463 lines
20 KiB
Python
463 lines
20 KiB
Python
# coding=utf-8
|
|
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# 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.
|
|
"""Testing suite for the PyTorch M2M100 model."""
|
|
|
|
import copy
|
|
import tempfile
|
|
import unittest
|
|
|
|
import pytest
|
|
|
|
from transformers import M2M100Config, is_torch_available
|
|
from transformers.testing_utils import (
|
|
require_flash_attn,
|
|
require_sentencepiece,
|
|
require_tokenizers,
|
|
require_torch,
|
|
require_torch_fp16,
|
|
require_torch_gpu,
|
|
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 transformers import M2M100ForConditionalGeneration, M2M100Model, M2M100Tokenizer
|
|
from transformers.models.m2m_100.modeling_m2m_100 import M2M100Decoder, M2M100Encoder
|
|
|
|
|
|
def prepare_m2m_100_inputs_dict(
|
|
config,
|
|
input_ids,
|
|
decoder_input_ids,
|
|
attention_mask=None,
|
|
decoder_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 decoder_attention_mask is None:
|
|
decoder_attention_mask = decoder_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,
|
|
"decoder_input_ids": decoder_input_ids,
|
|
"attention_mask": attention_mask,
|
|
"decoder_attention_mask": attention_mask,
|
|
"head_mask": head_mask,
|
|
"decoder_head_mask": decoder_head_mask,
|
|
"cross_attn_head_mask": cross_attn_head_mask,
|
|
}
|
|
|
|
|
|
class M2M100ModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
seq_length=7,
|
|
is_training=True,
|
|
use_labels=False,
|
|
vocab_size=99,
|
|
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,
|
|
encoder_layerdrop=0.0,
|
|
decoder_layerdrop=0.0,
|
|
max_position_embeddings=20,
|
|
eos_token_id=2,
|
|
pad_token_id=1,
|
|
bos_token_id=0,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.seq_length = seq_length
|
|
self.is_training = is_training
|
|
self.use_labels = use_labels
|
|
self.vocab_size = vocab_size
|
|
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.encoder_layerdrop = encoder_layerdrop
|
|
self.decoder_layerdrop = decoder_layerdrop
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.eos_token_id = eos_token_id
|
|
self.pad_token_id = pad_token_id
|
|
self.bos_token_id = bos_token_id
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
input_ids[:, -1] = self.eos_token_id # Eos Token
|
|
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
# we need to clamp the input ids here to avoid having pad token in between
|
|
# this is because for M2M100 the position_ids are prepared such that
|
|
# all pad tokens have pos id = 2 and rest are between 2..seq_length
|
|
# and the seq_length here is seq_length - num_pad_tokens
|
|
# but when using past, there is no way of knowing if the past input ids had
|
|
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
|
|
# position_ids being off by num_pad_tokens in past input
|
|
input_ids = input_ids.clamp(self.pad_token_id + 1)
|
|
decoder_input_ids = decoder_input_ids.clamp(self.pad_token_id + 1)
|
|
|
|
config = self.get_config()
|
|
inputs_dict = prepare_m2m_100_inputs_dict(config, input_ids, decoder_input_ids)
|
|
return config, inputs_dict
|
|
|
|
def get_config(self):
|
|
return M2M100Config(
|
|
vocab_size=self.vocab_size,
|
|
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,
|
|
encoder_layerdrop=self.encoder_layerdrop,
|
|
decoder_layerdrop=self.decoder_layerdrop,
|
|
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()
|
|
return config, inputs_dict
|
|
|
|
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
|
|
model = M2M100Model(config=config).get_decoder().to(torch_device).eval()
|
|
input_ids = inputs_dict["input_ids"]
|
|
attention_mask = inputs_dict["attention_mask"]
|
|
head_mask = inputs_dict["head_mask"]
|
|
|
|
# first forward pass
|
|
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
|
|
|
|
output, past_key_values = outputs.to_tuple()
|
|
|
|
# create hypothetical multiple next token and extent to next_input_ids
|
|
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
|
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
|
|
|
|
# append to next input_ids and
|
|
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
|
|
|
|
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
|
|
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
|
|
"last_hidden_state"
|
|
]
|
|
|
|
# select random slice
|
|
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
|
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
