-
Notifications
You must be signed in to change notification settings - Fork 1
/
pretrain_gpt.py
328 lines (277 loc) · 13.5 KB
/
pretrain_gpt.py
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
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
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
323
324
325
326
327
328
# coding=utf-8
# Copyright (c) 2020, 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.
"""Pretrain GPT"""
import torch
import math
from functools import partial
from megatron import get_args
from megatron import print_rank_0
from megatron import get_timers
from megatron import get_tokenizer
from megatron import mpu
from megatron.data.gpt_dataset import build_train_valid_test_datasets
from megatron.model import GPTModel, GPTModelPipe
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import average_losses_across_data_parallel_group
import deepspeed
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.accelerator.real_accelerator import get_accelerator
import os
import subprocess
from torch import nn
import torch.nn.functional as F
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
print_rank_0('building GPT model ...')
see_memory_usage(f"Before Building Model", force=True)
args = get_args()
with deepspeed.zero.Init(data_parallel_group=mpu.get_data_parallel_group(),
remote_device=None if args.remote_device == 'none' else args.remote_device,
config_dict_or_path=args.deepspeed_config,
enabled=args.zero_stage == 3,
mpu=mpu):
if args.deepspeed and not args.no_pipeline_parallel:
model = GPTModelPipe(
num_tokentypes=0,
parallel_output=True
)
# This is a hack to give us a reference to get_batch_pipe from within training.py
# We need to call model.set_batch_fn after deepspeed.initialize
model._megatron_batch_fn = get_batch_pipe
# Predompute the attention mask and store it in args. This avoids having to
# pipeline it as an activation during training. The mask is constant, and thus
# we can reuse it.
attention_mask = torch.tril(torch.ones(
(1, args.seq_length, args.seq_length), device=get_accelerator().current_device_name())).view(
1, 1, args.seq_length, args.seq_length)
# Convert attention mask to binary:
attention_mask = (attention_mask < 0.5)
if args.fp16:
attention_mask = attention_mask.half()
elif args.bf16:
attention_mask = attention_mask.bfloat16()
# Attention mask must be bool.
args.attn_mask = attention_mask.to(torch.bool)
else:
model = GPTModel(
num_tokentypes=0,
parallel_output=True,
pre_process=pre_process,
post_process=post_process
)
see_memory_usage(f"After Building Model", force=True)
return model
def get_batch(data_iterator):
"""Generate a batch"""
args = get_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['text']
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b['text'].long()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss)
return tokens, labels, loss_mask, attention_mask, position_ids
def data_post_process(data, data_sampler_state_dict):
args = get_args()
if args.data_efficiency_curriculum_learning:
if 'seqlen_truncate' in data_sampler_state_dict['current_difficulties']:
args.data_efficiency_curriculum_learning_seqlen_type = 'seqlen_truncate'
current_seqlen = data_sampler_state_dict['current_difficulties']['seqlen_truncate']
if current_seqlen < args.seq_length:
data['text'] = data['text'][:, :(current_seqlen+1)].contiguous()
elif 'seqlen_reshape' in data_sampler_state_dict['current_difficulties']:
args.data_efficiency_curriculum_learning_seqlen_type = 'seqlen_reshape'
current_seqlen = data_sampler_state_dict['current_difficulties']['seqlen_reshape']
if current_seqlen < args.seq_length:
orig_num_token = torch.numel(data['text'])
reshape_len = (data['text'].size()[1] // (current_seqlen+1)) * (current_seqlen+1)
data['text'] = torch.cat((data['text'][:, :reshape_len].contiguous().view(-1, current_seqlen+1),
data['text'][:, -(current_seqlen+1):]), 0).contiguous()
num_row = math.ceil(orig_num_token / (current_seqlen+1))
num_row = min(num_row, data['text'].size()[0])
if num_row > 1 and num_row % 2 != 0:
num_row -= 1
data['text'] = data['text'][:num_row, :].contiguous()
else:
args.data_efficiency_curriculum_learning_seqlen_type = None
return data
def get_batch_pipe(data):
"""Modification of `get_batch` to work on `next(data_iterator)` instead of `data_iterator`"""
args = get_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['text']
