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Convert benchmarks to ruff format (#18068)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-05-13 14:43:29 +01:00
committed by GitHub
parent b922c2ebd2
commit 009d9e7590
41 changed files with 3980 additions and 2938 deletions
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4
.buildkite/pyproject.toml
View File

@ -1,13 +1,9 @@
# This local pyproject file is part of the migration from yapf to ruff format.
# It uses the same core rules as the main pyproject.toml file, but with the
# following differences:
# - isort profile is set to black
# - ruff line length is overridden to 88
# - deprecated typing ignores (UP006, UP035) have been removed
[tool.isort]
profile = "black"
[tool.ruff]
line-length = 88
exclude = [

4
.pre-commit-config.yaml
View File

@ -17,7 +17,7 @@ repos:
- id: ruff
args: [--output-format, github, --fix]
- id: ruff-format
files: ^(.buildkite).*
files: ^(.buildkite|benchmarks)/.*
- repo: https://github.com/codespell-project/codespell
rev: v2.4.1
hooks:
@ -28,8 +28,6 @@ repos:
rev: 6.0.1
hooks:
- id: isort
# necessary during the transition from yapf to ruff format
args: [--resolve-all-configs, --config-root, .]
- repo: https://github.com/pre-commit/mirrors-clang-format
rev: v20.1.3
hooks:

189
benchmarks/backend_request_func.py
View File

@ -12,8 +12,7 @@ from typing import Optional, Union
import aiohttp
import huggingface_hub.constants
from tqdm.asyncio import tqdm
from transformers import (AutoTokenizer, PreTrainedTokenizer,
PreTrainedTokenizerFast)
from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
# NOTE(simon): do not import vLLM here so the benchmark script
# can run without vLLM installed.
@ -43,8 +42,7 @@ class RequestFuncOutput:
latency: float = 0.0
output_tokens: int = 0
ttft: float = 0.0 # Time to first token
itl: list[float] = field(
default_factory=list) # list of inter-token latencies
itl: list[float] = field(default_factory=list) # list of inter-token latencies
tpot: float = 0.0 # avg next-token latencies
prompt_len: int = 0
error: str = ""
@ -57,8 +55,9 @@ async def async_request_tgi(
api_url = request_func_input.api_url
assert api_url.endswith("generate_stream")
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
async with aiohttp.ClientSession(
trust_env=True, timeout=AIOHTTP_TIMEOUT
) as session:
params = {
"max_new_tokens": request_func_input.output_len,
"do_sample": True,
@ -105,8 +104,7 @@ async def async_request_tgi(
# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)
output.itl.append(timestamp - most_recent_timestamp)
most_recent_timestamp = timestamp
@ -133,8 +131,9 @@ async def async_request_trt_llm(
api_url = request_func_input.api_url
assert api_url.endswith("generate_stream")
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
async with aiohttp.ClientSession(
trust_env=True, timeout=AIOHTTP_TIMEOUT
) as session:
payload = {
"accumulate_tokens": True,
"text_input": request_func_input.prompt,
@ -159,8 +158,7 @@ async def async_request_trt_llm(
if not chunk_bytes:
continue
chunk = chunk_bytes.decode("utf-8").removeprefix(
"data:")
chunk = chunk_bytes.decode("utf-8").removeprefix("data:")
data = json.loads(chunk)
output.generated_text += data["text_output"]
@ -172,8 +170,7 @@ async def async_request_trt_llm(
# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)
output.itl.append(timestamp - most_recent_timestamp)
most_recent_timestamp = timestamp
@ -197,9 +194,9 @@ async def async_request_deepspeed_mii(
request_func_input: RequestFuncInput,
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
async with aiohttp.ClientSession(
trust_env=True, timeout=AIOHTTP_TIMEOUT
) as session:
payload = {
"model": request_func_input.model,
"prompt": request_func_input.prompt,
@ -217,19 +214,21 @@ async def async_request_deepspeed_mii(
st = time.perf_counter()
try:
async with session.post(url=request_func_input.api_url,
json=payload) as response:
async with session.post(
url=request_func_input.api_url, json=payload
) as response:
if response.status == 200:
parsed_resp = await response.json()
output.latency = time.perf_counter() - st
if "choices" in parsed_resp:
output.generated_text = parsed_resp["choices"][0][
"text"]
output.generated_text = parsed_resp["choices"][0]["text"]
elif "text" in parsed_resp:
output.generated_text = parsed_resp["text"][0]
else:
output.error = ("Unexpected response format: "
"neither 'choices' nor 'text' found")
output.error = (
"Unexpected response format: "
"neither 'choices' nor 'text' found"
)
output.success = False
output.success = True
else:
@ -250,15 +249,17 @@ async def async_request_openai_completions(
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith(
("completions", "profile")
), "OpenAI Completions API URL must end with 'completions' or 'profile'."
assert api_url.endswith(("completions", "profile")), (
"OpenAI Completions API URL must end with 'completions' or 'profile'."
)
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
async with aiohttp.ClientSession(
trust_env=True, timeout=AIOHTTP_TIMEOUT
) as session:
payload = {
"model": request_func_input.model_name \
if request_func_input.model_name else request_func_input.model,
"model": request_func_input.model_name
if request_func_input.model_name
else request_func_input.model,
"prompt": request_func_input.prompt,
"temperature": 0.0,
"repetition_penalty": 1.0,
@ -273,9 +274,7 @@ async def async_request_openai_completions(
payload["ignore_eos"] = request_func_input.ignore_eos
if request_func_input.extra_body:
payload.update(request_func_input.extra_body)
headers = {
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
}
headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
output = RequestFuncOutput()
output.prompt_len = request_func_input.prompt_len
@ -284,8 +283,9 @@ async def async_request_openai_completions(
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url, json=payload,
headers=headers) as response:
async with session.post(
url=api_url, json=payload, headers=headers
) as response:
if response.status == 200:
first_chunk_received = False
async for chunk_bytes in response.content:
@ -293,8 +293,7 @@ async def async_request_openai_completions(
if not chunk_bytes:
continue
chunk = chunk_bytes.decode("utf-8").removeprefix(
"data: ")
chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
if chunk != "[DONE]":
data = json.loads(chunk)
@ -314,21 +313,20 @@ async def async_request_openai_completions(
# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)
output.itl.append(timestamp - most_recent_timestamp)
most_recent_timestamp = timestamp
generated_text += text or ""
elif usage := data.get("usage"):
output.output_tokens = usage.get(
"completion_tokens")
output.output_tokens = usage.get("completion_tokens")
if first_chunk_received:
output.success = True
else:
output.success = False
output.error = (
"Never received a valid chunk to calculate TTFT."
"This response will be marked as failed!")
"This response will be marked as failed!"
)
output.generated_text = generated_text
output.latency = most_recent_timestamp - st
else:
@ -349,23 +347,22 @@ async def async_request_openai_chat_completions(
pbar: Optional[tqdm] = None,
) -> RequestFuncOutput:
api_url = request_func_input.api_url
assert api_url.endswith(
("chat/completions", "profile")
), "OpenAI Chat Completions API URL must end with 'chat/completions'."
assert api_url.endswith(("chat/completions", "profile")), (
"OpenAI Chat Completions API URL must end with 'chat/completions'."
)
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
async with aiohttp.ClientSession(
trust_env=True, timeout=AIOHTTP_TIMEOUT
) as session:
content = [{"type": "text", "text": request_func_input.prompt}]
if request_func_input.multi_modal_content:
content.append(request_func_input.multi_modal_content)
payload = {
"model": request_func_input.model_name \
if request_func_input.model_name else request_func_input.model,
"model": request_func_input.model_name
if request_func_input.model_name
else request_func_input.model,
"messages": [
{
"role": "user",
"content": content
},
{"role": "user", "content": content},
],
"temperature": 0.0,
"max_completion_tokens": request_func_input.output_len,
@ -391,16 +388,16 @@ async def async_request_openai_chat_completions(
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url, json=payload,
headers=headers) 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()
if not chunk_bytes:
continue
chunk = chunk_bytes.decode("utf-8").removeprefix(
"data: ")
chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
if chunk != "[DONE]":
timestamp = time.perf_counter()
data = json.loads(chunk)
@ -414,13 +411,11 @@ async def async_request_openai_chat_completions(
# Decoding phase
else:
output.itl.append(timestamp -
most_recent_timestamp)
output.itl.append(timestamp - most_recent_timestamp)
generated_text += content or ""
elif usage := data.get("usage"):
output.output_tokens = usage.get(
"completion_tokens")
output.output_tokens = usage.get("completion_tokens")
most_recent_timestamp = timestamp
@ -446,25 +441,28 @@ async def async_request_openai_audio(
) -> RequestFuncOutput:
# Lazy import without PlaceholderModule to avoid vllm dep.
import soundfile
api_url = request_func_input.api_url
assert api_url.endswith(
("transcriptions", "translations"
)), "OpenAI Chat Completions API URL must end with 'transcriptions' "
assert api_url.endswith(("transcriptions", "translations")), (
"OpenAI Chat Completions API URL must end with 'transcriptions' "
)
"or `translations`."
async with aiohttp.ClientSession(trust_env=True,
timeout=AIOHTTP_TIMEOUT) as session:
async with aiohttp.ClientSession(
trust_env=True, timeout=AIOHTTP_TIMEOUT
) as session:
content = [{"type": "text", "text": request_func_input.prompt}]
payload = {
"model": request_func_input.model_name \
if request_func_input.model_name else request_func_input.model,
"model": request_func_input.model_name
if request_func_input.model_name
else request_func_input.model,
"temperature": 0.0,
"max_completion_tokens": request_func_input.output_len,
"stream": True,
"language": "en",
# Flattened due to multipart/form-data
"stream_include_usage": True,
"stream_continuous_usage_stats": True
"stream_continuous_usage_stats": True,
}
if request_func_input.extra_body:
payload.update(request_func_input.extra_body)
@ -479,9 +477,9 @@ async def async_request_openai_audio(
buffer.seek(0)
return buffer
with to_bytes(*request_func_input.multi_modal_content['audio']) as f:
with to_bytes(*request_func_input.multi_modal_content["audio"]) as f:
form = aiohttp.FormData()
form.add_field('file', f, content_type='audio/wav')
form.add_field("file", f, content_type="audio/wav")
for key, value in payload.items():
form.add_field(key, str(value))
@ -493,24 +491,22 @@ async def async_request_openai_audio(
st = time.perf_counter()
most_recent_timestamp = st
try:
async with session.post(url=api_url,
data=form,
headers=headers) as response:
async with session.post(
url=api_url, data=form, headers=headers
) as response:
if response.status == 200:
async for chunk_bytes in response.content:
chunk_bytes = chunk_bytes.strip()
if not chunk_bytes:
continue
chunk = chunk_bytes.decode("utf-8").removeprefix(
"data: ")
chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
if chunk != "[DONE]":
timestamp = time.perf_counter()
data = json.loads(chunk)
if choices := data.get("choices"):
content = choices[0]["delta"].get(
"content")
content = choices[0]["delta"].get("content")
# First token
if ttft == 0.0:
ttft = timestamp - st
@ -519,12 +515,14 @@ async def async_request_openai_audio(
# Decoding phase
else:
output.itl.append(
timestamp - most_recent_timestamp)
timestamp - most_recent_timestamp
)
generated_text += content or ""
elif usage := data.get("usage"):
output.output_tokens = usage.get(
"completion_tokens")
"completion_tokens"
)
most_recent_timestamp = timestamp
@ -545,7 +543,7 @@ async def async_request_openai_audio(
def get_model(pretrained_model_name_or_path: str) -> str:
if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
if os.getenv("VLLM_USE_MODELSCOPE", "False").lower() == "true":
from modelscope import snapshot_download
from vllm.model_executor.model_loader.weight_utils import get_lock
@ -556,7 +554,8 @@ def get_model(pretrained_model_name_or_path: str) -> str:
model_path = snapshot_download(
model_id=pretrained_model_name_or_path,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"],
)
return model_path
return pretrained_model_name_or_path
@ -569,23 +568,23 @@ def get_tokenizer(
**kwargs,
) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
if pretrained_model_name_or_path is not None and not os.path.exists(
pretrained_model_name_or_path):
pretrained_model_name_or_path = get_model(
pretrained_model_name_or_path)
pretrained_model_name_or_path
):
pretrained_model_name_or_path = get_model(pretrained_model_name_or_path)
if tokenizer_mode == "slow":
if kwargs.get("use_fast", False):
raise ValueError(
"Cannot use the fast tokenizer in slow tokenizer mode.")
raise ValueError("Cannot use the fast tokenizer in slow tokenizer mode.")
kwargs["use_fast"] = False
if tokenizer_mode == "mistral":
try:
from vllm.transformers_utils.tokenizer import MistralTokenizer
except ImportError as e:
raise ImportError("MistralTokenizer requires vllm package.\n"
"Please install it with `pip install vllm` "
"to use mistral tokenizer mode.") from e
return MistralTokenizer.from_pretrained(
str(pretrained_model_name_or_path))
raise ImportError(
"MistralTokenizer requires vllm package.\n"
"Please install it with `pip install vllm` "
"to use mistral tokenizer mode."
) from e
return MistralTokenizer.from_pretrained(str(pretrained_model_name_or_path))
else:
return AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
@ -608,7 +607,7 @@ ASYNC_REQUEST_FUNCS = {
}
OPENAI_COMPATIBLE_BACKENDS = [
k for k, v in ASYNC_REQUEST_FUNCS.items()
if v in (async_request_openai_completions,
async_request_openai_chat_completions)
k
for k, v in ASYNC_REQUEST_FUNCS.items()
if v in (async_request_openai_completions, async_request_openai_chat_completions)
]

353
benchmarks/benchmark_dataset.py
View File

@ -82,14 +82,12 @@ class BenchmarkDataset(ABC):
self.dataset_path = dataset_path
# Set the random seed, ensuring that a None value is replaced with the
# default seed.
self.random_seed = (random_seed
if random_seed is not None else self.DEFAULT_SEED)
self.random_seed = random_seed if random_seed is not None else self.DEFAULT_SEED
self.data = None
def apply_multimodal_chat_transformation(
self,
prompt: str,
mm_content: Optional[MultiModalDataDict] = None) -> list[dict]:
self, prompt: str, mm_content: Optional[MultiModalDataDict] = None
) -> list[dict]:
"""
Transform a prompt and optional multimodal content into a chat format.
This method is used for chat models that expect a specific conversation
@ -111,8 +109,7 @@ class BenchmarkDataset(ABC):
NotImplementedError: If a subclass does not implement this method.
"""
# TODO (jenniferzhao): add support for downloading data
raise NotImplementedError(
"load_data must be implemented in subclasses.")
raise NotImplementedError("load_data must be implemented in subclasses.")
def get_random_lora_request(
self,
@ -158,8 +155,9 @@ class BenchmarkDataset(ABC):
return lora_request, lora_tokenizer_cache[lora_id] or tokenizer
@abstractmethod
def sample(self, tokenizer: PreTrainedTokenizerBase,
num_requests: int) -> list[SampleRequest]:
def sample(
self, tokenizer: PreTrainedTokenizerBase, num_requests: int
) -> list[SampleRequest]:
"""
Abstract method to generate sample requests from the dataset.
@ -177,8 +175,9 @@ class BenchmarkDataset(ABC):
"""
raise NotImplementedError("sample must be implemented in subclasses.")
def maybe_oversample_requests(self, requests: list[SampleRequest],
num_requests: int) -> None:
def maybe_oversample_requests(
self, requests: list[SampleRequest], num_requests: int
) -> None:
"""
Oversamples the list of requests if its size is less than the desired
number.
@ -189,11 +188,9 @@ class BenchmarkDataset(ABC):
"""
if len(requests) < num_requests:
random.seed(self.random_seed)
additional = random.choices(requests,
k=num_requests - len(requests))
additional = random.choices(requests, k=num_requests - len(requests))
requests.extend(additional)
logger.info("Oversampled requests to reach %d total samples.",
num_requests)
logger.info("Oversampled requests to reach %d total samples.", num_requests)
# -----------------------------------------------------------------------------
@ -218,14 +215,14 @@ def is_valid_sequence(
"""
# Check for invalid conditions
prompt_too_short = prompt_len < min_len
output_too_short = (not skip_min_output_len_check) and (output_len
< min_len)
output_too_short = (not skip_min_output_len_check) and (output_len < min_len)
prompt_too_long = prompt_len > max_prompt_len
combined_too_long = (prompt_len + output_len) > max_total_len
# Return True if none of the invalid conditions are met
return not (prompt_too_short or output_too_short or prompt_too_long
or combined_too_long)
return not (
prompt_too_short or output_too_short or prompt_too_long or combined_too_long
)
@cache
@ -257,28 +254,28 @@ def process_image(image: Any) -> Mapping[str, Any]:
Raises:
ValueError: If the input is not a supported type.
"""
if isinstance(image, dict) and 'bytes' in image:
image = Image.open(BytesIO(image['bytes']))
if isinstance(image, dict) and "bytes" in image:
image = Image.open(BytesIO(image["bytes"]))
if isinstance(image, Image.Image):
image = image.convert("RGB")
with io.BytesIO() as image_data:
image.save(image_data, format="JPEG")
image_base64 = base64.b64encode(
image_data.getvalue()).decode("utf-8")
image_base64 = base64.b64encode(image_data.getvalue()).decode("utf-8")
return {
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_base64}"
},
"image_url": {"url": f"data:image/jpeg;base64,{image_base64}"},
}
if isinstance(image, str):
image_url = (image if image.startswith(
("http://", "file://")) else f"file://{image}")
image_url = (
image if image.startswith(("http://", "file://")) else f"file://{image}"
)
return {"type": "image_url", "image_url": {"url": image_url}}
raise ValueError(f"Invalid image input {image}. Must be a PIL.Image.Image"
" or str or dictionary with raw image bytes.")
raise ValueError(
f"Invalid image input {image}. Must be a PIL.Image.Image"
" or str or dictionary with raw image bytes."
)
# -----------------------------------------------------------------------------
@ -318,8 +315,11 @@ class RandomDataset(BenchmarkDataset):
num_special_tokens = tokenizer.num_special_tokens_to_add()
real_input_len = input_len - num_special_tokens
prefix_token_ids = (np.random.randint(
0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
prefix_token_ids = (
np.random.randint(0, vocab_size, size=prefix_len).tolist()
if prefix_len > 0
else []
)
# New sampling logic: [X * (1 - b), X * (1 + b)]
input_low = int(real_input_len * (1 - range_ratio))
@ -329,21 +329,17 @@ class RandomDataset(BenchmarkDataset):
# Add logging for debugging
logger.info("Sampling input_len from [%s, %s]", input_low, input_high)
logger.info("Sampling output_len from [%s, %s]", output_low,
output_high)
logger.info("Sampling output_len from [%s, %s]", output_low, output_high)
input_lens = np.random.randint(input_low,
input_high + 1,
size=num_requests)
output_lens = np.random.randint(output_low,
output_high + 1,
size=num_requests)
input_lens = np.random.randint(input_low, input_high + 1, size=num_requests)
output_lens = np.random.randint(output_low, output_high + 1, size=num_requests)
offsets = np.random.randint(0, vocab_size, size=num_requests)
requests = []
for i in range(num_requests):
inner_seq = ((offsets[i] + i + np.arange(input_lens[i])) %
vocab_size).tolist()
inner_seq = (
(offsets[i] + i + np.arange(input_lens[i])) % vocab_size
).tolist()
token_sequence = prefix_token_ids + inner_seq
prompt = tokenizer.decode(token_sequence)
# After decoding the prompt we have to encode and decode it again.
@ -354,8 +350,9 @@ class RandomDataset(BenchmarkDataset):
# [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
# To avoid uncontrolled change of the prompt length,
# the encoded sequence is truncated before being decode again.
re_encoded_sequence = tokenizer.encode(
prompt, add_special_tokens=False)[:input_lens[i]]
re_encoded_sequence = tokenizer.encode(prompt, add_special_tokens=False)[
: input_lens[i]
]
prompt = tokenizer.decode(re_encoded_sequence)
total_input_len = prefix_len + int(input_lens[i])
requests.append(
@ -363,7 +360,8 @@ class RandomDataset(BenchmarkDataset):
prompt=prompt,
prompt_len=total_input_len,
expected_output_len=int(output_lens[i]),
))
)
)
return requests
@ -390,7 +388,8 @@ class ShareGPTDataset(BenchmarkDataset):
self.data = json.load(f)
# Filter entries with at least two conversation turns.
self.data = [
entry for entry in self.data
entry
for entry in self.data
if "conversations" in entry and len(entry["conversations"]) >= 2
]
random.seed(self.random_seed)
@ -416,27 +415,28 @@ class ShareGPTDataset(BenchmarkDataset):
)
lora_request, tokenizer = self.get_random_lora_request(
tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
)
prompt_ids = tokenizer(prompt).input_ids
completion_ids = tokenizer(completion).input_ids
prompt_len = len(prompt_ids)
new_output_len = (len(completion_ids)
if output_len is None else output_len)
if not is_valid_sequence(prompt_len,
new_output_len,
skip_min_output_len_check=output_len
is not None):
new_output_len = len(completion_ids) if output_len is None else output_len
if not is_valid_sequence(
prompt_len,
new_output_len,
skip_min_output_len_check=output_len is not None,
):
continue
if enable_multimodal_chat:
prompt = self.apply_multimodal_chat_transformation(
prompt, None)
prompt = self.apply_multimodal_chat_transformation(prompt, None)
samples.append(
SampleRequest(
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=new_output_len,
lora_request=lora_request,
))
)
)
self.maybe_oversample_requests(samples, num_requests)
return samples
@ -482,20 +482,20 @@ class SonnetDataset(BenchmarkDataset):
) -> list:
# Calculate average token length for a poem line.
tokenized_lines = [tokenizer(line).input_ids for line in self.data]
avg_len = sum(len(tokens)
for tokens in tokenized_lines) / len(tokenized_lines)
avg_len = sum(len(tokens) for tokens in tokenized_lines) / len(tokenized_lines)
# Build the base prompt.
base_prompt = "Pick as many lines as you can from these poem lines:\n"
base_msg = [{"role": "user", "content": base_prompt}]
base_fmt = tokenizer.apply_chat_template(base_msg,
add_generation_prompt=True,
tokenize=False)
base_fmt = tokenizer.apply_chat_template(
base_msg, add_generation_prompt=True, tokenize=False
)
base_offset = len(tokenizer(base_fmt).input_ids)
if input_len <= base_offset:
raise ValueError(
f"'input_len' must be higher than the base prompt length "
f"({base_offset}).")
f"({base_offset})."
)
# Determine how many poem lines to use.
num_input_lines = round((input_len - base_offset) / avg_len)
@ -504,21 +504,23 @@ class SonnetDataset(BenchmarkDataset):
samples = []
while len(samples) < num_requests:
extra_lines = random.choices(self.data,
k=num_input_lines - num_prefix_lines)
extra_lines = random.choices(
self.data, k=num_input_lines - num_prefix_lines
)
prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
msg = [{"role": "user", "content": prompt}]
prompt_formatted = tokenizer.apply_chat_template(
msg, add_generation_prompt=True, tokenize=False)
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=prompt_formatted if return_prompt_formatted else prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
))
)
)
return samples
@ -538,7 +540,9 @@ class BurstGPTDataset(BenchmarkDataset):
super().__init__(**kwargs)
self.load_data()
def load_data(self, ):
def load_data(
self,
):
if self.dataset_path is None:
raise ValueError("dataset_path must be provided for loading data.")
@ -552,8 +556,7 @@ class BurstGPTDataset(BenchmarkDataset):
def _sample_loaded_data(self, num_requests: int) -> list:
if num_requests <= len(self.data):
data = self.data.sample(n=num_requests,
random_state=self.random_seed)
data = self.data.sample(n=num_requests, random_state=self.random_seed)
else:
data = self.data.sample(
n=num_requests,
@ -577,7 +580,8 @@ class BurstGPTDataset(BenchmarkDataset):
input_len = int(data[i][2])
output_len = int(data[i][3])
lora_req, tokenizer = self.get_random_lora_request(
tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
)
vocab_size = tokenizer.vocab_size
# Generate a synthetic prompt: a list of token IDs computed as (i +
# j) modulo vocab_size.
@ -589,7 +593,8 @@ class BurstGPTDataset(BenchmarkDataset):
prompt_len=input_len,
expected_output_len=output_len,
lora_request=lora_req,
))
)
)
return samples
@ -632,20 +637,23 @@ class HuggingFaceDataset(BenchmarkDataset):
class ConversationDataset(HuggingFaceDataset):
"""Dataset for conversation data with multimodal support."""
SUPPORTED_DATASET_PATHS = {
'lmms-lab/LLaVA-OneVision-Data', 'Aeala/ShareGPT_Vicuna_unfiltered'
"lmms-lab/LLaVA-OneVision-Data",
"Aeala/ShareGPT_Vicuna_unfiltered",
}
IS_MULTIMODAL = True
def sample(self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
**kwargs) -> list:
def sample(
self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
**kwargs,
) -> list:
# Filter examples with at least 2 conversations
filtered_data = self.data.filter(
lambda x: len(x["conversations"]) >= 2)
filtered_data = self.data.filter(lambda x: len(x["conversations"]) >= 2)
sampled_requests = []
dynamic_output = output_len is None
@ -661,24 +669,22 @@ class ConversationDataset(HuggingFaceDataset):
completion_len = len(completion_ids)
output_len = completion_len if dynamic_output else output_len
assert isinstance(output_len, int) and output_len > 0
if dynamic_output and not is_valid_sequence(
prompt_len, completion_len):
if dynamic_output and not is_valid_sequence(prompt_len, completion_len):
continue
mm_content = process_image(
item["image"]) if "image" in item else None
mm_content = process_image(item["image"]) if "image" in item else None
if enable_multimodal_chat:
# Note: when chat is enabled the request prompt_len is no longer
# accurate and we will be using request output to count the
# actual prompt len and output len
prompt = self.apply_multimodal_chat_transformation(
prompt, mm_content)
prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
sampled_requests.append(
SampleRequest(
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=mm_content,
))
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
@ -695,10 +701,8 @@ class VisionArenaDataset(HuggingFaceDataset):
DEFAULT_OUTPUT_LEN = 128
SUPPORTED_DATASET_PATHS = {
"lmarena-ai/VisionArena-Chat":
lambda x: x["conversation"][0][0]["content"],
"lmarena-ai/vision-arena-bench-v0.1":
lambda x: x["turns"][0][0]["content"]
"lmarena-ai/VisionArena-Chat": lambda x: x["conversation"][0][0]["content"],
"lmarena-ai/vision-arena-bench-v0.1": lambda x: x["turns"][0][0]["content"],
}
IS_MULTIMODAL = True
@ -710,16 +714,14 @@ class VisionArenaDataset(HuggingFaceDataset):
enable_multimodal_chat: bool = False,
**kwargs,
) -> list:
output_len = (output_len
if output_len is not None else self.DEFAULT_OUTPUT_LEN)
output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
sampled_requests = []
for item in self.data:
if len(sampled_requests) >= num_requests:
break
parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
if parser_fn is None:
raise ValueError(
f"Unsupported dataset path: {self.dataset_path}")
raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
prompt = parser_fn(item)
mm_content = process_image(item["images"][0])
prompt_len = len(tokenizer(prompt).input_ids)
@ -727,15 +729,15 @@ class VisionArenaDataset(HuggingFaceDataset):
# Note: when chat is enabled the request prompt_len is no longer
# accurate and we will be using request output to count the
# actual prompt len
prompt = self.apply_multimodal_chat_transformation(
prompt, mm_content)
prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
sampled_requests.append(
SampleRequest(
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=mm_content,
))
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
@ -760,14 +762,15 @@ class InstructCoderDataset(HuggingFaceDataset):
"likaixin/InstructCoder",
}
def sample(self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
**kwargs) -> list:
output_len = (output_len
if output_len is not None else self.DEFAULT_OUTPUT_LEN)
def sample(
self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
**kwargs,
) -> list:
output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
sampled_requests = []
for item in self.data:
if len(sampled_requests) >= num_requests:
@ -779,7 +782,8 @@ class InstructCoderDataset(HuggingFaceDataset):
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
))
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
@ -794,38 +798,38 @@ class MTBenchDataset(HuggingFaceDataset):
MT-Bench Dataset.
https://huggingface.co/datasets/philschmid/mt-bench
We create a single turn dataset for MT-Bench.
We create a single turn dataset for MT-Bench.
This is similar to Spec decoding benchmark setup in vLLM
https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
""" # noqa: E501
""" # noqa: E501
DEFAULT_OUTPUT_LEN = 256 # avg len used in SD bench in vLLM
SUPPORTED_DATASET_PATHS = {
"philschmid/mt-bench",
}
def sample(self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
**kwargs) -> list:
output_len = (output_len
if output_len is not None else self.DEFAULT_OUTPUT_LEN)
def sample(
self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
enable_multimodal_chat: bool = False,
**kwargs,
) -> list:
output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
sampled_requests = []
for item in self.data:
if len(sampled_requests) >= num_requests:
break
prompt = item['turns'][0]
prompt = item["turns"][0]
# apply template
prompt = tokenizer.apply_chat_template([{
"role": "user",
"content": prompt
}],
add_generation_prompt=True,
tokenize=False)
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
add_generation_prompt=True,
tokenize=False,
)
prompt_len = len(tokenizer(prompt).input_ids)
sampled_requests.append(
@ -833,7 +837,8 @@ class MTBenchDataset(HuggingFaceDataset):
prompt=prompt,
prompt_len=prompt_len,
expected_output_len=output_len,
))
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
@ -847,23 +852,27 @@ class AIMODataset(HuggingFaceDataset):
"""
Dataset class for processing a AIMO dataset with reasoning questions.
"""
SUPPORTED_DATASET_PATHS = {
"AI-MO/aimo-validation-aime", "AI-MO/NuminaMath-1.5",
"AI-MO/NuminaMath-CoT"
"AI-MO/aimo-validation-aime",
"AI-MO/NuminaMath-1.5",
"AI-MO/NuminaMath-CoT",
}
def sample(self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
**kwargs) -> list:
def sample(
self,
tokenizer: PreTrainedTokenizerBase,
num_requests: int,
output_len: Optional[int] = None,
**kwargs,
) -> list:
sampled_requests = []
dynamic_output = output_len is None
for item in self.data:
if len(sampled_requests) >= num_requests:
break
prompt, completion = item['problem'], item["solution"]
prompt, completion = item["problem"], item["solution"]
prompt_ids = tokenizer(prompt).input_ids
completion_ids = tokenizer(completion).input_ids
@ -871,10 +880,9 @@ class AIMODataset(HuggingFaceDataset):
completion_len = len(completion_ids)
output_len = completion_len if dynamic_output else output_len
assert isinstance(output_len, int) and output_len > 0
if dynamic_output and not is_valid_sequence(prompt_len,
completion_len,
max_prompt_len=2048,
max_total_len=32000):
if dynamic_output and not is_valid_sequence(
prompt_len, completion_len, max_prompt_len=2048, max_total_len=32000
):
continue
sampled_requests.append(
SampleRequest(
@ -882,7 +890,8 @@ class AIMODataset(HuggingFaceDataset):
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=None,
))
)
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests
@ -905,25 +914,25 @@ You are a code completion assistant and your task is to analyze user edits and t
### Response:
""" # noqa: E501
""" # noqa: E501
def _format_zeta_prompt(
sample: dict,
original_start_marker: str = "<|editable_region_start|>") -> dict:
sample: dict, original_start_marker: str = "<|editable_region_start|>"
) -> dict:
"""Format the zeta prompt for the Next Edit Prediction (NEP) dataset.
This function formats examples from the NEP dataset
into prompts and expected outputs. It could be
This function formats examples from the NEP dataset
into prompts and expected outputs. It could be
further extended to support more NEP datasets.
Args:
sample: The dataset sample containing events,
sample: The dataset sample containing events,
inputs, and outputs.
original_start_marker: The marker indicating the
start of the editable region. Defaults to
original_start_marker: The marker indicating the
start of the editable region. Defaults to
"<|editable_region_start|>".
Returns:
A dictionary with the formatted prompts and expected outputs.
"""
@ -953,10 +962,8 @@ class NextEditPredictionDataset(HuggingFaceDataset):
"zed-industries/zeta": _format_zeta_prompt,
}
def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int,
**kwargs):
formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(
self.dataset_path)
def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int, **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 = []
@ -967,8 +974,10 @@ class NextEditPredictionDataset(HuggingFaceDataset):
prompt=sample["prompt"],
prompt_len=len(tokenizer(sample["prompt"]).input_ids),
expected_output_len=len(
tokenizer(sample["expected_output"]).input_ids),
))
tokenizer(sample["expected_output"]).input_ids
),
)
)
if len(samples) >= num_requests:
break
self.maybe_oversample_requests(samples, num_requests)
@ -997,18 +1006,22 @@ class ASRDataset(HuggingFaceDataset):
| AMI | Meetings | Spontaneous | ihm, sdm |
+----------------+----------------------------------------+--------------------------+-----------------------------+
""" # noqa: E501
""" # noqa: E501
SUPPORTED_DATASET_PATHS = {
"openslr/librispeech_asr", "facebook/voxpopuli", "LIUM/tedlium",
"edinburghcstr/ami", "speechcolab/gigaspeech", "kensho/spgispeech"
"openslr/librispeech_asr",
"facebook/voxpopuli",
"LIUM/tedlium",
"edinburghcstr/ami",
"speechcolab/gigaspeech",
"kensho/spgispeech",
}
DEFAULT_OUTPUT_LEN = 128
IS_MULTIMODAL = True
# TODO Whisper-specific. Abstract interface when more models are supported.
TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|>"\
"<|notimestamps|>"
TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
skip_long_audios: bool = True
def sample(
@ -1019,8 +1032,8 @@ class ASRDataset(HuggingFaceDataset):
**kwargs,
) -> list:
import librosa
output_len = (output_len
if output_len is not None else self.DEFAULT_OUTPUT_LEN)
output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
prompt = ASRDataset.TRANSCRIPTION_PREAMBLE
prompt_len = len(tokenizer(prompt).input_ids)
sampled_requests = []
@ -1043,10 +1056,14 @@ class ASRDataset(HuggingFaceDataset):
prompt_len=prompt_len,
expected_output_len=output_len,
multi_modal_data=mm_content,
))
)
)
if skipped:
logger.warning("%d samples discarded from dataset due to" \
" their length being greater than" \
" what Whisper supports.", skipped)
logger.warning(
"%d samples discarded from dataset due to"
" their length being greater than"
" what Whisper supports.",
skipped,
)
self.maybe_oversample_requests(sampled_requests, num_requests)
return sampled_requests

75
benchmarks/benchmark_latency.py
View File

@ -11,9 +11,9 @@ from typing import Any, Optional
import numpy as np
import torch
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
from tqdm import tqdm
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
from vllm import LLM, SamplingParams
from vllm.engine.arg_utils import EngineArgs
from vllm.inputs import PromptType
@ -21,13 +21,14 @@ from vllm.sampling_params import BeamSearchParams
from vllm.utils import FlexibleArgumentParser
def save_to_pytorch_benchmark_format(args: argparse.Namespace,
results: dict[str, Any]) -> None:
def save_to_pytorch_benchmark_format(
args: argparse.Namespace, results: dict[str, Any]
) -> None:
pt_records = convert_to_pytorch_benchmark_format(
args=args,
metrics={"latency": results["latencies"]},
extra_info={k: results[k]
for k in ["avg_latency", "percentiles"]})
extra_info={k: results[k] for k in ["avg_latency", "percentiles"]},
)
if pt_records:
pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
write_to_json(pt_file, pt_records)
@ -42,9 +43,11 @@ def main(args: argparse.Namespace):
# the engine will automatically process the request in multiple batches.
llm = LLM(**dataclasses.asdict(engine_args))
assert llm.llm_engine.model_config.max_model_len >= (
args.input_len +
args.output_len), ("Please ensure that max_model_len is greater than"
" the sum of input_len and output_len.")
args.input_len + args.output_len
), (
"Please ensure that max_model_len is greater than"
" the sum of input_len and output_len."
)
sampling_params = SamplingParams(
n=args.n,
@ -55,18 +58,16 @@ def main(args: argparse.Namespace):
detokenize=not args.disable_detokenize,
)
print(sampling_params)
dummy_prompt_token_ids = np.random.randint(10000,
size=(args.batch_size,
args.input_len))
dummy_prompts: list[PromptType] = [{
"prompt_token_ids": batch
} for batch in dummy_prompt_token_ids.tolist()]
dummy_prompt_token_ids = np.random.randint(
10000, size=(args.batch_size, args.input_len)
)
dummy_prompts: list[PromptType] = [
{"prompt_token_ids": batch} for batch in dummy_prompt_token_ids.tolist()
]
def llm_generate():
if not args.use_beam_search:
llm.generate(dummy_prompts,
sampling_params=sampling_params,
use_tqdm=False)
llm.generate(dummy_prompts, sampling_params=sampling_params, use_tqdm=False)
else:
llm.beam_search(
dummy_prompts,
@ -80,12 +81,13 @@ def main(args: argparse.Namespace):
def run_to_completion(profile_dir: Optional[str] = None):
if profile_dir:
with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
on_trace_ready=torch.profiler.tensorboard_trace_handler(
str(profile_dir)),
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
on_trace_ready=torch.profiler.tensorboard_trace_handler(
str(profile_dir)
),
) as p:
llm_generate()
print(p.key_averages().table(sort_by="self_cuda_time_total"))
@ -103,8 +105,9 @@ def main(args: argparse.Namespace):
if args.profile:
profile_dir = args.profile_result_dir
if not profile_dir:
profile_dir = (Path(".") / "vllm_benchmark_result" /
f"latency_result_{time.time()}")
profile_dir = (
Path(".") / "vllm_benchmark_result" / f"latency_result_{time.time()}"
)
print(f"Profiling (results will be saved to '{profile_dir}')...")
run_to_completion(profile_dir=profile_dir)
return
@ -135,7 +138,8 @@ def main(args: argparse.Namespace):
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark the latency of processing a single batch of "
"requests till completion.")
"requests till completion."
)
parser.add_argument("--input-len", type=int, default=32)
parser.add_argument("--output-len", type=int, default=128)
parser.add_argument("--batch-size", type=int, default=8)
@ -152,10 +156,9 @@ if __name__ == "__main__":
default=10,
help="Number of iterations to run for warmup.",
)
parser.add_argument("--num-iters",
type=int,
default=30,
help="Number of iterations to run.")
parser.add_argument(
"--num-iters", type=int, default=30, help="Number of iterations to run."
)
parser.add_argument(
"--profile",
action="store_true",
@ -165,8 +168,10 @@ if __name__ == "__main__":
"--profile-result-dir",
type=str,
default=None,
help=("path to save the pytorch profiler output. Can be visualized "
"with ui.perfetto.dev or Tensorboard."),
help=(
"path to save the pytorch profiler output. Can be visualized "
"with ui.perfetto.dev or Tensorboard."
),
)
parser.add_argument(
"--output-json",
@ -177,8 +182,10 @@ if __name__ == "__main__":
parser.add_argument(
"--disable-detokenize",
action="store_true",
help=("Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"),
help=(
"Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"
),
)
parser = EngineArgs.add_cli_args(parser)

