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Multiprocess Parallel Random Data Generation for Benchmark Serving. #1038

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180 changes: 152 additions & 28 deletions utils/bench_serving/benchmark_serving.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@
import warnings
from dataclasses import dataclass
from datetime import datetime
from multiprocessing import Pool, cpu_count
from typing import Any, AsyncGenerator, Collection, Dict, List, Optional, Tuple

import numpy as np
Expand Down Expand Up @@ -88,6 +89,64 @@ class BenchmarkMetrics:
percentiles_e2el_ms: List[Tuple[float, float]]


# --- Multiprocessing helpers for sample_random_requests ---
_worker_tokenizer = None


def _init_tokenizer_worker(tokenizer_id, tokenizer_mode, trust_remote_code):
"""Initialize tokenizer once per worker process."""
global _worker_tokenizer
_worker_tokenizer = get_tokenizer(
tokenizer_id,
tokenizer_mode=tokenizer_mode,
trust_remote_code=trust_remote_code,
)


def _process_prompt_chunk(chunk_args):
"""Generate a chunk of random prompts in a worker process."""
(indices, prefix_token_ids, input_lens, output_lens, offsets,
prefix_len, vocab_size, use_chat_template, seed) = chunk_args

rng = np.random.RandomState(seed)
tokenizer = _worker_tokenizer

results = []
for local_idx, global_idx in enumerate(indices):
tgt_prompt_len = prefix_len + input_lens[local_idx]
prompt_token_ids = prefix_token_ids + [
(offsets[local_idx] + global_idx + j) % vocab_size
for j in range(input_lens[local_idx])
]
prompt = tokenizer.decode(prompt_token_ids)

max_retries = 10
for _ in range(max_retries):
prompt_token_ids = tokenizer.encode(prompt, add_special_tokens=False)
if len(prompt_token_ids) < tgt_prompt_len:
num_extras = tgt_prompt_len - len(prompt_token_ids)
prompt_token_ids.extend(
rng.randint(0, vocab_size, size=num_extras).tolist())
elif len(prompt_token_ids) > tgt_prompt_len:
prompt_token_ids = prompt_token_ids[:tgt_prompt_len]
else:
break
prompt = tokenizer.decode(prompt_token_ids)

if use_chat_template:
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
add_generation_prompt=True,
tokenize=False,
)

prompt_len = len(tokenizer.encode(prompt, add_special_tokens=False))
mismatch = prompt_len - tgt_prompt_len
results.append((prompt, prompt_len, output_lens[local_idx], None, mismatch))

return results


def sample_random_requests(
prefix_len: int,
input_len: int,
Expand All @@ -96,8 +155,13 @@ def sample_random_requests(
range_ratio: float,
tokenizer: PreTrainedTokenizerBase,
use_chat_template: bool = False,
tokenizer_id: Optional[str] = None,
tokenizer_mode: str = "auto",
trust_remote_code: bool = False,
num_workers: int = 0,
) -> List[Tuple[str, int, int]]:
prefix_token_ids = np.random.randint(0, tokenizer.vocab_size, size=prefix_len).tolist()
vocab_size = tokenizer.vocab_size
prefix_token_ids = np.random.randint(0, vocab_size, size=prefix_len).tolist()

if use_chat_template:
chat_template_dummy = tokenizer.apply_chat_template(
Expand All @@ -117,37 +181,85 @@ def sample_uniform(seq_len):

input_lens = sample_uniform(input_len)
output_lens = sample_uniform(output_len)
offsets = np.random.randint(0, tokenizer.vocab_size, size=num_prompts)

input_requests = []
mismatches = []
for i in range(num_prompts):
tgt_prompt_len = prefix_len + input_lens[i]
prompt_token_ids = prefix_token_ids + [(offsets[i] + i + j) % tokenizer.vocab_size for j in range(input_lens[i])]
prompt = tokenizer.decode(prompt_token_ids)

max_retries = 10
for _ in range(max_retries):
prompt_token_ids = tokenizer.encode(prompt, add_special_tokens=False)
if len(prompt_token_ids) < tgt_prompt_len:
num_extras = tgt_prompt_len - len(prompt_token_ids)
prompt_token_ids.extend(np.random.randint(0, tokenizer.vocab_size, size=num_extras).tolist())
elif len(prompt_token_ids) > tgt_prompt_len:
prompt_token_ids = prompt_token_ids[:tgt_prompt_len]
else:
offsets = np.random.randint(0, vocab_size, size=num_prompts)

