bert_model.py 10.7 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
#
# 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.

"""BERT model."""

import torch

Mohammad's avatar
Mohammad committed
20
from megatron import get_args
mohammad's avatar
mohammad committed
21
from megatron import mpu
22
23
from megatron.model.language_model import parallel_lm_logits
from megatron.model.language_model import get_language_model
24
from megatron.model import import_layernorm
25
from megatron.model.utils import openai_gelu, erf_gelu
26
27
28
from megatron.model.utils import get_linear_layer
from megatron.model.utils import init_method_normal
from megatron.model.utils import scaled_init_method_normal
29
from .module import MegatronModule
30

Neel Kant's avatar
Neel Kant committed
31

32
def bert_extended_attention_mask(attention_mask):
Neel Kant's avatar
Neel Kant committed
33
34
35
36
37
38
39
40
41
42
    # We create a 3D attention mask from a 2D tensor mask.
    # [b, 1, s]
    attention_mask_b1s = attention_mask.unsqueeze(1)
    # [b, s, 1]
    attention_mask_bs1 = attention_mask.unsqueeze(2)
    # [b, s, s]
    attention_mask_bss = attention_mask_b1s * attention_mask_bs1
    # [b, 1, s, s]
    extended_attention_mask = attention_mask_bss.unsqueeze(1)

43
44
    # Convert attention mask to binary:
    extended_attention_mask = (extended_attention_mask < 0.5)
Neel Kant's avatar
Neel Kant committed
45

46
    return extended_attention_mask
Neel Kant's avatar
Neel Kant committed
47
48
49
50
51
52
53
54
55
56
57

def bert_position_ids(token_ids):
    # Create position ids
    seq_length = token_ids.size(1)
    position_ids = torch.arange(seq_length, dtype=torch.long,
                                device=token_ids.device)
    position_ids = position_ids.unsqueeze(0).expand_as(token_ids)

    return position_ids


58
59
60
61
62
63
64
65
class BertLMHead(MegatronModule):
    """Masked LM head for Bert

    Arguments:
        mpu_vocab_size: model parallel size of vocabulary.
        hidden_size: hidden size
        init_method: init method for weight initialization
        layernorm_epsilon: tolerance for layer norm divisions
66
        parallel_output: whether output logits being distributed or not.
67
    """
Neel Kant's avatar
Neel Kant committed
68

69
70
71
72
73
    def __init__(self, mpu_vocab_size, hidden_size, init_method,
                 layernorm_epsilon, parallel_output):

        super(BertLMHead, self).__init__()

74
        args = get_args()
Neel Kant's avatar
Neel Kant committed
75

76
        self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size))
77
        self.bias.tensor_model_parallel = True
Mohammad Shoeybi's avatar
Mohammad Shoeybi committed
78
79
        self.bias.partition_dim = 0
        self.bias.stride = 1
80
81
82
        self.parallel_output = parallel_output

        self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
83
        LayerNorm = import_layernorm(args.fp32_residual_connection)
84
        self.layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)
85
86
87
        self.gelu = torch.nn.functional.gelu
        if args.openai_gelu:
            self.gelu = openai_gelu
88
        elif args.onnx_safe:
Boris Fomitchev's avatar
Boris Fomitchev committed
89
            self.gelu = erf_gelu
90
91
92

    def forward(self, hidden_states, word_embeddings_weight):
        hidden_states = self.dense(hidden_states)
93
        hidden_states = self.gelu(hidden_states)
94
95
96
97
98
99
100
101
        hidden_states = self.layernorm(hidden_states)
        output = parallel_lm_logits(hidden_states,
                                    word_embeddings_weight,
                                    self.parallel_output,
                                    bias=self.bias)
        return output


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
def post_language_model_processing(lm_output, pooled_output,
                                   lm_head, binary_head,
                                   lm_labels,
                                   logit_weights,
                                   fp16_lm_cross_entropy):
    # Output.
    lm_logits = lm_head(
        lm_output, logit_weights)

    binary_logits = None
    if binary_head is not None:
        binary_logits = binary_head(pooled_output)

    if lm_labels is None:
        return lm_logits, binary_logits
    else:
        if fp16_lm_cross_entropy:
            assert lm_logits.dtype == torch.half
            lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits, lm_labels)
        else:
            lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits.float(),
                                                       lm_labels)
        return lm_loss, binary_logits


127
class BertModelBase(MegatronModule):
128
129
    """Bert Language model."""

Mohammad's avatar
Mohammad committed
130
    def __init__(self, num_tokentypes=2, add_binary_head=True,
131
                 parallel_output=True):
132
        super(BertModelBase, self).__init__()
Mohammad's avatar
Mohammad committed
133
        args = get_args()
134

mohammad's avatar
mohammad committed
135
        self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
136
137
        self.add_binary_head = add_binary_head
        self.parallel_output = parallel_output
138

Mohammad's avatar
Mohammad committed
139
140
141
        init_method = init_method_normal(args.init_method_std)
        scaled_init_method = scaled_init_method_normal(args.init_method_std,
                                                       args.num_layers)
Neel Kant's avatar
Neel Kant committed
142

143
144
        self.language_model, self._language_model_key = get_language_model(
            num_tokentypes=num_tokentypes,
145
            add_pooler=self.add_binary_head,
146
            init_method=init_method,
147
            scaled_init_method=scaled_init_method)
148

