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Commit 7de30014 authored by zhuwenwen's avatar zhuwenwen
Browse files

update perf

parent 2cfcb974
Show whitespace changes
Inline Side-by-side
Showing with 181 additions and 33 deletions
vllm/perf/backend_request_func.py
View file @ 7de30014
......@@ -34,6 +34,7 @@ class RequestFuncInput:
multi_modal_content: Optional[dict | list[dict]] = None
ignore_eos: bool = False
language: Optional[str] = None
request_id: Optional[str] = None
@dataclass
......@@ -71,6 +72,9 @@ async def async_request_tgi(
"inputs": request_func_input.prompt,
"parameters": params,
}
headers = None
if request_func_input.request_id:
headers = {"x-request-id": request_func_input.request_id}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
if request_func_input.ignore_eos:
......@@ -82,7 +86,9 @@ async def async_request_tgi(
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url, json=payload) as response:
async with session.post(
url=api_url, json=payload, headers=headers
) as response:
if response.status == 200:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
......@@ -145,6 +151,9 @@ async def async_request_trt_llm(
}
if request_func_input.ignore_eos:
payload["min_length"] = request_func_input.output_len
headers = None
if request_func_input.request_id:
headers = {"x-request-id": request_func_input.request_id}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
......@@ -152,7 +161,9 @@ async def async_request_trt_llm(
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url, json=payload) as response:
async with session.post(
url=api_url, json=payload, headers=headers
) as response:
if response.status == 200:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
......@@ -211,6 +222,8 @@ async def async_request_deepspeed_mii(
"top_p": 1.0,
}
headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
if request_func_input.request_id:
headers["x-request-id"] = request_func_input.request_id
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
......@@ -283,6 +296,8 @@ async def async_request_openai_completions(
if request_func_input.extra_body:
payload.update(request_func_input.extra_body)
headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
if request_func_input.request_id:
headers["x-request-id"] = request_func_input.request_id
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
......@@ -395,6 +410,8 @@ async def async_request_openai_chat_completions(
"Content-Type": "application/json",
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
}
if request_func_input.request_id:
headers["x-request-id"] = request_func_input.request_id
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
......@@ -491,6 +508,8 @@ async def async_request_openai_audio(
headers = {
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
}
if request_func_input.request_id:
headers["x-request-id"] = request_func_input.request_id
# Send audio file
def to_bytes(y, sr):
......
vllm/perf/benchmark_dataset.py
View file @ 7de30014
......@@ -19,6 +19,7 @@ import logging
import random
from abc import ABC, abstractmethod
from collections.abc import Mapping
from copy import deepcopy
from dataclasses import dataclass
from functools import cache
from io import BytesIO
......@@ -54,6 +55,7 @@ class SampleRequest:
expected_output_len: int
multi_modal_data: Optional[Union[MultiModalDataDict, dict, list[dict]]] = None
lora_request: Optional[LoRARequest] = None
request_id: Optional[str] = None
# -----------------------------------------------------------------------------
......@@ -155,7 +157,10 @@ class BenchmarkDataset(ABC):
@abstractmethod
def sample(
self, tokenizer: PreTrainedTokenizerBase, num_requests: int
self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
request_id_prefix: str = "",
) -> list[SampleRequest]:
"""
Abstract method to generate sample requests from the dataset.
......@@ -167,6 +172,7 @@ class BenchmarkDataset(ABC):
tokenizer (PreTrainedTokenizerBase): The tokenizer to be used
for processing the dataset's text.
num_requests (int): The number of sample requests to generate.
request_id_prefix (str) The prefix of request_id.
Returns:
list[SampleRequest]: A list of sample requests generated from the
......@@ -175,7 +181,10 @@ class BenchmarkDataset(ABC):
raise NotImplementedError("sample must be implemented in subclasses.")
def maybe_oversample_requests(
self, requests: list[SampleRequest], num_requests: int
self,
requests: list[SampleRequest],
num_requests: int,
request_id_prefix: str = "",
) -> None:
"""
Oversamples the list of requests if its size is less than the desired
......@@ -183,11 +192,18 @@ class BenchmarkDataset(ABC):
Args:
requests (List[SampleRequest]): The current list of sampled
requests. num_requests (int): The target number of requests.
requests.
num_requests (int): The target number of requests.
request_id_prefix (str) The prefix of the request ids.