81
benchmarks/benchmark_long_document_qa_throughput.py
View File

@ -76,7 +76,7 @@ def repeat_prompts(prompts, repeat_count, mode: str):
- 'random': Shuffle the prompts randomly after repetition.
- 'tile': Repeat the entire prompt list in sequence.
Example: [1, 2, 3] -> [1, 2, 3, 1, 2, 3].
- 'interleave': Repeat each prompt consecutively before moving to
- 'interleave': Repeat each prompt consecutively before moving to
the next. Example: [1, 2, 3] -> [1, 1, 2, 2, 3, 3].
Returns:
@ -86,20 +86,21 @@ def repeat_prompts(prompts, repeat_count, mode: str):
ValueError: If an invalid mode is provided.
"""
print("Repeat mode: ", mode)
if mode == 'random':
if mode == "random":
repeated_prompts = prompts * repeat_count
random.shuffle(repeated_prompts)
return repeated_prompts
elif mode == 'tile':
elif mode == "tile":
return prompts * repeat_count
elif mode == 'interleave':
elif mode == "interleave":
repeated_prompts = []
for prompt in prompts:
repeated_prompts.extend([prompt] * repeat_count)
return repeated_prompts
else:
raise ValueError(f"Invalid mode: {mode}, only support "
"'random', 'tile', 'interleave'")
raise ValueError(
f"Invalid mode: {mode}, only support 'random', 'tile', 'interleave'"
)
def main(args):
@ -109,16 +110,16 @@ def main(args):
# we append the document id at the beginning to avoid any of the document
# being the prefix of other documents
prompts = [
str(i) + ' '.join(['hi'] * args.document_length)
str(i) + " ".join(["hi"] * args.document_length)
for i in range(args.num_documents)
]
prompts = repeat_prompts(prompts, args.repeat_count, mode=args.repeat_mode)
warmup_prompts = [
"This is warm up request " + str(i) + \
' '.join(['hi'] * args.document_length)
for i in range(args.num_documents)]
"This is warm up request " + str(i) + " ".join(["hi"] * args.document_length)
for i in range(args.num_documents)
]
# Create the LLM engine
engine_args = EngineArgs.from_cli_args(args)
@ -142,42 +143,52 @@ def main(args):
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description=
'Benchmark the performance with or without automatic prefix caching.')
description="Benchmark the performance with or "
"without automatic prefix caching."
)
parser.add_argument(
'--document-length',
"--document-length",
type=int,
# Roughly the number of tokens for a system paper,
# excluding images
default=20000,
help='Range of input lengths for sampling prompts,'
'specified as "min:max" (e.g., "128:256").')
help="Range of input lengths for sampling prompts, "
'specified as "min:max" (e.g., "128:256").',
)
parser.add_argument('--num-documents',
type=int,
default=8,
help='Range of input lengths for sampling prompts,'
'specified as "min:max" (e.g., "128:256").')
parser.add_argument(
"--num-documents",
type=int,
default=8,
help="Range of input lengths for sampling prompts, "
'specified as "min:max" (e.g., "128:256").',
)
parser.add_argument('--output-len', type=int, default=10)
parser.add_argument("--output-len", type=int, default=10)
parser.add_argument('--repeat-count',
type=int,
default=2,
help='Number of times to repeat each prompt')
parser.add_argument(
"--repeat-count",
type=int,
default=2,
help="Number of times to repeat each prompt",
)
parser.add_argument("--repeat-mode",
type=str,
default='random',
help='The mode to repeat prompts. The supported '
'modes are "random", "tile", and "interleave". '
'See repeat_prompts() in the source code for details.')
parser.add_argument(
"--repeat-mode",
type=str,
default="random",
help="The mode to repeat prompts. The supported "
'modes are "random", "tile", and "interleave". '
"See repeat_prompts() in the source code for details.",
)
parser.add_argument("--shuffle-seed",
type=int,
default=0,
help='Random seed when the repeat mode is "random"')
parser.add_argument(
"--shuffle-seed",
type=int,
default=0,
help='Random seed when the repeat mode is "random"',
)
parser = EngineArgs.add_cli_args(parser)
args = parser.parse_args()

116
benchmarks/benchmark_prefix_caching.py
View File

@ -63,8 +63,7 @@ class Request:
output_len: int
def sample_tokens(tokenizer: PreTrainedTokenizerBase,
length: int) -> list[int]:
def sample_tokens(tokenizer: PreTrainedTokenizerBase, length: int) -> list[int]:
vocab = tokenizer.get_vocab()
all_special_ids = set(tokenizer.all_special_ids)
@ -91,8 +90,10 @@ def sample_requests_from_dataset(
# 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]
dataset = [
(data["conversations"][0]["value"], data["conversations"][1]["value"])
for data in dataset
]
# Shuffle the dataset.
random.shuffle(dataset)
@ -113,8 +114,9 @@ def sample_requests_from_dataset(
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)
output_len = (
len(completion_token_ids) if fixed_output_len is None else fixed_output_len
)
if min_len <= prompt_len <= max_len:
filtered_requests.append(Request(prompt, prompt_len, output_len))
@ -128,27 +130,27 @@ def sample_requests_from_random(
fixed_output_len: Optional[int],
prefix_len: int,
) -> list[Request]:
requests = []
prefix_token_ids = sample_tokens(tokenizer, prefix_len)
min_len, max_len = input_length_range
for i in range(num_requests):
unique_part_token_ids = sample_tokens(
tokenizer,
random.randint(min_len - prefix_len, max_len - prefix_len))
tokenizer, random.randint(min_len - prefix_len, max_len - prefix_len)
)
prompt_token_ids = prefix_token_ids + unique_part_token_ids
prompt = tokenizer.decode(prompt_token_ids)
prompt_len = len(prompt_token_ids)
assert (min_len <= prompt_len <= max_len
), f"prompt_len {prompt_len} out of range {min_len}:{max_len}"
assert min_len <= prompt_len <= max_len, (
f"prompt_len {prompt_len} out of range {min_len}:{max_len}"
)
requests.append(Request(prompt, prompt_len, fixed_output_len))
return requests
def repeat_and_sort_requests(requests: list[Request],
repeat_count: int,
sort: bool = False) -> list[str]:
def repeat_and_sort_requests(
requests: list[Request], repeat_count: int, sort: bool = False
) -> list[str]:
repeated_requests = requests * repeat_count
if sort:
repeated_requests.sort(key=lambda x: x[1])
@ -159,14 +161,14 @@ def repeat_and_sort_requests(requests: list[Request],
def main(args):
tokenizer = get_tokenizer(args.model, trust_remote_code=True)
input_length_range = tuple(map(int, args.input_length_range.split(':')))
input_length_range = tuple(map(int, args.input_length_range.split(":")))
random.seed(args.seed)
if args.dataset_path is not None:
if args.prefix_len > 0:
raise ValueError("prefix-len is not supported when "
"dataset-path is provided.")
print(f"Start to sample {args.num_prompts} prompts "
f"from {args.dataset_path}")
raise ValueError(
"prefix-len is not supported when dataset-path is provided."
)
print(f"Start to sample {args.num_prompts} prompts from {args.dataset_path}")
filtered_requests = sample_requests_from_dataset(
dataset_path=args.dataset_path,
num_requests=args.num_prompts,
@ -196,14 +198,16 @@ def main(args):
llm = LLM(**dataclasses.asdict(engine_args))
sampling_params = SamplingParams(temperature=0,
max_tokens=args.output_len,
detokenize=not args.disable_detokenize)
sampling_params = SamplingParams(
temperature=0,
max_tokens=args.output_len,
detokenize=not args.disable_detokenize,
)
print("Testing filtered requests")
prompts = repeat_and_sort_requests(filtered_requests,
repeat_count=args.repeat_count,
sort=args.sort)
prompts = repeat_and_sort_requests(
filtered_requests, repeat_count=args.repeat_count, sort=args.sort
)
print("------start generating------")
test_prefix(
@ -215,29 +219,35 @@ def main(args):
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description=
'Benchmark the performance with or without automatic prefix caching.')
parser.add_argument("--dataset-path",
type=str,
default=None,
help="Path to the dataset.")
parser.add_argument('--output-len', type=int, default=10)
parser.add_argument('--num-prompts',
type=int,
required=True,
help="Number of the prompts sampled from dataset")
parser.add_argument('--repeat-count',
type=int,
default=1,
help='Number of times to repeat each prompt')
parser.add_argument('--sort',
action='store_true',
help='Sort prompts by input length')
parser.add_argument('--input-length-range',
type=str,
required=True,
help='Range of input lengths for sampling prompts,'
'specified as "min:max" (e.g., "128:256").')
description="Benchmark the performance with or without "
"automatic prefix caching."
)
parser.add_argument(
"--dataset-path", type=str, default=None, help="Path to the dataset."
)
parser.add_argument("--output-len", type=int, default=10)
parser.add_argument(
"--num-prompts",
type=int,
required=True,
help="Number of the prompts sampled from dataset",
)
parser.add_argument(
"--repeat-count",
type=int,
default=1,
help="Number of times to repeat each prompt",
)
parser.add_argument(
"--sort", action="store_true", help="Sort prompts by input length"
)
parser.add_argument(
"--input-length-range",
type=str,
required=True,
help="Range of input lengths for sampling prompts,"
'specified as "min:max" (e.g., "128:256").',
)
parser.add_argument(
"--prefix-len",
type=int,
@ -248,10 +258,12 @@ if __name__ == "__main__":
"when dataset-path is not provided.",
)
parser.add_argument(
'--disable-detokenize',
action='store_true',
help=("Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"),
"--disable-detokenize",
action="store_true",
help=(
"Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"
),
)
parser = EngineArgs.add_cli_args(parser)

122
benchmarks/benchmark_prioritization.py
View File

@ -1,5 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
"""Benchmark offline prioritization."""
import argparse
import dataclasses
import json
@ -13,7 +14,7 @@ from vllm.engine.arg_utils import EngineArgs
from vllm.utils import FlexibleArgumentParser
#Select a equi-probable random priority
# Select a equi-probable random priority
def get_random_flag():
return 0 if random.random() < 0.5 else 1
@ -33,8 +34,10 @@ def sample_requests(
# 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]
dataset = [
(data["conversations"][0]["value"], data["conversations"][1]["value"])
for data in dataset
]
# Shuffle the dataset.
random.shuffle(dataset)
@ -51,8 +54,9 @@ def sample_requests(
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
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
@ -74,13 +78,16 @@ def run_vllm(
disable_detokenize: bool = False,
) -> float:
from vllm import LLM, SamplingParams
llm = LLM(**dataclasses.asdict(engine_args))
assert all(
llm.llm_engine.model_config.max_model_len >= (request[1] + request[2])
for request in requests), (
"Please ensure that max_model_len is greater than the sum of"
" input_len and output_len for all requests.")
for request in requests
), (
"Please ensure that max_model_len is greater than the sum of"
" input_len and output_len for all requests."
)
# Add the requests to the engine.
prompts = []
@ -97,7 +104,8 @@ def run_vllm(
ignore_eos=True,
max_tokens=output_len,
detokenize=not disable_detokenize,
))
)
)
start = time.perf_counter()
llm.generate(prompts, sampling_params, priority=priority, use_tqdm=True)
@ -111,26 +119,33 @@ def main(args: argparse.Namespace):
# Sample the requests.
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer, trust_remote_code=args.trust_remote_code)
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,
get_random_flag()) for _ in range(args.num_prompts)]
requests = [
(prompt, args.input_len, args.output_len, get_random_flag())
for _ in range(args.num_prompts)
]
else:
requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
args.output_len)
requests = sample_requests(
args.dataset, args.num_prompts, tokenizer, args.output_len
)
if args.backend == "vllm":
elapsed_time = run_vllm(requests, args.n,
EngineArgs.from_cli_args(args),
args.disable_detokenize)
elapsed_time = run_vllm(
requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
)
else:
raise ValueError(f"Unknown backend: {args.backend}")
total_num_tokens = sum(prompt_len + output_len
for _, prompt_len, output_len, priority in requests)
print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
f"{total_num_tokens / elapsed_time:.2f} tokens/s")
total_num_tokens = sum(
prompt_len + output_len for _, prompt_len, output_len, priority 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:
@ -147,41 +162,44 @@ def main(args: argparse.Namespace):
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("--n",
type=int,
default=1,
help="Number of generated sequences per prompt.")
parser.add_argument("--num-prompts",
type=int,
default=200,
help="Number of prompts to process.")
parser.add_argument(
'--output-json',
"--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(
"--n", type=int, default=1, help="Number of generated sequences per prompt."
)
parser.add_argument(
"--num-prompts", type=int, default=200, help="Number of prompts to process."
)
parser.add_argument(
"--output-json",
type=str,
default=None,
help='Path to save the throughput results in JSON format.')
help="Path to save the throughput results in JSON format.",
)
parser.add_argument(
'--disable-detokenize',
action='store_true',
help=("Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"),
"--disable-detokenize",
action="store_true",
help=(
"Do not detokenize responses (i.e. do not include "
"detokenization time in the latency measurement)"
),
)
parser = EngineArgs.add_cli_args(parser)

581
benchmarks/benchmark_serving.py
View File

@ -20,6 +20,7 @@ On the client side, run:
--endpoint /generate_stream
to the end of the command above.
"""
import argparse
import asyncio
import gc
@ -34,12 +35,16 @@ from datetime import datetime
from typing import Any, Optional
import numpy as np
from backend_request_func import (ASYNC_REQUEST_FUNCS,
OPENAI_COMPATIBLE_BACKENDS, RequestFuncInput,
RequestFuncOutput)
from tqdm.asyncio import tqdm
from transformers import PreTrainedTokenizerBase
from backend_request_func import (
ASYNC_REQUEST_FUNCS,
OPENAI_COMPATIBLE_BACKENDS,
RequestFuncInput,
RequestFuncOutput,
)
try:
from vllm.transformers_utils.tokenizer import get_tokenizer
except ImportError:
@ -50,12 +55,21 @@ try:
except ImportError:
from argparse import ArgumentParser as FlexibleArgumentParser
from benchmark_dataset import (AIMODataset, ASRDataset, BurstGPTDataset,
ConversationDataset, HuggingFaceDataset,
InstructCoderDataset, MTBenchDataset,
NextEditPredictionDataset, RandomDataset,
SampleRequest, ShareGPTDataset, SonnetDataset,
VisionArenaDataset)
from benchmark_dataset import (
AIMODataset,
ASRDataset,
BurstGPTDataset,
ConversationDataset,
HuggingFaceDataset,
InstructCoderDataset,
MTBenchDataset,
NextEditPredictionDataset,
RandomDataset,
SampleRequest,
ShareGPTDataset,
SonnetDataset,
VisionArenaDataset,
)
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@ -118,7 +132,8 @@ async def get_request(
# Calculate scale parameter theta to maintain the desired request_rate.
assert burstiness > 0, (
f"A positive burstiness factor is expected, but given {burstiness}.")
f"A positive burstiness factor is expected, but given {burstiness}."
)
theta = 1.0 / (request_rate * burstiness)
for request in input_requests:
@ -164,8 +179,10 @@ def calculate_metrics(
# bundled together
# Note : this may inflate the output token count slightly
output_len = len(
tokenizer(outputs[i].generated_text,
add_special_tokens=False).input_ids)
tokenizer(
outputs[i].generated_text, add_special_tokens=False
).input_ids
)
actual_output_lens.append(output_len)
total_input += input_requests[i].prompt_len
tpot = 0
@ -188,16 +205,19 @@ def calculate_metrics(
if "ttft" in goodput_config_dict:
valid_metrics.append(ttfts)
slo_values.append(goodput_config_dict["ttft"] /
MILLISECONDS_TO_SECONDS_CONVERSION)
slo_values.append(
goodput_config_dict["ttft"] / MILLISECONDS_TO_SECONDS_CONVERSION
)
if "tpot" in goodput_config_dict:
valid_metrics.append(all_tpots)
slo_values.append(goodput_config_dict["tpot"] /
MILLISECONDS_TO_SECONDS_CONVERSION)
slo_values.append(
goodput_config_dict["tpot"] / MILLISECONDS_TO_SECONDS_CONVERSION
)
if "e2el" in goodput_config_dict:
valid_metrics.append(e2els)
slo_values.append(goodput_config_dict["e2el"] /
MILLISECONDS_TO_SECONDS_CONVERSION)
slo_values.append(
goodput_config_dict["e2el"] / MILLISECONDS_TO_SECONDS_CONVERSION
)
for req_metric in zip(*valid_metrics):
is_good_req = all([s >= r for s, r in zip(slo_values, req_metric)])
@ -208,7 +228,8 @@ def calculate_metrics(
warnings.warn(
"All requests failed. This is likely due to a misconfiguration "
"on the benchmark arguments.",
stacklevel=2)
stacklevel=2,
)
metrics = BenchmarkMetrics(
completed=completed,
total_input=total_input,
@ -217,27 +238,31 @@ def calculate_metrics(
request_goodput=good_completed / dur_s,
output_throughput=sum(actual_output_lens) / dur_s,
total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s,
mean_ttft_ms=np.mean(ttfts or 0) *
1000, # ttfts is empty if streaming is not supported by backend
mean_ttft_ms=np.mean(ttfts or 0)
* 1000, # ttfts is empty if streaming is not supported by backend
std_ttft_ms=np.std(ttfts or 0) * 1000,
median_ttft_ms=np.median(ttfts or 0) * 1000,
percentiles_ttft_ms=[(p, np.percentile(ttfts or 0, p) * 1000)
for p in selected_percentiles],
percentiles_ttft_ms=[
(p, np.percentile(ttfts or 0, p) * 1000) for p in selected_percentiles
],
mean_tpot_ms=np.mean(tpots or 0) * 1000,
std_tpot_ms=np.std(tpots or 0) * 1000,
median_tpot_ms=np.median(tpots or 0) * 1000,
percentiles_tpot_ms=[(p, np.percentile(tpots or 0, p) * 1000)
for p in selected_percentiles],
percentiles_tpot_ms=[
(p, np.percentile(tpots or 0, p) * 1000) for p in selected_percentiles
],
mean_itl_ms=np.mean(itls or 0) * 1000,
std_itl_ms=np.std(itls or 0) * 1000,
median_itl_ms=np.median(itls or 0) * 1000,
percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
for p in selected_percentiles],
percentiles_itl_ms=[
(p, np.percentile(itls or 0, p) * 1000) for p in selected_percentiles
],
mean_e2el_ms=np.mean(e2els or 0) * 1000,
std_e2el_ms=np.std(e2els or 0) * 1000,
median_e2el_ms=np.median(e2els or 0) * 1000,
percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
for p in selected_percentiles],
percentiles_e2el_ms=[
(p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
],
)
return metrics, actual_output_lens
@ -270,10 +295,12 @@ async def benchmark(
raise ValueError(f"Unknown backend: {backend}")
print("Starting initial single prompt test run...")
test_prompt, test_prompt_len, test_output_len, test_mm_content = \
input_requests[0].prompt, input_requests[0].prompt_len, \
input_requests[0].expected_output_len, \
input_requests[0].multi_modal_data
test_prompt, test_prompt_len, test_output_len, test_mm_content = (
input_requests[0].prompt,
input_requests[0].prompt_len,
input_requests[0].expected_output_len,
input_requests[0].multi_modal_data,
)
assert test_mm_content is None or isinstance(test_mm_content, dict)
test_input = RequestFuncInput(
@ -293,36 +320,36 @@ async def benchmark(
if not test_output.success:
raise ValueError(
"Initial test run failed - Please make sure benchmark arguments "
f"are correctly specified. Error: {test_output.error}")
f"are correctly specified. Error: {test_output.error}"
)
else:
print("Initial test run completed. Starting main benchmark run...")
if lora_modules:
# For each input request, choose a LoRA module at random.
lora_modules = iter(
[random.choice(lora_modules) \
for _ in range(len(input_requests))])
[random.choice(lora_modules) for _ in range(len(input_requests))]
)
if profile:
print("Starting profiler...")
profile_input = RequestFuncInput(model=model_id,
model_name=model_name,
prompt=test_prompt,
api_url=base_url + "/start_profile",
prompt_len=test_prompt_len,
output_len=test_output_len,
logprobs=logprobs,
multi_modal_content=test_mm_content,
ignore_eos=ignore_eos,
extra_body=extra_body)
profile_input = RequestFuncInput(
model=model_id,
model_name=model_name,
prompt=test_prompt,
api_url=base_url + "/start_profile",
prompt_len=test_prompt_len,
output_len=test_output_len,
logprobs=logprobs,
multi_modal_content=test_mm_content,
ignore_eos=ignore_eos,
extra_body=extra_body,
)
profile_output = await request_func(request_func_input=profile_input)
if profile_output.success:
print("Profiler started")
if burstiness == 1.0:
distribution = "Poisson process"
else:
distribution = "Gamma distribution"
distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
print(f"Traffic request rate: {request_rate}")
print(f"Burstiness factor: {burstiness} ({distribution})")
@ -334,42 +361,45 @@ async def benchmark(
# and it will simplify the code in limited_request_func.
# semaphore = (asyncio.Semaphore(max_concurrency)
# if max_concurrency else contextlib.nullcontext())
semaphore = (asyncio.Semaphore(max_concurrency)
if max_concurrency else None)
semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else None
async def limited_request_func(request_func_input, pbar):
if semaphore is None:
return await request_func(request_func_input=request_func_input,
pbar=pbar)
return await request_func(request_func_input=request_func_input, pbar=pbar)
async with semaphore:
return await request_func(request_func_input=request_func_input,
pbar=pbar)
return await request_func(request_func_input=request_func_input, pbar=pbar)
benchmark_start_time = time.perf_counter()
tasks: list[asyncio.Task] = []
async for request in get_request(input_requests, request_rate, burstiness):
prompt, prompt_len, output_len, mm_content = request.prompt, \
request.prompt_len, request.expected_output_len, \
request.multi_modal_data
prompt, prompt_len, output_len, mm_content = (
request.prompt,
request.prompt_len,
request.expected_output_len,
request.multi_modal_data,
)
req_model_id, req_model_name = model_id, model_name
if lora_modules:
req_lora_module = next(lora_modules)
req_model_id, req_model_name = req_lora_module, req_lora_module
request_func_input = RequestFuncInput(model=req_model_id,
model_name=req_model_name,
prompt=prompt,
api_url=api_url,
prompt_len=prompt_len,
output_len=output_len,
logprobs=logprobs,
multi_modal_content=mm_content,
ignore_eos=ignore_eos,
extra_body=extra_body)
request_func_input = RequestFuncInput(
model=req_model_id,
model_name=req_model_name,
prompt=prompt,
api_url=api_url,
prompt_len=prompt_len,
output_len=output_len,
logprobs=logprobs,
multi_modal_content=mm_content,
ignore_eos=ignore_eos,
extra_body=extra_body,
)
tasks.append(
asyncio.create_task(
limited_request_func(request_func_input=request_func_input,
pbar=pbar)))
limited_request_func(request_func_input=request_func_input, pbar=pbar)
)
)
outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)
if profile:
@ -401,22 +431,32 @@ async def benchmark(
goodput_config_dict=goodput_config_dict,
)
print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, c='='))
print("{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
print("{:<40} {:<10.2f}".format("Benchmark duration (s):",
benchmark_duration))
print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
print("{:<40} {:<10}".format("Total generated tokens:",
metrics.total_output))
print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
metrics.request_throughput))
print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
print(
"{:<40} {:<10.2f}".format(
"Request throughput (req/s):", metrics.request_throughput
)
)
if goodput_config_dict:
print("{:<40} {:<10.2f}".format("Request goodput (req/s):",
metrics.request_goodput))
print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
metrics.output_throughput))
print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):",
metrics.total_token_throughput))
print(
"{:<40} {:<10.2f}".format(
"Request goodput (req/s):", metrics.request_goodput
)
)
print(
"{:<40} {:<10.2f}".format(
"Output token throughput (tok/s):", metrics.output_throughput
)
)
print(
"{:<40} {:<10.2f}".format(
"Total Token throughput (tok/s):", metrics.total_token_throughput
)
)
result = {
"duration": benchmark_duration,
@ -424,8 +464,7 @@ async def benchmark(
"total_input_tokens": metrics.total_input,
"total_output_tokens": metrics.total_output,
"request_throughput": metrics.request_throughput,
"request_goodput:":
metrics.request_goodput if goodput_config_dict else None,
"request_goodput:": metrics.request_goodput if goodput_config_dict else None,
"output_throughput": metrics.output_throughput,
"total_token_throughput": metrics.total_token_throughput,
"input_lens": [output.prompt_len for output in outputs],
@ -448,29 +487,35 @@ async def benchmark(
# metric.
if metric_attribute_name not in selected_percentile_metrics:
return
print("{s:{c}^{n}}".format(s=metric_header, n=50, c='-'))
print("{:<40} {:<10.2f}".format(
f"Mean {metric_name} (ms):",
getattr(metrics, f"mean_{metric_attribute_name}_ms")))
print("{:<40} {:<10.2f}".format(
f"Median {metric_name} (ms):",
getattr(metrics, f"median_{metric_attribute_name}_ms")))
print("{s:{c}^{n}}".format(s=metric_header, n=50, c="-"))
print(
"{:<40} {:<10.2f}".format(
f"Mean {metric_name} (ms):",
getattr(metrics, f"mean_{metric_attribute_name}_ms"),
)
)
print(
"{:<40} {:<10.2f}".format(
f"Median {metric_name} (ms):",
getattr(metrics, f"median_{metric_attribute_name}_ms"),
)
)
result[f"mean_{metric_attribute_name}_ms"] = getattr(
metrics, f"mean_{metric_attribute_name}_ms")
metrics, f"mean_{metric_attribute_name}_ms"
)
result[f"median_{metric_attribute_name}_ms"] = getattr(
metrics, f"median_{metric_attribute_name}_ms")
metrics, f"median_{metric_attribute_name}_ms"
)
result[f"std_{metric_attribute_name}_ms"] = getattr(
metrics, f"std_{metric_attribute_name}_ms")
for p, value in getattr(metrics,
f"percentiles_{metric_attribute_name}_ms"):
metrics, f"std_{metric_attribute_name}_ms"
)
for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}_ms"):
p_word = str(int(p)) if int(p) == p else str(p)
print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):",
value))
print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):", value))
result[f"p{p_word}_{metric_attribute_name}_ms"] = value
process_one_metric("ttft", "TTFT", "Time to First Token")
process_one_metric("tpot", "TPOT",
"Time per Output Token (excl. 1st token)")
process_one_metric("tpot", "TPOT", "Time per Output Token (excl. 1st token)")
process_one_metric("itl", "ITL", "Inter-token Latency")
process_one_metric("e2el", "E2EL", "End-to-end Latency")
@ -490,12 +535,14 @@ def check_goodput_args(args):
raise ValueError(
f"Invalid metric name found, {slo_name}: {slo_val}. "
"The service level objective name should be one of "
f"{str(VALID_NAMES)}. ")
f"{str(VALID_NAMES)}. "
)
if slo_val < 0:
raise ValueError(
f"Invalid value found, {slo_name}: {slo_val}. "
"The service level objective value should be "
"non-negative.")
"non-negative."
)
return goodput_config_dict
@ -508,31 +555,42 @@ def parse_goodput(slo_pairs):
except ValueError as err:
raise argparse.ArgumentTypeError(
"Invalid format found for service level objectives. "
"Specify service level objectives for goodput as \"KEY:VALUE\" "
'Specify service level objectives for goodput as "KEY:VALUE" '
"pairs, where the key is a metric name, and the value is a "
"number in milliseconds.") from err
"number in milliseconds."
) from err
return goodput_config_dict
def save_to_pytorch_benchmark_format(args: argparse.Namespace,
results: dict[str, Any],
file_name: str) -> None:
def save_to_pytorch_benchmark_format(
args: argparse.Namespace, results: dict[str, Any], file_name: str
) -> None:
metrics = [
"median_ttft_ms", "mean_ttft_ms", "std_ttft_ms", "p99_ttft_ms",
"mean_tpot_ms", "median_tpot_ms", "std_tpot_ms", "p99_tpot_ms",
"median_itl_ms", "mean_itl_ms", "std_itl_ms", "p99_itl_ms"
"median_ttft_ms",
"mean_ttft_ms",
"std_ttft_ms",
"p99_ttft_ms",
"mean_tpot_ms",
"median_tpot_ms",
"std_tpot_ms",
"p99_tpot_ms",
"median_itl_ms",
"mean_itl_ms",
"std_itl_ms",
"p99_itl_ms",
]
# These raw data might be useful, but they are rather big. They can be added
# later if needed
ignored_metrics = ["ttfts", "itls", "generated_texts", "errors"]
pt_records = convert_to_pytorch_benchmark_format(
args=args,
metrics={k: [results[k]]
for k in metrics},
metrics={k: [results[k]] for k in metrics},
extra_info={
k: results[k]
for k in results if k not in metrics and k not in ignored_metrics
})
for k in results
if k not in metrics and k not in ignored_metrics
},
)
if pt_records:
# Don't use json suffix here as we don't want CI to pick it up
pt_file = f"{os.path.splitext(file_name)[0]}.pytorch.json"
@ -557,34 +615,42 @@ def main(args: argparse.Namespace):
api_url = f"http://{args.host}:{args.port}{args.endpoint}"
base_url = f"http://{args.host}:{args.port}"
tokenizer = get_tokenizer(tokenizer_id,
tokenizer_mode=tokenizer_mode,
trust_remote_code=args.trust_remote_code)
tokenizer = get_tokenizer(
tokenizer_id,
tokenizer_mode=tokenizer_mode,
trust_remote_code=args.trust_remote_code,
)
if args.dataset_name is None:
raise ValueError(
"Please specify '--dataset-name' and the corresponding "
"'--dataset-path' if required.")
"'--dataset-path' if required."
)
if args.dataset_name == "sonnet":
dataset = SonnetDataset(dataset_path=args.dataset_path)
# For the "sonnet" dataset, formatting depends on the backend.
if args.backend == "openai-chat":
input_requests = dataset.sample(num_requests=args.num_prompts,
input_len=args.sonnet_input_len,
output_len=args.sonnet_output_len,
prefix_len=args.sonnet_prefix_len,
tokenizer=tokenizer,
return_prompt_formatted=False)
input_requests = dataset.sample(
num_requests=args.num_prompts,
input_len=args.sonnet_input_len,
output_len=args.sonnet_output_len,
prefix_len=args.sonnet_prefix_len,
tokenizer=tokenizer,
return_prompt_formatted=False,
)
else:
assert tokenizer.chat_template or tokenizer.default_chat_template, (
"Tokenizer/model must have chat template for sonnet dataset.")
input_requests = dataset.sample(num_requests=args.num_prompts,
input_len=args.sonnet_input_len,
output_len=args.sonnet_output_len,
prefix_len=args.sonnet_prefix_len,
tokenizer=tokenizer,
return_prompt_formatted=True)
"Tokenizer/model must have chat template for sonnet dataset."
)
input_requests = dataset.sample(
num_requests=args.num_prompts,
input_len=args.sonnet_input_len,
output_len=args.sonnet_output_len,
prefix_len=args.sonnet_prefix_len,
tokenizer=tokenizer,
return_prompt_formatted=True,
)
elif args.dataset_name == "hf":
# all following datasets are implemented from the
@ -611,23 +677,30 @@ def main(args: argparse.Namespace):
dataset_class = ASRDataset
args.hf_split = "train"
else:
supported_datasets = set([
dataset_name for cls in HuggingFaceDataset.__subclasses__()
for dataset_name in cls.SUPPORTED_DATASET_PATHS
])
supported_datasets = set(
[
dataset_name
for cls in HuggingFaceDataset.__subclasses__()
for dataset_name in cls.SUPPORTED_DATASET_PATHS
]
)
raise ValueError(
f"Unsupported dataset path: {args.dataset_path}. "
"Huggingface dataset only supports dataset_path"
f" from one of following: {supported_datasets}. "
"Please consider contributing if you would "
"like to add support for additional dataset formats.")
"like to add support for additional dataset formats."
)
if (dataset_class.IS_MULTIMODAL and backend not in \
["openai-chat", "openai-audio"]):
if dataset_class.IS_MULTIMODAL and backend not in [
"openai-chat",
"openai-audio",
]:
# multi-modal benchmark is only available on OpenAI Chat backend.
raise ValueError(
"Multi-modal content is only supported on 'openai-chat' and " \
"'openai-audio' backend.")
"Multi-modal content is only supported on 'openai-chat' and "
"'openai-audio' backend."
)
input_requests = dataset_class(
dataset_path=args.dataset_path,
dataset_subset=args.hf_subset,
@ -642,26 +715,24 @@ def main(args: argparse.Namespace):
else:
# For datasets that follow a similar structure, use a mapping.
dataset_mapping = {
"sharegpt":
lambda: ShareGPTDataset(random_seed=args.seed,
dataset_path=args.dataset_path).sample(
tokenizer=tokenizer,
num_requests=args.num_prompts,
output_len=args.sharegpt_output_len,
),
"burstgpt":
lambda: BurstGPTDataset(random_seed=args.seed,
dataset_path=args.dataset_path).
sample(tokenizer=tokenizer, num_requests=args.num_prompts),
"random":
lambda: RandomDataset(dataset_path=args.dataset_path).sample(
"sharegpt": lambda: ShareGPTDataset(
random_seed=args.seed, dataset_path=args.dataset_path
).sample(
tokenizer=tokenizer,
num_requests=args.num_prompts,
output_len=args.sharegpt_output_len,
),
"burstgpt": lambda: BurstGPTDataset(
random_seed=args.seed, dataset_path=args.dataset_path
).sample(tokenizer=tokenizer, num_requests=args.num_prompts),
"random": lambda: RandomDataset(dataset_path=args.dataset_path).sample(
tokenizer=tokenizer,
num_requests=args.num_prompts,
prefix_len=args.random_prefix_len,
input_len=args.random_input_len,
output_len=args.random_output_len,
range_ratio=args.random_range_ratio,
)
),
}
try:
@ -677,15 +748,16 @@ def main(args: argparse.Namespace):
"top_p": args.top_p,
"top_k": args.top_k,
"min_p": args.min_p,
"temperature": args.temperature
}.items() if v is not None
"temperature": args.temperature,
}.items()
if v is not None
}
# Sampling parameters are only supported by openai-compatible backend.
if sampling_params and args.backend not in OPENAI_COMPATIBLE_BACKENDS:
raise ValueError(
"Sampling parameters are only supported by openai-compatible "
"backends.")
"Sampling parameters are only supported by openai-compatible backends."
)
if "temperature" not in sampling_params:
sampling_params["temperature"] = 0.0 # Default to greedy decoding.
@ -709,15 +781,14 @@ def main(args: argparse.Namespace):
disable_tqdm=args.disable_tqdm,
profile=args.profile,
selected_percentile_metrics=args.percentile_metrics.split(","),
selected_percentiles=[
float(p) for p in args.metric_percentiles.split(",")
],
selected_percentiles=[float(p) for p in args.metric_percentiles.split(",")],
ignore_eos=args.ignore_eos,
goodput_config_dict=goodput_config_dict,
max_concurrency=args.max_concurrency,
lora_modules=args.lora_modules,
extra_body=sampling_params,
))
)
)
# Save config and results to json
if args.save_result or args.append_result:
@ -742,8 +813,9 @@ def main(args: argparse.Namespace):
"Invalid metadata format. Please use KEY=VALUE format."
)
# Traffic
result_json["request_rate"] = (args.request_rate if args.request_rate
< float("inf") else "inf")
result_json["request_rate"] = (
args.request_rate if args.request_rate < float("inf") else "inf"
)
result_json["burstiness"] = args.burstiness
result_json["max_concurrency"] = args.max_concurrency
@ -753,24 +825,31 @@ def main(args: argparse.Namespace):
if not args.save_detailed:
# Remove fields with too many data points
for field in [
"input_lens", "output_lens", "ttfts", "itls",
"generated_texts", "errors"
"input_lens",
"output_lens",
"ttfts",
"itls",
"generated_texts",
"errors",
]:
if field in result_json:
del result_json[field]
# Save to file
base_model_id = model_id.split("/")[-1]
max_concurrency_str = (f"-concurrency{args.max_concurrency}"
if args.max_concurrency is not None else "")
file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json" #noqa
max_concurrency_str = (
f"-concurrency{args.max_concurrency}"
if args.max_concurrency is not None
else ""
)
file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json" # noqa
if args.result_filename:
file_name = args.result_filename
if args.result_dir:
file_name = os.path.join(args.result_dir, file_name)
with open(file_name,
mode="a+" if args.append_result else "w",
encoding='utf-8') as outfile:
with open(
file_name, mode="a+" if args.append_result else "w", encoding="utf-8"
) as outfile:
# Append a newline.
if args.append_result and outfile.tell() != 0:
outfile.write("\n")
@ -780,7 +859,8 @@ def main(args: argparse.Namespace):
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark the online serving throughput.")
description="Benchmark the online serving throughput."
)
parser.add_argument(
"--backend",
type=str,
@ -809,11 +889,13 @@ if __name__ == "__main__":
choices=["sharegpt", "burstgpt", "sonnet", "random", "hf"],
help="Name of the dataset to benchmark on.",
)
parser.add_argument("--dataset-path",
type=str,
default=None,
help="Path to the sharegpt/sonnet dataset. "
"Or the huggingface dataset ID if using HF dataset.")
parser.add_argument(
"--dataset-path",
type=str,
default=None,
help="Path to the sharegpt/sonnet dataset. "
"Or the huggingface dataset ID if using HF dataset.",
)
parser.add_argument(
"--max-concurrency",
type=int,
@ -825,7 +907,8 @@ if __name__ == "__main__":
"initiated, this argument will control how many are actually allowed "
"to execute at a time. This means that when used in combination, the "
"actual request rate may be lower than specified with --request-rate, "
"if the server is not processing requests fast enough to keep up.")
"if the server is not processing requests fast enough to keep up.",
)
parser.add_argument(
"--model",
@ -836,8 +919,7 @@ if __name__ == "__main__":
parser.add_argument(
"--tokenizer",
type=str,
help=
"Name or path of the tokenizer, if not using the default tokenizer.", # noqa: E501
help="Name or path of the tokenizer, if not using the default tokenizer.", # noqa: E501
)
parser.add_argument("--use-beam-search", action="store_true")
parser.add_argument(
@ -850,11 +932,13 @@ if __name__ == "__main__":
"--logprobs",
type=int,
default=None,
help=("Number of logprobs-per-token to compute & return as part of "
"the request. If unspecified, then either (1) if beam search "
"is disabled, no logprobs are computed & a single dummy "
"logprob is returned for each token; or (2) if beam search "
"is enabled 1 logprob per token is computed"),
help=(
"Number of logprobs-per-token to compute & return as part of "
"the request. If unspecified, then either (1) if beam search "
"is disabled, no logprobs are computed & a single dummy "
"logprob is returned for each token; or (2) if beam search "
"is enabled 1 logprob per token is computed"
),
)
parser.add_argument(
"--request-rate",
@ -938,35 +1022,38 @@ if __name__ == "__main__":
"--ignore-eos",
action="store_true",
help="Set ignore_eos flag when sending the benchmark request."
"Warning: ignore_eos is not supported in deepspeed_mii and tgi.")
"Warning: ignore_eos is not supported in deepspeed_mii and tgi.",
)
parser.add_argument(
"--percentile-metrics",
type=str,
default="ttft,tpot,itl",
help="Comma-separated list of selected metrics to report percentils. "
"This argument specifies the metrics to report percentiles. "
"Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
"Default value is \"ttft,tpot,itl\".")
'Allowed metric names are "ttft", "tpot", "itl", "e2el". '
'Default value is "ttft,tpot,itl".',
)
parser.add_argument(
"--metric-percentiles",
type=str,
default="99",
help="Comma-separated list of percentiles for selected metrics. "
"To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
"Default value is \"99\". "
"Use \"--percentile-metrics\" to select metrics.",
'To report 25-th, 50-th, and 75-th percentiles, use "25,50,75". '
'Default value is "99". '
'Use "--percentile-metrics" to select metrics.',
)
parser.add_argument(
"--goodput",
nargs="+",
required=False,
help="Specify service level objectives for goodput as \"KEY:VALUE\" "
help='Specify service level objectives for goodput as "KEY:VALUE" '
"pairs, where the key is a metric name, and the value is in "
"milliseconds. Multiple \"KEY:VALUE\" pairs can be provided, "
'milliseconds. Multiple "KEY:VALUE" pairs can be provided, '
"separated by spaces. Allowed request level metric names are "
"\"ttft\", \"tpot\", \"e2el\". For more context on the definition of "
'"ttft", "tpot", "e2el". For more context on the definition of '
"goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
"and the blog: https://hao-ai-lab.github.io/blogs/distserve")
"and the blog: https://hao-ai-lab.github.io/blogs/distserve",
)
# group for dataset specific arguments
sonnet_group = parser.add_argument_group("sonnet dataset options")
@ -974,22 +1061,19 @@ if __name__ == "__main__":
"--sonnet-input-len",
type=int,
default=550,
help=
"Number of input tokens per request, used only for sonnet dataset.",
help="Number of input tokens per request, used only for sonnet dataset.",
)
sonnet_group.add_argument(
"--sonnet-output-len",
type=int,
default=150,
help=
"Number of output tokens per request, used only for sonnet dataset.",
help="Number of output tokens per request, used only for sonnet dataset.",
)
sonnet_group.add_argument(
"--sonnet-prefix-len",
type=int,
default=200,
help=
"Number of prefix tokens per request, used only for sonnet dataset.",
help="Number of prefix tokens per request, used only for sonnet dataset.",
)
sharegpt_group = parser.add_argument_group("sharegpt dataset options")
@ -998,22 +1082,21 @@ if __name__ == "__main__":
type=int,
default=None,
help="Output length for each request. Overrides the output length "
"from the ShareGPT dataset.")
"from the ShareGPT dataset.",
)
random_group = parser.add_argument_group("random dataset options")
random_group.add_argument(
"--random-input-len",
type=int,
default=1024,
help=
"Number of input tokens per request, used only for random sampling.",
help="Number of input tokens per request, used only for random sampling.",
)
random_group.add_argument(
"--random-output-len",
type=int,
default=128,
help=
"Number of output tokens per request, used only for random sampling.",
help="Number of output tokens per request, used only for random sampling.",
)
random_group.add_argument(
"--random-range-ratio",
@ -1028,23 +1111,23 @@ if __name__ == "__main__":
"--random-prefix-len",
type=int,
default=0,
help=("Number of fixed prefix tokens before the random context "
"in a request. "
"The total input length is the sum of `random-prefix-len` and "
"a random "
"context length sampled from [input_len * (1 - range_ratio), "
"input_len * (1 + range_ratio)]."),
help=(
"Number of fixed prefix tokens before the random context "
"in a request. "
"The total input length is the sum of `random-prefix-len` and "
"a random "
"context length sampled from [input_len * (1 - range_ratio), "
"input_len * (1 + range_ratio)]."
),
)
hf_group = parser.add_argument_group("hf dataset options")
hf_group.add_argument("--hf-subset",
type=str,
default=None,
help="Subset of the HF dataset.")
hf_group.add_argument("--hf-split",
type=str,
default=None,
help="Split of the HF dataset.")
hf_group.add_argument(
"--hf-subset", type=str, default=None, help="Subset of the HF dataset."
)
hf_group.add_argument(
"--hf-split", type=str, default=None, help="Split of the HF dataset."
)
hf_group.add_argument(
"--hf-output-len",
type=int,
@ -1058,52 +1141,58 @@ if __name__ == "__main__":
"--top-p",
type=float,
default=None,
help="Top-p sampling parameter. Only has effect on openai-compatible "
"backends.")
help="Top-p sampling parameter. Only has effect on openai-compatible backends.",
)
sampling_group.add_argument(
"--top-k",
type=int,
default=None,
help="Top-k sampling parameter. Only has effect on openai-compatible "
"backends.")
help="Top-k sampling parameter. Only has effect on openai-compatible backends.",
)
sampling_group.add_argument(
"--min-p",
type=float,
default=None,
help="Min-p sampling parameter. Only has effect on openai-compatible "
"backends.")
help="Min-p sampling parameter. Only has effect on openai-compatible backends.",
)
sampling_group.add_argument(
"--temperature",
type=float,
default=None,
help="Temperature sampling parameter. Only has effect on "
"openai-compatible backends. If not specified, default to greedy "
"decoding (i.e. temperature==0.0).")
"decoding (i.e. temperature==0.0).",
)
parser.add_argument(
'--tokenizer-mode',
"--tokenizer-mode",
type=str,
default="auto",
choices=['auto', 'slow', 'mistral', 'custom'],
choices=["auto", "slow", "mistral", "custom"],
help='The tokenizer mode.\n\n* "auto" will use the '
'fast tokenizer if available.\n* "slow" will '
'always use the slow tokenizer. \n* '
"always use the slow tokenizer. \n* "
'"mistral" will always use the `mistral_common` tokenizer. \n*'
'"custom" will use --tokenizer to select the preregistered tokenizer.')
'"custom" will use --tokenizer to select the preregistered tokenizer.',
)
parser.add_argument("--served-model-name",
type=str,
default=None,
help="The model name used in the API. "
"If not specified, the model name will be the "
"same as the ``--model`` argument. ")
parser.add_argument(
"--served-model-name",
type=str,
default=None,
help="The model name used in the API. "
"If not specified, the model name will be the "
"same as the ``--model`` argument. ",
)
parser.add_argument("--lora-modules",
nargs='+',
default=None,
help="A subset of LoRA module names passed in when "
"launching the server. For each request, the "
"script chooses a LoRA module at random.")
parser.add_argument(
"--lora-modules",
nargs="+",
default=None,
help="A subset of LoRA module names passed in when "
"launching the server. For each request, the "
"script chooses a LoRA module at random.",
)
args = parser.parse_args()

