File size: 6,649 Bytes
48b5986 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import importlib
import numbers
import os
import sys
import time
import torch
from torch.nn.parameter import Parameter
sys.path.append(os.path.dirname(__file__))
try:
fastfold_layer_norm_cuda = importlib.import_module("fastfold_layer_norm_cuda")
except ImportError:
from model.protenix.layer_norm.torch_ext_compile import compile
current_dir = os.path.dirname(__file__)
fastfold_layer_norm_cuda = compile(
name="fastfold_layer_norm_cuda",
sources=[
os.path.join(f"{current_dir}/kernel", file)
for file in ["layer_norm_cuda.cpp", "layer_norm_cuda_kernel.cu"]
],
extra_include_paths=[f"{current_dir}/kernel"],
build_directory=current_dir,
)
class FusedLayerNormAffineFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, input, weight, bias, normalized_shape, eps):
d = input.dtype
ctx.normalized_shape = normalized_shape
ctx.eps = eps
input_ = input.contiguous()
if weight is None:
if bias is None:
output, mean, invvar = fastfold_layer_norm_cuda.forward_none_affine(
input_, ctx.normalized_shape, ctx.eps
)
else:
output, mean, invvar = (
fastfold_layer_norm_cuda.forward_with_bias_affine(
input_, ctx.normalized_shape, bias.to(d), ctx.eps
)
)
else:
if bias is None:
output, mean, invvar = (
fastfold_layer_norm_cuda.forward_with_weight_affine(
input_, ctx.normalized_shape, weight.to(d), ctx.eps
)
)
else:
output, mean, invvar = (
fastfold_layer_norm_cuda.forward_with_both_affine(
input_, ctx.normalized_shape, weight.to(d), bias.to(d), ctx.eps
)
)
ctx.save_for_backward(input_, weight, bias, mean, invvar)
return output
@staticmethod
def backward(ctx, grad_output):
d = grad_output.dtype
input_, weight_, bias_, mean, invvar = ctx.saved_tensors
grad_input = grad_weight = grad_bias = None
if weight_ is None:
if bias_ is None:
grad_input, grad_weight, grad_bias = (
fastfold_layer_norm_cuda.backward_none_affine(
grad_output.contiguous(),
mean,
invvar,
input_,
ctx.normalized_shape,
ctx.eps,
)
)
else:
grad_input, grad_weight, grad_bias = (
fastfold_layer_norm_cuda.backward_with_bias_affine(
grad_output.contiguous(),
mean,
invvar,
input_,
ctx.normalized_shape,
bias_.to(dtype=d),
ctx.eps,
)
)
else:
if bias_ is None:
grad_input, grad_weight, grad_bias = (
fastfold_layer_norm_cuda.backward_with_weight_affine(
grad_output.contiguous(),
mean,
invvar,
input_,
ctx.normalized_shape,
weight_.to(dtype=d),
ctx.eps,
)
)
else:
grad_input, grad_weight, grad_bias = (
fastfold_layer_norm_cuda.backward_with_both_affine(
grad_output.contiguous(),
mean,
invvar,
input_,
ctx.normalized_shape,
weight_.to(dtype=d),
bias_.to(dtype=d),
ctx.eps,
)
)
return (
grad_input,
None if weight_ is None else grad_weight,
None if bias_ is None else grad_bias,
None,
None,
)
class FusedLayerNorm(torch.nn.Module):
def __init__(
self,
normalized_shape,
create_scale=True,
create_offset=True,
eps=1e-5,
):
"""
Args:
normalized_shape (int or list or torch.Size) input shape from an expected input of size
create_scale (bool) If set to False, the layer will not learn an additive weight, Default: True
create_offset (bool) If set to False, the layer will not learn an additive bias, Default: True
eps (float) a value added to the denominator for numerical stability. Default: 1e-5
"""
super(FusedLayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = torch.Size(normalized_shape)
self.eps = eps
if create_scale:
self.weight = Parameter(torch.ones(*normalized_shape))
else:
self.weight = None
if create_offset:
self.bias = Parameter(torch.zeros(*normalized_shape))
else:
self.bias = None
self.reset_parameters()
def reset_parameters(self):
if self.weight is not None:
torch.nn.init.ones_(self.weight)
if self.bias is not None:
torch.nn.init.zeros_(self.bias)
def forward(self, input):
return FusedLayerNormAffineFunction.apply(
input, self.weight, self.bias, self.normalized_shape, self.eps
)
if __name__ == "__main__":
dtype = torch.float32
data = torch.rand(10, 10).cuda().to(dtype=dtype)
data1 = data * 1
data.requires_grad = True
data1.requires_grad = True
layer_norm = (
FusedLayerNorm(10, create_scale=True, create_offset=True).cuda().to(dtype=dtype)
)
layer_norm_torch = torch.nn.LayerNorm(10).cuda().to(dtype=dtype)
out = layer_norm(data)
out1 = layer_norm_torch(data1)
# print(out - out1)
loss = out.sum()
loss.backward()
loss1 = out1.sum()
loss1.backward()
print(data.grad - data1.grad)
print(layer_norm.weight.grad - layer_norm_torch.weight.grad)
print(layer_norm.bias.grad - layer_norm_torch.bias.grad)
print(layer_norm.weight.grad, layer_norm.bias.grad)
|