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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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54
vllm_ascend/quantization/methods/w8a8_pdmix.py
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@@ -22,15 +22,14 @@ for prefill and decode phases:
- Decode (KV consumer): Uses static W8A8 quantization
"""
from typing import Any, Dict, Optional
from typing import Any
import torch
from vllm.config import get_current_vllm_config
from .base import AscendLinearScheme
from .registry import register_scheme
from .w8a8_dynamic import (AscendW8A8DynamicFusedMoEMethod,
AscendW8A8DynamicLinearMethod)
from .w8a8_dynamic import AscendW8A8DynamicFusedMoEMethod, AscendW8A8DynamicLinearMethod
from .w8a8_static import AscendW8A8LinearMethod
@@ -53,31 +52,27 @@ class AscendW8A8PDMixLinearMethod(AscendLinearScheme):
self._dynamic_method = AscendW8A8DynamicLinearMethod()
kv_transfer_config = get_current_vllm_config().kv_transfer_config
self._is_kv_consumer = (kv_transfer_config is not None
and kv_transfer_config.is_kv_consumer)
self._is_kv_consumer = kv_transfer_config is not None and kv_transfer_config.is_kv_consumer
def get_weight(self, input_size: int, output_size: int,
params_dtype: torch.dtype) -> Dict[str, Any]:
return self._static_method.get_weight(input_size, output_size,
params_dtype)
def get_weight(self, input_size: int, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
return self._static_method.get_weight(input_size, output_size, params_dtype)
def get_pertensor_param(self, params_dtype: torch.dtype) -> Dict[str, Any]:
def get_pertensor_param(self, params_dtype: torch.dtype) -> dict[str, Any]:
return self._static_method.get_pertensor_param(params_dtype)
def get_perchannel_param(
self,
output_size: int,
params_dtype: torch.dtype,
) -> Dict[str, Any]:
return self._static_method.get_perchannel_param(
output_size, params_dtype)
) -> dict[str, Any]:
return self._static_method.get_perchannel_param(output_size, params_dtype)
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:
if layer.is_kv_consumer:
return self._static_method.apply(layer, x, bias, tp_rank)
@@ -92,26 +87,15 @@ class AscendW8A8PDMixLinearMethod(AscendLinearScheme):
@register_scheme("W8A8_MIX", "moe")
class AscendW8A8PDMixFusedMoeMethod(AscendW8A8DynamicFusedMoEMethod):
def get_dynamic_quant_param(self, num_experts: int,
intermediate_size_per_partition: int,
hidden_sizes: int,
params_dtype: torch.dtype) -> Dict[str, Any]:
def get_dynamic_quant_param(
self, num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype
) -> dict[str, Any]:
param_dict = super().get_dynamic_quant_param(
num_experts, intermediate_size_per_partition, hidden_sizes,
params_dtype)
param_dict["w2_deq_scale"] = torch.empty(num_experts,
hidden_sizes,
dtype=torch.float32)
param_dict["w13_deq_scale"] = torch.empty(
num_experts,
2 * intermediate_size_per_partition,
dtype=torch.float32)
param_dict["w2_input_offset"] = torch.empty(num_experts,
1,
dtype=torch.int8)
param_dict["w13_input_offset"] = torch.empty(num_experts,
1,
dtype=torch.int8)
num_experts, intermediate_size_per_partition, hidden_sizes, params_dtype
)
param_dict["w2_deq_scale"] = torch.empty(num_experts, hidden_sizes, dtype=torch.float32)
param_dict["w13_deq_scale"] = torch.empty(num_experts, 2 * intermediate_size_per_partition, dtype=torch.float32)
param_dict["w2_input_offset"] = torch.empty(num_experts, 1, dtype=torch.int8)
param_dict["w13_input_offset"] = torch.empty(num_experts, 1, dtype=torch.int8)
return param_dict