# Copyright 2020 The HuggingFace Team. 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. import math from collections import OrderedDict import torch from packaging import version from torch import Tensor, nn from .utils import logging logger = logging.get_logger(__name__) class PytorchGELUTanh(nn.Module): """ A fast C implementation of the tanh approximation of the GeLU activation function. See https://arxiv.org/abs/1606.08415. This implementation is equivalent to NewGELU and FastGELU but much faster. However, it is not an exact numerical match due to rounding errors. """ def __init__(self): super().__init__() if version.parse(torch.__version__) < version.parse("1.12.0"): raise ImportError( f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use " "PytorchGELUTanh. Please upgrade torch." ) def forward(self, input: Tensor) -> Tensor: return nn.functional.gelu(input, approximate="tanh") class NewGELUActivation(nn.Module): """ Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 """ def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0)))) class GELUActivation(nn.Module): """ Original Implementation of the GELU activation function in Google BERT repo when initially created. For information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 """ def __init__(self, use_gelu_python: bool = False): super().__init__() if use_gelu_python: self.act = self._gelu_python else: self.act = nn.functional.gelu def _gelu_python(self, input: Tensor) -> Tensor: return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0))) def forward(self, input: Tensor) -> Tensor: return self.act(input) class FastGELUActivation(nn.Module): """ Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs """ def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input))) class QuickGELUActivation(nn.Module): """ Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs """ def forward(self, input: Tensor) -> Tensor: return input * torch.sigmoid(1.702 * input) class ClippedGELUActivation(nn.Module): """ Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to https://arxiv.org/abs/2004.09602. Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415 """ def __init__(self, min: float, max: float): if min > max: raise ValueError(f"min should be < max (got min: {min}, max: {max})") super().__init__() self.min = min self.max = max def forward(self, x: Tensor) -> Tensor: return torch.clip(gelu(x), self.min, self.max) class AccurateGELUActivation(nn.Module): """ Applies GELU approximation that is faster than default and more accurate than QuickGELU. See: https://github.com/hendrycks/GELUs Implemented along with MEGA (Moving Average Equipped Gated Attention) """ def __init__(self): super().__init__() self.precomputed_constant = math.sqrt(2 / math.pi) def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(input, 3)))) class SiLUActivation(nn.Module): """ See Gaussian Error Linear Units (Hendrycks et al., https://arxiv.org/abs/1606.08415) where the SiLU (Sigmoid Linear Unit) was originally introduced and coined, and see Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning (Elfwing et al., https://arxiv.org/abs/1702.03118) and Swish: a Self-Gated Activation Function (Ramachandran et al., https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with later. """ def forward(self, input: Tensor) -> Tensor: return nn.functional.silu(input) class MishActivation(nn.Module): """ See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also visit the official repository for the paper: https://github.com/digantamisra98/Mish """ def __init__(self): super().__init__() if version.parse(torch.__version__) < version.parse("1.9.0"): self.act = self._mish_python else: self.act = nn.functional.mish def _mish_python(self, input: Tensor) -> Tensor: return input * torch.tanh(nn.functional.softplus(input)) def forward(self, input: Tensor) -> Tensor: return self.act(input) class LinearActivation(nn.Module): """ Applies the linear activation function, i.e. forwarding input directly to output. """ def forward(self, input: Tensor) -> Tensor: return input class LaplaceActivation(nn.Module): """ Applies elementwise activation based on Laplace function, introduced in MEGA as an attention activation. See https://arxiv.org/abs/2209.10655 Inspired by squared relu, but with bounded range and gradient for better stability """ def forward(self, input, mu=0.707107, sigma=0.282095): input = (input - mu).div(sigma * math.sqrt(2.0)) return 0.5 * (1.0 + torch.erf(input)) class ReLUSquaredActivation(nn.Module): """ Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2 """ def forward(self, input): relu_applied = nn.functional.relu(input) squared = torch.square(relu_applied) return squared class ClassInstantier(OrderedDict): def __getitem__(self, key): content = super().__getitem__(key) cls, kwargs = content if isinstance(content, tuple) else (content, {}) return cls(**kwargs) ACT2CLS = { "gelu": GELUActivation, "gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}), "gelu_fast": FastGELUActivation, "gelu_new": NewGELUActivation, "gelu_python": (GELUActivation, {"use_gelu_python": True}), "gelu_pytorch_tanh": PytorchGELUTanh, "gelu_accurate": AccurateGELUActivation, "laplace": LaplaceActivation, "linear": LinearActivation, "mish": MishActivation, "quick_gelu": QuickGELUActivation, "relu": nn.ReLU, "relu2": ReLUSquaredActivation, "relu6": nn.ReLU6, "sigmoid": nn.Sigmoid, "silu": SiLUActivation, "swish": SiLUActivation, "tanh": nn.Tanh, } ACT2FN = ClassInstantier(ACT2CLS) def get_activation(activation_string): if activation_string in ACT2FN: return ACT2FN[activation_string] else: raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}") # For backwards compatibility with: from activations import gelu_python gelu_python = get_activation("gelu_python") gelu_new = get_activation("gelu_new") gelu = get_activation("gelu") gelu_fast = get_activation("gelu_fast") quick_gelu = get_activation("quick_gelu") silu = get_activation("silu") mish = get_activation("mish") linear_act = get_activation("linear")