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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #7) (#6023)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
|` vllm_ascend/quantization/compressed_tensors/compressed_tensors.py`|
|` vllm_ascend/quantization/quant_config.py`|
|` vllm_ascend/quantization/utils.py`|
|` vllm_ascend/quantization/w4a16.py`|
|` vllm_ascend/quantization/w4a4_flatquant_dynamic.py`|
|` vllm_ascend/quantization/w4a8_dynamic.py`|
|` vllm_ascend/quantization/w8a16.py`|
|` vllm_ascend/quantization/w8a8.py`|
|` vllm_ascend/quantization/w8a8_dynamic.py`|
|` vllm_ascend/quantization/w8a8_pdmix.py`|
|` vllm_ascend/quantization/w8a8mxfp8.py`|
|` vllm_ascend/sample/rejection_sampler.py`|
|` vllm_ascend/sample/sampler.py`|
|` vllm_ascend/worker/block_table.py`|

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

Signed-off-by: MrZ20 <2609716663@qq.com>
This commit is contained in:
SILONG ZENG
2026-02-06 14:56:53 +08:00
committed by GitHub
parent d0bc16859c
commit 99aedaff63
20 changed files with 997 additions and 1307 deletions
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43
vllm_ascend/quantization/methods/w8a8_mxfp8.py
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@@ -15,7 +15,7 @@
# limitations under the License.
#
from typing import Any, Dict, Optional
from typing import Any
import torch
import torch_npu
@@ -28,48 +28,37 @@ from .registry import register_scheme
@register_scheme("W8A8_MXFP8", "linear")
class AscendW8A8MXFP8DynamicLinearMethod(AscendLinearScheme):
"""Linear method for Ascend W8A8_MXFP8 (Microscaling FP8) quantization.
This scheme uses microscaling FP8 quantization with per-group scales.
The activation is dynamically quantized to FP8 (E4M3FN format) with
microscaling, and weights are stored in FP8 format with per-group scales.
"""
model_dtype = None
def __init__(self):
vllm_config = get_current_vllm_config()
self.group_size = vllm_config.quant_config.quant_description.get(
"group_size", 32)
self.group_size = vllm_config.quant_config.quant_description.get("group_size", 32)
def get_weight(self, input_size: int, output_size: int,
params_dtype: torch.dtype) -> Dict[str, Any]:
params_dict = {
"weight":
torch.empty(output_size, input_size, dtype=torch.float8_e4m3fn)
}
def get_weight(self, input_size: int, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
params_dict = {"weight": torch.empty(output_size, input_size, dtype=torch.float8_e4m3fn)}
return params_dict
def get_pergroup_param(self,
input_size: int,
output_size: int,
params_dtype: torch.dtype,
layer_type: Optional[str] = None) -> Dict[str, Any]:
def get_pergroup_param(
self, input_size: int, output_size: int, params_dtype: torch.dtype, layer_type: str | None = None
) -> dict[str, Any]:
params_dict = {}
params_dict["weight_scale"] = torch.empty(output_size,
input_size //
self.group_size,
dtype=torch.uint8)
params_dict["weight_scale"] = torch.empty(output_size, input_size // self.group_size, dtype=torch.uint8)
return params_dict
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
tp_rank: Optional[int] = 0,
bias: torch.Tensor | None = None,
tp_rank: int | None = 0,
) -> torch.Tensor:
quantized_x, dynamic_scale = torch_npu.npu_dynamic_mx_quant(
x, dst_type=torch.float8_e4m3fn)
quantized_x, dynamic_scale = torch_npu.npu_dynamic_mx_quant(x, dst_type=torch.float8_e4m3fn)
pertoken_scale = dynamic_scale
output_dtype = x.dtype
@@ -82,13 +71,13 @@ class AscendW8A8MXFP8DynamicLinearMethod(AscendLinearScheme):
pertoken_scale_dtype=torch_npu.float8_e8m0fnu,
bias=bias,
output_dtype=output_dtype,
group_sizes=[1, 1, self.group_size])
group_sizes=[1, 1, self.group_size],
)
return output
def process_weights_after_loading(self, layer):
n_dim, k_dim = layer.weight_scale.data.shape
layer.weight_scale.data = layer.weight_scale.data.reshape(
n_dim, k_dim // 2, 2)
layer.weight_scale.data = layer.weight_scale.data.reshape(n_dim, k_dim // 2, 2)
layer.weight.data = layer.weight.data.transpose(0, 1)
layer.weight_scale.data = layer.weight_scale.data.transpose(0, 1)