From 3b61cb450d899dc423feb264c297d4d18d701678 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Mon, 9 Dec 2024 12:38:46 -0800 Subject: [PATCH 01/79] [V1] Further reduce CPU overheads in flash-attn (#10989) Signed-off-by: Woosuk Kwon --- csrc/cache_kernels.cu | 14 ++++++++++++-- vllm/v1/attention/backends/flash_attn.py | 21 ++++++++++++++++----- 2 files changed, 28 insertions(+), 7 deletions(-) diff --git a/csrc/cache_kernels.cu b/csrc/cache_kernels.cu index 1be806bbfa43c..8a95279f9a25a 100644 --- a/csrc/cache_kernels.cu +++ b/csrc/cache_kernels.cu @@ -307,10 +307,20 @@ void reshape_and_cache_flash( torch::Tensor& key_cache, // [num_blocks, block_size, num_heads, head_size] torch::Tensor& value_cache, // [num_blocks, block_size, num_heads, head_size] - torch::Tensor& slot_mapping, // [num_tokens] + torch::Tensor& slot_mapping, // [num_tokens] or [num_actual_tokens] const std::string& kv_cache_dtype, const double k_scale, const double v_scale) { - int num_tokens = key.size(0); + // NOTE(woosuk): In vLLM V1, key.size(0) can be different from + // slot_mapping.size(0) because of padding for CUDA graphs. + // In vLLM V0, key.size(0) is always equal to slot_mapping.size(0) because + // both include padding. + // In vLLM V1, however, key.size(0) can be larger than slot_mapping.size(0) + // since key includes padding for CUDA graphs, while slot_mapping does not. + // In this case, slot_mapping.size(0) represents the actual number of tokens + // before padding. + // For compatibility with both cases, we use slot_mapping.size(0) as the + // number of tokens. + int num_tokens = slot_mapping.size(0); int num_heads = key.size(1); int head_size = key.size(2); int block_size = key_cache.size(1); diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py index d37989055c2e5..251a103e60f06 100644 --- a/vllm/v1/attention/backends/flash_attn.py +++ b/vllm/v1/attention/backends/flash_attn.py @@ -138,14 +138,25 @@ def forward( # Profiling run. return output - num_actual_tokens = attn_metadata.num_actual_tokens + # IMPORTANT! + # NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in + # eager-mode PyTorch. Thus, we need to be careful about any CPU overhead + # in this method. For example, `view` and `slice` (or `[:n]`) operations + # are surprisingly slow even in the case they do not invoke any GPU ops. + # Minimize the PyTorch ops in this method as much as possible. + # Whenever making a change in this method, please benchmark the + # performance to make sure it does not introduce any overhead. + num_actual_tokens = attn_metadata.num_actual_tokens # Reshape the input keys and values and store them in the cache. - key_cache = kv_cache[0] - value_cache = kv_cache[1] + # NOTE(woosuk): Here, key and value are padded while slot_mapping is + # not padded. However, we don't need to do key[:num_actual_tokens] and + # value[:num_actual_tokens] because the reshape_and_cache_flash op uses + # the slot_mapping's shape to determine the number of actual tokens. + key_cache, value_cache = kv_cache.unbind(0) torch.ops._C_cache_ops.reshape_and_cache_flash( - key[:num_actual_tokens], - value[:num_actual_tokens], + key, + value, key_cache, value_cache, attn_metadata.slot_mapping, From ca871491edb0fba11fe9aa94300bd8d282fa29e1 Mon Sep 17 00:00:00 2001 From: Jee Jee Li Date: Tue, 10 Dec 2024 04:54:44 +0800 Subject: [PATCH 02/79] [Misc][LoRA] Abstract PunicaWrapper (#10955) Signed-off-by: Jee Jee Li --- tests/lora/test_layers.py | 49 +- vllm/lora/layers.py | 7 +- vllm/lora/models.py | 8 +- vllm/lora/punica.py | 725 -------------------- vllm/lora/punica_wrapper/__init__.py | 7 + vllm/lora/punica_wrapper/punica_base.py | 480 +++++++++++++ vllm/lora/punica_wrapper/punica_gpu.py | 358 ++++++++++ vllm/lora/punica_wrapper/punica_selector.py | 14 + vllm/lora/punica_wrapper/utils.py | 159 +++++ 9 files changed, 1058 insertions(+), 749 deletions(-) delete mode 100644 vllm/lora/punica.py create mode 100644 vllm/lora/punica_wrapper/__init__.py create mode 100644 vllm/lora/punica_wrapper/punica_base.py create mode 100644 vllm/lora/punica_wrapper/punica_gpu.py create mode 100644 vllm/lora/punica_wrapper/punica_selector.py create mode 100644 vllm/lora/punica_wrapper/utils.py diff --git a/tests/lora/test_layers.py b/tests/lora/test_layers.py index a113e3f7abc1e..fb8c0b2a7ba26 100644 --- a/tests/lora/test_layers.py +++ b/tests/lora/test_layers.py @@ -28,7 +28,7 @@ # yapf: enable from vllm.lora.models import (LongContextLoRAContext, LoRALayerWeights, PackedLoRALayerWeights) -from vllm.lora.punica import PunicaWrapper +from vllm.lora.punica_wrapper import get_punica_wrapper from vllm.model_executor.layers.linear import (ColumnParallelLinear, MergedColumnParallelLinear, QKVParallelLinear, @@ -48,11 +48,12 @@ torch.float32: (5e-3, 5e-3), torch.bfloat16: (3e-2, 2e-2), } -CUDA_DEVICES = [ +# TODO: Modify this based on platform +DEVICES = [ f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2) ] -# We will launch different triton kernels between the prefill and decode +#For GPU, we will launch different triton kernels between the prefill and decode # stages, so we need to verify this. prefill stage(True) or decode stage(False) STAGES = [True, False] @@ -192,9 +193,18 @@ def create_random_inputs( return inputs, index_mapping, prompt_mapping +def check_punica_wrapper(punica_wrapper) -> bool: + if current_platform.is_cuda_alike(): + from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU + + return type(punica_wrapper) is PunicaWrapperGPU + else: + return False + + @torch.inference_mode() @pytest.mark.parametrize("num_loras", [1, 2, 4, 8]) -@pytest.mark.parametrize("device", CUDA_DEVICES) +@pytest.mark.parametrize("device", DEVICES) @pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 128000]) @pytest.mark.parametrize("stage", STAGES) def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None: @@ -205,7 +215,8 @@ def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None: torch.set_default_device(device) max_loras = 8 - punica_wrapper = PunicaWrapper(8192, 256, device) + punica_wrapper = get_punica_wrapper(8192, 256, device) + assert check_punica_wrapper(punica_wrapper) lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, lora_dtype=torch.float16) @@ -296,7 +307,7 @@ def create_random_embedding_layer(): # @pytest.mark.skip( # reason="Fails when loras are in any slot other than the first.") @pytest.mark.parametrize("num_loras", [1, 2, 4, 8]) -@pytest.mark.parametrize("device", CUDA_DEVICES) +@pytest.mark.parametrize("device", DEVICES) @pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 128000]) @pytest.mark.parametrize("stage", STAGES) def test_embeddings_with_new_embeddings(dist_init, num_loras, device, @@ -305,7 +316,8 @@ def test_embeddings_with_new_embeddings(dist_init, num_loras, device, torch.cuda.set_device(device) torch.set_default_device(device) max_loras = 8 - punica_wrapper = PunicaWrapper(8192, 256, device) + punica_wrapper = get_punica_wrapper(8192, 256, device) + assert check_punica_wrapper(punica_wrapper) lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, lora_dtype=torch.float16) @@ -432,7 +444,7 @@ def create_random_embedding_layer(): @torch.inference_mode() @pytest.mark.parametrize("num_loras", [1, 2, 4, 8]) -@pytest.mark.parametrize("device", CUDA_DEVICES) +@pytest.mark.parametrize("device", DEVICES) @pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 256512]) @pytest.mark.parametrize("stage", STAGES) def test_lm_head_logits_processor(dist_init, num_loras, device, vocab_size, @@ -441,7 +453,8 @@ def test_lm_head_logits_processor(dist_init, num_loras, device, vocab_size, torch.cuda.set_device(device) torch.set_default_device(device) max_loras = 8 - punica_wrapper = PunicaWrapper(8192, 256, device) + punica_wrapper = get_punica_wrapper(8192, 256, device) + assert check_punica_wrapper(punica_wrapper) lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, lora_dtype=torch.float16) @@ -563,7 +576,7 @@ def _pretest(): @torch.inference_mode() @pytest.mark.parametrize("num_loras", [1, 2, 4, 8]) -@pytest.mark.parametrize("device", CUDA_DEVICES) +@pytest.mark.parametrize("device", DEVICES) @pytest.mark.parametrize("stage", STAGES) @pytest.mark.parametrize("bias_enabled", [True, False]) def test_linear_replicated(dist_init, num_loras, device, stage, @@ -571,7 +584,8 @@ def test_linear_replicated(dist_init, num_loras, device, stage, torch.cuda.set_device(device) torch.set_default_device(device) - punica_wrapper = PunicaWrapper(8192, 256, device) + punica_wrapper = get_punica_wrapper(8192, 256, device) + assert check_punica_wrapper(punica_wrapper) max_loras = 8 lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, @@ -675,7 +689,7 @@ def create_random_linear_replicated_layer(): @pytest.mark.parametrize("num_loras", [1, 2, 4, 8]) @pytest.mark.parametrize("orientation", ["row", "column"]) @pytest.mark.parametrize("fully_shard", [True, False]) -@pytest.mark.parametrize("device", CUDA_DEVICES) +@pytest.mark.parametrize("device", DEVICES) @pytest.mark.parametrize("stage", STAGES) @pytest.mark.parametrize("bias_enabled", [True, False]) def test_linear_parallel(dist_init, num_loras, orientation, fully_shard, @@ -683,7 +697,8 @@ def test_linear_parallel(dist_init, num_loras, orientation, fully_shard, torch.cuda.set_device(device) torch.set_default_device(device) - punica_wrapper = PunicaWrapper(8192, 256, device) + punica_wrapper = get_punica_wrapper(8192, 256, device) + assert check_punica_wrapper(punica_wrapper) max_loras = 8 lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, @@ -797,7 +812,7 @@ def create_random_linear_parallel_layer(): @pytest.mark.parametrize("num_loras", [1, 2, 4, 8]) @pytest.mark.parametrize("repeats", [1, 2, 3]) @pytest.mark.parametrize("fully_shard", [True, False]) -@pytest.mark.parametrize("device", CUDA_DEVICES) +@pytest.mark.parametrize("device", DEVICES) @pytest.mark.parametrize("stage", STAGES) @pytest.mark.parametrize("bias_enabled", [True, False]) def test_column_parallel_packed(dist_init, num_loras, repeats, fully_shard, @@ -805,7 +820,8 @@ def test_column_parallel_packed(dist_init, num_loras, repeats, fully_shard, torch.cuda.set_device(device) torch.set_default_device(device) - punica_wrapper = PunicaWrapper(8192, 256, device) + punica_wrapper = get_punica_wrapper(8192, 256, device) + assert check_punica_wrapper(punica_wrapper) max_loras = 8 lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, @@ -963,7 +979,8 @@ def test_rotary_embedding_long_context(dist_init, num_loras, device, seed = 0 current_platform.seed_everything(seed) torch.set_default_device(device) - punica_wrapper = PunicaWrapper(8192, 256, device) + punica_wrapper = get_punica_wrapper(8192, 256, device) + assert check_punica_wrapper(punica_wrapper) max_loras = 8 lora_config = LoRAConfig(max_loras=max_loras, max_lora_rank=8, diff --git a/vllm/lora/layers.py b/vllm/lora/layers.py index 3e9c2ceb83eac..38cb846578d5c 100644 --- a/vllm/lora/layers.py +++ b/vllm/lora/layers.py @@ -17,7 +17,6 @@ tensor_model_parallel_all_reduce, tensor_model_parallel_gather) from vllm.distributed.utils import divide -from vllm.lora.punica import PunicaWrapper # yapf: disable from vllm.model_executor.layers.linear import (ColumnParallelLinear, LinearBase, @@ -33,7 +32,7 @@ VocabParallelEmbedding) if TYPE_CHECKING: - pass + from vllm.lora.punica_wrapper import PunicaWrapperBase def _get_lora_device(base_layer: nn.Module) -> torch.device: @@ -115,9 +114,9 @@ def set_lora( def set_mapping( self, - punica_wrapper: PunicaWrapper, + punica_wrapper, ): - self.punica_wrapper: PunicaWrapper = punica_wrapper + self.punica_wrapper: PunicaWrapperBase = punica_wrapper @classmethod def can_replace_layer( diff --git a/vllm/lora/models.py b/vllm/lora/models.py index 9855b57d0c9c9..49cd9f0c236ad 100644 --- a/vllm/lora/models.py +++ b/vllm/lora/models.py @@ -21,7 +21,7 @@ LinearScalingRotaryEmbeddingWithLora, LoRAMapping) from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights -from vllm.lora.punica import PunicaWrapper +from vllm.lora.punica_wrapper import get_punica_wrapper from vllm.lora.utils import (from_layer, from_layer_logits_processor, is_regex_target_modules, parse_fine_tuned_lora_name, replace_submodule) @@ -331,9 +331,9 @@ def __init__( self.lora_index_to_id: List[Optional[int]] = [None] * self.lora_slots self.vocab_size = vocab_size self.long_lora_context: Optional[LongContextLoRAContext] = None - self.punica_wrapper = PunicaWrapper(max_num_batched_tokens, - max_batches=self.max_num_seqs, - device=self.device) + self.punica_wrapper = get_punica_wrapper(max_num_batched_tokens, + max_batches=self.max_num_seqs, + device=self.device) # Scaling factor -> offset to the sin_cos_cache to it. # Used for long context lora. self.scaling_factor_to_offset: Dict[float, int] = {} diff --git a/vllm/lora/punica.py b/vllm/lora/punica.py deleted file mode 100644 index 563d1181d6fcb..0000000000000 --- a/vllm/lora/punica.py +++ /dev/null @@ -1,725 +0,0 @@ -""" -Based on: -Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). -Punica: Multi-Tenant LoRA Serving. -https://arxiv.org/abs/2310.18547 -""" - -from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union - -import torch - -from vllm.triton_utils import HAS_TRITON - -if HAS_TRITON: - from vllm.lora.ops.bgmv_expand import bgmv_expand - from vllm.lora.ops.bgmv_expand_slice import bgmv_expand_slice - from vllm.lora.ops.bgmv_shrink import bgmv_shrink - from vllm.lora.ops.sgmv_expand import sgmv_expand - from vllm.lora.ops.sgmv_expand_slice import sgmv_expand_slice - from vllm.lora.ops.sgmv_shrink import sgmv_shrink - -if TYPE_CHECKING: - # avoid circuit import - from vllm.lora.layers import LoRAMapping - from vllm.lora.models import LongContextLoRAContext - - -def compute_meta( - token_lora_tensor: torch.Tensor -) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int, bool]: - """ - Get the information required for the sgmv kernel. With the features: - 1. If consecutive requests in the batch use the same LoRA, this function - will combine them into a single request, improving sgmv kernel inference - performance. - 2. At the beginning of each prefill stage inference, recalculations are - needed based on the input, but only once. - """ - - lora_indices_tensor, seq_length_tensor = torch.unique_consecutive( - token_lora_tensor, return_counts=True) - cum_result = torch.cumsum(seq_length_tensor, dim=0) - b_seq_start_tensor = torch.zeros_like(seq_length_tensor) - b_seq_start_tensor[1:].copy_(cum_result[:-1]) - max_length = seq_length_tensor.max().item() - token_nums = seq_length_tensor.sum().item() - batch_size = lora_indices_tensor.size(0) - no_lora = False - # -1 means no lora should be applied. Use `no_lora` to determine whether - # the current step requires LoRA. If LoRA is not needed, the prefill stage - # does not need to launch the triton kernel, which can improve performance - if batch_size == 1 and lora_indices_tensor == -1: - no_lora = True - return (b_seq_start_tensor, seq_length_tensor, lora_indices_tensor, - batch_size, max_length, token_nums, no_lora) - - -# TODO see if this can be vectorized -def convert_mapping( - mapping: "LoRAMapping", - lora_index_to_id: List[Optional[int]], - max_loras: int, - vocab_size: int, - extra_vocab_size: int, - device: torch.device, - long_lora_context: Optional["LongContextLoRAContext"] = None, -) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, - Optional[torch.Tensor], List[int]]: - """Converts LoRAMapping to index tensors. - - Args: - mapping: LoRAMapping mapping rows in a batch to LoRA ids. - lora_index_to_id: List mapping LoRA ids to LoRA indices. - max_loras: Maximum number of LoRAs. - vocab_size: Model vocab size. - extra_vocab_size: Extra vocab size each LoRA can have. - long_lora_context: Passed if there are long context lora in a batch. - - Returns: - A tuple of tensors: - base_indices: Tensor of shape [batch_size] mapping batch rows to - LoRA indices. - sampler_indices: Tensor of shape [batch_size] mapping requests to - LoRA indices for sampler. For generation, this will be the - same as base_indicies. For prefill, this will map requests - to LoRA indices. - sampler_indices_padded: Tensor of shape [batch_size] mapping - requests to LoRA indices for sampler with padding. - Same as sampler_indicies, but -1 is replaced with - max_loras. - embeddings_indices: Tensor of shape [2, batch_size] mapping - requests to embedding indices. First row is for embeddings - added by the LoRAs, second row is for the LoRA.lora_a - embeddings. - long_lora_indices: Tensor of shape [batch_size] mapping - requests to RoPE offsets and rot dims for long LoRAs. - None if long context lora doesn't exist. - indices_len: List of lengths of the above tensors. It contains - (base_indices, sampler_indices, sampler_indices_padded, - embeddings_indices, long_lora_indices). - """ - index_mapping_indices: List[int] = list(mapping.index_mapping).copy() - embedding_indices = index_mapping_indices.copy() - lora_indices = index_mapping_indices.copy() - long_lora_offsets: Optional[torch.Tensor] = None - if long_lora_context: - long_lora_offsets = torch.zeros(len(index_mapping_indices), - device=device, - dtype=torch.long) - prompt_mapping: List[int] = [ - lora_index_to_id.index(x) if x > 0 else -1 - for x in mapping.prompt_mapping - ] - lora_idx = None - for i in range(len(index_mapping_indices)): - # TODO index can be slow. optimize - lora_idx = (lora_index_to_id.index(index_mapping_indices[i]) - if index_mapping_indices[i] > 0 else -1) - embedding_indices[i] = lora_idx if index_mapping_indices[i] > 0 else 0 - lora_indices[i] = lora_idx - if long_lora_context: - assert long_lora_offsets is not None - lora_offset: int = long_lora_context.offsets_by_lora_id.get( - index_mapping_indices[i], 0) - long_lora_offsets[i] = lora_offset - - indices_list: List[Union[List[int], torch.Tensor]] = [ - index_mapping_indices, - lora_indices, - embedding_indices, - ] - if long_lora_context: - assert long_lora_offsets is not None - indices_list.append(long_lora_offsets) - indices = torch.tensor(indices_list, dtype=torch.long, device=device) - prompt_mapping_tensor = torch.tensor(prompt_mapping, - dtype=torch.long, - device=device) - embeddings_indices = torch.stack([ - indices[2] * extra_vocab_size, - indices[2] * (vocab_size + extra_vocab_size), - ]) - embeddings_indices[embeddings_indices == -1] = max_loras - 1 - base_indices = indices[1] - sampler_indices = prompt_mapping_tensor - sampler_indices_padded = sampler_indices.clone() - sampler_indices_padded[sampler_indices_padded == -1] = max_loras - 1 - sampler_indices_padded = torch.arange( - 0, len(sampler_indices_padded), device=device, dtype=torch.long) + ( - sampler_indices_padded * len(sampler_indices_padded)) - long_lora_indices = None - long_lora_indices_len: Optional[int] = None - if long_lora_context: - long_lora_indices = indices[3] - long_lora_indices_len = long_lora_indices.shape[-1] - # Contain length of indices tensors. Used to index into each tensor. - indices_len = [ - base_indices.shape[-1], - sampler_indices.shape[-1], - sampler_indices_padded.shape[-1], - embeddings_indices.shape[-1], - ] - if long_lora_indices_len is not None: - indices_len.append(long_lora_indices_len) - else: - # If long_lora doesn't exist,append None - indices_len.append(None) - - return ( - base_indices, - sampler_indices, - sampler_indices_padded, - embeddings_indices, - long_lora_indices, - indices_len, - ) - - -class PunicaWrapper: - """ - PunicaWrapper is designed to manage and provide metadata for the punica - kernel. The main function is to maintain the state information for - Multi-LoRA, and to provide the interface for the punica kernel. - """ - - def __init__(self, max_num_batched_tokens: int, max_batches: int, - device: Union[torch.device, str]): - self._token_lora_indices = torch.empty(max_num_batched_tokens, - dtype=torch.long, - device=device) - self._sampler_indices = torch.empty(max_num_batched_tokens, - dtype=torch.long, - device=device) - self._sampler_indices_padded = torch.empty(max_num_batched_tokens, - dtype=torch.long, - device=device) - self._embeddings_indices = torch.empty(2, - max_num_batched_tokens, - dtype=torch.long, - device=device) - self._long_lora_indices = torch.empty(max_num_batched_tokens, - dtype=torch.long, - device=device) - - # 5 is the number of indicies tensors. - # base_indices, sampler_indices, sampler_indices_padded, - # embeddings_indices,long_lora_indices - self.indices_len: List[Optional[int]] = [None] * 5 - # these attributes are the information required for sgmv kernel - self._seq_start_locs = torch.empty(max_batches, - dtype=torch.long, - device=device) - self._seq_lengths = torch.empty(max_batches, - dtype=torch.long, - device=device) - self._lora_indices_per_batch = torch.empty(max_batches, - dtype=torch.long, - device=device) - self.device: torch.device = device - self.max_length: int = 0 - self.token_nums: int = 0 - self.batch_size: int = -1 - self.is_prefill = False - self.no_lora = False - - def update_metadata( - self, - mapping: "LoRAMapping", - lora_index_to_id: List[Optional[int]], - max_loras: int, - vocab_size: int, - extra_vocab_size: int, - long_lora_context: Optional["LongContextLoRAContext"] = None, - ): - - self._update_base_metadata(mapping, lora_index_to_id, max_loras, - vocab_size, extra_vocab_size, - long_lora_context) - if mapping.is_prefill: - # Update metadata required for prefill-related operators. - self._update_prefill_metada(self.token_lora_indices) - self.is_prefill = True - else: - self.is_prefill = False - - def _update_base_metadata( - self, - mapping: "LoRAMapping", - lora_index_to_id: List[Optional[int]], - max_loras: int, - vocab_size: int, - extra_vocab_size: int, - long_lora_context: Optional["LongContextLoRAContext"] = None, - ): - ( - base_indices, - sampler_indices, - sampler_indices_padded, - embeddings_indices, - long_lora_offsets_tensor, - indices_len, - ) = convert_mapping( - mapping, - lora_index_to_id, - max_loras, - vocab_size, - extra_vocab_size, - self.device, - long_lora_context, - ) - self._token_lora_indices[:base_indices.shape[0]].copy_(base_indices) - self._sampler_indices[:sampler_indices.shape[0]].copy_(sampler_indices) - self._sampler_indices_padded[:sampler_indices_padded.shape[0]].copy_( - sampler_indices_padded) - self._embeddings_indices[:embeddings_indices. - shape[0], :embeddings_indices.shape[1]].copy_( - embeddings_indices) - if long_lora_offsets_tensor is not None: - self._long_lora_indices[:long_lora_offsets_tensor.shape[0]].copy_( - long_lora_offsets_tensor) - else: - self._long_lora_indices.zero_() - self.indices_len[:] = indices_len - - def _update_prefill_metada(self, token_lora_tensor: torch.Tensor) -> None: - - (b_seq_start_tensor, seq_length_tensor, lora_indices_tensor, - batch_size, max_length, token_nums, - no_lora) = compute_meta(token_lora_tensor) - - self._seq_start_locs[:b_seq_start_tensor.shape[0]].copy_( - b_seq_start_tensor) - self._seq_lengths[:seq_length_tensor.shape[0]].copy_(seq_length_tensor) - self._lora_indices_per_batch[:lora_indices_tensor.shape[0]].copy_( - lora_indices_tensor) - self.batch_size = batch_size - self.max_length = max_length - self.token_nums = token_nums - self.no_lora = no_lora - - @property - def prefill_metadata( - self - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int]: - """ - This property provides a convenient way to access the necessary - metadata for prefill-related kernel computations. - 1. seq_start_locs: Tensor of sequence start positions. - 2. seq_lengths: Tensor of sequence lengths. - 3. lora_indices_per_batch: Tensor of lora indices, and an index of - -1 means no lora should be applied. - 4. batch_size: Batch size after clustering identical lora indices. - 5. max_length: The maximum sequence length in the batch. - 6. token_nums: The token numbers in the batch. - """ - return (self._seq_start_locs[:self.batch_size], - self._seq_lengths[:self.batch_size], - self._lora_indices_per_batch[:self.batch_size], - self.batch_size, self.max_length, self.token_nums) - - @property - def token_lora_indices(self) -> torch.Tensor: - """ - This property provides the lora indices corresponding to each token - in the batch. An index of -1 means no lora should be applied. - """ - token_lora_len = self.indices_len[0] - return self._token_lora_indices[:token_lora_len] - - @property - def sampler_indices(self) -> torch.Tensor: - """ - This property is used to access the lora indices specifically for - LogitsProcessorWithLoRA. - """ - sampler_indices_len = self.indices_len[1] - return self._sampler_indices[:sampler_indices_len] - - @property - def sampler_indices_padded(self) -> torch.Tensor: - """ - This property provides access to padded sampler indices. - """ - indices_padded_len = self.indices_len[2] - return self._sampler_indices_padded[:indices_padded_len] - - @property - def embeddings_indices(self) -> torch.Tensor: - """ - This property provides access to the indices used for lora embeddings, - specifically for VocabParallelEmbeddingWithLoRA. - """ - embeddings_indices_len = self.indices_len[3] - return self._embeddings_indices[:, :embeddings_indices_len] - - @property - def long_lora_indices(self) -> torch.Tensor: - """ - This property provides access to the indices used for long context - lora, specifically for LinearScalingRotaryEmbeddingWithLora. - """ - long_lora_len = self.indices_len[4] - return self._long_lora_indices[:long_lora_len] - - def _shrink_prefill( - self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - scale: float, - ): - #No LoRA request, so return directly - if self.no_lora: - return - sgmv_shrink( - x, - w_t_all, - y, - *self.prefill_metadata, - scale, - ) - - def _shrink_decode( - self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - scale: float, - ): - bgmv_shrink(x, w_t_all, y, self.token_lora_indices, scale) - - def _expand_prefill( - self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - add_input: bool, - ): - #No LoRA request, so return directly - if self.no_lora: - return - sgmv_expand( - x, - w_t_all, - y, - *self.prefill_metadata, - add_input, - ) - - def _expand_decode( - self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - add_input: bool, - ): - bgmv_expand(x, w_t_all, y, self.token_lora_indices, add_input) - - def _expand_slice_prefill( - self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - y_offset: Optional[int], - y_slice_size: Optional[int], - add_input: bool, - ): - #No LoRA request, so return directly - if self.no_lora: - return - sgmv_expand_slice( - x, - w_t_all, - y, - *self.prefill_metadata, - y_offset, - y_slice_size, - add_input, - ) - - def _expand_slice_decode( - self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - y_offset: Optional[int], - y_slice_size: Optional[int], - add_input: bool, - ): - bgmv_expand_slice(x, w_t_all, y, self.token_lora_indices, y_offset, - y_slice_size, add_input) - - def _apply_expand(self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - y_offset: Optional[int], - y_slice_size: Optional[int], - add_input: bool = True): - """ - Perform the ` y[:,y_offset:y_offset+y_slice_size]+=x@w_t_all` - computation, which is suitable for the - GEMM of lora'b. - """ - - expand_slice_fun: Callable = (self._expand_slice_prefill - if self.is_prefill else - self._expand_slice_decode) - expand_slice_fun(y, x, w_t_all, y_offset, y_slice_size, add_input) - - def _apply_bias( - self, - indices: torch.Tensor, - output: torch.Tensor, - output_slices: Tuple[int, ...], - lora_bias_stacked: Tuple[Optional[torch.Tensor], ...], - ): - """Applies bias to output - - Input shapes: - lora_bias_stacked: 3 element tuple of (num_loras, output_dim) - indices: (batch_size) - output: (batch_size, q_slice_size + 2*kv_slice_size) - output_slices: n-1 element tuple of (slice_size...), - where n is number of slices - """ - org_output = output - output = output.view(-1, output.shape[-1]) - indices = indices.view(-1) - - offset_left = 0 - for slice_idx, slice in enumerate(output_slices): - bias = lora_bias_stacked[slice_idx] - if bias is not None: - bias = bias.view(-1, bias.shape[-1]) - bias = bias[indices] - bias[indices == -1] = 0 - output[:, offset_left:offset_left + slice] += bias - offset_left += slice - - return output.view_as(org_output) - - def _apply_shrink( - self, - y: torch.Tensor, - x: torch.Tensor, - w_t_all: torch.Tensor, - scale: float, - ): - """ - Perform the ` y+=x@w_t_all` computation, which is suitable for the - GEMM of lora'a. - When `is_prefill is` true, it indicates that it is currently the - prefill stage, and the `_shrink_prefill` function should be called. - Otherwise, it is the decode stage, and the _shrink_decode function - should be called. - """ - y_org = y - y = y.view(-1, y.shape[-1]) - shrink_fun: Callable = (self._shrink_prefill - if self.is_prefill else self._shrink_decode) - shrink_fun(y, x, w_t_all, scale) - y = y.view_as(y_org) - - def add_shrink( - self, - y: Union[Tuple[torch.Tensor, ...], torch.Tensor], - x: torch.Tensor, - lora_a_stacked: Tuple[torch.Tensor, ...], - scale: float, - ): - """ - Performs GEMM for multiple slices of lora_a. - When `is_prefill is` true, it indicates that it is currently the - prefill stage, and the `_shrink_prefill` function should be called. - Otherwise, it is the decode stage, and the _shrink_decode function - should be called. - - Semantics: - for i in range(len(lora_a_stacked)): - y[i] += (x @ lora_a_stacked[i]) * scale - - Args: - y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors - x (torch.Tensor): Input tensor - lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights - scale (float): Scaling factor for the operation - """ - - x = x.view(-1, x.shape[-1]) - # TODO fuse these kernels - for slice_idx in range(len(lora_a_stacked)): - self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx], - scale) - - def add_expand( - self, - y: torch.Tensor, - x: Union[Tuple[torch.Tensor, ...], torch.Tensor], - lora_b_stacked: Tuple[torch.Tensor, ...], - lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], - output_slices: Tuple[int, ...], - offset_start: int = 0, - add_input=True, - ) -> None: - """ - Performs GEMM and bias addition for multiple slices of lora_b. - - Semantics: - for i in range(len(lora_b_stacked)): - slice = output_slices[i] - y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] + - lora_bias_stacked[i] - offset += slice - - Args: - y (torch.Tensor): Output tensor. - x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors - lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight - lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): - bias's weight - output_slices (Tuple[int, ...]): Every slice's size - add_input (bool): Defaults to True. - """ - y_org = y - y = y.view(-1, y.shape[-1]) - offset_left = offset_start - if lora_bias_stacked is not None: - self._apply_bias(self.token_lora_indices, y, output_slices, - lora_bias_stacked) - for slice_idx in range(len(lora_b_stacked)): - self._apply_expand( - y, - x[slice_idx], - lora_b_stacked[slice_idx], - offset_left, - output_slices[slice_idx], - add_input=add_input, - ) - offset_left += output_slices[slice_idx] - y = y.view_as(y_org) - - def add_lora_embedding( - self, - y: torch.Tensor, - x: torch.Tensor, - lora_b_stacked: torch.Tensor, - add_input: bool = True, - ): - """ - Applies lora specifically for VocabParallelEmbeddingWithLoRA. - - Semantics: - y += x @ lora_b_stacked - - Args: - y (torch.Tensor): Output tensor. - x (torch.Tensor): Input tensor. - lora_b_stacked (torch.Tensor): lora_b's weights. - add_input (bool): Default to True. - - """ - - # Embedding layer only need expand op - expand_fun: Callable = (self._expand_prefill - if self.is_prefill else self._expand_decode) - expand_fun(y, x, lora_b_stacked, add_input) - - def add_lora_linear( - self, - y: torch.Tensor, - x: torch.Tensor, - lora_a_stacked: Tuple[torch.Tensor, ...], - lora_b_stacked: Tuple[torch.Tensor, ...], - lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], - scale: float, - output_slices: Tuple[int, ...], - *, - buffer: Optional[Tuple[torch.Tensor, ...]] = None) -> None: - """ - Applicable to linear-related lora. - - Semantics: - for i in range(len(lora_a_stacked)): - y[i] += ( - x[i].unsqueeze(0) - @ lora_a_stacked[indices[i], layer_idx, :, :] - @ lora_b_stacked[indices[i], layer_idx, :, :] - * scale - ).squeeze(0)+lora_bias_stacked[i] - - Args: - y (torch.Tensor): Output tensor. Will be changed in-place. - x (torch.Tensor): Input tensor - lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight. - lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight. - lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias. - scale (float): Scaling factor. - output_slices (Tuple[int, ...]): Every slice's size. - buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None. - """ - - assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices) - if lora_bias_stacked is not None: - assert len(lora_bias_stacked) == len(output_slices) - y = self._apply_bias(self.token_lora_indices, y, output_slices, - lora_bias_stacked) - - if buffer is None: - r = lora_b_stacked[0].size(-1) - # We set the buffer to be float32 by default ,refer to: - # https://github.com/triton-lang/triton/issues/1387 - buffer = tuple( - torch.zeros( - (x.size(0), r), dtype=torch.float32, device=x.device) - for _ in range(len(output_slices))) - self.add_shrink(buffer, x, lora_a_stacked, scale) - self.add_expand(y, - buffer, - lora_b_stacked, - None, - output_slices, - add_input=True) - - def add_lora_logits(self, - y: torch.Tensor, - x: torch.Tensor, - lora_a_stacked: torch.Tensor, - lora_b_stacked: torch.Tensor, - scale, - *, - buffer: Optional[torch.Tensor] = None) -> None: - """ - Applies lora specifically for LogitsProcessorWithLoRA. - - Semantics: - buffer = (x @ lora_a_stacked) * scale - y += buffer @ lora_b_stacked - - Args: - y (torch.Tensor): Output tensor. - x (torch.Tensor): Input tensor. - lora_a_stacked (torch.Tensor): lora_a's weights. - lora_b_stacked (torch.Tensor):lora_b's weights. - scale (float): Scaling factor. - buffer (Optional[torch.Tensor]):Default to None. - """ - y_org = y - y = y.view(-1, y.shape[-1]) - x = x.view(-1, x.shape[-1]) - r = lora_b_stacked.size(-1) - if buffer is None: - # We set the buffer to be float32 by default ,refer to: - # https://github.com/triton-lang/triton/issues/1387 - buffer = torch.zeros((x.size(0), r), - dtype=torch.float32, - device=x.device) - # LogitsProcessorWithLoRA always using bgmv. - bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale) - bgmv_expand(buffer, - lora_b_stacked, - y, - self.sampler_indices, - add_inputs=True) - y = y.view_as(y_org) diff --git a/vllm/lora/punica_wrapper/__init__.py b/vllm/lora/punica_wrapper/__init__.py new file mode 100644 index 0000000000000..48ada3926ea46 --- /dev/null +++ b/vllm/lora/punica_wrapper/__init__.py @@ -0,0 +1,7 @@ +from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase +from vllm.lora.punica_wrapper.punica_selector import get_punica_wrapper + +__all__ = [ + "PunicaWrapperBase", + "get_punica_wrapper", +] diff --git a/vllm/lora/punica_wrapper/punica_base.py b/vllm/lora/punica_wrapper/punica_base.py new file mode 100644 index 0000000000000..0a5a84bdd8deb --- /dev/null +++ b/vllm/lora/punica_wrapper/punica_base.py @@ -0,0 +1,480 @@ +""" +Based on: +Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). +Punica: Multi-Tenant LoRA Serving. +https://arxiv.org/abs/2310.18547 +""" + +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, List, Optional, Tuple, Union + +import torch + +from .utils import compute_meta, convert_mapping + +if TYPE_CHECKING: + # avoid circuit import + from vllm.lora.layers import LoRAMapping + from vllm.lora.models import LongContextLoRAContext + + +class PunicaWrapperABC(ABC): + """ + PunicaWrapper ABC. + """ + + @abstractmethod + def update_metadata( + self, + mapping: "LoRAMapping", + lora_index_to_id: List[Optional[int]], + max_loras: int, + vocab_size: int, + extra_vocab_size: int, + long_lora_context: Optional["LongContextLoRAContext"] = None, + **kwargs, + ) -> None: + """ + Update the lora-related metadata + """ + raise NotImplementedError + + @abstractmethod + def add_shrink( + self, + y: Union[Tuple[torch.Tensor, ...], torch.Tensor], + x: torch.Tensor, + lora_a_stacked: Tuple[torch.Tensor, ...], + scale: float, + **kwargs, + ) -> None: + """ + Performs GEMM for multiple slices of lora_a. + """ + + raise NotImplementedError + + @abstractmethod + def add_expand( + self, + y: torch.Tensor, + x: Union[Tuple[torch.Tensor, ...], torch.Tensor], + lora_b_stacked: Tuple[torch.Tensor, ...], + lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], + output_slices: Tuple[int, ...], + offset_start: int = 0, + add_input=True, + **kwargs, + ) -> None: + """ + Performs GEMM and bias addition for multiple slices of lora_b. + """ + raise NotImplementedError + + @abstractmethod + def add_lora_embedding( + self, + y: torch.Tensor, + x: torch.Tensor, + lora_b_stacked: torch.Tensor, + add_input: bool = True, + **kwargs, + ) -> None: + """ + Applies lora specifically for VocabParallelEmbeddingWithLoRA, + and this layer only requires the expand operation. + """ + raise NotImplementedError + + @abstractmethod + def add_lora_linear(self, + y: torch.Tensor, + x: torch.Tensor, + lora_a_stacked: Tuple[torch.Tensor, ...], + lora_b_stacked: Tuple[torch.Tensor, ...], + lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], + scale: float, + output_slices: Tuple[int, ...], + *, + buffer: Optional[Tuple[torch.Tensor, ...]] = None, + **kwargs) -> None: + """ + Applicable to linear-related lora. + """ + + raise NotImplementedError + + @abstractmethod + def add_lora_logits(self, + y: torch.Tensor, + x: torch.Tensor, + lora_a_stacked: torch.Tensor, + lora_b_stacked: torch.Tensor, + scale, + *, + buffer: Optional[torch.Tensor] = None, + **kwargs) -> None: + """ + Applies lora specifically for LogitsProcessorWithLoRA. + """ + raise NotImplementedError + + +class PunicaWrapperBase(PunicaWrapperABC): + """ + PunicaWrapperBase is designed to manage and provide metadata for the punica + kernel. The main function is to maintain the state information for + Multi-LoRA, and to provide the interface for the punica. + """ + + def __init__(self, max_num_batched_tokens: int, max_batches: int, + device: Union[torch.device, str], **kwargs): + self._token_lora_indices = torch.empty(max_num_batched_tokens, + dtype=torch.long, + device=device) + self._sampler_indices = torch.empty(max_num_batched_tokens, + dtype=torch.long, + device=device) + self._sampler_indices_padded = torch.empty(max_num_batched_tokens, + dtype=torch.long, + device=device) + self._embeddings_indices = torch.empty(2, + max_num_batched_tokens, + dtype=torch.long, + device=device) + self._long_lora_indices = torch.empty(max_num_batched_tokens, + dtype=torch.long, + device=device) + + # 5 is the number of indicies tensors. + # base_indices, sampler_indices, sampler_indices_padded, + # embeddings_indices,long_lora_indices + self.indices_len: List[Optional[int]] = [None] * 5 + # these attributes are the information required for sgmv kernel + self._seq_start_locs = torch.empty(max_batches, + dtype=torch.long, + device=device) + self._seq_lengths = torch.empty(max_batches, + dtype=torch.long, + device=device) + self._lora_indices_per_batch = torch.empty(max_batches, + dtype=torch.long, + device=device) + self.device: torch.device = device + self.max_length: int = 0 + self.token_nums: int = 0 + self.batch_size: int = -1 + self.is_prefill = False + self.no_lora = False + + def _update_base_metadata( + self, + mapping: "LoRAMapping", + lora_index_to_id: List[Optional[int]], + max_loras: int, + vocab_size: int, + extra_vocab_size: int, + long_lora_context: Optional["LongContextLoRAContext"] = None, + ): + ( + base_indices, + sampler_indices, + sampler_indices_padded, + embeddings_indices, + long_lora_offsets_tensor, + indices_len, + ) = convert_mapping( + mapping, + lora_index_to_id, + max_loras, + vocab_size, + extra_vocab_size, + self.device, + long_lora_context, + ) + self._token_lora_indices[:base_indices.shape[0]].copy_(base_indices) + self._sampler_indices[:sampler_indices.shape[0]].copy_(sampler_indices) + self._sampler_indices_padded[:sampler_indices_padded.shape[0]].copy_( + sampler_indices_padded) + self._embeddings_indices[:embeddings_indices. + shape[0], :embeddings_indices.shape[1]].copy_( + embeddings_indices) + if long_lora_offsets_tensor is not None: + self._long_lora_indices[:long_lora_offsets_tensor.shape[0]].copy_( + long_lora_offsets_tensor) + else: + self._long_lora_indices.zero_() + self.indices_len[:] = indices_len + + def _update_prefill_metada(self, token_lora_tensor: torch.Tensor) -> None: + + (b_seq_start_tensor, seq_length_tensor, lora_indices_tensor, + batch_size, max_length, token_nums, + no_lora) = compute_meta(token_lora_tensor) + + self._seq_start_locs[:b_seq_start_tensor.shape[0]].copy_( + b_seq_start_tensor) + self._seq_lengths[:seq_length_tensor.shape[0]].copy_(seq_length_tensor) + self._lora_indices_per_batch[:lora_indices_tensor.shape[0]].copy_( + lora_indices_tensor) + self.batch_size = batch_size + self.max_length = max_length + self.token_nums = token_nums + self.no_lora = no_lora + + def _apply_bias( + self, + indices: torch.Tensor, + output: torch.Tensor, + output_slices: Tuple[int, ...], + lora_bias_stacked: Tuple[Optional[torch.Tensor], ...], + ): + """Applies bias to output + + Input shapes: + lora_bias_stacked: 3 element tuple of (num_loras, output_dim) + indices: (batch_size) + output: (batch_size, q_slice_size + 2*kv_slice_size) + output_slices: n-1 element tuple of (slice_size...), + where n is number of slices + """ + org_output = output + output = output.view(-1, output.shape[-1]) + indices = indices.view(-1) + + offset_left = 0 + for slice_idx, slice in enumerate(output_slices): + bias = lora_bias_stacked[slice_idx] + if bias is not None: + bias = bias.view(-1, bias.shape[-1]) + bias = bias[indices] + bias[indices == -1] = 0 + output[:, offset_left:offset_left + slice] += bias + offset_left += slice + + return output.view_as(org_output) + + @property + def prefill_metadata( + self + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int]: + """ + This property provides a convenient way to access the necessary + metadata for prefill-related kernel computations. + 1. seq_start_locs: Tensor of sequence start positions. + 2. seq_lengths: Tensor of sequence lengths. + 3. lora_indices_per_batch: Tensor of lora indices, and an index of + -1 means no lora should be applied. + 4. batch_size: Batch size after clustering identical lora indices. + 5. max_length: The maximum sequence length in the batch. + 6. token_nums: The token numbers in the batch. + """ + return (self._seq_start_locs[:self.batch_size], + self._seq_lengths[:self.batch_size], + self._lora_indices_per_batch[:self.batch_size], + self.batch_size, self.max_length, self.token_nums) + + @property + def token_lora_indices(self) -> torch.Tensor: + """ + This property provides the lora indices corresponding to each token + in the batch. An index of -1 means no lora should be applied. + """ + token_lora_len = self.indices_len[0] + return self._token_lora_indices[:token_lora_len] + + @property + def sampler_indices(self) -> torch.Tensor: + """ + This property is used to access the lora indices specifically for + LogitsProcessorWithLoRA. + """ + sampler_indices_len = self.indices_len[1] + return self._sampler_indices[:sampler_indices_len] + + @property + def sampler_indices_padded(self) -> torch.Tensor: + """ + This property provides access to padded sampler indices. + """ + indices_padded_len = self.indices_len[2] + return self._sampler_indices_padded[:indices_padded_len] + + @property + def embeddings_indices(self) -> torch.Tensor: + """ + This property provides access to the indices used for lora embeddings, + specifically for VocabParallelEmbeddingWithLoRA. + """ + embeddings_indices_len = self.indices_len[3] + return self._embeddings_indices[:, :embeddings_indices_len] + + @property + def long_lora_indices(self) -> torch.Tensor: + """ + This property provides access to the indices used for long context + lora, specifically for LinearScalingRotaryEmbeddingWithLora. + """ + long_lora_len = self.indices_len[4] + return self._long_lora_indices[:long_lora_len] + + def update_metadata( + self, + mapping: "LoRAMapping", + lora_index_to_id: List[Optional[int]], + max_loras: int, + vocab_size: int, + extra_vocab_size: int, + long_lora_context: Optional["LongContextLoRAContext"] = None, + **kwargs): + + self._update_base_metadata(mapping, lora_index_to_id, max_loras, + vocab_size, extra_vocab_size, + long_lora_context) + if mapping.is_prefill: + # Update metadata required for prefill-related operators. + self._update_prefill_metada(self.token_lora_indices) + self.is_prefill = True + else: + self.is_prefill = False + + @abstractmethod + def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor], + x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...], + scale: float, **kwargs) -> None: + """ + Performs GEMM for multiple slices of lora_a. + + Semantics: + for i in range(len(lora_a_stacked)): + y[i] += (x @ lora_a_stacked[i]) * scale + + Args: + y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors + x (torch.Tensor): Input tensor + lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights + scale (float): Scaling factor for the operation + + """ + # TODO: implement it based on torch ops + raise NotImplementedError + + @abstractmethod + def add_expand(self, + y: torch.Tensor, + x: Union[Tuple[torch.Tensor, ...], torch.Tensor], + lora_b_stacked: Tuple[torch.Tensor, ...], + lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], + output_slices: Tuple[int, ...], + offset_start: int = 0, + add_input=True, + **kwargs) -> None: + """ + Performs GEMM and bias addition for multiple slices of lora_b. + + Semantics: + for i in range(len(lora_b_stacked)): + slice = output_slices[i] + y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] + + lora_bias_stacked[i] + offset += slice + + Args: + y (torch.Tensor): Output tensor. + x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors + lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight + lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): + bias's weight + output_slices (Tuple[int, ...]): Every slice's size + add_input (bool): Defaults to True. + + """ + # TODO: implement it based on torch ops + raise NotImplementedError + + @abstractmethod + def add_lora_embedding(self, + y: torch.Tensor, + x: torch.Tensor, + lora_b_stacked: torch.Tensor, + add_input: bool = True, + **kwargs) -> None: + """ + Applies lora specifically for VocabParallelEmbeddingWithLoRA. + and this layer only requires the expand operation. + Semantics: + y += x @ lora_b_stacked + + Args: + y (torch.Tensor): Output tensor. + x (torch.Tensor): Input tensor. + lora_b_stacked (torch.Tensor): lora_b's weights. + add_input (bool): Default to True. + """ + # TODO: implement it based on torch ops + raise NotImplementedError + + @abstractmethod + def add_lora_linear(self, + y: torch.Tensor, + x: torch.Tensor, + lora_a_stacked: Tuple[torch.Tensor, ...], + lora_b_stacked: Tuple[torch.Tensor, ...], + lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], + scale: float, + output_slices: Tuple[int, ...], + *, + buffer: Optional[Tuple[torch.Tensor, ...]] = None, + **kwargs) -> None: + """ + Applicable to linear-related lora. + + Semantics: + for i in range(len(lora_a_stacked)): + y[i] += ( + x[i].unsqueeze(0) + @ lora_a_stacked[indices[i], layer_idx, :, :] + @ lora_b_stacked[indices[i], layer_idx, :, :] + * scale + ).squeeze(0)+lora_bias_stacked[i] + + Args: + y (torch.Tensor): Output tensor. Will be changed in-place. + x (torch.Tensor): Input tensor + lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight. + lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight. + lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias. + scale (float): Scaling factor. + output_slices (Tuple[int, ...]): Every slice's size. + buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None. + """ + # TODO: implement it based on torch ops + raise NotImplementedError + + @abstractmethod + def add_lora_logits(self, + y: torch.Tensor, + x: torch.Tensor, + lora_a_stacked: torch.Tensor, + lora_b_stacked: torch.Tensor, + scale, + *, + buffer: Optional[torch.Tensor] = None, + **kwargs) -> None: + """ + Applies lora specifically for LogitsProcessorWithLoRA. + + Semantics: + buffer = (x @ lora_a_stacked) * scale + y += buffer @ lora_b_stacked + + Args: + y (torch.Tensor): Output tensor. + x (torch.Tensor): Input tensor. + lora_a_stacked (torch.Tensor): lora_a's weights. + lora_b_stacked (torch.Tensor):lora_b's weights. + scale (float): Scaling factor. + buffer (Optional[torch.Tensor]):Default to None. + """ + # TODO: implement it based on torch ops + raise NotImplementedError diff --git a/vllm/lora/punica_wrapper/punica_gpu.py b/vllm/lora/punica_wrapper/punica_gpu.py new file mode 100644 index 0000000000000..b2af29de129ce --- /dev/null +++ b/vllm/lora/punica_wrapper/punica_gpu.py @@ -0,0 +1,358 @@ +""" +Based on: +Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). +Punica: Multi-Tenant LoRA Serving. +https://arxiv.org/abs/2310.18547 +""" + +from typing import Callable, Optional, Tuple, Union, final + +import torch + +from vllm.triton_utils import HAS_TRITON + +if HAS_TRITON: + from vllm.lora.ops.bgmv_expand import bgmv_expand + from vllm.lora.ops.bgmv_expand_slice import bgmv_expand_slice + from vllm.lora.ops.bgmv_shrink import bgmv_shrink + from vllm.lora.ops.sgmv_expand import sgmv_expand + from vllm.lora.ops.sgmv_expand_slice import sgmv_expand_slice + from vllm.lora.ops.sgmv_shrink import sgmv_shrink + +from .punica_base import PunicaWrapperBase + + +@final +class PunicaWrapperGPU(PunicaWrapperBase): + """ + PunicaWrapperGPU is designed to manage and provide metadata for the punica + kernel. The main function is to maintain the state information for + Multi-LoRA, and to provide the interface for the punica triton kernel. + """ + + def __init__(self, max_num_batched_tokens: int, max_batches: int, + device: Union[torch.device, str], **kwargs): + PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches, + device) + + def _shrink_prefill( + self, + y: torch.Tensor, + x: torch.Tensor, + w_t_all: torch.Tensor, + scale: float, + ): + #No LoRA request, so return directly + if self.no_lora: + return + sgmv_shrink( + x, + w_t_all, + y, + *self.prefill_metadata, + scale, + ) + + def _shrink_decode( + self, + y: torch.Tensor, + x: torch.Tensor, + w_t_all: torch.Tensor, + scale: float, + ): + bgmv_shrink(x, w_t_all, y, self.token_lora_indices, scale) + + def _expand_prefill( + self, + y: torch.Tensor, + x: torch.Tensor, + w_t_all: torch.Tensor, + add_input: bool, + ): + #No LoRA request, so return directly + if self.no_lora: + return + sgmv_expand( + x, + w_t_all, + y, + *self.prefill_metadata, + add_input, + ) + + def _expand_decode( + self, + y: torch.Tensor, + x: torch.Tensor, + w_t_all: torch.Tensor, + add_input: bool, + ): + bgmv_expand(x, w_t_all, y, self.token_lora_indices, add_input) + + def _expand_slice_prefill( + self, + y: torch.Tensor, + x: torch.Tensor, + w_t_all: torch.Tensor, + y_offset: Optional[int], + y_slice_size: Optional[int], + add_input: bool, + ): + #No LoRA request, so return directly + if self.no_lora: + return + sgmv_expand_slice( + x, + w_t_all, + y, + *self.prefill_metadata, + y_offset, + y_slice_size, + add_input, + ) + + def _expand_slice_decode( + self, + y: torch.Tensor, + x: torch.Tensor, + w_t_all: torch.Tensor, + y_offset: Optional[int], + y_slice_size: Optional[int], + add_input: bool, + ): + bgmv_expand_slice(x, w_t_all, y, self.token_lora_indices, y_offset, + y_slice_size, add_input) + + def _apply_expand( + self, + y: torch.Tensor, + x: torch.Tensor, + w_t_all: torch.Tensor, + y_offset: Optional[int], + y_slice_size: Optional[int], + add_input: bool = True, + ): + """ + Perform the ` y[:,y_offset:y_offset+y_slice_size]+=x@w_t_all` + computation, which is suitable for the + GEMM of lora'b. + """ + + expand_slice_fun: Callable = (self._expand_slice_prefill + if self.is_prefill else + self._expand_slice_decode) + expand_slice_fun(y, x, w_t_all, y_offset, y_slice_size, add_input) + + def _apply_shrink(self, y: torch.Tensor, x: torch.Tensor, + w_t_all: torch.Tensor, scale: float): + """ + Perform the ` y+=x@w_t_all` computation, which is suitable for the + GEMM of lora'a. + When `is_prefill is` true, it indicates that it is currently the + prefill stage, and the `_shrink_prefill` function should be called. + Otherwise, it is the decode stage, and the _shrink_decode function + should be called. + """ + y_org = y + y = y.view(-1, y.shape[-1]) + shrink_fun: Callable = (self._shrink_prefill + if self.is_prefill else self._shrink_decode) + shrink_fun(y, x, w_t_all, scale) + y = y.view_as(y_org) + + def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor], + x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...], + scale: float, **kwargs): + """ + Performs GEMM for multiple slices of lora_a. + When `is_prefill is` true, it indicates that it is currently the + prefill stage, and the `_shrink_prefill` function should be called. + Otherwise, it is the decode stage, and the _shrink_decode function + should be called. + + Semantics: + for i in range(len(lora_a_stacked)): + y[i] += (x @ lora_a_stacked[i]) * scale + + Args: + y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors + x (torch.Tensor): Input tensor + lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights + scale (float): Scaling factor for the operation + """ + + x = x.view(-1, x.shape[-1]) + # TODO fuse these kernels + for slice_idx in range(len(lora_a_stacked)): + self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx], + scale) + + def add_expand(self, + y: torch.Tensor, + x: Union[Tuple[torch.Tensor, ...], torch.Tensor], + lora_b_stacked: Tuple[torch.Tensor, ...], + lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], + output_slices: Tuple[int, ...], + offset_start: int = 0, + add_input=True, + **kwargs) -> None: + """ + Performs GEMM and bias addition for multiple slices of lora_b. + + Semantics: + for i in range(len(lora_b_stacked)): + slice = output_slices[i] + y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] + + lora_bias_stacked[i] + offset += slice + + Args: + y (torch.Tensor): Output tensor. + x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors + lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight + lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): + bias's weight + output_slices (Tuple[int, ...]): Every slice's size + add_input (bool): Defaults to True. + """ + y_org = y + y = y.view(-1, y.shape[-1]) + offset_left = offset_start + if lora_bias_stacked is not None: + self._apply_bias(self.token_lora_indices, y, output_slices, + lora_bias_stacked) + for slice_idx in range(len(lora_b_stacked)): + self._apply_expand( + y, + x[slice_idx], + lora_b_stacked[slice_idx], + offset_left, + output_slices[slice_idx], + add_input=add_input, + ) + offset_left += output_slices[slice_idx] + y = y.view_as(y_org) + + def add_lora_embedding(self, + y: torch.Tensor, + x: torch.Tensor, + lora_b_stacked: torch.Tensor, + add_input: bool = True, + **kwargs) -> None: + """ + Applies lora specifically for VocabParallelEmbeddingWithLoRA. + + Semantics: + y += x @ lora_b_stacked + + Args: + y (torch.Tensor): Output tensor. + x (torch.Tensor): Input tensor. + lora_b_stacked (torch.Tensor): lora_b's weights. + add_input (bool): Default to True. + """ + + # Embedding layer only need expand op + expand_fun: Callable = (self._expand_prefill + if self.is_prefill else self._expand_decode) + expand_fun(y, x, lora_b_stacked, add_input) + + def add_lora_linear(self, + y: torch.Tensor, + x: torch.Tensor, + lora_a_stacked: Tuple[torch.Tensor, ...], + lora_b_stacked: Tuple[torch.Tensor, ...], + lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]], + scale: float, + output_slices: Tuple[int, ...], + *, + buffer: Optional[Tuple[torch.Tensor, ...]] = None, + **kwargs) -> None: + """ + Applicable to linear-related lora. + + Semantics: + for i in range(len(lora_a_stacked)): + y[i] += ( + x[i].unsqueeze(0) + @ lora_a_stacked[indices[i], layer_idx, :, :] + @ lora_b_stacked[indices[i], layer_idx, :, :] + * scale + ).squeeze(0)+lora_bias_stacked[i] + + Args: + y (torch.Tensor): Output tensor. Will be changed in-place. + x (torch.Tensor): Input tensor + lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight. + lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight. + lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias. + scale (float): Scaling factor. + output_slices (Tuple[int, ...]): Every slice's size. + buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None. + """ + + assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices) + if lora_bias_stacked is not None: + assert len(lora_bias_stacked) == len(output_slices) + y = self._apply_bias(self.token_lora_indices, y, output_slices, + lora_bias_stacked) + + if buffer is None: + r = lora_b_stacked[0].size(-1) + # We set the buffer to be float32 by default ,refer to: + # https://github.com/triton-lang/triton/issues/1387 + buffer = tuple( + torch.zeros( + (x.size(0), r), dtype=torch.float32, device=x.device) + for _ in range(len(output_slices))) + self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs) + self.add_expand(y, + buffer, + lora_b_stacked, + None, + output_slices, + add_input=True, + **kwargs) + + def add_lora_logits(self, + y: torch.Tensor, + x: torch.Tensor, + lora_a_stacked: torch.Tensor, + lora_b_stacked: torch.Tensor, + scale, + *, + buffer: Optional[torch.Tensor] = None, + **kwargs) -> None: + """ + Applies lora specifically for LogitsProcessorWithLoRA. + + Semantics: + buffer = (x @ lora_a_stacked) * scale + y += buffer @ lora_b_stacked + + Args: + y (torch.Tensor): Output tensor. + x (torch.Tensor): Input tensor. + lora_a_stacked (torch.Tensor): lora_a's weights. + lora_b_stacked (torch.Tensor):lora_b's weights. + scale (float): Scaling factor. + buffer (Optional[torch.Tensor]):Default to None. + """ + y_org = y + y = y.view(-1, y.shape[-1]) + x = x.view(-1, x.shape[-1]) + r = lora_b_stacked.size(-1) + if buffer is None: + # We set the buffer to be float32 by default ,refer to: + # https://github.com/triton-lang/triton/issues/1387 + buffer = torch.zeros((x.size(0), r), + dtype=torch.float32, + device=x.device) + # LogitsProcessorWithLoRA always using bgmv. + bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale) + bgmv_expand(buffer, + lora_b_stacked, + y, + self.sampler_indices, + add_inputs=True) + y = y.view_as(y_org) diff --git a/vllm/lora/punica_wrapper/punica_selector.py b/vllm/lora/punica_wrapper/punica_selector.py new file mode 100644 index 0000000000000..df6c1bdc7dd71 --- /dev/null +++ b/vllm/lora/punica_wrapper/punica_selector.py @@ -0,0 +1,14 @@ +from vllm.platforms import current_platform +from vllm.utils import print_info_once + +from .punica_base import PunicaWrapperBase + + +def get_punica_wrapper(*args, **kwargs) -> PunicaWrapperBase: + if current_platform.is_cuda_alike(): + # Lazy import to avoid ImportError + from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU + print_info_once("Using PunicaWrapperGPU.") + return PunicaWrapperGPU(*args, **kwargs) + else: + raise NotImplementedError diff --git a/vllm/lora/punica_wrapper/utils.py b/vllm/lora/punica_wrapper/utils.py new file mode 100644 index 0000000000000..7360c8c09e3ac --- /dev/null +++ b/vllm/lora/punica_wrapper/utils.py @@ -0,0 +1,159 @@ +from typing import TYPE_CHECKING, List, Optional, Tuple, Union + +import torch + +if TYPE_CHECKING: + # avoid circuit import + from vllm.lora.layers import LoRAMapping + from vllm.lora.models import LongContextLoRAContext + + +def compute_meta( + token_lora_tensor: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int, bool]: + """ + Get the information required for the sgmv kernel. With the features: + 1. If consecutive requests in the batch use the same LoRA, this function + will combine them into a single request, improving sgmv kernel inference + performance. + 2. At the beginning of each prefill stage inference, recalculations are + needed based on the input, but only once. + """ + + lora_indices_tensor, seq_length_tensor = torch.unique_consecutive( + token_lora_tensor, return_counts=True) + cum_result = torch.cumsum(seq_length_tensor, dim=0) + b_seq_start_tensor = torch.zeros_like(seq_length_tensor) + b_seq_start_tensor[1:].copy_(cum_result[:-1]) + max_length = seq_length_tensor.max().item() + token_nums = seq_length_tensor.sum().item() + batch_size = lora_indices_tensor.size(0) + no_lora = False + # -1 means no lora should be applied. Use `no_lora` to determine whether + # the current step requires LoRA. If LoRA is not needed, the prefill stage + # does not need to launch the triton kernel, which can improve performance + if batch_size == 1 and lora_indices_tensor == -1: + no_lora = True + return (b_seq_start_tensor, seq_length_tensor, lora_indices_tensor, + batch_size, max_length, token_nums, no_lora) + + +# TODO see if this can be vectorized +def convert_mapping( + mapping: "LoRAMapping", + lora_index_to_id: List[Optional[int]], + max_loras: int, + vocab_size: int, + extra_vocab_size: int, + device: torch.device, + long_lora_context: Optional["LongContextLoRAContext"] = None, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, + Optional[torch.Tensor], List[int]]: + """Converts LoRAMapping to index tensors. + + Args: + mapping: LoRAMapping mapping rows in a batch to LoRA ids. + lora_index_to_id: List mapping LoRA ids to LoRA indices. + max_loras: Maximum number of LoRAs. + vocab_size: Model vocab size. + extra_vocab_size: Extra vocab size each LoRA can have. + long_lora_context: Passed if there are long context lora in a batch. + + Returns: + A tuple of tensors: + base_indices: Tensor of shape [batch_size] mapping batch rows to + LoRA indices. + sampler_indices: Tensor of shape [batch_size] mapping requests to + LoRA indices for sampler. For generation, this will be the + same as base_indicies. For prefill, this will map requests + to LoRA indices. + sampler_indices_padded: Tensor of shape [batch_size] mapping + requests to LoRA indices for sampler with padding. + Same as sampler_indicies, but -1 is replaced with + max_loras. + embeddings_indices: Tensor of shape [2, batch_size] mapping + requests to embedding indices. First row is for embeddings + added by the LoRAs, second row is for the LoRA.lora_a + embeddings. + long_lora_indices: Tensor of shape [batch_size] mapping + requests to RoPE offsets and rot dims for long LoRAs. + None if long context lora doesn't exist. + indices_len: List of lengths of the above tensors. It contains + (base_indices, sampler_indices, sampler_indices_padded, + embeddings_indices, long_lora_indices). + """ + index_mapping_indices: List[int] = list(mapping.index_mapping).copy() + embedding_indices = index_mapping_indices.copy() + lora_indices = index_mapping_indices.copy() + long_lora_offsets: Optional[torch.Tensor] = None + if long_lora_context: + long_lora_offsets = torch.zeros(len(index_mapping_indices), + device=device, + dtype=torch.long) + prompt_mapping: List[int] = [ + lora_index_to_id.index(x) if x > 0 else -1 + for x in mapping.prompt_mapping + ] + lora_idx = None + for i in range(len(index_mapping_indices)): + # TODO index can be slow. optimize + lora_idx = (lora_index_to_id.index(index_mapping_indices[i]) + if index_mapping_indices[i] > 0 else -1) + embedding_indices[i] = lora_idx if index_mapping_indices[i] > 0 else 0 + lora_indices[i] = lora_idx + if long_lora_context: + assert long_lora_offsets is not None + lora_offset: int = long_lora_context.offsets_by_lora_id.get( + index_mapping_indices[i], 0) + long_lora_offsets[i] = lora_offset + + indices_list: List[Union[List[int], torch.Tensor]] = [ + index_mapping_indices, + lora_indices, + embedding_indices, + ] + if long_lora_context: + assert long_lora_offsets is not None + indices_list.append(long_lora_offsets) + indices = torch.tensor(indices_list, dtype=torch.long, device=device) + prompt_mapping_tensor = torch.tensor(prompt_mapping, + dtype=torch.long, + device=device) + embeddings_indices = torch.stack([ + indices[2] * extra_vocab_size, + indices[2] * (vocab_size + extra_vocab_size), + ]) + embeddings_indices[embeddings_indices == -1] = max_loras - 1 + base_indices = indices[1] + sampler_indices = prompt_mapping_tensor + sampler_indices_padded = sampler_indices.clone() + sampler_indices_padded[sampler_indices_padded == -1] = max_loras - 1 + sampler_indices_padded = torch.arange( + 0, len(sampler_indices_padded), device=device, dtype=torch.long) + ( + sampler_indices_padded * len(sampler_indices_padded)) + long_lora_indices = None + long_lora_indices_len: Optional[int] = None + if long_lora_context: + long_lora_indices = indices[3] + long_lora_indices_len = long_lora_indices.shape[-1] + # Contain length of indices tensors. Used to index into each tensor. + indices_len = [ + base_indices.shape[-1], + sampler_indices.shape[-1], + sampler_indices_padded.shape[-1], + embeddings_indices.shape[-1], + ] + if long_lora_indices_len is not None: + indices_len.append(long_lora_indices_len) + else: + # If long_lora doesn't exist,append None + indices_len.append(None) + + return ( + base_indices, + sampler_indices, + sampler_indices_padded, + embeddings_indices, + long_lora_indices, + indices_len, + ) From a811dd660856a5c222a1447fe1d93deccbc162fd Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Tue, 10 Dec 2024 04:55:10 +0800 Subject: [PATCH 03/79] [Model] merged input processor for Phi-3-Vision models (#10977) Signed-off-by: Isotr0py <2037008807@qq.com> Co-authored-by: Cyrus Leung --- tests/entrypoints/openai/test_vision.py | 4 +- .../openai/test_vision_embedding.py | 4 +- .../mm_processor_kwargs/test_phi3v.py | 136 ++------ tests/multimodal/test_processor_kwargs.py | 169 +++++----- vllm/inputs/registry.py | 4 +- vllm/model_executor/models/phi3v.py | 298 +++++------------- vllm/multimodal/processing.py | 29 +- 7 files changed, 235 insertions(+), 409 deletions(-) diff --git a/tests/entrypoints/openai/test_vision.py b/tests/entrypoints/openai/test_vision.py index 157d873a75b4d..a0b6edd566561 100644 --- a/tests/entrypoints/openai/test_vision.py +++ b/tests/entrypoints/openai/test_vision.py @@ -89,7 +89,7 @@ async def test_single_chat_session_image(client: openai.AsyncOpenAI, choice = chat_completion.choices[0] assert choice.finish_reason == "length" assert chat_completion.usage == openai.types.CompletionUsage( - completion_tokens=10, prompt_tokens=772, total_tokens=782) + completion_tokens=10, prompt_tokens=775, total_tokens=785) message = choice.message message = chat_completion.choices[0].message @@ -181,7 +181,7 @@ async def test_single_chat_session_image_base64encoded( choice = chat_completion.choices[0] assert choice.finish_reason == "length" assert chat_completion.usage == openai.types.CompletionUsage( - completion_tokens=10, prompt_tokens=772, total_tokens=782) + completion_tokens=10, prompt_tokens=775, total_tokens=785) message = choice.message message = chat_completion.choices[0].message diff --git a/tests/entrypoints/openai/test_vision_embedding.py b/tests/entrypoints/openai/test_vision_embedding.py index d0c43b47bf0af..425f2a10ec855 100644 --- a/tests/entrypoints/openai/test_vision_embedding.py +++ b/tests/entrypoints/openai/test_vision_embedding.py @@ -95,5 +95,5 @@ async def test_image_embedding(server: RemoteOpenAIServer, model_name: str, assert len(embeddings["data"]) == 1 assert len(embeddings["data"][0]["embedding"]) == 3072 assert embeddings["usage"]["completion_tokens"] == 0 - assert embeddings["usage"]["prompt_tokens"] == 762 - assert embeddings["usage"]["total_tokens"] == 762 + assert embeddings["usage"]["prompt_tokens"] == 765 + assert embeddings["usage"]["total_tokens"] == 765 diff --git a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_phi3v.py b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_phi3v.py index 60a8f63eb5faa..c16192a1e1438 100644 --- a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_phi3v.py +++ b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_phi3v.py @@ -2,12 +2,10 @@ from typing import Optional import pytest -import torch -from transformers import AutoImageProcessor, AutoTokenizer +from transformers import AutoTokenizer -from vllm.inputs import InputContext, token_inputs +from vllm.inputs import InputContext, InputProcessingContext from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID -from vllm.multimodal import MultiModalRegistry from .....conftest import _ImageAssets from ....utils import build_model_context @@ -17,15 +15,9 @@ # Wrap lazy imports to avoid initializing CUDA during test collection @pytest.fixture() -def input_processor_for_phi3v(): - from vllm.model_executor.models.phi3v import input_processor_for_phi3v - return input_processor_for_phi3v - - -@pytest.fixture() -def dummy_data_for_phi3v(): - from vllm.model_executor.models.phi3v import dummy_data_for_phi3v - return dummy_data_for_phi3v +def processor_for_phi3v(): + from vllm.model_executor.models.phi3v import Phi3VProcessor + return Phi3VProcessor @pytest.fixture() @@ -34,53 +26,6 @@ def get_max_phi3v_image_tokens(): return get_max_phi3v_image_tokens -@pytest.mark.parametrize("model", models) -@pytest.mark.parametrize("num_crops", [4, 16, None]) -def test_input_mapper_override(model: str, image_assets: _ImageAssets, - num_crops: Optional[int]): - """Ensure that the [default] input mapper handles num_crops properly.""" - # We pass the processor kwargs here since for this model, we fall back to - # the default mapper; this will fall back to the HF mapper and forward - # mm_processor_kwargs to it. - mm_processor_kwargs = { - "num_crops": num_crops - } if num_crops is not None else {} - ctx = build_model_context( - model_name=model, - tokenizer_name=model, - trust_remote_code=True, - mm_processor_kwargs=mm_processor_kwargs, - ) - - hf_processor = AutoImageProcessor.from_pretrained(model, - trust_remote_code=True, - **mm_processor_kwargs) - - mm_registry = MultiModalRegistry() - mm_registry.init_mm_limits_per_prompt(ctx.model_config) - - image = image_assets[0].pil_image - hf_result = hf_processor.preprocess( - image, - return_tensors="pt", - ) - - vllm_result = mm_registry.map_input( - ctx.model_config, - {"image": image}, - ) - - assert torch.all(hf_result["image_sizes"] == vllm_result["image_sizes"]) - assert torch.all( - hf_result["num_img_tokens"] == vllm_result["num_img_tokens"]) - - # For pixel values, the second axis should be the num_crops + 1 - # for the rescaled original image. The default value in VLLM falls - # back to the HF config, which is why we compare to the processor num_crops - assert torch.all(hf_result["pixel_values"] == vllm_result["pixel_values"]) - assert vllm_result["pixel_values"].shape[1] == hf_processor.num_crops + 1 - - @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("num_crops,expected_max_tokens", [ (4, 781), @@ -112,48 +57,20 @@ def test_max_tokens_override(get_max_phi3v_image_tokens, model: str, @pytest.mark.parametrize("model", models) -@pytest.mark.parametrize("num_crops,toks_per_img,num_imgs", [ - (4, 781, 1), - (4, 781, 2), - (16, 2653, 1), - (16, 2653, 2), -]) -def test_dummy_data_override(dummy_data_for_phi3v, model: str, num_crops: int, - toks_per_img: int, num_imgs: int): - """Ensure dummy_data_for_phi3v handles num_crops properly.""" - # Same as the previous test - don't initialize mm_processor_kwargs - # in this test and assume that the kwargs will be correctly expanded by - # the partial when calling the dummy data func. - ctx = build_model_context( - model_name=model, - tokenizer_name=model, - trust_remote_code=True, - mm_processor_kwargs=None, - ) - - dummy_data = dummy_data_for_phi3v( - ctx=ctx, - seq_len=8192, # Should be bigger than num_imgs * toks_per_img - mm_counts={"image": num_imgs}, - num_crops=num_crops, - ) - sequence_data = dummy_data.seq_data - # Ensure we have the right number of placeholders per num_crops size - img_tok_count = sequence_data.get_token_ids().count(_IMAGE_TOKEN_ID) - assert img_tok_count == toks_per_img * num_imgs - - -@pytest.mark.parametrize("model", models) -@pytest.mark.parametrize("num_crops,expected_toks_per_img,num_imgs", [ - (4, 757, 1), - (4, 757, 2), - (16, 1921, 1), - (16, 1921, 2), -]) -def test_input_processor_override(input_processor_for_phi3v, - image_assets: _ImageAssets, model: str, - num_crops: int, expected_toks_per_img: int, - num_imgs: int): +@pytest.mark.parametrize( + "num_crops,expected_toks_per_img,num_imgs", + [ + (4, 757, 1), + (4, 757, 2), + (16, 1921, 1), + (16, 1921, 2), + # the default num_crops of phi-3.5-vision is 4 + (None, 757, 2), + (None, 757, 2), + ]) +def test_processor_override(processor_for_phi3v, image_assets: _ImageAssets, + model: str, num_crops: Optional[int], + expected_toks_per_img: int, num_imgs: int): """Ensure input_processor_for_phi3v handles num_crops properly.""" # Same as the previous test - don't initialize mm_processor_kwargs # in this test and assume that the kwargs will be correctly expanded by @@ -163,19 +80,20 @@ def test_input_processor_override(input_processor_for_phi3v, tokenizer_name=model, trust_remote_code=True, ) - tokenizer = AutoTokenizer.from_pretrained(model) + tokenizer = AutoTokenizer.from_pretrained(model, trust_remote_code=True) + ctx = InputProcessingContext(ctx.model_config, tokenizer) # Build the image str / prompt based on the number of images we pass img_str = "".join([f"<|image_{idx}|>\n" for idx in range(1, num_imgs + 1)]) prompt = f"<|user|>\n{img_str}<|end|>\n<|assistant|>\n" images = [image_assets[0].pil_image] * num_imgs - inputs = token_inputs(prompt_token_ids=tokenizer.encode(prompt), - prompt=prompt, - multi_modal_data={"image": images}) + mm_data = {"image": images} + mm_processor_kwargs = {} + if num_crops is not None: + mm_processor_kwargs = {"num_crops": num_crops} - processed_inputs = input_processor_for_phi3v(ctx, - inputs, - num_crops=num_crops) + processor = processor_for_phi3v(ctx) + processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs) # Ensure we have the right number of placeholders per num_crops size img_tok_count = processed_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID) diff --git a/tests/multimodal/test_processor_kwargs.py b/tests/multimodal/test_processor_kwargs.py index e6c8793989e13..d141cdf1f083b 100644 --- a/tests/multimodal/test_processor_kwargs.py +++ b/tests/multimodal/test_processor_kwargs.py @@ -15,13 +15,13 @@ # Used for fast tests where the model doesn't matter DUMMY_MODEL_ID = "facebook/opt-125m" # Used for tests that need a multimodal model -MULTIMODAL_MODEL_ID = "microsoft/Phi-3.5-vision-instruct" +MULTIMODAL_MODEL_ID = "OpenGVLab/InternVL2-2B" # For mm_processor_kwargs - we test overrides by defining mocks for each place # it is used, and ensuring that we can pass processor kwargs an override value # to receive the intended result for things like sequence length etc. -DEFAULT_NUM_CROPS = 4 -NUM_CROPS_OVERRIDE = 16 +DEFAULT_MAX_DYNAMIC_PATCH = 6 +MAX_DYNAMIC_PATCH_OVERRIDE = 4 # Mocks for all of the places that we use the mm_processor_kwargs @@ -33,10 +33,11 @@ def use_processor_mock(): def custom_processor(ctx: InputContext, inputs: DecoderOnlyInputs, *, - num_crops=DEFAULT_NUM_CROPS): + max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH): # For testing purposes, we don't worry about the prompt - return token_inputs(prompt_token_ids=[], - mm_processor_kwargs={"num_crops": num_crops}) + return token_inputs( + prompt_token_ids=[], + mm_processor_kwargs={"max_dynamic_patch": max_dynamic_patch}) with patch("vllm.inputs.registry.InputRegistry._get_model_input_processor", return_value=custom_processor): @@ -52,9 +53,9 @@ def custom_dummy_data_factory(self, seq_len: int, mm_counts: Mapping[str, int], *, - num_crops=DEFAULT_NUM_CROPS): + max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH): seq_data = SequenceData( - array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * num_crops)) + array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * max_dynamic_patch)) return DummyData(seq_data, None) with patch( @@ -65,15 +66,15 @@ def custom_dummy_data_factory(self, # Lazy import to avoid CUDA reinitialization error def mm_model_cls(): - from vllm.model_executor.models.phi3v import Phi3VForCausalLM + from vllm.model_executor.models.internvl import InternVLChatModel - return Phi3VForCausalLM + return InternVLChatModel # lambda whose signature matches max token calcs extra & mapper + extra kwargs -get_num_crops = lambda ctx, *, num_crops=DEFAULT_NUM_CROPS: num_crops -custom_mapper = lambda ctx, data, *, num_crops=DEFAULT_NUM_CROPS: { - "pixel_values": torch.zeros(size=(1, num_crops + 1, 3, 336, 336)) +get_max_dynamic_patch = lambda ctx, *, max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH: max_dynamic_patch # noqa: E501 +custom_mapper = lambda ctx, data, *, max_dynamic_patch=DEFAULT_MAX_DYNAMIC_PATCH: { # noqa: E501 + "pixel_values": torch.zeros(size=(1, max_dynamic_patch + 1, 3, 448, 448)) } @@ -88,27 +89,28 @@ def test_default_processor_is_a_noop(): assert proc_inputs is proc_outputs -def _get_num_crops_info(init_num_crops: int, inference_num_crops: int): - """Get the init / inference kwargs and expected num_crops for this test.""" - # If we have a value for num_crops, pass the override value and make +def _get_max_dynamic_patch_info(init_max_dynamic_patch: int, + inference_max_dynamic_patch: int): + """Get the init / inference kwargs and expected max_dynamic_patch.""" + # If we have a value for max_dynamic_patch, pass the override value and make # sure we get that value as a return-value from out mock processor, # otherwise fall back to the default value - init_kwargs = None if init_num_crops is None else { - "num_crops": init_num_crops + init_kwargs = None if init_max_dynamic_patch is None else { + "max_dynamic_patch": init_max_dynamic_patch } - inference_kwargs = None if inference_num_crops is None else { - "num_crops": inference_num_crops + inference_kwargs = None if inference_max_dynamic_patch is None else { + "max_dynamic_patch": inference_max_dynamic_patch } - if inference_num_crops is not None: - expected_seq_count = inference_num_crops - elif init_num_crops is not None: - expected_seq_count = init_num_crops + if inference_max_dynamic_patch is not None: + expected_seq_count = inference_max_dynamic_patch + elif init_max_dynamic_patch is not None: + expected_seq_count = init_max_dynamic_patch else: - expected_seq_count = DEFAULT_NUM_CROPS + expected_seq_count = DEFAULT_MAX_DYNAMIC_PATCH return init_kwargs, inference_kwargs, expected_seq_count -def _get_processed_num_crops( +def _get_processed_max_dynamic_patch( processor: Callable[[ProcessorInputs], ProcessorInputs], inference_kwargs: Optional[Dict[str, int]], ) -> int: @@ -120,27 +122,30 @@ def _get_processed_num_crops( assert "type" in processed_inputs assert processed_inputs["type"] == "token" assert "mm_processor_kwargs" in processed_inputs - return processed_inputs["mm_processor_kwargs"]["num_crops"] + return processed_inputs["mm_processor_kwargs"]["max_dynamic_patch"] -@pytest.mark.parametrize("init_num_crops,inference_num_crops", [ - (None, None), - (NUM_CROPS_OVERRIDE, None), - (DEFAULT_NUM_CROPS, NUM_CROPS_OVERRIDE), -]) -def test_input_processor_kwargs(use_processor_mock, init_num_crops, - inference_num_crops): +@pytest.mark.parametrize( + "init_max_dynamic_patch,inference_max_dynamic_patch", [ + (None, None), + (MAX_DYNAMIC_PATCH_OVERRIDE, None), + (DEFAULT_MAX_DYNAMIC_PATCH, MAX_DYNAMIC_PATCH_OVERRIDE), + ]) +def test_input_processor_kwargs(use_processor_mock, init_max_dynamic_patch, + inference_max_dynamic_patch): """Ensure input processors can use processor kwargs.""" dummy_registry = InputRegistry() - init_kwargs, inference_kwargs, expected_seq_count = _get_num_crops_info( - init_num_crops, inference_num_crops) + (init_kwargs, inference_kwargs, + expected_seq_count) = _get_max_dynamic_patch_info( + init_max_dynamic_patch, inference_max_dynamic_patch) ctx = build_model_context(DUMMY_MODEL_ID, mm_processor_kwargs=init_kwargs) processor = dummy_registry.create_input_processor(ctx.model_config) - num_crops_val = _get_processed_num_crops(processor, inference_kwargs) + max_dynamic_patch_val = _get_processed_max_dynamic_patch( + processor, inference_kwargs) - assert num_crops_val == expected_seq_count + assert max_dynamic_patch_val == expected_seq_count @pytest.mark.parametrize( @@ -165,18 +170,21 @@ def test_processor_with_sad_kwarg_overrides(use_processor_mock, processor = dummy_registry.create_input_processor(ctx.model_config) # Should filter out the inference time kwargs - num_crops_val = _get_processed_num_crops(processor, mm_processor_kwargs) - assert num_crops_val == DEFAULT_NUM_CROPS + max_dynamic_patch_val = _get_processed_max_dynamic_patch( + processor, mm_processor_kwargs) + assert max_dynamic_patch_val == DEFAULT_MAX_DYNAMIC_PATCH ### Test overrides for the dummy data -@pytest.mark.parametrize("num_crops", [None, NUM_CROPS_OVERRIDE]) -def test_dummy_data_kwarg_overrides(use_dummy_data_mock, num_crops): +@pytest.mark.parametrize("max_dynamic_patch", + [None, MAX_DYNAMIC_PATCH_OVERRIDE]) +def test_dummy_data_kwarg_overrides(use_dummy_data_mock, max_dynamic_patch): """Ensure dummy data factories can use processor kwargs.""" - mm_processor_kwargs = None if num_crops is None else { - "num_crops": num_crops + mm_processor_kwargs = None if max_dynamic_patch is None else { + "max_dynamic_patch": max_dynamic_patch } - expected_seq_count = DEFAULT_NUM_CROPS if num_crops is None else num_crops + expected_seq_count = (DEFAULT_MAX_DYNAMIC_PATCH + if max_dynamic_patch is None else max_dynamic_patch) dummy_registry = InputRegistry() ctx = build_model_context(DUMMY_MODEL_ID, mm_processor_kwargs=mm_processor_kwargs) @@ -217,17 +225,20 @@ def test_dummy_data_with_sad_kwarg_overrides(use_dummy_data_mock, # len is solely dependent on the value of the mm_processor_kwargs. dummy_data = dummy_registry.dummy_data_for_profiling( ctx.model_config, seq_len=-1, mm_registry=mm_registry) - assert len(dummy_data.seq_data.prompt_token_ids) == DEFAULT_NUM_CROPS + assert len( + dummy_data.seq_data.prompt_token_ids) == DEFAULT_MAX_DYNAMIC_PATCH ### Test overrides for the max token count per multimodal instance -@pytest.mark.parametrize("num_crops", [None, NUM_CROPS_OVERRIDE]) -def test_max_tokens_kwarg_overrides(num_crops): +@pytest.mark.parametrize("max_dynamic_patch", + [None, MAX_DYNAMIC_PATCH_OVERRIDE]) +def test_max_tokens_kwarg_overrides(max_dynamic_patch): """Ensure max token calcs can use processor kwargs.""" - mm_processor_kwargs = None if num_crops is None else { - "num_crops": num_crops + mm_processor_kwargs = None if max_dynamic_patch is None else { + "max_dynamic_patch": max_dynamic_patch } - expected_seq_count = DEFAULT_NUM_CROPS if num_crops is None else num_crops + expected_seq_count = (DEFAULT_MAX_DYNAMIC_PATCH + if max_dynamic_patch is None else max_dynamic_patch) ctx = build_model_context(MULTIMODAL_MODEL_ID, task="generate", @@ -239,11 +250,11 @@ def test_max_tokens_kwarg_overrides(num_crops): mm_registry.init_mm_limits_per_prompt(ctx.model_config) # Patch the image registry for phi3v with our lambda that is compatible # with overrides, then ensure that calling the method correctly echos - # our num_crops value back from the mm_processor_kwargs. + # our max_dynamic_patch value back from the mm_processor_kwargs. with patch.object( mm_registry._get_plugin("image"), "_max_mm_tokens", - {mm_model_cls(): get_num_crops}, + {mm_model_cls(): get_max_dynamic_patch}, ): max_multimodal_tokens = mm_registry.get_max_multimodal_tokens( ctx.model_config) @@ -279,26 +290,29 @@ def test_max_tokens_with_sad_kwarg_overrides(mm_processor_kwargs): with patch.object( mm_registry._get_plugin("image"), "_max_mm_tokens", - {mm_model_cls(): get_num_crops}, + {mm_model_cls(): get_max_dynamic_patch}, ): max_multimodal_tokens = mm_registry.get_max_multimodal_tokens( ctx.model_config) - assert max_multimodal_tokens == DEFAULT_NUM_CROPS + assert max_multimodal_tokens == DEFAULT_MAX_DYNAMIC_PATCH ### Test overrides for the mapper -@pytest.mark.parametrize("num_crops", [DEFAULT_NUM_CROPS, NUM_CROPS_OVERRIDE]) -def test_default_mapper_with_processor_kwargs(image_assets, num_crops): +@pytest.mark.parametrize( + "max_dynamic_patch", + [DEFAULT_MAX_DYNAMIC_PATCH, MAX_DYNAMIC_PATCH_OVERRIDE]) +def test_default_mapper_with_processor_kwargs(image_assets, max_dynamic_patch): """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}) + ctx = build_model_context( + MULTIMODAL_MODEL_ID, + task="generate", + trust_remote_code=True, + mm_processor_kwargs={"max_dynamic_patch": max_dynamic_patch}, + limit_mm_per_prompt={"image": 1}) mm_registry = MultiModalRegistry() mm_registry.init_mm_limits_per_prompt(ctx.model_config) @@ -307,20 +321,22 @@ def test_default_mapper_with_processor_kwargs(image_assets, num_crops): mm_inputs = {"image": image} mapped_inputs = mm_registry.map_input(ctx.model_config, mm_inputs) - # Phi3v pixel vals should have shape: [batch, num_crops+1, 3, 336, 336] - assert mapped_inputs["pixel_values"].shape[1] == num_crops + 1 + # pixel vals should have shape: [batch, max_dynamic_patch+1, ...] + assert mapped_inputs["pixel_values"].shape[1] == max_dynamic_patch + 1 -@pytest.mark.parametrize("init_num_crops,inference_num_crops", [ - (None, None), - (NUM_CROPS_OVERRIDE, None), - (DEFAULT_NUM_CROPS, NUM_CROPS_OVERRIDE), -]) -def test_custom_mapper_kwarg_overrides(image_assets, init_num_crops, - inference_num_crops): +@pytest.mark.parametrize( + "init_max_dynamic_patch,inference_max_dynamic_patch", [ + (None, None), + (MAX_DYNAMIC_PATCH_OVERRIDE, None), + (DEFAULT_MAX_DYNAMIC_PATCH, MAX_DYNAMIC_PATCH_OVERRIDE), + ]) +def test_custom_mapper_kwarg_overrides(image_assets, init_max_dynamic_patch, + inference_max_dynamic_patch): """Ensure custom mappers can use processor kwargs.""" - init_kwargs, inference_kwargs, expected_seq_count = _get_num_crops_info( - init_num_crops, inference_num_crops) + (init_kwargs, inference_kwargs, + expected_seq_count) = _get_max_dynamic_patch_info( + init_max_dynamic_patch, inference_max_dynamic_patch) ctx = build_model_context(MULTIMODAL_MODEL_ID, task="generate", @@ -335,7 +351,7 @@ def test_custom_mapper_kwarg_overrides(image_assets, init_num_crops, # Patch the image registry for phi3v with our lambda that is compatible # with overrides, then ensure that calling the method correctly echos - # our num_crops value back from the mm_processor_kwargs. + # our max_dynamic_patch value back from the mm_processor_kwargs. mm_registry._get_plugin("image").register_input_mapper(custom_mapper)( mm_model_cls()) mapped_inputs = mm_registry.map_input(ctx.model_config, mm_inputs, @@ -373,11 +389,12 @@ def test_custom_mapper_with_sad_kwarg_overrides(image_assets, # Patch the image registry for phi3v with our lambda that is compatible # with overrides, then ensure that calling the method correctly echos - # our num_crops value back from the mm_processor_kwargs. + # our max_dynamic_patch value back from the mm_processor_kwargs. mm_registry._get_plugin("image").register_input_mapper(custom_mapper)( mm_model_cls()) # Should filter out the inference time kwargs mapped_inputs = mm_registry.map_input( ctx.model_config, mm_inputs, mm_processor_kwargs=mm_processor_kwargs) - assert mapped_inputs["pixel_values"].shape[1] == DEFAULT_NUM_CROPS + 1 + assert mapped_inputs["pixel_values"].shape[1] == ( + DEFAULT_MAX_DYNAMIC_PATCH + 1) diff --git a/vllm/inputs/registry.py b/vllm/inputs/registry.py index 646554c72481a..0dfed3b7e61bf 100644 --- a/vllm/inputs/registry.py +++ b/vllm/inputs/registry.py @@ -69,12 +69,12 @@ class InputProcessingContext(InputContext): tokenizer: AnyTokenizer """The tokenizer used to tokenize the inputs.""" - def get_hf_processor(self) -> ProcessorMixin: + def get_hf_processor(self, **kwargs) -> ProcessorMixin: return cached_get_processor( self.model_config.tokenizer, tokenizer=self.tokenizer, # Override the tokenizer with ours trust_remote_code=self.model_config.trust_remote_code, - ) + **kwargs) N = TypeVar("N", bound=Type[nn.Module]) diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index eef23029a2aca..3c7854ce388ab 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -12,22 +12,18 @@ # 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. -import itertools -import re -from functools import cached_property, lru_cache -from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional, Set, - Tuple, TypedDict, Union) +from functools import cached_property +from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple, + TypedDict, Union) -import numpy as np import torch import torch.nn as nn -from PIL import Image -from transformers import CLIPVisionConfig, PretrainedConfig +from transformers import (BatchFeature, CLIPVisionConfig, PretrainedConfig, + ProcessorMixin) from vllm.attention import AttentionMetadata -from vllm.config import ModelConfig, VllmConfig -from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData, - InputContext, token_inputs) +from vllm.config import VllmConfig +from vllm.inputs import InputContext from vllm.logger import init_logger from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler @@ -36,12 +32,18 @@ from vllm.model_executor.models.clip import CLIPVisionModel from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.multimodal import MULTIMODAL_REGISTRY -from vllm.multimodal.inputs import NestedTensors, PlaceholderRange -from vllm.multimodal.utils import cached_get_tokenizer, repeat_and_pad_token +from vllm.multimodal.image import cached_get_image_processor +from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors +from vllm.multimodal.processing import (BaseMultiModalProcessor, + InputProcessingContext, + ModalityProcessingMetadata, + MultiModalDataDict, + MultiModalProcessingMetadata, + PromptReplacement) from vllm.sequence import IntermediateTensors from vllm.utils import is_list_of -from .clip import dummy_image_for_clip, dummy_seq_data_for_clip +from .clip import dummy_image_for_clip from .interfaces import SupportsMultiModal, SupportsPP from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, init_vllm_registered_model, maybe_prefix, @@ -303,231 +305,99 @@ def add_image_newline(self, image_features_hd): return image_features_hd_newline -# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L57 -def _calc_padded_size(*, width: int, height: int, padding_unit: int = 336): - target_height = int(np.ceil(height / padding_unit) * padding_unit) - top_padding = int((target_height - height) / 2) - bottom_padding = target_height - height - top_padding - padded_width = width - padded_height = height + top_padding + bottom_padding - return padded_width, padded_height - - -# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L90 -def _calc_hd_transform_size(*, width: int, height: int, hd_num: int): - transposed = False - if width < height: - width, height = height, width - transposed = True - - ratio = width / height - scale = 1 - while scale * np.ceil(scale / ratio) <= hd_num: - scale += 1 - scale -= 1 - - new_width = int(scale * 336) - new_height = int(new_width / ratio) - - padded_width, padded_height = _calc_padded_size(width=new_width, - height=new_height) - - if transposed: - padded_width, padded_height = padded_height, padded_width - - return padded_width, padded_height - - -# Based on https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_processing_phi3_v.py#L181 -def get_phi3v_image_feature_size( - hf_config: Dict[str, Any], - *, - input_height: int, - input_width: int, - num_crops: int, -) -> int: - if num_crops is None: - num_crops = hf_config.get("num_crops", 16) - new_width, new_height = _calc_hd_transform_size(width=input_width, - height=input_height, - hd_num=num_crops) - - return (new_height // 336 * new_width // 336 + 1) * 144 + 1 \ - + (new_height // 336 + 1) * 12 - - def get_max_phi3v_image_tokens(ctx: InputContext, *, num_crops: Optional[int] = None): + mm_processor_kwargs = {} + if num_crops is not None: + mm_processor_kwargs["num_crops"] = num_crops - return get_phi3v_image_feature_size( - ctx.get_hf_image_processor_config(), - input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT, - input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH, - num_crops=num_crops, + model_config = ctx.model_config + image_processor = cached_get_image_processor( + model_config.model, + trust_remote_code=model_config.trust_remote_code, + **mm_processor_kwargs, + ) + + num_tokens = image_processor.calc_num_image_tokens_from_image_size( + width=MAX_IMAGE_FEATURE_SIZE_WIDTH, + height=MAX_IMAGE_FEATURE_SIZE_HEIGHT, ) + return num_tokens -def dummy_data_for_phi3v(ctx: InputContext, - seq_len: int, - mm_counts: Mapping[str, int], - *, - num_crops: Optional[int] = None): +def dummy_mm_kwargs_for_phi3v(ctx: InputProcessingContext, + mm_counts: Mapping[str, int]): num_images = mm_counts["image"] - image_feature_size = get_max_phi3v_image_tokens(ctx, num_crops=num_crops) - - seq_data, ranges = dummy_seq_data_for_clip( - CLIP_VIT_LARGE_PATCH14_336_CONFIG, - seq_len, - num_images, - image_token_id=_IMAGE_TOKEN_ID, - image_feature_size_override=image_feature_size, - ) - mm_data = dummy_image_for_clip( + data = dummy_image_for_clip( CLIP_VIT_LARGE_PATCH14_336_CONFIG, num_images, image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH, image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT, ) - return DummyData(seq_data, mm_data, ranges) - + hf_processor = ctx.get_hf_processor() + image_processor = hf_processor.image_processor # type: ignore + hf_inputs = image_processor.preprocess(data['image'], return_tensors="pt") -@lru_cache -def _get_image_placeholder_token_id_candidates( - model_config: ModelConfig, - idx: int, -) -> List[List[int]]: - assert idx > 0 + return MultiModalKwargs(**hf_inputs) - tokenizer = cached_get_tokenizer(model_config.tokenizer) - # This is used when the image token is at the start of the string - start_candidate = tokenizer.encode(f"<|image_{idx}|>", - add_special_tokens=False) +def create_metadata_for_phi3v( + ctx: InputProcessingContext) -> MultiModalProcessingMetadata: + return { + "image": + ModalityProcessingMetadata(prompt_repls=[ + PromptReplacement(target=[_IMAGE_TOKEN_ID], + repl_unit=[_IMAGE_TOKEN_ID], + repl_count=get_max_phi3v_image_tokens(ctx)), + ]), + } - # This is used when the image token is in the middle of the string - # We need to get the token for "<", not "▁<" - # https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/raw/main/tokenizer.json - a_token_id, = tokenizer.encode("a", add_special_tokens=False) - a_token_id_, *middle_candidate = tokenizer.encode(f"a<|image_{idx}|>", - add_special_tokens=False) - assert a_token_id == a_token_id_ - return [start_candidate, middle_candidate] +class Phi3VProcessor(BaseMultiModalProcessor): + def __init__(self, ctx: InputProcessingContext) -> None: + super().__init__( + ctx=ctx, + metadata=create_metadata_for_phi3v(ctx), + ) -def input_processor_for_phi3v(ctx: InputContext, - inputs: DecoderOnlyInputs, - *, - num_crops: Optional[int] = None): - multi_modal_data = inputs.get("multi_modal_data") - if multi_modal_data is None or "image" not in multi_modal_data: - return inputs - - model_config = ctx.model_config - hf_config = ctx.get_hf_image_processor_config() - - image_data = multi_modal_data["image"] - if isinstance(image_data, Image.Image): - w, h = image_data.size - image_feature_size = [ - get_phi3v_image_feature_size(hf_config, - input_width=w, - input_height=h, - num_crops=num_crops) - ] - image_data = [image_data] - elif is_list_of(image_data, Image.Image): - image_feature_size = [] - for image in image_data: - w, h = image.size - image_feature_size.append( - get_phi3v_image_feature_size(hf_config, - input_width=w, - input_height=h, - num_crops=num_crops)) - elif isinstance(image_data, torch.Tensor): - image_feature_size = [image_data.shape[0]] - image_data = [image_data] - elif is_list_of(image_data, torch.Tensor): - image_feature_size = [item.shape[0] for item in image_data] - else: - raise TypeError(f"Invalid image type: {type(image_data)}") - - prompt = inputs.get("prompt") - if prompt is None: - # for async server request, we assume prompt and its token_ids is always - # in correct format. And num_image_tags == len(image_data) always True. - image_idx = range(1, len(image_data) + 1) - new_prompt = None - else: - image_idx = sorted(map(int, re.findall(r"<\|image_(\d+)\|>+", prompt))) - if prompt.count("<|image|>") > 0: - logger.warning("Please follow the prompt format that is " - "documented on HuggingFace which does not involve " - "repeating <|image|> tokens.") - elif (num_image_tags := len(image_idx)) > 1: - assert num_image_tags == len( - image_data), "The count of image_placeholder not match image's" - new_prompt = prompt - - prompt_token_ids = inputs["prompt_token_ids"].copy() - - # masked placeholder with image token id - for idx in image_idx: - candidates = _get_image_placeholder_token_id_candidates(model_config, - idx=idx) - - for candidate in candidates: - for i in range(len(prompt_token_ids) - len(candidate) + 1): - if prompt_token_ids[i:i + len(candidate)] == candidate: - prompt_token_ids[i:i + - len(candidate)] = ([_IMAGE_TOKEN_ID] * - len(candidate)) - break - - # merge consecutive tag ids - merged_token_ids: List[int] = [] - for is_placeholder, token_ids in itertools.groupby( - prompt_token_ids, lambda x: x == _IMAGE_TOKEN_ID): - if is_placeholder: - merged_token_ids.append(_IMAGE_TOKEN_ID) - else: - merged_token_ids.extend(list(token_ids)) - - # TODO: Move this to utils or integrate with clip. - new_token_ids: List[int] = [] - placeholder_ranges: List[PlaceholderRange] = [] - placeholder_idx = 0 - while merged_token_ids: - token_id = merged_token_ids.pop(0) - if token_id == _IMAGE_TOKEN_ID: - replacement_ids = repeat_and_pad_token( - _IMAGE_TOKEN_ID, - repeat_count=image_feature_size[placeholder_idx], - ) - placeholder_ranges.append({ - "offset": len(new_token_ids), - "length": len(replacement_ids) - }) - new_token_ids.extend(replacement_ids) - placeholder_idx += 1 - else: - new_token_ids.append(token_id) - - # 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, - multi_modal_placeholders={"image": placeholder_ranges}) + def _get_hf_processor( + self, + *, + num_crops: Optional[int] = None, + ) -> ProcessorMixin: + if num_crops is not None: + return self.ctx.get_hf_processor(num_crops=num_crops) + return self.ctx.get_hf_processor() + + def _apply_hf_processor( + self, + prompt: str, + mm_data: MultiModalDataDict, + mm_processor_kwargs: Mapping[str, object], + ) -> BatchFeature: + processed_outputs = super()._apply_hf_processor( + prompt, mm_data, mm_processor_kwargs) + # Phi3v processor has inserted -1, -2 etc as placeholder in prompt_ids, + # which will cause OverflowError when decoding the prompt_ids. + # Therefore, we need to do an early replacement here + token_ids = processed_outputs['input_ids'] + token_ids[token_ids < 0] = _IMAGE_TOKEN_ID + processed_outputs['input_ids'] = token_ids + return processed_outputs + + def _get_dummy_mm_kwargs( + self, + mm_counts: Mapping[str, int], + ) -> MultiModalKwargs: + return dummy_mm_kwargs_for_phi3v(self.ctx, mm_counts) -@MULTIMODAL_REGISTRY.register_image_input_mapper() @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_phi3v_image_tokens) -@INPUT_REGISTRY.register_dummy_data(dummy_data_for_phi3v) -@INPUT_REGISTRY.register_input_processor(input_processor_for_phi3v) +@MULTIMODAL_REGISTRY.register_processor(Phi3VProcessor) class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): diff --git a/vllm/multimodal/processing.py b/vllm/multimodal/processing.py index c3a95d60e6fe6..922c83b6fd8a9 100644 --- a/vllm/multimodal/processing.py +++ b/vllm/multimodal/processing.py @@ -3,7 +3,8 @@ from collections.abc import Callable, ItemsView, Iterable, Mapping, Sequence from dataclasses import dataclass from functools import lru_cache -from typing import Any, Generic, NamedTuple, Optional, Protocol, TypeVar, Union +from typing import (Any, Dict, Generic, NamedTuple, Optional, Protocol, + TypeVar, Union, cast) import torch from transformers import BatchFeature, ProcessorMixin @@ -11,7 +12,8 @@ from vllm.inputs import DummyData, InputProcessingContext from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer -from vllm.utils import flatten_2d_lists, full_groupby, is_list_of +from vllm.utils import (flatten_2d_lists, full_groupby, is_list_of, + resolve_mm_processor_kwargs) from .inputs import (AudioItem, ImageItem, MultiModalDataDict, MultiModalInputsV2, MultiModalKwargs, PlaceholderRange, @@ -543,8 +545,14 @@ def __init__( self.ctx = ctx self.metadata = metadata + self.init_mm_processor_kwargs = (ctx.model_config.mm_processor_kwargs + or {}) - def _get_hf_processor(self) -> ProcessorMixin: + def _get_hf_processor( + self, + **mm_processor_kwargs: Mapping[str, object], + ) -> ProcessorMixin: + # by default, we won't pass any kwargs to the processor initialization return self.ctx.get_hf_processor() def _get_tokenizer(self) -> AnyTokenizer: @@ -581,7 +589,13 @@ def _apply_hf_processor( mm_data: MultiModalDataDict, mm_processor_kwargs: Mapping[str, object], ) -> BatchFeature: - hf_processor = self._get_hf_processor() + # some mm_processor_kwargs may be used in processor initialization + # instead of processor call + processor_init_kwargs = { + **self.init_mm_processor_kwargs, + **mm_processor_kwargs, + } + hf_processor = self._get_hf_processor(**processor_init_kwargs) processor_data = dict[str, Any]() passthrough_data = dict[str, Any]() @@ -601,6 +615,13 @@ def _apply_hf_processor( else: processor_data[k] = v + # filter mm_processor_kwargs used in processor call + mm_processor_kwargs = resolve_mm_processor_kwargs( + self.init_mm_processor_kwargs, + cast(Dict[str, Any], mm_processor_kwargs), + hf_processor, + ) + try: hf_inputs = hf_processor( text=prompt, # type: ignore From cbcbdb1ceb9c219d13b2386e101992c399410551 Mon Sep 17 00:00:00 2001 From: Konrad Zawora Date: Mon, 9 Dec 2024 22:21:06 +0100 Subject: [PATCH 04/79] [Bugfix][Hardware][Gaudi] Bump vllm_hpu_extension version (#11028) Signed-off-by: Konrad Zawora --- requirements-hpu.txt | 2 +- vllm/attention/backends/hpu_attn.py | 11 +++++++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/requirements-hpu.txt b/requirements-hpu.txt index 4674efb812cfd..17d40d0ee131a 100644 --- a/requirements-hpu.txt +++ b/requirements-hpu.txt @@ -8,4 +8,4 @@ pandas tabulate setuptools>=61 setuptools-scm>=8 -vllm-hpu-extension @ git+https://github.com/HabanaAI/vllm-hpu-extension.git@fd7f2e6 +vllm-hpu-extension @ git+https://github.com/HabanaAI/vllm-hpu-extension.git@e096d6f diff --git a/vllm/attention/backends/hpu_attn.py b/vllm/attention/backends/hpu_attn.py index 2c62e565c04c7..f90d15d4207e7 100644 --- a/vllm/attention/backends/hpu_attn.py +++ b/vllm/attention/backends/hpu_attn.py @@ -111,8 +111,16 @@ def __init__( self.matmul_qk = Matmul() self.softmax = Softmax() self.matmul_av = Matmul() + self.batch2block_matmul = Matmul() + self.block2batch_matmul = Matmul() + # NOTE(kzawora): Contiguous PA is off until model runner supports it self.k_cache = VLLMKVCache() + self.k_cache.use_contiguous_pa = False self.v_cache = VLLMKVCache() + self.v_cache.use_contiguous_pa = False + # NOTE(kzawora): Pipelined PA is off until model runner supports it + ops.pa_impl = ops.pa + self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads self.sliding_window = sliding_window self.alibi_slopes = alibi_slopes @@ -228,9 +236,12 @@ def forward( block_mapping=attn_metadata.block_mapping, block_bias=attn_metadata.attn_bias, block_scales=attn_metadata.block_scales, + block_groups=None, scale=self.scale, matmul_qk_op=self.matmul_qk, matmul_av_op=self.matmul_av, + batch2block_matmul_op=self.batch2block_matmul, + block2batch_matmul_op=self.block2batch_matmul, keys_fetch_func=self.k_cache.fetch_from_cache, values_fetch_func=self.v_cache.fetch_from_cache) # Reshape the output tensor. From 1a2f8fb828f0444705db319786b2e901159f184e Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 9 Dec 2024 13:47:24 -0800 Subject: [PATCH 05/79] [v1] fix use compile sizes (#11000) Signed-off-by: youkaichao --- vllm/config.py | 1 + vllm/v1/worker/gpu_model_runner.py | 3 +++ 2 files changed, 4 insertions(+) diff --git a/vllm/config.py b/vllm/config.py index 29f0839dcabba..5fb9563fcf3a3 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -2522,6 +2522,7 @@ def __post_init__(self): self.compilation_config.custom_ops = ["none"] self.compilation_config.use_cudagraph = True self.compilation_config.use_inductor = True + self.compilation_config.cudagraph_num_of_warmups = 1 self.compilation_config.pass_config.enable_fusion = False self.compilation_config.pass_config.enable_reshape = False self.compilation_config.level = CompilationLevel.PIECEWISE diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 7f95be06188e3..c601aca13feaf 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -582,6 +582,9 @@ def capture_model(self) -> None: # can reuse the memory pool allocated for the large shapes. with graph_capture(): for num_tokens in reversed(self.cudagraph_batch_sizes): + for _ in range(self.vllm_config.compilation_config. + cudagraph_num_of_warmups): + self._dummy_run(self.model, num_tokens, self.kv_caches) self._dummy_run(self.model, num_tokens, self.kv_caches) end_time = time.perf_counter() From 9c6459e4cb020ec1ad9ea08cac9309b83d432fc8 Mon Sep 17 00:00:00 2001 From: xendo Date: Mon, 9 Dec 2024 22:53:24 +0100 Subject: [PATCH 06/79] [Neuron] Upgrade neuron to 2.20.2 (#11016) Signed-off-by: Jerzy Zagorski Co-authored-by: Jerzy Zagorski --- Dockerfile.neuron | 3 ++- vllm/utils.