khalidsaifullaah
commited on
Commit
•
3f395b9
1
Parent(s):
d458774
Saving weights and logs of step 2500
Browse files- events.out.tfevents.1626027152.t1v-n-934dd7d5-w-0.42551.3.v2 +3 -0
- flax_model.msgpack +3 -0
- run.sh +21 -0
- run_2.sh +22 -0
- run_clm_flax_v2.py +823 -0
- utils.py +122 -0
events.out.tfevents.1626027152.t1v-n-934dd7d5-w-0.42551.3.v2
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version https://git-lfs.github.com/spec/v1
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oid sha256:6014b2439019a454ff299b5d33dd83d9f8e5dfa5508901303ac09b2ac318a8e1
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size 367914
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flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:064a2c476ff8b960a62039cf6ee5ad1450f4e7848dfad669dddf51d21c496847
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size 497764120
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run.sh
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#!/usr/bin/env bash
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python run_clm_flax.py \
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--output_dir="${MODEL_DIR}" \
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--model_type="gpt2" \
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--config_name="${MODEL_DIR}" \
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--tokenizer_name="${MODEL_DIR}" \
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--dataset_name="mc4" \
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--dataset_config_name="bn" \
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--do_train --do_eval \
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--block_size="512" \
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--per_device_train_batch_size="64" \
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--per_device_eval_batch_size="64" \
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--learning_rate="5e-3" --warmup_steps="1000" \
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--adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
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--overwrite_output_dir \
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--num_train_epochs="50" \
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--logging_steps="500" \
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--save_steps="2500" \
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--eval_steps="2500" \
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--preprocessing_num_workers="90" \
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--push_to_hub
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run_2.sh
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#!/usr/bin/env bash
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python run_clm_flax_v2.py \
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--output_dir="${MODEL_DIR}" \
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--model_type="gpt2" \
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--config_name="${MODEL_DIR}" \
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--tokenizer_name="${MODEL_DIR}" \
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--dataset_name="mc4" \
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--dataset_config_name="bn" \
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--do_train --do_eval \
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--block_size="512" \
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--per_device_train_batch_size="64" \
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--per_device_eval_batch_size="64" \
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--learning_rate="5e-3" --warmup_steps="1000" \
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--adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
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--overwrite_output_dir \
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--max_steps="100000" \
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--decay_steps="100000" \
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--logging_steps="50" \
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--save_steps="50" \
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--eval_steps="50" \
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--max_eval_samples 100 \
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--push_to_hub
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run_clm_flax_v2.py
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Team All rights reserved.
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#
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5 |
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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7 |
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# You may obtain a copy of the License at
|
8 |
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#
|
9 |
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# http://www.apache.org/licenses/LICENSE-2.0
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10 |
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#
|
11 |
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# Unless required by applicable law or agreed to in writing, software
|
12 |
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# distributed under the License is distributed on an "AS IS" BASIS,
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13 |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
14 |
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# See the License for the specific language governing permissions and
|
15 |
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# limitations under the License.
|
16 |
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"""
|
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+
Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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19 |
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https://huggingface.co/models?filter=causal-lm
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20 |
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"""
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21 |
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# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
|
22 |
+
|
23 |
+
from ast import Str
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24 |
+
import logging
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25 |
+
import math
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26 |
+
import os
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27 |
+
import sys
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28 |
+
import time
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29 |
+
from dataclasses import dataclass, field
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30 |
+
from pathlib import Path
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31 |
+
from typing import Callable, Optional
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32 |
+
import json
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33 |
+
import shutil
|
34 |
+
from collections import defaultdict
|
35 |
+
from flax import training
|
36 |
+
import numpy as np
|
37 |
+
import datasets
|
38 |
+
from datasets import Dataset, load_dataset
|
39 |
+
from tqdm import tqdm
|
40 |
+
|
41 |
+
import jax
|
42 |
+
import jax.profiler
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43 |
+
import jax.numpy as jnp
|
44 |
+
import optax
|
45 |
+
import transformers
|
46 |
+
from flax import jax_utils, traverse_util
|
47 |
+
from flax.jax_utils import unreplicate
|
48 |
+
from flax.training import train_state
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49 |
+
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
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50 |
+
from flax.training.checkpoints import save_checkpoint, restore_checkpoint
|
51 |
+
from flax.serialization import to_bytes, from_bytes
|
52 |
+
from transformers import (
|
53 |
+
CONFIG_MAPPING,
|
54 |
+
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
|
55 |
+
AutoConfig,
|
56 |
+
AutoTokenizer,
|
57 |
+
FlaxAutoModelForCausalLM,
|
58 |
+
HfArgumentParser,
|
59 |
+
TrainingArguments,
|
60 |
+
is_tensorboard_available,
|
61 |
+
)
|
62 |
+
from transformers.testing_utils import CaptureLogger
|
63 |
+
|
64 |
+
from importlib.util import find_spec
|
65 |
+
from utils import PrefetchDataloader, make_batch
|
66 |
+
|
67 |
+
logger = logging.getLogger(__name__)
|
68 |
+
|
69 |
+
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
|
70 |
+
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
|
71 |
+
|
72 |
+
|
73 |
+
@dataclass
|
74 |
+
class ModelArguments:
|
75 |
+
"""
|
76 |
+
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
|
77 |
+
"""
|
78 |
+
|
79 |
+
model_name_or_path: Optional[str] = field(
|
80 |
+
default=None,
|
81 |
+
metadata={
|
82 |
+
"help": "The model checkpoint for weights initialization."
|
83 |
+
"Don't set if you want to train a model from scratch."