487
benchmarks/benchmark_serving_structured_output.py
View File

@ -19,6 +19,7 @@ On the client side, run:
--endpoint /generate_stream
to the end of the command above.
"""
import argparse
import asyncio
import copy
@ -36,11 +37,15 @@ from typing import Optional
import datasets
import numpy as np
import pandas as pd
from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
RequestFuncOutput)
from tqdm.asyncio import tqdm
from transformers import PreTrainedTokenizerBase
from backend_request_func import (
ASYNC_REQUEST_FUNCS,
RequestFuncInput,
RequestFuncOutput,
)
try:
from vllm.transformers_utils.tokenizer import get_tokenizer
except ImportError:
@ -52,7 +57,8 @@ except ImportError:
from argparse import ArgumentParser as FlexibleArgumentParser
from vllm.v1.structured_output.backend_xgrammar import (
has_xgrammar_unsupported_json_features)
has_xgrammar_unsupported_json_features,
)
MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@ -98,6 +104,7 @@ class SampleRequest:
prompt_len: The length of the prompt in tokens.
expected_output_len: The expected length of the output in tokens.
"""
prompt: str
prompt_len: int
expected_output_len: int
@ -106,32 +113,28 @@ class SampleRequest:
completion: str = None
def sample_requests(tokenizer: PreTrainedTokenizerBase,
args: argparse.Namespace) -> list[SampleRequest]:
if args.dataset == 'json' or args.dataset == 'json-unique':
def sample_requests(
tokenizer: PreTrainedTokenizerBase, args: argparse.Namespace
) -> list[SampleRequest]:
if args.dataset == "json" or args.dataset == "json-unique":
if args.json_schema_path is None:
dir_path = os.path.dirname(os.path.realpath(__file__))
args.json_schema_path = os.path.join(dir_path,
"structured_schemas",
"structured_schema_1.json")
args.json_schema_path = os.path.join(
dir_path, "structured_schemas", "structured_schema_1.json"
)
json_schemas = []
with open(args.json_schema_path) as f:
schema = json.load(f)
if args.dataset == 'json-unique':
json_schemas = [
copy.deepcopy(schema) for _ in range(args.num_prompts)
]
if args.dataset == "json-unique":
json_schemas = [copy.deepcopy(schema) for _ in range(args.num_prompts)]
for i in range(len(json_schemas)):
if "properties" not in json_schemas[i]:
json_schemas[i]["properties"] = {}
json_schemas[i]["properties"][
f"__optional_field_{uuid.uuid4()}"] = {
"type":
"string",
"description":
"An unique optional field to avoid cached schemas"
}
json_schemas[i]["properties"][f"__optional_field_{uuid.uuid4()}"] = {
"type": "string",
"description": "An unique optional field to avoid cached schemas",
}
else:
json_schemas = [schema] * args.num_prompts
@ -142,11 +145,13 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
return json_schemas[index % len(json_schemas)]
requests = [
SampleRequest(prompt=gen_prompt(i),
prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
expected_output_len=args.output_len,
schema=get_schema(i),
structure_type=args.structure_type)
SampleRequest(
prompt=gen_prompt(i),
prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
expected_output_len=args.output_len,
schema=get_schema(i),
structure_type=args.structure_type,
)
for i in range(args.num_prompts)
]
@ -170,11 +175,13 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
input_len = len(tokenizer(prompt).input_ids)
print(f"Input length of the prompt: {input_len} tokens")
requests = [
SampleRequest(prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=schema,
structure_type=args.structure_type)
SampleRequest(
prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=schema,
structure_type=args.structure_type,
)
for _ in range(args.num_prompts)
]
@ -188,11 +195,13 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
input_len = len(tokenizer(prompt).input_ids)
print(f"Input length of the prompt: {input_len} tokens")
requests = [
SampleRequest(prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=regex,
structure_type=args.structure_type)
SampleRequest(
prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=regex,
structure_type=args.structure_type,
)
for _ in range(args.num_prompts)
]
@ -203,48 +212,55 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
input_len = len(tokenizer(prompt).input_ids)
print(f"Input length of the prompt: {input_len} tokens")
requests = [
SampleRequest(prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=choice,
structure_type=args.structure_type)
SampleRequest(
prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=choice,
structure_type=args.structure_type,
)
for _ in range(args.num_prompts)
]
elif args.dataset == "xgrammar_bench":
requests: list[SampleRequest] = []
dataset = datasets.load_dataset("NousResearch/json-mode-eval",
split="train")
dataset = datasets.load_dataset("NousResearch/json-mode-eval", split="train")
full_dataset_len = len(dataset)
def _filter_func(item):
import json
schema = json.loads(item["schema"])
return not has_xgrammar_unsupported_json_features(schema)
dataset = dataset.filter(_filter_func)
num_filtered_out = full_dataset_len - len(dataset)
print(f"dataset has {len(dataset)} entries after filtering "
f"out {num_filtered_out} entries with unsupported features")
print(
f"dataset has {len(dataset)} entries after filtering "
f"out {num_filtered_out} entries with unsupported features"
)
len_dataset = len(dataset)
for data_point_idx in range(args.num_prompts):
idx = data_point_idx
while idx >= len_dataset:
idx -= len_dataset
schema = dataset["schema"][idx]
prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
tokenize=False,
add_generation_prompt=True)
prompt = tokenizer.apply_chat_template(
dataset["prompt"][idx], tokenize=False, add_generation_prompt=True
)
input_len = len(tokenizer(prompt).input_ids)
completion = dataset["completion"][idx]
requests.append(
SampleRequest(prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=schema,
structure_type=args.structure_type,
completion=completion))
SampleRequest(
prompt=prompt,
prompt_len=input_len,
expected_output_len=args.output_len,
schema=schema,
structure_type=args.structure_type,
completion=completion,
)
)
return requests
@ -276,7 +292,8 @@ async def get_request(
# Calculate scale parameter theta to maintain the desired request_rate.
assert burstiness > 0, (
f"A positive burstiness factor is expected, but given {burstiness}.")
f"A positive burstiness factor is expected, but given {burstiness}."
)
theta = 1.0 / (request_rate * burstiness)
for i, request in enumerate(input_requests):
@ -318,8 +335,8 @@ def calculate_metrics(
# multiple output tokens may be bundled together
# Note : this may inflate the output token count slightly
output_len = len(
tokenizer(outputs[i].generated_text,
add_special_tokens=False).input_ids)
tokenizer(outputs[i].generated_text, add_special_tokens=False).input_ids
)
actual_output_lens.append(output_len)
total_input += input_requests[i].prompt_len
tpot = 0
@ -343,16 +360,19 @@ def calculate_metrics(
if "ttft" in goodput_config_dict:
valid_metrics.append(ttfts)
slo_values.append(goodput_config_dict["ttft"] /
MILLISECONDS_TO_SECONDS_CONVERSION)
slo_values.append(
goodput_config_dict["ttft"] / MILLISECONDS_TO_SECONDS_CONVERSION
)
if "tpot" in goodput_config_dict:
valid_metrics.append(all_tpots)
slo_values.append(goodput_config_dict["tpot"] /
MILLISECONDS_TO_SECONDS_CONVERSION)
slo_values.append(
goodput_config_dict["tpot"] / MILLISECONDS_TO_SECONDS_CONVERSION
)
if "e2el" in goodput_config_dict:
valid_metrics.append(e2els)
slo_values.append(goodput_config_dict["e2el"] /
MILLISECONDS_TO_SECONDS_CONVERSION)
slo_values.append(
goodput_config_dict["e2el"] / MILLISECONDS_TO_SECONDS_CONVERSION
)
for req_metric in zip(*valid_metrics):
is_good_req = all([s >= r for s, r in zip(slo_values, req_metric)])
@ -363,7 +383,8 @@ def calculate_metrics(
warnings.warn(
"All requests failed. This is likely due to a misconfiguration "
"on the benchmark arguments.",
stacklevel=2)
stacklevel=2,
)
metrics = BenchmarkMetrics(
completed=completed,
total_input=total_input,
@ -372,27 +393,31 @@ def calculate_metrics(
request_goodput=good_completed / dur_s,
output_throughput=sum(actual_output_lens) / dur_s,
total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s,
mean_ttft_ms=np.mean(ttfts or 0) *
1000, # ttfts is empty if streaming is not supported by backend
mean_ttft_ms=np.mean(ttfts or 0)
* 1000, # ttfts is empty if streaming is not supported by backend
std_ttft_ms=np.std(ttfts or 0) * 1000,
median_ttft_ms=np.median(ttfts or 0) * 1000,
percentiles_ttft_ms=[(p, np.percentile(ttfts or 0, p) * 1000)
for p in selected_percentiles],
percentiles_ttft_ms=[
(p, np.percentile(ttfts or 0, p) * 1000) for p in selected_percentiles
],
mean_tpot_ms=np.mean(tpots or 0) * 1000,
std_tpot_ms=np.std(tpots or 0) * 1000,
median_tpot_ms=np.median(tpots or 0) * 1000,
percentiles_tpot_ms=[(p, np.percentile(tpots or 0, p) * 1000)
for p in selected_percentiles],
percentiles_tpot_ms=[
(p, np.percentile(tpots or 0, p) * 1000) for p in selected_percentiles
],
mean_itl_ms=np.mean(itls or 0) * 1000,
std_itl_ms=np.std(itls or 0) * 1000,
median_itl_ms=np.median(itls or 0) * 1000,
percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
for p in selected_percentiles],
percentiles_itl_ms=[
(p, np.percentile(itls or 0, p) * 1000) for p in selected_percentiles
],
mean_e2el_ms=np.mean(e2els or 0) * 1000,
std_e2el_ms=np.std(e2els or 0) * 1000,
median_e2el_ms=np.median(e2els or 0) * 1000,
percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
for p in selected_percentiles],
percentiles_e2el_ms=[
(p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
],
)
return metrics, actual_output_lens
@ -429,12 +454,13 @@ async def benchmark(
print("Starting initial single prompt test run...")
structured_output_req_idx = random.sample(
range(len(input_requests)),
int(len(input_requests) * structured_output_ratio))
range(len(input_requests)), int(len(input_requests) * structured_output_ratio)
)
test_request = input_requests[0]
test_req_extra_body = (prepare_extra_body(test_request)
if 0 in structured_output_req_idx else None)
test_req_extra_body = (
prepare_extra_body(test_request) if 0 in structured_output_req_idx else None
)
test_input = RequestFuncInput(
model=model_id,
prompt=test_request.prompt,
@ -448,7 +474,8 @@ async def benchmark(
if not test_output.success:
raise ValueError(
"Initial test run failed - Please make sure benchmark arguments "
f"are correctly specified. Error: {test_output.error}")
f"are correctly specified. Error: {test_output.error}"
)
else:
print("Initial test run completed. Starting main benchmark run...")
@ -467,10 +494,7 @@ async def benchmark(
if profile_output.success:
print("Profiler started")
if burstiness == 1.0:
distribution = "Poisson process"
else:
distribution = "Gamma distribution"
distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
print(f"Traffic request rate: {request_rate}")
print(f"Burstiness factor: {burstiness} ({distribution})")
@ -482,24 +506,21 @@ async def benchmark(
# and it will simplify the code in limited_request_func.
# semaphore = (asyncio.Semaphore(max_concurrency)
# if max_concurrency else contextlib.nullcontext())
semaphore = (asyncio.Semaphore(max_concurrency)
if max_concurrency else None)
semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else None
async def limited_request_func(request_func_input, pbar):
if semaphore is None:
return await request_func(request_func_input=request_func_input,
pbar=pbar)
return await request_func(request_func_input=request_func_input, pbar=pbar)
async with semaphore:
return await request_func(request_func_input=request_func_input,
pbar=pbar)
return await request_func(request_func_input=request_func_input, pbar=pbar)
benchmark_start_time = time.perf_counter()
tasks: list[asyncio.Task] = []
expected: list[str] = []
async for i, request in get_request(input_requests, request_rate,
burstiness):
extra_body = prepare_extra_body(
request) if i in structured_output_req_idx else None
async for i, request in get_request(input_requests, request_rate, burstiness):
extra_body = (
prepare_extra_body(request) if i in structured_output_req_idx else None
)
request_func_input = RequestFuncInput(
model=model_id,
prompt=request.prompt,
@ -512,8 +533,9 @@ async def benchmark(
expected.append(request.completion)
tasks.append(
asyncio.create_task(
limited_request_func(request_func_input=request_func_input,
pbar=pbar)))
limited_request_func(request_func_input=request_func_input, pbar=pbar)
)
)
outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)
if profile:
@ -545,54 +567,58 @@ async def benchmark(
goodput_config_dict=goodput_config_dict,
)
print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, c='='))
print("{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
print("{:<40} {:<10.2f}".format("Benchmark duration (s):",
benchmark_duration))
print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
print("{:<40} {:<10}".format("Total generated tokens:",
metrics.total_output))
print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
metrics.request_throughput))
print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
print(
"{:<40} {:<10.2f}".format(
"Request throughput (req/s):", metrics.request_throughput
)
)
if goodput_config_dict:
print("{:<40} {:<10.2f}".format("Request goodput (req/s):",
metrics.request_goodput))
print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
metrics.output_throughput))
print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):",
metrics.total_token_throughput))
print(
"{:<40} {:<10.2f}".format(
"Request goodput (req/s):", metrics.request_goodput
)
)
print(
"{:<40} {:<10.2f}".format(
"Output token throughput (tok/s):", metrics.output_throughput
)
)
print(
"{:<40} {:<10.2f}".format(
"Total Token throughput (tok/s):", metrics.total_token_throughput
)
)
result = {
"duration":
benchmark_duration,
"completed":
metrics.completed,
"total_input_tokens":
metrics.total_input,
"total_output_tokens":
metrics.total_output,
"request_throughput":
metrics.request_throughput,
"output_throughput":
metrics.output_throughput,
"total_token_throughput":
metrics.total_token_throughput,
"ttft_description":
pd.Series([output.ttft for output in outputs]).describe().to_dict(),
"tpot_description":
pd.Series([output.tpot for output in outputs]).describe().to_dict(),
"duration": benchmark_duration,
"completed": metrics.completed,
"total_input_tokens": metrics.total_input,
"total_output_tokens": metrics.total_output,
"request_throughput": metrics.request_throughput,
"output_throughput": metrics.output_throughput,
"total_token_throughput": metrics.total_token_throughput,
"ttft_description": pd.Series([output.ttft for output in outputs])
.describe()
.to_dict(),
"tpot_description": pd.Series([output.tpot for output in outputs])
.describe()
.to_dict(),
"input_lens": [output.prompt_len for output in outputs],
"output_lens":
actual_output_lens,
"output_lens": actual_output_lens,
"ttfts": [output.ttft for output in outputs],
"itls": [output.itl for output in outputs],
"errors": [output.error for output in outputs],
}
ret = [{
'generated': output.generated_text,
'expected': gt
} for output, gt in zip(outputs, expected)]
ret = [
{"generated": output.generated_text, "expected": gt}
for output, gt in zip(outputs, expected)
]
def process_one_metric(
# E.g., "ttft"
@ -606,29 +632,35 @@ async def benchmark(
# metric.
if metric_attribute_name not in selected_percentile_metrics:
return
print("{s:{c}^{n}}".format(s=metric_header, n=50, c='-'))
print("{:<40} {:<10.2f}".format(
f"Mean {metric_name} (ms):",
getattr(metrics, f"mean_{metric_attribute_name}_ms")))
print("{:<40} {:<10.2f}".format(
f"Median {metric_name} (ms):",
getattr(metrics, f"median_{metric_attribute_name}_ms")))
print("{s:{c}^{n}}".format(s=metric_header, n=50, c="-"))
print(
"{:<40} {:<10.2f}".format(
f"Mean {metric_name} (ms):",
getattr(metrics, f"mean_{metric_attribute_name}_ms"),
)
)
print(
"{:<40} {:<10.2f}".format(
f"Median {metric_name} (ms):",
getattr(metrics, f"median_{metric_attribute_name}_ms"),
)
)
result[f"mean_{metric_attribute_name}_ms"] = getattr(
metrics, f"mean_{metric_attribute_name}_ms")
metrics, f"mean_{metric_attribute_name}_ms"
)
result[f"median_{metric_attribute_name}_ms"] = getattr(
metrics, f"median_{metric_attribute_name}_ms")
metrics, f"median_{metric_attribute_name}_ms"
)
result[f"std_{metric_attribute_name}_ms"] = getattr(
metrics, f"std_{metric_attribute_name}_ms")
for p, value in getattr(metrics,
f"percentiles_{metric_attribute_name}_ms"):
metrics, f"std_{metric_attribute_name}_ms"
)
for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}_ms"):
p_word = str(int(p)) if int(p) == p else str(p)
print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):",
value))
print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):", value))
result[f"p{p_word}_{metric_attribute_name}_ms"] = value
process_one_metric("ttft", "TTFT", "Time to First Token")
process_one_metric("tpot", "TPOT",
"Time per Output Token (excl. 1st token)")
process_one_metric("tpot", "TPOT", "Time per Output Token (excl. 1st token)")
process_one_metric("itl", "ITL", "Inter-token Latency")
process_one_metric("e2el", "E2EL", "End-to-end Latency")
@ -638,13 +670,13 @@ async def benchmark(
def evaluate(ret, args):
def _eval_correctness_json(expected, actual):
# extract json string from string using regex
import re
actual = actual.replace('\n', '').replace(' ', '').strip()
actual = actual.replace("\n", "").replace(" ", "").strip()
try:
actual = re.search(r'\{.*\}', actual).group()
actual = re.search(r"\{.*\}", actual).group()
actual = json.loads(actual)
except Exception:
return False
@ -656,28 +688,32 @@ def evaluate(ret, args):
def _eval_correctness_regex(expected, actual):
import re
return re.match(args.regex, actual) is not None
def _eval_correctness(expected, actual):
if args.structure_type == 'guided_json':
if args.structure_type == "guided_json":
return _eval_correctness_json(expected, actual)
elif args.structure_type == 'guided_regex':
elif args.structure_type == "guided_regex":
return _eval_correctness_regex(expected, actual)
elif args.structure_type == 'guided_choice':
elif args.structure_type == "guided_choice":
return _eval_correctness_choice(expected, actual)
else:
return None
scores = []
for res in ret:
score = _eval_correctness(res['expected'], res['generated'])
res['correctness'] = score
score = _eval_correctness(res["expected"], res["generated"])
res["correctness"] = score
scores.append(score)
not_none_scores = [score for score in scores if score is not None]
return (sum(not_none_scores) / len(not_none_scores) *
100) if len(not_none_scores) > 0 else None
return (
(sum(not_none_scores) / len(not_none_scores) * 100)
if len(not_none_scores) > 0
else None
)
def parse_goodput(slo_pairs):
@ -689,9 +725,10 @@ def parse_goodput(slo_pairs):
except ValueError as err:
raise argparse.ArgumentTypeError(
"Invalid format found for service level objectives. "
"Specify service level objectives for goodput as \"KEY:VALUE\" "
'Specify service level objectives for goodput as "KEY:VALUE" '
"pairs, where the key is a metric name, and the value is a "
"number in milliseconds.") from err
"number in milliseconds."
) from err
return goodput_config_dict
@ -705,12 +742,14 @@ def check_goodput_args(args):
raise ValueError(
f"Invalid metric name found, {slo_name}: {slo_val}. "
"The service level objective name should be one of "
f"{str(VALID_NAMES)}. ")
f"{str(VALID_NAMES)}. "
)
if slo_val < 0:
raise ValueError(
f"Invalid value found, {slo_name}: {slo_val}. "
"The service level objective value should be "
"non-negative.")
"non-negative."
)
return goodput_config_dict
@ -736,19 +775,19 @@ def main(args: argparse.Namespace):
tokenizer_mode=args.tokenizer_mode,
)
if args.dataset == 'grammar':
args.structure_type = 'guided_grammar'
elif args.dataset == 'regex':
args.structure_type = 'guided_regex'
elif args.dataset == 'choice':
args.structure_type = 'guided_choice'
if args.dataset == "grammar":
args.structure_type = "guided_grammar"
elif args.dataset == "regex":
args.structure_type = "guided_regex"
elif args.dataset == "choice":
args.structure_type = "guided_choice"
else:
args.structure_type = 'guided_json'
args.structure_type = "guided_json"
if args.no_structured_output:
args.structured_output_ratio = 0
if args.save_results:
result_file_name = f'{args.structured_output_ratio}guided'
result_file_name = f"{args.structured_output_ratio}guided"
result_file_name += f"_{backend}"
result_file_name += f"_{args.request_rate}qps"
result_file_name += f"_{args.model.split('/')[-1]}"
@ -776,36 +815,29 @@ def main(args: argparse.Namespace):
disable_tqdm=args.disable_tqdm,
profile=args.profile,
selected_percentile_metrics=args.percentile_metrics.split(","),
selected_percentiles=[
float(p) for p in args.metric_percentiles.split(",")
],
selected_percentiles=[float(p) for p in args.metric_percentiles.split(",")],
ignore_eos=args.ignore_eos,
max_concurrency=args.max_concurrency,
structured_output_ratio=args.structured_output_ratio,
goodput_config_dict=goodput_config_dict,
))
)
)
# Save config and results to json
score = evaluate(ret, args)
print("correct_rate(%)", score, '\n')
print("correct_rate(%)", score, "\n")
if args.save_results:
results = {
"backend":
backend,
"model_id":
model_id,
"tokenizer_id":
tokenizer_id,
"num_prompts":
args.num_prompts,
"request_rate":
args.request_rate if args.request_rate < float("inf") else "inf",
"burstiness":
args.burstiness,
"max_concurrency":
args.max_concurrency,
"correct_rate(%)":
score
"backend": backend,
"model_id": model_id,
"tokenizer_id": tokenizer_id,
"num_prompts": args.num_prompts,
"request_rate": args.request_rate
if args.request_rate < float("inf")
else "inf",
"burstiness": args.burstiness,
"max_concurrency": args.max_concurrency,
"correct_rate(%)": score,
}
results = {"outputs": ret, **results, **benchmark_result}
@ -814,13 +846,14 @@ def main(args: argparse.Namespace):
result_file_name = args.result_filename
if args.result_dir:
result_file_name = os.path.join(args.result_dir, result_file_name)
with open(result_file_name, "w", encoding='utf-8') as outfile:
with open(result_file_name, "w", encoding="utf-8") as outfile:
json.dump(results, outfile, indent=4)
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark the online serving throughput.")
description="Benchmark the online serving throughput."
)
parser.add_argument(
"--backend",
type=str,
@ -842,16 +875,14 @@ if __name__ == "__main__":
default="/v1/completions",
help="API endpoint.",
)
parser.add_argument("--dataset",
default='json',
choices=[
'json', 'json-unique', 'grammar', 'regex',
'choice', 'xgrammar_bench'
])
parser.add_argument("--json-schema-path",
type=str,
default=None,
help="Path to json schema.")
parser.add_argument(
"--dataset",
default="json",
choices=["json", "json-unique", "grammar", "regex", "choice", "xgrammar_bench"],
)
parser.add_argument(
"--json-schema-path", type=str, default=None, help="Path to json schema."
)
parser.add_argument(
"--max-concurrency",
type=int,
@ -863,7 +894,8 @@ if __name__ == "__main__":
"initiated, this argument will control how many are actually allowed "
"to execute at a time. This means that when used in combination, the "
"actual request rate may be lower than specified with --request-rate, "
"if the server is not processing requests fast enough to keep up.")
"if the server is not processing requests fast enough to keep up.",
)
parser.add_argument(
"--model",
type=str,
@ -873,15 +905,13 @@ if __name__ == "__main__":
parser.add_argument(
"--tokenizer",
type=str,
help=
"Name or path of the tokenizer, if not using the default tokenizer.", # noqa: E501
help="Name or path of the tokenizer, if not using the default tokenizer.", # noqa: E501
)
parser.add_argument(
"--tokenizer-mode",
type=str,
default="auto",
help=
"Name or path of the tokenizer, if not using the default tokenizer.", # noqa: E501
help="Name or path of the tokenizer, if not using the default tokenizer.", # noqa: E501
)
parser.add_argument(
"--num-prompts",
@ -958,44 +988,51 @@ if __name__ == "__main__":
"--ignore-eos",
action="store_true",
help="Set ignore_eos flag when sending the benchmark request."
"Warning: ignore_eos is not supported in deepspeed_mii and tgi.")
"Warning: ignore_eos is not supported in deepspeed_mii and tgi.",
)
parser.add_argument(
"--percentile-metrics",
type=str,
default="ttft,tpot,itl",
help="Comma-separated list of selected metrics to report percentils. "
"This argument specifies the metrics to report percentiles. "
"Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
"Default value is \"ttft,tpot,itl\".")
'Allowed metric names are "ttft", "tpot", "itl", "e2el". '
'Default value is "ttft,tpot,itl".',
)
parser.add_argument(
"--metric-percentiles",
type=str,
default="99",
help="Comma-separated list of percentiles for selected metrics. "
"To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
"Default value is \"99\". "
"Use \"--percentile-metrics\" to select metrics.",
'To report 25-th, 50-th, and 75-th percentiles, use "25,50,75". '
'Default value is "99". '
'Use "--percentile-metrics" to select metrics.',
)
parser.add_argument(
"--goodput",
nargs="+",
required=False,
help="Specify service level objectives for goodput as \"KEY:VALUE\" "
help='Specify service level objectives for goodput as "KEY:VALUE" '
"pairs, where the key is a metric name, and the value is in "
"milliseconds. Multiple \"KEY:VALUE\" pairs can be provided, "
'milliseconds. Multiple "KEY:VALUE" pairs can be provided, '
"separated by spaces. Allowed request level metric names are "
"\"ttft\", \"tpot\", \"e2el\". For more context on the definition of "
'"ttft", "tpot", "e2el". For more context on the definition of '
"goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
"and the blog: https://hao-ai-lab.github.io/blogs/distserve")
"and the blog: https://hao-ai-lab.github.io/blogs/distserve",
)
parser.add_argument("--no-structured-output",
action='store_true',
default=False,
help="Whether to disable JSON decoding or not.")
parser.add_argument("--structured-output-ratio",
type=float,
default=1.0,
help="Ratio of Structured Outputs requests")
parser.add_argument(
"--no-structured-output",
action="store_true",
default=False,
help="Whether to disable JSON decoding or not.",
)
parser.add_argument(
"--structured-output-ratio",
type=float,
default=1.0,
help="Ratio of Structured Outputs requests",
)
args = parser.parse_args()
main(args)

