615 lines
23 KiB
Python
615 lines
23 KiB
Python
# coding=utf-8
|
|
# Copyright 2020 Huggingface
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import tempfile
|
|
import unittest
|
|
|
|
import timeout_decorator # noqa
|
|
from parameterized import parameterized
|
|
|
|
from transformers import FSMTConfig, is_torch_available
|
|
from transformers.testing_utils import (
|
|
require_sentencepiece,
|
|
require_tokenizers,
|
|
require_torch,
|
|
require_torch_fp16,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
from transformers.utils import cached_property
|
|
|
|
from ...generation.test_utils import GenerationTesterMixin
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, ids_tensor
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
from torch import nn
|
|
|
|
from transformers import FSMTForConditionalGeneration, FSMTModel, FSMTTokenizer
|
|
from transformers.models.fsmt.modeling_fsmt import (
|
|
SinusoidalPositionalEmbedding,
|
|
_prepare_fsmt_decoder_inputs,
|
|
invert_mask,
|
|
shift_tokens_right,
|
|
)
|
|
from transformers.pipelines import TranslationPipeline
|
|
|
|
|
|
class FSMTModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
src_vocab_size=99,
|
|
tgt_vocab_size=99,
|
|
langs=["ru", "en"],
|
|
batch_size=13,
|
|
seq_length=7,
|
|
is_training=False,
|
|
use_labels=False,
|
|
hidden_size=16,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
intermediate_size=4,
|
|
hidden_act="relu",
|
|
hidden_dropout_prob=0.1,
|
|
attention_probs_dropout_prob=0.1,
|
|
max_position_embeddings=20,
|
|
bos_token_id=0,
|
|
pad_token_id=1,
|
|
eos_token_id=2,
|
|
):
|
|
self.parent = parent
|
|
self.src_vocab_size = src_vocab_size
|
|
self.tgt_vocab_size = tgt_vocab_size
|
|
self.langs = langs
|
|
self.batch_size = batch_size
|
|
self.seq_length = seq_length
|
|
self.is_training = is_training
|
|
self.use_labels = use_labels
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.hidden_act = hidden_act
|
|
self.hidden_dropout_prob = hidden_dropout_prob
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.bos_token_id = bos_token_id
|
|
self.pad_token_id = pad_token_id
|
|
self.eos_token_id = eos_token_id
|
|
torch.manual_seed(0)
|
|
|
|
# hack needed for modeling_common tests - despite not really having this attribute in this model
|
|
self.vocab_size = self.src_vocab_size
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids = ids_tensor([self.batch_size, self.seq_length], self.src_vocab_size).clamp(
|
|
3,
|
|
)
|
|
input_ids[:, -1] = 2 # Eos Token
|
|
|
|
config = self.get_config()
|
|
inputs_dict = prepare_fsmt_inputs_dict(config, input_ids)
|
|
return config, inputs_dict
|
|
|
|
def get_config(self):
|
|
return FSMTConfig(
|
|
vocab_size=self.src_vocab_size, # hack needed for common tests
|
|
src_vocab_size=self.src_vocab_size,
|
|
tgt_vocab_size=self.tgt_vocab_size,
|
|
langs=self.langs,
|
|
d_model=self.hidden_size,
|
|
encoder_layers=self.num_hidden_layers,
|
|
decoder_layers=self.num_hidden_layers,
|
|
encoder_attention_heads=self.num_attention_heads,
|
|
decoder_attention_heads=self.num_attention_heads,
|
|
encoder_ffn_dim=self.intermediate_size,
|
|
decoder_ffn_dim=self.intermediate_size,
|
|
dropout=self.hidden_dropout_prob,
|
|
attention_dropout=self.attention_probs_dropout_prob,
|
|
max_position_embeddings=self.max_position_embeddings,
|
|
eos_token_id=self.eos_token_id,
|
|
bos_token_id=self.bos_token_id,
