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Commit c71ac7cc authored by jerrrrry's avatar jerrrrry
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# 使用官方光源基础镜像
FROM 2.4.1-ubuntu22.04-dtk25.04-py3.10:vllm0.6.2
# 创建目标目录
RUN mkdir -p /workspace/test/results
# 设置工作目录/workspace/test/results方便挂载结果
WORKDIR /workspace/test/results
# 将主机上的 test.sh 复制到容器中的 /workspace/test
COPY ./run.sh /workspace/test
COPY ./benchmark_throughput_0.6.2.py /workspace/test
COPY ./topo.xml /workspace/test
COPY ./models-to-test.cfg /workspace/test
# 确保 test.sh 有可执行权限
RUN chmod +x /workspace/test/run.sh
# 设置容器启动时运行的命令
# 使用单个 CMD 执行所有命令
CMD bash -c "\
rocm-bandwidth-test > rocm-bandwidth-test.txt && \
hy-smi > hy-smi.txt && \
hy-smi -c > hy-smi-c.txt && \
pip list > pip-list.txt && \
lscpu > lscpu.txt && \
bash /workspace/test/run.sh > test.log 2>&1"
\ No newline at end of file
benchmark_throughput_0.6.2.py 0 → 100644
View file @ c71ac7cc
"""Benchmark offline inference throughput."""
import argparse
import json
import random
import time
from typing import List, Optional, Tuple
import numpy as np
import torch
import uvloop
from tqdm import tqdm
from transformers import (AutoModelForCausalLM, AutoTokenizer,
PreTrainedTokenizerBase)
from vllm.inputs import PromptInputs
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.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 run_vllm(
warmup_requests: List[Tuple[str, int, int]],
requests_json: List[Tuple[str, int, int]],
model: str,
tokenizer: str,
quantization: Optional[str],
tensor_parallel_size: int,
seed: int,
n: int,
use_beam_search: bool,
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,
use_v2_block_manager: bool = False,
download_dir: Optional[str] = None,
load_format: str = EngineArgs.load_format,
disable_async_output_proc: bool = False,
use_new_beam_search_impl: 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,
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,
use_v2_block_manager=use_v2_block_manager,
disable_async_output_proc=disable_async_output_proc,
)
# warmup
warmup_prompts = []
warmup_sampling_params = []
for prompt, _, output_len in warmup_requests:
warmup_prompts.append(prompt)
warmup_sampling_params.append(
SamplingParams(
n=n,
temperature=0.0 if use_beam_search else 1.0,
top_p=1.0,
use_beam_search=use_beam_search,
ignore_eos=True,
max_tokens=output_len,
))
print("Warming up...")
for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
llm.generate(warmup_prompts, warmup_sampling_params, use_tqdm=True)
info_json={}
for ELEprompt in args.num_prompts:
for ELEinput,ELEoutput in zip(args.input_len,args.output_len):
info={}
requests=requests_json["{}_{}_{}".format(ELEprompt,ELEinput,ELEoutput)]
# 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=0.0 if use_beam_search else 1.0,
top_p=1.0,
use_beam_search=use_beam_search,
ignore_eos=True,
max_tokens=output_len,
))
if not use_new_beam_search_impl:
start = time.perf_counter()
real_output = llm.generate(prompts, sampling_params, use_tqdm=True)
end = time.perf_counter()
else:
assert use_beam_search
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,
beam_width=n,
max_tokens=output_len,
ignore_eos=True)
end = time.perf_counter()
total_ttfts = []
total_tpops = []
total_output_token_throughput = []
total_inout_token_throughput = []
for output in real_output:
ttft_ = output.metrics.first_token_time - output.metrics.arrival_time
tpop_ = (output.metrics.finished_time - output.metrics.arrival_time - ttft_) / (ELEoutput-1)
output_token_throughput = (ELEoutput) / (output.metrics.finished_time - output.metrics.arrival_time)
inout_token_throughput = (ELEoutput + ELEinput) / (output.metrics.finished_time - output.metrics.arrival_time)
total_ttfts.append(ttft_)
total_tpops.append(tpop_)
total_output_token_throughput.append(output_token_throughput)
total_inout_token_throughput.append(inout_token_throughput)
