mirror of
https://github.com/allenai/olmocr.git
synced 2025-10-31 10:04:26 +00:00
710 lines
26 KiB
Python
710 lines
26 KiB
Python
"""Benchmark offline inference throughput."""
|
|
import argparse
|
|
import json
|
|
import random
|
|
import time
|
|
import base64
|
|
|
|
from typing import List, Optional, Tuple
|
|
from PIL import Image
|
|
from io import BytesIO
|
|
|
|
import torch
|
|
import uvloop
|
|
from tqdm import tqdm
|
|
from transformers import (AutoModelForCausalLM, AutoTokenizer,
|
|
PreTrainedTokenizerBase, AutoProcessor)
|
|
|
|
from vllm import TokensPrompt
|
|
from vllm.engine.arg_utils import DEVICE_OPTIONS, AsyncEngineArgs, EngineArgs
|
|
from vllm.entrypoints.openai.api_server import (
|
|
build_async_engine_client_from_engine_args)
|
|
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
|
|
from vllm.sampling_params import BeamSearchParams
|
|
from vllm.utils import FlexibleArgumentParser, merge_async_iterators
|
|
|
|
|
|
def sample_requests(
|
|
dataset_path: str,
|
|
num_requests: int,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
fixed_output_len: Optional[int],
|
|
) -> List[Tuple[str, int, int]]:
|
|
if fixed_output_len is not None and fixed_output_len < 4:
|
|
raise ValueError("output_len too small")
|
|
|
|
# Load the dataset.
|
|
with open(dataset_path) as f:
|
|
dataset = json.load(f)
|
|
# Filter out the conversations with less than 2 turns.
|
|
dataset = [data for data in dataset if len(data["conversations"]) >= 2]
|
|
# Only keep the first two turns of each conversation.
|
|
dataset = [(data["conversations"][0]["value"],
|
|
data["conversations"][1]["value"]) for data in dataset]
|
|
|
|
# Shuffle the dataset.
|
|
random.shuffle(dataset)
|
|
|
|
# Filter out sequences that are too long or too short
|
|
filtered_dataset: List[Tuple[str, int, int]] = []
|
|
for i in range(len(dataset)):
|
|
if len(filtered_dataset) == num_requests:
|
|
break
|
|
|
|
# Tokenize the prompts and completions.
|
|
prompt = dataset[i][0]
|
|
prompt_token_ids = tokenizer(prompt).input_ids
|
|
completion = dataset[i][1]
|
|
completion_token_ids = tokenizer(completion).input_ids
|
|
prompt_len = len(prompt_token_ids)
|
|
output_len = len(completion_token_ids
|
|
) if fixed_output_len is None else fixed_output_len
|
|
if prompt_len < 4 or output_len < 4:
|
|
# Prune too short sequences.
|
|
continue
|
|
if prompt_len > 1024 or prompt_len + output_len > 2048:
