-
Notifications
You must be signed in to change notification settings - Fork 58
/
input_pipeline.py
311 lines (269 loc) · 10.7 KB
/
input_pipeline.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
"""Input pipeline for a WMT dataset."""
import functools
import os
from typing import Dict, List, Optional, Union
import tensorflow as tf
import tensorflow_datasets as tfds
from algorithmic_efficiency import data_utils
from algorithmic_efficiency.pytorch_utils import pytorch_setup
from algorithmic_efficiency.workloads.wmt import tokenizer
RANK = pytorch_setup()[1]
# Avoid multithreading in all processes but the first (rank 0).
AUTOTUNE = tf.data.AUTOTUNE if RANK == 0 else None
Features = Dict[str, tf.Tensor]
TFDS_SPLIT_NAME = {
'train': 'train',
'eval_train': 'train',
'validation': 'validation',
'test': 'test',
}
def normalize_feature_names(ds_info, features: Features) -> Features:
"""Normalizes feature names to 'inputs' and 'targets'."""
input_lang, target_lang = ds_info.supervised_keys
features['inputs'] = features.pop(input_lang)
features['targets'] = features.pop(target_lang)
return features
def pack_dataset(dataset: tf.data.Dataset,
key2length: Union[int, Dict[str, int]],
keys: Optional[List[str]] = None) -> tf.data.Dataset:
"""Creates a 'packed' version of a dataset on-the-fly.
Adapted from the mesh-tf implementation.
This is meant to replace the irritation of having to create a separate
"packed" version of a dataset to train efficiently on TPU.
Each example in the output dataset represents several examples in the
input dataset.
For each key in the input dataset, two additional keys are created:
<key>_segmentation: an int32 tensor identifying the parts
representing the original example.
<key>_position: an int32 tensor identifying the position within the original
example.
Example:
Two input examples get combined to form an output example.
The input examples are:
{"inputs": [8, 7, 1, 0], "targets":[4, 1, 0]}
{"inputs": [2, 3, 4, 1], "targets":[5, 6, 1]}
The output example is:
{
"inputs": [8, 7, 1, 2, 3, 4, 1, 0, 0, 0]
"inputs_segmentations": [1, 1, 1, 2, 2, 2, 2, 0, 0, 0]
"inputs_positions": [0, 1, 2, 0, 1, 2, 3, 0, 0, 0]
"targets": [4, 1, 5, 6, 1, 0, 0, 0, 0, 0]
"targets_segmentations": [1, 1, 2, 2, 2, 0, 0, 0, 0, 0]
"targets_positions": [0, 1, 0, 1, 2, 0, 0, 0, 0, 0]
}
0 represents padding in both the inputs and the outputs.
Sequences in the incoming examples are truncated to length "length", and the
sequences in the output examples all have fixed (padded) length "length".
Args:
dataset: a tf.data.Dataset
key2length: an integer, or a dict from feature-key to integer
keys: a list of strings (e.g. ["inputs", "targets"])
Returns:
a tf.data.Dataset
"""
shapes = tf.nest.map_structure(lambda spec: spec.shape, dataset.element_spec)
if keys is None:
keys = list(shapes.keys())
for k in keys:
if k not in shapes:
raise ValueError(
f'Key {k} not found in dataset. Available keys are {shapes.keys()}')
if not shapes[k].is_compatible_with(tf.TensorShape([None])):
raise ValueError('Tensors to be packed must be one-dimensional.')
# make sure that the length dictionary contains all keys as well as the
# keys suffixed by "_segmentation" and "_position"
if isinstance(key2length, int):
key2length = {k: key2length for k in keys}
for k in keys:
for suffix in ['_segmentation', '_position']:
key2length[k + suffix] = key2length[k]
# trim to length
dataset = dataset.map(
lambda x: {k: x[k][:key2length[k]] for k in keys},
num_parallel_calls=AUTOTUNE)
# Setting batch_size=length ensures that the concatenated sequences (if they
# have length >=1) are sufficient to fill at least one packed example.
batch_size = max(key2length.values())
dataset = dataset.padded_batch(
batch_size, padded_shapes={k: [-1] for k in keys})
dataset = _pack_with_tf_ops(dataset, keys, key2length)
# Set the Tensor shapes correctly since they get lost in the process.
def my_fn(x):
return {k: tf.reshape(v, [key2length[k]]) for k, v in x.items()}
return dataset.map(my_fn, num_parallel_calls=AUTOTUNE)
def _pack_with_tf_ops(dataset: tf.data.Dataset,
keys: List[str],
key2length: Dict[str, int]) -> tf.data.Dataset:
"""Helper-function for packing a dataset which has already been batched.
Helper for pack_dataset() Uses tf.while_loop.
Args:
dataset: a dataset containing padded batches of examples.
keys: a list of strings
key2length: an dict from feature-key to integer
Returns:
a dataset.
"""
empty_example = {}
for k in keys:
empty_example[k] = tf.zeros([0], dtype=tf.int32)
empty_example[k + '_position'] = tf.zeros([0], dtype=tf.int32)
keys_etc = empty_example.keys()
def write_packed_example(partial, outputs):
new_partial = empty_example.copy()
new_outputs = {}
for k in keys_etc:
new_outputs[k] = outputs[k].write(
outputs[k].size(),
tf.pad(partial[k], [[0, key2length[k] - tf.size(partial[k])]]))
return new_partial, new_outputs
def map_fn(x):
"""Internal function to flat_map over.
Consumes a batch of input examples and produces a variable number of output
examples.