# 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)
total_num_tokens = sum(prompt_len + output_len
for _, prompt_len, output_len in requests)
total_output_tokens = sum(output_len
for _, prompt_len, output_len in requests)
info["elapsed_time"] = np.around(end - start,2)
info["Throughput"] = np.around(len(requests) / info['elapsed_time'],2)
info["total_tokens"] = np.around(total_num_tokens / info['elapsed_time'],2)
info["output_tokens"] = np.around(total_output_tokens / info['elapsed_time'],2)
info["ttft_mean"] = np.around(np.mean(total_ttfts),5)
info["ttft_median"] = np.around(np.median(total_ttfts or 0),5)
info["ttft_p99"] = np.around(np.percentile(total_ttfts or 0, 99),5)
info["tpop_mean"] = np.around(np.mean(total_tpops),4)
info["tpop_median"] = np.around(np.median(total_tpops or 0),5)
info["tpop_p99"] = np.around(np.percentile(total_tpops or 0, 99),5)
info["output_token_throughput_mean"] = np.around(np.mean(total_output_token_throughput),2)
info["output_token_throughput_median"] = np.around(np.median(total_output_token_throughput or 0),2)
info["output_token_throughput_p99"] = np.around(np.percentile(total_output_token_throughput or 0, 99),2)
info["inout_token_throughput_mean"] = np.around(np.mean(total_inout_token_throughput),2)
info["inout_token_throughput_median"] = np.around(np.median(total_inout_token_throughput or 0),2)
info["inout_token_throughput_p99"] = np.around(np.percentile(total_inout_token_throughput or 0, 99),2)
info_json["{}_{}_{}".format(ELEprompt,ELEinput,ELEoutput)] = info
print("promt:{},input:{},output:{}".format(ELEprompt,ELEinput,ELEoutput))
print(f"Latency: {info['elapsed_time']:.2f} s")
print(f"Throughput: {len(requests) / info['elapsed_time']:.2f} requests/s, "
f"{total_num_tokens / info['elapsed_time']:.2f} total tokens/s, "
f"{total_output_tokens / info['elapsed_time']:.2f} output tokens/s")
print("==============================================")
print(f"total_out_tokens: {total_output_tokens: .2f} tokens")
print(f"elapsed_time: {info['elapsed_time']: .2f} s") # 总耗时
print(f"TTFT_mean: {info['ttft_mean']: .5f} s") # 首字延时
print(f"ttft_p99: {info['ttft_p99']: .5f} s")
print(f"ttft_median: {info['ttft_median']: .5f} s")
print(f"TPOP_mean: {info['tpop_mean']: .5f} s") # 单字decode时间
print(f"tpop_median: {info['tpop_median']: .5f} s")
print(f"tpop_p99: {info['tpop_p99']: .5f} s")
print(f"output_token_throughput_mean: {info['output_token_throughput_mean']:.2f} tokens/s") # 单路生成吞吐
print(f"output_token_throughput_median: {info['output_token_throughput_median']:.2f} tokens/s")
print(f"output_token_throughput_p99: {info['output_token_throughput_p99']:.2f} tokens/s")
print(f"inout_token_throughput_mean: {info['inout_token_throughput_mean']:.2f} tokens/s") # 单路总吞吐
print(f"tinout_token_throughput_median: {info['inout_token_throughput_median']:.2f} tokens/s")
print(f"inout_token_throughput_p99: {info['inout_token_throughput_p99']:.2f} tokens/s")
print("==============================================")
print("\n")
return info_json
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,
use_beam_search: bool,
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,
use_v2_block_manager: bool = False,
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,
use_v2_block_manager=use_v2_block_manager,
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=0.0 if use_beam_search else 1.0,
top_p=1.0,
use_beam_search=use_beam_search,
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,
use_beam_search: bool,
max_batch_size: int,
trust_remote_code: bool,
) -> float:
assert not use_beam_search
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=not use_beam_search,
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)
warmup_prompt = "hi" * 10
warmup_requests = [(warmup_prompt, 10, 10)
for _ in range(1)]
if args.dataset is None:
requests_json={}
for ELEprompt in args.num_prompts:
for ELEinput,ELEoutput in zip(args.input_len,args.output_len):
