FastDeploy/fastdeploy/model_executor/layers/activation.py

"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""

from typing import Optional

import paddle
from paddle import nn
from paddle.incubate.nn.functional import fused_bias_act, swiglu

from fastdeploy.config import FDConfig
from fastdeploy.platforms import current_platform


class SiluAndMul(nn.Layer):
    """
    SiluAndMul Layer
    """

    def __init__(
        self,
        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:
            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): Activation method to be applied. Defaults to "gelu".
            dequant_scales (Optional[Tensor]): Dequantization scales, used in
                quantization scenarios.
            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.

        Raises:
            ValueError: If the default data type is not supported (only float32, float16,
                and bfloat16 are supported).
        """
        super().__init__()

        if (
            current_platform.is_cuda()
            or current_platform.is_xpu()
            or current_platform.is_iluvatar()
            or current_platform.is_dcu()
            or current_platform.is_maca()
        ):
            self.forward = self.forward_cuda
        elif current_platform.is_gcu():
            self.forward = self.forward_gcu
        elif current_platform.is_intel_hpu():
            self.forward = self.forward_intel_hpu
        else:
            raise NotImplementedError

        self.bias = bias
        act_method = act_method.lower()
        if act_method == "silu":
            act_method = "swiglu"

        self.act_method = act_method
        self.dequant_scales = dequant_scales
        self.shift = shift
        self.smooth = smooth
        self.quant_scale = quant_scale
        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":
            self._fuse_kernel_compute_dtype = "bf16"
        elif self._dtype == "float16":
            self._fuse_kernel_compute_dtype = "fp16"
        elif self._dtype == "float32":
            self._fuse_kernel_compute_dtype = "fp32"
        else:
            raise ValueError(
                f"Just support float32, float16 and \
                    bfloat16 as default dtype, but received {self._dtype}"
            )

        # fp8 is not support smooth quantization
        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: paddle.Tensor) -> paddle.Tensor:
        """
        Forward propagation of the custom activation layer.

        Args:
            x (Tensor): Input tensor to the activation layer.

        Returns:
            Tensor: Output tensor.
        """
        return fused_bias_act(
            x,
            bias=self.bias,
            act_method=self.act_method,
            compute_dtype=self._fuse_kernel_compute_dtype,
            dequant_scales=self.dequant_scales,
            shift=self.shift,
            smooth=self.smooth,
            quant_scale=self.quant_scale,
            quant_round_type=self.quant_round_type,
            quant_max_bound=self.quant_max_bound,
            quant_min_bound=self.quant_min_bound,
        )

    def forward_gcu(self, x):
        """
        Forward propagation of the custom activation layer.

        Args:
            x (Tensor): Input tensor to the activation layer.

        Returns:
            Tensor: Output tensor.
        """
        out = swiglu(x)
        if self.bias is not None:
            out = out + self.bias
        return out

    def forward_intel_hpu(self, x):
        """
        Forward propagation of the custom activation layer.
        Args:
            x (Tensor): Input tensor to the activation layer.
        Returns:
            Tensor: Output tensor.
        """
        return


def get_act_fn(act_fn_name: str) -> nn.Layer:
    """Get an activation function by name."""
    act_fn_name = act_fn_name.lower()

    if act_fn_name.startswith("paddle.nn.Layer"):
        activation_name = act_fn_name.split(".")[-1]
        if activation_name == "identity":
            return nn.Identity()
        act_fn_name = activation_name

    activation_map = {
        "gelu": nn.GELU(),
        "relu": nn.ReLU(),
        "silu": nn.Silu(),
        "tanh": nn.Tanh(),
        "sigmoid": nn.Sigmoid(),
    }
    if act_fn_name in activation_map:
        return activation_map[act_fn_name]
    else:
        raise ValueError(f"Activation function {act_fn_name!r} is not supported.")