|
|
# Prune too long sequences.
|
|
continue
|
|
filtered_dataset.append((prompt, prompt_len, output_len))
|
|
|
|
return filtered_dataset
|
|
|
|
|
|
def sample_mm_requests_qwen2vl(
|
|
dataset_path: str,
|
|
num_requests: int,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
fixed_output_len: Optional[int],
|
|
):
|
|
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
|
|
|
|
with open(dataset_path, "r") as f:
|
|
json_data = [json.loads(line) for line in f.readlines() if len(line.strip()) > 0]
|
|
|
|
result = []
|
|
|
|
for data in tqdm(json_data):
|
|
text = processor.apply_chat_template(
|
|
data["chat_messages"], tokenize=False, add_generation_prompt=True
|
|
)
|
|
|
|
raw_b64 = data["chat_messages"][0]["content"][1]["image_url"]["url"]
|
|
main_image = Image.open(BytesIO(base64.b64decode(raw_b64[raw_b64.find(",") + 1:])))
|
|
|
|
# Process inputs using processor
|
|
inputs = processor(
|
|
text=[text],
|
|
#images=[main_image], # Don't pad out the image tokens yet, since that happens later inside of birr
|
|
padding=True,
|
|
return_tensors="np",
|
|
)
|
|
|
|
# print(inputs)
|
|
|
|
tokens = inputs["input_ids"][0]
|
|
prompt_len = len(tokens)
|
|
|
|
result.append((TokensPrompt(
|
|
dict(
|
|
prompt_token_ids=tokens,
|
|
multi_modal_data=dict(image=dict(image_embeds=torch.randn(1036, 3584), image_grid_thw=torch.tensor([[1, 74, 56]]))),
|
|
# multi_modal_data=dict(image=main_image)
|
|
)
|
|
), prompt_len, fixed_output_len))
|
|
|
|
if len(result) >= num_requests:
|
|
break
|
|
|
|
return result
|
|
|
|
|
|
def sample_mm_requests_phi3(
|
|
dataset_path: str,
|
|
num_requests: int,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
fixed_output_len: Optional[int],
|
|
):
|
|
processor = AutoProcessor.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True)
|
|
|
|
with open(dataset_path, "r") as f:
|
|
json_data = [json.loads(line) for line in f.readlines() if len(line.strip()) > 0]
|
|
|
|
result = []
|
|
|
|
for data in tqdm(json_data):
|
|
inputs = processor.tokenizer.apply_chat_template([
|
|
{"role": "user", "content": "<|image_1|>\n" + data["chat_messages"][0]["content"][0]["text"] }
|
|
], tokenize=True, add_generation_prompt=True
|
|
)
|
|
|
|
raw_b64 = data["chat_messages"][0]["content"][1]["image_url"]["url"]
|
|
main_image = Image.open(BytesIO(base64.b64decode(raw_b64[raw_b64.find(",") + 1:])))
|
|
|
|
|
|
#tokens = inputs["input_ids"][0]
|
|
tokens = inputs
|
|
prompt_len = len(tokens)
|
|
|
|
result.append((TokensPrompt(
|
|
dict(
|
|
prompt_token_ids=tokens,
|
|
multi_modal_data=dict(image=main_image),
|
|
)
|
|
), prompt_len, fixed_output_len))
|
|
|
|
if len(result) >= num_requests:
|
|
break
|
|
|
|
return result
|
|
|
|
|
|
def sample_mm_requests_molmo(
|
|
dataset_path: str,
|
|
num_requests: int,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
fixed_output_len: Optional[int],
|
|
):
|
|
processor = AutoProcessor.from_pretrained(
|
|
'allenai/Molmo-7B-D-0924',
|
|
trust_remote_code=True,
|
|
torch_dtype='auto',
|
|
device_map='auto'
|
|
)
|
|
|
|
with open(dataset_path, "r") as f:
|
|
json_data = [json.loads(line) for line in f.readlines() if len(line.strip()) > 0]
|
|
|
|
result = []
|
|
|
|
for data in tqdm(json_data):
|
|
raw_b64 = data["chat_messages"][0]["content"][1]["image_url"]["url"]
|
|