"""
if len(requests) < num_requests:
random.seed(self.random_seed)
additional = random.choices(requests, k=num_requests - len(requests))
additional = deepcopy(
random.choices(requests, k=num_requests - len(requests))
)
for i in range(len(additional)):
req = additional[i]
req.request_id = request_id_prefix + str(len(requests) + i)
requests.extend(additional)
logger.info("Oversampled requests to reach %d total samples.", num_requests)
......@@ -277,6 +293,41 @@ def process_image(image: Any) -> Mapping[str, Any]:
)
def process_video(video: Any) -> Mapping[str, Any]:
"""
Process a single video input and return a multimedia content dictionary.
Supports the following input types:
1. Dictionary with raw video bytes: - Expects a dict with a 'bytes' key
containing raw video data.
2. String input: - Treats the string as a URL or local file path. -
Prepends "file://" if the string doesn't start with "http://" or
"file://". - Returns a dictionary with the image URL.
Raises:
ValueError: If the input is not a supported type.
"""
if isinstance(video, dict) and "bytes" in video:
video_bytes = video["bytes"]
video_base64 = base64.b64encode(video_bytes).decode("utf-8")
return {
"type": "video_url",
"video_url": {"url": f"data:video/mp4;base64,{video_base64}"},
}
if isinstance(video, str):
video_url = (
video if video.startswith(("http://", "file://")) else f"file://{video}"
)
return {"type": "video_url", "video_url": {"url": video_url}}
raise ValueError(
f"Invalid video input {video}. Must be a string of local path/remote url, or a dictionary with raw video bytes in the form of `{{'bytes': raw_video_bytes}}`." # noqa: E501
)
# -----------------------------------------------------------------------------
# Random Dataset Implementation (Synthetic Data)
# -----------------------------------------------------------------------------
......@@ -303,6 +354,7 @@ class RandomDataset(BenchmarkDataset):
range_ratio: float = DEFAULT_RANGE_RATIO,
input_len: int = DEFAULT_INPUT_LEN,
output_len: int = DEFAULT_OUTPUT_LEN,
request_id_prefix: str = "",
**kwargs,
) -> list[SampleRequest]:
# Enforce range_ratio < 1
......@@ -363,8 +415,10 @@ class RandomDataset(BenchmarkDataset):
prompt=prompt,
prompt_len=total_input_len,
expected_output_len=int(output_lens[i]),
request_id=request_id_prefix + str(i),
)
)
return requests
......@@ -406,9 +460,11 @@ class ShareGPTDataset(BenchmarkDataset):
max_loras: Optional[int] = None,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
request_id_prefix: str = "",
**kwargs,
) -> list:
samples: list = []
ind = 0
for entry in self.data:
if len(samples) >= num_requests:
break
......@@ -430,9 +486,10 @@ class ShareGPTDataset(BenchmarkDataset):
skip_min_output_len_check=output_len is not None,
):
continue
# TODO: Also support ShareGPT4Video.
if image_path := entry.get("image"):
mm_content = process_image(image_path)
elif video_path := entry.get("video"):
mm_content = process_video(video_path)
else:
mm_content = None
if enable_multimodal_chat:
......@@ -444,9 +501,11 @@ class ShareGPTDataset(BenchmarkDataset):
expected_output_len=new_output_len,
lora_request=lora_request,
multi_modal_data=mm_content,
request_id=request_id_prefix + str(ind),
)
)
self.maybe_oversample_requests(samples, num_requests)
ind += 1
self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
return samples
......@@ -512,10 +571,11 @@ class CustomDataset(BenchmarkDataset):
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
skip_chat_template: bool = False,
request_id_prefix: str = "",
**kwargs,
) -> list:
sampled_requests = []
for item in self.data:
for i, item in enumerate(self.data):
if len(sampled_requests) >= num_requests:
break
prompt = item["prompt"]
......@@ -534,9 +594,12 @@ class CustomDataset(BenchmarkDataset):
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
request_id=request_id_prefix + str(i),
)
)
self.maybe_oversample_requests(
sampled_requests, num_requests, request_id_prefix
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
......@@ -578,6 +641,7 @@ class SonnetDataset(BenchmarkDataset):
input_len: int = DEFAULT_INPUT_LEN,
output_len: int = DEFAULT_OUTPUT_LEN,
return_prompt_formatted: bool = False,
request_id_prefix: str = "",
**kwargs,
) -> list:
# Calculate average token length for a poem line.
......@@ -603,6 +667,7 @@ class SonnetDataset(BenchmarkDataset):
prefix_lines = self.data[:num_prefix_lines]
samples = []
ind = 0
while len(samples) < num_requests:
extra_lines = random.choices(
self.data, k=num_input_lines - num_prefix_lines
......@@ -613,14 +678,17 @@ class SonnetDataset(BenchmarkDataset):
msg, add_generation_prompt=True, tokenize=False
)
prompt_len = len(tokenizer(prompt_formatted).input_ids)
if prompt_len <= input_len:
samples.append(
SampleRequest(
prompt=prompt_formatted if return_prompt_formatted else prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
request_id=request_id_prefix + str(ind),
)
)
ind += 1
return samples
......@@ -672,6 +740,7 @@ class BurstGPTDataset(BenchmarkDataset):
num_requests: int,
max_loras: Optional[int] = None,
lora_path: Optional[str] = None,
request_id_prefix: str = "",
**kwargs,
) -> list[SampleRequest]:
samples = []
......@@ -693,6 +762,7 @@ class BurstGPTDataset(BenchmarkDataset):
prompt_len=input_len,
expected_output_len=output_len,
lora_request=lora_req,
request_id=request_id_prefix + str(i),
)
)
return samples
......@@ -752,12 +822,14 @@ class ConversationDataset(HuggingFaceDataset):
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
request_id_prefix: str = "",
**kwargs,
) -> list:
# Filter examples with at least 2 conversations
filtered_data = self.data.filter(lambda x: len(x["conversations"]) >= 2)
sampled_requests = []
dynamic_output = output_len is None
ind = 0
for item in filtered_data:
if len(sampled_requests) >= num_requests:
......@@ -785,9 +857,13 @@ class ConversationDataset(HuggingFaceDataset):
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=mm_content,
request_id=request_id_prefix + str(ind),
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
ind += 1
self.maybe_oversample_requests(
sampled_requests, num_requests, request_id_prefix
)
return sampled_requests
......@@ -814,11 +890,12 @@ class VisionArenaDataset(HuggingFaceDataset):
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
request_id_prefix: str = "",
**kwargs,
) -> list:
output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
sampled_requests = []
for item in self.data:
for i, item in enumerate(self.data):
if len(sampled_requests) >= num_requests:
break
parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
......@@ -838,9 +915,12 @@ class VisionArenaDataset(HuggingFaceDataset):
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=mm_content,
request_id=request_id_prefix + str(i),
)
)
self.maybe_oversample_requests(
sampled_requests, num_requests, request_id_prefix
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
......@@ -870,15 +950,18 @@ class InstructCoderDataset(HuggingFaceDataset):
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
request_id_prefix: str = "",
**kwargs,
) -> list:
output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
sampled_requests = []
for item in self.data:
for i, item in enumerate(self.data):
if len(sampled_requests) >= num_requests:
break
prompt = f"{item['input']}\n\n{item['instruction']} Just output \
the code, do not include any explanation."
prompt = (
f"{item['input']}\n\n{item['instruction']} Just output "
"the code, do not include any explanation."
