ydshieh
commited on
Commit
•
5306066
1
Parent(s):
283180e
upload debug.py
Browse files
debug.py
ADDED
@@ -0,0 +1,1343 @@
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1 |
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#!/usr/bin/env python
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# coding=utf-8
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3 |
+
# Copyright 2021 The HuggingFace Team All rights reserved.
|
4 |
+
#
|
5 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
6 |
+
# you may not use this file except in compliance with the License.
|
7 |
+
# You may obtain a copy of the License at
|
8 |
+
#
|
9 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
10 |
+
#
|
11 |
+
# Unless required by applicable law or agreed to in writing, software
|
12 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
13 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
14 |
+
# See the License for the specific language governing permissions and
|
15 |
+
# limitations under the License.
|
16 |
+
"""
|
17 |
+
Fine-tuning the library models for summarization.
|
18 |
+
"""
|
19 |
+
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
|
20 |
+
import json
|
21 |
+
import logging
|
22 |
+
import os
|
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+
import sys
|
24 |
+
import time
|
25 |
+
from dataclasses import dataclass, field
|
26 |
+
import datetime
|
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+
from functools import partial
|
28 |
+
from pathlib import Path
|
29 |
+
from typing import Callable, Optional
|
30 |
+
|
31 |
+
import datasets
|
32 |
+
import nltk # Here to have a nice missing dependency error message early on
|
33 |
+
import numpy as np
|
34 |
+
from datasets import Dataset, load_dataset, load_metric
|
35 |
+
from tqdm import tqdm
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36 |
+
from PIL import Image
|
37 |
+
|
38 |
+
import jax
|
39 |
+
import jax.numpy as jnp
|
40 |
+
import optax
|
41 |
+
import transformers
|
42 |
+
from filelock import FileLock
|
43 |
+
from flax import jax_utils, traverse_util
|
44 |
+
from flax.jax_utils import unreplicate
|
45 |
+
from flax.training import train_state
|
46 |
+
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
|
47 |
+
from huggingface_hub import Repository
|
48 |
+
from transformers import (
|
49 |
+
CONFIG_MAPPING,
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50 |
+
FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
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51 |
+
AutoConfig,
|
52 |
+
AutoFeatureExtractor,
|
53 |
+
AutoTokenizer,
|
54 |
+
HfArgumentParser,
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55 |
+
TrainingArguments,
|
56 |
+
is_tensorboard_available,
|
57 |
+
FlaxAutoModelForVision2Seq,
|
58 |
+
)
|
59 |
+
from transformers.file_utils import get_full_repo_name, is_offline_mode
|
60 |
+
|
61 |
+
|
62 |
+
logger = logging.getLogger(__name__)
|
63 |
+
|
64 |
+
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
65 |
+
|
66 |
+
|
67 |
+
try:
|
68 |
+
nltk.data.find("tokenizers/punkt")
|
69 |
+
except (LookupError, OSError):
|
70 |
+
if is_offline_mode():
|
71 |
+
raise LookupError(
|
72 |
+
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
|
73 |
+
)
|
74 |
+
with FileLock(".lock") as lock:
|
75 |
+
nltk.download("punkt", quiet=True)
|
76 |
+
|
77 |
+
|
78 |
+
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING.keys())
|
79 |
+
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
|
80 |
+
|
81 |
+
|
82 |
+
# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
|
83 |
+
def shift_tokens_right(input_ids: np.ndarray, pad_token_id: int, decoder_start_token_id: int) -> np.ndarray:
|
84 |
+
"""
|
85 |
+
Shift input ids one token to the right.
|
86 |
+
"""
|
87 |
+
shifted_input_ids = np.zeros_like(input_ids)
|
88 |
+
shifted_input_ids[:, 1:] = input_ids[:, :-1]
|
89 |
+
shifted_input_ids[:, 0] = decoder_start_token_id
|
90 |
+
|
91 |
+
shifted_input_ids = np.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
|
92 |
+
return shifted_input_ids
|
93 |
+
|
94 |
+
|
95 |
+
@dataclass
|
96 |
+
class CustomTrainingArguments(TrainingArguments):
|
97 |
+
|
98 |
+
do_predict_during_training: bool = field(default=None, metadata={"help": "???"})
|
99 |
+
do_predict_after_evaluation: bool = field(default=None, metadata={"help": "???"})
|
100 |
+
block_size: int = field(default=None, metadata={"help": "???"})
|
101 |
+
|
102 |
+
|
103 |
+
@dataclass
|
104 |
+
class ModelArguments:
|
105 |
+
"""
|
106 |
+
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
|
107 |
+
"""
|
108 |
+
|
109 |
+
model_name_or_path: Optional[str] = field(
|
110 |
+
default=None,
|
111 |
+
metadata={
|
112 |
+
"help": "The model checkpoint for weights initialization."
|
113 |
+
"Don't set if you want to train a model from scratch."
|
114 |
+
},
|
115 |
+
)
|
116 |
+
encoder_model_name_or_path: Optional[str] = field(
|
117 |
+
default=None,
|
118 |
+
metadata={
|
119 |
+
"help": "The encoder model checkpoint for weights initialization."
|
120 |
+
"Don't set if you want to train a model from scratch."
|
121 |
+
},
|
122 |
+
)
|
123 |
+
decoder_model_name_or_path: Optional[str] = field(
|
124 |
+
default=None,
|
125 |
+
metadata={
|
126 |
+
"help": "The decoder model checkpoint for weights initialization."
|
127 |
+
"Don't set if you want to train a model from scratch."
|
128 |
+
},
|
129 |
+
)
|
130 |
+
model_type: Optional[str] = field(
|
131 |
+
default='vision-encoder-decoder',
|
132 |
+
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
|
133 |
+
)
|
134 |
+
encoder_model_type: Optional[str] = field(
|
135 |
+
default=None,
|
136 |
+
metadata={"help": "If training from scratch, pass a encoder model type from the list: " + ", ".join(MODEL_TYPES)},
|
137 |
+
)
|
138 |
+
decoder_model_type: Optional[str] = field(
|
139 |
+
default=None,
|
140 |
+
metadata={"help": "If training from scratch, pass a decoder model type from the list: " + ", ".join(MODEL_TYPES)},
|
141 |
+
)
|
142 |
+
config_name: Optional[str] = field(
|
143 |
+
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
144 |
+
)
|
145 |
+
encoder_config_name: Optional[str] = field(
|
146 |
+
default=None, metadata={"help": "Pretrained config name or path if not the same as encoder_model_name"}
|
147 |
+
)
|
148 |
+
decoder_config_name: Optional[str] = field(
|
149 |
+
default=None, metadata={"help": "Pretrained config name or path if not the same as decoder_model_name"}
|
150 |
+
)
|
151 |
+
feature_extractor_name: Optional[str] = field(
|
152 |
+
default=None, metadata={"help": "Pretrained feature extractor_name name or path if not the same as encoder_model_name"}
|
153 |
+
)
|
154 |
+
tokenizer_name: Optional[str] = field(
|
155 |
+
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as decoder_model_name"}
|
156 |
+
)
|
157 |
+
cache_dir: Optional[str] = field(
|
158 |
+
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
159 |
+
)
|
160 |
+
use_fast_tokenizer: bool = field(
|
161 |
+
default=True,
|
162 |
+
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
163 |
+
)
|
164 |
+
dtype: Optional[str] = field(
|
165 |
+
default="float32",
|
166 |
+
metadata={
|
167 |
+
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
|
168 |
+
},
|
169 |
+
)
|
170 |
+
|
171 |
+
|
172 |
+
@dataclass
|
173 |
+
class DataTrainingArguments:
|
174 |
+
"""
|
175 |
+
Arguments pertaining to what data we are going to input our model for training and eval.