main_image = Image.open(BytesIO(base64.b64decode(raw_b64[raw_b64.find(",") + 1:])))
|
|
|
|
inputs = inputs = processor.process(
|
|
images=[main_image],
|
|
text=data["chat_messages"][0]["content"][0]["text"]
|
|
)
|
|
|
|
#print(inputs)
|
|
|
|
# Molmo has max size of 4096 which is lower than our dataset was generated for
|
|
tokens = inputs["input_ids"][:2000]
|
|
#tokens = inputs
|
|
prompt_len = len(tokens)
|
|
|
|
result.append((TokensPrompt(
|
|
dict(
|
|
prompt_token_ids=tokens,
|
|
multi_modal_data=dict(image=main_image),
|
|
)
|
|
), prompt_len, fixed_output_len))
|
|
|
|
if len(result) >= num_requests:
|
|
break
|
|
|
|
return result
|
|
|
|
def run_vllm(
|
|
requests: List[Tuple[str, int, int]],
|
|
model: str,
|
|
tokenizer: str,
|
|
quantization: Optional[str],
|
|
tensor_parallel_size: int,
|
|
seed: int,
|
|
n: int,
|
|
trust_remote_code: bool,
|
|
dtype: str,
|
|
max_model_len: Optional[int],
|
|
enforce_eager: bool,
|
|
kv_cache_dtype: str,
|
|
quantization_param_path: Optional[str],
|
|
device: str,
|
|
enable_prefix_caching: bool,
|
|
enable_chunked_prefill: bool,
|
|
max_num_batched_tokens: int,
|
|
distributed_executor_backend: Optional[str],
|
|
gpu_memory_utilization: float = 0.9,
|
|
num_scheduler_steps: int = 1,
|
|
download_dir: Optional[str] = None,
|
|
load_format: str = EngineArgs.load_format,
|
|
disable_async_output_proc: bool = False,
|
|
) -> float:
|
|
from vllm import LLM, SamplingParams
|
|
llm = LLM(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
quantization=quantization,
|
|
tensor_parallel_size=tensor_parallel_size,
|
|
seed=seed,
|
|
trust_remote_code=trust_remote_code,
|
|
dtype=dtype,
|
|
|
|
# speculative_model="[ngram]",
|
|
# num_speculative_tokens=1,
|
|
# ngram_prompt_lookup_max=5,
|
|
|
|
max_model_len=max_model_len,
|
|
gpu_memory_utilization=gpu_memory_utilization,
|
|
enforce_eager=enforce_eager,
|
|
kv_cache_dtype=kv_cache_dtype,
|
|
quantization_param_path=quantization_param_path,
|
|
device=device,
|
|
enable_prefix_caching=enable_prefix_caching,
|
|
download_dir=download_dir,
|
|
enable_chunked_prefill=enable_chunked_prefill,
|
|
max_num_batched_tokens=max_num_batched_tokens,
|
|
distributed_executor_backend=distributed_executor_backend,
|
|
load_format=load_format,
|
|
num_scheduler_steps=num_scheduler_steps,
|
|
disable_async_output_proc=disable_async_output_proc,
|
|
)
|
|
|
|
# Add the requests to the engine.
|
|
prompts: List[str] = []
|
|
sampling_params: List[SamplingParams] = []
|
|
for prompt, _, output_len in requests:
|
|
prompts.append(prompt)
|
|
sampling_params.append(
|
|
SamplingParams(
|
|
n=n,
|
|
temperature=1.0,
|
|
top_p=1.0,
|
|
ignore_eos=True,
|
|
max_tokens=output_len,
|
|
))
|
|
|
|
use_beam_search = False
|
|
|
|
if not use_beam_search:
|
|
start = time.perf_counter()
|
|
llm.generate(prompts, sampling_params, use_tqdm=True)
|
|
end = time.perf_counter()
|
|
else:
|
|
prompts = [prompt for prompt, _, _ 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
|
|
start = time.perf_counter()
|
|
llm.beam_search(
|
|
prompts,
|
|
BeamSearchParams(
|
|
beam_width=n,
|
|
max_tokens=output_len,
|
|
ignore_eos=True,
|
|
))
|
|
end = time.perf_counter()
|
|
return end - start
|
|
|
|
|
|
async def run_vllm_async(
|
|
requests: List[Tuple[str, int, int]],
|
|
model: str,
|
|
tokenizer: str,
|
|
quantization: Optional[str],
|
|
tensor_parallel_size: int,