|
|
pad_token_id=self.pad_token_id,
|
|
)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config, inputs_dict = self.prepare_config_and_inputs()
|
|
inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"]
|
|
inputs_dict["decoder_attention_mask"] = inputs_dict["attention_mask"]
|
|
inputs_dict["use_cache"] = False
|
|
return config, inputs_dict
|
|
|
|
|
|
def prepare_fsmt_inputs_dict(
|
|
config,
|
|
input_ids,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
decoder_head_mask=None,
|
|
cross_attn_head_mask=None,
|
|
):
|
|
if attention_mask is None:
|
|
attention_mask = input_ids.ne(config.pad_token_id)
|
|
if head_mask is None:
|
|
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
|
|
if decoder_head_mask is None:
|
|
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
|
|
if cross_attn_head_mask is None:
|
|
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
|
|
return {
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"head_mask": head_mask,
|
|
"decoder_head_mask": decoder_head_mask,
|
|
}
|
|
|
|
|
|
@require_torch
|
|
class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (FSMTModel, FSMTForConditionalGeneration) if is_torch_available() else ()
|
|
all_generative_model_classes = (FSMTForConditionalGeneration,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"conversational": FSMTForConditionalGeneration,
|
|
"feature-extraction": FSMTModel,
|
|
"summarization": FSMTForConditionalGeneration,
|
|
"text2text-generation": FSMTForConditionalGeneration,
|
|
"translation": FSMTForConditionalGeneration,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
is_encoder_decoder = True
|
|
test_pruning = False
|
|
test_missing_keys = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = FSMTModelTester(self)
|
|
self.langs = ["en", "ru"]
|
|
config = {
|
|
"langs": self.langs,
|
|
"src_vocab_size": 10,
|
|
"tgt_vocab_size": 20,
|
|
}
|
|
# XXX: hack to appease to all other models requiring `vocab_size`
|
|
config["vocab_size"] = 99 # no such thing in FSMT
|
|
self.config_tester = ConfigTester(self, config_class=FSMTConfig, **config)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
# XXX: override test_model_common_attributes / different Embedding type
|
|
def test_model_common_attributes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding))
|
|
model.set_input_embeddings(nn.Embedding(10, 10))
|
|
x = model.get_output_embeddings()
|
|
self.assertTrue(x is None or isinstance(x, nn.modules.sparse.Embedding))
|
|
|
|
def test_initialization_more(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
|
|
model = FSMTModel(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
# test init
|
|
# self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item())
|
|
|
|
def _check_var(module):
|
|
"""Check that we initialized various parameters from N(0, config.init_std)."""
|
|
self.assertAlmostEqual(torch.std(module.weight).item(), config.init_std, 2)
|
|
|
|
_check_var(model.encoder.embed_tokens)
|
|
_check_var(model.encoder.layers[0].self_attn.k_proj)
|
|
_check_var(model.encoder.layers[0].fc1)
|
|
# XXX: different std for fairseq version of SinusoidalPositionalEmbedding
|
|
# self.assertAlmostEqual(torch.std(model.encoder.embed_positions.weights).item(), config.init_std, 2)
|
|
|
|
def test_advanced_inputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
|
|
config.use_cache = False
|
|