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/Dockerfile.neuron b/Dockerfile.neuron index 76dbd4c04d3f3..77162bc82de62 100644 --- a/Dockerfile.neuron +++ b/Dockerfile.neuron @@ -1,5 +1,6 @@ # default base image -ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.1.2-neuronx-py310-sdk2.20.0-ubuntu20.04" +# https://gallery.ecr.aws/neuron/pytorch-inference-neuronx +ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.1.2-neuronx-py310-sdk2.20.2-ubuntu20.04" FROM $BASE_IMAGE diff --git a/vllm/utils.py b/vllm/utils.py index 1f19d9eacd16d..2bb1fb2af40f4 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -1628,7 +1628,7 @@ def direct_register_custom_op( library object. If you want to bind the operator to a different library, make sure the library object is alive when the operator is used. """ - if is_in_doc_build(): + if is_in_doc_build() or not supports_custom_op(): return import torch.library if hasattr(torch.library, "infer_schema"): From b63ba848323efd88207b12d7582501d525503b8a Mon Sep 17 00:00:00 2001 From: Gregory Shtrasberg <156009573+gshtras@users.noreply.github.com> Date: Mon, 9 Dec 2024 17:00:29 -0500 Subject: [PATCH 07/79] [ROCm][bugfix] scpecilative decoding worker class (#11035) Signed-off-by: Gregory Shtrasberg --- vllm/platforms/rocm.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py index 66674e3ebe91f..0133f26a0b1bc 100644 --- a/vllm/platforms/rocm.py +++ b/vllm/platforms/rocm.py @@ -93,6 +93,8 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: elif vllm_config.speculative_config: parallel_config.worker_cls = \ "vllm.spec_decode.spec_decode_worker.create_spec_worker" + parallel_config.sd_worker_cls = \ + "vllm.worker.worker.Worker" else: parallel_config.worker_cls = "vllm.worker.worker.Worker" From 5ed5d5f128d26a48c1b1db16c319fcb96c93799d Mon Sep 17 00:00:00 2001 From: Richard Liu <39319471+richardsliu@users.noreply.github.com> Date: Mon, 9 Dec 2024 15:07:48 -0800 Subject: [PATCH 08/79] Build tpu image in release pipeline (#10936) Signed-off-by: Richard Liu Co-authored-by: Kevin H. Luu --- .buildkite/release-pipeline.yaml | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/.buildkite/release-pipeline.yaml b/.buildkite/release-pipeline.yaml index 93e118fb3eab8..2de6fceb0c3fe 100644 --- a/.buildkite/release-pipeline.yaml +++ b/.buildkite/release-pipeline.yaml @@ -39,3 +39,19 @@ steps: - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7" - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.1.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain ." - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT" + + - label: "Build and publish TPU release image" + depends_on: ~ + if: build.env("NIGHTLY") == "1" + agents: + queue: tpu_queue_postmerge + commands: + - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f Dockerfile.tpu ." + - "docker push vllm/vllm-tpu:nightly" + - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT" + plugins: + - docker-login#v3.0.0: + username: vllm + password-env: DOCKERHUB_TOKEN + env: + DOCKER_BUILDKIT: "1" From 6faec545057e6152e92e8ab619fc018e20864943 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Mon, 9 Dec 2024 15:08:19 -0800 Subject: [PATCH 09/79] [V1] Do not store `None` in self.generators (#11038) Signed-off-by: Woosuk Kwon --- vllm/v1/worker/gpu_input_batch.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/vllm/v1/worker/gpu_input_batch.py b/vllm/v1/worker/gpu_input_batch.py index 457784bb0287c..25d95ac6e26af 100644 --- a/vllm/v1/worker/gpu_input_batch.py +++ b/vllm/v1/worker/gpu_input_batch.py @@ -102,6 +102,8 @@ def __init__( self.top_k_reqs: Set[str] = set() # req_index -> generator + # NOTE(woosuk): The indices of the requests that do not have their own + # generator should not be included in the dictionary. self.generators: Dict[int, torch.Generator] = {} self.num_logprobs: Dict[str, int] = {} @@ -147,7 +149,10 @@ def add_request( if sampling_params.top_k > 0: self.top_k_reqs.add(req_id) - self.generators[req_index] = request.generator + # NOTE(woosuk): self.generators should not include the requests that + # do not have their own generator. + if request.generator is not None: + self.generators[req_index] = request.generator num_logprobs = sampling_params.logprobs if num_logprobs is not None and num_logprobs > 0: From 6d525288c1a40ee70f9cff2fe08657f23bae88dc Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Mon, 9 Dec 2024 20:15:34 -0500 Subject: [PATCH 10/79] [Docs] Add dedicated tool calling page to docs (#10554) Signed-off-by: mgoin Co-authored-by: Tyler Michael Smith --- docs/source/index.rst | 1 + .../serving/openai_compatible_server.md | 217 ------------- docs/source/usage/tool_calling.md | 287 ++++++++++++++++++ 3 files changed, 288 insertions(+), 217 deletions(-) create mode 100644 docs/source/usage/tool_calling.md diff --git a/docs/source/index.rst b/docs/source/index.rst index 86b1eed2d26ba..c45c941b00e20 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -102,6 +102,7 @@ Documentation usage/lora usage/multimodal_inputs + usage/tool_calling usage/structured_outputs usage/spec_decode usage/compatibility_matrix diff --git a/docs/source/serving/openai_compatible_server.md b/docs/source/serving/openai_compatible_server.md index d75e90807ca1d..f75653106cf66 100644 --- a/docs/source/serving/openai_compatible_server.md +++ b/docs/source/serving/openai_compatible_server.md @@ -361,220 +361,3 @@ $ vllm serve SOME_MODEL --config config.yaml **NOTE** In case an argument is supplied simultaneously using command line and the config file, the value from the commandline will take precedence. The order of priorities is `command line > config file values > defaults`. - ---- - -## Tool calling in the chat completion API -vLLM currently supports named function calling, as well as the `auto` and `none` options for the `tool_choice` field in the chat completion API. The `tool_choice` option `required` is **not yet supported** but on the roadmap. - -It is the callers responsibility to prompt the model with the tool information, vLLM will not automatically manipulate the prompt. -Please see below for recommended configuration and chat templates to use when function calling is to be used with the different models. - - -### Named Function Calling -vLLM supports named function calling in the chat completion API by default. It does so using Outlines, so this is -enabled by default, and will work with any supported model. You are guaranteed a validly-parsable function call - not a -high-quality one. - -vLLM will use guided decoding to ensure the response matches the tool parameter object defined by the JSON schema in the `tools` parameter. - -To use a named function, you need to define the functions in the `tools` parameter of the chat completion request, and -specify the `name` of one of the tools in the `tool_choice` parameter of the chat completion request. - - -### Automatic Function Calling -To enable this feature, you should set the following flags: -* `--enable-auto-tool-choice` -- **mandatory** Auto tool choice. tells vLLM that you want to enable the model to generate its own tool calls when it -deems appropriate. -* `--tool-call-parser` -- select the tool parser to use (listed below). Additional tool parsers -will continue to be added in the future, and also can register your own tool parsers in the `--tool-parser-plugin`. -* `--tool-parser-plugin` -- **optional** tool parser plugin used to register user defined tool parsers into vllm, the registered tool parser name can be specified in `--tool-call-parser`. -* `--chat-template` -- **optional** for auto tool choice. the path to the chat template which handles `tool`-role messages and `assistant`-role messages -that contain previously generated tool calls. Hermes, Mistral and Llama models have tool-compatible chat templates in their -`tokenizer_config.json` files, but you can specify a custom template. This argument can be set to `tool_use` if your model has a tool use-specific chat -template configured in the `tokenizer_config.json`. In this case, it will be used per the `transformers` specification. More on this [here](https://huggingface.co/docs/transformers/en/chat_templating#why-do-some-models-have-multiple-templates) -from HuggingFace; and you can find an example of this in a `tokenizer_config.json` [here](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B/blob/main/tokenizer_config.json) - -If your favorite tool-calling model is not supported, please feel free to contribute a parser & tool use chat template! - - -#### Hermes Models (`hermes`) - -All Nous Research Hermes-series models newer than Hermes 2 Pro should be supported. -* `NousResearch/Hermes-2-Pro-*` -* `NousResearch/Hermes-2-Theta-*` -* `NousResearch/Hermes-3-*` - - -_Note that the Hermes 2 **Theta** models are known to have degraded tool call quality & capabilities due to the merge -step in their creation_. - -Flags: `--tool-call-parser hermes` - - -#### Mistral Models (`mistral`) - -Supported models: -* `mistralai/Mistral-7B-Instruct-v0.3` (confirmed) -* Additional mistral function-calling models are compatible as well. - -Known issues: -1. Mistral 7B struggles to generate parallel tool calls correctly. -2. Mistral's `tokenizer_config.json` chat template requires tool call IDs that are exactly 9 digits, which is -much shorter than what vLLM generates. Since an exception is thrown when this condition -is not met, the following additional chat templates are provided: - -* `examples/tool_chat_template_mistral.jinja` - this is the "official" Mistral chat template, but tweaked so that -it works with vLLM's tool call IDs (provided `tool_call_id` fields are truncated to the last 9 digits) -* `examples/tool_chat_template_mistral_parallel.jinja` - this is a "better" version that adds a tool-use system prompt -when tools are provided, that results in much better reliability when working with parallel tool calling. - - -Recommended flags: `--tool-call-parser mistral --chat-template examples/tool_chat_template_mistral_parallel.jinja` - - -#### Llama Models (`llama3_json`) - -Supported models: -* `meta-llama/Meta-Llama-3.1-8B-Instruct` -* `meta-llama/Meta-Llama-3.1-70B-Instruct` -* `meta-llama/Meta-Llama-3.1-405B-Instruct` -* `meta-llama/Meta-Llama-3.1-405B-Instruct-FP8` - -The tool calling that is supported is the [JSON based tool calling](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#json-based-tool-calling). For [pythonic tool calling](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/text_prompt_format.md#zero-shot-function-calling) in Llama-3.2 models, see the `pythonic` tool parser below. -Other tool calling formats like the built in python tool calling or custom tool calling are not supported. - -Known issues: -1. Parallel tool calls are not supported. -2. The model can generate parameters with a wrong format, such as generating - an array serialized as string instead of an array. - -The `tool_chat_template_llama3_json.jinja` file contains the "official" Llama chat template, but tweaked so that -it works better with vLLM. - -Recommended flags: `--tool-call-parser llama3_json --chat-template examples/tool_chat_template_llama3_json.jinja` - -#### IBM Granite - -Supported models: -* `ibm-granite/granite-3.0-8b-instruct` - -Recommended flags: `--tool-call-parser granite --chat-template examples/tool_chat_template_granite.jinja` - -`examples/tool_chat_template_granite.jinja`: this is a modified chat template from the original on Huggingface. Parallel function calls are supported. - -* `ibm-granite/granite-20b-functioncalling` - -Recommended flags: `--tool-call-parser granite-20b-fc --chat-template examples/tool_chat_template_granite_20b_fc.jinja` - -`examples/tool_chat_template_granite_20b_fc.jinja`: this is a modified chat template from the original on Huggingface, which is not vLLM compatible. It blends function description elements from the Hermes template and follows the same system prompt as "Response Generation" mode from [the paper](https://arxiv.org/abs/2407.00121). Parallel function calls are supported. - - -#### InternLM Models (`internlm`) - -Supported models: -* `internlm/internlm2_5-7b-chat` (confirmed) -* Additional internlm2.5 function-calling models are compatible as well - -Known issues: -* Although this implementation also supports InternLM2, the tool call results are not stable when testing with the `internlm/internlm2-chat-7b` model. - -Recommended flags: `--tool-call-parser internlm --chat-template examples/tool_chat_template_internlm2_tool.jinja` - - -#### Jamba Models (`jamba`) -AI21's Jamba-1.5 models are supported. -* `ai21labs/AI21-Jamba-1.5-Mini` -* `ai21labs/AI21-Jamba-1.5-Large` - - -Flags: `--tool-call-parser jamba` - - -#### Models with Pythonic Tool Calls (`pythonic`) - -A growing number of models output a python list to represent tool calls instead of using JSON. This has the advantage of inherently supporting parallel tool calls and removing ambiguity around the JSON schema required for tool calls. The `pythonic` tool parser can support such models. - -As a concrete example, these models may look up the weather in San Francisco and Seattle by generating: -```python -[get_weather(city='San Francisco', metric='celsius'), get_weather(city='Seattle', metric='celsius')] -``` - -Limitations: -* The model must not generate both text and tool calls in the same generation. This may not be hard to change for a specific model, but the community currently lacks consensus on which tokens to emit when starting and ending tool calls. (In particular, the Llama 3.2 models emit no such tokens.) -* Llama's smaller models struggle to use tools effectively. - -Example supported models: -* `meta-llama/Llama-3.2-1B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`) -* `meta-llama/Llama-3.2-3B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`) -* `Team-ACE/ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`) -* `fixie-ai/ultravox-v0_4-ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`) - -Flags: `--tool-call-parser pythonic --chat-template {see_above}` - ---- -**WARNING** -Llama's smaller models frequently fail to emit tool calls in the correct format. Your mileage may vary. - ---- - - -### How to write a tool parser plugin - -A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py. - -Here is a summary of a plugin file: - -```python - -# import the required packages - -# define a tool parser and register it to vllm -# the name list in register_module can be used -# in --tool-call-parser. you can define as many -# tool parsers as you want here. -@ToolParserManager.register_module(["example"]) -class ExampleToolParser(ToolParser): - def __init__(self, tokenizer: AnyTokenizer): - super().__init__(tokenizer) - - # adjust request. e.g.: set skip special tokens - # to False for tool call output. - def adjust_request( - self, request: ChatCompletionRequest) -> ChatCompletionRequest: - return request - - # implement the tool call parse for stream call - def extract_tool_calls_streaming( - self, - previous_text: str, - current_text: str, - delta_text: str, - previous_token_ids: Sequence[int], - current_token_ids: Sequence[int], - delta_token_ids: Sequence[int], - request: ChatCompletionRequest, - ) -> Union[DeltaMessage, None]: - return delta - - # implement the tool parse for non-stream call - def extract_tool_calls( - self, - model_output: str, - request: ChatCompletionRequest, - ) -> ExtractedToolCallInformation: - return ExtractedToolCallInformation(tools_called=False, - tool_calls=[], - content=text) - - -``` - -Then you can use this plugin in the command line like this. -``` - --enable-auto-tool-choice \ - --tool-parser-plugin - --tool-call-parser example \ - --chat-template \ -``` - diff --git a/docs/source/usage/tool_calling.md b/docs/source/usage/tool_calling.md new file mode 100644 index 0000000000000..f8be023307b0c --- /dev/null +++ b/docs/source/usage/tool_calling.md @@ -0,0 +1,287 @@ +# Tool Calling + +vLLM currently supports named function calling, as well as the `auto` and `none` options for the `tool_choice` field in the chat completion API. The `tool_choice` option `required` is **not yet supported** but on the roadmap. + +## Quickstart + +Start the server with tool calling enabled. This example uses Meta's Llama 3.1 8B model, so we need to use the llama3 tool calling chat template from the vLLM examples directory: + +```bash +vllm serve meta-llama/Llama-3.1-8B-Instruct \ + --enable-auto-tool-choice \ + --tool-call-parser llama3_json \ + --chat-template examples/tool_chat_template_llama3_json.jinja +``` + +Next, make a request to the model that should result in it using the available tools: + +```python +from openai import OpenAI +import json + +client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy") + +def get_weather(location: str, unit: str): + return f"Getting the weather for {location} in {unit}..." +tool_functions = {"get_weather": get_weather} + +tools = [{ + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"}, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]} + }, + "required": ["location", "unit"] + } + } +}] + +response = client.chat.completions.create( + model=client.models.list().data[0].id, + messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}], + tools=tools, + tool_choice="auto" +) + +tool_call = response.choices[0].message.tool_calls[0].function +print(f"Function called: {tool_call.name}") +print(f"Arguments: {tool_call.arguments}") +print(f"Result: {get_weather(**json.loads(tool_call.arguments))}") +``` + +Example output: +``` +Function called: get_weather +Arguments: {"location": "San Francisco, CA", "unit": "fahrenheit"} +Result: Getting the weather for San Francisco, CA in fahrenheit... +``` + +This example demonstrates: +- Setting up the server with tool calling enabled +- Defining an actual function to handle tool calls +- Making a request with `tool_choice="auto"` +- Handling the structured response and executing the corresponding function + +You can also specify a particular function using named function calling by setting `tool_choice={"type": "function", "function": {"name": "get_weather"}}`. Note that this will use the guided decoding backend - so the first time this is used, there will be several seconds of latency (or more) as the FSM is compiled for the first time before it is cached for subsequent requests. + +Remember that it's the callers responsibility to: +1. Define appropriate tools in the request +2. Include relevant context in the chat messages +3. Handle the tool calls in your application logic + +For more advanced usage, including parallel tool calls and different model-specific parsers, see the sections below. + +## Named Function Calling +vLLM supports named function calling in the chat completion API by default. It does so using Outlines through guided decoding, so this is +enabled by default, and will work with any supported model. You are guaranteed a validly-parsable function call - not a +high-quality one. + +vLLM will use guided decoding to ensure the response matches the tool parameter object defined by the JSON schema in the `tools` parameter. +For best results, we recommend ensuring that the expected output format / schema is specified in the prompt to ensure that the model's intended generation is aligned with the schema that it's being forced to generate by the guided decoding backend. + +To use a named function, you need to define the functions in the `tools` parameter of the chat completion request, and +specify the `name` of one of the tools in the `tool_choice` parameter of the chat completion request. + + +## Automatic Function Calling + +To enable this feature, you should set the following flags: +* `--enable-auto-tool-choice` -- **mandatory** Auto tool choice. tells vLLM that you want to enable the model to generate its own tool calls when it +deems appropriate. +* `--tool-call-parser` -- select the tool parser to use (listed below). Additional tool parsers +will continue to be added in the future, and also can register your own tool parsers in the `--tool-parser-plugin`. +* `--tool-parser-plugin` -- **optional** tool parser plugin used to register user defined tool parsers into vllm, the registered tool parser name can be specified in `--tool-call-parser`. +* `--chat-template` -- **optional** for auto tool choice. the path to the chat template which handles `tool`-role messages and `assistant`-role messages +that contain previously generated tool calls. Hermes, Mistral and Llama models have tool-compatible chat templates in their +`tokenizer_config.json` files, but you can specify a custom template. This argument can be set to `tool_use` if your model has a tool use-specific chat +template configured in the `tokenizer_config.json`. In this case, it will be used per the `transformers` specification. More on this [here](https://huggingface.co/docs/transformers/en/chat_templating#why-do-some-models-have-multiple-templates) +from HuggingFace; and you can find an example of this in a `tokenizer_config.json` [here](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B/blob/main/tokenizer_config.json) + +If your favorite tool-calling model is not supported, please feel free to contribute a parser & tool use chat template! + + +### Hermes Models (`hermes`) + +All Nous Research Hermes-series models newer than Hermes 2 Pro should be supported. +* `NousResearch/Hermes-2-Pro-*` +* `NousResearch/Hermes-2-Theta-*` +* `NousResearch/Hermes-3-*` + + +_Note that the Hermes 2 **Theta** models are known to have degraded tool call quality & capabilities due to the merge +step in their creation_. + +Flags: `--tool-call-parser hermes` + + +### Mistral Models (`mistral`) + +Supported models: +* `mistralai/Mistral-7B-Instruct-v0.3` (confirmed) +* Additional mistral function-calling models are compatible as well. + +Known issues: +1. Mistral 7B struggles to generate parallel tool calls correctly. +2. Mistral's `tokenizer_config.json` chat template requires tool call IDs that are exactly 9 digits, which is +much shorter than what vLLM generates. Since an exception is thrown when this condition +is not met, the following additional chat templates are provided: + +* `examples/tool_chat_template_mistral.jinja` - this is the "official" Mistral chat template, but tweaked so that +it works with vLLM's tool call IDs (provided `tool_call_id` fields are truncated to the last 9 digits) +* `examples/tool_chat_template_mistral_parallel.jinja` - this is a "better" version that adds a tool-use system prompt +when tools are provided, that results in much better reliability when working with parallel tool calling. + + +Recommended flags: `--tool-call-parser mistral --chat-template examples/tool_chat_template_mistral_parallel.jinja` + + +### Llama Models (`llama3_json`) + +Supported models: +* `meta-llama/Meta-Llama-3.1-8B-Instruct` +* `meta-llama/Meta-Llama-3.1-70B-Instruct` +* `meta-llama/Meta-Llama-3.1-405B-Instruct` +* `meta-llama/Meta-Llama-3.1-405B-Instruct-FP8` + +The tool calling that is supported is the [JSON based tool calling](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/#json-based-tool-calling). For [pythonic tool calling](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/text_prompt_format.md#zero-shot-function-calling) in Llama-3.2 models, see the `pythonic` tool parser below. +Other tool calling formats like the built in python tool calling or custom tool calling are not supported. + +Known issues: +1. Parallel tool calls are not supported. +2. The model can generate parameters with a wrong format, such as generating + an array serialized as string instead of an array. + +The `tool_chat_template_llama3_json.jinja` file contains the "official" Llama chat template, but tweaked so that +it works better with vLLM. + +Recommended flags: `--tool-call-parser llama3_json --chat-template examples/tool_chat_template_llama3_json.jinja` + +#### IBM Granite + +Supported models: +* `ibm-granite/granite-3.0-8b-instruct` + +Recommended flags: `--tool-call-parser granite --chat-template examples/tool_chat_template_granite.jinja` + +`examples/tool_chat_template_granite.jinja`: this is a modified chat template from the original on Huggingface. Parallel function calls are supported. + +* `ibm-granite/granite-20b-functioncalling` + +Recommended flags: `--tool-call-parser granite-20b-fc --chat-template examples/tool_chat_template_granite_20b_fc.jinja` + +`examples/tool_chat_template_granite_20b_fc.jinja`: this is a modified chat template from the original on Huggingface, which is not vLLM compatible. It blends function description elements from the Hermes template and follows the same system prompt as "Response Generation" mode from [the paper](https://arxiv.org/abs/2407.00121). Parallel function calls are supported. + + +### InternLM Models (`internlm`) + +Supported models: +* `internlm/internlm2_5-7b-chat` (confirmed) +* Additional internlm2.5 function-calling models are compatible as well + +Known issues: +* Although this implementation also supports InternLM2, the tool call results are not stable when testing with the `internlm/internlm2-chat-7b` model. + +Recommended flags: `--tool-call-parser internlm --chat-template examples/tool_chat_template_internlm2_tool.jinja` + + +### Jamba Models (`jamba`) +AI21's Jamba-1.5 models are supported. +* `ai21labs/AI21-Jamba-1.5-Mini` +* `ai21labs/AI21-Jamba-1.5-Large` + + +Flags: `--tool-call-parser jamba` + + +### Models with Pythonic Tool Calls (`pythonic`) + +A growing number of models output a python list to represent tool calls instead of using JSON. This has the advantage of inherently supporting parallel tool calls and removing ambiguity around the JSON schema required for tool calls. The `pythonic` tool parser can support such models. + +As a concrete example, these models may look up the weather in San Francisco and Seattle by generating: +```python +[get_weather(city='San Francisco', metric='celsius'), get_weather(city='Seattle', metric='celsius')] +``` + +Limitations: +* The model must not generate both text and tool calls in the same generation. This may not be hard to change for a specific model, but the community currently lacks consensus on which tokens to emit when starting and ending tool calls. (In particular, the Llama 3.2 models emit no such tokens.) +* Llama's smaller models struggle to use tools effectively. + +Example supported models: +* `meta-llama/Llama-3.2-1B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`) +* `meta-llama/Llama-3.2-3B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`) +* `Team-ACE/ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`) +* `fixie-ai/ultravox-v0_4-ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`) + +Flags: `--tool-call-parser pythonic --chat-template {see_above}` + +--- +**WARNING** +Llama's smaller models frequently fail to emit tool calls in the correct format. Your mileage may vary. + +--- + + +## How to write a tool parser plugin + +A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py. + +Here is a summary of a plugin file: + +```python + +# import the required packages + +# define a tool parser and register it to vllm +# the name list in register_module can be used +# in --tool-call-parser. you can define as many +# tool parsers as you want here. +@ToolParserManager.register_module(["example"]) +class ExampleToolParser(ToolParser): + def __init__(self, tokenizer: AnyTokenizer): + super().__init__(tokenizer) + + # adjust request. e.g.: set skip special tokens + # to False for tool call output. + def adjust_request( + self, request: ChatCompletionRequest) -> ChatCompletionRequest: + return request + + # implement the tool call parse for stream call + def extract_tool_calls_streaming( + self, + previous_text: str, + current_text: str, + delta_text: str, + previous_token_ids: Sequence[int], + current_token_ids: Sequence[int], + delta_token_ids: Sequence[int], + request: ChatCompletionRequest, + ) -> Union[DeltaMessage, None]: + return delta + + # implement the tool parse for non-stream call + def extract_tool_calls( + self, + model_output: str, + request: ChatCompletionRequest, + ) -> ExtractedToolCallInformation: + return ExtractedToolCallInformation(tools_called=False, + tool_calls=[], + content=text) + + +``` + +Then you can use this plugin in the command line like this. +``` + --enable-auto-tool-choice \ + --tool-parser-plugin + --tool-call-parser example \ + --chat-template \ +``` + From d1f6d1c8af892c7269f113711783374eebb52511 Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Tue, 10 Dec 2024 10:23:07 +0800 Subject: [PATCH 11/79] [Model] Add has_weight to RMSNorm and re-enable weights loading tracker for Mamba (#10739) Signed-off-by: Isotr0py <2037008807@qq.com> --- vllm/model_executor/layers/layernorm.py | 11 ++++++-- .../layers/mamba/mamba_mixer.py | 26 +++++++++++++------ vllm/model_executor/models/mamba.py | 9 +++++-- 3 files changed, 34 insertions(+), 12 deletions(-) diff --git a/vllm/model_executor/layers/layernorm.py b/vllm/model_executor/layers/layernorm.py index 345919c5d1636..43ea4eb5a4d1a 100644 --- a/vllm/model_executor/layers/layernorm.py +++ b/vllm/model_executor/layers/layernorm.py @@ -20,6 +20,7 @@ def __init__( hidden_size: int, eps: float = 1e-6, var_hidden_size: Optional[int] = None, + has_weight: bool = True, ) -> None: super().__init__() @@ -27,7 +28,11 @@ 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)) + self.has_weight = has_weight + + self.weight = torch.ones(hidden_size) + if self.has_weight: + self.weight = nn.Parameter(self.weight) def forward_native( self, @@ -59,7 +64,9 @@ def forward_native( variance = x_var.pow(2).mean(dim=-1, keepdim=True) x = x * torch.rsqrt(variance + self.variance_epsilon) - x = x.to(orig_dtype) * self.weight + x = x.to(orig_dtype) + if self.has_weight: + x = x * self.weight if residual is None: return x else: diff --git a/vllm/model_executor/layers/mamba/mamba_mixer.py b/vllm/model_executor/layers/mamba/mamba_mixer.py index 8ef0a6cdf2c52..10bec75f49fdf 100644 --- a/vllm/model_executor/layers/mamba/mamba_mixer.py +++ b/vllm/model_executor/layers/mamba/mamba_mixer.py @@ -40,6 +40,7 @@ def __init__(self, use_conv_bias: bool, use_bias: bool, use_rms_norm: bool, + rms_norm_has_weight: bool = True, rms_norm_eps: float = 1e-5, activation="silu"): super().__init__() @@ -105,14 +106,23 @@ def A_weight_loader(param: Parameter, loaded_weight: torch.Tensor): input_is_parallel=True, ) - self.dt_layernorm = RMSNorm(time_step_rank, - eps=rms_norm_eps) if use_rms_norm else None - - self.b_layernorm = RMSNorm(ssm_state_size, - eps=rms_norm_eps) if use_rms_norm else None - - self.c_layernorm = RMSNorm(ssm_state_size, - eps=rms_norm_eps) if use_rms_norm else None + self.dt_layernorm = RMSNorm( + time_step_rank, + eps=rms_norm_eps, + has_weight=rms_norm_has_weight, + ) if use_rms_norm else None + + self.b_layernorm = RMSNorm( + ssm_state_size, + eps=rms_norm_eps, + has_weight=rms_norm_has_weight, + ) if use_rms_norm else None + + self.c_layernorm = RMSNorm( + ssm_state_size, + eps=rms_norm_eps, + has_weight=rms_norm_has_weight, + ) if use_rms_norm else None def forward_native(self, hidden_states: torch.Tensor, attn_metadata: AttentionMetadata, diff --git a/vllm/model_executor/models/mamba.py b/vllm/model_executor/models/mamba.py index b32032e411b0a..8bdcd2c5aad1f 100644 --- a/vllm/model_executor/models/mamba.py +++ b/vllm/model_executor/models/mamba.py @@ -1,5 +1,5 @@ """PyTorch MAMBA model.""" -from typing import Iterable, List, Optional, Tuple +from typing import Iterable, List, Optional, Set, Tuple import torch from torch import nn @@ -47,6 +47,7 @@ def __init__(self, use_conv_bias=config.use_conv_bias, use_bias=config.use_bias, use_rms_norm=self.is_falcon_mamba, + rms_norm_has_weight=not self.is_falcon_mamba, rms_norm_eps=mixer_rms_eps, activation=config.hidden_act) @@ -241,8 +242,10 @@ def sample( next_tokens = self.sampler(logits, sampling_metadata) return next_tokens - def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + def load_weights(self, weights: Iterable[Tuple[str, + torch.Tensor]]) -> Set[str]: params_dict = dict(self.named_parameters()) + loaded_params: Set[str] = set() for name, loaded_weight in weights: if "A_log" in name: name = name.replace("A_log", "A") @@ -254,3 +257,5 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) + loaded_params.add(name) + return loaded_params From 391d7b2763df0b90a975c7232f38c4de4be2ff85 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Tue, 10 Dec 2024 13:45:47 +0800 Subject: [PATCH 12/79] [Bugfix] Fix usage of `deprecated` decorator (#11025) Signed-off-by: DarkLight1337 --- vllm/engine/llm_engine.py | 8 +-- vllm/engine/multiprocessing/__init__.py | 8 +-- vllm/engine/multiprocessing/client.py | 16 +++--- vllm/entrypoints/llm.py | 72 ++++++++++++------------- 4 files changed, 52 insertions(+), 52 deletions(-) diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 560f84a008291..8fc69d96d321e 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -677,12 +677,10 @@ def stop_remote_worker_execution_loop(self) -> None: self.model_executor.stop_remote_worker_execution_loop() @overload - @deprecated("'inputs' will be renamed to 'prompt") def add_request( self, request_id: str, - *, - inputs: PromptType, + prompt: PromptType, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, @@ -693,10 +691,12 @@ def add_request( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def add_request( self, request_id: str, - prompt: PromptType, + *, + inputs: PromptType, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, diff --git a/vllm/engine/multiprocessing/__init__.py b/vllm/engine/multiprocessing/__init__.py index 7020012e8bb86..420f540d0b5f4 100644 --- a/vllm/engine/multiprocessing/__init__.py +++ b/vllm/engine/multiprocessing/__init__.py @@ -35,11 +35,9 @@ class RPCProcessRequest: priority: int = 0 @overload - @deprecated("'inputs' will be renamed to 'prompt") def __init__( self, - *, - inputs: PromptType, + prompt: PromptType, params: Union[SamplingParams, PoolingParams], request_id: str, lora_request: Optional[LoRARequest] = None, @@ -50,9 +48,11 @@ def __init__( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def __init__( self, - prompt: PromptType, + *, + inputs: PromptType, params: Union[SamplingParams, PoolingParams], request_id: str, lora_request: Optional[LoRARequest] = None, diff --git a/vllm/engine/multiprocessing/client.py b/vllm/engine/multiprocessing/client.py index 7e4f81b2cf8e2..32bd83305bb8f 100644 --- a/vllm/engine/multiprocessing/client.py +++ b/vllm/engine/multiprocessing/client.py @@ -415,11 +415,9 @@ def dead_error(self) -> BaseException: return ENGINE_DEAD_ERROR(self._errored_with) @overload - @deprecated("'inputs' will be renamed to 'prompt") def generate( self, - *, - inputs: PromptType, + prompt: PromptType, sampling_params: SamplingParams, request_id: str, lora_request: Optional[LoRARequest] = None, @@ -430,9 +428,11 @@ def generate( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def generate( self, - prompt: PromptType, + *, + inputs: PromptType, sampling_params: SamplingParams, request_id: str, lora_request: Optional[LoRARequest] = None, @@ -487,11 +487,9 @@ def generate( prompt_adapter_request, priority) @overload - @deprecated("'inputs' will be renamed to 'prompt") def encode( self, - *, - inputs: PromptType, + prompt: PromptType, pooling_params: PoolingParams, request_id: str, lora_request: Optional[LoRARequest] = None, @@ -501,9 +499,11 @@ def encode( ... @overload + @deprecated("'inputs' will be renamed to 'prompt") def encode( self, - prompt: PromptType, + *, + inputs: PromptType, pooling_params: PoolingParams, request_id: str, lora_request: Optional[LoRARequest] = None, diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index 8de30ccd18a11..2a02187223a33 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -252,8 +252,21 @@ def set_tokenizer(self, tokenizer: AnyTokenizer) -> None: else: tokenizer_group.tokenizer = get_cached_tokenizer(tokenizer) + @overload + def generate( + self, + prompts: Union[PromptType, Sequence[PromptType]], + /, + *, + sampling_params: Optional[Union[SamplingParams, + Sequence[SamplingParams]]] = None, + use_tqdm: bool = True, + lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, + ) -> List[RequestOutput]: + ... + @overload # LEGACY: single (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: str, @@ -266,7 +279,7 @@ def generate( ... @overload # LEGACY: multi (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: List[str], @@ -279,7 +292,7 @@ def generate( ... @overload # LEGACY: single (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: Optional[str] = None, @@ -293,7 +306,7 @@ def generate( ... @overload # LEGACY: multi (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: Optional[List[str]] = None, @@ -307,7 +320,7 @@ def generate( ... @overload # LEGACY: single or multi token ids [pos-only] - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def generate( self, prompts: None, @@ -318,19 +331,6 @@ def generate( ) -> List[RequestOutput]: ... - @overload - def generate( - self, - prompts: Union[PromptType, Sequence[PromptType]], - /, - *, - sampling_params: Optional[Union[SamplingParams, - Sequence[SamplingParams]]] = None, - use_tqdm: bool = True, - lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, - ) -> List[RequestOutput]: - ... - @deprecate_kwargs( "prompt_token_ids", is_deprecated=lambda: LLM.DEPRECATE_LEGACY, @@ -672,8 +672,21 @@ def chat( lora_request=lora_request, ) + @overload + def encode( + self, + prompts: Union[PromptType, Sequence[PromptType]], + /, + *, + pooling_params: Optional[Union[PoolingParams, + Sequence[PoolingParams]]] = None, + use_tqdm: bool = True, + lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, + ) -> List[PoolingRequestOutput]: + ... + @overload # LEGACY: single (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: str, @@ -686,7 +699,7 @@ def encode( ... @overload # LEGACY: multi (prompt + optional token ids) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: List[str], @@ -699,7 +712,7 @@ def encode( ... @overload # LEGACY: single (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: Optional[str] = None, @@ -713,7 +726,7 @@ def encode( ... @overload # LEGACY: multi (token ids + optional prompt) - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: Optional[List[str]] = None, @@ -727,7 +740,7 @@ def encode( ... @overload # LEGACY: single or multi token ids [pos-only] - @deprecated("'prompt_token_ids' will become part of 'prompts") + @deprecated("'prompt_token_ids' will become part of 'prompts'") def encode( self, prompts: None, @@ -738,19 +751,6 @@ def encode( ) -> List[PoolingRequestOutput]: ... - @overload - def encode( - self, - prompts: Union[PromptType, Sequence[PromptType]], - /, - *, - pooling_params: Optional[Union[PoolingParams, - Sequence[PoolingParams]]] = None, - use_tqdm: bool = True, - lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None, - ) -> List[PoolingRequestOutput]: - ... - @deprecate_kwargs( "prompt_token_ids", is_deprecated=lambda: LLM.DEPRECATE_LEGACY, From 980ad394a83a6f12c576a035922db3c2e743beff Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Mon, 9 Dec 2024 22:46:29 -0700 Subject: [PATCH 13/79] [Frontend] Use request id from header (#10968) Signed-off-by: Joe Runde --- docs/requirements-docs.txt | 1 + vllm/entrypoints/openai/api_server.py | 4 ++-- vllm/entrypoints/openai/serving_chat.py | 3 ++- vllm/entrypoints/openai/serving_completion.py | 4 ++-- vllm/entrypoints/openai/serving_embedding.py | 4 ++-- vllm/entrypoints/openai/serving_engine.py | 11 ++++++++++- vllm/entrypoints/openai/serving_score.py | 4 ++-- vllm/entrypoints/openai/serving_tokenization.py | 9 ++++++--- 8 files changed, 27 insertions(+), 13 deletions(-) diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index 5c80645b405ae..ca2da4cd66d2d 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -16,5 +16,6 @@ mistral_common >= 1.5.0 aiohttp starlette openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args +fastapi # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args partial-json-parser # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args requests diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index c7bc30040279c..0f93eb54111ad 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -305,7 +305,7 @@ async def health(raw_request: Request) -> Response: async def tokenize(request: TokenizeRequest, raw_request: Request): handler = tokenization(raw_request) - generator = await handler.create_tokenize(request) + generator = await handler.create_tokenize(request, raw_request) if isinstance(generator, ErrorResponse): return JSONResponse(content=generator.model_dump(), status_code=generator.code) @@ -319,7 +319,7 @@ async def tokenize(request: TokenizeRequest, raw_request: Request): async def detokenize(request: DetokenizeRequest, raw_request: Request): handler = tokenization(raw_request) - generator = await handler.create_detokenize(request) + generator = await handler.create_detokenize(request, raw_request) if isinstance(generator, ErrorResponse): return JSONResponse(content=generator.model_dump(), status_code=generator.code) diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index 54ca0463bcab1..0af7613a473a4 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -176,7 +176,8 @@ async def create_chat_completion( logger.exception("Error in preprocessing prompt inputs") return self.create_error_response(str(e)) - request_id = f"chatcmpl-{request.request_id}" + request_id = "chatcmpl-" \ + f"{self._base_request_id(raw_request, request.request_id)}" request_metadata = RequestResponseMetadata(request_id=request_id) if raw_request: diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index fc1c4908d6650..c54d5f07cf58c 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -30,7 +30,7 @@ from vllm.sampling_params import BeamSearchParams, SamplingParams from vllm.sequence import Logprob from vllm.transformers_utils.tokenizer import AnyTokenizer -from vllm.utils import merge_async_iterators, random_uuid +from vllm.utils import merge_async_iterators logger = init_logger(__name__) @@ -86,7 +86,7 @@ async def create_completion( "suffix is not currently supported") model_name = self.base_model_paths[0].name - request_id = f"cmpl-{random_uuid()}" + request_id = f"cmpl-{self._base_request_id(raw_request)}" created_time = int(time.time()) request_metadata = RequestResponseMetadata(request_id=request_id) diff --git a/vllm/entrypoints/openai/serving_embedding.py b/vllm/entrypoints/openai/serving_embedding.py index 2cbb252610e39..3f7b75e893cad 100644 --- a/vllm/entrypoints/openai/serving_embedding.py +++ b/vllm/entrypoints/openai/serving_embedding.py @@ -19,7 +19,7 @@ from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing from vllm.logger import init_logger from vllm.outputs import PoolingOutput, PoolingRequestOutput -from vllm.utils import merge_async_iterators, random_uuid +from vllm.utils import merge_async_iterators logger = init_logger(__name__) @@ -110,7 +110,7 @@ async def create_embedding( "dimensions is currently not supported") model_name = request.model - request_id = f"embd-{random_uuid()}" + request_id = f"embd-{self._base_request_id(raw_request)}" created_time = int(time.monotonic()) truncate_prompt_tokens = None diff --git a/vllm/entrypoints/openai/serving_engine.py b/vllm/entrypoints/openai/serving_engine.py index 8232c6116c1bd..63f27b955461e 100644 --- a/vllm/entrypoints/openai/serving_engine.py +++ b/vllm/entrypoints/openai/serving_engine.py @@ -6,6 +6,7 @@ from typing import (Any, Callable, Dict, Iterable, Iterator, List, Mapping, Optional, Sequence, Tuple, TypedDict, Union) +from fastapi import Request from pydantic import Field from starlette.datastructures import Headers from typing_extensions import Annotated @@ -47,7 +48,7 @@ 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 AtomicCounter, is_list_of, make_async +from vllm.utils import AtomicCounter, is_list_of, make_async, random_uuid logger = init_logger(__name__) @@ -565,6 +566,14 @@ async def _get_trace_headers( return None + @staticmethod + def _base_request_id(raw_request: Request, + default: Optional[str] = None) -> Optional[str]: + """Pulls the request id to use from a header, if provided""" + default = default or random_uuid() + return raw_request.headers.get( + "X-Request-Id", default) if raw_request is not None else default + @staticmethod def _get_decoded_token(logprob: Logprob, token_id: int, diff --git a/vllm/entrypoints/openai/serving_score.py b/vllm/entrypoints/openai/serving_score.py index a1f14449ba9c3..fed06fa452955 100644 --- a/vllm/entrypoints/openai/serving_score.py +++ b/vllm/entrypoints/openai/serving_score.py @@ -15,7 +15,7 @@ from vllm.logger import init_logger from vllm.outputs import PoolingRequestOutput from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer -from vllm.utils import make_async, merge_async_iterators, random_uuid +from vllm.utils import make_async, merge_async_iterators logger = init_logger(__name__) @@ -102,7 +102,7 @@ async def create_score( return error_check_ret model_name = request.model - request_id = f"score-{random_uuid()}" + request_id = f"score-{self._base_request_id(raw_request)}" created_time = int(time.monotonic()) truncate_prompt_tokens = request.truncate_prompt_tokens diff --git a/vllm/entrypoints/openai/serving_tokenization.py b/vllm/entrypoints/openai/serving_tokenization.py index 9c3dc2c98b2dd..2e849333680d4 100644 --- a/vllm/entrypoints/openai/serving_tokenization.py +++ b/vllm/entrypoints/openai/serving_tokenization.py @@ -1,5 +1,7 @@ from typing import Final, List, Optional, Union +from fastapi import Request + from vllm.config import ModelConfig from vllm.engine.protocol import EngineClient from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption @@ -17,7 +19,6 @@ LoRAModulePath, OpenAIServing) from vllm.logger import init_logger -from vllm.utils import random_uuid logger = init_logger(__name__) @@ -48,12 +49,13 @@ def __init__( async def create_tokenize( self, request: TokenizeRequest, + raw_request: Request, ) -> Union[TokenizeResponse, ErrorResponse]: error_check_ret = await self._check_model(request) if error_check_ret is not None: return error_check_ret - request_id = f"tokn-{random_uuid()}" + request_id = f"tokn-{self._base_request_id(raw_request)}" try: ( @@ -112,12 +114,13 @@ async def create_tokenize( async def create_detokenize( self, request: DetokenizeRequest, + raw_request: Request, ) -> Union[DetokenizeResponse, ErrorResponse]: error_check_ret = await self._check_model(request) if error_check_ret is not None: return error_check_ret - request_id = f"tokn-{random_uuid()}" + request_id = f"tokn-{self._base_request_id(raw_request)}" ( lora_request, From bc192a2b099558ec94864974b2a91b84c271a84d Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Tue, 10 Dec 2024 07:09:32 +0100 Subject: [PATCH 14/79] [Pixtral] Improve loading (#11040) --- vllm/model_executor/models/pixtral.py | 56 ++++++++++++--------------- 1 file changed, 25 insertions(+), 31 deletions(-) diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index c6786c363ab4a..94a4ab882c1a9 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -1,6 +1,5 @@ from dataclasses import dataclass, fields from functools import cached_property -from itertools import tee from typing import Iterable, List, Mapping, Optional, Set, Tuple, Union import numpy @@ -359,38 +358,33 @@ def is_vision_encoder_weights(weight: Tuple[str, torch.Tensor]): def is_vision_lang_adapter_weights(weight: Tuple[str, torch.Tensor]): return weight[0].startswith("vision_language_adapter") - def is_vision_weights(weight: Tuple[str, torch.Tensor]): - return is_vision_encoder_weights( - weight) or is_vision_lang_adapter_weights(weight) - - llm_weights, vision_encoder_weights, vision_lang_adapter_weights = tee( - weights, 3) - - # llm - llm_weights = filter(lambda x: not is_vision_weights(x), llm_weights) - self.language_model.load_weights(llm_weights) - - # vision encoder - vision_encoder_weights = filter(is_vision_encoder_weights, - vision_encoder_weights) + # Get references to parameters for direct loading vision_encoder_dict = dict(self.vision_encoder.named_parameters()) - for name, loaded_weight in vision_encoder_weights: - # cut 'vision_encoder.' - name = '.'.join(name.split(".")[1:]) - param = vision_encoder_dict[name] - - default_weight_loader(param, loaded_weight) - - # adapter - vision_lang_adapter_weights = filter(is_vision_lang_adapter_weights, - vision_lang_adapter_weights) - vision_lang_adpter_dict = dict( + vision_lang_adapter_dict = dict( self.vision_language_adapter.named_parameters()) - for name, loaded_weight in vision_lang_adapter_weights: - # cut 'vision_language_adapter.' - name = '.'.join(name.split(".")[1:]) - param = vision_lang_adpter_dict[name] - default_weight_loader(param, loaded_weight) + + def llm_weights_generator(): + # Single pass over weights + for name, w in weights: + if is_vision_encoder_weights((name, w)): + # Load vision encoder weights directly + trimmed_name = '.'.join(name.split(".")[1:]) + param = vision_encoder_dict[trimmed_name] + with torch.no_grad(): + default_weight_loader(param, w) + elif is_vision_lang_adapter_weights((name, w)): + # Load vision-language adapter weights directly + trimmed_name = '.'.join(name.split(".")[1:]) + param = vision_lang_adapter_dict[trimmed_name] + with torch.no_grad(): + default_weight_loader(param, w) + else: + # LLM weights: yield them to be loaded + # by language_model.load_weights + yield (name, w) + + # Now we call the language model load with the generator + self.language_model.load_weights(llm_weights_generator()) # Vision encoder From 28b3a1c7e596c08efac0fcfa59a629d16197be30 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Tue, 10 Dec 2024 01:28:14 -0500 Subject: [PATCH 15/79] [V1] Multiprocessing Tensor Parallel Support for v1 (#9856) Signed-off-by: Tyler Michael Smith --- .../test_basic_correctness.py | 16 + tests/conftest.py | 11 +- .../device_communicators/shm_broadcast.py | 76 ++-- vllm/executor/multiproc_gpu_executor.py | 47 +-- vllm/executor/multiproc_worker_utils.py | 42 ++ .../model_executor/layers/logits_processor.py | 5 +- vllm/platforms/cuda.py | 28 +- vllm/utils.py | 26 ++ vllm/v1/core/scheduler.py | 4 +- vllm/v1/engine/async_llm.py | 18 +- vllm/v1/engine/core.py | 74 ++-- vllm/v1/engine/core_client.py | 13 +- vllm/v1/engine/llm_engine.py | 19 +- vllm/v1/executor/abstract.py | 48 +++ vllm/v1/executor/multiproc_executor.py | 375 ++++++++++++++++++ .../{gpu_executor.py => uniproc_executor.py} | 12 +- vllm/v1/outputs.py | 6 +- vllm/v1/sample/sampler.py | 3 +- vllm/v1/utils.py | 33 +- vllm/v1/worker/gpu_model_runner.py | 12 +- vllm/v1/worker/gpu_worker.py | 11 +- 21 files changed, 733 insertions(+), 146 deletions(-) create mode 100644 vllm/v1/executor/abstract.py create mode 100644 vllm/v1/executor/multiproc_executor.py rename vllm/v1/executor/{gpu_executor.py => uniproc_executor.py} (90%) diff --git a/tests/basic_correctness/test_basic_correctness.py b/tests/basic_correctness/test_basic_correctness.py index fcba253d159f3..11d05cefb7313 100644 --- a/tests/basic_correctness/test_basic_correctness.py +++ b/tests/basic_correctness/test_basic_correctness.py @@ -26,6 +26,14 @@ TARGET_TEST_SUITE = os.environ.get("TARGET_TEST_SUITE", "L4") +@pytest.fixture(autouse=True) +def v1(run_with_both_engines): + # Simple autouse wrapper to run both engines for each test + # This can be promoted up to conftest.py to run for every + # test in a package + pass + + def test_vllm_gc_ed(): """Verify vllm instance is GC'ed when it is deleted""" llm = LLM("facebook/opt-125m") @@ -36,6 +44,7 @@ def test_vllm_gc_ed(): assert weak_llm() is None +@pytest.mark.skip_v1 @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS", "FLASHINFER"]) @pytest.mark.parametrize("dtype", ["half"]) @@ -118,6 +127,11 @@ def test_models_distributed( if attention_backend: os.environ["VLLM_ATTENTION_BACKEND"] = attention_backend + # Import VLLM_USE_V1 dynamically to handle patching + from vllm.envs import VLLM_USE_V1 + if VLLM_USE_V1 and distributed_executor_backend != "mp": + pytest.skip(f"Skip {distributed_executor_backend} for V1") + dtype = "half" max_tokens = 5 @@ -143,6 +157,7 @@ def test_models_distributed( ) +@pytest.mark.skip_v1 def test_model_with_failure(vllm_runner) -> None: try: with patch("vllm.model_executor.models.opt.OPTForCausalLM.forward", @@ -169,6 +184,7 @@ def test_model_with_failure(vllm_runner) -> None: os.remove(filename) +@pytest.mark.skip_v1 def test_failure_with_async_out_proc(vllm_runner) -> None: filename = None diff --git a/tests/conftest.py b/tests/conftest.py index d6be8f5b00af8..7606e0f11dfeb 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,7 +5,6 @@ from enum import Enum from typing import (Any, Callable, Dict, List, Optional, Tuple, Type, TypedDict, TypeVar, Union) -from unittest.mock import patch import numpy as np import pytest @@ -110,7 +109,7 @@ def prompts(self, prompts: _VideoAssetPrompts) -> List[str]: @pytest.fixture(params=[True, False]) -def run_with_both_engines(request): +def run_with_both_engines(request, monkeypatch): # Automatically runs tests twice, once with V1 and once without use_v1 = request.param # Tests decorated with `@skip_v1` are only run without v1 @@ -119,11 +118,11 @@ def run_with_both_engines(request): if use_v1: if skip_v1: pytest.skip("Skipping test on vllm V1") - with patch('vllm.envs.VLLM_USE_V1', True): - yield + monkeypatch.setenv('VLLM_USE_V1', '1') else: - with patch('vllm.envs.VLLM_USE_V1', False): - yield + monkeypatch.setenv('VLLM_USE_V1', '0') + + yield @pytest.fixture(autouse=True) diff --git a/vllm/distributed/device_communicators/shm_broadcast.py b/vllm/distributed/device_communicators/shm_broadcast.py index 2ff1a1ead99c1..9a2d8918d96e5 100644 --- a/vllm/distributed/device_communicators/shm_broadcast.py +++ b/vllm/distributed/device_communicators/shm_broadcast.py @@ -1,10 +1,11 @@ import os import pickle +import sys import time from contextlib import contextmanager from dataclasses import dataclass, field from multiprocessing import shared_memory -from typing import List, Optional +from typing import List, Optional, Tuple from unittest.mock import patch import torch @@ -21,6 +22,20 @@ logger = init_logger(__name__) +# We prefer to use os.sched_yield as it results in tighter polling loops, +# measured to be around 3e-7 seconds. However on earlier versions of Python +# os.sched_yield() does not release the GIL, so we fall back to time.sleep(0) +USE_SCHED_YIELD = ((sys.version_info[:3] >= (3, 11, 1)) + or (sys.version_info[:2] == (3, 10) + and sys.version_info[2] >= 8)) + + +def sched_yield(): + if USE_SCHED_YIELD: + os.sched_yield() + else: + time.sleep(0) + class ShmRingBuffer: @@ -114,11 +129,14 @@ def __init__(self, # and we should suppress the error pass + def handle(self): + return (self.n_reader, self.max_chunk_bytes, self.max_chunks, + self.shared_memory.name) + def __reduce__(self): return ( self.