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- # 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
-
- from transformers import is_torch_available
- from transformers.file_utils import cached_property
- from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
-
- from .test_configuration_common import ConfigTester
- from .test_generation_utils import GenerationTesterMixin
- from .test_modeling_common import ModelTesterMixin, ids_tensor
-
-
- if is_torch_available():
- import torch
-
- from transformers import M2M100Config, 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,
- }
-
-
- @require_torch
- 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 = 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,
- )
- inputs_dict = prepare_m2m_100_inputs_dict(config, input_ids, decoder_input_ids)
- return config, inputs_dict
-
- 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, unittest.TestCase):
- all_model_classes = (
- (
- M2M100Model,
- M2M100ForConditionalGeneration,
- )
- if is_torch_available()
- else ()
- )
- all_generative_model_classes = (M2M100ForConditionalGeneration,) if is_torch_available() else ()
- is_encoder_decoder = True
- test_pruning = False
- test_missing_keys = 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]
-
- 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)
- if torch_device == "cuda":
- 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
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