435
benchmarks/benchmark_throughput.py
View File

@ -1,5 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
"""Benchmark offline inference throughput."""
import argparse
import dataclasses
import json
@ -11,18 +12,25 @@ from typing import Any, Optional, Union
import torch
import uvloop
from benchmark_dataset import (AIMODataset, BurstGPTDataset,
ConversationDataset, InstructCoderDataset,
RandomDataset, SampleRequest, ShareGPTDataset,
SonnetDataset, VisionArenaDataset)
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
from tqdm import tqdm
from transformers import (AutoModelForCausalLM, AutoTokenizer,
PreTrainedTokenizerBase)
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase
from benchmark_dataset import (
AIMODataset,
BurstGPTDataset,
ConversationDataset,
InstructCoderDataset,
RandomDataset,
SampleRequest,
ShareGPTDataset,
SonnetDataset,
VisionArenaDataset,
)
from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
from vllm.entrypoints.openai.api_server import (
build_async_engine_client_from_engine_args)
build_async_engine_client_from_engine_args,
)
from vllm.inputs import TextPrompt, TokensPrompt
from vllm.lora.request import LoRARequest
from vllm.outputs import RequestOutput
@ -37,23 +45,30 @@ def run_vllm(
disable_detokenize: bool = False,
) -> tuple[float, Optional[list[RequestOutput]]]:
from vllm import LLM, SamplingParams
llm = LLM(**dataclasses.asdict(engine_args))
assert all(
llm.llm_engine.model_config.max_model_len >= (
request.prompt_len + request.expected_output_len)
for request in requests), (
"Please ensure that max_model_len is greater than the sum of"
" prompt_len and expected_output_len for all requests.")
llm.llm_engine.model_config.max_model_len
>= (request.prompt_len + request.expected_output_len)
for request in requests
), (
"Please ensure that max_model_len is greater than the sum of"
" prompt_len and expected_output_len for all requests."
)
# Add the requests to the engine.
prompts: list[Union[TextPrompt, TokensPrompt]] = []
sampling_params: list[SamplingParams] = []
for request in requests:
prompts.append(
TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
multi_modal_data=request.multi_modal_data)
if "prompt_token_ids" in request.prompt else \
TextPrompt(prompt=request.prompt,
multi_modal_data=request.multi_modal_data))
TokensPrompt(
prompt_token_ids=request.prompt["prompt_token_ids"],
multi_modal_data=request.multi_modal_data,
)
if "prompt_token_ids" in request.prompt
else TextPrompt(
prompt=request.prompt, multi_modal_data=request.multi_modal_data
)
)
sampling_params.append(
SamplingParams(
n=n,
@ -62,7 +77,8 @@ def run_vllm(
ignore_eos=True,
max_tokens=request.expected_output_len,
detokenize=not disable_detokenize,
))
)
)
lora_requests: Optional[list[LoRARequest]] = None
if engine_args.enable_lora:
lora_requests = [request.lora_request for request in requests]
@ -72,10 +88,9 @@ def run_vllm(
outputs = None
if not use_beam_search:
start = time.perf_counter()
outputs = llm.generate(prompts,
sampling_params,
lora_request=lora_requests,
use_tqdm=True)
outputs = llm.generate(
prompts, sampling_params, lora_request=lora_requests, use_tqdm=True
)
end = time.perf_counter()
else:
assert lora_requests is None, "BeamSearch API does not support LoRA"
@ -91,30 +106,35 @@ def run_vllm(
beam_width=n,
max_tokens=output_len,
ignore_eos=True,
))
),
)
end = time.perf_counter()
return end - start, outputs
def run_vllm_chat(
requests: list[SampleRequest],
n: int,
engine_args: EngineArgs,
disable_detokenize: bool = False) -> tuple[float, list[RequestOutput]]:
requests: list[SampleRequest],
n: int,
engine_args: EngineArgs,
disable_detokenize: bool = False,
) -> tuple[float, list[RequestOutput]]:
"""
Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
multimodal models as it properly handles multimodal inputs and chat
formatting. For non-multimodal models, use run_vllm() instead.
"""
from vllm import LLM, SamplingParams
llm = LLM(**dataclasses.asdict(engine_args))
assert all(
llm.llm_engine.model_config.max_model_len >= (
request.prompt_len + request.expected_output_len)
for request in requests), (
"Please ensure that max_model_len is greater than the sum of "
"prompt_len and expected_output_len for all requests.")
llm.llm_engine.model_config.max_model_len
>= (request.prompt_len + request.expected_output_len)
for request in requests
), (
"Please ensure that max_model_len is greater than the sum of "
"prompt_len and expected_output_len for all requests."
)
prompts = []
sampling_params: list[SamplingParams] = []
@ -128,7 +148,8 @@ def run_vllm_chat(
ignore_eos=True,
max_tokens=request.expected_output_len,
detokenize=not disable_detokenize,
))
)
)
start = time.perf_counter()
outputs = llm.chat(prompts, sampling_params, use_tqdm=True)
end = time.perf_counter()
@ -145,14 +166,17 @@ async def run_vllm_async(
from vllm import SamplingParams
async with build_async_engine_client_from_engine_args(
engine_args, disable_frontend_multiprocessing) as llm:
engine_args, disable_frontend_multiprocessing
) as llm:
model_config = await llm.get_model_config()
assert all(
model_config.max_model_len >= (request.prompt_len +
request.expected_output_len)
for request in requests), (
"Please ensure that max_model_len is greater than the sum of"
" prompt_len and expected_output_len for all requests.")
model_config.max_model_len
>= (request.prompt_len + request.expected_output_len)
for request in requests
), (
"Please ensure that max_model_len is greater than the sum of"
" prompt_len and expected_output_len for all requests."
)
# Add the requests to the engine.
prompts: list[Union[TextPrompt, TokensPrompt]] = []
@ -160,11 +184,15 @@ async def run_vllm_async(
lora_requests: list[Optional[LoRARequest]] = []
for request in requests:
prompts.append(
TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
multi_modal_data=request.multi_modal_data)
if "prompt_token_ids" in request.prompt else \
TextPrompt(prompt=request.prompt,
multi_modal_data=request.multi_modal_data))
TokensPrompt(
prompt_token_ids=request.prompt["prompt_token_ids"],
multi_modal_data=request.multi_modal_data,
)
if "prompt_token_ids" in request.prompt
else TextPrompt(
prompt=request.prompt, multi_modal_data=request.multi_modal_data
)
)
sampling_params.append(
SamplingParams(
n=n,
@ -173,17 +201,16 @@ async def run_vllm_async(
ignore_eos=True,
max_tokens=request.expected_output_len,
detokenize=not disable_detokenize,
))
)
)
lora_requests.append(request.lora_request)
generators = []
start = time.perf_counter()
for i, (prompt, sp,
lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
generator = llm.generate(prompt,
sp,
lora_request=lr,
request_id=f"test{i}")
for i, (prompt, sp, lr) in enumerate(
zip(prompts, sampling_params, lora_requests)
):
generator = llm.generate(prompt, sp, lora_request=lr, request_id=f"test{i}")
generators.append(generator)
all_gens = merge_async_iterators(*generators)
async for i, res in all_gens:
@ -202,7 +229,8 @@ def run_hf(
disable_detokenize: bool = False,
) -> float:
llm = AutoModelForCausalLM.from_pretrained(
model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
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
@ -225,14 +253,15 @@ def run_hf(
# Check if we can add more requests to the batch.
next_prompt_len = requests[i + 1].prompt_len
next_output_len = requests[i + 1].expected_output_len
if (max(max_prompt_len, next_prompt_len) +
max(max_output_len, next_output_len)) <= 2048:
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
input_ids = tokenizer(batch, return_tensors="pt", padding=True).input_ids
llm_outputs = llm.generate(
input_ids=input_ids.cuda(),
do_sample=True,
@ -262,6 +291,7 @@ def run_mii(
output_len: int,
) -> float:
from mii import client, serve
llm = serve(model, tensor_parallel=tensor_parallel_size)
prompts = [request.prompt for request in requests]
@ -273,8 +303,9 @@ def run_mii(
return end - start
def save_to_pytorch_benchmark_format(args: argparse.Namespace,
results: dict[str, Any]) -> None:
def save_to_pytorch_benchmark_format(
args: argparse.Namespace, results: dict[str, Any]
) -> None:
pt_records = convert_to_pytorch_benchmark_format(
args=args,
metrics={
@ -282,9 +313,9 @@ def save_to_pytorch_benchmark_format(args: argparse.Namespace,
"tokens_per_second": [results["tokens_per_second"]],
},
extra_info={
k: results[k]
for k in ["elapsed_time", "num_requests", "total_num_tokens"]
})
k: results[k] for k in ["elapsed_time", "num_requests", "total_num_tokens"]
},
)
if pt_records:
# Don't use json suffix here as we don't want CI to pick it up
pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
@ -316,7 +347,8 @@ def get_requests(args, tokenizer):
sample_kwargs["enable_multimodal_chat"] = True
elif args.dataset_name == "sonnet":
assert tokenizer.chat_template or tokenizer.default_chat_template, (
"Tokenizer/model must have chat template for sonnet dataset.")
"Tokenizer/model must have chat template for sonnet dataset."
)
dataset_cls = SonnetDataset
sample_kwargs["prefix_len"] = args.prefix_len
sample_kwargs["return_prompt_formatted"] = True
@ -325,21 +357,21 @@ def get_requests(args, tokenizer):
elif args.dataset_name == "hf":
if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
dataset_cls = VisionArenaDataset
common_kwargs['dataset_subset'] = None
common_kwargs['dataset_split'] = "train"
common_kwargs["dataset_subset"] = None
common_kwargs["dataset_split"] = "train"
sample_kwargs["enable_multimodal_chat"] = True
elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
dataset_cls = InstructCoderDataset
common_kwargs['dataset_split'] = "train"
common_kwargs["dataset_split"] = "train"
elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
dataset_cls = ConversationDataset
common_kwargs['dataset_subset'] = args.hf_subset
common_kwargs['dataset_split'] = args.hf_split
common_kwargs["dataset_subset"] = args.hf_subset
common_kwargs["dataset_split"] = args.hf_split
sample_kwargs["enable_multimodal_chat"] = True
elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
dataset_cls = AIMODataset
common_kwargs['dataset_subset'] = None
common_kwargs['dataset_split'] = "train"
common_kwargs["dataset_subset"] = None
common_kwargs["dataset_split"] = "train"
else:
raise ValueError(f"Unknown dataset name: {args.dataset_name}")
# Remove None values
@ -354,10 +386,10 @@ def main(args: argparse.Namespace):
random.seed(args.seed)
# Sample the requests.
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer, trust_remote_code=args.trust_remote_code)
args.tokenizer, trust_remote_code=args.trust_remote_code
)
requests = get_requests(args, tokenizer)
is_multi_modal = any(request.multi_modal_data is not None
for request in requests)
is_multi_modal = any(request.multi_modal_data is not None for request in requests)
request_outputs: Optional[list[RequestOutput]] = None
if args.backend == "vllm":
if args.async_engine:
@ -368,23 +400,34 @@ def main(args: argparse.Namespace):
AsyncEngineArgs.from_cli_args(args),
args.disable_frontend_multiprocessing,
args.disable_detokenize,
))
)
)
else:
elapsed_time, request_outputs = run_vllm(
requests, args.n, EngineArgs.from_cli_args(args),
args.disable_detokenize)
requests,
args.n,
EngineArgs.from_cli_args(args),
args.disable_detokenize,
)
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,
args.disable_detokenize)
elapsed_time = run_hf(
requests,
args.model,
tokenizer,
args.n,
args.hf_max_batch_size,
args.trust_remote_code,
args.disable_detokenize,
)
elif args.backend == "mii":
elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
args.output_len)
elapsed_time = run_mii(
requests, args.model, args.tensor_parallel_size, args.output_len
)
elif args.backend == "vllm-chat":
elapsed_time, request_outputs = run_vllm_chat(
requests, args.n, EngineArgs.from_cli_args(args),
args.disable_detokenize)
requests, args.n, EngineArgs.from_cli_args(args), args.disable_detokenize
)
else:
raise ValueError(f"Unknown backend: {args.backend}")
@ -396,28 +439,31 @@ def main(args: argparse.Namespace):
for ro in request_outputs:
if not isinstance(ro, RequestOutput):
continue
total_prompt_tokens += len(
ro.prompt_token_ids) if ro.prompt_token_ids else 0
total_output_tokens += sum(
len(o.token_ids) for o in ro.outputs if o)
total_prompt_tokens += (
len(ro.prompt_token_ids) if ro.prompt_token_ids else 0
)
total_output_tokens += sum(len(o.token_ids) for o in ro.outputs if o)
total_num_tokens = total_prompt_tokens + total_output_tokens
else:
total_num_tokens = sum(r.prompt_len + r.expected_output_len
for r in requests)
total_num_tokens = sum(r.prompt_len + r.expected_output_len for r in requests)
total_output_tokens = sum(r.expected_output_len for r in requests)
total_prompt_tokens = total_num_tokens - total_output_tokens
if is_multi_modal and args.backend != "vllm-chat":
print("\033[91mWARNING\033[0m: Multi-modal request with "
f"{args.backend} backend detected. The "
"following metrics are not accurate because image tokens are not"
" counted. See vllm-project/vllm/issues/9778 for details.")
print(
"\033[91mWARNING\033[0m: Multi-modal request with "
f"{args.backend} backend detected. The "
"following metrics are not accurate because image tokens are not"
" counted. See vllm-project/vllm/issues/9778 for details."
)
# TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
# vllm-chat backend counts the image tokens now
print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
print(
f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
f"{total_output_tokens / elapsed_time:.2f} output tokens/s"
)
print(f"Total num prompt tokens: {total_prompt_tokens}")
print(f"Total num output tokens: {total_output_tokens}")
@ -445,7 +491,8 @@ def validate_args(args):
warnings.warn(
"The '--dataset' argument will be deprecated in the next release. "
"Please use '--dataset-name' and '--dataset-path' instead.",
stacklevel=2)
stacklevel=2,
)
args.dataset_path = args.dataset
if not getattr(args, "tokenizer", None):
@ -458,9 +505,8 @@ def validate_args(args):
# === Dataset Configuration ===
if not args.dataset and not args.dataset_path:
print(
"When dataset path is not set, it will default to random dataset")
args.dataset_name = 'random'
print("When dataset path is not set, it will default to random dataset")
args.dataset_name = "random"
if args.input_len is None:
raise ValueError("input_len must be provided for a random dataset")
@ -468,41 +514,55 @@ def validate_args(args):
# --hf-subset and --hf-split: only used
# when dataset_name is 'hf'
if args.dataset_name != "hf" and (
getattr(args, "hf_subset", None) is not None
or getattr(args, "hf_split", None) is not None):
warnings.warn("--hf-subset and --hf-split will be ignored \
getattr(args, "hf_subset", None) is not None
or getattr(args, "hf_split", None) is not None
):
warnings.warn(
"--hf-subset and --hf-split will be ignored \
since --dataset-name is not 'hf'.",
stacklevel=2)
stacklevel=2,
)
elif args.dataset_name == "hf":
if args.dataset_path in (
VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
| ConversationDataset.SUPPORTED_DATASET_PATHS):
assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend." #noqa: E501
elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
| AIMODataset.SUPPORTED_DATASET_PATHS):
assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend." #noqa: E501
VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
| ConversationDataset.SUPPORTED_DATASET_PATHS
):
assert args.backend == "vllm-chat", (
f"{args.dataset_path} needs to use vllm-chat as the backend."
) # noqa: E501
elif args.dataset_path in (
InstructCoderDataset.SUPPORTED_DATASET_PATHS
| AIMODataset.SUPPORTED_DATASET_PATHS
):
assert args.backend == "vllm", (
f"{args.dataset_path} needs to use vllm as the backend."
) # noqa: E501
else:
raise ValueError(
f"{args.dataset_path} is not supported by hf dataset.")
raise ValueError(f"{args.dataset_path} is not supported by hf dataset.")
# --random-range-ratio: only used when dataset_name is 'random'
if args.dataset_name != 'random' and args.random_range_ratio is not None:
warnings.warn("--random-range-ratio will be ignored since \
if args.dataset_name != "random" and args.random_range_ratio is not None:
warnings.warn(
"--random-range-ratio will be ignored since \
--dataset-name is not 'random'.",
stacklevel=2)
stacklevel=2,
)
# --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
# set.
if args.dataset_name not in {"random", "sonnet", None
} and args.prefix_len is not None:
warnings.warn("--prefix-len will be ignored since --dataset-name\
if (
args.dataset_name not in {"random", "sonnet", None}
and args.prefix_len is not None
):
warnings.warn(
"--prefix-len will be ignored since --dataset-name\
is not 'random', 'sonnet', or not set.",
stacklevel=2)
stacklevel=2,
)
# === LoRA Settings ===
if getattr(args, "enable_lora", False) and args.backend != "vllm":
raise ValueError(
"LoRA benchmarking is only supported for vLLM backend")
raise ValueError("LoRA benchmarking is only supported for vLLM backend")
if getattr(args, "enable_lora", False) and args.lora_path is None:
raise ValueError("LoRA path must be provided when enable_lora is True")
@ -512,8 +572,10 @@ def validate_args(args):
if args.backend != "hf" and args.hf_max_batch_size is not None:
raise ValueError("HF max batch size is only for HF backend.")
if args.backend in {"hf", "mii"} and getattr(args, "quantization",
None) is not None:
if (
args.backend in {"hf", "mii"}
and getattr(args, "quantization", None) is not None
):
raise ValueError("Quantization is only for vLLM backend.")
if args.backend == "mii" and args.dtype != "auto":
@ -521,29 +583,32 @@ def validate_args(args):
if args.backend == "mii" and args.n != 1:
raise ValueError("n must be 1 for MII backend.")
if args.backend == "mii" and args.tokenizer != args.model:
raise ValueError(
"Tokenizer must be the same as the model for MII backend.")
raise ValueError("Tokenizer must be the same as the model for MII backend.")
# --data-parallel is not supported currently.
# 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")
please use benchmark serving instead"
)
if __name__ == "__main__":
parser = FlexibleArgumentParser(description="Benchmark the throughput.")
parser.add_argument("--backend",
type=str,
choices=["vllm", "hf", "mii", "vllm-chat"],
default="vllm")
parser.add_argument(
"--backend",
type=str,
choices=["vllm", "hf", "mii", "vllm-chat"],
default="vllm",
)
parser.add_argument(
"--dataset-name",
type=str,
choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
help="Name of the dataset to benchmark on.",
default="sharegpt")
default="sharegpt",
)
parser.add_argument(
"--dataset",
type=str,
@ -551,57 +616,70 @@ if __name__ == "__main__":
help="Path to the ShareGPT dataset, will be deprecated in\
the next release. The dataset is expected to "
"be a json in form of list[dict[..., conversations: "
"list[dict[..., value: <prompt_or_response>]]]]")
parser.add_argument("--dataset-path",
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("--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("--hf-max-batch-size",
type=int,
default=None,
help="Maximum batch size for HF backend.")
"list[dict[..., value: <prompt_or_response>]]]]",
)
parser.add_argument(
'--output-json',
"--dataset-path", 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(
"--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(
"--hf-max-batch-size",
type=int,
default=None,
help="Maximum batch size for HF backend.",
)
parser.add_argument(
"--output-json",
type=str,
default=None,
help='Path to save the throughput results in JSON format.')
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.")
help="Path to save the throughput results in JSON format.",
)
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.",
)
parser.add_argument(
"--disable-detokenize",
action="store_true",
help=("Do not detokenize the response (i.e. do not include "
"detokenization time in the measurement)"))
help=(
"Do not detokenize the response (i.e. do not include "
"detokenization time in the measurement)"
),
)
# LoRA
parser.add_argument(
"--lora-path",
type=str,
default=None,
help="Path to the LoRA adapters to use. This can be an absolute path, "
"a relative path, or a Hugging Face model identifier.")
"a relative path, or a Hugging Face model identifier.",
)
parser.add_argument(
"--prefix-len",
type=int,
@ -615,7 +693,8 @@ if __name__ == "__main__":
f"prefix_len (default: {SonnetDataset.DEFAULT_PREFIX_LEN}) "
"controls how much of the input is fixed lines versus "
"random lines, but the total input length remains approximately "
"input_len tokens.")
"input_len tokens.",
)
# random dataset
parser.add_argument(
"--random-range-ratio",
@ -629,14 +708,12 @@ if __name__ == "__main__":
)
# hf dtaset
parser.add_argument("--hf-subset",
type=str,
default=None,
help="Subset of the HF dataset.")
parser.add_argument("--hf-split",
type=str,
default=None,
help="Split of the HF dataset.")
parser.add_argument(
"--hf-subset", type=str, default=None, help="Subset of the HF dataset."
)
parser.add_argument(
"--hf-split", type=str, default=None, help="Split of the HF dataset."
)
parser = AsyncEngineArgs.add_cli_args(parser)
args = parser.parse_args()

15
benchmarks/benchmark_utils.py
View File

@ -7,9 +7,9 @@ import os
from typing import Any
def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
metrics: dict[str, list],
extra_info: dict[str, Any]) -> list:
def convert_to_pytorch_benchmark_format(
args: argparse.Namespace, metrics: dict[str, list], extra_info: dict[str, Any]
) -> list:
"""
Save the benchmark results in the format used by PyTorch OSS benchmark with
on metric per record
@ -37,12 +37,12 @@ def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
},
}
tp = record["benchmark"]["extra_info"]["args"].get(
"tensor_parallel_size")
tp = record["benchmark"]["extra_info"]["args"].get("tensor_parallel_size")
# Save tensor_parallel_size parameter if it's part of the metadata
if not tp and "tensor_parallel_size" in extra_info:
record["benchmark"]["extra_info"]["args"][
"tensor_parallel_size"] = extra_info["tensor_parallel_size"]
record["benchmark"]["extra_info"]["args"]["tensor_parallel_size"] = (
extra_info["tensor_parallel_size"]
)
records.append(record)
@ -50,7 +50,6 @@ def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
class InfEncoder(json.JSONEncoder):
def clear_inf(self, o: Any):
if isinstance(o, dict):
return {k: self.clear_inf(v) for k, v in o.items()}

370
benchmarks/cutlass_benchmarks/sparse_benchmarks.py
View File

@ -23,8 +23,9 @@ DEFAULT_TP_SIZES = [1]
# bench
def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
**kwargs) -> TMeasurement:
def bench_fn(
label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
) -> TMeasurement:
min_run_time = 1
globals = {
@ -41,16 +42,18 @@ def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
).blocked_autorange(min_run_time=min_run_time)
def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
sub_label: str) -> Iterable[TMeasurement]:
def bench_int8(
dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
) -> Iterable[TMeasurement]:
assert dtype == torch.int8
b_compressed, e, a, b = make_rand_sparse_tensors(torch.int8, m, n, k)
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
torch.bfloat16)
out = ops.cutlass_scaled_sparse_mm(
a, b_compressed, e, scale_a, scale_b, torch.bfloat16
)
out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16)
if not torch.allclose(out, out_ref):
@ -63,54 +66,107 @@ def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
timers = []
# pytorch impl - bfloat16
timers.append(
bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
torch.mm, a.to(dtype=torch.bfloat16),
b.to(dtype=torch.bfloat16)))
bench_fn(
label,
sub_label,
"pytorch_bf16_bf16_bf16_matmul-no-scales",
torch.mm,
a.to(dtype=torch.bfloat16),
b.to(dtype=torch.bfloat16),
)
)
# pytorch impl - float16
timers.append(
bench_fn(label, sub_label,
"pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm,
a.to(dtype=torch.float16), b.to(dtype=torch.float16)))
bench_fn(
label,
sub_label,
"pytorch_fp16_fp16_fp16_matmul-no-scales",
torch.mm,
a.to(dtype=torch.float16),
b.to(dtype=torch.float16),
)
)
# cutlass impl
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
torch.bfloat16))
bench_fn(
label,
sub_label,
"cutlass_i8_i8_bf16_scaled_mm",
ops.cutlass_scaled_mm,
a,
b,
scale_a,
scale_b,
torch.bfloat16,
)
)
# cutlass with bias
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
bias))
bench_fn(
label,
sub_label,
"cutlass_i8_i8_bf16_scaled_mm_bias",
ops.cutlass_scaled_mm,
a,
b,
scale_a,
scale_b,
torch.bfloat16,
bias,
)
)
# cutlass sparse impl
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm",
ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
scale_b, torch.bfloat16))
bench_fn(
label,
sub_label,
"cutlass_i8_i8_bf16_scaled_sparse_mm",
ops.cutlass_scaled_sparse_mm,
a,
b_compressed,
e,
scale_a,
scale_b,
torch.bfloat16,
)
)
# cutlass sparse with bias
timers.append(
bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_sparse_mm_bias",
ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
scale_b, torch.bfloat16, bias))
bench_fn(
label,
sub_label,
"cutlass_i8_i8_bf16_scaled_sparse_mm_bias",
ops.cutlass_scaled_sparse_mm,
a,
b_compressed,
e,
scale_a,
scale_b,
torch.bfloat16,
bias,
)
)
return timers
def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
sub_label: str) -> Iterable[TMeasurement]:
def bench_fp8(
dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
) -> Iterable[TMeasurement]:
assert dtype == torch.float8_e4m3fn
b_compressed, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n,
k)
b_compressed, e, a, b = make_rand_sparse_tensors(torch.float8_e4m3fn, m, n, k)
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
out = ops.cutlass_scaled_sparse_mm(a, b_compressed, e, scale_a, scale_b,
torch.bfloat16)
out = ops.cutlass_scaled_sparse_mm(
a, b_compressed, e, scale_a, scale_b, torch.bfloat16
)
out_ref = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16)
if not torch.allclose(out, out_ref):
@ -124,97 +180,165 @@ def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
# pytorch impl w. bf16
timers.append(
bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
torch.mm, a.to(dtype=torch.bfloat16, device="cuda"),
b.to(dtype=torch.bfloat16, device="cuda")))
bench_fn(
label,
sub_label,
"pytorch_bf16_bf16_bf16_matmul-no-scales",
torch.mm,
a.to(dtype=torch.bfloat16, device="cuda"),
b.to(dtype=torch.bfloat16, device="cuda"),
)
)
# pytorch impl: bf16 output, without fp8 fast accum
timers.append(
bench_fn(label,
sub_label,
"pytorch_fp8_fp8_bf16_scaled_mm",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.bfloat16))
bench_fn(
label,
sub_label,
"pytorch_fp8_fp8_bf16_scaled_mm",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.bfloat16,
)
)
# pytorch impl: bf16 output, with fp8 fast accum
timers.append(
bench_fn(label,
sub_label,
"pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.bfloat16,
use_fast_accum=True))
bench_fn(
label,
sub_label,
"pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.bfloat16,
use_fast_accum=True,
)
)
# pytorch impl: fp16 output, without fp8 fast accum
timers.append(
bench_fn(label,
sub_label,
"pytorch_fp8_fp8_fp16_scaled_mm",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.float16))
bench_fn(
label,
sub_label,
"pytorch_fp8_fp8_fp16_scaled_mm",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.float16,
)
)
# pytorch impl: fp16 output, with fp8 fast accum
timers.append(
bench_fn(label,
sub_label,
"pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.float16,
use_fast_accum=True))
bench_fn(
label,
sub_label,
"pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
torch._scaled_mm,
a,
b,
scale_a=scale_a,
scale_b=scale_b,
out_dtype=torch.float16,
use_fast_accum=True,
)
)
# cutlass impl: bf16 output
timers.append(
bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm",
ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
torch.bfloat16))
bench_fn(
label,
sub_label,
"cutlass_fp8_fp8_bf16_scaled_mm",
ops.cutlass_scaled_mm,
a,
b,
scale_a,
scale_b,
torch.bfloat16,
)
)
# cutlass impl: bf16 output
timers.append(
bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_sparse_mm",
ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
scale_b, torch.bfloat16))
bench_fn(
label,
sub_label,
"cutlass_fp8_fp8_bf16_scaled_sparse_mm",
ops.cutlass_scaled_sparse_mm,
a,
b_compressed,
e,
scale_a,
scale_b,
torch.bfloat16,
)
)
# cutlass impl: fp16 output
timers.append(
bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_sparse_mm",
ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
scale_b, torch.float16))
bench_fn(
label,
sub_label,
"cutlass_fp8_fp8_fp16_scaled_sparse_mm",
ops.cutlass_scaled_sparse_mm,
a,
b_compressed,
e,
scale_a,
scale_b,
torch.float16,
)
)
# cutlass impl: bf16 output, with bias
timers.append(
bench_fn(label, sub_label,
"cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
scale_b, torch.bfloat16, bias))
bench_fn(
label,
sub_label,
"cutlass_fp8_fp8_bf16_scaled_sparse_mm_bias",
ops.cutlass_scaled_sparse_mm,
a,
b_compressed,
e,
scale_a,
scale_b,
torch.bfloat16,
bias,
)
)
# cutlass impl: fp16 output, with bias
timers.append(
bench_fn(label, sub_label,
"cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
ops.cutlass_scaled_sparse_mm, a, b_compressed, e, scale_a,
scale_b, torch.float16, bias.to(dtype=torch.float16)))
bench_fn(
label,
sub_label,
"cutlass_fp8_fp8_fp16_scaled_sparse_mm_bias",
ops.cutlass_scaled_sparse_mm,
a,
b_compressed,
e,
scale_a,
scale_b,
torch.float16,
bias.to(dtype=torch.float16),
)
)
return timers
def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str,
sub_label: str) -> Iterable[TMeasurement]:
def bench(
dtype: torch.dtype, m: int, k: int, n: int, label: str, sub_label: str
) -> Iterable[TMeasurement]:
if dtype == torch.int8:
return bench_int8(dtype, m, k, n, label, sub_label)
if dtype == torch.float8_e4m3fn:
@ -228,12 +352,12 @@ def print_timers(timers: Iterable[TMeasurement]):
compare.print()
def run(dtype: torch.dtype,
MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
def run(
dtype: torch.dtype, MKNs: Iterable[tuple[int, int, int]]
) -> Iterable[TMeasurement]:
results = []
for m, k, n in MKNs:
timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm",
f"MKN=({m}x{k}x{n})")
timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm", f"MKN=({m}x{k}x{n})")
print_timers(timers)
results.extend(timers)
@ -241,10 +365,12 @@ def run(dtype: torch.dtype,
# output makers
def make_output(data: Iterable[TMeasurement],
MKNs: Iterable[tuple[int, int, int]],
base_description: str,
timestamp=None):
def make_output(
data: Iterable[TMeasurement],
MKNs: Iterable[tuple[int, int, int]],
base_description: str,
timestamp=None,
):
print(f"== All Results {base_description} ====")
print_timers(data)
@ -258,8 +384,7 @@ def make_output(data: Iterable[TMeasurement],
def run_square_bench(args):
dim_sizes = list(
range(args.dim_start, args.dim_end + 1, args.dim_increment))
dim_sizes = list(range(args.dim_start, args.dim_end + 1, args.dim_increment))
MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
data = run(args.dtype, MKNs)
@ -319,7 +444,7 @@ def run_model_bench(args):
pkl.dump(all_data, f)
if __name__ == '__main__':
if __name__ == "__main__":
def to_torch_dtype(dt):
if dt == "int8":
@ -344,12 +469,15 @@ Benchmark Cutlass GEMM.
Output:
- a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
""", # noqa: E501
formatter_class=argparse.RawTextHelpFormatter)
formatter_class=argparse.RawTextHelpFormatter,
)
parser.add_argument("--dtype",
type=to_torch_dtype,
required=True,
help="Available options are ['int8', 'fp8']")
parser.add_argument(
"--dtype",
type=to_torch_dtype,
required=True,
help="Available options are ['int8', 'fp8']",
)
subparsers = parser.add_subparsers(dest="cmd")
square_parser = subparsers.add_parser("square_bench")
@ -368,19 +496,19 @@ Benchmark Cutlass GEMM.
range_parser.set_defaults(func=run_range_bench)
model_parser = subparsers.add_parser("model_bench")
model_parser.add_argument("--models",
nargs="+",
type=str,
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys())
model_parser.add_argument("--tp-sizes",
nargs="+",
type=int,
default=DEFAULT_TP_SIZES)
model_parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
model_parser.add_argument(
"--models",
nargs="+",
type=str,
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys(),
)
model_parser.add_argument(
"--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
)
model_parser.add_argument(
"--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
)
model_parser.set_defaults(func=run_model_bench)
args = parser.parse_args()

33
benchmarks/cutlass_benchmarks/utils.py
View File

@ -10,8 +10,9 @@ import vllm._custom_ops as ops
def to_fp8(tensor: torch.Tensor) -> torch.Tensor:
finfo = torch.finfo(torch.float8_e4m3fn)
return torch.round(tensor.clamp(
min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
dtype=torch.float8_e4m3fn
)
def to_int8(tensor: torch.Tensor) -> torch.Tensor:
@ -26,10 +27,11 @@ def to_fp16(tensor: torch.Tensor) -> torch.Tensor:
return tensor.to(dtype=torch.float16)
def make_rand_tensors(dtype: torch.dtype, m: int, n: int,
k: int) -> tuple[torch.Tensor, torch.Tensor]:
a = torch.randn((m, k), device='cuda') * 5
b = torch.randn((n, k), device='cuda').t() * 5
def make_rand_tensors(
dtype: torch.dtype, m: int, n: int, k: int
) -> tuple[torch.Tensor, torch.Tensor]:
a = torch.randn((m, k), device="cuda") * 5
b = torch.randn((n, k), device="cuda").t() * 5
if dtype == torch.int8:
return to_int8(a), to_int8(b)
@ -49,9 +51,7 @@ def prune_to_2_4(tensor):
# Create binary mask
mask = torch.zeros_like(reshaped)
mask.scatter_(dim=1,
index=indices,
src=torch.ones_like(indices, dtype=mask.dtype))
mask.scatter_(dim=1, index=indices, src=torch.ones_like(indices, dtype=mask.dtype))
# Apply mask and reshape back
pruned = reshaped * mask
@ -62,10 +62,11 @@ def prune_to_2_4(tensor):
return pruned.reshape(original_shape)
def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
k: int) -> tuple[torch.Tensor, torch.Tensor]:
a = torch.randn((m, k), device='cuda') * 5
b = torch.randn((n, k), device='cuda').t() * 5
def make_rand_sparse_tensors(
dtype: torch.dtype, m: int, n: int, k: int
) -> tuple[torch.Tensor, torch.Tensor]:
a = torch.randn((m, k), device="cuda") * 5
b = torch.randn((n, k), device="cuda").t() * 5
b = prune_to_2_4(b.t()).t()
@ -86,9 +87,9 @@ def make_rand_sparse_tensors(dtype: torch.dtype, m: int, n: int,
return b_compressed, e, a, b
def make_n_rand_sparse_tensors(num_tensors: int, dtype: torch.dtype,
m: int, n: int, k: int) -> \
tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
def make_n_rand_sparse_tensors(
num_tensors: int, dtype: torch.dtype, m: int, n: int, k: int
) -> tuple[Iterable[torch.Tensor], Iterable[torch.Tensor]]:
ABs = []
for _ in range(num_tensors):
b_comp, e, a, b = make_rand_sparse_tensors(dtype, m, n, k)