|
84 |
+
},
|
85 |
+
)
|
86 |
+
model_type: Optional[str] = field(
|
87 |
+
default=None,
|
88 |
+
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
|
89 |
+
)
|
90 |
+
config_name: Optional[str] = field(
|
91 |
+
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
92 |
+
)
|
93 |
+
tokenizer_name: Optional[str] = field(
|
94 |
+
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
95 |
+
)
|
96 |
+
cache_dir: Optional[str] = field(
|
97 |
+
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
98 |
+
)
|
99 |
+
use_fast_tokenizer: bool = field(
|
100 |
+
default=True,
|
101 |
+
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
102 |
+
)
|
103 |
+
dtype: Optional[str] = field(
|
104 |
+
default="float32",
|
105 |
+
metadata={
|
106 |
+
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
|
107 |
+
},
|
108 |
+
)
|
109 |
+
save_optimizer: Optional[bool] = field(
|
110 |
+
default=True,
|
111 |
+
metadata={"help": "Whether to store full train state including optimizer."},
|
112 |
+
)
|
113 |
+
repo_path_or_name: Optional[str] = field(
|
114 |
+
default=None,
|
115 |
+
metadata={"help": "Path to the modelhub repo directory"},
|
116 |
+
)
|
117 |
+
repo_url: Optional[str] = field(
|
118 |
+
default=None,
|
119 |
+
metadata={"help": "URL of the modelhub repo"},
|
120 |
+
)
|
121 |
+
decay_steps: int = field(default=None, metadata={"help":"Number of steps from peak to final learning rate"})
|
122 |
+
|
123 |
+
@dataclass
|
124 |
+
class DataTrainingArguments:
|
125 |
+
"""
|
126 |
+
Arguments pertaining to what data we are going to input our model for training and eval.
|
127 |
+
"""
|
128 |
+
|
129 |
+
dataset_name: Optional[str] = field(
|
130 |
+
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
131 |
+
)
|
132 |
+
dataset_config_name: Optional[str] = field(
|
133 |
+
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
134 |
+
)
|
135 |
+
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
|
136 |
+
validation_file: Optional[str] = field(
|
137 |
+
default=None,
|
138 |
+
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
|
139 |
+
)
|
140 |
+
data_dir: Optional[str] = field(default=None, metadata={"help": "Path to data directory."})
|
141 |
+
max_train_samples: Optional[int] = field(
|
142 |
+
default=None,
|
143 |
+
metadata={
|
144 |
+
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
145 |
+
"value if set."
|
146 |
+
},
|
147 |
+
)
|
148 |
+
max_eval_samples: Optional[int] = field(
|
149 |
+
default=None,
|
150 |
+
metadata={
|
151 |
+
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
152 |
+
"value if set."
|
153 |
+
},
|
154 |
+
)
|
155 |
+
overwrite_cache: bool = field(
|
156 |
+
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
157 |
+
)
|
158 |
+
validation_split_percentage: Optional[int] = field(
|
159 |
+
default=5,
|
160 |
+
metadata={
|
161 |
+
"help": "The percentage of the train set used as validation set in case there's no validation split"
|
162 |
+
},
|
163 |
+
)
|
164 |
+
block_size: Optional[int] = field(
|
165 |
+
default=None,
|
166 |
+
metadata={
|
167 |
+
"help": "Optional input sequence length after tokenization. "
|
168 |
+
"The training dataset will be truncated in block of this size for training. "
|
169 |
+
"Default to the model max input length for single sentence inputs (take into account special tokens)."
|
170 |
+
},
|
171 |
+
)
|
172 |
+
overwrite_cache: bool = field(
|
173 |
+
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
174 |
+
)
|
175 |
+
preprocessing_num_workers: Optional[int] = field(
|
176 |
+
default=None,
|
177 |
+
metadata={"help": "The number of processes to use for the preprocessing."},
|
178 |
+
)
|
179 |
+
text_column_name: Optional[str] = field(
|
180 |
+
default='text',
|
181 |
+
metadata={"help": "Column containing main text data."},
|
182 |
+
)
|
183 |
+
shuffle_buffer_size: int = field(
|
184 |
+
default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
|
185 |
+
)
|
186 |
+
num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
|
187 |
+
num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
|
188 |
+
prefetch_buffer: int = field(default=8, metadata={"help": "The number of batches to prefetch for loading"})
|
189 |
+
|
190 |
+
def __post_init__(self):
|
191 |
+
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
|
192 |
+
raise ValueError("Need either a dataset name or a training/validation file.")
|
193 |
+
else:
|
194 |
+
if self.train_file is not None:
|
195 |
+
extension = self.train_file.split(".")[-1]
|
196 |
+
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
|
197 |
+
if self.validation_file is not None:
|
198 |
+
extension = self.validation_file.split(".")[-1]
|
199 |
+
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
|
200 |
+
|
201 |
+
|
202 |
+
class TrainState(train_state.TrainState):
|
203 |
+
dropout_rng: jnp.ndarray
|
204 |
+
|
205 |
+
def replicate(self):
|
206 |
+
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
|
207 |
+
|
208 |
+
# the below functions are not used now, probably to be removed
|
209 |
+
def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
|
210 |
+
num_samples = len(samples_idx)
|
211 |
+
samples_to_remove = num_samples % batch_size
|
212 |
+
|
213 |
+
if samples_to_remove != 0:
|
214 |
+
samples_idx = samples_idx[:-samples_to_remove]
|
215 |
+
sections_split = num_samples // batch_size
|
216 |
+
batch_idx = np.split(samples_idx, sections_split)
|
217 |
+
return batch_idx
|
218 |
+
|
219 |
+
|
220 |
+
def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
|
221 |
+
"""
|
222 |
+
The training iterator is advanced so that after groupifying the samples,
|
223 |
+
`num_samples` of length `max_seq_length` are returned.
