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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 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, | |
"leaky_relu": nn.LeakyReLU, | |
"linear": LinearActivation, | |
"mish": MishActivation, | |
"quick_gelu": QuickGELUActivation, | |
"relu": nn.ReLU, | |
"relu2": ReLUSquaredActivation, | |
"relu6": nn.ReLU6, | |
"sigmoid": nn.Sigmoid, | |
"silu": nn.SiLU, | |
"swish": nn.SiLU, | |
"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") | |