Spaces:
Sleeping
Sleeping
File size: 10,095 Bytes
8273cb9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import functools
import unittest
from typing import Any, Dict, Sequence
import fairseq
import fairseq.options
import fairseq.tasks
import torch
from tests.utils import dummy_dictionary
VOCAB_SIZE = 100
@fairseq.tasks.register_task("fake_task")
class FakeTask(fairseq.tasks.LegacyFairseqTask):
def __init__(self, args):
super().__init__(args)
self.dictionary = dummy_dictionary(VOCAB_SIZE - 4)
assert len(self.dictionary) == VOCAB_SIZE
@property
def source_dictionary(self):
return self.dictionary
@property
def target_dictionary(self):
return self.dictionary
@functools.lru_cache()
def get_toy_model(
device: str,
architecture: str = "roberta_enc_dec",
**extra_args: Any,
):
assert device in ("gpu", "cpu")
kwargs = {
"arch": architecture,
# Use characteristics dimensions
"encoder_layers": 3,
"encoder_embed_dim": 12,
"encoder_ffn_embed_dim": 14,
"encoder_attention_heads": 4,
"decoder_layers": 3,
"decoder_embed_dim": 12,
"decoder_ffn_embed_dim": 14,
"decoder_attention_heads": 4,
# Disable dropout so we have comparable tests.
"dropout": 0,
"attention_dropout": 0,
"activation_dropout": 0,
"encoder_layerdrop": 0,
# required args
"tokens_per_sample": 256,
"data": "/tmp/test_roberta",
}
kwargs.update(extra_args)
fake_task = FakeTask(kwargs)
args = fairseq.options.get_args(
task="online_backtranslation",
mono_langs="en,ro",
valid_lang_pairs="en-ro",
**kwargs,
)
torch.manual_seed(0)
model = fake_task.build_model(args)
if device == "gpu":
model.cuda()
return fake_task, model
def mk_sample(
lang: str, device: str, tok: Sequence[int] = None, batch_size: int = 2
) -> Dict[str, Any]:
assert device in ("gpu", "cpu")
if not tok:
if lang == "en":
tok = [10, 11, 12, 13, 14, 15, 2]
else:
tok = [20, 21, 22, 23, 24, 25, 26, 27, 2]
batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size)
if device == "gpu":
batch = batch.cuda()
sample = {
"net_input": {
"src_tokens": batch,
"prev_output_tokens": batch,
"src_lengths": torch.tensor(
[len(tok)] * batch_size, dtype=torch.long, device=batch.device
),
},
"target": batch[:, 1:],
}
return sample
def cpu_gpu(fn):
def helper(self):
fn(self, "cpu")
if torch.cuda.is_available():
fn(self, "gpu")
return helper
def architectures(fn):
def helper(self):
for arch in ["roberta_enc_dec", "transformer"]:
fn(self, arch)
return helper
class RobertaTest(unittest.TestCase):
def assertTensorEqual(self, t1, t2, delta: float = 1e-6):
self.assertEqual(t1.size(), t2.size(), "size mismatch")
if delta == 0.0:
self.assertEqual(t1.ne(t2).long().sum(), 0)
else:
self.assertEqual(((t2 - t1).abs() > delta).long().sum(), 0)
def assertSharing(self, model, link_groups: Sequence[Sequence[str]]):
ids = {}
for group in link_groups:
group_ids = {name: id(params(model, name)) for name in group}
shared_id = group_ids[group[0]]
self.assertEqual(group_ids, {name: shared_id for name in group})
self.assertNotIn(shared_id, ids)
ids[shared_id] = group
def test_roberta_shared_params(self):
_, roberta = get_toy_model("cpu", architecture="roberta")
self.assertSharing(
roberta,
[
[
"encoder.sentence_encoder.embed_tokens.weight",
"encoder.lm_head.weight",
]
],
)
_, roberta = get_toy_model(
"cpu", architecture="roberta", untie_weights_roberta=True
)
self.assertSharing(
roberta,
[
["encoder.sentence_encoder.embed_tokens.weight"],
["encoder.lm_head.weight"],
],
)
def test_roberta_enc_dec_shared_params(self):
# 3 distinct embeddings
_, enc_dec = get_toy_model("cpu", architecture="roberta_enc_dec")
self.assertSharing(
enc_dec,
[
["encoder.embed_tokens.weight"],
["decoder.embed_tokens.weight"],
["decoder.output_projection.weight"],
],
)
# 2 distinct embeddings, one for encoder, one for decoder
_, enc_dec = get_toy_model(
"cpu", architecture="roberta_enc_dec", share_decoder_input_output_embed=True
)
self.assertSharing(
enc_dec,
[