|
224 |
+
"""
|
225 |
+
num_total_tokens = max_seq_length * num_samples
|
226 |
+
samples = defaultdict(list)
|
227 |
+
|
228 |
+
i = 0
|
229 |
+
while i < num_total_tokens:
|
230 |
+
tokenized_samples = next(train_iterator)
|
231 |
+
i += len(tokenized_samples["input_ids"])
|
232 |
+
|
233 |
+
# concatenate tokenized samples to list
|
234 |
+
samples = {k: samples[k] + tokenized_samples[k] for k in tokenized_samples.keys()}
|
235 |
+
|
236 |
+
# Concatenated tokens are split to lists of length `max_seq_length`.
|
237 |
+
# Note that remainedr of % max_seq_length are thrown away.
|
238 |
+
def group_texts(examples):
|
239 |
+
result = {
|
240 |
+
k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
|
241 |
+
for k, t in examples.items()
|
242 |
+
}
|
243 |
+
return result
|
244 |
+
|
245 |
+
grouped_samples = group_texts(samples)
|
246 |
+
return grouped_samples
|
247 |
+
|
248 |
+
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
|
249 |
+
"""
|
250 |
+
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
|
251 |
+
Shuffle batches if `shuffle` is `True`.
|
252 |
+
"""
|
253 |
+
steps_per_epoch = len(dataset) // batch_size
|
254 |
+
|
255 |
+
if shuffle:
|
256 |
+
batch_idx = jax.random.permutation(rng, len(dataset))
|
257 |
+
else:
|
258 |
+
batch_idx = jnp.arange(len(dataset))
|
259 |
+
|
260 |
+
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
261 |
+
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
|
262 |
+
|
263 |
+
for idx in batch_idx:
|
264 |
+
batch = dataset[idx]
|
265 |
+
batch = {k: jnp.array(v) for k, v in batch.items()}
|
266 |
+
|
267 |
+
batch = shard(batch)
|
268 |
+
|
269 |
+
yield batch
|
270 |
+
|
271 |
+
|
272 |
+
def write_train_metric(summary_writer, train_metrics, train_time, step):
|
273 |
+
summary_writer.scalar("train_time", train_time, step)
|
274 |
+
|
275 |
+
train_metrics = get_metrics(train_metrics)
|
276 |
+
for key, vals in train_metrics.items():
|
277 |
+
tag = f"train_{key}"
|
278 |
+
for i, val in enumerate(vals):
|
279 |
+
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
280 |
+
|
281 |
+
|
282 |
+
def write_eval_metric(summary_writer, eval_metrics, step):
|
283 |
+
for metric_name, value in eval_metrics.items():
|
284 |
+
summary_writer.scalar(f"eval_{metric_name}", value, step)
|
285 |
+
|
286 |
+
|
287 |
+
def create_learning_rate_fn(
|
288 |
+
num_train_steps: int, train_batch_size: int, num_warmup_steps: int, learning_rate: float
|
289 |
+
) -> Callable[[int], jnp.array]:
|
290 |
+
"""Returns a linear warmup, linear_decay learning rate function."""
|
291 |
+
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
|
292 |
+
decay_fn = optax.linear_schedule(
|
293 |
+
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
|
294 |
+
)
|
295 |
+
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
|
296 |
+
return schedule_fn
|
297 |
+
def gpt3_schedule(warmup_steps,
|
298 |
+
total_steps,
|
299 |
+
peak_lr,
|
300 |
+
end_lr):
|
301 |
+
def sch(step):
|
302 |
+
warmup_pct = jnp.clip(step, 0, warmup_steps) / warmup_steps
|
303 |
+
anneal_pct = jnp.clip(step - warmup_steps, 0, total_steps) / total_steps
|
304 |
+
|
305 |
+
return warmup_pct * peak_lr - (peak_lr - end_lr) * (1 - jnp.cos(jnp.pi * anneal_pct)) / 2
|
306 |
+
|
307 |
+
return sch
|
308 |
+
|
309 |
+
# utils
|
310 |
+
def mb_item(x):
|
311 |
+
return x.item() if hasattr(x, "item") else x
|
312 |
+
|
313 |
+
#checkpoint functions
|
314 |
+
def save_model_checkpoint(model, save_dir, state, with_opt=True, push_to_hub=False):
|
315 |
+
"""
|
316 |
+
If `push_to_hub` is True, will save to `save_dir`. Otherwise will save to `save_dir/ckpt-{step}`.
|
317 |
+
"""
|
318 |
+
state = jax_utils.unreplicate(state)
|
319 |
+
logger.info(f"SAVING CHECKPOINT IN {save_dir}...")
|
320 |
+
if not push_to_hub:
|
321 |
+
save_dir = f"{save_dir}/ckpt-{mb_item(state.step)-1}"
|
322 |
+
model.save_pretrained(
|
323 |
+
save_dir,
|
324 |
+
params=state.params,
|
325 |
+
push_to_hub=push_to_hub,
|
326 |
+
commit_message=f"Saving weights and logs at step {mb_item(state.step)-1}",
|
327 |
+
)
|
328 |
+
if with_opt:
|
329 |
+
with open(os.path.join(save_dir, "opt_state.msgpack"), "wb") as f:
|
330 |
+
f.write(to_bytes(state.opt_state))
|
331 |
+
with open(os.path.join(save_dir, "training_state.json"), "w") as f:
|
332 |
+
json.dump({"step": state.step.item()}, f)
|
333 |
+
logger.info("checkpoint saved")
|
334 |
+
|
335 |
+
def restore_model_checkpoint(save_dir, state):
|
336 |
+
logger.info(f"RESTORING CHECKPOINT FROM {save_dir}...")