274
benchmarks/cutlass_benchmarks/w8a8_benchmarks.py
View File

@ -16,7 +16,8 @@ from weight_shapes import WEIGHT_SHAPES
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
w8a8_block_fp8_matmul)
w8a8_block_fp8_matmul,
)
from vllm.utils import FlexibleArgumentParser
DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@ -25,8 +26,9 @@ DEFAULT_TP_SIZES = [1]
# bench
def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
**kwargs) -> TMeasurement:
def bench_fn(
label: str, sub_label: str, description: str, fn: Callable, *args, **kwargs
) -> TMeasurement:
min_run_time = 1
globals = {
@ -44,45 +46,48 @@ def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
def bench_int8(
dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[list[str]] = None,
) -> Iterable[TMeasurement]:
"""Benchmark INT8-based kernels."""
assert dtype == torch.int8
a, b = make_rand_tensors(torch.int8, m, n, k)
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
azp = torch.zeros((m, ), device="cuda", dtype=torch.int32)
azp_adj = torch.zeros((n, ), device="cuda", dtype=torch.int32)
bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
azp = torch.zeros((m,), device="cuda", dtype=torch.int32)
azp_adj = torch.zeros((n,), device="cuda", dtype=torch.int32)
bench_fns = {
"pytorch_bf16_bf16_bf16_matmul-no-scales":
lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
),
"pytorch_fp16_fp16_fp16_matmul-no-scales":
lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
"cutlass_i8_i8_bf16_scaled_mm":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
"cutlass_i8_i8_bf16_scaled_mm_bias":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
bias),
"cutlass_i8_i8_bf16_scaled_mm_azp":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj),
"cutlass_i8_i8_bf16_scaled_mm_azp_bias":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj, None, bias),
"cutlass_i8_i8_bf16_scaled_mm_azp_pt":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj, azp),
"cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias":
lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
bfloat16, azp_adj, azp, bias),
"pytorch_bf16_bf16_bf16_matmul-no-scales": lambda: torch.mm(
a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
),
"pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
a.to(dtype=torch.float16), b.to(dtype=torch.float16)
),
"cutlass_i8_i8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
a, b, scale_a, scale_b, torch.bfloat16
),
"cutlass_i8_i8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
a, b, scale_a, scale_b, torch.bfloat16, bias
),
"cutlass_i8_i8_bf16_scaled_mm_azp": lambda: ops.cutlass_scaled_mm_azp(
a, b, scale_a, scale_b, torch.bfloat16, azp_adj
),
"cutlass_i8_i8_bf16_scaled_mm_azp_bias": lambda: ops.cutlass_scaled_mm_azp(
a, b, scale_a, scale_b, torch.bfloat16, azp_adj, None, bias
),
"cutlass_i8_i8_bf16_scaled_mm_azp_pt": lambda: ops.cutlass_scaled_mm_azp(
a, b, scale_a, scale_b, torch.bfloat16, azp_adj, azp
),
"cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias": lambda: ops.cutlass_scaled_mm_azp(
a, b, scale_a, scale_b, torch.bfloat16, azp_adj, azp, bias
),
}
timers = []
@ -96,73 +101,65 @@ def bench_int8(
def bench_fp8(
dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[list[str]] = None,
) -> Iterable[TMeasurement]:
"""Benchmark FP8-based kernels."""
assert dtype == torch.float8_e4m3fn
a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
a_cont = a.contiguous()
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
block_scale_a = torch.rand((m, k // 128),
device="cuda",
dtype=torch.float32)
block_scale_b = torch.rand((k // 128, n // 128),
device="cuda",
dtype=torch.float32)
block_scale_a = torch.rand((m, k // 128), device="cuda", dtype=torch.float32)
block_scale_b = torch.rand((k // 128, n // 128), device="cuda", dtype=torch.float32)
block_scale_a_M_major = block_scale_a.t().contiguous().t()
block_scale_b_K_major = block_scale_b.t().contiguous().t()
bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)
print(m, k, n)
bench_fns = {
"pytorch_bf16_bf16_bf16_matmul-no-scales":
lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
),
"pytorch_fp16_fp16_fp16_matmul-no-scales":
lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
"pytorch_fp8_fp8_fp16_scaled_mm":
lambda: torch._scaled_mm(
a, b, scale_a, scale_b, out_dtype=torch.float16),
"pytorch_fp8_fp8_fp16_scaled_mm_fast_accum":
lambda: torch._scaled_mm(a,
b,
scale_a,
scale_b,
out_dtype=torch.float16,
use_fast_accum=True),
"pytorch_fp8_fp8_bf16_scaled_mm":
lambda: torch._scaled_mm(
a, b, scale_a, scale_b, out_dtype=torch.bfloat16),
"pytorch_fp8_fp8_bf16_scaled_mm_fast_accum":
lambda: torch._scaled_mm(a,
b,
scale_a,
scale_b,
out_dtype=torch.bfloat16,
use_fast_accum=True),
"cutlass_fp8_fp8_bf16_scaled_mm":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
"cutlass_fp8_fp8_fp16_scaled_mm":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16),
"cutlass_fp8_fp8_bf16_scaled_mm_bias":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
bias),
"cutlass_fp8_fp8_fp16_scaled_mm_bias":
lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16,
bias.to(dtype=torch.float16)),
"triton_fp8_fp8_fp16_scaled_mm_blockwise":
lambda: w8a8_block_fp8_matmul(a_cont, b.t(), block_scale_a,
block_scale_b.t(), (128, 128)),
"cutlass_fp8_fp8_fp16_scaled_mm_blockwise":
lambda: ops.cutlass_scaled_mm(a, b, block_scale_a_M_major,
block_scale_b_K_major, torch.float16),
"pytorch_bf16_bf16_bf16_matmul-no-scales": lambda: torch.mm(
a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
),
"pytorch_fp16_fp16_fp16_matmul-no-scales": lambda: torch.mm(
a.to(dtype=torch.float16), b.to(dtype=torch.float16)
),
"pytorch_fp8_fp8_fp16_scaled_mm": lambda: torch._scaled_mm(
a, b, scale_a, scale_b, out_dtype=torch.float16
),
"pytorch_fp8_fp8_fp16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
a, b, scale_a, scale_b, out_dtype=torch.float16, use_fast_accum=True
),
"pytorch_fp8_fp8_bf16_scaled_mm": lambda: torch._scaled_mm(
a, b, scale_a, scale_b, out_dtype=torch.bfloat16
),
"pytorch_fp8_fp8_bf16_scaled_mm_fast_accum": lambda: torch._scaled_mm(
a, b, scale_a, scale_b, out_dtype=torch.bfloat16, use_fast_accum=True
),
"cutlass_fp8_fp8_bf16_scaled_mm": lambda: ops.cutlass_scaled_mm(
a, b, scale_a, scale_b, torch.bfloat16
),
"cutlass_fp8_fp8_fp16_scaled_mm": lambda: ops.cutlass_scaled_mm(
a, b, scale_a, scale_b, torch.float16
),
"cutlass_fp8_fp8_bf16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
a, b, scale_a, scale_b, torch.bfloat16, bias
),
"cutlass_fp8_fp8_fp16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm(
a, b, scale_a, scale_b, torch.float16, bias.to(dtype=torch.float16)
),
"triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_block_fp8_matmul(
a_cont, b.t(), block_scale_a, block_scale_b.t(), (128, 128)
),
"cutlass_fp8_fp8_fp16_scaled_mm_blockwise": lambda: ops.cutlass_scaled_mm(
a, b, block_scale_a_M_major, block_scale_b_K_major, torch.float16
),
}
timers = []
@ -175,13 +172,15 @@ def bench_fp8(
return timers
def bench(dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
def bench(
dtype: torch.dtype,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
bench_kernels: Optional[list[str]] = None,
) -> Iterable[TMeasurement]:
if dtype == torch.int8:
return bench_int8(dtype, m, k, n, label, sub_label, bench_kernels)
if dtype == torch.float8_e4m3fn:
@ -195,27 +194,33 @@ def print_timers(timers: Iterable[TMeasurement]):
compare.print()
def run(dtype: torch.dtype,
MKNs: Iterable[tuple[int, int, int]],
bench_kernels: Optional[list[str]] = None) -> Iterable[TMeasurement]:
def run(
dtype: torch.dtype,
MKNs: Iterable[tuple[int, int, int]],
bench_kernels: Optional[list[str]] = None,
) -> Iterable[TMeasurement]:
results = []
for m, k, n in MKNs:
timers = bench(dtype,
m,
k,
n,
f"scaled-{dtype}-gemm",
f"MKN=({m}x{k}x{n})",
bench_kernels=bench_kernels)
timers = bench(
dtype,
m,
k,
n,
f"scaled-{dtype}-gemm",
f"MKN=({m}x{k}x{n})",
bench_kernels=bench_kernels,
)
print_timers(timers)
results.extend(timers)
return results
def make_output(data: Iterable[TMeasurement],
MKNs: Iterable[tuple[int, int, int]],
base_description: str,
timestamp=None):
def make_output(
data: Iterable[TMeasurement],
MKNs: Iterable[tuple[int, int, int]],
base_description: str,
timestamp=None,
):
print(f"== All Results {base_description} ====")
print_timers(data)
@ -226,8 +231,7 @@ def make_output(data: Iterable[TMeasurement],
def run_square_bench(args):
dim_sizes = list(
range(args.dim_start, args.dim_end + 1, args.dim_increment))
dim_sizes = list(range(args.dim_start, args.dim_end + 1, args.dim_increment))
MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
data = run(args.dtype, MKNs, bench_kernels=args.kernels)
make_output(data, MKNs, f"square_bench-{args.dtype}")
@ -285,7 +289,7 @@ def run_model_bench(args):
pkl.dump(all_data, f)
if __name__ == '__main__':
if __name__ == "__main__":
def to_torch_dtype(dt):
if dt == "int8":
@ -310,19 +314,21 @@ Benchmark Cutlass GEMM.
Output:
- a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
""", # noqa: E501
formatter_class=argparse.RawTextHelpFormatter)
formatter_class=argparse.RawTextHelpFormatter,
)
parser.add_argument("--dtype",
type=to_torch_dtype,
required=True,
help="Available options are ['int8', 'fp8']")
parser.add_argument(
"--dtype",
type=to_torch_dtype,
required=True,
help="Available options are ['int8', 'fp8']",
)
parser.add_argument(
"--kernels",
nargs="+",
type=str,
default=None,
help=
"Exact names of the kernels to benchmark. If not set, runs all kernels."
help="Exact names of the kernels to benchmark. If not set, runs all kernels.",
)
subparsers = parser.add_subparsers(dest="cmd")
@ -343,19 +349,19 @@ Benchmark Cutlass GEMM.
range_parser.set_defaults(func=run_range_bench)
model_parser = subparsers.add_parser("model_bench")
model_parser.add_argument("--models",
nargs="+",
type=str,
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys())
model_parser.add_argument("--tp-sizes",
nargs="+",
type=int,
default=DEFAULT_TP_SIZES)
model_parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
model_parser.add_argument(
"--models",
nargs="+",
type=str,
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys(),
)
model_parser.add_argument(
"--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
)
model_parser.add_argument(
"--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
)
model_parser.set_defaults(func=run_model_bench)
args = parser.parse_args()

2
benchmarks/cutlass_benchmarks/weight_shapes.py
View File

@ -42,4 +42,4 @@ WEIGHT_SHAPES = {
([8192, 57344], 1),
([28672, 8192], 0),
],
}
}

27
benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py
View File

@ -12,39 +12,37 @@ app = Quart(__name__)
async def forward_request(url, data):
async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
headers = {
"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
}
async with session.post(url=url, json=data,
headers=headers) as response:
headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
async with session.post(url=url, json=data, headers=headers) as response:
if response.status == 200:
# if response.headers.get('Transfer-Encoding') == 'chunked':
if True:
async for chunk_bytes in response.content.iter_chunked(
1024):
async for chunk_bytes in response.content.iter_chunked(1024):
yield chunk_bytes
else:
content = await response.read()
yield content
@app.route('/v1/completions', methods=['POST'])
@app.route("/v1/completions", methods=["POST"])
async def handle_request():
try:
original_request_data = await request.get_json()
prefill_request = original_request_data.copy()
# change max_tokens = 1 to let it only do prefill
prefill_request['max_tokens'] = 1
prefill_request["max_tokens"] = 1
# finish prefill
async for _ in forward_request('http://localhost:8100/v1/completions',
prefill_request):
async for _ in forward_request(
"http://localhost:8100/v1/completions", prefill_request
):
continue
# return decode
generator = forward_request('http://localhost:8200/v1/completions',
original_request_data)
generator = forward_request(
"http://localhost:8200/v1/completions", original_request_data
)
response = await make_response(generator)
response.timeout = None
@ -53,11 +51,12 @@ async def handle_request():
except Exception as e:
import sys
import traceback
exc_info = sys.exc_info()
print("Error occurred in disagg prefill proxy server")
print(e)
print("".join(traceback.format_exception(*exc_info)))
if __name__ == '__main__':
if __name__ == "__main__":
app.run(port=8000)

20
benchmarks/disagg_benchmarks/round_robin_proxy.py
View File

@ -8,7 +8,6 @@ from aiohttp import web
class RoundRobinProxy:
def __init__(self, target_ports):
self.target_ports = target_ports
self.port_cycle = itertools.cycle(self.target_ports)
@ -21,14 +20,15 @@ class RoundRobinProxy:
try:
# Forward the request
async with session.request(
method=request.method,
url=target_url,
headers=request.headers,
data=request.content,
method=request.method,
url=target_url,
headers=request.headers,
data=request.content,
) as response:
# Start sending the response
resp = web.StreamResponse(status=response.status,
headers=response.headers)
resp = web.StreamResponse(
status=response.status, headers=response.headers
)
await resp.prepare(request)
# Stream the response content
@ -45,11 +45,11 @@ class RoundRobinProxy:
async def main():
proxy = RoundRobinProxy([8100, 8200])
app = web.Application()
app.router.add_route('*', '/{path:.*}', proxy.handle_request)
app.router.add_route("*", "/{path:.*}", proxy.handle_request)
runner = web.AppRunner(app)
await runner.setup()
site = web.TCPSite(runner, 'localhost', 8000)
site = web.TCPSite(runner, "localhost", 8000)
await site.start()
print("Proxy server started on http://localhost:8000")
@ -58,5 +58,5 @@ async def main():
await asyncio.Event().wait()
if __name__ == '__main__':
if __name__ == "__main__":
asyncio.run(main())

44
benchmarks/disagg_benchmarks/visualize_benchmark_results.py
View File

@ -6,43 +6,41 @@ import matplotlib.pyplot as plt
import pandas as pd
if __name__ == "__main__":
data = []
for name in ['disagg_prefill', 'chunked_prefill']:
for name in ["disagg_prefill", "chunked_prefill"]:
for qps in [2, 4, 6, 8]:
with open(f"results/{name}-qps-{qps}.json") as f:
x = json.load(f)
x['name'] = name
x['qps'] = qps
x["name"] = name
x["qps"] = qps
data.append(x)
df = pd.DataFrame.from_dict(data)
dis_df = df[df['name'] == 'disagg_prefill']
chu_df = df[df['name'] == 'chunked_prefill']
dis_df = df[df["name"] == "disagg_prefill"]
chu_df = df[df["name"] == "chunked_prefill"]
plt.style.use('bmh')
plt.rcParams['font.size'] = 20
plt.style.use("bmh")
plt.rcParams["font.size"] = 20
for key in [
'mean_ttft_ms', 'median_ttft_ms', 'p99_ttft_ms', 'mean_itl_ms',
'median_itl_ms', 'p99_itl_ms'
"mean_ttft_ms",
"median_ttft_ms",
"p99_ttft_ms",
"mean_itl_ms",
"median_itl_ms",
"p99_itl_ms",
]:
fig, ax = plt.subplots(figsize=(11, 7))
plt.plot(dis_df['qps'],
dis_df[key],
label='disagg_prefill',
marker='o',
linewidth=4)
plt.plot(chu_df['qps'],
chu_df[key],
label='chunked_prefill',
marker='o',
linewidth=4)
plt.plot(
dis_df["qps"], dis_df[key], label="disagg_prefill", marker="o", linewidth=4
)
plt.plot(
chu_df["qps"], chu_df[key], label="chunked_prefill", marker="o", linewidth=4
)
ax.legend()
ax.set_xlabel('QPS')
ax.set_xlabel("QPS")
ax.set_ylabel(key)
ax.set_ylim(bottom=0)
fig.savefig(f'results/{key}.png')
fig.savefig(f"results/{key}.png")
plt.close(fig)

141
benchmarks/fused_kernels/layernorm_rms_benchmarks.py
View File

@ -24,10 +24,12 @@ class bench_params_t:
dtype: torch.dtype
def description(self):
return (f'N {self.num_tokens} '
f'x D {self.hidden_size} '
f'x R {self.add_residual} '
f'x DT {self.dtype}')
return (
f"N {self.num_tokens} "
f"x D {self.hidden_size} "
f"x R {self.add_residual} "
f"x DT {self.dtype}"
)
def get_bench_params() -> list[bench_params_t]:
@ -38,15 +40,19 @@ def get_bench_params() -> list[bench_params_t]:
DTYPES = [torch.bfloat16, torch.float]
combinations = product(NUM_TOKENS, HIDDEN_SIZES, ADD_RESIDUAL, DTYPES)
bench_params = list(map(lambda x: \
bench_params_t(x[0], x[1], x[2], x[3]), combinations))
bench_params = list(
map(lambda x: bench_params_t(x[0], x[1], x[2], x[3]), combinations)
)
return bench_params
# Reference impls
def unfused_int8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
residual: Optional[torch.Tensor],
quant_dtype: torch.dtype):
def unfused_int8_impl(
rms_norm_layer: RMSNorm,
x: torch.Tensor,
residual: Optional[torch.Tensor],
quant_dtype: torch.dtype,
):
# Norm
torch_out = None
if residual is None:
@ -58,9 +64,12 @@ def unfused_int8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
torch_out, _, _ = ops.scaled_int8_quant(torch_out)
def unfused_fp8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
residual: Optional[torch.Tensor],
quant_dtype: torch.dtype):
def unfused_fp8_impl(
rms_norm_layer: RMSNorm,
x: torch.Tensor,
residual: Optional[torch.Tensor],
quant_dtype: torch.dtype,
):
# Norm
torch_out = None
if residual is None:
@ -73,22 +82,27 @@ def unfused_fp8_impl(rms_norm_layer: RMSNorm, x: torch.Tensor,
def fused_impl(
rms_norm_layer: RMSNorm, # this stores the weights
x: torch.Tensor,
residual: Optional[torch.Tensor],
quant_dtype: torch.dtype):
out, _ = ops.rms_norm_dynamic_per_token_quant(x,
rms_norm_layer.weight,
1e-6,
quant_dtype,
residual=residual)
rms_norm_layer: RMSNorm, # this stores the weights
x: torch.Tensor,
residual: Optional[torch.Tensor],
quant_dtype: torch.dtype,
):
out, _ = ops.rms_norm_dynamic_per_token_quant(
x, rms_norm_layer.weight, 1e-6, quant_dtype, residual=residual
)
# Bench functions
def bench_fn(rms_norm_layer: RMSNorm, x: torch.Tensor, residual: torch.Tensor,
quant_dtype: torch.dtype, label: str, sub_label: str,
fn: Callable, description: str) -> TMeasurement:
def bench_fn(
rms_norm_layer: RMSNorm,
x: torch.Tensor,
residual: torch.Tensor,
quant_dtype: torch.dtype,
label: str,
sub_label: str,
fn: Callable,
description: str,
) -> TMeasurement:
min_run_time = 1
globals = {
@ -106,43 +120,81 @@ def bench_fn(rms_norm_layer: RMSNorm, x: torch.Tensor, residual: torch.Tensor,
description=description,
).blocked_autorange(min_run_time=min_run_time)
def bench(params: bench_params_t, label: str, sub_label: str) \
-> Iterable[TMeasurement]:
def bench(params: bench_params_t, label: str, sub_label: str) -> Iterable[TMeasurement]:
# Make inputs
layer = RMSNorm(params.hidden_size, 1e-6).to(dtype=params.dtype)
# Make weights
layer.weight.data.normal_(mean=1.0, std=0.1)
# Make inputs
scale = 1 / params.hidden_size
x = torch.randn(params.num_tokens,
params.hidden_size,
dtype=params.dtype,
device='cuda') * scale
residual = (torch.randn_like(x) * scale).to(device='cuda') \
if params.add_residual else None
x = (
torch.randn(
params.num_tokens, params.hidden_size, dtype=params.dtype, device="cuda"
)
* scale
)
residual = (
(torch.randn_like(x) * scale).to(device="cuda") if params.add_residual else None
)
timers = []
# unfused int8 impl.
timers.append(
bench_fn(layer, x, residual, torch.int8, label, sub_label,
unfused_int8_impl, "unfused_int8_impl"))
bench_fn(
layer,
x,
residual,
torch.int8,
label,
sub_label,
unfused_int8_impl,
"unfused_int8_impl",
)
)
# unfused fp8 impl.
timers.append(
bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
unfused_fp8_impl, "unfused_fp8_impl"))
bench_fn(
layer,
x,
residual,
torch.float8_e4m3fn,
label,
sub_label,
unfused_fp8_impl,
"unfused_fp8_impl",
)
)
# fused int8 impl.
timers.append(
bench_fn(layer, x, residual, torch.int8, label, sub_label, fused_impl,
"fused_int8_impl"))
bench_fn(
layer,
x,
residual,
torch.int8,
label,
sub_label,
fused_impl,
"fused_int8_impl",
)
)
# fused fp8 impl.
timers.append(
bench_fn(layer, x, residual, torch.float8_e4m3fn, label, sub_label,
fused_impl, "fused_fp8_impl"))
bench_fn(
layer,
x,
residual,
torch.float8_e4m3fn,
label,
sub_label,
fused_impl,
"fused_fp8_impl",
)
)
print_timers(timers)
@ -157,13 +209,12 @@ def print_timers(timers: Iterable[TMeasurement]):
def main():
torch.set_default_device('cuda')
torch.set_default_device("cuda")
bench_params = get_bench_params()
timers = []
for bp in tqdm(bench_params):
timers.extend(
bench(bp, "rms-norm-dynamic-per-token-quant", bp.description()))
timers.extend(bench(bp, "rms-norm-dynamic-per-token-quant", bp.description()))
print_timers(timers)
# pickle all the results
@ -172,5 +223,5 @@ def main():
pkl.dump(timers, f)
if __name__ == '__main__':
if __name__ == "__main__":
main()

194
benchmarks/kernels/benchmark_aqlm.py
View File

@ -9,32 +9,39 @@ import torch.nn.functional as F
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.aqlm import (
dequantize_weight, generic_dequantize_gemm, get_int_dtype,
optimized_dequantize_gemm)
dequantize_weight,
generic_dequantize_gemm,
get_int_dtype,
optimized_dequantize_gemm,
)
from vllm.utils import FlexibleArgumentParser
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def torch_mult(
input: torch.Tensor, # [..., in_features]
weights: torch.Tensor,
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
# [..., in_features]
input: torch.Tensor,
weights: torch.Tensor,
# [num_out_groups, 1, 1, 1]
scales: torch.Tensor,
) -> torch.Tensor:
output = F.linear(input, weights)
return output
def dequant_out_scale(
input: torch.Tensor, # [..., in_features]
codes: torch.IntTensor, # [num_out_groups, num_in_groups, num_codebooks]
codebooks: torch.
Tensor, # [num_codebooks, codebook_size, out_group_size, in_group_size]
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
# [..., in_features]
input: torch.Tensor,
# [num_out_groups, num_in_groups, num_codebooks]
codes: torch.IntTensor,
# [num_codebooks, codebook_size, out_group_size, in_group_size]
codebooks: torch.Tensor,
# [num_out_groups, 1, 1, 1]
scales: torch.Tensor,
output_partition_sizes: torch.IntTensor,
bias: Optional[torch.Tensor],
) -> torch.Tensor:
weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
if bias is None:
@ -46,40 +53,42 @@ def dequant_out_scale(
flattened_output *= b_scales
return flattened_output.view(orig_shape)
else:
b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
-1, weights.shape[1])
b_scales = scales.view(scales.shape[:-3] + (-1,)).expand(-1, weights.shape[1])
weights *= b_scales
return F.linear(input, weights, bias)
def dequant_weight_scale(
input: torch.Tensor, # [..., in_features]
codes: torch.IntTensor, # [num_out_groups, num_in_groups, num_codebooks]
codebooks: torch.
Tensor, # [num_codebooks, codebook_size, out_group_size, in_group_size]
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
# [..., in_features]
input: torch.Tensor,
# [num_out_groups, num_in_groups, num_codebooks]
codes: torch.IntTensor,
# [num_codebooks, codebook_size, out_group_size, in_group_size]
codebooks: torch.Tensor,
# [num_out_groups, 1, 1, 1]
scales: torch.Tensor,
output_partition_sizes: torch.IntTensor,
bias: Optional[torch.Tensor],
) -> torch.Tensor:
weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
-1, weights.shape[1])
b_scales = scales.view(scales.shape[:-3] + (-1,)).expand(-1, weights.shape[1])
weights *= b_scales
return F.linear(input, weights, bias)
def dequant_no_scale(
input: torch.Tensor, # [..., in_features]
codes: torch.IntTensor, # [num_out_groups, num_in_groups, num_codebooks]
codebooks: torch.
Tensor, # [num_codebooks, codebook_size, out_group_size, in_group_size]
scales: torch.Tensor, # [num_out_groups, 1, 1, 1]
# [..., in_features]
input: torch.Tensor,
# [num_out_groups, num_in_groups, num_codebooks]
codes: torch.IntTensor,
# [num_codebooks, codebook_size, out_group_size, in_group_size]
codebooks: torch.Tensor,
# [num_out_groups, 1, 1, 1]
scales: torch.Tensor,
output_partition_sizes: torch.IntTensor,
bias: Optional[torch.Tensor],
) -> torch.Tensor:
weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
return F.linear(input, weights, bias)
@ -89,23 +98,26 @@ def dequant_no_scale(
# the generic pytorch version.
# Just visual comparison.
def dequant_test(k: int, parts: torch.Tensor, nbooks: int, bits: int) -> None:
n = int(parts.sum().item())
device = torch.device('cuda:0')
device = torch.device("cuda:0")
code_range = (1 << bits) // 2
ingroups = 8
codes = torch.randint(-code_range,
code_range,
size=(n, k // ingroups, nbooks),
dtype=get_int_dtype(bits),
device=device)
codes = torch.randint(
-code_range,
code_range,
size=(n, k // ingroups, nbooks),
dtype=get_int_dtype(bits),
device=device,
)
codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
dtype=torch.float16,
device=device)
codebooks = torch.randn(
size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
dtype=torch.float16,
device=device,
)
count = 0
for index in range(16):
@ -138,24 +150,25 @@ def dequant_test(k: int, parts: torch.Tensor, nbooks: int, bits: int) -> None:
def main():
parser = FlexibleArgumentParser(description="Benchmark aqlm performance.")
# Add arguments
parser.add_argument("--nbooks",
type=int,
default=1,
help="Number of codebooks (default: 1)")
parser.add_argument("--bits",
type=int,
default=16,
help="Number of bits per code element (default: 16)")
parser.add_argument(
"--nbooks", type=int, default=1, help="Number of codebooks (default: 1)"
)
parser.add_argument(
"--bits",
type=int,
default=16,
help="Number of bits per code element (default: 16)",
)
parser.add_argument(
"--test",
type=bool,
default=False,
help="Run the decompression/dequant tester rather than benchmarking "
"(default: False)")
"(default: False)",
)
# Parse the arguments
args = parser.parse_args()
@ -165,7 +178,7 @@ def main():
bits = args.bits
if args.test:
dequant_test(4096, torch.tensor((4096, )), nbooks, bits)
dequant_test(4096, torch.tensor((4096,)), nbooks, bits)
return
# Otherwise, benchmark.
@ -184,31 +197,54 @@ def main():
with open(filename, "w") as f:
sys.stdout = f
print('m | k | n | n parts', end='')
print("m | k | n | n parts", end="")
for method in methods:
print(f" | {method.__name__.replace('_', ' ')} (µs)", end='')
print('')
print(f" | {method.__name__.replace('_', ' ')} (µs)", end="")
print("")
# These are reasonable prefill sizes.
ksandpartions = ((4096, (4096, 4096, 4096)), (4096, (4096, )),
(4096, (11008, 11008)), (11008, (4096, )))
ksandpartions = (
(4096, (4096, 4096, 4096)),
(4096, (4096,)),
(4096, (11008, 11008)),
(11008, (4096,)),
)
# reasonable ranges for m.
for m in [
1, 2, 4, 8, 10, 12, 14, 16, 24, 32, 48, 52, 56, 64, 96, 112,
128, 256, 512, 1024, 1536, 2048, 3072, 4096
1,
2,
4,
8,
10,
12,
14,
16,
24,
32,
48,
52,
56,
64,
96,
112,
128,
256,
512,
1024,
1536,
2048,
3072,
4096,
]:
print(f'{m}', file=sys.__stdout__)
print(f"{m}", file=sys.__stdout__)
for ksp in ksandpartions:
run_grid(m, ksp[0], torch.tensor(ksp[1]), nbooks, bits,
methods)
run_grid(m, ksp[0], torch.tensor(ksp[1]), nbooks, bits, methods)
sys.stdout = sys.__stdout__
def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int,
methods):
def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, methods):
# I didn't see visible improvements from increasing these, but feel free :)
num_warmup_trials = 1
num_trials = 1
@ -229,7 +265,7 @@ def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int,
)
n = parts.sum().item()
print(f'{m} | {k} | {n} | {parts.tolist()}', end='')
print(f"{m} | {k} | {n} | {parts.tolist()}", end="")
for method in methods:
best_time_us = 1e20
@ -249,32 +285,36 @@ def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int,
if kernel_dur_us < best_time_us:
best_time_us = kernel_dur_us
print(f' | {kernel_dur_us:.0f}', end='')
print(f" | {kernel_dur_us:.0f}", end="")
print('')
print("")
def run_timing(num_calls: int, m: int, k: int, parts: torch.Tensor,
nbooks: int, bits: int, method) -> float:
def run_timing(
num_calls: int, m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int, method
) -> float:
n = int(parts.sum().item())
device = torch.device('cuda:0')
device = torch.device("cuda:0")
input = torch.randn((1, m, k), dtype=torch.float16, device=device)
code_range = (1 << bits) // 2
ingroups = 8
codes = torch.randint(-code_range,
code_range,
size=(n, k // ingroups, nbooks),
dtype=get_int_dtype(bits),
device=device)
codes = torch.randint(
-code_range,
code_range,
size=(n, k // ingroups, nbooks),
dtype=get_int_dtype(bits),
device=device,
)
codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
dtype=torch.float16,
device=device)
codebooks = torch.randn(
size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
dtype=torch.float16,
device=device,
)
scales = torch.randn(size=(n, 1, 1, 1), dtype=torch.float16, device=device)

61
benchmarks/kernels/benchmark_bitblas.py
View File

@ -3,27 +3,33 @@
# Licensed under the MIT License.
from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
MINIMUM_BITBLAS_VERSION)
MINIMUM_BITBLAS_VERSION,
)
try:
import bitblas
if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
raise ImportError("bitblas version is wrong. Please "
f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
raise ImportError(
"bitblas version is wrong. Please "
f"install bitblas>={MINIMUM_BITBLAS_VERSION}"
)
except ImportError as e:
bitblas_import_exception = e
raise ValueError("Trying to use the bitblas backend, but could not import"
f"with the following error: {bitblas_import_exception}. "
"Please install bitblas through the following command: "
f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
) from bitblas_import_exception
raise ValueError(
"Trying to use the bitblas backend, but could not import"
f"with the following error: {bitblas_import_exception}. "
"Please install bitblas through the following command: "
f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
) from bitblas_import_exception
from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target
from vllm.utils import FlexibleArgumentParser
parser = FlexibleArgumentParser(
description="Benchmark BitBLAS int4 on a specific target.")
description="Benchmark BitBLAS int4 on a specific target."
)
# Add arguments to the parser
parser.add_argument(
@ -32,10 +38,9 @@ parser.add_argument(
default=auto_detect_nvidia_target(),
help="Specify the target device for benchmarking.",
)
parser.add_argument("--group_size",
type=int,
default=None,
help="Group size for grouped quantization.")
parser.add_argument(
"--group_size", type=int, default=None, help="Group size for grouped quantization."
)
parser.add_argument(
"--A_dtype",
type=str,
@ -82,17 +87,17 @@ parser.add_argument(
choices=["nt", "nn"],
help="Matrix layout, 'nt' for non-transpose A and transpose W.",
)
parser.add_argument("--with_bias",
action="store_true",
help="Include bias in the benchmark.")
parser.add_argument(
"--with_bias", action="store_true", help="Include bias in the benchmark."
)
parser.add_argument(
"--with_scaling",
action="store_true",
help="Include scaling factor in the quantization.",
)
parser.add_argument("--with_zeros",
action="store_true",
help="Include zeros in the quantization.")
parser.add_argument(
"--with_zeros", action="store_true", help="Include zeros in the quantization."
)
parser.add_argument(
"--zeros_mode",
type=str,
@ -170,8 +175,7 @@ shapes = [
]
# Build test shapes with all the shared arguments
test_shapes = [(MatmulConfig, Matmul, (*shape, *shared_args))
for shape in shapes]
test_shapes = [(MatmulConfig, Matmul, (*shape, *shared_args)) for shape in shapes]
benchmark_sets = []
benchmark_sets.extend(test_shapes)
@ -206,12 +210,12 @@ for config_key, values in benchmark_results.items():
func_name = args_split[0]
input_args_str = "-".join(args_split[1:])
col_widths[0] = max(col_widths[0], len(func_name) + 2, len(headers[0]) + 2)
col_widths[1] = max(col_widths[1],
len(input_args_str) + 2,
len(headers[1]) + 2)
col_widths[2] = max(col_widths[2],
len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
len(headers[2]) + 2)
col_widths[1] = max(col_widths[1], len(input_args_str) + 2, len(headers[1]) + 2)
col_widths[2] = max(
col_widths[2],
len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
len(headers[2]) + 2,
)
# break only if you want to measure widths from a single example;
# otherwise, let it loop over all items.
@ -232,5 +236,6 @@ for config_key, values in benchmark_results.items():
f"{values['BitBLAS_top20_latency']:.3f} ms",
]
row_str = "".join(
[str(cell).ljust(col_widths[idx]) for idx, cell in enumerate(row)])
[str(cell).ljust(col_widths[idx]) for idx, cell in enumerate(row)]
)
print(row_str)