|
|
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
|
|
|
|
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
|
|
|
|
# test that outputs are equal for slice
|
|
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
|
|
|
|
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
|
|
model = M2M100Model(config=config).to(torch_device).eval()
|
|
outputs = model(**inputs_dict)
|
|
|
|
encoder_last_hidden_state = outputs.encoder_last_hidden_state
|
|
last_hidden_state = outputs.last_hidden_state
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
encoder = model.get_encoder()
|
|
encoder.save_pretrained(tmpdirname)
|
|
encoder = M2M100Encoder.from_pretrained(tmpdirname).to(torch_device)
|
|
|
|
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
|
|
0
|
|
]
|
|
|
|
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
decoder = model.get_decoder()
|
|
decoder.save_pretrained(tmpdirname)
|
|
decoder = M2M100Decoder.from_pretrained(tmpdirname).to(torch_device)
|
|
|
|
last_hidden_state_2 = decoder(
|
|
input_ids=inputs_dict["decoder_input_ids"],
|
|
attention_mask=inputs_dict["decoder_attention_mask"],
|
|
encoder_hidden_states=encoder_last_hidden_state,
|
|
encoder_attention_mask=inputs_dict["attention_mask"],
|
|
)[0]
|
|
|
|
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
|
|
|
|
|
|
@require_torch
|
|
class M2M100ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
M2M100Model,
|
|
M2M100ForConditionalGeneration,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
all_generative_model_classes = (M2M100ForConditionalGeneration,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"conversational": M2M100ForConditionalGeneration,
|
|
"feature-extraction": M2M100Model,
|
|
"summarization": M2M100ForConditionalGeneration,
|
|
"text2text-generation": M2M100ForConditionalGeneration,
|
|
"translation": M2M100ForConditionalGeneration,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
is_encoder_decoder = True
|
|
fx_compatible = True
|
|
test_pruning = False
|
|
test_missing_keys = False
|
|
|
|
# TODO: Fix the failed tests
|
|
def is_pipeline_test_to_skip(
|
|
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
|
|
):
|
|
if pipeline_test_casse_name == "TranslationPipelineTests":
|
|
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
|
|
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
|
|
return True
|
|
|
|
return False
|
|
|
|
def setUp(self):
|
|
self.model_tester = M2M100ModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=M2M100Config)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_save_load_strict(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"], [])
|
|
|
|
def test_decoder_model_past_with_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
|
|
|
def test_encoder_decoder_model_standalone(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
|
|
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
|
|
|
|
def test_inputs_embeds(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in (M2M100Model, M2M100ForConditionalGeneration):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
if not self.is_encoder_decoder:
|
|
input_ids = inputs["input_ids"]
|
|
del inputs["input_ids"]
|
|
else:
|
|
encoder_input_ids = inputs["input_ids"]
|
|
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
|
|
del inputs["input_ids"]
|
|
inputs.pop("decoder_input_ids", None)
|
|
|
|
wte = model.get_input_embeddings()
|
|
if not self.is_encoder_decoder:
|
|
inputs["inputs_embeds"] = wte(input_ids)
|
|
else:
|
|
inputs["inputs_embeds"] = wte(encoder_input_ids)
|
|
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
|
|
|
|
with torch.no_grad():
|
|
model(**inputs)[0]
|
|
|
|
@require_torch_fp16
|
|
def test_generate_fp16(self):
|
|
config, input_dict = self.model_tester.prepare_config_and_inputs()
|
|
input_ids = input_dict["input_ids"]
|
|
attention_mask = input_ids.ne(1).to(torch_device)
|
|
model = M2M100ForConditionalGeneration(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 _long_tensor(tok_lst):
|
|
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
|
|
|
|
|
|
TOLERANCE = 1e-4
|
|
|
|
|
|
@require_torch
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
@slow
|
|
class M2M100ModelIntegrationTests(unittest.TestCase):
|
|
@cached_property
|
|
def default_tokenizer(self):
|
|
return M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
|
|
|
|
def test_inference_no_head(self):
|
|