|
337 |
+
with open(os.path.join(save_dir, "flax_model.msgpack"), "rb") as f:
|
338 |
+
params = from_bytes(state.params, f.read())
|
339 |
+
|
340 |
+
with open(os.path.join(save_dir, "opt_state.msgpack"), "rb") as f:
|
341 |
+
opt_state = from_bytes(state.opt_state, f.read())
|
342 |
+
|
343 |
+
with open(os.path.join(save_dir, "training_state.json"), "r") as f:
|
344 |
+
training_state = json.load(f)
|
345 |
+
step = training_state["step"]
|
346 |
+
|
347 |
+
logger.info("checkpoint restored")
|
348 |
+
return state.replace(step=step, params=params, opt_state=opt_state), step
|
349 |
+
|
350 |
+
def rotate_checkpoints(ckpt_dir:str, save_total_limit:int):
|
351 |
+
"Removes older checkpoints so that `save_total_limit` checkpoints are kept"
|
352 |
+
# TODO: what to remove is decided using step number only, we might want to improve that
|
353 |
+
ckpts = [str(x) for x in Path(ckpt_dir).glob("ckpt-*")]
|
354 |
+
# sort checkpoints by step
|
355 |
+
ckpts_sorted = sorted(ckpts, key=lambda x: int(x.split('-')[-1]))
|
356 |
+
ckpts_to_delete = ckpts_sorted[:-save_total_limit]
|
357 |
+
for ckpt in ckpts_to_delete:
|
358 |
+
logger.info(f"Deleting older checkpoint [{ckpt}] due to save_total_limit ({save_total_limit})")
|
359 |
+
shutil.rmtree(ckpt)
|
360 |
+
|
361 |
+
def main():
|
362 |
+
# See all possible arguments in src/transformers/training_args.py
|
363 |
+
# or by passing the --help flag to this script.
|
364 |
+
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
365 |
+
|
366 |
+
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
367 |
+
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
368 |
+
# If we pass only one argument to the script and it's the path to a json file,
|
369 |
+
# let's parse it to get our arguments.
|
370 |
+
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
371 |
+
else:
|
372 |
+
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
373 |
+
|
374 |
+
if (
|
375 |
+
os.path.exists(training_args.output_dir)
|
376 |
+
and os.listdir(training_args.output_dir)
|
377 |
+
and training_args.do_train
|
378 |
+
and not training_args.overwrite_output_dir
|
379 |
+
):
|
380 |
+
raise ValueError(
|
381 |
+
f"Output directory ({training_args.output_dir}) already exists and is not empty."
|
382 |
+
"Use --overwrite_output_dir to overcome."
|
383 |
+
)
|
384 |
+
|
385 |
+
# Make one log on every process with the configuration for debugging.
|
386 |
+
logging.basicConfig(
|
387 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
388 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
389 |
+
level=logging.INFO,
|
390 |
+
)
|
391 |
+
# Setup logging, we only want one process per machine to log things on the screen.
|
392 |
+
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
|
393 |
+
if jax.process_index() == 0:
|
394 |
+
datasets.utils.logging.set_verbosity_warning()
|
395 |
+
transformers.utils.logging.set_verbosity_info()
|
396 |
+
else:
|
397 |
+
datasets.utils.logging.set_verbosity_error()
|
398 |
+
transformers.utils.logging.set_verbosity_error()
|
399 |
+
|
400 |
+
# Set the verbosity to info of the Transformers logger (on main process only):
|
401 |
+
logger.info(f"Training/evaluation parameters {training_args}")
|
402 |
+
|
403 |
+
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
404 |
+
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
405 |
+
# (the dataset will be downloaded automatically from the datasets Hub).
|
406 |
+
#
|
407 |
+
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
408 |
+
# 'text' is found. You can easily tweak this behavior (see below).
|
409 |
+
#
|
410 |
+
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
|
411 |
+
# download the dataset.
|
412 |
+
if data_args.dataset_name is not None:
|
413 |
+
# Downloading and loading a dataset from the hub.
|
414 |
+
train_dataset = load_dataset(
|
415 |
+
data_args.dataset_name,
|
416 |
+
data_args.dataset_config_name,
|
417 |
+
cache_dir=model_args.cache_dir,
|
418 |
+
streaming=True,
|
419 |
+
split="train"
|
420 |
+
)
|
421 |
+
eval_dataset = load_dataset(
|
422 |
+
data_args.dataset_name,
|
423 |
+
data_args.dataset_config_name,
|
424 |
+
cache_dir=model_args.cache_dir,
|
425 |
+
streaming=True,
|
426 |
+
split="validation"
|
427 |
+
)
|
428 |
+
|
429 |
+
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
430 |
+
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
431 |
+
|
432 |
+
# Load pretrained model and tokenizer
|
433 |
+
|
434 |
+
# Distributed training:
|
435 |
+
# The .from_pretrained methods guarantee that only one local process can concurrently
|
436 |
+
# download model & vocab.
|
437 |
+
if model_args.config_name:
|
438 |
+
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
|
439 |
+
elif model_args.model_name_or_path:
|
440 |
+
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
|
441 |
+
else:
|
442 |
+
config = CONFIG_MAPPING[model_args.model_type]()
|
443 |
+
logger.warning("You are instantiating a new config instance from scratch.")
|
444 |
+
|
445 |
+
if model_args.tokenizer_name:
|
446 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
447 |
+
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
448 |
+
)
|
449 |
+
elif model_args.model_name_or_path:
|
450 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
451 |
+
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
452 |
+
)
|
453 |
+
else:
|
454 |
+
raise ValueError(
|
455 |
+
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
|
456 |
+
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
457 |
+
)
|
458 |
+
|
459 |
+
if model_args.model_name_or_path:
|
460 |
+
model = FlaxAutoModelForCausalLM.from_pretrained(
|
461 |
+
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
462 |
+
)
|
463 |
+
else:
|
464 |
+
model = FlaxAutoModelForCausalLM.from_config(
|
465 |
+
config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
466 |
+
)