Args:
x: a single example
Returns:
a tf.data.Dataset
"""
partial = empty_example.copy()
i = tf.zeros([], dtype=tf.int32)
dynamic_batch_size = tf.shape(x[keys[0]])[0]
outputs = {}
for k in keys:
outputs[k] = tf.TensorArray(
tf.int32, size=0, dynamic_size=True, element_shape=[key2length[k]])
outputs[k + '_position'] = tf.TensorArray(
tf.int32, size=0, dynamic_size=True, element_shape=[key2length[k]])
def body_fn(i, partial, outputs):
"""Body function for while_loop.
Args:
i: integer scalar
partial: dictionary of Tensor (partially-constructed example)
outputs: dictionary of TensorArray
Returns:
A triple containing the new values of the inputs.
"""
can_append = True
one_example = {}
for k in keys:
val = tf.cast(x[k][i], tf.int32)
val = val[:tf.reduce_sum(tf.cast(tf.not_equal(val, 0), tf.int32))]
one_example[k] = val
for k in keys:
can_append = tf.logical_and(
can_append,
tf.less_equal(
tf.size(partial[k]) + tf.size(one_example[k]), key2length[k]))
def false_fn():
return write_packed_example(partial, outputs)
def true_fn():
return partial, outputs
partial, outputs = tf.cond(can_append, true_fn, false_fn)
new_partial = {}
for k in keys:
new_seq = one_example[k][:key2length[k]]
new_seq_len = tf.size(new_seq)
new_partial[k] = tf.concat([partial[k], new_seq], 0)
new_partial[k + '_position'] = tf.concat(
[partial[k + '_position'], tf.range(new_seq_len)], 0)
partial = new_partial
return i + 1, partial, outputs
# For loop over all examples in the batch.
i, partial, outputs = tf.while_loop(
cond=lambda *_: True,
body=body_fn,
loop_vars=(i, partial, outputs),
shape_invariants=(
tf.TensorShape([]),
{k: tf.TensorShape([None]) for k in keys_etc},
{k: tf.TensorShape(None) for k in keys_etc},
),
maximum_iterations=dynamic_batch_size)
_, outputs = write_packed_example(partial, outputs)
packed = {k: outputs[k].stack() for k in keys_etc}
for k in keys:
packed[k + '_segmentation'] = (
tf.cumsum(
tf.cast(tf.equal(packed[k + '_position'], 0), tf.int32), axis=1) *
tf.cast(tf.not_equal(packed[k], 0), tf.int32))
return packed
dataset = dataset.map(map_fn, num_parallel_calls=AUTOTUNE)
return dataset.unbatch()
def preprocess_wmt_data(dataset: tf.data.Dataset,
data_rng,
train: bool,
shuffle: bool,
shuffle_buffer_size: int = 1024,
max_length: int = 256,
global_batch_size: int = 128):
"""Shuffle and batch/pack the given dataset."""
def length_filter(max_len):
def filter_fn(x):
source, target = x['inputs'], x['targets']
l = tf.maximum(tf.shape(source)[0], tf.shape(target)[0])
return tf.less(l, max_len + 1)
return filter_fn
if max_length > 0:
dataset = dataset.filter(length_filter(max_length))
if shuffle:
dataset = dataset.shuffle(shuffle_buffer_size, seed=data_rng[0])
if train:
dataset = dataset.repeat()
dataset = pack_dataset(dataset, max_length)
dataset = dataset.batch(global_batch_size, drop_remainder=train)
else: # simple (static-shape) padded batching
dataset = dataset.padded_batch(
global_batch_size,
padded_shapes={'inputs': max_length, 'targets': max_length},
padding_values={'inputs': 0, 'targets': 0},
drop_remainder=False)
dataset = dataset.prefetch(AUTOTUNE)
return dataset
def get_wmt_dataset(data_rng,
split: str,
data_dir: str,
is_training: bool,
vocab_size: int,
global_batch_size: int,
num_batches: Optional[int] = None,
repeat_final_dataset: bool = False,
vocab_path: Optional[str] = None):
"""Load and return dataset of batched examples for use during training."""
if vocab_path is None:
vocab_path = os.path.join(data_dir, 'wmt_sentencepiece_model')
if split in ['validation', 'test']:
ds_name = 'wmt14_translate/de-en:1.0.0'
else:
ds_name = 'wmt17_translate/de-en:1.0.0'
dataset_builder = tfds.builder(ds_name, data_dir=data_dir)
ds = dataset_builder.as_dataset(
split=TFDS_SPLIT_NAME[split], shuffle_files=False)
# Avoid creating too many threads when using PyTorch DDP.
if RANK != 0:
options = tf.data.Options()
options.threading.private_threadpool_size = 1
ds = ds.with_options(options)
ds = ds.map(
functools.partial(normalize_feature_names, dataset_builder.info),
num_parallel_calls=AUTOTUNE)
# Load tf-text SentencePiece tokenizer.
sp_tokenizer = tokenizer.load_tokenizer(vocab_path=vocab_path)
ds = ds.map(tokenizer.TokenizeOp(sp_tokenizer), num_parallel_calls=AUTOTUNE)
shuffle = split in ['train', 'eval_train']
ds = preprocess_wmt_data(
ds,
data_rng,
train=is_training,
shuffle=shuffle,
global_batch_size=global_batch_size,
max_length=256)
if num_batches:
ds = ds.take(num_batches)
if repeat_final_dataset:
ds = ds.repeat()
ds = map(
functools.partial(
data_utils.shard_and_maybe_pad_np,
global_batch_size=global_batch_size),
ds)
return ds, sp_tokenizer