datatype = torch.int64
# Broadcast data.
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b['text'].long()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss)
if args.curriculum_learning_legacy and args.curriculum_seqlen < tokens.size()[1]:
# seqlen-based curriculum learning
# tokens, position_ids, labels, loss_mask have size [batch size, seqlen]
tokens = tokens[:, :args.curriculum_seqlen].contiguous()
position_ids = position_ids[:, :args.curriculum_seqlen].contiguous()
if labels is not None:
labels = labels[:, :args.curriculum_seqlen].contiguous()
loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()
return (tokens, position_ids, attention_mask), (labels, loss_mask)
def loss_func(loss_mask, moe_loss, mos_loss, output_tensor):
args = get_args()
losses = output_tensor.float()
loss_mask = loss_mask.view(-1).float()
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
if args.mos or args.kd:
# assert max(args.num_experts) >= 1
loss = loss + moe_loss + mos_loss
if args.mos:
return loss, {'total loss': loss, 'lm loss': averaged_loss[0], 'moe loss': moe_loss, 'mos loss': mos_loss}
elif args.kd:
return loss, {'total loss': loss, 'lm loss': averaged_loss[0], 'moe loss': moe_loss, 'kd loss': mos_loss}
print_rank_0('>>> total loss: {}, lm loss {}, kd loss {}'.format(loss, averaged_loss[0], mos_loss))
else:
if max(args.num_experts) <= 1:
return loss, {'lm loss': averaged_loss[0]}
else:
loss = loss + moe_loss
return loss, {'lm loss': averaged_loss[0], 'moe loss': moe_loss}
def calculate_mos_loss(args, stu_output, teacher_model, tokens, position_ids, attention_mask):
mos_loss = 0
alpha = args.kd_alpha_ce
beta = args.kd_beta_ce
kd_temp = args.kd_temp
if teacher_model:
with torch.no_grad():
if args.curriculum_learning_legacy and args.curriculum_seqlen < args.seq_length:
assert args.curriculum_seqlen is not None
curriculum_seqlen = args.curriculum_seqlen
tokens = tokens[:, :curriculum_seqlen].contiguous()
position_ids = position_ids[:, :curriculum_seqlen].contiguous()
attention_mask = attention_mask[:, :, :curriculum_seqlen, :curriculum_seqlen].contiguous()
# No need to truncate labels as we do not need it for the teacher logits
tea_output, *tea_other_losses = teacher_model(tokens, position_ids, attention_mask)
assert stu_output.size() == tea_output.size(), 'teacher and student output should match in size. Student: {}, Teacher: {}, CL seq length {}'.format(stu_output.size(), tea_output.size(), args.curriculum_seqlen)
student_logits = F.log_softmax(stu_output / kd_temp, dim=2)
tea_logits = F.softmax(tea_output / kd_temp, dim=2) # The target logits is expected to be probabilities. If we use log_softmax, then we need to set target_log to true when initializing the KLDivLoss.
mos_loss = kd_temp * kd_temp * nn.KLDivLoss(reduction='batchmean')(student_logits, tea_logits)
mos_loss = mos_loss.div(args.seq_length) * beta
return mos_loss
def forward_step(data_iterator, model):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch-generator').start()
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
timers('batch-generator').stop()
if args.data_efficiency_curriculum_learning:
args.curriculum_seqlen = tokens.size()[1]
if hasattr(args, 'data_efficiency_curriculum_learning_seqlen_type') and \
args.data_efficiency_curriculum_learning_seqlen_type == 'seqlen_reshape':
args.data_efficiency_curriculum_learning_numel = torch.numel(tokens)
if args.mos or args.kd:
# The forward func can return either the loss or the logits, depending on whether passing in the labels or not.
stu_output, *other_losses = model(tokens, position_ids, attention_mask)
if args.curriculum_learning_legacy and args.curriculum_seqlen < args.seq_length:
assert args.curriculum_seqlen is not None
labels = labels[:, :args.curriculum_seqlen].contiguous()
output_tensor = mpu.vocab_parallel_cross_entropy(stu_output.contiguous().float(), labels)
else:
output_tensor, *other_losses = model(tokens, position_ids, attention_mask,
labels=labels)
if args.curriculum_learning_legacy and args.curriculum_seqlen < args.seq_length:
loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()
moe_losses = []
for moe_loss in other_losses:
if moe_loss is not None:
moe_losses.append(moe_loss)
moe_loss = sum(moe_losses) * args.moe_loss_coeff
mos_loss = 0
if args.mos or args.kd:
assert model.training
if args.teacher_forward and args.teacher_model is not None:
mos_loss = calculate_mos_loss(args, stu_output,
args.teacher_model[0], tokens, position_ids, attention_mask)
# Output_tensor stores the standard loss, loos_func calculates the total loss.
return output_tensor, partial(loss_func, loss_mask, moe_loss, mos_loss)
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_args()
print_rank_0('> building train, validation, and test datasets '
'for GPT ...')
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
data_prefix=args.data_path,
data_impl=args.data_impl,
splits_string=args.split,
train_valid_test_num_samples=train_val_test_num_samples,
seq_length=args.seq_length,
seed=args.seed,
skip_warmup=(not args.mmap_warmup))
print_rank_0("> finished creating GPT datasets ...")
return train_ds, valid_ds, test_ds
def command_exists(cmd):
result = subprocess.Popen(f'type {cmd}', stdout=subprocess.PIPE, shell=True)
return result.wait() == 0
def git_ds_info():
from deepspeed.env_report import main as ds_report
ds_report()
# Write out version/git info
git_hash_cmd = "git rev-parse --short HEAD"
git_branch_cmd = "git rev-parse --abbrev-ref HEAD"
if command_exists('git'):
try:
result = subprocess.check_output(git_hash_cmd, shell=True)
git_hash = result.decode('utf-8').strip()
result = subprocess.check_output(git_branch_cmd, shell=True)
git_branch = result.decode('utf-8').strip()
except subprocess.CalledProcessError:
git_hash = "unknown"
git_branch = "unknown"
else:
git_hash = "unknown"
git_branch = "unknown"
print(f'**** Git info for Megatron: git_hash={git_hash} git_branch={git_branch} ****')
if __name__ == "__main__":
git_ds_info()
pretrain(train_valid_test_datasets_provider, model_provider, forward_step,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
data_post_process=data_post_process)