model = M2M100Model.from_pretrained("facebook/m2m100_418M").to(torch_device)
|
|
input_ids = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
|
|
decoder_input_ids = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
|
|
inputs_dict = prepare_m2m_100_inputs_dict(model.config, input_ids, decoder_input_ids)
|
|
with torch.no_grad():
|
|
output = model(**inputs_dict)[0]
|
|
expected_shape = torch.Size((1, 11, 1024))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
# change to expected output here
|
|
expected_slice = torch.tensor(
|
|
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]], device=torch_device
|
|
)
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
|
|
|
|
def test_inference_head(self):
|
|
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M").to(torch_device)
|
|
|
|
# change to intended input
|
|
input_ids = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
|
|
decoder_input_ids = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
|
|
inputs_dict = prepare_m2m_100_inputs_dict(model.config, input_ids, decoder_input_ids)
|
|
with torch.no_grad():
|
|
output = model(**inputs_dict)[0]
|
|
expected_shape = torch.Size((1, 11, model.config.vocab_size))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
# change to expected output here
|
|
expected_slice = torch.tensor(
|
|
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]], device=torch_device
|
|
)
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
|
|
|
|
def test_seq_to_seq_generation(self):
|
|
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M").to(torch_device)
|
|
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="fr", tgt_lang="en")
|
|
|
|
src_fr = [
|
|
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
|
|
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
|
|
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
|
|
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
|
|
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
|
|
]
|
|
|
|
# The below article tests that we don't add any hypotheses outside of the top n_beams
|
|
dct = tokenizer(src_fr, padding=True, return_tensors="pt")
|
|
|
|
hypotheses_batch = model.generate(
|
|
input_ids=dct["input_ids"].to(torch_device),
|
|
attention_mask=dct["attention_mask"].to(torch_device),
|
|
num_beams=5,
|
|
forced_bos_token_id=tokenizer.get_lang_id("en"),
|
|
)
|
|
|
|
expected_en = [
|
|
"The NSA case highlights the total absence of intelligence debate",
|
|
"I think there are two levels of response from the French government.",
|
|
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
|
|
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
|
|
" communications in France.",
|
|
]
|
|
|
|
generated = tokenizer.batch_decode(
|
|
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
|
|
)
|
|
assert generated == expected_en
|
|
|
|
@require_flash_attn
|
|
@require_torch_gpu
|
|
@pytest.mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_seq_to_seq_generation(self):
|
|
"""
|
|
Overwritting the common test as the test is flaky on tiny models
|
|
"""
|
|
model = M2M100ForConditionalGeneration.from_pretrained(
|
|
"facebook/m2m100_418M", attn_implementation="flash_attention_2"
|
|
).to(torch_device)
|
|
|
|
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="fr", tgt_lang="en")
|
|
|
|
src_fr = [
|
|
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
|
|
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
|
|
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
|
|
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
|
|
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
|
|
]
|
|
|
|
# The below article tests that we don't add any hypotheses outside of the top n_beams
|
|
dct = tokenizer(src_fr, padding=True, return_tensors="pt")
|
|
|
|
hypotheses_batch = model.generate(
|
|
input_ids=dct["input_ids"].to(torch_device),
|
|
attention_mask=dct["attention_mask"].to(torch_device),
|
|
num_beams=5,
|
|
forced_bos_token_id=tokenizer.get_lang_id("en"),
|
|
)
|
|
|
|
expected_en = [
|
|
"The NSA case highlights the total absence of intelligence debate",
|
|
"I think there are two levels of response from the French government.",
|
|
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
|
|
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
|
|
" communications in France.",
|
|
]
|
|
|
|
generated = tokenizer.batch_decode(
|
|
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
|
|
)
|
|
assert generated == expected_en
|