inputs_dict["input_ids"][:, -2:] = config.pad_token_id
|
|
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
|
|
config, inputs_dict["input_ids"]
|
|
)
|
|
model = FSMTModel(config).to(torch_device).eval()
|
|
|
|
decoder_features_with_created_mask = model(**inputs_dict)[0]
|
|
decoder_features_with_passed_mask = model(
|
|
decoder_attention_mask=invert_mask(decoder_attn_mask), decoder_input_ids=decoder_input_ids, **inputs_dict
|
|
)[0]
|
|
_assert_tensors_equal(decoder_features_with_passed_mask, decoder_features_with_created_mask)
|
|
useless_mask = torch.zeros_like(decoder_attn_mask)
|
|
decoder_features = model(decoder_attention_mask=useless_mask, **inputs_dict)[0]
|
|
self.assertTrue(isinstance(decoder_features, torch.Tensor)) # no hidden states or attentions
|
|
self.assertEqual(
|
|
decoder_features.size(),
|
|
(self.model_tester.batch_size, self.model_tester.seq_length, config.tgt_vocab_size),
|
|
)
|
|
if decoder_attn_mask.min().item() < -1e3: # some tokens were masked
|
|
self.assertFalse((decoder_features_with_created_mask == decoder_features).all().item())
|
|
|
|
# Test different encoder attention masks
|
|
decoder_features_with_long_encoder_mask = model(
|
|
inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"].long()
|
|
)[0]
|
|
_assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask)
|
|
|
|
def test_save_load_missing_keys(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
|
|
self.assertEqual(info["missing_keys"], [])
|
|
|
|
@unittest.skip("Test has a segmentation fault on torch 1.8.0")
|
|
def test_export_to_onnx(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
|
|
model = FSMTModel(config).to(torch_device)
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
torch.onnx.export(
|
|
model,
|
|
(inputs_dict["input_ids"], inputs_dict["attention_mask"]),
|
|
f"{tmpdirname}/fsmt_test.onnx",
|
|
export_params=True,
|
|
opset_version=12,
|
|
input_names=["input_ids", "attention_mask"],
|
|
)
|
|
|
|
def test_ensure_weights_are_shared(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
|
|
|
|
config.tie_word_embeddings = True
|
|
model = FSMTForConditionalGeneration(config)
|
|
|
|
# FSMT shares three weights.
|
|
# Not an issue to not have these correctly tied for torch.load, but it is an issue for safetensors.
|
|
self.assertEqual(
|
|
len(
|
|
{
|
|
model.get_output_embeddings().weight.data_ptr(),
|
|
model.get_input_embeddings().weight.data_ptr(),
|
|
model.base_model.decoder.output_projection.weight.data_ptr(),
|
|
}
|
|
),
|
|
1,
|
|
)
|
|
|
|
config.tie_word_embeddings = False
|
|
model = FSMTForConditionalGeneration(config)
|
|
|
|
# FSMT shares three weights.
|
|
# Not an issue to not have these correctly tied for torch.load, but it is an issue for safetensors.
|
|
self.assertEqual(
|
|
len(
|
|
{
|
|
model.get_output_embeddings().weight.data_ptr(),
|
|
model.get_input_embeddings().weight.data_ptr(),
|
|
model.base_model.decoder.output_projection.weight.data_ptr(),
|
|
}
|
|
),
|
|
2,
|
|
)
|
|
|
|
@unittest.skip("can't be implemented for FSMT due to dual vocab.")
|
|
def test_resize_tokens_embeddings(self):
|
|
pass
|
|
|
|
@unittest.skip("Passing inputs_embeds not implemented for FSMT.")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip("Input ids is required for FSMT.")
|
|
def test_inputs_embeds_matches_input_ids(self):
|
|
pass
|
|
|
|
@unittest.skip("model weights aren't tied in FSMT.")
