON format.') - parser.add_argument( - '--distributed-executor-backend', - choices=['ray', 'mp'], - default=None, - help='Backend to use for distributed serving. When more than 1 GPU ' - 'is used, will be automatically set to "ray" if installed ' - 'or "mp" (multiprocessing) otherwise.') - parser.add_argument( - '--load-format', - type=str, - default=EngineArgs.load_format, - choices=[ - 'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer', - 'bitsandbytes' - ], - help='The format of the model weights to load.\n\n' - '* "auto" will try to load the weights in the safetensors format ' - 'and fall back to the pytorch bin format if safetensors format ' - 'is not available.\n' - '* "pt" will load the weights in the pytorch bin format.\n' - '* "safetensors" will load the weights in the safetensors format.\n' - '* "npcache" will load the weights in pytorch format and store ' - 'a numpy cache to speed up the loading.\n' - '* "dummy" will initialize the weights with random values, ' - 'which is mainly for profiling.\n' - '* "tensorizer" will load the weights using tensorizer from ' - 'CoreWeave. See the Tensorize vLLM Model script in the Examples' - 'section for more information.\n' - '* "bitsandbytes" will load the weights using bitsandbytes ' - 'quantization.\n') - parser.add_argument( - "--disable-async-output-proc", - action='store_true', - default=False, - help="Disable async output processor for vLLM backend.") parser.add_argument("--async-engine", action='store_true', default=False, @@ -531,6 +323,7 @@ def main(args: argparse.Namespace): action='store_true', default=False, help="Disable decoupled async engine frontend.") + parser = AsyncEngineArgs.add_cli_args(parser) args = parser.parse_args() if args.tokenizer is None: args.tokenizer = args.model From d1e82408759067eca0ae55e548f6243a9e0aa12d Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Tue, 22 Oct 2024 18:41:13 -0400 Subject: [PATCH 055/222] [Bugfix] Fix spurious "No compiled cutlass_scaled_mm ..." for W8A8 on Turing (#9487) --- CMakeLists.txt | 4 ++-- csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu | 8 +++++--- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7f6d1c66b2cf7..a53a8575d01ca 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -252,7 +252,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") message(STATUS "Building Marlin kernels for archs: ${MARLIN_ARCHS}") else() message(STATUS "Not building Marlin kernels as no compatible archs found" - "in CUDA target architectures") + " in CUDA target architectures") endif() # @@ -432,7 +432,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") message(STATUS "Building Marlin MOE kernels for archs: ${MARLIN_MOE_ARCHS}") else() message(STATUS "Not building Marlin MOE kernels as no compatible archs found" - "in CUDA target architectures") + " in CUDA target architectures") endif() endif() diff --git a/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu b/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu index 1657f7d0b16e8..97a969cf5e3e0 100644 --- a/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu +++ b/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu @@ -137,9 +137,11 @@ void cutlass_scaled_mm(torch::Tensor& c, torch::Tensor const& a, return; } - // Turing - TORCH_CHECK(version_num >= 75); - cutlass_scaled_mm_sm75(c, a, b, a_scales, b_scales, bias); + if (version_num >= 75) { + // Turing + cutlass_scaled_mm_sm75(c, a, b, a_scales, b_scales, bias); + return; + } #endif TORCH_CHECK_NOT_IMPLEMENTED( From b17046e2982cad4cc205851c5af98375e0d1c3f3 Mon Sep 17 00:00:00 2001 From: yulei Date: Wed, 23 Oct 2024 06:43:03 +0800 Subject: [PATCH 056/222] [BugFix] Fix metrics error for --num-scheduler-steps > 1 (#8234) --- tests/metrics/test_metrics.py | 39 +++++++++++++++++++++++++++++++++++ vllm/engine/llm_engine.py | 9 ++++++++ 2 files changed, 48 insertions(+) diff --git a/tests/metrics/test_metrics.py b/tests/metrics/test_metrics.py index 92e6086e312f7..7a361ef320810 100644 --- a/tests/metrics/test_metrics.py +++ b/tests/metrics/test_metrics.py @@ -84,6 +84,45 @@ def test_metric_counter_generation_tokens( f"metric: {metric_count!r}") +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("max_tokens", [128, 129]) +@pytest.mark.parametrize("disable_async_output_proc", [True, False]) +def test_metric_counter_generation_tokens_multi_step( + vllm_runner, + example_prompts, + model: str, + max_tokens: int, + disable_async_output_proc: bool, +) -> None: + num_scheduler_steps = 8 + with vllm_runner( + model, + disable_log_stats=False, + gpu_memory_utilization=0.4, + num_scheduler_steps=num_scheduler_steps, + disable_async_output_proc=disable_async_output_proc, + ) as vllm_model: + vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) + tokenizer = vllm_model.model.get_tokenizer() + stat_logger = vllm_model.model.llm_engine.stat_loggers['prometheus'] + metric_count = stat_logger.metrics.counter_generation_tokens.labels( + **stat_logger.labels)._value.get() + vllm_generation_count = 0 + for i in range(len(example_prompts)): + vllm_output_ids, vllm_output_str = vllm_outputs[i] + prompt_ids = tokenizer.encode(example_prompts[i]) + # vllm_output_ids contains both prompt tokens and generation tokens. + # We're interested only in the count of the generation tokens. + vllm_generation_count += len(vllm_output_ids) - len(prompt_ids) + + # The multi-step scheduling will continue to execute forward even when + # encountering EOS, leading to slightly imprecise metrics. + assert abs(vllm_generation_count - metric_count) <\ + len(example_prompts) * num_scheduler_steps, \ + (f"generation token count: {vllm_generation_count!r}\n" + f"metric: {metric_count!r}") + + @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("dtype", ["float"]) @pytest.mark.parametrize( diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 3a29e6a9ae094..99beea932882d 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -1718,6 +1718,15 @@ def _get_stats(self, # TPOTs. latency = seq_group.get_last_latency(now) time_per_output_tokens_iter.append(latency) + if seq_group.state.current_step == 0: + # For async_output_proc, the do_log_stats() + # is called following init_multi_step(), which + # sets the current_step to zero. + actual_num_batched_tokens +=\ + seq_group.state.num_steps - 1 + else: + actual_num_batched_tokens +=\ + seq_group.state.current_step - 1 # Because of chunked prefill, we can have a single sequence # group that does multiple prompt_runs. To prevent logging From 208cb34c812585ce387d7aff82678a3776a66756 Mon Sep 17 00:00:00 2001 From: Seth Kimmel Date: Tue, 22 Oct 2024 15:43:25 -0700 Subject: [PATCH 057/222] [Doc]: Update tensorizer docs to include vllm[tensorizer] (#7889) Co-authored-by: Kaunil Dhruv --- docs/source/serving/tensorizer.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/source/serving/tensorizer.rst b/docs/source/serving/tensorizer.rst index a44696507fb9a..96a93db94871b 100644 --- a/docs/source/serving/tensorizer.rst +++ b/docs/source/serving/tensorizer.rst @@ -9,4 +9,7 @@ shorter Pod startup times and CPU memory usage. Tensor encryption is also suppor For more information on CoreWeave's Tensorizer, please refer to `CoreWeave's Tensorizer documentation `_. For more information on serializing a vLLM model, as well a general usage guide to using Tensorizer with vLLM, see -the `vLLM example script `_. \ No newline at end of file +the `vLLM example script `_. + +.. note:: + Note that to use this feature you will need to install `tensorizer` by running `pip install vllm[tensorizer]`. From 65050a40e63fb8d57f383ea833d8869f77e85c89 Mon Sep 17 00:00:00 2001 From: Chen Zhang Date: Tue, 22 Oct 2024 17:45:35 -0700 Subject: [PATCH 058/222] [Bugfix] Generate exactly input_len tokens in benchmark_throughput (#9592) --- benchmarks/benchmark_throughput.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/benchmarks/benchmark_throughput.py b/benchmarks/benchmark_throughput.py index 5cca92edb251b..24eb54e7b73bc 100644 --- a/benchmarks/benchmark_throughput.py +++ b/benchmarks/benchmark_throughput.py @@ -233,7 +233,16 @@ def main(args: argparse.Namespace): args.tokenizer, trust_remote_code=args.trust_remote_code) if args.dataset is None: # Synthesize a prompt with the given input length. - prompt = "hi" * (args.input_len - 1) + # As tokenizer may add additional tokens like BOS, we need to try + # different lengths to get the desired input length. + for i in range(-10, 10): + prompt = "hi " * (args.input_len + i) + tokenized_prompt = tokenizer(prompt).input_ids + if len(tokenized_prompt) == args.input_len: + break + else: + raise ValueError( + f"Failed to synthesize a prompt with {args.input_len} tokens.") requests = [(prompt, args.input_len, args.output_len) for _ in range(args.num_prompts)] else: From 29061ed9df84f1298806b2fc525ce4bc7eba1d29 Mon Sep 17 00:00:00 2001 From: Flex Wang Date: Tue, 22 Oct 2024 20:17:28 -0700 Subject: [PATCH 059/222] [Misc] Add an env var VLLM_LOGGING_PREFIX, if set, it will be prepend to all logging messages (#9590) --- vllm/envs.py | 5 +++++ vllm/logger.py | 4 +++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/vllm/envs.py b/vllm/envs.py index a20271229c567..ae6825f280073 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -27,6 +27,7 @@ VLLM_USAGE_SOURCE: str = "" VLLM_CONFIGURE_LOGGING: int = 1 VLLM_LOGGING_LEVEL: str = "INFO" + VLLM_LOGGING_PREFIX: str = "" VLLM_LOGGING_CONFIG_PATH: Optional[str] = None VLLM_TRACE_FUNCTION: int = 0 VLLM_ATTENTION_BACKEND: Optional[str] = None @@ -268,6 +269,10 @@ def get_default_config_root(): "VLLM_LOGGING_LEVEL": lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO"), + # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages + "VLLM_LOGGING_PREFIX": + lambda: os.getenv("VLLM_LOGGING_PREFIX", ""), + # Trace function calls # If set to 1, vllm will trace function calls # Useful for debugging diff --git a/vllm/logger.py b/vllm/logger.py index 77dddbfb60965..ccf09691a052a 100644 --- a/vllm/logger.py +++ b/vllm/logger.py @@ -15,8 +15,10 @@ VLLM_CONFIGURE_LOGGING = envs.VLLM_CONFIGURE_LOGGING VLLM_LOGGING_CONFIG_PATH = envs.VLLM_LOGGING_CONFIG_PATH VLLM_LOGGING_LEVEL = envs.VLLM_LOGGING_LEVEL +VLLM_LOGGING_PREFIX = envs.VLLM_LOGGING_PREFIX -_FORMAT = "%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s" +_FORMAT = (f"{VLLM_LOGGING_PREFIX}%(levelname)s %(asctime)s " + "%(filename)s:%(lineno)d] %(message)s") _DATE_FORMAT = "%m-%d %H:%M:%S" DEFAULT_LOGGING_CONFIG = { From 831540cf04b0b40cd1fe462356de4a30b831e4ea Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 23 Oct 2024 11:35:29 +0800 Subject: [PATCH 060/222] [Model] Support E5-V (#9576) --- docs/source/models/supported_models.rst | 14 ++ examples/offline_inference_vision_language.py | 6 +- ...ine_inference_vision_language_embedding.py | 190 ++++++++++++++++-- ...e_inference_vision_language_multi_image.py | 7 +- tests/conftest.py | 60 +++--- tests/models/embedding/utils.py | 3 +- .../vision_language/test_llava_next.py | 135 +++++++++++++ .../embedding/vision_language/test_phi3v.py | 93 +++++++-- vllm/model_executor/models/llava_next.py | 33 ++- vllm/model_executor/models/phi3v.py | 2 - vllm/model_executor/models/registry.py | 1 + vllm/model_executor/models/utils.py | 78 ++++++- 12 files changed, 532 insertions(+), 90 deletions(-) create mode 100644 tests/models/embedding/vision_language/test_llava_next.py diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 3d8df3c9f8c9f..ad153d2927d6c 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -334,6 +334,14 @@ The following modalities are supported depending on the model: - **V**\ ideo - **A**\ udio +Any combination of modalities joined by :code:`+` are supported. + +- e.g.: :code:`T + I` means that the model supports text-only, image-only, and text-with-image inputs. + +On the other hand, modalities separated by :code:`/` are mutually exclusive. + +- e.g.: :code:`T / I` means that the model supports text-only and image-only inputs, but not text-with-image inputs. + .. _supported_vlms: Text Generation @@ -484,6 +492,12 @@ Multimodal Embedding - Example HF Models - :ref:`LoRA ` - :ref:`PP ` + * - :code:`LlavaNextForConditionalGeneration` + - LLaVA-NeXT-based + - T / I + - :code:`royokong/e5-v` + - + - ✅︎ * - :code:`Phi3VForCausalLM` - Phi-3-Vision-based - T + I diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index 06b424abd50b5..610cc31db9c4e 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -1,6 +1,6 @@ """ -This example shows how to use vLLM for running offline inference -with the correct prompt format on vision language models. +This example shows how to use vLLM for running offline inference with +the correct prompt format on vision language models for text generation. For most models, the prompt format should follow corresponding examples on HuggingFace model repository. @@ -450,7 +450,7 @@ def main(args): if __name__ == "__main__": parser = FlexibleArgumentParser( description='Demo on using vLLM for offline inference with ' - 'vision language models') + 'vision language models for text generation') parser.add_argument('--model-type', '-m', type=str, diff --git a/examples/offline_inference_vision_language_embedding.py b/examples/offline_inference_vision_language_embedding.py index cfedd145a015d..e1732d045f949 100644 --- a/examples/offline_inference_vision_language_embedding.py +++ b/examples/offline_inference_vision_language_embedding.py @@ -1,22 +1,170 @@ +""" +This example shows how to use vLLM for running offline inference with +the correct prompt format on vision language models for multimodal embedding. + +For most models, the prompt format should follow corresponding examples +on HuggingFace model repository. +""" +from argparse import Namespace +from typing import Literal, NamedTuple, Optional, TypedDict, Union, get_args + +from PIL.Image import Image + from vllm import LLM -from vllm.assets.image import ImageAsset - -image = ImageAsset("cherry_blossom").pil_image.convert("RGB") -prompt = "<|image_1|> Represent the given image with the following question: What is in the image" # noqa: E501 - -# Create an LLM. -llm = LLM( - model="TIGER-Lab/VLM2Vec-Full", - task="embedding", - trust_remote_code=True, - max_model_len=4096, - max_num_seqs=2, - mm_processor_kwargs={"num_crops": 16}, -) - -# Generate embedding. The output is a list of EmbeddingRequestOutputs. -outputs = llm.encode({"prompt": prompt, "multi_modal_data": {"image": image}}) - -# Print the outputs. -for output in outputs: - print(output.outputs.embedding) # list of 3072 floats +from vllm.multimodal.utils import fetch_image +from vllm.utils import FlexibleArgumentParser + + +class TextQuery(TypedDict): + modality: Literal["text"] + text: str + + +class ImageQuery(TypedDict): + modality: Literal["image"] + image: Image + + +class TextImageQuery(TypedDict): + modality: Literal["text+image"] + text: str + image: Image + + +QueryModality = Literal["text", "image", "text+image"] +Query = Union[TextQuery, ImageQuery, TextImageQuery] + + +class ModelRequestData(NamedTuple): + llm: LLM + prompt: str + image: Optional[Image] + + +def run_e5_v(query: Query): + llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n' # noqa: E501 + + if query["modality"] == "text": + text = query["text"] + prompt = llama3_template.format( + f"{text}\nSummary above sentence in one word: ") + image = None + elif query["modality"] == "image": + prompt = llama3_template.format( + "\nSummary above image in one word: ") + image = query["image"] + else: + modality = query['modality'] + raise ValueError(f"Unsupported query modality: '{modality}'") + + llm = LLM( + model="royokong/e5-v", + task="embedding", + max_model_len=4096, + ) + + return ModelRequestData( + llm=llm, + prompt=prompt, + image=image, + ) + + +def run_vlm2vec(query: Query): + if query["modality"] == "text": + text = query["text"] + prompt = f"Find me an everyday image that matches the given caption: {text}" # noqa: E501 + image = None + elif query["modality"] == "image": + prompt = "<|image_1|> Find a day-to-day image that looks similar to the provided image." # noqa: E501 + image = query["image"] + elif query["modality"] == "text+image": + text = query["text"] + prompt = f"<|image_1|> Represent the given image with the following question: {text}" # noqa: E501 + image = query["image"] + else: + modality = query['modality'] + raise ValueError(f"Unsupported query modality: '{modality}'") + + llm = LLM( + model="TIGER-Lab/VLM2Vec-Full", + task="embedding", + trust_remote_code=True, + mm_processor_kwargs={"num_crops": 4}, + ) + + return ModelRequestData( + llm=llm, + prompt=prompt, + image=image, + ) + + +def get_query(modality: QueryModality): + if modality == "text": + return TextQuery(modality="text", text="A dog sitting in the grass") + + if modality == "image": + return ImageQuery( + modality="image", + image=fetch_image( + "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/American_Eskimo_Dog.jpg/360px-American_Eskimo_Dog.jpg" # noqa: E501 + ), + ) + + if modality == "text+image": + return TextImageQuery( + modality="text+image", + text="A cat standing in the snow.", + image=fetch_image( + "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/179px-Felis_catus-cat_on_snow.jpg" # noqa: E501 + ), + ) + + msg = f"Modality {modality} is not supported." + raise ValueError(msg) + + +def run_encode(model: str, modality: QueryModality): + query = get_query(modality) + req_data = model_example_map[model](query) + + mm_data = {} + if req_data.image is not None: + mm_data["image"] = req_data.image + + outputs = req_data.llm.encode({ + "prompt": req_data.prompt, + "multi_modal_data": mm_data, + }) + + for output in outputs: + print(output.outputs.embedding) + + +def main(args: Namespace): + run_encode(args.model_name, args.modality) + + +model_example_map = { + "e5_v": run_e5_v, + "vlm2vec": run_vlm2vec, +} + +if __name__ == "__main__": + parser = FlexibleArgumentParser( + description='Demo on using vLLM for offline inference with ' + 'vision language models for multimodal embedding') + parser.add_argument('--model-name', + '-m', + type=str, + default="vlm2vec", + choices=model_example_map.keys(), + help='The name of the embedding model.') + parser.add_argument('--modality', + type=str, + default="image", + choices=get_args(QueryModality), + help='Modality of the input.') + args = parser.parse_args() + main(args) diff --git a/examples/offline_inference_vision_language_multi_image.py b/examples/offline_inference_vision_language_multi_image.py index 69f590fb7950d..e28514bf403f7 100644 --- a/examples/offline_inference_vision_language_multi_image.py +++ b/examples/offline_inference_vision_language_multi_image.py @@ -1,7 +1,7 @@ """ This example shows how to use vLLM for running offline inference with -multi-image input on vision language models, using the chat template defined -by the model. +multi-image input on vision language models for text generation, +using the chat template defined by the model. """ from argparse import Namespace from typing import List, NamedTuple, Optional @@ -334,7 +334,8 @@ def main(args: Namespace): if __name__ == "__main__": parser = FlexibleArgumentParser( description='Demo on using vLLM for offline inference with ' - 'vision language models that support multi-image input') + 'vision language models that support multi-image input for text ' + 'generation') parser.add_argument('--model-type', '-m', type=str, diff --git a/tests/conftest.py b/tests/conftest.py index fc8bd1a473476..76f581e0363f7 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -43,10 +43,12 @@ _TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")] _LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")] -PromptImageInput = Union[List[Image.Image], List[List[Image.Image]]] -PromptAudioInput = Union[List[Tuple[np.ndarray, int]], - List[List[Tuple[np.ndarray, int]]]] -PromptVideoInput = Union[List[np.ndarray], List[List[np.ndarray]]] +_M = TypeVar("_M") +_PromptMultiModalInput = Union[List[_M], List[List[_M]]] + +PromptImageInput = _PromptMultiModalInput[Image.Image] +PromptAudioInput = _PromptMultiModalInput[Tuple[np.ndarray, int]] +PromptVideoInput = _PromptMultiModalInput[np.ndarray] def _read_prompts(filename: str) -> List[str]: @@ -318,12 +320,12 @@ def get_inputs( "text": prompt, "return_tensors": "pt", } - if images is not None and images[i] is not None: - processor_kwargs["images"] = images[i] - if videos is not None and videos[i] is not None: - processor_kwargs["videos"] = videos[i] - if audios is not None and audios[i] is not None: - audio, sr = audios[i] + if images is not None and (image := images[i]) is not None: + processor_kwargs["images"] = image + if videos is not None and (video := videos[i]) is not None: + processor_kwargs["videos"] = video + if audios is not None and (audio_tuple := audios[i]) is not None: + audio, sr = audio_tuple processor_kwargs["audio"] = audio processor_kwargs["sampling_rate"] = sr @@ -338,7 +340,7 @@ def generate( self, prompts: List[str], images: Optional[PromptImageInput] = None, - videos: Optional[List[np.ndarray]] = None, + videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, **kwargs: Any, ) -> List[Tuple[List[List[int]], List[str]]]: @@ -368,7 +370,7 @@ def generate_greedy( prompts: List[str], max_tokens: int, images: Optional[PromptImageInput] = None, - videos: Optional[List[np.ndarray]] = None, + videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, **kwargs: Any, ) -> List[Tuple[List[int], str]]: @@ -409,7 +411,7 @@ def generate_greedy_logprobs( prompts: List[str], max_tokens: int, images: Optional[PromptImageInput] = None, - videos: Optional[List[np.ndarray]] = None, + videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, **kwargs: Any, ) -> List[List[torch.Tensor]]: @@ -488,7 +490,7 @@ def generate_greedy_logprobs_limit( num_logprobs: int, images: Optional[PromptImageInput] = None, audios: Optional[PromptAudioInput] = None, - videos: Optional[List[np.ndarray]] = None, + videos: Optional[PromptVideoInput] = None, **kwargs: Any, ) -> List[TokensTextLogprobs]: all_inputs = self.get_inputs(prompts, @@ -657,15 +659,18 @@ def get_inputs( inputs = [TextPrompt(prompt=prompt) for prompt in prompts] if images is not None: for i, image in enumerate(images): - inputs[i]["multi_modal_data"] = {"image": image} + if image is not None: + inputs[i]["multi_modal_data"] = {"image": image} if videos is not None: for i, video in enumerate(videos): - inputs[i]["multi_modal_data"] = {"video": video} + if video is not None: + inputs[i]["multi_modal_data"] = {"video": video} if audios is not None: for i, audio in enumerate(audios): - inputs[i]["multi_modal_data"] = {"audio": audio} + if audio is not None: + inputs[i]["multi_modal_data"] = {"audio": audio} return inputs @@ -837,13 +842,20 @@ def generate_beam_search( returned_outputs.append((token_ids, texts)) return returned_outputs - def encode(self, prompts: List[str]) -> List[List[float]]: - req_outputs = self.model.encode(prompts) - outputs = [] - for req_output in req_outputs: - embedding = req_output.outputs.embedding - outputs.append(embedding) - return outputs + def encode( + self, + prompts: List[str], + images: Optional[PromptImageInput] = None, + videos: Optional[PromptVideoInput] = None, + audios: Optional[PromptAudioInput] = None, + ) -> List[List[float]]: + inputs = self.get_inputs(prompts, + images=images, + videos=videos, + audios=audios) + + req_outputs = self.model.encode(inputs) + return [req_output.outputs.embedding for req_output in req_outputs] def __enter__(self): return self diff --git a/tests/models/embedding/utils.py b/tests/models/embedding/utils.py index 2fcc2013d91ef..fd1c44d9c117e 100644 --- a/tests/models/embedding/utils.py +++ b/tests/models/embedding/utils.py @@ -16,7 +16,8 @@ def check_embeddings_close( for prompt_idx, (embeddings_0, embeddings_1) in enumerate( zip(embeddings_0_lst, embeddings_1_lst)): - assert len(embeddings_0) == len(embeddings_1) + assert len(embeddings_0) == len(embeddings_1), ( + f"Length mismatch: {len(embeddings_0)} vs. {len(embeddings_1)}") sim = F.cosine_similarity(torch.tensor(embeddings_0), torch.tensor(embeddings_1), diff --git a/tests/models/embedding/vision_language/test_llava_next.py b/tests/models/embedding/vision_language/test_llava_next.py new file mode 100644 index 0000000000000..52aef8c34d6f3 --- /dev/null +++ b/tests/models/embedding/vision_language/test_llava_next.py @@ -0,0 +1,135 @@ +from typing import List, Type + +import pytest +import torch.nn.functional as F +from transformers import AutoModelForVision2Seq + +from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner +from ....utils import large_gpu_test +from ..utils import check_embeddings_close + +llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n' # noqa: E501 + +HF_TEXT_PROMPTS = [ + # T -> X + llama3_template.format( + "The label of the object is stop sign\nSummary above sentence in one word: " # noqa: E501 + ), + # T -> X + llama3_template.format( + "cherry blossom\nSummary above sentence in one word: "), +] + +HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({ + # I -> X + "stop_sign": + llama3_template.format("\nSummary above image in one word: "), + # I -> X + "cherry_blossom": + llama3_template.format("\nSummary above image in one word: "), +}) + +MODELS = ["royokong/e5-v"] + + +def _run_test( + hf_runner: Type[HfRunner], + vllm_runner: Type[VllmRunner], + input_texts: List[str], + input_images: PromptImageInput, + model: str, + *, + dtype: str, +) -> None: + # NOTE: take care of the order. run vLLM first, and then run HF. + # vLLM needs a fresh new process without cuda initialization. + # if we run HF first, the cuda initialization will be done and it + # will hurt multiprocessing backend with fork method (the default method). + with vllm_runner(model, + task="embedding", + dtype=dtype, + max_model_len=4096, + enforce_eager=True) as vllm_model: + vllm_outputs = vllm_model.encode(input_texts, images=input_images) + + with hf_runner(model, dtype=dtype, + auto_cls=AutoModelForVision2Seq) as hf_model: + # Patch the issue where image_token_id + # exceeds the maximum allowed vocab size + hf_model.model.resize_token_embeddings( + hf_model.model.language_model.vocab_size + 1) + + all_inputs = hf_model.get_inputs(input_texts, images=input_images) + + all_outputs = [] + for inputs in all_inputs: + # Based on: https://huggingface.co/royokong/e5-v + outputs = hf_model.model( + **hf_model.wrap_device(inputs, + device=hf_model.model.device.type), + return_dict=True, + output_hidden_states=True, + ) + pooled_output = F.normalize(outputs.hidden_states[-1][0, -1, :], + dim=-1) + + all_outputs.append(pooled_output.tolist()) + + hf_outputs = all_outputs + + check_embeddings_close( + embeddings_0_lst=hf_outputs, + embeddings_1_lst=vllm_outputs, + name_0="hf", + name_1="vllm", + ) + + +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("dtype", ["half"]) +def test_models_text( + hf_runner, + vllm_runner, + image_assets, + model: str, + dtype: str, +) -> None: + input_texts_images = [(text, None) for text in HF_TEXT_PROMPTS] + input_texts = [text for text, _ in input_texts_images] + input_images = [image for _, image in input_texts_images] + + _run_test( + hf_runner, + vllm_runner, + input_texts, + input_images, # type: ignore + model, + dtype=dtype, + ) + + +@large_gpu_test(min_gb=48) +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("dtype", ["half"]) +def test_models_image( + hf_runner, + vllm_runner, + image_assets, + model: str, + dtype: str, +) -> None: + input_texts_images = [ + (text, asset.pil_image) + for text, asset in zip(HF_IMAGE_PROMPTS, image_assets) + ] + input_texts = [text for text, _ in input_texts_images] + input_images = [image for _, image in input_texts_images] + + _run_test( + hf_runner, + vllm_runner, + input_texts, + input_images, + model, + dtype=dtype, + ) diff --git a/tests/models/embedding/vision_language/test_phi3v.py b/tests/models/embedding/vision_language/test_phi3v.py index 0ca90e6bfa52e..ee411472ba284 100644 --- a/tests/models/embedding/vision_language/test_phi3v.py +++ b/tests/models/embedding/vision_language/test_phi3v.py @@ -1,42 +1,53 @@ +from typing import List, Type + import pytest import torch.nn.functional as F -from ....conftest import IMAGE_ASSETS +from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner +from ....utils import large_gpu_test from ..utils import check_embeddings_close +HF_TEXT_PROMPTS = [ + # T -> X + "Find me an everyday image that matches the given caption: The label of the object is stop sign", # noqa: E501 + # T -> X + "Retrieve an image of this caption: cherry blossom", +] + HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({ + # T + I -> X "stop_sign": "<|image_1|> Select the portion of the image that isolates the object of the given label: The label of the object is stop sign", # noqa: E501 + # I -> X "cherry_blossom": - "<|image_1|> Represent the given image with the following question: What is in the image", # noqa: E501 + "<|image_1|> Represent the given image for classification", # noqa: E501 }) MODELS = ["TIGER-Lab/VLM2Vec-Full"] -@pytest.mark.parametrize("model", MODELS) -@pytest.mark.parametrize("dtype", ["half"]) -def test_models( - hf_runner, - vllm_runner, - example_prompts, +def _run_test( + hf_runner: Type[HfRunner], + vllm_runner: Type[VllmRunner], + input_texts: List[str], + input_images: PromptImageInput, model: str, + *, dtype: str, ) -> None: # NOTE: take care of the order. run vLLM first, and then run HF. # vLLM needs a fresh new process without cuda initialization. # if we run HF first, the cuda initialization will be done and it # will hurt multiprocessing backend with fork method (the default method). - with vllm_runner(model, - task="embedding", - max_model_len=4096, - max_num_seqs=2, - dtype=dtype, + with vllm_runner(model, task="embedding", dtype=dtype, enforce_eager=True) as vllm_model: - vllm_outputs = vllm_model.encode(example_prompts) + vllm_outputs = vllm_model.encode(input_texts, images=input_images) - with hf_runner(model, dtype=dtype) as hf_model: - all_inputs = hf_model.get_inputs(example_prompts) + # use eager mode for hf runner, since phi3_v didn't work with flash_attn + hf_model_kwargs = {"_attn_implementation": "eager"} + with hf_runner(model, dtype=dtype, + model_kwargs=hf_model_kwargs) as hf_model: + all_inputs = hf_model.get_inputs(input_texts, images=input_images) all_outputs = [] for inputs in all_inputs: @@ -61,3 +72,53 @@ def test_models( name_0="hf", name_1="vllm", ) + + +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("dtype", ["half"]) +def test_models_text( + hf_runner, + vllm_runner, + image_assets, + model: str, + dtype: str, +) -> None: + input_texts_images = [(text, None) for text in HF_TEXT_PROMPTS] + input_texts = [text for text, _ in input_texts_images] + input_images = [image for _, image in input_texts_images] + + _run_test( + hf_runner, + vllm_runner, + input_texts, + input_images, # type: ignore + model, + dtype=dtype, + ) + + +@large_gpu_test(min_gb=48) +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("dtype", ["half"]) +def test_models_image( + hf_runner, + vllm_runner, + image_assets, + model: str, + dtype: str, +) -> None: + input_texts_images = [ + (text, asset.pil_image) + for text, asset in zip(HF_IMAGE_PROMPTS, image_assets) + ] + input_texts = [text for text, _ in input_texts_images] + input_images = [image for _, image in input_texts_images] + + _run_test( + hf_runner, + vllm_runner, + input_texts, + input_images, + model, + dtype=dtype, + ) diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index 4dd472b04bb1a..46cba8ebbc583 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -13,11 +13,13 @@ from vllm.attention import AttentionMetadata from vllm.config import CacheConfig, MultiModalConfig from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext +from vllm.model_executor.layers.pooler import Pooler, PoolingType from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.sampler import Sampler, SamplerOutput +from vllm.model_executor.pooling_metadata import PoolingMetadata from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY -from vllm.sequence import IntermediateTensors +from vllm.sequence import IntermediateTensors, PoolerOutput from vllm.utils import is_list_of from .clip import (CLIPVisionModel, dummy_image_for_clip, @@ -28,8 +30,8 @@ from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip, get_siglip_image_feature_size, get_siglip_patch_grid_length, input_processor_for_siglip) -from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - merge_multimodal_embeddings) +from .utils import (AutoWeightsLoader, embed_multimodal, flatten_bn, + init_vllm_registered_model) # Result in the max possible feature size (2x2 grid of 336x336px tiles) MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448 @@ -312,6 +314,10 @@ def __init__(self, self.language_model = init_vllm_registered_model( config.text_config, cache_config, quant_config) + # The same model class supports both language generation and embedding + # because the architecture name is the same + self._pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True) + self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) @@ -605,14 +611,12 @@ def forward( image_input = self._parse_and_validate_image_input(**kwargs) if image_input is not None: - vision_embeddings = self._process_image_input(image_input) - inputs_embeds = self.language_model.model.get_input_embeddings( - input_ids) - - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, vision_embeddings, - self.config.image_token_index) - + inputs_embeds = embed_multimodal( + input_ids, + self.config.image_token_index, + self.language_model.model.get_input_embeddings, + lambda _: self._process_image_input(image_input), + ) input_ids = None else: inputs_embeds = None @@ -641,6 +645,13 @@ def sample( ) -> Optional[SamplerOutput]: return self.language_model.sample(logits, sampling_metadata) + def pooler( + self, + hidden_states: torch.Tensor, + pooling_metadata: PoolingMetadata, + ) -> Optional[PoolerOutput]: + return self._pooler(hidden_states, pooling_metadata) + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): loader = AutoWeightsLoader(self) loader.load_weights(weights) diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index 91c14e32c946c..9a1083520efd2 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -467,8 +467,6 @@ def input_processor_for_phi3v(ctx: InputContext, prompt_token_ids = inputs["prompt_token_ids"].copy() - print("prompt_token_ids (old)", prompt_token_ids) - # masked placeholder with image token id for idx in image_idx: candidates = _get_image_placeholder_token_id_candidates(model_config, diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 8745e0cbd97b6..a255b2a2f3982 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -94,6 +94,7 @@ "MistralModel": ("llama", "LlamaEmbeddingModel"), "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"), # [Multimodal] + "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"), # noqa: E501 "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"), } diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index ec1d76d2117f3..d96e988fba384 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -1,7 +1,7 @@ import itertools from dataclasses import dataclass, field -from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional, - Protocol, Tuple, Union, overload) +from typing import (Any, Callable, Dict, Iterable, List, Literal, Mapping, + Optional, Protocol, Tuple, Union, overload) import torch import torch.nn as nn @@ -294,10 +294,11 @@ def _embedding_count_expression(embeddings: NestedTensors) -> str: _embedding_count_expression(inner) for inner in embeddings) -def merge_multimodal_embeddings(input_ids: torch.Tensor, - inputs_embeds: torch.Tensor, - multimodal_embeddings: NestedTensors, - placeholder_token_id: int) -> torch.Tensor: +def _merge_multimodal_embeddings( + inputs_embeds: torch.Tensor, + is_multimodal: torch.Tensor, + multimodal_embeddings: NestedTensors, +) -> torch.Tensor: """ Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the positions in ``inputs_embeds`` corresponding to placeholder tokens in @@ -306,8 +307,7 @@ def merge_multimodal_embeddings(input_ids: torch.Tensor, Note: This updates ``inputs_embeds`` in place. """ - mask = (input_ids == placeholder_token_id) - num_expected_tokens = mask.sum().item() + num_expected_tokens = is_multimodal.sum().item() assert isinstance(num_expected_tokens, int) flattened = _flatten_embeddings(multimodal_embeddings) @@ -317,10 +317,70 @@ def merge_multimodal_embeddings(input_ids: torch.Tensor, f"Attempted to assign {expr} = {flattened.shape[0]} " f"multimodal tokens to {num_expected_tokens} placeholders") - inputs_embeds[mask] = flattened + inputs_embeds[is_multimodal] = flattened return inputs_embeds +def embed_multimodal( + input_ids: torch.Tensor, + multimodal_token_id: int, + get_text_embeds: Callable[[torch.Tensor], torch.Tensor], + get_multimodal_embeds: Callable[[torch.Tensor], Union[torch.Tensor, + List[torch.Tensor]]], +) -> torch.Tensor: + """ + Embed token IDs and multimodal inputs and combine their embeddings. + + ``multimodal_token_id`` is used to determine whether a token ID should + be embedded using ``get_text_embeds`` or ``get_multimodal_embeds``. + + Compared to ``merge_multimodal_embeddings`, this avoids running + ``get_text_embeds`` on ``input_ids[input_ids == multimodal_token_id]`` + which causes issues when the placeholder token ID exceeds the + vocabulary size of the language model. + """ + is_multimodal = input_ids == multimodal_token_id + is_text = ~is_multimodal + + text_embeds = get_text_embeds(input_ids[is_text]) + multimodal_embeds = get_multimodal_embeds(input_ids[is_multimodal]) + + merged_embeds = torch.empty( + (input_ids.shape[0], text_embeds.shape[1]), + dtype=text_embeds.dtype, + device=text_embeds.device, + ) + + merged_embeds[is_text] = text_embeds + + return _merge_multimodal_embeddings( + merged_embeds, + is_multimodal, + multimodal_embeds, + ) + + +def merge_multimodal_embeddings( + input_ids: torch.Tensor, + inputs_embeds: torch.Tensor, + multimodal_embeddings: NestedTensors, + placeholder_token_id: int, +) -> torch.Tensor: + """ + Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the + positions in ``inputs_embeds`` corresponding to placeholder tokens in + ``input_ids``. + + Note: + This updates ``inputs_embeds`` in place. + """ + return _merge_multimodal_embeddings( + inputs_embeds, + (input_ids == placeholder_token_id), + multimodal_embeddings, + ) + + class LayerFn(Protocol): def __call__(self, prefix: str) -> torch.nn.Module: From 51c24c9736b1dbe65cb203deb9e56d4037eb1ec6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Luka=20Govedi=C4=8D?= Date: Wed, 23 Oct 2024 00:43:07 -0400 Subject: [PATCH 061/222] [Build] Fix `FetchContent` multiple build issue (#9596) Signed-off-by: luka --- CMakeLists.txt | 10 ++++++---- setup.py | 8 ++++++++ 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a53a8575d01ca..d1956f3d409b4 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -169,12 +169,12 @@ endif() # # Use FetchContent for C++ dependencies that are compiled as part of vLLM's build process. -# Configure it to place files in vllm/.deps, in order to play nicely with sccache. +# setup.py will override FETCHCONTENT_BASE_DIR to play nicely with sccache. +# Each dependency that produces build artifacts should override its BINARY_DIR to avoid +# conflicts between build types. It should instead be set to ${CMAKE_BINARY_DIR}/. # include(FetchContent) -get_filename_component(PROJECT_ROOT_DIR "${CMAKE_CURRENT_SOURCE_DIR}" ABSOLUTE) -file(MAKE_DIRECTORY "${FETCHCONTENT_BASE_DIR}") -set(FETCHCONTENT_BASE_DIR "${PROJECT_ROOT_DIR}/.deps") +file(MAKE_DIRECTORY ${FETCHCONTENT_BASE_DIR}) # Ensure the directory exists message(STATUS "FetchContent base directory: ${FETCHCONTENT_BASE_DIR}") # @@ -509,6 +509,8 @@ else() GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git GIT_TAG 013f0c4fc47e6574060879d9734c1df8c5c273bd GIT_PROGRESS TRUE + # Don't share the vllm-flash-attn build between build types + BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn ) endif() diff --git a/setup.py b/setup.py index d1f4b7f1c1119..8abeb0ba739db 100644 --- a/setup.py +++ b/setup.py @@ -157,6 +157,14 @@ def configure(self, ext: CMakeExtension) -> None: # on subsequent calls to python. cmake_args += ['-DVLLM_PYTHON_PATH={}'.format(":".join(sys.path))] + # Override the base directory for FetchContent downloads to $ROOT/.deps + # This allows sharing dependencies between profiles, + # and plays more nicely with sccache. + # To override this, set the FETCHCONTENT_BASE_DIR environment variable. + fc_base_dir = os.path.join(ROOT_DIR, ".deps") + fc_base_dir = os.environ.get("FETCHCONTENT_BASE_DIR", fc_base_dir) + cmake_args += ['-DFETCHCONTENT_BASE_DIR={}'.format(fc_base_dir)] + # # Setup parallelism and build tool # From 2394962d7083f1c1001dba9efefadb674321e688 Mon Sep 17 00:00:00 2001 From: Mengqing Cao Date: Wed, 23 Oct 2024 16:28:21 +0800 Subject: [PATCH 062/222] [Hardware][XPU] using current_platform.is_xpu (#9605) --- vllm/attention/selector.py | 6 +++--- vllm/config.py | 4 ++-- vllm/executor/ray_utils.py | 4 ++-- vllm/model_executor/custom_op.py | 4 ++-- vllm/utils.py | 29 +++-------------------------- vllm/worker/xpu_worker.py | 7 ++++--- 6 files changed, 16 insertions(+), 38 deletions(-) diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index 714c4f7fdb4e5..cd3c642b8c8a2 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -10,7 +10,7 @@ from vllm.attention.backends.abstract import AttentionBackend from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import STR_BACKEND_ENV_VAR, is_hip, is_openvino, is_xpu +from vllm.utils import STR_BACKEND_ENV_VAR, is_hip, is_openvino logger = init_logger(__name__) @@ -136,7 +136,7 @@ def get_attn_backend( from vllm.attention.backends.openvino import OpenVINOAttentionBackend return OpenVINOAttentionBackend elif backend == _Backend.IPEX: - assert is_xpu(), RuntimeError( + assert current_platform.is_xpu(), RuntimeError( "IPEX attention backend is only used for the XPU device.") logger.info("Using IPEX attention backend.") from vllm.attention.backends.ipex_attn import IpexAttnBackend @@ -198,7 +198,7 @@ def which_attn_to_use( logger.info("Cannot use %s backend on OpenVINO.", selected_backend) return _Backend.OPENVINO - if is_xpu(): + if current_platform.is_xpu(): if selected_backend != _Backend.IPEX: logger.info("Cannot use %s backend on XPU.", selected_backend) return _Backend.IPEX diff --git a/vllm/config.py b/vllm/config.py index 12935e77c2aa7..c569789c650ab 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -17,7 +17,7 @@ get_hf_image_processor_config, get_hf_text_config) from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory, - is_hip, is_openvino, is_xpu, print_warning_once) + is_hip, is_openvino, print_warning_once) if TYPE_CHECKING: from ray.util.placement_group import PlacementGroup @@ -1121,7 +1121,7 @@ def __init__(self, device: str = "auto") -> None: self.device_type = "tpu" elif current_platform.is_cpu(): self.device_type = "cpu" - elif is_xpu(): + elif current_platform.is_xpu(): self.device_type = "xpu" else: raise RuntimeError("Failed to infer device type") diff --git a/vllm/executor/ray_utils.py b/vllm/executor/ray_utils.py index 7e46acefc5b0e..0af7b3386d895 100644 --- a/vllm/executor/ray_utils.py +++ b/vllm/executor/ray_utils.py @@ -10,7 +10,7 @@ from vllm.logger import init_logger from vllm.platforms import current_platform from vllm.sequence import ExecuteModelRequest, IntermediateTensors -from vllm.utils import get_ip, is_hip, is_xpu +from vllm.utils import get_ip, is_hip from vllm.worker.worker_base import WorkerWrapperBase logger = init_logger(__name__) @@ -231,7 +231,7 @@ def initialize_ray_cluster( assert_ray_available() # Connect to a ray cluster. - if is_hip() or is_xpu(): + if is_hip() or current_platform.is_xpu(): ray.init(address=ray_address, ignore_reinit_error=True, num_gpus=parallel_config.world_size) diff --git a/vllm/model_executor/custom_op.py b/vllm/model_executor/custom_op.py index d7506d268e73b..71eed6eb68d78 100644 --- a/vllm/model_executor/custom_op.py +++ b/vllm/model_executor/custom_op.py @@ -7,7 +7,7 @@ from vllm.compilation.levels import CompilationLevel from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import is_hip, is_xpu, print_warning_once +from vllm.utils import is_hip, print_warning_once logger = init_logger(__name__) @@ -78,7 +78,7 @@ def dispatch_forward(self): return self.forward_cpu elif current_platform.is_tpu(): return self.forward_tpu - elif is_xpu(): + elif current_platform.is_xpu(): return self.forward_xpu else: return self.forward_cuda diff --git a/vllm/utils.py b/vllm/utils.py index 797c1bcfd5342..0e9b241b6f9f6 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -327,29 +327,6 @@ def is_openvino() -> bool: return False -@lru_cache(maxsize=None) -def is_xpu() -> bool: - from importlib.metadata import PackageNotFoundError, version - try: - is_xpu_flag = "xpu" in version("vllm") - except PackageNotFoundError: - return False - # vllm is not build with xpu - if not is_xpu_flag: - return False - try: - import intel_extension_for_pytorch as ipex # noqa: F401 - _import_ipex = True - except ImportError as e: - logger.warning("Import Error for IPEX: %s", e.msg) - _import_ipex = False - # ipex dependency is not ready - if not _import_ipex: - logger.warning("not found ipex lib") - return False - return hasattr(torch, "xpu") and torch.xpu.is_available() - - @lru_cache(maxsize=None) def get_max_shared_memory_bytes(gpu: int = 0) -> int: """Returns the maximum shared memory per thread block in bytes.""" @@ -379,7 +356,7 @@ def seed_everything(seed: int) -> None: if current_platform.is_cuda_alike(): torch.cuda.manual_seed_all(seed) - if is_xpu(): + if current_platform.is_xpu(): torch.xpu.manual_seed_all(seed) @@ -774,7 +751,7 @@ def is_pin_memory_available() -> bool: print_warning_once("Using 'pin_memory=False' as WSL is detected. " "This may slow down the performance.") return False - elif is_xpu(): + elif current_platform.is_xpu(): print_warning_once("Pin memory is not supported on XPU.") return False elif current_platform.is_neuron(): @@ -795,7 +772,7 @@ def current_memory_usage(self) -> float: if current_platform.is_cuda_alike(): torch.cuda.reset_peak_memory_stats(self.device) mem = torch.cuda.max_memory_allocated(self.device) - elif is_xpu(): + elif current_platform.is_xpu(): torch.xpu.reset_peak_memory_stats(self.device) # type: ignore mem = torch.xpu.max_memory_allocated(self.device) # type: ignore return mem diff --git a/vllm/worker/xpu_worker.py b/vllm/worker/xpu_worker.py index 9ad070d042a3d..917866f2d985b 100644 --- a/vllm/worker/xpu_worker.py +++ b/vllm/worker/xpu_worker.py @@ -17,7 +17,7 @@ from vllm.distributed.parallel_state import get_pp_group from vllm.logger import init_logger from vllm.model_executor import set_random_seed -from vllm.utils import is_xpu +from vllm.platforms import current_platform from vllm.worker.cache_engine import CacheEngine from vllm.worker.worker import Worker from vllm.worker.worker_base import LoraNotSupportedWorkerBase @@ -53,7 +53,7 @@ def __init__( observability_config: Optional[ObservabilityConfig] = None, ) -> None: assert device_config.device_type == "xpu" - assert is_xpu() + assert current_platform.is_xpu() self.model_config = model_config self.parallel_config = parallel_config @@ -91,7 +91,8 @@ def __init__( self.gpu_cache: Optional[List[List[torch.Tensor]]] def init_device(self) -> None: - if self.device_config.device.type == "xpu" and is_xpu(): + if self.device_config.device.type == "xpu" and current_platform.is_xpu( + ): self.device = torch.device(f"xpu:{self.local_rank}") torch.xpu.set_device(self.device) torch.xpu.empty_cache() From 3ff57ebfcacdd4f7690ed8f5693657de2bdedea8 Mon Sep 17 00:00:00 2001 From: Isotr0py <2037008807@qq.com> Date: Wed, 23 Oct 2024 18:42:47 +0800 Subject: [PATCH 063/222] [Model] Initialize Florence-2 language backbone support (#9555) --- examples/florence2_inference.py | 44 +++ tests/conftest.py | 28 +- .../vision_language/test_florence2.py | 102 +++++++ vllm/model_executor/models/florence2.py | 261 ++++++++++++++++++ vllm/model_executor/models/registry.py | 1 + 5 files changed, 428 insertions(+), 8 deletions(-) create mode 100644 examples/florence2_inference.py create mode 100644 tests/models/encoder_decoder/vision_language/test_florence2.py create mode 100644 vllm/model_executor/models/florence2.py diff --git a/examples/florence2_inference.py b/examples/florence2_inference.py new file mode 100644 index 0000000000000..b58ac2e1f7ed4 --- /dev/null +++ b/examples/florence2_inference.py @@ -0,0 +1,44 @@ +''' +Demonstrate prompting of text-to-text +encoder/decoder models, specifically Florence-2 +''' +# TODO(Isotr0py): +# Move to offline_inference_vision_language.py after porting vision backbone +from vllm import LLM, SamplingParams + +dtype = "float" + +# Create a Florence-2 encoder/decoder model instance +llm = LLM( + model="microsoft/Florence-2-base", + tokenizer="facebook/bart-base", + dtype=dtype, + trust_remote_code=True, +) + +prompts = [ + "", "", "", + "", "", "", + "", "", "" +] +# Create a sampling params object. +sampling_params = SamplingParams( + temperature=0, + top_p=1.0, + min_tokens=0, + max_tokens=20, +) + +# Generate output tokens from the prompts. The output is a list of +# RequestOutput objects that contain the prompt, generated +# text, and other information. +outputs = llm.generate(prompts, sampling_params) + +# Print the outputs. +for output in outputs: + prompt = output.prompt + encoder_prompt = output.encoder_prompt + generated_text = output.outputs[0].text + print(f"Encoder prompt: {encoder_prompt!r}, " + f"Decoder prompt: {prompt!r}, " + f"Generated text: {generated_text!r}") diff --git a/tests/conftest.py b/tests/conftest.py index 76f581e0363f7..b11bbcb4ab7d1 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -253,7 +253,9 @@ def __init__( dtype: str = "half", *, model_kwargs: Optional[Dict[str, Any]] = None, + is_embedding_model: bool = False, is_sentence_transformer: bool = False, + skip_tokenizer_init: bool = False, auto_cls: Type[_BaseAutoModelClass] = AutoModelForCausalLM, postprocess_inputs: Callable[[BatchEncoding], BatchEncoding] = identity, @@ -281,11 +283,12 @@ def __init__( **model_kwargs, )) - self.tokenizer = AutoTokenizer.from_pretrained( - model_name, - torch_dtype=torch_dtype, - trust_remote_code=True, - ) + if not skip_tokenizer_init: + self.tokenizer = AutoTokenizer.from_pretrained( + model_name, + torch_dtype=torch_dtype, + trust_remote_code=True, + ) # don't put this import at the top level # it will call torch.cuda.device_count() @@ -295,6 +298,8 @@ def __init__( torch_dtype=torch_dtype, trust_remote_code=True, ) + if skip_tokenizer_init: + self.tokenizer = self.processor.tokenizer self.postprocess_inputs = postprocess_inputs @@ -535,6 +540,7 @@ def generate_encoder_decoder_greedy_logprobs_limit( encoder_decoder_prompts: List[ExplicitEncoderDecoderPrompt[str, str]], max_tokens: int, num_logprobs: int, + images: Optional[PromptImageInput] = None, **kwargs: Any, ) -> List[TokensTextLogprobs]: ''' @@ -545,11 +551,17 @@ def generate_encoder_decoder_greedy_logprobs_limit( all_output_ids: List[List[int]] = [] all_output_strs: List[str] = [] - for (encoder_prompt, - decoder_prompt) in to_enc_dec_tuple_list(encoder_decoder_prompts): + for i, (encoder_prompt, decoder_prompt) in enumerate( + to_enc_dec_tuple_list(encoder_decoder_prompts)): + processor_kwargs: Dict[str, Any] = { + "text": encoder_prompt, + "return_tensors": "pt", + } + if images is not None and images[i] is not None: + processor_kwargs["images"] = images[i] encoder_input_ids = self.wrap_device( - self.tokenizer(encoder_prompt, return_tensors="pt").input_ids, + self.processor(**processor_kwargs).input_ids, device=self.model.device.type, ) diff --git a/tests/models/encoder_decoder/vision_language/test_florence2.py b/tests/models/encoder_decoder/vision_language/test_florence2.py new file mode 100644 index 0000000000000..483773f069133 --- /dev/null +++ b/tests/models/encoder_decoder/vision_language/test_florence2.py @@ -0,0 +1,102 @@ +from functools import partial +from typing import List, Optional, Tuple, Type + +import pytest +from PIL import Image + +from vllm.inputs.data import ExplicitEncoderDecoderPrompt +from vllm.sequence import SampleLogprobs + +from ....conftest import HfRunner, VllmRunner +from ...utils import check_logprobs_close + +Florence2Prompt = partial(ExplicitEncoderDecoderPrompt, + decoder_prompt=None, + mm_processor_kwargs=None) + +MODELS = ["microsoft/Florence-2-base"] +# Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer +# Therefore, we borrow the BartTokenizer from the original Bart model +TOKENIZER = "facebook/bart-base" +PROMPTS = [ + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), + Florence2Prompt(encoder_prompt=""), +] + + +def vllm_to_hf_output(vllm_output: Tuple[List[int], str, + Optional[SampleLogprobs]], ): + """Sanitize vllm output to be comparable with hf output.""" + output_ids, output_str, out_logprobs = vllm_output + + hf_output_str = "" + output_str + "" + + return output_ids, hf_output_str, out_logprobs + + +def run_test( + hf_runner: Type[HfRunner], + vllm_runner: Type[VllmRunner], + prompts: List[ExplicitEncoderDecoderPrompt], + model: str, + *, + dtype: str, + max_tokens: int, + num_logprobs: int, + tensor_parallel_size: int, + distributed_executor_backend: Optional[str] = None, +) -> None: + with vllm_runner(model, + tokenizer_name=TOKENIZER, + dtype=dtype, + tensor_parallel_size=tensor_parallel_size, + distributed_executor_backend=distributed_executor_backend, + enforce_eager=True) as vllm_model: + vllm_outputs = vllm_model.generate_encoder_decoder_greedy_logprobs( + prompts, max_tokens, num_logprobs) + + # Florence-2 processors require image inputs + dummy_image = Image.new(mode="RGB", size=(2, 2)) + with hf_runner(model, dtype=dtype, skip_tokenizer_init=True) as hf_model: + hf_model.model.get_output_embeddings = lambda: \ + hf_model.model.language_model.lm_head + hf_outputs = (hf_model.generate_encoder_decoder_greedy_logprobs_limit( + prompts, + max_tokens, + num_logprobs, + images=[dummy_image] * len(prompts), + )) + + check_logprobs_close( + outputs_0_lst=hf_outputs, + outputs_1_lst=[ + vllm_to_hf_output(vllm_output) for vllm_output in vllm_outputs + ], + name_0="hf", + name_1="vllm", + ) + + +@pytest.mark.parametrize("model", MODELS) +@pytest.mark.parametrize("dtype", ["float"]) +@pytest.mark.parametrize("max_tokens", [64]) +@pytest.mark.parametrize("num_logprobs", [5]) +def test_models(hf_runner, vllm_runner, model, dtype, max_tokens, + num_logprobs) -> None: + run_test( + hf_runner, + vllm_runner, + PROMPTS, + model, + dtype=dtype, + max_tokens=max_tokens, + num_logprobs=num_logprobs, + tensor_parallel_size=1, + ) diff --git a/vllm/model_executor/models/florence2.py b/vllm/model_executor/models/florence2.py new file mode 100644 index 0000000000000..6840ac8b9e303 --- /dev/null +++ b/vllm/model_executor/models/florence2.py @@ -0,0 +1,261 @@ +import math +from typing import Iterable, List, Optional, Tuple + +import torch +import torch.nn as nn +from transformers import PretrainedConfig + +from vllm.attention import AttentionMetadata +from vllm.config import CacheConfig +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) +from vllm.model_executor.layers.sampler import Sampler, SamplerOutput +from vllm.model_executor.model_loader.weight_utils import default_weight_loader +from vllm.model_executor.models.bart import (BartDecoder, BartEncoder, + BartParallelLMHead, + BartScaledWordEmbedding) +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.sequence import IntermediateTensors + +from .utils import AutoWeightsLoader + + +class Florence2LanguageModel(nn.Module): + + def __init__(self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None): + super().