layers.py 16.5 KB
Newer Older
1
# coding=utf-8
Mohammad's avatar
Mohammad committed
2
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
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
#
# 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.


# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch


import math

import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter

28
29
30
31
32
33
from .initialize import get_tensor_model_parallel_rank
from .initialize import get_tensor_model_parallel_world_size
from .mappings import copy_to_tensor_model_parallel_region
from .mappings import gather_from_tensor_model_parallel_region
from .mappings import reduce_from_tensor_model_parallel_region
from .mappings import scatter_to_tensor_model_parallel_region
34
35
36
37
from .random import get_cuda_rng_tracker
from .utils import divide
from .utils import split_tensor_along_last_dim
from .utils import VocabUtility
38
from megatron import get_args
39

mohammad's avatar
mohammad committed
40
41
42
43
44
45

_MODEL_PARALLEL_ATTRIBUTE_DEFAULTS = {'tensor_model_parallel': False,
                                      'partition_dim': -1,
                                      'partition_stride': 1}


mohammad's avatar
mohammad committed
46
47
48
49
50
51
def param_is_not_tensor_parallel_duplicate(param):
    return (hasattr(param, 'tensor_model_parallel') and
            param.tensor_model_parallel) or (
                get_tensor_model_parallel_rank() == 0)


mohammad's avatar
mohammad committed
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
def set_tensor_model_parallel_attributes(tensor, is_parallel, dim, stride):
    # Make sure the attributes are not set.
    for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
        assert not hasattr(tensor, attribute)
    # Set the attributes.
    setattr(tensor, 'tensor_model_parallel', is_parallel)
    setattr(tensor, 'partition_dim', dim)
    setattr(tensor, 'partition_stride', stride)


def set_defaults_if_not_set_tensor_model_parallel_attributes(tensor):
    def maybe_set(attribute, value):
        if not hasattr(tensor, attribute):
            setattr(tensor, attribute, value)
    for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
        maybe_set(attribute, _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS[attribute])


def copy_tensor_model_parallel_attributes(destination_tensor, source_tensor):
    def maybe_copy(attribute):
        if hasattr(source_tensor, attribute):
            setattr(destination_tensor, attribute,
                    getattr(source_tensor, attribute))
    for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
        maybe_copy(attribute)


79
80
81
82
def _initialize_affine_weight_gpu(weight, init_method,
                                  partition_dim, stride=1):
    """Initialize affine weight for model parallel on GPU."""

mohammad's avatar
mohammad committed
83
84
85
86
87
    set_tensor_model_parallel_attributes(tensor=weight,
                                         is_parallel=True,
                                         dim=partition_dim,
                                         stride=stride)

88
89
90
91
92
93
94
95
    with get_cuda_rng_tracker().fork():
        init_method(weight)


def _initialize_affine_weight_cpu(weight, output_size, input_size,
                                  per_partition_size, partition_dim,
                                  init_method, stride=1,
                                  return_master_weight=False):
96
97
98
99
    """Initialize affine weight for model parallel.

    Build the master weight on all processes and scatter
    the relevant chunk."""
Mohammad Shoeybi's avatar
Mohammad Shoeybi committed
100

mohammad's avatar
mohammad committed
101
102
103
104
    set_tensor_model_parallel_attributes(tensor=weight,
                                         is_parallel=True,
                                         dim=partition_dim,
                                         stride=stride)
Mohammad Shoeybi's avatar
Mohammad Shoeybi committed
105

106
107
    # Initialize master weight
    master_weight = torch.empty(output_size, input_size,
108
                                dtype=torch.float,
109
110
                                requires_grad=False)
    init_method(master_weight)
111
112
    args = get_args()
    master_weight = master_weight.to(dtype=args.params_dtype)
113
114
115
116
117

    # Split and copy
    per_partition_per_stride_size = divide(per_partition_size, stride)
    weight_list = torch.split(master_weight, per_partition_per_stride_size,
                              dim=partition_dim)
Jared Casper's avatar
Jared Casper committed
118
    rank = get_tensor_model_parallel_rank()
119
    world_size = get_tensor_model_parallel_world_size()
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
    my_weight_list = weight_list[rank::world_size]

    with torch.no_grad():
        torch.cat(my_weight_list, dim=partition_dim, out=weight)
    if return_master_weight:
        return master_weight
    return None


class VocabParallelEmbedding(torch.nn.Module):
    """Embedding parallelized in the vocabulary dimension.

