khalidsaifullaah commited on
Commit
9b64d7f
1 Parent(s): f12b40d

pytorch weights added

Browse files
Files changed (7) hide show
  1. .gitattributes +1 -0
  2. config.json +2 -0
  3. jax2tensor.py +21 -0
  4. pytorch_model.bin +3 -0
  5. run_2.sh +0 -22
  6. run_clm_flax_v2.py +0 -823
  7. utils.py +0 -122
.gitattributes CHANGED
@@ -15,3 +15,4 @@
15
  *.pt filter=lfs diff=lfs merge=lfs -text
16
  *.pth filter=lfs diff=lfs merge=lfs -text
17
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
15
  *.pt filter=lfs diff=lfs merge=lfs -text
16
  *.pth filter=lfs diff=lfs merge=lfs -text
17
  *tfevents* filter=lfs diff=lfs merge=lfs -text
18
+ pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
config.json CHANGED
@@ -1,4 +1,5 @@
1
  {
 
2
  "activation_function": "gelu_new",
3
  "architectures": [
4
  "GPT2LMHeadModel"
@@ -30,6 +31,7 @@
30
  "max_length": 50
31
  }
32
  },
 
33
  "transformers_version": "4.9.0.dev0",
34
  "use_cache": true,
35
  "vocab_size": 50257
 
1
  {
2
+ "_name_or_path": "./",
3
  "activation_function": "gelu_new",
4
  "architectures": [
5
  "GPT2LMHeadModel"
 
31
  "max_length": 50
32
  }
33
  },
34
+ "torch_dtype": "float32",
35
  "transformers_version": "4.9.0.dev0",
36
  "use_cache": true,
37
  "vocab_size": 50257
jax2tensor.py ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import numpy as np
3
+ import jax
4
+ import jax.numpy as jnp
5
+ from transformers import AutoTokenizer
6
+ from transformers import FlaxGPT2LMHeadModel
7
+ from transformers import GPT2LMHeadModel
8
+
9
+
10
+ model_fx = FlaxGPT2LMHeadModel.from_pretrained("./")
11
+ model_pt = GPT2LMHeadModel.from_pretrained("./", from_flax=True)
12
+
13
+ model_pt.save_pretrained("./")
14
+
15
+
16
+ input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32)
17
+ input_ids_pt = torch.tensor(input_ids)
18
+ logits_pt = model_pt(input_ids_pt).logits
19
+ print(logits_pt)
20
+ logits_fx = model_fx(input_ids).logits
21
+ print(logits_fx)
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6739f146a875869da1ed7f1fd23bb877f6be6e6d27a58ed84cfe626773d62aa1
3
+ size 510401385
run_2.sh DELETED
@@ -1,22 +0,0 @@
1
- #!/usr/bin/env bash
2
- python run_clm_flax_v2.py \
3
- --output_dir="${MODEL_DIR}" \
4
- --model_type="gpt2" \
5
- --config_name="${MODEL_DIR}" \
6
- --tokenizer_name="${MODEL_DIR}" \
7
- --dataset_name="mc4" \
8
- --dataset_config_name="bn" \
9
- --do_train --do_eval \
10
- --block_size="512" \
11
- --per_device_train_batch_size="64" \
12
- --per_device_eval_batch_size="64" \
13
- --learning_rate="5e-3" --warmup_steps="1000" \
14
- --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
15
- --overwrite_output_dir \
16
- --max_steps="100000" \
17
- --decay_steps="100000" \
18
- --logging_steps="50" \
19
- --save_steps="50" \
20
- --eval_steps="50" \
21
- --max_eval_samples 100 \
22
- --push_to_hub
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
run_clm_flax_v2.py DELETED
@@ -1,823 +0,0 @@
1
- #!/usr/bin/env python
2
- # coding=utf-8
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
- Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
18
- Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
19
- https://huggingface.co/models?filter=causal-lm
20
- """
21
- # 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
24
- import logging
25
- import math
26
- import os
27
- import sys
28
- import time
29
- from dataclasses import dataclass, field
30
- from pathlib import Path
31
- from typing import Callable, Optional
32
- import json
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
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
49
- from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
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 DELETED
@@ -1,122 +0,0 @@
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