|
176 |
+
"""
|
177 |
+
|
178 |
+
dataset_name: Optional[str] = field(
|
179 |
+
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
180 |
+
)
|
181 |
+
dataset_config_name: Optional[str] = field(
|
182 |
+
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
183 |
+
)
|
184 |
+
data_dir: Optional[str] = field(
|
185 |
+
default=None, metadata={"help": "The data directory of the dataset to use (via the datasets library)."}
|
186 |
+
)
|
187 |
+
image_column: Optional[str] = field(
|
188 |
+
default=None,
|
189 |
+
metadata={"help": "The name of the column in the datasets containing the full image file paths (for image captioning)."},
|
190 |
+
)
|
191 |
+
caption_column: Optional[str] = field(
|
192 |
+
default=None,
|
193 |
+
metadata={"help": "The name of the column in the datasets containing the image captions (for image captioning)."},
|
194 |
+
)
|
195 |
+
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
|
196 |
+
validation_file: Optional[str] = field(
|
197 |
+
default=None,
|
198 |
+
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
|
199 |
+
)
|
200 |
+
test_file: Optional[str] = field(
|
201 |
+
default=None,
|
202 |
+
metadata={"help": "An optional input predict data file to do prediction on (a text file)."},
|
203 |
+
)
|
204 |
+
max_source_length: Optional[int] = field(
|
205 |
+
default=1024,
|
206 |
+
metadata={
|
207 |
+
"help": "The maximum total input sequence length after tokenization. Sequences longer "
|
208 |
+
"than this will be truncated, sequences shorter will be padded."
|
209 |
+
},
|
210 |
+
)
|
211 |
+
max_target_length: Optional[int] = field(
|
212 |
+
default=128,
|
213 |
+
metadata={
|
214 |
+
"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
|
215 |
+
"than this will be truncated, sequences shorter will be padded."
|
216 |
+
},
|
217 |
+
)
|
218 |
+
val_max_target_length: Optional[int] = field(
|
219 |
+
default=None,
|
220 |
+
metadata={
|
221 |
+
"help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
|
222 |
+
"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
|
223 |
+
"This argument is also used to override the `max_length` param of `model.generate`, which is used "
|
224 |
+
"during evaluation."
|
225 |
+
},
|
226 |
+
)
|
227 |
+
max_train_samples: Optional[int] = field(
|
228 |
+
default=None,
|
229 |
+
metadata={
|
230 |
+
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
231 |
+
"value if set."
|
232 |
+
},
|
233 |
+
)
|
234 |
+
max_eval_samples: Optional[int] = field(
|
235 |
+
default=None,
|
236 |
+
metadata={
|
237 |
+
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
238 |
+
"value if set."
|
239 |
+
},
|
240 |
+
)
|
241 |
+
max_predict_samples: Optional[int] = field(
|
242 |
+
default=None,
|
243 |
+
metadata={
|
244 |
+
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
|
245 |
+
"value if set."
|
246 |
+
},
|
247 |
+
)
|
248 |
+
preprocessing_num_workers: Optional[int] = field(
|
249 |
+
default=None,
|
250 |
+
metadata={"help": "The number of processes to use for the preprocessing."},
|
251 |
+
)
|
252 |
+
predict_with_generate: bool = field(
|
253 |
+
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
|
254 |
+
)
|
255 |
+
num_beams: Optional[int] = field(
|
256 |
+
default=None,
|
257 |
+
metadata={
|
258 |
+
"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
|
259 |
+
"which is used during evaluation."
|
260 |
+
},
|
261 |
+
)
|
262 |
+
overwrite_cache: bool = field(
|
263 |
+
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
264 |
+
)
|
265 |
+
|
266 |
+
def __post_init__(self):
|
267 |
+
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
|
268 |
+
raise ValueError("Need either a dataset name or a training/validation file.")
|
269 |
+
else:
|
270 |
+
if self.train_file is not None:
|
271 |
+
extension = self.train_file.split(".")[-1]
|
272 |
+
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
|
273 |
+
if self.validation_file is not None:
|
274 |
+
extension = self.validation_file.split(".")[-1]
|
275 |
+
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
|
276 |
+
if self.val_max_target_length is None:
|
277 |
+
self.val_max_target_length = self.max_target_length
|
278 |
+
|
279 |
+
|
280 |
+
image_captioning_name_mapping = {
|
281 |
+
"image_caption_dataset.py": ("image_file", "caption"),
|
282 |
+
}
|
283 |
+
|
284 |
+
|
285 |
+
class TrainState(train_state.TrainState):
|
286 |
+
dropout_rng: jnp.ndarray
|
287 |
+
|
288 |
+
def replicate(self):
|
289 |
+
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
|
290 |
+
|
291 |
+
|
292 |
+
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
|
293 |
+
"""
|
294 |
+
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
|
295 |
+
Shuffle batches if `shuffle` is `True`.
|
296 |
+
"""
|
297 |
+
steps_per_epoch = len(dataset) // batch_size
|
298 |
+
|
299 |
+
if shuffle:
|
300 |
+
batch_idx = jax.random.permutation(rng, len(dataset))
|
301 |
+
else:
|
302 |
+
batch_idx = jnp.arange(len(dataset))
|
303 |
+
|
304 |
+
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
305 |
+
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
|
306 |
+
|
307 |
+
for idx in batch_idx:
|
308 |
+
batch = dataset[idx]
|
309 |
+
batch = {k: jnp.array(v) for k, v in batch.items()}
|
310 |
+
|
311 |
+
batch = shard(batch)
|
312 |
+
|
313 |
+
yield batch
|
314 |
+
|
315 |
+
|
316 |
+
def write_metric(summary_writer, mode, metrics, step, train_time=None):
|
317 |
+
|
318 |
+
if train_time:
|
319 |
+
summary_writer.scalar("train_time", train_time, step)
|
320 |
+
|
321 |
+
if mode == "train":
|
322 |
+
metrics = get_metrics(metrics)
|
323 |
+
for key, vals in metrics.items():
|
324 |
+
tag = f"{mode}_{key}"
|
325 |
+
for i, val in enumerate(vals):
|
326 |
+
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
327 |
+
|
328 |
+
elif mode in ["valid", "pred"]:
|
329 |
+
for metric_name, value in metrics.items():
|
330 |
+
summary_writer.scalar(f"{mode}_{metric_name}", value, step)
|
331 |
+
|
332 |
+
|
333 |
+
def create_learning_rate_fn(
|
334 |
+
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
|
335 |
+
) -> Callable[[int], jnp.array]:
|
336 |
+
"""Returns a linear warmup, linear_decay learning rate function."""
|
337 |
+
steps_per_epoch = train_ds_size // train_batch_size
|
338 |
+
num_train_steps = steps_per_epoch * num_train_epochs
|
339 |
+
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
|
340 |
+
decay_fn = optax.linear_schedule(
|
341 |
+
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
|
342 |
+
)
|
343 |
+
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
|
344 |
+
return schedule_fn
|
345 |
+
|
346 |
+
|
347 |
+
def main():
|
348 |
+
# See all possible arguments in src/transformers/training_args.py
|
349 |
+
# or by passing the --help flag to this script.
|
350 |
+
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
351 |
+
|
352 |
+
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments))
|
353 |
+
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
354 |
+
# If we pass only one argument to the script and it's the path to a json file,
|
355 |
+
# let's parse it to get our arguments.
|
356 |
+
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
357 |
+
else:
|
358 |
+
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
359 |
+
|
360 |
+
if (
|
361 |
+
os.path.exists(training_args.output_dir)
|
362 |
+
and os.listdir(training_args.output_dir)
|
363 |
+
and training_args.do_train
|
364 |
+
and not training_args.overwrite_output_dir
|
365 |
+
):
|
366 |
+
raise ValueError(
|
367 |
+
f"Output directory ({training_args.output_dir}) already exists and is not empty."
|
368 |
+
"Use --overwrite_output_dir to overcome."