    This is mainly adapted from torch.nn.Embedding and all the default
    values are kept.
    Arguments:
        num_embeddings: vocabulary size.
        embedding_dim: size of hidden state.
        init_method: method to initialize weights.
    """
Neel Kant's avatar
Neel Kant committed
139

140
141
142
143
144
145
146
147
148
149
150
151
152
    def __init__(self, num_embeddings, embedding_dim,
                 init_method=init.xavier_normal_):
        super(VocabParallelEmbedding, self).__init__()
        # Keep the input dimensions.
        self.num_embeddings = num_embeddings
        self.embedding_dim = embedding_dim
        # Set the detauls for compatibility.
        self.padding_idx = None
        self.max_norm = None
        self.norm_type = 2.
        self.scale_grad_by_freq = False
        self.sparse = False
        self._weight = None
153
        self.tensor_model_parallel_size = get_tensor_model_parallel_world_size()
154
155
156
        # Divide the weight matrix along the vocaburaly dimension.
        self.vocab_start_index, self.vocab_end_index = \
            VocabUtility.vocab_range_from_global_vocab_size(
157
158
                self.num_embeddings, get_tensor_model_parallel_rank(),
                self.tensor_model_parallel_size)
159
        self.num_embeddings_per_partition = self.vocab_end_index - \
Neel Kant's avatar
Neel Kant committed
160
            self.vocab_start_index
161

162
163
        # Allocate weights and initialize.
        args = get_args()
164
        if args.use_cpu_initialization:
165
166
167
            self.weight = Parameter(torch.empty(
                self.num_embeddings_per_partition, self.embedding_dim,
                dtype=args.params_dtype))
168
169
170
171
            if args.perform_initialization:
                _initialize_affine_weight_cpu(
                    self.weight, self.num_embeddings, self.embedding_dim,
                    self.num_embeddings_per_partition, 0, init_method)
172
173
174
175
        else:
            self.weight = Parameter(torch.empty(
                self.num_embeddings_per_partition, self.embedding_dim,
                device=torch.cuda.current_device(), dtype=args.params_dtype))
176
177
178
            if args.perform_initialization:
                _initialize_affine_weight_gpu(self.weight, init_method,
                                              partition_dim=0, stride=1)
179
180

    def forward(self, input_):
181
        if self.tensor_model_parallel_size > 1:
182
183
184
185
186
187
188
189
190
            # Build the mask.
            input_mask = (input_ < self.vocab_start_index) | \
                         (input_ >= self.vocab_end_index)
            # Mask the input.
            masked_input = input_.clone() - self.vocab_start_index
            masked_input[input_mask] = 0
        else:
            masked_input = input_
            # Get the embeddings.
191
192
193
194
195
        output_parallel = F.embedding(masked_input, self.weight,
                                      self.padding_idx, self.max_norm,
                                      self.norm_type, self.scale_grad_by_freq,
                                      self.sparse)
        # Mask the output embedding.
196
        if self.tensor_model_parallel_size > 1:
197
            output_parallel[input_mask, :] = 0.0
198
        # Reduce across all the model parallel GPUs.
199
        output = reduce_from_tensor_model_parallel_region(output_parallel)
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
        return output


class ColumnParallelLinear(torch.nn.Module):
    """Linear layer with column parallelism.

    The linear layer is defined as Y = XA + b. A is parallelized along
    its second dimension as A = [A_1, ..., A_p].