|
|
seed: int,
|
|
n: int,
|
|
trust_remote_code: bool,
|
|
dtype: str,
|
|
max_model_len: Optional[int],
|
|
enforce_eager: bool,
|
|
kv_cache_dtype: str,
|
|
quantization_param_path: Optional[str],
|
|
device: str,
|
|
enable_prefix_caching: bool,
|
|
enable_chunked_prefill: bool,
|
|
max_num_batched_tokens: int,
|
|
distributed_executor_backend: Optional[str],
|
|
gpu_memory_utilization: float = 0.9,
|
|
num_scheduler_steps: int = 1,
|
|
download_dir: Optional[str] = None,
|
|
load_format: str = EngineArgs.load_format,
|
|
disable_async_output_proc: bool = False,
|
|
disable_frontend_multiprocessing: bool = False,
|
|
) -> float:
|
|
from vllm import SamplingParams
|
|
engine_args = AsyncEngineArgs(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
quantization=quantization,
|
|
tensor_parallel_size=tensor_parallel_size,
|
|
seed=seed,
|
|
trust_remote_code=trust_remote_code,
|
|
dtype=dtype,
|
|
max_model_len=max_model_len,
|
|
gpu_memory_utilization=gpu_memory_utilization,
|
|
enforce_eager=enforce_eager,
|
|
kv_cache_dtype=kv_cache_dtype,
|
|
quantization_param_path=quantization_param_path,
|
|
device=device,
|
|
enable_prefix_caching=enable_prefix_caching,
|
|
download_dir=download_dir,
|
|
enable_chunked_prefill=enable_chunked_prefill,
|
|
max_num_batched_tokens=max_num_batched_tokens,
|
|
distributed_executor_backend=distributed_executor_backend,
|
|
load_format=load_format,
|
|
num_scheduler_steps=num_scheduler_steps,
|
|
disable_async_output_proc=disable_async_output_proc,
|
|
worker_use_ray=False,
|
|
disable_log_requests=True,
|
|
)
|
|
|
|
async with build_async_engine_client_from_engine_args(
|
|
engine_args, disable_frontend_multiprocessing) as llm:
|
|
|
|
# Add the requests to the engine.
|
|
prompts: List[str] = []
|
|
sampling_params: List[SamplingParams] = []
|
|
for prompt, _, output_len in requests:
|
|
prompts.append(prompt)
|
|
sampling_params.append(
|
|
SamplingParams(
|
|
n=n,
|
|
temperature=1.0,
|
|
top_p=1.0,
|
|
ignore_eos=True,
|
|
max_tokens=output_len,
|
|
))
|
|
|
|
generators = []
|
|
start = time.perf_counter()
|
|
for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
|
|
generator = llm.generate(prompt, sp, request_id=f"test{i}")
|
|
generators.append(generator)
|
|
all_gens = merge_async_iterators(*generators)
|
|
async for i, res in all_gens:
|
|
pass
|
|
end = time.perf_counter()
|
|
return end - start
|
|
|
|
|
|
def run_hf(
|
|
requests: List[Tuple[str, int, int]],
|
|
model: str,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
n: int,
|
|
max_batch_size: int,
|
|
trust_remote_code: bool,
|
|
) -> float:
|
|
llm = AutoModelForCausalLM.from_pretrained(
|
|
model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
|
|
if llm.config.model_type == "llama":
|
|
# To enable padding in the HF backend.
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
llm = llm.cuda()
|
|
|
|
pbar = tqdm(total=len(requests))
|
|
start = time.perf_counter()
|
|
batch: List[str] = []
|
|
max_prompt_len = 0
|
|
max_output_len = 0
|
|
for i in range(len(requests)):
|
|
prompt, prompt_len, output_len = requests[i]
|
|
# Add the prompt to the batch.
|
|
batch.append(prompt)
|
|
max_prompt_len = max(max_prompt_len, prompt_len)
|
|
max_output_len = max(max_output_len, output_len)
|
|
if len(batch) < max_batch_size and i != len(requests) - 1:
