Source code for modelzoo.common.pytorch.model_utils.activations

# Copyright 2022 Cerebras Systems.
#
# 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
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# 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.

# This code is adapted from
# https://github.com/huggingface/transformers/blob/master/src/transformers/activations.py
#
# Copyright 2022 Cerebras Systems.
#
# 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

import torch
from torch import nn

# TODO: Figure logging
# from .utils import logging
# logger = logging.get_logger(__name__)


[docs]def gelu_new(x): """ 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 """ return ( 0.5 * x * ( 1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * x * x * x)) ) )
[docs]def gelu_fast(x): return ( 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))) )
[docs]def quick_gelu(x): return x * torch.sigmoid(1.702 * x)
[docs]def squared_gelu(x): g = gelu(x) return g * g
[docs]def linear_act(x): return x
# GLU bivariate Activations implementation
[docs]def glu_bivariate_base_fn(x1, x2, activation_fn): assert ( x1.shape == x2.shape ), "GLU activation inputs must have the same shape" return x1 * activation_fn(x2)
[docs]def liglu(x1, x2): identity = lambda x: x return glu_bivariate_base_fn(x1, x2, identity)
[docs]def geglu(x1, x2): return glu_bivariate_base_fn(x1, x2, gelu)
[docs]def reglu(x1, x2): return glu_bivariate_base_fn(x1, x2, nn.functional.relu)
[docs]def swiglu(x1, x2): return glu_bivariate_base_fn(x1, x2, nn.functional.silu)
GLU_ACTIVATIONS = { "liglu", "geglu", "reglu", "swiglu", } ACT2FN = { "relu": nn.functional.relu, "leaky_relu": nn.functional.leaky_relu, "silu": nn.functional.silu, "swish": nn.functional.silu, "gelu": nn.functional.gelu, "tanh": torch.tanh, "gelu_new": gelu_new, "gelu_fast": gelu_fast, "quick_gelu": quick_gelu, "squared_gelu": squared_gelu, "mish": nn.functional.mish, "linear": linear_act, "sigmoid": torch.sigmoid, "relu6": nn.functional.relu6, "liglu": liglu, "geglu": geglu, "reglu": reglu, "swiglu": swiglu, None: linear_act, }
[docs]def get_activation(activation): if callable(activation): return activation if activation is not None: activation = activation.lower() if activation in ACT2FN: return ACT2FN[activation] else: raise KeyError( f"function {activation} not found in ACT2FN mapping {list(ACT2FN.keys())}" )
[docs]def is_glu_activation(activation): if hasattr(activation, "is_glu_activation"): return getattr(activation, "is_glu_activation") if isinstance(activation, str): activation = activation.lower() return activation in GLU_ACTIVATIONS