409
benchmarks/kernels/benchmark_cutlass_fp4_moe.py
View File

@ -5,6 +5,7 @@ kernel. The cutlass_moe_fp4 kernel takes in fp4 quantized weights and 16-bit
activations. The triton_moe kernel takes in fp8 weights(tensor scaled to fp8)
and 16-bit activations.
"""
import nvtx
import torch
import torch.utils.benchmark as benchmark
@ -12,8 +13,7 @@ import torch.utils.benchmark as benchmark
from vllm import _custom_ops as ops
from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
from vllm.model_executor.layers.fused_moe.fused_moe import (fused_experts,
fused_topk)
from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
from vllm.scalar_type import scalar_types
from vllm.utils import FlexibleArgumentParser
@ -38,19 +38,27 @@ FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
def to_fp8(tensor: torch.Tensor):
finfo = torch.finfo(torch.float8_e4m3fn)
return torch.round(tensor.clamp(
min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
dtype=torch.float8_e4m3fn
)
def bench_run(results: list[benchmark.Measurement], model: str,
num_experts: int, topk: int, per_act_token: bool,
per_out_ch: bool, mkn: tuple[int, int, int]):
def bench_run(
results: list[benchmark.Measurement],
model: str,
num_experts: int,
topk: int,
per_act_token: bool,
per_out_ch: bool,
mkn: tuple[int, int, int],
):
label = "NVFP4 Blockscaled CUTLASS MOE vs FP8 Tensor Scaled Triton"
sub_label = (
"{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, "
"MKN=({})".format(model, num_experts, topk, per_act_token, per_out_ch,
mkn))
"{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, MKN=({})".format(
model, num_experts, topk, per_act_token, per_out_ch, mkn
)
)
print(f"Testing: {sub_label}")
@ -64,18 +72,12 @@ def bench_run(results: list[benchmark.Measurement], model: str,
_, a_fp8_scale = ops.scaled_fp8_quant(a)
w1_fp8q = torch.empty((num_experts, 2 * n, k),
device=device,
dtype=torch.float8_e4m3fn)
w2_fp8q = torch.empty((num_experts, k, n),
device=device,
dtype=torch.float8_e4m3fn)
w1_fp8scale = torch.empty((num_experts, 1, 1),
device=device,
dtype=torch.float32)
w2_fp8scale = torch.empty((num_experts, 1, 1),
device=device,
dtype=torch.float32)
w1_fp8q = torch.empty(
(num_experts, 2 * n, k), device=device, dtype=torch.float8_e4m3fn
)
w2_fp8q = torch.empty((num_experts, k, n), device=device, dtype=torch.float8_e4m3fn)
w1_fp8scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
w2_fp8scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
for expert in range(num_experts):
w1_fp8q[expert], w1_fp8scale[expert] = ops.scaled_fp8_quant(w1[expert])
@ -91,26 +93,24 @@ def bench_run(results: list[benchmark.Measurement], model: str,
topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
quant_blocksize = 16
w1_blockscale = torch.empty((num_experts, 2 * n, k // quant_blocksize),
device=device,
dtype=torch.float8_e4m3fn)
w2_blockscale = torch.empty((num_experts, k, n // quant_blocksize),
device=device,
dtype=torch.float8_e4m3fn)
w1_blockscale = torch.empty(
(num_experts, 2 * n, k // quant_blocksize),
device=device,
dtype=torch.float8_e4m3fn,
)
w2_blockscale = torch.empty(
(num_experts, k, n // quant_blocksize), device=device, dtype=torch.float8_e4m3fn
)
# n_b_scales = 2 * n if per_out_ch else 1
# k_b_scales = k if per_out_ch else 1
w1_fp4 = torch.empty((num_experts, 2 * n, k // 2),
device=device,
dtype=torch.uint8)
w2_fp4 = torch.empty((num_experts, k, n // 2),
device=device,
dtype=torch.uint8)
w1_fp4 = torch.empty((num_experts, 2 * n, k // 2), device=device, dtype=torch.uint8)
w2_fp4 = torch.empty((num_experts, k, n // 2), device=device, dtype=torch.uint8)
w1_gs = torch.empty((num_experts, ), device=device, dtype=torch.float32)
w2_gs = torch.empty((num_experts, ), device=device, dtype=torch.float32)
a1_gs = torch.ones((num_experts, ), device=device, dtype=torch.float32)
a2_gs = torch.ones((num_experts, ), device=device, dtype=torch.float32)
w1_gs = torch.empty((num_experts,), device=device, dtype=torch.float32)
w2_gs = torch.empty((num_experts,), device=device, dtype=torch.float32)
a1_gs = torch.ones((num_experts,), device=device, dtype=torch.float32)
a2_gs = torch.ones((num_experts,), device=device, dtype=torch.float32)
for expert in range(num_experts):
w1_e = w1[expert]
@ -121,96 +121,141 @@ def bench_run(results: list[benchmark.Measurement], model: str,
w2_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
w1_fp4[expert], w1_blockscale[expert] = ops.scaled_fp4_quant(
w1_e, w1_gs[expert])
w1_e, w1_gs[expert]
)
w2_fp4[expert], w2_blockscale[expert] = ops.scaled_fp4_quant(
w2_e, w2_gs[expert])
w2_e, w2_gs[expert]
)
def run_triton_moe(a: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
topk_weights: torch.Tensor, topk_ids: torch.Tensor,
w1_scale: torch.Tensor, w2_scale: torch.Tensor,
a_fp8_scale: torch.Tensor, num_repeats: int):
def run_triton_moe(
a: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
a_fp8_scale: torch.Tensor,
num_repeats: int,
):
for _ in range(num_repeats):
fused_experts(a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_fp8_scale)
fused_experts(
a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_fp8_scale,
)
def run_cutlass_moe_fp4(a: torch.Tensor, w1_fp4: torch.Tensor,
w2_fp4: torch.Tensor, w1_blockscale: torch.Tensor,
w2_blockscale: torch.Tensor, w1_gs: torch.Tensor,
w2_gs: torch.Tensor, a1_gs: torch.Tensor,
a2_gs: torch.Tensor, topk_weights: torch.Tensor,
topk_ids: torch.Tensor, m: int, n: int, k: int,
e: int, device: torch.device, num_repeats: int):
def run_cutlass_moe_fp4(
a: torch.Tensor,
w1_fp4: torch.Tensor,
w2_fp4: torch.Tensor,
w1_blockscale: torch.Tensor,
w2_blockscale: torch.Tensor,
w1_gs: torch.Tensor,
w2_gs: torch.Tensor,
a1_gs: torch.Tensor,
a2_gs: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
m: int,
n: int,
k: int,
e: int,
device: torch.device,
num_repeats: int,
):
for _ in range(num_repeats):
with nvtx.annotate("cutlass_moe_fp4", color="green"):
cutlass_moe_fp4(a=a,
a1_gscale=a1_gs,
a2_gscale=a2_gs,
w1_fp4=w1_fp4,
w1_blockscale=w1_blockscale,
w1_alphas=w1_gs,
w2_fp4=w2_fp4,
w2_blockscale=w2_blockscale,
w2_alphas=w2_gs,
topk_weights=topk_weights,
topk_ids=topk_ids,
m=m,
n=n,
k=k,
e=num_experts,
device=device)
cutlass_moe_fp4(
a=a,
a1_gscale=a1_gs,
a2_gscale=a2_gs,
w1_fp4=w1_fp4,
w1_blockscale=w1_blockscale,
w1_alphas=w1_gs,
w2_fp4=w2_fp4,
w2_blockscale=w2_blockscale,
w2_alphas=w2_gs,
topk_weights=topk_weights,
topk_ids=topk_ids,
m=m,
n=n,
k=k,
e=num_experts,
device=device,
)
def run_cutlass_from_graph(
a: torch.Tensor, a1_gscale: torch.Tensor, w1_fp4: torch.Tensor,
w1_blockscale: torch.Tensor, w1_alphas: torch.Tensor,
a2_gscale: torch.Tensor, w2_fp4: torch.Tensor,
w2_blockscale: torch.Tensor, w2_alphas: torch.Tensor,
topk_weights: torch.Tensor, topk_ids: torch.Tensor, m: int, n: int,
k: int, e: int, device: torch.device):
a: torch.Tensor,
a1_gscale: torch.Tensor,
w1_fp4: torch.Tensor,
w1_blockscale: torch.Tensor,
w1_alphas: torch.Tensor,
a2_gscale: torch.Tensor,
w2_fp4: torch.Tensor,
w2_blockscale: torch.Tensor,
w2_alphas: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
m: int,
n: int,
k: int,
e: int,
device: torch.device,
):
with set_current_vllm_config(
VllmConfig(parallel_config=ParallelConfig(
pipeline_parallel_size=1))):
return cutlass_moe_fp4(a=a,
a1_gscale=a1_gs,
w1_fp4=w1_fp4,
w1_blockscale=w1_blockscale,
w1_alphas=w1_alphas,
a2_gscale=a2_gs,
w2_fp4=w2_fp4,
w2_blockscale=w2_blockscale,
w2_alphas=w2_alphas,
topk_weights=topk_weights,
topk_ids=topk_ids,
m=m,
n=n,
k=k,
e=num_experts,
device=device)
VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
):
return cutlass_moe_fp4(
a=a,
a1_gscale=a1_gs,
w1_fp4=w1_fp4,
w1_blockscale=w1_blockscale,
w1_alphas=w1_alphas,
a2_gscale=a2_gs,
w2_fp4=w2_fp4,
w2_blockscale=w2_blockscale,
w2_alphas=w2_alphas,
topk_weights=topk_weights,
topk_ids=topk_ids,
m=m,
n=n,
k=k,
e=num_experts,
device=device,
)
def run_triton_from_graph(a: torch.Tensor, w1: torch.Tensor,
w2: torch.Tensor, topk_weights: torch.Tensor,
topk_ids: torch.Tensor, w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
a_fp8_scale: torch.Tensor):
def run_triton_from_graph(
a: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
a_fp8_scale: torch.Tensor,
):
with set_current_vllm_config(
VllmConfig(parallel_config=ParallelConfig(
pipeline_parallel_size=1))):
return fused_experts(a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_fp8_scale)
VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
):
return fused_experts(
a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_fp8_scale,
)
def replay_graph(graph, num_repeats):
for _ in range(num_repeats):
@ -220,30 +265,39 @@ def bench_run(results: list[benchmark.Measurement], model: str,
cutlass_stream = torch.cuda.Stream()
cutlass_graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
run_cutlass_from_graph(a=a,
a1_gscale=a1_gs,
w1_fp4=w1_fp4,
w1_blockscale=w1_blockscale,
w1_alphas=w1_gs,
a2_gscale=a2_gs,
w2_fp4=w2_fp4,
w2_blockscale=w2_blockscale,
w2_alphas=w2_gs,
topk_weights=topk_weights,
topk_ids=topk_ids,
m=m,
n=n,
k=k,
e=num_experts,
device=device)
run_cutlass_from_graph(
a=a,
a1_gscale=a1_gs,
w1_fp4=w1_fp4,
w1_blockscale=w1_blockscale,
w1_alphas=w1_gs,
a2_gscale=a2_gs,
w2_fp4=w2_fp4,
w2_blockscale=w2_blockscale,
w2_alphas=w2_gs,
topk_weights=topk_weights,
topk_ids=topk_ids,
m=m,
n=n,
k=k,
e=num_experts,
device=device,
)
torch.cuda.synchronize()
triton_stream = torch.cuda.Stream()
triton_graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(triton_graph, stream=triton_stream):
run_triton_from_graph(a, w1_fp8q_notransp, w2_fp8q_notransp,
topk_weights, topk_ids, w1_fp8scale, w2_fp8scale,
a_fp8_scale)
run_triton_from_graph(
a,
w1_fp8q_notransp,
w2_fp8q_notransp,
topk_weights,
topk_ids,
w1_fp8scale,
w2_fp8scale,
a_fp8_scale,
)
torch.cuda.synchronize()
min_run_time = 5
@ -290,18 +344,27 @@ def bench_run(results: list[benchmark.Measurement], model: str,
}
# Warmup
run_triton_moe(a, w1_fp8q_notransp, w2_fp8q_notransp, topk_weights,
topk_ids, w1_fp8scale, w2_fp8scale, a_fp8_scale, num_warmup)
run_triton_moe(
a,
w1_fp8q_notransp,
w2_fp8q_notransp,
topk_weights,
topk_ids,
w1_fp8scale,
w2_fp8scale,
a_fp8_scale,
num_warmup,
)
results.append(
benchmark.Timer(
stmt=
"run_triton_moe(a, w1_fp8q_notransp, w2_fp8q_notransp, topk_weights, topk_ids, w1_fp8scale, w2_fp8scale, a_fp8_scale, num_runs)", # noqa: E501
stmt="run_triton_moe(a, w1_fp8q_notransp, w2_fp8q_notransp, topk_weights, topk_ids, w1_fp8scale, w2_fp8scale, a_fp8_scale, num_runs)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="triton_moe",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
# Warmup
replay_graph(triton_graph, num_warmup)
@ -313,23 +376,40 @@ def bench_run(results: list[benchmark.Measurement], model: str,
label=label,
sub_label=sub_label,
description="triton_moe_cuda_graphs",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
# Warmup
run_cutlass_moe_fp4(a, w1_fp4, w2_fp4, w1_blockscale, w2_blockscale, w1_gs,
w2_gs, a1_gs, a2_gs, topk_weights, topk_ids, m, n, k,
num_experts, device, num_warmup)
run_cutlass_moe_fp4(
a,
w1_fp4,
w2_fp4,
w1_blockscale,
w2_blockscale,
w1_gs,
w2_gs,
a1_gs,
a2_gs,
topk_weights,
topk_ids,
m,
n,
k,
num_experts,
device,
num_warmup,
)
results.append(
benchmark.Timer(
stmt=
"run_cutlass_moe_fp4(a, w1_fp4, w2_fp4, w1_blockscale, w2_blockscale, w1_alphas, w2_alphas, a1_gscale, a2_gscale, topk_weights, topk_ids, m, n, k, e, device, num_runs)", # noqa: E501
stmt="run_cutlass_moe_fp4(a, w1_fp4, w2_fp4, w1_blockscale, w2_blockscale, w1_alphas, w2_alphas, a1_gscale, a2_gscale, topk_weights, topk_ids, m, n, k, e, device, num_runs)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="cutlass_moe_fp4",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
# Warmup
replay_graph(cutlass_graph, num_warmup)
@ -341,7 +421,8 @@ def bench_run(results: list[benchmark.Measurement], model: str,
label=label,
sub_label=sub_label,
description="cutlass_moe_fp4_cuda_graphs",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
def main(args):
@ -369,8 +450,15 @@ def main(args):
for per_out_ch in PER_OUT_CH_OPTS:
for size_m in args.batch_sizes:
mkn = (size_m, size_k, size_n)
bench_run(results, model, num_experts, topk,
per_act_token, per_out_ch, mkn)
bench_run(
results,
model,
num_experts,
topk,
per_act_token,
per_out_ch,
mkn,
)
compare = benchmark.Compare(results)
compare.print()
@ -378,8 +466,8 @@ def main(args):
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark NVFP4 CUTLASS MOE across specified "
"models/shapes/batches")
description="Benchmark NVFP4 CUTLASS MOE across specified models/shapes/batches"
)
parser.add_argument(
"--models",
nargs="+",
@ -387,21 +475,14 @@ if __name__ == "__main__":
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES_MOE.keys(),
)
parser.add_argument("--tp-sizes",
nargs="+",
type=int,
default=DEFAULT_TP_SIZES)
parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
parser.add_argument("--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES)
parser.add_argument(
"--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
)
parser.add_argument("--limit-k", nargs="+", type=int, default=[])
parser.add_argument("--limit-n", nargs="+", type=int, default=[])
parser.add_argument("--limit-num-groups", nargs="+", type=int, default=[])
parser.add_argument("--limit-per-act-token",
nargs="+",
type=int,
default=[])
parser.add_argument("--limit-per-act-token", nargs="+", type=int, default=[])
parser.add_argument("--limit-per-out-ch", nargs="+", type=int, default=[])
args = parser.parse_args()

357
benchmarks/kernels/benchmark_grouped_gemm_cutlass.py
View File

@ -6,14 +6,18 @@ from benchmark_shapes import WEIGHT_SHAPES_MOE
from vllm import _custom_ops as ops
from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.fused_moe.fused_moe import (cutlass_moe_fp8,
fused_experts,
fused_topk)
from vllm.model_executor.layers.fused_moe.fused_moe import (
cutlass_moe_fp8,
fused_experts,
fused_topk,
)
from vllm.utils import FlexibleArgumentParser
DEFAULT_MODELS = [
"nm-testing/Mixtral-8x7B-Instruct-v0.1", "nm-testing/deepseekv2-lite",
"ibm-granite/granite-3.0-1b-a400m", "ibm-granite/granite-3.0-3b-a800m"
"nm-testing/Mixtral-8x7B-Instruct-v0.1",
"nm-testing/deepseekv2-lite",
"ibm-granite/granite-3.0-1b-a400m",
"ibm-granite/granite-3.0-3b-a800m",
]
DEFAULT_BATCH_SIZES = [1, 4, 8, 16, 32, 64, 128, 256, 512]
DEFAULT_TP_SIZES = [1]
@ -24,19 +28,27 @@ PER_OUT_CH_OPTS = [False]
def to_fp8(tensor: torch.Tensor):
finfo = torch.finfo(torch.float8_e4m3fn)
return torch.round(tensor.clamp(
min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
dtype=torch.float8_e4m3fn
)
def bench_run(results: list[benchmark.Measurement], model: str,
num_experts: int, topk: int, per_act_token: bool,
per_out_ch: bool, mkn: tuple[int, int, int]):
def bench_run(
results: list[benchmark.Measurement],
model: str,
num_experts: int,
topk: int,
per_act_token: bool,
per_out_ch: bool,
mkn: tuple[int, int, int],
):
label = "Quant Matmul"
sub_label = (
"{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, "
"MKN=({})".format(model, num_experts, topk, per_act_token, per_out_ch,
mkn))
"{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, MKN=({})".format(
model, num_experts, topk, per_act_token, per_out_ch, mkn
)
)
print(f"Testing: {sub_label}")
@ -50,35 +62,17 @@ def bench_run(results: list[benchmark.Measurement], model: str,
_, a_scale = ops.scaled_fp8_quant(a)
w1_q = torch.empty((num_experts, 2 * n, k),
device="cuda",
dtype=torch.float8_e4m3fn)
w2_q = torch.empty((num_experts, k, n),
device="cuda",
dtype=torch.float8_e4m3fn)
w1_scale = torch.empty((num_experts, 1, 1),
device="cuda",
dtype=torch.float32)
w2_scale = torch.empty((num_experts, 1, 1),
device="cuda",
dtype=torch.float32)
w1_q = torch.empty(
(num_experts, 2 * n, k), device="cuda", dtype=torch.float8_e4m3fn
)
w2_q = torch.empty((num_experts, k, n), device="cuda", dtype=torch.float8_e4m3fn)
w1_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)
w2_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)
ab_strides1 = torch.full((num_experts, ),
k,
device="cuda",
dtype=torch.int64)
c_strides1 = torch.full((num_experts, ),
2 * n,
device="cuda",
dtype=torch.int64)
ab_strides2 = torch.full((num_experts, ),
n,
device="cuda",
dtype=torch.int64)
c_strides2 = torch.full((num_experts, ),
k,
device="cuda",
dtype=torch.int64)
ab_strides1 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
c_strides1 = torch.full((num_experts,), 2 * n, device="cuda", dtype=torch.int64)
ab_strides2 = torch.full((num_experts,), n, device="cuda", dtype=torch.int64)
c_strides2 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
for expert in range(num_experts):
w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(w1[expert])
@ -91,82 +85,120 @@ def bench_run(results: list[benchmark.Measurement], model: str,
score = torch.randn((m, num_experts), device="cuda", dtype=dtype)
topk_weights, topk_ids, token_expert_indices = fused_topk(
a, score, topk, renormalize=False)
a, score, topk, renormalize=False
)
def run_triton_moe(a: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
topk_weights: torch.Tensor, topk_ids: torch.Tensor,
w1_scale: torch.Tensor, w2_scale: torch.Tensor,
a_scale: torch.Tensor, num_repeats: int):
def run_triton_moe(
a: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
a_scale: torch.Tensor,
num_repeats: int,
):
for _ in range(num_repeats):
fused_experts(a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_scale)
fused_experts(
a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_scale,
)
def run_cutlass_moe(a: torch.Tensor, a_scale: torch.Tensor,
w1: torch.Tensor, w2: torch.Tensor,
w1_scale: torch.Tensor, w2_scale: torch.Tensor,
topk_weights: torch.Tensor, topk_ids: torch.Tensor,
ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
ab_strides2: torch.Tensor, c_strides2: torch.Tensor,
num_repeats: int):
def run_cutlass_moe(
a: torch.Tensor,
a_scale: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
ab_strides1: torch.Tensor,
c_strides1: torch.Tensor,
ab_strides2: torch.Tensor,
c_strides2: torch.Tensor,
num_repeats: int,
):
for _ in range(num_repeats):
cutlass_moe_fp8(a,
w1,
w2,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
a1_scale=a_scale)
cutlass_moe_fp8(
a,
w1,
w2,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
a1_scale=a_scale,
)
def run_cutlass_from_graph(
a: torch.Tensor, a_scale: torch.Tensor, w1_q: torch.Tensor,
w2_q: torch.Tensor, w1_scale: torch.Tensor, w2_scale: torch.Tensor,
topk_weights: torch.Tensor, topk_ids: torch.Tensor,
ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
ab_strides2: torch.Tensor, c_strides2: torch.Tensor):
a: torch.Tensor,
a_scale: torch.Tensor,
w1_q: torch.Tensor,
w2_q: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
ab_strides1: torch.Tensor,
c_strides1: torch.Tensor,
ab_strides2: torch.Tensor,
c_strides2: torch.Tensor,
):
with set_current_vllm_config(
VllmConfig(parallel_config=ParallelConfig(
pipeline_parallel_size=1))):
return cutlass_moe_fp8(a,
w1_q,
w2_q,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
a1_scale=a_scale)
VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
):
return cutlass_moe_fp8(
a,
w1_q,
w2_q,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
a1_scale=a_scale,
)
def run_triton_from_graph(a: torch.Tensor, w1: torch.Tensor,
w2: torch.Tensor, topk_weights: torch.Tensor,
topk_ids: torch.Tensor, w1_scale: torch.Tensor,
w2_scale: torch.Tensor, a_scale: torch.Tensor):
def run_triton_from_graph(
a: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
a_scale: torch.Tensor,
):
with set_current_vllm_config(
VllmConfig(parallel_config=ParallelConfig(
pipeline_parallel_size=1))):
return fused_experts(a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_scale)
VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
):
return fused_experts(
a,
w1,
w2,
topk_weights,
topk_ids,
use_fp8_w8a8=True,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a_scale,
)
def replay_graph(graph, num_repeats):
for _ in range(num_repeats):
@ -176,16 +208,35 @@ def bench_run(results: list[benchmark.Measurement], model: str,
cutlass_stream = torch.cuda.Stream()
cutlass_graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
run_cutlass_from_graph(a, a_scale, w1_q, w2_q, w1_scale, w2_scale,
topk_weights, topk_ids, ab_strides1, c_strides1,
ab_strides2, c_strides2)
run_cutlass_from_graph(
a,
a_scale,
w1_q,
w2_q,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
)
torch.cuda.synchronize()
triton_stream = torch.cuda.Stream()
triton_graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(triton_graph, stream=triton_stream):
run_triton_from_graph(a, w1_q_notransp, w2_q_notransp, topk_weights,
topk_ids, w1_scale, w2_scale, a_scale)
run_triton_from_graph(
a,
w1_q_notransp,
w2_q_notransp,
topk_weights,
topk_ids,
w1_scale,
w2_scale,
a_scale,
)
torch.cuda.synchronize()
min_run_time = 5
@ -225,18 +276,27 @@ def bench_run(results: list[benchmark.Measurement], model: str,
}
# Warmup
run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids,
w1_scale, w2_scale, a_scale, num_warmup)
run_triton_moe(
a,
w1_q_notransp,
w2_q_notransp,
topk_weights,
topk_ids,
w1_scale,
w2_scale,
a_scale,
num_warmup,
)
results.append(
benchmark.Timer(
stmt=
"run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids, w1_scale, w2_scale, a_scale, num_runs)", # noqa: E501
stmt="run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids, w1_scale, w2_scale, a_scale, num_runs)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="triton_moe",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
# Warmup
replay_graph(triton_graph, num_warmup)
@ -248,22 +308,35 @@ def bench_run(results: list[benchmark.Measurement], model: str,
label=label,
sub_label=sub_label,
description="triton_moe_cuda_graphs",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
# Warmup
run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights,
topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2,
num_warmup)
run_cutlass_moe(
a,
a_scale,
w1_q,
w2_q,
w1_scale,
w2_scale,
topk_weights,
topk_ids,
ab_strides1,
c_strides1,
ab_strides2,
c_strides2,
num_warmup,
)
results.append(
benchmark.Timer(
stmt=
"run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2, num_runs)", # noqa: E501
stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2, num_runs)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="grouped_gemm_moe",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
# Warmup
replay_graph(cutlass_graph, num_warmup)
@ -275,7 +348,8 @@ def bench_run(results: list[benchmark.Measurement], model: str,
label=label,
sub_label=sub_label,
description="grouped_gemm_moe_cuda_graphs",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
def main(args):
@ -303,8 +377,15 @@ def main(args):
for per_out_ch in PER_OUT_CH_OPTS:
for size_m in DEFAULT_BATCH_SIZES:
mkn = (size_m, size_k, size_n)
bench_run(results, model, num_experts, topk,
per_act_token, per_out_ch, mkn)
bench_run(
results,
model,
num_experts,
topk,
per_act_token,
per_out_ch,
mkn,
)
compare = benchmark.Compare(results)
compare.print()
@ -312,7 +393,8 @@ def main(args):
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark Marlin across specified models/shapes/batches")
description="Benchmark Marlin across specified models/shapes/batches"
)
parser.add_argument(
"--models",
nargs="+",
@ -320,21 +402,14 @@ if __name__ == "__main__":
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES_MOE.keys(),
)
parser.add_argument("--tp-sizes",
nargs="+",
type=int,
default=DEFAULT_TP_SIZES)
parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
parser.add_argument("--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES)
parser.add_argument(
"--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
)
parser.add_argument("--limit-k", nargs="+", type=int, default=[])
parser.add_argument("--limit-n", nargs="+", type=int, default=[])
parser.add_argument("--limit-num-groups", nargs="+", type=int, default=[])
parser.add_argument("--limit-per-act-token",
nargs="+",
type=int,
default=[])
parser.add_argument("--limit-per-act-token", nargs="+", type=int, default=[])
parser.add_argument("--limit-per-out-ch", nargs="+", type=int, default=[])
args = parser.parse_args()

60
benchmarks/kernels/benchmark_layernorm.py
View File

@ -10,14 +10,16 @@ from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
@torch.inference_mode()
def main(num_tokens: int,
hidden_size: int,
add_residual: bool,
dtype: torch.dtype,
seed: int = 0,
do_profile: bool = False,
num_warmup_iters: int = 5,
num_iters: int = 100) -> None:
def main(
num_tokens: int,
hidden_size: int,
add_residual: bool,
dtype: torch.dtype,
seed: int = 0,
do_profile: bool = False,
num_warmup_iters: int = 5,
num_iters: int = 100,
) -> None:
current_platform.seed_everything(seed)
torch.set_default_device("cuda")
@ -56,33 +58,35 @@ def main(num_tokens: int,
print(f"Kernel running time: {latency * 1000000:.3f} us")
if __name__ == '__main__':
parser = FlexibleArgumentParser(
description="Benchmark the layernorm kernel.")
if __name__ == "__main__":
parser = FlexibleArgumentParser(description="Benchmark the layernorm kernel.")
parser.add_argument("--num-tokens", type=int, default=4096)
parser.add_argument("--hidden-size", type=int, default=8192)
parser.add_argument("--add-residual", action="store_true")
parser.add_argument("--dtype",
type=str,
choices=["half", "bfloat16", "float"],
default="half")
parser.add_argument(
"--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--profile", action="store_true")
parser.add_argument("--num-warmup-iters", type=int, default=5)
parser.add_argument("--num-iters",
type=int,
default=100,
help="Number of benchmark iterations. "
"If --profile is set, this number is ignored")
parser.add_argument(
"--num-iters",
type=int,
default=100,
help="Number of benchmark iterations. "
"If --profile is set, this number is ignored",
)
args = parser.parse_args()
print(args)
main(num_tokens=args.num_tokens,
hidden_size=args.hidden_size,
add_residual=args.add_residual,
dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
seed=args.seed,
do_profile=args.profile,
num_warmup_iters=args.num_warmup_iters,
num_iters=args.num_iters)
main(
num_tokens=args.num_tokens,
hidden_size=args.hidden_size,
add_residual=args.add_residual,
dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
seed=args.seed,
do_profile=args.profile,
num_warmup_iters=args.num_warmup_iters,
num_iters=args.num_iters,
)

647
benchmarks/kernels/benchmark_lora.py
View File

File diff suppressed because it is too large Load Diff

367
benchmarks/kernels/benchmark_machete.py
View File

@ -20,12 +20,18 @@ from weight_shapes import WEIGHT_SHAPES
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, marlin_permute_scales,
marlin_zero_points)
GPTQ_MARLIN_MAX_PARALLEL,
GPTQ_MARLIN_MIN_THREAD_N,
marlin_permute_scales,
marlin_zero_points,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
MarlinWorkspace)
MarlinWorkspace,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
pack_rows, quantize_weights)
pack_rows,
quantize_weights,
)
from vllm.scalar_type import ScalarType, scalar_types
from vllm.utils import FlexibleArgumentParser
@ -82,12 +88,14 @@ def rand_data(shape, dtype=torch.float16, scale=1):
return torch.randint(-15, 15, shape, dtype=dtype, device="cuda")
def quantize_and_pack(atype: torch.dtype,
w: torch.Tensor,
wtype: ScalarType,
stype: Optional[torch.dtype],
group_size: Optional[int],
zero_points: bool = False):
def quantize_and_pack(
atype: torch.dtype,
w: torch.Tensor,
wtype: ScalarType,
stype: Optional[torch.dtype],
group_size: Optional[int],
zero_points: bool = False,
):
assert wtype.is_integer(), "TODO: support floating point weights"
w_ref, w_q, w_s, w_zp = quantize_weights(
@ -96,21 +104,24 @@ def quantize_and_pack(atype: torch.dtype,
group_size=group_size,
zero_points=zero_points,
# to match how the kernel applies zps
ref_zero_points_after_scales=True)
ref_zero_points_after_scales=True,
)
w_q = pack_rows(w_q, wtype.size_bits, *w_q.shape)
return w_ref, w_q, w_s, w_zp
def create_bench_tensors(shape: tuple[int, int, int], types: TypeConfig,
group_size: Optional[int]) -> list[BenchmarkTensors]:
def create_bench_tensors(
shape: tuple[int, int, int], types: TypeConfig, group_size: Optional[int]
) -> list[BenchmarkTensors]:
m, n, k = shape
# we want to make sure that weights don't fit into L2 cache between runs so
# we construct enough weights to exceed L2 cache, which is 50mb on a H100
# so we target total weight size > 2*50mb
num_weights = math.ceil(2 * 50 * 1024**2 * 8 /
(k * n * types.weight_type.size_bits))
num_weights = math.ceil(
2 * 50 * 1024**2 * 8 / (k * n * types.weight_type.size_bits)
)
a = rand_data((m, k), types.act_type, scale=5)
@ -124,8 +135,13 @@ def create_bench_tensors(shape: tuple[int, int, int], types: TypeConfig,
w = w.to(torch.float16)
w_ref, w_q_packed, w_s, w_zp = quantize_and_pack(
a.dtype, w, types.weight_type, types.group_scale_type, group_size,
types.group_zero_type is not None)
a.dtype,
w,
types.weight_type,
types.group_scale_type,
group_size,
types.group_zero_type is not None,
)
if not a.dtype.is_floating_point:
aiinfo = torch.iinfo(a.dtype)
@ -133,21 +149,30 @@ def create_bench_tensors(shape: tuple[int, int, int], types: TypeConfig,
w_ref = w_ref.to(torch.float32)
w_ch_s = None if types.channel_scale_type is None else\
rand_data((n,), types.channel_scale_type)
w_tok_s = None if types.token_scale_type is None else\
rand_data((m,), types.token_scale_type)
w_ch_s = (
None
if types.channel_scale_type is None
else rand_data((n,), types.channel_scale_type)
)
w_tok_s = (
None
if types.token_scale_type is None
else rand_data((m,), types.token_scale_type)
)
benchmark_tensors.append(
BenchmarkTensors(w_ref=w_ref,
a=a,
w_q=w_q_packed,
wtype=types.weight_type,
w_g_s=w_s,
w_g_zp=w_zp,
group_size=group_size,
w_ch_s=w_ch_s,
w_tok_s=w_tok_s))
BenchmarkTensors(
w_ref=w_ref,
a=a,
w_q=w_q_packed,
wtype=types.weight_type,
w_g_s=w_s,
w_g_zp=w_zp,
group_size=group_size,
w_ch_s=w_ch_s,
w_tok_s=w_tok_s,
)
)
return benchmark_tensors
@ -170,50 +195,57 @@ def cutlass_scaled_mm_create_bench_fn(bt: BenchmarkTensors) -> Callable:
scale_b = torch.tensor(1.0, dtype=torch.float32, device=bt.a.device)
w_col_major = bt.w_ref.to(bt.a.dtype).t().contiguous().t()
return lambda: ops.cutlass_scaled_mm(
bt.a, w_col_major, scale_a, scale_b, out_dtype=torch.float16)
bt.a, w_col_major, scale_a, scale_b, out_dtype=torch.float16
)
def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
device = bt.a.device
workspace = MarlinWorkspace(bt.w_ref.shape[1], GPTQ_MARLIN_MIN_THREAD_N,
GPTQ_MARLIN_MAX_PARALLEL)
workspace = MarlinWorkspace(
bt.w_ref.shape[1], GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
)
if bt.w_g_zp is None:
w_zp = torch.empty(0, dtype=torch.int, device=device)
else:
w_zp = marlin_zero_points(bt.w_g_zp, bt.w_ref.shape[0],
bt.w_ref.shape[1], bt.wtype.size_bits)
w_zp = marlin_zero_points(
bt.w_g_zp, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.wtype.size_bits
)
if bt.group_size is None:
w_s = torch.tensor([], device="cuda", dtype=torch.half)
else:
w_s = marlin_permute_scales(bt.w_g_s, bt.w_ref.shape[0],
bt.w_ref.shape[1], bt.group_size)
w_s = marlin_permute_scales(
bt.w_g_s, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.group_size
)
sort_indices = torch.empty(0, dtype=torch.int, device=device)
g_idx = torch.empty(0, dtype=torch.int, device=device)
w_q = ops.gptq_marlin_repack(bt.w_q, sort_indices, bt.w_ref.shape[0],
bt.w_ref.shape[1], bt.wtype.size_bits)
w_q = ops.gptq_marlin_repack(
bt.w_q, sort_indices, bt.w_ref.shape[0], bt.w_ref.shape[1], bt.wtype.size_bits
)
if bt.a.dtype.is_floating_point:
assert bt.w_ch_s is None
assert bt.w_tok_s is None
assert bt.group_size is not None
fn = lambda: ops.gptq_marlin_gemm(a=bt.a,
b_q_weight=w_q,
b_scales=w_s,
b_zeros=w_zp,
g_idx=g_idx,
perm=sort_indices,
workspace=workspace.scratch,
b_q_type=bt.wtype,
size_m=bt.a.shape[0],
size_n=bt.w_ref.shape[1],
size_k=bt.w_ref.shape[0],
is_k_full=True,
is_zp_float=False)
fn = lambda: ops.gptq_marlin_gemm(
a=bt.a,
b_q_weight=w_q,
b_scales=w_s,
b_zeros=w_zp,
g_idx=g_idx,
perm=sort_indices,
workspace=workspace.scratch,
b_q_type=bt.wtype,
size_m=bt.a.shape[0],
size_n=bt.w_ref.shape[1],
size_k=bt.w_ref.shape[0],
is_k_full=True,
is_zp_float=False,
)
else:
assert bt.a.dtype == torch.int8
assert bt.wtype == scalar_types.uint4b8
@ -221,36 +253,35 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
if bt.w_ch_s is not None:
s_ch = bt.w_ch_s.to(torch.float32)
else:
s_ch = torch.ones(bt.w_ref.shape[1],
dtype=torch.float32,
device=device)
s_ch = torch.ones(bt.w_ref.shape[1], dtype=torch.float32, device=device)
if bt.w_tok_s is not None:
s_tok = bt.w_tok_s.to(torch.float32)
else:
s_tok = torch.ones(bt.a.shape[0],
dtype=torch.float32,
device=device)
s_tok = torch.ones(bt.a.shape[0], dtype=torch.float32, device=device)
fn = lambda: ops.marlin_qqq_gemm(a=bt.a,
b_q_weight=w_q,
s_group=w_s,
s_tok=s_tok,
s_ch=s_ch,
workspace=workspace.scratch,
size_m=bt.a.shape[0],
size_n=bt.w_ref.shape[1],
size_k=bt.w_ref.shape[0])
fn = lambda: ops.marlin_qqq_gemm(
a=bt.a,
b_q_weight=w_q,
s_group=w_s,
s_tok=s_tok,
s_ch=s_ch,
workspace=workspace.scratch,
size_m=bt.a.shape[0],
size_n=bt.w_ref.shape[1],
size_k=bt.w_ref.shape[0],
)
return fn
def machete_create_bench_fn(bt: BenchmarkTensors,
out_type=torch.dtype,
schedule=None) -> Callable:
def machete_create_bench_fn(
bt: BenchmarkTensors, out_type=torch.dtype, schedule=None
) -> Callable:
w_q = bt.w_q.t().contiguous().t() # make col major
w_q = ops.machete_prepack_B(w_q, bt.a.dtype, bt.wtype,
None if bt.w_g_s is None else bt.w_g_s.dtype)
w_q = ops.machete_prepack_B(
w_q, bt.a.dtype, bt.wtype, None if bt.w_g_s is None else bt.w_g_s.dtype
)
w_g_zp = bt.w_g_zp
if w_g_zp is not None:
@ -275,26 +306,24 @@ def machete_create_bench_fn(bt: BenchmarkTensors,
# bench
def bench_fns(label: str, sub_label: str, description: str,
fns: list[Callable]):
def bench_fns(label: str, sub_label: str, description: str, fns: list[Callable]):
min_run_time = 1 if not NVTX_PROFILE else 0.1
res = TBenchmark.Timer(
stmt="""
for fn in fns:
fn()
""",
globals={
"fns": fns
},
globals={"fns": fns},
label=label,
sub_label=sub_label,
description=description,
).blocked_autorange(min_run_time=min_run_time)
if NVTX_PROFILE:
with nvtx.annotate("mm-bench"), nvtx.annotate(
f"{label}|{sub_label}|{description}"):
with (
nvtx.annotate("mm-bench"),
nvtx.annotate(f"{label}|{sub_label}|{description}"),
):
fns[0]()
return res
@ -304,19 +333,20 @@ _SWEEP_SCHEDULES_RESULTS: Optional[pd.DataFrame] = None
_SWEEP_SCHEDULES_RESULTS_CSV: Optional[str] = None
def bench(types: TypeConfig,
group_size: int,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
sweep_schedules: bool = True) -> list[TMeasurement]:
def bench(
types: TypeConfig,
group_size: int,
m: int,
k: int,
n: int,
label: str,
sub_label: str,
sweep_schedules: bool = True,
) -> list[TMeasurement]:
benchmark_tensors = create_bench_tensors((m, n, k), types, group_size)
sub_label += f", L={len(benchmark_tensors)}"
name_type_string = f"W{types.weight_type}"+\
f"-A{terse_type_name(types.act_type)}"
name_type_string = f"W{types.weight_type}" + f"-A{terse_type_name(types.act_type)}"
if types.group_scale_type is not None:
name_type_string += f"-GS{terse_type_name(types.group_scale_type)}"
if types.group_zero_type is not None:
@ -332,31 +362,45 @@ def bench(types: TypeConfig,
# pytorch impl
timers.append(
bench_fns(
label, sub_label, "torch.matmul (fp16)",
[torch_matmul_f16_create_bench_fn(bt)
for bt in benchmark_tensors]))
label,
sub_label,
"torch.matmul (fp16)",
[torch_matmul_f16_create_bench_fn(bt) for bt in benchmark_tensors],
)
)
if types.act_type == torch.int8 or types.act_type == torch.float8_e4m3fn:
timers.append(
bench_fns(
label, sub_label,
f"cutlass_scaled_mm ({terse_type_name(types.act_type)})", [
cutlass_scaled_mm_create_bench_fn(bt)
for bt in benchmark_tensors
]))
label,
sub_label,
f"cutlass_scaled_mm ({terse_type_name(types.act_type)})",
[cutlass_scaled_mm_create_bench_fn(bt) for bt in benchmark_tensors],
)
)
if types.act_type != torch.float8_e4m3fn:
timers.append(
bench_fns(label, sub_label, f"marlin ({name_type_string})",
[marlin_create_bench_fn(bt)
for bt in benchmark_tensors]))
bench_fns(
label,
sub_label,
f"marlin ({name_type_string})",
[marlin_create_bench_fn(bt) for bt in benchmark_tensors],
)
)
# machete
timers.append(
bench_fns(label, sub_label, f"machete ({name_type_string})", [
machete_create_bench_fn(bt, out_type=types.output_type)
for bt in benchmark_tensors
]))
bench_fns(
label,
sub_label,
f"machete ({name_type_string})",
[
machete_create_bench_fn(bt, out_type=types.output_type)
for bt in benchmark_tensors
],
)
)
if sweep_schedules:
global _SWEEP_SCHEDULES_RESULTS
@ -371,7 +415,8 @@ def bench(types: TypeConfig,
group_zeros_type=types.group_zero_type,
token_scales_type=types.token_scale_type,
channel_scales_type=types.channel_scale_type,
out_type=types.output_type)
out_type=types.output_type,
)
if schedules is None or len(schedules) == 0:
raise ValueError("No schedules found to sweep")
@ -383,11 +428,17 @@ def bench(types: TypeConfig,
if schedule_M >= 2 * max(m, 16) or schedule_M < m // 4:
continue
res = bench_fns(label, sub_label, "machete_best", [
machete_create_bench_fn(
bt, out_type=types.output_type, schedule=schedule)
for bt in benchmark_tensors
])
res = bench_fns(
label,
sub_label,
"machete_best",
[
machete_create_bench_fn(
bt, out_type=types.output_type, schedule=schedule
)
for bt in benchmark_tensors
],
)
results_row = {
"M": m,
@ -398,10 +449,8 @@ def bench(types: TypeConfig,
"median": res.median,
}
if _SWEEP_SCHEDULES_RESULTS is None:
_SWEEP_SCHEDULES_RESULTS = pd.DataFrame(
columns=results_row.keys())
_SWEEP_SCHEDULES_RESULTS.\
loc[len(_SWEEP_SCHEDULES_RESULTS)] = results_row
_SWEEP_SCHEDULES_RESULTS = pd.DataFrame(columns=results_row.keys())
_SWEEP_SCHEDULES_RESULTS.loc[len(_SWEEP_SCHEDULES_RESULTS)] = results_row
print(f" {res.median:5.5} ", schedule)
if not best or res.median < best.median:
@ -422,8 +471,9 @@ def print_timers(timers: list[TMeasurement]):
def run(args, MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
types = TypeConfig(
act_type=args.act_type,
weight_type=scalar_types.uint4b8 if args.group_zero_type is None \
else scalar_types.uint4,
weight_type=scalar_types.uint4b8
if args.group_zero_type is None
else scalar_types.uint4,
output_type=args.out_type,
group_scale_type=args.group_scale_type,
group_zero_type=args.group_zero_type,
@ -433,14 +483,16 @@ def run(args, MKNs: Iterable[tuple[int, int, int]]) -> Iterable[TMeasurement]:
results: list[TMeasurement] = []
for m, k, n in MKNs:
timers = bench(types,
args.group_size,
m,
k,
n,
f"{args.act_type}-gemm",
f"MKN=({m}x{k}x{n})",
sweep_schedules=args.sweep_schedules)
timers = bench(
types,
args.group_size,
m,
k,
n,
f"{args.act_type}-gemm",
f"MKN=({m}x{k}x{n})",
sweep_schedules=args.sweep_schedules,
)
print_timers(timers)
results.extend(timers)
@ -454,7 +506,6 @@ def make_output(
base_description: str,
timestamp=None,
):
print(f"== All Results {base_description} ====")
print_timers(data)
@ -468,8 +519,7 @@ def make_output(
def run_square_bench(args):
dim_sizes = list(
range(args.dim_start, args.dim_end + 1, args.dim_increment))
dim_sizes = list(range(args.dim_start, args.dim_end + 1, args.dim_increment))
MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
data = run(args.dtype, args.sweep_schedules, MKNs)
@ -479,8 +529,9 @@ def run_square_bench(args):
def run_range_bench(args):
m_start, k_start, n_start = (int(x) for x in args.dim_start.split(","))
m_end, k_end, n_end = (int(x) for x in args.dim_end.split(","))
m_increment, k_increment, n_increment = \
(int(x) for x in args.dim_increment.split(","))
m_increment, k_increment, n_increment = (
int(x) for x in args.dim_increment.split(",")
)
Ms = list(range(m_start, m_end + 1, m_increment))
Ks = list(range(k_start, k_end + 1, k_increment))
Ns = list(range(n_start, n_end + 1, n_increment))
@ -492,7 +543,6 @@ def run_range_bench(args):
def run_model_bench(args):
print("Benchmarking models:")
for i, model in enumerate(args.models):
print(f"[{i}] {model}")
@ -535,10 +585,13 @@ def run_model_bench(args):
with open(f"model_bench-{type_string}-{timestr}.pkl", "wb") as f:
args_dict = vars(args)
args_dict.pop("func")
pkl.dump({
"args": args_dict,
"results": all_results,
}, f)
pkl.dump(
{
"args": args_dict,
"results": all_results,
},
f,
)
if __name__ == "__main__":
@ -554,7 +607,6 @@ if __name__ == "__main__":
}[dt]
class ToTorchDtype(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
setattr(namespace, self.dest, to_torch_dtype(values))
@ -580,32 +632,32 @@ Benchmark Machete GEMM.
"--act-type",
action=ToTorchDtype,
required=True,
choices=['bfloat16', 'float16', 'int8', 'float8_e4m3fn'],
choices=["bfloat16", "float16", "int8", "float8_e4m3fn"],
)
parser.add_argument(
"--group-scale-type",
action=ToTorchDtype,
choices=['bfloat16', 'float16'],
choices=["bfloat16", "float16"],
)
parser.add_argument(
"--group-zero-type",
type=to_torch_dtype,
choices=['bfloat16', 'float16'],
choices=["bfloat16", "float16"],
)
parser.add_argument(
"--channel-scale-type",
action=ToTorchDtype,
choices=['float'],
choices=["float"],
)
parser.add_argument(
"--token-scale-type",
action=ToTorchDtype,
choices=['float'],
choices=["float"],
)
parser.add_argument(
"--out-type",
action=ToTorchDtype,
choices=['bfloat16', 'float16'],
choices=["bfloat16", "float16"],
)
parser.add_argument(
"--group-size",
@ -618,9 +670,11 @@ Benchmark Machete GEMM.
action="store_true",
help="Run a sweep over all supported schedules",
)
parser.add_argument("--sweep-csv-out",
help="CSV to store sweep results",
default="sch_sweep_results.csv")
parser.add_argument(
"--sweep-csv-out",
help="CSV to store sweep results",
default="sch_sweep_results.csv",
)
subparsers = parser.add_subparsers(dest="cmd", required=True)
square_parser = subparsers.add_parser("square_bench")
@ -634,17 +688,20 @@ Benchmark Machete GEMM.
"--dim-start",
type=str,
required=True,
help="Start value for M,K,N as common separated list")
help="Start value for M,K,N as common separated list",
)
range_parser.add_argument(
"--dim-end",
type=str,
required=True,
help="End value (inclusive) for M,K,N as common separated list")
help="End value (inclusive) for M,K,N as common separated list",
)
range_parser.add_argument(
"--dim-increment",
type=str,
required=True,
help="Increment value for M,K,N as common separated list")
help="Increment value for M,K,N as common separated list",
)
range_parser.set_defaults(func=run_range_bench)
model_parser = subparsers.add_parser("model_bench")
@ -655,14 +712,12 @@ Benchmark Machete GEMM.
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys(),
)
model_parser.add_argument("--tp-sizes",
nargs="+",
type=int,
default=DEFAULT_TP_SIZES)
model_parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
model_parser.add_argument(
"--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES
)
model_parser.add_argument(
"--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
)
model_parser.set_defaults(func=run_model_bench)
args = parser.parse_args()