)
# apply template
prompt = tokenizer.apply_chat_template(
......@@ -892,9 +975,12 @@ class InstructCoderDataset(HuggingFaceDataset):
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
request_id=request_id_prefix + str(i),
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
self.maybe_oversample_requests(
sampled_requests, num_requests, request_id_prefix
)
return sampled_requests
......@@ -924,12 +1010,13 @@ class MTBenchDataset(HuggingFaceDataset):
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
request_id_prefix: str = "",
**kwargs,
) -> list:
output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
sampled_requests = []
for item in self.data:
for i, item in enumerate(self.data):
if len(sampled_requests) >= num_requests:
break
prompt = item["turns"][0]
......@@ -947,9 +1034,12 @@ class MTBenchDataset(HuggingFaceDataset):
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
request_id=request_id_prefix + str(i),
)
)
self.maybe_oversample_requests(
sampled_requests, num_requests, request_id_prefix
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
......@@ -974,10 +1064,12 @@ class AIMODataset(HuggingFaceDataset):
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
request_id_prefix: str = "",
**kwargs,
) -> list:
sampled_requests = []
dynamic_output = output_len is None
ind = 0
for item in self.data:
if len(sampled_requests) >= num_requests:
......@@ -1000,9 +1092,13 @@ class AIMODataset(HuggingFaceDataset):
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=None,
request_id=request_id_prefix + str(ind),
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
ind += 1
self.maybe_oversample_requests(
sampled_requests, num_requests, request_id_prefix
)
return sampled_requests
......@@ -1072,12 +1168,18 @@ class NextEditPredictionDataset(HuggingFaceDataset):
"zed-industries/zeta": _format_zeta_prompt,
}
def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int, **kwargs):
def sample(
self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
request_id_prefix: str = "",
**kwargs,
):
formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(self.dataset_path)
if formatting_prompt_func is None:
raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
samples = []
for sample in self.data:
for i, sample in enumerate(self.data):
sample = formatting_prompt_func(sample)
samples.append(
SampleRequest(
......@@ -1086,11 +1188,12 @@ class NextEditPredictionDataset(HuggingFaceDataset):
expected_output_len=len(
tokenizer(sample["expected_output"]).input_ids
),
request_id=request_id_prefix + str(i),
)
)
if len(samples) >= num_requests:
break
self.maybe_oversample_requests(samples, num_requests)
self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
return samples
......@@ -1139,6 +1242,7 @@ class ASRDataset(HuggingFaceDataset):
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
request_id_prefix: str = "",
**kwargs,
) -> list:
import librosa
......@@ -1148,6 +1252,7 @@ class ASRDataset(HuggingFaceDataset):
prompt_len = len(tokenizer(prompt).input_ids)
sampled_requests = []
skipped = 0
ind = 0
for item in self.data:
if len(sampled_requests) >= num_requests:
break
......@@ -1166,8 +1271,10 @@ class ASRDataset(HuggingFaceDataset):
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=mm_content,
request_id=request_id_prefix + str(ind),
)
)
ind += 1
if skipped:
logger.warning(
"%d samples discarded from dataset due to"
......@@ -1175,5 +1282,7 @@ class ASRDataset(HuggingFaceDataset):
" what Whisper supports.",
skipped,
)
self.maybe_oversample_requests(sampled_requests, num_requests)
self.maybe_oversample_requests(
sampled_requests, num_requests, request_id_prefix
)
return sampled_requests
\ No newline at end of file
vllm/perf/benchmark_moe.py
View file @ 7de30014
......@@ -489,8 +489,10 @@ class BenchmarkWorker:
)
# NOTE(woosuk): The current naming convention uses w2.shape[2], which
# is the intermediate size after silu_and_mul.
block_n = block_quant_shape[0] if block_quant_shape else None
block_k = block_quant_shape[1] if block_quant_shape else None
op_config = get_moe_configs(
num_experts, shard_intermediate_size // 2, dtype_str, use_nn_moe=nn_moe
num_experts, shard_intermediate_size // 2, dtype_str, block_n, block_k, use_nn_moe=nn_moe
)
if op_config is None:
config = get_default_config(
......@@ -500,8 +502,8 @@ class BenchmarkWorker:
hidden_size,
topk,
dtype_str,
is_marlin=False,
use_nn_moe=nn_moe
block_quant_shape,
use_nn_moe=nn_moe,
)
else:
config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
......