["encoder.embed_tokens.weight"],
[
"decoder.embed_tokens.weight",
"decoder.output_projection.weight",
],
],
)
# shared embeddings
_, enc_dec = get_toy_model(
"cpu", architecture="roberta_enc_dec", share_all_embeddings=True
)
self.assertSharing(
enc_dec,
[
[
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"decoder.output_projection.weight",
]
],
)
def test_roberta_max_positions_is_correctly_set(self):
device = "cpu"
task, model = get_toy_model(device)
max_pos = model.max_decoder_positions()
self.assertEqual(max_pos, 256)
self.assertEqual(max_pos, model.decoder.max_positions())
self.assertEqual(max_pos, model.encoder.max_positions())
self.assertEqual(max_pos, model.encoder.embed_positions.max_positions)
sentence = [31 for _ in range(max_pos)]
sample = mk_sample("en", device, sentence, batch_size=1)
self.assertEqual(list(sample["net_input"]["src_lengths"]), [max_pos])
self.assertEqual(len(sample["net_input"]["src_tokens"][0]), max_pos)
x, _ = model.forward(**sample["net_input"])
self.assertEqual(x.shape, (1, max_pos, VOCAB_SIZE))
@cpu_gpu
def test_roberta_forward_backward(self, device: str):
_, model = get_toy_model(device)
sample = mk_sample("en", device)
en_tokens = sample["net_input"]["src_tokens"]
(bs, l) = en_tokens.shape
# Forward
logits, _ = model(**sample["net_input"])
self.assertEqual(logits.shape, (bs, l, VOCAB_SIZE))
# Backward
loss = logits.sum()
loss.backward()
@cpu_gpu
def test_roberta_forward_backward_bs1(self, device: str):
_, model = get_toy_model(device)
sample = mk_sample("en", device, batch_size=1)
o, _ = model.forward(**sample["net_input"])
loss = o.sum()
sample2 = mk_sample("ro", device, batch_size=1)
o, _ = model.forward(**sample2["net_input"])
loss += o.sum()
loss.backward()
@cpu_gpu
def test_roberta_batching(self, device: str):
"""
Checks that the batch of size 2 give twice the same results than the batch of size 1.
"""
_, model = get_toy_model(device)
sample = mk_sample("en", device, batch_size=1)
slen = sample["net_input"]["src_lengths"][0]
sample2 = mk_sample("en", device, batch_size=2)
with torch.no_grad():
z = model.encoder.forward(
sample["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
)
z = z["encoder_out"][-1]
logits, _ = model.forward(**sample["net_input"])
z2 = model.encoder.forward(
sample2["net_input"]["src_tokens"], sample["net_input"]["src_lengths"]
)
z2 = z2["encoder_out"][-1]
logits2, _ = model.forward(**sample2["net_input"])
self.assertEqual(z.shape, (slen, 1, 12))
self.assertEqual(z2.shape, (slen, 2, 12))
self.assertTensorEqual(logits2[0], logits2[1])
self.assertTensorEqual(logits[0], logits2[0])
@cpu_gpu
def test_roberta_incremental_decoder(self, device: str):
"""
Checks that incremental decoding yields the same result than non incremental one.
"""
task, model = get_toy_model(device)
en_sample = mk_sample("en", device)
en_tokens = en_sample["net_input"]["src_tokens"]
ro_sample = mk_sample("ro", device)
ro_tokens = ro_sample["net_input"]["src_tokens"]
en_enc = model.encoder.forward(
en_tokens, src_lengths=en_sample["net_input"]["src_lengths"]
)
(bs, tgt_len) = ro_tokens.shape
# Decode without incremental state
ro_dec, _ = model.decoder.forward(ro_tokens, encoder_out=en_enc)
self.assertEqual(ro_dec.shape, (bs, tgt_len, VOCAB_SIZE))
self.assertTensorEqual(ro_dec[0], ro_dec[1])
# Decode with incremental state
inc_state = {}
ro_dec_inc = []
for i in range(tgt_len):
ro, _ = model.decoder.forward(
ro_tokens[:, : i + 1], encoder_out=en_enc, incremental_state=inc_state
)
self.assertEqual(ro.shape, (bs, 1, VOCAB_SIZE))
ro_dec_inc.append(ro)
for i in range(tgt_len):
# Intra-batch
self.assertTensorEqual(ro_dec_inc[i][0], ro_dec_inc[i][1])
# Incremental vs non-incremental
self.assertTensorEqual(ro_dec_inc[i][:, 0], ro_dec[:, i])
def params(model, name):
if "." not in name:
return getattr(model, name)
prefix, name = name.split(".", 1)
return params(getattr(model, prefix), name)
|