# Synthesize a prompt with the given input length.
prompt = "hi" * (ELEinput - 1)
requests = [(prompt, ELEinput, ELEoutput)
for _ in range(ELEprompt)]
print("type(requests):",type(requests))
requests_json["{}_{}_{}".format(ELEprompt,ELEinput,ELEoutput)]=requests
else:
requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
args.output_len)
if args.backend == "vllm":
if args.async_engine:
run_args = [
requests, args.model, args.tokenizer, args.quantization,
args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
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.use_v2_block_manager, args.download_dir, args.load_format,
args.disable_async_output_proc
]
else:
run_args = [
warmup_requests, requests_json, args.model, args.tokenizer, args.quantization,
args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
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.use_v2_block_manager, 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:
info_json = run_vllm(*run_args, args.use_new_beam_search_impl)
elif args.backend == "hf":
assert args.tensor_parallel_size == 1
elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
args.use_beam_search, 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)
# if args.dataset is None:
# total_out_tokens = args.output_len * args.num_prompts
# else:
# total_out_tokens = sum(output_len for _, _, output_len in requests)
# print(f"Latency: {elapsed_time:.2f} s")
# print(f"All Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
# f"{total_num_tokens / elapsed_time:.2f} tokens/s")
# print(f"Generate Throughput: {total_out_tokens / elapsed_time:.2f} tokens/s")
with open(args.output_json,"w") as f:
title="bs_in_out"
data_keys=info_json[list(info_json.keys())[0]].keys()
keys_string = ','.join(data_keys)
title=title+","+keys_string
f.write(title)
f.write("\n")
for key, value in info_json.items():
values_as_strings = [str(value) for value in info_json[key].values()]
values_string = ','.join(values_as_strings)
key=key+","+values_string
f.writelines(key)
f.write("\n")
# 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,
nargs="*",
default=None,
help="Input prompt length for each request")
parser.add_argument("--output-len",
type=int,
nargs="*",
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("--use-beam-search", action="store_true")
parser.add_argument('--num-iters-warmup',
type=int,
default=1,
help='Number of iterations to run for warmup.')
parser.add_argument("--use-new-beam-search-impl", action="store_true")
parser.add_argument("--num-prompts",
nargs="*",
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 (hcu) 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 (hcu), 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("--use-v2-block-manager",
action='store_true',
help="Enable block manager v2.")
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.use_beam_search:
raise ValueError("Beam search is not supported 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)
\ No newline at end of file
models-to-test.cfg 0 → 100644
View file @ c71ac7cc
# 格式说明:
# 模型名称;模型路径;tp;batch;prompt_tokens;completion_tokens;dtype;max_model_len;gpu_memory_utilization
# 多个值用逗号分隔
DeepSeek-R1-Distill-Qwen-1.5B;/workspace/llms/DeepSeek-R1-Distill-Qwen-1.5B;1;1,2,4;128,512,1024;1024,1024,1024;float16;32768;0.95
DeepSeek-R1-Distill-Qwen-7B;/workspace/llms/DeepSeek-R1-Distill-Qwen-7B;4;1,2;128,512;512,1024;bfloat16;4096;0.95
DeepSeek-R1-Distill-Llama-8B;/workspace/llms/DeepSeek-R1-Distill-Llama-8B;1;1,2,4,8;128,256,512,1024;256,512,1024,2048;float16;8192;0.95
\ No newline at end of file
run.sh 0 → 100644
View file @ c71ac7cc
export HIP_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export ROCBLAS_COMPUTETYPE_FP16R=0
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export NCCL_ALGO=Ring
export NCCL_LAUNCH_MODE=GROUP
export NCCL_NCHANNELS_PER_PEER=16
export NCCL_MAX_NCHANNELS=16
export NCCL_MIN_NCHANNELS=16
export NCCL_IB_TIMEOUT=22
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NCCL_IB_HCA=mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_5:1,mlx5_6:1,mlx5_7:1,mlx5_8:1,mlx5_9:1
export NCCL_P2P_LEVEL=SYS
export NCCL_NET_GDR_LEVEL=7
export NCCL_NET_GDR_READ=1
export RCCL_SDMA_COPY_ENABLE=0
export SENDRECV_STREAM_WITH_COMPUTE=1
export NCCL_TOPO_FILE="/workspace/test/topo.xml"
export LD_LIBRARY_PATH=/usr/local/lib/python3.10/site-packages/torch/lib/:$LD_LIBRARY_PATH
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export VLLM_NUMA_BIND=1
export VLLM_RANK0_NUMA=3
export VLLM_RANK1_NUMA=1
export VLLM_RANK2_NUMA=1
export VLLM_RANK3_NUMA=0
export VLLM_RANK4_NUMA=7
export VLLM_RANK5_NUMA=5
export VLLM_RANK6_NUMA=5
export VLLM_RANK7_NUMA=4
export VLLM_RPC_TIMEOUT=100000
#!/bin/bash
# 模型配置文件路径
MODELS_CONFIG="/workspace/test/models-to-test.cfg"
# 结果目录
RESULTS_DIR="/workspace/test/results"
# 读取配置文件，跳过注释和空行
while IFS= read -r line || [[ -n "$line" ]]; do
# 跳过注释行和空行
if [[ "$line" =~ ^# ]] || [[ -z "$line" ]]; then
continue
fi
# 解析配置行
IFS=';' read -ra CONFIG <<< "$line"
model_name="${CONFIG[0]}"
model_path="${CONFIG[1]}"
tp="${CONFIG[2]}"
batch="${CONFIG[3]//,/ }" # 将逗号替换为空格
prompt_tokens="${CONFIG[4]//,/ }"
completion_tokens="${CONFIG[5]//,/ }"