|
369 |
+
)
|
370 |
+
|
371 |
+
# Make one log on every process with the configuration for debugging.
|
372 |
+
logging.basicConfig(
|
373 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
374 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
375 |
+
level=logging.INFO,
|
376 |
+
)
|
377 |
+
# Setup logging, we only want one process per machine to log things on the screen.
|
378 |
+
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
|
379 |
+
if jax.process_index() == 0:
|
380 |
+
datasets.utils.logging.set_verbosity_warning()
|
381 |
+
transformers.utils.logging.set_verbosity_info()
|
382 |
+
else:
|
383 |
+
datasets.utils.logging.set_verbosity_error()
|
384 |
+
transformers.utils.logging.set_verbosity_error()
|
385 |
+
|
386 |
+
# Set the verbosity to info of the Transformers logger (on main process only):
|
387 |
+
logger.info(f"Training/evaluation parameters {training_args}")
|
388 |
+
|
389 |
+
# Handle the repository creation
|
390 |
+
if training_args.push_to_hub:
|
391 |
+
if training_args.hub_model_id is None:
|
392 |
+
repo_name = get_full_repo_name(
|
393 |
+
Path(training_args.output_dir).absolute().name, token=training_args.hub_token
|
394 |
+
)
|
395 |
+
else:
|
396 |
+
repo_name = training_args.hub_model_id
|
397 |
+
repo = Repository(training_args.output_dir, clone_from=repo_name)
|
398 |
+
|
399 |
+
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
|
400 |
+
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
401 |
+
# (the dataset will be downloaded automatically from the datasets Hub).
|
402 |
+
#
|
403 |
+
# For CSV/JSON files this script will use the first column for the full texts and the second column for the
|
404 |
+
# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
|
405 |
+
#
|
406 |
+
if data_args.dataset_name is not None:
|
407 |
+
# Downloading and loading a dataset from the hub.
|
408 |
+
dataset = load_dataset(
|
409 |
+
data_args.dataset_name, data_args.dataset_config_name, keep_in_memory=False, data_dir=data_args.data_dir,
|
410 |
+
cache_dir="./dataset_cache/"
|
411 |
+
)
|
412 |
+
else:
|
413 |
+
data_files = {}
|
414 |
+
if data_args.train_file is not None:
|
415 |
+
data_files["train"] = data_args.train_file
|
416 |
+
extension = data_args.train_file.split(".")[-1]
|
417 |
+
if data_args.validation_file is not None:
|
418 |
+
data_files["validation"] = data_args.validation_file
|
419 |
+
extension = data_args.validation_file.split(".")[-1]
|
420 |
+
if data_args.test_file is not None:
|
421 |
+
data_files["test"] = data_args.test_file
|
422 |
+
extension = data_args.test_file.split(".")[-1]
|
423 |
+
# TODO: Check
|
424 |
+
dataset = load_dataset(extension, data_files=data_files, cache_dir="./dataset_cache/", data_dir=data_args.data_dir, )
|
425 |
+
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
426 |
+
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
427 |
+
|
428 |
+
# Load pretrained model and tokenizer
|
429 |
+
|
430 |
+
encoder_cache_dir, decoder_cache_dir = None, None
|
431 |
+
if model_args.cache_dir:
|
432 |
+
encoder_cache_dir = os.path.join(model_args.cache_dir, "encoder")
|
433 |
+
decoder_cache_dir = os.path.join(model_args.cache_dir, "decoder")
|
434 |
+
|
435 |
+
# Use explicit specified config
|
436 |
+
if model_args.config_name:
|
437 |
+
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
|
438 |
+
# Use pretrained model's config
|
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 |
+
# Use specified `model_type` (default to `vision-encoder-decoder`)
|
442 |
+
else:
|
443 |
+
|
444 |
+
if not model_args.model_type in MODEL_TYPES:
|
445 |
+
raise ValueError(
|
446 |
+
f"Unrecognized model identifier: {model_args.model_type}. Should contain one of {', '.join(MODEL_TYPES)}."
|
447 |
+
)
|
448 |
+
config_class = CONFIG_MAPPING[model_args.model_type]
|
449 |
+
|
450 |
+
# Deal with encoder-decoder models that require specifying encoder/decoder
|
451 |
+
if hasattr(config_class, "from_encoder_decoder_configs"):
|
452 |
+
|
453 |
+
# Use explicit specified encoder config
|
454 |
+
if model_args.encoder_config_name:
|
455 |
+
encoder_config = AutoConfig.from_pretrained(model_args.encoder_config_name, cache_dir=encoder_cache_dir)
|
456 |
+
# Use pretrained encoder model's config
|
457 |
+
elif model_args.encoder_model_name_or_path:
|
458 |
+
encoder_config = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path, cache_dir=encoder_cache_dir)
|
459 |
+
# Use specified encoder model type
|
460 |
+
elif model_args.encoder_model_type:
|
461 |
+
encoder_config = AutoConfig.for_model(model_args.encoder_model_type)
|
462 |
+
logger.warning("You are instantiating a new config instance from scratch for the encoder.")
|
463 |
+
else:
|
464 |
+
raise ValueError("Encoder Config: if pretrained config or model location is not provided, `encoder_model_type` is required.")
|
465 |
+
|
466 |
+
# Use explicit specified decoder config
|
467 |
+
if model_args.decoder_config_name:
|
468 |
+
decoder_config = AutoConfig.from_pretrained(model_args.decoder_config_name, cache_dir=decoder_cache_dir)
|
469 |
+
# Use pretrained decoder model's config
|
470 |
+
elif model_args.decoder_model_name_or_path:
|
471 |
+
decoder_config = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path, cache_dir=decoder_cache_dir)
|
472 |
+
# Use specified decoder model type
|
473 |
+
elif model_args.decoder_model_type:
|
474 |
+
decoder_config = AutoConfig.for_model(model_args.decoder_model_type)
|
475 |
+
logger.warning("You are instantiating a new config instance from scratch for the decoder.")
|
476 |
+
else:
|
477 |
+
raise ValueError("Decoder Config: if pretrained config or model location is not provided, `decoder_model_type` is required.")
|
478 |
+
|
479 |
+
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
|
480 |
+
decoder_config.is_decoder = True
|
481 |
+
decoder_config.add_cross_attention = True
|
482 |
+
|
483 |
+
config = config_class.from_encoder_decoder_configs(encoder_config, decoder_config)
|
484 |
+
# For self-contained model
|
485 |
+
else:
|
486 |
+
config = config_class()
|
487 |
+
logger.warning("You are instantiating a new config instance from scratch.")
|
488 |
+
|
489 |
+
decoder_start_token_id = getattr(config, "decoder_start_token_id", None)
|
490 |
+
if not decoder_start_token_id and getattr(config, "decoder", None):
|
491 |
+
decoder_start_token_id = getattr(config.decoder, "decoder_start_token_id", None)
|
492 |
+
bos_token_id = getattr(config, "bos_token_id", None)
|
493 |
+
if not bos_token_id and getattr(config, "decoder", None):
|
494 |
+
bos_token_id = getattr(config.decoder, "bos_token_id", None)
|
495 |
+
eos_token_id = getattr(config, "eos_token_id", None)
|
496 |
+
if not eos_token_id and getattr(config, "decoder", None):
|
497 |
+
eos_token_id = getattr(config.decoder, "eos_token_id", None)
|
498 |
+
pad_token_id = getattr(config, "pad_token_id", None)
|
499 |
+
if not pad_token_id and getattr(config, "decoder", None):
|
500 |
+
pad_token_id = getattr(config.decoder, "pad_token_id", None)
|
501 |
+
|
502 |
+
if decoder_start_token_id is None:
|
503 |
+
decoder_start_token_id = bos_token_id
|
504 |
+
if pad_token_id is None:
|
505 |
+
pad_token_id = eos_token_id
|
506 |
+
|
507 |
+
if getattr(config, "decoder", None):
|
508 |
+
config.decoder.decoder_start_token_id = decoder_start_token_id
|
509 |
+
config.decoder.bos_token_id = bos_token_id
|
510 |
+
config.decoder.eos_token_id = eos_token_id
|
511 |
+
config.decoder.pad_token_id = pad_token_id
|
512 |
+
|
513 |
+
# Set `encoder-decoder` (top-level) specific config
|
514 |
+
config.decoder_start_token_id = decoder_start_token_id
|
515 |
+
config.bos_token_id = bos_token_id
|
516 |
+
config.eos_token_id = eos_token_id
|
517 |
+
config.pad_token_id = pad_token_id
|
518 |
+
|
519 |
+
if model_args.model_name_or_path:
|
520 |
+
model = FlaxAutoModelForVision2Seq.from_pretrained(
|
521 |
+
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
522 |
+
)
|
523 |
+
else:
|
524 |
+
# model_class = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING[config.__class__]
|
525 |
+
model = FlaxAutoModelForVision2Seq.from_config(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
|
526 |
+
model_class = model.__class__
|
527 |
+
|
528 |
+
# encoder_class = FlaxAutoModel
|
529 |
+
# decoder_class = FlaxAutoModelForCausalLM
|
530 |
+
module = model.module.bind(model.params)
|
531 |
+
encoder_class_name = type(module.encoder).__name__.replace("Module", "Model")
|
532 |
+
decoder_class_name = type(module.decoder).__name__.replace("Module", "Model")
|
533 |
+
encoder_class = getattr(transformers, encoder_class_name, None)
|
534 |
+
decoder_class = getattr(transformers, decoder_class_name, None)
|
535 |
+
|
536 |
+
if hasattr(model_class, "from_encoder_decoder_pretrained"):
|
537 |
+
|
538 |
+
if model_args.encoder_model_name_or_path:
|
539 |
+
encoder = encoder_class.from_pretrained(
|
540 |
+
model_args.encoder_model_name_or_path,
|
541 |
+
config=config.encoder,
|
542 |
+
seed=training_args.seed,
|
543 |
+
dtype=getattr(jnp, model_args.dtype)
|
544 |
+
)
|
545 |
+
else:
|
546 |
+
encoder = encoder_class(config=config.encoder, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
|
547 |
+
logger.warning("You are instantiating a new model instance from scratch for the encoder.")