|
|
def test_tie_model_weights(self):
|
|
pass
|
|
|
|
@unittest.skip("TODO: Decoder embeddings cannot be resized at the moment")
|
|
def test_resize_embeddings_untied(self):
|
|
pass
|
|
|
|
|
|
@require_torch
|
|
class FSMTHeadTests(unittest.TestCase):
|
|
src_vocab_size = 99
|
|
tgt_vocab_size = 99
|
|
langs = ["ru", "en"]
|
|
|
|
def _get_config(self):
|
|
return FSMTConfig(
|
|
src_vocab_size=self.src_vocab_size,
|
|
tgt_vocab_size=self.tgt_vocab_size,
|
|
langs=self.langs,
|
|
d_model=24,
|
|
encoder_layers=2,
|
|
decoder_layers=2,
|
|
encoder_attention_heads=2,
|
|
decoder_attention_heads=2,
|
|
encoder_ffn_dim=32,
|
|
decoder_ffn_dim=32,
|
|
max_position_embeddings=48,
|
|
eos_token_id=2,
|
|
pad_token_id=1,
|
|
bos_token_id=0,
|
|
)
|
|
|
|
def _get_config_and_data(self):
|
|
input_ids = torch.tensor(
|
|
[
|
|
[71, 82, 18, 33, 46, 91, 2],
|
|
[68, 34, 26, 58, 30, 82, 2],
|
|
[5, 97, 17, 39, 94, 40, 2],
|
|
[76, 83, 94, 25, 70, 78, 2],
|
|
[87, 59, 41, 35, 48, 66, 2],
|
|
[55, 13, 16, 58, 5, 2, 1], # note padding
|
|
[64, 27, 31, 51, 12, 75, 2],
|
|
[52, 64, 86, 17, 83, 39, 2],
|
|
[48, 61, 9, 24, 71, 82, 2],
|
|
[26, 1, 60, 48, 22, 13, 2],
|
|
[21, 5, 62, 28, 14, 76, 2],
|
|
[45, 98, 37, 86, 59, 48, 2],
|
|
[70, 70, 50, 9, 28, 0, 2],
|
|
],
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
|
|
batch_size = input_ids.shape[0]
|
|
config = self._get_config()
|
|
return config, input_ids, batch_size
|
|
|
|
def test_generate_beam_search(self):
|
|
input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], dtype=torch.long, device=torch_device)
|
|
config = self._get_config()
|
|
lm_model = FSMTForConditionalGeneration(config).to(torch_device)
|
|
lm_model.eval()
|
|
|
|
max_length = 5
|
|
new_input_ids = lm_model.generate(
|
|
input_ids.clone(),
|
|
do_sample=True,
|
|
num_return_sequences=1,
|
|
num_beams=2,
|
|
no_repeat_ngram_size=3,
|
|
max_length=max_length,
|
|
)
|
|
self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length))
|
|
|
|
def test_shift_tokens_right(self):
|
|
input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long)
|
|
shifted = shift_tokens_right(input_ids, 1)
|
|
n_pad_before = input_ids.eq(1).float().sum()
|
|
n_pad_after = shifted.eq(1).float().sum()
|
|
self.assertEqual(shifted.shape, input_ids.shape)
|
|
self.assertEqual(n_pad_after, n_pad_before - 1)
|
|
self.assertTrue(torch.eq(shifted[:, 0], 2).all())
|
|
|
|
@require_torch_fp16
|
|
def test_generate_fp16(self):
|
|
config, input_ids, batch_size = self._get_config_and_data()
|
|
attention_mask = input_ids.ne(1).to(torch_device)
|
|
model = FSMTForConditionalGeneration(config).eval().to(torch_device)
|
|
model.half()
|
|
model.generate(input_ids, attention_mask=attention_mask)
|
|
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
|
|
|
|
def test_dummy_inputs(self):
|
|
config, *_ = self._get_config_and_data()
|
|
model = FSMTForConditionalGeneration(config).eval().to(torch_device)
|
|
model(**model.dummy_inputs)
|
|
|
|
def test_prepare_fsmt_decoder_inputs(self):
|
|
config, *_ = self._get_config_and_data()
|
|
input_ids = _long_tensor(([4, 4, 2]))
|
|
decoder_input_ids = _long_tensor([[26388, 2, config.pad_token_id]])
|
|
causal_mask_dtype = torch.float32
|
|
ignore = torch.finfo(causal_mask_dtype).min
|
|