|
|
# Check if we can add more requests to the batch.
|
|
_, next_prompt_len, next_output_len = requests[i + 1]
|
|
if (max(max_prompt_len, next_prompt_len) +
|
|
max(max_output_len, next_output_len)) <= 2048:
|
|
# We can add more requests to the batch.
|
|
continue
|
|
|
|
# Generate the sequences.
|
|
input_ids = tokenizer(batch, return_tensors="pt",
|
|
padding=True).input_ids
|
|
llm_outputs = llm.generate(
|
|
input_ids=input_ids.cuda(),
|
|
do_sample=True,
|
|
num_return_sequences=n,
|
|
temperature=1.0,
|
|
top_p=1.0,
|
|
use_cache=True,
|
|
max_new_tokens=max_output_len,
|
|
)
|
|
# Include the decoding time.
|
|
tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
|
|
pbar.update(len(batch))
|
|
|
|
# Clear the batch.
|
|
batch = []
|
|
max_prompt_len = 0
|
|
max_output_len = 0
|
|
end = time.perf_counter()
|
|
return end - start
|
|
|
|
|
|
def run_mii(
|
|
requests: List[Tuple[str, int, int]],
|
|
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]
|
|
|
|
start = time.perf_counter()
|
|
llm.generate(prompts, max_new_tokens=output_len)
|
|
end = time.perf_counter()
|
|
client = client(model)
|
|
client.terminate_server()
|
|
return end - start
|
|
|
|
|
|
def main(args: argparse.Namespace):
|
|
print(args)
|
|
random.seed(args.seed)
|
|
|
|
# Sample the requests.
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
args.tokenizer, trust_remote_code=args.trust_remote_code)
|
|
if args.dataset is None:
|
|
# Synthesize a prompt with the given input length.
|
|
prompt = "hi" * (args.input_len - 1)
|
|
requests = [(prompt, args.input_len, args.output_len)
|
|
for _ in range(args.num_prompts)]
|
|
else:
|
|
# requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
|
|
# args.output_len)
|
|
requests = sample_mm_requests_qwen2vl(args.dataset, args.num_prompts, tokenizer,
|
|
args.output_len)
|
|
|
|
if args.backend == "vllm":
|
|
run_args = [
|
|
requests, args.model, args.tokenizer, args.quantization,
|
|
args.tensor_parallel_size, args.seed, args.n,
|
|
args.trust_remote_code, args.dtype, args.max_model_len,
|
|
args.enforce_eager, args.kv_cache_dtype,
|
|
args.quantization_param_path, args.device,
|
|
args.enable_prefix_caching, args.enable_chunked_prefill,
|
|
args.max_num_batched_tokens, args.distributed_executor_backend,
|
|
args.gpu_memory_utilization, args.num_scheduler_steps,
|
|
args.download_dir, args.load_format, args.disable_async_output_proc
|
|
]
|
|
|
|
if args.async_engine:
|
|
run_args.append(args.disable_frontend_multiprocessing)
|
|
elapsed_time = uvloop.run(run_vllm_async(*run_args))
|
|
else:
|
|
elapsed_time = run_vllm(*run_args)
|
|
elif args.backend == "hf":
|
|
assert args.tensor_parallel_size == 1
|
|
elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
|
|
args.hf_max_batch_size, args.trust_remote_code)
|
|
elif args.backend == "mii":
|
|
elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
|
|
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)
|
|
print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
|
|
f"{total_num_tokens / elapsed_time:.2f} tokens/s")
|
|
|
|
# Output JSON results if specified
|
|
if args.output_json:
|
|
results = {
|
|
"elapsed_time": elapsed_time,
|
|
"num_requests": len(requests),
|
|
"total_num_tokens": total_num_tokens,
|
|
"requests_per_second": len(requests) / elapsed_time,
|
|
"tokens_per_second": total_num_tokens / elapsed_time,
|
|
}
|
|
with open(args.output_json, "w") as f:
|
|
json.dump(results, f, indent=4)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = FlexibleArgumentParser(description="Benchmark the throughput.")