__init__() + self.config = config + + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.shared = BartScaledWordEmbedding(self.vocab_size, config.d_model) + self.encoder = BartEncoder(config, + cache_config=cache_config, + quant_config=quant_config) + self.decoder = BartDecoder(config, + cache_config=cache_config, + quant_config=quant_config) + + if self.config.tie_word_embeddings: + self.encoder.embed_tokens.weight = self.shared.weight + self.decoder.embed_tokens.weight = self.shared.weight + + def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, + encoder_input_ids: torch.Tensor, + encoder_positions: torch.Tensor, kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata) -> torch.Tensor: + r""" + Args: + input_ids + Indices of *decoder* input sequence tokens in the vocabulary. + Padding will be ignored by default should you + provide it. + positions + Positions of *decoder* input sequence tokens. + encoder_input_ids + Indices of *encoder* input sequence tokens in the vocabulary. + encoder_positions: + Positions of *encoder* input sequence tokens. + kv_caches: + Layer-wise list of KV cache tensors + attn_metadata: + vLLM Attention metadata structure + Returns: + Model output torch.Tensor + """ + + encoder_hidden_states = None + + if encoder_input_ids.numel() > 0: + # Run encoder attention if a non-zero number of encoder tokens + # are provided as input + encoder_hidden_states = self.encoder(input_ids=encoder_input_ids, + positions=encoder_positions, + kv_caches=kv_caches, + attn_metadata=attn_metadata) + + # decoder outputs consists of + # (dec_features, past_key_value, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + decoder_input_ids=input_ids, + decoder_positions=positions, + encoder_hidden_states=encoder_hidden_states, + kv_caches=kv_caches, + attn_metadata=attn_metadata) + + return decoder_outputs + + +class Florence2LanguageForConditionalGeneration(nn.Module): + + def __init__(self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None): + super().__init__() + self.config = config + self.model = Florence2LanguageModel(config, + cache_config=cache_config, + quant_config=quant_config) + embed_scale = math.sqrt( + config.d_model) if config.scale_embedding else 1.0 + + self.vocab_size = config.vocab_size + self.lm_head = BartParallelLMHead(self.vocab_size, + config.d_model, + embed_scale=embed_scale) + + self.logits_processor = LogitsProcessor(self.vocab_size, + config.vocab_size) + self.sampler = Sampler() + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + encoder_input_ids: torch.Tensor, + encoder_positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + **kwargs, + ) -> torch.Tensor: + r""" + Args: + input_ids + torch.Tensor of *decoder* input token ids. + positions + torch.Tensor of *decoder* position indices. + encoder_input_ids + torch.Tensor of *encoder* input token ids. + encoder_positions + torch.Tensor of *encoder* position indices + kv_caches: + Layer-wise list of KV cache tensors + attn_metadata: + vLLM Attention metadata structure + Returns: + Output torch.Tensor + """ + return self.model(input_ids, positions, encoder_input_ids, + encoder_positions, kv_caches, attn_metadata) + + def compute_logits( + self, + hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[torch.Tensor]: + logits = self.logits_processor(self.lm_head, hidden_states, + sampling_metadata) + return logits + + def sample(self, logits: torch.Tensor, + sampling_metadata: SamplingMetadata) -> SamplerOutput: + next_tokens = self.sampler(logits, sampling_metadata) + return next_tokens + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + stacked_params_mapping = [ + # (param_name, shard_name, shard_id) + ("qkv_proj", "q_proj", "q"), + ("qkv_proj", "k_proj", "k"), + ("qkv_proj", "v_proj", "v"), + ] + + params_dict = dict(self.named_parameters()) + for name, loaded_weight in weights: + for (param_name, weight_name, shard_id) in stacked_params_mapping: + if weight_name not in name: + continue + + param = params_dict[name.replace(weight_name, param_name)] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + if "final_logits_bias" in name: + continue + if self.config.tie_word_embeddings and "embed_tokens" in name: + continue + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) + + +class Florence2ForConditionalGeneration(nn.Module): + + def __init__(self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None): + super().__init__() + + # TODO(Isotr0py): Add vision backbone + self.language_model = Florence2LanguageForConditionalGeneration( + config=config.text_config, + cache_config=cache_config, + quant_config=quant_config) + + @property + def sampler(self): + return self.language_model.sampler + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors] = None, + *, + encoder_input_ids: torch.Tensor, + encoder_positions: torch.Tensor, + **kwargs, + ) -> torch.Tensor: + r""" + Args: + input_ids + torch.Tensor of *decoder* input token ids. + positions + torch.Tensor of *decoder* position indices. + encoder_input_ids + torch.Tensor of *encoder* input token ids. + encoder_positions + torch.Tensor of *encoder* position indices + kv_caches: + Layer-wise list of KV cache tensors + attn_metadata: + vLLM Attention metadata structure + Returns: + Output torch.Tensor + """ + return self.language_model(input_ids, positions, encoder_input_ids, + encoder_positions, kv_caches, attn_metadata) + + def compute_logits( + self, + hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[torch.Tensor]: + return self.language_model.compute_logits(hidden_states, + sampling_metadata) + + def sample( + self, + logits: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> SamplerOutput: + return self.language_model.sample(logits, sampling_metadata) + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + skip_prefixes = [ + 'image_projection', "vision_tower", "image_proj_norm", + "image_pos_embed", "visual_temporal_embed" + ] + loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes) + loader.load_weights(weights) diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index a255b2a2f3982..787c65743e894 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -85,6 +85,7 @@ # [Encoder-decoder] "BartModel": ("bart", "BartForConditionalGeneration"), "BartForConditionalGeneration": ("bart", "BartForConditionalGeneration"), + "Florence2ForConditionalGeneration": ("florence2", "Florence2ForConditionalGeneration"), # noqa: E501 } _EMBEDDING_MODELS = { From c18e1a34189812af21aa504f9166de5ed4a86675 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 23 Oct 2024 19:27:37 +0800 Subject: [PATCH 064/222] [VLM] Enable overriding whether post layernorm is used in vision encoder + fix quant args (#9217) Co-authored-by: Isotr0py <2037008807@qq.com> --- .../model_executor/layers/quantization/awq.py | 20 ++- vllm/model_executor/models/blip.py | 87 +++++++++---- vllm/model_executor/models/blip2.py | 2 +- vllm/model_executor/models/clip.py | 104 ++++++++++----- .../models/idefics2_vision_model.py | 51 ++++++-- vllm/model_executor/models/intern_vit.py | 41 ++++-- vllm/model_executor/models/internvl.py | 41 +++++- vllm/model_executor/models/llava.py | 32 ++++- vllm/model_executor/models/llava_next.py | 30 +---- .../model_executor/models/llava_next_video.py | 29 +---- vllm/model_executor/models/llava_onevision.py | 29 +---- vllm/model_executor/models/minicpmv.py | 33 +++-- vllm/model_executor/models/mllama.py | 120 +++++++++++++----- vllm/model_executor/models/nvlm_d.py | 5 + vllm/model_executor/models/paligemma.py | 3 +- vllm/model_executor/models/phi3v.py | 15 ++- vllm/model_executor/models/pixtral.py | 90 +++++++++++-- vllm/model_executor/models/siglip.py | 72 ++++++++--- 18 files changed, 551 insertions(+), 253 deletions(-) diff --git a/vllm/model_executor/layers/quantization/awq.py b/vllm/model_executor/layers/quantization/awq.py index 410b3cb5321cb..38dd1f2e10fcd 100644 --- a/vllm/model_executor/layers/quantization/awq.py +++ b/vllm/model_executor/layers/quantization/awq.py @@ -3,7 +3,8 @@ import torch from vllm import _custom_ops as ops -from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase +from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase, + UnquantizedLinearMethod) from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.parameter import (GroupQuantScaleParameter, @@ -21,10 +22,12 @@ def __init__( weight_bits: int, group_size: int, zero_point: bool, + modules_to_not_convert: Optional[List[str]] = None, ) -> None: self.weight_bits = weight_bits self.group_size = group_size self.zero_point = zero_point + self.modules_to_not_convert = modules_to_not_convert or [] if self.weight_bits != 4: raise ValueError( @@ -35,7 +38,8 @@ def __init__( def __repr__(self) -> str: return (f"AWQConfig(weight_bits={self.weight_bits}, " f"group_size={self.group_size}, " - f"zero_point={self.zero_point})") + f"zero_point={self.zero_point}, " + f"modules_to_not_convert={self.modules_to_not_convert})") def get_name(self) -> str: return "awq" @@ -61,11 +65,15 @@ def from_config(cls, config: Dict[str, Any]) -> "AWQConfig": weight_bits = cls.get_from_keys(config, ["w_bit", "bits"]) group_size = cls.get_from_keys(config, ["q_group_size", "group_size"]) zero_point = cls.get_from_keys(config, ["zero_point"]) - return cls(weight_bits, group_size, zero_point) + modules_to_not_convert = cls.get_from_keys_or( + config, ["modules_to_not_convert"], None) + return cls(weight_bits, group_size, zero_point, modules_to_not_convert) def get_quant_method(self, layer: torch.nn.Module, - prefix: str) -> Optional["AWQLinearMethod"]: + prefix: str) -> Optional["LinearMethodBase"]: if isinstance(layer, LinearBase): + if is_layer_skipped_awq(prefix, self.modules_to_not_convert): + return UnquantizedLinearMethod() return AWQLinearMethod(self) return None @@ -73,6 +81,10 @@ def get_scaled_act_names(self) -> List[str]: return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"] +def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]): + return any(module_name in prefix for module_name in modules_to_not_convert) + + class AWQLinearMethod(LinearMethodBase): """Linear method for AWQ. diff --git a/vllm/model_executor/models/blip.py b/vllm/model_executor/models/blip.py index 778162dd63ca6..1f2d7384076ed 100644 --- a/vllm/model_executor/models/blip.py +++ b/vllm/model_executor/models/blip.py @@ -122,7 +122,7 @@ def input_processor_for_blip( # Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/blip/modeling_blip.py#L164 # noqa class BlipVisionEmbeddings(nn.Module): - def __init__(self, config: BlipVisionConfig): + def __init__(self, config: Union[BlipVisionConfig, Blip2VisionConfig]): super().__init__() self.config = config @@ -167,9 +167,10 @@ class BlipParallelAttention(nn.Module): def __init__( self, - config: BlipVisionConfig, + config: Union[BlipVisionConfig, Blip2VisionConfig], quant_config: Optional[QuantizationConfig] = None, - ): + prefix: str = "", + ) -> None: super().__init__() self.config = config self.embed_dim = config.hidden_size @@ -189,11 +190,13 @@ def __init__( self.num_heads, bias=config.qkv_bias, quant_config=quant_config, + prefix=f"{prefix}.qkv", ) self.projection = RowParallelLinear( self.embed_dim, self.embed_dim, quant_config=quant_config, + prefix=f"{prefix}.projection", ) self.tp_size = get_tensor_model_parallel_world_size() @@ -235,9 +238,12 @@ def forward( class BlipMLP(nn.Module): - def __init__(self, - config: BlipVisionConfig, - quant_config: Optional[QuantizationConfig] = None): + def __init__( + self, + config: BlipVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() self.config = config @@ -246,11 +252,13 @@ def __init__(self, self.fc1 = ColumnParallelLinear(config.hidden_size, config.intermediate_size, bias=True, - quant_config=quant_config) + quant_config=quant_config, + prefix=f"{prefix}.fc1") self.fc2 = RowParallelLinear(config.intermediate_size, config.hidden_size, bias=True, - quant_config=quant_config) + quant_config=quant_config, + prefix=f"{prefix}.fc2") def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states, _ = self.fc1(hidden_states) @@ -262,24 +270,32 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class BlipEncoderLayer(nn.Module): - def __init__(self, - config: BlipVisionConfig, - quant_config: Optional[QuantizationConfig] = None): + def __init__( + self, + config: BlipVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() # fallback to sdpa attention if tp unavailable num_heads = config.num_attention_heads tp_size = get_tensor_model_parallel_world_size() if USE_XFORMERS_OPS and num_heads % tp_size == 0: - self.self_attn = BlipParallelAttention(config, - quant_config=quant_config) + self.self_attn = BlipParallelAttention( + config, + quant_config=quant_config, + prefix=f"{prefix}.self_attn", + ) else: # Blip doesn't have SDPA attention implemented in transformers # use eager attention instead for cpu backend self.self_attn = BlipAttention(config) self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.mlp = BlipMLP(config, quant_config=quant_config) + self.mlp = BlipMLP(config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) @@ -307,10 +323,13 @@ class BlipEncoder(nn.Module): config: BlipConfig """ - def __init__(self, - config: BlipVisionConfig, - quant_config: Optional[QuantizationConfig] = None, - num_hidden_layers_override: Optional[int] = None): + def __init__( + self, + config: BlipVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + num_hidden_layers_override: Optional[int] = None, + prefix: str = "", + ) -> None: super().__init__() self.config = config @@ -321,8 +340,10 @@ def __init__(self, num_hidden_layers = num_hidden_layers_override self.layers = nn.ModuleList([ - BlipEncoderLayer(config=config, quant_config=quant_config) - for _ in range(num_hidden_layers) + BlipEncoderLayer(config=config, + quant_config=quant_config, + prefix=f"{prefix}.layers.{layer_idx}") + for layer_idx in range(num_hidden_layers) ]) def forward(self, inputs_embeds: torch.Tensor): @@ -337,10 +358,15 @@ class BlipVisionModel(nn.Module): config_class = BlipVisionConfig main_input_name = "pixel_values" - def __init__(self, - config: BlipVisionConfig, - quant_config: Optional[QuantizationConfig] = None, - num_hidden_layers_override: Optional[int] = None): + def __init__( + self, + config: BlipVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + num_hidden_layers_override: Optional[int] = None, + require_post_norm: Optional[bool] = None, + prefix: str = "", + ) -> None: super().__init__() tp_size = get_tensor_model_parallel_world_size() @@ -354,19 +380,24 @@ def __init__(self, config=config, quant_config=quant_config, num_hidden_layers_override=num_hidden_layers_override, + prefix=f"{prefix}.encoder", ) + num_hidden_layers = config.num_hidden_layers if len(self.encoder.layers) > config.num_hidden_layers: raise ValueError( - f"The original encoder only has {config.num_hidden_layers} " + f"The original encoder only has {num_hidden_layers} " f"layers, but you requested {len(self.encoder.layers)} layers." ) - elif len(self.encoder.layers) == config.num_hidden_layers: + + # If possible, skip post_layernorm to conserve memory + if require_post_norm is None: + require_post_norm = len(self.encoder.layers) == num_hidden_layers + + if require_post_norm: self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) else: - # post_layernorm is unused when we extract intermediate features - # In this case, we can skip it to conserve memory self.post_layernorm = None def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py index d6fe7d150336a..cd2013e91514d 100644 --- a/vllm/model_executor/models/blip2.py +++ b/vllm/model_executor/models/blip2.py @@ -490,7 +490,7 @@ def __init__(self, self.multimodal_config = multimodal_config # TODO: Optionally initializes this for supporting embeddings. - self.vision_model = BlipVisionModel(config.vision_config) + self.vision_model = BlipVisionModel(config.vision_config, quant_config) self.query_tokens = nn.Parameter( torch.zeros(1, config.num_query_tokens, diff --git a/vllm/model_executor/models/clip.py b/vllm/model_executor/models/clip.py index 7b0981d611b25..6b45cb384d4a0 100644 --- a/vllm/model_executor/models/clip.py +++ b/vllm/model_executor/models/clip.py @@ -192,6 +192,7 @@ def __init__( self, config: CLIPVisionConfig, quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", ): super().__init__() self.config = config @@ -211,12 +212,14 @@ def __init__( head_size=self.head_dim, total_num_heads=self.num_heads, quant_config=quant_config, + prefix=f"{prefix}.qkv_proj", ) self.out_proj = RowParallelLinear( input_size=self.embed_dim, output_size=self.embed_dim, quant_config=quant_config, + prefix=f"{prefix}.out_proj", ) self.tp_size = get_tensor_model_parallel_world_size() @@ -259,20 +262,25 @@ def forward( class CLIPMLP(nn.Module): - def __init__(self, - config: CLIPVisionConfig, - quant_config: Optional[QuantizationConfig] = None): + def __init__( + self, + config: CLIPVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() self.config = config self.activation_fn = get_act_fn(config.hidden_act) self.fc1 = ColumnParallelLinear(config.hidden_size, config.intermediate_size, bias=True, - quant_config=quant_config) + quant_config=quant_config, + prefix=f"{prefix}.fc1") self.fc2 = RowParallelLinear(config.intermediate_size, config.hidden_size, bias=True, - quant_config=quant_config) + quant_config=quant_config, + prefix=f"{prefix}.fc2") def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states, _ = self.fc1(hidden_states) @@ -284,21 +292,29 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class CLIPEncoderLayer(nn.Module): - def __init__(self, - config: CLIPVisionConfig, - quant_config: Optional[QuantizationConfig] = None): + def __init__( + self, + config: CLIPVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() num_heads = config.num_attention_heads tp_size = get_tensor_model_parallel_world_size() if USE_XFORMERS_OPS and num_heads % tp_size == 0: - self.self_attn = CLIPParallelAttention(config, - quant_config=quant_config) + self.self_attn = CLIPParallelAttention( + config, + quant_config=quant_config, + prefix=f"{prefix}.self_attn", + ) else: self.self_attn = CLIPSdpaAttention(config) self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.mlp = CLIPMLP(config, quant_config=quant_config) + self.mlp = CLIPMLP(config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) @@ -327,11 +343,15 @@ class CLIPEncoder(nn.Module): config: CLIPConfig """ - def __init__(self, - config: CLIPVisionConfig, - quant_config: Optional[QuantizationConfig] = None, - num_hidden_layers_override: Optional[int] = None): + def __init__( + self, + config: CLIPVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + num_hidden_layers_override: Optional[int] = None, + prefix: str = "", + ) -> None: super().__init__() + self.config = config if num_hidden_layers_override is None: @@ -339,8 +359,10 @@ def __init__(self, else: num_hidden_layers = num_hidden_layers_override self.layers = nn.ModuleList([ - CLIPEncoderLayer(config=config, quant_config=quant_config) - for _ in range(num_hidden_layers) + CLIPEncoderLayer(config=config, + quant_config=quant_config, + prefix=f"{prefix}.layers.{layer_idx}") + for layer_idx in range(num_hidden_layers) ]) def forward(self, inputs_embeds: torch.Tensor): @@ -354,11 +376,17 @@ def forward(self, inputs_embeds: torch.Tensor): class CLIPVisionTransformer(nn.Module): - def __init__(self, - config: CLIPVisionConfig, - quant_config: Optional[QuantizationConfig] = None, - num_hidden_layers_override: Optional[int] = None): + def __init__( + self, + config: CLIPVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + num_hidden_layers_override: Optional[int] = None, + require_post_norm: Optional[bool] = None, + prefix: str = "", + ) -> None: super().__init__() + self.config = config embed_dim = config.hidden_size @@ -370,19 +398,25 @@ def __init__(self, self.encoder = CLIPEncoder( config=config, quant_config=quant_config, - num_hidden_layers_override=num_hidden_layers_override) + num_hidden_layers_override=num_hidden_layers_override, + prefix=f"{prefix}.encoder", + ) + num_hidden_layers = config.num_hidden_layers if len(self.encoder.layers) > config.num_hidden_layers: raise ValueError( - f"The original encoder only has {config.num_hidden_layers} " + f"The original encoder only has {num_hidden_layers} " f"layers, but you requested {len(self.encoder.layers)} layers." ) - elif len(self.encoder.layers) == config.num_hidden_layers: + + # If possible, skip post_layernorm to conserve memory + if require_post_norm is None: + require_post_norm = len(self.encoder.layers) == num_hidden_layers + + if require_post_norm: self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) else: - # post_layernorm is unused when we extract intermediate features - # In this case, we can skip it to conserve memory self.post_layernorm = None def forward( @@ -405,10 +439,15 @@ class CLIPVisionModel(nn.Module): config_class = CLIPVisionConfig main_input_name = "pixel_values" - def __init__(self, - config: CLIPVisionConfig, - quant_config: Optional[QuantizationConfig] = None, - num_hidden_layers_override: Optional[int] = None): + def __init__( + self, + config: CLIPVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + num_hidden_layers_override: Optional[int] = None, + require_post_norm: Optional[bool] = None, + prefix: str = "", + ) -> None: super().__init__() tp_size = get_tensor_model_parallel_world_size() @@ -418,7 +457,10 @@ def __init__(self, self.vision_model = CLIPVisionTransformer( config=config, quant_config=quant_config, - num_hidden_layers_override=num_hidden_layers_override) + num_hidden_layers_override=num_hidden_layers_override, + require_post_norm=require_post_norm, + prefix=f"{prefix}.vision_model", + ) def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: return self.vision_model(pixel_values) diff --git a/vllm/model_executor/models/idefics2_vision_model.py b/vllm/model_executor/models/idefics2_vision_model.py index 3b0b6febaa48c..43f4f29814e6d 100644 --- a/vllm/model_executor/models/idefics2_vision_model.py +++ b/vllm/model_executor/models/idefics2_vision_model.py @@ -113,7 +113,8 @@ def __init__( self, config: Idefics2Config, quant_config: Optional[QuantizationConfig] = None, - ): + prefix: str = "", + ) -> None: super().__init__() self.config = config self.embed_dim = config.hidden_size @@ -130,12 +131,14 @@ def __init__( self.head_dim, self.num_heads, quant_config=quant_config, + prefix=f"{prefix}.qkv_proj", ) self.out_proj = RowParallelLinear( self.embed_dim, self.embed_dim, bias=True, quant_config=quant_config, + prefix=f"{prefix}.out_proj", ) self.tp_size = get_tensor_model_parallel_world_size() self.num_heads_per_partition = divide(self.num_heads, self.tp_size) @@ -178,7 +181,8 @@ def __init__( self, config: Idefics2Config, quant_config: Optional[QuantizationConfig] = None, - ): + prefix: str = "", + ) -> None: super().__init__() self.config = config self.activation_fn = get_act_fn(config.hidden_act) @@ -187,12 +191,14 @@ def __init__( config.intermediate_size, bias=True, quant_config=quant_config, + prefix=f"{prefix}.fc1", ) self.fc2 = RowParallelLinear( config.intermediate_size, config.hidden_size, bias=True, quant_config=quant_config, + prefix=f"{prefix}.fc2", ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: @@ -204,13 +210,22 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class Idefics2EncoderLayer(nn.Module): - def __init__(self, config: Idefics2Config): + def __init__( + self, + config: Idefics2Config, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() self.embed_dim = config.hidden_size - self.self_attn = Idefics2VisionAttention(config) + self.self_attn = Idefics2VisionAttention(config, + quant_config=quant_config, + prefix=f"{prefix}.self_attn") self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) - self.mlp = Idefics2VisionMLP(config) + self.mlp = Idefics2VisionMLP(config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) @@ -245,12 +260,20 @@ class Idefics2Encoder(nn.Module): config: Idefics2Config """ - def __init__(self, config: Idefics2Config): + def __init__( + self, + config: Idefics2Config, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() + self.config = config self.layers = nn.ModuleList([ - Idefics2EncoderLayer(config) - for _ in range(config.num_hidden_layers) + Idefics2EncoderLayer(config, + quant_config=quant_config, + prefix=f"{prefix}.layers.{layer_idx}") + for layer_idx in range(config.num_hidden_layers) ]) def forward( @@ -275,12 +298,20 @@ def forward( class Idefics2VisionTransformer(nn.Module): - def __init__(self, config: Idefics2VisionConfig): + def __init__( + self, + config: Idefics2VisionConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() + embed_dim = config.hidden_size self.config = config self.embeddings = Idefics2VisionEmbeddings(config) - self.encoder = Idefics2Encoder(config) + self.encoder = Idefics2Encoder(config, + quant_config=quant_config, + prefix=f"{prefix}.encoder") self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) diff --git a/vllm/model_executor/models/intern_vit.py b/vllm/model_executor/models/intern_vit.py index b59671e914e7d..9761635d2a6c2 100644 --- a/vllm/model_executor/models/intern_vit.py +++ b/vllm/model_executor/models/intern_vit.py @@ -137,6 +137,7 @@ def __init__( quant_config: Optional[QuantizationConfig] = None, *, num_dummy_heads: int = 0, + prefix: str = "", ) -> None: super().__init__() @@ -165,6 +166,7 @@ def __init__( num_dummy_heads + self.num_heads, bias=config.qkv_bias, quant_config=quant_config, + prefix=f"{prefix}.qkv", ) self.qk_normalization = config.qk_normalization @@ -181,6 +183,7 @@ def __init__( self.dummy_dim, self.embed_dim, quant_config=quant_config, + prefix=f"{prefix}.proj", ) def _apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor): @@ -284,20 +287,26 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class InternMLP(nn.Module): - def __init__(self, - config: PretrainedConfig, - quant_config: Optional[QuantizationConfig] = None): + def __init__( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: super().__init__() + self.config = config self.activation_fn = get_act_fn(config.hidden_act) self.fc1 = ColumnParallelLinear(config.hidden_size, config.intermediate_size, bias=True, - quant_config=quant_config) + quant_config=quant_config, + prefix=f"{prefix}.fc1") self.fc2 = RowParallelLinear(config.intermediate_size, config.hidden_size, bias=True, - quant_config=quant_config) + quant_config=quant_config, + prefix=f"{prefix}.fc2") def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states, _ = self.fc1(hidden_states) @@ -315,6 +324,7 @@ def __init__( quant_config: Optional[QuantizationConfig] = None, *, num_dummy_heads: int = 0, + prefix: str = "", ) -> None: super().__init__() @@ -324,9 +334,12 @@ def __init__( self.attn = self._init_attn(config, quant_config, - num_dummy_heads=num_dummy_heads) + num_dummy_heads=num_dummy_heads, + prefix=f"{prefix}.attn") - self.mlp = InternMLP(config, quant_config=quant_config) + self.mlp = InternMLP(config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps) self.norm2 = NORM2FN[self.norm_type](self.embed_dim, @@ -343,6 +356,7 @@ def _init_attn( quant_config: Optional[QuantizationConfig], *, num_dummy_heads: int, + prefix: str = "", ): # fallback to sdpa attention if tp unavailable tp_size = get_tensor_model_parallel_world_size() @@ -351,7 +365,8 @@ def _init_attn( if USE_XFORMERS_OPS and (num_heads + num_dummy_heads) % tp_size == 0: return InternParallelAttention(config, quant_config=quant_config, - num_dummy_heads=num_dummy_heads) + num_dummy_heads=num_dummy_heads, + prefix=prefix) return InternSdpaAttention(config, num_dummy_heads=num_dummy_heads) @@ -377,6 +392,7 @@ def __init__( *, num_hidden_layers_override: Optional[int] = None, num_dummy_heads: int = 0, + prefix: str = "", ): super().__init__() @@ -390,8 +406,9 @@ def __init__( self.layers = nn.ModuleList([ InternVisionEncoderLayer(config, quant_config, - num_dummy_heads=num_dummy_heads) - for _ in range(num_hidden_layers) + num_dummy_heads=num_dummy_heads, + prefix=f"{prefix}.layers.{layer_idx}") + for layer_idx in range(num_hidden_layers) ]) def forward(self, inputs_embeds: torch.Tensor): @@ -412,7 +429,8 @@ def __init__( *, num_hidden_layers_override: Optional[int] = None, num_dummy_heads: int = 0, - ): + prefix: str = "", + ) -> None: super().__init__() self.config = config @@ -423,6 +441,7 @@ def __init__( quant_config=quant_config, num_hidden_layers_override=num_hidden_layers_override, num_dummy_heads=num_dummy_heads, + prefix=f"{prefix}.encoder", ) def get_input_embeddings(self): diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index a80e00e34957c..3ae37d9fe5d85 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -19,7 +19,8 @@ from vllm.config import CacheConfig, MultiModalConfig from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext, token_inputs) -from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.layers.quantization import (AWQConfig, + QuantizationConfig) from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.models.intern_vit import (InternVisionModel, InternVisionPatchModel) @@ -418,11 +419,11 @@ def __init__(self, self.config = config self.multimodal_config = multimodal_config + self._patch_quant_config(config, quant_config) image_size = config.force_image_size or config.vision_config.image_size patch_size = config.vision_config.patch_size self.patch_size = patch_size - self.select_layer = config.select_layer self.num_image_token = int( (image_size // patch_size)**2 * (config.downsample_ratio**2)) self.downsample_ratio = config.downsample_ratio @@ -430,7 +431,12 @@ def __init__(self, self.llm_arch_name = config.text_config.architectures[0] self.is_mono = self.llm_arch_name == 'InternLM2VEForCausalLM' - self.vision_model = self._init_vision_model(config, self.is_mono) + self.vision_model = self._init_vision_model( + config, + quant_config=quant_config, + is_mono=self.is_mono, + prefix="vision_model", + ) self.language_model = init_vllm_registered_model( config.text_config, cache_config, quant_config) @@ -441,6 +447,18 @@ def __init__(self, self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) + def _patch_quant_config(self, config: PretrainedConfig, + quant_config: QuantizationConfig): + # the awq models from OpenGVLab missing `modules_to_not_convert` + # patch the quant_config to add `modules_to_not_convert` back + if isinstance(quant_config, AWQConfig): + text_config = config.text_config + llm_quant_config = getattr(text_config, "quantization_config", + None) + if (not quant_config.modules_to_not_convert) and \ + (llm_quant_config is not None): + quant_config.modules_to_not_convert.append("vision_model") + @cached_property def sampler(self): if hasattr(self.language_model, "sampler"): @@ -448,17 +466,28 @@ def sampler(self): return Sampler() - def _init_vision_model(self, config: PretrainedConfig, is_mono: bool): + def _init_vision_model( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig], + *, + is_mono: bool, + prefix: str, + ): if not is_mono: - vision_feature_layer = self.select_layer + vision_feature_layer = config.select_layer if vision_feature_layer < 0: num_hidden_layers = config.vision_config.num_hidden_layers \ + vision_feature_layer + 1 else: num_hidden_layers = vision_feature_layer + 1 + return InternVisionModel( config.vision_config, - num_hidden_layers_override=num_hidden_layers) + quant_config=quant_config, + num_hidden_layers_override=num_hidden_layers, + prefix=prefix, + ) else: return InternVisionPatchModel(config.vision_config) diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index a666dcba290f2..83e869efa4712 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -1,12 +1,12 @@ from functools import cached_property -from typing import (Iterable, List, Literal, Mapping, Optional, Tuple, - TypedDict, Union) +from typing import (Iterable, List, Literal, Mapping, Optional, Protocol, + Tuple, TypedDict, Union) import torch import torch.nn as nn from PIL import Image from transformers import (CLIPVisionConfig, LlavaConfig, PixtralVisionConfig, - SiglipVisionConfig) + PretrainedConfig, SiglipVisionConfig) from vllm.attention import AttentionMetadata from vllm.config import CacheConfig, MultiModalConfig @@ -200,7 +200,17 @@ def input_processor_for_llava(ctx: InputContext, inputs: DecoderOnlyInputs): raise NotImplementedError(msg) -def _init_vision_tower(hf_config: LlavaConfig): +class LlavaLikeConfig(Protocol): + vision_config: PretrainedConfig + vision_feature_layer: int + + +def init_vision_tower_for_llava( + hf_config: LlavaLikeConfig, + quant_config: Optional[QuantizationConfig], + *, + require_post_norm: Optional[bool] = None, +): vision_config = hf_config.vision_config # Initialize the vision tower only up to the required feature layer @@ -214,16 +224,24 @@ def _init_vision_tower(hf_config: LlavaConfig): if isinstance(vision_config, CLIPVisionConfig): return CLIPVisionModel( vision_config, + quant_config, num_hidden_layers_override=num_hidden_layers, + require_post_norm=require_post_norm, ) elif isinstance(vision_config, SiglipVisionConfig): return SiglipVisionModel( vision_config, + quant_config, num_hidden_layers_override=num_hidden_layers, + require_post_norm=require_post_norm, ) elif isinstance(vision_config, PixtralVisionConfig): - # TODO: allow layer override? - return PixtralHFVisionModel(vision_config) + return PixtralHFVisionModel( + vision_config, + quant_config, + num_hidden_layers_override=num_hidden_layers, + require_post_norm=require_post_norm, + ) msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) @@ -255,7 +273,7 @@ def __init__(self, config.projector_hidden_act = "gelu" # TODO: Optionally initializes this for supporting embeddings. - self.vision_tower = _init_vision_tower(config) + self.vision_tower = init_vision_tower_for_llava(config, quant_config) self.multi_modal_projector = LlavaMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, text_hidden_size=config.text_config.hidden_size, diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index 46cba8ebbc583..d33d4ac5bfaed 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -26,7 +26,7 @@ dummy_seq_data_for_clip, get_clip_image_feature_size, get_clip_patch_grid_length, input_processor_for_clip) from .interfaces import SupportsMultiModal, SupportsPP -from .llava import LlavaMultiModalProjector +from .llava import LlavaMultiModalProjector, init_vision_tower_for_llava from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip, get_siglip_image_feature_size, get_siglip_patch_grid_length, input_processor_for_siglip) @@ -259,32 +259,6 @@ def input_processor_for_llava_next(ctx: InputContext, raise NotImplementedError(msg) -def _init_vision_tower(hf_config: LlavaNextConfig): - vision_config = hf_config.vision_config - - # Initialize the vision tower only up to the required feature layer - vision_feature_layer = hf_config.vision_feature_layer - if vision_feature_layer < 0: - num_hidden_layers = hf_config.vision_config.num_hidden_layers \ - + vision_feature_layer + 1 - else: - num_hidden_layers = vision_feature_layer + 1 - - if isinstance(vision_config, CLIPVisionConfig): - return CLIPVisionModel( - vision_config, - num_hidden_layers_override=num_hidden_layers, - ) - elif isinstance(vision_config, SiglipVisionConfig): - return SiglipVisionModel( - vision_config, - num_hidden_layers_override=num_hidden_layers, - ) - - msg = f"Unsupported vision config: {type(vision_config)}" - raise NotImplementedError(msg) - - @MULTIMODAL_REGISTRY.register_image_input_mapper() @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_next_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_next) @@ -303,7 +277,7 @@ def __init__(self, self.multimodal_config = multimodal_config # TODO: Optionally initializes this for supporting embeddings. - self.vision_tower = _init_vision_tower(config) + self.vision_tower = init_vision_tower_for_llava(config, quant_config) self.image_newline = nn.Parameter( torch.empty(config.text_config.hidden_size)) self.multi_modal_projector = LlavaMultiModalProjector( diff --git a/vllm/model_executor/models/llava_next_video.py b/vllm/model_executor/models/llava_next_video.py index 4a354b616c2f6..d02cf9044dfc0 100644 --- a/vllm/model_executor/models/llava_next_video.py +++ b/vllm/model_executor/models/llava_next_video.py @@ -26,6 +26,7 @@ from .clip import dummy_image_for_clip, dummy_seq_data_for_clip from .interfaces import SupportsMultiModal, SupportsPP +from .llava import init_vision_tower_for_llava from .siglip import (SiglipVisionModel, dummy_image_for_siglip, dummy_seq_data_for_siglip) from .utils import (AutoWeightsLoader, init_vllm_registered_model, @@ -179,32 +180,6 @@ def input_processor_for_llava_next_video(ctx: InputContext, raise NotImplementedError(msg) -def _init_vision_tower(hf_config: LlavaNextVideoConfig): - vision_config = hf_config.vision_config - - # Initialize the vision tower only up to the required feature layer - vision_feature_layer = hf_config.vision_feature_layer - if vision_feature_layer < 0: - num_hidden_layers = hf_config.vision_config.num_hidden_layers \ - + vision_feature_layer + 1 - else: - num_hidden_layers = vision_feature_layer + 1 - - if isinstance(vision_config, CLIPVisionConfig): - return CLIPVisionModel( - vision_config, - num_hidden_layers_override=num_hidden_layers, - ) - elif isinstance(vision_config, SiglipVisionConfig): - return SiglipVisionModel( - vision_config, - num_hidden_layers_override=num_hidden_layers, - ) - - msg = f"Unsupported vision config: {type(vision_config)}" - raise NotImplementedError(msg) - - # adopted from transformers modeling_llava_next_video.py class LlavaNextVideoPooler(nn.Module): @@ -281,7 +256,7 @@ def __init__(self, self.multimodal_config = multimodal_config # Initialize the vision tower only up to the required feature layer - self.vision_tower = _init_vision_tower(config) + self.vision_tower = init_vision_tower_for_llava(config, quant_config) self.vision_resampler = LlavaNextVideoPooler(config) self.multi_modal_projector = LlavaNextMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, diff --git a/vllm/model_executor/models/llava_onevision.py b/vllm/model_executor/models/llava_onevision.py index 5bd3055ca181a..10aa8049a2347 100644 --- a/vllm/model_executor/models/llava_onevision.py +++ b/vllm/model_executor/models/llava_onevision.py @@ -31,6 +31,7 @@ dummy_video_for_clip, get_clip_image_feature_size, get_clip_patch_grid_length, input_processor_for_clip) from .interfaces import SupportsMultiModal, SupportsPP +from .llava import init_vision_tower_for_llava from .siglip import (SiglipVisionModel, dummy_seq_data_for_siglip, dummy_video_for_siglip, get_siglip_image_feature_size, get_siglip_patch_grid_length, input_processor_for_siglip) @@ -357,32 +358,6 @@ def input_processor_for_llava_onevision(ctx: InputContext, raise NotImplementedError(msg) -def _init_vision_tower(hf_config: LlavaOnevisionConfig): - vision_config = hf_config.vision_config - - # Initialize the vision tower only up to the required feature layer - vision_feature_layer = hf_config.vision_feature_layer - if vision_feature_layer < 0: - num_hidden_layers = hf_config.vision_config.num_hidden_layers \ - + vision_feature_layer + 1 - else: - num_hidden_layers = vision_feature_layer + 1 - - if isinstance(vision_config, CLIPVisionConfig): - return CLIPVisionModel( - vision_config, - num_hidden_layers_override=num_hidden_layers, - ) - elif isinstance(vision_config, SiglipVisionConfig): - return SiglipVisionModel( - vision_config, - num_hidden_layers_override=num_hidden_layers, - ) - - msg = f"Unsupported vision config: {type(vision_config)}" - raise NotImplementedError(msg) - - class LlavaOnevisionMultiModalProjector(nn.Module): def __init__(self, config: LlavaOnevisionConfig): @@ -425,7 +400,7 @@ def __init__(self, self.multimodal_config = multimodal_config # Initialize the vision tower only up to the required feature layer - self.vision_tower = _init_vision_tower(config) + self.vision_tower = init_vision_tower_for_llava(config, quant_config) self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config) self.language_model = init_vllm_registered_model( config.text_config, cache_config, quant_config) diff --git a/vllm/model_executor/models/minicpmv.py b/vllm/model_executor/models/minicpmv.py index ca7c2be5a038e..2ec51dc4647f5 100644 --- a/vllm/model_executor/models/minicpmv.py +++ b/vllm/model_executor/models/minicpmv.py @@ -395,7 +395,7 @@ def __init__( self.version = get_version_by_config(self.config) self.llm = self.init_llm(config, cache_config, quant_config) - self.vpm = self.init_vision_module() + self.vpm = self.init_vision_module(config, quant_config) param_dtype = torch.get_default_dtype() self.vpm.to(dtype=param_dtype) self.vision_dim = (self.vpm.embed_dim if self.version == (2, 0) else @@ -647,7 +647,11 @@ def init_llm( ) -> nn.Module: raise NotImplementedError - def init_vision_module(self) -> nn.Module: + def init_vision_module( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig], + ) -> nn.Module: raise NotImplementedError def init_resampler(self, embed_dim: int, vision_dim: int) -> nn.Module: @@ -693,7 +697,11 @@ def init_llm( quant_config=quant_config), name="model") - def init_vision_module(self) -> nn.Module: + def init_vision_module( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig], + ) -> nn.Module: # TODO :refactor this vision model try: import timm @@ -817,8 +825,13 @@ def init_llm( quant_config=quant_config), name="model") - def init_vision_module(self) -> nn.Module: - model = Idefics2VisionTransformer(self.config.vision_config) + def init_vision_module( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig], + ) -> nn.Module: + model = Idefics2VisionTransformer(config.vision_config, + quant_config=quant_config) if self.config.drop_vision_last_layer: model.encoder.layers = model.encoder.layers[:-1] return model @@ -929,9 +942,13 @@ def init_llm( quant_config=quant_config), name="model") - def init_vision_module(self) -> nn.Module: - - model = Idefics2VisionTransformer(self.config.vision_config) + def init_vision_module( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig], + ) -> nn.Module: + model = Idefics2VisionTransformer(config.vision_config, + quant_config=quant_config) if self.config.drop_vision_last_layer: model.encoder.layers = model.encoder.layers[:-1] return model diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py index 378231f14455a..23e2b520e5b40 100644 --- a/vllm/model_executor/models/mllama.py +++ b/vllm/model_executor/models/mllama.py @@ -379,9 +379,13 @@ def forward( class MllamaVisionEncoderLayer(nn.Module): - def __init__(self, - config: config_mllama.MllamaVisionConfig, - is_gated: bool = False): + def __init__( + self, + config: config_mllama.MllamaVisionConfig, + quant_config: Optional[QuantizationConfig], + prefix: str = "", + is_gated: bool = False, + ) -> None: super().__init__() self.hidden_size = config.hidden_size @@ -390,7 +394,9 @@ def __init__(self, self.intermediate_size = config.intermediate_size self.self_attn = MllamaVisionSdpaAttention(config) - self.mlp = CLIPMLP(config) + self.mlp = CLIPMLP(config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") self.input_layernorm = nn.LayerNorm(self.hidden_size, eps=config.norm_eps) @@ -427,16 +433,23 @@ def forward( class MllamaVisionEncoder(nn.Module): - def __init__(self, - config: config_mllama.MllamaVisionConfig, - num_layers=32, - is_gated=False, - output_hidden_states=None): + def __init__( + self, + config: config_mllama.MllamaVisionConfig, + quant_config: Optional[QuantizationConfig], + num_layers: int = 32, + is_gated: bool = False, + output_hidden_states=None, + prefix: str = "", + ) -> None: super().__init__() self.config = config self.layers = nn.ModuleList([ - MllamaVisionEncoderLayer(config, is_gated) - for _ in range(num_layers) + MllamaVisionEncoderLayer(config, + quant_config=quant_config, + is_gated=is_gated, + prefix=f"{prefix}.layers.{layer_idx}") + for layer_idx in range(num_layers) ]) self.output_hidden_states = output_hidden_states or [] @@ -463,8 +476,14 @@ def forward( class MllamaVisionModel(nn.Module): - def __init__(self, config: config_mllama.MllamaVisionConfig): + def __init__( + self, + config: config_mllama.MllamaVisionConfig, + quant_config: Optional[QuantizationConfig], + prefix: str = "", + ) -> None: super().__init__() + self.image_size = config.image_size self.patch_size = config.patch_size self.max_num_tiles = config.max_num_tiles @@ -500,12 +519,19 @@ def __init__(self, config: config_mllama.MllamaVisionConfig): # encoders self.transformer = MllamaVisionEncoder( config, + quant_config, config.num_hidden_layers, is_gated=False, - output_hidden_states=config.intermediate_layers_indices) - self.global_transformer = MllamaVisionEncoder(config, - config.num_global_layers, - is_gated=True) + output_hidden_states=config.intermediate_layers_indices, + prefix=f"{prefix}.transformer", + ) + self.global_transformer = MllamaVisionEncoder( + config, + quant_config, + config.num_global_layers, + is_gated=True, + prefix=f"{prefix}.global_transformer", + ) def apply_class_embedding(self, hidden_state: torch.Tensor) -> torch.Tensor: @@ -648,6 +674,7 @@ def __init__( config: Optional[config_mllama.MllamaTextConfig] = None, layer_idx: Optional[int] = None, quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", ): super().__init__() self.config = config @@ -673,6 +700,7 @@ def __init__( self.num_key_value_heads, bias=False, quant_config=quant_config, + prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( self.num_heads * self.head_dim, @@ -680,6 +708,7 @@ def __init__( bias=False, input_is_parallel=True, quant_config=quant_config, + prefix=f"{prefix}.o_proj", ) # vllm.model_executor.layers.layernorm.RMSNorm has precision issue, # use huggingface's instead @@ -692,6 +721,7 @@ def __init__( self.head_dim, self.scaling, self.num_local_key_value_heads, + prefix=f"{prefix}.attn", ) def forward( @@ -791,15 +821,21 @@ class MllamaCrossAttentionDecoderLayer(torch.nn.Module): """Cross-attention transformer block with tanh-gated attention and feedforward.""" - def __init__(self, config: config_mllama.MllamaTextConfig, layer_idx: int, - quant_config: Optional[QuantizationConfig]) \ - -> None: + def __init__( + self, + config: config_mllama.MllamaTextConfig, + layer_idx: int, + quant_config: Optional[QuantizationConfig], + prefix: str = "", + ) -> None: super().__init__() + self.layer_idx = layer_idx self.cross_attn = MllamaTextCrossAttention( config=config, layer_idx=layer_idx, quant_config=quant_config, + prefix=f"{prefix}.cross_attn", ) self.input_layernorm = RMSNorm(config.hidden_size, @@ -811,6 +847,7 @@ def __init__(self, config: config_mllama.MllamaTextConfig, layer_idx: int, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, + prefix=f"{prefix}.mlp", ) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) @@ -854,10 +891,15 @@ class MllamaTextModel(nn.Module): config_class = config_mllama.MllamaTextConfig base_model_prefix = "model" - def __init__(self, config: config_mllama.MllamaTextConfig, - cache_config: Optional[CacheConfig], - quant_config: Optional[QuantizationConfig]): + def __init__( + self, + config: config_mllama.MllamaTextConfig, + cache_config: Optional[CacheConfig], + quant_config: Optional[QuantizationConfig], + prefix: str = "", + ) -> None: super().__init__() + self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding(config.vocab_size + 8, @@ -869,13 +911,20 @@ def __init__(self, config: config_mllama.MllamaTextConfig, if layer_idx in self.cross_attention_layers: layers.append( MllamaCrossAttentionDecoderLayer( - config, layer_idx, quant_config=quant_config)) + config, + layer_idx, + quant_config=quant_config, + prefix=f"{prefix}.layers.{layer_idx}", + )) else: # TODO: force LlamaDecoderLayer to config.attention_bias=False layers.append( - LlamaDecoderLayer(config, - cache_config=cache_config, - quant_config=quant_config)) + LlamaDecoderLayer( + config, + cache_config=cache_config, + quant_config=quant_config, + prefix=f"{prefix}.layers.{layer_idx}", + )) self.layers = nn.ModuleList(layers) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) @@ -932,12 +981,19 @@ class MllamaForCausalLM(nn.Module): "MllamaCrossAttentionDecoderLayer", "MllamaSelfAttentionDecoderLayer" ] - def __init__(self, config: config_mllama.MllamaTextConfig, - cache_config: Optional[CacheConfig], - quant_config: Optional[QuantizationConfig]): + def __init__( + self, + config: config_mllama.MllamaTextConfig, + cache_config: Optional[CacheConfig], + quant_config: Optional[QuantizationConfig], + prefix: str = "", + ) -> None: super().__init__() self.vocab_size = config.vocab_size - self.model = MllamaTextModel(config, cache_config, quant_config) + self.model = MllamaTextModel(config, + cache_config, + quant_config, + prefix=f"{prefix}.model") self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, @@ -994,11 +1050,13 @@ def __init__(self, config.pad_token_id if config.pad_token_id is not None else -1 self.image_size = config.vision_config.image_size - self.vision_model = MllamaVisionModel(config.vision_config) + self.vision_model = MllamaVisionModel(config.vision_config, + quant_config) self.language_model = MllamaForCausalLM( config.text_config, cache_config=cache_config, quant_config=quant_config, + prefix="language_model", ) self.multi_modal_projector = nn.Linear( config.vision_config.vision_output_dim, diff --git a/vllm/model_executor/models/nvlm_d.py b/vllm/model_executor/models/nvlm_d.py index a52e3cb6039be..3e3c3b05879fb 100644 --- a/vllm/model_executor/models/nvlm_d.py +++ b/vllm/model_executor/models/nvlm_d.py @@ -4,10 +4,13 @@ # Copyright (c) 2024 NVIDIA # Licensed under Apache 2.0 License [see LICENSE for details] # -------------------------------------------------------- +from typing import Optional + import torch.nn as nn from transformers import PretrainedConfig from vllm.inputs import INPUT_REGISTRY +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.multimodal import MULTIMODAL_REGISTRY from .intern_vit import InternVisionModel @@ -56,9 +59,11 @@ def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential: ) def _init_vision_model(self, config: PretrainedConfig, + quant_config: Optional[QuantizationConfig], num_hidden_layers: int): # We added additional dummy heads to the original num of heads to make # the number of heads divisible by 8. return InternVisionModel(config.vision_config, + quant_config=quant_config, num_hidden_layers_override=num_hidden_layers, num_dummy_heads=7) diff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py index 7806cd6ab4608..7a62a098a4525 100644 --- a/vllm/model_executor/models/paligemma.py +++ b/vllm/model_executor/models/paligemma.py @@ -142,7 +142,8 @@ def __init__(self, self.config = config self.multimodal_config = multimodal_config - self.vision_tower = SiglipVisionModel(config.vision_config) + self.vision_tower = SiglipVisionModel(config.vision_config, + quant_config) self.multi_modal_projector = PaliGemmaMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, projection_dim=config.vision_config.projection_dim) diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index 9a1083520efd2..855a9b17585a4 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -70,7 +70,8 @@ projection_dim=768) -def _init_img_processor(hf_config: PretrainedConfig): +def _init_img_processor(hf_config: PretrainedConfig, + quant_config: Optional[QuantizationConfig]): clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG layer_idx = hf_config.img_processor.get('layer_idx', -2) @@ -82,7 +83,10 @@ def _init_img_processor(hf_config: PretrainedConfig): num_hidden_layers = layer_idx + 1 img_processor = CLIPVisionModel( - clip_config, num_hidden_layers_override=num_hidden_layers) + clip_config, + quant_config, + num_hidden_layers_override=num_hidden_layers, + ) return img_processor @@ -148,14 +152,15 @@ def get_img_features(self, class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase): """Phi3 Image embedding with HD transform.""" - def __init__(self, config: PretrainedConfig) -> None: + def __init__(self, config: PretrainedConfig, + quant_config: Optional[QuantizationConfig]) -> None: super().__init__() # n_embed or hidden_size hidden_size = config.n_embd if hasattr( config, 'n_embd') else config.hidden_size - self.img_processor = _init_img_processor(config) + self.img_processor = _init_img_processor(config, quant_config) image_dim_out = config.img_processor['image_dim_out'] self.num_img_tokens = config.img_processor['num_img_tokens'] @@ -535,7 +540,7 @@ def __init__(self, ) # TODO: Optionally initializes this for supporting input embeddings. - self.vision_embed_tokens = Phi3HDImageEmbedding(config) + self.vision_embed_tokens = Phi3HDImageEmbedding(config, quant_config) self.language_model = LlamaForCausalLM(config, cache_config, quant_config) diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index f33871c0d5acc..18dbee94e10b0 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -767,9 +767,17 @@ def input_processor_for_pixtral_hf( class PixtralHFMLP(nn.Module): - def __init__(self, config: PixtralVisionConfig): + def __init__( + self, + config: PixtralVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + prefix: str = "", + ) -> None: super().__init__() + assert config.intermediate_size is not None + # TODO: Use quant_config and prefix after optimizing this self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) @@ -787,8 +795,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class PixtralHFAttention(nn.Module): - def __init__(self, config: PixtralVisionConfig): + def __init__( + self, + config: PixtralVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + prefix: str = "", + ) -> None: super().__init__() + self.config = config assert not config.hidden_size % config.num_attention_heads self.n_heads = config.num_attention_heads @@ -796,6 +811,7 @@ def __init__(self, config: PixtralVisionConfig): self.scale = self.head_dim**-0.5 + # TODO: Use quant_config and prefix after optimizing this self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False) @@ -840,11 +856,22 @@ def forward( class PixtralHFTransformerBlock(nn.Module): - def __init__(self, config: PixtralVisionConfig): + def __init__( + self, + config: PixtralVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + prefix: str = "", + ) -> None: super().__init__() + self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5) - self.attention = PixtralHFAttention(config) - self.feed_forward = PixtralHFMLP(config) + self.attention = PixtralHFAttention(config, + quant_config=quant_config, + prefix=f"{prefix}.attention") + self.feed_forward = PixtralHFMLP(config, + quant_config=quant_config, + prefix=f"{prefix}.feed_forward") self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5) def forward( @@ -864,11 +891,27 @@ def forward( class PixtralHFTransformer(nn.Module): - def __init__(self, config: PixtralVisionConfig): + def __init__( + self, + config: PixtralVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + num_hidden_layers_override: Optional[int] = None, + prefix: str = "", + ) -> None: super().__init__() - self.layers = torch.nn.ModuleList() - for _ in range(config.num_hidden_layers): - self.layers.append(PixtralHFTransformerBlock(config)) + + if num_hidden_layers_override is None: + num_hidden_layers = config.num_hidden_layers + else: + num_hidden_layers = num_hidden_layers_override + + self.layers = nn.ModuleList([ + PixtralHFTransformerBlock(config=config, + quant_config=quant_config, + prefix=f"{prefix}.layers.{layer_idx}") + for layer_idx in range(num_hidden_layers) + ]) def forward( self, @@ -883,7 +926,15 @@ def forward( class PixtralHFVisionModel(nn.Module): - def __init__(self, config: PixtralVisionConfig): + def __init__( + self, + config: PixtralVisionConfig, + quant_config: Optional[QuantizationConfig] = None, + *, + num_hidden_layers_override: Optional[int] = None, + require_post_norm: Optional[bool] = None, + prefix: str = "", + ) -> None: super().__init__() self.config = config @@ -895,7 +946,24 @@ def __init__(self, config: PixtralVisionConfig): bias=False, ) self.ln_pre = RMSNorm(config.hidden_size, eps=1e-5) - self.transformer = PixtralHFTransformer(config) + self.transformer = PixtralHFTransformer( + config, + quant_config, + num_hidden_layers_override=num_hidden_layers_override, + prefix=f"{prefix}.transformer", + ) + + num_hidden_layers = config.num_hidden_layers + if len(self.transformer.layers) > config.num_hidden_layers: + raise ValueError( + f"The original encoder only has {num_hidden_layers} " + f"layers, but you requested {len(self.transformer.layers)} " + "layers.") + + if require_post_norm is True: + msg = "PixtralHFVisionModel does not have post-layernorm" + raise ValueError(msg) + self.dtype = next(self.parameters()).dtype self.device = next(self.parameters()).device self.patch_positional_embedding = PixtralRotaryEmbedding( diff --git a/vllm/model_executor/models/siglip.py b/vllm/model_executor/models/siglip.py index e717ab108c77b..91277b0ccd145 100644 --- a/vllm/model_executor/models/siglip.py +++ b/vllm/model_executor/models/siglip.py @@ -248,8 +248,10 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, - ): + prefix: str = "", + ) -> None: super().