[Misc] Refactor benchmark_throughput.py

benchmarks/benchmark_throughput.py

@@ -4,7 +4,7 @@
 import json
 import random
 import time
-from typing import List, Optional, Tuple
+from typing import List, Optional
 
 import torch
 import uvloop
@@ -15,16 +15,35 @@
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.entrypoints.openai.api_server import (
     build_async_engine_client_from_engine_args)
+from vllm.inputs import TextPrompt
+from vllm.multimodal import MultiModalDataDict
 from vllm.sampling_params import BeamSearchParams
 from vllm.utils import FlexibleArgumentParser, merge_async_iterators
 
 
+@dataclasses.dataclass
+class SampleRequest:
+    """A class representing a single inference request for benchmarking.
+
+    Attributes:
+        prompt: The input text prompt for the model.
+        multi_modal_data: Optional dictionary containing multi-modal data (e.g.
+            images).
+        prompt_len: The length of the prompt in tokens.
+        expected_output_len: The expected length of the output in tokens.
+    """
+    prompt: str
+    prompt_len: int
+    expected_output_len: int
+    multi_modal_data: Optional[MultiModalDataDict] = None
+
+
 def sample_requests(
     dataset_path: str,
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
     fixed_output_len: Optional[int],
-) -> List[Tuple[str, int, int]]:
+) -> List[SampleRequest]:
     if fixed_output_len is not None and fixed_output_len < 4:
         raise ValueError("output_len too small")
 
@@ -41,7 +60,7 @@ def sample_requests(
     random.shuffle(dataset)
 
     # Filter out sequences that are too long or too short
-    filtered_dataset: List[Tuple[str, int, int]] = []
+    filtered_dataset: List[SampleRequest] = []
     for i in range(len(dataset)):
         if len(filtered_dataset) == num_requests:
             break
@@ -60,31 +79,34 @@ def sample_requests(
         if prompt_len > 1024 or prompt_len + output_len > 2048:
             # Prune too long sequences.
             continue
-        filtered_dataset.append((prompt, prompt_len, output_len))
+        filtered_dataset.append(
+            SampleRequest(prompt=prompt,
+                          prompt_len=prompt_len,
+                          expected_output_len=output_len))
 
     return filtered_dataset
 
 
 def run_vllm(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
     n: int,
     engine_args: EngineArgs,
 ) -> float:
     from vllm import LLM, SamplingParams
     llm = LLM(**dataclasses.asdict(engine_args))
 
     # Add the requests to the engine.
-    prompts: List[str] = []
+    prompts: List[TextPrompt] = []
     sampling_params: List[SamplingParams] = []
-    for prompt, _, output_len in requests:
-        prompts.append(prompt)
+    for request in requests:
+        prompts.append(TextPrompt(prompt=request.prompt))
         sampling_params.append(
             SamplingParams(
                 n=n,
                 temperature=1.0,
                 top_p=1.0,
                 ignore_eos=True,
-                max_tokens=output_len,
+                max_tokens=request.expected_output_len,
             ))
 
     use_beam_search = False
@@ -94,11 +116,11 @@ def run_vllm(
         llm.generate(prompts, sampling_params, use_tqdm=True)
         end = time.perf_counter()
     else:
-        prompts = [prompt for prompt, _, _ in requests]
+        prompts = [request.prompt for request in requests]
         # output_len should be the same for all requests.
         output_len = requests[0][2]
-        for prompt, input_len, _output_len in requests:
-            assert _output_len == output_len
+        for request in requests:
+            assert request.expected_output_len == output_len
         start = time.perf_counter()
         llm.beam_search(
             prompts,
@@ -112,7 +134,7 @@ def run_vllm(
 
 
 async def run_vllm_async(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
     n: int,
     engine_args: AsyncEngineArgs,
     disable_frontend_multiprocessing: bool = False,
@@ -123,17 +145,17 @@ async def run_vllm_async(
             engine_args, disable_frontend_multiprocessing) as llm:
 
         # Add the requests to the engine.
-        prompts: List[str] = []
+        prompts: List[TextPrompt] = []
         sampling_params: List[SamplingParams] = []
-        for prompt, _, output_len in requests:
-            prompts.append(prompt)
+        for request in requests:
+            prompts.append(TextPrompt(prompt=request.prompt))
             sampling_params.append(
                 SamplingParams(
                     n=n,
                     temperature=1.0,
                     top_p=1.0,
                     ignore_eos=True,
-                    max_tokens=output_len,
+                    max_tokens=request.expected_output_len,
                 ))
 
         generators = []
@@ -149,7 +171,7 @@ async def run_vllm_async(
 
 
 def run_hf(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
     model: str,
     tokenizer: PreTrainedTokenizerBase,
     n: int,
@@ -207,14 +229,14 @@ def run_hf(
 
 
 def run_mii(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
     model: str,
     tensor_parallel_size: int,
     output_len: int,
 ) -> float:
     from mii import client, serve
     llm = serve(model, tensor_parallel=tensor_parallel_size)
-    prompts = [prompt for prompt, _, _ in requests]
+    prompts = [request.prompt for request in requests]
 
     start = time.perf_counter()
     llm.generate(prompts, max_new_tokens=output_len)
@@ -243,8 +265,12 @@ def main(args: argparse.Namespace):
         else:
             raise ValueError(
                 f"Failed to synthesize a prompt with {args.input_len} tokens.")
-        requests = [(prompt, args.input_len, args.output_len)
-                    for _ in range(args.num_prompts)]
+        requests = [
+            SampleRequest(prompt=prompt,
+                          prompt_len=args.input_len,
+                          expected_output_len=args.output_len)
+            for _ in range(args.num_prompts)
+        ]
     else:
         requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
                                    args.output_len)
@@ -270,9 +296,10 @@ def main(args: argparse.Namespace):
                                args.output_len)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
-    total_num_tokens = sum(prompt_len + output_len
-                           for _, prompt_len, output_len in requests)
-    total_output_tokens = sum(output_len for _, _, output_len in requests)
+    total_num_tokens = sum(request.prompt_len + request.expected_output_len
+                           for request in requests)
+    total_output_tokens = sum(request.expected_output_len
+                              for request in requests)
     print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
           f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
           f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
@@ -299,7 +326,9 @@ def main(args: argparse.Namespace):
     parser.add_argument("--dataset",
                         type=str,
                         default=None,
-                        help="Path to the dataset.")
+                        help="Path to the dataset. The dataset is expected to "
+                        "be a json in form of List[Dict[..., conversations: "
+                        "List[Dict[..., value: <prompt_or_response>]]]]")
     parser.add_argument("--input-len",
                         type=int,
                         default=None,