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
|
|
config, input_ids, decoder_input_ids, causal_mask_dtype=causal_mask_dtype
|
|
)
|
|
expected_causal_mask = torch.tensor(
|
|
[[0, ignore, ignore], [0, 0, ignore], [0, 0, 0]] # never attend to the final token, because its pad
|
|
).to(input_ids.device)
|
|
self.assertEqual(decoder_attn_mask.size(), decoder_input_ids.size())
|
|
self.assertTrue(torch.eq(expected_causal_mask, causal_mask).all())
|
|
|
|
|
|
def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
|
|
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
|
|
if a is None and b is None:
|
|
return True
|
|
try:
|
|
if torch.allclose(a, b, atol=atol):
|
|
return True
|
|
raise
|
|
except Exception:
|
|
if len(prefix) > 0:
|
|
prefix = f"{prefix}: "
|
|
raise AssertionError(f"{prefix}{a} != {b}")
|
|
|
|
|
|
def _long_tensor(tok_lst):
|
|
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
|
|
|
|
|
|
TOLERANCE = 1e-4
|
|
|
|
|
|
pairs = [
|
|
["en-ru"],
|
|
["ru-en"],
|
|
["en-de"],
|
|
["de-en"],
|
|
]
|
|
|
|
|
|
@require_torch
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class FSMTModelIntegrationTests(unittest.TestCase):
|
|
tokenizers_cache = {}
|
|
models_cache = {}
|
|
default_mname = "facebook/wmt19-en-ru"
|
|
|
|
@cached_property
|
|
def default_tokenizer(self):
|
|
return self.get_tokenizer(self.default_mname)
|
|
|
|
@cached_property
|
|
def default_model(self):
|
|
return self.get_model(self.default_mname)
|
|
|
|
def get_tokenizer(self, mname):
|
|
if mname not in self.tokenizers_cache:
|
|
self.tokenizers_cache[mname] = FSMTTokenizer.from_pretrained(mname)
|
|
return self.tokenizers_cache[mname]
|
|
|
|
def get_model(self, mname):
|
|
if mname not in self.models_cache:
|
|
self.models_cache[mname] = FSMTForConditionalGeneration.from_pretrained(mname).to(torch_device)
|
|
if torch_device == "cuda":
|
|
self.models_cache[mname].half()
|
|
return self.models_cache[mname]
|
|
|
|
@slow
|
|
def test_inference_no_head(self):
|
|
tokenizer = self.default_tokenizer
|
|
model = FSMTModel.from_pretrained(self.default_mname).to(torch_device)
|
|
|
|
src_text = "My friend computer will translate this for me"
|
|
input_ids = tokenizer([src_text], return_tensors="pt")["input_ids"]
|
|
input_ids = _long_tensor(input_ids).to(torch_device)
|
|
inputs_dict = prepare_fsmt_inputs_dict(model.config, input_ids)
|
|
with torch.no_grad():
|
|
output = model(**inputs_dict)[0]
|
|
expected_shape = torch.Size((1, 10, model.config.tgt_vocab_size))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
# expected numbers were generated when en-ru model, using just fairseq's model4.pt
|
|
# may have to adjust if switched to a different checkpoint
|
|
expected_slice = torch.tensor(
|
|
[[-1.5753, -1.5753, 2.8975], [-0.9540, -0.9540, 1.0299], [-3.3131, -3.3131, 0.5219]]
|
|
).to(torch_device)
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
|
|
|
|
def translation_setup(self, pair):
|
|
text = {
|
|
"en": "Machine learning is great, isn't it?",
|
|
"ru": "Машинное обучение - это здорово, не так ли?",
|
|
"de": "Maschinelles Lernen ist großartig, oder?",
|
|
}
|
|
|
|
src, tgt = pair.split("-")
|
|
print(f"Testing {src} -> {tgt}")
|
|
mname = f"facebook/wmt19-{pair}"
|
|
|
|
src_text = text[src]
|
|
tgt_text = text[tgt]