__init__() + self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads @@ -266,12 +268,14 @@ def __init__( head_size=self.head_dim, total_num_heads=self.num_heads, quant_config=quant_config, + prefix=f"{prefix}.qkv_proj", ) self.out_proj = RowParallelLinear( input_size=self.embed_dim, output_size=self.embed_dim, quant_config=quant_config, + prefix=f"{prefix}.out_proj", ) self.tp_size = get_tensor_model_parallel_world_size() @@ -314,8 +318,10 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, - ): + prefix: str = "", + ) -> None: super().__init__() + self.config = config self.activation_fn = get_act_fn(config.hidden_act) @@ -326,11 +332,13 @@ def __init__( config.hidden_size, config.intermediate_size, quant_config=quant_config if quantizable else None, + prefix=f"{prefix}.fc1", ) self.fc2 = RowParallelLinear( config.intermediate_size, config.hidden_size, quant_config=quant_config if quantizable else None, + prefix=f"{prefix}.fc2", ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: @@ -346,15 +354,20 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, - ): + prefix: str = "", + ) -> None: super().__init__() + self.embed_dim = config.hidden_size num_heads = config.num_attention_heads tp_size = get_tensor_model_parallel_world_size() if USE_XFORMERS_OPS and num_heads % tp_size == 0: - self.self_attn = SiglipParallelAttention(config, - quant_config=quant_config) + self.self_attn = SiglipParallelAttention( + config, + quant_config=quant_config, + prefix=f"{prefix}.self_attn", + ) else: self.self_attn = SiglipSdpaAttention(config) @@ -363,6 +376,7 @@ def __init__( self.mlp = SiglipMLP( config, quant_config=quant_config, + prefix=f"{prefix}.mlp", ) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) @@ -392,8 +406,10 @@ def __init__( config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, num_hidden_layers_override: Optional[int] = None, - ): + prefix: str = "", + ) -> None: super().__init__() + self.config = config if num_hidden_layers_override is None: @@ -402,8 +418,10 @@ def __init__( num_hidden_layers = num_hidden_layers_override self.layers = nn.ModuleList([ - SiglipEncoderLayer(config, quant_config=quant_config) - for _ in range(num_hidden_layers) + SiglipEncoderLayer(config, + quant_config=quant_config, + prefix=f"{prefix}.layers.{layer_idx}") + for layer_idx in range(num_hidden_layers) ]) def forward( @@ -424,7 +442,8 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, - ): + prefix: str = "", + ) -> None: super().__init__() self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) @@ -433,7 +452,9 @@ def __init__( config.hidden_size, config.num_attention_heads, batch_first=True) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.mlp = SiglipMLP(config=config, quant_config=quant_config) + self.mlp = SiglipMLP(config=config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: batch_size = hidden_state.shape[0] @@ -454,9 +475,13 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, + *, num_hidden_layers_override: Optional[int] = None, - ): + require_post_norm: Optional[bool] = None, + prefix: str = "", + ) -> None: super().__init__() + self.config = config embed_dim = config.hidden_size @@ -465,26 +490,34 @@ def __init__( config, quant_config=quant_config, num_hidden_layers_override=num_hidden_layers_override, + prefix=f"{prefix}.encoder", ) + num_hidden_layers = config.num_hidden_layers if len(self.encoder.layers) > config.num_hidden_layers: raise ValueError( - f"The original encoder only has {config.num_hidden_layers} " + f"The original encoder only has {num_hidden_layers} " f"layers, but you requested {len(self.encoder.layers)} layers." ) - elif len(self.encoder.layers) == config.num_hidden_layers: + + # If possible, skip post_layernorm to conserve memory + if require_post_norm is None: + require_post_norm = len(self.encoder.layers) == num_hidden_layers + + if require_post_norm: self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) else: - # post_layernorm is unused when we extract intermediate features - # In this case, we can skip it to conserve memory self.post_layernorm = None self.use_head = (True if not hasattr(config, "vision_use_head") else config.vision_use_head) if self.use_head: self.head = SiglipMultiheadAttentionPoolingHead( - config=config, quant_config=quant_config) + config=config, + quant_config=quant_config, + prefix=f"{prefix}.head", + ) def forward( self, @@ -517,8 +550,11 @@ def __init__( self, config: SiglipVisionConfig, quant_config: Optional[QuantizationConfig] = None, + *, num_hidden_layers_override: Optional[int] = None, - ): + require_post_norm: Optional[bool] = None, + prefix: str = "", + ) -> None: super().__init__() num_heads = config.num_attention_heads @@ -529,6 +565,8 @@ def __init__( config, quant_config, num_hidden_layers_override=num_hidden_layers_override, + require_post_norm=require_post_norm, + prefix=f"{prefix}.vision_model", ) def get_input_embeddings(self) -> nn.Module: From 31a08f5bd231c2ac547e9bb6b6490282d2e76f83 Mon Sep 17 00:00:00 2001 From: Alex Brooks Date: Wed, 23 Oct 2024 08:05:18 -0600 Subject: [PATCH 065/222] [Model] Add min_pixels / max_pixels to Qwen2VL as mm_processor_kwargs (#9612) Signed-off-by: Alex-Brooks --- examples/offline_inference_vision_language.py | 5 + .../vision_language/test_qwen2_vl.py | 160 ++++++++++++++++++ vllm/model_executor/models/qwen2_vl.py | 89 ++++++++-- 3 files changed, 236 insertions(+), 18 deletions(-) create mode 100644 tests/models/decoder_only/vision_language/test_qwen2_vl.py diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index 610cc31db9c4e..83d2548a506e4 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -267,6 +267,11 @@ def run_qwen2_vl(question: str, modality: str): model=model_name, max_model_len=8192, max_num_seqs=5, + # Note - mm_processor_kwargs can also be passed to generate/chat calls + mm_processor_kwargs={ + "min_pixels": 28 * 28, + "max_pixels": 1280 * 28 * 28, + }, ) prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n" diff --git a/tests/models/decoder_only/vision_language/test_qwen2_vl.py b/tests/models/decoder_only/vision_language/test_qwen2_vl.py new file mode 100644 index 0000000000000..d3de5fb26d4b8 --- /dev/null +++ b/tests/models/decoder_only/vision_language/test_qwen2_vl.py @@ -0,0 +1,160 @@ +from typing import Any, Dict, Tuple + +import pytest +import torch +from PIL.Image import Image +from transformers import AutoTokenizer + +from vllm.inputs import InputContext, token_inputs +from vllm.multimodal import MultiModalRegistry + +from ....conftest import _ImageAssets +from ...utils import build_model_context + +MODEL = "Qwen/Qwen2-VL-2B-Instruct" +MIN_PIXELS = "min_pixels" +MAX_PIXELS = "max_pixels" + + +# Fixtures lazy import to avoid initializing CUDA during test collection +# NOTE: Qwen2vl supports multiple input modalities, so it registers multiple +# input mappers. +@pytest.fixture() +def image_input_mapper_for_qwen2_vl(): + from vllm.model_executor.models.qwen2_vl import ( + image_input_mapper_for_qwen2_vl) + return image_input_mapper_for_qwen2_vl + + +@pytest.fixture() +def input_processor_for_qwen2_vl(): + from vllm.model_executor.models.qwen2_vl import ( + input_processor_for_qwen2_vl) + return input_processor_for_qwen2_vl + + +@pytest.fixture() +def qwen2_vl_context() -> InputContext: + return build_model_context(model_name=MODEL) + + +@pytest.fixture() +def get_max_qwen2_vl_image_tokens(): + from vllm.model_executor.models.qwen2_vl import ( + get_max_qwen2_vl_image_tokens) + return get_max_qwen2_vl_image_tokens + + +@pytest.fixture() +def dummy_data_for_qwen2_vl(): + from vllm.model_executor.models.qwen2_vl import dummy_data_for_qwen2_vl + return dummy_data_for_qwen2_vl + + +@pytest.mark.parametrize("mm_processor_kwargs,expected_max_tokens", [ + ({}, 1225), + ({ + MIN_PIXELS: 64**2, + MAX_PIXELS: 512**2 + }, 324), +]) +def test_qwen2_vl_max_image_tokens(get_max_qwen2_vl_image_tokens, + qwen2_vl_context: InputContext, + mm_processor_kwargs: Dict[str, Any], + expected_max_tokens: int): + """Ensure that the max token calc handles min/max pixels properly.""" + actual_max_tokens = get_max_qwen2_vl_image_tokens(qwen2_vl_context, + **mm_processor_kwargs) + assert actual_max_tokens == expected_max_tokens + + +@pytest.mark.parametrize("mm_processor_kwargs,token_count,img_size", [ + [{}, 1225, (980, 980)], + [{ + MIN_PIXELS: 64**2, + MAX_PIXELS: 512**2 + }, 324, (504, 504)], +]) +def test_qwen2_vl_dummy_data(dummy_data_for_qwen2_vl, + qwen2_vl_context: InputContext, + mm_processor_kwargs: Dict[str, Any], + token_count: int, img_size: Tuple[int, int]): + """Ensure that the dummy data handles min/max pixels properly.""" + seq_len = 3000 + hf_config = qwen2_vl_context.get_hf_config() + image_token_id = hf_config.image_token_id + + # NOTE: video value is required, but isn't actually used + # when making the dummy data except for error handling currently + seq_data, mm_data = dummy_data_for_qwen2_vl(qwen2_vl_context, seq_len, { + "image": 1, + "video": 0 + }, **mm_processor_kwargs) + + # Ensure we have the right number of placeholders for min/max pixel values + assert seq_data.get_token_ids().count(image_token_id) == token_count + + # Ensure the images were resized correctly + image = mm_data["image"] + assert isinstance(image, Image) + assert image.size == img_size + + +@pytest.mark.parametrize("mm_processor_kwargs,num_placeholders", [ + ({}, 1426), + ({ + MIN_PIXELS: 64**2, + MAX_PIXELS: 512**2 + }, 330), +]) +def test_input_processor(input_processor_for_qwen2_vl, + qwen2_vl_context: InputContext, + image_assets: _ImageAssets, num_placeholders: int, + mm_processor_kwargs: Dict[str, Any]): + """Ensure that the image processor handles min/max pixels properly.""" + tokenizer = AutoTokenizer.from_pretrained(MODEL) + prompt = "<|vision_start|><|image_pad|><|vision_end|>" + + image = image_assets[0].pil_image + hf_config = qwen2_vl_context.get_hf_config() + image_token_id = hf_config.image_token_id + + inputs = token_inputs(prompt_token_ids=tokenizer.encode(prompt), + prompt=prompt, + multi_modal_data={"image": [image]}) + + processed_inputs = input_processor_for_qwen2_vl(qwen2_vl_context, inputs, + **mm_processor_kwargs) + assert processed_inputs["prompt_token_ids"].count( + image_token_id) == num_placeholders + assert len(processed_inputs["multi_modal_data"]["image"]) == 1 + + +@pytest.mark.parametrize("mm_processor_kwargs,pixels_shape", [ + ({}, [5704, 1176]), + ({ + MIN_PIXELS: 64**2, + MAX_PIXELS: 512**2 + }, [1320, 1176]), +]) +def test_image_mapper_override(qwen2_vl_context: InputContext, + image_assets: _ImageAssets, + mm_processor_kwargs: Dict[str, Any], + pixels_shape: Tuple[int, int]): + """Ensure that the image mapper handles min/max pixels properly.""" + mm_registry = MultiModalRegistry() + mm_registry.init_mm_limits_per_prompt(qwen2_vl_context.model_config) + + image = image_assets[0].pil_image + + mapped_output = mm_registry.map_input( + qwen2_vl_context.model_config, + {"image": image}, + mm_processor_kwargs=mm_processor_kwargs, + ) + + # Dimension 0 of pixel values should match the product of image_grid_thw + actual_pixels_shape = mapped_output["pixel_values"].shape + assert list(actual_pixels_shape) == pixels_shape + assert actual_pixels_shape[0] == torch.prod( + mapped_output["image_grid_thw"]) diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index 9cca6b65e3277..3dc955b12ba0e 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -549,6 +549,9 @@ def mm_input_mapper_for_qwen2_vl( ctx: InputContext, data: MultiModalData[object], data_type_key: str, + *, + min_pixels: Optional[int] = None, + max_pixels: Optional[int] = None, ) -> MultiModalInputs: """Input mapper for Qwen2-VL.""" if data_type_key == "image" and isinstance(data, dict): @@ -557,8 +560,19 @@ def mm_input_mapper_for_qwen2_vl( "image_grid_thw": data.get("image_grid_thw"), }) model_config = ctx.model_config + # Handle mm processor kwargs; we pass these at creation time + # because preprocess() in transformers doesn't expose them + mm_processor_kwargs = {} + if min_pixels: + mm_processor_kwargs["min_pixels"] = min_pixels + if max_pixels: + mm_processor_kwargs["max_pixels"] = max_pixels + image_processor = cached_get_image_processor( - model_config.model, trust_remote_code=model_config.trust_remote_code) + model_config.model, + trust_remote_code=model_config.trust_remote_code, + **mm_processor_kwargs, + ) if image_processor is None: raise RuntimeError("No HuggingFace processor is available " "to process the image object") @@ -631,25 +645,36 @@ def _get_max_image_info( image_processor, data_type_key: str = "image", mm_count: int = 1, + min_pixels: Optional[int] = None, + max_pixels: Optional[int] = None, ): + # Limit min / max pixels unless they're explicitly provided + if min_pixels is None: + min_pixels = max(image_processor.min_pixels, 28 * 28) + if max_pixels is None: + max_pixels = min(image_processor.max_pixels, 1280 * 28 * 28) + return _get_vision_info( image_processor, height=9999999, width=9999999, - - # Limit min / max pixels. - min_pixels=max(image_processor.min_pixels, 28 * 28), - max_pixels=min(image_processor.max_pixels, 1280 * 28 * 28), + min_pixels=min_pixels, + max_pixels=max_pixels, data_type_key=data_type_key, mm_count=mm_count, ) -def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str) -> int: +def get_max_qwen2_vl_mm_tokens(ctx: InputContext, + data_type_key: str, + *, + min_pixels=None, + max_pixels=None) -> int: image_processor = cached_get_image_processor(ctx.model_config.model) max_resized_height, max_resized_width, max_llm_image_tokens = \ _get_max_image_info(image_processor, data_type_key=data_type_key, - mm_count=1) + mm_count=1, min_pixels=min_pixels, + max_pixels=max_pixels) return max_llm_image_tokens @@ -660,14 +685,20 @@ def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str) -> int: def dummy_data_for_qwen2_vl( - ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int] + ctx: InputContext, + seq_len: int, + mm_counts: Mapping[str, int], + *, + min_pixels: Optional[int] = None, + max_pixels: Optional[int] = None ) -> Tuple[SequenceData, Optional[MultiModalDataDict]]: image_processor = cached_get_image_processor(ctx.model_config.model) num_images = mm_counts["image"] max_resized_height, max_resized_width, max_llm_image_tokens = \ _get_max_image_info(image_processor, data_type_key="image", - mm_count=num_images) + mm_count=num_images, min_pixels=min_pixels, + max_pixels=max_pixels) if seq_len - max_llm_image_tokens - 2 < 0: raise RuntimeError( f"Qwen2-VL cannot process {num_images} images in a prompt, " @@ -678,10 +709,11 @@ def dummy_data_for_qwen2_vl( num_videos = mm_counts["video"] max_resized_height, max_resized_width, max_llm_video_tokens = \ _get_max_image_info(image_processor, data_type_key="video", - mm_count=num_videos) + mm_count=num_videos, min_pixels=min_pixels, + max_pixels=max_pixels) if seq_len - max_llm_video_tokens - 2 < 0: raise RuntimeError( - f"Qwen2-VL cannot process {num_images} videos in a prompt, " + f"Qwen2-VL cannot process {num_videos} videos in a prompt, " "please increase max_model_len or reduce video limit by " "--limit-mm-per-prompt.") @@ -706,6 +738,8 @@ def _get_llm_num_vision_tokens( mm_inputs: list, data_type_key: str, image_processor, + min_pixels: int, + max_pixels: int, ): """Get number of vision tokens of multimodal inputs. @@ -715,12 +749,13 @@ def _get_llm_num_vision_tokens( image = to_numpy_array(mm_inputs[0]) input_data_format = infer_channel_dimension_format(image) height, width = get_image_size(image, channel_dim=input_data_format) + _, _, llm_num_vision_tokens = _get_vision_info( image_processor, height=height, width=width, - min_pixels=image_processor.min_pixels, - max_pixels=image_processor.max_pixels, + min_pixels=min_pixels, + max_pixels=max_pixels, do_resize=image_processor.do_resize, data_type_key=data_type_key, mm_count=len(mm_inputs), @@ -730,7 +765,8 @@ def _get_llm_num_vision_tokens( def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable, data_type_key: str, image_processor: Any, - prompt_token_ids: List[int]) -> List[int]: + prompt_token_ids: List[int], min_pixels: Optional[int], + max_pixels: Optional[int]) -> List[int]: """ Expand pad tokens for multi-modal inputs (e.g., images or videos). @@ -741,6 +777,8 @@ def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable, data_type_key (str): The type of the multi-modal input. image_processor (Any): The image processor used to process the inputs. prompt_token_ids (List[int]): The list of token IDs in the prompt. + min_pixels (int): min pixels to used for img processing + max_pixels (int): max pixels to be used for img processing Returns: List[int]: The list of token IDs for the multi-modal inputs. @@ -757,6 +795,8 @@ def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable, [data] if data_type_key == "image" else data, data_type_key=data_type_key, image_processor=image_processor, + min_pixels=min_pixels, + max_pixels=max_pixels, ) if cnt == 0: end_idx = indices[cnt] @@ -773,6 +813,9 @@ def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable, def input_processor_for_qwen2_vl( ctx: InputContext, inputs: DecoderOnlyInputs, + *, + min_pixels: Optional[int] = None, + max_pixels: Optional[int] = None, ) -> DecoderOnlyInputs: multi_modal_data = inputs.get("multi_modal_data", None) if multi_modal_data is None: @@ -783,6 +826,10 @@ def input_processor_for_qwen2_vl( processor = cached_get_processor(ctx.model_config.model) image_processor = processor.image_processor + # Apply processor kwarg overrides for image processor options + min_pixels = min_pixels if min_pixels else image_processor.min_pixels + max_pixels = max_pixels if max_pixels else image_processor.max_pixels + hf_config = ctx.get_hf_config(Qwen2VLConfig) # To avoid redundant processing of vision objects (resize, rescale, etc.), @@ -830,16 +877,22 @@ def input_processor_for_qwen2_vl( else: prompt_token_ids = _expand_pad_tokens(image_inputs, hf_config.image_token_id, - make_batched_images, "image", + make_batched_images, + "image", image_processor, - prompt_token_ids) + prompt_token_ids, + min_pixels=min_pixels, + max_pixels=max_pixels) if video_inputs is not None: prompt_token_ids = _expand_pad_tokens(video_inputs, hf_config.video_token_id, - make_batched_videos, "video", + make_batched_videos, + "video", image_processor, - prompt_token_ids) + prompt_token_ids, + min_pixels=min_pixels, + max_pixels=max_pixels) return token_inputs( prompt_token_ids=prompt_token_ids, From e7116c017c86cb547f4d1888edaf13a9be2a4562 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 23 Oct 2024 22:09:04 +0800 Subject: [PATCH 066/222] [Bugfix] Fix `_init_vision_model` in NVLM_D model (#9611) Co-authored-by: Isotr0py <2037008807@qq.com> --- vllm/model_executor/models/nvlm_d.py | 37 +++++++++++++++++++++------- 1 file changed, 28 insertions(+), 9 deletions(-) diff --git a/vllm/model_executor/models/nvlm_d.py b/vllm/model_executor/models/nvlm_d.py index 3e3c3b05879fb..df4fd0a3256e9 100644 --- a/vllm/model_executor/models/nvlm_d.py +++ b/vllm/model_executor/models/nvlm_d.py @@ -58,12 +58,31 @@ def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential: nn.Linear(llm_intermediate_size, llm_hidden_size, bias=False), ) - def _init_vision_model(self, config: PretrainedConfig, - quant_config: Optional[QuantizationConfig], - num_hidden_layers: int): - # We added additional dummy heads to the original num of heads to make - # the number of heads divisible by 8. - return InternVisionModel(config.vision_config, - quant_config=quant_config, - num_hidden_layers_override=num_hidden_layers, - num_dummy_heads=7) + def _init_vision_model( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig], + *, + is_mono: bool, + prefix: str, + ): + if not is_mono: + vision_feature_layer = config.select_layer + if vision_feature_layer < 0: + num_hidden_layers = config.vision_config.num_hidden_layers \ + + vision_feature_layer + 1 + else: + num_hidden_layers = vision_feature_layer + 1 + + # We added additional dummy heads to the original num of heads to + # make the number of heads divisible by 8. + return InternVisionModel( + config.vision_config, + quant_config=quant_config, + num_hidden_layers_override=num_hidden_layers, + num_dummy_heads=7, + prefix=prefix, + ) + else: + msg = "Monolith mode is not applicable to NVLM_D" + raise NotImplementedError(msg) From dbdd3b5e5ace989923a5abb549780564980bc11e Mon Sep 17 00:00:00 2001 From: youkaichao Date: Wed, 23 Oct 2024 09:14:44 -0700 Subject: [PATCH 067/222] [misc] comment to avoid future confusion about baichuan (#9620) Signed-off-by: youkaichao --- vllm/model_executor/models/baichuan.py | 8 ++++++-- vllm/model_executor/models/registry.py | 6 ++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/vllm/model_executor/models/baichuan.py b/vllm/model_executor/models/baichuan.py index 54ed548ba8bc7..767230aeacc35 100644 --- a/vllm/model_executor/models/baichuan.py +++ b/vllm/model_executor/models/baichuan.py @@ -432,7 +432,9 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): class BaichuanForCausalLM(BaiChuanBaseForCausalLM): - """Baichuan 13B and Baichuan2 7B/13B.""" + """Baichuan 13B and Baichuan2 7B/13B. + NOTE: the class name has a lower case 'c'. + """ def __init__( self, @@ -450,7 +452,9 @@ def __init__( class BaiChuanForCausalLM(BaiChuanBaseForCausalLM): - """Baichuan 7B.""" + """Baichuan 7B. + NOTE: the class name has an upper case 'C'. + """ def __init__( self, diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 787c65743e894..db58414299070 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -26,8 +26,10 @@ "AquilaModel": ("llama", "LlamaForCausalLM"), "AquilaForCausalLM": ("llama", "LlamaForCausalLM"), # AquilaChat2 "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"), - "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"), # baichuan-7b - "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"), # baichuan-13b + # baichuan-7b, upper case 'C' in the class name + "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"), + # baichuan-13b, lower case 'c' in the class name + "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"), "BloomForCausalLM": ("bloom", "BloomForCausalLM"), # ChatGLMModel supports multimodal "CohereForCausalLM": ("commandr", "CohereForCausalLM"), From e5ac6a4199fd967d2655310712cee6e642e91bd7 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Wed, 23 Oct 2024 12:40:43 -0400 Subject: [PATCH 068/222] [Bugfix] Fix divide by zero when serving Mamba models (#9617) Signed-off-by: Tyler Michael Smith --- vllm/engine/llm_engine.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 99beea932882d..167efa51e3e2f 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -1612,7 +1612,7 @@ def _get_stats(self, # KV Cache Usage in % num_total_gpu = self.cache_config.num_gpu_blocks gpu_cache_usage_sys = 0. - if num_total_gpu is not None: + if num_total_gpu: # Guard against both None and 0 num_free_gpu = sum( scheduler.block_manager.get_num_free_gpu_blocks() for scheduler in self.scheduler) @@ -1620,7 +1620,7 @@ def _get_stats(self, num_total_cpu = self.cache_config.num_cpu_blocks cpu_cache_usage_sys = 0. - if num_total_cpu is not None and num_total_cpu > 0: + if num_total_cpu: # Guard against both None and 0 num_free_cpu = sum( scheduler.block_manager.get_num_free_cpu_blocks() for scheduler in self.scheduler) From fd0e2cfdb2e0fa6ee2822a73141441de51114f2a Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Wed, 23 Oct 2024 12:47:20 -0400 Subject: [PATCH 069/222] [Misc] Separate total and output tokens in benchmark_throughput.py (#8914) --- benchmarks/benchmark_throughput.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/benchmarks/benchmark_throughput.py b/benchmarks/benchmark_throughput.py index 24eb54e7b73bc..ee41c8ea38382 100644 --- a/benchmarks/benchmark_throughput.py +++ b/benchmarks/benchmark_throughput.py @@ -272,8 +272,10 @@ def main(args: argparse.Namespace): raise ValueError(f"Unknown backend: {args.backend}") total_num_tokens = sum(prompt_len + output_len for _, prompt_len, output_len in requests) + total_output_tokens = sum(output_len for _, _, output_len in requests) print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, " - f"{total_num_tokens / elapsed_time:.2f} tokens/s") + f"{total_num_tokens / elapsed_time:.2f} total tokens/s, " + f"{total_output_tokens / elapsed_time:.2f} output tokens/s") # Output JSON results if specified if args.output_json: From 9013e24f7b09a19405c6856b88c004afd4e3fc57 Mon Sep 17 00:00:00 2001 From: Yongzao <532741407@qq.com> Date: Thu, 24 Oct 2024 01:07:48 +0800 Subject: [PATCH 070/222] [torch.compile] Adding torch compile annotations to some models (#9614) --- vllm/model_executor/models/baichuan.py | 2 ++ vllm/model_executor/models/bloom.py | 2 ++ vllm/model_executor/models/commandr.py | 2 ++ vllm/model_executor/models/exaone.py | 2 ++ vllm/model_executor/models/gemma.py | 2 ++ vllm/model_executor/models/gpt2.py | 2 ++ 6 files changed, 12 insertions(+) diff --git a/vllm/model_executor/models/baichuan.py b/vllm/model_executor/models/baichuan.py index 767230aeacc35..f2cfdf8ffd30a 100644 --- a/vllm/model_executor/models/baichuan.py +++ b/vllm/model_executor/models/baichuan.py @@ -26,6 +26,7 @@ from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) @@ -250,6 +251,7 @@ def forward( return hidden_states, residual +@support_torch_compile class BaiChuanModel(nn.Module): def __init__(self, diff --git a/vllm/model_executor/models/bloom.py b/vllm/model_executor/models/bloom.py index b2c9e221690b3..77ab7de6165fb 100644 --- a/vllm/model_executor/models/bloom.py +++ b/vllm/model_executor/models/bloom.py @@ -24,6 +24,7 @@ from transformers import BloomConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) @@ -218,6 +219,7 @@ def forward( return output +@support_torch_compile class BloomModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/commandr.py b/vllm/model_executor/models/commandr.py index 578cd2f04861b..348e6d20f3297 100644 --- a/vllm/model_executor/models/commandr.py +++ b/vllm/model_executor/models/commandr.py @@ -28,6 +28,7 @@ from transformers import CohereConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import SiluAndMul @@ -250,6 +251,7 @@ def forward( return hidden_states, residual +@support_torch_compile class CohereModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/exaone.py b/vllm/model_executor/models/exaone.py index dfb8fe55d2fb8..4126ceb7117d4 100644 --- a/vllm/model_executor/models/exaone.py +++ b/vllm/model_executor/models/exaone.py @@ -29,6 +29,7 @@ from torch import nn from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) @@ -311,6 +312,7 @@ def forward( return hidden_states, residual +@support_torch_compile class ExaoneModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/gemma.py b/vllm/model_executor/models/gemma.py index 91e556db70a0b..436bd45d53f35 100644 --- a/vllm/model_executor/models/gemma.py +++ b/vllm/model_executor/models/gemma.py @@ -22,6 +22,7 @@ from transformers import GemmaConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.logger import init_logger @@ -239,6 +240,7 @@ def forward( return hidden_states, residual +@support_torch_compile class GemmaModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/gpt2.py b/vllm/model_executor/models/gpt2.py index 975502340e5f9..3330d84021368 100644 --- a/vllm/model_executor/models/gpt2.py +++ b/vllm/model_executor/models/gpt2.py @@ -24,6 +24,7 @@ from transformers import GPT2Config from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed.parallel_state import ( get_pp_group, get_tensor_model_parallel_world_size) @@ -182,6 +183,7 @@ def forward( return hidden_states +@support_torch_compile class GPT2Model(nn.Module): def __init__( From 150b779081381124609a30383b5f87dbd6d110e5 Mon Sep 17 00:00:00 2001 From: Alex Brooks Date: Wed, 23 Oct 2024 11:28:57 -0600 Subject: [PATCH 071/222] [Frontend] Enable Online Multi-image Support for MLlama (#9393) Signed-off-by: Alex-Brooks Co-authored-by: Cyrus Leung --- tests/entrypoints/test_chat_utils.py | 176 +++++++++++++++++++++++++++ vllm/entrypoints/chat_utils.py | 91 ++++++++------ 2 files changed, 230 insertions(+), 37 deletions(-) diff --git a/tests/entrypoints/test_chat_utils.py b/tests/entrypoints/test_chat_utils.py index 1d8c328b73259..f64743e065fc8 100644 --- a/tests/entrypoints/test_chat_utils.py +++ b/tests/entrypoints/test_chat_utils.py @@ -8,11 +8,13 @@ from vllm.config import ModelConfig from vllm.entrypoints.chat_utils import (parse_chat_messages, parse_chat_messages_futures) +from vllm.entrypoints.llm import apply_hf_chat_template from vllm.multimodal import MultiModalDataDict from vllm.multimodal.utils import encode_image_base64 from vllm.transformers_utils.tokenizer_group import TokenizerGroup PHI3V_MODEL_ID = "microsoft/Phi-3.5-vision-instruct" +MLLAMA_MODEL_ID = "meta-llama/Llama-3.2-11B-Vision-Instruct" @pytest.fixture(scope="module") @@ -39,6 +41,30 @@ def phi3v_tokenizer(): ) +@pytest.fixture(scope="module") +def mllama_model_config(): + return ModelConfig(MLLAMA_MODEL_ID, + task="generate", + tokenizer=MLLAMA_MODEL_ID, + tokenizer_mode="auto", + trust_remote_code=True, + dtype="bfloat16", + seed=0, + limit_mm_per_prompt={ + "image": 2, + }) + + +@pytest.fixture(scope="module") +def mllama_tokenizer(): + return TokenizerGroup( + MLLAMA_MODEL_ID, + enable_lora=False, + max_num_seqs=5, + max_input_length=None, + ) + + @pytest.fixture(scope="module") def image_url(): image = ImageAsset('cherry_blossom') @@ -414,3 +440,153 @@ def test_parse_chat_messages_multiple_images_uncommon_input( "<|image_1|>\n<|image_2|>\nWhat's in these images?" }] _assert_mm_data_is_image_input(mm_data, 2) + + +### Mllama currently wraps images / texts as interleaved dictionaries +def test_mllama_single_image( + mllama_model_config, + mllama_tokenizer, + image_url, +): + """Ensures that a single image is parsed correctly mllama.""" + conversation, mm_data = parse_chat_messages([{ + "role": + "user", + "content": [{ + 'type': 'text', + 'text': 'The content of this image is:' + }, { + "image_url": image_url + }] + }], mllama_model_config, mllama_tokenizer) + _assert_mm_data_is_image_input(mm_data, 1) + assert conversation == [{ + 'role': + 'user', + 'content': [{ + 'type': 'text', + 'text': 'The content of this image is:' + }, { + 'type': 'image' + }] + }] + + +def test_mllama_interleaved_images( + mllama_model_config, + mllama_tokenizer, + image_url, +): + """Ensures that multiple image are parsed as interleaved dicts.""" + conversation, mm_data = parse_chat_messages([{ + "role": + "user", + "content": [ + { + 'type': 'text', + 'text': 'The content of the first image is:' + }, + { + "image_url": image_url + }, + { + 'type': 'text', + 'text': 'The content of the second image is:' + }, + { + "image_url": image_url + }, + ] + }], mllama_model_config, mllama_tokenizer) + _assert_mm_data_is_image_input(mm_data, 2) + assert conversation == [{ + 'role': + 'user', + 'content': [{ + 'type': 'text', + 'text': 'The content of the first image is:' + }, { + 'type': 'image' + }, { + 'type': 'text', + 'text': 'The content of the second image is:' + }, { + 'type': 'image' + }] + }] + + +@pytest.mark.parametrize("model", [MLLAMA_MODEL_ID]) +def test_multimodal_image_parsing_matches_hf(model, image_url): + """Checks end to end hf alignment for multimodal [image] parsing.""" + + def get_conversation(is_hf: bool): + img_part = {"type": "image_url", "image_url": {"url": image_url}} + if is_hf: + img_part = {'type': 'image'} + return [{ + 'role': + 'user', + 'content': [ + { + 'type': 'text', + 'text': 'The content of the first image is:' + }, + img_part, + { + 'type': 'text', + 'text': 'The content of the second image is:' + }, + img_part, + { + 'type': 'text', + 'text': 'What animal is in the first image?' + }, + ] + }] + + # Build a config for the model + model_config = ModelConfig(model, + task="generate", + tokenizer=MLLAMA_MODEL_ID, + tokenizer_mode="auto", + trust_remote_code=True, + dtype="bfloat16", + seed=0, + limit_mm_per_prompt={ + "image": 2, + }) + + # Build the tokenizer group and grab the underlying tokenizer + tokenizer_group = TokenizerGroup( + MLLAMA_MODEL_ID, + enable_lora=False, + max_num_seqs=5, + max_input_length=None, + ) + tokenizer = tokenizer_group.tokenizer + + # Build and parse a conversation with {"type": "image"} using the tokenizer + hf_conversation = get_conversation(is_hf=True) + hf_result = tokenizer.apply_chat_template( + hf_conversation, + tokenize=False, + add_generation_prompt=True, + ) + + # Now parse with vLLMs chat utils & apply the template + vllm_conversation = get_conversation(is_hf=False) + conversation, _ = parse_chat_messages( + vllm_conversation, + model_config, + tokenizer_group, + ) + + vllm_result = apply_hf_chat_template( + tokenizer, + conversation=conversation, + chat_template=None, + add_generation_prompt=True, + ) + + assert hf_result == vllm_result diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index f64af27a957be..ddc5e0b90e858 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -483,53 +483,70 @@ def _parse_chat_message_content_parts( parts: Iterable[ChatCompletionContentPartParam], mm_tracker: BaseMultiModalItemTracker, ) -> List[ConversationMessage]: - texts: List[str] = [] + content: List[Union[str, Dict[str, str]]] = [] mm_parser = mm_tracker.create_parser() keep_multimodal_content = \ mm_tracker._model_config.hf_config.model_type in \ MODEL_KEEP_MULTI_MODAL_CONTENT - has_image = False for part in parts: - if isinstance(part, str): # Handle plain text parts - text = _TextParser(part) - texts.append(text) - else: # Handle structured dictionary parts - part_type, content = _parse_chat_message_content_mm_part(part) - - # if part_type is text/refusal/image_url/audio_url but - # content is empty, logg a warning and skip - if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content: - logger.warning("Skipping multimodal part " - "with empty / unparsable content.") - continue - - if part_type in ("text", "refusal"): - texts.append(content) - elif part_type == "image_url": - mm_parser.parse_image(content) - has_image = True - elif part_type == "audio_url": - mm_parser.parse_audio(content) - else: - raise NotImplementedError(f"Unknown part type: {part_type}") + parse_res = _parse_chat_message_content_part( + part, mm_parser, wrap_dicts=keep_multimodal_content) + if parse_res: + content.append(parse_res) - text_prompt = "\n".join(texts) if keep_multimodal_content: - text_prompt = "\n".join(texts) - role_content = [{'type': 'text', 'text': text_prompt}] - - if has_image: - role_content = [{'type': 'image'}] + role_content + # Parsing wraps images and texts as interleaved dictionaries return [ConversationMessage(role=role, - content=role_content)] # type: ignore - else: - mm_placeholder_counts = mm_parser.mm_placeholder_counts() - if mm_placeholder_counts: - text_prompt = _get_full_multimodal_text_prompt( - mm_placeholder_counts, text_prompt) - return [ConversationMessage(role=role, content=text_prompt)] + content=content)] # type: ignore + texts = cast(List[str], content) + text_prompt = "\n".join(texts) + mm_placeholder_counts = mm_parser.mm_placeholder_counts() + if mm_placeholder_counts: + text_prompt = _get_full_multimodal_text_prompt(mm_placeholder_counts, + text_prompt) + return [ConversationMessage(role=role, content=text_prompt)] + + +def _parse_chat_message_content_part( + part: ChatCompletionContentPartParam, + mm_parser: BaseMultiModalContentParser, + wrap_dicts: bool) -> Optional[Union[str, Dict[str, str]]]: + """Parses a single part of a conversation. If wrap_dicts is True, + structured dictionary pieces for texts and images will be + wrapped in dictionaries, i.e., {"type": "text", "text", ...} and + {"type": "image"}, respectively. Otherwise multimodal data will be + handled by mm_parser, and texts will be returned as strings to be joined + with multimodal placeholders. + """ + if isinstance(part, str): # Handle plain text parts + text = _TextParser(part) + return text + + # Handle structured dictionary parts + part_type, content = _parse_chat_message_content_mm_part(part) + + # if part_type is text/refusal/image_url/audio_url but + # content is empty, log a warning and skip + if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content: + logger.warning( + "Skipping multimodal part (type: '%s')" + "with empty / unparsable content.", part_type) + return None + + if part_type in ("text", "refusal"): + return {'type': 'text', 'text': content} if wrap_dicts else content + + if part_type == "image_url": + mm_parser.parse_image(content) + return {'type': 'image'} if wrap_dicts else None + + if part_type == "audio_url": + mm_parser.parse_audio(content) + return {'type': 'audio'} if wrap_dicts else None + + raise NotImplementedError(f"Unknown part type: {part_type}") # No need to validate using Pydantic again From fc6c27462614924dca90898ef762d6c56c0874ba Mon Sep 17 00:00:00 2001 From: Yunfei Chu Date: Thu, 24 Oct 2024 01:54:22 +0800 Subject: [PATCH 072/222] [Model] Add Qwen2-Audio model support (#9248) Co-authored-by: DarkLight1337 --- docs/source/models/supported_models.rst | 6 + examples/offline_inference_audio_language.py | 54 ++- tests/distributed/test_pipeline_parallel.py | 1 + vllm/entrypoints/chat_utils.py | 5 +- vllm/model_executor/models/qwen2_audio.py | 462 +++++++++++++++++++ vllm/model_executor/models/registry.py | 1 + vllm/model_executor/models/ultravox.py | 3 + 7 files changed, 515 insertions(+), 17 deletions(-) create mode 100644 vllm/model_executor/models/qwen2_audio.py diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index ad153d2927d6c..456269261300e 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -459,6 +459,12 @@ Text Generation - :code:`Qwen/Qwen-VL`, :code:`Qwen/Qwen-VL-Chat`, etc. - - ✅︎ + * - :code:`Qwen2AudioForConditionalGeneration` + - Qwen2-Audio + - T + A\ :sup:`+` + - :code:`Qwen/Qwen2-Audio-7B-Instruct` + - + - ✅︎ * - :code:`Qwen2VLForConditionalGeneration` - Qwen2-VL - T + I\ :sup:`E+` + V\ :sup:`+` diff --git a/examples/offline_inference_audio_language.py b/examples/offline_inference_audio_language.py index 1c6ac06123bbb..37ec667d96a77 100644 --- a/examples/offline_inference_audio_language.py +++ b/examples/offline_inference_audio_language.py @@ -12,14 +12,15 @@ from vllm.utils import FlexibleArgumentParser audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")] -question_per_audio_count = [ - "What is recited in the audio?", - "What sport and what nursery rhyme are referenced?" -] +question_per_audio_count = { + 0: "What is 1+1?", + 1: "What is recited in the audio?", + 2: "What sport and what nursery rhyme are referenced?" +} # Ultravox 0.3 -def run_ultravox(question, audio_count): +def run_ultravox(question: str, audio_count: int): model_name = "fixie-ai/ultravox-v0_3" tokenizer = AutoTokenizer.from_pretrained(model_name) @@ -42,9 +43,29 @@ def run_ultravox(question, audio_count): return llm, prompt, stop_token_ids -model_example_map = { - "ultravox": run_ultravox, -} +# Qwen2-Audio +def run_qwen2_audio(question: str, audio_count: int): + model_name = "Qwen/Qwen2-Audio-7B-Instruct" + + llm = LLM(model=model_name, + max_model_len=4096, + max_num_seqs=5, + limit_mm_per_prompt={"audio": audio_count}) + + audio_in_prompt = "".join([ + f"Audio {idx+1}: " + f"<|audio_bos|><|AUDIO|><|audio_eos|>\n" for idx in range(audio_count) + ]) + + prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n" + "<|im_start|>user\n" + f"{audio_in_prompt}{question}<|im_end|>\n" + "<|im_start|>assistant\n") + stop_token_ids = None + return llm, prompt, stop_token_ids + + +model_example_map = {"ultravox": run_ultravox, "qwen2_audio": run_qwen2_audio} def main(args): @@ -54,7 +75,7 @@ def main(args): audio_count = args.num_audios llm, prompt, stop_token_ids = model_example_map[model]( - question_per_audio_count[audio_count - 1], audio_count) + question_per_audio_count[audio_count], audio_count) # We set temperature to 0.2 so that outputs can be different # even when all prompts are identical when running batch inference. @@ -62,16 +83,17 @@ def main(args): max_tokens=64, stop_token_ids=stop_token_ids) - assert args.num_prompts > 0 - inputs = { - "prompt": prompt, - "multi_modal_data": { + mm_data = {} + if audio_count > 0: + mm_data = { "audio": [ asset.audio_and_sample_rate for asset in audio_assets[:audio_count] ] - }, - } + } + + assert args.num_prompts > 0 + inputs = {"prompt": prompt, "multi_modal_data": mm_data} if args.num_prompts > 1: # Batch inference inputs = [inputs] * args.num_prompts @@ -100,7 +122,7 @@ def main(args): parser.add_argument("--num-audios", type=int, default=1, - choices=[1, 2], + choices=[0, 1, 2], help="Number of audio items per prompt.") args = parser.parse_args() diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index 49c80bd640423..a93cdbe1cf2a2 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -199,6 +199,7 @@ def iter_params(self, model_name: str): "microsoft/Phi-3-vision-128k-instruct": PPTestSettings.fast(trust_remote_code=True), # noqa: E501 "mistralai/Pixtral-12B-2409": PPTestSettings.fast(tp_base=2, tokenizer_mode="mistral"), # noqa: E501 "Qwen/Qwen-VL-Chat": PPTestSettings.fast(trust_remote_code=True), + "Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(), "Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(), "fixie-ai/ultravox-v0_3": PPTestSettings.fast(), } diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index ddc5e0b90e858..faa493d518a7c 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -196,7 +196,10 @@ def _placeholder_str(self, modality: ModalityStr, elif modality == "audio": if model_type == "ultravox": return "<|reserved_special_token_0|>" - raise TypeError(f"Unknown {modality} model type: {model_type}") + if model_type == "qwen2_audio": + return (f"Audio {current_count}: " + f"<|audio_bos|><|AUDIO|><|audio_eos|>") + raise TypeError(f"Unknown model type: {model_type}") elif modality == "video": if model_type == "qwen2_vl": return "<|vision_start|><|video_pad|><|vision_end|>" diff --git a/vllm/model_executor/models/qwen2_audio.py b/vllm/model_executor/models/qwen2_audio.py new file mode 100644 index 0000000000000..3d049eeb920b7 --- /dev/null +++ b/vllm/model_executor/models/qwen2_audio.py @@ -0,0 +1,462 @@ +# coding=utf-8 +# Copyright 2024 The Qwen team. +# Copyright 2023 The vLLM team. +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Inference-only Qwen2-Audio model compatible with HuggingFace weights.""" +from functools import lru_cache +from typing import Iterable, List, Mapping, Optional, Tuple, TypedDict, Union + +import librosa +import numpy as np +import torch +import torch.nn as nn +from transformers import Qwen2AudioConfig, Qwen2AudioEncoder + +from vllm.attention import AttentionMetadata +from vllm.config import CacheConfig, MultiModalConfig +from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext, + token_inputs) +from vllm.logger import init_logger +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) +from vllm.model_executor.layers.sampler import Sampler, SamplerOutput +from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead +from vllm.model_executor.model_loader.weight_utils import ( + default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.models.qwen2 import Qwen2Model +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs +from vllm.sequence import IntermediateTensors, SequenceData + +from .interfaces import SupportsMultiModal, SupportsPP + +logger = init_logger(__name__) + +_KEYS_TO_MODIFY_MAPPING = { + "language_model.lm_head": "lm_head", + "language_model.model": "language_model", +} + + +# # === Audio Inputs === # +class Qwen2AudioInputs(TypedDict): + input_features: torch.Tensor + """Shape: + `(num_audios, num_mel_bins, 3000)` + """ + + feature_attention_mask: torch.Tensor + """Shape: `(num_audios, 3000)` + """ + + +# === Audio Encoder === # + + +class Qwen2AudioMultiModalProjector(nn.Module): + + def __init__(self, audio_hidden_size: int, text_hidden_size: int): + super().__init__() + self.linear = nn.Linear(audio_hidden_size, text_hidden_size, bias=True) + + def forward(self, audio_features): + hidden_states = self.linear(audio_features) + return hidden_states + + +def dummy_data_for_qwen2_audio(ctx: InputContext, seq_len: int, + mm_counts: Mapping[str, int]): + num_audios = mm_counts["audio"] + max_llm_audio_tokens = get_max_qwen2_audio_audio_tokens(ctx) * num_audios + if seq_len - max_llm_audio_tokens - 2 < 0: + raise RuntimeError( + f"Qwen2-Audio cannot process {num_audios} audios in a prompt, " + "please increase max_model_len or reduce audio limit by " + "--limit-mm-per-prompt.") + + audio_token_index = ctx.model_config.hf_config.audio_token_index + + dummy_seqdata = SequenceData.from_prompt_token_counts( + (audio_token_index, max_llm_audio_tokens), + (0, seq_len - max_llm_audio_tokens), + ) + dummy_audio = np.full((max_llm_audio_tokens * 2 * 2 * 160, ), 0.) + return dummy_seqdata, {"audio": [(dummy_audio, 16000)] * num_audios} + + +def get_processor( + processor_name: str, + *args, + trust_remote_code: bool = False, + **kwargs, +): + """Gets a processor for the given model name via HuggingFace. + + Derived from `vllm.transformers_utils.image_processor.get_image_processor`. + """ + # don't put this import at the top level + # it will call torch.cuda.device_count() + from transformers import AutoProcessor + + try: + processor = AutoProcessor.from_pretrained( + processor_name, + *args, + trust_remote_code=trust_remote_code, + **kwargs) + except ValueError as e: + # If the error pertains to the processor class not existing or not + # currently being imported, suggest using the --trust-remote-code flag. + # Unlike AutoTokenizer, AutoProcessor does not separate such errors + if not trust_remote_code: + err_msg = ( + "Failed to load the processor. If the processor is " + "a custom processor not yet available in the HuggingFace " + "transformers library, consider setting " + "`trust_remote_code=True` in LLM or using the " + "`--trust-remote-code` flag in the CLI.") + raise RuntimeError(err_msg) from e + else: + raise e + + return processor + + +cached_get_processor = lru_cache(get_processor) + + +def _get_feat_extract_output_lengths(input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers + and the output length of the audio encoder + """ + input_lengths = (input_lengths - 1) // 2 + 1 + output_lengths = (input_lengths - 2) // 2 + 1 + return input_lengths, output_lengths + + +def get_max_qwen2_audio_audio_tokens(ctx: InputContext) -> int: + max_source_position = ( + ctx.model_config.hf_config.audio_config.max_source_positions) + output_lengths = (max_source_position - 2) // 2 + 1 + return output_lengths + + +def input_processor_for_qwen2_audio( + ctx: InputContext, inputs: DecoderOnlyInputs) -> DecoderOnlyInputs: + multi_modal_data = inputs.get("multi_modal_data") + if multi_modal_data is None or "audio" not in multi_modal_data: + return inputs + + audios = multi_modal_data["audio"] + if not isinstance(audios, list): + audios = [audios] + + if len(audios) == 0: + return inputs + + processor = cached_get_processor(ctx.model_config.model) + resampled_audios = [ + librosa.resample(audio, + orig_sr=sampling_rate, + target_sr=processor.feature_extractor.sampling_rate) + for audio, sampling_rate in audios + ] + audio_input_lengths = np.array( + [min(3000, _.shape[0] // 160 + 1) for _ in resampled_audios]) + + audio_feat_lengths, audio_output_lengths = _get_feat_extract_output_lengths( + audio_input_lengths) + + audio_token_index = ctx.model_config.hf_config.audio_token_index + + input_ids = inputs['prompt_token_ids'] + + new_input_ids = [] + audio_num = input_ids.count(audio_token_index) + assert len(audio_input_lengths) == audio_num, \ + (f'The text input contains {audio_num} audio tokens, ' + f'but {len(audio_input_lengths)} audios provided') + start = 0 + for audio_idx in range(audio_num): + end = input_ids.index(audio_token_index, start) + new_input_ids.extend(input_ids[start:end]) # text part + + new_input_ids.extend([audio_token_index] * + audio_output_lengths[audio_idx]) + start = end + 1 + new_input_ids.extend(input_ids[start:]) + + return token_inputs( + prompt_token_ids=new_input_ids, + prompt=inputs['prompt'], + multi_modal_data=multi_modal_data, + ) + + +def input_mapper_for_qwen2_audio( + ctx: InputContext, + multi_modal_data: Union[np.ndarray, List[np.ndarray]], +) -> MultiModalInputs: + """Input mapper for Qwen2-Audio.""" + if not isinstance(multi_modal_data, list): + multi_modal_data = [multi_modal_data] + + if len(multi_modal_data) == 0: + return MultiModalInputs() + + processor = cached_get_processor(ctx.model_config.model) + audio_feature_extractor = processor.feature_extractor + if audio_feature_extractor is None: + raise RuntimeError( + "No HuggingFace audio_feature_extractor is available " + "to process the audio object") + + try: + resampled_audios = [ + librosa.resample( + audio, + orig_sr=sampling_rate, + target_sr=processor.feature_extractor.sampling_rate) + for audio, sampling_rate in multi_modal_data + ] + batch_data = audio_feature_extractor(resampled_audios, + sampling_rate=16000, + return_attention_mask=True, + padding="max_length", + return_tensors="pt").data + batch_data["feature_attention_mask"] = batch_data.pop("attention_mask") + except Exception: + logger.error("Failed to process audio (%s)", multi_modal_data) + raise + + return MultiModalInputs(batch_data) + + +@INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen2_audio) +@INPUT_REGISTRY.register_input_processor(input_processor_for_qwen2_audio) +@MULTIMODAL_REGISTRY.register_input_mapper("audio", + input_mapper_for_qwen2_audio) +@MULTIMODAL_REGISTRY.register_max_multimodal_tokens( + "audio", get_max_qwen2_audio_audio_tokens) +class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal, + SupportsPP): + + def __init__(self, + config: Qwen2AudioConfig, + multimodal_config: MultiModalConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None) -> None: + super().__init__() + + self.config = config + self.multimodal_config = multimodal_config + + self.audio_tower = Qwen2AudioEncoder(config.audio_config) + self.multi_modal_projector = Qwen2AudioMultiModalProjector( + config.audio_config.d_model, config.text_config.hidden_size) + + self.quant_config = quant_config + + self.language_model = Qwen2Model(config.text_config, cache_config, + quant_config) + self.unpadded_vocab_size = config.text_config.vocab_size + if config.text_config.tie_word_embeddings: + self.lm_head = self.language_model.embed_tokens + else: + self.lm_head = ParallelLMHead(config.text_config.vocab_size, + config.text_config.hidden_size, + quant_config=quant_config) + logit_scale = getattr(config, "logit_scale", 1.0) + self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, + config.text_config.vocab_size, + logit_scale) + self.sampler = Sampler() + + self.make_empty_intermediate_tensors = ( + self.language_model.make_empty_intermediate_tensors) + + def _validate_and_reshape_mm_tensor(self, + mm_input: Union[torch.Tensor, + List[torch.Tensor]], + name: str) -> torch.Tensor: + if not isinstance(mm_input, (torch.Tensor, list)): + raise ValueError(f"Incorrect type of {name}. " + f"Got type: {type(mm_input)}") + if isinstance(mm_input, torch.Tensor): + return torch.concat(list(mm_input)) + else: + return torch.concat(mm_input) + + def _parse_and_validate_audio_input( + self, **kwargs: object) -> Optional[Qwen2AudioInputs]: + input_features = kwargs.pop('input_features', None) + feature_attention_mask = kwargs.pop('feature_attention_mask', None) + if input_features is None: + return None + input_features = self._validate_and_reshape_mm_tensor( + input_features, 'input_features') + feature_attention_mask = self._validate_and_reshape_mm_tensor( + feature_attention_mask, 'feature_attention_mask') + if not isinstance(input_features, (torch.Tensor, list)): + raise ValueError("Incorrect type of audio input features. " + f"Got type: {type(input_features)}") + return Qwen2AudioInputs(input_features=input_features, + feature_attention_mask=feature_attention_mask) + + def _process_audio_input(self, + audio_input: Qwen2AudioInputs) -> torch.Tensor: + + input_features = audio_input["input_features"] + feature_attention_mask = audio_input["feature_attention_mask"] + + audio_feat_lengths, audio_output_lengths = ( + self.audio_tower._get_feat_extract_output_lengths( + feature_attention_mask.sum(-1))) + + batch_size, _, max_mel_seq_len = input_features.shape + max_seq_len = (max_mel_seq_len - 2) // 2 + 1 + # Create a sequence tensor of shape (batch_size, max_seq_len) + seq_range = (torch.arange( + 0, + max_seq_len, + dtype=audio_feat_lengths.dtype, + device=audio_feat_lengths.device).unsqueeze(0).expand( + batch_size, max_seq_len)) + lengths_expand = audio_feat_lengths.unsqueeze(-1).expand( + batch_size, max_seq_len) + # Create mask + padding_mask = seq_range >= lengths_expand + + audio_attention_mask_ = padding_mask.view( + batch_size, 1, 1, max_seq_len).expand(batch_size, 1, max_seq_len, + max_seq_len) + audio_attention_mask = audio_attention_mask_.to( + dtype=self.audio_tower.conv1.weight.dtype, + device=self.audio_tower.conv1.weight.device) + audio_attention_mask[audio_attention_mask_] = float("-inf") + + audio_outputs = self.audio_tower(input_features, + attention_mask=audio_attention_mask) + selected_audio_feature = audio_outputs.last_hidden_state + audio_features = self.multi_modal_projector(selected_audio_feature) + num_audios, max_audio_tokens, embed_dim = audio_features.shape + audio_features_mask = torch.arange(max_audio_tokens).expand( + num_audios, max_audio_tokens + ).to(audio_output_lengths.device) < audio_output_lengths.unsqueeze(1) + masked_audio_features = audio_features[audio_features_mask].view( + -1, embed_dim) + + return masked_audio_features + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors] = None, + **kwargs: object, + ) -> Union[torch.Tensor, IntermediateTensors]: + if intermediate_tensors is not None: + input_ids = None + inputs_embeds = None + else: + audio_input = self._parse_and_validate_audio_input(**kwargs) + + if audio_input is None: + inputs_embeds = None + else: + inputs_embeds = self.language_model.embed_tokens(input_ids) + masked_audio_features = self._process_audio_input(audio_input) + # merge llm embeddings and audio features + mask = (input_ids == self.config.audio_token_index) + inputs_embeds[mask, :] = masked_audio_features + + input_ids = None + + hidden_states = self.language_model( + input_ids=input_ids, + positions=positions, + kv_caches=kv_caches, + attn_metadata=attn_metadata, + intermediate_tensors=intermediate_tensors, + inputs_embeds=inputs_embeds, + ) + return hidden_states + + def compute_logits(self, hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata) -> torch.Tensor: + logits = self.logits_processor(self.lm_head, hidden_states, + sampling_metadata) + return logits + + def sample( + self, + logits: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[SamplerOutput]: + next_tokens = self.sampler(logits, sampling_metadata) + return next_tokens + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + stacked_params_mapping = [ + # (param_name, shard_name, shard_id) + ("qkv_proj", "q_proj", "q"), + ("qkv_proj", "k_proj", "k"), + ("qkv_proj", "v_proj", "v"), + ("gate_up_proj", "gate_proj", 0), + ("gate_up_proj", "up_proj", 1), + ] + params_dict = dict(self.named_parameters(remove_duplicate=False)) + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + if (self.config.text_config.tie_word_embeddings + and "lm_head.weight" in name): + continue + for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items(): + if key_to_modify in name: + name = name.replace(key_to_modify, new_key) + for (param_name, weight_name, shard_id) in stacked_params_mapping: + if weight_name not in name or 'audio' in name: + continue + name = name.replace(weight_name, param_name) + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + # Remapping the name of FP8 kv-scale. + name = maybe_remap_kv_scale_name(name, params_dict) + if name is None: + continue + + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index db58414299070..717615988a907 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -121,6 +121,7 @@ "PixtralForConditionalGeneration": ("pixtral", "PixtralForConditionalGeneration"), # noqa: E501 "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"), "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"), # noqa: E501 + "Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"), # noqa: E501 "UltravoxModel": ("ultravox", "UltravoxModel"), # [Encoder-decoder] "MllamaForConditionalGeneration": ("mllama", "MllamaForConditionalGeneration"), # noqa: E501 diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py index 49c32cbeaa366..5f33b872beecb 100644 --- a/vllm/model_executor/models/ultravox.py +++ b/vllm/model_executor/models/ultravox.py @@ -117,6 +117,9 @@ def input_mapper_for_ultravox(ctx: InputContext, data: object): if not isinstance(data, list): data = [data] + if len(data) == 0: + return MultiModalInputs() + # If the audio inputs are embeddings, no need for preprocessing if is_list_of(data, torch.Tensor, check="all"): return MultiModalInputs({"audio_embeds": data}) From b548d7a5f4aabd1ee7ba90a80ccee0ca5c401524 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Wed, 23 Oct 2024 18:45:26 -0400 Subject: [PATCH 073/222] [CI/Build] Add bot to close stale issues and PRs (#9436) --- .github/workflows/stale.yml | 47 +++++++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 .github/workflows/stale.yml diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml new file mode 100644 index 0000000000000..becf2f4f74616 --- /dev/null +++ b/.github/workflows/stale.yml @@ -0,0 +1,47 @@ +name: 'Close inactive issues and PRs' + +on: + schedule: + # Daily at 1:30 AM UTC + - cron: '30 1 * * *' + +jobs: + close-issues-and-pull-requests: + permissions: + issues: write + pull-requests: write + runs-on: ubuntu-latest + steps: + - uses: actions/stale@28ca1036281a5e5922ead5184a1bbf96e5fc984e # v9.0.0 + with: + exempt-draft-pr: true + exempt-issue-labels: 'keep-open' + exempt-pr-labels: 'keep-open' + + labels-to-add-when-unstale: 'unstale' + labels-to-remove-when-stale: 'unstale' + + days-before-issue-stale: 90 + days-before-issue-close: 30 + stale-issue-label: 'stale' + stale-issue-message: > + This issue has been automatically marked as stale because it has not + had any activity within 90 days. It will be automatically closed if no + further activity occurs within 30 days. Leave a comment if + you feel this issue should remain open. Thank you! + close-issue-message: > + This issue has been automatically closed due to inactivity. Please + feel free to reopen if you feel it is still relevant. Thank you! + + days-before-pr-stale: 90 + days-before-pr-close: 30 + stale-pr-label: 'stale' + stale-pr-message: > + This pull request has been automatically marked as stale because it + has not had any activity within 90 days. It will be automatically + closed if no further activity occurs within 30 days. Leave a comment + if you feel this pull request should remain open. Thank you! + close-pr-message: > + This pull request has been automatically closed due to inactivity. + Please feel free to reopen if you intend to continue working on it. + Thank you! From bb01f2915eb3ade94b086033d7f2a6fe7de3c067 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Wed, 23 Oct 2024 22:03:44 -0400 Subject: [PATCH 074/222] [Bugfix][Model] Fix Mllama SDPA illegal memory access for batched multi-image (#9626) Signed-off-by: mgoin --- vllm/model_executor/models/mllama.