    Arguments:
        input_size: first dimension of matrix A.
        output_size: second dimension of matrix A.
        bias: If true, add bias
        gather_output: If true, call all-gether on output and make Y avaiable
                       to all GPUs, otherwise, every GPU will have its output
                       which is Y_i = XA_i
        init_method: method to initialize weights. Note that bias is always set
                     to zero.
        stride: For the strided linear layers.
        keep_master_weight_for_test: This was added for testing and should be
                                     set to False. It returns the master weights
                                     used for initialization.
222
        skip_bias_add: This was added to enable performance optimations where bias
223
                       can be fused with other elementwise operations. we skip
224
                       adding bias but instead return it.
225
    """
Neel Kant's avatar
Neel Kant committed
226

227
228
    def __init__(self, input_size, output_size, bias=True, gather_output=True,
                 init_method=init.xavier_normal_, stride=1,
229
230
                 keep_master_weight_for_test=False,
                 skip_bias_add=False):
231
232
233
234
235
236
237
        super(ColumnParallelLinear, self).__init__()

        # Keep input parameters
        self.input_size = input_size
        self.output_size = output_size
        self.gather_output = gather_output
        # Divide the weight matrix along the last dimension.
238
        world_size = get_tensor_model_parallel_world_size()
239
        self.output_size_per_partition = divide(output_size, world_size)
240
        self.skip_bias_add = skip_bias_add
241
242
243
244

        # Parameters.
        # Note: torch.nn.functional.linear performs XA^T + b and as a result
        # we allocate the transpose.
245
246
        # Initialize weight.
        args = get_args()
247
        if args.use_cpu_initialization:
248
249
250
            self.weight = Parameter(torch.empty(self.output_size_per_partition,
                                                self.input_size,
                                                dtype=args.params_dtype))
251
252
253
254
255
            if args.perform_initialization:
                self.master_weight = _initialize_affine_weight_cpu(
                    self.weight, self.output_size, self.input_size,
                    self.output_size_per_partition, 0, init_method,
                    stride=stride, return_master_weight=keep_master_weight_for_test)
256
257
258
259
        else:
            self.weight = Parameter(torch.empty(
                self.output_size_per_partition, self.input_size,
                device=torch.cuda.current_device(), dtype=args.params_dtype))
260
261
262
            if args.perform_initialization:
                _initialize_affine_weight_gpu(self.weight, init_method,
                                              partition_dim=0, stride=stride)
hwijeen's avatar
hwijeen committed
263

264
        if bias:
265
            if args.use_cpu_initialization:
266
267
268
269
270
271
272
                self.bias = Parameter(torch.empty(
                    self.output_size_per_partition, dtype=args.params_dtype))
            else:
                self.bias = Parameter(torch.empty(
                    self.output_size_per_partition,
                    device=torch.cuda.current_device(),
                    dtype=args.params_dtype))
273
            set_tensor_model_parallel_attributes(self.bias, True, 0, stride)
274
275
276
277
278
279
            # Always initialize bias to zero.
            with torch.no_grad():
                self.bias.zero_()
        else:
            self.register_parameter('bias', None)

280

281
282
283

    def forward(self, input_):
        # Set up backprop all-reduce.
284
        input_parallel = copy_to_tensor_model_parallel_region(input_)
285
        # Matrix multiply.
286
287
288

        bias = self.bias if not self.skip_bias_add else None
        output_parallel = F.linear(input_parallel, self.weight, bias)
289
290
        if self.gather_output:
            # All-gather across the partitions.
291
            output = gather_from_tensor_model_parallel_region(output_parallel)
292
        else:
hwijeen's avatar
hwijeen committed
293
            output = output_parallel
294
295
        output_bias = self.bias if self.skip_bias_add else None
        return output, output_bias
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322


class RowParallelLinear(torch.nn.Module):
    """Linear layer with row parallelism.