__class__, - (self.n_reader, self.max_chunk_bytes, self.max_chunks, - self.shared_memory.name), + self.handle(), ) def __del__(self): @@ -147,7 +165,7 @@ class Handle: connect_ip: str local_reader_ranks: List[int] = field(default_factory=list) - buffer: Optional[ShmRingBuffer] = None + buffer_handle: Optional[Tuple[int, int, int, str]] = None local_subscribe_port: Optional[int] = None remote_subscribe_port: Optional[int] = None @@ -228,7 +246,7 @@ def __init__( self.handle = Handle( connect_ip=connect_ip, local_reader_ranks=local_reader_ranks, - buffer=self.buffer, + buffer_handle=self.buffer.handle(), local_subscribe_port=local_subscribe_port, remote_subscribe_port=remote_subscribe_port, ) @@ -247,8 +265,8 @@ def create_from_handle(handle: Handle, rank) -> "MessageQueue": context = Context() if rank in handle.local_reader_ranks: - assert handle.buffer is not None - self.buffer = handle.buffer + assert handle.buffer_handle is not None + self.buffer = ShmRingBuffer(*handle.buffer_handle) self.current_idx = 0 self.local_reader_rank = handle.local_reader_ranks.index(rank) self._is_local_reader = True @@ -314,7 +332,7 @@ def wait_until_ready(self): assert recv == b"READY" @contextmanager - def acquire_write(self): + def acquire_write(self, timeout: Optional[float] = None): assert self._is_writer, "Only writers can acquire write" start_time = time.monotonic() n_warning = 1 @@ -329,16 +347,20 @@ def acquire_write(self): # we need to wait until it is read by all readers # Release the processor to other threads - os.sched_yield() + sched_yield() - # if we wait for a long time, we should warn the user + # if we wait for a long time, log a message if (time.monotonic() - start_time > VLLM_RINGBUFFER_WARNING_INTERVAL * n_warning): - logger.warning( - "No available block found in %s second. ", - VLLM_RINGBUFFER_WARNING_INTERVAL) + logger.debug("No available block found in %s second. ", + VLLM_RINGBUFFER_WARNING_INTERVAL) n_warning += 1 + # if we time out, raise an exception + if (timeout is not None + and time.monotonic() - start_time > timeout): + raise TimeoutError + continue # found a block that is either # (1) not written @@ -365,7 +387,7 @@ def acquire_write(self): break @contextmanager - def acquire_read(self): + def acquire_read(self, timeout: Optional[float] = None): assert self._is_local_reader, "Only readers can acquire read" start_time = time.monotonic() n_warning = 1 @@ -383,16 +405,20 @@ def acquire_read(self): # we need to wait until it is written # Release the processor to other threads - os.sched_yield() + sched_yield() - # if we wait for a long time, we should warn the user + # if we wait for a long time, log a message if (time.monotonic() - start_time > VLLM_RINGBUFFER_WARNING_INTERVAL * n_warning): - logger.warning( - "No available block found in %s second. ", - VLLM_RINGBUFFER_WARNING_INTERVAL) + logger.debug("No available block found in %s second. ", + VLLM_RINGBUFFER_WARNING_INTERVAL) n_warning += 1 + # if we time out, raise an exception + if (timeout is not None + and time.monotonic() - start_time > timeout): + raise TimeoutError + continue # found a block that is not read by this reader # let caller read from the buffer @@ -406,24 +432,26 @@ def acquire_read(self): 1) % self.buffer.max_chunks break - def enqueue(self, obj): + def enqueue(self, obj, timeout: Optional[float] = None): + """ Write to message queue with optional timeout (in seconds) """ assert self._is_writer, "Only writers can enqueue" serialized_obj = pickle.dumps(obj, protocol=pickle.HIGHEST_PROTOCOL) if self.n_local_reader > 0: if len(serialized_obj) >= self.buffer.max_chunk_bytes: - with self.acquire_write() as buf: + with self.acquire_write(timeout) as buf: buf[0] = 1 # overflow self.local_socket.send(serialized_obj) else: - with self.acquire_write() as buf: + with self.acquire_write(timeout) as buf: buf[0] = 0 # not overflow buf[1:len(serialized_obj) + 1] = serialized_obj if self.n_remote_reader > 0: self.remote_socket.send(serialized_obj) - def dequeue(self): + def dequeue(self, timeout: Optional[float] = None): + """ Read from message queue with optional timeout (in seconds) """ if self._is_local_reader: - with self.acquire_read() as buf: + with self.acquire_read(timeout) as buf: overflow = buf[0] == 1 if not overflow: # no need to know the size of serialized object diff --git a/vllm/executor/multiproc_gpu_executor.py b/vllm/executor/multiproc_gpu_executor.py index c450209f0eb91..fc58163cade64 100644 --- a/vllm/executor/multiproc_gpu_executor.py +++ b/vllm/executor/multiproc_gpu_executor.py @@ -3,25 +3,19 @@ from functools import partial from typing import Any, List, Optional -import torch - from vllm.executor.distributed_gpu_executor import ( # yapf: disable DistributedGPUExecutor, DistributedGPUExecutorAsync) from vllm.executor.gpu_executor import create_worker -from vllm.executor.multiproc_worker_utils import (ProcessWorkerWrapper, - ResultHandler, WorkerMonitor) +from vllm.executor.multiproc_worker_utils import ( + ProcessWorkerWrapper, ResultHandler, WorkerMonitor, + set_multiprocessing_worker_envs) from vllm.logger import init_logger from vllm.model_executor.layers.sampler import SamplerOutput from vllm.sequence import ExecuteModelRequest -from vllm.triton_utils.importing import HAS_TRITON from vllm.utils import (_run_task_with_lock, cuda_device_count_stateless, - cuda_is_initialized, get_distributed_init_method, - get_open_port, make_async, + get_distributed_init_method, get_open_port, make_async, update_environment_variables) -if HAS_TRITON: - from vllm.triton_utils import maybe_set_triton_cache_manager - logger = init_logger(__name__) @@ -37,30 +31,8 @@ def _init_executor(self) -> None: world_size = self.parallel_config.world_size tensor_parallel_size = self.parallel_config.tensor_parallel_size - # Disable torch async compiling which won't work with daemonic processes - os.environ["TORCHINDUCTOR_COMPILE_THREADS"] = "1" - - # Configure thread parallelism if OMP_NUM_THREADS isn't set - # - # Helps to avoid CPU contention. The default of spawning a thread per - # core combined with multiprocessing for each GPU can have a negative - # impact on performance. The contention is amplified when running in a - # container where CPU limits can cause throttling. - default_omp_num_threads = 1 - if "OMP_NUM_THREADS" not in os.environ and ( - current_parallelism := - torch.get_num_threads()) > default_omp_num_threads: - logger.warning( - "Reducing Torch parallelism from %d threads to %d to avoid " - "unnecessary CPU contention. Set OMP_NUM_THREADS in the " - "external environment to tune this value as needed.", - current_parallelism, default_omp_num_threads) - os.environ["OMP_NUM_THREADS"] = str(default_omp_num_threads) - torch.set_num_threads(default_omp_num_threads) - - # workaround for https://github.com/vllm-project/vllm/issues/6103 - if HAS_TRITON and world_size > 1: - maybe_set_triton_cache_manager() + # Set multiprocessing envs that are common to V0 and V1 + set_multiprocessing_worker_envs(self.parallel_config) # Multiprocessing-based executor does not support multi-node setting. # Since it only works for single node, we can use the loopback address @@ -122,13 +94,6 @@ def _check_executor_parameters(self): "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size)))) }) - if (cuda_is_initialized() - and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") != "spawn"): - logger.warning("CUDA was previously initialized. We must use " - "the `spawn` multiprocessing start method. Setting " - "VLLM_WORKER_MULTIPROC_METHOD to 'spawn'.") - os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" - cuda_device_count = cuda_device_count_stateless() # Use confusing message for more common TP-only case. assert tensor_parallel_size <= cuda_device_count, ( diff --git a/vllm/executor/multiproc_worker_utils.py b/vllm/executor/multiproc_worker_utils.py index 884267d23dfc8..fe475db6d3f57 100644 --- a/vllm/executor/multiproc_worker_utils.py +++ b/vllm/executor/multiproc_worker_utils.py @@ -11,8 +11,15 @@ from typing import (Any, Callable, Dict, Generic, List, Optional, TextIO, TypeVar, Union) +import torch + import vllm.envs as envs from vllm.logger import init_logger +from vllm.triton_utils.importing import HAS_TRITON +from vllm.utils import cuda_is_initialized + +if HAS_TRITON: + from vllm.triton_utils import maybe_set_triton_cache_manager logger = init_logger(__name__) @@ -270,3 +277,38 @@ def write_with_prefix(s: str): def get_mp_context(): mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD return multiprocessing.get_context(mp_method) + + +def set_multiprocessing_worker_envs(parallel_config): + """ Set up environment variables that should be used when there are workers + in a multiprocessing environment. This should be called by the parent + process before worker processes are created""" + + if (cuda_is_initialized() + and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") != "spawn"): + logger.warning("CUDA was previously initialized. We must use " + "the `spawn` multiprocessing start method. Setting " + "VLLM_WORKER_MULTIPROC_METHOD to 'spawn'.") + os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" + + # Configure thread parallelism if OMP_NUM_THREADS isn't set + # + # Helps to avoid CPU contention. The default of spawning a thread per + # core combined with multiprocessing for each GPU can have a negative + # impact on performance. The contention is amplified when running in a + # container where CPU limits can cause throttling. + default_omp_num_threads = 1 + if "OMP_NUM_THREADS" not in os.environ and ( + current_parallelism := + torch.get_num_threads()) > default_omp_num_threads: + logger.warning( + "Reducing Torch parallelism from %d threads to %d to avoid " + "unnecessary CPU contention. Set OMP_NUM_THREADS in the " + "external environment to tune this value as needed.", + current_parallelism, default_omp_num_threads) + os.environ["OMP_NUM_THREADS"] = str(default_omp_num_threads) + torch.set_num_threads(default_omp_num_threads) + + # workaround for https://github.com/vllm-project/vllm/issues/6103 + if HAS_TRITON and parallel_config.world_size > 1: + maybe_set_triton_cache_manager() diff --git a/vllm/model_executor/layers/logits_processor.py b/vllm/model_executor/layers/logits_processor.py index fb76b1b17925e..2bc7e458494f7 100644 --- a/vllm/model_executor/layers/logits_processor.py +++ b/vllm/model_executor/layers/logits_processor.py @@ -5,6 +5,7 @@ import torch import torch.nn as nn +import vllm.envs as envs from vllm.distributed import (tensor_model_parallel_all_gather, tensor_model_parallel_gather) from vllm.model_executor.layers.vocab_parallel_embedding import ( @@ -42,7 +43,9 @@ def __init__(self, # Soft cap the logits. Used in Gemma 2. self.soft_cap = soft_cap # Whether to use gather or all-gather to gather the logits. - self.use_gather = not current_platform.is_tpu() + + self.use_gather = not current_platform.is_tpu( + ) and not envs.VLLM_USE_V1 def forward( self, diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index edaf377b501df..10f83fd304281 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -12,6 +12,7 @@ # import custom ops, trigger op registration import vllm._C # noqa +import vllm.envs as envs from vllm.logger import init_logger from .interface import DeviceCapability, Platform, PlatformEnum @@ -110,17 +111,28 @@ def log_warnings(cls): def check_and_update_config(cls, vllm_config: VllmConfig) -> None: parallel_config = vllm_config.parallel_config scheduler_config = vllm_config.scheduler_config + if parallel_config.worker_cls == "auto": if scheduler_config.is_multi_step: - parallel_config.worker_cls = \ - "vllm.worker.multi_step_worker.MultiStepWorker" + if envs.VLLM_USE_V1: + raise NotImplementedError + else: + parallel_config.worker_cls = \ + "vllm.worker.multi_step_worker.MultiStepWorker" elif vllm_config.speculative_config: - parallel_config.worker_cls = \ - "vllm.spec_decode.spec_decode_worker.create_spec_worker" - parallel_config.sd_worker_cls = \ - "vllm.worker.worker.Worker" + if envs.VLLM_USE_V1: + raise NotImplementedError + else: + parallel_config.worker_cls = \ + "vllm.spec_decode.spec_decode_worker.create_spec_worker" + parallel_config.sd_worker_cls = \ + "vllm.worker.worker.Worker" else: - parallel_config.worker_cls = "vllm.worker.worker.Worker" + if envs.VLLM_USE_V1: + parallel_config.worker_cls = \ + "vllm.v1.worker.gpu_worker.Worker" + else: + parallel_config.worker_cls = "vllm.worker.worker.Worker" # NVML utils @@ -249,4 +261,4 @@ def is_full_nvlink(cls, physical_device_ids: List[int]) -> bool: if not isinstance(pynvml, _MockModule): CudaPlatform.log_warnings() except ModuleNotFoundError: - CudaPlatform.log_warnings() \ No newline at end of file + CudaPlatform.log_warnings() diff --git a/vllm/utils.py b/vllm/utils.py index 2bb1fb2af40f4..7cdb2cb320b05 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -10,6 +10,7 @@ import inspect import ipaddress import os +import signal import socket import subprocess import sys @@ -1652,3 +1653,28 @@ def resolve_obj_by_qualname(qualname: str) -> Any: module_name, obj_name = qualname.rsplit(".", 1) module = importlib.import_module(module_name) return getattr(module, obj_name) + + +def kill_process_tree(pid: int): + """ + Kills all descendant processes of the given pid by sending SIGKILL. + + Args: + pid (int): Process ID of the parent process + """ + try: + parent = psutil.Process(pid) + except psutil.NoSuchProcess: + return + + # Get all children recursively + children = parent.children(recursive=True) + + # Send SIGKILL to all children first + for child in children: + with contextlib.suppress(ProcessLookupError): + os.kill(child.pid, signal.SIGKILL) + + # Finally kill the parent + with contextlib.suppress(ProcessLookupError): + os.kill(pid, signal.SIGKILL) diff --git a/vllm/v1/core/scheduler.py b/vllm/v1/core/scheduler.py index 1203d35fc985f..a3e85c20cc664 100644 --- a/vllm/v1/core/scheduler.py +++ b/vllm/v1/core/scheduler.py @@ -5,6 +5,8 @@ from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig from vllm.logger import init_logger +from vllm.multimodal import MultiModalKwargs +from vllm.multimodal.base import PlaceholderRange from vllm.sampling_params import SamplingParams from vllm.v1.core.encoder_cache_manager import EncoderCacheManager from vllm.v1.core.kv_cache_manager import KVCacheManager @@ -383,7 +385,7 @@ def update_from_output( model_runner_output: "ModelRunnerOutput", ) -> List[EngineCoreOutput]: # NOTE(woosuk): This method doesn't consider speculative decoding. - sampled_token_ids = model_runner_output.sampled_token_ids_cpu.tolist() + sampled_token_ids = model_runner_output.sampled_token_ids num_scheduled_tokens = scheduler_output.num_scheduled_tokens new_running: List[Request] = [] engine_core_outputs: List[EngineCoreOutput] = [] diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py index 0bcccda2bf329..26fd650aee4b7 100644 --- a/vllm/v1/engine/async_llm.py +++ b/vllm/v1/engine/async_llm.py @@ -20,7 +20,7 @@ from vllm.v1.engine.core_client import EngineCoreClient from vllm.v1.engine.detokenizer import Detokenizer from vllm.v1.engine.processor import Processor -from vllm.v1.executor.gpu_executor import GPUExecutor +from vllm.v1.executor.abstract import Executor logger = init_logger(__name__) @@ -30,7 +30,7 @@ class AsyncLLM(EngineClient): def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, @@ -119,14 +119,24 @@ def from_engine_args( def shutdown(self): """Shutdown, cleaning up the background proc and IPC.""" - self.engine_core.shutdown() + if engine_core := getattr(self, "engine_core", None): + engine_core.shutdown() if handler := getattr(self, "output_handler", None): handler.cancel() @classmethod def _get_executor_cls(cls, vllm_config: VllmConfig): - return GPUExecutor + distributed_executor_backend = ( + vllm_config.parallel_config.distributed_executor_backend) + if distributed_executor_backend == "mp": + from vllm.v1.executor.multiproc_executor import MultiprocExecutor + executor_class = MultiprocExecutor + else: + assert (distributed_executor_backend is None) + from vllm.v1.executor.uniproc_executor import UniprocExecutor + executor_class = UniprocExecutor + return executor_class async def add_request( self, diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index 751eb3b40a68d..fdb241e6753fb 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -1,12 +1,12 @@ import multiprocessing import pickle import queue +import signal import threading import time -from contextlib import contextmanager from multiprocessing.process import BaseProcess from multiprocessing.sharedctypes import Synchronized -from typing import Any, Iterator, List, Tuple, Type, Union +from typing import List, Tuple, Type, Union import zmq import zmq.asyncio @@ -20,9 +20,10 @@ EngineCoreProfile, EngineCoreRequest, EngineCoreRequestType) from vllm.v1.engine.mm_input_mapper import MMInputMapper -from vllm.v1.executor.gpu_executor import GPUExecutor +from vllm.v1.executor.abstract import Executor from vllm.v1.request import Request, RequestStatus from vllm.v1.serial_utils import PickleEncoder +from vllm.v1.utils import make_zmq_socket from vllm.version import __version__ as VLLM_VERSION logger = init_logger(__name__) @@ -38,7 +39,7 @@ class EngineCore: def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], usage_context: UsageContext, ): assert vllm_config.model_config.task != "embedding" @@ -80,7 +81,7 @@ def _initialize_kv_caches(self, num_gpu_blocks = num_gpu_blocks_override num_cpu_blocks = 0 - self.model_executor.initialize_cache(num_gpu_blocks) + self.model_executor.initialize(num_gpu_blocks) elapsed = time.time() - start logger.info(("init engine (profile, create kv cache, " "warmup model) took %.2f seconds"), elapsed) @@ -112,8 +113,11 @@ def step(self) -> List[EngineCoreOutput]: scheduler_output, output) return engine_core_outputs + def shutdown(self): + self.model_executor.shutdown() + def profile(self, is_start=True): - self.model_executor.worker.profile(is_start) + self.model_executor.profile(is_start) class EngineCoreProc(EngineCore): @@ -124,7 +128,7 @@ class EngineCoreProc(EngineCore): def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], usage_context: UsageContext, input_path: str, output_path: str, @@ -151,32 +155,9 @@ def __init__( daemon=True).start() # Send Readiness signal to EngineClient. - with self.make_socket(ready_path, zmq.constants.PUSH) as ready_socket: + with make_zmq_socket(ready_path, zmq.constants.PUSH) as ready_socket: ready_socket.send_string(EngineCoreProc.READY_STR) - @contextmanager - def make_socket(self, path: str, type: Any) -> Iterator[zmq.Socket]: - """Context manager for use """ - - ctx = zmq.Context() - try: - socket = ctx.socket(type) - - if type == zmq.constants.PULL: - socket.connect(path) - elif type == zmq.constants.PUSH: - socket.bind(path) - else: - raise ValueError(f"Unknown Socket Type: {type}") - - yield socket - - except KeyboardInterrupt: - logger.debug("EngineCore had Keyboard Interrupt.") - - finally: - ctx.destroy(linger=0) - @staticmethod def wait_for_startup( proc: BaseProcess, @@ -209,7 +190,7 @@ def wait_for_startup( @staticmethod def make_engine_core_process( vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], usage_context: UsageContext, input_path: str, output_path: str, @@ -244,17 +225,38 @@ def make_engine_core_process( def run_engine_core(*args, **kwargs): """Launch EngineCore busy loop in background process.""" + # Signal handler used for graceful termination. + # SystemExit exception is only raised once to allow this and worker + # processes to terminate without error + shutdown_requested = False + + def signal_handler(signum, frame): + nonlocal shutdown_requested + if not shutdown_requested: + shutdown_requested = True + raise SystemExit() + + # Either SIGTERM or SIGINT will terminate the engine_core + signal.signal(signal.SIGTERM, signal_handler) + signal.signal(signal.SIGINT, signal_handler) + + engine_core = None try: engine_core = EngineCoreProc(*args, **kwargs) engine_core.run_busy_loop() - except KeyboardInterrupt: + except SystemExit: logger.debug("EngineCore interrupted.") except BaseException as e: logger.exception(e) raise e + finally: + if engine_core is not None: + engine_core.shutdown() + engine_core = None + def run_busy_loop(self): """Core busy loop of the EngineCore.""" @@ -272,6 +274,8 @@ def run_busy_loop(self): logger.debug("EngineCore busy loop waiting.") if self.should_shutdown: return + except BaseException: + raise # 2) Handle any new client requests (Abort or Add). while not self.input_queue.empty(): @@ -321,7 +325,7 @@ def process_input_socket(self, input_path: str): decoder_add_req = PickleEncoder() decoder_abort_req = PickleEncoder() - with self.make_socket(input_path, zmq.constants.PULL) as socket: + with make_zmq_socket(input_path, zmq.constants.PULL) as socket: while True: # (RequestType, RequestData) type_frame, data_frame = socket.recv_multipart(copy=False) @@ -349,7 +353,7 @@ def process_output_socket(self, output_path: str): # Reuse send buffer. buffer = bytearray() - with self.make_socket(output_path, zmq.constants.PUSH) as socket: + with make_zmq_socket(output_path, zmq.constants.PUSH) as socket: while True: engine_core_outputs = self.output_queue.get() outputs = EngineCoreOutputs(outputs=engine_core_outputs) diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py index 835963f7ee86c..ee89cece73141 100644 --- a/vllm/v1/engine/core_client.py +++ b/vllm/v1/engine/core_client.py @@ -1,5 +1,4 @@ import multiprocessing -import time from typing import List, Union import msgspec @@ -7,7 +6,7 @@ import zmq.asyncio from vllm.logger import init_logger -from vllm.utils import get_open_zmq_ipc_path +from vllm.utils import get_open_zmq_ipc_path, kill_process_tree from vllm.v1.engine import (EngineCoreOutput, EngineCoreOutputs, EngineCoreProfile, EngineCoreRequest, EngineCoreRequestType) @@ -99,6 +98,12 @@ def add_request(self, request: EngineCoreRequest) -> None: def abort_requests(self, request_ids: List[str]) -> None: self.engine_core.abort_requests(request_ids) + def shutdown(self): + self.engine_core.shutdown() + + def __del__(self): + self.shutdown() + async def profile(self, is_start=True) -> None: self.engine_core.profile(is_start) @@ -163,10 +168,10 @@ def shutdown(self): # Shutdown the process if needed. if hasattr(self, "proc") and self.proc.is_alive(): self.proc.terminate() + self.proc.join(5) - time.sleep(5) if self.proc.is_alive(): - self.proc.kill() + kill_process_tree(self.proc.pid) def __del__(self): self.shutdown() diff --git a/vllm/v1/engine/llm_engine.py b/vllm/v1/engine/llm_engine.py index 994e68669108e..1b3a9f12d009e 100644 --- a/vllm/v1/engine/llm_engine.py +++ b/vllm/v1/engine/llm_engine.py @@ -20,7 +20,7 @@ from vllm.v1.engine.core_client import EngineCoreClient from vllm.v1.engine.detokenizer import Detokenizer from vllm.v1.engine.processor import Processor -from vllm.v1.executor.gpu_executor import GPUExecutor +from vllm.v1.executor.abstract import Executor logger = init_logger(__name__) @@ -33,7 +33,7 @@ class LLMEngine: def __init__( self, vllm_config: VllmConfig, - executor_class: Type[GPUExecutor], + executor_class: Type[Executor], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, @@ -104,10 +104,17 @@ def from_engine_args( @classmethod def _get_executor_cls(cls, vllm_config: VllmConfig): - return GPUExecutor - - def stop_remote_worker_execution_loop(self) -> None: - raise NotImplementedError("TP not implemented yet.") + distributed_executor_backend = ( + vllm_config.parallel_config.distributed_executor_backend) + if distributed_executor_backend == "mp": + from vllm.v1.executor.multiproc_executor import MultiprocExecutor + executor_class = MultiprocExecutor + else: + assert (distributed_executor_backend is None) + from vllm.v1.executor.uniproc_executor import UniprocExecutor + executor_class = UniprocExecutor + + return executor_class def get_num_unfinished_requests(self) -> int: return self.detokenizer.get_num_unfinished_requests() diff --git a/vllm/v1/executor/abstract.py b/vllm/v1/executor/abstract.py new file mode 100644 index 0000000000000..9cd267581ad18 --- /dev/null +++ b/vllm/v1/executor/abstract.py @@ -0,0 +1,48 @@ +from abc import ABC, abstractmethod +from typing import Dict, Optional, Tuple + +from vllm.config import VllmConfig +from vllm.v1.outputs import ModelRunnerOutput + + +class Executor(ABC): + """Abstract class for executors.""" + + @abstractmethod + def __init__(self, vllm_config: VllmConfig) -> None: + raise NotImplementedError + + @abstractmethod + def initialize(self, num_gpu_blocks: int) -> None: + raise NotImplementedError + + @abstractmethod + def determine_num_available_blocks(self) -> Tuple[int, int]: + raise NotImplementedError + + @abstractmethod + def execute_model( + self, + scheduler_output, + ) -> ModelRunnerOutput: + raise NotImplementedError + + @abstractmethod + def profile(self, is_start=True): + raise NotImplementedError + + @abstractmethod + def shutdown(self): + pass + + @abstractmethod + def check_health(self) -> None: + raise NotImplementedError + + @abstractmethod + def collective_rpc(self, + method: str, + timeout: Optional[float] = None, + args: Tuple = (), + kwargs: Optional[Dict] = None) -> []: + raise NotImplementedError diff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py new file mode 100644 index 0000000000000..f8f3d583618cf --- /dev/null +++ b/vllm/v1/executor/multiproc_executor.py @@ -0,0 +1,375 @@ +import atexit +import os +import pickle +import signal +import sys +import time +from dataclasses import dataclass +from enum import Enum, auto +from multiprocessing.process import BaseProcess +from typing import Dict, List, Optional, Tuple + +import zmq + +from vllm.config import VllmConfig +from vllm.distributed import (destroy_distributed_environment, + destroy_model_parallel) +from vllm.distributed.device_communicators.shm_broadcast import (Handle, + MessageQueue) +from vllm.executor.multiproc_worker_utils import ( + _add_prefix, get_mp_context, set_multiprocessing_worker_envs) +from vllm.logger import init_logger +from vllm.utils import (get_distributed_init_method, get_open_port, + get_open_zmq_ipc_path) +from vllm.v1.outputs import ModelRunnerOutput +from vllm.v1.utils import make_zmq_socket +from vllm.worker.worker_base import WorkerWrapperBase + +logger = init_logger(__name__) + +POLLING_TIMEOUT_MS = 5000 +POLLING_TIMEOUT_S = POLLING_TIMEOUT_MS // 1000 + + +class MultiprocExecutor: + + def __init__(self, vllm_config: VllmConfig) -> None: + # Call self.shutdown at exit to clean up + # and ensure workers will be terminated. + atexit.register(self.shutdown) + + self.vllm_config = vllm_config + self.parallel_config = vllm_config.parallel_config + + self.world_size = self.parallel_config.world_size + tensor_parallel_size = self.parallel_config.tensor_parallel_size + assert self.world_size == tensor_parallel_size, ( + f"world_size ({self.world_size}) must be equal to the " + f"tensor_parallel_size ({tensor_parallel_size}). " + f"Pipeline parallelism is not yet implemented in v1") + + # Set multiprocessing envs that are common to V0 and V1 + set_multiprocessing_worker_envs(self.parallel_config) + + # Multiprocessing-based executor does not support multi-node setting. + # Since it only works for single node, we can use the loopback address + # 127.0.0.1 for communication. + distributed_init_method = get_distributed_init_method( + "127.0.0.1", get_open_port()) + + # Initialize worker and set up message queues for SchedulerOutputs + # and ModelRunnerOutputs + self.rpc_broadcast_mq = MessageQueue(self.world_size, self.world_size) + scheduler_output_handle = self.rpc_broadcast_mq.export_handle() + + # Create workers + self.workers: List[WorkerProcHandle] = [] + for rank in range(self.world_size): + worker = WorkerProc.make_worker_process(vllm_config, rank, rank, + distributed_init_method, + scheduler_output_handle) + self.workers.append(worker) + + # Ensure message queues are ready. Will deadlock if re-ordered + # Must be kept consistent with the WorkerProc + self.rpc_broadcast_mq.wait_until_ready() + for w in self.workers: + w.worker_response_mq.wait_until_ready() + + def initialize(self, num_gpu_blocks: int) -> None: + """ + Initialize the KV caches and begin the model execution loop of the + underlying workers. + """ + self.collective_rpc("initialize_cache", args=(num_gpu_blocks, )) + self.collective_rpc("compile_or_warm_up_model") + + def determine_num_available_blocks(self) -> Tuple[int, int]: + """ + Determine the number of available KV blocks by invoking the + underlying worker. + """ + num_blocks = self.collective_rpc("determine_num_available_blocks") + + # Since we use a shared centralized controller, we take the minimum + # number of blocks across all workers to make sure all the memory + # operators can be applied to all workers. + num_gpu_blocks = min(b[0] for b in num_blocks) + num_cpu_blocks = min(b[1] for b in num_blocks) + + return num_gpu_blocks, num_cpu_blocks + + def collective_rpc(self, + method: str, + timeout: Optional[float] = None, + args: Tuple = (), + kwargs: Optional[Dict] = None) -> []: + """ + Execute an RPC call on workers. + + Args: + method: Name of the worker method to execute + timeout: Maximum time in seconds to wait for execution. Rases a + TimeoutError on timeout. None means wait indefinitely. + args: Positional arguments to pass to the worker method + kwargs: Keyword arguments to pass to the worker method + + Returns: + List of results from each worker + """ + start_time = time.monotonic() + kwargs = kwargs or {} + + try: + self.rpc_broadcast_mq.enqueue((method, args, kwargs)) + + responses = [None] * self.world_size + for w in self.workers: + dequeue_timeout = timeout - (time.monotonic() - start_time() + ) if timeout is not None else None + status, result = w.worker_response_mq.dequeue( + timeout=dequeue_timeout) + + if status != WorkerProc.ResponseStatus.SUCCESS: + if isinstance(result, Exception): + raise result + else: + raise RuntimeError("Worker failed") + + responses[w.rank] = result + + return responses + except TimeoutError as e: + raise TimeoutError(f"RPC call to {method} timed out.") from e + except Exception as e: + # Re-raise any other exceptions + raise e + + def execute_model( + self, + scheduler_output, + ) -> ModelRunnerOutput: + model_output = self.collective_rpc("execute_model", + args=(scheduler_output, ))[0] + return model_output + + def profile(self, is_start=True): + self.collective_rpc("profile", args=(is_start, )) + return + + def _ensure_worker_termination(self): + """Ensure that all worker processes are terminated. Assumes workers have + received termination requests. Waits for processing, then sends + termination and kill signals if needed.""" + + def wait_for_termination(procs, timeout): + start_time = time.time() + while time.time() - start_time < timeout: + if all(not proc.is_alive() for proc in procs): + return True + time.sleep(0.1) + return False + + # Send SIGTERM if still running + active_procs = [w.proc for w in self.workers if w.proc.is_alive()] + self.workers = None + for p in active_procs: + p.terminate() + if wait_for_termination(active_procs, 4): + return + + # Send SIGKILL if still running + active_procs = [p for p in active_procs if p.is_alive()] + for p in active_procs: + p.kill() + + def shutdown(self): + """Properly shut down the executor and its workers""" + if (hasattr(self, 'workers') and self.workers is not None): + for w in self.workers: #TODO: not sure if needed + w.worker_response_mq = None + self._ensure_worker_termination() + + self.rpc_broadcast_mq = None + + def check_health(self) -> None: + self.collective_rpc("check_health", timeout=10) + return + + +@dataclass +class WorkerProcHandle: + proc: BaseProcess + rank: int + ready_path: str + worker_response_mq: MessageQueue # The worker process writes to this MQ + + +class WorkerProc: + """Wrapper that runs one Worker in a separate process.""" + + READY_STR = "READY" + + def __init__( + self, + vllm_config: VllmConfig, + local_rank: int, + rank: int, + distributed_init_method: str, + input_shm_handle: Handle, + ready_path: str, + ): + self.rank = rank + wrapper = WorkerWrapperBase(vllm_config=vllm_config) + wrapper.init_worker(vllm_config, local_rank, rank, + distributed_init_method) + self.worker = wrapper.worker + + pid = os.getpid() + _add_prefix(sys.stdout, f"VllmWorker rank={rank}", pid) + _add_prefix(sys.stderr, f"VllmWorker rank={rank}", pid) + + # Initialize MessageQueue for receiving SchedulerOutput + self.rpc_broadcast_mq = MessageQueue.create_from_handle( + input_shm_handle, self.worker.rank) + + # Initializes a message queue for sending the model output + self.worker_response_mq = MessageQueue(1, 1) + worker_response_mq_handle = self.worker_response_mq.export_handle() + + # Send Readiness signal to EngineCore process. + with make_zmq_socket(ready_path, zmq.constants.PUSH) as ready_socket: + payload = pickle.dumps(worker_response_mq_handle, + protocol=pickle.HIGHEST_PROTOCOL) + ready_socket.send_string(WorkerProc.READY_STR) + ready_socket.send(payload) + + self.worker.initialize() + self.worker.load_model() + + @staticmethod + def make_worker_process( + vllm_config: VllmConfig, + local_rank: int, + rank: int, + distributed_init_method: str, + input_shm_handle, # Receive SchedulerOutput + ) -> WorkerProcHandle: + context = get_mp_context() + + # ZMQ path for worker to send ready message and shm_broadcast handle + # back to core process. + ready_path = get_open_zmq_ipc_path() + + process_kwargs = { + "vllm_config": vllm_config, + "local_rank": local_rank, + "rank": rank, + "distributed_init_method": distributed_init_method, + "input_shm_handle": input_shm_handle, + "ready_path": ready_path, + } + # Run EngineCore busy loop in background process. + proc = context.Process(target=WorkerProc.worker_main, + kwargs=process_kwargs, + daemon=True) + proc.start() + + # Wait for startup + worker_response_mq_handle = WorkerProc.wait_for_startup( + proc, ready_path) + + worker_response_mq = MessageQueue.create_from_handle( + worker_response_mq_handle, 0) + + return WorkerProcHandle(proc, rank, ready_path, worker_response_mq) + + def shutdown(self): + self.rpc_broadcast_mq = None + self.worker_response_mq = None + destroy_model_parallel() + destroy_distributed_environment() + + @staticmethod + def worker_main(*args, **kwargs): + """ Worker initialization and execution loops. + This runs a background process """ + + # Signal handler used for graceful termination. + # SystemExit exception is only raised once to allow this and worker + # processes to terminate without error + shutdown_requested = False + + def signal_handler(signum, frame): + nonlocal shutdown_requested + if not shutdown_requested: + shutdown_requested = True + raise SystemExit() + + # Either SIGTERM or SIGINT will terminate the worker + signal.signal(signal.SIGTERM, signal_handler) + signal.signal(signal.SIGINT, signal_handler) + + worker = None + try: + worker = WorkerProc(*args, **kwargs) + + # Ensure message queues are ready. Will deadlock if re-ordered. + # Must be kept consistent with the Executor + worker.rpc_broadcast_mq.wait_until_ready() + worker.worker_response_mq.wait_until_ready() + + worker.worker_busy_loop() + + except SystemExit: + logger.debug("Worker interrupted.") + + except BaseException as e: + logger.exception(e) + raise + + finally: + # Clean up once worker exits busy loop + if worker is not None: + worker.shutdown() + worker = None + + @staticmethod + def wait_for_startup( + proc: BaseProcess, + ready_path: str, + ) -> Optional[Handle]: + """Wait until the Worker is ready.""" + with make_zmq_socket(ready_path, zmq.constants.PULL) as socket: + + # Wait for Worker to send READY. + while socket.poll(timeout=POLLING_TIMEOUT_MS) == 0: + logger.debug("Waiting for WorkerProc to startup.") + + if not proc.is_alive(): + raise RuntimeError("WorkerProc failed to start.") + + message = socket.recv_string() + assert message == WorkerProc.READY_STR + handle_frame = socket.recv(copy=False) + handle = pickle.loads(handle_frame.buffer) + return handle + + class ResponseStatus(Enum): + SUCCESS = auto() + FAILURE = auto() + + def worker_busy_loop(self): + """Main busy loop for Multiprocessing Workers""" + while True: + method, args, kwargs = self.rpc_broadcast_mq.dequeue() + + try: + output = getattr(self.worker, method)(*args, **kwargs) + except BaseException as e: + self.worker_response_mq.enqueue( + (WorkerProc.ResponseStatus.FAILURE, e)) + continue + + self.worker_response_mq.enqueue( + (WorkerProc.ResponseStatus.SUCCESS, output)) diff --git a/vllm/v1/executor/gpu_executor.py b/vllm/v1/executor/uniproc_executor.py similarity index 90% rename from vllm/v1/executor/gpu_executor.py rename to vllm/v1/executor/uniproc_executor.py index f71fa16b16e27..9b1d9a40950c6 100644 --- a/vllm/v1/executor/gpu_executor.py +++ b/vllm/v1/executor/uniproc_executor.py @@ -10,7 +10,7 @@ logger = init_logger(__name__) -class GPUExecutor: +class UniprocExecutor: def __init__(self, vllm_config: VllmConfig) -> None: self.vllm_config = vllm_config @@ -54,7 +54,7 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: """ return self.worker.determine_num_available_blocks() - def initialize_cache(self, num_gpu_blocks: int) -> None: + def initialize(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 @@ -71,7 +71,13 @@ def execute_model( output = self.worker.execute_model(scheduler_output) return output + def profile(self, is_start: bool = True): + self.worker.profile(is_start) + + def shutdown(self): + self.worker = None + def check_health(self) -> None: - # GPUExecutor will always be healthy as long as + # UniprocExecutor will always be healthy as long as # it's running. return diff --git a/vllm/v1/outputs.py b/vllm/v1/outputs.py index 8574987728844..acc3a944e21b9 100644 --- a/vllm/v1/outputs.py +++ b/vllm/v1/outputs.py @@ -8,7 +8,7 @@ class SamplerOutput: # [num_reqs] - sampled_token_ids: torch.Tensor + sampled_token_ids: List[int] # [num_reqs, max_num_logprobs + 1] logprob_token_ids: Optional[torch.Tensor] @@ -20,6 +20,8 @@ class SamplerOutput: prompt_logprobs: Optional[torch.Tensor] +# ModelRunnerOutput is serialized and sent to the scheduler process. +# This is expensive for torch.Tensor so prefer to use List instead. @dataclass class ModelRunnerOutput: @@ -29,7 +31,7 @@ class ModelRunnerOutput: req_id_to_index: Dict[str, int] # [num_reqs] - sampled_token_ids_cpu: torch.Tensor + sampled_token_ids: List[int] # [num_reqs, max_num_logprobs + 1] logprob_token_ids_cpu: Optional[torch.Tensor] diff --git a/vllm/v1/sample/sampler.py b/vllm/v1/sample/sampler.py index 927f274541c4d..d1a755be01ff7 100644 --- a/vllm/v1/sample/sampler.py +++ b/vllm/v1/sample/sampler.py @@ -37,8 +37,9 @@ def forward( topk_logprobs = None topk_indices = None + # NOTE: CPU-GPU synchronization happens here. sampler_output = SamplerOutput( - sampled_token_ids=sampled, + sampled_token_ids=sampled.tolist(), logprob_token_ids=topk_indices, logprobs=topk_logprobs, prompt_logprob_token_ids=None, diff --git a/vllm/v1/utils.py b/vllm/v1/utils.py index 4b26749712e32..6e7a7d4fe12cd 100644 --- a/vllm/v1/utils.py +++ b/vllm/v1/utils.py @@ -1,4 +1,11 @@ -from typing import Generic, List, TypeVar, overload +from contextlib import contextmanager +from typing import Any, Generic, Iterator, List, TypeVar, overload + +import zmq + +from vllm.logger import init_logger + +logger = init_logger(__name__) T = TypeVar("T") @@ -62,3 +69,27 @@ def __contains__(self, item): def __len__(self): return len(self._x) + + +@contextmanager +def make_zmq_socket(path: str, type: Any) -> Iterator[zmq.Socket]: + """Context manager for a ZMQ socket""" + + ctx = zmq.Context() + try: + socket = ctx.socket(type) + + if type == zmq.constants.PULL: + socket.connect(path) + elif type == zmq.constants.PUSH: + socket.bind(path) + else: + raise ValueError(f"Unknown Socket Type: {type}") + + yield socket + + except KeyboardInterrupt: + logger.debug("Worker had Keyboard Interrupt.") + + finally: + ctx.destroy(linger=0) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index c601aca13feaf..0a5adfb28c9bd 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -34,6 +34,7 @@ class GPUModelRunner: def __init__( self, vllm_config: VllmConfig, + device: torch.device, input_registry: InputRegistry = INPUT_REGISTRY, ): self.vllm_config = vllm_config @@ -43,7 +44,6 @@ def __init__( self.load_config = vllm_config.load_config self.parallel_config = vllm_config.parallel_config self.scheduler_config = vllm_config.scheduler_config - self.device_config = vllm_config.device_config self.speculative_config = vllm_config.speculative_config self.prompt_adapter_config = vllm_config.prompt_adapter_config self.observability_config = vllm_config.observability_config @@ -52,7 +52,7 @@ def __init__( cache_config = self.cache_config scheduler_config = self.scheduler_config parallel_config = self.parallel_config - self.device = self.device_config.device + self.device = device self.pin_memory = is_pin_memory_available() self.dtype = self.model_config.dtype if cache_config.cache_dtype == "auto": @@ -477,9 +477,7 @@ def execute_model( 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() + sampled_token_ids = sampler_output.sampled_token_ids # 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 @@ -490,7 +488,7 @@ def execute_model( 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] + token_id = sampled_token_ids[i] self.input_batch.token_ids_cpu[i, seq_len] = token_id req_state.output_token_ids.append(token_id) else: @@ -512,7 +510,7 @@ def execute_model( 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, + sampled_token_ids=sampled_token_ids, logprob_token_ids_cpu=logprob_token_ids, logprobs_cpu=logprobs, ) diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py index d33b55a8a9f9a..d32848c3775ae 100644 --- a/vllm/v1/worker/gpu_worker.py +++ b/vllm/v1/worker/gpu_worker.py @@ -15,6 +15,7 @@ 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.core.scheduler import SchedulerOutput from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.worker.gpu_model_runner import GPUModelRunner @@ -56,7 +57,6 @@ def __init__( from vllm.utils import init_cached_hf_modules init_cached_hf_modules() - self.model_runner = GPUModelRunner(vllm_config) # Torch profiler. Enabled and configured through env vars: # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace if envs.VLLM_TORCH_PROFILER_DIR: @@ -103,6 +103,9 @@ def initialize(self): # Set random seed. set_random_seed(self.model_config.seed) + # Construct the model runner + self.model_runner = GPUModelRunner(self.vllm_config, self.device) + def load_model(self) -> None: self.model_runner.load_model() @@ -198,7 +201,7 @@ def execute_model( scheduler_output: "SchedulerOutput", ) -> ModelRunnerOutput: output = self.model_runner.execute_model(scheduler_output) - # TODO(woosuk): Send the output to the engine process. + return output if self.rank == 0 else None return output def profile(self, is_start=True): @@ -209,6 +212,10 @@ def profile(self, is_start=True): else: self.profiler.stop() + def check_health(self) -> None: + # worker will always be healthy as long as it's running. + return + def init_worker_distributed_environment( parallel_config: ParallelConfig, From ebf778061db4e67c6903f8d6e8ad97c3db0174d8 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 9 Dec 2024 22:35:36 -0800 Subject: [PATCH 16/79] monitor metrics of tokens per step using cudagraph batchsizes (#11031) Signed-off-by: youkaichao --- tests/metrics/test_metrics.py | 2 +- vllm/engine/llm_engine.py | 6 ++++-- vllm/engine/metrics.py | 25 ++++++++++++++++--------- vllm/engine/metrics_types.py | 3 ++- 4 files changed, 23 insertions(+), 13 deletions(-) diff --git a/tests/metrics/test_metrics.py b/tests/metrics/test_metrics.py index 4a824c7acef21..b3c7850556f90 100644 --- a/tests/metrics/test_metrics.py +++ b/tests/metrics/test_metrics.py @@ -411,7 +411,7 @@ def log(self, *args, **kwargs): logger = _RayPrometheusStatLogger( local_interval=0.5, labels=dict(model_name=engine.model_config.served_model_name), - max_model_len=engine.model_config.max_model_len) + vllm_config=engine.vllm_config) engine.add_logger("ray", logger) for i, prompt in enumerate(example_prompts): engine.add_request( diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 8fc69d96d321e..6eca304b45f07 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -232,6 +232,7 @@ def __init__( use_cached_outputs: bool = False, ) -> None: + self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.cache_config = vllm_config.cache_config self.lora_config = vllm_config.lora_config @@ -385,13 +386,14 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: self.stat_loggers = { "logging": LoggingStatLogger( - local_interval=_LOCAL_LOGGING_INTERVAL_SEC), + local_interval=_LOCAL_LOGGING_INTERVAL_SEC, + vllm_config=vllm_config), "prometheus": PrometheusStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict( model_name=self.model_config.served_model_name), - max_model_len=self.model_config.max_model_len), + vllm_config=vllm_config), } self.stat_loggers["prometheus"].info("cache_config", self.cache_config) diff --git a/vllm/engine/metrics.py b/vllm/engine/metrics.py index a5ae21c3966a7..c8aec8dd3afa3 100644 --- a/vllm/engine/metrics.py +++ b/vllm/engine/metrics.py @@ -6,6 +6,7 @@ import numpy as np import prometheus_client +from vllm.config import VllmConfig from vllm.engine.metrics_types import (StatLoggerBase, Stats, SupportsMetricsInfo) from vllm.executor.ray_utils import ray @@ -44,10 +45,12 @@ class Metrics: _counter_cls = prometheus_client.Counter _histogram_cls = prometheus_client.Histogram - def __init__(self, labelnames: List[str], max_model_len: int): + def __init__(self, labelnames: List[str], vllm_config: VllmConfig): # Unregister any existing vLLM collectors (for CI/CD) self._unregister_vllm_metrics() + max_model_len = vllm_config.model_config.max_model_len + # System stats # Scheduler State self.gauge_scheduler_running = self._gauge_cls( @@ -115,11 +118,15 @@ def __init__(self, labelnames: List[str], max_model_len: int): name="vllm:tokens_total", documentation="Number of prefill plus generation tokens processed.", labelnames=labelnames) + buckets = [1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8096] + if not vllm_config.model_config.enforce_eager: + buckets = vllm_config.compilation_config.capture_sizes.copy() + buckets.sort() self.histogram_iteration_tokens = self._histogram_cls( name="vllm:iteration_tokens_total", documentation="Histogram of number of tokens per engine_step.", labelnames=labelnames, - buckets=[1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8096]) + buckets=buckets) self.histogram_time_to_first_token = self._histogram_cls( name="vllm:time_to_first_token_seconds", documentation="Histogram of time to first token in seconds.", @@ -361,10 +368,10 @@ class RayMetrics(Metrics): _histogram_cls: Type[prometheus_client.Histogram] = cast( Type[prometheus_client.Histogram], _RayHistogramWrapper) - def __init__(self, labelnames: List[str], max_model_len: int): + def __init__(self, labelnames: List[str], vllm_config: VllmConfig): if ray_metrics is None: raise ImportError("RayMetrics requires Ray to be installed.") - super().__init__(labelnames, max_model_len) + super().__init__(labelnames, vllm_config) def _unregister_vllm_metrics(self) -> None: # No-op on purpose @@ -421,8 +428,8 @@ def get_throughput(tracked_stats: List[int], now: float, class LoggingStatLogger(StatLoggerBase): """LoggingStatLogger is used in LLMEngine to log to Stdout.""" - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) + def __init__(self, local_interval: float, vllm_config: VllmConfig) -> None: + super().__init__(local_interval, vllm_config) self.last_prompt_throughput: Optional[float] = None self.last_generation_throughput: Optional[float] = None @@ -515,12 +522,12 @@ class PrometheusStatLogger(StatLoggerBase): _gauge_cls = prometheus_client.Gauge def __init__(self, local_interval: float, labels: Dict[str, str], - max_model_len: int) -> None: - super().__init__(local_interval) + vllm_config: VllmConfig) -> None: + super().__init__(local_interval, vllm_config) # Prometheus metrics self.labels = labels self.metrics = self._metrics_cls(labelnames=list(labels.keys()), - max_model_len=max_model_len) + vllm_config=vllm_config) def _log_gauge(self, gauge, data: Union[int, float]) -> None: # Convenience function for logging to gauge. diff --git a/vllm/engine/metrics_types.py b/vllm/engine/metrics_types.py index 5f7ec3bbcb269..5c7a430d11c5a 100644 --- a/vllm/engine/metrics_types.py +++ b/vllm/engine/metrics_types.py @@ -16,6 +16,7 @@ from dataclasses import dataclass from typing import Dict, List, Optional, Protocol +from vllm.config import VllmConfig from vllm.spec_decode.metrics import SpecDecodeWorkerMetrics @@ -77,7 +78,7 @@ def metrics_info(self) -> Dict[str, str]: class StatLoggerBase(ABC): """Base class for StatLogger.""" - def __init__(self, local_interval: float) -> None: + def __init__(self, local_interval: float, vllm_config: VllmConfig) -> None: # Tracked stats over current local logging interval. self.num_prompt_tokens: List[int] = [] self.num_generation_tokens: List[int] = [] From e35879c27601b09aab49e054786ce2a459f7a384 Mon Sep 17 00:00:00 2001 From: Jeff Cook Date: Mon, 9 Dec 2024 23:54:22 -0700 Subject: [PATCH 17/79] [Bugfix] Fix xgrammar failing to read a vocab_size from LlavaConfig on PixtralHF. (#11043) --- vllm/model_executor/guided_decoding/xgrammar_decoding.