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py index 23e2b520e5b40..475364f322c62 100644 --- a/vllm/model_executor/models/mllama.py +++ b/vllm/model_executor/models/mllama.py @@ -795,17 +795,19 @@ def attention_with_mask( kv_len = k.shape[0] q = q.transpose(0, 1).view(self.num_local_key_value_heads, self.num_key_value_groups, q_len, - self.head_dim) + self.head_dim).contiguous() k = k.transpose(0, 1)[:, None, :, :].expand(self.num_local_key_value_heads, self.num_key_value_groups, - kv_len, self.head_dim) + kv_len, + self.head_dim).contiguous() v = v.transpose(0, 1)[:, None, :, :].expand(self.num_local_key_value_heads, self.num_key_value_groups, - kv_len, self.head_dim) + kv_len, + self.head_dim).contiguous() attention_mask = attention_mask.view(1, 1, q_len, kv_len) output = F.scaled_dot_product_attention(q, k, From b7df53cd42f3eab007b4f287c151960858e949df Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Wed, 23 Oct 2024 22:07:44 -0400 Subject: [PATCH 075/222] [Bugfix] Use "vision_model" prefix for MllamaVisionModel (#9628) Signed-off-by: mgoin --- vllm/model_executor/models/mllama.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py index 475364f322c62..44ef49729c969 100644 --- a/vllm/model_executor/models/mllama.py +++ b/vllm/model_executor/models/mllama.py @@ -1053,7 +1053,8 @@ def __init__(self, self.image_size = config.vision_config.image_size self.vision_model = MllamaVisionModel(config.vision_config, - quant_config) + quant_config, + prefix="vision_model") self.language_model = MllamaForCausalLM( config.text_config, cache_config=cache_config, From 33bab4106011b4c4b4b68640676a076a2bcccfed Mon Sep 17 00:00:00 2001 From: Vinay R Damodaran Date: Thu, 24 Oct 2024 01:05:49 -0400 Subject: [PATCH 076/222] [Bugfix]: Make chat content text allow type content (#9358) Signed-off-by: Vinay Damodaran --- .../serving/openai_compatible_server.md | 17 +++++++ tests/entrypoints/openai/test_serving_chat.py | 1 + tests/entrypoints/test_chat_utils.py | 48 ++++++++++++++++++- vllm/config.py | 2 + vllm/engine/arg_utils.py | 10 ++++ vllm/engine/llm_engine.py | 3 +- vllm/entrypoints/chat_utils.py | 31 ++++++++---- vllm/entrypoints/openai/serving_chat.py | 7 ++- 8 files changed, 107 insertions(+), 12 deletions(-) diff --git a/docs/source/serving/openai_compatible_server.md b/docs/source/serving/openai_compatible_server.md index cc8e539a8a6d3..413c87ab28755 100644 --- a/docs/source/serving/openai_compatible_server.md +++ b/docs/source/serving/openai_compatible_server.md @@ -103,6 +103,23 @@ vllm serve --chat-template ./path-to-chat-template.jinja vLLM community provides a set of chat templates for popular models. You can find them in the examples directory [here](https://github.com/vllm-project/vllm/tree/main/examples/) +With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies +both a `type` and a `text` field. An example is provided below: +```python +completion = client.chat.completions.create( + model="NousResearch/Meta-Llama-3-8B-Instruct", + messages=[ + {"role": "user", "content": [{"type": "text", "text": "Classify this sentiment: vLLM is wonderful!"}]} + ] +) +``` +Most chat templates for LLMs expect the `content` to be a `string` but there are some newer models like +`meta-llama/Llama-Guard-3-1B` that expect the content to be parsed with the new OpenAI spec. In order to choose which +format the content needs to be parsed in by vLLM, please use the `--chat-template-text-format` argument to specify +between `string` or `openai`. The default value is `string` and vLLM internally converts both spec formats to match +this, unless explicitly specified. + + ## Command line arguments for the server ```{argparse} diff --git a/tests/entrypoints/openai/test_serving_chat.py b/tests/entrypoints/openai/test_serving_chat.py index d9342fad9f018..e969d33775d86 100644 --- a/tests/entrypoints/openai/test_serving_chat.py +++ b/tests/entrypoints/openai/test_serving_chat.py @@ -26,6 +26,7 @@ class MockModelConfig: tokenizer = MODEL_NAME trust_remote_code = False tokenizer_mode = "auto" + chat_template_text_format = "string" max_model_len = 100 tokenizer_revision = None multimodal_config = MultiModalConfig() diff --git a/tests/entrypoints/test_chat_utils.py b/tests/entrypoints/test_chat_utils.py index f64743e065fc8..5fa466f8f041f 100644 --- a/tests/entrypoints/test_chat_utils.py +++ b/tests/entrypoints/test_chat_utils.py @@ -17,7 +17,7 @@ MLLAMA_MODEL_ID = "meta-llama/Llama-3.2-11B-Vision-Instruct" -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def phi3v_model_config(): return ModelConfig(PHI3V_MODEL_ID, task="generate", @@ -26,6 +26,7 @@ def phi3v_model_config(): trust_remote_code=True, dtype="bfloat16", seed=0, + chat_template_text_format="string", limit_mm_per_prompt={ "image": 2, }) @@ -330,6 +331,51 @@ def test_parse_chat_messages_multiple_images_across_messages( _assert_mm_data_is_image_input(mm_data, 2) +def test_parse_chat_messages_context_text_format( + phi3v_model_config, + phi3v_tokenizer, +): + phi3v_model_config.chat_template_text_format = "openai" + conversation, mm_data = parse_chat_messages( + [{ + "role": "user", + "content": [{ + "type": "text", + "text": "What's in this text?" + }] + }, { + "role": "assistant", + "content": "Some stuff." + }, { + "role": "user", + "content": "What about this one?" + }], phi3v_model_config, phi3v_tokenizer) + + assert conversation == [ + { + "role": "user", + "content": [{ + "type": "text", + "text": "What's in this text?" + }] + }, + { + "role": "assistant", + "content": [{ + "type": "text", + "text": "Some stuff." + }] + }, + { + "role": "user", + "content": [{ + "type": "text", + "text": "What about this one?" + }] + }, + ] + + def test_parse_chat_messages_rejects_too_many_images_in_one_message( phi3v_model_config, phi3v_tokenizer, diff --git a/vllm/config.py b/vllm/config.py index c569789c650ab..25f841231dedd 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -142,6 +142,7 @@ def __init__(self, use_async_output_proc: bool = True, override_neuron_config: Optional[Dict[str, Any]] = None, config_format: ConfigFormat = ConfigFormat.AUTO, + chat_template_text_format: str = "string", mm_processor_kwargs: Optional[Dict[str, Any]] = None) -> None: self.model = model self.tokenizer = tokenizer @@ -176,6 +177,7 @@ def __init__(self, self.model, revision) self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype) self.use_async_output_proc = use_async_output_proc + self.chat_template_text_format = chat_template_text_format self.mm_processor_kwargs = mm_processor_kwargs # Set enforce_eager to False if the value is unset. diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index a5cfaf3977a4f..c49f475b9ee61 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -89,6 +89,7 @@ class EngineArgs: task: TaskOption = "auto" skip_tokenizer_init: bool = False tokenizer_mode: str = 'auto' + chat_template_text_format: str = 'string' trust_remote_code: bool = False download_dir: Optional[str] = None load_format: str = 'auto' @@ -250,6 +251,14 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: 'fast tokenizer if available.\n* "slow" will ' 'always use the slow tokenizer. \n* ' '"mistral" will always use the `mistral_common` tokenizer.') + parser.add_argument( + '--chat-template-text-format', + type=str, + default=EngineArgs.chat_template_text_format, + choices=['string', 'openai'], + help='The format to render text content within a chat template. ' + '"string" will keep the content field as a string whereas ' + '"openai" will parse content in the current OpenAI format.') parser.add_argument('--trust-remote-code', action='store_true', help='Trust remote code from huggingface.') @@ -858,6 +867,7 @@ def create_model_config(self) -> ModelConfig: # We know this is not None because we set it in __post_init__ tokenizer=cast(str, self.tokenizer), tokenizer_mode=self.tokenizer_mode, + chat_template_text_format=self.chat_template_text_format, trust_remote_code=self.trust_remote_code, dtype=self.dtype, seed=self.seed, diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 167efa51e3e2f..0d73ed7c8e7ab 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -254,7 +254,7 @@ def __init__( "num_scheduler_steps=%d, chunked_prefill_enabled=%s " "multi_step_stream_outputs=%s, enable_prefix_caching=%s, " "use_async_output_proc=%s, use_cached_outputs=%s, " - "mm_processor_kwargs=%s)", + "chat_template_text_format=%s, mm_processor_kwargs=%s)", VLLM_VERSION, model_config.model, speculative_config, @@ -289,6 +289,7 @@ def __init__( cache_config.enable_prefix_caching, model_config.use_async_output_proc, use_cached_outputs, + model_config.chat_template_text_format, model_config.mm_processor_kwargs, ) # TODO(woosuk): Print more configs in debug mode. diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index faa493d518a7c..fef6a91414db6 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -121,7 +121,7 @@ class ConversationMessage(TypedDict, total=False): role: Required[str] """The role of the message's author.""" - content: Optional[str] + content: Union[Optional[str], List[Dict[str, str]]] """The contents of the message""" tool_call_id: Optional[str] @@ -431,7 +431,7 @@ def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int], def _parse_chat_message_content_mm_part( part: ChatCompletionContentPartParam) -> Tuple[str, str]: """ - Parses a given multi modal content part based on its type. + Parses a given multi-modal content part based on its type. Args: part: A dict containing the content part, with a potential 'type' field. @@ -485,21 +485,26 @@ def _parse_chat_message_content_parts( role: str, parts: Iterable[ChatCompletionContentPartParam], mm_tracker: BaseMultiModalItemTracker, + chat_template_text_format: str, ) -> List[ConversationMessage]: content: List[Union[str, Dict[str, str]]] = [] mm_parser = mm_tracker.create_parser() - keep_multimodal_content = \ + wrap_dicts = \ mm_tracker._model_config.hf_config.model_type in \ - MODEL_KEEP_MULTI_MODAL_CONTENT + MODEL_KEEP_MULTI_MODAL_CONTENT or \ + (chat_template_text_format == "openai") for part in parts: parse_res = _parse_chat_message_content_part( - part, mm_parser, wrap_dicts=keep_multimodal_content) + part, + mm_parser, + wrap_dicts=wrap_dicts, + ) if parse_res: content.append(parse_res) - if keep_multimodal_content: + if wrap_dicts: # Parsing wraps images and texts as interleaved dictionaries return [ConversationMessage(role=role, content=content)] # type: ignore @@ -560,6 +565,7 @@ def _parse_chat_message_content_part( def _parse_chat_message_content( message: ChatCompletionMessageParam, mm_tracker: BaseMultiModalItemTracker, + chat_template_text_format: str, ) -> List[ConversationMessage]: role = message["role"] content = message.get("content") @@ -575,6 +581,7 @@ def _parse_chat_message_content( role, content, # type: ignore mm_tracker, + chat_template_text_format, ) for result_msg in result: @@ -618,7 +625,11 @@ def parse_chat_messages( mm_tracker = MultiModalItemTracker(model_config, tokenizer) for msg in messages: - sub_messages = _parse_chat_message_content(msg, mm_tracker) + sub_messages = _parse_chat_message_content( + msg, + mm_tracker, + model_config.chat_template_text_format, + ) conversation.extend(sub_messages) @@ -636,7 +647,11 @@ def parse_chat_messages_futures( mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer) for msg in messages: - sub_messages = _parse_chat_message_content(msg, mm_tracker) + sub_messages = _parse_chat_message_content( + msg, + mm_tracker, + model_config.chat_template_text_format, + ) conversation.extend(sub_messages) diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index b9b240b64850e..cd2883a3b323b 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -384,7 +384,7 @@ async def chat_completion_stream_generator( # Send response to echo the input portion of the # last message if request.echo or request.continue_final_message: - last_msg_content: str = "" + last_msg_content: Union[str, List[Dict[str, str]]] = "" if conversation and "content" in conversation[ -1] and conversation[-1].get("role") == role: last_msg_content = conversation[-1]["content"] or "" @@ -724,10 +724,13 @@ async def chat_completion_full_generator( choices.append(choice_data) if request.echo or request.continue_final_message: - last_msg_content = "" + last_msg_content: Union[str, List[Dict[str, str]]] = "" if conversation and "content" in conversation[-1] and conversation[ -1].get("role") == role: last_msg_content = conversation[-1]["content"] or "" + if isinstance(last_msg_content, list): + last_msg_content = "\n".join(msg['text'] + for msg in last_msg_content) for choice in choices: full_message = last_msg_content + (choice.message.content From 056a68c7dbaff03252d2f8c058d3fb700565ad1f Mon Sep 17 00:00:00 2001 From: Yan Ma Date: Thu, 24 Oct 2024 13:14:00 +0800 Subject: [PATCH 077/222] [XPU] avoid triton import for xpu (#9440) Co-authored-by: Cyrus Leung Co-authored-by: Cyrus Leung --- vllm/triton_utils/importing.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/vllm/triton_utils/importing.py b/vllm/triton_utils/importing.py index ef7ca149266b6..36315abcdfcda 100644 --- a/vllm/triton_utils/importing.py +++ b/vllm/triton_utils/importing.py @@ -5,10 +5,12 @@ logger = init_logger(__name__) -# neuron has too old torch -HAS_TRITON = find_spec( - "triton") is not None and not current_platform.is_neuron() +HAS_TRITON = ( + find_spec("triton") is not None + and not current_platform.is_xpu() # Not compatible + and not current_platform.is_neuron() # neuron has too old torch +) if not HAS_TRITON: - logger.info("Triton not installed; certain GPU-related functions" - " will not be available.") + logger.info("Triton not installed or not compatible; certain GPU-related" + " functions will not be available.") From 836e8ef6eeafcd1e24b25c990da6331f48a95fd2 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Thu, 24 Oct 2024 14:12:05 +0800 Subject: [PATCH 078/222] [Bugfix] Fix PP for ChatGLM and Molmo (#9422) --- docs/source/models/supported_models.rst | 2 +- tests/distributed/test_pipeline_parallel.py | 37 +++--- vllm/model_executor/models/chatglm.py | 129 ++++++++++++-------- vllm/model_executor/models/molmo.py | 73 +++++++---- vllm/model_executor/models/qwen2_rm.py | 3 +- vllm/model_executor/models/qwen2_vl.py | 23 ++-- vllm/model_executor/models/utils.py | 54 ++++++-- 7 files changed, 197 insertions(+), 124 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 456269261300e..c92d65110f464 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -425,7 +425,7 @@ Text Generation - * - :code:`MolmoForCausalLM` - Molmo - - Image + - T + I - :code:`allenai/Molmo-7B-D-0924`, :code:`allenai/Molmo-72B-0924`, etc. - - ✅︎ diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index a93cdbe1cf2a2..8d0190e37ef13 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -118,11 +118,8 @@ def iter_params(self, model_name: str): # The values displayed here are only a rough indicator of the size of the model # yapf: disable -GENERATION_MODEL_SETTINGS = { - # [DETAILED TESTS] - "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(), - "microsoft/Phi-3-mini-4k-instruct": PPTestSettings.detailed(trust_remote_code=True, multi_node_only=True), # noqa: E501 - # [FAST TESTS] +TEXT_GENERATION_MODELS = { + # [Decoder-only] # Uses Llama # "BAAI/AquilaChat-7B": PPTestSettings.fast(), "Snowflake/snowflake-arctic-instruct": PPTestSettings.fast(tp_base=8, trust_remote_code=True), # noqa: E501 @@ -151,6 +148,7 @@ def iter_params(self, model_name: str): "core42/jais-13b-chat": PPTestSettings.fast(), # TODO: Implement PP # "ai21labs/AI21-Jamba-1.5-Mini": PPTestSettings.fast(), + "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(), "openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(trust_remote_code=True), "openbmb/MiniCPM3-4B": PPTestSettings.fast(trust_remote_code=True), # Uses Llama @@ -163,6 +161,7 @@ def iter_params(self, model_name: str): "facebook/opt-iml-max-1.3b": PPTestSettings.fast(), "OrionStarAI/Orion-14B-Chat": PPTestSettings.fast(trust_remote_code=True), "microsoft/phi-2": PPTestSettings.fast(), + "microsoft/Phi-3-mini-4k-instruct": PPTestSettings.detailed(trust_remote_code=True, multi_node_only=True), # noqa: E501 "microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(trust_remote_code=True), # noqa: E501 "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.fast(trust_remote_code=True), # noqa: E501 "adept/persimmon-8b-chat": PPTestSettings.fast(), @@ -174,40 +173,40 @@ def iter_params(self, model_name: str): "upstage/solar-pro-preview-instruct": PPTestSettings.fast(tp_base=2), # FIXME: Cannot load tokenizer in latest transformers version # "xverse/XVERSE-7B-Chat": PPTestSettings.fast(trust_remote_code=True), + # [Encoder-only] + # TODO: Implement PP + # "facebook/bart-base": PPTestSettings.fast(), } -EMBEDDING_MODEL_SETTINGS = { # type: ignore[var-annotated] - # [FAST TESTS] +EMBEDDING_MODELS = { # type: ignore[var-annotated] + # [Text-only] "intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(), "BAAI/bge-multilingual-gemma2": PPTestSettings.fast(), "Qwen/Qwen2.5-Math-RM-72B": PPTestSettings.fast(tp_base=4, trust_remote_code=True), # noqa: E501 } -MULTIMODAL_MODEL_SETTINGS = { - # [FAST TESTS] +MULTIMODAL_MODELS = { + # [Decoder-only] "Salesforce/blip2-opt-2.7b": PPTestSettings.fast(), "facebook/chameleon-7b": PPTestSettings.fast(), "adept/fuyu-8b": PPTestSettings.fast(), + "THUDM/glm-4v-9b": PPTestSettings.fast(trust_remote_code=True), "OpenGVLab/InternVL2-1B": PPTestSettings.fast(trust_remote_code=True), "llava-hf/llava-1.5-7b-hf": PPTestSettings.fast(), "llava-hf/llava-v1.6-mistral-7b-hf": PPTestSettings.fast(), "llava-hf/LLaVA-NeXT-Video-7B-hf": PPTestSettings.fast(), "llava-hf/llava-onevision-qwen2-0.5b-ov-hf": PPTestSettings.fast(), "openbmb/MiniCPM-Llama3-V-2_5": PPTestSettings.fast(trust_remote_code=True), - # TODO: Implement PP - # "meta-llama/Llama-3.2-11B-Vision-Instruct": PPTestSettings.fast(), + "allenai/Molmo-7B-D-0924": PPTestSettings.fast(trust_remote_code=True), "microsoft/Phi-3-vision-128k-instruct": PPTestSettings.fast(trust_remote_code=True), # noqa: E501 "mistralai/Pixtral-12B-2409": PPTestSettings.fast(tp_base=2, tokenizer_mode="mistral"), # noqa: E501 "Qwen/Qwen-VL-Chat": PPTestSettings.fast(trust_remote_code=True), "Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(), "Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(), "fixie-ai/ultravox-v0_3": PPTestSettings.fast(), -} - -CONDITIONAL_GENERATION_MODEL_SETTINGS = { # type: ignore[var-annotated] - # [FAST TESTS] + # [Encoder-decoder] # TODO: Implement PP - # "facebook/bart-base": PPTestSettings.fast(), + # "meta-llama/Llama-3.2-11B-Vision-Instruct": PPTestSettings.fast(), } # yapf: enable @@ -323,7 +322,7 @@ def _compare_tp( ("model_name", "parallel_setup", "distributed_backend", "task", "test_options"), [ - params for model_name, settings in GENERATION_MODEL_SETTINGS.items() + params for model_name, settings in TEXT_GENERATION_MODELS.items() for params in settings.iter_params(model_name) if model_name in TEST_MODELS ], @@ -350,7 +349,7 @@ def test_tp_language_generation( ("model_name", "parallel_setup", "distributed_backend", "task", "test_options"), [ - params for model_name, settings in EMBEDDING_MODEL_SETTINGS.items() + params for model_name, settings in EMBEDDING_MODELS.items() for params in settings.iter_params(model_name) if model_name in TEST_MODELS ], @@ -377,7 +376,7 @@ def test_tp_language_embedding( ("model_name", "parallel_setup", "distributed_backend", "task", "test_options"), [ - params for model_name, settings in MULTIMODAL_MODEL_SETTINGS.items() + params for model_name, settings in MULTIMODAL_MODELS.items() for params in settings.iter_params(model_name) if model_name in TEST_MODELS ], diff --git a/vllm/model_executor/models/chatglm.py b/vllm/model_executor/models/chatglm.py index 8283975b9d8e2..ca90d10e9f9fb 100644 --- a/vllm/model_executor/models/chatglm.py +++ b/vllm/model_executor/models/chatglm.py @@ -13,8 +13,9 @@ from vllm.attention import Attention, AttentionMetadata from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig -from vllm.distributed import get_tensor_model_parallel_world_size -from vllm.inputs import INPUT_REGISTRY, DecoderOnlyInputs, InputContext +from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size +from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext, + token_inputs) from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm @@ -22,8 +23,7 @@ QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.layers.vocab_parallel_embedding import ( @@ -39,7 +39,9 @@ SequenceData) from vllm.transformers_utils.configs import ChatGLMConfig -from .interfaces import SupportsLoRA, SupportsMultiModal +from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP +from .utils import (is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, make_layers) logger = init_logger(__name__) @@ -150,6 +152,10 @@ def find_all_positions(input_ids: List[int], target: int) -> List[int]: def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs): + multi_modal_data = inputs.get("multi_modal_data") + if multi_modal_data is None or "image" not in multi_modal_data: + return inputs + hf_config = ctx.get_hf_config(ChatGLMConfig) vision_config = getattr(hf_config, 'vision_config', None) @@ -161,8 +167,8 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs): msg = f"Unsupported vision config: {type(vision_config)}" raise NotImplementedError(msg) - input_ids = inputs.get("prompt_token_ids") - position_ids = inputs.get("position_ids") + input_ids = inputs["prompt_token_ids"] + tokenizer = cached_get_tokenizer( ctx.model_config.model, trust_remote_code=ctx.model_config.trust_remote_code) @@ -171,20 +177,19 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs): raw_batch_data = tokenizer.apply_chat_template( conversation=[{ "role": "user", - "image": inputs['multi_modal_data']["image"], - "content": inputs['prompt'] + "image": multi_modal_data["image"], + "content": inputs['prompt'], }], add_generation_prompt=True, tokenize=True, return_tensors="pt", - return_dict=True).data + return_dict=True, + ).data except Exception: logger.error("Failed to process content (%s)", inputs['prompt']) raise input_ids = raw_batch_data['input_ids'][0].tolist() - if position_ids is None: - position_ids = list(range(len(input_ids))) boi_token_id = hf_config.boi_token_id eoi_token_id = hf_config.eoi_token_id boi_positions = find_all_positions(input_ids, boi_token_id) @@ -193,7 +198,6 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs): assert len(boi_positions) == len(eoi_positions) new_input_ids = [] - new_position_ids = [] final_processed_position = 0 final_processed_position = 0 @@ -201,29 +205,28 @@ def input_processor_for_glmv(ctx: InputContext, inputs: DecoderOnlyInputs): assert boi_position < eoi_position new_input_ids.extend(input_ids[final_processed_position:boi_position + 1]) - new_position_ids.extend( - list(range(final_processed_position, boi_position + 1))) new_input_ids.extend([input_ids[boi_position + 1]] * image_placeholder_length) - new_position_ids.extend([boi_position + 1] * image_placeholder_length) final_processed_position = eoi_position new_input_ids.extend(input_ids[final_processed_position:]) - new_position_ids.extend( - list(range(final_processed_position, len(input_ids)))) - assert len(new_input_ids) == len(new_position_ids) + prompt = inputs.get("prompt") + if prompt is None: + prompt = tokenizer.decode(new_input_ids) - inputs["prompt_token_ids"] = new_input_ids - inputs["position_ids"] = new_position_ids - return inputs + return token_inputs( + prompt_token_ids=new_input_ids, + prompt=prompt, + multi_modal_data=multi_modal_data, + ) class GLMAttention(nn.Module): def __init__( self, - config, + config: ChatGLMConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ): @@ -314,7 +317,7 @@ class GLMMLP(nn.Module): def __init__( self, - config, + config: ChatGLMConfig, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() @@ -357,7 +360,7 @@ class GLMBlock(nn.Module): def __init__( self, - config, + config: ChatGLMConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ): @@ -428,9 +431,10 @@ class GLMTransformer(nn.Module): def __init__( self, - config, + config: ChatGLMConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", ): super().__init__() self.post_layer_norm = config.post_layer_norm @@ -439,10 +443,11 @@ def __init__( self.num_layers = config.num_layers # Transformer layers. - self.layers = nn.ModuleList([ - GLMBlock(config, cache_config, quant_config) - for i in range(self.num_layers) - ]) + self.start_layer, self.end_layer, self.layers = make_layers( + self.num_layers, + lambda prefix: GLMBlock(config, cache_config, quant_config), + prefix=f"{prefix}.layers", + ) if self.post_layer_norm: layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm @@ -450,6 +455,10 @@ def __init__( self.final_layernorm = layer_norm_func( config.hidden_size, eps=config.layernorm_epsilon) + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory(["hidden_states"], + config.hidden_size)) + def forward( self, hidden_states: torch.Tensor, @@ -457,16 +466,16 @@ def forward( kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, ) -> torch.Tensor: - for i in range(self.num_layers): + for i in range(self.start_layer, self.end_layer): layer = self.layers[i] hidden_states = layer( hidden_states=hidden_states, position_ids=position_ids, - kv_cache=kv_caches[i], + kv_cache=kv_caches[i - self.start_layer], attn_metadata=attn_metadata, ) # Final layer norm. - if self.post_layer_norm: + if get_pp_group().is_last_rank and self.post_layer_norm: hidden_states = self.final_layernorm(hidden_states) return hidden_states @@ -476,7 +485,7 @@ class ChatGLMModel(nn.Module): def __init__( self, - config, + config: ChatGLMConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ): @@ -504,6 +513,9 @@ def __init__( else: self.vision = None + self.make_empty_intermediate_tensors = ( + self.encoder.make_empty_intermediate_tensors) + def _parse_and_validate_image_input( self, **kwargs: object) -> GLMImagePixelInputs: @@ -529,24 +541,26 @@ def forward( intermediate_tensors: Optional[IntermediateTensors] = None, **kwargs: object, ) -> torch.Tensor: - - inputs_embeds = self.embedding(input_ids) - image_input = self._parse_and_validate_image_input(**kwargs) - - if image_input["pixel_values"] is not None: - pixel_values = image_input["pixel_values"].to( - dtype=inputs_embeds.dtype) - image_embeds = self.vision(pixel_values) - - boi_token_id = self.config.boi_token_id - eoi_token_id = self.config.eoi_token_id - - inputs_embeds = merge_glm_vision_embeddings( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - vision_embeddings=image_embeds, - boi_token_id=boi_token_id, - eoi_token_id=eoi_token_id) + if intermediate_tensors is None: + inputs_embeds = self.embedding(input_ids) + image_input = self._parse_and_validate_image_input(**kwargs) + + if image_input["pixel_values"] is not None: + pixel_values = image_input["pixel_values"].to( + dtype=inputs_embeds.dtype) + image_embeds = self.vision(pixel_values) + + boi_token_id = self.config.boi_token_id + eoi_token_id = self.config.eoi_token_id + + inputs_embeds = merge_glm_vision_embeddings( + input_ids=input_ids, + inputs_embeds=inputs_embeds, + vision_embeddings=image_embeds, + boi_token_id=boi_token_id, + eoi_token_id=eoi_token_id) + else: + inputs_embeds = intermediate_tensors["hidden_states"] # Run encoder. hidden_states = self.encoder( @@ -555,6 +569,9 @@ def forward( kv_caches=kv_caches, attn_metadata=attn_metadata, ) + + if not get_pp_group().is_last_rank: + return IntermediateTensors({"hidden_states": hidden_states}) return hidden_states @@ -562,7 +579,8 @@ def forward( @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv) @INPUT_REGISTRY.register_input_processor(input_processor_for_glmv) -class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal): +class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, + SupportsMultiModal): packed_modules_mapping = { "query_key_value": ["query_key_value"], "dense_h_to_4h": ["dense_h_to_4h"] @@ -610,7 +628,8 @@ def forward(self, intermediate_tensors: Optional[IntermediateTensors] = None, **kwargs) -> torch.Tensor: hidden_states = self.transformer(input_ids, positions, kv_caches, - attn_metadata, **kwargs) + attn_metadata, intermediate_tensors, + **kwargs) return hidden_states def compute_logits( @@ -656,6 +675,8 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue + if is_pp_missing_parameter(name, self): + continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) diff --git a/vllm/model_executor/models/molmo.py b/vllm/model_executor/models/molmo.py index 7369de79f5083..3c34227767e05 100644 --- a/vllm/model_executor/models/molmo.py +++ b/vllm/model_executor/models/molmo.py @@ -30,21 +30,21 @@ QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.models.interfaces import SupportsMultiModal -from vllm.model_executor.models.utils import make_layers from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalInputs +from vllm.multimodal.utils import cached_get_tokenizer from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors, SequenceData) from vllm.transformers_utils.processor import get_processor -from .utils import get_vit_attn_backend +from .interfaces import SupportsMultiModal, SupportsPP +from .utils import (get_vit_attn_backend, + make_empty_intermediate_tensors_factory, make_layers) # TODO: hard-coded for now. Consider making it configurable. VIT_LAYERS = [-2, -9] @@ -744,6 +744,10 @@ def __init__( assert config.layer_norm_type == "rms" self.norm = RMSNorm(config.hidden_size, config.layer_norm_eps) + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory( + ["hidden_states", "residual"], config.hidden_size)) + def forward( self, input_ids: torch.Tensor, @@ -925,16 +929,19 @@ def pad_images( def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs): - prompt = inputs.get("prompt", None) - multi_modal_data = inputs.get("multi_modal_data", None) - if multi_modal_data is not None: - image = multi_modal_data.get("image", None) - else: - image = None + prompt = inputs.get("prompt") + multi_modal_data = inputs.get("multi_modal_data") + image = None if multi_modal_data is None else multi_modal_data.get("image") + processor = cached_get_processor(ctx.model_config.model, trust_remote_code=True, revision=ctx.model_config.code_revision) + model_config = ctx.model_config + tokenizer = cached_get_tokenizer( + model_config.tokenizer, + trust_remote_code=model_config.trust_remote_code) + # NOTE: message formatting for raw text prompt is only applied for # offline inference; for online inference, the prompt is always in # instruction format and tokenized. @@ -997,9 +1004,13 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs): multi_modal_data = dict(image=image_data) + prompt = inputs.get("prompt") + if prompt is None: + prompt = tokenizer.decode(out["input_ids"]) + return token_inputs( prompt_token_ids=out["input_ids"], - prompt=inputs["prompt"], + prompt=prompt, multi_modal_data=multi_modal_data, ) @@ -1008,7 +1019,7 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs): @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_molmo_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_molmo) @INPUT_REGISTRY.register_input_processor(input_processor_for_molmo) -class MolmoForCausalLM(nn.Module, SupportsMultiModal): +class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP): def __init__( self, @@ -1040,6 +1051,9 @@ def __init__( or config.vocab_size) self.sampler = Sampler() + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors) + def _parse_and_validate_image_input( self, **kwargs: object, @@ -1123,31 +1137,36 @@ def forward( positions: torch.LongTensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors] = None, **kwargs: object, ) -> SamplerOutput: + if intermediate_tensors is not None: + input_ids = None + inputs_embeds = None + else: + image_input = self._parse_and_validate_image_input(**kwargs) - image_input = self._parse_and_validate_image_input(**kwargs) - - if image_input is not None: - inputs_embeds = self.model.embed_tokens(input_ids) - image_features = self._process_image_input(image_input) + if image_input is not None: + inputs_embeds = self.model.embed_tokens(input_ids) + image_features = self._process_image_input(image_input) - inputs_embeds = self._merge_multimodal_embeddings( - inputs_embeds, - image_features, - image_input["image_input_idx"], - image_input["seq_len"], - ) + inputs_embeds = self._merge_multimodal_embeddings( + inputs_embeds, + image_features, + image_input["image_input_idx"], + image_input["seq_len"], + ) - input_ids = None - else: - inputs_embeds = None + input_ids = None + else: + inputs_embeds = None hidden_states = self.model( input_ids=input_ids, positions=positions, kv_caches=kv_caches, attn_metadata=attn_metadata, + intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds, ) diff --git a/vllm/model_executor/models/qwen2_rm.py b/vllm/model_executor/models/qwen2_rm.py index 7dcf52a56e985..ee0eeb9db3808 100644 --- a/vllm/model_executor/models/qwen2_rm.py +++ b/vllm/model_executor/models/qwen2_rm.py @@ -119,5 +119,6 @@ def pooler( return self._pooler(hidden_states, pooling_metadata) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): - loader = AutoWeightsLoader(self) + loader = AutoWeightsLoader(self, + ignore_unexpected_prefixes=["lm_head."]) loader.load_weights(weights) diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index 3dc955b12ba0e..4e60fe70b25f1 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -61,6 +61,7 @@ MultiModalInputs) from vllm.multimodal.base import MultiModalData from vllm.multimodal.image import cached_get_image_processor +from vllm.multimodal.utils import cached_get_tokenizer from vllm.sequence import IntermediateTensors, SequenceData from vllm.transformers_utils.config import uses_mrope from vllm.transformers_utils.processor import cached_get_processor @@ -817,7 +818,7 @@ def input_processor_for_qwen2_vl( min_pixels: Optional[int] = None, max_pixels: Optional[int] = None, ) -> DecoderOnlyInputs: - multi_modal_data = inputs.get("multi_modal_data", None) + multi_modal_data = inputs.get("multi_modal_data") if multi_modal_data is None: return inputs @@ -830,6 +831,7 @@ def input_processor_for_qwen2_vl( min_pixels = min_pixels if min_pixels else image_processor.min_pixels max_pixels = max_pixels if max_pixels else image_processor.max_pixels + model_config = ctx.model_config hf_config = ctx.get_hf_config(Qwen2VLConfig) # To avoid redundant processing of vision objects (resize, rescale, etc.), @@ -845,14 +847,11 @@ def input_processor_for_qwen2_vl( # return_tensors="pt") # prompt_token_ids = inputs["input_ids"][0].tolist() - prompt_token_ids = inputs.get("prompt_token_ids", None) - if prompt_token_ids is None: - prompt = inputs["prompt"] - prompt_token_ids = processor.tokenizer( - prompt, - padding=True, - return_tensors=None, - )["input_ids"] + tokenizer = cached_get_tokenizer( + model_config.tokenizer, + trust_remote_code=model_config.trust_remote_code) + + prompt_token_ids = inputs["prompt_token_ids"] # Expand image pad tokens. @@ -894,9 +893,13 @@ def input_processor_for_qwen2_vl( min_pixels=min_pixels, max_pixels=max_pixels) + prompt = inputs.get("prompt") + if prompt is None: + prompt = tokenizer.decode(prompt_token_ids) + return token_inputs( prompt_token_ids=prompt_token_ids, - prompt=inputs["prompt"], + prompt=prompt, multi_modal_data=multi_modal_data, ) diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index d96e988fba384..6995f5805c5e1 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -79,6 +79,9 @@ class AutoWeightsLoader: Similarly, the weight loading logic for individual parameters can be overridden by defining a ``weight_loader`` method. + + Detailed weight loading information can be viewed by setting the + environment variable ``VLLM_LOGGING_LEVEL=DEBUG``. """ def __init__( @@ -136,20 +139,27 @@ def _load_param( weight_qualname = self._get_qualname(base_prefix, weight_name) if self._can_skip(weight_qualname): + logger.debug("Skipping weight %s", weight_qualname) + continue if weight_name != "": - if not self._can_ignore_unexpected(weight_qualname): - raise ValueError( - f"Attempted to load nested weight '{weight_qualname}' " - f"into a single parameter '{base_prefix}'") + if self._can_ignore_unexpected(weight_qualname): + logger.debug("Ignoring weight %s", weight_qualname) - continue + continue + + raise ValueError( + f"Attempted to load nested weight '{weight_qualname}' " + f"into a single parameter '{base_prefix}'") weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, weight_data) + logger.debug("Loaded weight %s with shape %s", weight_qualname, + param.shape) + yield weight_qualname def _load_module( @@ -175,21 +185,41 @@ def _load_module( for child_prefix, child_weights in self._groupby_prefix(weights): prefix = self._get_qualname(base_prefix, child_prefix) - if self._can_skip(prefix): - continue - if child_prefix in child_modules: + if self._can_skip(prefix + "."): + logger.debug("Skipping module %s", prefix) + + continue + yield from self._load_module(prefix, child_modules[child_prefix], child_weights) elif child_prefix in child_params: + if self._can_skip(prefix): + logger.debug("Skipping param %s", prefix) + + continue + yield from self._load_param(prefix, child_params[child_prefix], child_weights) else: - if not self._can_ignore_unexpected(prefix): - msg = (f"There is no module or parameter named '{prefix}' " - f"in {type(self.module).__name__}") - raise ValueError(msg) + can_skip_module = self._can_skip(prefix + ".") + can_skip_param = self._can_skip(prefix) + if can_skip_module or can_skip_param: + logger.debug("Skipping missing %s", prefix) + + continue + + can_ignore_module = self._can_ignore_unexpected(prefix + ".") + can_ignore_param = self._can_ignore_unexpected(prefix) + if can_ignore_module or can_ignore_param: + logger.debug("Ignoring missing %s", prefix) + + continue + + msg = (f"There is no module or parameter named '{prefix}' " + f"in {type(self.module).__name__}") + raise ValueError(msg) def load_weights( self, From 3770071eb4dc97eb728ad68adde027769ee31afe Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Wed, 23 Oct 2024 23:33:22 -0700 Subject: [PATCH 079/222] [V1][Bugfix] Clean up requests when aborted (#9629) Signed-off-by: Woosuk Kwon --- vllm/v1/engine/llm_engine.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/vllm/v1/engine/llm_engine.py b/vllm/v1/engine/llm_engine.py index 511b417086c63..072e52bcd686a 100644 --- a/vllm/v1/engine/llm_engine.py +++ b/vllm/v1/engine/llm_engine.py @@ -300,6 +300,7 @@ def add_request( def abort_request(self, request_id: Union[str, Iterable[str]]) -> None: self.scheduler.finish_requests(request_id, RequestStatus.FINISHED_ABORTED) + self._free_request(request_id) def get_num_unfinished_requests(self) -> int: """Gets the number of unfinished requests.""" @@ -361,6 +362,11 @@ def recv_from_detokenizer(self) -> List[RequestOutput]: num_reqs = len(detokenizer_output.req_ids) for i in range(num_reqs): req_id = detokenizer_output.req_ids[i] + if req_id not in self.requests: + # The request has been aborted while the detokenizer was + # processing the outputs. + continue + req = self.requests[req_id] req.output_text += detokenizer_output.detokenized_texts[i] @@ -373,9 +379,7 @@ def recv_from_detokenizer(self) -> List[RequestOutput]: req_outputs.append(req_output) if finished: - del self.requests[req_id] - del self.num_lagged_steps[req_id] - del self.request_outputs[req_id] + self._free_request(req_id) return req_outputs def terminate_detokenizer(self) -> None: @@ -440,6 +444,11 @@ def _make_request_output( req_output.finished = finished return req_output + def _free_request(self, request_id: str) -> None: + self.requests.pop(request_id, None) + self.num_lagged_steps.pop(request_id, None) + self.request_outputs.pop(request_id, None) + def check_health(self) -> None: if self.tokenizer: self.tokenizer.check_health() From 4fdc581f9e5740ba10b16ebf8a4c467e65bb9822 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Thu, 24 Oct 2024 00:16:44 -0700 Subject: [PATCH 080/222] [core] simplify seq group code (#9569) Co-authored-by: Zhuohan Li --- tests/core/test_chunked_prefill_scheduler.py | 153 -------------- tests/core/test_scheduler.py | 204 +------------------ vllm/core/scheduler.py | 2 +- vllm/engine/llm_engine.py | 40 ++-- vllm/engine/output_processor/single_step.py | 127 ++---------- vllm/sequence.py | 102 ++-------- 6 files changed, 62 insertions(+), 566 deletions(-) diff --git a/tests/core/test_chunked_prefill_scheduler.py b/tests/core/test_chunked_prefill_scheduler.py index 308dad1850c9a..acd82065ae457 100644 --- a/tests/core/test_chunked_prefill_scheduler.py +++ b/tests/core/test_chunked_prefill_scheduler.py @@ -4,7 +4,6 @@ import pytest # noqa from vllm.config import CacheConfig, SchedulerConfig -from vllm.core.interfaces import AllocStatus from vllm.core.scheduler import Scheduler from vllm.sequence import Logprob, SequenceGroup @@ -347,158 +346,6 @@ def test_prompt_limit_exceed(): assert out.ignored_seq_groups[0] == seq_group -def test_swap(): - """Verify swapping works with chunked prefill requests""" - block_size = 4 - max_seqs = 30 - max_model_len = 200 - max_num_batched_tokens = 30 - scheduler_config = SchedulerConfig( - "generate", - max_num_batched_tokens, - max_seqs, - max_model_len, - enable_chunked_prefill=True, - ) - cache_config = CacheConfig(block_size, 1.0, 1, "auto") - cache_config.num_cpu_blocks = 16 - cache_config.num_gpu_blocks = 16 - scheduler = Scheduler(scheduler_config, cache_config, None) - - _, seq_group = create_dummy_prompt("1", - prompt_length=60, - best_of=2, - block_size=block_size) - scheduler.add_seq_group(seq_group) - _, out = schedule_and_update_computed_tokens(scheduler) - # The request is chunked. - # prefill scheduled now. - assert len(out.scheduled_seq_groups) == 1 - assert out.num_prefill_groups == 1 - assert seq_group.is_prefill() - assert out.num_batched_tokens == max_num_batched_tokens - - # The last request should be swapped out. - scheduler.block_manager.can_append_slots = MagicMock() - - def cannot_append_second_group(seq_group, num_lookahead_slots): - return seq_group.request_id != "1" - - scheduler.block_manager.can_append_slots.side_effect = ( - cannot_append_second_group) - - # The running prefill is now swapped. - _, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 0 - assert out.num_batched_tokens == 0 - assert out.blocks_to_swap_out != [] - assert out.blocks_to_swap_in == [] - - # Add 1 more task. Swap should be prioritized over new prefill. - _, seq_group = create_dummy_prompt("2", prompt_length=60) - scheduler.add_seq_group(seq_group) - _, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 1 - # 3 decodes. It is swapped in. - assert out.num_batched_tokens == 30 - assert out.blocks_to_swap_in != [] - assert out.blocks_to_swap_out == [] - - -def test_running_prefill_prioritized_over_swap(): - block_size = 4 - max_seqs = 30 - max_model_len = 200 - max_num_batched_tokens = 30 - scheduler_config = SchedulerConfig( - "generate", - max_num_batched_tokens, - max_seqs, - max_model_len, - enable_chunked_prefill=True, - ) - cache_config = CacheConfig(block_size, 1.0, 1, "auto") - cache_config.num_cpu_blocks = 32 - cache_config.num_gpu_blocks = 32 - scheduler = Scheduler(scheduler_config, cache_config, None) - - _, seq_group = create_dummy_prompt("1", - prompt_length=60, - best_of=2, - block_size=block_size) - scheduler.add_seq_group(seq_group) - _, out = schedule_and_update_computed_tokens(scheduler) - # The request is chunked. - # prefill scheduled now. - assert len(out.scheduled_seq_groups) == 1 - assert out.num_prefill_groups == 1 - assert seq_group.is_prefill() - assert out.num_batched_tokens == max_num_batched_tokens - - # The request should be swapped out. - scheduler.block_manager.can_append_slots = MagicMock() - - def cannot_append_second_group(seq_group, num_lookahead_slots): - return seq_group.request_id != "1" - - scheduler.block_manager.can_append_slots.side_effect = ( - cannot_append_second_group) - - # The running prefill is now swapped. - _, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 0 - assert out.num_batched_tokens == 0 - assert out.blocks_to_swap_out != [] - assert out.blocks_to_swap_in == [] - - # Add 1 more task. Swap is not possible, so prefill is running. - scheduler.block_manager.can_swap_in = MagicMock() - scheduler.block_manager.can_swap_in.return_value = AllocStatus.LATER - - _, seq_group2 = create_dummy_prompt("2", - prompt_length=60, - block_size=block_size) - scheduler.add_seq_group(seq_group2) - _, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 1 - # 3 decodes. It is swapped in. - assert out.num_batched_tokens == 30 - assert out.blocks_to_swap_in == [] - assert out.blocks_to_swap_out == [] - assert out.scheduled_seq_groups[0].seq_group == seq_group2 - - # Now although swap is possible, running prefill is prioritized. - scheduler.block_manager.can_swap_in.return_value = AllocStatus.OK - _, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 1 - # 3 decodes. It is swapped in. - assert out.num_batched_tokens == 30 - assert out.blocks_to_swap_in == [] - assert out.blocks_to_swap_out == [] - assert not seq_group2.is_prefill() - assert out.scheduled_seq_groups[0].seq_group == seq_group2 - append_new_token(seq_group2, 1) - - # Decoding is prioritized. - _, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 1 - # 3 decodes. It is swapped in. - assert out.num_batched_tokens == 1 - assert out.blocks_to_swap_in == [] - assert out.blocks_to_swap_out == [] - assert not seq_group2.is_prefill() - assert out.scheduled_seq_groups[0].seq_group == seq_group2 - append_new_token(seq_group2, 1) - - # Since we abort the sequence group, we can finally swap. - scheduler.abort_seq_group(seq_group2.request_id) - _, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 1 - assert out.num_batched_tokens == 30 - assert out.blocks_to_swap_in != [] - assert out.blocks_to_swap_out == [] - - def test_chunked_prefill_preempt(): """Verify preempt works with chunked prefill requests""" block_size = 4 diff --git a/tests/core/test_scheduler.py b/tests/core/test_scheduler.py index 00b6349b9f8c5..5ff32be611592 100644 --- a/tests/core/test_scheduler.py +++ b/tests/core/test_scheduler.py @@ -10,7 +10,7 @@ from vllm.core.interfaces import AllocStatus from vllm.core.scheduler import Scheduler, SchedulingBudget from vllm.lora.request import LoRARequest -from vllm.sequence import SequenceGroup, SequenceStatus +from vllm.sequence import SequenceGroup from .utils import (append_new_token, append_new_token_seq_group, create_dummy_prompt, get_sequence_groups, @@ -296,55 +296,6 @@ def test_scheduler_delay_factor(): append_new_token(out, 1) -def test_swapped_out_prioritized(): - block_size = 4 - scheduler = initialize_scheduler(max_num_seqs=6, - block_size=block_size, - num_cpu_blocks=64, - num_gpu_blocks=64) - # best_of=2 * 3 == 6 sequences. - for i in range(3): - _, seq_group = create_dummy_prompt(str(i), - prompt_length=60, - best_of=2, - block_size=block_size) - scheduler.add_seq_group(seq_group) - seq_group_meta, out = schedule_and_update_computed_tokens(scheduler) - # prefill scheduled now. - assert len(out.scheduled_seq_groups) == 3 - append_new_token(out, 1) - - # The last request should be swapped out. - scheduler.block_manager.can_append_slots = MagicMock() - - def cannot_append_second_group(seq_group, num_lookahead_slots): - return seq_group.request_id != "2" - - scheduler.block_manager.can_append_slots.side_effect = ( - cannot_append_second_group) - - seq_group_meta, out = schedule_and_update_computed_tokens(scheduler) - assert len(out.scheduled_seq_groups) == 2 - assert out.num_batched_tokens == 2 - assert out.blocks_to_swap_out != [] - assert out.blocks_to_swap_in == [] - append_new_token(out, 1) - - # Add 1 more task. Swap should be prioritized over prefill. - _, seq_group = create_dummy_prompt(str(i), - prompt_length=60, - best_of=2, - block_size=block_size) - scheduler.add_seq_group(seq_group) - seq_group_meta, out = schedule_and_update_computed_tokens(scheduler) - append_new_token(out, 1) - assert len(out.scheduled_seq_groups) == 3 - # 3 decodes. It is swapped in. - assert out.num_batched_tokens == 3 - assert out.blocks_to_swap_in != [] - assert out.blocks_to_swap_out == [] - - def initialize_scheduler( *, max_num_seqs=1000, @@ -646,60 +597,6 @@ def cannot_append_second_group(seq_group, num_lookahead_slots): assert output.blocks_to_copy == [] -def test_decode_swap_beam_search(): - """ - Test best_of > 1 swap out blocks - """ - block_size = 4 - scheduler = initialize_scheduler(block_size=block_size, - num_gpu_blocks=64, - num_cpu_blocks=64) - curr_loras = None - budget = create_token_budget() - for i in range(3): - _, seq_group = create_dummy_prompt(str(i), - prompt_length=60, - best_of=2, - block_size=block_size) - scheduler._allocate_and_set_running(seq_group) - scheduler._add_seq_group_to_running(seq_group) - append_new_token_seq_group(60, seq_group, 1) - budget.add_num_seqs(seq_group.request_id, - seq_group.get_max_num_running_seqs()) - budget.add_num_batched_tokens( - seq_group.request_id, seq_group.num_seqs(SequenceStatus.RUNNING)) - - # The last request should be swapped out. - scheduler.block_manager.can_append_slots = MagicMock() - - def cannot_append_second_group(seq_group, num_lookahead_slots): - return seq_group.request_id != "2" - - scheduler.block_manager.can_append_slots.side_effect = ( - cannot_append_second_group) - scheduler.block_manager.swap_out = MagicMock() - expected_swap_mapping = [("5", "7")] - scheduler.block_manager.swap_out.return_value = expected_swap_mapping - - output = scheduler._schedule_running(budget, curr_loras) - remainig_running = scheduler.running - assert len(remainig_running) == 0 - assert len(output.decode_seq_groups) == 2 - assert len(output.prefill_seq_groups) == 0 - assert output.decode_seq_groups[0].seq_group.request_id == "0" - assert output.decode_seq_groups[1].seq_group.request_id == "1" - assert len(output.preempted) == 0 - assert len(output.swapped_out) == 1 - # Budget should refledct preempted requests. - assert budget.num_batched_tokens == 2 - # since there are 2 sequences, 2 should be subtracted. - assert budget.num_curr_seqs == 4 - # Both should be preempted, not swapped. - assert output.blocks_to_swap_out == expected_swap_mapping - # Nothing is copied. - assert output.blocks_to_copy == [] - - def test_schedule_decode_blocks_to_copy_update(): """ Verify blocks_to_copy is updated. @@ -736,105 +633,6 @@ def test_schedule_decode_blocks_to_copy_update(): assert output.blocks_to_copy == [(2, 3)] -def test_schedule_swapped_simple(): - block_size = 4 - scheduler = initialize_scheduler(block_size=block_size) - curr_loras = None - blocks_to_swap_out: List[Tuple[int, int]] = [] - _, seq_group = create_dummy_prompt("1", - prompt_length=4, - best_of=2, - block_size=block_size) - scheduler._allocate_and_set_running(seq_group) - append_new_token_seq_group(4, seq_group, 1) - scheduler._swap_out(seq_group, blocks_to_swap_out) - scheduler._add_seq_group_to_swapped(seq_group) - - budget = create_token_budget() - output = scheduler._schedule_swapped(budget, curr_loras) - remaining_swapped = scheduler.swapped - assert len(remaining_swapped) == 0 - assert budget.num_batched_tokens == 1 - assert budget.num_curr_seqs == 2 - assert len(output.decode_seq_groups) == 1 - assert len(output.prefill_seq_groups) == 0 - # swap in is the reverse of swap out - blocks_to_swap_in_reverse = [] - for swapin, swapout in output.blocks_to_swap_in: - blocks_to_swap_in_reverse.append((swapout, swapin)) - assert blocks_to_swap_out == blocks_to_swap_in_reverse - - -def test_schedule_swapped_max_token_budget(): - block_size = 4 - scheduler = initialize_scheduler(block_size=block_size, - num_cpu_blocks=32, - num_gpu_blocks=32) - curr_loras = None - blocks_to_swap_out: List[Tuple[int, int]] = [] - for i in range(2): - _, seq_group = create_dummy_prompt(str(i), prompt_length=60, best_of=2) - scheduler._allocate_and_set_running(seq_group) - append_new_token_seq_group(60, seq_group, 1) - scheduler._swap_out(seq_group, blocks_to_swap_out) - scheduler._add_seq_group_to_swapped(seq_group) - - budget = create_token_budget(token_budget=1) - output = scheduler._schedule_swapped(budget, curr_loras) - remaining_swapped = scheduler.swapped - assert len(remaining_swapped) == 1 - assert budget.num_batched_tokens == 1 - assert budget.num_curr_seqs == 2 - assert len(output.decode_seq_groups) == 1 - assert len(output.prefill_seq_groups) == 0 - - # Verify num_batched_tokens are respected. - budget = create_token_budget(token_budget=1) - add_token_budget(budget, 1, 0) - output = scheduler._schedule_swapped(budget, curr_loras) - remaining_swapped = scheduler.swapped - assert len(remaining_swapped) == 1 - assert budget.num_batched_tokens == 1 - assert budget.num_curr_seqs == 0 - assert len(output.decode_seq_groups) == 0 - assert len(output.prefill_seq_groups) == 0 - - -def test_schedule_swapped_max_seqs(): - block_size = 4 - scheduler = initialize_scheduler(block_size=block_size, - num_cpu_blocks=64, - num_gpu_blocks=64) - curr_loras = None - blocks_to_swap_out: List[Tuple[int, int]] = [] - for i in range(4): - _, seq_group = create_dummy_prompt(str(i), - prompt_length=60, - block_size=4) - scheduler._allocate_and_set_running(seq_group) - append_new_token_seq_group(60, seq_group, 1) - scheduler._swap_out(seq_group, blocks_to_swap_out) - scheduler._add_seq_group_to_swapped(seq_group) - - budget = create_token_budget(max_num_seqs=2) - output = scheduler._schedule_swapped(budget, curr_loras) - remaining_swapped = scheduler.swapped - assert len(remaining_swapped) == 2 - assert budget.num_batched_tokens == 2 - assert budget.num_curr_seqs == 2 - assert len(output.decode_seq_groups) == 2 - assert len(output.prefill_seq_groups) == 0 - - # Verify num_curr_seqs are respected. - output = scheduler._schedule_swapped(budget, curr_loras) - remaining_swapped = scheduler.swapped - assert len(remaining_swapped) == 2 - assert budget.num_batched_tokens == 2 - assert budget.num_curr_seqs == 2 - assert len(output.decode_seq_groups) == 0 - assert len(output.prefill_seq_groups) == 0 - - def test_schedule_swapped_max_loras(): block_size = 4 lora_config = LoRAConfig(max_lora_rank=8, max_loras=1) diff --git a/vllm/core/scheduler.py b/vllm/core/scheduler.py index 8d3fce106dd2c..88733b8f53b86 100644 --- a/vllm/core/scheduler.py +++ b/vllm/core/scheduler.py @@ -290,7 +290,7 @@ def scheduler_running_outputs_builder(): def scheduled_seq_group_builder(): - return ScheduledSequenceGroup(SequenceGroup("", [], -1), + return ScheduledSequenceGroup(SequenceGroup.