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polish code with new pre-commit rule (#2923)

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Zero Rains
2025-07-19 23:19:27 +08:00
committed by GitHub
parent b8676d71a8
commit 25698d56d1
424 changed files with 14307 additions and 13518 deletions
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61
benchmarks/benchmark_dataset.py
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@@ -26,9 +26,9 @@ from abc import ABC, abstractmethod
from collections.abc import Mapping
from dataclasses import dataclass
from io import BytesIO
from typing import Any, Callable, Optional, Union
from PIL import Image
from typing import Any, Optional, Union
from PIL import Image
logger = logging.getLogger(__name__)
@@ -38,6 +38,7 @@ class SampleRequest:
"""
Represents a single inference request for benchmarking.
"""
no: int
prompt: Union[str, Any]
history_QA: Union[str, Any]
@@ -48,6 +49,7 @@ class SampleRequest:
class BenchmarkDataset(ABC):
"""BenchmarkDataset"""
DEFAULT_SEED = 0
IS_MULTIMODAL = False
@@ -68,8 +70,7 @@ 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
self.hyperparameter_path = hyperparameter_path
self.hyperparameters = {}
@@ -85,8 +86,7 @@ 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.")
@abstractmethod
def sample(self, num_requests: int) -> list[SampleRequest]:
@@ -105,8 +105,7 @@ 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.
@@ -117,11 +116,9 @@ 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)
def is_valid_sequence(
@@ -141,14 +138,12 @@ 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)
def process_image(image: Any) -> Mapping[str, Any]:
@@ -171,28 +166,25 @@ 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."
)
class EBDataset(BenchmarkDataset):
@@ -243,8 +235,7 @@ class EBDataset(BenchmarkDataset):
new_output_len = int(entry["max_dec_len"])
if enable_multimodal_chat:
prompt = self.apply_multimodal_chat_transformation(
prompt, None)
prompt = self.apply_multimodal_chat_transformation(prompt, None)
samples.append(
SampleRequest(
no=cnt,
@@ -252,17 +243,20 @@ class EBDataset(BenchmarkDataset):
prompt_len=self.prompt_len,
history_QA=[],
expected_output_len=new_output_len,
))
)
)
cnt += 1
self.maybe_oversample_requests(samples, num_requests)
return samples
class EBChatDataset(BenchmarkDataset):
"""
Implements the ShareGPT dataset. Loads data from a JSON file and generates
sample requests based on conversation turns.
"""
prompt_len: int
def __init__(self, **kwargs) -> None:
@@ -296,8 +290,7 @@ class EBChatDataset(BenchmarkDataset):
new_output_len = int(entry.get("max_tokens", 12288))
if enable_multimodal_chat:
prompt = self.apply_multimodal_chat_transformation(
prompt, None)
prompt = self.apply_multimodal_chat_transformation(prompt, None)
samples.append(
SampleRequest(
no=cnt,
@@ -306,9 +299,9 @@ class EBChatDataset(BenchmarkDataset):
prompt_len=0,
history_QA=history_QA,
expected_output_len=new_output_len,
))
)
)
cnt += 1
self.maybe_oversample_requests(samples, num_requests)
return samples