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# Copyright (c) 2024, EleutherAI
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# Copyright (c) 2024, NVIDIA CORPORATION. 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 torch
import torch.nn.functional as F
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)
def get_activation(neox_args):
"""retrieves the activation function specified in neox_args and whether or not the activation is gated"""
is_gated = False
if neox_args.activation == "geglu":
is_gated = True
activation_func = F.gelu
elif neox_args.activation == "reglu":
is_gated = True
activation_func = F.relu
elif neox_args.activation == "bilinear":
is_gated = True
activation_func = lambda x: x
elif neox_args.activation == "swiglu":
is_gated = True
activation_func = swish
elif neox_args.activation == "glu":
is_gated = True
activation_func = F.sigmoid
elif neox_args.activation == "gelu":
if neox_args.onnx_safe and neox_args.bias_gelu_fusion:
raise ValueError("onnx_safe + bias_gelu_fusion not compatible")
if neox_args.onnx_safe:
activation_func = erf_gelu
elif neox_args.bias_gelu_fusion:
activation_func = bias_gelu_impl
else:
activation_func = F.gelu
elif neox_args.activation == "relu":
activation_func = F.relu
elif neox_args.activation == "softsign":
activation_func = F.softsign
elif neox_args.activation == "swish":
activation_func = swish
elif neox_args.activation == "mish":
activation_func = mish
elif neox_args.activation == "silu":
activation_func = F.silu
else:
raise ValueError(f"Activation function {neox_args.activation} not recognized")
return activation_func, is_gated
###### BIAS GELU FUSION/ NO AUTOGRAD ################
# 1/sqrt(2*pi)-> 0.3989423
# 1/sqrt(2) -> 0.70710678
# sqrt(2/pi) -> 0.79788456
# this function is tanh approximation of gelu
# actual gelu is:
# x * 0.5 * (1.0 + torch.erf(x * 0.70710678))
@torch.jit.script
def bias_gelu(bias, y):
x = bias + y
return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
# gradient of tanh approximation of gelu
# gradient of actual gelu is:
# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x)
@torch.jit.script
def bias_gelu_back(g, bias, y):
x = bias + y
tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
# sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
ff = 0.5 * x * (
(1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)
) + 0.5 * (1 + tanh_out)
return ff * g
class GeLUFunction(torch.autograd.Function):
@staticmethod
# bias is an optional argument
def forward(ctx, input, bias):
ctx.save_for_backward(input, bias)
return bias_gelu(bias, input)
@staticmethod
def backward(ctx, grad_output):
input, bias = ctx.saved_tensors
tmp = bias_gelu_back(grad_output, bias, input)
return tmp, tmp
bias_gelu_impl = GeLUFunction.apply
# This is actually Python equivalent of torch.nn.functional.gelu(), also with type hints for ONNX exporter
@torch.jit.script
def erf_gelu(x):
return (
x
* 0.5
* (
torch.erf(x / 1.41421).to(dtype=x.dtype)
+ torch.ones_like(x).to(dtype=x.dtype)
)
)
@torch.jit.script
def swish(x, beta: float = 1.0):
return x * torch.sigmoid(beta * x)
@torch.jit.script
def mish(x):
return x * torch.tanh(F.softplus(x))
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