190
benchmarks/kernels/benchmark_marlin.py
View File

@ -6,19 +6,34 @@ from benchmark_shapes import WEIGHT_SHAPES
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N,
GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES)
GPTQ_MARLIN_24_MAX_PARALLEL,
GPTQ_MARLIN_24_MIN_THREAD_N,
GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES,
GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES,
)
from vllm.model_executor.layers.quantization.utils.allspark_utils import (
ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD, ALLSPARK_SUPPORTED_QUANT_TYPES)
ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD,
ALLSPARK_SUPPORTED_QUANT_TYPES,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
MARLIN_SUPPORTED_GROUP_SIZES, query_marlin_supported_quant_types)
GPTQ_MARLIN_MAX_PARALLEL,
GPTQ_MARLIN_MIN_THREAD_N,
MARLIN_SUPPORTED_GROUP_SIZES,
query_marlin_supported_quant_types,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
MarlinWorkspace, marlin_quantize)
MarlinWorkspace,
marlin_quantize,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
marlin_24_quantize)
marlin_24_quantize,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
gptq_pack, gptq_quantize_weights, quantize_weights, sort_weights)
gptq_pack,
gptq_quantize_weights,
quantize_weights,
sort_weights,
)
from vllm.scalar_type import ScalarType
from vllm.utils import FlexibleArgumentParser
@ -29,22 +44,29 @@ ACT_ORDER_OPTS = [False, True]
K_FULL_OPTS = [False, True]
def bench_run(results: list[benchmark.Measurement], model: str,
act_order: bool, is_k_full: bool, quant_type: ScalarType,
group_size: int, size_m: int, size_k: int, size_n: int):
def bench_run(
results: list[benchmark.Measurement],
model: str,
act_order: bool,
is_k_full: bool,
quant_type: ScalarType,
group_size: int,
size_m: int,
size_k: int,
size_n: int,
):
label = "Quant Matmul"
sub_label = ("{}, act={} k_full={}, q={}, g={}, "
"MKN=({}x{}x{})".format(model, act_order, is_k_full,
str(quant_type), group_size, size_m,
size_k, size_n))
sub_label = "{}, act={} k_full={}, q={}, g={}, MKN=({}x{}x{})".format(
model, act_order, is_k_full, str(quant_type), group_size, size_m, size_k, size_n
)
print(f"Testing: {sub_label}")
a = torch.randn(size_m, size_k).to(torch.half).cuda()
b = torch.rand(size_k, size_n).to(torch.half).cuda()
a_tmp = (torch.zeros(size_m, size_k).to(torch.half).cuda())
a_tmp = torch.zeros(size_m, size_k).to(torch.half).cuda()
# Marlin quant
(
@ -57,14 +79,16 @@ def bench_run(results: list[benchmark.Measurement], model: str,
) = marlin_quantize(b, quant_type, group_size, act_order)
# Marlin_24 quant
(marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta,
marlin_24_s) = marlin_24_quantize(b, quant_type, group_size)
(marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s) = (
marlin_24_quantize(b, quant_type, group_size)
)
marlin_zp = torch.empty(0, dtype=torch.int, device=b.device)
# GPTQ quant
(w_ref, q_w, s, g_idx,
rand_perm) = gptq_quantize_weights(b, quant_type, group_size, act_order)
(w_ref, q_w, s, g_idx, rand_perm) = gptq_quantize_weights(
b, quant_type, group_size, act_order
)
q_w_gptq = gptq_pack(q_w, quant_type.size_bits, size_k, size_n)
# For act_order, sort the "weights" and "g_idx"
@ -74,32 +98,37 @@ def bench_run(results: list[benchmark.Measurement], model: str,
(q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)
# Prepare
marlin_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_MIN_THREAD_N,
GPTQ_MARLIN_MAX_PARALLEL)
marlin_workspace = MarlinWorkspace(
size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
)
marlin_24_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_24_MIN_THREAD_N,
GPTQ_MARLIN_24_MAX_PARALLEL)
marlin_24_workspace = MarlinWorkspace(
size_n, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_MAX_PARALLEL
)
marlin_zp = torch.zeros_like(marlin_s, dtype=torch.int)
# AllSpark W8A16 quant
as_supported_case = (quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
and group_size == -1 and not act_order and is_k_full)
as_supported_case = (
quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
and group_size == -1
and not act_order
and is_k_full
)
if as_supported_case:
properties = torch.cuda.get_device_properties(b.device.index)
sm_count = properties.multi_processor_count
sm_version = properties.major * 10 + properties.minor
supported_arch = (sm_version >= 80 and sm_version < 90)
supported_arch = sm_version >= 80 and sm_version < 90
as_supported_case = as_supported_case and supported_arch
if supported_arch:
has_zp = False
w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size,
has_zp)
w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size, has_zp)
qw = qw.to(torch.uint8)
qw_reorder, s_reorder, zp_reorder = \
ops.allspark_repack_weight(
qw, s, zp, has_zp)
qw_reorder, s_reorder, zp_reorder = ops.allspark_repack_weight(
qw, s, zp, has_zp
)
CUBLAS_M_THRESHOLD = ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD
globals = {
@ -136,8 +165,7 @@ def bench_run(results: list[benchmark.Measurement], model: str,
"zp_reorder": zp_reorder if as_supported_case else None,
"sm_count": sm_count if as_supported_case else None,
"sm_version": sm_version if as_supported_case else None,
"CUBLAS_M_THRESHOLD":
CUBLAS_M_THRESHOLD if as_supported_case else None,
"CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD if as_supported_case else None,
# Kernels
"gptq_marlin_gemm": ops.gptq_marlin_gemm,
"gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
@ -158,60 +186,63 @@ def bench_run(results: list[benchmark.Measurement], model: str,
label=label,
sub_label=sub_label,
description="pytorch_gemm",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
results.append(
benchmark.Timer(
stmt=
"output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)", # noqa: E501
stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_gemm_fp16",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
results.append(
benchmark.Timer(
stmt=
"output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)", # noqa: E501
stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_gemm_fp32",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
if (quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES):
if (
quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
):
results.append(
benchmark.Timer(
stmt=
"output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, quant_type, size_m, size_n, size_k)", # noqa: E501
stmt="output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, quant_type, size_m, size_n, size_k)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_24_gemm",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
results.append(
benchmark.Timer(
stmt=
"q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)", # noqa: E501
stmt="q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="gptq_marlin_repack",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
if as_supported_case:
results.append(
benchmark.Timer(
stmt=
"output = allspark_w8a16_gemm(a, qw_reorder, s_reorder, zp_reorder, size_n, group_size, sm_count, sm_version, CUBLAS_M_THRESHOLD, False, True)", # noqa: E501
stmt="output = allspark_w8a16_gemm(a, qw_reorder, s_reorder, zp_reorder, size_n, group_size, sm_count, sm_version, CUBLAS_M_THRESHOLD, False, True)", # noqa: E501
globals=globals,
label=label,
sub_label=sub_label,
description="allspark_w8a16_gemm_fp32",
).blocked_autorange(min_run_time=min_run_time))
).blocked_autorange(min_run_time=min_run_time)
)
def main(args):
@ -233,37 +264,50 @@ def main(args):
continue
for act_order in ACT_ORDER_OPTS:
if len(args.limit_act_order
) > 0 and act_order not in args.limit_act_order:
if (
len(args.limit_act_order) > 0
and act_order not in args.limit_act_order
):
continue
for is_k_full in K_FULL_OPTS:
if len(args.limit_k_full
) > 0 and is_k_full not in args.limit_k_full:
if (
len(args.limit_k_full) > 0
and is_k_full not in args.limit_k_full
):
continue
for quant_type in query_marlin_supported_quant_types(
False):
if len(args.limit_num_bits) > 0 and \
quant_type.size_bits not in args.limit_num_bits:
for quant_type in query_marlin_supported_quant_types(False):
if (
len(args.limit_num_bits) > 0
and quant_type.size_bits not in args.limit_num_bits
):
continue
for group_size in MARLIN_SUPPORTED_GROUP_SIZES:
if len(
args.limit_group_size
) > 0 and group_size not in args.limit_group_size:
if (
len(args.limit_group_size) > 0
and group_size not in args.limit_group_size
):
continue
# For act_order, the group_size must be less than
# size_k
if act_order and (group_size == size_k
or group_size == -1):
if act_order and (group_size == size_k or group_size == -1):
continue
for size_m in args.batch_sizes:
bench_run(results, model, act_order, is_k_full,
quant_type, group_size, size_m,
size_k, size_n)
bench_run(
results,
model,
act_order,
is_k_full,
quant_type,
group_size,
size_m,
size_k,
size_n,
)
compare = benchmark.Compare(results)
compare.print()
@ -274,7 +318,8 @@ def main(args):
#
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark Marlin across specified models/shapes/batches")
description="Benchmark Marlin across specified models/shapes/batches"
)
parser.add_argument(
"--models",
nargs="+",
@ -282,10 +327,9 @@ if __name__ == "__main__":
default=DEFAULT_MODELS,
choices=WEIGHT_SHAPES.keys(),
)
parser.add_argument("--batch-sizes",
nargs="+",
type=int,
default=DEFAULT_BATCH_SIZES)
parser.add_argument(
"--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
)
parser.add_argument("--limit-k", nargs="+", type=int, default=[])
parser.add_argument("--limit-n", nargs="+", type=int, default=[])
parser.add_argument("--limit-group-size", nargs="+", type=int, default=[])

402
benchmarks/kernels/benchmark_moe.py
View File

@ -31,56 +31,60 @@ class BenchmarkConfig(TypedDict):
num_stages: int
def benchmark_config(config: BenchmarkConfig,
num_tokens: int,
num_experts: int,
shard_intermediate_size: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
num_iters: int = 100,
block_quant_shape: List[int] = None,
use_deep_gemm: bool = False) -> float:
def benchmark_config(
config: BenchmarkConfig,
num_tokens: int,
num_experts: int,
shard_intermediate_size: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
num_iters: int = 100,
block_quant_shape: List[int] = None,
use_deep_gemm: bool = False,
) -> float:
init_dtype = torch.float16 if use_fp8_w8a8 else dtype
x = torch.randn(num_tokens, hidden_size, dtype=dtype)
if use_int8_w8a16:
w1 = torch.randint(-127,
127, (
num_experts,
shard_intermediate_size,
hidden_size,
),
dtype=torch.int8)
w2 = torch.randint(-127,
127, (
num_experts,
hidden_size,
shard_intermediate_size // 2,
),
dtype=torch.int8)
w1 = torch.randint(
-127,
127,
(
num_experts,
shard_intermediate_size,
hidden_size,
),
dtype=torch.int8,
)
w2 = torch.randint(
-127,
127,
(
num_experts,
hidden_size,
shard_intermediate_size // 2,
),
dtype=torch.int8,
)
else:
w1 = torch.randn(num_experts,
shard_intermediate_size,
hidden_size,
dtype=init_dtype)
w2 = torch.randn(num_experts,
hidden_size,
shard_intermediate_size // 2,
dtype=init_dtype)
gating_output = torch.randn(num_iters,
num_tokens,
num_experts,
dtype=torch.float32)
w1 = torch.randn(
num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
)
w2 = torch.randn(
num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
)
gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)
w1_scale = None
w2_scale = None
a1_scale = None
a2_scale = None
if use_int8_w8a16:
w1_scale = torch.randn((num_experts, 2 * shard_intermediate_size),
dtype=torch.float32)
w1_scale = torch.randn(
(num_experts, 2 * shard_intermediate_size), dtype=torch.float32
)
w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
if use_fp8_w8a8:
if block_quant_shape:
@ -93,10 +97,14 @@ def benchmark_config(config: BenchmarkConfig,
n_tiles_w2 = (K + block_n - 1) // block_n
k_tiles_w1 = (K + block_k - 1) // block_k
k_tiles_w2 = (N + block_k - 1) // block_k
w1_scale = torch.rand((E, n_tiles_w1, k_tiles_w1),
dtype=torch.float32) * factor_for_scale
w2_scale = torch.rand((E, n_tiles_w2, k_tiles_w2),
dtype=torch.float32) * factor_for_scale
w1_scale = (
torch.rand((E, n_tiles_w1, k_tiles_w1), dtype=torch.float32)
* factor_for_scale
)
w2_scale = (
torch.rand((E, n_tiles_w2, k_tiles_w2), dtype=torch.float32)
* factor_for_scale
)
else:
w1_scale = torch.randn(num_experts, dtype=torch.float32)
w2_scale = torch.randn(num_experts, dtype=torch.float32)
@ -114,10 +122,12 @@ def benchmark_config(config: BenchmarkConfig,
def run():
from vllm.model_executor.layers.fused_moe import override_config
with override_config(config):
if use_deep_gemm:
topk_weights, topk_ids, token_expert_indices = fused_topk(
x, input_gating, topk, False)
x, input_gating, topk, False
)
return fused_experts(
x,
w1,
@ -213,8 +223,7 @@ def get_rocm_tuning_space(use_fp16):
return param_ranges
def get_configs_compute_bound(use_fp16,
block_quant_shape) -> list[dict[str, int]]:
def get_configs_compute_bound(use_fp16, block_quant_shape) -> list[dict[str, int]]:
configs: list[BenchmarkConfig] = []
if current_platform.is_rocm():
@ -250,20 +259,25 @@ def get_configs_compute_bound(use_fp16,
if block_quant_shape is not None and not use_fp16:
block_n, block_k = block_quant_shape[0], block_quant_shape[1]
for config in configs[:]:
if config["BLOCK_SIZE_K"] % block_k != 0 or config[
"BLOCK_SIZE_N"] % block_n != 0:
if (
config["BLOCK_SIZE_K"] % block_k != 0
or config["BLOCK_SIZE_N"] % block_n != 0
):
configs.remove(config)
return configs
def prune_rocm_search_space(num_tokens, shard_intermediate_size, hidden_size,
search_space, is_fp16, topk):
def prune_rocm_search_space(
num_tokens, shard_intermediate_size, hidden_size, search_space, is_fp16, topk
):
N1, K1 = shard_intermediate_size, hidden_size
N2, K2 = hidden_size, shard_intermediate_size // 2
pruned_space_1 = prune_rocm_configs(num_tokens * topk, N1, K1,
search_space, is_fp16)
pruned_space_2 = prune_rocm_configs(num_tokens * topk, N2, K2,
search_space, is_fp16)
pruned_space_1 = prune_rocm_configs(
num_tokens * topk, N1, K1, search_space, is_fp16
)
pruned_space_2 = prune_rocm_configs(
num_tokens * topk, N2, K2, search_space, is_fp16
)
search_space = merge_unique_dicts(pruned_space_1, pruned_space_2)
return search_space
@ -301,14 +315,14 @@ def prune_rocm_configs(M, N, K, configs, is_fp16=True):
SPLIT_K = config.get("SPLIT_K", 1)
GROUP_M = config.get("GROUP_SIZE_M")
if is_fp16:
if (matrix_instr_nonkdim > BLOCK_SIZE_M
or matrix_instr_nonkdim > BLOCK_SIZE_N):
if (
matrix_instr_nonkdim > BLOCK_SIZE_M
or matrix_instr_nonkdim > BLOCK_SIZE_N
):
continue
if (matrix_instr_nonkdim >= M
and matrix_instr_nonkdim != BLOCK_SIZE_M):
if matrix_instr_nonkdim >= M and matrix_instr_nonkdim != BLOCK_SIZE_M:
continue
if (matrix_instr_nonkdim >= N
and matrix_instr_nonkdim != BLOCK_SIZE_N):
if matrix_instr_nonkdim >= N and matrix_instr_nonkdim != BLOCK_SIZE_N:
continue
# Skip BLOCK_SIZE that is too large compare to M/N
# unless BLOCK_SIZE is already small enough
@ -329,8 +343,10 @@ def prune_rocm_configs(M, N, K, configs, is_fp16=True):
continue
# out of shared memory resource
# TODO (zhanglx): This does not consider the LDS usage in the epilogue
LDS = (BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a +
BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b)
LDS = (
BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a
+ BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b
)
if LDS > 65536:
continue
# Skip small block sizes and num_warps for large gemm
@ -364,7 +380,6 @@ def merge_unique_dicts(list1, list2):
@ray.remote(num_gpus=1)
class BenchmarkWorker:
def __init__(self, seed: int) -> None:
torch.set_default_device("cuda")
current_platform.seed_everything(seed)
@ -388,36 +403,40 @@ class BenchmarkWorker:
use_deep_gemm: bool = False,
) -> tuple[dict[str, int], float]:
current_platform.seed_everything(self.seed)
dtype_str = get_config_dtype_str(dtype,
use_int8_w8a16=use_int8_w8a16,
use_fp8_w8a8=use_fp8_w8a8)
dtype_str = get_config_dtype_str(
dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
)
# NOTE(woosuk): The current naming convention uses w2.shape[2], which
# is the intermediate size after silu_and_mul.
op_config = get_moe_configs(num_experts, shard_intermediate_size // 2,
dtype_str)
op_config = get_moe_configs(
num_experts, shard_intermediate_size // 2, dtype_str
)
if op_config is None:
config = get_default_config(num_tokens,
num_experts,
shard_intermediate_size,
hidden_size,
topk,
dtype_str,
is_marlin=False)
config = get_default_config(
num_tokens,
num_experts,
shard_intermediate_size,
hidden_size,
topk,
dtype_str,
is_marlin=False,
)
else:
config = op_config[min(op_config.keys(),
key=lambda x: abs(x - num_tokens))]
kernel_time = benchmark_config(config,
num_tokens,
num_experts,
shard_intermediate_size,
hidden_size,
topk,
dtype,
use_fp8_w8a8,
use_int8_w8a16,
num_iters=100,
block_quant_shape=block_quant_shape,
use_deep_gemm=use_deep_gemm)
config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
kernel_time = benchmark_config(
config,
num_tokens,
num_experts,
shard_intermediate_size,
hidden_size,
topk,
dtype,
use_fp8_w8a8,
use_int8_w8a16,
num_iters=100,
block_quant_shape=block_quant_shape,
use_deep_gemm=use_deep_gemm,
)
return config, kernel_time
def tune(
@ -438,10 +457,14 @@ class BenchmarkWorker:
best_time = float("inf")
if current_platform.is_rocm():
is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
search_space = prune_rocm_search_space(num_tokens,
shard_intermediate_size,
hidden_size, search_space,
is_fp16, topk)
search_space = prune_rocm_search_space(
num_tokens,
shard_intermediate_size,
hidden_size,
search_space,
is_fp16,
topk,
)
need_device_guard = False
if current_platform.is_rocm():
@ -449,8 +472,7 @@ class BenchmarkWorker:
if visible_device != f"{self.device_id}":
need_device_guard = True
with torch.cuda.device(
self.device_id) if need_device_guard else nullcontext():
with torch.cuda.device(self.device_id) if need_device_guard else nullcontext():
for config in tqdm(search_space):
try:
kernel_time = benchmark_config(
@ -465,7 +487,8 @@ class BenchmarkWorker:
use_int8_w8a16,
num_iters=20,
block_quant_shape=block_quant_shape,
use_deep_gemm=use_deep_gemm)
use_deep_gemm=use_deep_gemm,
)
except triton.runtime.autotuner.OutOfResources:
# Some configurations may be invalid and fail to compile.
continue
@ -481,42 +504,44 @@ class BenchmarkWorker:
def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
return {
"BLOCK_SIZE_M":
config["BLOCK_SIZE_M"],
"BLOCK_SIZE_N":
config["BLOCK_SIZE_N"],
"BLOCK_SIZE_K":
config["BLOCK_SIZE_K"],
"GROUP_SIZE_M":
config["GROUP_SIZE_M"],
"num_warps":
config["num_warps"],
"num_stages":
config["num_stages"],
**({
"waves_per_eu": config["waves_per_eu"]
} if "waves_per_eu" in config else {}),
**({
"matrix_instr_nonkdim": config["matrix_instr_nonkdim"]
} if "matrix_instr_nonkdim" in config else {}),
**({
"kpack": config["kpack"]
} if "kpack" in config else {}),
"BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
"BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
"BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
"GROUP_SIZE_M": config["GROUP_SIZE_M"],
"num_warps": config["num_warps"],
"num_stages": config["num_stages"],
**(
{"waves_per_eu": config["waves_per_eu"]} if "waves_per_eu" in config else {}
),
**(
{"matrix_instr_nonkdim": config["matrix_instr_nonkdim"]}
if "matrix_instr_nonkdim" in config
else {}
),
**({"kpack": config["kpack"]} if "kpack" in config else {}),
}
def save_configs(configs: dict[int, BenchmarkConfig], num_experts: int,
shard_intermediate_size: int, hidden_size: int, topk: int,
dtype: torch.dtype, use_fp8_w8a8: bool, use_int8_w8a16: bool,
block_quant_shape: List[int]) -> None:
dtype_str = get_config_dtype_str(dtype,
use_int8_w8a16=use_int8_w8a16,
use_fp8_w8a8=use_fp8_w8a8)
def save_configs(
configs: dict[int, BenchmarkConfig],
num_experts: int,
shard_intermediate_size: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
block_quant_shape: List[int],
) -> None:
dtype_str = get_config_dtype_str(
dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
)
# NOTE(woosuk): The current naming convention uses w2.shape[2], which
# is the intermediate size after silu_and_mul.
filename = get_config_file_name(num_experts, shard_intermediate_size // 2,
dtype_str, block_quant_shape)
filename = get_config_file_name(
num_experts, shard_intermediate_size // 2, dtype_str, block_quant_shape
)
print(f"Writing best config to {filename}...")
with open(filename, "w") as f:
@ -525,18 +550,16 @@ def save_configs(configs: dict[int, BenchmarkConfig], num_experts: int,
def get_weight_block_size_safety(config, default_value=None):
quantization_config = getattr(config, 'quantization_config', {})
quantization_config = getattr(config, "quantization_config", {})
if isinstance(quantization_config, dict):
return quantization_config.get('weight_block_size', default_value)
return quantization_config.get("weight_block_size", default_value)
return default_value
def main(args: argparse.Namespace):
print(args)
config = get_config(model=args.model,
trust_remote_code=args.trust_remote_code)
config = get_config(model=args.model, trust_remote_code=args.trust_remote_code)
if args.model_prefix:
config = getattr(config, args.model_prefix)
config = SimpleNamespace(**config)
@ -551,14 +574,12 @@ def main(args: argparse.Namespace):
topk = config.num_experts_per_tok
intermediate_size = config.intermediate_size
shard_intermediate_size = 2 * intermediate_size // args.tp_size
elif (config.architectures[0]
in ("DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM")):
elif config.architectures[0] in ("DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM"):
E = config.n_routed_experts
topk = config.num_experts_per_tok
intermediate_size = config.moe_intermediate_size
shard_intermediate_size = 2 * intermediate_size // args.tp_size
elif config.architectures[0] in ("Qwen2MoeForCausalLM",
"Qwen3MoeForCausalLM"):
elif config.architectures[0] in ("Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"):
E = config.num_experts
topk = config.num_experts_per_tok
intermediate_size = config.moe_intermediate_size
@ -573,16 +594,35 @@ def main(args: argparse.Namespace):
shard_intermediate_size = 2 * intermediate_size // args.tp_size
hidden_size = config.hidden_size
dtype = torch.float16 if current_platform.is_rocm() else getattr(
torch, config.torch_dtype)
dtype = (
torch.float16
if current_platform.is_rocm()
else getattr(torch, config.torch_dtype)
)
use_fp8_w8a8 = args.dtype == "fp8_w8a8"
use_int8_w8a16 = args.dtype == "int8_w8a16"
block_quant_shape = get_weight_block_size_safety(config)
if args.batch_size is None:
batch_sizes = [
1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
2048, 3072, 4096
1,
2,
4,
8,
16,
24,
32,
48,
64,
96,
128,
256,
512,
1024,
1536,
2048,
3072,
4096,
]
else:
batch_sizes = [args.batch_size]
@ -593,7 +633,8 @@ def main(args: argparse.Namespace):
# Ray will set ROCR_VISIBLE_DEVICES for device visibility
logger.warning(
"Ray uses ROCR_VISIBLE_DEVICES to control device accessibility."
"Replacing HIP_VISIBLE_DEVICES with ROCR_VISIBLE_DEVICES.")
"Replacing HIP_VISIBLE_DEVICES with ROCR_VISIBLE_DEVICES."
)
val = os.environ["HIP_VISIBLE_DEVICES"]
os.environ["ROCR_VISIBLE_DEVICES"] = val
del os.environ["HIP_VISIBLE_DEVICES"]
@ -620,25 +661,59 @@ def main(args: argparse.Namespace):
start = time.time()
configs = _distribute(
"tune", [(batch_size, E, shard_intermediate_size, hidden_size,
topk, dtype, use_fp8_w8a8, use_int8_w8a16, search_space,
block_quant_shape, use_deep_gemm)
for batch_size in batch_sizes])
"tune",
[
(
batch_size,
E,
shard_intermediate_size,
hidden_size,
topk,
dtype,
use_fp8_w8a8,
use_int8_w8a16,
search_space,
block_quant_shape,
use_deep_gemm,
)
for batch_size in batch_sizes
],
)
best_configs = {
M: sort_config(config)
for M, config in zip(batch_sizes, configs)
M: sort_config(config) for M, config in zip(batch_sizes, configs)
}
save_configs(best_configs, E, shard_intermediate_size, hidden_size,
topk, dtype, use_fp8_w8a8, use_int8_w8a16,
block_quant_shape)
save_configs(
best_configs,
E,
shard_intermediate_size,
hidden_size,
topk,
dtype,
use_fp8_w8a8,
use_int8_w8a16,
block_quant_shape,
)
end = time.time()
print(f"Tuning took {end - start:.2f} seconds")
else:
outputs = _distribute(
"benchmark",
[(batch_size, E, shard_intermediate_size, hidden_size, topk, dtype,
use_fp8_w8a8, use_int8_w8a16, block_quant_shape, use_deep_gemm)
for batch_size in batch_sizes])
[
(
batch_size,
E,
shard_intermediate_size,
hidden_size,
topk,
dtype,
use_fp8_w8a8,
use_int8_w8a16,
block_quant_shape,
use_deep_gemm,
)
for batch_size in batch_sizes
],
)
for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
print(f"Batch size: {batch_size}, config: {config}")
@ -647,18 +722,15 @@ def main(args: argparse.Namespace):
if __name__ == "__main__":
parser = FlexibleArgumentParser()
parser.add_argument("--model",
type=str,
default="mistralai/Mixtral-8x7B-Instruct-v0.1")
parser.add_argument("--tp-size",
"-tp",
"--tensor-parallel-size",
type=int,
default=2)
parser.add_argument("--dtype",
type=str,
choices=["auto", "fp8_w8a8", "int8_w8a16"],
default="auto")
parser.add_argument(
"--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
)
parser.add_argument(
"--tp-size", "-tp", "--tensor-parallel-size", type=int, default=2
)
parser.add_argument(
"--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
)
parser.add_argument("--use-deep-gemm", action="store_true")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--batch-size", type=int, required=False)