dtype="${CONFIG[6]}"
max_model_len="${CONFIG[7]}"
gpu_memory_utilization="${CONFIG[8]}"
echo "开始测试模型: $model_name"
echo "模型路径: $model_path"
echo "参数配置:"
echo " tensor_parallel_size: $tp"
echo " batch_sizes: $batch"
echo " prompt_tokens: $prompt_tokens"
echo " completion_tokens: $completion_tokens"
echo " dtype: $dtype"
echo " max_model_len: $max_model_len"
echo " gpu_memory_utilization: $gpu_memory_utilization"
# 创建模型专属结果目录
model_result_dir="${RESULTS_DIR}/${model_name}"
mkdir -p "$model_result_dir"
# 运行基准测试
python /workspace/test/benchmark_throughput_0.6.2.py \
--model "$model_path" \
--tensor-parallel-size "$tp" \
--num-prompts $batch \
--input-len $prompt_tokens \
--output-len $completion_tokens \
--dtype "$dtype" \
--trust-remote-code \
--max-model-len "$max_model_len" \
--gpu-memory-utilization "$gpu_memory_utilization" \
--output-json "${model_result_dir}/${model_name}_tp${tp}.txt" \
2>&1 | tee "${model_result_dir}/${model_name}_tp${tp}.log"
echo "完成测试模型: $model_name"
echo "结果保存在: $model_result_dir"
echo "----------------------------------------"
done < "$MODELS_CONFIG"
\ No newline at end of file
start.sh 0 → 100644
View file @ c71ac7cc
docker build -t vllm-test . && docker run -v /public/opendas/DL_DATA/llm-models:/workspace/llms -v /usr/local/hyhal:/usr/local/hyhal:ro --ipc=host --device=/dev/kfd --device=/dev/mkfd --device=/dev/dri --shm-size=500G --security-opt seccomp=unconfined -v /opt/hyhal:/opt/hyhal:ro -v $PWD/results:/workspace/test/results vllm-test
\ No newline at end of file
topo.xml 0 → 100644
View file @ c71ac7cc
<system version="2">
<cpu numaid="3" affinity="00000000,00000000,ffff0000,00000000,00000000,00000000,ffff0000,00000000" arch="x86_64" vendor="HygonGenuine" familyid="159" modelid="4">
<pci busid="0000:99:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:9d:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:9f:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="0" sm="93" gcn="gfx936" arch="169983" rank="0" gdr="1">
<xgmi target="0000:56:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:5d:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:05:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:e5:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:ca:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:b1:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:c1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
<pci busid="0000:51:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:54:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:56:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="1" sm="93" gcn="gfx936" arch="169983" rank="1" gdr="1">
<xgmi target="0000:9f:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:5d:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:05:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:e5:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:ca:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:b1:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:c1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
</pci>
<pci busid="0000:9b:00.0" class="0x020000" vendor="0x15b3" device="0x1021" subsystem_vendor="0x15b3" subsystem_device="0x0022" link_speed="32.0 GT/s PCIe" link_width="16">
<nic>
<net name="mlx5_2" dev="2" speed="200000" port="1" latency="0.000000" guid="0x2227a1000373255c" maxconn="131072" gdr="1"/>
<net name="mlx5_3" dev="3" speed="200000" port="2" latency="0.000000" guid="0x2227a1000373255c" maxconn="131072" gdr="1"/>
</nic>
</pci>
</pci>
</cpu>
<cpu numaid="0" affinity="00000000,00000000,00000000,0000ffff,00000000,00000000,00000000,0000ffff" arch="x86_64" vendor="HygonGenuine" familyid="159" modelid="4">
<pci busid="0000:01:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:03:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:05:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="3" sm="93" gcn="gfx936" arch="169983" rank="3" gdr="1">
<xgmi target="0000:9f:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:56:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:5d:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:e5:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:ca:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:b1:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:c1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
<pci busid="0000:59:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:5b:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:5d:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="2" sm="93" gcn="gfx936" arch="169983" rank="2" gdr="1">
<xgmi target="0000:9f:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:56:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:05:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:e5:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:ca:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:b1:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:c1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
</pci>
<pci busid="0000:06:00.0" class="0x020000" vendor="0x15b3" device="0x1021" subsystem_vendor="0x15b3" subsystem_device="0x0022" link_speed="32.0 GT/s PCIe" link_width="16">