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vllm/model_executor/guided_decoding/xgrammar_decoding.py b/vllm/model_executor/guided_decoding/xgrammar_decoding.py index b59a2269d2cd5..80e88dd5b4b37 100644 --- a/vllm/model_executor/guided_decoding/xgrammar_decoding.py +++ b/vllm/model_executor/guided_decoding/xgrammar_decoding.py @@ -148,7 +148,7 @@ def from_guided_params(cls, else: json_str = guided_params.json return cls(json_str=json_str, - vocab_size=model_config.hf_config.vocab_size, + vocab_size=model_config.hf_text_config.vocab_size, encoded_vocab=encoded_vocab, stop_token_ids=stop_token_ids, backend_str=backend_str, @@ -168,7 +168,7 @@ def from_guided_params(cls, else: grammar_str = guided_params.grammar return cls(grammar_str=grammar_str, - vocab_size=model_config.hf_config.vocab_size, + vocab_size=model_config.hf_text_config.vocab_size, encoded_vocab=encoded_vocab, stop_token_ids=stop_token_ids, backend_str=backend_str, @@ -176,7 +176,7 @@ def from_guided_params(cls, max_threads=max_threads) elif guided_params.json_object: return cls(json_object=True, - vocab_size=model_config.hf_config.vocab_size, + vocab_size=model_config.hf_text_config.vocab_size, encoded_vocab=encoded_vocab, stop_token_ids=stop_token_ids, backend_str=backend_str, From bfd610430c04d2962a03a2db304fb13b09b4f1b3 Mon Sep 17 00:00:00 2001 From: Diego Marinho Date: Tue, 10 Dec 2024 18:08:10 +1100 Subject: [PATCH 18/79] Update README.md (#11034) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index cfeb24cbb5823..ed5161ccffb45 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,7 @@ Easy, fast, and cheap LLM serving for everyone --- *Latest News* 🔥 +- [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone! - [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing). - [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there! - [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users! From 82c73fd5104e010c2c98820f3e761e1e4f36c135 Mon Sep 17 00:00:00 2001 From: Gene Der Su Date: Mon, 9 Dec 2024 23:41:11 -0800 Subject: [PATCH 19/79] [Bugfix] cuda error running llama 3.2 (#11047) --- vllm/platforms/cuda.py | 35 ++++++++++++++++++++++++++++------- 1 file changed, 28 insertions(+), 7 deletions(-) diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 10f83fd304281..ae1fd6d5ce068 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -4,7 +4,8 @@ import os from functools import lru_cache, wraps -from typing import TYPE_CHECKING, Callable, List, Optional, TypeVar +from typing import (TYPE_CHECKING, Callable, List, Optional, Tuple, TypeVar, + Union) import pynvml import torch @@ -78,7 +79,9 @@ class CudaPlatformBase(Platform): dispatch_key: str = "CUDA" @classmethod - def get_device_capability(cls, device_id: int = 0) -> DeviceCapability: + def get_device_capability(cls, + device_id: int = 0 + ) -> Optional[DeviceCapability]: raise NotImplementedError @classmethod @@ -144,11 +147,29 @@ class NvmlCudaPlatform(CudaPlatformBase): @classmethod @lru_cache(maxsize=8) @with_nvml_context - def get_device_capability(cls, device_id: int = 0) -> DeviceCapability: - physical_device_id = device_id_to_physical_device_id(device_id) - handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id) - major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle) - return DeviceCapability(major=major, minor=minor) + def get_device_capability(cls, + device_id: int = 0 + ) -> Optional[DeviceCapability]: + try: + physical_device_id = device_id_to_physical_device_id(device_id) + handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id) + major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle) + return DeviceCapability(major=major, minor=minor) + except RuntimeError: + return None + + @classmethod + @lru_cache(maxsize=8) + @with_nvml_context + def has_device_capability( + cls, + capability: Union[Tuple[int, int], int], + device_id: int = 0, + ) -> bool: + try: + return super().has_device_capability(capability, device_id) + except RuntimeError: + return False @classmethod @lru_cache(maxsize=8) From fe2e10c71b98a43ccde0e8aba0d4fe0d23369538 Mon Sep 17 00:00:00 2001 From: Maxime Fournioux <55544262+mfournioux@users.noreply.github.com> Date: Tue, 10 Dec 2024 10:19:27 +0100 Subject: [PATCH 20/79] Add example of helm chart for vllm deployment on k8s (#9199) Signed-off-by: Maxime Fournioux <55544262+mfournioux@users.noreply.github.com> --- .github/workflows/lint-and-deploy.yaml | 81 ++++++ docs/source/index.rst | 1 + .../serving/architecture_helm_deployment.png | Bin 0 -> 991484 bytes docs/source/serving/deploying_with_helm.rst | 253 +++++++++++++++++ examples/chart-helm/.helmignore | 6 + examples/chart-helm/Chart.yaml | 21 ++ examples/chart-helm/ct.yaml | 3 + examples/chart-helm/lintconf.yaml | 42 +++ examples/chart-helm/templates/_helpers.tpl | 164 +++++++++++ examples/chart-helm/templates/configmap.yaml | 11 + .../chart-helm/templates/custom-objects.yaml | 6 + examples/chart-helm/templates/deployment.yaml | 122 ++++++++ examples/chart-helm/templates/hpa.yaml | 31 ++ examples/chart-helm/templates/job.yaml | 37 +++ .../templates/poddisruptionbudget.yaml | 7 + examples/chart-helm/templates/pvc.yaml | 13 + examples/chart-helm/templates/secrets.yaml | 10 + examples/chart-helm/templates/service.yaml | 14 + examples/chart-helm/values.schema.json | 265 ++++++++++++++++++ examples/chart-helm/values.yaml | 119 ++++++++ 20 files changed, 1206 insertions(+) create mode 100644 .github/workflows/lint-and-deploy.yaml create mode 100644 docs/source/serving/architecture_helm_deployment.png create mode 100644 docs/source/serving/deploying_with_helm.rst create mode 100644 examples/chart-helm/.helmignore create mode 100644 examples/chart-helm/Chart.yaml create mode 100644 examples/chart-helm/ct.yaml create mode 100644 examples/chart-helm/lintconf.yaml create mode 100644 examples/chart-helm/templates/_helpers.tpl create mode 100644 examples/chart-helm/templates/configmap.yaml create mode 100644 examples/chart-helm/templates/custom-objects.yaml create mode 100644 examples/chart-helm/templates/deployment.yaml create mode 100644 examples/chart-helm/templates/hpa.yaml create mode 100644 examples/chart-helm/templates/job.yaml create mode 100644 examples/chart-helm/templates/poddisruptionbudget.yaml create mode 100644 examples/chart-helm/templates/pvc.yaml create mode 100644 examples/chart-helm/templates/secrets.yaml create mode 100644 examples/chart-helm/templates/service.yaml create mode 100644 examples/chart-helm/values.schema.json create mode 100644 examples/chart-helm/values.yaml diff --git a/.github/workflows/lint-and-deploy.yaml b/.github/workflows/lint-and-deploy.yaml new file mode 100644 index 0000000000000..ab6f6e5d2060d --- /dev/null +++ b/.github/workflows/lint-and-deploy.yaml @@ -0,0 +1,81 @@ +name: Lint and Deploy Charts + +on: pull_request + +jobs: + lint-and-deploy: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + with: + fetch-depth: 0 + + - name: Set up Helm + uses: azure/setup-helm@fe7b79cd5ee1e45176fcad797de68ecaf3ca4814 # v4.2.0 + with: + version: v3.14.4 + + #Python is required because ct lint runs Yamale and yamllint which require Python. + - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 + with: + python-version: '3.13' + + - name: Set up chart-testing + uses: helm/chart-testing-action@e6669bcd63d7cb57cb4380c33043eebe5d111992 # v2.6.1 + with: + version: v3.10.1 + + - name: Run chart-testing (lint) + run: ct lint --target-branch ${{ github.event.repository.default_branch }} --chart-dirs examples/chart-helm --charts examples/chart-helm + + - name: Setup minio + run: | + docker network create vllm-net + docker run -d -p 9000:9000 --name minio --net vllm-net \ + -e "MINIO_ACCESS_KEY=minioadmin" \ + -e "MINIO_SECRET_KEY=minioadmin" \ + -v /tmp/data:/data \ + -v /tmp/config:/root/.minio \ + minio/minio server /data + export AWS_ACCESS_KEY_ID=minioadmin + export AWS_SECRET_ACCESS_KEY=minioadmin + export AWS_EC2_METADATA_DISABLED=true + mkdir opt-125m + cd opt-125m && curl -O -Ls "https://huggingface.co/facebook/opt-125m/resolve/main/{pytorch_model.bin,config.json,generation_config.json,merges.txt,special_tokens_map.json,tokenizer_config.json,vocab.json}" && cd .. + aws --endpoint-url http://127.0.0.1:9000/ s3 mb s3://testbucket + aws --endpoint-url http://127.0.0.1:9000/ s3 cp opt-125m/ s3://testbucket/opt-125m --recursive + + - name: Create kind cluster + uses: helm/kind-action@0025e74a8c7512023d06dc019c617aa3cf561fde # v1.10.0 + + - name: Build the Docker image vllm cpu + run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env . + + - name: Configuration of docker images, network and namespace for the kind cluster + run: | + docker pull amazon/aws-cli:2.6.4 + kind load docker-image amazon/aws-cli:2.6.4 --name chart-testing + kind load docker-image vllm-cpu-env:latest --name chart-testing + docker network connect vllm-net "$(docker ps -aqf "name=chart-testing-control-plane")" + kubectl create ns ns-vllm + + - name: Run chart-testing (install) + run: | + export AWS_ACCESS_KEY_ID=minioadmin + export AWS_SECRET_ACCESS_KEY=minioadmin + helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/chart-helm -f examples/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env" + + - name: curl test + run: | + kubectl -n ns-vllm port-forward service/test-vllm-service 8001:80 & + sleep 10 + CODE="$(curl -v -f --location http://localhost:8001/v1/completions \ + --header "Content-Type: application/json" \ + --data '{ + "model": "opt-125m", + "prompt": "San Francisco is a", + "max_tokens": 7, + "temperature": 0 + }'):$CODE" + echo "$CODE" \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index c45c941b00e20..ebf1361976c5e 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -82,6 +82,7 @@ Documentation serving/openai_compatible_server serving/deploying_with_docker serving/deploying_with_k8s + serving/deploying_with_helm serving/deploying_with_nginx serving/distributed_serving serving/metrics diff 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zFdK8kdJ7<_hjkOjT9ue%rzQYVbfa|sV9RMBpJ5+)T@Tbq8upO@2H`|?sv|C2CEGe82#D+u6?$ZFG)^8}jXZ1+j6Nlf8}g(AU`5mkzy2sXOAxr9F9E X&+FC3YT^A1{ZqZHrIfFD!~cH(b|Z=* literal 0 HcmV?d00001 diff --git a/docs/source/serving/deploying_with_helm.rst b/docs/source/serving/deploying_with_helm.rst new file mode 100644 index 0000000000000..21b17e881b945 --- /dev/null +++ b/docs/source/serving/deploying_with_helm.rst @@ -0,0 +1,253 @@ +.. _deploying_with_helm: + +Deploying with Helm +=================== + +A Helm chart to deploy vLLM for Kubernetes + +Helm is a package manager for Kubernetes. It will help you to deploy vLLM on k8s and automate the deployment of vLLMm Kubernetes applications. With Helm, you can deploy the same framework architecture with different configurations to multiple namespaces by overriding variables values. + +This guide will walk you through the process of deploying vLLM with Helm, including the necessary prerequisites, steps for helm install and documentation on architecture and values file. + +Prerequisites +------------- +Before you begin, ensure that you have the following: + +- A running Kubernetes cluster +- NVIDIA Kubernetes Device Plugin (``k8s-device-plugin``): This can be found at `https://github.com/NVIDIA/k8s-device-plugin `__ +- Available GPU resources in your cluster +- S3 with the model which will be deployed + +Installing the chart +-------------------- + +To install the chart with the release name ``test-vllm``: + +.. code-block:: console + + helm upgrade --install --create-namespace --namespace=ns-vllm test-vllm . -f values.yaml --set secrets.s3endpoint=$ACCESS_POINT --set secrets.s3buckername=$BUCKET --set secrets.s3accesskeyid=$ACCESS_KEY --set secrets.s3accesskey=$SECRET_KEY + +Uninstalling the Chart +---------------------- + +To uninstall the ``test-vllm`` deployment: + +.. code-block:: console + + helm uninstall test-vllm --namespace=ns-vllm + +The command removes all the Kubernetes components associated with the +chart **including persistent volumes** and deletes the release. + +Architecture +------------ + +.. image:: architecture_helm_deployment.png + +Values +------ + +.. list-table:: Values + :widths: 25 25 25 25 + :header-rows: 1 + + * - Key + - Type + - Default + - Description + * - autoscaling + - object + - {"enabled":false,"maxReplicas":100,"minReplicas":1,"targetCPUUtilizationPercentage":80} + - Autoscaling configuration + * - autoscaling.enabled + - bool + - false + - Enable autoscaling + * - autoscaling.maxReplicas + - int + - 100 + - Maximum replicas + * - autoscaling.minReplicas + - int + - 1 + - Minimum replicas + * - autoscaling.targetCPUUtilizationPercentage + - int + - 80 + - Target CPU utilization for autoscaling + * - configs + - object + - {} + - Configmap + * - containerPort + - int + - 8000 + - Container port + * - customObjects + - list + - [] + - Custom Objects configuration + * - deploymentStrategy + - object + - {} + - Deployment strategy configuration + * - externalConfigs + - list + - [] + - External configuration + * - extraContainers + - list + - [] + - Additional containers configuration + * - extraInit + - object + - {"pvcStorage":"1Gi","s3modelpath":"relative_s3_model_path/opt-125m", "awsEc2MetadataDisabled": true} + - Additional configuration for the init container + * - extraInit.pvcStorage + - string + - "50Gi" + - Storage size of the s3 + * - extraInit.s3modelpath + - string + - "relative_s3_model_path/opt-125m" + - Path of the model on the s3 which hosts model weights and config files + * - extraInit.awsEc2MetadataDisabled + - boolean + - true + - Disables the use of the Amazon EC2 instance metadata service + * - extraPorts + - list + - [] + - Additional ports configuration + * - gpuModels + - list + - ["TYPE_GPU_USED"] + - Type of gpu used + * - image + - object + - {"command":["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"],"repository":"vllm/vllm-openai","tag":"latest"} + - Image configuration + * - image.command + - list + - ["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"] + - Container launch command + * - image.repository + - string + - "vllm/vllm-openai" + - Image repository + * - image.tag + - string + - "latest" + - Image tag + * - livenessProbe + - object + - {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":15,"periodSeconds":10} + - Liveness probe configuration + * - livenessProbe.failureThreshold + - int + - 3 + - Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive + * - livenessProbe.httpGet + - object + - {"path":"/health","port":8000} + - Configuration of the Kubelet http request on the server + * - livenessProbe.httpGet.path + - string + - "/health" + - Path to access on the HTTP server + * - livenessProbe.httpGet.port + - int + - 8000 + - Name or number of the port to access on the container, on which the server is listening + * - livenessProbe.initialDelaySeconds + - int + - 15 + - Number of seconds after the container has started before liveness probe is initiated + * - livenessProbe.periodSeconds + - int + - 10 + - How often (in seconds) to perform the liveness probe + * - maxUnavailablePodDisruptionBudget + - string + - "" + - Disruption Budget Configuration + * - readinessProbe + - object + - {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":5,"periodSeconds":5} + - Readiness probe configuration + * - readinessProbe.failureThreshold + - int + - 3 + - Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready + * - readinessProbe.httpGet + - object + - {"path":"/health","port":8000} + - Configuration of the Kubelet http request on the server + * - readinessProbe.httpGet.path + - string + - "/health" + - Path to access on the HTTP server + * - readinessProbe.httpGet.port + - int + - 8000 + - Name or number of the port to access on the container, on which the server is listening + * - readinessProbe.initialDelaySeconds + - int + - 5 + - Number of seconds after the container has started before readiness probe is initiated + * - readinessProbe.periodSeconds + - int + - 5 + - How often (in seconds) to perform the readiness probe + * - replicaCount + - int + - 1 + - Number of replicas + * - resources + - object + - {"limits":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1},"requests":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1}} + - Resource configuration + * - resources.limits."nvidia.com/gpu" + - int + - 1 + - Number of gpus used + * - resources.limits.cpu + - int + - 4 + - Number of CPUs + * - resources.limits.memory + - string + - "16Gi" + - CPU memory configuration + * - resources.requests."nvidia.com/gpu" + - int + - 1 + - Number of gpus used + * - resources.requests.cpu + - int + - 4 + - Number of CPUs + * - resources.requests.memory + - string + - "16Gi" + - CPU memory configuration + * - secrets + - object + - {} + - Secrets configuration + * - serviceName + - string + - + - Service name + * - servicePort + - int + - 80 + - Service port + * - labels.environment + - string + - test + - Environment name + * - labels.release + - string + - test + - Release name diff --git a/examples/chart-helm/.helmignore b/examples/chart-helm/.helmignore new file mode 100644 index 0000000000000..2d1303b784cb8 --- /dev/null +++ b/examples/chart-helm/.helmignore @@ -0,0 +1,6 @@ +*.png +.git/ +ct.yaml +lintconf.yaml +values.schema.json +/workflows \ No newline at end of file diff --git a/examples/chart-helm/Chart.yaml b/examples/chart-helm/Chart.yaml new file mode 100644 index 0000000000000..fb0f06f6d2701 --- /dev/null +++ b/examples/chart-helm/Chart.yaml @@ -0,0 +1,21 @@ +apiVersion: v2 +name: chart-vllm +description: Chart vllm + +# A chart can be either an 'application' or a 'library' chart. +# +# Application charts are a collection of templates that can be packaged into versioned archives +# to be deployed. +# +# Library charts provide useful utilities or functions for the chart developer. They're included as +# a dependency of application charts to inject those utilities and functions into the rendering +# pipeline. Library charts do not define any templates and therefore cannot be deployed. +type: application + +# This is the chart version. This version number should be incremented each time you make changes +# to the chart and its templates, including the app version. +# Versions are expected to follow Semantic Versioning (https://semver.org/) +version: 0.0.1 + +maintainers: + - name: mfournioux diff --git a/examples/chart-helm/ct.yaml b/examples/chart-helm/ct.yaml new file mode 100644 index 0000000000000..d273e118203ad --- /dev/null +++ b/examples/chart-helm/ct.yaml @@ -0,0 +1,3 @@ +chart-dirs: + - charts +validate-maintainers: false \ No newline at end of file diff --git a/examples/chart-helm/lintconf.yaml b/examples/chart-helm/lintconf.yaml new file mode 100644 index 0000000000000..c8e8c5d7d9767 --- /dev/null +++ b/examples/chart-helm/lintconf.yaml @@ -0,0 +1,42 @@ +--- +rules: + braces: + min-spaces-inside: 0 + max-spaces-inside: 0 + min-spaces-inside-empty: -1 + max-spaces-inside-empty: -1 + brackets: + min-spaces-inside: 0 + max-spaces-inside: 0 + min-spaces-inside-empty: -1 + max-spaces-inside-empty: -1 + colons: + max-spaces-before: 0 + max-spaces-after: 1 + commas: + max-spaces-before: 0 + min-spaces-after: 1 + max-spaces-after: 1 + comments: + require-starting-space: true + min-spaces-from-content: 2 + document-end: disable + document-start: disable # No --- to start a file + empty-lines: + max: 2 + max-start: 0 + max-end: 0 + hyphens: + max-spaces-after: 1 + indentation: + spaces: consistent + indent-sequences: whatever # - list indentation will handle both indentation and without + check-multi-line-strings: false + key-duplicates: enable + line-length: disable # Lines can be any length + new-line-at-end-of-file: disable + new-lines: + type: unix + trailing-spaces: enable + truthy: + level: warning \ No newline at end of file diff --git a/examples/chart-helm/templates/_helpers.tpl b/examples/chart-helm/templates/_helpers.tpl new file mode 100644 index 0000000000000..a9690bad3c945 --- /dev/null +++ b/examples/chart-helm/templates/_helpers.tpl @@ -0,0 +1,164 @@ +{{/* +Define ports for the pods +*/}} +{{- define "chart.container-port" -}} +{{- default "8000" .Values.containerPort }} +{{- end }} + +{{/* +Define service name +*/}} +{{- define "chart.service-name" -}} +{{- if .Values.serviceName }} +{{- .Values.serviceName | lower | trim }} +{{- else }} +"{{ .Release.Name }}-service" +{{- end }} +{{- end }} + +{{/* +Define service port +*/}} +{{- define "chart.service-port" -}} +{{- if .Values.servicePort }} +{{- .Values.servicePort }} +{{- else }} +{{- include "chart.container-port" . }} +{{- end }} +{{- end }} + +{{/* +Define service port name +*/}} +{{- define "chart.service-port-name" -}} +"service-port" +{{- end }} + +{{/* +Define container port name +*/}} +{{- define "chart.container-port-name" -}} +"container-port" +{{- end }} + +{{/* +Define deployment strategy +*/}} +{{- define "chart.strategy" -}} +strategy: +{{- if not .Values.deploymentStrategy }} + rollingUpdate: + maxSurge: 100% + maxUnavailable: 0 +{{- else }} +{{ toYaml .Values.deploymentStrategy | indent 2 }} +{{- end }} +{{- end }} + +{{/* +Define additional ports +*/}} +{{- define "chart.extraPorts" }} +{{- with .Values.extraPorts }} +{{ toYaml . }} +{{- end }} +{{- end }} + +{{/* +Define chart external ConfigMaps and Secrets +*/}} +{{- define "chart.externalConfigs" -}} +{{- with .Values.externalConfigs -}} +{{ toYaml . }} +{{- end }} +{{- end }} + + +{{/* +Define liveness et readiness probes +*/}} +{{- define "chart.probes" -}} +{{- if .Values.readinessProbe }} +readinessProbe: +{{- with .Values.readinessProbe }} +{{- toYaml . | nindent 2 }} +{{- end }} +{{- end }} +{{- if .Values.livenessProbe }} +livenessProbe: +{{- with .Values.livenessProbe }} +{{- toYaml . | nindent 2 }} +{{- end }} +{{- end }} +{{- end }} + +{{/* +Define resources +*/}} +{{- define "chart.resources" -}} +requests: + memory: {{ required "Value 'resources.requests.memory' must be defined !" .Values.resources.requests.memory | quote }} + cpu: {{ required "Value 'resources.requests.cpu' must be defined !" .Values.resources.requests.cpu | quote }} + {{- if and (gt (int (index .Values.resources.requests "nvidia.com/gpu")) 0) (gt (int (index .Values.resources.limits "nvidia.com/gpu")) 0) }} + nvidia.com/gpu: {{ required "Value 'resources.requests.nvidia.com/gpu' must be defined !" (index .Values.resources.requests "nvidia.com/gpu") | quote }} + {{- end }} +limits: + memory: {{ required "Value 'resources.limits.memory' must be defined !" .Values.resources.limits.memory | quote }} + cpu: {{ required "Value 'resources.limits.cpu' must be defined !" .Values.resources.limits.cpu | quote }} + {{- if and (gt (int (index .Values.resources.requests "nvidia.com/gpu")) 0) (gt (int (index .Values.resources.limits "nvidia.com/gpu")) 0) }} + nvidia.com/gpu: {{ required "Value 'resources.limits.nvidia.com/gpu' must be defined !" (index .Values.resources.limits "nvidia.com/gpu") | quote }} + {{- end }} +{{- end }} + + +{{/* +Define User used for the main container +*/}} +{{- define "chart.user" }} +{{- if .Values.image.runAsUser }} +runAsUser: +{{- with .Values.runAsUser }} +{{- toYaml . | nindent 2 }} +{{- end }} +{{- end }} +{{- end }} + +{{- define "chart.extraInitImage" -}} +"amazon/aws-cli:2.6.4" +{{- end }} + +{{- define "chart.extraInitEnv" -}} +- name: S3_ENDPOINT_URL + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3endpoint +- name: S3_BUCKET_NAME + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3bucketname +- name: AWS_ACCESS_KEY_ID + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3accesskeyid +- name: AWS_SECRET_ACCESS_KEY + valueFrom: + secretKeyRef: + name: {{ .Release.Name }}-secrets + key: s3accesskey +- name: S3_PATH + value: "{{ .Values.extraInit.s3modelpath }}" +- name: AWS_EC2_METADATA_DISABLED + value: "{{ .Values.extraInit.awsEc2MetadataDisabled }}" +{{- end }} + +{{/* + Define chart labels +*/}} +{{- define "chart.labels" -}} +{{- with .Values.labels -}} +{{ toYaml . }} +{{- end }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/configmap.yaml b/examples/chart-helm/templates/configmap.yaml new file mode 100644 index 0000000000000..cc5d03782f878 --- /dev/null +++ b/examples/chart-helm/templates/configmap.yaml @@ -0,0 +1,11 @@ +{{- if .Values.configs -}} +apiVersion: v1 +kind: ConfigMap +metadata: + name: "{{ .Release.Name }}-configs" + namespace: {{ .Release.Namespace }} +data: + {{- with .Values.configs }} + {{- toYaml . | nindent 2 }} + {{- end }} +{{- end -}} \ No newline at end of file diff --git a/examples/chart-helm/templates/custom-objects.yaml b/examples/chart-helm/templates/custom-objects.yaml new file mode 100644 index 0000000000000..8a65ffd0e552d --- /dev/null +++ b/examples/chart-helm/templates/custom-objects.yaml @@ -0,0 +1,6 @@ +{{- if .Values.customObjects }} +{{- range .Values.customObjects }} +{{- tpl (. | toYaml) $ }} +--- +{{- end }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/deployment.yaml b/examples/chart-helm/templates/deployment.yaml new file mode 100644 index 0000000000000..536983b587be2 --- /dev/null +++ b/examples/chart-helm/templates/deployment.yaml @@ -0,0 +1,122 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: "{{ .Release.Name }}-deployment-vllm" + namespace: {{ .Release.Namespace }} + labels: + {{- include "chart.labels" . | nindent 4 }} +spec: + replicas: {{ .Values.replicaCount }} + {{- include "chart.strategy" . | nindent 2 }} + selector: + matchLabels: + environment: "test" + release: "test" + progressDeadlineSeconds: 1200 + template: + metadata: + labels: + environment: "test" + release: "test" + spec: + containers: + - name: "vllm" + image: "{{ required "Required value 'image.repository' must be defined !" .Values.image.repository }}:{{ required "Required value 'image.tag' must be defined !" .Values.image.tag }}" + {{- if .Values.image.command }} + command : + {{- with .Values.image.command }} + {{- toYaml . | nindent 10 }} + {{- end }} + {{- end }} + securityContext: + {{- if .Values.image.securityContext }} + {{- with .Values.image.securityContext }} + {{- toYaml . | nindent 12 }} + {{- end }} + {{- else }} + runAsNonRoot: false + {{- include "chart.user" . | indent 12 }} + {{- end }} + imagePullPolicy: IfNotPresent + {{- if .Values.image.env }} + env : + {{- with .Values.image.env }} + {{- toYaml . | nindent 10 }} + {{- end }} + {{- else }} + env: [] + {{- end }} + {{- if or .Values.externalConfigs .Values.configs .Values.secrets }} + envFrom: + {{- if .Values.configs }} + - configMapRef: + name: "{{ .Release.Name }}-configs" + {{- end }} + {{- if .Values.secrets}} + - secretRef: + name: "{{ .Release.Name }}-secrets" + {{- end }} + {{- include "chart.externalConfigs" . | nindent 12 }} + {{- end }} + ports: + - name: {{ include "chart.container-port-name" . }} + containerPort: {{ include "chart.container-port" . }} + {{- include "chart.extraPorts" . | nindent 12 }} + {{- include "chart.probes" . | indent 10 }} + resources: {{- include "chart.resources" . | nindent 12 }} + volumeMounts: + - name: {{ .Release.Name }}-storage + mountPath: /data + + {{- with .Values.extraContainers }} + {{ toYaml . | nindent 8 }} + {{- end }} + + {{- if .Values.extraInit }} + initContainers: + - name: wait-download-model + image: {{ include "chart.extraInitImage" . }} + command: + - /bin/bash + args: + - -eucx + - while aws --endpoint-url $S3_ENDPOINT_URL s3 sync --dryrun s3://$S3_BUCKET_NAME/$S3_PATH /data | grep -q download; do sleep 10; done + env: {{- include "chart.extraInitEnv" . | nindent 10 }} + resources: + requests: + cpu: 200m + memory: 1Gi + limits: + cpu: 500m + memory: 2Gi + volumeMounts: + - name: {{ .Release.Name }}-storage + mountPath: /data + {{- end }} + volumes: + - name: {{ .Release.Name }}-storage + persistentVolumeClaim: + claimName: {{ .Release.Name }}-storage-claim + + {{- with .Values.nodeSelector }} + nodeSelector: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.tolerations }} + tolerations: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- if and (gt (int (index .Values.resources.requests "nvidia.com/gpu")) 0) (gt (int (index .Values.resources.limits "nvidia.com/gpu")) 0) }} + runtimeClassName: nvidia + affinity: + nodeAffinity: + requiredDuringSchedulingIgnoredDuringExecution: + nodeSelectorTerms: + - matchExpressions: + - key: nvidia.com/gpu.product + operator: In + {{- with .Values.gpuModels }} + values: + {{- toYaml . | nindent 20 }} + {{- end }} + {{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/hpa.yaml b/examples/chart-helm/templates/hpa.yaml new file mode 100644 index 0000000000000..5ca94c8213541 --- /dev/null +++ b/examples/chart-helm/templates/hpa.yaml @@ -0,0 +1,31 @@ +{{- if .Values.autoscaling.enabled }} +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: "{{ .Release.Name }}-hpa" + namespace: {{ .Release.Namespace }} +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: vllm + minReplicas: {{ .Values.autoscaling.minReplicas }} + maxReplicas: {{ .Values.autoscaling.maxReplicas }} + metrics: + {{- if .Values.autoscaling.targetCPUUtilizationPercentage }} + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: {{ .Values.autoscaling.targetCPUUtilizationPercentage }} + {{- end }} + {{- if .Values.autoscaling.targetMemoryUtilizationPercentage }} + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: {{ .Values.autoscaling.targetMemoryUtilizationPercentage }} + {{- end }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/job.yaml b/examples/chart-helm/templates/job.yaml new file mode 100644 index 0000000000000..f9ea3541e78d2 --- /dev/null +++ b/examples/chart-helm/templates/job.yaml @@ -0,0 +1,37 @@ +{{- if .Values.extraInit }} +apiVersion: batch/v1 +kind: Job +metadata: + name: "{{ .Release.Name }}-init-vllm" + namespace: {{ .Release.Namespace }} +spec: + ttlSecondsAfterFinished: 100 + template: + metadata: + name: init-vllm + spec: + containers: + - name: job-download-model + image: {{ include "chart.extraInitImage" . }} + command: + - /bin/bash + args: + - -eucx + - aws --endpoint-url $S3_ENDPOINT_URL s3 sync s3://$S3_BUCKET_NAME/$S3_PATH /data + env: {{- include "chart.extraInitEnv" . | nindent 8 }} + volumeMounts: + - name: {{ .Release.Name }}-storage + mountPath: /data + resources: + requests: + cpu: 200m + memory: 1Gi + limits: + cpu: 500m + memory: 2Gi + restartPolicy: OnFailure + volumes: + - name: {{ .Release.Name }}-storage + persistentVolumeClaim: + claimName: "{{ .Release.Name }}-storage-claim" +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/poddisruptionbudget.yaml b/examples/chart-helm/templates/poddisruptionbudget.yaml new file mode 100644 index 0000000000000..512bac727da87 --- /dev/null +++ b/examples/chart-helm/templates/poddisruptionbudget.yaml @@ -0,0 +1,7 @@ +apiVersion: policy/v1 +kind: PodDisruptionBudget +metadata: + name: "{{ .Release.Name }}-pdb" + namespace: {{ .Release.Namespace }} +spec: + maxUnavailable: {{ default 1 .Values.maxUnavailablePodDisruptionBudget }} \ No newline at end of file diff --git a/examples/chart-helm/templates/pvc.yaml b/examples/chart-helm/templates/pvc.yaml new file mode 100644 index 0000000000000..e8d203a7a5ace --- /dev/null +++ b/examples/chart-helm/templates/pvc.yaml @@ -0,0 +1,13 @@ +{{- if .Values.extraInit }} +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: "{{ .Release.Name }}-storage-claim" + namespace: {{ .Release.Namespace }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: {{ .Values.extraInit.pvcStorage }} +{{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/secrets.yaml b/examples/chart-helm/templates/secrets.yaml new file mode 100644 index 0000000000000..4e88e747b616a --- /dev/null +++ b/examples/chart-helm/templates/secrets.yaml @@ -0,0 +1,10 @@ +apiVersion: v1 +kind: Secret +metadata: + name: "{{ .Release.Name }}-secrets" + namespace: {{ .Release.Namespace }} +type: Opaque +data: + {{- range $key, $val := .Values.secrets }} + {{ $key }}: {{ $val | b64enc | quote }} + {{- end }} \ No newline at end of file diff --git a/examples/chart-helm/templates/service.yaml b/examples/chart-helm/templates/service.yaml new file mode 100644 index 0000000000000..12d0f68b03a35 --- /dev/null +++ b/examples/chart-helm/templates/service.yaml @@ -0,0 +1,14 @@ +apiVersion: v1 +kind: Service +metadata: + name: "{{ .Release.Name }}-service" + namespace: {{ .Release.Namespace }} +spec: + type: ClusterIP + ports: + - name: {{ include "chart.service-port-name" . }} + port: {{ include "chart.service-port" . }} + targetPort: {{ include "chart.container-port-name" . }} + protocol: TCP + selector: + {{- include "chart.labels" . | nindent 4 }} \ No newline at end of file diff --git a/examples/chart-helm/values.schema.json b/examples/chart-helm/values.schema.json new file mode 100644 index 0000000000000..812d54bde1397 --- /dev/null +++ b/examples/chart-helm/values.schema.json @@ -0,0 +1,265 @@ +{ + "$schema": "http://json-schema.org/schema#", + "type": "object", + "properties": { + "image": { + "type": "object", + "properties": { + "repository": { + "type": "string" + }, + "tag": { + "type": "string" + }, + "command": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "required": [ + "command", + "repository", + "tag" + ] + }, + "containerPort": { + "type": "integer" + }, + "serviceName": { + "type": "null" + }, + "servicePort": { + "type": "integer" + }, + "extraPorts": { + "type": "array" + }, + "replicaCount": { + "type": "integer" + }, + "deploymentStrategy": { + "type": "object" + }, + "resources": { + "type": "object", + "properties": { + "requests": { + "type": "object", + "properties": { + "cpu": { + "type": "integer" + }, + "memory": { + "type": "string" + }, + "nvidia.com/gpu": { + "type": "integer" + } + }, + "required": [ + "cpu", + "memory", + "nvidia.com/gpu" + ] + }, + "limits": { + "type": "object", + "properties": { + "cpu": { + "type": "integer" + }, + "memory": { + "type": "string" + }, + "nvidia.com/gpu": { + "type": "integer" + } + }, + "required": [ + "cpu", + "memory", + "nvidia.com/gpu" + ] + } + }, + "required": [ + "limits", + "requests" + ] + }, + "gpuModels": { + "type": "array", + "items": { + "type": "string" + } + }, + "autoscaling": { + "type": "object", + "properties": { + "enabled": { + "type": "boolean" + }, + "minReplicas": { + "type": "integer" + }, + "maxReplicas": { + "type": "integer" + }, + "targetCPUUtilizationPercentage": { + "type": "integer" + } + }, + "required": [ + "enabled", + "maxReplicas", + "minReplicas", + "targetCPUUtilizationPercentage" + ] + }, + "configs": { + "type": "object" + }, + "secrets": { + "type": "object" + }, + "externalConfigs": { + "type": "array" + }, + "customObjects": { + "type": "array" + }, + "maxUnavailablePodDisruptionBudget": { + "type": "string" + }, + "extraInit": { + "type": "object", + "properties": { + "s3modelpath": { + "type": "string" + }, + "pvcStorage": { + "type": "string" + }, + "awsEc2MetadataDisabled": { + "type": "boolean" + } + }, + "required": [ + "pvcStorage", + "s3modelpath", + "awsEc2MetadataDisabled" + ] + }, + "extraContainers": { + "type": "array" + }, + "readinessProbe": { + "type": "object", + "properties": { + "initialDelaySeconds": { + "type": "integer" + }, + "periodSeconds": { + "type": "integer" + }, + "failureThreshold": { + "type": "integer" + }, + "httpGet": { + "type": "object", + "properties": { + "path": { + "type": "string" + }, + "port": { + "type": "integer" + } + }, + "required": [ + "path", + "port" + ] + } + }, + "required": [ + "failureThreshold", + "httpGet", + "initialDelaySeconds", + "periodSeconds" + ] + }, + "livenessProbe": { + "type": "object", + "properties": { + "initialDelaySeconds": { + "type": "integer" + }, + "failureThreshold": { + "type": "integer" + }, + "periodSeconds": { + "type": "integer" + }, + "httpGet": { + "type": "object", + "properties": { + "path": { + "type": "string" + }, + "port": { + "type": "integer" + } + }, + "required": [ + "path", + "port" + ] + } + }, + "required": [ + "failureThreshold", + "httpGet", + "initialDelaySeconds", + "periodSeconds" + ] + }, + "labels": { + "type": "object", + "properties": { + "environment": { + "type": "string" + }, + "release": { + "type": "string" + } + }, + "required": [ + "environment", + "release" + ] + } + }, + "required": [ + "autoscaling", + "configs", + "containerPort", + "customObjects", + "deploymentStrategy", + "externalConfigs", + "extraContainers", + "extraInit", + "extraPorts", + "gpuModels", + "image", + "labels", + "livenessProbe", + "maxUnavailablePodDisruptionBudget", + "readinessProbe", + "replicaCount", + "resources", + "secrets", + "servicePort" + ] +} \ No newline at end of file diff --git a/examples/chart-helm/values.yaml b/examples/chart-helm/values.yaml new file mode 100644 index 0000000000000..9c48e7d061bf7 --- /dev/null +++ b/examples/chart-helm/values.yaml @@ -0,0 +1,119 @@ +# -- Default values for chart vllm +# -- Declare variables to be passed into your templates. + +# -- Image configuration +image: + # -- Image repository + repository: "vllm/vllm-openai" + # -- Image tag + tag: "latest" + # -- Container launch command + command: ["vllm", "serve", "/data/", "--served-model-name", "opt-125m", "--dtype", "bfloat16", "--host", "0.0.0.0", "--port", "8000"] + +# -- Container port +containerPort: 8000 +# -- Service name +serviceName: +# -- Service port +servicePort: 80 +# -- Additional ports configuration +extraPorts: [] + +# -- Number of replicas +replicaCount: 1 + +# -- Deployment strategy configuration +deploymentStrategy: {} + +# -- Resource configuration +resources: + requests: + # -- Number of CPUs + cpu: 4 + # -- CPU memory configuration + memory: 16Gi + # -- Number of gpus used + nvidia.com/gpu: 1 + limits: + # -- Number of CPUs + cpu: 4 + # -- CPU memory configuration + memory: 16Gi + # -- Number of gpus used + nvidia.com/gpu: 1 + +# -- Type of gpu used +gpuModels: + - "TYPE_GPU_USED" + +# -- Autoscaling configuration +autoscaling: + # -- Enable autoscaling + enabled: false + # -- Minimum replicas + minReplicas: 1 + # -- Maximum replicas + maxReplicas: 100 + # -- Target CPU utilization for autoscaling + targetCPUUtilizationPercentage: 80 + # targetMemoryUtilizationPercentage: 80 + +# -- Configmap +configs: {} + +# -- Secrets configuration +secrets: {} + +# -- External configuration +externalConfigs: [] + +# -- Custom Objects configuration +customObjects: [] + +# -- Disruption Budget Configuration +maxUnavailablePodDisruptionBudget: "" + +# -- Additional configuration for the init container +extraInit: + # -- Path of the model on the s3 which hosts model weights and config files + s3modelpath: "relative_s3_model_path/opt-125m" + # -- Storage size of the s3 + pvcStorage: "1Gi" + awsEc2MetadataDisabled: true + +# -- Additional containers configuration +extraContainers: [] + +# -- Readiness probe configuration +readinessProbe: + # -- Number of seconds after the container has started before readiness probe is initiated + initialDelaySeconds: 5 + # -- How often (in seconds) to perform the readiness probe + periodSeconds: 5 + # -- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready + failureThreshold: 3 + # -- Configuration of the Kubelet http request on the server + httpGet: + # -- Path to access on the HTTP server + path: /health + # -- Name or number of the port to access on the container, on which the server is listening + port: 8000 + +# -- Liveness probe configuration +livenessProbe: + # -- Number of seconds after the container has started before liveness probe is initiated + initialDelaySeconds: 15 + # -- Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive + failureThreshold: 3 + # -- How often (in seconds) to perform the liveness probe + periodSeconds: 10 + # -- Configuration of the Kubelet http request on the server + httpGet: + # -- Path to access on the HTTP server + path: /health + # -- Name or number of the port to access on the container, on which the server is listening + port: 8000 + +labels: + environment: "test" + release: "test" From beb16b2c810a87b28e7b8a7aa29d26f842f654b9 Mon Sep 17 00:00:00 2001 From: Travis Johnson Date: Tue, 10 Dec 2024 03:27:11 -0700 Subject: [PATCH 21/79] [Bugfix] Handle <|tool_call|> token in granite tool parser (#11039) Signed-off-by: Travis Johnson --- vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py index b5854ca39ab47..00917c866e496 100644 --- a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py +++ b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py @@ -35,11 +35,13 @@ class GraniteToolParser(ToolParser): def __init__(self, tokenizer: AnyTokenizer): super().__init__(tokenizer) + self.bot_token = "<|tool_call|>" def extract_tool_calls( self, model_output: str, request: ChatCompletionRequest) -> ExtractedToolCallInformation: - stripped = model_output.strip() + # remove whitespace and the BOT token if it exists + stripped = model_output.strip().removeprefix(self.bot_token).lstrip() if not stripped or stripped[0] != '[': return ExtractedToolCallInformation(tools_called=False, tool_calls=[], From d05f88679bedd73939251a17c3d785a354b2946c Mon Sep 17 00:00:00 2001 From: Jee Jee Li Date: Tue, 10 Dec 2024 19:12:01 +0800 Subject: [PATCH 22/79] [Misc][LoRA] Add PEFTHelper for LoRA (#11003) Signed-off-by: Jee Jee Li --- tests/lora/test_lora_manager.py | 58 +++++++++++++++++++++++++-- vllm/lora/lora.py | 18 +++++++++ vllm/lora/models.py | 42 ++++++++------------ vllm/lora/peft_helper.py | 70 +++++++++++++++++++++++++++++++++ 4 files changed, 160 insertions(+), 28 deletions(-) create mode 100644 vllm/lora/peft_helper.py diff --git a/tests/lora/test_lora_manager.py b/tests/lora/test_lora_manager.py index 8d109b2c81503..0b76f466702fc 100644 --- a/tests/lora/test_lora_manager.py +++ b/tests/lora/test_lora_manager.py @@ -1,3 +1,4 @@ +import json import os from typing import Dict, List @@ -13,6 +14,7 @@ from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights from vllm.lora.models import (LoRAMapping, LoRAModel, LoRAModelManager, LRUCacheLoRAModelManager) +from vllm.lora.peft_helper import PEFTHelper from vllm.lora.request import LoRARequest from vllm.lora.worker_manager import (LRUCacheWorkerLoRAManager, WorkerLoRAManager) @@ -30,18 +32,68 @@ ] +def test_peft_helper(sql_lora_files): + lora_config_path = os.path.join(sql_lora_files, "adapter_config.json") + with open(lora_config_path) as f: + config = json.load(f) + peft_helper = PEFTHelper.from_dict(config) + assert peft_helper.r == 8 + assert peft_helper.lora_alpha == 16 + assert peft_helper.target_modules == [ + "q_proj", + "v_proj", + "k_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + "embed_tokens", + "lm_head", + ] + + expected_error = "vLLM only supports modules_to_save being None." + with pytest.raises(ValueError, match=expected_error): + config = dict( + r=8, + lora_alpha=16, + target_modules=["gate_proj"], + modules_to_save=["lm_head"], + ) + PEFTHelper.from_dict(config) + expected_error = "vLLM does not yet support RSLoRA." + with pytest.raises(ValueError, match=expected_error): + config = dict(r=8, + lora_alpha=16, + target_modules=["gate_proj"], + use_rslora=True) + PEFTHelper.from_dict(config) + + expected_error = "vLLM does not yet support DoRA." + with pytest.raises(ValueError, match=expected_error): + config = dict(r=8, + lora_alpha=16, + target_modules=["gate_proj"], + use_dora=True) + PEFTHelper.from_dict(config) + + @pytest.mark.parametrize("device", CUDA_DEVICES) def test_from_lora_tensors(sql_lora_files, device): tensors = load_file( os.path.join(sql_lora_files, "adapter_model.safetensors")) new_embeddings = load_file( os.path.join(sql_lora_files, "new_embeddings.safetensors")) + + lora_config_path = os.path.join(sql_lora_files, "adapter_config.json") + with open(lora_config_path) as f: + config = json.load(f) + + peft_helper = PEFTHelper.from_dict(config) lora_model = LoRAModel.from_lora_tensors( 1, - 8, - 16, tensors, - device, + peft_helper=peft_helper, + device=device, embeddings=new_embeddings, embedding_modules=EMBEDDING_MODULES, embedding_padding_modules=EMBEDDING_PADDING_MODULES) diff --git a/vllm/lora/lora.py b/vllm/lora/lora.py index b648312ba76ec..dde347b78bf81 100644 --- a/vllm/lora/lora.py +++ b/vllm/lora/lora.py @@ -4,6 +4,7 @@ import torch import torch.types +from vllm.lora.peft_helper import PEFTHelper from vllm.utils import is_pin_memory_available @@ -59,6 +60,23 @@ def extra_vocab_size(self) -> int: return self.embeddings_tensor.shape[ 0] if self.embeddings_tensor is not None else 0 + @classmethod + def from_config( + cls, + module_name: str, + peft_helper: PEFTHelper, + embeddings_tensor: Optional[torch.Tensor] = None, + ) -> "LoRALayerWeights": + return cls( + module_name, + peft_helper.r, + peft_helper.lora_alpha, + None, + None, + None, + embeddings_tensor, + ) + @classmethod def create_dummy_lora_weights( cls, diff --git a/vllm/lora/models.py b/vllm/lora/models.py index 49cd9f0c236ad..70806a77b9fff 100644 --- a/vllm/lora/models.py +++ b/vllm/lora/models.py @@ -21,6 +21,7 @@ LinearScalingRotaryEmbeddingWithLora, LoRAMapping) from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights +from vllm.lora.peft_helper import PEFTHelper from vllm.lora.punica_wrapper import get_punica_wrapper from vllm.lora.utils import (from_layer, from_layer_logits_processor, is_regex_target_modules, @@ -104,14 +105,12 @@ def get_lora(self, module_name: str) -> Optional[LoRALayerWeights]: def from_lora_tensors( cls, lora_model_id: int, - rank: int, - lora_alpha: int, tensors: Dict[str, torch.Tensor], + peft_helper: PEFTHelper, device: str = "cuda", dtype: Optional[torch.dtype] = None, embeddings: Optional[Dict[str, torch.Tensor]] = None, target_embedding_padding: Optional[int] = None, - scaling_factor: Optional[float] = None, embedding_modules: Optional[Dict[str, str]] = None, embedding_padding_modules: Optional[List[str]] = None, ) -> "LoRAModel": @@ -135,10 +134,9 @@ def from_lora_tensors( if pin_memory: lora_embeddings_tensor = ( lora_embeddings_tensor.pin_memory()) - loras[module_name] = LoRALayerWeights(module_name, rank, - lora_alpha, None, None, - None, - lora_embeddings_tensor) + loras[module_name] = LoRALayerWeights.from_config( + module_name, peft_helper, lora_embeddings_tensor) + if is_bias: loras[module_name].bias = tensor.to(device=device, dtype=dtype).t() @@ -170,7 +168,11 @@ def from_lora_tensors( for lora in loras.values(): lora.optimize() - return cls(lora_model_id, rank, loras, scaling_factor=scaling_factor) + + return cls(lora_model_id, + peft_helper.r, + loras, + scaling_factor=peft_helper.vllm_scaling_factor) @classmethod def from_local_checkpoint( @@ -212,6 +214,9 @@ def from_local_checkpoint( "new_embeddings.bin") with open(lora_config_path) as f: config = json.load(f) + + config["vllm_max_position_embeddings"] = max_position_embeddings + peft_helper = PEFTHelper.from_dict(config) if os.path.isfile(lora_tensor_path): tensors: Dict[str, torch.Tensor] = {} # Find unexpected modules. @@ -242,7 +247,7 @@ def from_local_checkpoint( # When a bin file is provided, we rely on config to find unexpected # modules. unexpected_modules = [] - target_modules = config["target_modules"] + target_modules = peft_helper.target_modules if not isinstance(target_modules, list): target_modules = [target_modules] for module in target_modules: @@ -256,7 +261,7 @@ def from_local_checkpoint( # https://github.com/vllm-project/vllm/pull/5909. But there's no # other better mechanism. if unexpected_modules and not is_regex_target_modules( - config["target_modules"], expected_lora_modules): + peft_helper.target_modules, expected_lora_modules): raise ValueError( f"While loading {lora_dir}, expected" f" target modules in {expected_lora_modules}" @@ -274,30 +279,17 @@ def from_local_checkpoint( embeddings = torch.load(new_embeddings_bin_file_path, map_location=device) - rank = config["r"] - lora_alpha = config["lora_alpha"] - context_length = config.get("context_length", None) - scaling_factor = None - if context_length: - if max_position_embeddings is None: - max_position_embeddings = context_length - scaling_factor = float( - math.ceil(context_length / max_position_embeddings)) - return cls.from_lora_tensors( lora_model_id=get_lora_id() if lora_model_id is None else lora_model_id, - rank=rank, - lora_alpha=lora_alpha, tensors=tensors, + peft_helper=peft_helper, device=device, dtype=dtype, embeddings=embeddings, target_embedding_padding=target_embedding_padding, - scaling_factor=scaling_factor, embedding_modules=embedding_modules, - embedding_padding_modules=embedding_padding_modules, - ) + embedding_padding_modules=embedding_padding_modules) class LoRAModelManager(AdapterModelManager): diff --git a/vllm/lora/peft_helper.py b/vllm/lora/peft_helper.py new file mode 100644 index 0000000000000..edf4ba5659575 --- /dev/null +++ b/vllm/lora/peft_helper.py @@ -0,0 +1,70 @@ +# Adapted from: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/config.py + +import math +from dataclasses import MISSING, dataclass, field, fields +from typing import Literal, Optional, Union + + +@dataclass +class PEFTHelper: + # Required fields + r: int + lora_alpha: int + target_modules: Union[list[str], str] + + bias: Literal["none", "all", "lora_only"] = field(default="none") + modules_to_save: Optional[list[str]] = field(default=None) + use_rslora: bool = field(default=False) + use_dora: bool = field(default=False) + # long lora field + context_length: int = field(default=0) + # Extra vllm field, start with 'vllm_' to avoid conflict + vllm_max_position_embeddings: Optional[int] = field(default=False) + vllm_scaling_factor: Optional[float] = field(default=None) + + def _validate_features(self): + error_msg = [] + + if self.modules_to_save: + error_msg.append("vLLM only supports modules_to_save being None.") + if self.use_rslora: + error_msg.append("vLLM does not yet support RSLoRA.") + + if self.use_dora: + error_msg.append("vLLM does not yet support DoRA.") + + if error_msg: + raise ValueError(f"{', '.join(error_msg)}") + + def __post_init__(self): + self._validate_features() + if self.context_length: + if self.vllm_max_position_embeddings is None: + self.vllm_max_position_embeddings = self.context_length + self.vllm_scaling_factor = float( + math.ceil(self.context_length / + self.vllm_max_position_embeddings)) + + @classmethod + def from_dict(cls, config_dict: dict) -> "PEFTHelper": + # Get all field information from the class + class_fields = {f.name: f for f in fields(cls)} + # Check for required fields + required_fields = { + name + for name, f in class_fields.items() + if f.default is MISSING and f.default_factory is MISSING + } + + # Identify any missing required fields + missing_fields = required_fields - set(config_dict.keys()) + if missing_fields: + raise ValueError( + f"Missing required configuration fields: {missing_fields}") + + # Filter out fields that aren't defined in the class + filtered_dict = { + k: v + for k, v in config_dict.items() if k in class_fields + } + return cls(**filtered_dict) From 9b9cef3145381721fa950c89718fe71849ac2a55 Mon Sep 17 00:00:00 2001 From: Joe Runde Date: Tue, 10 Dec 2024 09:38:23 -0700 Subject: [PATCH 23/79] [Bugfix] Backport request id validation to v0 (#11036) Signed-off-by: Joe Runde --- vllm/engine/multiprocessing/client.py | 4 ++++ vllm/v1/engine/async_llm.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/vllm/engine/multiprocessing/client.py b/vllm/engine/multiprocessing/client.py index 32bd83305bb8f..a729023bc00bb 100644 --- a/vllm/engine/multiprocessing/client.py +++ b/vllm/engine/multiprocessing/client.py @@ -576,6 +576,10 @@ async def _process_request( if self._errored_with is not None: raise ENGINE_DEAD_ERROR(self._errored_with) + # Ensure the request id is unique among running requests + if request_id in self.output_queues: + raise ValueError(f"Request {request_id} already exists") + # Constructing guided decoding logits processors is expensive, so we do # it here to avoid contending with cpu resources and the GIL on the # backend process. diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py index 26fd650aee4b7..24cafeff63d1e 100644 --- a/vllm/v1/engine/async_llm.py +++ b/vllm/v1/engine/async_llm.py @@ -152,7 +152,7 @@ async def add_request( """Add new request to the AsyncLLM.""" if self.detokenizer.is_request_active(request_id): - raise KeyError(f"Request {request_id} already exists.") + raise ValueError(f"Request {request_id} already exists.") # 1) Create a new AsyncStream for the request. stream = self._add_request_to_streams(request_id) From 250ee65d72a0c7b86ec5cea9cbe9377da21d6439 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fl=C3=A1via=20B=C3=A9o?