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| # 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") | |