|
548 |
+
|
549 |
+
if model_args.decoder_model_name_or_path:
|
550 |
+
decoder = decoder_class.from_pretrained(
|
551 |
+
model_args.decoder_model_name_or_path,
|
552 |
+
config=config.decoder,
|
553 |
+
seed=training_args.seed,
|
554 |
+
dtype=getattr(jnp, model_args.dtype)
|
555 |
+
)
|
556 |
+
else:
|
557 |
+
decoder = decoder_class(config=config.decoder, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
|
558 |
+
logger.warning("You are instantiating a new model instance from scratch for the decoder.")
|
559 |
+
|
560 |
+
model = model_class.from_encoder_decoder_pretrained(
|
561 |
+
model_args.encoder_model_name_or_path,
|
562 |
+
model_args.decoder_model_name_or_path,
|
563 |
+
encoder_model=encoder,
|
564 |
+
decoder_model=decoder,
|
565 |
+
encoder_config=config.encoder,
|
566 |
+
decoder_config=config.decoder,
|
567 |
+
encoder_seed=training_args.seed,
|
568 |
+
decoder_seed=training_args.seed,
|
569 |
+
encoder_dtype=getattr(jnp, model_args.dtype),
|
570 |
+
decoder_dtype=getattr(jnp, model_args.dtype),
|
571 |
+
)
|
572 |
+
|
573 |
+
# Set `encoder-decoder` (top-level) specific config
|
574 |
+
model.config.decoder_start_token_id = decoder_start_token_id
|
575 |
+
model.config.bos_token_id = bos_token_id
|
576 |
+
model.config.eos_token_id = eos_token_id
|
577 |
+
model.config.pad_token_id = pad_token_id
|
578 |
+
|
579 |
+
else:
|
580 |
+
logger.warning("You are instantiating a new model instance from scratch.")
|
581 |
+
|
582 |
+
feature_extractor = None
|
583 |
+
if model_args.feature_extractor_name:
|
584 |
+
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
585 |
+
model_args.feature_extractor_name, cache_dir=model_args.cache_dir,
|
586 |
+
)
|
587 |
+
elif model_args.model_name_or_path:
|
588 |
+
try:
|
589 |
+
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
590 |
+
model_args.model_name_or_path, cache_dir=model_args.cache_dir
|
591 |
+
)
|
592 |
+
except ValueError as e:
|
593 |
+
logger.warning(e)
|
594 |
+
# Check encoder
|
595 |
+
if not feature_extractor:
|
596 |
+
if model_args.encoder_model_name_or_path:
|
597 |
+
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
598 |
+
model_args.encoder_model_name_or_path, cache_dir=model_args.cache_dir
|
599 |
+
)
|
600 |
+
else:
|
601 |
+
raise ValueError(
|
602 |
+
"You are instantiating a new feature extractor from scratch. This is not supported by this script."
|
603 |
+
"You can do it from another script, save it, and load it from here, using --feature_extractor_name."
|
604 |
+
)
|
605 |
+
|
606 |
+
def get_tokenizer():
|
607 |
+
|
608 |
+
tokenizer = None
|
609 |
+
if model_args.tokenizer_name:
|
610 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
611 |
+
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
612 |
+
)
|
613 |
+
elif model_args.model_name_or_path:
|
614 |
+
try:
|
615 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
616 |
+
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
617 |
+
)
|
618 |
+
except ValueError as e:
|
619 |
+
logger.warning(e)
|
620 |
+
|
621 |
+
# Check decoder
|
622 |
+
if not tokenizer:
|
623 |
+
if model_args.decoder_model_name_or_path:
|
624 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
625 |
+
model_args.decoder_model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
626 |
+
)
|
627 |
+
else:
|
628 |
+
raise ValueError(
|
629 |
+
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
|
630 |
+
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
631 |
+
)
|
632 |
+
tokenizer.pad_token = tokenizer.convert_ids_to_tokens(config.pad_token_id)
|
633 |
+
|
634 |
+
return tokenizer
|
635 |
+
|
636 |
+
tokenizer = get_tokenizer()
|
637 |
+
|
638 |
+
# Preprocessing the datasets.
|
639 |
+
# We need to tokenize inputs and targets.
|
640 |
+
if training_args.do_train:
|
641 |
+
column_names = dataset["train"].column_names
|
642 |
+
elif training_args.do_eval:
|
643 |
+
column_names = dataset["validation"].column_names
|
644 |
+
elif training_args.do_predict:
|
645 |
+
column_names = dataset["test"].column_names
|
646 |
+
else:
|
647 |
+
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
|
648 |
+
return
|
649 |
+
|
650 |
+
# Get the column names for input/target.
|
651 |
+
dataset_columns = image_captioning_name_mapping.get(data_args.dataset_name, None)
|
652 |
+
if data_args.image_column is None:
|
653 |
+
assert dataset_columns is not None
|
654 |
+
image_column = dataset_columns[0]
|
655 |
+
else:
|
656 |
+
image_column = data_args.image_column
|
657 |
+
if image_column not in column_names:
|
658 |
+
raise ValueError(
|
659 |
+
f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}"
|
660 |
+
)
|
661 |
+
if data_args.caption_column is None:
|
662 |
+
assert dataset_columns is not None
|
663 |
+
caption_column = dataset_columns[1]
|
664 |
+
else:
|
665 |
+
caption_column = data_args.caption_column
|
666 |
+
if caption_column not in column_names:
|
667 |
+
raise ValueError(
|
668 |
+
f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}"
|
669 |
+
)
|
670 |
+
|
671 |
+
# In Flax, for seq2seq models we need to pass `decoder_input_ids`
|
672 |
+
# as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here
|
673 |
+
# for that dynamically import the `shift_tokens_right` function from the model file
|
674 |
+
model_module = __import__(model.__module__, fromlist=["shift_tokens_right"])
|
675 |
+
shift_tokens_right_fn = getattr(model_module, "shift_tokens_right", shift_tokens_right)
|
676 |
+
|
677 |
+
def filter_fn(examples):
|
678 |
+
|
679 |
+
bools = []
|
680 |
+
for image_file in examples[image_column]:
|
681 |
+
with Image.open(image_file) as image:
|
682 |
+
try:
|
683 |
+
feature_extractor(images=image, return_tensors="np")
|
684 |
+
bools.append(True)
|
685 |
+
except:
|
686 |
+
bools.append(False)
|
687 |
+
|
688 |
+
return bools
|
689 |
+
|
690 |
+
# Setting padding="max_length" as we need fixed length inputs for jitted functions
|
691 |
+
def tokenization_fn(examples, max_target_length):
|
692 |
+
|
693 |
+
captions = []
|
694 |
+
for caption in examples[caption_column]:
|
695 |
+
captions.append(caption.lower() + ' ' + tokenizer.eos_token)
|
696 |
+
|
697 |
+
targets = captions
|
698 |
+
|
699 |
+
model_inputs = {}
|
700 |
+
|
701 |
+
# Setup the tokenizer for targets
|
702 |
+
with tokenizer.as_target_tokenizer():
|
703 |
+
labels = tokenizer(
|
704 |
+
targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np"
|
705 |
+
)
|
706 |
+
|
707 |
+
model_inputs["labels"] = labels["input_ids"]
|
708 |
+
decoder_input_ids = shift_tokens_right_fn(
|
709 |
+
labels["input_ids"], config.pad_token_id, config.decoder_start_token_id
|
710 |
+
)
|
711 |
+
model_inputs["decoder_input_ids"] = np.asarray(decoder_input_ids)
|
712 |
+
|
713 |
+
# We need decoder_attention_mask so we can ignore pad tokens from loss
|