|
|
parser.add_argument("--backend",
|
|
type=str,
|
|
choices=["vllm", "hf", "mii"],
|
|
default="vllm")
|
|
parser.add_argument("--dataset",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the dataset.")
|
|
parser.add_argument("--input-len",
|
|
type=int,
|
|
default=None,
|
|
help="Input prompt length for each request")
|
|
parser.add_argument("--output-len",
|
|
type=int,
|
|
default=None,
|
|
help="Output length for each request. Overrides the "
|
|
"output length from the dataset.")
|
|
parser.add_argument("--model", type=str, default="facebook/opt-125m")
|
|
parser.add_argument("--tokenizer", type=str, default=None)
|
|
parser.add_argument('--quantization',
|
|
'-q',
|
|
choices=[*QUANTIZATION_METHODS, None],
|
|
default=None)
|
|
parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
|
|
parser.add_argument("--n",
|
|
type=int,
|
|
default=1,
|
|
help="Number of generated sequences per prompt.")
|
|
parser.add_argument("--num-prompts",
|
|
type=int,
|
|
default=1000,
|
|
help="Number of prompts to process.")
|
|
parser.add_argument("--seed", type=int, default=0)
|
|
parser.add_argument("--hf-max-batch-size",
|
|
type=int,
|
|
default=None,
|
|
help="Maximum batch size for HF backend.")
|
|
parser.add_argument('--trust-remote-code',
|
|
action='store_true',
|
|
help='trust remote code from huggingface')
|
|
parser.add_argument(
|
|
'--max-model-len',
|
|
type=int,
|
|
default=None,
|
|
help='Maximum length of a sequence (including prompt and output). '
|
|
'If None, will be derived from the model.')
|
|
parser.add_argument(
|
|
'--dtype',
|
|
type=str,
|
|
default='auto',
|
|
choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
|
|
help='data type for model weights and activations. '
|
|
'The "auto" option will use FP16 precision '
|
|
'for FP32 and FP16 models, and BF16 precision '
|
|
'for BF16 models.')
|
|
parser.add_argument('--gpu-memory-utilization',
|
|
type=float,
|
|
default=0.9,
|
|
help='the fraction of GPU memory to be used for '
|
|
'the model executor, which can range from 0 to 1.'
|
|
'If unspecified, will use the default value of 0.9.')
|
|
parser.add_argument("--enforce-eager",
|
|
action="store_true",
|
|
help="enforce eager execution")
|
|
parser.add_argument(
|
|
'--kv-cache-dtype',
|
|
type=str,
|
|
choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
|
|
default="auto",
|
|
help='Data type for kv cache storage. If "auto", will use model '
|
|
'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
|
|
'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
|
|
parser.add_argument(
|
|
'--quantization-param-path',
|
|
type=str,
|
|
default=None,
|
|
help='Path to the JSON file containing the KV cache scaling factors. '
|
|
'This should generally be supplied, when KV cache dtype is FP8. '
|
|
'Otherwise, KV cache scaling factors default to 1.0, which may cause '
|
|
'accuracy issues. FP8_E5M2 (without scaling) is only supported on '
|
|
'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is '
|
|
'instead supported for common inference criteria.')
|
|
parser.add_argument("--device",
|
|
type=str,
|
|
default="auto",
|
|
choices=DEVICE_OPTIONS,
|
|
help='device type for vLLM execution')
|
|
parser.add_argument(
|
|
"--num-scheduler-steps",
|
|
type=int,
|
|
default=1,
|
|
help="Maximum number of forward steps per scheduler call.")
|
|
parser.add_argument(
|
|
"--enable-prefix-caching",
|
|
action='store_true',
|
|
help="Enable automatic prefix caching for vLLM backend.")
|
|
parser.add_argument("--enable-chunked-prefill",
|
|
action='store_true',
|
|
help="enable chunked prefill for vLLM backend.")