    The linear layer is defined as Y = XA + b. A is parallelized along
    its first dimension and X along its second dimension as:
               -   -
              | A_1 |
              | .   |
          A = | .   |        X = [X_1, ..., X_p]
              | .   |
              | A_p |
               -   -
    Arguments:
        input_size: first dimension of matrix A.
        output_size: second dimension of matrix A.
        bias: If true, add bias. Note that bias is not parallelized.
        input_is_parallel: If true, we assume that the input is already
                           split across the GPUs and we do not split
                           again.
        init_method: method to initialize weights. Note that bias is always set
                     to zero.
        stride: For the strided linear layers.
        keep_master_weight_for_test: This was added for testing and should be
                                     set to False. It returns the master weights
                                     used for initialization.
hwijeen's avatar
hwijeen committed
323
324
        skip_bias_add: This was added to enable performance optimization where bias
                       can be fused with other elementwise operations. We skip
325
                       adding bias but instead return it.
326
    """
Neel Kant's avatar
Neel Kant committed
327

328
329
330
    def __init__(self, input_size, output_size, bias=True,
                 input_is_parallel=False,
                 init_method=init.xavier_normal_, stride=1,
331
332
                 keep_master_weight_for_test=False,
                 skip_bias_add=False):
333
334
335
336
337
338
339
        super(RowParallelLinear, self).__init__()

        # Keep input parameters
        self.input_size = input_size
        self.output_size = output_size
        self.input_is_parallel = input_is_parallel
        # Divide the weight matrix along the last dimension.
340
        world_size = get_tensor_model_parallel_world_size()
341
        self.input_size_per_partition = divide(input_size, world_size)
342
        self.skip_bias_add = skip_bias_add
343
344
345
346

        # Parameters.
        # Note: torch.nn.functional.linear performs XA^T + b and as a result
        # we allocate the transpose.
347
348
        # Initialize weight.
        args = get_args()
349
        if args.use_cpu_initialization:
350
351
352
            self.weight = Parameter(torch.empty(self.output_size,
                                                self.input_size_per_partition,
                                                dtype=args.params_dtype))
353
354
355
356
357
            if args.perform_initialization:
                self.master_weight = _initialize_affine_weight_cpu(
                    self.weight, self.output_size, self.input_size,
                    self.input_size_per_partition, 1, init_method,
                    stride=stride, return_master_weight=keep_master_weight_for_test)
358
359
360
361
        else:
            self.weight = Parameter(torch.empty(
                self.output_size, self.input_size_per_partition,
                device=torch.cuda.current_device(), dtype=args.params_dtype))
362
363
364
            if args.perform_initialization:
                _initialize_affine_weight_gpu(self.weight, init_method,
                                              partition_dim=1, stride=stride)
365
        if bias:
366
            if args.use_cpu_initialization:
367
368
369
370
371
372
                self.bias = Parameter(torch.empty(self.output_size,
                                                  dtype=args.params_dtype))
            else:
                self.bias = Parameter(torch.empty(
                    self.output_size, device=torch.cuda.current_device(),
                    dtype=args.params_dtype))
373
374
375
376
377
378
            # Always initialize bias to zero.
            with torch.no_grad():
                self.bias.zero_()
        else:
            self.register_parameter('bias', None)

379

380
381
382
383
384
385

    def forward(self, input_):
        # Set up backprop all-reduce.
        if self.input_is_parallel:
            input_parallel = input_
        else:
386
            input_parallel = scatter_to_tensor_model_parallel_region(input_)
387
388
389
        # Matrix multiply.
        output_parallel = F.linear(input_parallel, self.weight)
        # All-reduce across all the partitions.
390
        output_ = reduce_from_tensor_model_parallel_region(output_parallel)
391
392
393
        if not self.skip_bias_add:
            output = output_ + self.bias if self.bias is not None else output_
            output_bias = None
394
395
        else:
            output = output_
396
397
            output_bias = self.bias
        return output, output_bias