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