<nic>
<net name="mlx5_4" dev="4" speed="200000" port="1" latency="0.000000" guid="0x8228a1000373255c" maxconn="131072" gdr="1"/>
<net name="mlx5_5" dev="5" speed="200000" port="2" latency="0.000000" guid="0x8228a1000373255c" maxconn="131072" gdr="1"/>
</nic>
</pci>
</pci>
</cpu>
<cpu numaid="7" affinity="7fff0000,00000000,00000000,00000000,ffff0000,00000000,00000000,00000000" arch="x86_64" vendor="HygonGenuine" familyid="159" modelid="4">
<pci busid="0000:e1:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:e3:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:e5:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="4" sm="93" gcn="gfx936" arch="169983" rank="4" gdr="1">
<xgmi target="0000:9f:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:56:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:5d:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:05:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:ca:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:b1:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:c1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
<pci busid="0000:bd:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:bf:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:c1:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="5" sm="93" gcn="gfx936" arch="169983" rank="5" gdr="1">
<xgmi target="0000:9f:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:56:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:5d:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:05:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:e5:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:ca:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:b1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
</pci>
<pci busid="0000:e6:00.0" class="0x020000" vendor="0x15b3" device="0x1021" subsystem_vendor="0x15b3" subsystem_device="0x0022" link_speed="32.0 GT/s PCIe" link_width="16">
<nic>
<net name="mlx5_6" dev="6" speed="200000" port="1" latency="0.000000" guid="0x6227a1000373255c" maxconn="131072" gdr="1"/>
<net name="mlx5_7" dev="7" speed="200000" port="2" latency="0.000000" guid="0x6227a1000373255c" maxconn="131072" gdr="1"/>
</nic>
</pci>
</pci>
</cpu>
<cpu numaid="4" affinity="00000000,0000ffff,00000000,00000000,00000000,0000ffff,00000000,00000000" arch="x86_64" vendor="HygonGenuine" familyid="159" modelid="4">
<pci busid="0000:ab:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:af:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:b1:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="7" sm="93" gcn="gfx936" arch="169983" rank="7" gdr="1">
<xgmi target="0000:9f:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:56:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:5d:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:05:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:e5:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:ca:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:c1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
<pci busid="0000:c5:00.0" class="0x060400" vendor="0x1000" device="0xc030" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:c8:00.0" class="0x060400" vendor="0x1d94" device="0x23b7" subsystem_vendor="0x1000" subsystem_device="0x100b" link_speed="32.0 GT/s PCIe" link_width="16">
<pci busid="0000:ca:00.0" class="0x0b4000" vendor="0x1d94" device="0x6320" subsystem_vendor="0x1d94" subsystem_device="0x6310" link_speed="32.0 GT/s PCIe" link_width="16">
<gpu dev="6" sm="93" gcn="gfx936" arch="169983" rank="6" gdr="1">
<xgmi target="0000:9f:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:56:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:5d:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:05:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:e5:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:b1:00.0" count="7" tclass="0x0b4000"/>
<xgmi target="0000:c1:00.0" count="7" tclass="0x0b4000"/>
</gpu>
</pci>
</pci>
</pci>
<pci busid="0000:ad:00.0" class="0x020000" vendor="0x15b3" device="0x1021" subsystem_vendor="0x15b3" subsystem_device="0x0022" link_speed="32.0 GT/s PCIe" link_width="16">
<nic>
<net name="mlx5_8" dev="8" speed="200000" port="1" latency="0.000000" guid="0xd226a1000373255c" maxconn="131072" gdr="1"/>
<net name="mlx5_9" dev="9" speed="200000" port="2" latency="0.000000" guid="0xd226a1000373255c" maxconn="131072" gdr="1"/>
</nic>
</pci>
</pci>
</cpu>
<cpu numaid="2" affinity="00000000,00000000,0000ffff,00000000,00000000,00000000,0000ffff,00000000" arch="x86_64" vendor="HygonGenuine" familyid="159" modelid="4">
<pci busid="0000:71:00.0" class="0x020000" vendor="0x15b3" device="0xa2dc" subsystem_vendor="0x15b3" subsystem_device="0x0009" link_speed="32.0 GT/s PCIe" link_width="16">
<nic>
<net name="mlx5_0" dev="0" speed="200000" port="1" latency="0.000000" guid="0xc0d00a000324e9b8" maxconn="131072" gdr="1"/>
<net name="mlx5_1" dev="1" speed="40000" port="2" latency="0.000000" guid="0xc0d00a000324e9b8" maxconn="131072" gdr="1"/>
</nic>
</pci>
</cpu>
</system>
\ No newline at end of file
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