= <119421251+flaviabeo@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:38:15 -0300 Subject: [PATCH 24/79] [BUG] Remove token param #10921 (#11022) Signed-off-by: Flavia Beo --- vllm/transformers_utils/config.py | 63 ++++++++++++++----------------- 1 file changed, 29 insertions(+), 34 deletions(-) diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 3da99bcbee9ae..4529cf27ef565 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -1,5 +1,6 @@ import enum import json +import os from pathlib import Path from typing import Any, Dict, Optional, Type, Union @@ -41,6 +42,7 @@ from transformers import AutoConfig MISTRAL_CONFIG_NAME = "params.json" +HF_TOKEN = os.getenv('HF_TOKEN', None) logger = init_logger(__name__) @@ -77,8 +79,8 @@ class ConfigFormat(str, enum.Enum): MISTRAL = "mistral" -def file_or_path_exists(model: Union[str, Path], config_name, revision, - token) -> bool: +def file_or_path_exists(model: Union[str, Path], config_name: str, + revision: Optional[str]) -> bool: if Path(model).exists(): return (Path(model) / config_name).is_file() @@ -93,7 +95,10 @@ def file_or_path_exists(model: Union[str, Path], config_name, revision, # NB: file_exists will only check for the existence of the config file on # hf_hub. This will fail in offline mode. try: - return file_exists(model, config_name, revision=revision, token=token) + return file_exists(model, + config_name, + revision=revision, + token=HF_TOKEN) except huggingface_hub.errors.OfflineModeIsEnabled: # Don't raise in offline mode, all we know is that we don't have this # file cached. @@ -161,7 +166,6 @@ def get_config( revision: Optional[str] = None, code_revision: Optional[str] = None, config_format: ConfigFormat = ConfigFormat.AUTO, - token: Optional[str] = None, **kwargs, ) -> PretrainedConfig: # Separate model folder from file path for GGUF models @@ -173,19 +177,20 @@ def get_config( if config_format == ConfigFormat.AUTO: if is_gguf or file_or_path_exists( - model, HF_CONFIG_NAME, revision=revision, token=token): + model, HF_CONFIG_NAME, revision=revision): config_format = ConfigFormat.HF - elif file_or_path_exists(model, - MISTRAL_CONFIG_NAME, - revision=revision, - token=token): + elif file_or_path_exists(model, MISTRAL_CONFIG_NAME, + revision=revision): config_format = ConfigFormat.MISTRAL else: # If we're in offline mode and found no valid config format, then # raise an offline mode error to indicate to the user that they # don't have files cached and may need to go online. # This is conveniently triggered by calling file_exists(). - file_exists(model, HF_CONFIG_NAME, revision=revision, token=token) + file_exists(model, + HF_CONFIG_NAME, + revision=revision, + token=HF_TOKEN) raise ValueError(f"No supported config format found in {model}") @@ -194,7 +199,7 @@ def get_config( model, revision=revision, code_revision=code_revision, - token=token, + token=HF_TOKEN, **kwargs, ) @@ -206,7 +211,7 @@ def get_config( model, revision=revision, code_revision=code_revision, - token=token, + token=HF_TOKEN, **kwargs, ) else: @@ -216,7 +221,7 @@ def get_config( trust_remote_code=trust_remote_code, revision=revision, code_revision=code_revision, - token=token, + token=HF_TOKEN, **kwargs, ) except ValueError as e: @@ -234,7 +239,7 @@ def get_config( raise e elif config_format == ConfigFormat.MISTRAL: - config = load_params_config(model, revision, token=token, **kwargs) + config = load_params_config(model, revision, token=HF_TOKEN, **kwargs) else: raise ValueError(f"Unsupported config format: {config_format}") @@ -256,8 +261,7 @@ def get_config( def get_hf_file_to_dict(file_name: str, model: Union[str, Path], - revision: Optional[str] = 'main', - token: Optional[str] = None): + revision: Optional[str] = 'main'): """ Downloads a file from the Hugging Face Hub and returns its contents as a dictionary. @@ -266,7 +270,6 @@ def get_hf_file_to_dict(file_name: str, - file_name (str): The name of the file to download. - model (str): The name of the model on the Hugging Face Hub. - revision (str): The specific version of the model. - - token (str): The Hugging Face authentication token. Returns: - config_dict (dict): A dictionary containing @@ -276,8 +279,7 @@ def get_hf_file_to_dict(file_name: str, if file_or_path_exists(model=model, config_name=file_name, - revision=revision, - token=token): + revision=revision): if not file_path.is_file(): try: @@ -296,9 +298,7 @@ def get_hf_file_to_dict(file_name: str, return None -def get_pooling_config(model: str, - revision: Optional[str] = 'main', - token: Optional[str] = None): +def get_pooling_config(model: str, revision: Optional[str] = 'main'): """ This function gets the pooling and normalize config from the model - only applies to @@ -315,8 +315,7 @@ def get_pooling_config(model: str, """ modules_file_name = "modules.json" - modules_dict = get_hf_file_to_dict(modules_file_name, model, revision, - token) + modules_dict = get_hf_file_to_dict(modules_file_name, model, revision) if modules_dict is None: return None @@ -332,8 +331,7 @@ def get_pooling_config(model: str, if pooling: pooling_file_name = "{}/config.json".format(pooling["path"]) - pooling_dict = get_hf_file_to_dict(pooling_file_name, model, revision, - token) + pooling_dict = get_hf_file_to_dict(pooling_file_name, model, revision) pooling_type_name = next( (item for item, val in pooling_dict.items() if val is True), None) @@ -368,8 +366,8 @@ def get_pooling_config_name(pooling_name: str) -> Union[str, None]: def get_sentence_transformer_tokenizer_config(model: str, - revision: Optional[str] = 'main', - token: Optional[str] = None): + revision: Optional[str] = 'main' + ): """ Returns the tokenization configuration dictionary for a given Sentence Transformer BERT model. @@ -379,7 +377,6 @@ def get_sentence_transformer_tokenizer_config(model: str, BERT model. - revision (str, optional): The revision of the m odel to use. Defaults to 'main'. - - token (str): A Hugging Face access token. Returns: - dict: A dictionary containing the configuration parameters @@ -394,7 +391,7 @@ def get_sentence_transformer_tokenizer_config(model: str, "sentence_xlm-roberta_config.json", "sentence_xlnet_config.json", ]: - encoder_dict = get_hf_file_to_dict(config_name, model, revision, token) + encoder_dict = get_hf_file_to_dict(config_name, model, revision) if encoder_dict: break @@ -474,16 +471,14 @@ def _reduce_config(config: VllmConfig): exc_info=e) -def load_params_config(model: Union[str, Path], - revision: Optional[str], - token: Optional[str] = None, +def load_params_config(model: Union[str, Path], revision: Optional[str], **kwargs) -> PretrainedConfig: # This function loads a params.json config which # should be used when loading models in mistral format config_file_name = "params.json" - config_dict = get_hf_file_to_dict(config_file_name, model, revision, token) + config_dict = get_hf_file_to_dict(config_file_name, model, revision) assert isinstance(config_dict, dict) config_mapping = { From e7391949267a4eff3d84f02119f442f46b16d163 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Tue, 10 Dec 2024 15:08:16 -0500 Subject: [PATCH 25/79] [Core] Update to outlines >= 0.1.8 (#10576) Signed-off-by: Russell Bryant --- requirements-common.txt | 2 +- .../guided_decoding/outlines_logits_processors.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements-common.txt b/requirements-common.txt index 112528880c0ac..c71fc458aca13 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -18,7 +18,7 @@ prometheus_client >= 0.18.0 prometheus-fastapi-instrumentator >= 7.0.0 tiktoken >= 0.6.0 # Required for DBRX tokenizer lm-format-enforcer >= 0.10.9, < 0.11 -outlines >= 0.0.43, < 0.1 +outlines >= 0.1.8 xgrammar >= 0.1.6; platform_machine == "x86_64" typing_extensions >= 4.10 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317 diff --git a/vllm/model_executor/guided_decoding/outlines_logits_processors.py b/vllm/model_executor/guided_decoding/outlines_logits_processors.py index e1309c31f77e7..1f0dbe024609d 100644 --- a/vllm/model_executor/guided_decoding/outlines_logits_processors.py +++ b/vllm/model_executor/guided_decoding/outlines_logits_processors.py @@ -99,7 +99,7 @@ class RegexLogitsProcessor(BaseLogitsProcessor): def _get_guide(cls, regex_string: str, tokenizer: PreTrainedTokenizerBase) -> Guide: tokenizer = _adapt_tokenizer(tokenizer) - return RegexGuide(regex_string, tokenizer) + return RegexGuide.from_regex(regex_string, tokenizer) def __init__(self, regex_string: str, tokenizer: PreTrainedTokenizerBase): """Compile the FSM that drives the regex-structured generation. From 75f89dc44c6e44cc28bae59d5b40a588735b507b Mon Sep 17 00:00:00 2001 From: youkaichao Date: Tue, 10 Dec 2024 12:40:52 -0800 Subject: [PATCH 26/79] [torch.compile] add a flag to track batchsize statistics (#11059) Signed-off-by: youkaichao --- vllm/envs.py | 3 +++ vllm/forward_context.py | 32 +++++++++++++++++++++++- vllm/v1/attention/backends/flash_attn.py | 1 + vllm/v1/worker/gpu_model_runner.py | 2 ++ 4 files changed, 37 insertions(+), 1 deletion(-) diff --git a/vllm/envs.py b/vllm/envs.py index ab12a7b48dc53..be5d9985b63a4 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -69,6 +69,7 @@ VLLM_DISABLED_KERNELS: List[str] = [] VLLM_USE_V1: bool = False VLLM_ENABLE_V1_MULTIPROCESSING: bool = False + VLLM_LOG_BATCHSIZE_INTERVAL: float = -1 def get_default_cache_root(): @@ -452,6 +453,8 @@ def get_default_config_root(): # If set, enable multiprocessing in LLM for the V1 code path. "VLLM_ENABLE_V1_MULTIPROCESSING": lambda: bool(int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0"))), + "VLLM_LOG_BATCHSIZE_INTERVAL": + lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")), } # end-env-vars-definition diff --git a/vllm/forward_context.py b/vllm/forward_context.py index aaa3e4bb3a1e8..cd136f43c0c57 100644 --- a/vllm/forward_context.py +++ b/vllm/forward_context.py @@ -1,8 +1,19 @@ +import time +from collections import Counter from contextlib import contextmanager from dataclasses import dataclass from typing import Any, Dict, Optional +import vllm.envs as envs from vllm.config import VllmConfig +from vllm.logger import init_logger + +logger = init_logger(__name__) + +track_batchsize: bool = envs.VLLM_LOG_BATCHSIZE_INTERVAL >= 0 +batchsize_counter: Counter = Counter() +last_logging_time: float = 0 +batchsize_logging_interval: float = envs.VLLM_LOG_BATCHSIZE_INTERVAL @dataclass @@ -26,7 +37,26 @@ def get_forward_context() -> ForwardContext: @contextmanager def set_forward_context(context: Any, vllm_config: VllmConfig): """A context manager that stores the current forward context, - can be attention metadata, etc.""" + can be attention metadata, etc. + Here we can inject common logic for every model forward pass. + """ + global track_batchsize, batchsize_counter + global last_logging_time, batchsize_logging_interval + if track_batchsize and context is not None: + if hasattr(context, "num_prefill_tokens"): + # for v0 attention backends + batchsize = context.num_prefill_tokens + context.num_decode_tokens + else: + # for v1 attention backends + batchsize = context.num_input_tokens + batchsize_counter[batchsize] += 1 + if time.monotonic() - last_logging_time > batchsize_logging_interval: + last_logging_time = time.monotonic() + sorted_data = sorted(batchsize_counter.items(), + key=lambda x: x[1], + reverse=True) + logger.info("Batchsize distribution (batchsize, count): %s", + sorted_data) global _forward_context prev_context = _forward_context _forward_context = ForwardContext( diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py index 251a103e60f06..c9f04ace644c7 100644 --- a/vllm/v1/attention/backends/flash_attn.py +++ b/vllm/v1/attention/backends/flash_attn.py @@ -56,6 +56,7 @@ class FlashAttentionMetadata: seq_start_loc: torch.Tensor block_table: torch.Tensor slot_mapping: torch.Tensor + num_input_tokens: int = 0 # Number of tokens including padding. class FlashAttentionImpl(AttentionImpl): diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 0a5adfb28c9bd..a3335fa838352 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -445,6 +445,8 @@ def execute_model( # Eager mode. num_input_tokens = num_scheduled_tokens + attn_metadata.num_input_tokens = num_input_tokens + # Get the inputs embeds. if encoder_outputs: inputs_embeds = self.model.get_input_embeddings( From 134810b3d9a05510622282479f0f9e2114b88017 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Tue, 10 Dec 2024 14:41:23 -0800 Subject: [PATCH 27/79] [V1][Bugfix] Always set enable_chunked_prefill = True for V1 (#11061) Signed-off-by: Woosuk Kwon --- vllm/engine/arg_utils.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 3db069ec64ee4..7b9adc401abcf 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -122,7 +122,7 @@ class EngineArgs: cpu_offload_gb: float = 0 # GiB gpu_memory_utilization: float = 0.90 max_num_batched_tokens: Optional[int] = None - max_num_seqs: int = 256 + max_num_seqs: Optional[int] = None max_logprobs: int = 20 # Default value for OpenAI Chat Completions API disable_log_stats: bool = False revision: Optional[str] = None @@ -205,6 +205,9 @@ def __post_init__(self): # by user. if self.enable_prefix_caching is None: self.enable_prefix_caching = bool(envs.VLLM_USE_V1) + # Override max_num_seqs if it's not set by user. + if self.max_num_seqs is None: + self.max_num_seqs = 256 if not envs.VLLM_USE_V1 else 1024 # support `EngineArgs(compilation_config={...})` # without having to manually construct a @@ -1225,19 +1228,19 @@ def _override_v1_engine_args(self, usage_context: UsageContext) -> None: """ assert envs.VLLM_USE_V1, "V1 is not enabled" + # V1 always uses chunked prefills. + self.enable_chunked_prefill = True + # When no user override, set the default values based on the usage + # context. + # TODO(woosuk): Tune the default values for different hardware. if self.max_num_batched_tokens is None: - # When no user override, set the default values based on the - # usage context. if usage_context == UsageContext.LLM_CLASS: - logger.warning("Setting max_num_batched_tokens to 8192 " - "for LLM_CLASS usage context.") - self.max_num_seqs = 1024 self.max_num_batched_tokens = 8192 elif usage_context == UsageContext.OPENAI_API_SERVER: - logger.warning("Setting max_num_batched_tokens to 2048 " - "for OPENAI_API_SERVER usage context.") - self.max_num_seqs = 1024 self.max_num_batched_tokens = 2048 + logger.warning( + "Setting max_num_batched_tokens to %d for %s usage context.", + self.max_num_batched_tokens, usage_context.value) def _override_v1_engine_config(self, engine_config: VllmConfig) -> None: """ From 9a93973708d7f52f1d1439f8f32b8c1514d18b86 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Tue, 10 Dec 2024 19:16:22 -0500 Subject: [PATCH 28/79] [Bugfix] Fix Mamba multistep (#11071) Signed-off-by: Tyler Michael Smith --- vllm/attention/backends/placeholder_attn.py | 64 ++++++++++++++++++++- vllm/worker/multi_step_model_runner.py | 4 +- 2 files changed, 66 insertions(+), 2 deletions(-) diff --git a/vllm/attention/backends/placeholder_attn.py b/vllm/attention/backends/placeholder_attn.py index 888adbffb8578..658039bfc3365 100644 --- a/vllm/attention/backends/placeholder_attn.py +++ b/vllm/attention/backends/placeholder_attn.py @@ -11,7 +11,8 @@ from vllm.multimodal import MultiModalPlaceholderMap if TYPE_CHECKING: - from vllm.worker.model_runner import ModelInputForGPUBuilder + from vllm.worker.model_runner import (ModelInputForGPUBuilder, + ModelInputForGPUWithSamplingMetadata) # Placeholder attention backend for models like Mamba and embedding models that # lack attention. @@ -186,6 +187,67 @@ def decode_metadata(self) -> Optional["PlaceholderAttentionMetadata"]: ) return self._cached_decode_metadata + def advance_step(self, + model_input: "ModelInputForGPUWithSamplingMetadata", + sampled_token_ids: Optional[torch.Tensor], + block_size: int, + num_seqs: int, + num_queries: int, + turn_prefills_into_decodes: bool = False): + """ + Update metadata in-place to advance one decode step. + """ + # When using cudagraph, the num_seqs is padded to the next captured + # batch sized, but num_queries tracks the actual number of requests in + # the batch. For --enforce-eager mode, num_seqs == num_queries + if num_seqs != num_queries: + assert num_seqs > num_queries + assert self.use_cuda_graph + + assert not turn_prefills_into_decodes, \ + ("Multi-Step + Chunked-Prefill is not supported for attention-free" + "models. turn_prefills_into_decodes is a " + "Multi-Step + Chunked-Prefill specific parameter.") + + assert self.seq_lens is not None + assert self.max_decode_seq_len == max(self.seq_lens) + + assert self.num_prefills == 0 + assert self.num_prefill_tokens == 0 + assert self.num_decode_tokens == num_seqs + + assert self.seq_lens is not None + assert len(self.seq_lens) == num_seqs + assert self.seq_lens_tensor is not None + assert self.seq_lens_tensor.shape == (num_seqs, ) + assert self.max_query_len == 1 + assert self.max_prefill_seq_len == 0 + + assert self.query_start_loc is not None + assert self.query_start_loc.shape == (num_queries + 1, ) + assert self.seq_start_loc is not None + assert self.seq_start_loc.shape == (num_seqs + 1, ) + + assert self.context_lens_tensor is not None + assert self.context_lens_tensor.shape == (num_queries, ) + + assert self.block_tables is not None + + # Update query lengths. Note that we update only queries and not seqs, + # since tensors may be padded due to captured cuda graph batch size + for i in range(num_queries): + self.seq_lens[i] += 1 + self.max_decode_seq_len = max(self.seq_lens) + + # Update sequences, masking off entries greater than num_queries + device = self.seq_lens_tensor.device + mask = torch.arange(self.seq_lens_tensor.size(0), + device=device) < num_queries + self.seq_lens_tensor += mask.to(self.seq_lens_tensor.dtype) + if sampled_token_ids is not None: + model_input.input_tokens.masked_scatter_( + mask, sampled_token_ids[:num_queries]) + class PlaceholderAttentionMetadataBuilder( AttentionMetadataBuilder[PlaceholderAttentionMetadata]): diff --git a/vllm/worker/multi_step_model_runner.py b/vllm/worker/multi_step_model_runner.py index 3ca0d88a42183..e08a61e31fe42 100644 --- a/vllm/worker/multi_step_model_runner.py +++ b/vllm/worker/multi_step_model_runner.py @@ -29,7 +29,9 @@ logger = init_logger(__name__) -MULTI_STEP_ATTENTION_BACKENDS = ["FLASH_ATTN", "ROCM_FLASH", "FLASHINFER"] +MULTI_STEP_ATTENTION_BACKENDS = [ + "FLASH_ATTN", "ROCM_FLASH", "FLASHINFER", "NO_ATTENTION" +] MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS = ["FLASH_ATTN"] def _get_supported_attention_backends(chunked_prefill_enabled: bool) \ From d5c5154fcf4c5d65551c98e458cbb027e5f4b672 Mon Sep 17 00:00:00 2001 From: Aurick Qiao Date: Tue, 10 Dec 2024 21:09:20 -0500 Subject: [PATCH 29/79] [Misc] LoRA + Chunked Prefill (#9057) --- tests/lora/test_chatglm3_tp.py | 9 ++++++--- tests/lora/test_gemma.py | 3 ++- tests/lora/test_llama_tp.py | 6 +++++- tests/lora/test_long_context.py | 3 ++- tests/lora/test_minicpmv.py | 3 ++- tests/lora/test_minicpmv_tp.py | 2 ++ tests/lora/test_mixtral.py | 1 + tests/lora/test_phi.py | 3 ++- tests/lora/test_quant_model.py | 9 ++++++--- vllm/config.py | 3 ++- vllm/core/scheduler.py | 15 ++++++++++++--- vllm/worker/model_runner.py | 12 +++++++----- 12 files changed, 49 insertions(+), 20 deletions(-) diff --git a/tests/lora/test_chatglm3_tp.py b/tests/lora/test_chatglm3_tp.py index f17464573459f..49a527b99ac16 100644 --- a/tests/lora/test_chatglm3_tp.py +++ b/tests/lora/test_chatglm3_tp.py @@ -53,7 +53,8 @@ def test_chatglm3_lora(chatglm3_lora_files): max_loras=4, max_lora_rank=64, tensor_parallel_size=1, - trust_remote_code=True) + trust_remote_code=True, + enable_chunked_prefill=True) output1 = do_sample(llm, chatglm3_lora_files, lora_id=1) for i in range(len(EXPECTED_LORA_OUTPUT)): @@ -73,7 +74,8 @@ def test_chatglm3_lora_tp4(chatglm3_lora_files): max_lora_rank=64, tensor_parallel_size=4, trust_remote_code=True, - fully_sharded_loras=False) + fully_sharded_loras=False, + enable_chunked_prefill=True) output1 = do_sample(llm, chatglm3_lora_files, lora_id=1) for i in range(len(EXPECTED_LORA_OUTPUT)): @@ -93,7 +95,8 @@ def test_chatglm3_lora_tp4_fully_sharded_loras(chatglm3_lora_files): max_lora_rank=64, tensor_parallel_size=4, trust_remote_code=True, - fully_sharded_loras=True) + fully_sharded_loras=True, + enable_chunked_prefill=True) output1 = do_sample(llm, chatglm3_lora_files, lora_id=1) for i in range(len(EXPECTED_LORA_OUTPUT)): assert output1[i] == EXPECTED_LORA_OUTPUT[i] diff --git a/tests/lora/test_gemma.py b/tests/lora/test_gemma.py index 15ec66b0f5502..5ae705e474ec6 100644 --- a/tests/lora/test_gemma.py +++ b/tests/lora/test_gemma.py @@ -37,7 +37,8 @@ def test_gemma_lora(gemma_lora_files): llm = vllm.LLM(MODEL_PATH, max_model_len=1024, enable_lora=True, - max_loras=4) + max_loras=4, + enable_chunked_prefill=True) expected_lora_output = [ "more important than knowledge.\nAuthor: Albert Einstein\n", diff --git a/tests/lora/test_llama_tp.py b/tests/lora/test_llama_tp.py index d3ca7f878191a..dfeac380951d8 100644 --- a/tests/lora/test_llama_tp.py +++ b/tests/lora/test_llama_tp.py @@ -78,7 +78,8 @@ def test_llama_lora(sql_lora_files): enable_lora=True, max_num_seqs=16, max_loras=4, - tensor_parallel_size=1) + tensor_parallel_size=1, + enable_chunked_prefill=True) generate_and_test(llm, sql_lora_files) @@ -120,6 +121,7 @@ def test_llama_lora_tp4(sql_lora_files): max_num_seqs=16, max_loras=4, tensor_parallel_size=4, + enable_chunked_prefill=True, ) generate_and_test(llm, sql_lora_files) @@ -135,6 +137,7 @@ def test_llama_lora_tp4_fully_sharded_loras(sql_lora_files): max_loras=4, tensor_parallel_size=4, fully_sharded_loras=True, + enable_chunked_prefill=True, ) generate_and_test(llm, sql_lora_files) @@ -151,5 +154,6 @@ def test_llama_lora_tp4_fully_sharded_enable_bias(sql_lora_files): tensor_parallel_size=4, fully_sharded_loras=True, enable_lora_bias=True, + enable_chunked_prefill=True, ) generate_and_test(llm, sql_lora_files) diff --git a/tests/lora/test_long_context.py b/tests/lora/test_long_context.py index eada902c891f7..e7a34f2ced7ed 100644 --- a/tests/lora/test_long_context.py +++ b/tests/lora/test_long_context.py @@ -124,7 +124,8 @@ def lora_llm(long_context_infos): tensor_parallel_size=4, # FIXME enable async output processor disable_async_output_proc=True, - distributed_executor_backend="mp") + distributed_executor_backend="mp", + enable_chunked_prefill=True) yield llm del llm diff --git a/tests/lora/test_minicpmv.py b/tests/lora/test_minicpmv.py index 2c45ce5141f7d..1f3de9edc0d0f 100644 --- a/tests/lora/test_minicpmv.py +++ b/tests/lora/test_minicpmv.py @@ -67,7 +67,8 @@ 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 + gpu_memory_utilization=0.97, # This model is pretty big for CI gpus + enable_chunked_prefill=True, ) output1 = do_sample(llm, minicpmv_lora_files, lora_id=1) for i in range(len(EXPECTED_OUTPUT)): diff --git a/tests/lora/test_minicpmv_tp.py b/tests/lora/test_minicpmv_tp.py index ba29e562e58ec..930f177953a5f 100644 --- a/tests/lora/test_minicpmv_tp.py +++ b/tests/lora/test_minicpmv_tp.py @@ -69,6 +69,7 @@ def test_minicpmv_tp2(minicpmv_lora_files, fully_sharded): tensor_parallel_size=2, trust_remote_code=True, fully_sharded_loras=fully_sharded, + enable_chunked_prefill=True, ) output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1) @@ -89,6 +90,7 @@ def test_minicpmv_tp4(minicpmv_lora_files, fully_sharded): tensor_parallel_size=4, trust_remote_code=True, fully_sharded_loras=fully_sharded, + enable_chunked_prefill=True, ) output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1) for i in range(len(EXPECTED_OUTPUT)): diff --git a/tests/lora/test_mixtral.py b/tests/lora/test_mixtral.py index dddc299da446b..150221dfce6ab 100644 --- a/tests/lora/test_mixtral.py +++ b/tests/lora/test_mixtral.py @@ -47,6 +47,7 @@ def test_mixtral_lora(mixtral_lora_files, tp_size): max_loras=4, distributed_executor_backend="ray", tensor_parallel_size=tp_size, + enable_chunked_prefill=True, ) expected_lora_output = [ diff --git a/tests/lora/test_phi.py b/tests/lora/test_phi.py index 733eff48a9bf3..5a3fcb8d690d9 100644 --- a/tests/lora/test_phi.py +++ b/tests/lora/test_phi.py @@ -53,7 +53,8 @@ def test_phi2_lora(phi2_lora_files): max_model_len=1024, enable_lora=True, max_loras=2, - enforce_eager=True) + enforce_eager=True, + enable_chunked_prefill=True) expected_lora_output = [ "SELECT catalog_publisher, COUNT(*) as num_catalogs FROM catalogs GROUP BY catalog_publisher ORDER BY num_catalogs DESC LIMIT 1;", # noqa: E501 diff --git a/tests/lora/test_quant_model.py b/tests/lora/test_quant_model.py index 5432fa4ad0d3a..026269667b473 100644 --- a/tests/lora/test_quant_model.py +++ b/tests/lora/test_quant_model.py @@ -84,7 +84,8 @@ def test_quant_model_lora(tinyllama_lora_files, num_gpus_available, model, tensor_parallel_size=tp_size, gpu_memory_utilization=0.2, #avoid OOM quantization=model.quantization, - trust_remote_code=True) + trust_remote_code=True, + enable_chunked_prefill=True) if model.quantization is None: expected_no_lora_output = [ @@ -176,7 +177,8 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available, tensor_parallel_size=1, gpu_memory_utilization=0.2, #avoid OOM quantization=model.quantization, - trust_remote_code=True) + trust_remote_code=True, + enable_chunked_prefill=True) output_tp1 = do_sample(llm_tp1, tinyllama_lora_files, lora_id=1) del llm_tp1 @@ -189,7 +191,8 @@ def test_quant_model_tp_equality(tinyllama_lora_files, num_gpus_available, max_loras=4, tensor_parallel_size=2, gpu_memory_utilization=0.2, #avoid OOM - quantization=model.quantization) + quantization=model.quantization, + enable_chunked_prefill=True) output_tp2 = do_sample(llm_tp2, tinyllama_lora_files, lora_id=1) del llm_tp2 diff --git a/vllm/config.py b/vllm/config.py index 5fb9563fcf3a3..c66ddbb47f22e 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -1698,7 +1698,8 @@ def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig): # Reminder: Please update docs/source/usage/compatibility_matrix.rst # If the feature combo become valid if scheduler_config.chunked_prefill_enabled: - raise ValueError("LoRA is not supported with chunked prefill yet.") + logger.warning("LoRA with chunked prefill is still experimental " + "and may be unstable.") @dataclass diff --git a/vllm/core/scheduler.py b/vllm/core/scheduler.py index d23009dae01ee..94c62743883ec 100644 --- a/vllm/core/scheduler.py +++ b/vllm/core/scheduler.py @@ -166,9 +166,18 @@ def is_empty(self) -> bool: and not self.blocks_to_swap_out and not self.blocks_to_copy) def _sort_by_lora_ids(self): - self.scheduled_seq_groups = sorted( - self.scheduled_seq_groups, - key=lambda g: (g.seq_group.lora_int_id, g.seq_group.request_id)) + assert 0 <= self.num_prefill_groups <= len(self.scheduled_seq_groups) + + def key_fn(group: ScheduledSequenceGroup): + key = (group.seq_group.lora_int_id, group.seq_group.request_id) + if 0 < self.num_prefill_groups < len(self.scheduled_seq_groups): + # Sort sequence groups so that all prefills come before all + # decodes as required by chunked prefill. + return (not group.seq_group.is_prefill(), *key) + return key + + self.scheduled_seq_groups = sorted(self.scheduled_seq_groups, + key=key_fn) @property def lora_requests(self) -> Set[LoRARequest]: diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index 1bc5f65c7127f..551b84435fdc0 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -622,11 +622,13 @@ def _compute_lora_input(self, inter_data: InterDataForSeqGroup, inter_data.lora_requests.add(seq_group_metadata.lora_request) query_len = inter_data.query_lens[seq_idx] inter_data.lora_index_mapping.append([lora_id] * query_len) - inter_data.lora_prompt_mapping.append( - [lora_id] * - (query_len if seq_group_metadata.sampling_params - and seq_group_metadata.sampling_params.prompt_logprobs is not None - else 1)) + sampling_params = seq_group_metadata.sampling_params + if sampling_params and sampling_params.prompt_logprobs is not None: + inter_data.lora_prompt_mapping.append([lora_id] * query_len) + elif not self.chunked_prefill_enabled or seq_group_metadata.do_sample: + inter_data.lora_prompt_mapping.append([lora_id]) + else: + inter_data.lora_prompt_mapping.append([]) def _compute_prompt_adapter_input( self, inter_data: InterDataForSeqGroup, From ffa48c9146fda1e8810d1cfa159e1d70aadae6c6 Mon Sep 17 00:00:00 2001 From: Mor Zusman Date: Wed, 11 Dec 2024 04:53:37 +0200 Subject: [PATCH 30/79] [Model] PP support for Mamba-like models (#10992) Signed-off-by: mzusman --- docs/source/models/supported_models.rst | 6 +- tests/distributed/test_pipeline_parallel.py | 6 +- vllm/config.py | 58 +++++++++---- vllm/model_executor/models/interfaces.py | 37 ++++++++ vllm/model_executor/models/jamba.py | 93 ++++++++++++++------- vllm/model_executor/models/mamba.py | 68 ++++++++++----- vllm/model_executor/models/registry.py | 11 ++- vllm/utils.py | 5 ++ vllm/v1/worker/gpu_model_runner.py | 8 +- vllm/v1/worker/gpu_worker.py | 6 +- vllm/worker/cache_engine.py | 12 +-- 11 files changed, 229 insertions(+), 81 deletions(-) diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 4e5b10967e3bb..6540e023c1ab0 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -128,7 +128,7 @@ Text Generation - 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. @@ -193,7 +193,7 @@ Text Generation - Jamba - :code:`ai21labs/AI21-Jamba-1.5-Large`, :code:`ai21labs/AI21-Jamba-1.5-Mini`, :code:`ai21labs/Jamba-v0.1`, etc. - ✅︎ - - + - ✅︎ * - :code:`LlamaForCausalLM` - Llama 3.1, Llama 3, Llama 2, LLaMA, Yi - :code:`meta-llama/Meta-Llama-3.1-405B-Instruct`, :code:`meta-llama/Meta-Llama-3.1-70B`, :code:`meta-llama/Meta-Llama-3-70B-Instruct`, :code:`meta-llama/Llama-2-70b-hf`, :code:`01-ai/Yi-34B`, etc. @@ -203,7 +203,7 @@ Text Generation - Mamba - :code:`state-spaces/mamba-130m-hf`, :code:`state-spaces/mamba-790m-hf`, :code:`state-spaces/mamba-2.8b-hf`, etc. - - - + - ✅︎ * - :code:`MiniCPMForCausalLM` - MiniCPM - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, :code:`openbmb/MiniCPM-S-1B-sft`, etc. diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py index b818ca921fcb0..85d408efafe96 100644 --- a/tests/distributed/test_pipeline_parallel.py +++ b/tests/distributed/test_pipeline_parallel.py @@ -156,13 +156,13 @@ def iter_params(self, model_name: str): # "internlm/internlm-chat-7b": PPTestSettings.fast(), "internlm/internlm2-chat-7b": PPTestSettings.fast(trust_remote_code=True), "inceptionai/jais-13b-chat": PPTestSettings.fast(), - # TODO: Implement PP - # "ai21labs/AI21-Jamba-1.5-Mini": PPTestSettings.fast(), + "ai21labs/Jamba-tiny-dev": 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 # "mistralai/Mistral-7B-Instruct-v0.1": PPTestSettings.fast(), + "state-spaces/mamba-130m-hf": PPTestSettings.fast(), "mistralai/Mixtral-8x7B-Instruct-v0.1": PPTestSettings.fast(tp_base=4), "mosaicml/mpt-7b": PPTestSettings.fast(), "nvidia/Minitron-8B-Base": PPTestSettings.fast(), @@ -234,6 +234,8 @@ def iter_params(self, model_name: str): "OpenGVLab/InternVL2-1B", "microsoft/Phi-3-vision-128k-instruct", "fixie-ai/ultravox-v0_3", + # [LANGUAGE GENERATION - HYBRID ARCH] + "ai21labs/Jamba-tiny-dev", ] diff --git a/vllm/config.py b/vllm/config.py index c66ddbb47f22e..2a9f0ebae997d 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -27,8 +27,8 @@ ConfigFormat, get_config, get_hf_image_processor_config, get_hf_text_config, get_pooling_config, get_sentence_transformer_tokenizer_config, is_encoder_decoder, uses_mrope) -from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory, - print_warning_once, random_uuid, +from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless, + get_cpu_memory, print_warning_once, random_uuid, resolve_obj_by_qualname) if TYPE_CHECKING: @@ -284,6 +284,7 @@ def __init__( self._verify_tokenizer_mode() self.is_attention_free = self._init_attention_free() + self.is_hybrid = self._init_is_hybrid() self.has_inner_state = self._init_has_inner_state() if current_platform.is_neuron(): @@ -340,6 +341,10 @@ def _init_attention_free(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.is_attention_free_model(architectures) + def _init_is_hybrid(self) -> bool: + architectures = getattr(self.hf_config, "architectures", []) + return ModelRegistry.is_hybrid_model(architectures) + def _init_has_inner_state(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.model_has_inner_state(architectures) @@ -669,26 +674,51 @@ def get_num_attention_heads(self, num_heads = getattr(self.hf_text_config, "num_attention_heads", 0) return num_heads // parallel_config.tensor_parallel_size - def get_num_layers(self, parallel_config: "ParallelConfig") -> int: + def get_layers_start_end_indices( + self, parallel_config: "ParallelConfig") -> Tuple[int, int]: from vllm.distributed.utils import get_pp_indices total_num_hidden_layers = getattr(self.hf_text_config, "num_hidden_layers", 0) pp_rank = parallel_config.rank // parallel_config.tensor_parallel_size pp_size = parallel_config.pipeline_parallel_size start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size) - return end - start - - def get_num_attention_layers(self, - parallel_config: "ParallelConfig") -> int: - if self.is_attention_free: - return 0 + return start, end - num_layers = self.get_num_layers(parallel_config) + def get_num_layers(self, parallel_config: "ParallelConfig") -> int: + start, end = self.get_layers_start_end_indices(parallel_config) + return end - start - # Transformers supports layers_block_type @property - layers = getattr(self.hf_config, "layers_block_type", - ["attention"] * num_layers) - return len([t for t in layers if t == "attention"]) + def get_num_layers_by_block_type( + self, + parallel_config: "ParallelConfig", + block_type: LayerBlockType = LayerBlockType.attention, + ) -> int: + # This function relies on 'layers_block_type' in hf_config, + # for w/o this attribute, we will need to have workarounds like so + attn_block_type = block_type == LayerBlockType.attention + is_transformer = not self.is_hybrid and not self.is_attention_free + start, end = self.get_layers_start_end_indices(parallel_config) + + if is_transformer: + # Handle the basic case first + return end - start if attn_block_type else 0 + elif self.is_attention_free: + # Attention free + # Note that this code assumes there + # is only one type of attention-free block type. + return 0 if attn_block_type else end - start + else: + # Hybrid model + layers_block_type_value = getattr(self.hf_config, + "layers_block_type", None) + if layers_block_type_value is None: + raise ValueError("The model is an hybrid without a" + "layers_block_type in the hf_config," + "cannot determine the num of " + f"{block_type.value} layers") + + return sum(t == block_type.value + for t in layers_block_type_value[start:end]) def get_multimodal_config(self) -> "MultiModalConfig": """ diff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py index c3979eab905db..70b78fe64f2d8 100644 --- a/vllm/model_executor/models/interfaces.py +++ b/vllm/model_executor/models/interfaces.py @@ -363,6 +363,43 @@ def is_attention_free( return isinstance(model, IsAttentionFree) +@runtime_checkable +class IsHybrid(Protocol): + """The interface required for all models like Jamba that have both + attention and mamba blocks, indicates that + hf_config has 'layers_block_type'""" + + is_hybrid: ClassVar[Literal[True]] = True + """ + A flag that indicates this model has both mamba and attention blocks + , also indicates that the model's hf_config has + 'layers_block_type' """ + + +@runtime_checkable +class _IsHybridType(Protocol): + is_hybrid: ClassVar[Literal[True]] + + +@overload +def is_hybrid(model: object) -> TypeIs[IsHybrid]: + ... + + +@overload +def is_hybrid(model: Type[object]) -> TypeIs[Type[IsHybrid]]: + ... + + +def is_hybrid( + model: Union[Type[object], object] +) -> Union[TypeIs[Type[IsHybrid]], TypeIs[IsHybrid]]: + if isinstance(model, type): + return isinstance(model, _IsHybridType) + + return isinstance(model, IsHybrid) + + @runtime_checkable class SupportsCrossEncoding(Protocol): """The interface required for all models that support cross encoding.""" diff --git a/vllm/model_executor/models/jamba.py b/vllm/model_executor/models/jamba.py index 5d5e8ae1ee532..6bb4c13ab35df 100644 --- a/vllm/model_executor/models/jamba.py +++ b/vllm/model_executor/models/jamba.py @@ -9,6 +9,7 @@ from vllm.attention.layer import Attention from vllm.config import _BATCH_SIZES_TO_CAPTURE, CacheConfig, VllmConfig from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.distributed.parallel_state import get_pp_group from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (QKVParallelLinear, @@ -25,9 +26,12 @@ MambaCacheParams) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors +from vllm.utils import LayerBlockType -from .interfaces import HasInnerState, SupportsLoRA -from .utils import maybe_prefix +from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP +from .utils import (is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, make_layers, + maybe_prefix) KVCache = Tuple[torch.Tensor, torch.Tensor] @@ -281,16 +285,24 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): org_num_embeddings=config.vocab_size, ) - decoder_layers = [] - for i in range(config.num_hidden_layers): - layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]] - decoder_layers.append( - layer_class(config, - layer_idx=i, - cache_config=cache_config, - quant_config=quant_config, - prefix=f"{prefix}.layers.{i}")) - self.layers = nn.ModuleList(decoder_layers) + def get_layer(prefix: str): + layer_idx = int(prefix.rsplit(".", 1)[1]) + layer_class = ALL_DECODER_LAYER_TYPES[ + config.layers_block_type[layer_idx]] + return layer_class( + config, + layer_idx, + cache_config, + quant_config=quant_config, + prefix=prefix, + ) + + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers") + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory( + ["hidden_states", "residual"], config.hidden_size)) + self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) @@ -304,26 +316,34 @@ def forward( kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, mamba_cache_params: MambaCacheParams, + intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: - if inputs_embeds is not None: - hidden_states = inputs_embeds + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + residual = None else: - hidden_states = self.get_input_embeddings(input_ids) - residual = None - for i in range(len(self.layers)): + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] + + kv_cache_index = 0 + mamba_cache_index = 0 + for i in range(self.start_layer, self.end_layer): layer = self.layers[i] kv_cache = None layer_mamba_cache_params = None if isinstance(layer, JambaAttentionDecoderLayer): - kv_cache = kv_caches[(i - self.config.attn_layer_offset) // - self.config.attn_layer_period] + kv_cache = kv_caches[kv_cache_index] + kv_cache_index += 1 if isinstance(layer, JambaMambaDecoderLayer): - current_state_layer = i - (1 + - (i - self.config.attn_layer_offset) - // self.config.attn_layer_period) + current_state_layer = mamba_cache_index layer_mamba_cache_params = mamba_cache_params.at_layer_idx( current_state_layer) + mamba_cache_index += 1 hidden_states, residual = layer( positions=positions, @@ -332,11 +352,17 @@ def forward( attn_metadata=attn_metadata, residual=residual, mamba_cache_params=layer_mamba_cache_params) + if not get_pp_group().is_last_rank: + return IntermediateTensors({ + "hidden_states": hidden_states, + "residual": residual + }) hidden_states, _ = self.final_layernorm(hidden_states, residual) return hidden_states -class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA): +class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP, + IsHybrid): packed_modules_mapping = { "qkv_proj": [ "q_proj", @@ -368,6 +394,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() self.config = config + self.vllm_config = vllm_config + self.model_config = vllm_config.model_config self.scheduler_config = scheduler_config self.model = JambaModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) @@ -390,6 +418,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config.vocab_size) self.sampler = get_sampler() + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) @@ -406,10 +437,8 @@ def forward(self, self.scheduler_config.max_num_seqs) if self.scheduler_config else max(_BATCH_SIZES_TO_CAPTURE) + 2) - layers_type = self.config.layers_block_type - num_mamba_layers = sum( - [layer_type == "mamba" for layer_type in layers_type]) - + num_mamba_layers = self.model_config.get_num_layers_by_block_type( + self.vllm_config.parallel_config, LayerBlockType.mamba) self.mamba_cache = MambaCacheManager( self.lm_head.weight.dtype, num_mamba_layers, max_batch_size, *self._get_mamba_cache_shape()) @@ -423,7 +452,7 @@ def forward(self, state_indices_tensor) hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata, mamba_cache_params, - inputs_embeds) + intermediate_tensors, inputs_embeds) return hidden_states def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs): @@ -504,8 +533,12 @@ def load_weights(self, weights: Iterable[Tuple[str, 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 + # Skip layers on other devices. + if is_pp_missing_parameter(name, self): + continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) @@ -520,6 +553,8 @@ def load_weights(self, weights: Iterable[Tuple[str, if weight_name not in name: continue + if is_pp_missing_parameter(name, self): + continue name = name.replace(weight_name, param_name) param = params_dict[name] weight_loader = param.weight_loader @@ -533,6 +568,8 @@ def load_weights(self, weights: Iterable[Tuple[str, # 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", diff --git a/vllm/model_executor/models/mamba.py b/vllm/model_executor/models/mamba.py index 8bdcd2c5aad1f..1f5cd02711899 100644 --- a/vllm/model_executor/models/mamba.py +++ b/vllm/model_executor/models/mamba.py @@ -8,6 +8,7 @@ from vllm.attention.backends.abstract import AttentionMetadata from vllm.config import _BATCH_SIZES_TO_CAPTURE, CacheConfig, VllmConfig from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.distributed.parallel_state import get_pp_group from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer @@ -18,13 +19,16 @@ DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.interfaces import (HasInnerState, - IsAttentionFree) + IsAttentionFree, SupportsPP) from vllm.model_executor.models.mamba_cache import (MambaCacheManager, MambaCacheParams) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors +from vllm.utils import LayerBlockType -from .utils import maybe_prefix +from .utils import (is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, make_layers, + maybe_prefix) KVCache = Tuple[torch.Tensor, torch.Tensor] @@ -95,15 +99,17 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): org_num_embeddings=config.vocab_size, ) - decoder_layers = [] - for i in range(config.num_hidden_layers): - decoder_layers.append( - MambaDecoderLayer(config, - cache_config=cache_config, - quant_config=quant_config)) - self.layers = nn.ModuleList(decoder_layers) + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, + lambda prefix: MambaDecoderLayer( + config, cache_config=cache_config, quant_config=quant_config), + prefix=f"{prefix}.layers") + self.norm_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory( + ["hidden_states", "residual"], config.hidden_size)) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embeddings(input_ids) @@ -114,29 +120,40 @@ def forward( positions: torch.Tensor, attn_metadata: AttentionMetadata, mamba_cache_params: MambaCacheParams, + intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: - - if inputs_embeds is not None: - hidden_states = inputs_embeds + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + residual = None else: - hidden_states = self.get_input_embeddings(input_ids) - residual = None + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] - for i in range(len(self.layers)): + for i in range(self.start_layer, self.end_layer): layer = self.layers[i] hidden_states, residual = layer( positions=positions, hidden_states=hidden_states, attn_metadata=attn_metadata, residual=residual, - mamba_cache_params=mamba_cache_params.at_layer_idx(i)) + mamba_cache_params=mamba_cache_params.at_layer_idx( + i - self.start_layer)) + if not get_pp_group().is_last_rank: + return IntermediateTensors({ + "hidden_states": hidden_states, + "residual": residual + }) hidden_states, _ = self.norm_f(hidden_states, residual) return hidden_states -class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree): +class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config = vllm_config.model_config.hf_config @@ -148,7 +165,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() self.config = config + self.vllm_config = vllm_config self.scheduler_config = scheduler_config + self.model_config = vllm_config.model_config self.backbone = MambaModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "backbone")) self.unpadded_vocab_size = config.vocab_size @@ -174,6 +193,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config.vocab_size) self.sampler = get_sampler() + self.make_empty_intermediate_tensors = ( + self.backbone.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.backbone.get_input_embeddings(input_ids) @@ -189,9 +211,12 @@ def forward(self, max_batch_size = (VllmConfig.get_graph_batch_size( self.scheduler_config.max_num_seqs) if self.scheduler_config else max(_BATCH_SIZES_TO_CAPTURE) + 2) + + num_mamba_layers = self.model_config.get_num_layers_by_block_type( + self.vllm_config.parallel_config, LayerBlockType.mamba) self.mamba_cache = MambaCacheManager( - self.lm_head.weight.dtype, self.config.num_hidden_layers, - max_batch_size, *self._get_mamba_cache_shape()) + self.lm_head.weight.dtype, num_mamba_layers, max_batch_size, + *self._get_mamba_cache_shape()) ( mamba_cache_tensors, @@ -204,7 +229,8 @@ def forward(self, state_indices_tensor) hidden_states = self.backbone(input_ids, positions, attn_metadata, - mamba_cache_params, inputs_embeds) + mamba_cache_params, intermediate_tensors, + inputs_embeds) return hidden_states @@ -252,6 +278,8 @@ def load_weights(self, weights: Iterable[Tuple[str, # 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", diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index e69596aa915b5..4beea4641f5ab 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -21,7 +21,7 @@ from vllm.platforms import current_platform from .adapters import as_embedding_model -from .interfaces import (has_inner_state, is_attention_free, +from .interfaces import (has_inner_state, is_attention_free, is_hybrid, supports_cross_encoding, supports_multimodal, supports_pp) from .interfaces_base import is_pooling_model, is_text_generation_model @@ -218,6 +218,7 @@ class _ModelInfo: supports_pp: bool has_inner_state: bool is_attention_free: bool + is_hybrid: bool @staticmethod def from_model_cls(model: Type[nn.Module]) -> "_ModelInfo": @@ -239,6 +240,7 @@ def from_model_cls(model: Type[nn.Module]) -> "_ModelInfo": supports_pp=supports_pp(model), has_inner_state=has_inner_state(model), is_attention_free=is_attention_free(model), + is_hybrid=is_hybrid(model), ) @@ -484,6 +486,13 @@ def is_attention_free_model( model_cls, _ = self.inspect_model_cls(architectures) return model_cls.is_attention_free + def is_hybrid_model( + self, + architectures: Union[str, List[str]], + ) -> bool: + model_cls, _ = self.inspect_model_cls(architectures) + return model_cls.is_hybrid + ModelRegistry = _ModelRegistry({ model_arch: _LazyRegisteredModel( diff --git a/vllm/utils.py b/vllm/utils.py index 7cdb2cb320b05..1882264c19775 100644 --- a/vllm/utils.py +++ b/vllm/utils.py @@ -170,6 +170,11 @@ class Device(enum.Enum): CPU = enum.auto() +class LayerBlockType(enum.Enum): + attention = "attention" + mamba = "mamba" + + class Counter: def __init__(self, start: int = 0) -> None: diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index a3335fa838352..8d9976ded7c5e 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -15,8 +15,8 @@ from vllm.model_executor.model_loader import get_model from vllm.multimodal import MultiModalKwargs from vllm.sampling_params import SamplingType -from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, cdiv, - is_pin_memory_available) +from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, + LayerBlockType, cdiv, is_pin_memory_available) from vllm.v1.attention.backends.flash_attn import (FlashAttentionBackend, FlashAttentionMetadata) from vllm.v1.outputs import ModelRunnerOutput @@ -68,8 +68,8 @@ def __init__( 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_attn_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) self.num_kv_heads = model_config.get_num_kv_heads(parallel_config) self.head_size = model_config.get_head_size() self.hidden_size = model_config.get_hidden_size() diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py index d32848c3775ae..49e415ab72e0b 100644 --- a/vllm/v1/worker/gpu_worker.py +++ b/vllm/v1/worker/gpu_worker.py @@ -14,7 +14,7 @@ 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.utils import STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType, get_dtype_size from vllm.v1.core.scheduler import SchedulerOutput from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.worker.gpu_model_runner import GPUModelRunner @@ -260,8 +260,8 @@ def _get_cache_block_size( ) -> 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) + num_attention_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) key_cache_block = cache_config.block_size * num_heads * head_size value_cache_block = key_cache_block diff --git a/vllm/worker/cache_engine.py b/vllm/worker/cache_engine.py index ac3270d1c9909..7ccd4571b19df 100644 --- a/vllm/worker/cache_engine.py +++ b/vllm/worker/cache_engine.py @@ -6,8 +6,8 @@ from vllm.attention import get_attn_backend from vllm.config import CacheConfig, DeviceConfig, ModelConfig, ParallelConfig from vllm.logger import init_logger -from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size, - is_pin_memory_available) +from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType, + get_dtype_size, is_pin_memory_available) logger = init_logger(__name__) @@ -34,8 +34,8 @@ def __init__( self.head_size = model_config.get_head_size() # Models like Jamba, have mixed typed layers, E.g Mamba - self.num_attention_layers = model_config.get_num_attention_layers( - parallel_config) + self.num_attention_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) self.num_kv_heads = model_config.get_num_kv_heads(parallel_config) self.block_size = cache_config.block_size @@ -105,8 +105,8 @@ def get_cache_block_size( ) -> 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) + num_attention_layers = model_config.get_num_layers_by_block_type( + parallel_config, LayerBlockType.attention) key_cache_block = cache_config.block_size * num_heads * head_size value_cache_block = key_cache_block From e39400a4b60d28ff5c0a1a5194068c928adcaf98 Mon Sep 17 00:00:00 2001 From: Maximilien de Bayser Date: Wed, 11 Dec 2024 01:51:40 -0300 Subject: [PATCH 31/79] Fix streaming for granite tool call when <|tool_call|> is present (#11069) Signed-off-by: Max de Bayser --- vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py index 00917c866e496..dae481a2154a1 100644 --- a/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py +++ b/vllm/entrypoints/openai/tool_parsers/granite_tool_parser.py @@ -88,7 +88,11 @@ def extract_tool_calls_streaming( ) -> Union[DeltaMessage, None]: start_idx = consume_space(0, current_text) - if not current_text or current_text[start_idx] != '[': + if current_text[start_idx:].startswith(self.bot_token): + start_idx = consume_space(start_idx + len(self.bot_token), + current_text) + if not current_text or start_idx >= len(current_text)\ + or current_text[start_idx] != '[': return DeltaMessage(content=delta_text) # bit mask flags for partial JSON parsing. If the name hasn't been From 2e33fe419186c65a18da6668972d61d7bbc31564 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 11 Dec 2024 13:02:02 +0800 Subject: [PATCH 32/79] [CI/Build] Check transformers v4.47 (#10991) Signed-off-by: DarkLight1337 --- requirements-test.txt | 4 ++-- .../vision_language/mm_processor_kwargs/test_idefics3.py | 9 --------- .../models/embedding/vision_language/test_llava_next.py | 2 +- 3 files changed, 3 insertions(+), 12 deletions(-) diff --git a/requirements-test.txt b/requirements-test.txt index 38a064bca449a..8ceb705cdffd7 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -506,7 +506,7 @@ tiktoken==0.7.0 # mistral-common timm==1.0.11 # via -r requirements-test.in -tokenizers==0.20.3 +tokenizers==0.21.0 # via transformers torch==2.5.1 # via @@ -534,7 +534,7 @@ tqdm==4.66.6 # transformers tqdm-multiprocess==0.0.11 # via lm-eval -transformers==4.46.3 +transformers==4.47.0 # via # lm-eval # peft diff --git a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py index 31896bfd13e8c..c71a2d359043d 100644 --- a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py +++ b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_idefics3.py @@ -3,7 +3,6 @@ import pytest import torch -import transformers from transformers import AutoImageProcessor, AutoTokenizer from vllm.inputs import InputContext, token_inputs @@ -36,8 +35,6 @@ def get_max_idefics3_image_tokens(): return get_max_idefics3_image_tokens -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge", [None, 168, 336, 400, 2 * 336]) def test_input_mapper_override(model: str, image_assets: _ImageAssets, @@ -77,8 +74,6 @@ def test_input_mapper_override(model: str, image_assets: _ImageAssets, assert torch.all(hf_result["pixel_values"] == vllm_result["pixel_values"]) -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge, expected_max_tokens", [ (None, 2873), @@ -107,8 +102,6 @@ def test_max_tokens_override(get_max_idefics3_image_tokens, model: str, assert expected_max_tokens == actual_max_tokens -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge, toks_per_img, num_imgs", [ (168, 169, 1), @@ -143,8 +136,6 @@ def test_dummy_data_override(dummy_data_for_idefics3, model: str, assert img_tok_count == toks_per_img * num_imgs -@pytest.mark.skipif(transformers.