vllm/perf/benchmark_serving.py
View file @ 7de30014
......@@ -375,11 +375,12 @@ async def benchmark(
rps_change_events.append({"rps": rps_val, "timestamp": timestamp})
last_int_rps = current_int_rps
prompt, prompt_len, output_len, mm_content = (
prompt, prompt_len, output_len, mm_content, request_id = (
request.prompt,
request.prompt_len,
request.expected_output_len,
request.multi_modal_data,
request.request_id,
)
req_model_id, req_model_name = model_id, model_name
if lora_modules:
......@@ -397,6 +398,7 @@ async def benchmark(
multi_modal_content=mm_content,
ignore_eos=ignore_eos,
extra_body=extra_body,
request_id=request_id,
)
task = limited_request_func(request_func_input=request_func_input, pbar=pbar)
tasks.append(asyncio.create_task(task))
......@@ -665,6 +667,7 @@ def main(args: argparse.Namespace):
tokenizer=tokenizer,
output_len=args.custom_output_len,
skip_chat_template=args.custom_skip_chat_template,
request_id_prefix=args.request_id_prefix,
)
elif args.dataset_name == "sonnet":
......@@ -678,6 +681,7 @@ def main(args: argparse.Namespace):
prefix_len=args.sonnet_prefix_len,
tokenizer=tokenizer,
return_prompt_formatted=False,
request_id_prefix=args.request_id_prefix,
)
else:
assert tokenizer.chat_template or tokenizer.default_chat_template, (
......@@ -690,6 +694,7 @@ def main(args: argparse.Namespace):
prefix_len=args.sonnet_prefix_len,
tokenizer=tokenizer,
return_prompt_formatted=True,
request_id_prefix=args.request_id_prefix,
)
elif args.dataset_name == "hf":
......@@ -751,6 +756,7 @@ def main(args: argparse.Namespace):
num_requests=args.num_prompts,
tokenizer=tokenizer,
output_len=args.hf_output_len,
request_id_prefix=args.request_id_prefix,
)
else:
......@@ -762,10 +768,15 @@ def main(args: argparse.Namespace):
tokenizer=tokenizer,
num_requests=args.num_prompts,
output_len=args.sharegpt_output_len,
request_id_prefix=args.request_id_prefix,
),
"burstgpt": lambda: BurstGPTDataset(
random_seed=args.seed, dataset_path=args.dataset_path
).sample(tokenizer=tokenizer, num_requests=args.num_prompts),
).sample(
tokenizer=tokenizer,
num_requests=args.num_prompts,
request_id_prefix=args.request_id_prefix,
),
"random": lambda: RandomDataset(dataset_path=args.dataset_path).sample(
tokenizer=tokenizer,
num_requests=args.num_prompts,
......@@ -773,6 +784,7 @@ def main(args: argparse.Namespace):
input_len=args.random_input_len,
output_len=args.random_output_len,
range_ratio=args.random_range_ratio,
request_id_prefix=args.request_id_prefix,
),
}
......@@ -1118,6 +1130,13 @@ def create_argument_parser():
"goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
"and the blog: https://hao-ai-lab.github.io/blogs/distserve",
)
parser.add_argument(
"--request-id-prefix",
type=str,
required=False,
default="benchmark-serving",
help="Specify the prefix of request id.",
)
# group for dataset specific arguments
custom_group = parser.add_argument_group("custom dataset options")
......
vllm/perf/benchmark_throughput.py
View file @ 7de30014
......@@ -150,7 +150,6 @@ def run_vllm(
end = time.perf_counter()
else:
assert lora_requests is None, "BeamSearch API does not support LoRA"
prompts = [request.prompt for request in requests]
# output_len should be the same for all requests.
output_len = requests[0].expected_output_len
for request in requests:
......@@ -653,8 +652,8 @@ def validate_args(args):
# https://github.com/vllm-project/vllm/issues/16222
if args.data_parallel_size > 1:
raise ValueError(
"Data parallel is not supported in offline benchmark, \
please use benchmark serving instead"
"Data parallel is not supported in offline benchmark, "
"please use benchmark serving instead"
)
......
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