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

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This commit is contained in:
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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35
fastdeploy/model_executor/layers/normalization.py
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@@ -102,8 +102,7 @@ class RMSNorm(nn.Layer):
dtype=self._norm_weight_dtype,
)
def load_state_dict(self, state_dict: Dict[str,
paddle.Tensor | np.ndarray]):
def load_state_dict(self, state_dict: Dict[str, paddle.Tensor | np.ndarray]):
"""
Load the checkpoint state dictionary into the layer.
@@ -112,15 +111,10 @@ class RMSNorm(nn.Layer):
"""
# weight
weight_tensor = paddle.cast(
get_tensor(state_dict.pop(self.weight_key)),
self._norm_weight_dtype)
weight_tensor = paddle.cast(get_tensor(state_dict.pop(self.weight_key)), self._norm_weight_dtype)
self.weight.set_value(weight_tensor)
def forward(
self,
x,
residual_input: Optional[paddle.Tensor] = None) -> paddle.Tensor:
def forward(self, x, residual_input: Optional[paddle.Tensor] = None) -> paddle.Tensor:
"""
Defines the forward computation of the layer.
@@ -140,9 +134,7 @@ class RMSNorm(nn.Layer):
if current_platform.is_gcu():
if residual_input is None:
return rms_norm(x, self.weight, self.eps)
norm_out = self.norm_func(
x, residual_input, self.weight, self.eps
)
norm_out = self.norm_func(x, residual_input, self.weight, self.eps)
else:
norm_out = self.norm_func(
x,
@@ -152,7 +144,7 @@ class RMSNorm(nn.Layer):
begin_norm_axis=self.begin_norm_axis,
bias=self.bias,
residual=residual_input,
quant_scale=-1 if self.quant_scale is None else self.quant_scale,
quant_scale=(-1 if self.quant_scale is None else self.quant_scale),
quant_round_type=self.quant_round_type,
quant_max_bound=self.quant_max_bound,
quant_min_bound=self.quant_min_bound,
@@ -242,8 +234,7 @@ class LayerNorm(nn.Layer):
dtype=self._norm_weight_dtype,
)
def load_state_dict(self, state_dict: Dict[str,
paddle.Tensor | np.ndarray]):
def load_state_dict(self, state_dict: Dict[str, paddle.Tensor | np.ndarray]):
"""
Load the checkpoint state dictionary into the layer.
@@ -252,22 +243,18 @@ class LayerNorm(nn.Layer):
"""
# weight
weight_tensor = paddle.cast(
get_tensor(state_dict.pop(self.weight_key)),
self._norm_weight_dtype)
weight_tensor = paddle.cast(get_tensor(state_dict.pop(self.weight_key)), self._norm_weight_dtype)
self.weight.set_value(weight_tensor)
# bias
if self.with_bias:
bias_tensor = paddle.cast(
get_tensor(state_dict.pop(self.bias_key)),
self._norm_weight_dtype)
self._norm_weight_dtype,
)
self.bias.set_value(bias_tensor)
def forward(
self,
x,
residual_input: Optional[paddle.Tensor] = None) -> paddle.Tensor:
def forward(self, x, residual_input: Optional[paddle.Tensor] = None) -> paddle.Tensor:
"""
Defines the forward computation of the layer.
@@ -326,7 +313,7 @@ class LayerNorm(nn.Layer):
begin_norm_axis=1,
bias=self.bias,
residual=residual_input,
quant_scale=-1 if self.quant_scale is None else self.quant_scale,
quant_scale=(-1 if self.quant_scale is None else self.quant_scale),
quant_round_type=self.quant_round_type,
quant_max_bound=self.quant_max_bound,
quant_min_bound=self.quant_min_bound,