__new__(SequenceGroup), token_chunk_size=0) # return ScheduledSequenceGroup(seq_group=None, token_chunk_size=0) diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 0d73ed7c8e7ab..1dd0f097c74ff 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -647,10 +647,24 @@ def _add_processed_request( prompt_adapter_request: Optional[PromptAdapterRequest], trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, - ) -> SequenceGroup: + ) -> Optional[SequenceGroup]: """Add a processed request to the engine's request pool. return the created sequence group. """ + if isinstance(params, SamplingParams) and params.n > 1: + ParallelSampleSequenceGroup.add_request( + request_id, + self, + params, + processed_inputs=processed_inputs, + arrival_time=arrival_time, + lora_request=lora_request, + trace_headers=trace_headers, + prompt_adapter_request=prompt_adapter_request, + priority=priority, + ) + return None + self._validate_model_inputs(processed_inputs) # Create the sequences. block_size = self.cache_config.block_size @@ -721,7 +735,7 @@ def add_request( trace_headers: Optional[Mapping[str, str]] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, priority: int = 0, - ) -> Optional[SequenceGroup]: + ) -> None: ... @overload @@ -735,7 +749,7 @@ def add_request( trace_headers: Optional[Mapping[str, str]] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, priority: int = 0, - ) -> Optional[SequenceGroup]: + ) -> None: ... @deprecate_kwargs( @@ -754,7 +768,7 @@ def add_request( priority: int = 0, *, inputs: Optional[PromptType] = None, # DEPRECATED - ) -> Optional[SequenceGroup]: + ) -> None: """Add a request to the engine's request pool. The request is added to the request pool and will be processed by the @@ -798,22 +812,6 @@ def add_request( >>> # continue the request processing >>> ... """ - - if isinstance(params, SamplingParams) and params.n > 1: - ParallelSampleSequenceGroup.add_request( - request_id, - self, - params, - prompt=prompt, - arrival_time=arrival_time, - lora_request=lora_request, - trace_headers=trace_headers, - prompt_adapter_request=prompt_adapter_request, - priority=priority, - inputs=inputs, - ) - return None - if inputs is not None: prompt = inputs assert prompt is not None and params is not None @@ -844,7 +842,7 @@ def add_request( processed_inputs["mm_processor_kwargs"] = preprocessed_inputs.get( "mm_processor_kwargs") - return self._add_processed_request( + self._add_processed_request( request_id=request_id, processed_inputs=processed_inputs, params=params, diff --git a/vllm/engine/output_processor/single_step.py b/vllm/engine/output_processor/single_step.py index 9f8ebaf1f4d8c..da3185f33dbe9 100644 --- a/vllm/engine/output_processor/single_step.py +++ b/vllm/engine/output_processor/single_step.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Tuple +from typing import List from vllm.config import SchedulerConfig from vllm.core.scheduler import Scheduler @@ -6,9 +6,8 @@ SequenceGroupOutputProcessor) from vllm.engine.output_processor.stop_checker import StopChecker from vllm.logger import init_logger -from vllm.sequence import (CompletionSequenceGroupOutput, Sequence, - SequenceGroup, SequenceGroupOutput, SequenceOutput, - SequenceStatus) +from vllm.sequence import (CompletionSequenceGroupOutput, SequenceGroup, + SequenceGroupOutput) from vllm.transformers_utils.detokenizer import Detokenizer from vllm.utils import Counter @@ -114,104 +113,22 @@ def _process_sequence_group_outputs(self, seq_group: SequenceGroup, outputs: SequenceGroupOutput, is_async: bool) -> None: sampling_params = seq_group.sampling_params - if sampling_params.n == 1: - # only have one output sample - sample = outputs.samples[0] - # only have one sequence - seq = seq_group.seqs[0] - if not is_async: - seq.append_token_id(sample.output_token, sample.logprobs) - if sampling_params.detokenize and self.detokenizer: - new_char_count = self.detokenizer.decode_sequence_inplace( - seq, sampling_params) - else: - new_char_count = 0 - self.stop_checker.maybe_stop_sequence( - seq, - new_char_count, - sampling_params, - lora_req=seq_group.lora_request, - ) - if seq.is_finished(): - for scheduler in self.scheduler: - scheduler.free_seq(seq) - return - - # TODO: Add support for async for beam search - assert not is_async - - # Process samples - samples = outputs.samples - parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING) - parent_child_dict: Dict[int, List[SequenceOutput]] = { - parent_seq.seq_id: [] - for parent_seq in parent_seqs - } - for sample in samples: - # Guard against a KeyError which can occur if the request was - # aborted while the output was generated - if (child_list := - parent_child_dict.get(sample.parent_seq_id)) is not None: - child_list.append(sample) - # List of (child, parent) - child_seqs: List[Tuple[Sequence, Sequence]] = [] - - # Process the child samples for each parent sequence - for parent in parent_seqs: - child_samples: List[SequenceOutput] = parent_child_dict[ - parent.seq_id] - if len(child_samples) == 0: - # This parent sequence has no children samples. Remove - # the parent sequence from the sequence group since it will - # not be used in the future iterations. - parent.status = SequenceStatus.FINISHED_ABORTED - seq_group.remove(parent.seq_id) - for scheduler in self.scheduler: - scheduler.free_seq(parent) - continue - # Fork the parent sequence if there are multiple child samples. - for child_sample in child_samples[:-1]: - new_child_seq_id: int = next(self.seq_counter) - child = parent.fork(new_child_seq_id) - child.append_token_id(child_sample.output_token, - child_sample.logprobs) - child_seqs.append((child, parent)) - # Continue the parent sequence for the last child sample. - # We reuse the parent sequence here to reduce redundant memory - # copies, especially when using non-beam search sampling methods. - last_child_sample = child_samples[-1] - parent.append_token_id(last_child_sample.output_token, - last_child_sample.logprobs) - child_seqs.append((parent, parent)) - - for seq, _ in child_seqs: - if sampling_params.detokenize and self.detokenizer: - new_char_count = self.detokenizer.decode_sequence_inplace( - seq, sampling_params) - else: - new_char_count = 0 - self.stop_checker.maybe_stop_sequence( - seq, - new_char_count, - sampling_params, - lora_req=seq_group.lora_request, - ) - - # For newly created child sequences, add them to the sequence group - # and fork them in block manager if they are not finished. - for seq, parent in child_seqs: - if seq is not parent: - seq_group.add(seq) - if not seq.is_finished(): - for scheduler in self.scheduler: - scheduler.fork_seq(parent, seq) - - # Free the finished and selected parent sequences' memory in block - # manager. Keep them in the sequence group as candidate output. - # NOTE: we need to fork the new sequences before freeing the - # old sequences. - for seq, parent in child_seqs: - if seq is parent and seq.is_finished(): - for scheduler in self.scheduler: - scheduler.free_seq(seq) - return + + sample = outputs.samples[0] + seq = seq_group.first_seq + if not is_async: + seq.append_token_id(sample.output_token, sample.logprobs) + if sampling_params.detokenize and self.detokenizer: + new_char_count = self.detokenizer.decode_sequence_inplace( + seq, sampling_params) + else: + new_char_count = 0 + self.stop_checker.maybe_stop_sequence( + seq, + new_char_count, + sampling_params, + lora_req=seq_group.lora_request, + ) + if seq.is_finished(): + for scheduler in self.scheduler: + scheduler.free_seq(seq) diff --git a/vllm/sequence.py b/vllm/sequence.py index 93f58f00ef77b..fc936fbab0ea7 100644 --- a/vllm/sequence.py +++ b/vllm/sequence.py @@ -681,6 +681,7 @@ def __init__( ) -> None: self.request_id = request_id self.seqs = seqs + self.first_seq = seqs[0] self.arrival_time = arrival_time self.is_single_seq = len(seqs) == 1 self.seqs_dict = {seq.seq_id: seq for seq in seqs} @@ -705,15 +706,11 @@ def __init__( @property def prompt(self) -> Optional[str]: - # All sequences in the group should have the same prompt. - # We use the prompt of an arbitrary sequence. - return self.seqs[0].prompt + return self.first_seq.prompt @property def prompt_token_ids(self) -> List[int]: - # All sequences in the group should have the same prompt. - # We use the prompt of an arbitrary sequence. - return self.seqs[0].prompt_token_ids + return self.first_seq.prompt_token_ids @property def encoder_prompt(self) -> Optional[str]: @@ -733,17 +730,11 @@ def encoder_prompt_token_ids(self) -> Optional[List[int]]: @property def multi_modal_data(self) -> "MultiModalDataDict": - # All sequences in the group should have the same multi-modal data. - # We use the multi-modal data of an arbitrary sequence. - return self.seqs[0].multi_modal_data + return self.first_seq.multi_modal_data @property def mm_processor_kwargs(self) -> Dict[str, Any]: - # As with multi-modal data, all sequences in the group should have the - # same processor kwargs (i.e., mm_processor_kwargs are optionally - # provided per request; note that are independent of whether the model - # decoder-only or an encoder-decoder). - return self.seqs[0].mm_processor_kwargs + return self.first_seq.mm_processor_kwargs @property def lora_int_id(self) -> int: @@ -808,7 +799,7 @@ def maybe_set_first_token_time(self, time: float) -> None: # in TPOT, rather than recalculating TTFT (since from the ) # POV of the user, there is simply a long generation delay. if (self.metrics.first_token_time is None - and self.seqs[0].get_output_len() == 1): + and self.first_seq.get_output_len() == 1): self.metrics.first_token_time = time def maybe_set_first_scheduled_time(self, time: float) -> None: @@ -825,18 +816,7 @@ def set_finished_time(self, time: Optional[float]) -> None: def get_max_num_running_seqs(self) -> int: """The maximum number of sequences running in parallel in the remaining lifetime of the request.""" - if self.sampling_params: - n = self.sampling_params.n - assert isinstance(n, int) - if n > self.num_seqs(): - # At prompt stage, the sequence group is not yet filled up - # and only have one sequence running. However, in the - # generation stage, we will have `n` sequences - # running. - return n - # At sampling stages, return the number of actual sequences - # that are not finished yet. - return self.num_unfinished_seqs() + return 0 if self.first_seq.is_finished() else 1 def get_seqs( self, @@ -845,10 +825,7 @@ def get_seqs( if status is None: return self.seqs - if self.is_single_seq: - return self.seqs if self.seqs[0].status == status else [] - - return [seq for seq in self.seqs if seq.status == status] + return self.seqs if self.first_seq.status == status else [] def is_encoder_decoder(self) -> bool: return self.encoder_seq is not None @@ -856,29 +833,20 @@ def is_encoder_decoder(self) -> bool: def get_encoder_seq(self) -> Optional[Sequence]: return self.encoder_seq - def get_unfinished_seqs(self) -> List[Sequence]: - if self.is_single_seq: - return self.seqs if not self.seqs[0].is_finished() else [] - - return [seq for seq in self.seqs if not seq.is_finished()] - def get_finished_seqs(self) -> List[Sequence]: - if self.is_single_seq: - return self.seqs if self.seqs[0].is_finished() else [] - - return [seq for seq in self.seqs if seq.is_finished()] + return self.seqs if self.first_seq.is_finished() else [] def update_num_computed_tokens(self, num_new_computed_tokens: int): """Update number of tokens computed so far.""" - for seq in self.seqs: - if not seq.is_finished(): - seq.data.update_num_computed_tokens(num_new_computed_tokens) + seq = self.first_seq + if not seq.is_finished(): + seq.data.update_num_computed_tokens(num_new_computed_tokens) def get_num_uncomputed_tokens(self) -> int: num_uncomputed_tokens = 0 - for seq in self.seqs: - if not seq.is_finished(): - num_uncomputed_tokens += seq.data.get_num_uncomputed_tokens() + seq = self.first_seq + if not seq.is_finished(): + num_uncomputed_tokens += seq.data.get_num_uncomputed_tokens() return num_uncomputed_tokens def num_seqs(self, status: Optional[SequenceStatus] = None) -> int: @@ -892,46 +860,14 @@ def num_seqs(self, status: Optional[SequenceStatus] = None) -> int: return len(self.get_seqs(status)) - def num_unfinished_seqs(self) -> int: - if self.is_single_seq: - return 1 if not self.seqs[0].is_finished() else 0 - - return len(self.get_unfinished_seqs()) - def num_finished_seqs(self) -> int: - if self.is_single_seq: - return 1 if self.seqs[0].is_finished() else 0 - - return len(self.get_finished_seqs()) - - def find(self, seq_id: int) -> Sequence: - if seq_id not in self.seqs_dict: - raise ValueError(f"Sequence {seq_id} not found.") - return self.seqs_dict[seq_id] - - def add(self, seq: Sequence) -> None: - if seq.seq_id in self.seqs_dict: - raise ValueError(f"Sequence {seq.seq_id} already exists.") - self.seqs_dict[seq.seq_id] = seq - self.seqs.append(seq) - self.is_single_seq = len(self.seqs) == 1 - - def remove(self, seq_id: int) -> None: - seq = self.seqs_dict.pop(seq_id, None) - if seq is None: - raise ValueError(f"Sequence {seq_id} not found.") - self.seqs.remove(seq) - self.is_single_seq = len(self.seqs) == 1 + return 1 if self.first_seq.is_finished() else 0 def is_finished(self) -> bool: - if self.is_single_seq: - return self.seqs[0].is_finished() - - return all(seq.is_finished() for seq in self.seqs) + return self.first_seq.is_finished() def is_prefill(self) -> bool: - # Every sequence should be in the same stage. - return self.seqs[0].is_prefill() + return self.first_seq.is_prefill() def __repr__(self) -> str: return (f"SequenceGroup(request_id={self.request_id}, " @@ -1455,7 +1391,7 @@ def add_request(request_id: str, engine, params, **kwargs): for i in range(original_params.n): request_id_i = f"{request_id}_parallel_sample_{i}" group.seq_id_to_index[request_id_i] = i - seq_group = engine.add_request( + seq_group = engine._add_processed_request( request_id_i, params=params, **kwargs, From 8a02cd045ac661481ba2672846e09f5b57110f40 Mon Sep 17 00:00:00 2001 From: Yongzao <532741407@qq.com> Date: Thu, 24 Oct 2024 15:54:57 +0800 Subject: [PATCH 081/222] [torch.compile] Adding torch compile annotations to some models (#9639) Signed-off-by: youkaichao Co-authored-by: youkaichao --- docs/source/models/supported_models.rst | 2 +- tests/distributed/test_pipeline_parallel.py | 2 +- vllm/model_executor/models/jais.py | 4 +++- vllm/model_executor/models/minicpm.py | 2 ++ vllm/model_executor/models/mpt.py | 2 ++ vllm/model_executor/models/nemotron.py | 2 ++ vllm/model_executor/models/olmo.py | 2 ++ 7 files changed, 13 insertions(+), 3 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index c92d65110f464..a5ce33e548b18 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -144,7 +144,7 @@ Text Generation - ✅︎ * - :code:`JAISLMHeadModel` - Jais - - :code:`core42/jais-13b`, :code:`core42/jais-13b-chat`, :code:`core42/jais-30b-v3`, :code:`core42/jais-30b-chat-v3`, etc. + - :code:`inceptionai/jais-13b`, :code:`inceptionai/jais-13b-chat`, :code:`inceptionai/jais-30b-v3`, :code:`inceptionai/jais-30b-chat-v3`, etc. - - ✅︎ * - :code:`JambaForCausalLM` diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index 8d0190e37ef13..214448bf4320e 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -145,7 +145,7 @@ def iter_params(self, model_name: str): # Uses Llama # "internlm/internlm-chat-7b": PPTestSettings.fast(), "internlm/internlm2-chat-7b": PPTestSettings.fast(trust_remote_code=True), - "core42/jais-13b-chat": PPTestSettings.fast(), + "inceptionai/jais-13b-chat": PPTestSettings.fast(), # TODO: Implement PP # "ai21labs/AI21-Jamba-1.5-Mini": PPTestSettings.fast(), "meta-llama/Meta-Llama-3-8B": PPTestSettings.detailed(), diff --git a/vllm/model_executor/models/jais.py b/vllm/model_executor/models/jais.py index c5e5393442e30..b947f24a693b5 100644 --- a/vllm/model_executor/models/jais.py +++ b/vllm/model_executor/models/jais.py @@ -1,6 +1,6 @@ # coding=utf-8 # Adapted from -# https://huggingface.co/core42/jais-30b-chat-v3/blob/main/modeling_jais.py +# https://huggingface.co/inceptionai/jais-30b-chat-v3/blob/main/modeling_jais.py # Copyright 2023 The vLLM team. # Copyright 2023 the Jais authors and HuggingFace Inc. team. All rights # reserved. @@ -26,6 +26,7 @@ from torch import nn from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) @@ -212,6 +213,7 @@ def forward( return hidden_states +@support_torch_compile class JAISModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/minicpm.py b/vllm/model_executor/models/minicpm.py index decd90b682a1e..03fb036020f2f 100644 --- a/vllm/model_executor/models/minicpm.py +++ b/vllm/model_executor/models/minicpm.py @@ -29,6 +29,7 @@ from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, @@ -348,6 +349,7 @@ def forward( return hidden_states, None +@support_torch_compile class MiniCPMModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/mpt.py b/vllm/model_executor/models/mpt.py index e3d3937b13fa0..ee802030a5ef3 100644 --- a/vllm/model_executor/models/mpt.py +++ b/vllm/model_executor/models/mpt.py @@ -7,6 +7,7 @@ import torch.nn as nn from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) @@ -204,6 +205,7 @@ def forward( return hidden_states +@support_torch_compile class MPTModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/nemotron.py b/vllm/model_executor/models/nemotron.py index 14515e16e34ac..72a09129fed63 100644 --- a/vllm/model_executor/models/nemotron.py +++ b/vllm/model_executor/models/nemotron.py @@ -27,6 +27,7 @@ from torch import nn from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -290,6 +291,7 @@ def forward( return hidden_states, residual +@support_torch_compile class NemotronModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/olmo.py b/vllm/model_executor/models/olmo.py index 5ca7c66f5407d..90ab8abcb84b4 100644 --- a/vllm/model_executor/models/olmo.py +++ b/vllm/model_executor/models/olmo.py @@ -28,6 +28,7 @@ from transformers import OlmoConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import SiluAndMul @@ -221,6 +222,7 @@ def forward( return hidden_states +@support_torch_compile class OlmoModel(nn.Module): def __init__(self, From 295a061fb34ec6fb251abf1dbece5b1bb7dc9006 Mon Sep 17 00:00:00 2001 From: Jee Jee Li Date: Thu, 24 Oct 2024 16:18:27 +0800 Subject: [PATCH 082/222] [Kernel] add kernel for FATReLU (#9610) Signed-off-by: Jee Jee Li --- csrc/activation_kernels.cu | 42 ++++++++++++++++++++++++ csrc/ops.h | 3 ++ csrc/torch_bindings.cpp | 4 +++ tests/kernels/test_activation.py | 23 +++++++++---- vllm/_custom_ops.py | 6 ++++ vllm/model_executor/layers/activation.py | 8 ++++- 6 files changed, 78 insertions(+), 8 deletions(-) diff --git a/csrc/activation_kernels.cu b/csrc/activation_kernels.cu index 5ed1dc3b8f792..839dc36ba4e29 100644 --- a/csrc/activation_kernels.cu +++ b/csrc/activation_kernels.cu @@ -89,6 +89,48 @@ void gelu_tanh_and_mul(torch::Tensor& out, // [..., d] namespace vllm { +template +__device__ __forceinline__ T fatrelu_kernel(const T& x, const float threshold) { + const float f = (float)x; + return (T)(f > threshold ? f : 0.0f); +} + +template +__global__ void act_and_mul_kernel_with_param( + scalar_t* __restrict__ out, const scalar_t* __restrict__ input, const int d, + const float param) { + const int64_t token_idx = blockIdx.x; + for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) { + const scalar_t x = VLLM_LDG(&input[token_idx * 2 * d + idx]); + const scalar_t y = VLLM_LDG(&input[token_idx * 2 * d + d + idx]); + out[token_idx * d + idx] = ACT_FN(x, param) * y; + } +} + +} // namespace vllm + +#define LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(KERNEL, PARAM) \ + int d = input.size(-1) / 2; \ + int64_t num_tokens = input.numel() / input.size(-1); \ + dim3 grid(num_tokens); \ + dim3 block(std::min(d, 1024)); \ + const at::cuda::OptionalCUDAGuard device_guard(device_of(input)); \ + const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); \ + VLLM_DISPATCH_FLOATING_TYPES( \ + input.scalar_type(), "act_and_mul_kernel_with_param", [&] { \ + vllm::act_and_mul_kernel_with_param> \ + <<>>(out.data_ptr(), \ + input.data_ptr(), d, \ + PARAM); \ + }); + +void fatrelu_and_mul(torch::Tensor& out, // [..., d], + torch::Tensor& input, // [..., 2 * d] + double threshold) { + LAUNCH_ACTIVATION_GATE_KERNEL_WITH_PARAM(vllm::fatrelu_kernel, threshold); +} +namespace vllm { + // Element-wise activation kernel template. template __global__ void activation_kernel( diff --git a/csrc/ops.h b/csrc/ops.h index c10c34e085750..11a2970695545 100644 --- a/csrc/ops.h +++ b/csrc/ops.h @@ -48,6 +48,9 @@ void gelu_and_mul(torch::Tensor& out, torch::Tensor& input); void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input); +void fatrelu_and_mul(torch::Tensor& out, torch::Tensor& input, + double threshold); + void gelu_new(torch::Tensor& out, torch::Tensor& input); void gelu_fast(torch::Tensor& out, torch::Tensor& input); diff --git a/csrc/torch_bindings.cpp b/csrc/torch_bindings.cpp index b999028fe06a9..826f918c82e78 100644 --- a/csrc/torch_bindings.cpp +++ b/csrc/torch_bindings.cpp @@ -60,6 +60,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) { ops.def("gelu_tanh_and_mul(Tensor! out, Tensor input) -> ()"); ops.impl("gelu_tanh_and_mul", torch::kCUDA, &gelu_tanh_and_mul); + // FATReLU implementation. + ops.def("fatrelu_and_mul(Tensor! out, Tensor input, float threshold) -> ()"); + ops.impl("fatrelu_and_mul", torch::kCUDA, &fatrelu_and_mul); + // GELU implementation used in GPT-2. ops.def("gelu_new(Tensor! out, Tensor input) -> ()"); ops.impl("gelu_new", torch::kCUDA, &gelu_new); diff --git a/tests/kernels/test_activation.py b/tests/kernels/test_activation.py index 9b476585fa19e..0e3d3c3a2e987 100644 --- a/tests/kernels/test_activation.py +++ b/tests/kernels/test_activation.py @@ -1,12 +1,13 @@ +import random from typing import Type import pytest import torch from tests.kernels.utils import opcheck -from vllm.model_executor.layers.activation import (FastGELU, GeluAndMul, - NewGELU, QuickGELU, - SiluAndMul) +from vllm.model_executor.layers.activation import (FastGELU, FatreluAndMul, + GeluAndMul, NewGELU, + QuickGELU, SiluAndMul) from vllm.utils import seed_everything from .allclose_default import get_default_atol, get_default_rtol @@ -20,7 +21,8 @@ ] -@pytest.mark.parametrize("activation", ["silu", "gelu", "gelu_tanh"]) +@pytest.mark.parametrize("activation", + ["silu", "gelu", "gelu_tanh", "fatrelu"]) @pytest.mark.parametrize("num_tokens", NUM_TOKENS) @pytest.mark.parametrize("d", D) @pytest.mark.parametrize("dtype", DTYPES) @@ -47,16 +49,23 @@ def test_act_and_mul( elif activation == "gelu_tanh": layer = GeluAndMul(approximate="tanh") fn = torch.ops._C.gelu_tanh_and_mul + elif activation == "fatrelu": + threshold = random.uniform(0, 1) + layer = FatreluAndMul(threshold) + fn = torch.ops._C.fatrelu_and_mul out = layer(x) ref_out = layer.forward_native(x) - # The SiLU and GELU implementations are equivalent to the native PyTorch - # implementations, so we can do exact comparison. + # The SiLU, GELU and FatReLU implementations are equivalent to the native + # PyTorch implementations, so we can do exact comparison. torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0) d = x.shape[-1] // 2 output_shape = (x.shape[:-1] + (d, )) out = torch.empty(output_shape, dtype=x.dtype, device=x.device) - opcheck(fn, (out, x)) + if activation == "fatrelu": + opcheck(fn, (out, x, threshold)) + else: + opcheck(fn, (out, x)) @pytest.mark.parametrize("activation", [(FastGELU, torch.ops._C.gelu_fast), diff --git a/vllm/_custom_ops.py b/vllm/_custom_ops.py index a25f7abca5498..60f458096c70c 100644 --- a/vllm/_custom_ops.py +++ b/vllm/_custom_ops.py @@ -79,6 +79,12 @@ def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None: torch.ops._C.gelu_tanh_and_mul(out, x) +def fatrelu_and_mul(out: torch.Tensor, + x: torch.Tensor, + threshold: float = 0.0) -> None: + torch.ops._C.fatrelu_and_mul(out, x, threshold) + + def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> None: torch.ops._C.gelu_fast(out, x) diff --git a/vllm/model_executor/layers/activation.py b/vllm/model_executor/layers/activation.py index 8de3385a257f8..658a3700f33d6 100644 --- a/vllm/model_executor/layers/activation.py +++ b/vllm/model_executor/layers/activation.py @@ -39,7 +39,13 @@ def forward_native(self, x: torch.Tensor) -> torch.Tensor: return x1 * x2 def forward_cuda(self, x: torch.Tensor) -> torch.Tensor: - return self.forward_native(x) + from vllm import _custom_ops as ops + + d = x.shape[-1] // 2 + output_shape = (x.shape[:-1] + (d, )) + out = torch.empty(output_shape, dtype=x.dtype, device=x.device) + ops.fatrelu_and_mul(out, x, self.threshold) + return out @CustomOp.register("silu_and_mul") From ad6f78053ed33b2386713b574976523858a879b5 Mon Sep 17 00:00:00 2001 From: Yongzao <532741407@qq.com> Date: Thu, 24 Oct 2024 16:32:15 +0800 Subject: [PATCH 083/222] [torch.compile] expanding support and fix allgather compilation (#9637) Signed-off-by: youkaichao Co-authored-by: youkaichao --- vllm/distributed/parallel_state.py | 7 ++++++- vllm/model_executor/models/gpt_bigcode.py | 2 ++ vllm/model_executor/models/gpt_j.py | 2 ++ vllm/model_executor/models/gpt_neox.py | 2 ++ vllm/model_executor/models/granite.py | 2 ++ vllm/model_executor/models/internlm2.py | 2 ++ 6 files changed, 16 insertions(+), 1 deletion(-) diff --git a/vllm/distributed/parallel_state.py b/vllm/distributed/parallel_state.py index ab47d62921d2c..ec39856b6f67c 100644 --- a/vllm/distributed/parallel_state.py +++ b/vllm/distributed/parallel_state.py @@ -392,8 +392,12 @@ def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor: # Convert negative dim to positive. dim += input_.dim() input_size = input_.size() + # NOTE: we have to use concat-style all-gather here, + # stack-style all-gather has compatibility issues with + # torch.compile . see https://github.com/pytorch/pytorch/issues/138795 + output_size = (input_size[0] * world_size, ) + input_size[1:] # Allocate output tensor. - output_tensor = torch.empty((world_size, ) + input_size, + output_tensor = torch.empty(output_size, dtype=input_.dtype, device=input_.device) # All-gather. @@ -401,6 +405,7 @@ def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor: input_, group=self.device_group) # Reshape + output_tensor = output_tensor.reshape((world_size, ) + input_size) output_tensor = output_tensor.movedim(0, dim) output_tensor = output_tensor.reshape(input_size[:dim] + (world_size * diff --git a/vllm/model_executor/models/gpt_bigcode.py b/vllm/model_executor/models/gpt_bigcode.py index 6c4a04667c5da..24c79a8855475 100644 --- a/vllm/model_executor/models/gpt_bigcode.py +++ b/vllm/model_executor/models/gpt_bigcode.py @@ -25,6 +25,7 @@ from transformers import GPTBigCodeConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -187,6 +188,7 @@ def forward( return hidden_states +@support_torch_compile class GPTBigCodeModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/gpt_j.py b/vllm/model_executor/models/gpt_j.py index d40bf8c88ee19..0451d16b6c738 100644 --- a/vllm/model_executor/models/gpt_j.py +++ b/vllm/model_executor/models/gpt_j.py @@ -23,6 +23,7 @@ from transformers import GPTJConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -174,6 +175,7 @@ def forward( return hidden_states +@support_torch_compile class GPTJModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/gpt_neox.py b/vllm/model_executor/models/gpt_neox.py index 23a1ca06cc69e..1bccef7a5f173 100644 --- a/vllm/model_executor/models/gpt_neox.py +++ b/vllm/model_executor/models/gpt_neox.py @@ -23,6 +23,7 @@ from transformers import GPTNeoXConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -187,6 +188,7 @@ def forward( return hidden_states +@support_torch_compile class GPTNeoXModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/granite.py b/vllm/model_executor/models/granite.py index dcf4f5b27704a..5a397ed8ff6a0 100644 --- a/vllm/model_executor/models/granite.py +++ b/vllm/model_executor/models/granite.py @@ -28,6 +28,7 @@ from transformers import GraniteConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) @@ -254,6 +255,7 @@ def forward( return hidden_states +@support_torch_compile class GraniteModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/internlm2.py b/vllm/model_executor/models/internlm2.py index f6cde44e9d83d..9a77e48626ca5 100644 --- a/vllm/model_executor/models/internlm2.py +++ b/vllm/model_executor/models/internlm2.py @@ -7,6 +7,7 @@ from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, @@ -230,6 +231,7 @@ def forward( return hidden_states, residual +@support_torch_compile class InternLM2Model(nn.Module): def __init__( From b979143d5bbe35192b55875f04a24de4108eb514 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Thu, 24 Oct 2024 17:43:59 +0800 Subject: [PATCH 084/222] [Doc] Move additional tips/notes to the top (#9647) --- docs/source/models/supported_models.rst | 79 ++++++++++++------------- 1 file changed, 39 insertions(+), 40 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index a5ce33e548b18..98d804052b575 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -3,10 +3,47 @@ Supported Models ================ -vLLM supports a variety of generative Transformer models in `HuggingFace (HF) Transformers `_. -The following is the list of model architectures that are currently supported by vLLM. +vLLM supports a variety of generative and embedding models from `HuggingFace (HF) Transformers `_. +This page lists the model architectures that are currently supported by vLLM. Alongside each architecture, we include some popular models that use it. +For other models, you can check the :code:`config.json` file inside the model repository. +If the :code:`"architectures"` field contains a model architecture listed below, then it should be supported in theory. + +.. tip:: + The easiest way to check if your model is really supported at runtime is to run the program below: + + .. code-block:: python + + from vllm import LLM + + llm = LLM(model=...) # Name or path of your model + output = llm.generate("Hello, my name is") + print(output) + + If vLLM successfully generates text, it indicates that your model is supported. + +Otherwise, please refer to :ref:`Adding a New Model ` and :ref:`Enabling Multimodal Inputs ` +for instructions on how to implement your model in vLLM. +Alternatively, you can `open an issue on GitHub `_ to request vLLM support. + +.. note:: + To use models from `ModelScope `_ instead of HuggingFace Hub, set an environment variable: + + .. code-block:: shell + + $ export VLLM_USE_MODELSCOPE=True + + And use with :code:`trust_remote_code=True`. + + .. code-block:: python + + from vllm import LLM + + llm = LLM(model=..., revision=..., trust_remote_code=True) # Name or path of your model + output = llm.generate("Hello, my name is") + print(output) + Text-only Language Models ^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -515,44 +552,6 @@ Multimodal Embedding Some model architectures support both generation and embedding tasks. In this case, you have to pass :code:`--task embedding` to run the model in embedding mode. ----- - -If your model uses one of the above model architectures, you can seamlessly run your model with vLLM. -Otherwise, please refer to :ref:`Adding a New Model ` and :ref:`Enabling Multimodal Inputs ` -for instructions on how to implement support for your model. -Alternatively, you can raise an issue on our `GitHub `_ project. - -.. tip:: - The easiest way to check if your model is supported is to run the program below: - - .. code-block:: python - - from vllm import LLM - - llm = LLM(model=...) # Name or path of your model - output = llm.generate("Hello, my name is") - print(output) - - If vLLM successfully generates text, it indicates that your model is supported. - -.. tip:: - To use models from `ModelScope `_ instead of HuggingFace Hub, set an environment variable: - - .. code-block:: shell - - $ export VLLM_USE_MODELSCOPE=True - - And use with :code:`trust_remote_code=True`. - - .. code-block:: python - - from vllm import LLM - - llm = LLM(model=..., revision=..., trust_remote_code=True) # Name or path of your model - output = llm.generate("Hello, my name is") - print(output) - - Model Support Policy ===================== From f58454968fe1c5ddf84199b341a6ed5c99f0c0cc Mon Sep 17 00:00:00 2001 From: litianjian <45817262+litianjian@users.noreply.github.com> Date: Thu, 24 Oct 2024 22:52:07 +0800 Subject: [PATCH 085/222] [Bugfix]Disable the post_norm layer of the vision encoder for LLaVA models (#9653) --- vllm/model_executor/models/llava.py | 3 ++- vllm/model_executor/models/llava_next.py | 3 ++- vllm/model_executor/models/llava_next_video.py | 3 ++- vllm/model_executor/models/llava_onevision.py | 3 ++- 4 files changed, 8 insertions(+), 4 deletions(-) diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index 83e869efa4712..b005d83c17f90 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -273,7 +273,8 @@ def __init__(self, config.projector_hidden_act = "gelu" # TODO: Optionally initializes this for supporting embeddings. - self.vision_tower = init_vision_tower_for_llava(config, quant_config) + self.vision_tower = init_vision_tower_for_llava( + config, quant_config, require_post_norm=False) self.multi_modal_projector = LlavaMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, text_hidden_size=config.text_config.hidden_size, diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index d33d4ac5bfaed..9466e72ecc639 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -277,7 +277,8 @@ def __init__(self, self.multimodal_config = multimodal_config # TODO: Optionally initializes this for supporting embeddings. - self.vision_tower = init_vision_tower_for_llava(config, quant_config) + self.vision_tower = init_vision_tower_for_llava( + config, quant_config, require_post_norm=False) self.image_newline = nn.Parameter( torch.empty(config.text_config.hidden_size)) self.multi_modal_projector = LlavaMultiModalProjector( diff --git a/vllm/model_executor/models/llava_next_video.py b/vllm/model_executor/models/llava_next_video.py index d02cf9044dfc0..43eec43d56643 100644 --- a/vllm/model_executor/models/llava_next_video.py +++ b/vllm/model_executor/models/llava_next_video.py @@ -256,7 +256,8 @@ def __init__(self, self.multimodal_config = multimodal_config # Initialize the vision tower only up to the required feature layer - self.vision_tower = init_vision_tower_for_llava(config, quant_config) + self.vision_tower = init_vision_tower_for_llava( + config, quant_config, require_post_norm=False) self.vision_resampler = LlavaNextVideoPooler(config) self.multi_modal_projector = LlavaNextMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, diff --git a/vllm/model_executor/models/llava_onevision.py b/vllm/model_executor/models/llava_onevision.py index 10aa8049a2347..47e62409072e5 100644 --- a/vllm/model_executor/models/llava_onevision.py +++ b/vllm/model_executor/models/llava_onevision.py @@ -400,7 +400,8 @@ def __init__(self, self.multimodal_config = multimodal_config # Initialize the vision tower only up to the required feature layer - self.vision_tower = init_vision_tower_for_llava(config, quant_config) + self.vision_tower = init_vision_tower_for_llava( + config, quant_config, require_post_norm=False) self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config) self.language_model = init_vllm_registered_model( config.text_config, cache_config, quant_config) From de662d32b5d928d30e8923db548ed1fd94206158 Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Thu, 24 Oct 2024 17:17:45 +0100 Subject: [PATCH 086/222] Increase operation per run limit for "Close inactive issues and PRs" workflow (#9661) Signed-off-by: Harry Mellor --- .github/workflows/stale.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index becf2f4f74616..2418c61bdcf63 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -14,6 +14,10 @@ jobs: steps: - uses: actions/stale@28ca1036281a5e5922ead5184a1bbf96e5fc984e # v9.0.0 with: + # Increasing this value ensures that changes to this workflow + # propagate to all issues and PRs in days rather than months + operations-per-run: 1000 + exempt-draft-pr: true exempt-issue-labels: 'keep-open' exempt-pr-labels: 'keep-open' From d27cfbf791ef01483db9c45e215f3f299e54a079 Mon Sep 17 00:00:00 2001 From: Yongzao <532741407@qq.com> Date: Fri, 25 Oct 2024 00:31:42 +0800 Subject: [PATCH 087/222] [torch.compile] Adding torch compile annotations to some models (#9641) Signed-off-by: youkaichao Co-authored-by: youkaichao --- tests/distributed/test_pipeline_parallel.py | 3 ++- vllm/model_executor/models/opt.py | 2 ++ vllm/model_executor/models/orion.py | 18 ++++++++---------- vllm/model_executor/models/persimmon.py | 2 ++ vllm/model_executor/models/solar.py | 2 ++ vllm/model_executor/models/starcoder2.py | 2 ++ vllm/model_executor/models/xverse.py | 3 +++ 7 files changed, 21 insertions(+), 11 deletions(-) diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index 214448bf4320e..ed6360f9d6148 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -171,7 +171,8 @@ def iter_params(self, model_name: str): "stabilityai/stablelm-3b-4e1t": PPTestSettings.fast(), "bigcode/starcoder2-3b": PPTestSettings.fast(), "upstage/solar-pro-preview-instruct": PPTestSettings.fast(tp_base=2), - # FIXME: Cannot load tokenizer in latest transformers version + # FIXME: Cannot load tokenizer in latest transformers version. + # Need to use tokenizer from `meta-llama/Llama-2-7b-chat-hf` # "xverse/XVERSE-7B-Chat": PPTestSettings.fast(trust_remote_code=True), # [Encoder-only] # TODO: Implement PP diff --git a/vllm/model_executor/models/opt.py b/vllm/model_executor/models/opt.py index 3bcdb0d87fd52..37c3fa919124e 100644 --- a/vllm/model_executor/models/opt.py +++ b/vllm/model_executor/models/opt.py @@ -24,6 +24,7 @@ from transformers import OPTConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -279,6 +280,7 @@ def forward( return hidden_states +@support_torch_compile class OPTModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/orion.py b/vllm/model_executor/models/orion.py index 0913193f73a48..055407587c598 100644 --- a/vllm/model_executor/models/orion.py +++ b/vllm/model_executor/models/orion.py @@ -11,6 +11,7 @@ from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import SiluAndMul @@ -184,7 +185,6 @@ def forward( hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, - residual: Optional[torch.Tensor], ) -> Tuple[torch.Tensor, torch.Tensor]: # Self Attention residual = hidden_states @@ -203,9 +203,10 @@ def forward( hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states - return hidden_states, None + return hidden_states +@support_torch_compile class OrionModel(nn.Module): def __init__( @@ -233,8 +234,9 @@ def __init__( prefix=f"{prefix}.layers") self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps) self.make_empty_intermediate_tensors = ( - make_empty_intermediate_tensors_factory( - ["hidden_states", "residual"], config.hidden_size)) + make_empty_intermediate_tensors_factory([ + "hidden_states", + ], config.hidden_size)) def forward( self, @@ -246,24 +248,20 @@ def forward( ) -> Union[torch.Tensor, IntermediateTensors]: if get_pp_group().is_first_rank: hidden_states = self.embed_tokens(input_ids) - residual = None else: - assert intermediate_tensors + assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] - residual = intermediate_tensors["residual"] for i in range(self.start_layer, self.end_layer): layer = self.layers[i] - hidden_states, residual = layer( + hidden_states = layer( positions, hidden_states, kv_caches[i - self.start_layer], attn_metadata, - residual, ) if not get_pp_group().is_last_rank: return IntermediateTensors({ "hidden_states": hidden_states, - "residual": residual }) hidden_states = self.norm(hidden_states) return hidden_states diff --git a/vllm/model_executor/models/persimmon.py b/vllm/model_executor/models/persimmon.py index b625d19f6447d..fc9ef15db26c0 100644 --- a/vllm/model_executor/models/persimmon.py +++ b/vllm/model_executor/models/persimmon.py @@ -27,6 +27,7 @@ from transformers import PersimmonConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -209,6 +210,7 @@ def forward( return outputs +@support_torch_compile class PersimmonModel(nn.Module): def __init__(self, diff --git a/vllm/model_executor/models/solar.py b/vllm/model_executor/models/solar.py index b9298ed031144..5a3dd3c02b85b 100644 --- a/vllm/model_executor/models/solar.py +++ b/vllm/model_executor/models/solar.py @@ -29,6 +29,7 @@ from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) @@ -263,6 +264,7 @@ def forward( return hidden_states, residual +@support_torch_compile class SolarModel(nn.Module): def __init__( diff --git a/vllm/model_executor/models/starcoder2.py b/vllm/model_executor/models/starcoder2.py index 81dd7c4daa5e9..8f0644bca3e2e 100644 --- a/vllm/model_executor/models/starcoder2.py +++ b/vllm/model_executor/models/starcoder2.py @@ -25,6 +25,7 @@ from transformers import Starcoder2Config from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -193,6 +194,7 @@ def forward( return hidden_states +@support_torch_compile class Starcoder2Model(nn.Module): def __init__(self, diff --git a/vllm/model_executor/models/xverse.py b/vllm/model_executor/models/xverse.py index 3bded82033c08..036789642d3c4 100644 --- a/vllm/model_executor/models/xverse.py +++ b/vllm/model_executor/models/xverse.py @@ -27,6 +27,7 @@ from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import SiluAndMul @@ -220,6 +221,7 @@ def forward( return hidden_states, residual +@support_torch_compile class XverseModel(nn.Module): def __init__( @@ -266,6 +268,7 @@ def forward( residual = None else: hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] for i in range(self.start_layer, self.end_layer): layer = self.layers[i] hidden_states, residual = layer( From c866e0079de05cf6aee5931f3b9e200e8cbcf26c Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Fri, 25 Oct 2024 01:40:40 +0800 Subject: [PATCH 088/222] [CI/Build] Fix VLM test failures when using transformers v4.46 (#9666) --- tests/conftest.py | 16 +++++++++------- .../vision_language/test_chameleon.py | 5 +++++ .../vision_language/test_minicpmv.py | 4 ++-- .../vision_language/test_paligemma.py | 15 ++++++++++++--- 4 files changed, 28 insertions(+), 12 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index b11bbcb4ab7d1..6adff5e2328c4 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -232,20 +232,22 @@ def video_assets() -> _VideoAssets: return VIDEO_ASSETS -_T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding, BatchFeature) +_T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding, BatchFeature, dict) class HfRunner: - def wrap_device(self, input: _T, device: Optional[str] = None) -> _T: + def wrap_device(self, x: _T, device: Optional[str] = None) -> _T: if device is None: - return self.wrap_device( - input, "cpu" if current_platform.is_cpu() else "cuda") + device = "cpu" if current_platform.is_cpu() else "cuda" - if hasattr(input, "device") and input.device.type == device: - return input + if isinstance(x, dict): + return {k: self.wrap_device(v, device) for k, v in x.items()} - return input.to(device) + if hasattr(x, "device") and x.device.type == device: + return x + + return x.to(device) def __init__( self, diff --git a/tests/models/decoder_only/vision_language/test_chameleon.py b/tests/models/decoder_only/vision_language/test_chameleon.py index 8334451970a4f..4bd678b9f21c4 100644 --- a/tests/models/decoder_only/vision_language/test_chameleon.py +++ b/tests/models/decoder_only/vision_language/test_chameleon.py @@ -1,6 +1,7 @@ from typing import List, Optional, Type import pytest +import transformers from transformers import AutoModelForVision2Seq, BatchEncoding from vllm.multimodal.utils import rescale_image_size @@ -93,6 +94,10 @@ def process(hf_inputs: BatchEncoding): ) +@pytest.mark.skipif( + transformers.__version__.startswith("4.46.0"), + reason="Model broken in HF, see huggingface/transformers#34379", +) @pytest.mark.parametrize("model", models) @pytest.mark.parametrize( "size_factors", diff --git a/tests/models/decoder_only/vision_language/test_minicpmv.py b/tests/models/decoder_only/vision_language/test_minicpmv.py index 1d4e752052273..d3a0561f65797 100644 --- a/tests/models/decoder_only/vision_language/test_minicpmv.py +++ b/tests/models/decoder_only/vision_language/test_minicpmv.py @@ -32,8 +32,8 @@ models = ["openbmb/MiniCPM-Llama3-V-2_5"] -def _wrap_inputs(hf_inputs: BatchEncoding) -> BatchEncoding: - return BatchEncoding({"model_inputs": hf_inputs}) +def _wrap_inputs(hf_inputs: BatchEncoding): + return {"model_inputs": hf_inputs} def trunc_hf_output(hf_output: Tuple[List[int], str, diff --git a/tests/models/decoder_only/vision_language/test_paligemma.py b/tests/models/decoder_only/vision_language/test_paligemma.py index d7e29ea76ba4e..a3ca0845e5ff8 100644 --- a/tests/models/decoder_only/vision_language/test_paligemma.py +++ b/tests/models/decoder_only/vision_language/test_paligemma.py @@ -2,11 +2,12 @@ from typing import List, Optional, Tuple, Type import pytest -from transformers import AutoConfig, AutoModelForVision2Seq, AutoTokenizer +from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer, + BatchEncoding) from vllm.multimodal.utils import rescale_image_size from vllm.sequence import SampleLogprobs -from vllm.utils import is_hip +from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, is_hip from ....conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets from ...utils import check_logprobs_close @@ -74,6 +75,7 @@ def run_test( Note, the text input is also adjusted to abide by vllm contract. The text output is sanitized to be able to compare with hf. """ + torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype] images = [asset.pil_image for asset in image_assets] inputs_per_image = [( @@ -100,7 +102,14 @@ def run_test( for prompts, images in inputs_per_image ] - with hf_runner(model, dtype=dtype, + def process(hf_inputs: BatchEncoding): + hf_inputs["pixel_values"] = hf_inputs["pixel_values"] \ + .to(torch_dtype) # type: ignore + return hf_inputs + + with hf_runner(model, + dtype=dtype, + postprocess_inputs=process, auto_cls=AutoModelForVision2Seq) as hf_model: hf_outputs_per_image = [ hf_model.generate_greedy_logprobs_limit(prompts, From 722d46edb974315c7d2d8feed75520ea7a30d7fa Mon Sep 17 00:00:00 2001 From: Alex Brooks Date: Thu, 24 Oct 2024 11:42:24 -0600 Subject: [PATCH 089/222] [Model] Compute Llava Next Max Tokens / Dummy Data From Gridpoints (#9650) Signed-off-by: Alex-Brooks --- .../vision_language/test_llava_next.py | 66 ++++++++++++++++++- vllm/model_executor/models/llava_next.py | 41 ++++++++---- 2 files changed, 93 insertions(+), 14 deletions(-) diff --git a/tests/models/decoder_only/vision_language/test_llava_next.py b/tests/models/decoder_only/vision_language/test_llava_next.py index f833fe0c8bbb4..aa9b297c5dd4e 100644 --- a/tests/models/decoder_only/vision_language/test_llava_next.py +++ b/tests/models/decoder_only/vision_language/test_llava_next.py @@ -3,12 +3,13 @@ import pytest from transformers import AutoConfig, AutoModelForVision2Seq, AutoTokenizer +from vllm.inputs import InputContext from vllm.multimodal.utils import rescale_image_size from vllm.sequence import SampleLogprobs from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner, _ImageAssets) -from ...utils import check_logprobs_close +from ...utils import build_model_context, check_logprobs_close _LIMIT_IMAGE_PER_PROMPT = 4 @@ -22,6 +23,19 @@ models = ["llava-hf/llava-v1.6-mistral-7b-hf"] +@pytest.fixture() +def get_max_llava_next_image_tokens(): + from vllm.model_executor.models.llava_next import ( + get_max_llava_next_image_tokens) + return get_max_llava_next_image_tokens + + +@pytest.fixture() +def dummy_data_for_llava_next(): + from vllm.model_executor.models.llava_next import dummy_data_for_llava_next + return dummy_data_for_llava_next + + def vllm_to_hf_output(vllm_output: Tuple[List[int], str, Optional[SampleLogprobs]], model: str): @@ -281,3 +295,53 @@ def test_models_multiple_image_inputs(hf_runner, vllm_runner, image_assets, num_logprobs=num_logprobs, tensor_parallel_size=1, ) + + +@pytest.mark.parametrize("gridpoints,expected_max_tokens", [ + ([[336, 336]], 1176), + ([[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]], 2928), +]) +def test_get_max_llava_next_image_tokens(gridpoints, expected_max_tokens, + get_max_llava_next_image_tokens): + ctx = build_model_context(model_name="llava-hf/llava-v1.6-mistral-7b-hf") + + # Update the config image_grid_pinpoints + # and calculate the resulting max tokens + ctx.model_config.hf_config.image_grid_pinpoints = gridpoints + + actual_max_tokens = get_max_llava_next_image_tokens( + InputContext(ctx.model_config)) + + assert expected_max_tokens == actual_max_tokens + + +@pytest.mark.parametrize( + "gridpoints,expected_size", + [ + # One point; it has to be the largest + ([[336, 336]], (336, 336)), + # Default for most llava next models; the 2x2 tile is the largest + ([[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]], + (672, 672)), + # If two rectangular gridpoints are the same, the more vertical + # one has the higher feature count due to newline features + ([[336, 672], [672, 336]], (672, 336)) + ]) +def test_dummy_data_for_llava_next_feature_size(dummy_data_for_llava_next, + gridpoints, expected_size): + ctx = build_model_context(model_name="llava-hf/llava-v1.6-mistral-7b-hf") + + # Update the config image_grid_pinpoints + ctx.model_config.hf_config.image_grid_pinpoints = gridpoints + seq_len = 5000 # bigger than the max feature size for any image + + seq_data, mm_data = dummy_data_for_llava_next( + ctx, + seq_len=seq_len, + mm_counts={"image": 1}, + ) + + # The dummy data dims should match the gridpoint with the biggest feat size + assert mm_data["image"].height == expected_size[0] + assert mm_data["image"].width == expected_size[1] + assert len(seq_data.get_token_ids()) >= seq_len diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index 9466e72ecc639..2a582deeaa2c9 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -33,9 +33,6 @@ from .utils import (AutoWeightsLoader, embed_multimodal, flatten_bn, init_vllm_registered_model) -# Result in the max possible feature size (2x2 grid of 336x336px tiles) -MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448 - class LlavaNextImagePixelInputs(TypedDict): type: Literal["pixel_values"] @@ -149,11 +146,28 @@ def get_llava_next_image_feature_size( def get_max_llava_next_image_tokens(ctx: InputContext): - return get_llava_next_image_feature_size( - ctx.get_hf_config(LlavaNextConfig), - input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT, - input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH, - ) + """Compute the max feature size for all possible image grid pinpoints.""" + return _get_pinpoint_with_largest_features(ctx)[0] + + +def _get_pinpoint_with_largest_features( + ctx: InputContext) -> Tuple[int, Tuple[int, int]]: + """Get the grid pinpoint with the largest features & its feature size.""" + hf_config = ctx.get_hf_config(LlavaNextConfig) + largest_feature_size = 0 + largest_feature_pinpoint = None + for (height, width) in hf_config.image_grid_pinpoints: + feat_size = get_llava_next_image_feature_size( + hf_config, + input_height=height, + input_width=width, + ) + if feat_size > largest_feature_size: + largest_feature_size = feat_size + largest_feature_pinpoint = (height, width) + if not largest_feature_size or largest_feature_pinpoint is None: + raise ValueError("Cannot have a largest feature size of 0!") + return largest_feature_size, largest_feature_pinpoint def dummy_data_for_llava_next(ctx: InputContext, seq_len: int, @@ -162,7 +176,8 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int, vision_config = hf_config.vision_config num_images = mm_counts["image"] - image_feature_size = get_max_llava_next_image_tokens(ctx) + image_feature_size, pinpoint = _get_pinpoint_with_largest_features(ctx) + max_feat_height, max_feat_width = pinpoint if isinstance(vision_config, CLIPVisionConfig): seq_data = dummy_seq_data_for_clip( @@ -176,8 +191,8 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int, mm_data = dummy_image_for_clip( vision_config, num_images, - image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH, - image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT, + image_width_override=max_feat_width, + image_height_override=max_feat_height, ) return seq_data, mm_data @@ -193,8 +208,8 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int, mm_data = dummy_image_for_siglip( vision_config, num_images, - image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH, - image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT, + image_width_override=max_feat_width, + image_height_override=max_feat_height, ) return seq_data, mm_data From e26d37a185fd33c3f91d0035611c26cfb03883da Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Thu, 24 Oct 2024 13:44:38 -0400 Subject: [PATCH 090/222] [Log][Bugfix] Fix default value check for `image_url.detail` (#9663) --- vllm/entrypoints/chat_utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index fef6a91414db6..ce36f20760f4c 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -452,7 +452,8 @@ def _parse_chat_message_content_mm_part( content = MM_PARSER_MAP[part_type](part) # Special case for 'image_url.detail' - if part_type == "image_url" and part.get("detail") != "auto": + # We only support 'auto', which is the default + if part_type == "image_url" and part.get("detail", "auto") != "auto": logger.warning("'image_url.detail' is currently not supported " "and will be ignored.") From 59449095ab536febe9ff341b2a88a4fed572a70f Mon Sep 17 00:00:00 2001 From: Charlie Fu Date: Thu, 24 Oct 2024 17:37:52 -0500 Subject: [PATCH 091/222] [Performance][Kernel] Fused_moe Performance Improvement (#9384) Signed-off-by: charlifu --- CMakeLists.txt | 2 +- .../moe_align_sum_kernels.cu} | 98 ++++++++++++++++--- csrc/moe/moe_ops.h | 7 ++ csrc/moe/torch_bindings.cpp | 14 +++ csrc/ops.h | 5 - csrc/torch_bindings.cpp | 9 -- tests/kernels/test_moe.py | 6 +- vllm/_custom_ops.py | 10 +- .../layers/fused_moe/fused_moe.py | 5 +- 9 files changed, 118 insertions(+), 38 deletions(-) rename csrc/{moe_align_block_size_kernels.cu => moe/moe_align_sum_kernels.cu} (59%) diff --git a/CMakeLists.txt b/CMakeLists.txt index d1956f3d409b4..fc4ac10b7669a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -195,7 +195,6 @@ set(VLLM_EXT_SRC "csrc/quantization/compressed_tensors/int8_quant_kernels.cu" "csrc/quantization/fp8/common.cu" "csrc/cuda_utils_kernels.cu" - "csrc/moe_align_block_size_kernels.cu" "csrc/prepare_inputs/advance_step.cu" "csrc/torch_bindings.cpp") @@ -405,6 +404,7 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1) set(VLLM_MOE_EXT_SRC "csrc/moe/torch_bindings.cpp" + "csrc/moe/moe_align_sum_kernels.cu" "csrc/moe/topk_softmax_kernels.cu") set_gencode_flags_for_srcs( diff --git a/csrc/moe_align_block_size_kernels.cu b/csrc/moe/moe_align_sum_kernels.cu similarity index 59% rename from csrc/moe_align_block_size_kernels.cu rename to csrc/moe/moe_align_sum_kernels.cu index 1f8d75da83bb8..fff7ce34c838a 100644 --- a/csrc/moe_align_block_size_kernels.cu +++ b/csrc/moe/moe_align_sum_kernels.cu @@ -1,15 +1,17 @@ #include #include +#include #include #include -#include "cuda_compat.h" -#include "dispatch_utils.h" +#include "../cuda_compat.h" +#include "../dispatch_utils.h" #define CEILDIV(x, y) (((x) + (y) - 1) / (y)) namespace vllm { +namespace moe { namespace { __device__ __forceinline__ int32_t index(int32_t total_col, int32_t row, @@ -32,10 +34,10 @@ __global__ void moe_align_block_size_kernel(scalar_t* __restrict__ topk_ids, extern __shared__ int32_t shared_mem[]; int32_t* tokens_cnts = - shared_mem; // 2d tensor with shape (num_experts + 1, num_experts) + shared_mem; // 2d tensor with shape (blockDim.x + 1, num_experts) int32_t* cumsum = - shared_mem + (num_experts + 1) * - num_experts; // 1d tensor with shape (num_experts + 1) + shared_mem + + (blockDim.x + 1) * num_experts; // 1d tensor with shape (num_experts + 1) for (int i = 0; i < num_experts; ++i) { tokens_cnts[index(num_experts, threadIdx.x + 1, i)] = 0; @@ -53,10 +55,12 @@ __global__ void moe_align_block_size_kernel(scalar_t* __restrict__ topk_ids, __syncthreads(); // For each expert we accumulate the token counts from the different threads. - tokens_cnts[index(num_experts, 0, threadIdx.x)] = 0; - for (int i = 1; i <= blockDim.x; ++i) { - tokens_cnts[index(num_experts, i, threadIdx.x)] += - tokens_cnts[index(num_experts, i - 1, threadIdx.x)]; + if (threadIdx.x < num_experts) { + tokens_cnts[index(num_experts, 0, threadIdx.x)] = 0; + for (int i = 1; i <= blockDim.x; ++i) { + tokens_cnts[index(num_experts, i, threadIdx.x)] += + tokens_cnts[index(num_experts, i - 1, threadIdx.x)]; + } } __syncthreads(); @@ -79,9 +83,11 @@ __global__ void moe_align_block_size_kernel(scalar_t* __restrict__ topk_ids, * For each expert, each thread processes the tokens of the corresponding * blocks and stores the corresponding expert_id for each block. */ - for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1]; - i += block_size) { - expert_ids[i / block_size] = threadIdx.x; + if (threadIdx.x < num_experts) { + for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1]; + i += block_size) { + expert_ids[i / block_size] = threadIdx.x; + } } /** @@ -106,6 +112,24 @@ __global__ void moe_align_block_size_kernel(scalar_t* __restrict__ topk_ids, ++tokens_cnts[index(num_experts, threadIdx.x, expert_id)]; } } + +template +__global__ void moe_sum_kernel( + scalar_t* __restrict__ out, // [..., d] + const scalar_t* __restrict__ input, // [..., topk, d] + const int d) { + const int64_t token_idx = blockIdx.x; + for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) { + scalar_t x = 0.0; +#pragma unroll + for (int k = 0; k < TOPK; ++k) { + x += VLLM_LDG(&input[token_idx * TOPK * d + k * d + idx]); + } + out[token_idx * d + idx] = x; + } +} + +} // namespace moe } // namespace vllm void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts, @@ -117,18 +141,62 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts, topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] { // calc needed amount of shared mem for `tokens_cnts` and `cumsum` // tensors + const int32_t num_thread = max((int32_t)num_experts, WARP_SIZE); const int32_t shared_mem = - ((num_experts + 1) * num_experts + (num_experts + 1)) * + ((num_thread + 1) * num_experts + (num_experts + 1)) * sizeof(int32_t); // set dynamic shared mem - auto kernel = vllm::moe_align_block_size_kernel; + auto kernel = vllm::moe::moe_align_block_size_kernel; AT_CUDA_CHECK(VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize( (void*)kernel, shared_mem)); - kernel<<<1, num_experts, shared_mem, stream>>>( + kernel<<<1, num_thread, shared_mem, stream>>>( topk_ids.data_ptr(), sorted_token_ids.data_ptr(), experts_ids.data_ptr(), num_tokens_post_pad.data_ptr(), num_experts, block_size, topk_ids.numel()); }); } + +void moe_sum(torch::Tensor& input, // [num_tokens, topk, hidden_size] + torch::Tensor& output) // [num_tokens, hidden_size] +{ + const int hidden_size = input.size(-1); + const int num_tokens = output.numel() / hidden_size; + const int topk = input.size(1); + + dim3 grid(num_tokens); + dim3 block(std::min(hidden_size, 1024)); + const at::cuda::OptionalCUDAGuard device_guard(device_of(output)); + const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + switch (topk) { + case 2: + VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe_sum_kernel", [&] { + vllm::moe::moe_sum_kernel<<>>( + output.data_ptr(), input.data_ptr(), + hidden_size); + }); + break; + + case 3: + VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe_sum_kernel", [&] { + vllm::moe::moe_sum_kernel<<>>( + output.data_ptr(), input.data_ptr(), + hidden_size); + }); + break; + + case 4: + VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe_sum_kernel", [&] { + vllm::moe::moe_sum_kernel<<>>( + output.data_ptr(), input.data_ptr(), + hidden_size); + }); + break; + + default: + at::sum_out(output, input, 1); + break; + } +} diff --git a/csrc/moe/moe_ops.h b/csrc/moe/moe_ops.h index a251730aa765a..596cc0aa6c855 100644 --- a/csrc/moe/moe_ops.h +++ b/csrc/moe/moe_ops.h @@ -5,3 +5,10 @@ void topk_softmax(torch::Tensor& topk_weights, torch::Tensor& topk_indices, torch::Tensor& token_expert_indices, torch::Tensor& gating_output); + +void moe_sum(torch::Tensor& input, torch::Tensor& output); + +void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts, + int64_t block_size, torch::Tensor sorted_token_ids, + torch::Tensor experts_ids, + torch::Tensor num_tokens_post_pad); diff --git a/csrc/moe/torch_bindings.cpp b/csrc/moe/torch_bindings.cpp index 019c6cedd3d80..f3a558c14ab93 100644 --- a/csrc/moe/torch_bindings.cpp +++ b/csrc/moe/torch_bindings.cpp @@ -8,6 +8,20 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) { "token_expert_indices, Tensor gating_output) -> ()"); m.impl("topk_softmax", torch::kCUDA, &topk_softmax); + // Calculate the result of moe by summing up the partial results + // from all selected experts. + m.def("moe_sum(Tensor! input, Tensor output) -> ()"); + m.impl("moe_sum", torch::kCUDA, &moe_sum); + + // Aligning the number of tokens to be processed by each expert such + // that it is divisible by the block size. + m.def( + "moe_align_block_size(Tensor topk_ids, int num_experts," + " int block_size, Tensor! sorted_token_ids," + " Tensor! experts_ids," + " Tensor! num_tokens_post_pad) -> ()"); + m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size); + #ifndef USE_ROCM m.def( "marlin_gemm_moe(Tensor! a, Tensor! b_q_weights, Tensor! sorted_ids, " diff --git a/csrc/ops.h b/csrc/ops.h index 11a2970695545..f737f50c2ec96 100644 --- a/csrc/ops.h +++ b/csrc/ops.h @@ -145,11 +145,6 @@ void dynamic_per_token_scaled_fp8_quant( torch::Tensor& out, torch::Tensor const& input, torch::Tensor& scale, c10::optional const& scale_ub); -void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts, - int64_t block_size, torch::Tensor sorted_token_ids, - torch::Tensor experts_ids, - torch::Tensor num_tokens_post_pad); - void selective_scan_fwd(const torch::Tensor& u, const torch::Tensor& delta, const torch::Tensor& A, const torch::Tensor& B, const torch::Tensor& C, diff --git a/csrc/torch_bindings.cpp b/csrc/torch_bindings.cpp index 826f918c82e78..e704ff629fd6e 100644 --- a/csrc/torch_bindings.cpp +++ b/csrc/torch_bindings.cpp @@ -336,15 +336,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) { ops.impl("dynamic_per_token_scaled_fp8_quant", torch::kCUDA, &dynamic_per_token_scaled_fp8_quant); - // Aligning the number of tokens to be processed by each expert such - // that it is divisible by the block size. - ops.def( - "moe_align_block_size(Tensor topk_ids, int num_experts," - " int block_size, Tensor! sorted_token_ids," - " Tensor! experts_ids," - " Tensor! num_tokens_post_pad) -> ()"); - ops.