149
        self.initialize_word_embeddings(init_method_normal)
150
        if mpu.is_pipeline_last_stage():
151
152
153
154
155
156
157
158
159
160
161
            self.lm_head = BertLMHead(
                self.word_embeddings_weight().size(0),
                args.hidden_size, init_method, args.layernorm_epsilon, parallel_output)
            self._lm_head_key = 'lm_head'
            self.binary_head = None
            if self.add_binary_head:
                self.binary_head = get_linear_layer(args.hidden_size, 2,
                                                    init_method)
                self._binary_head_key = 'binary_head'

    def forward(self, bert_model_input, attention_mask,
mohammad's avatar
mohammad committed
162
                tokentype_ids=None, lm_labels=None):
163

164
        extended_attention_mask = bert_extended_attention_mask(attention_mask)
165
166

        kwargs = {}
167
        if mpu.is_pipeline_first_stage():
168
169
170
171
            input_ids = bert_model_input
            position_ids = bert_position_ids(input_ids)
            args = [input_ids, position_ids, extended_attention_mask]
            kwargs['tokentype_ids'] = tokentype_ids
172
        else:
173
174
            args = [bert_model_input, extended_attention_mask]
        lm_output = self.language_model(*args, **kwargs)
175
        if mpu.is_pipeline_last_stage() and self.add_binary_head:
176
            lm_output, pooled_output = lm_output
mohammad's avatar
mohammad committed
177
        else:
178
179
            pooled_output = None

180
        if mpu.is_pipeline_last_stage():
181
182
183
184
185
186
187
            return post_language_model_processing(lm_output, pooled_output,
                                                  self.lm_head, self.binary_head,
                                                  lm_labels,
                                                  self.word_embeddings_weight(),
                                                  self.fp16_lm_cross_entropy)
        else:
            return lm_output
188
189
190
191
192
193
194
195
196
197


    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                       keep_vars=False):
        """For easy load when model is combined with other heads,
        add an extra key."""

        state_dict_ = {}
        state_dict_[self._language_model_key] \
            = self.language_model.state_dict_for_save_checkpoint(
198
            destination, prefix, keep_vars)
199
        if mpu.is_pipeline_last_stage():
200
201
202
            state_dict_[self._lm_head_key] \
                = self.lm_head.state_dict_for_save_checkpoint(
                destination, prefix, keep_vars)
203
        if mpu.is_pipeline_last_stage() and self.add_binary_head:
204
205
            state_dict_[self._binary_head_key] \
                = self.binary_head.state_dict(destination, prefix, keep_vars)
206
        # Save word_embeddings.
207
        if mpu.is_pipeline_last_stage() and not mpu.is_pipeline_first_stage():
208
209
            state_dict_[self._word_embeddings_for_head_key] \
                = self.word_embeddings.state_dict(destination, prefix, keep_vars)
210
211
212
213
214
215
216
        return state_dict_

    def load_state_dict(self, state_dict, strict=True):
        """Customized load."""

        self.language_model.load_state_dict(
            state_dict[self._language_model_key], strict=strict)
217
        if mpu.is_pipeline_last_stage():
218
219
            self.lm_head.load_state_dict(
                state_dict[self._lm_head_key], strict=strict)
220
        if mpu.is_pipeline_last_stage() and self.add_binary_head:
Neel Kant's avatar
Neel Kant committed
221
222
            self.binary_head.load_state_dict(
                state_dict[self._binary_head_key], strict=strict)
223
        # Load word_embeddings.
224
        if mpu.is_pipeline_last_stage() and not mpu.is_pipeline_first_stage():
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
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
            self.word_embeddings.load_state_dict(
                state_dict[self._word_embeddings_for_head_key], strict=strict)


class BertModel(BertModelBase):

    def __init__(self, num_tokentypes=2, add_binary_head=True,
                 parallel_output=True):
        super(BertModel, self).__init__(
            num_tokentypes=num_tokentypes,
            add_binary_head=add_binary_head,
            parallel_output=parallel_output)

    def forward(self, input_ids, attention_mask,
                tokentype_ids=None, lm_labels=None):
        return super(BertModel, self).forward(
            input_ids,
            attention_mask,
            tokentype_ids=tokentype_ids,
            lm_labels=lm_labels)


class BertModelFirstStage(BertModelBase):

    def __init__(self, num_tokentypes=2):
        super(BertModelFirstStage, self).__init__(
            num_tokentypes=num_tokentypes)

    def forward(self, input_ids, attention_mask,
                tokentype_ids=None):
        return super(BertModelFirstStage, self).forward(
            input_ids,
            attention_mask,
            tokentype_ids=tokentype_ids)


class BertModelIntermediateStage(BertModelBase):

    def __init__(self, num_tokentypes=2):
        super(BertModelIntermediateStage, self).__init__(
            num_tokentypes=num_tokentypes)

    def forward(self, hidden_state, attention_mask):
        return super(BertModelIntermediateStage, self).forward(
            hidden_state,
            attention_mask)


class BertModelLastStage(BertModelBase):

    def __init__(self, num_tokentypes=2, add_binary_head=True,
                 parallel_output=True):
        super(BertModelLastStage, self).__init__(
            num_tokentypes=num_tokentypes,
            add_binary_head=add_binary_head,
            parallel_output=parallel_output)

    def forward(self, hidden_state, attention_mask,
                lm_labels=None):
        return super(BertModelLastStage, self).forward(
            hidden_state,
            attention_mask,
            lm_labels=lm_labels)