__version__ < "4.46.0", - reason="Model introduced in HF >= 4.46.0") @pytest.mark.parametrize("model", models) @pytest.mark.parametrize("longest_edge,expected_toks_per_img,num_imgs", [ (336, 169 * (1**2 + 1), 1), diff --git a/tests/models/embedding/vision_language/test_llava_next.py b/tests/models/embedding/vision_language/test_llava_next.py index 329c6ba279f89..693abd7252d5e 100644 --- a/tests/models/embedding/vision_language/test_llava_next.py +++ b/tests/models/embedding/vision_language/test_llava_next.py @@ -86,7 +86,7 @@ def _run_test( ) -@pytest.mark.skipif(transformers.__version__.startswith("4.46"), +@pytest.mark.skipif(transformers.__version__ >= "4.46", reason="Model broken with changes in transformers 4.46") @pytest.mark.core_model @pytest.mark.parametrize("model", MODELS) From 3fb4b4f1634a896653acc12c72b8e5d6d87a8f82 Mon Sep 17 00:00:00 2001 From: "Kevin H. Luu" Date: Wed, 11 Dec 2024 00:39:53 -0800 Subject: [PATCH 33/79] [ci/build] Fix AMD CI dependencies (#11087) --- requirements-rocm.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements-rocm.txt b/requirements-rocm.txt index 121123611d2da..ccc9062341772 100644 --- a/requirements-rocm.txt +++ b/requirements-rocm.txt @@ -5,7 +5,8 @@ awscli boto3 botocore +datasets ray >= 2.10.0 peft pytest-asyncio -tensorizer>=2.9.0 \ No newline at end of file +tensorizer>=2.9.0 From 9974fca047bb332ec68377be4579ea515a300d69 Mon Sep 17 00:00:00 2001 From: "Kevin H. Luu" Date: Wed, 11 Dec 2024 01:01:53 -0800 Subject: [PATCH 34/79] [ci/build] Fix entrypoints test and pin outlines version (#11088) --- requirements-common.txt | 2 +- .../guided_decoding/outlines_logits_processors.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements-common.txt b/requirements-common.txt index c71fc458aca13..792cd58e80669 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -18,7 +18,7 @@ prometheus_client >= 0.18.0 prometheus-fastapi-instrumentator >= 7.0.0 tiktoken >= 0.6.0 # Required for DBRX tokenizer lm-format-enforcer >= 0.10.9, < 0.11 -outlines >= 0.1.8 +outlines == 0.1.9 xgrammar >= 0.1.6; platform_machine == "x86_64" typing_extensions >= 4.10 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317 diff --git a/vllm/model_executor/guided_decoding/outlines_logits_processors.py b/vllm/model_executor/guided_decoding/outlines_logits_processors.py index 1f0dbe024609d..b63fed1c8a8c3 100644 --- a/vllm/model_executor/guided_decoding/outlines_logits_processors.py +++ b/vllm/model_executor/guided_decoding/outlines_logits_processors.py @@ -25,7 +25,7 @@ from outlines import grammars from outlines.caching import cache from outlines.fsm.guide import CFGGuide, Generate, Guide, RegexGuide, Write -from outlines.fsm.json_schema import build_regex_from_schema +from outlines_core.fsm.json_schema import build_regex_from_schema from pydantic import BaseModel from transformers import PreTrainedTokenizerBase From 61b1d2f6aef8e29c6a0d795a9c6682d525f4d8cc Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Wed, 11 Dec 2024 04:26:36 -0500 Subject: [PATCH 35/79] [Core] v1: Use atexit to handle engine core client shutdown (#11076) Signed-off-by: Russell Bryant --- vllm/v1/engine/core_client.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py index ee89cece73141..4d96b323d1662 100644 --- a/vllm/v1/engine/core_client.py +++ b/vllm/v1/engine/core_client.py @@ -1,3 +1,4 @@ +import atexit import multiprocessing from typing import List, Union @@ -157,6 +158,7 @@ def __init__( should_shutdown=self.should_shutdown, **kwargs, ) + atexit.register(self.shutdown) def shutdown(self): # Send shutdown signal to background process. From 2e32f5d28db3cd79f6a421f640e083be1f9468b7 Mon Sep 17 00:00:00 2001 From: B-201 Date: Wed, 11 Dec 2024 17:27:07 +0800 Subject: [PATCH 36/79] [Bugfix] Fix Idefics3 fails during multi-image inference (#11080) Signed-off-by: B-201 --- vllm/model_executor/models/idefics3.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/vllm/model_executor/models/idefics3.py b/vllm/model_executor/models/idefics3.py index e5d2edbd81eb1..17e772e7faa32 100644 --- a/vllm/model_executor/models/idefics3.py +++ b/vllm/model_executor/models/idefics3.py @@ -60,7 +60,8 @@ class Idefics3ImagePixelInputs(TypedDict): type: Literal["pixel_values"] data: torch.Tensor """ - Shape: `(batch_size * num_images, num_channels, height, width)` + Shape: `(batch_size * num_images * num_patches, + num_channels, height, width)` """ pixel_attention_mask: Optional[torch.BoolTensor] @@ -520,13 +521,17 @@ def _parse_and_validate_image_input( raise ValueError("Incorrect type of pixel values. " f"Got type: {type(pixel_values)}") - return Idefics3ImagePixelInputs(type="pixel_values", - data=self._validate_pixel_values( - flatten_bn(pixel_values, - concat=True)), - pixel_attention_mask=flatten_bn( - pixel_attention_mask, - concat=True)) + if isinstance(pixel_values, list): + pixel_values = torch.cat(pixel_values, dim=1) + pixel_attention_mask = torch.cat(pixel_attention_mask, dim=1) + else: + pixel_values = flatten_bn(pixel_values) + pixel_attention_mask = flatten_bn(pixel_attention_mask) + + return Idefics3ImagePixelInputs( + type="pixel_values", + data=self._validate_pixel_values(pixel_values), + pixel_attention_mask=pixel_attention_mask) raise AssertionError("This line should be unreachable.") From 40766ca1b8b0ef92e220595bda96c4336b597e5b Mon Sep 17 00:00:00 2001 From: Rafael Vasquez Date: Wed, 11 Dec 2024 04:27:39 -0500 Subject: [PATCH 37/79] [Bugfix]: Clamp `-inf` logprob values in prompt_logprobs (#11073) Signed-off-by: Rafael Vasquez --- vllm/entrypoints/openai/serving_completion.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/vllm/entrypoints/openai/serving_completion.py b/vllm/entrypoints/openai/serving_completion.py index c54d5f07cf58c..ee97d35f2b087 100644 --- a/vllm/entrypoints/openai/serving_completion.py +++ b/vllm/entrypoints/openai/serving_completion.py @@ -392,6 +392,12 @@ def request_output_to_completion_response( prompt_token_ids = final_res.prompt_token_ids assert prompt_token_ids is not None prompt_logprobs = final_res.prompt_logprobs + if prompt_logprobs: + for logprob_dict in prompt_logprobs: + if logprob_dict: + for logprob_values in logprob_dict.values(): + if logprob_values.logprob == float('-inf'): + logprob_values.logprob = -9999.0 prompt_text = final_res.prompt token_ids: GenericSequence[int] From 8f10d5e3930f05c2057a831cd80ba24c52b8ceef Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 11 Dec 2024 17:28:00 +0800 Subject: [PATCH 38/79] [Misc] Split up pooling tasks (#10820) Signed-off-by: DarkLight1337 --- docs/source/index.rst | 2 + docs/source/models/generative_models.rst | 146 ++++++++++++++++ docs/source/models/pooling_models.rst | 99 +++++++++++ docs/source/models/supported_models.rst | 157 ++++++++++++------ docs/source/usage/compatibility_matrix.rst | 12 +- examples/offline_inference_embedding.py | 7 +- ...ine_inference_vision_language_embedding.py | 4 +- tests/compile/test_basic_correctness.py | 4 +- tests/core/test_scheduler_encoder_decoder.py | 2 +- .../openai/test_vision_embedding.py | 2 +- .../embedding/language/test_embedding.py | 2 +- .../models/embedding/language/test_scoring.py | 12 +- .../vision_language/test_dse_qwen2_vl.py | 2 +- .../vision_language/test_llava_next.py | 2 +- .../embedding/vision_language/test_phi3v.py | 2 +- tests/test_config.py | 17 +- vllm/config.py | 137 ++++++++++----- vllm/core/scheduler.py | 2 +- vllm/engine/arg_utils.py | 7 +- vllm/engine/llm_engine.py | 4 +- vllm/entrypoints/llm.py | 53 +++--- vllm/entrypoints/openai/api_server.py | 8 +- vllm/entrypoints/openai/run_batch.py | 4 +- vllm/model_executor/model_loader/utils.py | 2 +- vllm/v1/engine/core.py | 2 +- vllm/worker/cpu_worker.py | 2 +- vllm/worker/worker.py | 2 +- 27 files changed, 527 insertions(+), 168 deletions(-) create mode 100644 docs/source/models/generative_models.rst create mode 100644 docs/source/models/pooling_models.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index ebf1361976c5e..842013d6d49c4 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -94,6 +94,8 @@ Documentation :caption: Models models/supported_models + models/generative_models + models/pooling_models models/adding_model models/enabling_multimodal_inputs diff --git a/docs/source/models/generative_models.rst b/docs/source/models/generative_models.rst new file mode 100644 index 0000000000000..fb71185600863 --- /dev/null +++ b/docs/source/models/generative_models.rst @@ -0,0 +1,146 @@ +.. _generative_models: + +Generative Models +================= + +vLLM provides first-class support for generative models, which covers most of LLMs. + +In vLLM, generative models implement the :class:`~vllm.model_executor.models.VllmModelForTextGeneration` interface. +Based on the final hidden states of the input, these models output log probabilities of the tokens to generate, +which are then passed through :class:`~vllm.model_executor.layers.Sampler` to obtain the final text. + +Offline Inference +----------------- + +The :class:`~vllm.LLM` class provides various methods for offline inference. +See :ref:`Engine Arguments ` for a list of options when initializing the model. + +For generative models, the only supported :code:`task` option is :code:`"generate"`. +Usually, this is automatically inferred so you don't have to specify it. + +``LLM.generate`` +^^^^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.generate` method is available to all generative models in vLLM. +It is similar to `its counterpart in HF Transformers `__, +except that tokenization and detokenization are also performed automatically. + +.. code-block:: python + + llm = LLM(model="facebook/opt-125m") + outputs = llm.generate("Hello, my name is") + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +You can optionally control the language generation by passing :class:`~vllm.SamplingParams`. +For example, you can use greedy sampling by setting :code:`temperature=0`: + +.. code-block:: python + + llm = LLM(model="facebook/opt-125m") + params = SamplingParams(temperature=0) + outputs = llm.generate("Hello, my name is", params) + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +A code example can be found in `examples/offline_inference.py `_. + +``LLM.beam_search`` +^^^^^^^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.beam_search` method implements `beam search `__ on top of :class:`~vllm.LLM.generate`. +For example, to search using 5 beams and output at most 50 tokens: + +.. code-block:: python + + llm = LLM(model="facebook/opt-125m") + params = BeamSearchParams(beam_width=5, max_tokens=50) + outputs = llm.generate("Hello, my name is", params) + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +``LLM.chat`` +^^^^^^^^^^^^ + +The :class:`~vllm.LLM.chat` method implements chat functionality on top of :class:`~vllm.LLM.generate`. +In particular, it accepts input similar to `OpenAI Chat Completions API `__ +and automatically applies the model's `chat template `__ to format the prompt. + +.. important:: + + In general, only instruction-tuned models have a chat template. + Base models may perform poorly as they are not trained to respond to the chat conversation. + +.. code-block:: python + + llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") + conversation = [ + { + "role": "system", + "content": "You are a helpful assistant" + }, + { + "role": "user", + "content": "Hello" + }, + { + "role": "assistant", + "content": "Hello! How can I assist you today?" + }, + { + "role": "user", + "content": "Write an essay about the importance of higher education.", + }, + ] + outputs = llm.chat(conversation) + + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + +A code example can be found in `examples/offline_inference_chat.py `_. + +If the model doesn't have a chat template or you want to specify another one, +you can explicitly pass a chat template: + +.. code-block:: python + + from vllm.entrypoints.chat_utils import load_chat_template + + # You can find a list of existing chat templates under `examples/` + custom_template = load_chat_template(chat_template="") + print("Loaded chat template:", custom_template) + + outputs = llm.chat(conversation, chat_template=custom_template) + +Online Inference +---------------- + +Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference. +Please click on the above link for more details on how to launch the server. + +Completions API +^^^^^^^^^^^^^^^ + +Our Completions API is similar to ``LLM.generate`` but only accepts text. +It is compatible with `OpenAI Completions API `__ +so that you can use OpenAI client to interact with it. +A code example can be found in `examples/openai_completion_client.py `_. + +Chat API +^^^^^^^^ + +Our Chat API is similar to ``LLM.chat``, accepting both text and :ref:`multi-modal inputs `. +It is compatible with `OpenAI Chat Completions API `__ +so that you can use OpenAI client to interact with it. +A code example can be found in `examples/openai_chat_completion_client.py `_. diff --git a/docs/source/models/pooling_models.rst b/docs/source/models/pooling_models.rst new file mode 100644 index 0000000000000..7fa66274c3c5a --- /dev/null +++ b/docs/source/models/pooling_models.rst @@ -0,0 +1,99 @@ +.. _pooling_models: + +Pooling Models +============== + +vLLM also supports pooling models, including embedding, reranking and reward models. + +In vLLM, pooling models implement the :class:`~vllm.model_executor.models.VllmModelForPooling` interface. +These models use a :class:`~vllm.model_executor.layers.Pooler` to aggregate the final hidden states of the input +before returning them. + +.. note:: + + We currently support pooling models primarily as a matter of convenience. + As shown in the :ref:`Compatibility Matrix `, most vLLM features are not applicable to + pooling models as they only work on the generation or decode stage, so performance may not improve as much. + +Offline Inference +----------------- + +The :class:`~vllm.LLM` class provides various methods for offline inference. +See :ref:`Engine Arguments ` for a list of options when initializing the model. + +For pooling models, we support the following :code:`task` options: + +- Embedding (:code:`"embed"` / :code:`"embedding"`) +- Classification (:code:`"classify"`) +- Sentence Pair Scoring (:code:`"score"`) +- Reward Modeling (:code:`"reward"`) + +The selected task determines the default :class:`~vllm.model_executor.layers.Pooler` that is used: + +- Embedding: Extract only the hidden states corresponding to the last token, and apply normalization. +- Classification: Extract only the hidden states corresponding to the last token, and apply softmax. +- Sentence Pair Scoring: Extract only the hidden states corresponding to the last token, and apply softmax. +- Reward Modeling: Extract all of the hidden states and return them directly. + +When loading `Sentence Transformers `__ models, +we attempt to override the default pooler based on its Sentence Transformers configuration file (:code:`modules.json`). + +You can customize the model's pooling method via the :code:`override_pooler_config` option, +which takes priority over both the model's and Sentence Transformers's defaults. + +``LLM.encode`` +^^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.encode` method is available to all pooling models in vLLM. +It returns the aggregated hidden states directly. + +.. code-block:: python + + llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed") + outputs = llm.encode("Hello, my name is") + + outputs = model.encode(prompts) + for output in outputs: + embeddings = output.outputs.embedding + print(f"Prompt: {prompt!r}, Embeddings (size={len(embeddings)}: {embeddings!r}") + +A code example can be found in `examples/offline_inference_embedding.py `_. + +``LLM.score`` +^^^^^^^^^^^^^ + +The :class:`~vllm.LLM.score` method outputs similarity scores between sentence pairs. +It is primarily designed for `cross-encoder models `__. +These types of models serve as rerankers between candidate query-document pairs in RAG systems. + +.. note:: + + vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG. + To handle RAG at a higher level, you should use integration frameworks such as `LangChain `_. + +You can use `these tests `_ as reference. + +Online Inference +---------------- + +Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference. +Please click on the above link for more details on how to launch the server. + +Embeddings API +^^^^^^^^^^^^^^ + +Our Embeddings API is similar to ``LLM.encode``, accepting both text and :ref:`multi-modal inputs `. + +The text-only API is compatible with `OpenAI Embeddings API `__ +so that you can use OpenAI client to interact with it. +A code example can be found in `examples/openai_embedding_client.py `_. + +The multi-modal API is an extension of the `OpenAI Embeddings API `__ +that incorporates `OpenAI Chat Completions API `__, +so it is not part of the OpenAI standard. Please see :ref:`this page ` for more details on how to use it. + +Score API +^^^^^^^^^ + +Our Score API is similar to ``LLM.score``. +Please see `this page <../serving/openai_compatible_server.html#score-api-for-cross-encoder-models>`__ for more details on how to use it. diff --git a/docs/source/models/supported_models.rst b/docs/source/models/supported_models.rst index 6540e023c1ab0..b9957cf9563b1 100644 --- a/docs/source/models/supported_models.rst +++ b/docs/source/models/supported_models.rst @@ -3,11 +3,21 @@ Supported Models ================ -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. +vLLM supports generative and pooling models across various tasks. +If a model supports more than one task, you can set the task via the :code:`--task` argument. + +For each task, we list the model architectures that have been implemented in 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. +Loading a Model +^^^^^^^^^^^^^^^ + +HuggingFace Hub ++++++++++++++++ + +By default, vLLM loads models from `HuggingFace (HF) Hub `_. + +To determine whether a given model is supported, you can check the :code:`config.json` file inside the HF repository. If the :code:`"architectures"` field contains a model architecture listed below, then it should be supported in theory. .. tip:: @@ -17,38 +27,57 @@ If the :code:`"architectures"` field contains a model architecture listed below, from vllm import LLM - llm = LLM(model=...) # Name or path of your model + # For generative models (task=generate) only + llm = LLM(model=..., task="generate") # 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. + # For pooling models (task={embed,classify,reward}) only + llm = LLM(model=..., task="embed") # Name or path of your model + output = llm.encode("Hello, my name is") + print(output) + + If vLLM successfully returns text (for generative models) or hidden states (for pooling models), 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: +ModelScope +++++++++++ - .. code-block:: shell +To use models from `ModelScope `_ instead of HuggingFace Hub, set an environment variable: - $ export VLLM_USE_MODELSCOPE=True +.. code-block:: shell - And use with :code:`trust_remote_code=True`. + $ export VLLM_USE_MODELSCOPE=True - .. code-block:: python +And use with :code:`trust_remote_code=True`. - from vllm import LLM +.. code-block:: python - llm = LLM(model=..., revision=..., trust_remote_code=True) # Name or path of your model - output = llm.generate("Hello, my name is") - print(output) + from vllm import LLM + + llm = LLM(model=..., revision=..., task=..., trust_remote_code=True) -Text-only Language Models -^^^^^^^^^^^^^^^^^^^^^^^^^ + # For generative models (task=generate) only + output = llm.generate("Hello, my name is") + print(output) -Text Generation ---------------- + # For pooling models (task={embed,classify,reward}) only + output = llm.encode("Hello, my name is") + print(output) + +List of Text-only Language Models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Generative Models ++++++++++++++++++ + +See :ref:`this page ` for more information on how to use generative models. + +Text Generation (``--task generate``) +------------------------------------- .. list-table:: :widths: 25 25 50 5 5 @@ -328,8 +357,24 @@ Text Generation .. note:: Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096. -Text Embedding --------------- +Pooling Models +++++++++++++++ + +See :ref:`this page ` for more information on how to use pooling models. + +.. important:: + Since some model architectures support both generative and pooling tasks, + you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode. + +Text Embedding (``--task embed``) +--------------------------------- + +Any text generation model can be converted into an embedding model by passing :code:`--task embed`. + +.. note:: + To get the best results, you should use pooling models that are specifically trained as such. + +The following table lists those that are tested in vLLM. .. list-table:: :widths: 25 25 50 5 5 @@ -371,13 +416,6 @@ 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. - -.. tip:: - You can override the model's pooling method by passing :code:`--override-pooler-config`. - .. note:: :code:`ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config. You should manually set mean pooling by passing :code:`--override-pooler-config '{"pooling_type": "MEAN"}'`. @@ -389,8 +427,8 @@ Text Embedding On the other hand, its 1.5B variant (:code:`Alibaba-NLP/gte-Qwen2-1.5B-instruct`) uses causal attention despite being described otherwise on its model card. -Reward Modeling ---------------- +Reward Modeling (``--task reward``) +----------------------------------- .. list-table:: :widths: 25 25 50 5 5 @@ -416,11 +454,8 @@ Reward Modeling For process-supervised reward models such as :code:`peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly, e.g.: :code:`--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`. -.. note:: - As an interim measure, these models are supported in both offline and online inference via Embeddings API. - -Classification ---------------- +Classification (``--task classify``) +------------------------------------ .. list-table:: :widths: 25 25 50 5 5 @@ -437,11 +472,8 @@ Classification - ✅︎ - ✅︎ -.. note:: - As an interim measure, these models are supported in both offline and online inference via Embeddings API. - -Sentence Pair Scoring ---------------------- +Sentence Pair Scoring (``--task score``) +---------------------------------------- .. list-table:: :widths: 25 25 50 5 5 @@ -468,13 +500,10 @@ Sentence Pair Scoring - - -.. note:: - These models are supported in both offline and online inference via Score API. - .. _supported_mm_models: -Multimodal Language Models -^^^^^^^^^^^^^^^^^^^^^^^^^^ +List of Multimodal Language Models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The following modalities are supported depending on the model: @@ -491,8 +520,15 @@ 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. -Text Generation ---------------- +See :ref:`this page ` on how to pass multi-modal inputs to the model. + +Generative Models ++++++++++++++++++ + +See :ref:`this page ` for more information on how to use generative models. + +Text Generation (``--task generate``) +------------------------------------- .. list-table:: :widths: 25 25 15 20 5 5 5 @@ -696,8 +732,24 @@ Text Generation The official :code:`openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now. For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630 -Multimodal Embedding --------------------- +Pooling Models +++++++++++++++ + +See :ref:`this page ` for more information on how to use pooling models. + +.. important:: + Since some model architectures support both generative and pooling tasks, + you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode. + +Text Embedding (``--task embed``) +--------------------------------- + +Any text generation model can be converted into an embedding model by passing :code:`--task embed`. + +.. note:: + To get the best results, you should use pooling models that are specifically trained as such. + +The following table lists those that are tested in vLLM. .. list-table:: :widths: 25 25 15 25 5 5 @@ -728,12 +780,7 @@ 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. - -.. tip:: - You can override the model's pooling method by passing :code:`--override-pooler-config`. +---- Model Support Policy ===================== diff --git a/docs/source/usage/compatibility_matrix.rst b/docs/source/usage/compatibility_matrix.rst index a93632ff36fb8..04dd72b1e3527 100644 --- a/docs/source/usage/compatibility_matrix.rst +++ b/docs/source/usage/compatibility_matrix.rst @@ -39,13 +39,13 @@ Feature x Feature - :abbr:`prmpt adptr (Prompt Adapter)` - :ref:`SD ` - CUDA graph - - :abbr:`emd (Embedding Models)` + - :abbr:`pooling (Pooling Models)` - :abbr:`enc-dec (Encoder-Decoder Models)` - :abbr:`logP (Logprobs)` - :abbr:`prmpt logP (Prompt Logprobs)` - :abbr:`async output (Async Output Processing)` - multi-step - - :abbr:`mm (Multimodal)` + - :abbr:`mm (Multimodal Inputs)` - best-of - beam-search - :abbr:`guided dec (Guided Decoding)` @@ -151,7 +151,7 @@ Feature x Feature - - - - * - :abbr:`emd (Embedding Models)` + * - :abbr:`pooling (Pooling Models)` - ✗ - ✗ - ✗ @@ -253,7 +253,7 @@ Feature x Feature - - - - * - :abbr:`mm (Multimodal)` + * - :abbr:`mm (Multimodal Inputs)` - ✅ - `✗ `__ - `✗ `__ @@ -386,7 +386,7 @@ Feature x Hardware - ✅ - ✗ - ✅ - * - :abbr:`emd (Embedding Models)` + * - :abbr:`pooling (Pooling Models)` - ✅ - ✅ - ✅ @@ -402,7 +402,7 @@ Feature x Hardware - ✅ - ✅ - ✗ - * - :abbr:`mm (Multimodal)` + * - :abbr:`mm (Multimodal Inputs)` - ✅ - ✅ - ✅ diff --git a/examples/offline_inference_embedding.py b/examples/offline_inference_embedding.py index ae158eef2ca4c..17f6d992073d7 100644 --- a/examples/offline_inference_embedding.py +++ b/examples/offline_inference_embedding.py @@ -9,7 +9,12 @@ ] # Create an LLM. -model = LLM(model="intfloat/e5-mistral-7b-instruct", enforce_eager=True) +model = LLM( + model="intfloat/e5-mistral-7b-instruct", + task="embed", # You should pass task="embed" for embedding models + enforce_eager=True, +) + # Generate embedding. The output is a list of PoolingRequestOutputs. outputs = model.encode(prompts) # Print the outputs. diff --git a/examples/offline_inference_vision_language_embedding.py b/examples/offline_inference_vision_language_embedding.py index e1732d045f949..bf466109f0981 100644 --- a/examples/offline_inference_vision_language_embedding.py +++ b/examples/offline_inference_vision_language_embedding.py @@ -59,7 +59,7 @@ def run_e5_v(query: Query): llm = LLM( model="royokong/e5-v", - task="embedding", + task="embed", max_model_len=4096, ) @@ -88,7 +88,7 @@ def run_vlm2vec(query: Query): llm = LLM( model="TIGER-Lab/VLM2Vec-Full", - task="embedding", + task="embed", trust_remote_code=True, mm_processor_kwargs={"num_crops": 4}, ) diff --git a/tests/compile/test_basic_correctness.py b/tests/compile/test_basic_correctness.py index 99781c55b672e..87d5aefea6cb4 100644 --- a/tests/compile/test_basic_correctness.py +++ b/tests/compile/test_basic_correctness.py @@ -55,7 +55,7 @@ class TestSetting: # embedding model TestSetting( model="BAAI/bge-multilingual-gemma2", - model_args=["--task", "embedding"], + model_args=["--task", "embed"], pp_size=1, tp_size=1, attn_backend="FLASHINFER", @@ -65,7 +65,7 @@ class TestSetting: # encoder-based embedding model (BERT) TestSetting( model="BAAI/bge-base-en-v1.5", - model_args=["--task", "embedding"], + model_args=["--task", "embed"], pp_size=1, tp_size=1, attn_backend="XFORMERS", diff --git a/tests/core/test_scheduler_encoder_decoder.py b/tests/core/test_scheduler_encoder_decoder.py index 7cd0416d321ef..16bea54936bc8 100644 --- a/tests/core/test_scheduler_encoder_decoder.py +++ b/tests/core/test_scheduler_encoder_decoder.py @@ -37,7 +37,7 @@ def test_scheduler_schedule_simple_encoder_decoder(): num_seq_group = 4 max_model_len = 16 scheduler_config = SchedulerConfig( - task="generate", + "generate", max_num_batched_tokens=64, max_num_seqs=num_seq_group, max_model_len=max_model_len, diff --git a/tests/entrypoints/openai/test_vision_embedding.py b/tests/entrypoints/openai/test_vision_embedding.py index 425f2a10ec855..43c63daacb17f 100644 --- a/tests/entrypoints/openai/test_vision_embedding.py +++ b/tests/entrypoints/openai/test_vision_embedding.py @@ -27,7 +27,7 @@ def server(): args = [ "--task", - "embedding", + "embed", "--dtype", "bfloat16", "--max-model-len", diff --git a/tests/models/embedding/language/test_embedding.py b/tests/models/embedding/language/test_embedding.py index 5ef8540265d14..f458ef5ef556d 100644 --- a/tests/models/embedding/language/test_embedding.py +++ b/tests/models/embedding/language/test_embedding.py @@ -54,7 +54,7 @@ def test_models( hf_outputs = hf_model.encode(example_prompts) with vllm_runner(model, - task="embedding", + task="embed", dtype=dtype, max_model_len=None, **vllm_extra_kwargs) as vllm_model: diff --git a/tests/models/embedding/language/test_scoring.py b/tests/models/embedding/language/test_scoring.py index 30fa5ea7b36c0..0c3115d195fc1 100644 --- a/tests/models/embedding/language/test_scoring.py +++ b/tests/models/embedding/language/test_scoring.py @@ -35,9 +35,7 @@ def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str): with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model: hf_outputs = hf_model.predict([text_pair]).tolist() - with vllm_runner(model_name, - task="embedding", - dtype=dtype, + with vllm_runner(model_name, task="score", dtype=dtype, max_model_len=None) as vllm_model: vllm_outputs = vllm_model.score(text_pair[0], text_pair[1]) @@ -58,9 +56,7 @@ def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str): with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model: hf_outputs = hf_model.predict(text_pairs).tolist() - with vllm_runner(model_name, - task="embedding", - dtype=dtype, + with vllm_runner(model_name, task="score", dtype=dtype, max_model_len=None) as vllm_model: vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2) @@ -82,9 +78,7 @@ def test_llm_N_to_N(vllm_runner, hf_runner, model_name, dtype: str): with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model: hf_outputs = hf_model.predict(text_pairs).tolist() - with vllm_runner(model_name, - task="embedding", - dtype=dtype, + with vllm_runner(model_name, task="score", dtype=dtype, max_model_len=None) as vllm_model: vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2) diff --git a/tests/models/embedding/vision_language/test_dse_qwen2_vl.py b/tests/models/embedding/vision_language/test_dse_qwen2_vl.py index 3dd8cb729f8a6..2641987b25a3a 100644 --- a/tests/models/embedding/vision_language/test_dse_qwen2_vl.py +++ b/tests/models/embedding/vision_language/test_dse_qwen2_vl.py @@ -93,7 +93,7 @@ def _run_test( # 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", + task="embed", dtype=dtype, enforce_eager=True, max_model_len=8192) as vllm_model: diff --git a/tests/models/embedding/vision_language/test_llava_next.py b/tests/models/embedding/vision_language/test_llava_next.py index 693abd7252d5e..f4cd8b81a0d7d 100644 --- a/tests/models/embedding/vision_language/test_llava_next.py +++ b/tests/models/embedding/vision_language/test_llava_next.py @@ -47,7 +47,7 @@ def _run_test( # 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", + task="embed", dtype=dtype, max_model_len=4096, enforce_eager=True) as vllm_model: diff --git a/tests/models/embedding/vision_language/test_phi3v.py b/tests/models/embedding/vision_language/test_phi3v.py index 6145aff1a5ea2..9374c23dd6ffe 100644 --- a/tests/models/embedding/vision_language/test_phi3v.py +++ b/tests/models/embedding/vision_language/test_phi3v.py @@ -39,7 +39,7 @@ def _run_test( # 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, + with vllm_runner(model, task="embed", dtype=dtype, enforce_eager=True) as vllm_model: vllm_outputs = vllm_model.encode(input_texts, images=input_images) diff --git a/tests/test_config.py b/tests/test_config.py index 45b0b938af215..4518adfc31bfc 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -7,11 +7,17 @@ from vllm.platforms import current_platform -@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): +@pytest.mark.parametrize( + ("model_id", "expected_runner_type", "expected_task"), + [ + ("facebook/opt-125m", "generate", "generate"), + ("intfloat/e5-mistral-7b-instruct", "pooling", "embed"), + ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify"), + ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "score"), + ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "reward"), + ], +) +def test_auto_task(model_id, expected_runner_type, expected_task): config = ModelConfig( model_id, task="auto", @@ -22,6 +28,7 @@ def test_auto_task(model_id, expected_task): dtype="float16", ) + assert config.runner_type == expected_runner_type assert config.task == expected_task diff --git a/vllm/config.py b/vllm/config.py index 2a9f0ebae997d..2d9a76fe7ddb1 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -45,13 +45,27 @@ logger = init_logger(__name__) -_EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 +_POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120 -TaskOption = Literal["auto", "generate", "embedding"] +TaskOption = Literal["auto", "generate", "embedding", "embed", "classify", + "score", "reward"] -# "draft" is only used internally for speculative decoding -_Task = Literal["generate", "embedding", "draft"] +_ResolvedTask = Literal["generate", "embed", "classify", "score", "reward", + "draft"] + +RunnerType = Literal["generate", "pooling", "draft"] + +_RUNNER_TASKS: Dict[RunnerType, List[_ResolvedTask]] = { + "generate": ["generate"], + "pooling": ["embed", "classify", "score", "reward"], + "draft": ["draft"], +} + +_TASK_RUNNER: Dict[_ResolvedTask, RunnerType] = { + task: runner + for runner, tasks in _RUNNER_TASKS.items() for task in tasks +} HfOverrides = Union[Dict[str, Any], Callable[[PretrainedConfig], PretrainedConfig]] @@ -144,7 +158,7 @@ class ModelConfig: def __init__( self, model: str, - task: Union[TaskOption, _Task], + task: Union[TaskOption, Literal["draft"]], tokenizer: str, tokenizer_mode: str, trust_remote_code: bool, @@ -295,6 +309,7 @@ def __init__( supported_tasks, task = self._resolve_task(task, self.hf_config) self.supported_tasks = supported_tasks self.task: Final = task + self.pooler_config = self._init_pooler_config(override_pooler_config) self._verify_quantization() @@ -323,7 +338,7 @@ def _init_pooler_config( override_pooler_config: Optional["PoolerConfig"], ) -> Optional["PoolerConfig"]: - if self.task == "embedding": + if self.runner_type == "pooling": user_config = override_pooler_config or PoolerConfig() base_config = get_pooling_config(self.model, self.revision) @@ -357,60 +372,90 @@ def _verify_tokenizer_mode(self) -> None: "either 'auto', 'slow' or 'mistral'.") self.tokenizer_mode = tokenizer_mode + def _get_preferred_task( + self, + architectures: List[str], + supported_tasks: Set[_ResolvedTask], + ) -> Optional[_ResolvedTask]: + model_id = self.model + if get_pooling_config(model_id, self.revision): + return "embed" + if ModelRegistry.is_cross_encoder_model(architectures): + return "score" + + suffix_to_preferred_task: List[Tuple[str, _ResolvedTask]] = [ + # Other models follow this pattern + ("ForCausalLM", "generate"), + ("ForConditionalGeneration", "generate"), + ("ForSequenceClassification", "classify"), + ("ChatModel", "generate"), + ("LMHeadModel", "generate"), + ("EmbeddingModel", "embed"), + ("RewardModel", "reward"), + ] + _, arch = ModelRegistry.inspect_model_cls(architectures) + + for suffix, pref_task in suffix_to_preferred_task: + if arch.endswith(suffix) and pref_task in supported_tasks: + return pref_task + + return None + def _resolve_task( self, - task_option: Union[TaskOption, _Task], + task_option: Union[TaskOption, Literal["draft"]], hf_config: PretrainedConfig, - ) -> Tuple[Set[_Task], _Task]: + ) -> Tuple[Set[_ResolvedTask], _ResolvedTask]: if task_option == "draft": return {"draft"}, "draft" architectures = getattr(hf_config, "architectures", []) - task_support: Dict[_Task, bool] = { + runner_support: Dict[RunnerType, 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_pooling_model(architectures), + "pooling": ModelRegistry.is_pooling_model(architectures), } - supported_tasks_lst: List[_Task] = [ - task for task, is_supported in task_support.items() if is_supported + supported_runner_types_lst: List[RunnerType] = [ + runner_type + for runner_type, is_supported in runner_support.items() + if is_supported + ] + + supported_tasks_lst: List[_ResolvedTask] = [ + task for runner_type in supported_runner_types_lst + for task in _RUNNER_TASKS[runner_type] ] supported_tasks = set(supported_tasks_lst) if task_option == "auto": selected_task = next(iter(supported_tasks_lst)) - if len(supported_tasks) > 1: - suffix_to_preferred_task: List[Tuple[str, _Task]] = [ - # Hardcode the models that are exceptions - ("AquilaModel", "generate"), - ("ChatGLMModel", "generate"), - # Other models follow this pattern - ("ForCausalLM", "generate"), - ("ForConditionalGeneration", "generate"), - ("ChatModel", "generate"), - ("LMHeadModel", "generate"), - ("EmbeddingModel", "embedding"), - ("RewardModel", "embedding"), - ("ForSequenceClassification", "embedding"), - ] - info, arch = ModelRegistry.inspect_model_cls(architectures) - - for suffix, pref_task in suffix_to_preferred_task: - if arch.endswith(suffix) and pref_task in supported_tasks: - selected_task = pref_task - break - else: - if (arch.endswith("Model") - and info.architecture.endswith("ForCausalLM") - and "embedding" in supported_tasks): - selected_task = "embedding" + if len(supported_tasks_lst) > 1: + preferred_task = self._get_preferred_task( + architectures, supported_tasks) + if preferred_task is not None: + selected_task = preferred_task logger.info( "This model supports multiple tasks: %s. " "Defaulting to '%s'.", supported_tasks, selected_task) else: + # Aliases + if task_option == "embedding": + preferred_task = self._get_preferred_task( + architectures, supported_tasks) + if preferred_task != "embed": + msg = ("The 'embedding' task will be restricted to " + "embedding models in a future release. Please " + "pass `--task classify`, `--task score`, or " + "`--task reward` explicitly for other pooling " + "models.") + warnings.warn(msg, DeprecationWarning, stacklevel=2) + + task_option = preferred_task or "embed" + if task_option not in supported_tasks: msg = ( f"This model does not support the '{task_option}' task. " @@ -533,7 +578,7 @@ 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.task == "embedding": + if self.runner_type == "pooling": self.use_async_output_proc = False # Reminder: Please update docs/source/usage/compatibility_matrix.rst @@ -750,6 +795,14 @@ def is_cross_encoder(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.is_cross_encoder_model(architectures) + @property + def supported_runner_types(self) -> Set[RunnerType]: + return {_TASK_RUNNER[task] for task in self.supported_tasks} + + @property + def runner_type(self) -> RunnerType: + return _TASK_RUNNER[self.task] + class CacheConfig: """Configuration for the KV cache. @@ -1096,7 +1149,7 @@ def _verify_args(self) -> None: class SchedulerConfig: """Scheduler configuration.""" - task: str = "generate" # The task to use the model for. + runner_type: str = "generate" # The runner type to launch for the model. # Maximum number of tokens to be processed in a single iteration. max_num_batched_tokens: int = field(default=None) # type: ignore @@ -1164,11 +1217,11 @@ def __post_init__(self) -> None: # for higher throughput. self.max_num_batched_tokens = max(self.max_model_len, 2048) - if self.task == "embedding": - # For embedding, choose specific value for higher throughput + if self.runner_type == "pooling": + # Choose specific value for higher throughput self.max_num_batched_tokens = max( self.max_num_batched_tokens, - _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS, + _POOLING_MODEL_MAX_NUM_BATCHED_TOKENS, ) if self.is_multimodal_model: # The value needs to be at least the number of multimodal tokens diff --git a/vllm/core/scheduler.py b/vllm/core/scheduler.py index 94c62743883ec..c3bc6becf0995 100644 --- a/vllm/core/scheduler.py +++ b/vllm/core/scheduler.py @@ -337,7 +337,7 @@ def __init__( self.lora_config = lora_config version = "selfattn" - if (self.scheduler_config.task == "embedding" + if (self.scheduler_config.runner_type == "pooling" or self.cache_config.is_attention_free): version = "placeholder" diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 7b9adc401abcf..d485c2a9e7208 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -1066,7 +1066,7 @@ def create_engine_config(self, if (is_gpu and not use_sliding_window and not use_spec_decode and not self.enable_lora and not self.enable_prompt_adapter - and model_config.task != "embedding"): + and model_config.runner_type != "pooling"): self.enable_chunked_prefill = True logger.warning( "Chunked prefill is enabled by default for models with " @@ -1083,7 +1083,8 @@ def create_engine_config(self, "errors during the initial memory profiling phase, or result " "in low performance due to small KV cache space. Consider " "setting --max-model-len to a smaller value.", max_model_len) - elif self.enable_chunked_prefill and model_config.task == "embedding": + elif (self.enable_chunked_prefill + and model_config.runner_type == "pooling"): msg = "Chunked prefill is not supported for embedding models" raise ValueError(msg) @@ -1144,7 +1145,7 @@ def create_engine_config(self, " please file an issue with detailed information.") scheduler_config = SchedulerConfig( - task=model_config.task, + runner_type=model_config.runner_type, max_num_batched_tokens=self.max_num_batched_tokens, max_num_seqs=self.max_num_seqs, max_model_len=model_config.max_model_len, diff --git a/vllm/engine/llm_engine.py b/vllm/engine/llm_engine.py index 6eca304b45f07..9be30c635cb2c 100644 --- a/vllm/engine/llm_engine.py +++ b/vllm/engine/llm_engine.py @@ -288,7 +288,7 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: self.model_executor = executor_class(vllm_config=vllm_config, ) - if self.model_config.task != "embedding": + if self.model_config.runner_type != "pooling": self._initialize_kv_caches() # If usage stat is enabled, collect relevant info. @@ -1123,7 +1123,7 @@ def _process_model_outputs(self, seq_group.metrics.model_execute_time = ( o.model_execute_time) - if self.model_config.task == "embedding": + if self.model_config.runner_type == "pooling": self._process_sequence_group_outputs(seq_group, output) else: self.output_processor.process_prompt_logprob(seq_group, output) diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index 2a02187223a33..0bec978c4869c 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -381,19 +381,20 @@ def generate( considered legacy and may be deprecated in the future. You should instead pass them via the ``inputs`` parameter. """ - task = self.llm_engine.model_config.task - if task != "generate": + runner_type = self.llm_engine.model_config.runner_type + if runner_type != "generate": messages = [ "LLM.generate() is only supported for (conditional) generation " "models (XForCausalLM, XForConditionalGeneration).", ] - supported_tasks = self.llm_engine.model_config.supported_tasks - if "generate" in supported_tasks: + supported_runner_types = self.llm_engine.model_config \ + .supported_runner_types + if "generate" in supported_runner_types: messages.append( - "Your model supports the 'generate' task, but is " - f"currently initialized for the '{task}' task. Please " - "initialize the model using `--task generate`.") + "Your model supports the 'generate' runner, but is " + f"currently initialized for the '{runner_type}' runner. " + "Please initialize vLLM using `--task generate`.") raise ValueError(" ".join(messages)) @@ -793,16 +794,18 @@ def encode( considered legacy and may be deprecated in the future. You should instead pass them via the ``inputs`` parameter. """ - task = self.llm_engine.model_config.task - if task != "embedding": - messages = ["LLM.encode() is only supported for embedding models."] + runner_type = self.llm_engine.model_config.runner_type + if runner_type != "pooling": + messages = ["LLM.encode() is only supported for pooling models."] - supported_tasks = self.llm_engine.model_config.supported_tasks - if "embedding" in supported_tasks: + supported_runner_types = self.llm_engine.model_config \ + .supported_runner_types + if "pooling" in supported_runner_types: messages.append( - "Your model supports the 'embedding' task, but is " - f"currently initialized for the '{task}' task. Please " - "initialize the model using `--task embedding`.") + "Your model supports the 'pooling' runner, but is " + f"currently initialized for the '{runner_type}' runner. " + "Please initialize vLLM using `--task embed`, " + "`--task classify`, `--task score` etc.") raise ValueError(" ".join(messages)) @@ -864,21 +867,23 @@ def score( A list of ``PoolingRequestOutput`` objects containing the generated scores in the same order as the input prompts. """ - task = self.llm_engine.model_config.task - if task != "embedding": - messages = ["LLM.score() is only supported for embedding models."] + runner_type = self.llm_engine.model_config.runner_type + if runner_type != "pooling": + messages = ["LLM.score() is only supported for pooling models."] - supported_tasks = self.llm_engine.model_config.supported_tasks - if "embedding" in supported_tasks: + supported_runner_types = self.llm_engine.model_config \ + .supported_runner_types + if "pooling" in supported_runner_types: messages.append( - "Your model supports the 'embedding' task, but is " - f"currently initialized for the '{task}' task. Please " - "initialize the model using `--task embedding`.") + "Your model supports the 'pooling' runner, but is " + f"currently initialized for the '{runner_type}' runner. " + "Please initialize vLLM using `--task embed`, " + "`--task classify`, `--task score` etc.") raise ValueError(" ".join(messages)) if not self.llm_engine.model_config.is_cross_encoder: - raise ValueError("Your model does not support the cross encoding") + raise ValueError("Your model does not support cross encoding") tokenizer = self.llm_engine.get_tokenizer() diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index 0f93eb54111ad..a345f8caeeed2 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -573,7 +573,7 @@ def init_app_state( enable_auto_tools=args.enable_auto_tool_choice, tool_parser=args.tool_call_parser, enable_prompt_tokens_details=args.enable_prompt_tokens_details, - ) if model_config.task == "generate" else None + ) if model_config.runner_type == "generate" else None state.openai_serving_completion = OpenAIServingCompletion( engine_client, model_config, @@ -582,7 +582,7 @@ def init_app_state( prompt_adapters=args.prompt_adapters, request_logger=request_logger, return_tokens_as_token_ids=args.return_tokens_as_token_ids, - ) if model_config.task == "generate" else None + ) if model_config.runner_type == "generate" else None state.openai_serving_embedding = OpenAIServingEmbedding( engine_client, model_config, @@ -590,13 +590,13 @@ def init_app_state( request_logger=request_logger, chat_template=resolved_chat_template, chat_template_content_format=args.chat_template_content_format, - ) if model_config.task == "embedding" else None + ) if model_config.runner_type == "pooling" else None state.openai_serving_scores = OpenAIServingScores( engine_client, model_config, base_model_paths, request_logger=request_logger - ) if (model_config.task == "embedding" \ + ) if (model_config.runner_type == "pooling" \ and model_config.is_cross_encoder) else None state.openai_serving_tokenization = OpenAIServingTokenization( engine_client, diff --git a/vllm/entrypoints/openai/run_batch.py b/vllm/entrypoints/openai/run_batch.py index 00cdb3b6839f5..675daf54c0d0d 100644 --- a/vllm/entrypoints/openai/run_batch.py +++ b/vllm/entrypoints/openai/run_batch.py @@ -224,7 +224,7 @@ async def main(args): chat_template=None, chat_template_content_format="auto", enable_prompt_tokens_details=args.enable_prompt_tokens_details, - ) if model_config.task == "generate" else None + ) if model_config.runner_type == "generate" else None openai_serving_embedding = OpenAIServingEmbedding( engine, model_config, @@ -232,7 +232,7 @@ async def main(args): request_logger=request_logger, chat_template=None, chat_template_content_format="auto", - ) if model_config.task == "embedding" else None + ) if model_config.runner_type == "pooling" else None tracker = BatchProgressTracker() logger.info("Reading batch from %s...", args.input_file) diff --git a/vllm/model_executor/model_loader/utils.py b/vllm/model_executor/model_loader/utils.py index cfb89e0f336bc..f15e7176b3d50 100644 --- a/vllm/model_executor/model_loader/utils.py +++ b/vllm/model_executor/model_loader/utils.py @@ -35,7 +35,7 @@ def get_model_architecture( architectures = ["QuantMixtralForCausalLM"] model_cls, arch = ModelRegistry.resolve_model_cls(architectures) - if model_config.task == "embedding": + if model_config.runner_type == "pooling": model_cls = as_embedding_model(model_cls) return model_cls, arch diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index fdb241e6753fb..55a5c4dff3a5c 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -42,7 +42,7 @@ def __init__( executor_class: Type[Executor], usage_context: UsageContext, ): - assert vllm_config.model_config.task != "embedding" + assert vllm_config.model_config.runner_type != "pooling" logger.info("Initializing an LLM engine (v%s) with config: %s", VLLM_VERSION, vllm_config) diff --git a/vllm/worker/cpu_worker.py b/vllm/worker/cpu_worker.py index 4fad1a3f4caeb..ba3d4a130a80b 100644 --- a/vllm/worker/cpu_worker.py +++ b/vllm/worker/cpu_worker.py @@ -163,7 +163,7 @@ def __init__( not in ["medusa", "mlp_speculator", "eagle"]) \ else {"return_hidden_states": True} ModelRunnerClass: Type[CPUModelRunnerBase] = CPUModelRunner - if self.model_config.task == "embedding": + if self.model_config.runner_type == "pooling": ModelRunnerClass = CPUPoolingModelRunner elif self.model_config.is_encoder_decoder: ModelRunnerClass = CPUEncoderDecoderModelRunner diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index 094dd5a5d08b3..832b9903b7abc 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -75,7 +75,7 @@ def __init__( else {"return_hidden_states": True} ModelRunnerClass: Type[GPUModelRunnerBase] = ModelRunner - if model_config.task == "embedding": + if model_config.runner_type == "pooling": ModelRunnerClass = PoolingModelRunner elif self.model_config.is_encoder_decoder: ModelRunnerClass = EncoderDecoderModelRunner From cad5c0a6eda057eeece87a42fff49fef3e18a2ac Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 11 Dec 2024 21:36:27 +0800 Subject: [PATCH 39/79] [Doc] Update docs to refer to pooling models (#11093) Signed-off-by: DarkLight1337 --- docs/source/usage/faq.rst | 7 ++++++- vllm/attention/backends/placeholder_attn.py | 2 +- vllm/config.py | 8 ++++---- vllm/core/placeholder_block_space_manager.py | 2 +- vllm/engine/arg_utils.py | 4 ++-- vllm/engine/async_llm_engine.py | 2 +- vllm/engine/multiprocessing/client.py | 2 +- vllm/engine/protocol.py | 2 +- vllm/entrypoints/openai/serving_score.py | 2 +- vllm/sequence.py | 6 +++--- vllm/v1/engine/processor.py | 2 +- vllm/worker/cpu_worker.py | 2 +- vllm/worker/hpu_worker.py | 4 ++-- vllm/worker/worker.py | 2 +- 14 files changed, 26 insertions(+), 21 deletions(-) diff --git a/docs/source/usage/faq.rst b/docs/source/usage/faq.rst index ce327abd5fa20..d88da32092924 100644 --- a/docs/source/usage/faq.rst +++ b/docs/source/usage/faq.rst @@ -11,7 +11,12 @@ A: Assuming that you're referring to using OpenAI compatible server to serve mul Q: Which model to use for offline