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size); - // Compute int8 quantized tensor for given scaling factor. ops.def( "static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale," diff --git a/tests/kernels/test_moe.py b/tests/kernels/test_moe.py index b87fbc3f1937e..c0053071258ea 100644 --- a/tests/kernels/test_moe.py +++ b/tests/kernels/test_moe.py @@ -19,7 +19,7 @@ marlin_quantize) from vllm.model_executor.models.mixtral import MixtralMoE from vllm.scalar_type import scalar_types -from vllm.utils import seed_everything +from vllm.utils import is_hip, seed_everything @pytest.mark.parametrize("m", [1024 * 128, 512, 222, 33, 1]) @@ -103,6 +103,7 @@ def test_mixtral_moe(dtype: torch.dtype): @pytest.mark.parametrize("act_order", [True, False]) @pytest.mark.parametrize("num_bits", [4, 8]) @pytest.mark.parametrize("is_k_full", [True, False]) +@pytest.mark.skipif(is_hip(), reason="Skip for rocm") def test_fused_marlin_moe( m: int, n: int, @@ -255,6 +256,7 @@ def test_fused_marlin_moe( @pytest.mark.parametrize("act_order", [True, False]) @pytest.mark.parametrize("num_bits", [4, 8]) @pytest.mark.parametrize("is_k_full", [True, False]) +@pytest.mark.skipif(is_hip(), reason="Skip for rocm") def test_single_marlin_moe_multiply( m: int, n: int, @@ -345,6 +347,6 @@ def test_moe_align_block_size_opcheck(): dtype=torch.int32, device=topk_ids.device) - opcheck(torch.ops._C.moe_align_block_size, + opcheck(torch.ops._moe_C.moe_align_block_size, (topk_ids, num_experts, block_size, sorted_ids, expert_ids, num_tokens_post_pad)) diff --git a/vllm/_custom_ops.py b/vllm/_custom_ops.py index 60f458096c70c..f57414bd5197e 100644 --- a/vllm/_custom_ops.py +++ b/vllm/_custom_ops.py @@ -813,13 +813,17 @@ def selective_scan_fwd(u: torch.Tensor, delta: torch.Tensor, A: torch.Tensor, # moe +def moe_sum(input: torch.Tensor, output: torch.Tensor): + torch.ops._moe_C.moe_sum(input, output) + + def moe_align_block_size(topk_ids: torch.Tensor, num_experts: int, block_size: int, sorted_token_ids: torch.Tensor, experts_ids: torch.Tensor, num_tokens_post_pad: torch.Tensor) -> None: - torch.ops._C.moe_align_block_size(topk_ids, num_experts, block_size, - sorted_token_ids, experts_ids, - num_tokens_post_pad) + torch.ops._moe_C.moe_align_block_size(topk_ids, num_experts, block_size, + sorted_token_ids, experts_ids, + num_tokens_post_pad) def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor, diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py index b1d3bc0a5f054..90a4209b5bce5 100644 --- a/vllm/model_executor/layers/fused_moe/fused_moe.py +++ b/vllm/model_executor/layers/fused_moe/fused_moe.py @@ -589,9 +589,8 @@ def fused_experts(hidden_states: torch.Tensor, use_fp8_w8a8=use_fp8_w8a8, use_int8_w8a16=use_int8_w8a16) - torch.sum(intermediate_cache3.view(*intermediate_cache3.shape), - dim=1, - out=out_hidden_states[begin_chunk_idx:end_chunk_idx]) + ops.moe_sum(intermediate_cache3.view(*intermediate_cache3.shape), + out_hidden_states[begin_chunk_idx:end_chunk_idx]) return out_hidden_states From c91ed47c436f2d45299bed5eacd257e8cbc7c312 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Thu, 24 Oct 2024 18:38:05 -0400 Subject: [PATCH 092/222] [Bugfix] Remove xformers requirement for Pixtral (#9597) Signed-off-by: mgoin --- vllm/model_executor/models/pixtral.py | 65 +++++++++++++++++++-------- 1 file changed, 46 insertions(+), 19 deletions(-) diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index 18dbee94e10b0..a9dbb3823743a 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -14,8 +14,6 @@ _num_image_tokens) from transformers.models.pixtral.modeling_pixtral import ( PixtralRotaryEmbedding, apply_rotary_pos_emb, position_ids_in_meshgrid) -from xformers.ops.fmha import memory_efficient_attention -from xformers.ops.fmha.attn_bias import BlockDiagonalMask from vllm.attention import AttentionMetadata from vllm.config import CacheConfig, ModelConfig, MultiModalConfig @@ -38,6 +36,12 @@ from .interfaces import SupportsMultiModal, SupportsPP from .utils import init_vllm_registered_model +try: + from xformers import ops as xops + USE_XFORMERS_OPS = True +except ImportError: + USE_XFORMERS_OPS = False + def get_max_pixtral_image_tokens(ctx: InputContext): tokenizer = cached_get_tokenizer( @@ -416,7 +420,7 @@ def __init__(self, args: VisionEncoderArgs): def forward( self, x: torch.Tensor, - mask: BlockDiagonalMask, + mask: torch.Tensor, freqs_cis: torch.Tensor, ) -> torch.Tensor: batch, patches, _ = x.shape @@ -427,7 +431,7 @@ def forward( v = v.reshape(batch, patches, self.n_heads, self.head_dim) q, k = apply_rotary_emb_vit(q, k, freqs_cis=freqs_cis) - out = memory_efficient_attention(q, k, v, attn_bias=mask) + out = xops.memory_efficient_attention(q, k, v, attn_bias=mask) out = out.reshape(batch, patches, self.n_heads * self.head_dim) return self.wo(out) @@ -444,7 +448,7 @@ def __init__(self, args: VisionEncoderArgs): def forward( self, x: torch.Tensor, - mask: BlockDiagonalMask, + mask: torch.Tensor, freqs_cis: torch.Tensor, ) -> torch.Tensor: r = self.attention.forward(self.attention_norm(x), @@ -467,7 +471,7 @@ def __init__(self, args: VisionEncoderArgs): def forward( self, x: torch.Tensor, - mask: BlockDiagonalMask, + mask: torch.Tensor, freqs_cis: Optional[torch.Tensor], ) -> torch.Tensor: for layer in self.layers: @@ -562,8 +566,12 @@ def forward( freqs_cis = self.freqs_cis[positions[:, 0], positions[:, 1]] # pass through Transformer with a block diagonal mask delimiting images - mask = BlockDiagonalMask.from_seqlens( - [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], ) + if USE_XFORMERS_OPS: + mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens( + [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], ) + else: + raise ImportError("Xformers is required for Pixtral inference " + "with the Mistral format") out = self.transformer(patch_embeds, mask=mask, freqs_cis=freqs_cis) # remove batch dimension of the single sequence @@ -828,7 +836,7 @@ def __init__( def forward( self, hidden_states: torch.Tensor, - attention_mask: BlockDiagonalMask, + attention_mask: torch.Tensor, position_embeddings: torch.Tensor, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: batch, patches, _ = hidden_states.size() @@ -843,12 +851,23 @@ def forward( cos, sin = position_embeddings q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0) - # Transpose q and k back for attention - q = q.transpose(1, 2).contiguous() - k = k.transpose(1, 2).contiguous() - v = v.reshape(batch, patches, self.n_heads, self.head_dim) + if USE_XFORMERS_OPS: + # Transpose q and k back for attention + q = q.transpose(1, 2).contiguous() + k = k.transpose(1, 2).contiguous() + v = v.reshape(batch, patches, self.n_heads, self.head_dim) + + out = xops.memory_efficient_attention(q, + k, + v, + attn_bias=attention_mask) + else: + v = v.reshape(batch, patches, self.n_heads, + self.head_dim).transpose(1, 2) + out = nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=attention_mask) + out = out.transpose(1, 2) - out = memory_efficient_attention(q, k, v, attn_bias=attention_mask) out = out.reshape(batch, patches, self.n_heads * self.head_dim) return self.o_proj(out) @@ -877,7 +896,7 @@ def __init__( def forward( self, hidden_states: torch.Tensor, - attention_mask: BlockDiagonalMask, + attention_mask: torch.Tensor, position_embeddings: torch.Tensor, ) -> torch.Tensor: r = self.attention.forward(self.attention_norm(hidden_states), @@ -916,7 +935,7 @@ def __init__( def forward( self, x: torch.Tensor, - attention_mask: BlockDiagonalMask, + attention_mask: torch.Tensor, position_embeddings: torch.Tensor, ) -> torch.Tensor: for layer in self.layers: @@ -1000,11 +1019,19 @@ def forward( patch_embeds_list, max_width=self.config.image_size // self.config.patch_size).to( self.device) - position_embedding = self.patch_positional_embedding( patch_embeds, position_ids) - attention_mask = BlockDiagonalMask.from_seqlens( - [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], ) + + if USE_XFORMERS_OPS: + attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens( + [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], ) + else: + from transformers.models.pixtral.modeling_pixtral import ( + generate_block_attention_mask) + attention_mask = generate_block_attention_mask( + [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], + patch_embeds) + out = self.transformer(patch_embeds, attention_mask, position_embedding) From 9f7b4ba86578fbb0b6e80a2b0c1a334d88787a57 Mon Sep 17 00:00:00 2001 From: "Kevin H. Luu" Date: Thu, 24 Oct 2024 17:59:00 -1000 Subject: [PATCH 093/222] [ci/Build] Skip Chameleon for transformers 4.46.0 on broadcast test #9675 (#9676) --- tests/models/decoder_only/vision_language/test_broadcast.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/models/decoder_only/vision_language/test_broadcast.py b/tests/models/decoder_only/vision_language/test_broadcast.py index d01490d74bd4d..fd7af4a8b0b29 100644 --- a/tests/models/decoder_only/vision_language/test_broadcast.py +++ b/tests/models/decoder_only/vision_language/test_broadcast.py @@ -1,4 +1,5 @@ import pytest +import transformers from ....utils import multi_gpu_test @@ -23,6 +24,9 @@ def test_models(hf_runner, vllm_runner, image_assets, elif model.startswith("llava-hf/llava-v1.6"): from .test_llava_next import models, run_test # type: ignore[no-redef] elif model.startswith("facebook/chameleon"): + if transformers.__version__.startswith("4.46.0"): + pytest.skip("Model broken in HF, " + "see huggingface/transformers#34379") from .test_chameleon import models, run_test # type: ignore[no-redef] else: raise NotImplementedError(f"Unsupported model: {model}") From a6f37218619df39760624d541bf7911ab911f792 Mon Sep 17 00:00:00 2001 From: Will Johnson Date: Fri, 25 Oct 2024 01:00:17 -0400 Subject: [PATCH 094/222] [Model] add a lora module for granite 3.0 MoE models (#9673) --- vllm/model_executor/models/granitemoe.py | 1 + 1 file changed, 1 insertion(+) diff --git a/vllm/model_executor/models/granitemoe.py b/vllm/model_executor/models/granitemoe.py index 5266951794a80..fd0d4c89a28fe 100644 --- a/vllm/model_executor/models/granitemoe.py +++ b/vllm/model_executor/models/granitemoe.py @@ -324,6 +324,7 @@ class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP): "o_proj", "embed_tokens", "lm_head", + "layer", ] embedding_modules = { "embed_tokens": "input_embeddings", From 9645b9f646024b1e416ed5a61cfba7d14d54b571 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Thu, 24 Oct 2024 22:20:37 -0700 Subject: [PATCH 095/222] [V1] Support sliding window attention (#9679) Signed-off-by: Woosuk Kwon --- vllm/v1/attention/backends/flash_attn.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py index 0530b1a6762ce..ec07464e6a12a 100644 --- a/vllm/v1/attention/backends/flash_attn.py +++ b/vllm/v1/attention/backends/flash_attn.py @@ -82,8 +82,10 @@ def __init__( if alibi_slopes is not None: alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32) self.alibi_slopes = alibi_slopes - self.sliding_window = ((sliding_window, sliding_window) - if sliding_window is not None else (-1, -1)) + if sliding_window is None: + self.sliding_window = (-1, -1) + else: + self.sliding_window = (sliding_window - 1, 0) self.kv_cache_dtype = kv_cache_dtype if logits_soft_cap is None: # In flash-attn, setting logits_soft_cap as 0 means no soft cap. @@ -93,12 +95,6 @@ def __init__( assert self.num_heads % self.num_kv_heads == 0 self.num_queries_per_kv = self.num_heads // self.num_kv_heads - if sliding_window is not None: - # NOTE(woosuk): flash-attn's sliding window does not work with - # paged KV cache. - raise ValueError( - "Sliding window is not supported in FlashAttention.") - support_head_sizes = FlashAttentionBackend.get_supported_head_sizes() if head_size not in support_head_sizes: raise ValueError( From ca0d92227e3a5e5880dde67da9d96c6d06454328 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Fri, 25 Oct 2024 15:40:33 -0400 Subject: [PATCH 096/222] [Bugfix] Fix compressed_tensors_moe bad config.strategy (#9677) --- .../quantization/compressed_tensors/compressed_tensors_moe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py index 733eece4b5fa6..c21aaa40ff2cc 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py @@ -245,7 +245,7 @@ def __init__( config = self.quant_config.target_scheme_map["Linear"].get("weights") self.num_bits = config.num_bits self.packed_factor = 32 // config.num_bits - self.strategy = config.strategy.value + self.strategy = config.strategy self.group_size = config.group_size assert config.symmetric, ( "Only symmetric quantization is supported for MoE") From 228cfbd03fd1ad9b26001817a6d414cc9f2c22ae Mon Sep 17 00:00:00 2001 From: Rafael Vasquez Date: Fri, 25 Oct 2024 17:32:10 -0400 Subject: [PATCH 097/222] [Doc] Improve quickstart documentation (#9256) Signed-off-by: Rafael Vasquez --- docs/source/getting_started/quickstart.rst | 98 ++++++++++++---------- 1 file changed, 52 insertions(+), 46 deletions(-) diff --git a/docs/source/getting_started/quickstart.rst b/docs/source/getting_started/quickstart.rst index 80b19ac672936..f0e6cddf09ef7 100644 --- a/docs/source/getting_started/quickstart.rst +++ b/docs/source/getting_started/quickstart.rst @@ -1,38 +1,50 @@ .. _quickstart: +========== Quickstart ========== -This guide shows how to use vLLM to: +This guide will help you quickly get started with vLLM to: -* run offline batched inference on a dataset; -* build an API server for a large language model; -* start an OpenAI-compatible API server. +* :ref:`Run offline batched inference ` +* :ref:`Run OpenAI-compatible inference ` -Be sure to complete the :ref:`installation instructions ` before continuing with this guide. +Prerequisites +-------------- +- OS: Linux +- Python: 3.8 - 3.12 +- GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.) -.. note:: +Installation +-------------- + +You can install vLLM using pip. It's recommended to use `conda `_ to create and manage Python environments. + +.. code-block:: console - By default, vLLM downloads model from `HuggingFace `_. If you would like to use models from `ModelScope `_ in the following examples, please set the environment variable: + $ conda create -n myenv python=3.10 -y + $ conda activate myenv + $ pip install vllm - .. code-block:: shell +Please refer to the :ref:`installation documentation ` for more details on installing vLLM. - export VLLM_USE_MODELSCOPE=True +.. _offline_batched_inference: Offline Batched Inference ------------------------- -We first show an example of using vLLM for offline batched inference on a dataset. In other words, we use vLLM to generate texts for a list of input prompts. +With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). The example script for this section can be found `here `__. + +The first line of this example imports the classes :class:`~vllm.LLM` and :class:`~vllm.SamplingParams`: -Import :class:`~vllm.LLM` and :class:`~vllm.SamplingParams` from vLLM. -The :class:`~vllm.LLM` class is the main class for running offline inference with vLLM engine. -The :class:`~vllm.SamplingParams` class specifies the parameters for the sampling process. +- :class:`~vllm.LLM` is the main class for running offline inference with vLLM engine. +- :class:`~vllm.SamplingParams` specifies the parameters for the sampling process. .. code-block:: python from vllm import LLM, SamplingParams -Define the list of input prompts and the sampling parameters for generation. The sampling temperature is set to 0.8 and the nucleus sampling probability is set to 0.95. For more information about the sampling parameters, refer to the `class definition `_. +The next section defines a list of input prompts and sampling parameters for text generation. The `sampling temperature `_ is set to ``0.8`` and the `nucleus sampling probability `_ is set to ``0.95``. You can find more information about the sampling parameters `here `__. .. code-block:: python @@ -44,46 +56,46 @@ Define the list of input prompts and the sampling parameters for generation. The ] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) -Initialize vLLM's engine for offline inference with the :class:`~vllm.LLM` class and the `OPT-125M model `_. The list of supported models can be found at :ref:`supported models `. +The :class:`~vllm.LLM` class initializes vLLM's engine and the `OPT-125M model `_ for offline inference. The list of supported models can be found :ref:`here `. .. code-block:: python llm = LLM(model="facebook/opt-125m") -Call ``llm.generate`` to generate the outputs. It adds the input prompts to vLLM engine's waiting queue and executes the vLLM engine to generate the outputs with high throughput. The outputs are returned as a list of ``RequestOutput`` objects, which include all the output tokens. +.. note:: + + By default, vLLM downloads models from `HuggingFace `_. If you would like to use models from `ModelScope `_, set the environment variable ``VLLM_USE_MODELSCOPE`` before initializing the engine. + +Now, the fun part! The outputs are generated using ``llm.generate``. It adds the input prompts to the vLLM engine's waiting queue and executes the vLLM engine to generate the outputs with high throughput. The outputs are returned as a list of ``RequestOutput`` objects, which include all of the output tokens. .. code-block:: python outputs = llm.generate(prompts, sampling_params) - # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") - -The code example can also be found in `examples/offline_inference.py `_. +.. _openai_compatible_server: OpenAI-Compatible Server ------------------------ vLLM can be deployed as a server that implements the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. -By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time (OPT-125M in the command below) and implements `list models `_, `create chat completion `_, and `create completion `_ endpoints. We are actively adding support for more endpoints. +By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time and implements endpoints such as `list models `_, `create chat completion `_, and `create completion `_ endpoints. -Start the server: +Run the following command to start the vLLM server with the `Qwen2.5-1.5B-Instruct `_ model: .. code-block:: console - $ vllm serve facebook/opt-125m + $ vllm serve Qwen/Qwen2.5-1.5B-Instruct -By default, the server uses a predefined chat template stored in the tokenizer. You can override this template by using the ``--chat-template`` argument: - -.. code-block:: console +.. note:: - $ vllm serve facebook/opt-125m --chat-template ./examples/template_chatml.jinja + By default, the server uses a predefined chat template stored in the tokenizer. You can learn about overriding it `here `__. -This server can be queried in the same format as OpenAI API. For example, list the models: +This server can be queried in the same format as OpenAI API. For example, to list the models: .. code-block:: console @@ -91,17 +103,17 @@ This server can be queried in the same format as OpenAI API. For example, list t You can pass in the argument ``--api-key`` or environment variable ``VLLM_API_KEY`` to enable the server to check for API key in the header. -Using OpenAI Completions API with vLLM -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +OpenAI Completions API with vLLM +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Query the model with input prompts: +Once your server is started, you can query the model with input prompts: .. code-block:: console $ curl http://localhost:8000/v1/completions \ $ -H "Content-Type: application/json" \ $ -d '{ - $ "model": "facebook/opt-125m", + $ "model": "Qwen/Qwen2.5-1.5B-Instruct", $ "prompt": "San Francisco is a", $ "max_tokens": 7, $ "temperature": 0 @@ -120,36 +132,32 @@ Since this server is compatible with OpenAI API, you can use it as a drop-in rep api_key=openai_api_key, base_url=openai_api_base, ) - completion = client.completions.create(model="facebook/opt-125m", + completion = client.completions.create(model="Qwen/Qwen2.5-1.5B-Instruct", prompt="San Francisco is a") print("Completion result:", completion) -For a more detailed client example, refer to `examples/openai_completion_client.py `_. - -Using OpenAI Chat API with vLLM -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +A more detailed client example can be found `here `__. -The vLLM server is designed to support the OpenAI Chat API, allowing you to engage in dynamic conversations with the model. The chat interface is a more interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. This is useful for tasks that require context or more detailed explanations. +OpenAI Chat API with vLLM +~~~~~~~~~~~~~~~~~~~~~~~~~~ -Querying the model using OpenAI Chat API: +vLLM is designed to also support the OpenAI Chat API. The chat interface is a more dynamic, interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. This is useful for tasks that require context or more detailed explanations. -You can use the `create chat completion `_ endpoint to communicate with the model in a chat-like interface: +You can use the `create chat completion `_ endpoint to interact with the model: .. code-block:: console $ curl http://localhost:8000/v1/chat/completions \ $ -H "Content-Type: application/json" \ $ -d '{ - $ "model": "facebook/opt-125m", + $ "model": "Qwen/Qwen2.5-1.5B-Instruct", $ "messages": [ $ {"role": "system", "content": "You are a helpful assistant."}, $ {"role": "user", "content": "Who won the world series in 2020?"} $ ] $ }' -Python Client Example: - -Using the `openai` python package, you can also communicate with the model in a chat-like manner: +Alternatively, you can use the `openai` python package: .. code-block:: python @@ -164,12 +172,10 @@ Using the `openai` python package, you can also communicate with the model in a ) chat_response = client.chat.completions.create( - model="facebook/opt-125m", + model="Qwen/Qwen2.5-1.5B-Instruct", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me a joke."}, ] ) print("Chat response:", chat_response) - -For more in-depth examples and advanced features of the chat API, you can refer to the official OpenAI documentation. From 6567e13724110fac2042d06a9e4c01fd822e8909 Mon Sep 17 00:00:00 2001 From: Travis Johnson Date: Fri, 25 Oct 2024 16:42:56 -0600 Subject: [PATCH 098/222] [Bugfix] Fix crash with llama 3.2 vision models and guided decoding (#9631) Signed-off-by: Travis Johnson Co-authored-by: pavlo-ruban Co-authored-by: Nick Hill --- .../guided_decoding/outlines_logits_processors.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/vllm/model_executor/guided_decoding/outlines_logits_processors.py b/vllm/model_executor/guided_decoding/outlines_logits_processors.py index c28bd71c9f682..e1309c31f77e7 100644 --- a/vllm/model_executor/guided_decoding/outlines_logits_processors.py +++ b/vllm/model_executor/guided_decoding/outlines_logits_processors.py @@ -15,11 +15,11 @@ # limitations under the License. import copy import json -import math from collections import defaultdict from functools import lru_cache from typing import Callable, DefaultDict, Dict, List, Union +import numpy as np import torch from lark import Lark from outlines import grammars @@ -77,9 +77,17 @@ def __call__(self, input_ids: List[int], f"Unsupported instruction type {type(instruction)}") mask = torch.full((scores.shape[-1], ), - -math.inf, + -torch.inf, device=scores.device) - mask[allowed_tokens] = 0 + # The tokenizer may support more token ids than the model can generate, + # eg. Llama 3.2 Vision models have an `<|image|>` token with id 128256 + # but scores.shape == torch.Size([128256]) + # Using NumPy is faster for filtering token ids + allowed_tokens = np.array(allowed_tokens, dtype=np.int64) + allowed_tokens = torch.tensor(allowed_tokens, device=scores.device) + allowed_tokens = allowed_tokens.masked_select( + allowed_tokens < scores.shape[-1]) + mask.index_fill_(0, allowed_tokens, 0) scores.add_(mask) return scores From 067e77f9a87c3466fce41c8fe8710fddc69ec26c Mon Sep 17 00:00:00 2001 From: Sam Stoelinga Date: Fri, 25 Oct 2024 22:05:47 -0700 Subject: [PATCH 099/222] [Bugfix] Steaming continuous_usage_stats default to False (#9709) Signed-off-by: Sam Stoelinga --- vllm/entrypoints/openai/protocol.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 733decf80a711..a212c0d608ddb 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -127,7 +127,7 @@ class ResponseFormat(OpenAIBaseModel): class StreamOptions(OpenAIBaseModel): include_usage: Optional[bool] = True - continuous_usage_stats: Optional[bool] = True + continuous_usage_stats: Optional[bool] = False class FunctionDefinition(OpenAIBaseModel): From 5cbdccd151ef50e3fc040690248a8d86d3b93c2a Mon Sep 17 00:00:00 2001 From: Mengqing Cao Date: Sat, 26 Oct 2024 18:59:06 +0800 Subject: [PATCH 100/222] [Hardware][openvino] is_openvino --> current_platform.is_openvino (#9716) --- tests/kernels/test_attention_selector.py | 3 +- vllm/attention/selector.py | 4 +-- vllm/config.py | 4 +-- vllm/executor/openvino_executor.py | 20 +++++-------- vllm/model_executor/model_loader/openvino.py | 4 +-- vllm/platforms/__init__.py | 10 +++++++ vllm/platforms/interface.py | 4 +++ vllm/platforms/openvino.py | 31 ++++++++++++++++++++ vllm/utils.py | 11 +------ vllm/worker/openvino_worker.py | 16 +++++----- 10 files changed, 69 insertions(+), 38 deletions(-) create mode 100644 vllm/platforms/openvino.py diff --git a/tests/kernels/test_attention_selector.py b/tests/kernels/test_attention_selector.py index 8bcee98403775..df3e770e260e0 100644 --- a/tests/kernels/test_attention_selector.py +++ b/tests/kernels/test_attention_selector.py @@ -30,7 +30,8 @@ def test_env(name: str, device: str, monkeypatch): False) assert backend.name == "ROCM_FLASH" elif device == "openvino": - with patch("vllm.attention.selector.is_openvino", return_value=True): + with patch("vllm.attention.selector.current_platform.is_openvino", + return_value=True): backend = which_attn_to_use(16, torch.float16, torch.float16, 16, False) assert backend.name == "OPENVINO" diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index cd3c642b8c8a2..10d4509b38279 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -10,7 +10,7 @@ from vllm.attention.backends.abstract import AttentionBackend from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import STR_BACKEND_ENV_VAR, is_hip, is_openvino +from vllm.utils import STR_BACKEND_ENV_VAR, is_hip logger = init_logger(__name__) @@ -193,7 +193,7 @@ def which_attn_to_use( logger.info("Cannot use %s backend on CPU.", selected_backend) return _Backend.TORCH_SDPA - if is_openvino(): + if current_platform.is_openvino(): if selected_backend != _Backend.OPENVINO: logger.info("Cannot use %s backend on OpenVINO.", selected_backend) return _Backend.OPENVINO diff --git a/vllm/config.py b/vllm/config.py index 25f841231dedd..a1fba98233b80 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -17,7 +17,7 @@ get_hf_image_processor_config, get_hf_text_config) from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory, - is_hip, is_openvino, print_warning_once) + is_hip, print_warning_once) if TYPE_CHECKING: from ray.util.placement_group import PlacementGroup @@ -1117,7 +1117,7 @@ def __init__(self, device: str = "auto") -> None: self.device_type = "cuda" elif current_platform.is_neuron(): self.device_type = "neuron" - elif is_openvino(): + elif current_platform.is_openvino(): self.device_type = "openvino" elif current_platform.is_tpu(): self.device_type = "tpu" diff --git a/vllm/executor/openvino_executor.py b/vllm/executor/openvino_executor.py index 4a39839a03199..d0c0333854dae 100644 --- a/vllm/executor/openvino_executor.py +++ b/vllm/executor/openvino_executor.py @@ -10,6 +10,7 @@ from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.model_executor.layers.sampler import SamplerOutput +from vllm.platforms import current_platform from vllm.sequence import ExecuteModelRequest from vllm.utils import (GiB_bytes, get_distributed_init_method, get_ip, get_open_port, make_async) @@ -17,14 +18,6 @@ logger = init_logger(__name__) -def is_openvino_cpu() -> bool: - return "CPU" in envs.VLLM_OPENVINO_DEVICE - - -def is_openvino_gpu() -> bool: - return "GPU" in envs.VLLM_OPENVINO_DEVICE - - class OpenVINOExecutor(ExecutorBase): uses_ray: bool = False @@ -32,7 +25,8 @@ class OpenVINOExecutor(ExecutorBase): def _init_executor(self) -> None: assert self.device_config.device_type == "openvino" assert self.lora_config is None, "OpenVINO backend doesn't support LoRA" - assert is_openvino_cpu() or is_openvino_gpu(), \ + assert current_platform.is_openvino_cpu() or \ + current_platform.is_openvino_gpu(), \ "OpenVINO backend supports only CPU and GPU devices" self.ov_core = ov.Core() @@ -163,7 +157,7 @@ def _verify_and_get_model_config(config: ModelConfig) -> ModelConfig: def _verify_and_get_cache_config(ov_core: ov.Core, config: CacheConfig) -> CacheConfig: if envs.VLLM_OPENVINO_CPU_KV_CACHE_PRECISION == "u8": - if not is_openvino_cpu(): + if not current_platform.is_openvino_cpu(): logger.info("VLLM_OPENVINO_CPU_KV_CACHE_PRECISION is" "ignored for GPU, f16 data type will be used.") config.cache_dtype = ov.Type.f16 @@ -172,7 +166,7 @@ def _verify_and_get_cache_config(ov_core: ov.Core, "VLLM_OPENVINO_CPU_KV_CACHE_PRECISION env var.") config.cache_dtype = ov.Type.u8 else: - if is_openvino_cpu(): + if current_platform.is_openvino_cpu(): ov_device = envs.VLLM_OPENVINO_DEVICE inference_precision = ov_core.get_property( ov_device, hints.inference_precision) @@ -183,7 +177,7 @@ def _verify_and_get_cache_config(ov_core: ov.Core, else: config.cache_dtype = ov.Type.f16 - if is_openvino_cpu(): + if current_platform.is_openvino_cpu(): if config.block_size != 32: logger.info( f"OpenVINO CPU optimal block size is 32, overriding currently set {config.block_size}" # noqa: G004, E501 @@ -198,7 +192,7 @@ def _verify_and_get_cache_config(ov_core: ov.Core, kv_cache_space = envs.VLLM_OPENVINO_KVCACHE_SPACE if kv_cache_space >= 0: - if kv_cache_space == 0 and is_openvino_cpu(): + if kv_cache_space == 0 and current_platform.is_openvino_cpu(): config.openvino_kvcache_space_bytes = 4 * GiB_bytes # type: ignore logger.warning( "Environment variable VLLM_OPENVINO_KVCACHE_SPACE (GB) " diff --git a/vllm/model_executor/model_loader/openvino.py b/vllm/model_executor/model_loader/openvino.py index 88b7ac46e5541..8ada2210d0d51 100644 --- a/vllm/model_executor/model_loader/openvino.py +++ b/vllm/model_executor/model_loader/openvino.py @@ -12,12 +12,12 @@ import vllm.envs as envs from vllm.attention.backends.openvino import OpenVINOAttentionMetadata from vllm.config import DeviceConfig, ModelConfig -from vllm.executor.openvino_executor import is_openvino_cpu from vllm.logger import init_logger from vllm.model_executor.layers.logits_processor import (LogitsProcessor, _prune_hidden_states) from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.platforms import current_platform logger = init_logger(__name__) @@ -136,7 +136,7 @@ def __init__( ov_device = envs.VLLM_OPENVINO_DEVICE paged_attention_transformation(pt_model.model) _modify_cache_parameters(pt_model.model, kv_cache_dtype, - is_openvino_cpu()) + current_platform.is_openvino_cpu()) ov_compiled = ov_core.compile_model(pt_model.model, ov_device) self.ov_request = ov_compiled.create_infer_request() diff --git a/vllm/platforms/__init__.py b/vllm/platforms/__init__.py index 58912158139bd..7e9f8b1297b80 100644 --- a/vllm/platforms/__init__.py +++ b/vllm/platforms/__init__.py @@ -65,6 +65,13 @@ except ImportError: pass +is_openvino = False +try: + from importlib.metadata import version + is_openvino = "openvino" in version("vllm") +except Exception: + pass + if is_tpu: # people might install pytorch built with cuda but run on tpu # so we need to check tpu first @@ -85,6 +92,9 @@ elif is_neuron: from .neuron import NeuronPlatform current_platform = NeuronPlatform() +elif is_openvino: + from .openvino import OpenVinoPlatform + current_platform = OpenVinoPlatform() else: current_platform = UnspecifiedPlatform() diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py index d36367f2bc9c1..7c933385d6ff6 100644 --- a/vllm/platforms/interface.py +++ b/vllm/platforms/interface.py @@ -11,6 +11,7 @@ class PlatformEnum(enum.Enum): XPU = enum.auto() CPU = enum.auto() NEURON = enum.auto() + OPENVINO = enum.auto() UNSPECIFIED = enum.auto() @@ -52,6 +53,9 @@ def is_cpu(self) -> bool: def is_neuron(self) -> bool: return self._enum == PlatformEnum.NEURON + def is_openvino(self) -> bool: + return self._enum == PlatformEnum.OPENVINO + def is_cuda_alike(self) -> bool: """Stateless version of :func:`torch.cuda.is_available`.""" return self._enum in (PlatformEnum.CUDA, PlatformEnum.ROCM) diff --git a/vllm/platforms/openvino.py b/vllm/platforms/openvino.py new file mode 100644 index 0000000000000..35dbe22abf7ff --- /dev/null +++ b/vllm/platforms/openvino.py @@ -0,0 +1,31 @@ +import torch + +import vllm.envs as envs +from vllm.utils import print_warning_once + +from .interface import Platform, PlatformEnum + + +class OpenVinoPlatform(Platform): + _enum = PlatformEnum.OPENVINO + + @classmethod + def get_device_name(self, device_id: int = 0) -> str: + return "openvino" + + @classmethod + def inference_mode(self): + return torch.inference_mode(mode=True) + + @classmethod + def is_openvino_cpu(self) -> bool: + return "CPU" in envs.VLLM_OPENVINO_DEVICE + + @classmethod + def is_openvino_gpu(self) -> bool: + return "GPU" in envs.VLLM_OPENVINO_DEVICE + + @classmethod + def is_pin_memory_available(self) -> bool: + print_warning_once("Pin memory is not supported on OpenViNO.") + return False diff --git a/vllm/utils.py b/vllm/utils.py index 0e9b241b6f9f6..fba9804289b94 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -318,15 +318,6 @@ def is_hip() -> bool: return torch.version.hip is not None -@lru_cache(maxsize=None) -def is_openvino() -> bool: - from importlib.metadata import PackageNotFoundError, version - try: - return "openvino" in version("vllm") - except PackageNotFoundError: - return False - - @lru_cache(maxsize=None) def get_max_shared_memory_bytes(gpu: int = 0) -> int: """Returns the maximum shared memory per thread block in bytes.""" @@ -757,7 +748,7 @@ def is_pin_memory_available() -> bool: elif current_platform.is_neuron(): print_warning_once("Pin memory is not supported on Neuron.") return False - elif current_platform.is_cpu() or is_openvino(): + elif current_platform.is_cpu() or current_platform.is_openvino(): return False return True diff --git a/vllm/worker/openvino_worker.py b/vllm/worker/openvino_worker.py index bc245d19663d6..a420d390c1ae4 100644 --- a/vllm/worker/openvino_worker.py +++ b/vllm/worker/openvino_worker.py @@ -13,12 +13,12 @@ from vllm.distributed import (broadcast_tensor_dict, ensure_model_parallel_initialized, init_distributed_environment) -from vllm.executor.openvino_executor import is_openvino_cpu from vllm.inputs import INPUT_REGISTRY from vllm.logger import init_logger from vllm.model_executor import set_random_seed from vllm.model_executor.layers.sampler import SamplerOutput from vllm.multimodal import MULTIMODAL_REGISTRY +from vllm.platforms import current_platform from vllm.sampling_params import SamplingParams from vllm.sequence import ExecuteModelRequest, SequenceGroupMetadata from vllm.worker.openvino_model_runner import OpenVINOModelRunner @@ -99,7 +99,7 @@ def _allocate_kv_cache( num_blocks, self.block_size, self.num_kv_heads, self.head_size)[1:] kv_cache: List[Tuple[ov.Tensor, ov.Tensor]] = [] - if is_openvino_cpu(): + if current_platform.is_openvino_cpu(): for _ in range(self.num_layers): key_blocks = ov.Tensor(self.cache_config.cache_dtype, k_block_shape) @@ -141,7 +141,7 @@ def _allocate_swap_cache( if num_blocks == 0: return swap_cache - assert not is_openvino_cpu(), \ + assert not current_platform.is_openvino_cpu(), \ "CPU device isn't supposed to have swap cache" # Update key_cache shape: @@ -285,7 +285,7 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: cache_block_size = self.get_cache_block_size_bytes() kvcache_space_bytes = self.cache_config.openvino_kvcache_space_bytes - if is_openvino_cpu(): + if current_platform.is_openvino_cpu(): num_device_blocks = int(kvcache_space_bytes // cache_block_size) num_swap_blocks = 0 else: @@ -322,7 +322,7 @@ def initialize_cache(self, num_gpu_blocks: int, num_device_blocks = num_gpu_blocks num_swap_blocks = num_cpu_blocks - if is_openvino_cpu(): + if current_platform.is_openvino_cpu(): assert (num_swap_blocks == 0 ), f"{type(self)} does not support swappable cache for CPU" @@ -366,7 +366,7 @@ def _init_cache_engine(self) -> None: assert self.kv_cache is not None # Populate the cache to warmup the memory - if is_openvino_cpu(): + if current_platform.is_openvino_cpu(): for key_cache, value_cache in self.kv_cache: key_cache.data[:] = 0 value_cache.data[:] = 0 @@ -414,7 +414,7 @@ def execute_model( blocks_to_swap_in = data["blocks_to_swap_in"] blocks_to_swap_out = data["blocks_to_swap_out"] - if is_openvino_cpu(): + if current_platform.is_openvino_cpu(): assert len(execute_model_req.blocks_to_swap_in) == 0 assert len(execute_model_req.blocks_to_swap_out) == 0 else: @@ -466,7 +466,7 @@ def get_cache_block_size_bytes(self) -> int: def profile_run(self) -> int: ov_device = envs.VLLM_OPENVINO_DEVICE - assert not is_openvino_cpu(), \ + assert not current_platform.is_openvino_cpu(), \ "CPU device isn't supposed to use profile run." import openvino.properties.device as device From 55137e8ee32509b2fa3b83d5caaee018a929f82d Mon Sep 17 00:00:00 2001 From: ErkinSagiroglu <52523336+MErkinSag@users.noreply.github.com> Date: Sat, 26 Oct 2024 13:12:57 +0100 Subject: [PATCH 101/222] Fix: MI100 Support By Bypassing Custom Paged Attention (#9560) --- vllm/attention/backends/rocm_flash_attn.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/vllm/attention/backends/rocm_flash_attn.py b/vllm/attention/backends/rocm_flash_attn.py index c2aec4aaa74e7..30859dfa60634 100644 --- a/vllm/attention/backends/rocm_flash_attn.py +++ b/vllm/attention/backends/rocm_flash_attn.py @@ -21,7 +21,10 @@ logger = init_logger(__name__) _PARTITION_SIZE_ROCM = 512 -_ON_NAVI = "gfx1" in torch.cuda.get_device_properties("cuda").gcnArchName +_GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName +_ON_NAVI = "gfx1" in _GPU_ARCH +_ON_MI250_MI300 = any(arch in _GPU_ARCH + for arch in ["gfx90a", "gfx940", "gfx941", "gfx942"]) class ROCmFlashAttentionBackend(AttentionBackend): @@ -662,7 +665,8 @@ def _use_rocm_custom_paged_attention(qtype: torch.dtype, head_size: int, block_size: int, gqa_ratio: int, max_seq_len: int) -> bool: # rocm custom page attention not support on navi (gfx1*) - return (not _ON_NAVI and (qtype == torch.half or qtype == torch.bfloat16) + return (_ON_MI250_MI300 and not _ON_NAVI + and (qtype == torch.half or qtype == torch.bfloat16) and (head_size == 64 or head_size == 128) and (block_size == 16 or block_size == 32) and (gqa_ratio >= 1 and gqa_ratio <= 16) and max_seq_len <= 32768) From 07e981fdf43bb7a7186c782a5ad6b99b36c2fc19 Mon Sep 17 00:00:00 2001 From: Vasiliy Alekseev Date: Sat, 26 Oct 2024 19:29:38 +0300 Subject: [PATCH 102/222] [Frontend] Bad words sampling parameter (#9717) Signed-off-by: Vasily Alexeev --- tests/samplers/test_no_bad_words.py | 185 ++++++++++++++++++ vllm/engine/llm_engine.py | 13 +- vllm/logits_process.py | 119 +++++++++++ .../guided_decoding/__init__.py | 3 +- .../lm_format_enforcer_decoding.py | 3 +- vllm/sampling_params.py | 32 +-- 6 files changed, 339 insertions(+), 16 deletions(-) create mode 100644 tests/samplers/test_no_bad_words.py create mode 100644 vllm/logits_process.py diff --git a/tests/samplers/test_no_bad_words.py b/tests/samplers/test_no_bad_words.py new file mode 100644 index 0000000000000..4190cf7cd7664 --- /dev/null +++ b/tests/samplers/test_no_bad_words.py @@ -0,0 +1,185 @@ +"""Make sure bad_words works. + +Run `pytest tests/samplers/test_no_bad_words.py`. + +""" +from typing import List, Optional + +from transformers import AutoTokenizer + +from vllm import LLM, SamplingParams + + +def _generate( + model: LLM, + prompt: str, + num_prompt_tokens: int, + temperature: float = 0, + bad_words: Optional[List[str]] = None, +) -> List[int]: + sampling_params = SamplingParams( + temperature=temperature, + bad_words=bad_words, + ) + + # [([output_token_ids, ], [output_text, ]), ] + output = model.generate([prompt], sampling_params=sampling_params) + + output_token_ids = output[0][0][0][num_prompt_tokens:] + # [0] first (and only) request output + # [0] token_ids (not text) + # [0] first (and only) output completion + + return output_token_ids + + +class TestOneTokenBadWord: + MODEL = "TheBloke/Llama-2-7B-fp16" + + PROMPT = "Hi! How are" + TARGET_TOKEN = "you" + + def setup_method(self, method): + self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL, + add_prefix_space=True) + + self.num_prompt_tokens = len(self._encode(self.PROMPT)) + self.target_token_id = self._encode(self.TARGET_TOKEN, + add_special_tokens=False)[0] + + def test_one_token_bad_word(self, vllm_runner): + with vllm_runner(self.MODEL) as llm: + output_token_ids = self._generate(llm) + assert output_token_ids[0] == self.target_token_id + + output_token_ids = self._generate(llm, + bad_words=[self.TARGET_TOKEN]) + assert self.target_token_id not in output_token_ids + + def _generate(self, + model: LLM, + bad_words: Optional[List[str]] = None) -> List[int]: + return _generate( + model=model, + prompt=self.PROMPT, + num_prompt_tokens=self.num_prompt_tokens, + bad_words=bad_words, + ) + + def _encode(self, + prompt: str, + add_special_tokens: bool = True) -> List[int]: + return self.tokenizer(prompt, + add_special_tokens=add_special_tokens).input_ids + + +class TestTwoTokenBadWord: + # Another model (with a different tokenizer behaviour) + MODEL = "openai-community/gpt2" + + PROMPT = "How old are you? I am 10" + TARGET_TOKEN1 = "years" + TARGET_TOKEN2 = "old" + NEIGHBOUR_TOKEN2 = "older" + + def setup_method(self, method): + self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL, + add_prefix_space=True) + + self.num_prompt_tokens = len(self._encode(self.PROMPT)) + self.target_token_id1 = self._encode(self.TARGET_TOKEN1, + add_special_tokens=False)[0] + self.target_token_id2 = self._encode(self.TARGET_TOKEN2, + add_special_tokens=False)[0] + self.neighbour_token_id2 = self._encode(self.NEIGHBOUR_TOKEN2, + add_special_tokens=False)[0] + + def test_two_token_bad_word(self, vllm_runner): + with vllm_runner(self.MODEL) as llm: + output_token_ids = self._generate(llm) + assert output_token_ids[:2] == [ + self.target_token_id1, self.target_token_id2 + ] + + output_token_ids = self._generate(llm, + bad_words=[self.TARGET_TOKEN1]) + assert self.target_token_id1 not in output_token_ids + + output_token_ids = self._generate(llm, + bad_words=[self.TARGET_TOKEN2]) + assert output_token_ids[0] == self.target_token_id1 + assert self.target_token_id2 not in output_token_ids + + output_token_ids = self._generate( + llm, bad_words=[f'{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}']) + assert output_token_ids[0] == self.target_token_id1 + assert output_token_ids[:2] != [ + self.target_token_id1, self.target_token_id2 + ] + assert not self._contains( + output_token_ids, + [self.target_token_id1, self.target_token_id2]) + # Model dependent behaviour + assert output_token_ids[:2] == [ + self.target_token_id1, self.neighbour_token_id2 + ] + + output_token_ids = self._generate( + llm, + bad_words=[ + f'{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}', + f'{self.TARGET_TOKEN1} {self.NEIGHBOUR_TOKEN2}' + ]) + assert output_token_ids[0] == self.target_token_id1 + assert output_token_ids[:2] != [ + self.target_token_id1, self.target_token_id2 + ] + assert not self._contains( + output_token_ids, + [self.target_token_id1, self.target_token_id2]) + assert output_token_ids[:2] != [ + self.target_token_id1, self.neighbour_token_id2 + ] + assert not self._contains( + output_token_ids, + [self.target_token_id1, self.neighbour_token_id2]) + assert ((self.target_token_id2 in output_token_ids) + or (self.neighbour_token_id2 in output_token_ids)) + + def _generate(self, + model: LLM, + bad_words: Optional[List[str]] = None) -> List[int]: + return _generate( + model=model, + prompt=self.PROMPT, + num_prompt_tokens=self.num_prompt_tokens, + bad_words=bad_words, + ) + + @staticmethod + def _contains(sequence: List[int], subsequence: List[int]) -> bool: + searched = False + + for start in range(len(sequence)): + end = start + len(subsequence) + current_subsequence = sequence[start:end] + + if len(current_subsequence) < len(subsequence): + continue + + searched = True + + assert len(current_subsequence) == len(subsequence) + + if current_subsequence == subsequence: + return True + + assert searched, "All subsequences did not match in length..." + + return False + + def _encode(self, + prompt: str, + add_special_tokens: bool = True) -> List[int]: + return self.tokenizer(prompt, + add_special_tokens=add_special_tokens).input_ids diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 1dd0f097c74ff..ede77f04b1db9 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -26,7 +26,8 @@ SequenceGroupOutputProcessor) from vllm.engine.output_processor.stop_checker import StopChecker from vllm.engine.output_processor.util import create_output_by_sequence_group -from vllm.entrypoints.openai.logits_processors import get_logits_processors +from vllm.entrypoints.openai.logits_processors import ( + get_logits_processors as get_openai_logits_processors) from vllm.executor.executor_base import ExecutorBase from vllm.executor.gpu_executor import GPUExecutor from vllm.executor.ray_utils import initialize_ray_cluster @@ -34,6 +35,7 @@ EncoderDecoderInputs, InputRegistry, PromptType) from vllm.inputs.preprocess import InputPreprocessor from vllm.logger import init_logger +from vllm.logits_process import get_bad_words_logits_processors from vllm.lora.request import LoRARequest from vllm.model_executor.guided_decoding import ( get_local_guided_decoding_logits_processor) @@ -1963,6 +1965,7 @@ def _build_logits_processors( logits_processors field. Returns the modified sampling params.""" logits_processors = [] + if (guided_decoding := sampling_params.guided_decoding) is not None: logger.debug( @@ -1984,7 +1987,7 @@ def _build_logits_processors( if (sampling_params.logit_bias or sampling_params.allowed_token_ids): tokenizer = self.get_tokenizer(lora_request=lora_request) - processors = get_logits_processors( + processors = get_openai_logits_processors( logit_bias=sampling_params.logit_bias, allowed_token_ids=sampling_params.allowed_token_ids, tokenizer=tokenizer) @@ -1994,6 +1997,12 @@ def _build_logits_processors( sampling_params.logit_bias = None sampling_params.allowed_token_ids = None + if len(sampling_params.bad_words) > 0: + tokenizer = self.get_tokenizer(lora_request) + processors = get_bad_words_logits_processors( + bad_words=sampling_params.bad_words, tokenizer=tokenizer) + logits_processors.extend(processors) + if logits_processors: if sampling_params.logits_processors is None: sampling_params.logits_processors = logits_processors diff --git a/vllm/logits_process.py b/vllm/logits_process.py new file mode 100644 index 0000000000000..7716ccd27e253 --- /dev/null +++ b/vllm/logits_process.py @@ -0,0 +1,119 @@ +from typing import Callable, List, Tuple, Union + +import torch + +from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer + +LogitsProcessor = Union[Callable[[List[int], torch.Tensor], torch.Tensor], + Callable[[List[int], List[int], torch.Tensor], + torch.Tensor]] +"""LogitsProcessor is a function that takes a list +of previously generated tokens, the logits tensor +for the next token and, optionally, prompt tokens as a +first argument, and returns a modified tensor of logits +to sample from.""" + + +def get_bad_words_logits_processors( + bad_words: List[str], + tokenizer: AnyTokenizer) -> List[LogitsProcessor]: + bad_words_ids: List[List[int]] = list() + + for bad_word in bad_words: + # To prohibit words both at the beginning + # and in the middle of text + # (related to add_prefix_space tokenizer parameter) + for add_prefix_space in [False, True]: + prefix = " " if add_prefix_space else "" + prompt = prefix + bad_word.lstrip() + + if isinstance(tokenizer, MistralTokenizer): + # Mistral tokenizers should not add special tokens + prompt_token_ids = tokenizer.encode(prompt=prompt) + else: + prompt_token_ids = tokenizer.encode(text=prompt, + add_special_tokens=False) + + # If no space at the beginning + # or if prefix space produces a new word token + if (not add_prefix_space) or ( + add_prefix_space + and prompt_token_ids[0] != bad_words_ids[-1][0] + and len(prompt_token_ids) == len(bad_words_ids[-1])): + bad_words_ids.append(prompt_token_ids) + + return [NoBadWordsLogitsProcessor(bad_words_ids=bad_words_ids)] + + +class NoBadWordsLogitsProcessor: + _SMALLEST_LOGIT = float("-inf") + _NEUTRAL_LOGIT = 0.0 + + def __init__(self, bad_words_ids: List[List[int]]): + self.bad_words_ids = bad_words_ids + self.word_bias: torch.FloatTensor = None + + def __call__( + self, + past_tokens_ids: Union[List[int], Tuple[int]], + logits: torch.FloatTensor, + ) -> torch.Tensor: + if self.word_bias is None: + self._init_word_bias(logits=logits) + + last_token_bias = torch.zeros_like(logits) + + for bad_word_ids in self.bad_words_ids: + if len(bad_word_ids) == 1: # 1-token words already processed + continue + + if len(bad_word_ids) > len(past_tokens_ids) + 1: + continue + + prefix_length = len(bad_word_ids) - 1 + last_token_id = bad_word_ids[-1] + actual_prefix = past_tokens_ids[-prefix_length:] + expected_prefix = bad_word_ids[:prefix_length] + + assert len(actual_prefix) == len(expected_prefix) + + is_match = tuple(actual_prefix) == tuple(expected_prefix) + last_token_bias[last_token_id] += (self._SMALLEST_LOGIT if is_match + else self._NEUTRAL_LOGIT) + + logits = logits + self.word_bias + last_token_bias + + return logits + + def _init_word_bias(self, logits: torch.FloatTensor) -> None: + # Code based on NoBadWordsLogitsProcessor and SequenceBiasLogitsProcessor # noqa: E501 + # from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py + + vocab_size = logits.shape[-1] + + self._check_token_ids_bounds(vocab_size=vocab_size) + + self.word_bias = torch.zeros((vocab_size, ), + dtype=torch.float, + device=logits.device) + + for bad_word_ids in self.bad_words_ids: + if len(bad_word_ids) == 1: + bad_word_id = bad_word_ids[-1] + self.word_bias[bad_word_id] = self._SMALLEST_LOGIT + + def _check_token_ids_bounds(self, vocab_size: int) -> None: + invalid_token_ids = [] + + for bad_word_ids in self.bad_words_ids: + for token_id in bad_word_ids: + if token_id < 0 or token_id >= vocab_size: + invalid_token_ids.append(token_id) + + if len(invalid_token_ids) > 0: + raise ValueError( + f"The model vocabulary size is {vocab_size}," + f" but the following tokens" + f" were specified as bad: {invalid_token_ids}." + f" All token id values should be integers satisfying:" + f" 0 <= token_id < {vocab_size}.") diff --git a/vllm/model_executor/guided_decoding/__init__.py b/vllm/model_executor/guided_decoding/__init__.py index 368436aa14613..d7b67425fcbc0 100644 --- a/vllm/model_executor/guided_decoding/__init__.py +++ b/vllm/model_executor/guided_decoding/__init__.py @@ -1,6 +1,7 @@ from typing import Optional -from vllm.sampling_params import GuidedDecodingParams, LogitsProcessor +from vllm.logits_process import LogitsProcessor +from vllm.sampling_params import GuidedDecodingParams async def get_guided_decoding_logits_processor( diff --git a/vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py b/vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py index cf2162ed7720d..a17e75a80300f 100644 --- a/vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py +++ b/vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py @@ -9,7 +9,8 @@ build_vllm_logits_processor, build_vllm_token_enforcer_tokenizer_data) from transformers import PreTrainedTokenizerBase -from vllm.sampling_params import GuidedDecodingParams, LogitsProcessor +from vllm.logits_process import LogitsProcessor +from vllm.sampling_params import GuidedDecodingParams def get_local_lm_format_enforcer_guided_decoding_logits_processor( diff --git a/vllm/sampling_params.py b/vllm/sampling_params.py index 9993cec13d649..bac32c991a0e3 100644 --- a/vllm/sampling_params.py +++ b/vllm/sampling_params.py @@ -3,14 +3,14 @@ from dataclasses import dataclass from enum import Enum, IntEnum from functools import cached_property -from typing import Any, Callable, Dict, List, Optional, Set, Union +from typing import Any, Dict, List, Optional, Set, Union import msgspec -import torch from pydantic import BaseModel from typing_extensions import Annotated from vllm.logger import init_logger +from vllm.logits_process import LogitsProcessor logger = init_logger(__name__) @@ -24,16 +24,6 @@ class SamplingType(IntEnum): RANDOM_SEED = 2 -LogitsProcessor = Union[Callable[[List[int], torch.Tensor], torch.Tensor], - Callable[[List[int], List[int], torch.Tensor], - torch.Tensor]] -"""LogitsProcessor is a function that takes a list -of previously generated tokens, the logits tensor -for the next token and, optionally, prompt tokens as a -first argument, and returns a modified tensor of logits -to sample from.""" - - # maybe make msgspec? @dataclass class GuidedDecodingParams: @@ -139,6 +129,10 @@ class SamplingParams( stop_token_ids: List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens. + bad_words: List of words that are not allowed to be generated. + More precisely, only the last token of a corresponding + token sequence is not allowed when the next generated token + can complete the sequence. include_stop_str_in_output: Whether to include the stop strings in output text. Defaults to False. ignore_eos: Whether to ignore the EOS token and continue generating @@ -186,6 +180,7 @@ class SamplingParams( seed: Optional[int] = None stop: Optional[Union[str, List[str]]] = None stop_token_ids: Optional[List[int]] = None + bad_words: Optional[List[str]] = None ignore_eos: bool = False max_tokens: Optional[int] = 16 min_tokens: int = 0 @@ -228,6 +223,7 @@ def from_optional( seed: Optional[int] = None, stop: Optional[Union[str, List[str]]] = None, stop_token_ids: Optional[List[int]] = None, + bad_words: Optional[List[str]] = None, include_stop_str_in_output: bool = False, ignore_eos: bool = False, max_tokens: Optional[int] = 16, @@ -267,6 +263,7 @@ def from_optional( seed=seed, stop=stop, stop_token_ids=stop_token_ids, + bad_words=bad_words, include_stop_str_in_output=include_stop_str_in_output, ignore_eos=ignore_eos, max_tokens=max_tokens, @@ -298,26 +295,36 @@ def __post_init__(self) -> None: f"got n={self.n} and best_of={self.best_of}.") self._real_n = self.n self.n = self.best_of + if 0 < self.temperature < _MAX_TEMP: logger.warning( "temperature %s is less than %s, which may cause numerical " "errors nan or inf in tensors. We have maxed it out to %s.", self.temperature, _MAX_TEMP, _MAX_TEMP) self.temperature = max(self.temperature, _MAX_TEMP) + if self.seed == -1: self.seed = None else: self.seed = self.seed + if self.stop is None: self.stop = [] elif isinstance(self.stop, str): self.stop = [self.stop] else: self.stop = list(self.stop) + if self.stop_token_ids is None: self.stop_token_ids = [] else: self.stop_token_ids = list(self.stop_token_ids) + + if self.bad_words is None: + self.bad_words = [] + else: + self.bad_words = list(self.bad_words) + self.logprobs = 1 if self.logprobs is True else self.logprobs self.prompt_logprobs = (1 if self.prompt_logprobs is True else self.prompt_logprobs) @@ -468,6 +475,7 @@ def __repr__(self) -> str: f"seed={self.seed}, " f"stop={self.stop}, " f"stop_token_ids={self.stop_token_ids}, " + f"bad_words={self.bad_words}, " f"include_stop_str_in_output={self.include_stop_str_in_output}, " f"ignore_eos={self.ignore_eos}, " f"max_tokens={self.max_tokens}, " From 6650e6a930dbdf1cd4def9b58e952376400ccfcf Mon Sep 17 00:00:00 2001 From: kakao-kevin-us Date: Sun, 27 Oct 2024 02:53:35 +0900 Subject: [PATCH 103/222] [Model] Add classification Task with Qwen2ForSequenceClassification (#9704) Signed-off-by: Kevin-Yang Co-authored-by: Kevin-Yang --- docs/source/models/supported_models.rst | 22 ++++ tests/conftest.py | 19 ++++ .../embedding/language/test_cls_models.py | 53 +++++++++ vllm/model_executor/layers/pooler.py | 9 +- vllm/model_executor/models/qwen2_cls.py | 107 ++++++++++++++++++ vllm/model_executor/models/registry.py | 2 + 6 files changed, 211 insertions(+), 1 deletion(-) create mode 100644 tests/models/embedding/language/test_cls_models.py create mode 100644 vllm/model_executor/models/qwen2_cls.py diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 98d804052b575..ff893b613f150 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -361,6 +361,28 @@ Reward Modeling .. note:: As an interim measure, these models are supported via Embeddings API. See `this RFC `_ for upcoming changes. +Classification +--------------- + +.. list-table:: + :widths: 25 25 50 5 5 + :header-rows: 1 + + * - Architecture + - Models + - Example HF Models + - :ref:`LoRA ` + - :ref:`PP ` + * - :code:`Qwen2ForSequenceClassification` + - Qwen2-based + - :code:`jason9693/Qwen2.5-1.5B-apeach`, etc. + - + - ✅︎ + +.. note:: + As an interim measure, these models are supported via Embeddings API. It will be supported via Classification API in the future (no reference APIs exist now). + + Multimodal Language Models ^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/tests/conftest.py b/tests/conftest.py index 6adff5e2328c4..2fce2d772c6ed 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -343,6 +343,17 @@ def get_inputs( return all_inputs + def classify(self, prompts: List[str]) -> List[str]: + # output is final logits + all_inputs = self.get_inputs(prompts) + outputs = [] + for inputs in all_inputs: + output = self.model(**self.wrap_device(inputs)) + logits = output.logits.softmax(dim=-1)[0].tolist() + outputs.append(logits) + + return outputs + def generate( self, prompts: List[str], @@ -688,6 +699,14 @@ def get_inputs( return inputs + def classify(self, prompts: List[str]) -> List[str]: + req_outputs = self.model.encode(prompts) + outputs = [] + for req_output in req_outputs: + embedding = req_output.outputs.embedding + outputs.append(embedding) + return outputs + def generate( self, prompts: List[str], diff --git a/tests/models/embedding/language/test_cls_models.py b/tests/models/embedding/language/test_cls_models.py new file mode 100644 index 0000000000000..d8ca6d361f0e3 --- /dev/null +++ b/tests/models/embedding/language/test_cls_models.py @@ -0,0 +1,53 @@ +"""Compare the outputs of HF and vLLM when using greedy sampling. + +This test only tests small models. Big models such as 7B should be tested from +test_big_models.py because it could use a larger instance to run tests. + +Run `pytest tests/models/test_cls_models.py`. +""" +import pytest +import torch +from transformers import AutoModelForSequenceClassification + +CLASSIFICATION_MODELS = ["jason9693/Qwen2.5-1.5B-apeach"] + + +@pytest.mark.parametrize("model", CLASSIFICATION_MODELS) +@pytest.mark.parametrize("dtype", ["float"]) +def test_classification_models( + hf_runner, + vllm_runner, + example_prompts, + model: str, + dtype: str, +) -> None: + with hf_runner(model, + dtype=dtype, + auto_cls=AutoModelForSequenceClassification) as hf_model: + hf_outputs = hf_model.classify(example_prompts) + + with vllm_runner(model, dtype=dtype) as vllm_model: + vllm_outputs = vllm_model.classify(example_prompts) + + print(hf_outputs, vllm_outputs) + + # check logits difference + for hf_output, vllm_output in zip(hf_outputs, vllm_outputs): + hf_output = torch.tensor(hf_output) + vllm_output = torch.tensor(vllm_output) + + assert torch.allclose(hf_output, vllm_output, 1e-3) + + +@pytest.mark.parametrize("model", CLASSIFICATION_MODELS) +@pytest.mark.parametrize("dtype", ["float"]) +def test_classification_model_print( + vllm_runner, + model: str, + dtype: str, +) -> None: + with vllm_runner(model, dtype=dtype) as vllm_model: + # This test is for verifying whether the model's extra_repr + # can be printed correctly. + print(vllm_model.model.llm_engine.model_executor.driver_worker. + model_runner.model) diff --git a/vllm/model_executor/layers/pooler.py b/vllm/model_executor/layers/pooler.py index 3455a4ccf282f..0a1df9cb699ae 100644 --- a/vllm/model_executor/layers/pooler.py +++ b/vllm/model_executor/layers/pooler.py @@ -28,11 +28,15 @@ class Pooler(nn.Module): normalize: Whether to normalize the pooled data. """ - def __init__(self, pooling_type: PoolingType, normalize: bool): + def __init__(self, + pooling_type: PoolingType, + normalize: bool, + softmax: bool = False): super().__init__() self.pooling_type = pooling_type self.normalize = normalize + self.softmax = softmax def forward( self, @@ -64,6 +68,9 @@ def forward( if self.normalize: pooled_data = nn.functional.normalize(pooled_data, p=2, dim=1) + if self.softmax: + pooled_data = nn.functional.softmax(pooled_data, dim=-1) + pooled_outputs = [ EmbeddingSequenceGroupOutput(data.tolist()) for data in pooled_data ] diff --git a/vllm/model_executor/models/qwen2_cls.py b/vllm/model_executor/models/qwen2_cls.py new file mode 100644 index 0000000000000..e10c6dbbb6472 --- /dev/null +++ b/vllm/model_executor/models/qwen2_cls.py @@ -0,0 +1,107 @@ +# coding=utf-8 +# Adapted from +# https://huggingface.co/Qwen/Qwen2.5-Math-RM-72B/blob/main/modeling_qwen2_rm.py +# Copyright 2024 Kakao Corp. (Kanana-X Team) +# Copyright 2024 The Qwen team. +# Copyright 2023 The vLLM team. +"""Inference-only Qwen2-Classification model compatible with HF weights.""" +from typing import Iterable, List, Optional, Tuple + +import torch +from torch import nn +from transformers import Qwen2Config + +from vllm.attention import AttentionMetadata +from vllm.config import CacheConfig, LoRAConfig +from vllm.model_executor.layers.linear import RowParallelLinear +from vllm.model_executor.layers.pooler import Pooler, PoolingType +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) +from vllm.model_executor.models.qwen2 import Qwen2Model +from vllm.model_executor.pooling_metadata import PoolingMetadata +from vllm.sequence import IntermediateTensors, PoolerOutput + +from .utils import AutoWeightsLoader + + +class Qwen2ForSequenceClassification(nn.Module): + packed_modules_mapping = { + "qkv_proj": [ + "q_proj", + "k_proj", + "v_proj", + ], + "gate_up_proj": [ + "gate_proj", + "up_proj", + ], + } + + # LoRA specific attributes + supported_lora_modules = [ + "qkv_proj", + "o_proj", + "gate_up_proj", + "down_proj", + ] + embedding_modules = {} + embedding_padding_modules = [] + + def __init__( + self, + config: Qwen2Config, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + lora_config: Optional[LoRAConfig] = None, + ) -> None: + # TODO (@robertgshaw2): see if this can be moved out + if (cache_config.sliding_window is not None + and hasattr(config, "max_window_layers")): + raise ValueError("Sliding window for some but all layers is not " + "supported. This model uses sliding window " + "but `max_window_layers` = %s is less than " + "`num_hidden_layers` = %s. Please open an issue " + "to discuss this feature." % ( + config.max_window_layers, + config.num_hidden_layers, + )) + + super().__init__() + + self.config = config + self.lora_config = lora_config + + self.quant_config = quant_config + self.model = Qwen2Model(config, cache_config, quant_config) + + self.score = RowParallelLinear(config.hidden_size, + config.num_labels, + quant_config=quant_config) + self._pooler = Pooler(pooling_type=PoolingType.LAST, + normalize=False, + softmax=True) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + intermediate_tensors: Optional[IntermediateTensors] = None, + ) -> torch.Tensor: + hidden_states = self.model(input_ids, positions, kv_caches, + attn_metadata, intermediate_tensors) + logits, _ = self.score(hidden_states) + return logits + + def pooler( + self, + hidden_states: torch.Tensor, + pooling_metadata: PoolingMetadata, + ) -> Optional[PoolerOutput]: + return self._pooler(hidden_states, pooling_metadata) + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + loader = AutoWeightsLoader(self, + ignore_unexpected_prefixes=["lm_head."]) + loader.load_weights(weights) diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 717615988a907..f6713ab0898f0 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -96,6 +96,8 @@ "Gemma2Model": ("gemma2", "Gemma2EmbeddingModel"), "MistralModel": ("llama", "LlamaEmbeddingModel"), "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"), + "Qwen2ForSequenceClassification": ( + "qwen2_cls", "Qwen2ForSequenceClassification"), # [Multimodal] "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"), # noqa: E501 "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"), From 67a6882da474a45dde0d35b3789e096e7bd0fd4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=A7=91=E8=8B=B1?= Date: Sun, 27 Oct 2024 12:18:03 +0800 Subject: [PATCH 104/222] [Misc] SpecDecodeWorker supports profiling (#9719) Signed-off-by: Abatom --- vllm/spec_decode/spec_decode_worker.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/vllm/spec_decode/spec_decode_worker.py b/vllm/spec_decode/spec_decode_worker.py index 316db43502d3b..9f7ef2f8d851c 100644 --- a/vllm/spec_decode/spec_decode_worker.py +++ b/vllm/spec_decode/spec_decode_worker.py @@ -1038,6 +1038,14 @@ def get_cache_block_size_bytes(self): """ raise NotImplementedError + def start_profile(self): + if isinstance(self.scorer_worker, Worker): + self.scorer_worker.start_profile() + + def stop_profile(self): + if isinstance(self.scorer_worker, Worker): + self.scorer_worker.stop_profile() + def split_num_cache_blocks_evenly(scorer_cache_block_size_bytes: int, proposer_cache_block_size_bytes: int, From 8549c82660cfa59a13cccd622f8afcc29cbd4281 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Sun, 27 Oct 2024 00:19:28 -0700 Subject: [PATCH 105/222] [core] cudagraph output with tensor weak reference (#9724) Signed-off-by: youkaichao --- csrc/ops.h | 24 +++++++++++++++++++++ csrc/torch_bindings.cpp | 3 +++ vllm/utils.py | 9 ++++++++ vllm/worker/model_runner.py | 42 +++++++++++++------------------------ 4 files changed, 50 insertions(+), 28 deletions(-) diff --git a/csrc/ops.h b/csrc/ops.h index f737f50c2ec96..c50eb39a3dacc 100644 --- a/csrc/ops.h +++ b/csrc/ops.h @@ -5,6 +5,30 @@ #include "core/scalar_type.hpp" +#include + +torch::Tensor weak_ref_tensor(torch::Tensor& tensor) { + // Ensure tensor is on CUDA + if (!tensor.is_cuda()) { + throw std::runtime_error("Tensor must be on CUDA device"); + } + + // Get the raw data pointer + void* data_ptr = tensor.data_ptr(); + + // Get tensor sizes and strides + std::vector sizes = tensor.sizes().vec(); + std::vector strides = tensor.strides().vec(); + + // Get tensor options (dtype, device) + auto options = tensor.options(); + + // Create a new tensor from the raw data pointer + auto new_tensor = torch::from_blob(data_ptr, sizes, strides, options); + + return new_tensor; +} + void paged_attention_v1( torch::Tensor& out, torch::Tensor& query, torch::Tensor& key_cache, torch::Tensor& value_cache, int64_t num_kv_heads, double scale, diff --git a/csrc/torch_bindings.cpp b/csrc/torch_bindings.cpp index e704ff629fd6e..b8185c24d5628 100644 --- a/csrc/torch_bindings.cpp +++ b/csrc/torch_bindings.cpp @@ -18,6 +18,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) { // vLLM custom ops + ops.def("weak_ref_tensor(Tensor input) -> Tensor"); + ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor); + // Attention ops // Compute the attention between an input query and the cached // keys/values using PagedAttention. diff --git a/vllm/utils.py b/vllm/utils.py index fba9804289b94..1f75de89d0cc2 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -1479,3 +1479,12 @@ def __iter__(self): def __len__(self): return len(self._factory) + + +def weak_ref_tensor(tensor: torch.Tensor) -> torch.Tensor: + """ + Create a weak reference to a tensor. + The new tensor will share the same data as the original tensor, + but will not keep the original tensor alive. + """ + return torch.ops._C.weak_ref_tensor(tensor) diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index 8b74f06e77be0..4a287e3741d0f 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -50,7 +50,7 @@ from vllm.transformers_utils.config import uses_mrope from vllm.utils import (DeviceMemoryProfiler, PyObjectCache, async_tensor_h2d, flatten_2d_lists, is_hip, is_pin_memory_available, - supports_dynamo) + supports_dynamo, weak_ref_tensor) from vllm.worker.model_runner_base import ( ModelRunnerBase, ModelRunnerInputBase, ModelRunnerInputBuilderBase, _add_attn_metadata_broadcastable_dict, @@ -1426,12 +1426,6 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None: dtype=self.model_config.dtype, device=self.device) - # Prepare buffer for outputs. These will be reused for all batch sizes. - # It will be filled after the first graph capture. - hidden_or_intermediate_states: List[Optional[torch.Tensor]] = [ - None - ] * self.parallel_config.pipeline_parallel_size - graph_batch_size = self.max_batchsize_to_capture batch_size_capture_list = [ bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size @@ -1474,12 +1468,6 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None: input_tokens[:batch_size], "positions": input_positions[..., :batch_size], - "hidden_or_intermediate_states": - hidden_or_intermediate_states[ - virtual_engine] # type: ignore - [:batch_size] - if hidden_or_intermediate_states[virtual_engine] - is not None else None, "intermediate_inputs": intermediate_inputs[:batch_size] if intermediate_inputs is not None else None, @@ -1762,15 +1750,13 @@ def capture( self, input_ids: torch.Tensor, positions: torch.Tensor, - hidden_or_intermediate_states: Optional[Union[IntermediateTensors, - torch.Tensor]], intermediate_inputs: Optional[IntermediateTensors], kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, memory_pool: Optional[Tuple[int, int]], stream: torch.cuda.Stream, **kwargs, - ) -> Union[torch.Tensor, IntermediateTensors]: + ): assert self._graph is None # Run the model a few times without capturing the graph. # This is to make sure that the captured graph does not include the @@ -1799,20 +1785,21 @@ def capture( intermediate_tensors=intermediate_inputs, **kwargs, ) - if hidden_or_intermediate_states is not None: - if get_pp_group().is_last_rank: - hidden_or_intermediate_states.copy_( - output_hidden_or_intermediate_states) - else: - for key in hidden_or_intermediate_states.tensors: - hidden_or_intermediate_states[key].copy_( - output_hidden_or_intermediate_states[key]) - else: - hidden_or_intermediate_states = ( + + if isinstance(output_hidden_or_intermediate_states, torch.Tensor): + hidden_or_intermediate_states = weak_ref_tensor( output_hidden_or_intermediate_states) + elif isinstance(output_hidden_or_intermediate_states, + IntermediateTensors): + hidden_or_intermediate_states = IntermediateTensors( + tensors={ + key: weak_ref_tensor(value) + for key, value in + output_hidden_or_intermediate_states.tensors.items() + }) del output_hidden_or_intermediate_states - # make sure `output_hidden_states` is deleted + # make sure `output_hidden_or_intermediate_states` is deleted # in the graph's memory pool gc.collect() torch.cuda.synchronize() @@ -1837,7 +1824,6 @@ def capture( } else: self.output_buffers = hidden_or_intermediate_states - return hidden_or_intermediate_states def forward( self, From 3cb07a36a20f9af11346650559470d685e9dc711 Mon Sep 17 00:00:00 2001 From: bnellnm <49004751+bnellnm@users.noreply.github.com> Date: Sun, 27 Oct 2024 05:44:24 -0400 Subject: [PATCH 106/222] [Misc] Upgrade to pytorch 2.5 (#9588) Signed-off-by: Bill Nell Signed-off-by: youkaichao Co-authored-by: youkaichao --- CMakeLists.txt | 4 +- cmake/utils.cmake | 6 +-- pyproject.toml | 2 +- requirements-build.txt | 2 +- requirements-cuda.txt | 6 +-- requirements-openvino.txt | 2 +- .../decoder_only/language/test_big_models.py | 46 ++++++++++++++----- vllm/platforms/cuda.py | 5 ++ 8 files changed, 48 insertions(+), 25 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index fc4ac10b7669a..1a6a311e97633 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -49,7 +49,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx11 # requirements.txt files and should be kept consistent. The ROCm torch # versions are derived from Dockerfile.rocm # -set(TORCH_SUPPORTED_VERSION_CUDA "2.4.0") +set(TORCH_SUPPORTED_VERSION_CUDA "2.5.0") set(TORCH_SUPPORTED_VERSION_ROCM "2.5.0") # @@ -507,7 +507,7 @@ else() FetchContent_Declare( vllm-flash-attn GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git - GIT_TAG 013f0c4fc47e6574060879d9734c1df8c5c273bd + GIT_TAG 5259c586c403a4e4d8bf69973c159b40cc346fb9 GIT_PROGRESS TRUE # Don't share the vllm-flash-attn build between build types BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn diff --git a/cmake/utils.cmake b/cmake/utils.cmake index 24bb7299338ac..40430dae10c5b 100644 --- a/cmake/utils.cmake +++ b/cmake/utils.cmake @@ -424,11 +424,7 @@ function (define_gpu_extension_target GPU_MOD_NAME) # Don't use `TORCH_LIBRARIES` for CUDA since it pulls in a bunch of # dependencies that are not necessary and may not be installed. if (GPU_LANGUAGE STREQUAL "CUDA") - if ("${CUDA_CUDA_LIB}" STREQUAL "") - set(CUDA_CUDA_LIB "${CUDA_CUDA_LIBRARY}") - endif() - target_link_libraries(${GPU_MOD_NAME} PRIVATE ${CUDA_CUDA_LIB} - ${CUDA_LIBRARIES}) + target_link_libraries(${GPU_MOD_NAME} PRIVATE CUDA::cudart CUDA::cuda_driver) else() target_link_libraries(${GPU_MOD_NAME} PRIVATE ${TORCH_LIBRARIES}) endif() diff --git a/pyproject.toml b/pyproject.toml index e0c56ab79cad0..e78f5652f486b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ requires = [ "packaging", "setuptools>=61", "setuptools-scm>=8.0", - "torch == 2.4.0", + "torch == 2.5.0", "wheel", "jinja2", ] diff --git a/requirements-build.txt b/requirements-build.txt index 6144a56da8c47..ea2b688bb3108 100644 --- a/requirements-build.txt +++ b/requirements-build.txt @@ -4,6 +4,6 @@ ninja packaging setuptools>=61 setuptools-scm>=8 -torch==2.4.0 +torch==2.5.0 wheel jinja2 diff --git a/requirements-cuda.txt b/requirements-cuda.txt index 3b3c2f876919e..92fa303d687a2 100644 --- a/requirements-cuda.txt +++ b/requirements-cuda.txt @@ -4,7 +4,7 @@ # Dependencies for NVIDIA GPUs ray >= 2.9 nvidia-ml-py # for pynvml package -torch == 2.4.0 +torch == 2.5.0 # These must be updated alongside torch -torchvision == 0.19 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version -xformers == 0.0.27.post2; platform_system == 'Linux' and platform_machine == 'x86_64' # Requires PyTorch 2.4.0 +torchvision == 0.20 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version +xformers == 0.0.28.post2; platform_system == 'Linux' and platform_machine == 'x86_64' # Requires PyTorch 2.5.0 diff --git a/requirements-openvino.txt b/requirements-openvino.txt index ac54cf0c3288f..7ad0d1e7f704b 100644 --- a/requirements-openvino.txt +++ b/requirements-openvino.txt @@ -1,7 +1,7 @@ # Common dependencies -r requirements-common.txt -torch == 2.4.0 # should be aligned with "common" vLLM torch version +torch == 2.5.0 # should be aligned with "common" vLLM torch version openvino >= 2024.4.0 # since 2024.4.0 both CPU and GPU support Paged Attention optimum @ git+https://github.com/huggingface/optimum.git@main # latest optimum is used to support latest transformers version diff --git a/tests/models/decoder_only/language/test_big_models.py b/tests/models/decoder_only/language/test_big_models.py index 75625b35209ce..fcfc159e4f5a0 100644 --- a/tests/models/decoder_only/language/test_big_models.py +++ b/tests/models/decoder_only/language/test_big_models.py @@ -8,7 +8,7 @@ from vllm.platforms import current_platform -from ...utils import check_outputs_equal +from ...utils import check_logprobs_close, check_outputs_equal MODELS = [ "meta-llama/Llama-2-7b-hf", @@ -43,18 +43,40 @@ def test_models( dtype: str, max_tokens: int, ) -> None: - with hf_runner(model, dtype=dtype) as hf_model: - hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) - with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model: - vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) - - check_outputs_equal( - outputs_0_lst=hf_outputs, - outputs_1_lst=vllm_outputs, - name_0="hf", - name_1="vllm", - ) + if model == "openbmb/MiniCPM3-4B": + # the output becomes slightly different when upgrading to + # pytorch 2.5 . Changing to logprobs checks instead of exact + # output checks. + NUM_LOG_PROBS = 8 + with hf_runner(model, dtype=dtype) as hf_model: + hf_outputs = hf_model.generate_greedy_logprobs_limit( + example_prompts, max_tokens, NUM_LOG_PROBS) + + with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model: + vllm_outputs = vllm_model.generate_greedy_logprobs( + example_prompts, max_tokens, NUM_LOG_PROBS) + + check_logprobs_close( + outputs_0_lst=hf_outputs, + outputs_1_lst=vllm_outputs, + name_0="hf", + name_1="vllm", + ) + else: + with hf_runner(model, dtype=dtype) as hf_model: + hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) + + with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model: + vllm_outputs = vllm_model.generate_greedy(example_prompts, + max_tokens) + + check_outputs_equal( + outputs_0_lst=hf_outputs, + outputs_1_lst=vllm_outputs, + name_0="hf", + name_1="vllm", + ) @pytest.mark.parametrize("model", MODELS) diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 30bbf5107475d..9c5212ace1346 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -7,6 +7,7 @@ from typing import Callable, List, Tuple, TypeVar import pynvml +import torch from typing_extensions import ParamSpec from vllm.logger import init_logger @@ -26,6 +27,10 @@ " and cause errors. See https://pypi.org/project/pynvml " "for more information.") +# pytorch 2.5 uses cudnn sdpa by default, which will cause crash on some models +# see https://github.com/huggingface/diffusers/issues/9704 for details +torch.backends.cuda.enable_cudnn_sdp(False) + # NVML utils # Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`, # all the related functions work on real physical device ids. From e130c40e4eba63ee8f04d493d83bca8c59b5ada5 Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Sun, 27 Oct 2024 17:30:03 +0000 Subject: [PATCH 107/222] Fix cache management in "Close inactive issues and PRs" actions workflow (#9734) --- .github/workflows/stale.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index 2418c61bdcf63..81e7c9b050760 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -10,6 +10,7 @@ jobs: permissions: issues: write pull-requests: write + actions: write runs-on: ubuntu-latest steps: - uses: actions/stale@28ca1036281a5e5922ead5184a1bbf96e5fc984e # v9.0.0 From 34a9941620d00879599a51609225452b705bae89 Mon Sep 17 00:00:00 2001 From: madt2709 <55849102+madt2709@users.noreply.github.com> Date: Sun, 27 Oct 2024 10:46:41 -0700 Subject: [PATCH 108/222] [Bugfix] Fix load config when using bools (#9533) --- tests/data/test_config.yaml | 2 ++ tests/test_utils.py | 6 +++++- vllm/engine/arg_utils.py | 14 +------------- vllm/utils.py | 35 +++++++++++++++++++++++++++-------- 4 files changed, 35 insertions(+), 22 deletions(-) diff --git a/tests/data/test_config.yaml b/tests/data/test_config.yaml index 42f4f6f7bb992..5090e8f357bb8 100644 --- a/tests/data/test_config.yaml +++ b/tests/data/test_config.yaml @@ -1,3 +1,5 @@ port: 12312 served_model_name: mymodel tensor_parallel_size: 2 +trust_remote_code: true +multi_step_stream_outputs: false diff --git a/tests/test_utils.py b/tests/test_utils.py index 0fed8e678fc76..a731b11eae81c 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -6,7 +6,7 @@ import pytest -from vllm.utils import (FlexibleArgumentParser, deprecate_kwargs, +from vllm.utils import (FlexibleArgumentParser, StoreBoolean, deprecate_kwargs, get_open_port, merge_async_iterators, supports_kw) from .utils import error_on_warning @@ -141,6 +141,8 @@ def parser_with_config(): parser.add_argument('--config', type=str) parser.add_argument('--port', type=int) parser.add_argument('--tensor-parallel-size', type=int) + parser.add_argument('--trust-remote-code', action='store_true') + parser.add_argument('--multi-step-stream-outputs', action=StoreBoolean) return parser @@ -214,6 +216,8 @@ def test_config_args(parser_with_config): args = parser_with_config.parse_args( ['serve', 'mymodel', '--config', './data/test_config.yaml']) assert args.tensor_parallel_size == 2 + assert args.trust_remote_code + assert not args.multi_step_stream_outputs def test_config_file(parser_with_config): diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index c49f475b9ee61..38687809a31f6 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -19,7 +19,7 @@ from vllm.transformers_utils.config import ( maybe_register_config_serialize_by_value) from vllm.transformers_utils.utils import check_gguf_file -from vllm.utils import FlexibleArgumentParser +from vllm.utils import FlexibleArgumentParser, StoreBoolean if TYPE_CHECKING: from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup @@ -1144,18 +1144,6 @@ def add_cli_args(parser: FlexibleArgumentParser, return parser -class StoreBoolean(argparse.Action): - - def __call__(self, parser, namespace, values, option_string=None): - if values.lower() == "true": - setattr(namespace, self.dest, True) - elif values.lower() == "false": - setattr(namespace, self.dest, False) - else: - raise ValueError(f"Invalid boolean value: {values}. " - "Expected 'true' or 'false'.") - - # These functions are used by sphinx to build the documentation def _engine_args_parser(): return EngineArgs.add_cli_args(FlexibleArgumentParser()) diff --git a/vllm/utils.py b/vllm/utils.py index 1f75de89d0cc2..d4f2c936ca9cc 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -1155,6 +1155,18 @@ def wrapper(*args: P.args, **kwargs: P.kwargs) -> None: return wrapper +class StoreBoolean(argparse.Action): + + def __call__(self, parser, namespace, values, option_string=None): + if values.lower() == "true": + setattr(namespace, self.dest, True) + elif values.lower() == "false": + setattr(namespace, self.dest, False) + else: + raise ValueError(f"Invalid boolean value: {values}. " + "Expected 'true' or 'false'.") + + class FlexibleArgumentParser(argparse.ArgumentParser): """ArgumentParser that allows both underscore and dash in names.""" @@ -1163,7 +1175,7 @@ def parse_args(self, args=None, namespace=None): args = sys.argv[1:] if '--config' in args: - args = FlexibleArgumentParser._pull_args_from_config(args) + args = self._pull_args_from_config(args) # Convert underscores to dashes and vice versa in argument names processed_args = [] @@ -1181,8 +1193,7 @@ def parse_args(self, args=None, namespace=None): return super().parse_args(processed_args, namespace) - @staticmethod - def _pull_args_from_config(args: List[str]) -> List[str]: + def _pull_args_from_config(self, args: List[str]) -> List[str]: """Method to pull arguments specified in the config file into the command-line args variable. @@ -1226,7 +1237,7 @@ def _pull_args_from_config(args: List[str]) -> List[str]: file_path = args[index + 1] - config_args = FlexibleArgumentParser._load_config_file(file_path) + config_args = self._load_config_file(file_path) # 0th index is for {serve,chat,complete} # followed by model_tag (only for serve) @@ -1247,8 +1258,7 @@ def _pull_args_from_config(args: List[str]) -> List[str]: return args - @staticmethod - def _load_config_file(file_path: str) -> List[str]: + def _load_config_file(self, file_path: str) -> List[str]: """Loads a yaml file and returns the key value pairs as a flattened list with argparse like pattern ```yaml @@ -1282,9 +1292,18 @@ def _load_config_file(file_path: str) -> List[str]: Make sure path is correct", file_path) raise ex + store_boolean_arguments = [ + action.dest for action in self._actions + if isinstance(action, StoreBoolean) + ] + for key, value in config.items(): - processed_args.append('--' + key) - processed_args.append(str(value)) + if isinstance(value, bool) and key not in store_boolean_arguments: + if value: + processed_args.append('--' + key) + else: + processed_args.append('--' + key) + processed_args.append(str(value)) return processed_args From 4e2d95e372ad5fbef7b27c66d527c37477c0c8bb Mon Sep 17 00:00:00 2001 From: wangshuai09 <391746016@qq.com> Date: Mon, 28 Oct 2024 12:07:00 +0800 Subject: [PATCH 109/222] [Hardware][ROCM] using current_platform.is_rocm (#9642) Signed-off-by: wangshuai09 <391746016@qq.com> --- .../test_basic_correctness.py | 4 +- tests/compile/utils.py | 4 +- tests/kernels/quant_utils.py | 17 +++-- tests/kernels/test_attention.py | 23 +++--- tests/kernels/test_attention_selector.py | 3 +- tests/kernels/test_blocksparse_attention.py | 7 +- tests/kernels/test_encoder_decoder_attn.py | 76 ++++++++++--------- tests/kernels/test_moe.py | 7 +- tests/lora/test_gemma.py | 5 +- tests/lora/test_quant_model.py | 4 +- .../vision_language/test_paligemma.py | 9 ++- .../vision_language/test_phi3v.py | 3 +- .../e2e/test_integration_dist_tp2.py | 4 +- tests/utils.py | 4 +- vllm/_custom_ops.py | 8 +- .../ops/blocksparse_attention/interface.py | 6 +- vllm/attention/selector.py | 4 +- vllm/config.py | 49 ++++++------ vllm/executor/ray_utils.py | 4 +- vllm/model_executor/custom_op.py | 4 +- .../compressed_tensors_moe.py | 5 +- .../schemes/compressed_tensors_w8a8_fp8.py | 6 +- .../layers/quantization/fbgemm_fp8.py | 3 +- .../model_executor/layers/quantization/fp8.py | 10 +-- .../layers/quantization/utils/w8a8_utils.py | 6 +- vllm/model_executor/models/exaone.py | 4 +- vllm/model_executor/models/granite.py | 4 +- vllm/model_executor/models/llama.py | 4 +- vllm/model_executor/models/registry.py | 4 +- vllm/model_executor/models/solar.py | 4 +- vllm/utils.py | 6 +- vllm/worker/model_runner.py | 9 ++- 32 files changed, 162 insertions(+), 148 deletions(-) diff --git a/tests/basic_correctness/test_basic_correctness.py b/tests/basic_correctness/test_basic_correctness.py index 3c2ca1bddd906..79647589d5204 100644 --- a/tests/basic_correctness/test_basic_correctness.py +++ b/tests/basic_correctness/test_basic_correctness.py @@ -11,7 +11,7 @@ import pytest from vllm import LLM -from vllm.utils import is_hip +from vllm.platforms import current_platform from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata from ..models.utils import check_outputs_equal @@ -51,7 +51,7 @@ def test_models( enforce_eager: bool, ) -> None: - if backend == "FLASHINFER" and is_hip(): + if backend == "FLASHINFER" and current_platform.is_rocm(): pytest.skip("Flashinfer does not support ROCm/HIP.") os.environ["VLLM_ATTENTION_BACKEND"] = backend diff --git a/tests/compile/utils.py b/tests/compile/utils.py index c69343b51ae02..64fc08e80de3b 100644 --- a/tests/compile/utils.py +++ b/tests/compile/utils.py @@ -5,7 +5,7 @@ from tests.quantization.utils import is_quant_method_supported from vllm import LLM, SamplingParams from vllm.compilation.levels import CompilationLevel -from vllm.utils import is_hip +from vllm.platforms import current_platform TEST_MODELS = [ ("facebook/opt-125m", {}), @@ -55,7 +55,7 @@ "quantization": "marlin" })) -if not is_hip() and is_quant_method_supported("awq"): +if not current_platform.is_rocm() and is_quant_method_supported("awq"): TEST_MODELS.append(("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", { "quantization": "AWQ" })) diff --git a/tests/kernels/quant_utils.py b/tests/kernels/quant_utils.py index 8f6a54ff5979c..f2358940fc7b8 100644 --- a/tests/kernels/quant_utils.py +++ b/tests/kernels/quant_utils.py @@ -2,12 +2,13 @@ import torch -from vllm.utils import is_hip +from vllm.platforms import current_platform # Using the default value (240.0) from pytorch will cause accuracy # issue on dynamic quantization models. Here use 224.0 for rocm. ROCM_FP8_MAX = 224.0 -FP8_DTYPE = torch.float8_e4m3fnuz if is_hip() else torch.float8_e4m3fn +FP8_DTYPE = torch.float8_e4m3fnuz if current_platform.is_rocm() \ + else torch.float8_e4m3fn def as_float32_tensor(x: Union[float, torch.tensor]) -> torch.tensor: @@ -24,8 +25,10 @@ def ref_dynamic_per_token_quant(x: torch.tensor, qtype_traits = torch.iinfo(quant_dtype) if quant_dtype == torch.int8 \ else torch.finfo(quant_dtype) - qtype_traits_max = ROCM_FP8_MAX if is_hip() else qtype_traits.max - qtype_traits_min = -ROCM_FP8_MAX if is_hip() else qtype_traits.min + qtype_traits_max = ROCM_FP8_MAX if current_platform.is_rocm() \ + else qtype_traits.max + qtype_traits_min = -ROCM_FP8_MAX if current_platform.is_rocm() \ + else qtype_traits.min qtype_max = as_float32_tensor(qtype_traits_max) s_1 = as_float32_tensor(1.0) s_512 = as_float32_tensor(512.0) @@ -66,8 +69,10 @@ def ref_dynamic_per_tensor_fp8_quant(x: torch.tensor) \ -> Tuple[torch.tensor, torch.tensor]: fp8_traits = torch.finfo(FP8_DTYPE) - fp8_traits_max = ROCM_FP8_MAX if is_hip() else fp8_traits.max - fp8_traits_min = -ROCM_FP8_MAX if is_hip() else fp8_traits.min + fp8_traits_max = ROCM_FP8_MAX if current_platform.is_rocm() \ + else fp8_traits.max + fp8_traits_min = -ROCM_FP8_MAX if current_platform.is_rocm() \ + else fp8_traits.min fp8_max = as_float32_tensor(fp8_traits_max) one = as_float32_tensor(1.0) diff --git a/tests/kernels/test_attention.py b/tests/kernels/test_attention.py index 52f1ecd176963..1604aa4d2d6e5 100644 --- a/tests/kernels/test_attention.py +++ b/tests/kernels/test_attention.py @@ -6,11 +6,12 @@ from tests.kernels.utils import opcheck from vllm import _custom_ops as ops -from vllm.utils import get_max_shared_memory_bytes, is_hip, seed_everything +from vllm.platforms import current_platform +from vllm.utils import get_max_shared_memory_bytes, seed_everything from .allclose_default import get_default_atol, get_default_rtol -if not is_hip(): +if not current_platform.is_rocm(): from xformers import ops as xops from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask @@ -23,8 +24,9 @@ NUM_BLOCKS = 4321 # Arbitrary values for testing PARTITION_SIZE = 512 # flshattF and tritonflashattF supported: {torch.float16, torch.bfloat16} -DTYPES = [torch.half, torch.bfloat16, torch.float - ] if not is_hip() else [torch.half, torch.bfloat16] +DTYPES = [ + torch.half, torch.bfloat16, torch.float +] if not current_platform.is_rocm() else [torch.half, torch.bfloat16] NUM_GEN_SEQS = [7] # Arbitrary values for testing NUM_PREFILL_SEQS = [3] # Arbitrary values for testing NUM_HEADS = [(40, 40), (64, 8)] # Arbitrary values for testing @@ -114,7 +116,8 @@ def ref_single_query_cached_kv_attention( @pytest.mark.parametrize( - "version", ["v1", "v2"] if not is_hip() else ["v1", "v2", "rocm"]) + "version", + ["v1", "v2"] if not current_platform.is_rocm() else ["v1", "v2", "rocm"]) @pytest.mark.parametrize("num_seqs", NUM_GEN_SEQS) @pytest.mark.parametrize("num_heads", NUM_HEADS) @pytest.mark.parametrize("head_size", HEAD_SIZES) @@ -317,8 +320,8 @@ def test_paged_attention( # NOTE(woosuk): Due to the kernel-level differences in the two # implementations, there is a small numerical difference in the two # outputs. Thus, we use a relaxed tolerance for the test. - atol = get_default_atol(output) if is_hip() else 1e-3 - rtol = get_default_rtol(output) if is_hip() else 1e-5 + atol = get_default_atol(output) if current_platform.is_rocm() else 1e-3 + rtol = get_default_rtol(output) if current_platform.is_rocm() else 1e-5 # NOTE(zhaoyang): FP8 KV Cache will introduce quantization error, # so we use a relaxed tolerance for the test. @@ -368,7 +371,7 @@ def ref_multi_query_kv_attention( @pytest.mark.parametrize("dtype", DTYPES) @pytest.mark.parametrize("seed", SEEDS) @pytest.mark.parametrize("device", CUDA_DEVICES) -@pytest.mark.skipif(is_hip(), +@pytest.mark.skipif(current_platform.is_rocm(), reason="Xformers backend is not supported on ROCm.") @torch.inference_mode() def test_multi_query_kv_attention( @@ -425,6 +428,6 @@ def test_multi_query_kv_attention( scale, dtype, ) - atol = get_default_atol(output) if is_hip() else 1e-3 - rtol = get_default_rtol(output) if is_hip() else 1e-5 + atol = get_default_atol(output) if current_platform.is_rocm() else 1e-3 + rtol = get_default_rtol(output) if current_platform.is_rocm() else 1e-5 torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol) diff --git a/tests/kernels/test_attention_selector.py b/tests/kernels/test_attention_selector.py index df3e770e260e0..3fe9ca0b0450f 100644 --- a/tests/kernels/test_attention_selector.py +++ b/tests/kernels/test_attention_selector.py @@ -25,7 +25,8 @@ def test_env(name: str, device: str, monkeypatch): False) assert backend.name == "TORCH_SDPA" elif device == "hip": - with patch("vllm.attention.selector.is_hip", return_value=True): + with patch("vllm.attention.selector.current_platform.is_rocm", + return_value=True): backend = which_attn_to_use(16, torch.float16, torch.float16, 16, False) assert backend.name == "ROCM_FLASH" diff --git a/tests/kernels/test_blocksparse_attention.py b/tests/kernels/test_blocksparse_attention.py index f3bd8f0524264..b65efb3abc230 100644 --- a/tests/kernels/test_blocksparse_attention.py +++ b/tests/kernels/test_blocksparse_attention.py @@ -7,7 +7,8 @@ from vllm import _custom_ops as ops from vllm.attention.ops.blocksparse_attention.interface import ( LocalStridedBlockSparseAttn) -from vllm.utils import get_max_shared_memory_bytes, is_hip, seed_everything +from vllm.platforms import current_platform +from vllm.utils import get_max_shared_memory_bytes, seed_everything from .allclose_default import get_default_atol, get_default_rtol @@ -316,8 +317,8 @@ def test_paged_attention( # NOTE(woosuk): Due to the kernel-level differences in the two # implementations, there is a small numerical difference in the two # outputs. Thus, we use a relaxed tolerance for the test. - atol = get_default_atol(output) if is_hip() else 1e-3 - rtol = get_default_rtol(output) if is_hip() else 1e-5 + atol = get_default_atol(output) if current_platform.is_rocm() else 1e-3 + rtol = get_default_rtol(output) if current_platform.is_rocm() else 1e-5 # NOTE(zhaoyang): FP8 KV Cache will introduce quantization error, # so we use a relaxed tolerance for the test. diff --git a/tests/kernels/test_encoder_decoder_attn.py b/tests/kernels/test_encoder_decoder_attn.py index 6b979d0558c46..bc99c5559d388 100644 --- a/tests/kernels/test_encoder_decoder_attn.py +++ b/tests/kernels/test_encoder_decoder_attn.py @@ -18,7 +18,7 @@ from vllm.attention.backends.utils import STR_NOT_IMPL_ENC_DEC_ROCM_HIP from vllm.attention.selector import (_Backend, global_force_attn_backend_context_manager) -from vllm.utils import is_hip +from vllm.platforms import current_platform # List of support backends for encoder/decoder models LIST_ENC_DEC_SUPPORTED_BACKENDS = [_Backend.XFORMERS] @@ -82,7 +82,7 @@ class TestResources(NamedTuple): will leverage attn_backend for the purpose of constructing backend-compatible attention metadata instances - + Attributes: * scale: 1/sqrt(d) scale factor for attn @@ -105,10 +105,10 @@ def _make_test_resources(test_pt: TestPoint, ) -> TestResources: Build key components for performing encoder/decoder attention test. Note that - (1) The Attention instance constructed here, automatically selects + (1) The Attention instance constructed here, automatically selects an attention backend class based on platform info & a set of canned heuristics, so - (2) The attention backend instance constructed here is thus *not + (2) The attention backend instance constructed here is thus *not the same backend instance* used by attn, but rather it is intended to be a *different instance* of the *same backend class*; therefore, @@ -156,7 +156,7 @@ def _encoder_attn_setup( ''' Set up test vectors & data structures for encoder attention test. - A triplet of synthetic query/key/value tensors are constructed. + A triplet of synthetic query/key/value tensors are constructed. Given this is an encoder attention test, the key & value sequences will have the same length as the corresponding queries. @@ -169,14 +169,14 @@ def _encoder_attn_setup( Arguments: * test_pt: TestPoint data structure; this function relies on the - following fields: batch_size, num_heads, head_size, + following fields: batch_size, num_heads, head_size, block_size, max_q_seq_len * test_rsrcs: TestResources data structure; this function relies on the scale field - + Returns: - + * PhaseTestParameters data structure comprising (1) packed query/key/value tensors, (2) the ideal output of attention computed using a naive implementation, and (3) KVCache field set to None @@ -265,7 +265,7 @@ def _decoder_attn_setup( Arguments: * test_pt: TestPoint data structure; this function relies on the - following fields: batch_size, num_heads, head_size, + following fields: batch_size, num_heads, head_size, block_size, max_q_seq_len * test_rsrcs: TestResources data structure; this function relies on the scale field @@ -275,14 +275,14 @@ def _decoder_attn_setup( * qkv: Unpacked (batch_size x padded_seq_len x num_heads x head_size) query/key/value tensors * Prefill-phase decoder self-attention PhaseTestParameters data structure, - including (1) packed (number_of_tokens x num_heads x head_size) + including (1) packed (number_of_tokens x num_heads x head_size) query/key/value tensors along with (2) ideal attention output - computed using a naive implementation, and (3) memory-mapping data + computed using a naive implementation, and (3) memory-mapping data structures appropriate for prefill phase. - * Decode-phase decoder self-attention PhaseTestParameters data structure, - including (1) packed (number_of_tokens x num_heads x head_size) - query/key/value tensors along with (2) ideal attention output - computed using a naive implementation, and (3) memory-mapping data + * Decode-phase decoder self-attention PhaseTestParameters data structure, + including (1) packed (number_of_tokens x num_heads x head_size) + query/key/value tensors along with (2) ideal attention output + computed using a naive implementation, and (3) memory-mapping data structures appropriate for decode phase. * max_block_idx: max physical address in decoder self-attention block-table (intended to be used as the base address for the encoder/ @@ -436,12 +436,12 @@ def _enc_dec_cross_attn_setup_reuses_query( This function also constructs the cross-attention KV cache memory mapping (slot mapping and block table), ensuring that the block table starts at - block_base_addr. + block_base_addr. Arguments: * decoder_qkv: pre-existing unpacked (batch_size x padded_seq_len x - num_heads x head_size) decoder self-attention inputs; + num_heads x head_size) decoder self-attention inputs; this function relies on the query and q_seq_lens fields * encoder_test_params: PhaseTestParameters data structure which was @@ -452,7 +452,7 @@ def _enc_dec_cross_attn_setup_reuses_query( self-attention; all fields including KV cache required * test_pt: TestPoint data structure; this function relies on the - following fields: batch_size, num_heads, head_size, + following fields: batch_size, num_heads, head_size, block_size, max_q_seq_len * test_rsrcs: TestResources data structure; this function relies on the scale field @@ -460,16 +460,16 @@ def _enc_dec_cross_attn_setup_reuses_query( Returns: - * Prefill-phase encoder/decoder cross-attention PhaseTestParameters data - structure, including (1) packed + * Prefill-phase encoder/decoder cross-attention PhaseTestParameters data + structure, including (1) packed (number_of_tokens x num_heads x head_size) query/key/value tensors - along with (2) ideal attention output computed using a + along with (2) ideal attention output computed using a naive implementation, and (3) memory-mapping data structures appropriate for prefill phase. - * Decode-phase encoder/decoder cross-attention PhaseTestParameters data + * Decode-phase encoder/decoder cross-attention PhaseTestParameters data structure, including (1) packed (number_of_tokens x num_heads x head_size) query/key/value tensors - along with (2) ideal attention output computed using a + along with (2) ideal attention output computed using a naive implementation, and (3) memory-mapping data structures appropriate for decode phase. ''' @@ -596,7 +596,7 @@ def _run_encoder_attention_test( ''' Run encoder attention. - attn.forward() is passed attn_type=AttentionType.ENCODER in order + attn.forward() is passed attn_type=AttentionType.ENCODER in order to configure the kernel invocation for encoder attention Requires attn_metadata.num_decode_tokens == 0 @@ -607,7 +607,7 @@ def _run_encoder_attention_test( * attn: Attention wrapper instance * encoder_test_params: encoder PhaseTestParameters data structure; this function relies on the packed - (number_of_tokens x num_heads x head_size) + (number_of_tokens x num_heads x head_size) query/key/value fields * attn_metadata: attention metadata for encoder/decoder-self attention @@ -646,7 +646,7 @@ def _run_decoder_self_attention_test( and attn (Attention wrapper instance) fields * decoder_test_params: decoder PhaseTestParameters data structure; this function relies on the packed - (number_of_tokens x num_heads x head_size) + (number_of_tokens x num_heads x head_size) query/key/value fields * attn_metadata: attention metadata for decoder-self attention (contains KV cache memory-mapping) @@ -694,11 +694,11 @@ def _run_encoder_decoder_cross_attention_test( and attn (Attention wrapper instance) fields * decoder_test_params: decoder PhaseTestParameters data structure; this function relies on the packed - (number_of_tokens x num_heads x head_size) + (number_of_tokens x num_heads x head_size) query field * cross_test_params: encoder/decoder PhaseTestParameters data structure; this function relies on the packed - (number_of_tokens x num_heads x head_size) + (number_of_tokens x num_heads x head_size) key/value fields * attn_metadata: attention metadata for encoder/decoder-self attention @@ -726,7 +726,8 @@ def _run_encoder_decoder_cross_attention_test( attn_type=attn_type) -@pytest.mark.skipif(is_hip(), reason=STR_NOT_IMPL_ENC_DEC_ROCM_HIP) +@pytest.mark.skipif(current_platform.is_rocm(), + reason=STR_NOT_IMPL_ENC_DEC_ROCM_HIP) @pytest.mark.parametrize("num_heads", NUM_HEADS) @pytest.mark.parametrize("head_size", HEAD_SIZES) @pytest.mark.parametrize("attn_backend", LIST_ENC_DEC_SUPPORTED_BACKENDS) @@ -755,7 +756,8 @@ def test_encoder_only( No KV cache is required for encoder-only attention. Note on ROCm/HIP: currently encoder/decoder models are not supported on - AMD GPUs, therefore this test simply is skipped if is_hip(). + AMD GPUs, therefore this test simply is skipped if + current_platform.is_rocm(). This test globally forces an override of the usual backend auto-selection process, forcing the specific backend-under-test @@ -811,7 +813,8 @@ def test_encoder_only( assert_actual_matches_ideal(enc_test_params, enc_pckd_act_out) -@pytest.mark.skipif(is_hip(), reason=STR_NOT_IMPL_ENC_DEC_ROCM_HIP) +@pytest.mark.skipif(current_platform.is_rocm(), + reason=STR_NOT_IMPL_ENC_DEC_ROCM_HIP) @pytest.mark.parametrize("num_heads", NUM_HEADS) @pytest.mark.parametrize("head_size", HEAD_SIZES) @pytest.mark.parametrize("attn_backend", LIST_ENC_DEC_SUPPORTED_BACKENDS) @@ -837,14 +840,14 @@ def test_e2e_enc_dec_attn( attributes for prefill-phase, and (2) an analogous attention metadata structure but for decode-phase * Test attention steps in the following order - + * Encoder attention * Prefill self-attention * Prefill cross-attention * Decode self-attention * Decode cross-attention - * Besides being reflective of realistic use-cases, this order would - exacerbate any accidental overlap in the self-/cross-attention + * Besides being reflective of realistic use-cases, this order would + exacerbate any accidental overlap in the self-/cross-attention block tables, which one hopes to avoid @@ -864,10 +867,11 @@ def test_e2e_enc_dec_attn( to be utilized. Note on ROCm/HIP: currently encoder/decoder models are not supported on - AMD GPUs, therefore this test simply is skipped if is_hip(). + AMD GPUs, therefore this test simply is skipped if + current_platform.is_rocm(). Note on metadata: there is a single attention metadata structure shared by - all prefill-phase attention operations (encoder, decoder, enc/dec cross), + all prefill-phase attention operations (encoder, decoder, enc/dec cross), and a single one shared by all decode-phase attention operations (decoder & enc/dec cross.) This is intended to reflect the behavior of EncoderDecoderModelRunner, which constructs a single attention metadata diff --git a/tests/kernels/test_moe.py