714 |
+
model_inputs["decoder_attention_mask"] = labels["attention_mask"]
|
715 |
+
|
716 |
+
model_inputs[image_column] = examples[image_column]
|
717 |
+
|
718 |
+
return model_inputs
|
719 |
+
|
720 |
+
def feature_extraction_fn(examples):
|
721 |
+
|
722 |
+
pixel_values = []
|
723 |
+
|
724 |
+
for image_file in examples[image_column]:
|
725 |
+
with Image.open(image_file) as image:
|
726 |
+
encoder_inputs = feature_extractor(images=image, return_tensors="np")
|
727 |
+
pixel_values.append(encoder_inputs.pixel_values)
|
728 |
+
|
729 |
+
pixel_values = np.concatenate(pixel_values)
|
730 |
+
|
731 |
+
model_inputs = examples
|
732 |
+
model_inputs['pixel_values'] = pixel_values
|
733 |
+
|
734 |
+
return model_inputs
|
735 |
+
|
736 |
+
features = datasets.Features(
|
737 |
+
{
|
738 |
+
"pixel_values": datasets.Array3D(
|
739 |
+
shape=(
|
740 |
+
getattr(config.encoder, "num_channels", 3),
|
741 |
+
config.encoder.image_size,
|
742 |
+
config.encoder.image_size,
|
743 |
+
),
|
744 |
+
dtype="float32",
|
745 |
+
),
|
746 |
+
"labels": datasets.Sequence(feature=datasets.Value(dtype='int32', id=None), length=-1, id=None),
|
747 |
+
"decoder_input_ids": datasets.Sequence(feature=datasets.Value(dtype='int32', id=None), length=-1, id=None),
|
748 |
+
"decoder_attention_mask": datasets.Sequence(feature=datasets.Value(dtype='int32', id=None), length=-1, id=None),
|
749 |
+
}
|
750 |
+
)
|
751 |
+
|
752 |
+
if training_args.do_train:
|
753 |
+
|
754 |
+
if "train" not in dataset:
|
755 |
+
raise ValueError("--do_train requires a train dataset")
|
756 |
+
train_dataset = dataset["train"]
|
757 |
+
train_dataset = datasets.concatenate_datasets([train_dataset] * 205)
|
758 |
+
|
759 |
+
# remove problematic examples
|
760 |
+
s = time.time()
|
761 |
+
train_dataset = train_dataset.filter(filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers)
|
762 |
+
e = time.time()
|
763 |
+
print(f'filter time: {e-s}')
|
764 |
+
print(len(train_dataset))
|
765 |
+
|
766 |
+
rng = jax.random.PRNGKey(training_args.seed)
|
767 |
+
rng, input_rng = jax.random.split(rng)
|
768 |
+
|
769 |
+
s = time.time()
|
770 |
+
indices_jax = jax.random.permutation(input_rng, len(train_dataset))
|
771 |
+
e = time.time()
|
772 |
+
print(f'get permutation indices for the whole dataset with jax - time: {e-s}')
|
773 |
+
|
774 |
+
s = time.time()
|
775 |
+
indices_np = np.random.permutation(len(train_dataset))
|
776 |
+
e = time.time()
|
777 |
+
print(f'get permutation indices for the whole dataset with np - time: {e-s}')
|
778 |
+
|
779 |
+
# indices = jnp.arange(len(ds))
|
780 |
+
|
781 |
+
block_size = 4096
|
782 |
+
for idx in range(4):
|
783 |
+
|
784 |
+
start_idx = block_size * idx
|
785 |
+
end_idx = block_size * (idx + 1)
|
786 |
+
|
787 |
+
s = time.time()
|
788 |
+
selected_indices_jax = indices_jax[start_idx:end_idx]
|
789 |
+
e = time.time()
|
790 |
+
print(f'get block indices with jax - time: {e-s}')
|
791 |
+
print(type(selected_indices_jax))
|
792 |
+
|
793 |
+
s = time.time()
|
794 |
+
selected_indices_np = indices_np[start_idx:end_idx]
|
795 |
+
e = time.time()
|
796 |
+
print(f'get block indices with np - time: {e-s}')
|
797 |
+
print(type(selected_indices_np))
|
798 |
+
|
799 |
+
|
800 |
+
s = time.time()
|
801 |
+
_ds = train_dataset.select(selected_indices_jax)
|
802 |
+
e = time.time()
|
803 |
+
print(f'select block with jax - time: {e-s}')
|
804 |
+
|
805 |
+
s = time.time()
|
806 |
+
_ds = train_dataset.select(selected_indices_np)
|
807 |
+
e = time.time()
|
808 |
+
print(f'select block with np - time: {e-s}')
|
809 |
+
|
810 |
+
s = time.time()
|
811 |
+
_selected_indices_np = np.array(selected_indices_jax)
|
812 |
+
e = time.time()
|
813 |
+
print(f'convert jax to np - time: {e-s}')
|
814 |
+
|
815 |
+
|
816 |
+
batch_size = 256
|
817 |
+
|
818 |
+
steps_per_epoch = len(_ds) // batch_size
|
819 |
+
|
820 |
+
s = time.time()
|
821 |
+
batch_idx_jax = jax.random.permutation(rng, len(_ds))
|
822 |
+
e = time.time()
|
823 |
+
print(f'get permutation indices for the block with jax - time: {e-s}')
|
824 |
+
# batch_idx = jnp.arange(len(dataset))
|
825 |
+
|
826 |
+
s = time.time()
|
827 |
+
batch_idx_np = np.random.permutation(len(_ds))
|
828 |
+
e = time.time()
|
829 |
+
print(f'get permutation indices for the block with np - time: {e-s}')
|
830 |
+
|
831 |
+
s = time.time()
|
832 |
+
batch_idx_jax = batch_idx_jax[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
833 |
+
e = time.time()
|
834 |
+
print(f'skip incomplete batch with jax - time: {e-s}')
|
835 |
+
|
836 |
+
s = time.time()
|
837 |
+
batch_idx_np = batch_idx_np[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
838 |
+
e = time.time()
|
839 |
+
print(f'skip incomplete batch with np - time: {e-s}')
|
840 |
+
|
841 |
+
s = time.time()
|
842 |
+
batch_idx_jax = batch_idx_jax.reshape((steps_per_epoch, batch_size))
|
843 |
+
e = time.time()
|
844 |
+
print(f'reshape block indices with jax - time: {e-s}')
|
845 |
+
|
846 |
+
s = time.time()
|
847 |
+
batch_idx_np = batch_idx_np.reshape((steps_per_epoch, batch_size))
|
848 |
+
e = time.time()
|
849 |
+
print(f'reshape block indices with np - time: {e-s}')
|
850 |
+
|
851 |
+
for idx in batch_idx_jax:
|
852 |
+
|
853 |
+
s = time.time()
|
854 |
+
batch = _ds[idx]
|
855 |
+
e = time.time()
|
856 |
+
print(f'get one batch with jax - time: {e-s}')
|
857 |
+
|
858 |
+
#s = time.time()
|
859 |
+
#batch = {k: jnp.array(v) for k, v in batch.items()}
|
860 |
+
#e = time.time()
|
861 |
+
#print(f'convert one batch to jnp time: {e-s}')
|
862 |
+
|
863 |
+
for idx in batch_idx_np:
|
864 |
+
|
865 |
+
s = time.time()
|
866 |
+
batch = _ds[idx]
|
867 |
+
e = time.time()
|
868 |
+
print(f'get one batch with np - time: {e-s}')
|
869 |
+
|
870 |
+
|
871 |
+
exit(0)
|
872 |
+
|
873 |
+
|
874 |
+
if training_args.do_predict:
|
875 |
+
if "test" not in dataset:
|
876 |
+
raise ValueError("--do_predict requires a test dataset")
|
877 |
+
predict_dataset = dataset["test"]
|
878 |
+
# remove problematic examples
|
879 |
+
predict_dataset = predict_dataset.filter(filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers)
|
880 |
+
if data_args.max_predict_samples is not None:
|
881 |
+
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
|
882 |
+
predict_dataset = predict_dataset.map(
|
883 |
+
tokenization_fn,
|
884 |
+
batched=True,
|
885 |
+
num_proc=data_args.preprocessing_num_workers,
|
886 |
+
# kept image paths
|
887 |
+
remove_columns=[x for x in column_names if x != image_column],
|
888 |
+
load_from_cache_file=not data_args.overwrite_cache,
|
889 |
+
desc=f"Running tokenizer on prediction dataset",
|
890 |
+
fn_kwargs={"max_target_length": data_args.val_max_target_length},
|
891 |
+
)
|
892 |
+
|
893 |
+
tokenizer = get_tokenizer()
