File size: 8,663 Bytes
d90b3a8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""This code is copied from NVIDIA apex:
https://github.com/NVIDIA/apex
with some changes. """
import numbers
import torch
from torch.nn.parameter import Parameter
from torch.nn import init
import importlib
from torch.nn import functional as F
import inspect
from megatron.utils import make_viewless_tensor
try:
from apex.contrib.layer_norm.layer_norm import FastLayerNormFN
HAVE_PERSIST_LAYER_NORM = True
except:
HAVE_PERSIST_LAYER_NORM = False
from apex.normalization.fused_layer_norm import (
FusedLayerNormAffineFunction,
FusedRMSNormAffineFunction,
)
global fused_layer_norm_cuda
fused_layer_norm_cuda = None
class MixedFusedLayerNorm(torch.nn.Module):
def __init__(
self,
normalized_shape,
eps=1e-5,
no_persist_layer_norm=True,
sequence_parallel=False,
apply_layernorm_1p=False,
mem_efficient_ln=True,
):
super(MixedFusedLayerNorm, self).__init__()
self.apply_layernorm_1p = apply_layernorm_1p
self.mem_efficient_ln = mem_efficient_ln
global fused_layer_norm_cuda
fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
# List of hiddens sizes supported in the persistent layer norm kernel
# If the hidden size is not supported, fall back to the non-persistent
# kernel.
persist_ln_hidden_sizes = [
1024,
1536,
2048,
2304,
3072,
3840,
4096,
5120,
6144,
8192,
10240,
12288,
12800,
15360,
16384,
18432,
20480,
24576,
25600,
30720,
32768,
40960,
49152,
65536,
]
if (
normalized_shape not in persist_ln_hidden_sizes
or not HAVE_PERSIST_LAYER_NORM
):
no_persist_layer_norm = True
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = torch.Size(normalized_shape)
self.eps = eps
self.weight = Parameter(torch.Tensor(*normalized_shape))
self.bias = Parameter(torch.Tensor(*normalized_shape))
self.reset_parameters()
self.no_persist_layer_norm = no_persist_layer_norm
self.sequence_parallel = sequence_parallel
# set sequence parallelism flag on weight and bias parameters
setattr(self.weight, "sequence_parallel", self.sequence_parallel)
setattr(self.bias, "sequence_parallel", self.sequence_parallel)
def reset_parameters(self):
if self.apply_layernorm_1p:
init.zeros_(self.weight)
init.zeros_(self.bias)
else:
init.ones_(self.weight)
init.zeros_(self.bias)
def forward(self, input):
weight = self.weight + 1 if self.apply_layernorm_1p else self.weight
# CPU path is here for unittest sake.
if not input.is_cuda:
print(
"WARNING! The input of FusedLayerNorm should be on the GPU."
"This warning should only be triggered in the FusedLayerNorm unit tests."
)
return F.layer_norm(
input, self.normalized_shape, weight, self.bias, self.eps
)
if self.no_persist_layer_norm:
# Apex does not have versions yet (https://github.com/NVIDIA/apex/pull/1648), so we need to inspect
# the function manually on whether the extra arg introduced in https://github.com/NVIDIA/apex/pull/1715 exists yet
if (
"memory_efficient"
in inspect.getfullargspec(FusedLayerNormAffineFunction.forward).args
):
return FusedLayerNormAffineFunction.apply(
input,
weight,
self.bias,
self.normalized_shape,
self.eps,
self.mem_efficient_ln,
)
else:
return FusedLayerNormAffineFunction.apply(
input, weight, self.bias, self.normalized_shape, self.eps
)
else:
output = FastLayerNormFN.apply(input, weight, self.bias, self.eps)
# Apex's fast layer norm function outputs a 'view' tensor (i.e., has
# a populated '_base' field). This will result in schedule.py's
# deallocate_output_tensor() throwing an error, so a viewless tensor is
# created to prevent this.
output = make_viewless_tensor(
inp=output, requires_grad=input.requires_grad, keep_graph=True
)
return output
class MixedFusedRMSNorm(torch.nn.Module):
def __init__(
self,
normalized_shape,
eps=1e-5,
no_persist_layer_norm=True,
sequence_parallel=False,
apply_rmsnorm_1p=False,
mem_efficient_rms=True,
):
super(MixedFusedRMSNorm, self).__init__()
self.apply_rmsnorm_1p = apply_rmsnorm_1p
self.mem_efficient_rms = mem_efficient_rms
self.norm_fn = FusedRMSNormAffineFunction
global fused_layer_norm_cuda
fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
# List of hiddens sizes supported in the persistent layer norm kernel
# If the hidden size is not supported, fall back to the non-persistent
# kernel.
persist_ln_hidden_sizes = [
1024,
1536,
2048,
2304,
3072,
3840,
4096,
5120,
6144,
8192,
10240,
12288,
12800,
15360,
16384,
18432,
20480,
24576,
25600,
30720,
32768,
40960,
49152,
65536,
]
if (
normalized_shape not in persist_ln_hidden_sizes
or not HAVE_PERSIST_LAYER_NORM
):
no_persist_layer_norm = True
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = torch.Size(normalized_shape)
self.eps = eps
self.scale = Parameter(torch.Tensor(*normalized_shape))
self.reset_parameters()
self.no_persist_layer_norm = no_persist_layer_norm
self.sequence_parallel = sequence_parallel
# set sequence parallelism flag on weight and bias parameters
setattr(self.scale, "sequence_parallel", self.sequence_parallel)
def reset_parameters(self):
if self.apply_rmsnorm_1p:
init.zeros_(self.scale)
else:
init.ones_(self.scale)
def forward(self, input):
weight = self.scale + 1 if self.apply_rmsnorm_1p else self.scale
# CPU path is here for unittest sake.
if not input.is_cuda:
print(
"WARNING! The input of FusedLayerNorm should be on the GPU."
"This warning should only be triggered in the FusedRMSNorm unit tests."
)
# Latest pytorch actually supports F.rms_norm but I don't want to break builds so...
return F.layer_norm(input, self.normalized_shape, weight, None, self.eps)
# Apex does not have versions yet (https://github.com/NVIDIA/apex/pull/1648), so we need to inspect
# the function manually on whether the extra arg introduced in https://github.com/NVIDIA/apex/pull/1715 exists yet
if "memory_efficient" in inspect.getfullargspec(self.norm_fn.forward).args:
return self.norm_fn.apply(
input,
weight,
self.normalized_shape,
self.eps,
self.mem_efficient_rms,
)
else:
return self.norm_fn.apply(input, weight, self.normalized_shape, self.eps)
# Apex's fast layer norm function outputs a 'view' tensor (i.e., has
# a populated '_base' field). This will result in schedule.py's
# deallocate_output_tensor() throwing an error, so a viewless tensor is
# created to prevent this.
output = make_viewless_tensor(
inp=output, requires_grad=input.requires_grad, keep_graph=True
)
return output
|