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Sync v2.0 version of code to github repo

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Jiang-Jia-Jun
2025-06-29 23:29:37 +00:00
parent d151496038
commit 92c2cfa2e7
597 changed files with 78776 additions and 22905 deletions
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43
fastdeploy/model_executor/layers/activation.py
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@@ -15,10 +15,13 @@
"""
# cipher_token=WjI1fQOvhN # do not edit this line
from typing import Optional
import paddle
from paddle import nn
from paddle.incubate.nn.functional import fused_bias_act
from fastdeploy.config import LLMConfig
from fastdeploy.config import FDConfig
from fastdeploy.platforms import current_platform
@@ -29,28 +32,27 @@ class SiluAndMul(nn.Layer):
def __init__(
self,
llm_config: LLMConfig,
bias=None,
act_method="gelu",
dequant_scales=None,
shift=None,
smooth=None,
quant_scale=-1,
fd_config: FDConfig,
bias: paddle.Tensor = None,
act_method: str = "gelu",
dequant_scales: Optional[paddle.Tensor] = None,
shift: Optional[paddle.Tensor] = None,
smooth: Optional[paddle.Tensor] = None,
quant_scale: float = -1,
):
"""
Initialize the activation layer with optional parameters for quantization, bias,
activation method, and more.
Args:
llm_config (Any): Arguments related to inference, including quantization
fd_config (Any): Arguments related to inference, including quantization
settings.
bias (Optional[Tensor]): Optional bias term to be added to the output.
act_method (str, optional): Activation method to be applied.
Defaults to "gelu".
dequant_scales (Optional[List[float]]): Dequantization scales, used in
act_method (str): Activation method to be applied. Defaults to "gelu".
dequant_scales (Optional[Tensor]): Dequantization scales, used in
quantization scenarios.
shift (Optional[float]): Shift factor, used in quantization scenarios.
smooth (Optional[float]): Smoothing factor, used for specific activation
shift (Optional[Tensor]): Shift factor, used in quantization scenarios.
smooth (Optional[Tensor]): Smoothing factor, used for specific activation
functions.
quant_scale (float, optional): Quantization scale, used in quantization
scenarios. Defaults to -1, indicating no quantization.
@@ -61,12 +63,13 @@ class SiluAndMul(nn.Layer):
"""
super().__init__()
if current_platform.is_cuda():
if current_platform.is_cuda() or current_platform.is_xpu():
self.forward = self.forward_cuda
else:
raise NotImplementedError
self.bias = bias
act_method = act_method.lower()
if act_method == "silu":
act_method = "swiglu"
@@ -75,9 +78,9 @@ class SiluAndMul(nn.Layer):
self.shift = shift
self.smooth = smooth
self.quant_scale = quant_scale
self.quant_round_type = llm_config.quant_config.quant_round_type
self.quant_max_bound = llm_config.quant_config.quant_max_bound
self.quant_min_bound = llm_config.quant_config.quant_min_bound
self.quant_round_type = fd_config.quant_config.quant_round_type if fd_config.quant_config else 0
self.quant_max_bound = fd_config.quant_config.quant_max_bound if fd_config.quant_config else 0
self.quant_min_bound = fd_config.quant_config.quant_min_bound if fd_config.quant_config else 0
self._dtype = self._helper.get_default_dtype()
if self._dtype == "bfloat16":
@@ -91,12 +94,12 @@ class SiluAndMul(nn.Layer):
bfloat16 as default dtype, but received {self._dtype}")
# fp8 is not support smooth quantization
if "float8" in llm_config.model_config.act_dtype:
if fd_config.quant_config and "fp8" in fd_config.quant_config.name():
self.dequant_scales = None
self.shift = None
self.smooth = None
def forward_cuda(self, x):
def forward_cuda(self, x: paddle.Tensor) -> paddle.Tensor:
"""
Forward propagation of the custom activation layer.