|
894 |
+
|
895 |
+
# Split the dataset into several chunks - each chunk is processed (.map) without cache to create a
|
896 |
+
# data loader separately (in a sequential order).
|
897 |
+
block_size = training_args.block_size
|
898 |
+
|
899 |
+
# Store some constant
|
900 |
+
|
901 |
+
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
|
902 |
+
|
903 |
+
if training_args.do_train:
|
904 |
+
steps_per_epoch = len(train_dataset) // train_batch_size
|
905 |
+
num_train_examples_per_epoch = steps_per_epoch * train_batch_size
|
906 |
+
num_epochs = int(training_args.num_train_epochs)
|
907 |
+
total_train_steps = steps_per_epoch * num_epochs
|
908 |
+
else:
|
909 |
+
num_train_examples_per_epoch = 0
|
910 |
+
|
911 |
+
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
|
912 |
+
|
913 |
+
if training_args.do_eval:
|
914 |
+
num_eval_examples = len(eval_dataset)
|
915 |
+
eval_steps = num_eval_examples // eval_batch_size + int(num_eval_examples % eval_batch_size > 0)
|
916 |
+
|
917 |
+
if training_args.do_predict:
|
918 |
+
num_test_examples = len(predict_dataset)
|
919 |
+
test_steps = num_test_examples // eval_batch_size + int(num_test_examples % eval_batch_size > 0)
|
920 |
+
|
921 |
+
def get_batch_iter(rng, ds, block_size, batch_size, shuffle=False, drop_last_batch=False, keep_in_memory=False, split=""):
|
922 |
+
|
923 |
+
if not block_size:
|
924 |
+
block_size = len(ds)
|
925 |
+
|
926 |
+
steps_per_split = block_size // batch_size
|
927 |
+
num_examples = len(ds)
|
928 |
+
steps = num_examples // batch_size + int(num_examples % batch_size > 0 and not drop_last_batch)
|
929 |
+
num_splits = steps // steps_per_split + int(steps % steps_per_split > 0)
|
930 |
+
|
931 |
+
if shuffle:
|
932 |
+
indices = jax.random.permutation(input_rng, len(ds))
|
933 |
+
else:
|
934 |
+
indices = jnp.arange(len(ds))
|
935 |
+
|
936 |
+
for idx in range(num_splits):
|
937 |
+
|
938 |
+
start_idx = block_size * idx
|
939 |
+
end_idx = block_size * (idx + 1)
|
940 |
+
|
941 |
+
selected_indices = indices[start_idx:end_idx]
|
942 |
+
|
943 |
+
_ds = ds.select(selected_indices)
|
944 |
+
|
945 |
+
names = {
|
946 |
+
"train": "train",
|
947 |
+
"valid": "validation",
|
948 |
+
"test": "prediction",
|
949 |
+
}
|
950 |
+
|
951 |
+
_ds =_ds.map(
|
952 |
+
feature_extraction_fn,
|
953 |
+
batched=True,
|
954 |
+
num_proc=data_args.preprocessing_num_workers,
|
955 |
+
remove_columns=[image_column],
|
956 |
+
load_from_cache_file=not data_args.overwrite_cache,
|
957 |
+
features=features,
|
958 |
+
keep_in_memory=keep_in_memory,
|
959 |
+
desc=f"Running feature extraction on {names[split]} dataset".replace(" ", " "),
|
960 |
+
)
|
961 |
+
_ds = _ds.with_format("numpy")
|
962 |
+
|
963 |
+
# No need to shuffle here
|
964 |
+
loader = data_loader(rng, _ds, batch_size=batch_size, shuffle=False)
|
965 |
+
|
966 |
+
for batch in loader:
|
967 |
+
yield batch
|
968 |
+
|
969 |
+
# Metric
|
970 |
+
metric = load_metric("rouge")
|
971 |
+
|
972 |
+
def postprocess_text(preds, labels):
|
973 |
+
preds = [pred.strip() for pred in preds]
|
974 |
+
labels = [label.strip() for label in labels]
|
975 |
+
|
976 |
+
# rougeLSum expects newline after each sentence
|
977 |
+
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
|
978 |
+
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
|
979 |
+
|
980 |
+
return preds, labels
|
981 |
+
|
982 |
+
def compute_metrics(preds, labels):
|
983 |
+
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
984 |
+
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
985 |
+
|
986 |
+
# Some simple post-processing
|
987 |
+
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
|
988 |
+
|
989 |
+
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
|
990 |
+
# Extract a few results from ROUGE
|
991 |
+
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
|
992 |
+
|
993 |
+
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
|
994 |
+
result["gen_len"] = np.mean(prediction_lens)
|
995 |
+
result = {k: round(v, 6) for k, v in result.items()}
|
996 |
+
|
997 |
+
return result, decoded_preds, decoded_labels
|
998 |
+
|
999 |
+
# Enable tensorboard only on the master node
|
1000 |
+
has_tensorboard = is_tensorboard_available()
|
1001 |
+
if has_tensorboard and jax.process_index() == 0:
|
1002 |
+
try:
|
1003 |
+
from flax.metrics.tensorboard import SummaryWriter
|
1004 |
+
|
1005 |
+
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
1006 |
+
except ImportError as ie:
|
1007 |
+
has_tensorboard = False
|
1008 |
+
logger.warning(
|
1009 |
+
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
1010 |
+
)
|
1011 |
+
else:
|
1012 |
+
logger.warning(
|
1013 |
+
"Unable to display metrics through TensorBoard because the package is not installed: "
|
1014 |
+
"Please run pip install tensorboard to enable."
|
1015 |
+
)
|
1016 |
+
|
1017 |
+
# Initialize our training
|
1018 |
+
rng = jax.random.PRNGKey(training_args.seed)
|
1019 |
+
rng, dropout_rng = jax.random.split(rng)
|
1020 |
+
|
1021 |
+
# Create learning rate schedule
|
1022 |
+
linear_decay_lr_schedule_fn = create_learning_rate_fn(
|
1023 |
+
num_train_examples_per_epoch,
|
1024 |
+
train_batch_size,
|
1025 |
+
training_args.num_train_epochs,
|
1026 |
+
training_args.warmup_steps,
|
1027 |
+
training_args.learning_rate,
|
1028 |
+
)