|
467 |
+
|
468 |
+
# Preprocessing the datasets.
|
469 |
+
# First we tokenize all the texts.
|
470 |
+
# column_names = eval_dataset.column_names
|
471 |
+
text_column_name = data_args.text_column_name # if data_args.text_column_name in column_names else column_names[0]
|
472 |
+
|
473 |
+
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
|
474 |
+
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
|
475 |
+
|
476 |
+
def tokenize_function(examples):
|
477 |
+
with CaptureLogger(tok_logger) as cl:
|
478 |
+
output = tokenizer(examples[text_column_name])
|
479 |
+
# clm input could be much much longer than block_size
|
480 |
+
if "Token indices sequence length is longer than the" in cl.out:
|
481 |
+
tok_logger.warning(
|
482 |
+
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
|
483 |
+
)
|
484 |
+
return output
|
485 |
+
|
486 |
+
tokenized_dataset = train_dataset.map(
|
487 |
+
tokenize_function,
|
488 |
+
batched=True,
|
489 |
+
)
|
490 |
+
tokenized_eval_dataset = eval_dataset.map(
|
491 |
+
tokenize_function,
|
492 |
+
batched=True,
|
493 |
+
# remove_columns=column_names,
|
494 |
+
# num_proc=data_args.preprocessing_num_workers,
|
495 |
+
# load_from_cache_file=not data_args.overwrite_cache,
|
496 |
+
)
|
497 |
+
|
498 |
+
if data_args.block_size is None:
|
499 |
+
block_size = tokenizer.model_max_length
|
500 |
+
if block_size > config.max_position_embeddings:
|
501 |
+
logger.warning(
|
502 |
+
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
|
503 |
+
"Picking 1024 instead. You can change that default value by passing --block_size xxx."
|
504 |
+
)
|
505 |
+
block_size = 1024
|
506 |
+
else:
|
507 |
+
if data_args.block_size > tokenizer.model_max_length:
|
508 |
+
logger.warning(
|
509 |
+
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
|
510 |
+
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
|
511 |
+
)
|
512 |
+
block_size = min(data_args.block_size, tokenizer.model_max_length)
|
513 |
+
|
514 |
+
# # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
|
515 |
+
def group_texts(examples):
|
516 |
+
# Concatenate all texts.
|
517 |
+
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
|
518 |
+
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
519 |
+
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
|
520 |
+
# customize this part to your needs.
|
521 |
+
total_length = (total_length // block_size) * block_size
|
522 |
+
# Split by chunks of max_len.
|
523 |
+
result = {
|
524 |
+
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
525 |
+
for k, t in concatenated_examples.items()
|
526 |
+
}
|
527 |
+
result["labels"] = result["input_ids"].copy()
|
528 |
+
return result
|
529 |
+
|
530 |
+
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
|
531 |
+
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
|
532 |
+
# to preprocess.
|
533 |
+
#
|
534 |
+
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
535 |
+
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
|
536 |
+
|
537 |
+
shuffle_seed = training_args.seed
|
538 |
+
# if training_args.do_train:
|
539 |
+
# if "train" not in tokenized_dataset:
|
540 |
+
# raise ValueError("--do_train requires a train dataset")
|
541 |
+
# train_dataset = tokenized_dataset
|
542 |
+
# if data_args.max_train_samples is not None:
|
543 |
+
# train_dataset = train_dataset.take(range(data_args.max_train_samples))
|
544 |
+
# train_dataset = train_dataset.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
|
545 |
+
# train_iter = iter(train_dataset)
|
546 |
+
|
547 |
+
|
548 |
+
train_loader = PrefetchDataloader(
|
549 |
+
tokenized_dataset,
|
550 |
+
training_args.max_steps * training_args.gradient_accumulation_steps,
|
551 |
+
int(training_args.per_device_train_batch_size) * jax.device_count(),
|
552 |
+
block_size,
|
553 |
+
prefetch_buffer=data_args.prefetch_buffer,
|
554 |
+
seed=shuffle_seed
|
555 |
+
)
|
556 |
+
# evaluation data is not in streaming mode
|
557 |
+
# if training_args.do_eval:
|
558 |
+
# eval_dataset = tokenized_eval_dataset.map(
|
559 |
+
# group_texts,
|
560 |
+
# batched=True,
|
561 |
+
# num_proc=data_args.preprocessing_num_workers,
|
562 |
+
# load_from_cache_file=not data_args.overwrite_cache,
|
563 |
+
# )
|
564 |
+
# if data_args.max_eval_samples is not None:
|
565 |
+
# eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
566 |
+
|
567 |
+
# Enable tensorboard only on the master node
|
568 |
+
has_tensorboard = is_tensorboard_available()
|
569 |
+
if has_tensorboard and jax.process_index() == 0:
|
570 |
+
try:
|
571 |
+
from flax.metrics.tensorboard import SummaryWriter
|
572 |
+
|
573 |
+
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
574 |
+
except ImportError as ie:
|
575 |
+
has_tensorboard = False
|
576 |
+
logger.warning(
|
577 |
+
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
578 |
+
)
|
579 |
+
else:
|
580 |
+
logger.warning(
|
581 |
+
"Unable to display metrics through TensorBoard because the package is not installed: "
|
582 |
+
"Please run pip install tensorboard to enable."
|
583 |
+
)
|
584 |
+
|
585 |
+
# enable wandb tracking
|
586 |
+
has_wandb = find_spec("wandb") is not None
|
587 |
+
if jax.process_index() == 0 and has_wandb and ("wandb" in training_args.report_to):
|
588 |
+
try:
|
589 |
+
import wandb
|
590 |
+
wandb.init(
|
591 |
+
name=training_args.run_name,
|
592 |
+
entity="wandb",
|
593 |
+
project="hf-flax-gpt-neo-copilot",
|
594 |
+
sync_tensorboard=True
|
595 |
+
)
|
596 |
+
wandb.config.update(training_args)
|
597 |
+
wandb.config.update(model_args)
|
598 |
+
wandb.config.update(data_args)
|
599 |
+
except ImportError as e:
|
600 |
+
print(e)
|
601 |
+
has_wandb = False
|
602 |
+
|
603 |
+
|
604 |
+
# Initialize our training
|
605 |
+
rng = jax.random.PRNGKey(training_args.seed)
|
606 |
+
rng, dropout_rng = jax.random.split(rng)
|
607 |
+
|
608 |
+
# Store some constant
|
609 |
+
num_epochs = int(training_args.num_train_epochs)
|
610 |
+
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() * training_args.gradient_accumulation_steps
|
611 |
+
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
|
612 |
+
total_train_steps = training_args.max_steps * training_args.gradient_accumulation_steps
|
613 |
+
|
614 |
+
# Create learning rate schedule
|
615 |
+
gpt3_schedule_fn = gpt3_schedule(
|
616 |
+
training_args.warmup_steps,
|
617 |
+
model_args.decay_steps,
|
618 |
+
training_args.learning_rate,
|
619 |
+
training_args.learning_rate / 10.
