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---------

Co-authored-by: Jiang-Jia-Jun <jiangjiajun@baidu.com>
This commit is contained in:
Jiang-Jia-Jun
2025-07-03 15:43:53 +08:00
committed by GitHub
parent d222248d00
commit 05c670e593
95 changed files with 9916 additions and 1312 deletions
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131
fastdeploy/model_executor/layers/normalization.py
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@@ -14,10 +14,15 @@
# limitations under the License.
"""
from typing import Callable, Dict, Optional
import numpy as np
import paddle
from paddle import nn
from paddle.incubate.nn.functional import fused_layer_norm, fused_rms_norm
from fastdeploy.config import FDConfig
from .utils import get_tensor
@@ -28,16 +33,16 @@ class RMSNorm(nn.Layer):
def __init__(
self,
fd_config,
hidden_size,
eps=1e-5,
prefix="",
linear_bias=None,
quant_scale=None,
begin_norm_axis=1,
):
fd_config: FDConfig,
hidden_size: int,
eps: float = 1e-5,
prefix: str = "",
linear_bias: paddle.Tensor = None,
quant_scale: float = None,
begin_norm_axis: int = 1,
) -> None:
"""
Initializes the normalization layer.
Initializes the RMSNormalization layer.
Args:
fd_config (FDConfig): Arguments related to inference, containing
@@ -45,33 +50,33 @@ class RMSNorm(nn.Layer):
num_attention_heads, and ffn_hidden_size.
hidden_size (int) : size of hidden state.
eps:(float, optional): Small value added to the variance to avoid division by zero. Defaults to 1e-5.
weight_key (str): Key name of weight in the pdparams state dict. Defaults to None, means no weight.
bias_key (str): Key name of bias in the pdparams state dict. Defaults to None, means no bias.
linear_bias (float, optional): Initial bias value for the linear layer (if used). Defaults to None.
prefix(str,optional):The name of current layer. Defaults to "".
linear_bias (paddle.Tensor,optional): Initial bias value for the linear layer (if used). Defaults to None.
quant_scale(float,optional):Quantization scale, used in quantization scenarios. Defaults to -1, indicating no quantization.
begin_norm_axis (int, optional): The axis along which to perform normalization. Defaults to 1.
Raises:
NotImplementedError: If the specified norm_type is not supported.
"""
super().__init__()
self.fd_config = fd_config
self.prefix = prefix
self.hidden_size = hidden_size
self.prefix: str = prefix
self.hidden_size: int = hidden_size
if len(prefix) == 0:
self.weight_key = None
self.weight_key: Optional[str] = None
else:
self.weight_key = f"{prefix}.weight"
self.with_weight = self.weight_key is not None
self.eps = eps
self.norm_func = fused_rms_norm
self.linear_bias = linear_bias
self.quant_scale = quant_scale
self._dtype = self._helper.get_default_dtype()
self._norm_weight_dtype = self._dtype
self.begin_norm_axis = begin_norm_axis
self.quant_round_type = self.fd_config.quant_config.quant_round_type if fd_config.quant_config else 0
self.quant_max_bound = self.fd_config.quant_config.quant_max_bound if fd_config.quant_config else 0
self.quant_min_bound = self.fd_config.quant_config.quant_min_bound if fd_config.quant_config else 0
self.begin_norm_axis = begin_norm_axis
self.weight_key: Optional[str] = f"{prefix}.weight"
self.with_weight: bool = self.weight_key is not None
self.eps: float = eps
self.norm_func: Callable = fused_rms_norm
self.linear_bias: Optional[paddle.Tensor] = linear_bias
self.quant_scale: Optional[float] = quant_scale
self._dtype: str = self._helper.get_default_dtype()
self._norm_weight_dtype: str = self._dtype
self.quant_round_type: int = self.fd_config.quant_config.quant_round_type if fd_config.quant_config else 0
self.quant_max_bound: int = self.fd_config.quant_config.quant_max_bound if fd_config.quant_config else 0
self.quant_min_bound: int = self.fd_config.quant_config.quant_min_bound if fd_config.quant_config else 0
self.begin_norm_axis: int = begin_norm_axis
self.init_weight()
@@ -88,7 +93,8 @@ class RMSNorm(nn.Layer):
dtype=self._norm_weight_dtype,
)
def load_state_dict(self, state_dict):
def load_state_dict(self, state_dict: Dict[str,
paddle.Tensor | np.ndarray]):
"""
Load the checkpoint state dictionary into the layer.