|
1029 |
+
|
1030 |
+
# We use Optax's "masking" functionality to not apply weight decay
|
1031 |
+
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
1032 |
+
# mask boolean with the same structure as the parameters.
|
1033 |
+
# The mask is True for parameters that should be decayed.
|
1034 |
+
# Note that this mask is specifically adapted for FlaxBart.
|
1035 |
+
# For FlaxT5, one should correct the layer norm parameter naming
|
1036 |
+
# accordingly - see `run_t5_mlm_flax.py` e.g.
|
1037 |
+
def decay_mask_fn(params):
|
1038 |
+
flat_params = traverse_util.flatten_dict(params)
|
1039 |
+
layer_norm_params = [
|
1040 |
+
(name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
|
1041 |
+
]
|
1042 |
+
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params}
|
1043 |
+
return traverse_util.unflatten_dict(flat_mask)
|
1044 |
+
|
1045 |
+
# create adam optimizer
|
1046 |
+
adamw = optax.adamw(
|
1047 |
+
learning_rate=linear_decay_lr_schedule_fn,
|
1048 |
+
b1=training_args.adam_beta1,
|
1049 |
+
b2=training_args.adam_beta2,
|
1050 |
+
eps=training_args.adam_epsilon,
|
1051 |
+
weight_decay=training_args.weight_decay,
|
1052 |
+
mask=decay_mask_fn,
|
1053 |
+
)
|
1054 |
+
|
1055 |
+
# Setup train state
|
1056 |
+
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
|
1057 |
+
|
1058 |
+
# label smoothed cross entropy
|
1059 |
+
def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
|
1060 |
+
"""
|
1061 |
+
The label smoothing implementation is adapted from Flax's official example:
|
1062 |
+
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
|
1063 |
+
"""
|
1064 |
+
vocab_size = logits.shape[-1]
|
1065 |
+
confidence = 1.0 - label_smoothing_factor
|
1066 |
+
low_confidence = (1.0 - confidence) / (vocab_size - 1)
|
1067 |
+
normalizing_constant = -(
|
1068 |
+
confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
|
1069 |
+
)
|
1070 |
+
soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
|
1071 |
+
|
1072 |
+
loss = optax.softmax_cross_entropy(logits, soft_labels)
|
1073 |
+
loss = loss - normalizing_constant
|
1074 |
+
|
1075 |
+
# ignore padded tokens from loss
|
1076 |
+
loss = loss * padding_mask
|
1077 |
+
loss = loss.sum() / padding_mask.sum()
|
1078 |
+
return loss
|
1079 |
+
|
1080 |
+
# Define gradient update step fn
|
1081 |
+
def train_step(state, batch, label_smoothing_factor=0.0):
|
1082 |
+
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
1083 |
+
|
1084 |
+
def compute_loss(params):
|
1085 |
+
labels = batch.pop("labels")
|
1086 |
+
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
1087 |
+
loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
|
1088 |
+
return loss
|
1089 |
+
|
1090 |
+
grad_fn = jax.value_and_grad(compute_loss)
|
1091 |
+
loss, grad = grad_fn(state.params)
|
1092 |
+
grad = jax.lax.pmean(grad, "batch")
|
1093 |
+
|
1094 |
+
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
1095 |
+
|
1096 |
+
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
|
1097 |
+
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
1098 |
+
|
1099 |
+
return new_state, metrics
|
1100 |
+
|
1101 |
+
# Define eval fn
|
1102 |
+
def eval_step(params, batch, label_smoothing_factor=0.0):
|
1103 |
+
labels = batch.pop("labels")
|
1104 |
+
logits = model(**batch, params=params, train=False)[0]
|
1105 |
+
loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
|
1106 |
+
|
1107 |
+
# summarize metrics
|
1108 |
+
metrics = {"loss": loss}
|
1109 |
+
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
1110 |
+
return metrics
|
1111 |
+
|
1112 |
+
# Define generation function
|
1113 |
+
max_length = (
|
1114 |
+
data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length
|
1115 |
+
)
|
1116 |
+
num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams
|
1117 |
+
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
|
1118 |
+
|
1119 |
+
def generate_step(params, batch):
|
1120 |
+
model.params = params
|
1121 |
+
output_ids = model.generate(batch['pixel_values'], **gen_kwargs)
|
1122 |
+
return output_ids.sequences
|
1123 |
+
|
1124 |
+
# Create parallel version of the train and eval step
|
1125 |
+
p_train_step = jax.pmap(
|
1126 |
+
partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,)
|
1127 |
+
)
|
1128 |
+
p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch")
|
1129 |
+
p_generate_step = jax.pmap(generate_step, "batch")
|
1130 |
+
|
1131 |
+
# Replicate the train state on each device
|
1132 |
+
state = state.replicate()
|
1133 |
+
|
1134 |
+
if training_args.do_train:
|
1135 |
+
logger.info("***** Running training *****")
|
1136 |
+
logger.info(f" Num train examples = {len(train_dataset)}")
|
1137 |
+
logger.info(f" Num train examples per epoch = {num_train_examples_per_epoch}")
|
1138 |
+
logger.info(f" Num Epochs = {num_epochs}")
|
1139 |
+
logger.info(f" Instantaneous train batch size per device = {training_args.per_device_train_batch_size}")
|
1140 |
+
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
|
1141 |
+
logger.info(f" Optimization steps per epoch = {steps_per_epoch}")
|
1142 |
+
logger.info(f" Total optimization steps = {total_train_steps}")
|
1143 |
+
if training_args.do_eval:
|
1144 |
+
logger.info(f" Num evaluation examples = {num_eval_examples}")
|
1145 |
+
logger.info(f" Instantaneous evaluation batch size per device = {training_args.per_device_eval_batch_size}")
|
1146 |
+
logger.info(f" Total evaluation batch size (w. parallel & distributed) = {eval_batch_size}")
|
1147 |
+
logger.info(f" Evaluation steps = {eval_steps}")
|
1148 |
+
if training_args.do_predict:
|
1149 |
+
logger.info(f" Num test examples = {num_test_examples}")
|
1150 |
+
logger.info(f" Instantaneous test batch size per device = {training_args.per_device_eval_batch_size}")
|
1151 |
+
logger.info(f" Total test batch size (w. parallel & distributed) = {eval_batch_size}")
|
1152 |
+
logger.info(f" Total train batch size (w. parallel & distributed) = {eval_batch_size}")
|
1153 |
+
logger.info(f" Test steps = {test_steps}")
|
1154 |
+
|
1155 |
+
# create output directory
|
1156 |
+
if not os.path.isdir(os.path.join(training_args.output_dir)):
|
1157 |
+
os.makedirs(os.path.join(training_args.output_dir), exist_ok=True)
|
1158 |
+
|
1159 |
+
def save_results(epoch, step):
|
1160 |
+
|
1161 |
+
# save checkpoint after each epoch and push checkpoint to the hub
|
1162 |
+
if jax.process_index() == 0:
|
1163 |
+
params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
|
1164 |
+
dir_name = f'ckpt_epoch_{epoch + 1}_step_{step}'
|
1165 |
+
model.save_pretrained(os.path.join(training_args.output_dir, dir_name), params=params)
|
1166 |
+
tokenizer.save_pretrained(os.path.join(training_args.output_dir, dir_name))
|
1167 |
+
if training_args.push_to_hub:
|
1168 |
+
commit_msg = f"Saving weights and logs of epoch {epoch + 1}- step {step}"
|
1169 |
+
repo.push_to_hub(commit_message=commit_msg, blocking=False)
|
1170 |
+
|
1171 |
+
def run_eval_or_test(rng, dataset, name, is_inside_training=True):
|
1172 |
+
|
1173 |
+
if name not in ["valid", "test"]:
|
1174 |
+
raise ValueError(f"`name` must be either \"valid\" or \"test\". Got {name} instead.")