|
620 |
+
)
|
621 |
+
|
622 |
+
# We use Optax's "masking" functionality to not apply weight decay
|
623 |
+
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
624 |
+
# mask boolean with the same structure as the parameters.
|
625 |
+
# The mask is True for parameters that should be decayed.
|
626 |
+
# Note that this mask is specifically adapted for FlaxGPT2.
|
627 |
+
# For other models, one should correct the layer norm parameter naming
|
628 |
+
# accordingly.
|
629 |
+
def decay_mask_fn(params):
|
630 |
+
flat_params = traverse_util.flatten_dict(params)
|
631 |
+
flat_mask = {
|
632 |
+
path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
|
633 |
+
for path in flat_params
|
634 |
+
}
|
635 |
+
return traverse_util.unflatten_dict(flat_mask)
|
636 |
+
|
637 |
+
# create optimizer
|
638 |
+
if training_args.adafactor:
|
639 |
+
# We use the default parameters here to initialize adafactor,
|
640 |
+
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
|
641 |
+
optimizer = optax.adafactor(
|
642 |
+
learning_rate=gpt3_schedule_fn,
|
643 |
+
)
|
644 |
+
else:
|
645 |
+
optimizer = optax.adamw(
|
646 |
+
learning_rate=gpt3_schedule_fn,
|
647 |
+
b1=training_args.adam_beta1,
|
648 |
+
b2=training_args.adam_beta2,
|
649 |
+
eps=training_args.adam_epsilon,
|
650 |
+
weight_decay=training_args.weight_decay,
|
651 |
+
mask=decay_mask_fn,
|
652 |
+
)
|
653 |
+
if training_args.gradient_accumulation_steps > 1:
|
654 |
+
optimizer = optax.MultiSteps(optimizer, training_args.gradient_accumulation_steps)
|
655 |
+
grad_accum_steps = training_args.gradient_accumulation_steps
|
656 |
+
|
657 |
+
# Setup train state
|
658 |
+
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng)
|
659 |
+
|
660 |
+
if training_args.resume_from_checkpoint:
|
661 |
+
state = restore_checkpoint(training_args.resume_from_checkpoint, state)
|
662 |
+
resume_step = mb_item(state.step)
|
663 |
+
else:
|
664 |
+
resume_step = 0
|
665 |
+
|
666 |
+
def loss_fn(logits, labels):
|
667 |
+
shift_logits = logits[..., :-1, :]
|
668 |
+
shift_labels = labels[..., 1:]
|
669 |
+
loss = optax.softmax_cross_entropy(shift_logits, onehot(shift_labels, shift_logits.shape[-1]))
|
670 |
+
return loss.mean()
|
671 |
+
|
672 |
+
# Define gradient update step fn
|
673 |
+
def train_step(state, batch):
|
674 |
+
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
675 |
+
|
676 |
+
def compute_loss(params):
|
677 |
+
labels = batch.pop("labels")
|
678 |
+
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
679 |
+
loss = loss_fn(logits, labels)
|
680 |
+
return loss
|
681 |
+
|
682 |
+
grad_fn = jax.value_and_grad(compute_loss)
|
683 |
+
loss, grad = grad_fn(state.params)
|
684 |
+
grad = jax.lax.pmean(grad, "batch")
|
685 |
+
|
686 |
+
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
687 |
+
|
688 |
+
metrics = {"loss": loss, "learning_rate": gpt3_schedule_fn(state.step // grad_accum_steps)}
|
689 |
+
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
690 |
+
|
691 |
+
return new_state, metrics
|
692 |
+
|
693 |
+
# Define eval fn
|
694 |
+
def eval_step(params, batch):
|
695 |
+
labels = batch.pop("labels")
|
696 |
+
logits = model(**batch, params=params, train=False)[0]
|
697 |
+
loss = loss_fn(logits, labels)
|
698 |
+
|
699 |
+
# summarize metrics
|
700 |
+
metrics = {"loss": loss}
|
701 |
+
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
702 |
+
return metrics
|
703 |
+
|
704 |
+
# Create parallel version of the train and eval step
|
705 |
+
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
|
706 |
+
p_eval_step = jax.pmap(eval_step, "batch")
|
707 |
+
|
708 |
+
# Replicate the train state on each device
|
709 |
+
state = state.replicate()
|
710 |
+
|
711 |
+
logger.info("***** Running training *****")
|
712 |
+
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
713 |
+
logger.info(f" Total train batch size (w. parallel, distributed and grad_accum) = {train_batch_size}")
|
714 |
+
logger.info(f" Total optimization steps = {training_args.max_steps}")
|
715 |
+
|
716 |
+
if not training_args.skip_memory_metrics:
|
717 |
+
server = jax.profiler.start_server(9999)
|
718 |
+
|
719 |
+
train_time = 0
|
720 |
+
train_metrics = []
|
721 |
+
# TODO: figure out training duration
|
722 |
+
steps = tqdm(range(training_args.max_steps), position=0, initial=resume_step)
|
723 |
+
for step in range(total_train_steps):
|
724 |
+
# ======================== Training ================================
|
725 |
+
train_start = time.time()
|
726 |
+
rng, input_rng = jax.random.split(rng)