@@ -102,7 +108,10 @@ class RMSNorm(nn.Layer):
self._norm_weight_dtype)
self.ln_weight.set_value(weight_tensor)
def forward(self, x, residual_input=None):
def forward(
self,
x,
residual_input: Optional[paddle.Tensor] = None) -> paddle.Tensor:
"""
Defines the forward computation of the layer.
@@ -140,18 +149,18 @@ class RMSNorm(nn.Layer):
class LayerNorm(nn.Layer):
"""
Normalization layer.
Initializes the LayerNormalization layer
"""
def __init__(
self,
fd_config,
hidden_size,
eps=1e-5,
fd_config: FDConfig,
hidden_size: int,
eps: float = 1e-5,
prefix="",
linear_bias=None,
quant_scale=None,
with_bias=False,
linear_bias: paddle.Tensor = None,
quant_scale: float = None,
with_bias: bool = False,
):
"""
Initializes the normalization layer.
@@ -160,35 +169,37 @@ class LayerNorm(nn.Layer):
fd_config (FDConfig): Arguments related to inference, containing
attributes such as weight_dtype, act_dtype, mp_size, hidden_size, head_dim,
num_attention_heads, and ffn_hidden_size.
prefix (str): Unique name of the layer, used for naming internal attributes,
you can give it any name you like.
hidden_size (int) : size of hidden state.
eps:(float, optional): Small value added to the variance to avoid division by zero. Defaults to 1e-5.
prefix (str): Unique name of the layer, used for naming internal attributes,
you can give it any name you like.
linear_bias (float, optional): Initial bias value for the linear layer (if used). Defaults to None.
quant_scale(float,optional):Quantization scale, used in quantization scenarios. Defaults to -1, indicating no quantization.
with_bias (bool):Whether to include bias or not. Defaults to False.
Raises:
NotImplementedError: If the specified norm_type is not supported.
"""
super().__init__()
self.fd_config = fd_config
self.prefix = prefix
self.hidden_size = hidden_size
self.prefix: str = prefix
self.hidden_size: int = hidden_size
if len(prefix) == 0:
self.weight_key = None
self.weight_key: Optional[str] = None
else:
self.weight_key = f"{prefix}.weight"
self.with_weight = self.weight_key is not None
self.bias_key = f"{prefix}.bias"
self.with_bias = with_bias
self.eps = eps
self.weight_key: Optional[str] = f"{prefix}.weight"
self.with_weight: bool = self.weight_key is not None
self.bias_key: str = f"{prefix}.bias"
self.with_bias: bool = with_bias
self.eps: float = eps
self.quant_scale: float = quant_scale
self.norm_func: Callable = fused_layer_norm
self.linear_bias: Optional[paddle.Tensor] = linear_bias
self._dtype: str = self._helper.get_default_dtype()
self._norm_weight_dtype: str = "float32"
self.norm_func = fused_layer_norm
self.linear_bias = linear_bias
self._dtype = self._helper.get_default_dtype()
self._norm_weight_dtype = "float32"
self.quant_round_type = self.fd_config.quant_config.quant_round_type if fd_config.quant_config else 0
self.quant_max_bound = self.fd_config.quant_config.quant_max_bound if fd_config.quant_config else 0
self.quant_min_bound = self.fd_config.quant_config.quant_min_bound if fd_config.quant_config else 0
self.quant_round_type: int = self.fd_config.quant_config.quant_round_type if fd_config.quant_config else 0
self.quant_max_bound: int = self.fd_config.quant_config.quant_max_bound if fd_config.quant_config else 0
self.quant_min_bound: int = self.fd_config.quant_config.quant_min_bound if fd_config.quant_config else 0
self.init_weight()
@@ -212,7 +223,8 @@ class LayerNorm(nn.Layer):
dtype=self._norm_weight_dtype,
)
def load_state_dict(self, state_dict):
def load_state_dict(self, state_dict: Dict[str,
paddle.Tensor | np.ndarray]):
"""
Load the checkpoint state dictionary into the layer.
@@ -233,7 +245,10 @@ class LayerNorm(nn.Layer):
self._norm_weight_dtype)
self.ln_bias.set_value(bias_tensor)
def forward(self, x, residual_input=None):
def forward(
self,
x,
residual_input: Optional[paddle.Tensor] = None) -> paddle.Tensor:
"""
Defines the forward computation of the layer.
@@ -259,7 +274,7 @@ class LayerNorm(nn.Layer):
begin_norm_axis=1,
bias=self.linear_bias,
residual=residual_input,
quant_scale=-1,
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,