[BUGFIX] Skip tokenization support for throughput benchmark (#12712)

Signed-off-by: root <root@banff-cyxtera-s73-5.ctr.dcgpu>
Signed-off-by: Aleksandr Malyshev <maleksan@amd.com>
Co-authored-by: root <root@banff-cyxtera-s73-5.ctr.dcgpu>
Co-authored-by: Aleksandr Malyshev <maleksan@amd.com>

benchmarks/benchmark_throughput.py

@@ -7,7 +7,7 @@ import os
 import random
 import time
 from functools import cache
-from typing import Any, Optional
+from typing import Any, Optional, Union
 
 import torch
 import uvloop
@@ -20,7 +20,7 @@ from transformers import (AutoModelForCausalLM, AutoTokenizer,
 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.inputs import TextPrompt, TokensPrompt
 from vllm.lora.request import LoRARequest
 from vllm.lora.utils import get_adapter_absolute_path
 from vllm.multimodal import MultiModalDataDict
@@ -178,10 +178,13 @@ def run_vllm(
             "Please ensure that max_model_len is greater than the sum of"
             " prompt_len and expected_output_len for all requests.")
     # Add the requests to the engine.
-    prompts: list[TextPrompt] = []
+    prompts: list[Union[TextPrompt, TokensPrompt]] = []
     sampling_params: list[SamplingParams] = []
     for request in requests:
         prompts.append(
+            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                       multi_modal_data=request.multi_modal_data)
+            if "prompt_token_ids" in request.prompt else \
             TextPrompt(prompt=request.prompt,
                        multi_modal_data=request.multi_modal_data))
         sampling_params.append(
@@ -242,11 +245,14 @@ async def run_vllm_async(
                 " prompt_len and expected_output_len for all requests.")
 
         # Add the requests to the engine.
-        prompts: list[TextPrompt] = []
+        prompts: list[Union[TextPrompt, TokensPrompt]] = []
         sampling_params: list[SamplingParams] = []
         lora_requests: list[Optional[LoRARequest]] = []
         for request in requests:
             prompts.append(
+                TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                        multi_modal_data=request.multi_modal_data)
+                if "prompt_token_ids" in request.prompt else \
                 TextPrompt(prompt=request.prompt,
                            multi_modal_data=request.multi_modal_data))
             sampling_params.append(
@@ -393,24 +399,29 @@ def main(args: argparse.Namespace):
                 random.randint(0, vocab_size - 1)
                 for _ in range(args.input_len)
             ]
-            # As tokenizer may add additional tokens like BOS, we need to try
-            # different lengths to get the desired input length.
-            for _ in range(5):  # Max attempts to correct
-                candidate_prompt = request_tokenizer.decode(candidate_ids)
-                tokenized_len = len(request_tokenizer.encode(candidate_prompt))
-
-                if tokenized_len == args.input_len:
-                    break
-
-                # Adjust length based on difference
-                diff = args.input_len - tokenized_len
-                if diff > 0:
-                    candidate_ids.extend([
-                        random.randint(100, vocab_size - 100)
-                        for _ in range(diff)
-                    ])
-                else:
-                    candidate_ids = candidate_ids[:diff]
+
+            candidate_prompt = {"prompt_token_ids": candidate_ids}
+
+            if not args.skip_tokenizer_init:
+                # As tokenizer may add additional tokens like BOS, we need
+                # to try different lengths to get the desired input length.
+                for _ in range(5):  # Max attempts to correct
+                    candidate_prompt = request_tokenizer.decode(candidate_ids)
+                    tokenized_len = len(
+                        request_tokenizer.encode(candidate_prompt))
+
+                    if tokenized_len == args.input_len:
+                        break
+
+                    # Adjust length based on difference
+                    diff = args.input_len - tokenized_len
+                    if diff > 0:
+                        candidate_ids.extend([
+                            random.randint(100, vocab_size - 100)
+                            for _ in range(diff)
+                        ])
+                    else:
+                        candidate_ids = candidate_ids[:diff]
             requests.append(
                 SampleRequest(prompt=candidate_prompt,
                               prompt_len=args.input_len,

vllm/engine/arg_utils.py

@@ -276,7 +276,9 @@ class EngineArgs:
         parser.add_argument(
             '--skip-tokenizer-init',
             action='store_true',
-            help='Skip initialization of tokenizer and detokenizer.')
+            help='Skip initialization of tokenizer and detokenizer. '
+            'Expects valid prompt_token_ids and None for prompt from '
+            'the input. The generated output will contain token ids.')
         parser.add_argument(
             '--revision',
             type=nullable_str,