|
727 |
+
|
728 |
+
cur_step = step
|
729 |
+
# skip to the step from which we are resuming
|
730 |
+
if cur_step < resume_step:
|
731 |
+
continue
|
732 |
+
|
733 |
+
# using advance_iter_and_group_samples seem to make training slower
|
734 |
+
# samples = advance_iter_and_group_samples(iter(tokenized_dataset), int(training_args.per_device_train_batch_size) * jax.device_count(), block_size)
|
735 |
+
# batch = shard(make_batch(samples))
|
736 |
+
batch = shard(next(train_loader))
|
737 |
+
# logger.info(f"{batch['input_ids'].shape}")
|
738 |
+
state, train_metric = p_train_step(state, batch)
|
739 |
+
train_metrics.append(train_metric)
|
740 |
+
if step % grad_accum_steps == 0:
|
741 |
+
steps.update(1)
|
742 |
+
|
743 |
+
if cur_step % (training_args.logging_steps * grad_accum_steps)== 0 and cur_step > 0:
|
744 |
+
# Save metrics
|
745 |
+
train_metric = unreplicate(train_metric)
|
746 |
+
train_time += time.time() - train_start
|
747 |
+
if has_tensorboard and jax.process_index() == 0:
|
748 |
+
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
|
749 |
+
if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
|
750 |
+
# TODO: add accumulation of metrics
|
751 |
+
_metrics = {k if k=="learning_rate" else f"train_{k}":mb_item(v.mean()) for k, v in train_metric.items()}
|
752 |
+
wandb.log({"training_step":cur_step, **_metrics}, commit=True)
|
753 |
+
|
754 |
+
steps.write(
|
755 |
+
f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
|
756 |
+
)
|
757 |
+
|
758 |
+
train_metrics = []
|
759 |
+
|
760 |
+
if cur_step % (training_args.eval_steps * grad_accum_steps) == 0 and cur_step > 0 and training_args.do_eval:
|
761 |
+
# ======================== Evaluating ==============================
|
762 |
+
eval_metrics = []
|
763 |
+
eval_steps = data_args.max_eval_samples # len(eval_dataset) // eval_batch_size
|
764 |
+
# eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
|
765 |
+
eval_loader = PrefetchDataloader(
|
766 |
+
tokenized_eval_dataset,
|
767 |
+
eval_steps,
|
768 |
+
eval_batch_size,
|
769 |
+
block_size,
|
770 |
+
prefetch_buffer=data_args.prefetch_buffer,
|
771 |
+
shuffle=False,
|
772 |
+
)
|
773 |
+
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
|
774 |
+
# Model forward
|
775 |
+
batch = shard(next(eval_loader))
|
776 |
+
metrics = p_eval_step(state.params, batch)
|
777 |
+
eval_metrics.append(metrics)
|
778 |
+
|
779 |
+
# normalize eval metrics
|
780 |
+
eval_metrics = get_metrics(eval_metrics)
|
781 |
+
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
|
782 |
+
|
783 |
+
try:
|
784 |
+
eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
|
785 |
+
except OverflowError:
|
786 |
+
eval_metrics["perplexity"] = float("inf")
|
787 |
+
# TODO: this needs to be closed properly
|
788 |
+
eval_loader.terminate()
|
789 |
+
# Print metrics and update progress bar
|
790 |
+
desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
|
791 |
+
steps.write(desc)
|
792 |
+
steps.desc = desc
|
793 |
+
|
794 |
+
# Save metrics
|
795 |
+
if has_tensorboard and jax.process_index() == 0:
|
796 |
+
# cur_step = epoch * (len(train_dataset) // train_batch_size)
|
797 |
+
write_eval_metric(summary_writer, eval_metrics, cur_step)
|
798 |
+
if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
|
799 |
+
_metrics = {f"eval_{k}":mb_item(v) for k, v in eval_metrics.items()}
|
800 |
+
wandb.log({"eval_step":cur_step, **_metrics})
|
801 |
+
|
802 |
+
if cur_step % (training_args.save_steps * grad_accum_steps) == 0 and cur_step > 0:
|
803 |
+
# save checkpoint after each epoch and push checkpoint to the hub
|
804 |
+
if jax.process_index() == 0:
|
805 |
+
print("*********", training_args.push_to_hub)
|
806 |
+
save_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
|
807 |
+
push_to_hub=training_args.push_to_hub)
|
808 |
+
if model_args.save_optimizer:
|
809 |
+
# this saves full state including optimizer
|
810 |
+
save_checkpoint(training_args.output_dir, jax_utils.unreplicate(state), cur_step, keep=training_args.save_total_limit, overwrite=False)
|
811 |
+
if training_args.save_total_limit is not None:
|
812 |
+
rotate_checkpoints(training_args.output_dir, training_args.save_total_limit)
|
813 |
+
|
814 |
+
train_loader.terminate()
|
815 |
+
# save model after training is over
|
816 |
+
save_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
|
817 |
+
push_to_hub=training_args.push_to_hub)
|
818 |
+
|
819 |
+
|
820 |
+
|
821 |
+
|
822 |
+
if __name__ == "__main__":
|
823 |
+
main()
|
utils.py
ADDED
@@ -0,0 +1,122 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
import threading
|
3 |
+
import queue
|
4 |
+
import multiprocessing
|
5 |
+
from collections import defaultdict
|
6 |
+
import jax
|
7 |
+
import jax.numpy as jnp
|
8 |
+
|
9 |
+
|
10 |
+
|
11 |
+
def make_batch(samples):
|
12 |
+
batch = {k:jnp.array(v) for k,v in samples.items()}
|
13 |
+
batch['labels'] = batch['input_ids'].copy()
|
14 |
+
return batch
|
15 |
+
|
16 |
+
class PrefetchDataloaderTread(threading.Thread):
|
17 |
+
"Prefetch dataloader for IterableDataset"
|
18 |
+
def __init__(self, dataset, max_steps, batch_size, sequence_length, prefetch_buffer=1, shuffle=True, shuffle_buffer=1000, seed=0):
|
19 |
+
super().__init__(daemon=True)
|
20 |
+
self.max_steps = max_steps
|
21 |
+
self.bs = batch_size
|
22 |
+
self.seq_len = sequence_length
|
23 |
+
self.max_length = batch_size * sequence_length
|
24 |
+
self.prefetch_buffer = prefetch_buffer
|
25 |
+
self.shuffle = shuffle
|
26 |
+
self.shuffle_buffer = shuffle_buffer
|
27 |
+
self.seed = seed
|
28 |
+
self.dataset = dataset
|
29 |
+
if shuffle:
|
30 |
+
shuffled_dataset = dataset.shuffle(shuffle_buffer, seed=self.seed)
|
31 |
+
self.seed += 1
|
32 |
+
self.ds_iter = iter(shuffled_dataset)
|
33 |
+
else:
|
34 |
+
self.ds_iter = iter(dataset)
|
35 |
+
self.queue = queue.Queue(prefetch_buffer)
|
36 |
+
self.rem = defaultdict(list)
|
37 |
+
self.start()
|
38 |
+
|
39 |
+
def __next__(self):
|
40 |
+
batch = self.queue.get()
|
41 |
+
return batch
|
42 |
+
|
43 |
+
def run(self):
|
44 |
+
i = 0
|
45 |
+
while True and i < self.max_steps:
|
46 |
+
i += 1
|
47 |
+
# prepair next batch
|
48 |
+
sample = self.rem.copy()
|
49 |
+
l = len(sample["input_ids"])
|
50 |
+
max_length = self.max_length
|
51 |
+
while l < max_length:
|
52 |
+
next_sample = next(self.ds_iter)
|
53 |
+
l += len(next_sample["input_ids"])
|
54 |
+
sample = {k:sample[k]+next_sample[k] for k in next_sample.keys()}
|
55 |
+
|
56 |
+
self.rem = {k:v[max_length:] for k,v in sample.items()}
|
57 |
+
sample = {k:v[:max_length] for k,v in sample.items()}
|
58 |
+
# regroup to shape [bs x seq_len]
|
59 |
+
samples = {k:np.array([v[i*self.seq_len:(i+1)*self.seq_len] for i in range(self.bs)]) for k,v in sample.items()}
|
60 |
+
|
61 |
+
self.queue.put(make_batch(samples))
|
62 |
+
self.queue.put(None)
|
63 |
+
|
64 |
+
def __iter__(self):
|
65 |
+
return self
|
66 |
+
|
67 |
+
|
68 |
+
class PrefetchDataloader(multiprocessing.Process):
|
69 |
+
"Prefetch dataloader for IterableDataset"
|
70 |
+
def __init__(self, dataset, max_steps, batch_size, sequence_length, prefetch_buffer=1, shuffle=True, shuffle_buffer=1000, seed=0):
|
71 |
+
super().__init__(daemon=True)
|
72 |
+
self.max_steps = max_steps
|
73 |
+
self.bs = batch_size
|
74 |
+
self.seq_len = sequence_length
|
75 |
+
self.max_length = batch_size * sequence_length
|
76 |
+
self.prefetch_buffer = prefetch_buffer
|
77 |
+
self.shuffle = shuffle
|
78 |
+
self.shuffle_buffer = shuffle_buffer
|
79 |
+
self.seed = seed
|
80 |
+
self.dataset = dataset
|
81 |
+
self.make_iter()
|
82 |
+
self.queue = multiprocessing.Queue(prefetch_buffer)
|
83 |
+
self.rem = defaultdict(list)
|
84 |
+
self.start()
|
85 |
+
|
86 |
+
def make_iter(self):
|
87 |
+
if self.shuffle:
|
88 |
+
shuffled_dataset = self.dataset.shuffle(self.shuffle_buffer, seed=self.seed)
|
89 |
+
self.seed += 1
|
90 |
+
self.ds_iter = iter(shuffled_dataset)
|
91 |
+
else:
|
92 |
+
self.ds_iter = iter(self.dataset)
|
93 |
+
|
94 |
+
def __next__(self):
|
95 |
+
return make_batch(self.queue.get())
|
96 |
+
|
97 |
+
def run(self):
|
98 |
+
i = 0
|
99 |
+
while True and i < self.max_steps:
|
100 |
+
# prepair next batch
|
101 |
+
sample = self.rem.copy()
|
102 |
+
l = len(sample["input_ids"])
|
103 |
+
max_length = self.max_length
|
104 |
+
while l < max_length:
|
105 |
+
try:
|
106 |
+
next_sample = next(self.ds_iter)
|
107 |
+
except StopIteration:
|
108 |
+
# reset generator if a pass through dataset is completed
|
109 |
+
self.make_iter()
|
110 |
+
l += len(next_sample["input_ids"])
|
111 |
+
sample = {k:sample[k]+next_sample[k] for k in next_sample.keys()}
|
112 |
+
|
113 |
+
self.rem = {k:v[max_length:] for k,v in sample.items()}
|
114 |
+
sample = {k:v[:max_length] for k,v in sample.items()}
|
115 |
+
# regroup to shape [bs x seq_len]
|
116 |
+
samples = {k:np.array([v[i*self.seq_len:(i+1)*self.seq_len] for i in range(self.bs)]) for k,v in sample.items()}
|
117 |
+
|
118 |
+
self.queue.put(samples)
|
119 |
+
self.queue.put(None)
|
120 |
+
|
121 |
+
def __iter__(self):
|
122 |
+
return self
|