|
1175 |
+
|
1176 |
+
logger.info(f"*** {'Predict' if name == 'test' else 'Evaluate'} ***")
|
1177 |
+
|
1178 |
+
metrics = []
|
1179 |
+
preds = []
|
1180 |
+
labels = []
|
1181 |
+
|
1182 |
+
batches = get_batch_iter(rng, dataset, block_size=block_size, batch_size=eval_batch_size, keep_in_memory=False, shuffle=False, split=name)
|
1183 |
+
steps = len(dataset) // eval_batch_size + int(len(dataset) % eval_batch_size > 0)
|
1184 |
+
for _ in tqdm(range(steps), desc=f"{'Predicting' if name == 'test' else 'Evaluating'}...", position=2, leave=False):
|
1185 |
+
# Model forward
|
1186 |
+
batch = next(batches)
|
1187 |
+
_labels = batch.get("labels", None)
|
1188 |
+
if name == "valid" and _labels is None:
|
1189 |
+
raise ValueError("Validation dataset requires `labels`")
|
1190 |
+
|
1191 |
+
if _labels is not None:
|
1192 |
+
_metrics = p_eval_step(state.params, batch)
|
1193 |
+
metrics.append(_metrics)
|
1194 |
+
|
1195 |
+
# generation
|
1196 |
+
if data_args.predict_with_generate:
|
1197 |
+
generated_ids = p_generate_step(state.params, batch)
|
1198 |
+
preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
|
1199 |
+
if _labels is not None:
|
1200 |
+
labels.extend(jax.device_get(_labels.reshape(-1, _labels.shape[-1])))
|
1201 |
+
|
1202 |
+
if metrics:
|
1203 |
+
# normalize metrics
|
1204 |
+
metrics = get_metrics(metrics)
|
1205 |
+
metrics = jax.tree_map(jnp.mean, metrics)
|
1206 |
+
|
1207 |
+
# compute ROUGE metrics
|
1208 |
+
generations = []
|
1209 |
+
rouge_desc = ""
|
1210 |
+
if data_args.predict_with_generate:
|
1211 |
+
if labels:
|
1212 |
+
rouge_metrics, decoded_preds, decoded_labels = compute_metrics(preds, labels)
|
1213 |
+
metrics.update(rouge_metrics)
|
1214 |
+
rouge_desc = " ".join([f"{'Predict' if name == 'test' else 'Eval'} {key}: {value} |" for key, value in rouge_metrics.items()])
|
1215 |
+
for pred, label in zip(decoded_preds, decoded_labels):
|
1216 |
+
pred = pred.replace("\n", " ")
|
1217 |
+
label = label.replace("\n", " ")
|
1218 |
+
generations.append({"label": label, "pred": pred})
|
1219 |
+
else:
|
1220 |
+
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
1221 |
+
# Some simple post-processing
|
1222 |
+
decoded_preds = [pred.strip() for pred in decoded_preds]
|
1223 |
+
# rougeLSum expects newline after each sentence
|
1224 |
+
decoded_preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in decoded_preds]
|
1225 |
+
for pred in decoded_preds:
|
1226 |
+
pred = pred.replace("\n", " ")
|
1227 |
+
generations.append({"pred": pred})
|
1228 |
+
|
1229 |
+
if metrics:
|
1230 |
+
# Print metrics and update progress bar
|
1231 |
+
desc = f"{'Predict' if name == 'test' else 'Eval'} Loss: {metrics['loss']} | {rouge_desc})"
|
1232 |
+
if is_inside_training:
|
1233 |
+
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Step: {cur_step} | " + desc
|
1234 |
+
epochs.write(desc)
|
1235 |
+
epochs.desc = desc
|
1236 |
+
logger.info(desc)
|
1237 |
+
|
1238 |
+
if jax.process_index() == 0:
|
1239 |
+
|
1240 |
+
ckpt_dir = ""
|
1241 |
+
if is_inside_training:
|
1242 |
+
ckpt_dir = f'ckpt_epoch_{epoch + 1}_step_{cur_step}'
|
1243 |
+
if not os.path.isdir(os.path.join(training_args.output_dir, ckpt_dir)):
|
1244 |
+
os.makedirs(os.path.join(training_args.output_dir, ckpt_dir), exist_ok=True)
|
1245 |
+
|
1246 |
+
if metrics:
|
1247 |
+
|
1248 |
+
# save final metrics in json
|
1249 |
+
metrics = {f"{name}_{metric_name}": round(value.item(), 6) for metric_name, value in metrics.items()}
|
1250 |
+
path = os.path.join(training_args.output_dir, ckpt_dir, f"{name}_results.json")
|
1251 |
+
with open(path, "w") as f:
|
1252 |
+
json.dump(metrics, f, indent=4, sort_keys=True)
|
1253 |
+
|
1254 |
+
# Update report
|
1255 |
+
with open(os.path.join(training_args.output_dir, 'report.txt'), 'a', encoding='UTF-8') as fp:
|
1256 |
+
fp.write(desc + '\n')
|
1257 |
+
|
1258 |
+
# Save metrics
|
1259 |
+
if has_tensorboard and is_inside_training:
|
1260 |
+
write_metric(summary_writer, name, metrics, cur_step)
|
1261 |
+
|
1262 |
+
# Save generations
|
1263 |
+
if generations:
|
1264 |
+
with open(os.path.join(training_args.output_dir, ckpt_dir, f'generation_{name}.json'), 'w', encoding='UTF-8') as fp:
|
1265 |
+
json.dump(generations, fp, ensure_ascii=False, indent=4)
|
1266 |
+
|
1267 |
+
input_rng = None
|
1268 |
+
|
1269 |
+
if training_args.do_train:
|
1270 |
+
|
1271 |
+
cur_step = 0
|
1272 |
+
train_time = 0
|
1273 |
+
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
1274 |
+
|
1275 |
+
for epoch in epochs:
|
1276 |
+
|
1277 |
+
# ======================== Training ================================
|
1278 |
+
|
1279 |
+
# Create sampling rng
|
1280 |
+
rng, input_rng = jax.random.split(rng)
|
1281 |
+
|
1282 |
+
train_metrics = []
|
1283 |
+
|
1284 |
+
train_batches = get_batch_iter(input_rng, train_dataset, block_size=block_size, batch_size=train_batch_size, keep_in_memory=True, shuffle=True, drop_last_batch=training_args.dataloader_drop_last, split="train")
|
1285 |
+
|
1286 |
+
# train
|
1287 |
+
for (batch_idx, _) in enumerate(tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False)):
|
1288 |
+
|
1289 |
+
cur_step += 1
|
1290 |
+
batch = next(train_batches)
|
1291 |
+
batch_start = time.time()
|
1292 |
+
state, train_metric = p_train_step(state, batch)
|
1293 |
+
train_metrics.append(train_metric)
|
1294 |
+
train_time += time.time() - batch_start
|
1295 |
+
|
1296 |
+
if cur_step % training_args.logging_steps == 0 or (training_args.eval_steps is not None and cur_step % training_args.eval_steps == 0) or cur_step % steps_per_epoch == 0:
|
1297 |
+
|
1298 |
+
time_per_step = train_time / cur_step
|
1299 |
+
|
1300 |
+
_train_metric = unreplicate(train_metric)
|
1301 |
+
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Step: {cur_step} | Loss: {_train_metric['loss']} | Learning Rate: {_train_metric['learning_rate']} | Time per step: {time_per_step})"
|
1302 |
+
epochs.desc = desc
|
1303 |
+
epochs.write(desc)
|
1304 |
+
logger.info(desc)
|
1305 |
+
with open(os.path.join(training_args.output_dir, 'report.txt'), 'a', encoding='UTF-8') as fp:
|
1306 |
+
fp.write(desc + '\n')
|
1307 |
+
|
1308 |
+
# Save metrics
|
1309 |
+
if has_tensorboard and jax.process_index() == 0:
|
1310 |
+
write_metric(summary_writer, "train", train_metrics, cur_step, train_time=train_time)
|
1311 |
+
|
1312 |
+
# ======================== Evaluating ==============================
|
1313 |
+
|
1314 |
+
if training_args.do_eval and ((training_args.eval_steps is not None and cur_step % training_args.eval_steps) or cur_step % steps_per_epoch == 0):
|
1315 |
+
run_eval_or_test(input_rng, eval_dataset, name="valid", is_inside_training=True)
|
1316 |
+
|
1317 |
+
# ======================== Prediction loop ==============================
|
1318 |
+
|
1319 |
+
# run prediction after evaluation if specified, otherwise only after each epoch
|
1320 |
+
if training_args.do_predict and training_args.do_predict_during_training and training_args.do_predict_after_evaluation:
|
1321 |
+
run_eval_or_test(input_rng, predict_dataset, name='test', is_inside_training=True)
|
1322 |
+
|
1323 |
+
# ======================== Save ==============================
|
1324 |
+
|
1325 |
+
save_results(epoch + 1, cur_step)
|
1326 |
+
|
1327 |
+
# run prediction after each epoch (if not done during training)
|
1328 |
+
if training_args.do_predict and training_args.do_predict_during_training and not training_args.do_predict_after_evaluation:
|
1329 |
+
run_eval_or_test(input_rng, predict_dataset, name='test', is_inside_training=True)
|
1330 |
+
save_results(epoch + 1, cur_step)
|
1331 |
+
|
1332 |
+
# Create sampling rng
|
1333 |
+
if input_rng is None:
|
1334 |
+
rng, input_rng = jax.random.split(rng)
|
1335 |
+
|
1336 |
+
# run prediction after each epoch (if not done during training)
|
1337 |
+
if training_args.do_predict and not (training_args.do_train and training_args.do_predict_during_training):
|
1338 |
+
run_eval_or_test(input_rng, predict_dataset, name='test', is_inside_training=False)
|
1339 |
+
|
1340 |
+
|
1341 |
+
if __name__ == "__main__":
|
1342 |
+
|
1343 |
+
main()
|