inference embedding? -A: If you want to use an embedding model, try: https://huggingface.co/intfloat/e5-mistral-7b-instruct. Instead models, such as Llama-3-8b, Mistral-7B-Instruct-v0.3, are generation models rather than an embedding model +A: You can try `e5-mistral-7b-instruct `__ and `BAAI/bge-base-en-v1.5 `__; +more are listed :ref:`here `. + +By extracting hidden states, vLLM can automatically convert text generation models like `Llama-3-8B `__, +`Mistral-7B-Instruct-v0.3 `__ into embedding models, +but they are expected be inferior to models that are specifically trained on embedding tasks. ---------------------------------------- diff --git a/vllm/attention/backends/placeholder_attn.py b/vllm/attention/backends/placeholder_attn.py index 658039bfc3365..534f79b3a60bf 100644 --- a/vllm/attention/backends/placeholder_attn.py +++ b/vllm/attention/backends/placeholder_attn.py @@ -14,7 +14,7 @@ from vllm.worker.model_runner import (ModelInputForGPUBuilder, ModelInputForGPUWithSamplingMetadata) -# Placeholder attention backend for models like Mamba and embedding models that +# Placeholder attention backend for models like Mamba and pooling models that # lack attention. diff --git a/vllm/config.py b/vllm/config.py index 2d9a76fe7ddb1..322c8f8990a40 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -152,7 +152,7 @@ class ModelConfig: this argument will be used to configure the neuron config that can not be gathered from the vllm arguments. override_pooler_config: Initialize non default pooling config or - override default pooling config for the embedding model. + override default pooling config for the pooling model. """ def __init__( @@ -576,7 +576,7 @@ def verify_async_output_proc(self, parallel_config, speculative_config, self.use_async_output_proc = False return - # Async postprocessor is not necessary with embedding mode + # Async postprocessor is not necessary for pooling models # since there is no token generation if self.runner_type == "pooling": self.use_async_output_proc = False @@ -1825,11 +1825,11 @@ class MultiModalConfig: @dataclass class PoolerConfig: - """Controls the behavior of output pooling in embedding models.""" + """Controls the behavior of output pooling in pooling models.""" pooling_type: Optional[str] = None """ - The pooling method of the embedding model. This should be a key in + The pooling method of the pooling model. This should be a key in :class:`vllm.model_executor.layers.pooler.PoolingType`. """ diff --git a/vllm/core/placeholder_block_space_manager.py b/vllm/core/placeholder_block_space_manager.py index 26d42b7f1790e..a47e594518534 100644 --- a/vllm/core/placeholder_block_space_manager.py +++ b/vllm/core/placeholder_block_space_manager.py @@ -8,7 +8,7 @@ class PlaceholderBlockSpaceManager(BlockSpaceManager): """A version of BlockSpaceManager for use in environments where block management is not required. - For example: embedding models or attention-free models like Mamba. + For example: pooling models or attention-free models like Mamba. This class provides the same interface as BlockSpaceManager, but its methods perform no actions or return simple values like True in specific diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index d485c2a9e7208..7337522bc9952 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -893,7 +893,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: '--override-pooler-config', type=PoolerConfig.from_json, default=None, - help="Override or set the pooling method in the embedding model. " + help="Override or set the pooling method for pooling models. " "e.g. {\"pooling_type\": \"mean\", \"normalize\": false}.'") parser.add_argument('--compilation-config', @@ -1085,7 +1085,7 @@ def create_engine_config(self, "setting --max-model-len to a smaller value.", max_model_len) elif (self.enable_chunked_prefill and model_config.runner_type == "pooling"): - msg = "Chunked prefill is not supported for embedding models" + msg = "Chunked prefill is not supported for pooling models" raise ValueError(msg) diff --git a/vllm/engine/async_llm_engine.py b/vllm/engine/async_llm_engine.py index 60dccd7a0812c..32396fd10188d 100644 --- a/vllm/engine/async_llm_engine.py +++ b/vllm/engine/async_llm_engine.py @@ -1085,7 +1085,7 @@ async def encode( trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, ) -> AsyncGenerator[PoolingRequestOutput, None]: - """Generate outputs for a request from an embedding model. + """Generate outputs for a request from a pooling model. Generate outputs for a request. This method is a coroutine. It adds the request into the waiting queue of the LLMEngine and streams the outputs diff --git a/vllm/engine/multiprocessing/client.py b/vllm/engine/multiprocessing/client.py index a729023bc00bb..0a046c71e86e8 100644 --- a/vllm/engine/multiprocessing/client.py +++ b/vllm/engine/multiprocessing/client.py @@ -527,7 +527,7 @@ def encode( *, inputs: Optional[PromptType] = None # DEPRECATED ) -> AsyncGenerator[PoolingRequestOutput, None]: - """Generate outputs for a request from an embedding model. + """Generate outputs for a request from a pooling model. Generate outputs for a request. This method is a coroutine. It adds the request into the waiting queue of the LLMEngine and streams the outputs diff --git a/vllm/engine/protocol.py b/vllm/engine/protocol.py index 4079de7d36793..a066836b92708 100644 --- a/vllm/engine/protocol.py +++ b/vllm/engine/protocol.py @@ -209,7 +209,7 @@ def encode( trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, ) -> AsyncGenerator[PoolingRequestOutput, None]: - """Generate outputs for a request from an embedding model.""" + """Generate outputs for a request from a pooling model.""" ... @abstractmethod diff --git a/vllm/entrypoints/openai/serving_score.py b/vllm/entrypoints/openai/serving_score.py index fed06fa452955..4929e720c00e4 100644 --- a/vllm/entrypoints/openai/serving_score.py +++ b/vllm/entrypoints/openai/serving_score.py @@ -119,7 +119,7 @@ async def create_score( if prompt_adapter_request is not None: raise NotImplementedError("Prompt adapter is not supported " - "for embedding models") + "for scoring models") if isinstance(tokenizer, MistralTokenizer): raise ValueError( diff --git a/vllm/sequence.py b/vllm/sequence.py index 669124319c4f4..b0f3c1cc3609f 100644 --- a/vllm/sequence.py +++ b/vllm/sequence.py @@ -618,9 +618,9 @@ class SequenceGroup: arrival_time: The arrival time of the request. lora_request: LoRA request. embeddings: The embeddings vectors of the prompt of the sequence group - for an embedding model. + for a pooling model. pooling_params: The pooling parameters used to generate the pooling - for an embedding model. + for a pooling model. encoder_seq: Optional, the single encoder sequence. Should be None unless you are working with an encoder/decoder model. trace_headers: OpenTelemetry trace headers. @@ -1102,7 +1102,7 @@ class PoolerOutput( msgspec.Struct, omit_defaults=True, # type: ignore[call-arg] array_like=True): # type: ignore[call-arg] - """The output from a pooling operation in the embedding model.""" + """The output from a pooling operation in the pooling model.""" outputs: List[EmbeddingSequenceGroupOutput] # lazy import to avoid circular import diff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py index 120fc64969552..e0e525b30a767 100644 --- a/vllm/v1/engine/processor.py +++ b/vllm/v1/engine/processor.py @@ -59,7 +59,7 @@ def process_inputs( priority: int = 0, ) -> Tuple[DetokenizerRequest, EngineCoreRequest]: - # TODO(woosuk): Support embedding mode. + # TODO(woosuk): Support pooling models. # TODO(woosuk): Check max_logprobs # TODO(woosuk): Support encoder-decoder models. diff --git a/vllm/worker/cpu_worker.py b/vllm/worker/cpu_worker.py index ba3d4a130a80b..09758a5d9accf 100644 --- a/vllm/worker/cpu_worker.py +++ b/vllm/worker/cpu_worker.py @@ -178,7 +178,7 @@ def __init__( # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[CPUCacheEngine] - # Initialize cpu_cache as embedding models don't initialize kv_caches + # Initialize cpu_cache as pooling models don't initialize kv_caches self.cpu_cache: Optional[List[List[torch.Tensor]]] = None # Torch profiler. Enabled and configured through env vars: diff --git a/vllm/worker/hpu_worker.py b/vllm/worker/hpu_worker.py index 493f7a9fad098..cca7cd50bfc7b 100644 --- a/vllm/worker/hpu_worker.py +++ b/vllm/worker/hpu_worker.py @@ -65,8 +65,8 @@ def __init__( # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[HPUCacheEngine] - # Initialize gpu_cache as embedding models don't initialize kv_caches - self.hpu_cache: Optional[List[List[torch.tensor]]] = None + # Initialize gpu_cache as pooling models don't initialize kv_caches + self.hpu_cache: Optional[List[List[torch.Tensor]]] = None # Torch profiler. Enabled and configured through env vars: # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace if envs.VLLM_TORCH_PROFILER_DIR: diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index 832b9903b7abc..a368bb9ee9a5b 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -91,7 +91,7 @@ def __init__( # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[CacheEngine] - # Initialize gpu_cache as embedding models don't initialize kv_caches + # Initialize gpu_cache as pooling models don't initialize kv_caches self.gpu_cache: Optional[List[List[torch.Tensor]]] = None self._seq_group_metadata_cache: Dict[str, SequenceGroupMetadata] = {} From b2f775456e4af7412308320a9c11e4dac3086205 Mon Sep 17 00:00:00 2001 From: hissu-hyvarinen Date: Wed, 11 Dec 2024 17:23:37 +0200 Subject: [PATCH 40/79] [CI/Build] Enable prefix caching test for AMD (#11098) Signed-off-by: Hissu Hyvarinen --- .buildkite/test-pipeline.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 8f57006214c88..df4fa7a6ee9ba 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -201,7 +201,7 @@ steps: - python3 offline_profile.py --model facebook/opt-125m - label: Prefix Caching Test # 9min - #mirror_hardwares: [amd] + mirror_hardwares: [amd] source_file_dependencies: - vllm/ - tests/prefix_caching From fd22220687af5ccd89d9f8f2812069ef0422244c Mon Sep 17 00:00:00 2001 From: bingps <46775742+bingps@users.noreply.github.com> Date: Wed, 11 Dec 2024 23:43:24 +0800 Subject: [PATCH 41/79] [Doc] Installed version of llmcompressor for int8/fp8 quantization (#11103) Signed-off-by: Guangda Liu Co-authored-by: Guangda Liu --- docs/source/quantization/fp8.rst | 2 +- docs/source/quantization/int8.rst | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/quantization/fp8.rst b/docs/source/quantization/fp8.rst index aacd07a34ad46..4dbf8e9d346e1 100644 --- a/docs/source/quantization/fp8.rst +++ b/docs/source/quantization/fp8.rst @@ -45,7 +45,7 @@ To produce performant FP8 quantized models with vLLM, you'll need to install the .. code-block:: console - $ pip install llmcompressor==0.1.0 + $ pip install llmcompressor Quantization Process -------------------- diff --git a/docs/source/quantization/int8.rst b/docs/source/quantization/int8.rst index 04fa308449507..aa5b251becb1c 100644 --- a/docs/source/quantization/int8.rst +++ b/docs/source/quantization/int8.rst @@ -19,7 +19,7 @@ To use INT8 quantization with vLLM, you'll need to install the `llm-compressor < .. code-block:: console - $ pip install llmcompressor==0.1.0 + $ pip install llmcompressor Quantization Process -------------------- @@ -142,4 +142,4 @@ Best Practices Troubleshooting and Support --------------------------- -If you encounter any issues or have feature requests, please open an issue on the ``vllm-project/llm-compressor`` GitHub repository. \ No newline at end of file +If you encounter any issues or have feature requests, please open an issue on the ``vllm-project/llm-compressor`` GitHub repository. From 91642db952458fbb6ae7c2d167757dc86b105991 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Wed, 11 Dec 2024 10:43:05 -0800 Subject: [PATCH 42/79] [torch.compile] use depyf to dump torch.compile internals (#10972) Signed-off-by: youkaichao --- requirements-common.txt | 1 + vllm/compilation/backends.py | 69 ++++++++++++++++++---------------- vllm/compilation/decorators.py | 2 +- vllm/compilation/monitor.py | 23 ++++++++++-- vllm/compilation/wrapper.py | 4 +- vllm/config.py | 6 ++- vllm/worker/model_runner.py | 3 +- 7 files changed, 66 insertions(+), 42 deletions(-) diff --git a/requirements-common.txt b/requirements-common.txt index 792cd58e80669..850b8f4101701 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -33,3 +33,4 @@ six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that need setuptools>=74.1.1; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12 einops # Required for Qwen2-VL. compressed-tensors == 0.8.0 # required for compressed-tensors +depyf==0.18.0 # required for profiling and debugging torch.compile diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py index f002a8ff905b1..09a3daa731829 100644 --- a/vllm/compilation/backends.py +++ b/vllm/compilation/backends.py @@ -9,7 +9,7 @@ import torch.fx as fx import vllm.envs as envs -from vllm.config import CompilationConfig +from vllm.config import CompilationConfig, VllmConfig from vllm.logger import init_logger from vllm.utils import weak_ref_tensors @@ -149,14 +149,15 @@ class PiecewiseCompileInterpreter(torch.fx.Interpreter): """ def __init__(self, module: torch.fx.GraphModule, - compile_submod_names: List[str], - compilation_configs: CompilationConfig, graph_pool): + compile_submod_names: List[str], vllm_config: VllmConfig, + graph_pool): super().__init__(module) from torch._guards import detect_fake_mode self.fake_mode = detect_fake_mode() self.compile_submod_names = compile_submod_names - self.compilation_configs = compilation_configs + self.compilation_config = vllm_config.compilation_config self.graph_pool = graph_pool + self.vllm_config = vllm_config def run(self, *args): fake_args = [ @@ -182,15 +183,15 @@ def call_module(self, target: torch.fx.node.Target, compiled_graph_for_general_shape = wrap_inductor( submod, args, - self.compilation_configs.inductor_compile_config, - self.compilation_configs, + self.compilation_config.inductor_compile_config, + self.compilation_config, graph_index=index, num_graphs=len(self.compile_submod_names), runtime_shape=None, - use_inductor=self.compilation_configs.use_inductor) + use_inductor=self.compilation_config.use_inductor) self.module.__dict__[target] = PiecewiseBackend( - submod, self.compilation_configs, self.graph_pool, index, + submod, self.vllm_config, self.graph_pool, index, len(self.compile_submod_names), sym_shape_indices, compiled_graph_for_general_shape) @@ -211,7 +212,8 @@ class VllmBackend: which handles the post-grad passes. """ - compilation_configs: CompilationConfig + vllm_config: VllmConfig + compilation_config: CompilationConfig graph_pool: Any _called: bool = False # the graph we compiled @@ -227,7 +229,7 @@ class VllmBackend: def __init__( self, - compilation_configs: CompilationConfig, + vllm_config: VllmConfig, ): global global_graph_pool if global_graph_pool is None: @@ -244,13 +246,14 @@ def __init__( self.sym_tensor_indices = [] self.input_buffers = [] - self.compilation_configs = compilation_configs + self.vllm_config = vllm_config + self.compilation_config = vllm_config.compilation_config # `torch.compile` is JIT compiled, so we don't need to # do anything here def configure_post_pass(self): - config = self.compilation_configs + config = self.compilation_config self.post_grad_pass_manager.configure(config.pass_config) # Post-grad custom passes are run using the post_grad_custom_post_pass @@ -271,7 +274,7 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: from .monitor import torch_compile_start_time dynamo_time = time.time() - torch_compile_start_time logger.info("Dynamo bytecode transform time: %.2f s", dynamo_time) - self.compilation_configs.compilation_time += dynamo_time + self.compilation_config.compilation_time += dynamo_time # we control the compilation process, each instance can only be # called once @@ -281,7 +284,7 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: self.configure_post_pass() self.split_gm, self.piecewise_graphs = split_graph( - graph, self.compilation_configs.splitting_ops) + graph, self.compilation_config.splitting_ops) from torch._dynamo.utils import lazy_format_graph_code logger.debug("%s", lazy_format_graph_code("before split", self.graph)) @@ -298,13 +301,13 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: # propagate the split graph to the piecewise backend, # compile submodules with symbolic shapes PiecewiseCompileInterpreter(self.split_gm, submod_names_to_compile, - self.compilation_configs, + self.vllm_config, self.graph_pool).run(*example_inputs) self._called = True - if not self.compilation_configs.use_cudagraph or \ - not self.compilation_configs.cudagraph_copy_inputs: + if not self.compilation_config.use_cudagraph or \ + not self.compilation_config.cudagraph_copy_inputs: return self.split_gm # if we need to copy input buffers for cudagraph @@ -364,10 +367,9 @@ class ConcreteSizeEntry: class PiecewiseBackend: - def __init__(self, graph: fx.GraphModule, - compilation_configs: CompilationConfig, graph_pool: Any, - piecewise_compile_index: int, total_piecewise_compiles: int, - sym_shape_indices: List[int], + def __init__(self, graph: fx.GraphModule, vllm_config: VllmConfig, + graph_pool: Any, piecewise_compile_index: int, + total_piecewise_compiles: int, sym_shape_indices: List[int], compiled_graph_for_general_shape: Callable): """ The backend for piecewise compilation. @@ -375,7 +377,7 @@ def __init__(self, graph: fx.GraphModule, We will compile `self.graph` once for the general shape, and then compile for different shapes specified in - `compilation_configs.compile_sizes`. + `compilation_config.compile_sizes`. Independently, we will capture cudagraph for different shapes. @@ -383,7 +385,8 @@ def __init__(self, graph: fx.GraphModule, compile it first, and then capture cudagraph. """ self.graph = graph - self.compilation_configs = compilation_configs + self.vllm_config = vllm_config + self.compilation_config = vllm_config.compilation_config self.graph_pool = graph_pool self.piecewise_compile_index = piecewise_compile_index self.total_piecewise_compiles = total_piecewise_compiles @@ -393,10 +396,10 @@ def __init__(self, graph: fx.GraphModule, piecewise_compile_index == total_piecewise_compiles - 1) self.compile_sizes: Set[int] = set( - self.compilation_configs.compile_sizes) + self.compilation_config.compile_sizes) self.capture_sizes: Set[int] = set( - self.compilation_configs.capture_sizes - ) if self.compilation_configs.use_cudagraph else set() + self.compilation_config.capture_sizes + ) if self.compilation_config.use_cudagraph else set() self.first_run_finished = False @@ -423,7 +426,7 @@ def __call__(self, *args) -> Any: self.first_run_finished = True # no specific sizes to compile if self.is_last_graph and not self.to_be_compiled_sizes: - end_monitoring_torch_compile(self.compilation_configs) + end_monitoring_torch_compile(self.vllm_config) return self.compiled_graph_for_general_shape(*args) runtime_shape = args[self.sym_shape_indices[0]] @@ -443,28 +446,28 @@ def __call__(self, *args) -> Any: entry.runnable = wrap_inductor( self.graph, args, - self.compilation_configs.inductor_compile_config, - self.compilation_configs, + self.compilation_config.inductor_compile_config, + self.compilation_config, graph_index=self.piecewise_compile_index, num_graphs=self.total_piecewise_compiles, runtime_shape=runtime_shape, - use_inductor=self.compilation_configs.use_inductor) + use_inductor=self.compilation_config.use_inductor) # finished compilations for all required shapes if self.is_last_graph and not self.to_be_compiled_sizes: - end_monitoring_torch_compile(self.compilation_configs) + end_monitoring_torch_compile(self.vllm_config) if not entry.use_cudagraph: return entry.runnable(*args) if entry.cudagraph is None: - if entry.num_finished_warmup < self.compilation_configs.cudagraph_num_of_warmups: # noqa + if entry.num_finished_warmup < self.compilation_config.cudagraph_num_of_warmups: # noqa entry.num_finished_warmup += 1 if self.is_first_graph: logger.debug( "Warming up %s/%s for shape %s", entry.num_finished_warmup, - self.compilation_configs.cudagraph_num_of_warmups, + self.compilation_config.cudagraph_num_of_warmups, runtime_shape) return entry.runnable(*args) diff --git a/vllm/compilation/decorators.py b/vllm/compilation/decorators.py index 938430fe2a501..805a217ee6ca1 100644 --- a/vllm/compilation/decorators.py +++ b/vllm/compilation/decorators.py @@ -185,7 +185,7 @@ def __call__(self, *args, **kwargs): "Unsupported dynamic dimensions" f" {dims} for argument {k} with type {type(arg)}.") # here, it is the starting point of the `torch.compile` process - start_monitoring_torch_compile(self.vllm_config.compilation_config) + start_monitoring_torch_compile(self.vllm_config) # if we don't use custom dispatcher, we can directly call the # compiled function and let torch.compile handle the dispatching, diff --git a/vllm/compilation/monitor.py b/vllm/compilation/monitor.py index 3348674b09af2..b97e40415b41b 100644 --- a/vllm/compilation/monitor.py +++ b/vllm/compilation/monitor.py @@ -1,19 +1,36 @@ +import os import time -from vllm.config import CompilationConfig, CompilationLevel +from vllm.config import CompilationConfig, CompilationLevel, VllmConfig from vllm.logger import init_logger logger = init_logger(__name__) +context_manager = None torch_compile_start_time: float = 0.0 -def start_monitoring_torch_compile(compilation_config: CompilationConfig): +def start_monitoring_torch_compile(vllm_config: VllmConfig): global torch_compile_start_time torch_compile_start_time = time.time() + compilation_config: CompilationConfig = vllm_config.compilation_config + if compilation_config.level == CompilationLevel.PIECEWISE and \ + compilation_config.debug_dump_path: + import depyf + path = os.path.join(compilation_config.debug_dump_path, + f"rank_{vllm_config.parallel_config.rank}") + global context_manager + context_manager = depyf.prepare_debug(path) + context_manager.__enter__() -def end_monitoring_torch_compile(compilation_config: CompilationConfig): + +def end_monitoring_torch_compile(vllm_config: VllmConfig): + compilation_config: CompilationConfig = vllm_config.compilation_config if compilation_config.level == CompilationLevel.PIECEWISE: logger.info("torch.compile takes %.2f s in total", compilation_config.compilation_time) + global context_manager + if context_manager is not None: + context_manager.__exit__(None, None, None) + context_manager = None diff --git a/vllm/compilation/wrapper.py b/vllm/compilation/wrapper.py index bc4d292fef402..c10241b483169 100644 --- a/vllm/compilation/wrapper.py +++ b/vllm/compilation/wrapper.py @@ -32,8 +32,8 @@ def __init__(self, # default compilation settings # compiling the forward method - backend = get_current_vllm_config( - ).compilation_config.init_backend() + vllm_config = get_current_vllm_config() + backend = vllm_config.compilation_config.init_backend(vllm_config) compiled_callable = torch.compile( self.forward, diff --git a/vllm/config.py b/vllm/config.py index 322c8f8990a40..7f9be5a3a98bc 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -2222,6 +2222,7 @@ class CompilationConfig(BaseModel): - 1: dynamo as is. - 2: dynamo once. - 3: piecewise compilation. + - debug_dump_path: the path to dump the debug information. - backend: the backend for compilation. It needs to be a string. - "" (empty string): use the default backend. - "eager"/"openxla"/...: use the specified backend registered in PyTorch. @@ -2289,6 +2290,7 @@ class CompilationConfig(BaseModel): certain small batchsizes, where inductor is good at optimizing. """ # noqa level: int = 0 + debug_dump_path: str = "" backend: str = "" custom_ops: List[str] = Field(default_factory=list) splitting_ops: List[str] = Field(default_factory=lambda: [ @@ -2394,7 +2396,7 @@ def model_post_init(self, __context: Any) -> None: self.static_forward_context = {} self.compilation_time = 0.0 - def init_backend(self) -> Union[str, Callable]: + def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]: if self.level == CompilationLevel.NO_COMPILATION: raise ValueError("No compilation level is set.") @@ -2413,7 +2415,7 @@ def init_backend(self) -> Union[str, Callable]: # merge with the config use_inductor assert self.level == CompilationLevel.PIECEWISE from vllm.compilation.backends import VllmBackend - return VllmBackend(self) + return VllmBackend(vllm_config) def init_with_cudagraph_sizes(self, sizes_to_specialize: List[int]): """To complete the initialization of config, diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py index 551b84435fdc0..26fd486130ce6 100644 --- a/vllm/worker/model_runner.py +++ b/vllm/worker/model_runner.py @@ -1162,7 +1162,8 @@ def load_model(self) -> None: if self.vllm_config.compilation_config.level ==\ CompilationLevel.DYNAMO_AS_IS and supports_dynamo(): - backend = self.vllm_config.compilation_config.init_backend() + backend = self.vllm_config.compilation_config.init_backend( + self.vllm_config) self.model = torch.compile( self.model, fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE, From d643c2aba1cd5421200f3a3bad1813dd067233b4 Mon Sep 17 00:00:00 2001 From: Woosuk Kwon Date: Wed, 11 Dec 2024 10:49:23 -0800 Subject: [PATCH 43/79] [V1] Use input_ids as input for text-only models (#11032) Signed-off-by: Woosuk Kwon --- vllm/v1/worker/gpu_model_runner.py | 68 +++++++++++++++++++++--------- 1 file changed, 47 insertions(+), 21 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 8d9976ded7c5e..e75be21ef2d91 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -61,6 +61,7 @@ def __init__( self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[ cache_config.cache_dtype] + self.is_multimodal_model = model_config.is_multimodal_model self.sliding_window = model_config.get_sliding_window() self.block_size = cache_config.block_size self.max_model_len = model_config.max_model_len @@ -103,6 +104,11 @@ def __init__( # The batch sizes in the config are in descending order. self.cudagraph_batch_sizes = list( reversed(self.vllm_config.compilation_config.capture_sizes)) + + # Persistent buffers for CUDA graphs. + self.input_ids = torch.zeros(self.max_num_tokens, + dtype=torch.int32, + device=self.device) self.positions = torch.zeros(self.max_num_tokens, dtype=torch.int64, device=self.device) @@ -310,7 +316,8 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"): 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) + self.input_ids[:total_num_scheduled_tokens].copy_(input_ids, + non_blocking=True) self.positions[:total_num_scheduled_tokens].copy_(positions, non_blocking=True) query_start_loc = query_start_loc.to(self.device, non_blocking=True) @@ -331,7 +338,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"): # token from the partial request. # TODO: Support prompt logprobs. logits_indices = query_start_loc[1:] - 1 - return input_ids, attn_metadata, logits_indices + return attn_metadata, logits_indices def _prepare_sampling( self, @@ -427,13 +434,15 @@ def execute_model( ) -> ModelRunnerOutput: self._update_states(scheduler_output) - # Run the encoder. - self._execute_encoder(scheduler_output) - encoder_outputs = self._gather_encoder_outputs(scheduler_output) + if self.is_multimodal_model: + # Run the multimodal encoder if any. + self._execute_encoder(scheduler_output) + encoder_outputs = self._gather_encoder_outputs(scheduler_output) + else: + encoder_outputs = [] # Prepare the decoder inputs. - input_ids, attn_metadata, logits_indices = self._prepare_inputs( - scheduler_output) + attn_metadata, logits_indices = self._prepare_inputs(scheduler_output) num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens if (self.use_cuda_graph and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]): @@ -444,29 +453,39 @@ def execute_model( else: # Eager mode. num_input_tokens = num_scheduled_tokens - attn_metadata.num_input_tokens = num_input_tokens - # Get the inputs embeds. - if encoder_outputs: - inputs_embeds = self.model.get_input_embeddings( - input_ids, encoder_outputs) + if self.is_multimodal_model: + # NOTE(woosuk): To unify token ids and soft tokens (vision + # embeddings), we always use embeddings (rather than token ids) + # as input to the multimodal model, even when the input is text. + input_ids = self.input_ids[:num_scheduled_tokens] + if encoder_outputs: + inputs_embeds = self.model.get_input_embeddings( + input_ids, encoder_outputs) + else: + inputs_embeds = self.model.get_input_embeddings(input_ids) + # TODO(woosuk): Avoid the copy. Optimize. + self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds) + inputs_embeds = self.inputs_embeds[:num_input_tokens] + input_ids = None else: - inputs_embeds = self.model.get_input_embeddings(input_ids) - # NOTE(woosuk): To unify token ids and soft tokens (vision embeddings), - # always use embeddings (rather than token ids) as input to the model. - # TODO(woosuk): Avoid the copy. Optimize. - self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds) + # For text-only models, we use token ids as input. + # While it is possible to use embeddings as input just like the + # multimodal models, it is not desirable for performance since + # then the embedding layer is not included in the CUDA graph. + input_ids = self.input_ids[:num_input_tokens] + inputs_embeds = None # Run the decoder. # Use persistent buffers for CUDA graphs. with set_forward_context(attn_metadata, self.vllm_config): hidden_states = self.model( - input_ids=None, + input_ids=input_ids, positions=self.positions[:num_input_tokens], kv_caches=self.kv_caches, attn_metadata=None, - inputs_embeds=self.inputs_embeds[:num_input_tokens], + inputs_embeds=inputs_embeds, ) hidden_states = hidden_states[:num_scheduled_tokens] hidden_states = hidden_states[logits_indices] @@ -534,13 +553,20 @@ def _dummy_run( num_tokens: int, kv_caches: List[torch.Tensor], ) -> torch.Tensor: + if self.is_multimodal_model: + input_ids = None + inputs_embeds = self.inputs_embeds[:num_tokens] + else: + input_ids = self.input_ids[:num_tokens] + inputs_embeds = None with set_forward_context(None, self.vllm_config): hidden_states = model( - input_ids=None, + input_ids=input_ids, positions=self.positions[:num_tokens], kv_caches=kv_caches, attn_metadata=None, - inputs_embeds=self.inputs_embeds[:num_tokens]) + inputs_embeds=inputs_embeds, + ) return hidden_states def profile_run(self) -> None: From 66aaa7722df3d7ef9e9bd2942cab5cd0d7473174 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Wed, 11 Dec 2024 10:59:50 -0800 Subject: [PATCH 44/79] [torch.compile] remove graph logging in ci (#11110) Signed-off-by: youkaichao --- vllm/compilation/backends.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py index 09a3daa731829..4a5dc337d01b8 100644 --- a/vllm/compilation/backends.py +++ b/vllm/compilation/backends.py @@ -287,9 +287,11 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: graph, self.compilation_config.splitting_ops) from torch._dynamo.utils import lazy_format_graph_code - logger.debug("%s", lazy_format_graph_code("before split", self.graph)) - logger.debug("%s", lazy_format_graph_code("after split", - self.split_gm)) + + # depyf will hook lazy_format_graph_code and dump the graph + # for debugging, no need to print the graph here + lazy_format_graph_code("before split", self.graph) + lazy_format_graph_code("after split", self.split_gm) compilation_counter.num_piecewise_graphs_seen += len( self.piecewise_graphs) From 72ff3a968682e6a3f7620ab59f2baf5e8eb2777b Mon Sep 17 00:00:00 2001 From: Rui Qiao <161574667+ruisearch42@users.noreply.github.com> Date: Wed, 11 Dec 2024 11:36:35 -0800 Subject: [PATCH 45/79] [core] Bump ray to use _overlap_gpu_communication in compiled graph tests (#10410) Signed-off-by: Rui Qiao Signed-off-by: Rui Qiao Co-authored-by: Rui Qiao --- requirements-test.in | 2 +- requirements-test.txt | 2 +- vllm/envs.py | 8 ++++++++ vllm/executor/ray_gpu_executor.py | 17 ++++++++++------- 4 files changed, 20 insertions(+), 9 deletions(-) diff --git a/requirements-test.in b/requirements-test.in index c0b228148ab31..57fddb416317e 100644 --- a/requirements-test.in +++ b/requirements-test.in @@ -13,7 +13,7 @@ einops # required for MPT, qwen-vl and Mamba httpx librosa # required for audio tests peft -ray[adag]==2.35 +ray[adag]==2.40.0 sentence-transformers # required for embedding tests soundfile # required for audio tests timm # required for internvl test diff --git a/requirements-test.txt b/requirements-test.txt index 8ceb705cdffd7..c786a1249bddb 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -410,7 +410,7 @@ pyyaml==6.0.2 # ray # timm # transformers -ray[adag]==2.35.0 +ray[adag]==2.40.0 # via -r requirements-test.in redis==5.2.0 # via tensorizer diff --git a/vllm/envs.py b/vllm/envs.py index be5d9985b63a4..bc8c1499e9534 100644 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -45,6 +45,7 @@ VLLM_USE_RAY_SPMD_WORKER: bool = False VLLM_USE_RAY_COMPILED_DAG: bool = False VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL: bool = True + VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = True VLLM_WORKER_MULTIPROC_METHOD: str = "fork" VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets") VLLM_IMAGE_FETCH_TIMEOUT: int = 5 @@ -337,6 +338,13 @@ def get_default_config_root(): lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL", "1")) ), + # If the env var is set, it enables GPU communication overlap in + # Ray's compiled DAG. This flag is ignored if + # VLLM_USE_RAY_COMPILED_DAG is not set. + "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": + lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "1")) + ), + # Use dedicated multiprocess context for workers. # Both spawn and fork work "VLLM_WORKER_MULTIPROC_METHOD": diff --git a/vllm/executor/ray_gpu_executor.py b/vllm/executor/ray_gpu_executor.py index 4263fb27265f6..4bf5cbbd18ffe 100644 --- a/vllm/executor/ray_gpu_executor.py +++ b/vllm/executor/ray_gpu_executor.py @@ -414,12 +414,10 @@ def _check_ray_adag_installation(self): import pkg_resources from packaging import version - required_version = version.parse("2.35") + required_version = version.parse("2.40") current_version = version.parse( pkg_resources.get_distribution("ray").version) - # TODO: update the constraint once we adapt to the backward - # incompatible API change from ray 2.36 - if current_version != required_version: + if current_version < required_version: raise ValueError(f"Ray version {required_version} is " f"required, but found {current_version}") @@ -445,6 +443,8 @@ def _compiled_ray_dag(self, enable_asyncio: bool): logger.info("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL = %s", envs.VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL) + logger.info("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM = %s", + envs.VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM) with InputNode() as input_data: # Example DAG: PP=2, TP=4 # (ExecuteModelReq, None) -> 0 -> (ExecuteModelReq, IntermediateOutput) -> 4 -> SamplerOutput # noqa: E501 @@ -480,7 +480,10 @@ def _compiled_ray_dag(self, enable_asyncio: bool): forward_dag = MultiOutputNode(outputs) - return forward_dag.experimental_compile(enable_asyncio=enable_asyncio) + return forward_dag.experimental_compile( + enable_asyncio=enable_asyncio, + _overlap_gpu_communication=envs. + VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM) def __del__(self): self.shutdown() @@ -507,8 +510,8 @@ async def execute_model_async( serialized_data = self.input_encoder.encode(execute_model_req) dag_future = await self.forward_dag.execute_async(serialized_data) - outputs = await dag_future - return self.output_decoder.decode(outputs[0]) + output = await dag_future[0] + return self.output_decoder.decode(output) async def _driver_execute_model_async( self, From d1e21a979bba4712f48dac1bbf410e0b57c92e7a Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Thu, 12 Dec 2024 06:18:16 +0800 Subject: [PATCH 46/79] [CI/Build] Split up VLM tests (#11083) Signed-off-by: DarkLight1337 --- .buildkite/test-pipeline.yaml | 32 ++++++--- pyproject.toml | 3 +- .../vision_language/test_models.py | 72 ++++++++++++------- tests/utils.py | 37 ++++++---- 4 files changed, 94 insertions(+), 50 deletions(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index df4fa7a6ee9ba..aca505178df06 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -321,7 +321,7 @@ steps: ##### models test ##### -- label: Basic Models Test # 30min +- label: Basic Models Test # 24min source_file_dependencies: - vllm/ - tests/models @@ -331,7 +331,7 @@ steps: - pytest -v -s models/test_registry.py - pytest -v -s models/test_initialization.py -- label: Language Models Test (Standard) # 42min +- label: Language Models Test (Standard) # 32min #mirror_hardwares: [amd] source_file_dependencies: - vllm/ @@ -342,7 +342,7 @@ steps: - pytest -v -s models/decoder_only/language -m 'core_model or quant_model' - pytest -v -s models/embedding/language -m core_model -- label: Language Models Test (Extended) # 50min +- label: Language Models Test (Extended) # 1h10min optional: true source_file_dependencies: - vllm/ @@ -353,7 +353,7 @@ steps: - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model' - pytest -v -s models/embedding/language -m 'not core_model' -- label: Multi-Modal Models Test (Standard) # 26min +- label: Multi-Modal Models Test (Standard) # 28min #mirror_hardwares: [amd] source_file_dependencies: - vllm/ @@ -369,7 +369,7 @@ steps: - pytest -v -s models/encoder_decoder/language -m core_model - pytest -v -s models/encoder_decoder/vision_language -m core_model -- label: Multi-Modal Models Test (Extended) # 1h15m +- label: Multi-Modal Models Test (Extended) 1 # 1h16m optional: true source_file_dependencies: - vllm/ @@ -380,14 +380,24 @@ steps: commands: - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model' + - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model' # HACK - run phi3v tests separately to sidestep this transformers bug # https://github.com/huggingface/transformers/issues/34307 - pytest -v -s models/decoder_only/vision_language/test_phi3v.py - - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model' + - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model' - pytest -v -s models/embedding/vision_language -m 'not core_model' - pytest -v -s models/encoder_decoder/language -m 'not core_model' - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model' +- label: Multi-Modal Models Test (Extended) 2 # 38m + optional: true + source_file_dependencies: + - vllm/ + - tests/models/decoder_only/vision_language + commands: + - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git + - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=1) and not core_model and not quant_model' + # This test is used only in PR development phase to test individual models and should never run on main - label: Custom Models Test optional: true @@ -446,11 +456,11 @@ steps: - pytest -v -s ./compile/test_basic_correctness.py - pytest -v -s ./compile/test_wrapper.py - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep -q 'Same node test passed' - - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m distributed_2_gpus + - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)' # Avoid importing model tests that cause CUDA reinitialization error - - pytest models/encoder_decoder/language/test_bart.py -v -s -m distributed_2_gpus - - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m distributed_2_gpus - - pytest models/decoder_only/vision_language/test_models.py -v -s -m distributed_2_gpus + - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)' + - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)' + - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)' - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py - pip install -e ./plugins/vllm_add_dummy_model - pytest -v -s distributed/test_distributed_oot.py @@ -540,7 +550,7 @@ steps: # see https://github.com/vllm-project/vllm/pull/5689 for details - pytest -v -s distributed/test_custom_all_reduce.py - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py - - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m distributed_2_gpus + - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)' - pytest -v -s -x lora/test_mixtral.py - label: LM Eval Large Models # optional diff --git a/pyproject.toml b/pyproject.toml index 253b706a774a7..c5a14ecf5aea9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -96,7 +96,8 @@ markers = [ "core_model: enable this model test in each PR instead of only nightly", "cpu_model: enable this model test in CPU tests", "quant_model: run this model test under Quantized category", - "distributed_2_gpus: run this test only in distributed tests for 2 GPUs", + "split: run this test as part of a split", + "distributed: run this test only in distributed GPU tests", "skip_v1: do not run this test with v1", "optional: optional tests that are automatically skipped, include --optional to run them", ] diff --git a/tests/models/decoder_only/vision_language/test_models.py b/tests/models/decoder_only/vision_language/test_models.py index ed8f34a677f84..3101d1d2ea831 100644 --- a/tests/models/decoder_only/vision_language/test_models.py +++ b/tests/models/decoder_only/vision_language/test_models.py @@ -1,7 +1,9 @@ """Common tests for testing .generate() functionality for single / multiple image, embedding, and video support for different VLMs in vLLM. """ +import math import os +from collections import defaultdict from pathlib import PosixPath from typing import Type @@ -10,11 +12,12 @@ from transformers.utils import is_flash_attn_2_available from vllm.platforms import current_platform -from vllm.utils import cuda_device_count_stateless, identity +from vllm.utils import identity from ....conftest import (IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets, _VideoAssets) -from ....utils import fork_new_process_for_each_test, large_gpu_mark +from ....utils import (fork_new_process_for_each_test, large_gpu_mark, + multi_gpu_marks) from ...utils import check_outputs_equal from .vlm_utils import custom_inputs, model_utils, runners from .vlm_utils.case_filtering import get_parametrized_options @@ -382,7 +385,7 @@ prompt_path_encoder=model_utils.qwen_prompt_path_encoder, ), ### Tensor parallel / multi-gpu broadcast tests - "broadcast-chameleon": VLMTestInfo( + "chameleon-broadcast": VLMTestInfo( models=["facebook/chameleon-7b"], prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:", max_model_len=4096, @@ -393,43 +396,25 @@ vllm_output_post_proc = lambda vllm_output, model: vllm_output[:2], hf_output_post_proc = lambda hf_output, model: hf_output[:2], comparator=check_outputs_equal, - marks=[ - pytest.mark.distributed_2_gpus, - pytest.mark.skipif( - cuda_device_count_stateless() < 2, - reason="Need at least 2 GPUs to run the test.", - ), - ], + marks=multi_gpu_marks(num_gpus=2), **COMMON_BROADCAST_SETTINGS # type: ignore ), - "broadcast-llava": VLMTestInfo( + "llava-broadcast": VLMTestInfo( models=["llava-hf/llava-1.5-7b-hf"], prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:", max_model_len=4096, auto_cls=AutoModelForVision2Seq, vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output, - marks=[ - pytest.mark.distributed_2_gpus, - pytest.mark.skipif( - cuda_device_count_stateless() < 2, - reason="Need at least 2 GPUs to run the test.", - ) - ], + marks=multi_gpu_marks(num_gpus=2), **COMMON_BROADCAST_SETTINGS # type: ignore ), - "broadcast-llava_next": VLMTestInfo( + "llava_next-broadcast": VLMTestInfo( models=["llava-hf/llava-v1.6-mistral-7b-hf"], prompt_formatter=lambda img_prompt: f"[INST] {img_prompt} [/INST]", max_model_len=10240, auto_cls=AutoModelForVision2Seq, vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output, - marks=[ - pytest.mark.distributed_2_gpus, - pytest.mark.skipif( - cuda_device_count_stateless() < 2, - reason="Need at least 2 GPUs to run the test.", - ) - ], + marks=multi_gpu_marks(num_gpus=2), **COMMON_BROADCAST_SETTINGS # type: ignore ), ### Custom input edge-cases for specific models @@ -468,6 +453,41 @@ # yapf: enable +def _mark_splits( + test_settings: dict[str, VLMTestInfo], + *, + num_groups: int, +) -> dict[str, VLMTestInfo]: + name_by_test_info_id = {id(v): k for k, v in test_settings.items()} + test_infos_by_model = defaultdict[str, list[VLMTestInfo]](list) + + for info in test_settings.values(): + for model in info.models: + test_infos_by_model[model].append(info) + + models = sorted(test_infos_by_model.keys()) + split_size = math.ceil(len(models) / num_groups) + + new_test_settings = dict[str, VLMTestInfo]() + + for i in range(num_groups): + models_in_group = models[i * split_size:(i + 1) * split_size] + + for model in models_in_group: + for info in test_infos_by_model[model]: + new_marks = (info.marks or []) + [pytest.mark.split(group=i)] + new_info = info._replace(marks=new_marks) + new_test_settings[name_by_test_info_id[id(info)]] = new_info + + missing_keys = test_settings.keys() - new_test_settings.keys() + assert not missing_keys, f"Missing keys: {missing_keys}" + + return new_test_settings + + +VLM_TEST_SETTINGS = _mark_splits(VLM_TEST_SETTINGS, num_groups=2) + + ### Test wrappers # Wrappers around the core test running func for: # - single image diff --git a/tests/utils.py b/tests/utils.py index a893667e144a6..afeb708f3bcdc 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -682,10 +682,12 @@ def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None: def large_gpu_mark(min_gb: int) -> pytest.MarkDecorator: - """Gets a pytest skipif mark, which triggers ig the the device doesn't have - meet a minimum memory requirement in gb; can be leveraged via - @large_gpu_test to skip tests in environments without enough resources, or - called when filtering tests to run directly. + """ + Get a pytest mark, which skips the test if the GPU doesn't meet + a minimum memory requirement in GB. + + This can be leveraged via `@large_gpu_test` to skip tests in environments + without enough resources, or called when filtering tests to run directly. """ try: if current_platform.is_cpu(): @@ -712,26 +714,37 @@ def large_gpu_test(*, min_gb: int): Currently, the CI machine uses L4 GPU which has 24 GB VRAM. """ - test_skipif = large_gpu_mark(min_gb) + mark = large_gpu_mark(min_gb) def wrapper(f: Callable[_P, None]) -> Callable[_P, None]: - return test_skipif(f) + return mark(f) return wrapper -def multi_gpu_test(*, num_gpus: int): - """ - Decorate a test to be run only when multiple GPUs are available. - """ - test_selector = getattr(pytest.mark, f"distributed_{num_gpus}_gpus") +def multi_gpu_marks(*, num_gpus: int): + """Get a collection of pytest marks to apply for `@multi_gpu_test`.""" + test_selector = pytest.mark.distributed(num_gpus=num_gpus) test_skipif = pytest.mark.skipif( cuda_device_count_stateless() < num_gpus, reason=f"Need at least {num_gpus} GPUs to run the test.", ) + return [test_selector, test_skipif] + + +def multi_gpu_test(*, num_gpus: int): + """ + Decorate a test to be run only when multiple GPUs are available. + """ + marks = multi_gpu_marks(num_gpus=num_gpus) + def wrapper(f: Callable[_P, None]) -> Callable[_P, None]: - return test_selector(test_skipif(fork_new_process_for_each_test(f))) + func = fork_new_process_for_each_test(f) + for mark in reversed(marks): + func = mark(func) + + return func return wrapper From 452a723bf2e8410ee9b47f82f90c7ea48aa6d14f Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Wed, 11 Dec 2024 18:34:54 -0500 Subject: [PATCH 47/79] [V1][Core] Remove should_shutdown to simplify core process termination (#11113) Signed-off-by: Tyler Michael Smith --- vllm/v1/engine/core.py | 13 ++----------- vllm/v1/engine/core_client.py | 6 ------ 2 files changed, 2 insertions(+), 17 deletions(-) diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index 55a5c4dff3a5c..a26ffe74a3ae8 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -5,7 +5,6 @@ import threading import time from multiprocessing.process import BaseProcess -from multiprocessing.sharedctypes import Synchronized from typing import List, Tuple, Type, Union import zmq @@ -133,13 +132,9 @@ def __init__( input_path: str, output_path: str, ready_path: str, - should_shutdown: Synchronized, ): super().__init__(vllm_config, executor_class, usage_context) - # Signal from main process to shutdown (multiprocessing.Value). - self.should_shutdown = should_shutdown - # Background Threads and Queues for IO. These enable us to # overlap ZMQ socket IO with GPU since they release the GIL, # and to overlap some serialization/deserialization with the @@ -195,7 +190,6 @@ def make_engine_core_process( input_path: str, output_path: str, ready_path: str, - should_shutdown: Synchronized, ) -> BaseProcess: # The current process might have CUDA context, # so we need to spawn a new process. @@ -210,7 +204,6 @@ def make_engine_core_process( "vllm_config": vllm_config, "executor_class": executor_class, "usage_context": usage_context, - "should_shutdown": should_shutdown } # Run EngineCore busy loop in background process. proc = context.Process(target=EngineCoreProc.run_engine_core, @@ -260,8 +253,8 @@ def signal_handler(signum, frame): def run_busy_loop(self): """Core busy loop of the EngineCore.""" - # Loop until we get a shutdown signal. - while not self.should_shutdown: + # Loop until process is sent a SIGINT or SIGTERM + while True: # 1) Poll the input queue until there is work to do. if not self.scheduler.has_unfinished_requests(): while True: @@ -272,8 +265,6 @@ def run_busy_loop(self): except queue.Empty: self._log_stats() logger.debug("EngineCore busy loop waiting.") - if self.should_shutdown: - return except BaseException: raise diff --git a/vllm/v1/engine/core_client.py b/vllm/v1/engine/core_client.py index 4d96b323d1662..1d5ddf4db4d7c 100644 --- a/vllm/v1/engine/core_client.py +++ b/vllm/v1/engine/core_client.py @@ -1,5 +1,4 @@ import atexit -import multiprocessing from typing import List, Union import msgspec @@ -149,21 +148,16 @@ def __init__( self.input_socket.bind(input_path) # Start EngineCore in background process. - self.should_shutdown = multiprocessing.Value('b', False, lock=False) self.proc = EngineCoreProc.make_engine_core_process( *args, input_path=input_path, output_path=output_path, ready_path=ready_path, - should_shutdown=self.should_shutdown, **kwargs, ) atexit.register(self.shutdown) def shutdown(self): - # Send shutdown signal to background process. - self.should_shutdown = True - # Shut down the zmq context. self.ctx.destroy(linger=0) From 4e116833686f3e0c0a223b05b5859ad76843a017 Mon Sep 17 00:00:00 2001 From: Alexander Matveev <59768536+alexm-neuralmagic@users.noreply.github.com> Date: Wed, 11 Dec 2024 19:55:30 -0500 Subject: [PATCH 48/79] [V1] VLM preprocessor hashing (#11020) Signed-off-by: Roger Wang Signed-off-by: Alexander Matveev Co-authored-by: Michael Goin Co-authored-by: Roger Wang --- examples/offline_inference_vision_language.py | 126 ++++++++++++-- requirements-common.txt | 1 + tests/v1/engine/test_engine_core.py | 1 + tests/v1/engine/test_engine_core_client.py | 1 + vllm/config.py | 10 +- vllm/engine/arg_utils.py | 8 + vllm/v1/engine/__init__.py | 3 +- vllm/v1/engine/core.py | 18 +- vllm/v1/engine/mm_input_mapper.py | 156 ++++++++++++++++-- vllm/v1/engine/processor.py | 35 ++-- vllm/v1/utils.py | 21 +++ 11 files changed, 332 insertions(+), 48 deletions(-) diff --git a/examples/offline_inference_vision_language.py b/examples/offline_inference_vision_language.py index c6a274ee5894b..5e210126dc8fe 100644 --- a/examples/offline_inference_vision_language.py +++ b/examples/offline_inference_vision_language.py @@ -5,6 +5,8 @@ For most models, the prompt format should follow corresponding examples on HuggingFace model repository. """ +import random + from transformers import AutoTokenizer from vllm import LLM, SamplingParams @@ -23,7 +25,9 @@ def run_llava(question: str, modality: str): prompt = f"USER: \n{question}\nASSISTANT:" - llm = LLM(model="llava-hf/llava-1.5-7b-hf", max_model_len=4096) + llm = LLM(model="llava-hf/llava-1.5-7b-hf", + max_model_len=4096, + mm_cache_preprocessor=args.mm_cache_preprocessor) stop_token_ids = None return llm, prompt, stop_token_ids @@ -33,7 +37,9 @@ def run_llava_next(question: str, modality: str): assert modality == "image" prompt = f"[INST] \n{question} [/INST]" - llm = LLM(model="llava-hf/llava-v1.6-mistral-7b-hf", max_model_len=8192) + llm = LLM(model="llava-hf/llava-v1.6-mistral-7b-hf", + max_model_len=8192, + mm_cache_preprocessor=args.mm_cache_preprocessor) stop_token_ids = None return llm, prompt, stop_token_ids @@ -44,7 +50,9 @@ def run_llava_next_video(question: str, modality: str): assert modality == "video" prompt = f"USER: