EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

[Lint]Style: Convert example to ruff format (#5863)

Browse Source 
### What this PR does / why we need it?
This PR fixes linting issues in the `example/` to align with the
project's Ruff configuration.

- vLLM version: v0.13.0
- vLLM main:
bde38c11df

Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain>
Co-authored-by: root <root@LAPTOP-VQKDDVMG.localdomain>
This commit is contained in:
SILONG ZENG
2026-01-13 20:46:50 +08:00
committed by GitHub
parent f7b904641e
commit 78d5ce3e01
23 changed files with 678 additions and 1037 deletions
Download Patch File Download Diff File Expand all files Collapse all files

72
examples/quantization/llm-compressor/w8a8_int8.py
View File

@@ -1,31 +1,25 @@
import os
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationScheme, QuantizationStrategy, QuantizationType
from datasets import load_dataset
from transformers import AutoModelForCausalLM, Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration, \
AutoTokenizer, AutoProcessor, AutoConfig, AutoImageProcessor
from llmcompressor import oneshot
from llmcompressor.modifiers.awq import AWQModifier
from llmcompressor.modifiers.quantization import GPTQModifier, QuantizationModifier
from compressed_tensors.quantization import QuantizationArgs, QuantizationScheme, QuantizationType, QuantizationStrategy
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
)
W8A8_W_cha_A_ten_static_symmetric = {
"group_0": QuantizationScheme(
targets=["Linear"],
weights=QuantizationArgs(
num_bits=8,
type=QuantizationType.INT,
strategy=QuantizationStrategy.CHANNEL,
symmetric=True,
dynamic=False
num_bits=8, type=QuantizationType.INT, strategy=QuantizationStrategy.CHANNEL, symmetric=True, dynamic=False
),
input_activations=QuantizationArgs(
num_bits=8,
type=QuantizationType.INT,
strategy=QuantizationStrategy.TENSOR,
symmetric=True,
dynamic=False
num_bits=8, type=QuantizationType.INT, strategy=QuantizationStrategy.TENSOR, symmetric=True, dynamic=False
),
),
}
@@ -53,19 +47,19 @@ TOKENIZER_DICT = {
def load_environment_variables():
env_vars = {
'model_path': "Qwen/Qwen3-32B",
'export_path': "/llm-compressor/export/GPTQ/W8A8_W_cha_A_ten_static_symmetric",
'modifier': "GPTQ",
'schemes': "W8A8_W_cha_A_ten_static_symmetric",
'calib_prompt_path': "HuggingFaceH4/ultrachat_200k"
"model_path": "Qwen/Qwen3-32B",
"export_path": "/llm-compressor/export/GPTQ/W8A8_W_cha_A_ten_static_symmetric",
"modifier": "GPTQ",
"schemes": "W8A8_W_cha_A_ten_static_symmetric",
"calib_prompt_path": "HuggingFaceH4/ultrachat_200k",
}
# verify export model path
if env_vars['export_path'] is None:
env_vars['export_path'] = env_vars['model_path'].rstrip("/") + "-" + env_vars['modifier']
if env_vars['schemes'] is not None:
env_vars['export_path'] += "-" + env_vars['schemes']
os.makedirs(env_vars['export_path'], exist_ok=True)
if env_vars["export_path"] is None:
env_vars["export_path"] = env_vars["model_path"].rstrip("/") + "-" + env_vars["modifier"]
if env_vars["schemes"] is not None:
env_vars["export_path"] += "-" + env_vars["schemes"]
os.makedirs(env_vars["export_path"], exist_ok=True)
return env_vars
@@ -74,19 +68,17 @@ def load_calibration_text_dataset(calib_prompt_path, tokenizer):
# Load dataset
for f in os.listdir(calib_prompt_path):
print(f)
if any(f.lower().endswith('.jsonl') for f in os.listdir(calib_prompt_path)):
ds = load_dataset('json', data_dir=calib_prompt_path, split='validation')
elif any(f.lower().endswith('.parquet') for f in os.listdir(calib_prompt_path)):
if any(f.lower().endswith(".jsonl") for f in os.listdir(calib_prompt_path)):
ds = load_dataset("json", data_dir=calib_prompt_path, split="validation")
elif any(f.lower().endswith(".parquet") for f in os.listdir(calib_prompt_path)):
ds = load_dataset("parquet", data_dir=calib_prompt_path, split="train[:512]")
else:
raise ValueError("Unsupported calibration file format: {}".format(
calib_prompt_path.split('.')[-1]))
raise ValueError("Unsupported calibration file format: {}".format(calib_prompt_path.split(".")[-1]))
# Preprocess dataset
def preprocess(example):
if tokenizer.chat_template is not None:
return {"text": tokenizer.apply_chat_template(
example["messages"], tokenize=False)}
return {"text": tokenizer.apply_chat_template(example["messages"], tokenize=False)}
else:
return {"text": example["messages"]}
@@ -118,8 +110,8 @@ def quantize_model(model, env_vars, dataset_dict=None):
# define a llmcompressor recipe
recipe = [
MODIFIER_DICT[env_vars['modifier']](
config_groups=SCHEMES_DICT[env_vars['schemes']],
MODIFIER_DICT[env_vars["modifier"]](
config_groups=SCHEMES_DICT[env_vars["schemes"]],
ignore=ignore,
),
]
@@ -138,18 +130,16 @@ def save_quantized_model(model, tokenizer, save_path, save_compressed=False):
tokenizer.save_pretrained(save_path)
if __name__ == '__main__':
if __name__ == "__main__":
# get environment variables
env_vars = load_environment_variables()
# support model type list
config = AutoConfig.from_pretrained(env_vars['model_path'], trust_remote_code=True)
config = AutoConfig.from_pretrained(env_vars["model_path"], trust_remote_code=True)
model_type = config.model_type
model = MODEL_DICT[model_type].from_pretrained(
env_vars['model_path'], torch_dtype="auto", trust_remote_code=True
)
tokenizer = TOKENIZER_DICT[model_type].from_pretrained(env_vars['model_path'], trust_remote_code=True)
model = MODEL_DICT[model_type].from_pretrained(env_vars["model_path"], torch_dtype="auto", trust_remote_code=True)
tokenizer = TOKENIZER_DICT[model_type].from_pretrained(env_vars["model_path"], trust_remote_code=True)
ds = load_calibration_text_dataset(env_vars["calib_prompt_path"], tokenizer)
@@ -157,4 +147,4 @@ if __name__ == '__main__':
quantize_model(model, env_vars, ds)
# save the quantized model
save_quantized_model(model, tokenizer, env_vars['export_path'], True)
save_quantized_model(model, tokenizer, env_vars["export_path"], True)