# 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 Speech2Text model.""" import copy import inspect import tempfile import unittest from datasets import load_dataset from transformers import Speech2TextConfig from transformers.testing_utils import ( is_torch_available, require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, require_torchaudio, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder def prepare_speech_to_text_inputs_dict( config, input_features, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, ): if attention_mask is None: attention_mask = input_features.ne(0) if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) return { # "input_ids": input_features, "input_features": input_features, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } @require_torch class Speech2TextModelTester: 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, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=32, input_feat_per_channel=24, input_channels=1, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=20, max_target_positions=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.num_conv_layers = num_conv_layers self.conv_kernel_sizes = conv_kernel_sizes self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels 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.max_source_positions = max_source_positions self.max_target_positions = max_target_positions 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_features = floats_tensor( [self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size ) attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2) config = self.get_config() inputs_dict = prepare_speech_to_text_inputs_dict( config, input_features=input_features, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, ) return config, inputs_dict def get_config(self): return Speech2TextConfig( 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, num_conv_layers=self.num_conv_layers, conv_kernel_sizes=self.conv_kernel_sizes, conv_channels=self.conv_channels, input_feat_per_channel=self.input_feat_per_channel, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, max_target_positions=self.max_target_positions, 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 get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for i in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths def create_and_check_model_forward(self, config, inputs_dict): model = Speech2TextModel(config=config).to(torch_device).eval() input_features = inputs_dict["input_features"] decoder_input_ids = inputs_dict["decoder_input_ids"] # first forward pass last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16)) def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = Speech2TextModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["decoder_input_ids"] attention_mask = inputs_dict["decoder_attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_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).clamp(2) 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 = Speech2TextModel(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 = Speech2TextEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder( inputs_dict["input_features"], 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 = Speech2TextDecoder.from_pretrained(tmpdirname).to(torch_device) encoder_attention_mask = encoder._get_feature_vector_attention_mask( encoder_last_hidden_state.shape[1], inputs_dict["attention_mask"] ) 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=encoder_attention_mask, )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) @require_torch class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Speech2TextModel, Speech2TextForConditionalGeneration) if is_torch_available() else () pipeline_model_mapping = ( {"automatic-speech-recognition": Speech2TextForConditionalGeneration, "feature-extraction": Speech2TextModel} if is_torch_available() else {} ) is_encoder_decoder = True test_missing_keys = False def setUp(self): self.model_tester = Speech2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Speech2TextConfig) self.maxDiff = 3000 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"], set()) def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) 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) @unittest.skip(reason="Not implemented currently") def test_inputs_embeds(self): pass @unittest.skip(reason="Training is not supported yet") def test_training(self): pass @unittest.skip def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_features = input_dict["input_features"] attention_mask = input_dict["attention_mask"] model = Speech2TextForConditionalGeneration(config).eval().to(torch_device) input_features = input_features.half() model.half() model.generate(input_features, attention_mask=attention_mask) model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_features", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend(["encoder_outputs"]) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length) subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_resize_tokens_embeddings(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is set to False") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # make sure that decoder_input_ids are resized if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_embeddings_untied(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is set to False") original_config.tie_word_embeddings = False # if model cannot untied embeddings -> leave test if original_config.tie_word_embeddings: self.skipTest(reason="Model cannot untie embeddings") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) @unittest.skip def test_generate_without_input_ids(self): pass @require_torch @require_torchaudio @require_sentencepiece @require_tokenizers @slow @unittest.skip("@eustlb broken in a weird way. To investigate later.") class Speech2TextModelIntegrationTests(unittest.TestCase): @classmethod def setUpClass(cls): model_name = "facebook/s2t-small-librispeech-asr" cls.model = Speech2TextForConditionalGeneration.from_pretrained(model_name, use_safetensors=False) cls.processor = Speech2TextProcessor.from_pretrained(model_name) # loads 4 samples ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") speech_samples = ds.sort("id").select(range(4))[:4]["audio"] cls.dataset = [x["array"] for x in speech_samples] def test_generation_librispeech(self): input_speech = [self.dataset[0]] input_features = self.processor(input_speech, return_tensors="pt").input_features.to(torch_device) generated_ids = self.model.generate(input_features) generated_transcript = self.processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel" ] self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS) def test_generation_librispeech_batched(self): input_speech = self.dataset inputs = self.processor(input_speech, return_tensors="pt", padding=True) input_features = inputs.input_features.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) generated_ids = self.model.generate(input_features, attention_mask=attention_mask) generated_transcripts = self.processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister cultar's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and roast beef looming before us" " similes drawn from eating and its results occur most readily to the mind", "he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it" " but little of rocky ithaca", ] self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)