|
|
parser.add_argument('--max-num-batched-tokens',
|
|
type=int,
|
|
default=None,
|
|
help='maximum number of batched tokens per '
|
|
'iteration')
|
|
parser.add_argument('--download-dir',
|
|
type=str,
|
|
default=None,
|
|
help='directory to download and load the weights, '
|
|
'default to the default cache dir of huggingface')
|
|
parser.add_argument(
|
|
'--output-json',
|
|
type=str,
|
|
default=None,
|
|
help='Path to save the throughput results in JSON format.')
|
|
parser.add_argument(
|
|
'--distributed-executor-backend',
|
|
choices=['ray', 'mp'],
|
|
default=None,
|
|
help='Backend to use for distributed serving. When more than 1 GPU '
|
|
'is used, will be automatically set to "ray" if installed '
|
|
'or "mp" (multiprocessing) otherwise.')
|
|
parser.add_argument(
|
|
'--load-format',
|
|
type=str,
|
|
default=EngineArgs.load_format,
|
|
choices=[
|
|
'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer',
|
|
'bitsandbytes'
|
|
],
|
|
help='The format of the model weights to load.\n\n'
|
|
'* "auto" will try to load the weights in the safetensors format '
|
|
'and fall back to the pytorch bin format if safetensors format '
|
|
'is not available.\n'
|
|
'* "pt" will load the weights in the pytorch bin format.\n'
|
|
'* "safetensors" will load the weights in the safetensors format.\n'
|
|
'* "npcache" will load the weights in pytorch format and store '
|
|
'a numpy cache to speed up the loading.\n'
|
|
'* "dummy" will initialize the weights with random values, '
|
|
'which is mainly for profiling.\n'
|
|
'* "tensorizer" will load the weights using tensorizer from '
|
|
'CoreWeave. See the Tensorize vLLM Model script in the Examples'
|
|
'section for more information.\n'
|
|
'* "bitsandbytes" will load the weights using bitsandbytes '
|
|
'quantization.\n')
|
|
parser.add_argument(
|
|
"--disable-async-output-proc",
|
|
action='store_true',
|
|
default=False,
|
|
help="Disable async output processor for vLLM backend.")
|
|
parser.add_argument("--async-engine",
|
|
action='store_true',
|
|
default=False,
|
|
help="Use vLLM async engine rather than LLM class.")
|
|
parser.add_argument("--disable-frontend-multiprocessing",
|
|
action='store_true',
|
|
default=False,
|
|
help="Disable decoupled async engine frontend.")
|
|
args = parser.parse_args()
|
|
if args.tokenizer is None:
|
|
args.tokenizer = args.model
|
|
if args.dataset is None:
|
|
assert args.input_len is not None
|
|
assert args.output_len is not None
|
|
else:
|
|
assert args.input_len is None
|
|
|
|
if args.backend == "vllm":
|
|
if args.hf_max_batch_size is not None:
|
|
raise ValueError("HF max batch size is only for HF backend.")
|
|
elif args.backend == "hf":
|
|
if args.hf_max_batch_size is None:
|
|
raise ValueError("HF max batch size is required for HF backend.")
|
|
if args.quantization is not None:
|
|
raise ValueError("Quantization is only for vLLM backend.")
|
|
elif args.backend == "mii":
|
|
if args.dtype != "auto":
|
|
raise ValueError("dtype must be auto for MII backend.")
|
|
if args.n != 1:
|
|
raise ValueError("n must be 1 for MII backend.")
|
|
if args.quantization is not None:
|
|
raise ValueError("Quantization is only for vLLM backend.")
|
|
if args.hf_max_batch_size is not None:
|
|
raise ValueError("HF max batch size is only for HF backend.")
|
|
if args.tokenizer != args.model:
|
|
raise ValueError("Tokenizer must be the same as the model for MII "
|
|
"backend.")
|
|
main(args)
|