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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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41
fastdeploy/model_executor/layers/quantization/wfp8afp8.py
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@@ -13,14 +13,19 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
from typing import Optional
import paddle
from fastdeploy.model_executor.layers.quantization.ops import (
cutlass_scaled_mm, scaled_fp8_quant)
cutlass_scaled_mm,
scaled_fp8_quant,
)
from fastdeploy.model_executor.layers.quantization.quant_base import (
QuantConfigBase, QuantMethodBase)
QuantConfigBase,
QuantMethodBase,
)
class WFP8AFP8Config(QuantConfigBase):
@@ -37,21 +42,18 @@ class WFP8AFP8Config(QuantConfigBase):
self.quant_round_type = 1
def name(self) -> str:
"""
"""
""" """
return "wfp8afp8"
@classmethod
def from_config(cls, config: dict) -> "WFP8AFP8Config":
"""
"""
""" """
weight_scale_dict = config.get("weight_scale_dict", None)
act_scale_dict = config.get("act_scale_dict", None)
return cls(weight_scale_dict, act_scale_dict)
def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
"""
"""
""" """
return WFP8AFP8LinearMethod(self)
@@ -68,8 +70,7 @@ class WFP8AFP8LinearMethod(QuantMethodBase):
self.quant_config = quant_config
def create_weights(self, layer):
"""
"""
""" """
layer.weight_shape.reverse()
layer.weight_dtype = "float8_e4m3fn"
# TODO(YuanRisheng): set weight logic should be moved to process_loaded_weights func
@@ -82,8 +83,7 @@ class WFP8AFP8LinearMethod(QuantMethodBase):
)
def process_loaded_weights(self, layer, weights) -> None:
"""
"""
""" """
if self.skip_quant:
weight_tensor = weights.cast(layer._dtype)
layer.weight.set_value(weight_tensor)
@@ -99,18 +99,21 @@ class WFP8AFP8LinearMethod(QuantMethodBase):
layer.weight_scale.set_value(weight_scale)
def apply(self, layer, x):
"""
"""
""" """
if self.skip_quant:
linear_out = paddle.matmul(x, layer.weight, False, True)
return linear_out
if self.use_per_token_if_dynamic:
out_type = x.dtype
a_q, a_scales = scaled_fp8_quant(
x, use_per_token_if_dynamic=self.use_per_token_if_dynamic)
linear_out = cutlass_scaled_mm(a_q, layer.weight, a_scales,
layer.weight_scale, out_type,
layer.bias)
a_q, a_scales = scaled_fp8_quant(x, use_per_token_if_dynamic=self.use_per_token_if_dynamic)
linear_out = cutlass_scaled_mm(
a_q,
layer.weight,
a_scales,
layer.weight_scale,
out_type,
layer.bias,
)
else:
raise NotImplementedError
return linear_out