# Decide whether to use multiprocessing
if num_workers <= 0:
num_workers = min(cpu_count() or 1, 8)
use_parallel = num_workers > 1 and tokenizer_id is not None

if use_parallel:
# Split work into chunks, one per worker
chunk_size = (num_prompts + num_workers - 1) // num_workers
chunk_args_list = []
for w in range(num_workers):
start = w * chunk_size
end = min(start + chunk_size, num_prompts)
if start >= num_prompts:
break
chunk_args_list.append((
list(range(start, end)),
prefix_token_ids,
input_lens[start:end],
output_lens[start:end],
offsets[start:end].tolist(),
prefix_len,
vocab_size,
use_chat_template,
int(np.random.randint(0, 2**31)),
))

actual_workers = len(chunk_args_list)
print(f"Generating {num_prompts} prompts using {actual_workers} worker processes...")
t0 = time.perf_counter()
with Pool(
processes=actual_workers,
initializer=_init_tokenizer_worker,
initargs=(tokenizer_id, tokenizer_mode, trust_remote_code),
) as pool:
chunk_results = pool.map(_process_prompt_chunk, chunk_args_list)

input_requests = []
mismatches = []
for chunk in chunk_results:
for prompt, prompt_len, out_len, mm_content, mismatch in chunk:
input_requests.append((prompt, prompt_len, out_len, mm_content))
mismatches.append(mismatch)
elapsed = time.perf_counter() - t0
print(f"Prompt generation completed in {elapsed:.1f}s")
else:
# Original serial path
if tokenizer_id is None and num_workers > 1:
print("Warning: tokenizer_id not provided, falling back to serial prompt generation.")
input_requests = []
mismatches = []
for i in range(num_prompts):
tgt_prompt_len = prefix_len + input_lens[i]
prompt_token_ids = prefix_token_ids + [(offsets[i] + i + j) % vocab_size for j in range(input_lens[i])]
prompt = tokenizer.decode(prompt_token_ids)

if use_chat_template:
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
add_generation_prompt=True,
tokenize=False,
)
max_retries = 10
for _ in range(max_retries):
prompt_token_ids = tokenizer.encode(prompt, add_special_tokens=False)
if len(prompt_token_ids) < tgt_prompt_len:
num_extras = tgt_prompt_len - len(prompt_token_ids)
prompt_token_ids.extend(np.random.randint(0, vocab_size, size=num_extras).tolist())
elif len(prompt_token_ids) > tgt_prompt_len:
prompt_token_ids = prompt_token_ids[:tgt_prompt_len]
else:
break
prompt = tokenizer.decode(prompt_token_ids)

prompt_len = len(tokenizer.encode(prompt, add_special_tokens=False))
mismatches.append(prompt_len - tgt_prompt_len)
input_requests.append((prompt, prompt_len, output_lens[i], None))
if use_chat_template:
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
add_generation_prompt=True,
tokenize=False,
)

prompt_len = len(tokenizer.encode(prompt, add_special_tokens=False))
mismatches.append(prompt_len - tgt_prompt_len)
input_requests.append((prompt, prompt_len, output_lens[i], None))

header_str = f'{"-"*19} Input/Output Length Statistics {"-"*19}'
print(header_str)
Expand Down Expand Up @@ -663,6 +775,10 @@ def main(args: argparse.Namespace):
range_ratio=args.random_range_ratio,
tokenizer=tokenizer,
use_chat_template=args.use_chat_template,
tokenizer_id=tokenizer_id,
tokenizer_mode=tokenizer_mode,
trust_remote_code=args.trust_remote_code,
num_workers=args.random_num_workers,
)

else:
Expand Down Expand Up @@ -1023,6 +1139,14 @@ def main(args: argparse.Namespace):
action="store_true",
help="Use chat template to format the prompt.",
)
random_group.add_argument(
'--random-num-workers',
type=int,
default=0,
help="Number of worker processes for parallel random prompt generation. "
"Only used with --dataset-name random. "
"0 (default) = auto (min(cpu_count, 8)). 1 = serial (no multiprocessing).",
)

hf_group = parser.add_argument_group("hf dataset options")
hf_group.add_argument("--hf-subset",
Expand Down