b/tests/kernels/test_moe.py index c0053071258ea..4bfc089c82179 100644 --- a/tests/kernels/test_moe.py +++ b/tests/kernels/test_moe.py @@ -18,8 +18,9 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils_test import ( marlin_quantize) from vllm.model_executor.models.mixtral import MixtralMoE +from vllm.platforms import current_platform from vllm.scalar_type import scalar_types -from vllm.utils import is_hip, seed_everything +from vllm.utils import seed_everything @pytest.mark.parametrize("m", [1024 * 128, 512, 222, 33, 1]) @@ -103,7 +104,7 @@ def test_mixtral_moe(dtype: torch.dtype): @pytest.mark.parametrize("act_order", [True, False]) @pytest.mark.parametrize("num_bits", [4, 8]) @pytest.mark.parametrize("is_k_full", [True, False]) -@pytest.mark.skipif(is_hip(), reason="Skip for rocm") +@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm") def test_fused_marlin_moe( m: int, n: int, @@ -256,7 +257,7 @@ def test_fused_marlin_moe( @pytest.mark.parametrize("act_order", [True, False]) @pytest.mark.parametrize("num_bits", [4, 8]) @pytest.mark.parametrize("is_k_full", [True, False]) -@pytest.mark.skipif(is_hip(), reason="Skip for rocm") +@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm") def test_single_marlin_moe_multiply( m: int, n: int, diff --git a/tests/lora/test_gemma.py b/tests/lora/test_gemma.py index f7c1d4f041c12..15ec66b0f5502 100644 --- a/tests/lora/test_gemma.py +++ b/tests/lora/test_gemma.py @@ -4,7 +4,7 @@ import vllm from vllm.lora.request import LoRARequest -from vllm.utils import is_hip +from vllm.platforms import current_platform MODEL_PATH = "google/gemma-7b" @@ -31,7 +31,8 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]: return generated_texts -@pytest.mark.xfail(is_hip(), reason="There can be output mismatch on ROCm") +@pytest.mark.xfail(current_platform.is_rocm(), + reason="There can be output mismatch on ROCm") def test_gemma_lora(gemma_lora_files): llm = vllm.LLM(MODEL_PATH, max_model_len=1024, diff --git a/tests/lora/test_quant_model.py b/tests/lora/test_quant_model.py index d004c65929418..5432fa4ad0d3a 100644 --- a/tests/lora/test_quant_model.py +++ b/tests/lora/test_quant_model.py @@ -8,7 +8,7 @@ import vllm from vllm.distributed import cleanup_dist_env_and_memory from vllm.lora.request import LoRARequest -from vllm.utils import is_hip +from vllm.platforms import current_platform @dataclass @@ -19,7 +19,7 @@ class ModelWithQuantization: MODELS: List[ModelWithQuantization] #AWQ quantization is currently not supported in ROCm. -if is_hip(): +if current_platform.is_rocm(): MODELS = [ ModelWithQuantization( model_path="TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", diff --git a/tests/models/decoder_only/vision_language/test_paligemma.py b/tests/models/decoder_only/vision_language/test_paligemma.py index a3ca0845e5ff8..69189ba2f25cb 100644 --- a/tests/models/decoder_only/vision_language/test_paligemma.py +++ b/tests/models/decoder_only/vision_language/test_paligemma.py @@ -6,8 +6,9 @@ BatchEncoding) from vllm.multimodal.utils import rescale_image_size +from vllm.platforms import current_platform from vllm.sequence import SampleLogprobs -from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, is_hip +from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE from ....conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets from ...utils import check_logprobs_close @@ -24,7 +25,7 @@ # ROCm Triton FA can run into compilation issues with these models due to, # excessive use of shared memory. Use other backends in the meantime. # FIXME (mattwong, gshtrasb, hongxiayan) -if is_hip(): +if current_platform.is_rocm(): os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0" @@ -70,7 +71,7 @@ def run_test( All the image fixtures for the test are from IMAGE_ASSETS. For huggingface runner, we provide the PIL images as input. - For vllm runner, we provide MultiModalDataDict objects + For vllm runner, we provide MultiModalDataDict objects and corresponding MultiModalConfig as input. Note, the text input is also adjusted to abide by vllm contract. The text output is sanitized to be able to compare with hf. @@ -151,7 +152,7 @@ def process(hf_inputs: BatchEncoding): pytest.param( "float", marks=pytest.mark.skipif( - is_hip(), + current_platform.is_rocm(), reason= "ROCm FA does not yet fully support 32-bit precision on PaliGemma") ), "half" diff --git a/tests/models/decoder_only/vision_language/test_phi3v.py b/tests/models/decoder_only/vision_language/test_phi3v.py index dfe10629f1c66..1840b4bb8574c 100644 --- a/tests/models/decoder_only/vision_language/test_phi3v.py +++ b/tests/models/decoder_only/vision_language/test_phi3v.py @@ -12,7 +12,6 @@ from vllm.multimodal.utils import rescale_image_size from vllm.platforms import current_platform from vllm.sequence import SampleLogprobs -from vllm.utils import is_hip from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner, _ImageAssets) @@ -56,7 +55,7 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str, # ROCm Triton FA can run into shared memory issues with these models, # use other backends in the meantime # FIXME (mattwong, gshtrasb, hongxiayan) -if is_hip(): +if current_platform.is_rocm(): os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0" diff --git a/tests/spec_decode/e2e/test_integration_dist_tp2.py b/tests/spec_decode/e2e/test_integration_dist_tp2.py index b829d1a5be784..25562ca85adf4 100644 --- a/tests/spec_decode/e2e/test_integration_dist_tp2.py +++ b/tests/spec_decode/e2e/test_integration_dist_tp2.py @@ -5,7 +5,7 @@ import pytest import torch -from vllm.utils import is_hip +from vllm.platforms import current_platform from .conftest import run_equality_correctness_test_tp @@ -51,7 +51,7 @@ def test_target_model_tp_gt_1(common_llm_kwargs, per_test_common_llm_kwargs, batch_size: int, output_len: int, seed: int): """Verify greedy equality when tensor parallelism is used. """ - if is_hip(): + if current_platform.is_rocm(): pytest.skip("hip is not well-supported yet") run_equality_correctness_test_tp("JackFram/llama-68m", common_llm_kwargs, diff --git a/tests/utils.py b/tests/utils.py index e983104e3cb0c..0c61891cfefec 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -26,7 +26,7 @@ from vllm.platforms import current_platform from vllm.transformers_utils.tokenizer import get_tokenizer from vllm.utils import (FlexibleArgumentParser, GB_bytes, - cuda_device_count_stateless, get_open_port, is_hip) + cuda_device_count_stateless, get_open_port) if current_platform.is_rocm(): from amdsmi import (amdsmi_get_gpu_vram_usage, @@ -487,7 +487,7 @@ def wait_for_gpu_memory_to_clear(devices: List[int], output: Dict[int, str] = {} output_raw: Dict[int, float] = {} for device in devices: - if is_hip(): + if current_platform.is_rocm(): dev_handle = amdsmi_get_processor_handles()[device] mem_info = amdsmi_get_gpu_vram_usage(dev_handle) gb_used = mem_info["vram_used"] / 2**10 diff --git a/vllm/_custom_ops.py b/vllm/_custom_ops.py index f57414bd5197e..46a2fb8bc80a2 100644 --- a/vllm/_custom_ops.py +++ b/vllm/_custom_ops.py @@ -659,11 +659,11 @@ def scaled_fp8_quant( Args: input: The input tensor to be quantized to FP8 scale: Optional scaling factor for the FP8 quantization - scale_ub: Optional upper bound for scaling factor in dynamic + scale_ub: Optional upper bound for scaling factor in dynamic per token case num_token_padding: If specified, pad the first dimension of the output to at least this value. - use_per_token_if_dynamic: Whether to do per_tensor or per_token + use_per_token_if_dynamic: Whether to do per_tensor or per_token in the dynamic quantization case. Returns: @@ -674,8 +674,8 @@ def scaled_fp8_quant( assert (input.ndim == 2) shape: Union[Tuple[int, int], torch.Size] = input.shape # For rocm, the output fp8 dtype is torch.float_e3m3fnuz - out_dtype: torch.dtype = torch.float8_e4m3fnuz if vllm.utils.is_hip() \ - else torch.float8_e4m3fn + out_dtype: torch.dtype = torch.float8_e4m3fnuz \ + if current_platform.is_rocm() else torch.float8_e4m3fn if num_token_padding: shape = (max(num_token_padding, input.shape[0]), shape[1]) output = torch.empty(shape, device=input.device, dtype=out_dtype) diff --git a/vllm/attention/ops/blocksparse_attention/interface.py b/vllm/attention/ops/blocksparse_attention/interface.py index e4dc576d27932..a98eb431ac7fc 100644 --- a/vllm/attention/ops/blocksparse_attention/interface.py +++ b/vllm/attention/ops/blocksparse_attention/interface.py @@ -3,7 +3,6 @@ import torch from vllm.platforms import current_platform -from vllm.utils import is_hip from .utils import (dense_to_crow_col, get_head_sliding_step, get_sparse_attn_mask) @@ -32,8 +31,9 @@ def __init__( ): super().__init__() if use_spda is None: - use_spda = is_hip() or current_platform.is_cpu() or not \ - IS_COMPUTE_8_OR_ABOVE + use_spda = current_platform.is_rocm() or \ + current_platform.is_cpu() or not \ + IS_COMPUTE_8_OR_ABOVE device = device or (torch.cuda.current_device() if current_platform.is_cuda_alike() else "cpu") device = torch.device(device) diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index 10d4509b38279..376b3136f0fb8 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -10,7 +10,7 @@ from vllm.attention.backends.abstract import AttentionBackend from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import STR_BACKEND_ENV_VAR, is_hip +from vllm.utils import STR_BACKEND_ENV_VAR logger = init_logger(__name__) @@ -208,7 +208,7 @@ def which_attn_to_use( logger.info("Cannot use %s backend on TPU.", selected_backend) return _Backend.PALLAS - if is_hip(): + if current_platform.is_rocm(): # AMD GPUs. selected_backend = (_Backend.ROCM_FLASH if selected_backend == _Backend.FLASH_ATTN else selected_backend) diff --git a/vllm/config.py b/vllm/config.py index a1fba98233b80..99a82c8f1b40b 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -17,7 +17,7 @@ get_hf_image_processor_config, get_hf_text_config) from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory, - is_hip, print_warning_once) + print_warning_once) if TYPE_CHECKING: from ray.util.placement_group import PlacementGroup @@ -43,7 +43,7 @@ class ModelConfig: Args: model: Name or path of the huggingface model to use. - It is also used as the content for `model_name` tag in metrics + It is also used as the content for `model_name` tag in metrics output when `served_model_name` is not specified. task: The task to use the model for. Each vLLM instance only supports one task, even if the same model can be used for multiple tasks. @@ -99,15 +99,15 @@ class ModelConfig: skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. served_model_name: The model name used in metrics tag `model_name`, - matches the model name exposed via the APIs. If multiple model - names provided, the first name will be used. If not specified, + matches the model name exposed via the APIs. If multiple model + names provided, the first name will be used. If not specified, the model name will be the same as `model`. - limit_mm_per_prompt: Maximum number of data instances per modality + limit_mm_per_prompt: Maximum number of data instances per modality per prompt. Only applicable for multimodal models. - override_neuron_config: Initialize non default neuron config or - override default neuron config that are specific to Neuron devices, - this argument will be used to configure the neuron config that - can not be gathered from the vllm arguments. + override_neuron_config: Initialize non default neuron config or + override default neuron config that are specific to Neuron devices, + this argument will be used to configure the neuron config that + can not be gathered from the vllm arguments. config_format: The config format which shall be loaded. Defaults to 'auto' which defaults to 'hf'. mm_processor_kwargs: Arguments to be forwarded to the model's processor @@ -350,7 +350,7 @@ def _verify_quantization(self) -> None: raise ValueError( f"Unknown quantization method: {self.quantization}. Must " f"be one of {supported_quantization}.") - if is_hip( + if current_platform.is_rocm( ) and self.quantization not in rocm_supported_quantization: raise ValueError( f"{self.quantization} quantization is currently not " @@ -365,7 +365,7 @@ def _verify_quantization(self) -> None: "%s quantization is not fully " "optimized yet. The speed can be slower than " "non-quantized models.", self.quantization) - if (self.quantization == "awq" and is_hip() + if (self.quantization == "awq" and current_platform.is_rocm() and not envs.VLLM_USE_TRITON_AWQ): logger.warning( "Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ" @@ -385,7 +385,7 @@ def _verify_cuda_graph(self) -> None: def _verify_bnb_config(self) -> None: """ - The current version of bitsandbytes (0.44.0) with 8-bit models does not + The current version of bitsandbytes (0.44.0) with 8-bit models does not yet support CUDA graph. """ is_bitsandbytes = self.quantization == "bitsandbytes" @@ -810,7 +810,7 @@ class LoadConfig: fast weight loading. "bitsandbytes" will load nf4 type weights. ignore_patterns: The list of patterns to ignore when loading the model. - Default to "original/**/*" to avoid repeated loading of llama's + Default to "original/**/*" to avoid repeated loading of llama's checkpoints. """ @@ -843,7 +843,8 @@ def _verify_load_format(self) -> None: self.load_format = LoadFormat(load_format) rocm_not_supported_load_format: List[str] = [] - if is_hip() and load_format in rocm_not_supported_load_format: + if current_platform.is_rocm( + ) and load_format in rocm_not_supported_load_format: rocm_supported_load_format = [ f for f in LoadFormat.__members__ if (f not in rocm_not_supported_load_format) @@ -967,7 +968,7 @@ def _verify_args(self) -> None: if self.use_ray: from vllm.executor import ray_utils ray_utils.assert_ray_available() - if is_hip(): + if current_platform.is_rocm(): self.disable_custom_all_reduce = True logger.info( "Disabled the custom all-reduce kernel because it is not " @@ -996,7 +997,7 @@ class SchedulerConfig: prompt latency) before scheduling next prompt. enable_chunked_prefill: If True, prefill requests can be chunked based on the remaining max_num_batched_tokens. - preemption_mode: Whether to perform preemption by swapping or + preemption_mode: Whether to perform preemption by swapping or recomputation. If not specified, we determine the mode as follows: We use recomputation by default since it incurs lower overhead than swapping. However, when the sequence group has multiple sequences @@ -1215,7 +1216,7 @@ def maybe_create_spec_config( typical_acceptance_sampler_posterior_threshold (Optional[float]): A threshold value that sets a lower bound on the posterior probability of a token in the target model for it to be - accepted. This threshold is used only when we use the + accepted. This threshold is used only when we use the TypicalAcceptanceSampler for token acceptance. typical_acceptance_sampler_posterior_alpha (Optional[float]): A scaling factor for the entropy-based threshold in the @@ -1225,7 +1226,7 @@ def maybe_create_spec_config( If set to False, token log probabilities are returned according to the log probability settings in SamplingParams. If not specified, it defaults to True. - + Returns: Optional["SpeculativeConfig"]: An instance of SpeculativeConfig if the necessary conditions are met, else None. @@ -1470,13 +1471,13 @@ def __init__( typical_acceptance_sampler_posterior_threshold (Optional[float]): A threshold value that sets a lower bound on the posterior probability of a token in the target model for it to be - accepted. This threshold is used only when we use the + accepted. This threshold is used only when we use the TypicalAcceptanceSampler for token acceptance. typical_acceptance_sampler_posterior_alpha (Optional[float]): A scaling factor for the entropy-based threshold in the TypicalAcceptanceSampler. disable_logprobs: If set to True, token log probabilities will not - be returned even if requested by sampling parameters. This + be returned even if requested by sampling parameters. This reduces latency by skipping logprob calculation in proposal sampling, target sampling, and after accepted tokens are determined. If set to False, log probabilities will be @@ -1843,10 +1844,10 @@ def get_min_sliding_window( def get_served_model_name(model: str, served_model_name: Optional[Union[str, List[str]]]): """ - If the input is a non-empty list, the first model_name in - `served_model_name` is taken. - If the input is a non-empty string, it is used directly. - For cases where the input is either an empty string or an + If the input is a non-empty list, the first model_name in + `served_model_name` is taken. + If the input is a non-empty string, it is used directly. + For cases where the input is either an empty string or an empty list, the fallback is to use `self.model`. """ if not served_model_name: diff --git a/vllm/executor/ray_utils.py b/vllm/executor/ray_utils.py index 0af7b3386d895..aa546ebada473 100644 --- a/vllm/executor/ray_utils.py +++ b/vllm/executor/ray_utils.py @@ -10,7 +10,7 @@ from vllm.logger import init_logger from vllm.platforms import current_platform from vllm.sequence import ExecuteModelRequest, IntermediateTensors -from vllm.utils import get_ip, is_hip +from vllm.utils import get_ip from vllm.worker.worker_base import WorkerWrapperBase logger = init_logger(__name__) @@ -231,7 +231,7 @@ def initialize_ray_cluster( assert_ray_available() # Connect to a ray cluster. - if is_hip() or current_platform.is_xpu(): + if current_platform.is_rocm() or current_platform.is_xpu(): ray.init(address=ray_address, ignore_reinit_error=True, num_gpus=parallel_config.world_size) diff --git a/vllm/model_executor/custom_op.py b/vllm/model_executor/custom_op.py index 71eed6eb68d78..83910339f3c9f 100644 --- a/vllm/model_executor/custom_op.py +++ b/vllm/model_executor/custom_op.py @@ -7,7 +7,7 @@ from vllm.compilation.levels import CompilationLevel from vllm.logger import init_logger from vllm.platforms import current_platform -from vllm.utils import is_hip, print_warning_once +from vllm.utils import print_warning_once logger = init_logger(__name__) @@ -72,7 +72,7 @@ def dispatch_forward(self): if not enabled: return self.forward_native - if is_hip(): + if current_platform.is_rocm(): return self.forward_hip elif current_platform.is_cpu(): return self.forward_cpu diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py index c21aaa40ff2cc..be3d3985a74ad 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py @@ -14,7 +14,8 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize) from vllm.model_executor.utils import set_weight_attrs -from vllm.utils import is_hip, print_warning_once +from vllm.platforms import current_platform +from vllm.utils import print_warning_once class GPTQMarlinState(Enum): @@ -150,7 +151,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None: layer.w2_input_scale.max(), requires_grad=False) # If rocm, normalize the weights and scales to e4m3fnuz - if is_hip(): + if current_platform.is_rocm(): # Normalize the weights and scales w13_weight, w13_weight_scale, w13_input_scale = \ normalize_e4m3fn_to_e4m3fnuz( diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index 7270b302ef965..73cc8ce0d2a4b 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -12,7 +12,7 @@ from vllm.model_executor.parameter import (ChannelQuantScaleParameter, ModelWeightParameter, PerTensorScaleParameter) -from vllm.utils import is_hip +from vllm.platforms import current_platform __all__ = ["CompressedTensorsW8A8Fp8"] @@ -40,7 +40,7 @@ def process_weights_after_loading(self, layer) -> None: logical_widths=layer.logical_widths, ) - if is_hip(): + if current_platform.is_rocm(): weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz( weight=weight, weight_scale=max_w_scale, @@ -56,7 +56,7 @@ def process_weights_after_loading(self, layer) -> None: elif self.strategy == QuantizationStrategy.CHANNEL: weight = layer.weight - if is_hip(): + if current_platform.is_rocm(): weight, weight_scale, input_scale = \ normalize_e4m3fn_to_e4m3fnuz( weight=weight, diff --git a/vllm/model_executor/layers/quantization/fbgemm_fp8.py b/vllm/model_executor/layers/quantization/fbgemm_fp8.py index f26907176ad1a..825d01d1b3551 100644 --- a/vllm/model_executor/layers/quantization/fbgemm_fp8.py +++ b/vllm/model_executor/layers/quantization/fbgemm_fp8.py @@ -19,7 +19,6 @@ from vllm.model_executor.parameter import (ChannelQuantScaleParameter, ModelWeightParameter) from vllm.platforms import current_platform -from vllm.utils import is_hip logger = init_logger(__name__) @@ -127,7 +126,7 @@ def process_weights_after_loading(self, layer: Module) -> None: weight = layer.weight - if is_hip(): + if current_platform.is_rocm(): weight, weight_scale, input_scale = \ normalize_e4m3fn_to_e4m3fnuz( weight=weight, diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index b5feb55db0e74..d34579b7099bb 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -26,7 +26,7 @@ PerTensorScaleParameter) from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform -from vllm.utils import is_hip, print_warning_once +from vllm.utils import print_warning_once ACTIVATION_SCHEMES = ["static", "dynamic"] @@ -123,7 +123,7 @@ def __init__(self, quant_config: Fp8Config): self.use_marlin = (not current_platform.has_device_capability(89) or envs.VLLM_TEST_FORCE_FP8_MARLIN) # Disable marlin for rocm - if is_hip(): + if current_platform.is_rocm(): self.use_marlin = False def create_weights( @@ -226,7 +226,7 @@ def process_weights_after_loading(self, layer: Module) -> None: weight_scale = layer.weight_scale # If rocm, use float8_e4m3fnuz. - if is_hip(): + if current_platform.is_rocm(): weight, weight_scale, input_scale = \ normalize_e4m3fn_to_e4m3fnuz( weight=weight, @@ -372,7 +372,7 @@ def process_weights_after_loading(self, layer: Module) -> None: if not self.quant_config.is_checkpoint_fp8_serialized: # If rocm, use float8_e4m3fnuz as dtype fp8_dtype = torch.float8_e4m3fnuz \ - if is_hip() else torch.float8_e4m3fn + if current_platform.is_rocm() else torch.float8_e4m3fn w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype) w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype) @@ -420,7 +420,7 @@ def process_weights_after_loading(self, layer: Module) -> None: layer.w2_input_scale = torch.nn.Parameter( layer.w2_input_scale.max(), requires_grad=False) # If rocm, normalize the weights and scales to e4m3fnuz - if is_hip(): + if current_platform.is_rocm(): # Normalize the weights and scales w13_weight, w13_weight_scale, w13_input_scale = \ normalize_e4m3fn_to_e4m3fnuz( diff --git a/vllm/model_executor/layers/quantization/utils/w8a8_utils.py b/vllm/model_executor/layers/quantization/utils/w8a8_utils.py index 411af922149fd..1879d2855d93d 100644 --- a/vllm/model_executor/layers/quantization/utils/w8a8_utils.py +++ b/vllm/model_executor/layers/quantization/utils/w8a8_utils.py @@ -4,16 +4,16 @@ from vllm import _custom_ops as ops from vllm.platforms import current_platform -from vllm.utils import is_hip # Input scaling factors are no longer optional in _scaled_mm starting # from pytorch 2.5. Allocating a dummy tensor to pass as input_scale -TORCH_DEVICE_IDENTITY = torch.ones(1).cuda() if is_hip() else None +TORCH_DEVICE_IDENTITY = torch.ones(1).cuda() \ + if current_platform.is_rocm() else None def cutlass_fp8_supported() -> bool: # cutlass is not supported on Rocm - if is_hip(): + if current_platform.is_rocm(): return False capability_tuple = current_platform.get_device_capability() diff --git a/vllm/model_executor/models/exaone.py b/vllm/model_executor/models/exaone.py index 4126ceb7117d4..22f194c776b69 100644 --- a/vllm/model_executor/models/exaone.py +++ b/vllm/model_executor/models/exaone.py @@ -49,9 +49,9 @@ from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name) from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs.exaone import ExaoneConfig -from vllm.utils import is_hip from .interfaces import SupportsLoRA, SupportsPP from .utils import (PPMissingLayer, is_pp_missing_parameter, @@ -595,7 +595,7 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None: if not isinstance(self.transformer.h[layer_idx], nn.Identity): layer_self_attn = self.transformer.h[layer_idx].attn - if is_hip(): + if current_platform.is_rocm(): # The scaling factor convention we are assuming is # quantized_value * scaling_factor ~= true_value # which is consistent with the practice of setting diff --git a/vllm/model_executor/models/granite.py b/vllm/model_executor/models/granite.py index 5a397ed8ff6a0..c968817747754 100644 --- a/vllm/model_executor/models/granite.py +++ b/vllm/model_executor/models/granite.py @@ -49,8 +49,8 @@ from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name) from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors -from vllm.utils import is_hip from .interfaces import SupportsLoRA, SupportsPP from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers @@ -534,7 +534,7 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None: if not isinstance(self.model.layers[layer_idx], nn.Identity): layer_self_attn = self.model.layers[layer_idx].self_attn - if is_hip(): + if current_platform.is_rocm(): # The scaling factor convention we are assuming is # quantized_value * scaling_factor ~= true_value # which is consistent with the practice of setting diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py index c346e3e808e3f..b0ca1fe006239 100644 --- a/vllm/model_executor/models/llama.py +++ b/vllm/model_executor/models/llama.py @@ -50,8 +50,8 @@ default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name) from vllm.model_executor.pooling_metadata import PoolingMetadata from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors, PoolerOutput -from vllm.utils import is_hip from .interfaces import SupportsLoRA, SupportsPP from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter, @@ -423,7 +423,7 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None: if not isinstance(self.layers[layer_idx], nn.Identity): layer_self_attn = self.layers[layer_idx].self_attn - if is_hip(): + if current_platform.is_rocm(): # The scaling factor convention we are assuming is # quantized_value * scaling_factor ~= true_value # which is consistent with the practice of setting diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index f6713ab0898f0..595a9256f958e 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -12,7 +12,7 @@ import torch.nn as nn from vllm.logger import init_logger -from vllm.utils import is_hip +from vllm.platforms import current_platform from .interfaces import (has_inner_state, is_attention_free, supports_multimodal, supports_pp) @@ -247,7 +247,7 @@ def _try_load_model_cls( model_arch: str, model: _BaseRegisteredModel, ) -> Optional[Type[nn.Module]]: - if is_hip(): + if current_platform.is_rocm(): if model_arch in _ROCM_UNSUPPORTED_MODELS: raise ValueError(f"Model architecture '{model_arch}' is not " "supported by ROCm for now.") diff --git a/vllm/model_executor/models/solar.py b/vllm/model_executor/models/solar.py index 5a3dd3c02b85b..e3e7ccb5cf179 100644 --- a/vllm/model_executor/models/solar.py +++ b/vllm/model_executor/models/solar.py @@ -49,8 +49,8 @@ from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name) from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors -from vllm.utils import is_hip from .interfaces import SupportsLoRA, SupportsPP from .utils import (PPMissingLayer, is_pp_missing_parameter, @@ -558,7 +558,7 @@ def load_kv_cache_scales(self, quantization_param_path: str) -> None: if not isinstance(self.model.layers[layer_idx], nn.Identity): layer_self_attn = self.model.layers[layer_idx].self_attn - if is_hip(): + if current_platform.is_rocm(): # The scaling factor convention we are assuming is # quantized_value * scaling_factor ~= true_value # which is consistent with the practice of setting diff --git a/vllm/utils.py b/vllm/utils.py index d4f2c936ca9cc..c3f9a6bdd8b80 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -314,10 +314,6 @@ def reset(self): self._index = 0 -def is_hip() -> bool: - return torch.version.hip is not None - - @lru_cache(maxsize=None) def get_max_shared_memory_bytes(gpu: int = 0) -> int: """Returns the maximum shared memory per thread block in bytes.""" @@ -1098,7 +1094,7 @@ def _cuda_device_count_stateless( if not torch.cuda._is_compiled(): return 0 - if is_hip(): + if current_platform.is_rocm(): # ROCm uses amdsmi instead of nvml for stateless device count # This requires a sufficiently modern version of Torch 2.4.0 raw_count = torch.cuda._device_count_amdsmi() if (hasattr( diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index 4a287e3741d0f..233a9e664d845 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -41,6 +41,7 @@ from vllm.model_executor.models.utils import set_cpu_offload_max_bytes from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs, MultiModalInputs, MultiModalRegistry) +from vllm.platforms import current_platform from vllm.prompt_adapter.layers import PromptAdapterMapping from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.prompt_adapter.worker_manager import ( @@ -49,7 +50,7 @@ from vllm.sequence import IntermediateTensors, SequenceGroupMetadata from vllm.transformers_utils.config import uses_mrope from vllm.utils import (DeviceMemoryProfiler, PyObjectCache, async_tensor_h2d, - flatten_2d_lists, is_hip, is_pin_memory_available, + flatten_2d_lists, is_pin_memory_available, supports_dynamo, weak_ref_tensor) from vllm.worker.model_runner_base import ( ModelRunnerBase, ModelRunnerInputBase, ModelRunnerInputBuilderBase, @@ -737,13 +738,13 @@ def _get_cuda_graph_pad_size(self, family of functions. Args: - num_seqs (int): Number of sequences scheduled to run. + num_seqs (int): Number of sequences scheduled to run. max_decode_seq_len (int): Greatest of all the decode sequence lengths. Used only in checking the viablility of using CUDA graphs. max_encoder_seq_len (int, optional): Greatest of all the encode sequence lengths. Defaults to 0. Used only in checking the - viability of using CUDA graphs. + viability of using CUDA graphs. Returns: int: Returns the determined number of padding sequences. If CUDA graphs is not viable, returns -1. @@ -1103,7 +1104,7 @@ def load_model(self) -> None: self.prompt_adapter_manager.create_prompt_adapter_manager( self.model)) - if self.kv_cache_dtype == "fp8" and is_hip(): + if self.kv_cache_dtype == "fp8" and current_platform.is_rocm(): # Currently only ROCm accepts kv-cache scaling factors # via quantization_param_path and this will be deprecated # in the future. From 32176fee733b76b295346870d717d44cb7102944 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Sun, 27 Oct 2024 21:58:04 -0700 Subject: [PATCH 110/222] [torch.compile] support moe models (#9632) Signed-off-by: youkaichao --- benchmarks/kernels/benchmark_moe.py | 33 +++--- tests/compile/test_basic_correctness.py | 4 +- tests/kernels/test_awq_marlin.py | 21 ++-- tests/kernels/test_moe.py | 7 +- .../layers/fused_moe/__init__.py | 28 ++++- .../layers/fused_moe/fused_marlin_moe.py | 51 +++++++-- .../layers/fused_moe/fused_moe.py | 100 ++++++++++++++++-- vllm/model_executor/layers/fused_moe/layer.py | 29 +++-- .../layers/quantization/awq_marlin.py | 7 +- .../compressed_tensors_moe.py | 7 +- .../layers/quantization/gptq_marlin.py | 6 +- vllm/model_executor/models/granitemoe.py | 2 + 12 files changed, 217 insertions(+), 78 deletions(-) diff --git a/benchmarks/kernels/benchmark_moe.py b/benchmarks/kernels/benchmark_moe.py index c2ad98b7e2656..4f88e8e6eb1a6 100644 --- a/benchmarks/kernels/benchmark_moe.py +++ b/benchmarks/kernels/benchmark_moe.py @@ -88,22 +88,23 @@ def prepare(i: int): input_gating.copy_(gating_output[i]) def run(): - fused_moe( - x, - w1, - w2, - input_gating, - topk, - renormalize=True, - inplace=True, - override_config=config, - use_fp8_w8a8=use_fp8_w8a8, - use_int8_w8a16=use_int8_w8a16, - w1_scale=w1_scale, - w2_scale=w2_scale, - a1_scale=a1_scale, - a2_scale=a2_scale, - ) + from vllm.model_executor.layers.fused_moe import override_config + with override_config(config): + fused_moe( + x, + w1, + w2, + input_gating, + topk, + renormalize=True, + inplace=True, + use_fp8_w8a8=use_fp8_w8a8, + use_int8_w8a16=use_int8_w8a16, + w1_scale=w1_scale, + w2_scale=w2_scale, + a1_scale=a1_scale, + a2_scale=a2_scale, + ) # JIT compilation & warmup run() diff --git a/tests/compile/test_basic_correctness.py b/tests/compile/test_basic_correctness.py index 77c56d91d0a8b..6aa27b24b4a6e 100644 --- a/tests/compile/test_basic_correctness.py +++ b/tests/compile/test_basic_correctness.py @@ -13,11 +13,11 @@ @pytest.mark.parametrize( "model, model_args, pp_size, tp_size, attn_backend, method, fullgraph", [ - ("meta-llama/Llama-3.2-1B", [], 2, 2, "FLASH_ATTN", "generate", True), + ("meta-llama/Llama-3.2-1B", [], 2, 2, "FLASHINFER", "generate", True), ("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", ["--quantization", "compressed-tensors" ], 1, 1, "FLASH_ATTN", "generate", True), - ("google/gemma-2-2b-it", [], 1, 2, "FLASHINFER", "generate", True), + ("ibm/PowerMoE-3b", [], 1, 2, "FLASH_ATTN", "generate", True), # TODO: add multi-modality test for llava ("llava-hf/llava-1.5-7b-hf", [], 2, 1, "FLASHINFER", "generate", False) ]) diff --git a/tests/kernels/test_awq_marlin.py b/tests/kernels/test_awq_marlin.py index 0f0a2b24563fd..59917dd2c58ad 100644 --- a/tests/kernels/test_awq_marlin.py +++ b/tests/kernels/test_awq_marlin.py @@ -5,11 +5,10 @@ import pytest import torch +import vllm.model_executor.layers.fused_moe # noqa from tests.kernels.utils import (compute_max_diff, stack_and_dev, torch_moe, torch_moe_single) from vllm import _custom_ops as ops -from vllm.model_executor.layers.fused_moe.fused_marlin_moe import ( - fused_marlin_moe, single_marlin_moe) from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk from vllm.model_executor.layers.quantization.utils.marlin_utils_test import ( awq_marlin_quantize) @@ -81,7 +80,7 @@ def test_fused_marlin_moe_awq( score = torch.randn((m, e), device="cuda", dtype=dtype) topk_weights, topk_ids = fused_topk(a, score, topk, False) - marlin_output = fused_marlin_moe( + marlin_output = torch.ops.vllm.fused_marlin_moe( a, qweight1, qweight2, @@ -150,14 +149,14 @@ def test_single_marlin_moe_multiply_awq( score = torch.randn((m, e), device="cuda", dtype=dtype) - marlin_output = single_marlin_moe(a, - qweight, - scales, - score, - topk, - renormalize=False, - w_zeros=zp, - num_bits=num_bits) + marlin_output = torch.ops.vllm.single_marlin_moe(a, + qweight, + scales, + score, + topk, + renormalize=False, + w_zeros=zp, + num_bits=num_bits) torch_output = torch_moe_single(a, w_ref.transpose(1, 2), score, topk) diff --git a/tests/kernels/test_moe.py b/tests/kernels/test_moe.py index 4bfc089c82179..70906ab2187bc 100644 --- a/tests/kernels/test_moe.py +++ b/tests/kernels/test_moe.py @@ -7,12 +7,11 @@ from transformers import MixtralConfig from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock +import vllm.model_executor.layers.fused_moe # noqa from tests.kernels.utils import (compute_max_diff, opcheck, stack_and_dev, torch_moe, torch_moe_single) from vllm import _custom_ops as ops from vllm.model_executor.layers.fused_moe import fused_moe -from vllm.model_executor.layers.fused_moe.fused_marlin_moe import ( - fused_marlin_moe, single_marlin_moe) from vllm.model_executor.layers.fused_moe.fused_moe import ( fused_topk, moe_align_block_size) from vllm.model_executor.layers.quantization.utils.marlin_utils_test import ( @@ -193,7 +192,7 @@ def test_fused_marlin_moe( topk, renormalize=False, ) - marlin_output = fused_marlin_moe( + marlin_output = torch.ops.vllm.fused_marlin_moe( a, qweight1, qweight2, @@ -309,7 +308,7 @@ def test_single_marlin_moe_multiply( sort_indices = stack_and_dev(sort_indices_l) score = torch.randn((m, e), device="cuda", dtype=dtype) - marlin_output = single_marlin_moe( + marlin_output = torch.ops.vllm.single_marlin_moe( a, qweight, scales, diff --git a/vllm/model_executor/layers/fused_moe/__init__.py b/vllm/model_executor/layers/fused_moe/__init__.py index e9b5703ca28be..c4223d12600ac 100644 --- a/vllm/model_executor/layers/fused_moe/__init__.py +++ b/vllm/model_executor/layers/fused_moe/__init__.py @@ -1,23 +1,43 @@ +from contextlib import contextmanager +from typing import Any, Dict, Optional + from vllm.model_executor.layers.fused_moe.layer import ( FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported) from vllm.triton_utils import HAS_TRITON +_config: Optional[Dict[str, Any]] = None + + +@contextmanager +def override_config(config): + global _config + old_config = _config + _config = config + yield + _config = old_config + + +def get_config() -> Optional[Dict[str, Any]]: + return _config + + __all__ = [ "FusedMoE", "FusedMoEMethodBase", "FusedMoeWeightScaleSupported", + "override_config", + "get_config", ] if HAS_TRITON: - from vllm.model_executor.layers.fused_moe.fused_marlin_moe import ( - fused_marlin_moe, single_marlin_moe) + # import to register the custom ops + import vllm.model_executor.layers.fused_moe.fused_marlin_moe # noqa + import vllm.model_executor.layers.fused_moe.fused_moe # noqa from vllm.model_executor.layers.fused_moe.fused_moe import ( fused_experts, fused_moe, fused_topk, get_config_file_name, grouped_topk) __all__ += [ - "fused_marlin_moe", - "single_marlin_moe", "fused_moe", "fused_topk", "fused_experts", diff --git a/vllm/model_executor/layers/fused_moe/fused_marlin_moe.py b/vllm/model_executor/layers/fused_moe/fused_marlin_moe.py index 5ae40a2af5a2b..93019d0d0abb6 100644 --- a/vllm/model_executor/layers/fused_moe/fused_marlin_moe.py +++ b/vllm/model_executor/layers/fused_moe/fused_marlin_moe.py @@ -1,6 +1,6 @@ """Fused MoE utilities for GPTQ.""" import functools -from typing import Any, Dict, Optional +from typing import Optional import torch @@ -18,6 +18,7 @@ def get_scalar_type(num_bits: int, has_zp: bool): return scalar_types.uint4b8 if num_bits == 4 else scalar_types.uint8b128 +@torch.library.custom_op("vllm::single_marlin_moe", mutates_args=[]) def single_marlin_moe( hidden_states: torch.Tensor, w: torch.Tensor, @@ -28,7 +29,6 @@ def single_marlin_moe( g_idx: Optional[torch.Tensor] = None, sort_indices: Optional[torch.Tensor] = None, w_zeros: Optional[torch.Tensor] = None, - override_config: Optional[Dict[str, Any]] = None, num_bits: int = 8, is_k_full: bool = True, ) -> torch.Tensor: @@ -49,8 +49,6 @@ def single_marlin_moe( - topk (int): The number of top-k experts to select. - renormalize (bool): If True, renormalize the top-k weights to sum to 1. - w_zeros (Optional[torch.Tensor]): Optional zero points to be used for w. - - override_config (Optional[Dict[str, Any]]): Optional override - for the kernel configuration. - num_bits (bool): The number of bits in expert weights quantization. Returns: @@ -79,7 +77,6 @@ def single_marlin_moe( w.shape, topk_ids.shape[1], None, - override_config=override_config, is_marlin=True) config = get_config_func(M) @@ -122,6 +119,24 @@ def single_marlin_moe( return torch.sum(intermediate_cache.view(*intermediate_cache.shape), dim=1) +@single_marlin_moe.register_fake +def _( + hidden_states: torch.Tensor, + w: torch.Tensor, + scales: torch.Tensor, + gating_output: torch.Tensor, + topk: int, + renormalize: bool, + g_idx: Optional[torch.Tensor] = None, + sort_indices: Optional[torch.Tensor] = None, + w_zeros: Optional[torch.Tensor] = None, + num_bits: int = 8, + is_k_full: bool = True, +) -> torch.Tensor: + return torch.empty_like(hidden_states) + + +@torch.library.custom_op("vllm::fused_marlin_moe", mutates_args=[]) def fused_marlin_moe( hidden_states: torch.Tensor, w1: torch.Tensor, @@ -137,7 +152,6 @@ def fused_marlin_moe( sort_indices2: Optional[torch.Tensor] = None, w1_zeros: Optional[torch.Tensor] = None, w2_zeros: Optional[torch.Tensor] = None, - override_config: Optional[Dict[str, Any]] = None, num_bits: int = 8, is_k_full: bool = True, ) -> torch.Tensor: @@ -161,8 +175,6 @@ def fused_marlin_moe( permutation. - topk_weights (torch.Tensor): Top-k weights. - topk_ids (torch.Tensor): Indices of topk-k elements. - - override_config (Optional[Dict[str, Any]]): Optional override - for the kernel configuration. - w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1. - w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2. - num_bits (bool): The number of bits in expert weights quantization. @@ -209,7 +221,6 @@ def fused_marlin_moe( w2.shape, topk_ids.shape[1], None, - override_config=override_config, is_marlin=True, ) config = get_config_func(M) @@ -311,3 +322,25 @@ def fused_marlin_moe( return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape), dim=1) + + +@fused_marlin_moe.register_fake +def _( + hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + w1_scale: torch.Tensor, + w2_scale: torch.Tensor, + gating_output: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + g_idx1: Optional[torch.Tensor] = None, + g_idx2: Optional[torch.Tensor] = None, + sort_indices1: Optional[torch.Tensor] = None, + sort_indices2: Optional[torch.Tensor] = None, + w1_zeros: Optional[torch.Tensor] = None, + w2_zeros: Optional[torch.Tensor] = None, + num_bits: int = 8, + is_k_full: bool = True, +) -> torch.Tensor: + return torch.empty_like(hidden_states) diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py index 90a4209b5bce5..1cf5c2253ca0b 100644 --- a/vllm/model_executor/layers/fused_moe/fused_moe.py +++ b/vllm/model_executor/layers/fused_moe/fused_moe.py @@ -358,9 +358,10 @@ def try_get_optimal_moe_config( top_k: int, dtype: Optional[str], M: int, - override_config: Optional[Dict[str, Any]] = None, is_marlin: bool = False, ): + from vllm.model_executor.layers.fused_moe import get_config + override_config = get_config() if override_config: config = override_config else: @@ -465,19 +466,109 @@ def get_config_dtype_str(dtype: torch.dtype, return None +@torch.library.custom_op("vllm::inplace_fused_experts", + mutates_args=["hidden_states"]) +def inplace_fused_experts(hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + use_fp8_w8a8: bool = False, + use_int8_w8a16: bool = False, + w1_scale: Optional[torch.Tensor] = None, + w2_scale: Optional[torch.Tensor] = None, + a1_scale: Optional[torch.Tensor] = None, + a2_scale: Optional[torch.Tensor] = None) -> None: + fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids, True, + use_fp8_w8a8, use_int8_w8a16, w1_scale, w2_scale, + a1_scale, a2_scale) + + +@inplace_fused_experts.register_fake +def _(hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + use_fp8_w8a8: bool = False, + use_int8_w8a16: bool = False, + w1_scale: Optional[torch.Tensor] = None, + w2_scale: Optional[torch.Tensor] = None, + a1_scale: Optional[torch.Tensor] = None, + a2_scale: Optional[torch.Tensor] = None) -> None: + pass + + +@torch.library.custom_op("vllm::outplace_fused_experts", mutates_args=[]) +def outplace_fused_experts( + hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + use_fp8_w8a8: bool = False, + use_int8_w8a16: bool = False, + w1_scale: Optional[torch.Tensor] = None, + w2_scale: Optional[torch.Tensor] = None, + a1_scale: Optional[torch.Tensor] = None, + a2_scale: Optional[torch.Tensor] = None) -> torch.Tensor: + return fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids, + False, use_fp8_w8a8, use_int8_w8a16, w1_scale, + w2_scale, a1_scale, a2_scale) + + +@outplace_fused_experts.register_fake +def _(hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + use_fp8_w8a8: bool = False, + use_int8_w8a16: bool = False, + w1_scale: Optional[torch.Tensor] = None, + w2_scale: Optional[torch.Tensor] = None, + a1_scale: Optional[torch.Tensor] = None, + a2_scale: Optional[torch.Tensor] = None) -> torch.Tensor: + return torch.empty_like(hidden_states) + + def fused_experts(hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, inplace: bool = False, - override_config: Optional[Dict[str, Any]] = None, use_fp8_w8a8: bool = False, use_int8_w8a16: bool = False, w1_scale: Optional[torch.Tensor] = None, w2_scale: Optional[torch.Tensor] = None, a1_scale: Optional[torch.Tensor] = None, a2_scale: Optional[torch.Tensor] = None): + if inplace: + torch.ops.vllm.inplace_fused_experts(hidden_states, w1, w2, + topk_weights, topk_ids, + use_fp8_w8a8, use_int8_w8a16, + w1_scale, w2_scale, a1_scale, + a2_scale) + return hidden_states + else: + return torch.ops.vllm.outplace_fused_experts( + hidden_states, w1, w2, topk_weights, topk_ids, use_fp8_w8a8, + use_int8_w8a16, w1_scale, w2_scale, a1_scale, a2_scale) + + +def fused_experts_impl(hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + inplace: bool = False, + use_fp8_w8a8: bool = False, + use_int8_w8a16: bool = False, + w1_scale: Optional[torch.Tensor] = None, + w2_scale: Optional[torch.Tensor] = None, + a1_scale: Optional[torch.Tensor] = None, + a2_scale: Optional[torch.Tensor] = None): # Check constraints. assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch" assert topk_weights.shape == topk_ids.shape, "topk shape mismatch" @@ -504,7 +595,6 @@ def fused_experts(hidden_states: torch.Tensor, w2.shape, topk_ids.shape[1], config_dtype, - override_config=override_config, ) config = get_config_func(M) @@ -602,7 +692,6 @@ def fused_moe( topk: int, renormalize: bool, inplace: bool = False, - override_config: Optional[Dict[str, Any]] = None, use_grouped_topk: bool = False, num_expert_group: Optional[int] = None, topk_group: Optional[int] = None, @@ -628,8 +717,6 @@ def fused_moe( - renormalize (bool): If True, renormalize the top-k weights to sum to 1. - inplace (bool): If True, perform the operation in-place. Defaults to False. - - override_config (Optional[Dict[str, Any]]): Optional override - for the kernel configuration. - num_expert_group: Optional[int]: additional parameter for grouped_topk - topk_group: Optional[int]: additional parameter for grouped_topk - use_grouped_topk: If True, use grouped_topk instead of fused_topk @@ -667,7 +754,6 @@ def fused_moe( topk_weights, topk_ids, inplace=inplace, - override_config=override_config, use_fp8_w8a8=use_fp8_w8a8, use_int8_w8a16=use_int8_w8a16, w1_scale=w1_scale, diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py index 8dd36620e3fa0..5570771ac917b 100644 --- a/vllm/model_executor/layers/fused_moe/layer.py +++ b/vllm/model_executor/layers/fused_moe/layer.py @@ -12,7 +12,16 @@ from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase) from vllm.model_executor.utils import set_weight_attrs - +from vllm.platforms import current_platform + +if current_platform.is_cuda_alike(): + from .fused_moe import fused_experts +else: + fused_experts = None # type: ignore +if current_platform.is_tpu(): + from .moe_pallas import fused_moe as fused_moe_pallas +else: + fused_moe_pallas = None # type: ignore logger = init_logger(__name__) @@ -96,9 +105,6 @@ def forward_cuda( num_expert_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None ) -> torch.Tensor: - from vllm.model_executor.layers.fused_moe.fused_moe import ( - fused_experts) - topk_weights, topk_ids = FusedMoE.select_experts( hidden_states=x, router_logits=router_logits, @@ -132,17 +138,18 @@ def forward_tpu( num_expert_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None ) -> torch.Tensor: - from vllm.model_executor.layers.fused_moe.moe_pallas import fused_moe assert not use_grouped_topk assert num_expert_group is None assert topk_group is None assert custom_routing_function is None - return fused_moe(hidden_states=x, - w1=layer.w13_weight, - w2=layer.w2_weight, - topk=top_k, - gating_output=router_logits, - renormalize=renormalize) + return fused_moe_pallas(hidden_states=x, + w1=layer.w13_weight, + w2=layer.w2_weight, + topk=top_k, + gating_output=router_logits, + renormalize=renormalize) + + forward_native = forward_cuda class FusedMoE(torch.nn.Module): diff --git a/vllm/model_executor/layers/quantization/awq_marlin.py b/vllm/model_executor/layers/quantization/awq_marlin.py index b3d93b285769c..95ec12daeeeb5 100644 --- a/vllm/model_executor/layers/quantization/awq_marlin.py +++ b/vllm/model_executor/layers/quantization/awq_marlin.py @@ -3,6 +3,7 @@ import torch from torch.nn import Parameter +import vllm.model_executor.layers.fused_moe # noqa from vllm import _custom_ops as ops from vllm.logger import init_logger from vllm.model_executor.layers.fused_moe.layer import ( @@ -435,10 +436,6 @@ def apply( topk_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None, ) -> torch.Tensor: - - from vllm.model_executor.layers.fused_moe.fused_marlin_moe import ( - fused_marlin_moe) - topk_weights, topk_ids = FusedMoE.select_experts( hidden_states=x, router_logits=router_logits, @@ -449,7 +446,7 @@ def apply( num_expert_group=num_expert_group, custom_routing_function=custom_routing_function) - return fused_marlin_moe( + return torch.ops.vllm.fused_marlin_moe( x, layer.w13_qweight, layer.w2_qweight, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py index be3d3985a74ad..dad04017d3212 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py @@ -6,6 +6,7 @@ from compressed_tensors import CompressionFormat from compressed_tensors.quantization import QuantizationStrategy +import vllm.model_executor.layers.fused_moe # noqa from vllm import _custom_ops as ops from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported) @@ -481,10 +482,6 @@ def apply( topk_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None, ) -> torch.Tensor: - - from vllm.model_executor.layers.fused_moe.fused_marlin_moe import ( - fused_marlin_moe) - topk_weights, topk_ids = FusedMoE.select_experts( hidden_states=x, router_logits=router_logits, @@ -495,7 +492,7 @@ def apply( num_expert_group=num_expert_group, custom_routing_function=custom_routing_function) - return fused_marlin_moe( + return torch.ops.vllm.fused_marlin_moe( x, layer.w13_weight_packed, layer.w2_weight_packed, diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index e77191796bd7e..b97dd108d6785 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -2,6 +2,7 @@ import torch +import vllm.model_executor.layers.fused_moe # noqa from vllm import _custom_ops as ops from vllm.logger import init_logger from vllm.model_executor.layers.fused_moe.layer import ( @@ -536,9 +537,6 @@ def apply( topk_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None, ) -> torch.Tensor: - from vllm.model_executor.layers.fused_moe.fused_marlin_moe import ( - fused_marlin_moe) - # The input must currently be float16 orig_dtype = x.dtype x = x.half() @@ -553,7 +551,7 @@ def apply( num_expert_group=num_expert_group, custom_routing_function=None) - return fused_marlin_moe( + return torch.ops.vllm.fused_marlin_moe( x, layer.w13_qweight, layer.w2_qweight, diff --git a/vllm/model_executor/models/granitemoe.py b/vllm/model_executor/models/granitemoe.py index fd0d4c89a28fe..5307bb21adb96 100644 --- a/vllm/model_executor/models/granitemoe.py +++ b/vllm/model_executor/models/granitemoe.py @@ -28,6 +28,7 @@ from transformers.models.granitemoe import GraniteMoeConfig from vllm.attention import Attention, AttentionMetadata +from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.model_executor.layers.fused_moe import FusedMoE @@ -244,6 +245,7 @@ def forward( return hidden_states +@support_torch_compile class GraniteMoeModel(nn.Module): def __init__( From feb92fbe4ab6803527df48658a87ebd00b99969f Mon Sep 17 00:00:00 2001 From: Robert Shaw <114415538+robertgshaw2-neuralmagic@users.noreply.github.com> Date: Mon, 28 Oct 2024 02:59:37 -0400 Subject: [PATCH 111/222] Fix beam search eos (#9627) --- vllm/engine/protocol.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/vllm/engine/protocol.py b/vllm/engine/protocol.py index 5c504e0f0217d..b00dd136d4a47 100644 --- a/vllm/engine/protocol.py +++ b/vllm/engine/protocol.py @@ -140,7 +140,12 @@ async def beam_search( best_beams = sorted_completed[:beam_width] for beam in best_beams: - beam.text = tokenizer.decode(beam.tokens[tokenized_length:]) + if (beam.tokens[-1] == tokenizer.eos_token_id and not ignore_eos): + # Skip the eos token in the text. + tokens = beam.tokens[tokenized_length:-1] + else: + tokens = beam.tokens[tokenized_length:] + beam.text = tokenizer.decode(tokens) beam_search_output = RequestOutput( request_id=request_id, From 2adb4409e0359039135b5aa6501994da12aa5a26 Mon Sep 17 00:00:00 2001 From: Yan Ma Date: Mon, 28 Oct 2024 15:13:03 +0800 Subject: [PATCH 112/222] [Bugfix] Fix ray instance detect issue (#9439) --- vllm/executor/ray_utils.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/vllm/executor/ray_utils.py b/vllm/executor/ray_utils.py index aa546ebada473..993d279890820 100644 --- a/vllm/executor/ray_utils.py +++ b/vllm/executor/ray_utils.py @@ -232,9 +232,16 @@ def initialize_ray_cluster( # Connect to a ray cluster. if current_platform.is_rocm() or current_platform.is_xpu(): - ray.init(address=ray_address, - ignore_reinit_error=True, - num_gpus=parallel_config.world_size) + # Try to connect existing ray instance and create a new one if not found + try: + ray.init("auto") + except ConnectionError: + logger.warning( + "No existing RAY instance detected. " + "A new instance will be launched with current node resources.") + ray.init(address=ray_address, + ignore_reinit_error=True, + num_gpus=parallel_config.world_size) else: ray.init(address=ray_address, ignore_reinit_error=True) From 8b0e4f2ad7b5a3ddd6d61acbe8ceb50b4ea3c309 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Mon, 28 Oct 2024 12:38:09 -0400 Subject: [PATCH 113/222] [CI/Build] Adopt Mergify for auto-labeling PRs (#9259) Signed-off-by: Russell Bryant --- .github/mergify.yml | 57 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 .github/mergify.yml diff --git a/.github/mergify.yml b/.github/mergify.yml new file mode 100644 index 0000000000000..2a3dee7c662d1 --- /dev/null +++ b/.github/mergify.yml @@ -0,0 +1,57 @@ +pull_request_rules: +- name: label-documentation + description: Automatically apply documentation label + conditions: + - or: + - files~=^[^/]+\.md$ + - files~=^docs/ + actions: + label: + add: + - documentation + +- name: label-ci-build + description: Automatically apply ci/build label + conditions: + - files~=^\.github/ + - files~=\.buildkite/ + - files~=^cmake/ + - files=CMakeLists.txt + - files~=^Dockerfile + - files~=^requirements.*\.txt + - files=setup.py + actions: + label: + add: + - ci/build + +- name: label-frontend + description: Automatically apply frontend label + conditions: + - files~=^vllm/entrypoints/ + actions: + label: + add: + - frontend + +- name: ping author on conflicts and add 'needs-rebase' label + conditions: + - conflict + - -closed + actions: + label: + add: + - needs-rebase + comment: + message: | + This pull request has merge conflicts that must be resolved before it can be + merged. @{{author}} please rebase it. https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork + +- name: remove 'needs-rebase' label when conflict is resolved + conditions: + - -conflict + - -closed + actions: + label: + remove: + - needs-rebase From 5f8d8075f957d5376b2f1cc451e35a2a757e95a5 Mon Sep 17 00:00:00 2001 From: litianjian <45817262+litianjian@users.noreply.github.com> Date: Tue, 29 Oct 2024 02:04:10 +0800 Subject: [PATCH 114/222] [Model][VLM] Add multi-video support for LLaVA-Onevision (#8905) Co-authored-by: litianjian Co-authored-by: DarkLight1337 --- .../vision_language/test_llava_onevision.py | 173 +++++------------- vllm/model_executor/models/clip.py | 4 +- vllm/model_executor/models/llava_onevision.py | 94 +++++++--- vllm/model_executor/models/siglip.py | 4 +- vllm/multimodal/video.py | 10 +- 5 files changed, 123 insertions(+), 162 deletions(-) diff --git a/tests/models/decoder_only/vision_language/test_llava_onevision.py b/tests/models/decoder_only/vision_language/test_llava_onevision.py index 367f25f446279..1616fd299b9aa 100644 --- a/tests/models/decoder_only/vision_language/test_llava_onevision.py +++ b/tests/models/decoder_only/vision_language/test_llava_onevision.py @@ -1,4 +1,4 @@ -from typing import List, Optional, Tuple, Type, overload +from typing import List, Optional, Tuple, Type import pytest from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer, @@ -9,9 +9,8 @@ from vllm.sequence import SampleLogprobs from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE -from ....conftest import (VIDEO_ASSETS, HfRunner, PromptImageInput, VllmRunner, - _VideoAssets) -from ....utils import large_gpu_test +from ....conftest import (VIDEO_ASSETS, HfRunner, PromptImageInput, + PromptVideoInput, VllmRunner) from ...utils import check_logprobs_close # Video test @@ -20,7 +19,7 @@ "<|im_start|>user\n