253
benchmarks/kernels/benchmark_moe_permute_unpermute.py
View File

@ -8,7 +8,9 @@ import torch
from transformers import AutoConfig
from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
_moe_permute, _moe_unpermute_and_reduce)
_moe_permute,
_moe_unpermute_and_reduce,
)
from vllm.model_executor.layers.fused_moe.fused_moe import *
from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import *
from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
@ -27,15 +29,17 @@ class BenchmarkConfig(TypedDict):
num_stages: int
def benchmark_permute(num_tokens: int,
num_experts: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
num_iters: int = 100,
use_customized_permute: bool = False) -> float:
def benchmark_permute(
num_tokens: int,
num_experts: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
num_iters: int = 100,
use_customized_permute: bool = False,
) -> float:
# init_dtype = torch.float16 if use_fp8_w8a8 else dtype
hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
# output_hidden_states = torch.empty_like(hidden_states)
@ -46,36 +50,41 @@ def benchmark_permute(num_tokens: int,
align_block_size = None
qhidden_states = hidden_states
gating_output = torch.randn(num_iters,
num_tokens,
num_experts,
dtype=torch.float32)
gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)
input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
topk_weights, topk_ids, token_expert_indices = fused_topk(
qhidden_states, input_gating, topk, False)
qhidden_states, input_gating, topk, False
)
def prepare(i: int):
input_gating.copy_(gating_output[i])
def run():
if use_customized_permute:
(permuted_hidden_states, first_token_off, inv_perm_idx,
m_indices) = moe_permute(
qhidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
token_expert_indices=token_expert_indices,
topk=topk,
n_expert=num_experts,
n_local_expert=num_experts,
expert_map=None,
align_block_size=align_block_size,
)
(permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = (
moe_permute(
qhidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
token_expert_indices=token_expert_indices,
topk=topk,
n_expert=num_experts,
n_local_expert=num_experts,
expert_map=None,
align_block_size=align_block_size,
)
)
else:
(permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
num_experts, None, align_block_size)
(
permuted_hidden_states,
a1q_scale,
sorted_token_ids,
expert_ids,
inv_perm,
) = _moe_permute(
qhidden_states, None, topk_ids, num_experts, None, align_block_size
)
# JIT compilation & warmup
run()
@ -111,15 +120,17 @@ def benchmark_permute(num_tokens: int,
return avg
def benchmark_unpermute(num_tokens: int,
num_experts: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
num_iters: int = 100,
use_customized_permute: bool = False) -> float:
def benchmark_unpermute(
num_tokens: int,
num_experts: int,
hidden_size: int,
topk: int,
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
num_iters: int = 100,
use_customized_permute: bool = False,
) -> float:
# init_dtype = torch.float16 if use_fp8_w8a8 else dtype
hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
output_hidden_states = torch.empty_like(hidden_states)
@ -133,46 +144,74 @@ def benchmark_unpermute(num_tokens: int,
input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
topk_weights, topk_ids, token_expert_indices = fused_topk(
qhidden_states, input_gating, topk, False)
qhidden_states, input_gating, topk, False
)
def prepare():
if use_customized_permute:
(permuted_hidden_states, first_token_off, inv_perm_idx,
m_indices) = moe_permute(
qhidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
token_expert_indices=token_expert_indices,
topk=topk,
n_expert=num_experts,
n_local_expert=num_experts,
expert_map=None,
align_block_size=align_block_size,
)
(permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = (
moe_permute(
qhidden_states,
topk_weights=topk_weights,
topk_ids=topk_ids,
token_expert_indices=token_expert_indices,
topk=topk,
n_expert=num_experts,
n_local_expert=num_experts,
expert_map=None,
align_block_size=align_block_size,
)
)
# convert to fp16/bf16 as gemm output
return (permuted_hidden_states.to(dtype), first_token_off,
inv_perm_idx, m_indices)
return (
permuted_hidden_states.to(dtype),
first_token_off,
inv_perm_idx,
m_indices,
)
else:
(permuted_qhidden_states, a1q_scale, sorted_token_ids, expert_ids,
inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
num_experts, None, align_block_size)
(
permuted_qhidden_states,
a1q_scale,
sorted_token_ids,
expert_ids,
inv_perm,
) = _moe_permute(
qhidden_states, None, topk_ids, num_experts, None, align_block_size
)
# convert to fp16/bf16 as gemm output
return (permuted_qhidden_states.to(dtype), a1q_scale,
sorted_token_ids, expert_ids, inv_perm)
return (
permuted_qhidden_states.to(dtype),
a1q_scale,
sorted_token_ids,
expert_ids,
inv_perm,
)
def run(input: tuple):
if use_customized_permute:
(permuted_hidden_states, first_token_off, inv_perm_idx,
m_indices) = input
moe_unpermute(permuted_hidden_states, topk_weights, topk_ids,
inv_perm_idx, first_token_off, topk, num_experts,
num_experts)
(permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = input
moe_unpermute(
permuted_hidden_states,
topk_weights,
topk_ids,
inv_perm_idx,
first_token_off,
topk,
num_experts,
num_experts,
)
else:
(permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
inv_perm) = input
_moe_unpermute_and_reduce(output_hidden_states,
permuted_hidden_states, inv_perm,
topk_weights)
(
permuted_hidden_states,
a1q_scale,
sorted_token_ids,
expert_ids,
inv_perm,
) = input
_moe_unpermute_and_reduce(
output_hidden_states, permuted_hidden_states, inv_perm, topk_weights
)
# JIT compilation & warmup
input = prepare()
@ -209,7 +248,6 @@ def benchmark_unpermute(num_tokens: int,
@ray.remote(num_gpus=1)
class BenchmarkWorker:
def __init__(self, seed: int) -> None:
torch.set_default_device("cuda")
current_platform.seed_everything(seed)
@ -241,7 +279,8 @@ class BenchmarkWorker:
use_fp8_w8a8,
use_int8_w8a16,
num_iters=100,
use_customized_permute=use_customized_permute)
use_customized_permute=use_customized_permute,
)
unpermute_time = benchmark_unpermute(
num_tokens,
num_experts,
@ -251,15 +290,15 @@ class BenchmarkWorker:
use_fp8_w8a8,
use_int8_w8a16,
num_iters=100,
use_customized_permute=use_customized_permute)
use_customized_permute=use_customized_permute,
)
return permute_time, unpermute_time
def get_weight_block_size_safety(config, default_value=None):
quantization_config = getattr(config, 'quantization_config', {})
quantization_config = getattr(config, "quantization_config", {})
if isinstance(quantization_config, dict):
return quantization_config.get('weight_block_size', default_value)
return quantization_config.get("weight_block_size", default_value)
return default_value
@ -267,20 +306,21 @@ def main(args: argparse.Namespace):
print(args)
config = AutoConfig.from_pretrained(
args.model, trust_remote_code=args.trust_remote_code)
args.model, trust_remote_code=args.trust_remote_code
)
if config.architectures[0] == "DbrxForCausalLM":
E = config.ffn_config.moe_num_experts
topk = config.ffn_config.moe_top_k
elif config.architectures[0] == "JambaForCausalLM":
E = config.num_experts
topk = config.num_experts_per_tok
elif (config.architectures[0] == "DeepseekV3ForCausalLM"
or config.architectures[0] == "DeepseekV2ForCausalLM"):
elif (
config.architectures[0] == "DeepseekV3ForCausalLM"
or config.architectures[0] == "DeepseekV2ForCausalLM"
):
E = config.n_routed_experts
topk = config.num_experts_per_tok
elif config.architectures[0] in [
"Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"
]:
elif config.architectures[0] in ["Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"]:
E = config.num_experts
topk = config.num_experts_per_tok
@ -299,8 +339,24 @@ def main(args: argparse.Namespace):
if args.batch_size is None:
batch_sizes = [
1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
2048, 3072, 4096
1,
2,
4,
8,
16,
24,
32,
48,
64,
96,
128,
256,
512,
1024,
1536,
2048,
3072,
4096,
]
else:
batch_sizes = [args.batch_size]
@ -321,9 +377,21 @@ def main(args: argparse.Namespace):
return ray.get(outputs)
outputs = _distribute(
"benchmark", [(batch_size, E, hidden_size, topk, dtype, use_fp8_w8a8,
use_int8_w8a16, use_customized_permute)
for batch_size in batch_sizes])
"benchmark",
[
(
batch_size,
E,
hidden_size,
topk,
dtype,
use_fp8_w8a8,
use_int8_w8a16,
use_customized_permute,
)
for batch_size in batch_sizes
],
)
for batch_size, (permute, unpermute) in zip(batch_sizes, outputs):
print(f"Batch size: {batch_size}")
@ -333,13 +401,12 @@ def main(args: argparse.Namespace):
if __name__ == "__main__":
parser = FlexibleArgumentParser()
parser.add_argument("--model",
type=str,
default="mistralai/Mixtral-8x7B-Instruct-v0.1")
parser.add_argument("--dtype",
type=str,
choices=["auto", "fp8_w8a8", "int8_w8a16"],
default="auto")
parser.add_argument(
"--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
)
parser.add_argument(
"--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
)
parser.add_argument("--use-customized-permute", action="store_true")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--batch-size", type=int, required=False)

97
benchmarks/kernels/benchmark_paged_attention.py
View File

@ -9,8 +9,11 @@ import torch
from vllm import _custom_ops as ops
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
create_kv_caches_with_random)
from vllm.utils import (
STR_DTYPE_TO_TORCH_DTYPE,
FlexibleArgumentParser,
create_kv_caches_with_random,
)
logger = init_logger(__name__)
@ -38,19 +41,15 @@ def main(
current_platform.seed_everything(seed)
scale = float(1.0 / (head_size**0.5))
query = torch.empty(num_seqs,
num_query_heads,
head_size,
dtype=dtype,
device=device)
query = torch.empty(
num_seqs, num_query_heads, head_size, dtype=dtype, device=device
)
query.uniform_(-scale, scale)
assert num_query_heads % num_kv_heads == 0
alibi_slopes = None
if use_alibi:
alibi_slopes = torch.randn(num_query_heads,
dtype=torch.float,
device=device)
alibi_slopes = torch.randn(num_query_heads, dtype=torch.float, device=device)
seq_lens = [seq_len for _ in range(num_seqs)]
max_seq_len = max(seq_lens)
@ -61,24 +60,23 @@ def main(
block_tables_lst: list[list[int]] = []
for _ in range(num_seqs):
block_table = [
random.randint(0, NUM_BLOCKS - 1)
for _ in range(max_num_blocks_per_seq)
random.randint(0, NUM_BLOCKS - 1) for _ in range(max_num_blocks_per_seq)
]
block_tables_lst.append(block_table)
block_tables = torch.tensor(block_tables_lst,
dtype=torch.int,
device=device)
block_tables = torch.tensor(block_tables_lst, dtype=torch.int, device=device)
# Create the KV cache.
key_caches, value_caches = create_kv_caches_with_random(NUM_BLOCKS,
block_size,
1,
num_kv_heads,
head_size,
kv_cache_dtype,
dtype,
device=device)
key_caches, value_caches = create_kv_caches_with_random(
NUM_BLOCKS,
block_size,
1,
num_kv_heads,
head_size,
kv_cache_dtype,
dtype,
device=device,
)
key_cache, value_cache = key_caches[0], value_caches[0]
# Prepare for the paged attention kernel.
@ -86,11 +84,8 @@ def main(
if version == "v2":
if current_platform.is_rocm():
global PARTITION_SIZE
if not args.custom_paged_attn:
PARTITION_SIZE = 1024
else:
PARTITION_SIZE = PARTITION_SIZE_ROCM
num_partitions = ((max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE)
PARTITION_SIZE = 1024 if not args.custom_paged_attn else PARTITION_SIZE_ROCM
num_partitions = (max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE
tmp_output = torch.empty(
size=(num_seqs, num_query_heads, num_partitions, head_size),
dtype=output.dtype,
@ -110,9 +105,7 @@ def main(
start_time = time.perf_counter()
# Using default kv_scale
k_scale = v_scale = torch.tensor(1.0,
dtype=torch.float32,
device=device)
k_scale = v_scale = torch.tensor(1.0, dtype=torch.float32, device=device)
for _ in range(num_iters):
if version == "v1":
@ -195,30 +188,29 @@ def main(
print(f"Kernel running time: {latency * 1000000:.3f} us")
if __name__ == '__main__':
logger.warning("This script benchmarks the paged attention kernel. "
"By default this is no longer used in vLLM inference.")
if __name__ == "__main__":
logger.warning(
"This script benchmarks the paged attention kernel. "
"By default this is no longer used in vLLM inference."
)
parser = FlexibleArgumentParser(
description="Benchmark the paged attention kernel.")
parser.add_argument("--version",
type=str,
choices=["v1", "v2"],
default="v2")
parser = FlexibleArgumentParser(description="Benchmark the paged attention kernel.")
parser.add_argument("--version", type=str, choices=["v1", "v2"], default="v2")
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--seq-len", type=int, default=4096)
parser.add_argument("--num-query-heads", type=int, default=64)
parser.add_argument("--num-kv-heads", type=int, default=8)
parser.add_argument("--head-size",
type=int,
choices=[64, 80, 96, 112, 120, 128, 192, 256],
default=128)
parser.add_argument(
"--head-size",
type=int,
choices=[64, 80, 96, 112, 120, 128, 192, 256],
default=128,
)
parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
parser.add_argument("--use-alibi", action="store_true")
parser.add_argument("--dtype",
type=str,
choices=["half", "bfloat16", "float"],
default="half")
parser.add_argument(
"--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--profile", action="store_true")
parser.add_argument(
@ -228,10 +220,11 @@ if __name__ == '__main__':
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("--custom-paged-attn",
action="store_true",
help="Use custom paged attention")
"ROCm (AMD GPU) supports fp8 (=fp8_e4m3)",
)
parser.add_argument(
"--custom-paged-attn", action="store_true", help="Use custom paged attention"
)
args = parser.parse_args()
print(args)

71
benchmarks/kernels/benchmark_quant.py
View File

@ -10,15 +10,17 @@ from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
@torch.inference_mode()
def main(num_tokens: int,
hidden_size: int,
static_scale: bool,
quant_dtype: torch.dtype,
dtype: torch.dtype,
seed: int = 0,
do_profile: bool = False,
num_warmup_iters: int = 5,
num_iters: int = 100) -> None:
def main(
num_tokens: int,
hidden_size: int,
static_scale: bool,
quant_dtype: torch.dtype,
dtype: torch.dtype,
seed: int = 0,
do_profile: bool = False,
num_warmup_iters: int = 5,
num_iters: int = 100,
) -> None:
current_platform.seed_everything(seed)
torch.set_default_device("cuda")
@ -56,7 +58,7 @@ def main(num_tokens: int,
print(f"Kernel running time: {latency * 1000000:.3f} us")
if __name__ == '__main__':
if __name__ == "__main__":
def to_torch_dtype(dt):
if dt == "int8":
@ -66,37 +68,40 @@ if __name__ == '__main__':
raise ValueError(f"Unsupported dtype: {dt}")
parser = FlexibleArgumentParser(
description="Benchmark the quantization (fp8 or int8) kernel.")
description="Benchmark the quantization (fp8 or int8) kernel."
)
parser.add_argument("--num-tokens", type=int, default=4096)
parser.add_argument("--hidden-size", type=int, default=8192)
parser.add_argument("--static-scale", action="store_true")
parser.add_argument("--quant-dtype",
type=str,
choices=["fp8", "int8"],
default="int8")
parser.add_argument("--dtype",
type=str,
choices=["half", "bfloat16", "float"],
default="half")
parser.add_argument(
"--quant-dtype", type=str, choices=["fp8", "int8"], default="int8"
)
parser.add_argument(
"--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--profile", action="store_true")
parser.add_argument("--num-warmup-iters", type=int, default=5)
parser.add_argument("--num-iters",
type=int,
default=100,
help="Number of benchmark iterations. "
"If --profile is set, this number is ignored")
parser.add_argument(
"--num-iters",
type=int,
default=100,
help="Number of benchmark iterations. "
"If --profile is set, this number is ignored",
)
args = parser.parse_args()
print(args)
main(num_tokens=args.num_tokens,
hidden_size=args.hidden_size,
static_scale=args.static_scale,
quant_dtype=to_torch_dtype(args.quant_dtype),
dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
seed=args.seed,
do_profile=args.profile,
num_warmup_iters=args.num_warmup_iters,
num_iters=args.num_iters)
main(
num_tokens=args.num_tokens,
hidden_size=args.hidden_size,
static_scale=args.static_scale,
quant_dtype=to_torch_dtype(args.quant_dtype),
dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
seed=args.seed,
do_profile=args.profile,
num_warmup_iters=args.num_warmup_iters,
num_iters=args.num_iters,
)

57
benchmarks/kernels/benchmark_rmsnorm.py
View File

@ -12,7 +12,6 @@ from vllm.triton_utils import triton
class HuggingFaceRMSNorm(nn.Module):
def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
@ -114,23 +113,19 @@ def rmsnorm_vllm(
def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
dtype = torch.bfloat16
x = torch.randn(batch_size,
seq_len,
hidden_size,
dtype=dtype,
device="cuda")
x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda")
weight = torch.ones(hidden_size, dtype=dtype, device="cuda")
residual = torch.randn_like(x) if use_residual else None
output_naive = rmsnorm_naive(
x.clone(), weight,
residual.clone() if residual is not None else None)
x.clone(), weight, residual.clone() if residual is not None else None
)
output_flashinfer = rmsnorm_flashinfer(
x.clone(), weight,
residual.clone() if residual is not None else None)
x.clone(), weight, residual.clone() if residual is not None else None
)
output_vllm = rmsnorm_vllm(
x.clone(), weight,
residual.clone() if residual is not None else None)
x.clone(), weight, residual.clone() if residual is not None else None
)
if use_residual:
output_naive = output_naive[0]
@ -141,9 +136,9 @@ def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
print(f"FlashInfer output={output_flashinfer}")
print(f"vLLM output={output_vllm}")
if torch.allclose(output_naive, output_flashinfer, atol=1e-2,
rtol=1e-2) and torch.allclose(
output_naive, output_vllm, atol=1e-2, rtol=1e-2):
if torch.allclose(
output_naive, output_flashinfer, atol=1e-2, rtol=1e-2
) and torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
print("✅ All implementations match")
else:
print("❌ Implementations differ")
@ -152,12 +147,10 @@ def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
batch_size_range = [2**i for i in range(0, 7, 2)]
seq_length_range = [2**i for i in range(6, 11, 1)]
head_num_range = [32, 48]
configs = list(
itertools.product(head_num_range, batch_size_range, seq_length_range))
configs = list(itertools.product(head_num_range, batch_size_range, seq_length_range))
def get_benchmark(use_residual):
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["head_num", "batch_size", "seq_len"],
@ -167,19 +160,15 @@ def get_benchmark(use_residual):
line_names=["HuggingFace", "FlashInfer", "vLLM"],
styles=[("blue", "-"), ("green", "-"), ("red", "-")],
ylabel="us",
plot_name=
f"rmsnorm-perf-{'with' if use_residual else 'without'}-residual",
plot_name=f"rmsnorm-perf-{'with' if use_residual else 'without'}-residual",
args={},
))
)
)
def benchmark(head_num, batch_size, seq_len, provider):
dtype = torch.bfloat16
hidden_size = head_num * 128 # assuming head_dim = 128
x = torch.randn(batch_size,
seq_len,
hidden_size,
dtype=dtype,
device="cuda")
x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda")
weight = torch.ones(hidden_size, dtype=dtype, device="cuda")
residual = torch.randn_like(x) if use_residual else None
@ -240,9 +229,9 @@ if __name__ == "__main__":
default=4096,
help="Hidden size (2nd dimension) of the sequence",
)
parser.add_argument("--use-residual",
action="store_true",
help="Whether to use residual connection")
parser.add_argument(
"--use-residual", action="store_true", help="Whether to use residual connection"
)
parser.add_argument(
"--save-path",
type=str,
@ -253,10 +242,12 @@ if __name__ == "__main__":
args = parser.parse_args()
# Run correctness test
calculate_diff(batch_size=args.batch_size,
seq_len=args.seq_len,
hidden_size=args.hidden_size,
use_residual=args.use_residual)
calculate_diff(
batch_size=args.batch_size,
seq_len=args.seq_len,
hidden_size=args.hidden_size,
use_residual=args.use_residual,
)
# Get the benchmark function with proper use_residual setting
benchmark = get_benchmark(args.use_residual)

83
benchmarks/kernels/benchmark_rope.py
View File

@ -6,8 +6,7 @@ from typing import Optional
import nvtx
import torch
from vllm.model_executor.layers.rotary_embedding import (RotaryEmbedding,
get_rope)
from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding, get_rope
from vllm.platforms import current_platform
from vllm.utils import FlexibleArgumentParser
@ -32,40 +31,49 @@ def benchmark_rope_kernels_multi_lora(
# silulating serving 4 LoRAs
scaling_factors = [1, 2, 4, 8]
# batched RoPE can take multiple scaling factors
batched_rope = get_rope(head_size, rotary_dim, max_position, base,
is_neox_style, {
"rope_type": "linear",
"factor": tuple(scaling_factors)
})
batched_rope = get_rope(
head_size,
rotary_dim,
max_position,
base,
is_neox_style,
{"rope_type": "linear", "factor": tuple(scaling_factors)},
)
# non-batched RoPE takes only one scaling factor, we create multiple
# instances to simulate the same behavior
non_batched_ropes: list[RotaryEmbedding] = []
for scaling_factor in scaling_factors:
non_batched_ropes.append(
get_rope(head_size, rotary_dim, max_position, base, is_neox_style,
{
"rope_type": "linear",
"factor": (scaling_factor, )
}))
get_rope(
head_size,
rotary_dim,
max_position,
base,
is_neox_style,
{"rope_type": "linear", "factor": (scaling_factor,)},
)
)
positions = torch.randint(0, max_position, (batch_size, seq_len))
query = torch.randn(batch_size,
seq_len,
num_heads * head_size,
dtype=dtype)
query = torch.randn(batch_size, seq_len, num_heads * head_size, dtype=dtype)
key = torch.randn_like(query)
# create query offsets for batched RoPE, we concat multiple kv cache
# together and each query needs to find the right kv cache of its type
offset_map = torch.tensor(
list(
accumulate([0] + [
max_position * scaling_factor * 2
for scaling_factor in scaling_factors[:-1]
])))
query_types = torch.randint(0,
len(scaling_factors), (batch_size, seq_len),
device=device)
accumulate(
[0]
+ [
max_position * scaling_factor * 2
for scaling_factor in scaling_factors[:-1]
]
)
)
)
query_types = torch.randint(
0, len(scaling_factors), (batch_size, seq_len), device=device
)
# map query types to offsets
query_offsets = offset_map[query_types]
# the kernel takes flattened offsets
@ -86,27 +94,28 @@ def benchmark_rope_kernels_multi_lora(
torch.cuda.synchronize()
if __name__ == '__main__':
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description="Benchmark the rotary embedding kernels.")
description="Benchmark the rotary embedding kernels."
)
parser.add_argument("--is-neox-style", type=bool, default=True)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--seq-len", type=int, default=512)
parser.add_argument("--num-heads", type=int, default=8)
parser.add_argument("--head-size",
type=int,
choices=[64, 80, 96, 112, 120, 128, 192, 256],
default=128)
parser.add_argument(
"--head-size",
type=int,
choices=[64, 80, 96, 112, 120, 128, 192, 256],
default=128,
)
parser.add_argument("--rotary-dim", type=int, choices=[16, 32], default=32)
parser.add_argument("--dtype",
type=str,
choices=["bfloat16", "float"],
default="float")
parser.add_argument(
"--dtype", type=str, choices=["bfloat16", "float"], default="float"
)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--device",
type=str,
choices=["cuda:0", "cuda:1"],
default="cuda:0")
parser.add_argument(
"--device", type=str, choices=["cuda:0", "cuda:1"], default="cuda:0"
)
args = parser.parse_args()
print(args)

113
benchmarks/kernels/benchmark_w8a8_block_fp8.py
View File

@ -14,14 +14,16 @@ import tqdm
import triton
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
_w8a8_block_fp8_matmul)
_w8a8_block_fp8_matmul,
)
from vllm.platforms import current_platform
from vllm.utils import FlexibleArgumentParser
mp.set_start_method("spawn", force=True)
assert current_platform.is_cuda(
), "Only support tune w8a8 block fp8 kernel on CUDA device."
assert current_platform.is_cuda(), (
"Only support tune w8a8 block fp8 kernel on CUDA device."
)
DTYPE_MAP = {
"float32": torch.float32,
@ -40,7 +42,7 @@ def w8a8_block_matmul(
config: dict[str, Any],
output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
"""This function performs matrix multiplication with
"""This function performs matrix multiplication with
block-wise quantization.
It takes two input tensors `A` and `B` with scales `As` and `Bs`.
@ -51,7 +53,7 @@ def w8a8_block_matmul(
B: The input tensor, e.g., weight.
As: The per-token-group quantization scale for `A`.
Bs: The per-block quantization scale for `B`.
block_size: The block size for per-block quantization.
block_size: The block size for per-block quantization.
It should be 2-dim, e.g., [128, 128].
output_dytpe: The dtype of the returned tensor.
@ -71,18 +73,18 @@ def w8a8_block_matmul(
assert triton.cdiv(N, block_n) == Bs.shape[0]
assert triton.cdiv(K, block_k) == Bs.shape[1]
C_shape = A.shape[:-1] + (N, )
C_shape = A.shape[:-1] + (N,)
C = A.new_empty(C_shape, dtype=output_dtype)
def grid(META):
return (triton.cdiv(M, META["BLOCK_SIZE_M"]) *
triton.cdiv(N, META["BLOCK_SIZE_N"]), )
return (
triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
)
if A.dtype == torch.float8_e4m3fn:
kernel = _w8a8_block_fp8_matmul
else:
raise RuntimeError(
"Currently, only support tune w8a8 block fp8 kernel.")
raise RuntimeError("Currently, only support tune w8a8 block fp8 kernel.")
kernel[grid](
A,
@ -119,14 +121,16 @@ def get_configs_compute_bound():
for block_n in [32, 64, 128, 256]:
for num_warps in [4, 8]:
for group_size in [1, 16, 32, 64]:
configs.append({
"BLOCK_SIZE_M": block_m,
"BLOCK_SIZE_N": block_n,
"BLOCK_SIZE_K": block_k,
"GROUP_SIZE_M": group_size,
"num_warps": num_warps,
"num_stages": num_stages,
})
configs.append(
{
"BLOCK_SIZE_M": block_m,
"BLOCK_SIZE_N": block_n,
"BLOCK_SIZE_K": block_k,
"GROUP_SIZE_M": group_size,
"num_warps": num_warps,
"num_stages": num_stages,
}
)
return configs
@ -165,15 +169,9 @@ def get_weight_shapes(tp_size):
return weight_shapes
def benchmark_config(A,
B,
As,
Bs,
block_size,
config,
out_dtype=torch.float16,
num_iters=10):
def benchmark_config(
A, B, As, Bs, block_size, config, out_dtype=torch.float16, num_iters=10
):
def run():
w8a8_block_matmul(A, B, As, Bs, block_size, config, out_dtype)
@ -206,26 +204,26 @@ def tune(M, N, K, block_size, out_dtype, search_space, input_type):
fp8_max, fp8_min = fp8_info.max, fp8_info.min
A_fp32 = (
(torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
fp8_max)
(torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
)
A = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
B_fp32 = (
(torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
fp8_max)
(torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
)
B = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
else:
raise RuntimeError(
"Currently, only support tune w8a8 block fp8 kernel.")
raise RuntimeError("Currently, only support tune w8a8 block fp8 kernel.")
block_n, block_k = block_size[0], block_size[1]
n_tiles = (N + block_n - 1) // block_n
k_tiles = (K + block_k - 1) // block_k
As = torch.rand(M, k_tiles, dtype=torch.float32,
device="cuda") * factor_for_scale
Bs = (torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda") *
factor_for_scale)
As = torch.rand(M, k_tiles, dtype=torch.float32, device="cuda") * factor_for_scale
Bs = (
torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda")
* factor_for_scale
)
best_config = None
best_time = float("inf")
@ -267,7 +265,8 @@ def save_configs(
device_name = current_platform.get_device_name().replace(" ", "_")
json_file_name = (
f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,"
f"block_shape=[{block_n},{block_k}].json")
f"block_shape=[{block_n},{block_k}].json"
)
config_file_path = os.path.join(save_path, json_file_name)
print(f"Writing best config to {config_file_path}...")
@ -295,8 +294,7 @@ def tune_on_gpu(args_dict):
search_space = get_configs_compute_bound()
search_space = [
config for config in search_space
if block_k % config["BLOCK_SIZE_K"] == 0
config for config in search_space if block_k % config["BLOCK_SIZE_K"] == 0
]
start = time.time()
@ -312,15 +310,11 @@ def tune_on_gpu(args_dict):
out_dtype,
search_space,
input_type,
) for batch_size in tqdm(batch_sizes,
desc=f"GPU {gpu_id} - Batch sizes")
)
for batch_size in tqdm(batch_sizes, desc=f"GPU {gpu_id} - Batch sizes")
]
best_configs = {
M: config
for M, config in zip(batch_sizes, benchmark_results)
}
save_configs(N, K, block_n, block_k, best_configs, save_path,
input_type)
best_configs = {M: config for M, config in zip(batch_sizes, benchmark_results)}
save_configs(N, K, block_n, block_k, best_configs, save_path, input_type)
end = time.time()
print(f"Tuning on GPU {gpu_id} took {end - start:.2f} seconds")
@ -376,13 +370,14 @@ def main(args):
process_args = []
for gpu_id in range(num_gpus):
process_args.append({
"gpu_id": gpu_id,
"batch_sizes": batches_per_gpu[gpu_id],
"weight_shapes":
weight_shapes, # Each GPU processes all weight shapes
"args": args,
})
process_args.append(
{
"gpu_id": gpu_id,
"batch_sizes": batches_per_gpu[gpu_id],
"weight_shapes": weight_shapes, # Each GPU processes all weight shapes
"args": args,
}
)
ctx = mp.get_context("spawn")
with ctx.Pool(num_gpus) as pool:
@ -398,13 +393,11 @@ Tune triton w8a8 block fp8 for DeepSeek-V3/DeepSeek-R1:
python3 benchmark_w8a8_block_fp8.py --tp-size 8 --input-type fp8
Then copy to model_executor/layers/quantization/utils/configs
""",
formatter_class=argparse.RawTextHelpFormatter)
formatter_class=argparse.RawTextHelpFormatter,
)
parser.add_argument("--tp-size", "-tp", type=int, default=8)
parser.add_argument("--input-type",
type=str,
choices=["fp8"],
default="fp8")
parser.add_argument("--input-type", type=str, choices=["fp8"], default="fp8")
parser.add_argument(
"--out-dtype",
type=str,

4
benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py
View File

@ -11,7 +11,9 @@ from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
# Import vLLM functions
from vllm import _custom_ops as ops
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
per_token_group_quant_fp8, w8a8_block_fp8_matmul)
per_token_group_quant_fp8,
w8a8_block_fp8_matmul,
)
from vllm.triton_utils import triton

33
benchmarks/kernels/graph_machete_bench.py
View File

@ -14,13 +14,14 @@ from vllm.utils import FlexibleArgumentParser
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description='Benchmark the latency of processing a single batch of '
'requests till completion.')
parser.add_argument('filename', type=str)
description="Benchmark the latency of processing a single batch of "
"requests till completion."
)
parser.add_argument("filename", type=str)
args = parser.parse_args()
with open(args.filename, 'rb') as f:
with open(args.filename, "rb") as f:
data = pickle.load(f)
raw_results: list[TMeasurement] = data["results"]
@ -38,11 +39,7 @@ if __name__ == "__main__":
raise Exception("MKN not found")
kernel = v.task_spec.description
results[KN].append({
"kernel": kernel,
"batch_size": M,
"median": v.median
})
results[KN].append({"kernel": kernel, "batch_size": M, "median": v.median})
rows = int(math.ceil(len(results) / 2))
fig, axs = plt.subplots(rows, 2, figsize=(12, 5 * rows))
@ -50,14 +47,16 @@ if __name__ == "__main__":
for axs_idx, (shape, data) in enumerate(results.items()):
plt.sca(axs[axs_idx])
df = pd.DataFrame(data)
sns.lineplot(data=df,
x="batch_size",
y="median",
hue="kernel",
style="kernel",
markers=True,
dashes=False,
palette="Dark2")
sns.lineplot(
data=df,
x="batch_size",
y="median",
hue="kernel",
style="kernel",
markers=True,
dashes=False,
palette="Dark2",
)
plt.title(f"Shape: {shape}")
plt.ylabel("time (median, s)")
plt.tight_layout()

52
benchmarks/kernels/utils.py
View File

@ -23,6 +23,7 @@ class ArgPool:
For every invocation during a benchmarking run, it will choose a
different value from the list.
"""
values: Iterable[Any]
def __getitem__(self, index):
@ -30,9 +31,7 @@ class ArgPool:
class Bench:
class ArgsIterator:
def __init__(self, args_list, kwargs_list):
assert len(args_list) == len(kwargs_list)
self.args_list = args_list
@ -53,10 +52,16 @@ class Bench:
def n_args(self):
return self.n
def __init__(self, cuda_graph_params: Optional[CudaGraphBenchParams],
label: str, sub_label: str, description: str, fn: Callable,
*args, **kwargs):
def __init__(
self,
cuda_graph_params: Optional[CudaGraphBenchParams],
label: str,
sub_label: str,
description: str,
fn: Callable,
*args,
**kwargs,
):
self.cuda_graph_params = cuda_graph_params
self.use_cuda_graph = self.cuda_graph_params is not None
self.label = label
@ -67,10 +72,8 @@ class Bench:
# Process args
self._args = args
self._kwargs = kwargs
self.args_list, self.kwargs_list = self.collapse_argpool(
*args, **kwargs)
self.args_iterator = self.ArgsIterator(self.args_list,
self.kwargs_list)
self.args_list, self.kwargs_list = self.collapse_argpool(*args, **kwargs)
self.args_iterator = self.ArgsIterator(self.args_list, self.kwargs_list)
# Cudagraph runner
self.g = None
@ -100,16 +103,13 @@ class Bench:
for i in range(argpool_size):
# collapse args; Just pick the ith value
args_list[i] = tuple([
arg[i] if isinstance(arg, ArgPool) else arg
for arg in args_list[i]
])
args_list[i] = tuple(
[arg[i] if isinstance(arg, ArgPool) else arg for arg in args_list[i]]
)
# collapse kwargs
kwargs_i = kwargs_list[i]
arg_pool_keys = [
k for k, v in kwargs_i.items() if isinstance(v, ArgPool)
]
arg_pool_keys = [k for k, v in kwargs_i.items() if isinstance(v, ArgPool)]
for k in arg_pool_keys:
# again just pick the ith value
kwargs_i[k] = kwargs_i[k][i]
@ -142,7 +142,7 @@ class Bench:
def run_cudagrah(self) -> TMeasurement:
assert self.use_cuda_graph
globals = {'g': self.g}
globals = {"g": self.g}
return TBenchmark.Timer(
stmt="g.replay()",
@ -162,15 +162,15 @@ class Bench:
has_arg_pool = self.args_iterator.n_args > 1
if has_arg_pool:
setup = '''
setup = """
args_iterator.reset()
args_it = args_iterator.__next__()
'''
stmt = '''
"""
stmt = """
args, kwargs = next(args_it)
fn(*args, **kwargs)
'''
globals = {'fn': self.fn, 'args_iterator': self.args_iterator}
"""
globals = {"fn": self.fn, "args_iterator": self.args_iterator}
else:
# no arg pool. Just use the args and kwargs directly
self.args_iterator.reset()
@ -178,10 +178,10 @@ class Bench:
args, kwargs = next(args_it)
setup = ""
stmt = '''
stmt = """
fn(*args, **kwargs)
'''
globals = {'fn': self.fn, 'args': args, 'kwargs': kwargs}
"""
globals = {"fn": self.fn, "args": args, "kwargs": kwargs}
return TBenchmark.Timer(
stmt=stmt,

36
benchmarks/overheads/benchmark_hashing.py
View File

@ -7,9 +7,8 @@ from vllm import LLM, SamplingParams
from vllm.utils import FlexibleArgumentParser
# A very long prompt, total number of tokens is about 15k.
LONG_PROMPT = ["You are an expert in large language models, aren't you?"
] * 1000
LONG_PROMPT = ' '.join(LONG_PROMPT)
LONG_PROMPT = ["You are an expert in large language models, aren't you?"] * 1000
LONG_PROMPT = " ".join(LONG_PROMPT)
def main(args):
@ -30,32 +29,35 @@ def main(args):
print("------start generating------")
for i in range(3):
profiler.runctx('llm.generate(LONG_PROMPT, sampling_params)',
globals(), locals())
profiler.runctx(
"llm.generate(LONG_PROMPT, sampling_params)", globals(), locals()
)
# analyze the runtime of hashing function
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative')
stats.sort_stats("cumulative")
total_time = 0
total_calls = 0
for func in stats.stats:
if 'hash_of_block' in func[2]:
if "hash_of_block" in func[2]:
total_time = stats.stats[func][3]
total_calls = stats.stats[func][0]
percentage = (total_time / stats.total_tt) * 100
print(f"Hashing took {total_time:.2f} seconds,"
f"{percentage:.2f}% of the total runtime.")
print(
f"Hashing took {total_time:.2f} seconds,{percentage:.2f}% of the total runtime."
)
if __name__ == "__main__":
parser = FlexibleArgumentParser(
description='Benchmark the performance of hashing function in'
'automatic prefix caching.')
parser.add_argument('--model', type=str, default='lmsys/longchat-7b-16k')
parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
parser.add_argument('--output-len', type=int, default=10)
parser.add_argument('--enable-prefix-caching',
action='store_true',
help='enable prefix caching')
description="Benchmark the performance of hashing function in"
"automatic prefix caching."
)
parser.add_argument("--model", type=str, default="lmsys/longchat-7b-16k")
parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
parser.add_argument("--output-len", type=int, default=10)
parser.add_argument(
"--enable-prefix-caching", action="store_true", help="enable prefix caching"
)
args = parser.parse_args()
main(args)

54
benchmarks/pyproject.toml Normal file
View File

@ -0,0 +1,54 @@
# This local pyproject file is part of the migration from yapf to ruff format.
# It uses the same core rules as the main pyproject.toml file, but with the
# following differences:
# - ruff line length is overridden to 88
# - deprecated typing ignores (UP006, UP035) have been removed
[tool.ruff]
line-length = 88
exclude = [
# External file, leaving license intact
"examples/other/fp8/quantizer/quantize.py",
"vllm/vllm_flash_attn/flash_attn_interface.pyi"
]
[tool.ruff.lint.per-file-ignores]
"vllm/third_party/**" = ["ALL"]
"vllm/version.py" = ["F401"]
"vllm/_version.py" = ["ALL"]
[tool.ruff.lint]
select = [
# pycodestyle
"E",
# Pyflakes
"F",
# pyupgrade
"UP",
# flake8-bugbear
"B",
# flake8-simplify
"SIM",
# isort
"I",
# flake8-logging-format
"G",
]
ignore = [
# star imports
"F405", "F403",
# lambda expression assignment
"E731",
# Loop control variable not used within loop body
"B007",
# f-string format
"UP032",
# Can remove once 3.10+ is the minimum Python version
"UP007",
]
[tool.ruff.lint.isort]
known-first-party = ["vllm"]
[tool.ruff.format]
docstring-code-format = true

5
pyproject.toml
View File

@ -54,6 +54,7 @@ include = ["vllm*"]
[tool.yapfignore]
ignore_patterns = [
".buildkite/**",
"benchmarks/**",
"build/**",
]
@ -155,6 +156,10 @@ ignore-words-list = "dout, te, indicies, subtile, ElementE"
skip = "tests/models/fixtures/*,tests/prompts/*,benchmarks/sonnet.txt,tests/lora/data/*,build/*,vllm/third_party/*"
[tool.isort]
skip_glob = [
".buildkite/*",
"benchmarks/*",
]
use_parentheses = true
skip_gitignore = true