|
|
|
|
tokenizer = self.get_tokenizer(mname)
|
|
model = self.get_model(mname)
|
|
return tokenizer, model, src_text, tgt_text
|
|
|
|
@parameterized.expand(pairs)
|
|
@slow
|
|
def test_translation_direct(self, pair):
|
|
tokenizer, model, src_text, tgt_text = self.translation_setup(pair)
|
|
|
|
input_ids = tokenizer.encode(src_text, return_tensors="pt").to(torch_device)
|
|
|
|
outputs = model.generate(input_ids)
|
|
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
|
assert decoded == tgt_text, f"\n\ngot: {decoded}\nexp: {tgt_text}\n"
|
|
|
|
@parameterized.expand(pairs)
|
|
@slow
|
|
def test_translation_pipeline(self, pair):
|
|
tokenizer, model, src_text, tgt_text = self.translation_setup(pair)
|
|
pipeline = TranslationPipeline(model, tokenizer, framework="pt", device=torch_device)
|
|
output = pipeline([src_text])
|
|
self.assertEqual([tgt_text], [x["translation_text"] for x in output])
|
|
|
|
|
|
@require_torch
|
|
class TestSinusoidalPositionalEmbeddings(unittest.TestCase):
|
|
padding_idx = 1
|
|
tolerance = 1e-4
|
|
|
|
def test_basic(self):
|
|
input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device)
|
|
emb1 = SinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6, padding_idx=self.padding_idx).to(
|
|
torch_device
|
|
)
|
|
emb = emb1(input_ids)
|
|
desired_weights = torch.tensor(
|
|
[
|
|
[9.0930e-01, 1.9999e-02, 2.0000e-04, -4.1615e-01, 9.9980e-01, 1.0000e00],
|
|
[1.4112e-01, 2.9995e-02, 3.0000e-04, -9.8999e-01, 9.9955e-01, 1.0000e00],
|
|
]
|
|
).to(torch_device)
|
|
self.assertTrue(
|
|
torch.allclose(emb[0], desired_weights, atol=self.tolerance),
|
|
msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n",
|
|
)
|
|
|
|
def test_odd_embed_dim(self):
|
|
# odd embedding_dim is allowed
|
|
SinusoidalPositionalEmbedding(num_positions=4, embedding_dim=5, padding_idx=self.padding_idx).to(torch_device)
|
|
|
|
# odd num_embeddings is allowed
|
|
SinusoidalPositionalEmbedding(num_positions=5, embedding_dim=4, padding_idx=self.padding_idx).to(torch_device)
|
|
|
|
@unittest.skip("different from marian (needs more research)")
|
|
def test_positional_emb_weights_against_marian(self):
|
|
desired_weights = torch.tensor(
|
|
[
|
|
[0, 0, 0, 0, 0],
|
|
[0.84147096, 0.82177866, 0.80180490, 0.78165019, 0.76140374],
|
|
[0.90929741, 0.93651021, 0.95829457, 0.97505713, 0.98720258],
|
|
]
|
|
)
|
|
emb1 = SinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512, padding_idx=self.padding_idx).to(
|
|
torch_device
|
|
)
|
|
weights = emb1.weights.data[:3, :5]
|
|
# XXX: only the 1st and 3rd lines match - this is testing against
|
|
# verbatim copy of SinusoidalPositionalEmbedding from fairseq
|
|
self.assertTrue(
|
|
torch.allclose(weights, desired_weights, atol=self.tolerance),
|
|
msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n",
|
|
)
|
|
|
|
# test that forward pass is just a lookup, there is no ignore padding logic
|
|
input_ids = torch.tensor(
|
|
[[4, 10, self.padding_idx, self.padding_idx, self.padding_idx]], dtype=torch.long, device=torch_device
|
|
)
|
|
no_cache_pad_zero = emb1(input_ids)[0]
|
|
# XXX: only the 1st line matches the 3rd
|
|
self.assertTrue(
|
|
torch.allclose(torch.tensor(desired_weights, device=torch_device), no_cache_pad_zero[:3, :5], atol=1e-3)
|
|
)
|