# 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 TensorFlow Speech2Text model. """ import inspect import unittest from transformers import Speech2TextConfig from transformers.file_utils import cached_property, is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from .test_configuration_common import ConfigTester from .test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel def prepare_speech_to_text_inputs_dict( config, input_features, 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 = tf.math.not_equal(input_features, 0) if decoder_attention_mask is None: decoder_attention_mask = tf.math.not_equal(decoder_input_ids, config.pad_token_id) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_features": input_features, "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_tf class TFSpeech2TextModelTester: 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 = tf.ones([self.batch_size, self.seq_length], dtype=tf.int64) decoder_input_ids = tf.math.maximum(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 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 _ in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFSpeech2TextModel(config=config).get_decoder() 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) _, (_, past_key_values) = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = tf.math.maximum(ids_tensor((self.batch_size, 3), config.vocab_size), 2) next_attn_mask = ids_tensor((self.batch_size, 3), 2, dtype=tf.int64) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-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 = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, atol=1e-2) @require_tf class TFSpeech2TextModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFSpeech2TextModel, TFSpeech2TextForConditionalGeneration) if is_tf_available() else () all_generative_model_classes = (TFSpeech2TextForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False test_onnx = False input_name = "input_ids" def setUp(self): self.model_tester = TFSpeech2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Speech2TextConfig) self.maxDiff = 3000 def test_config(self): self.config_tester.run_common_tests() 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) # not implemented currently def test_inputs_embeds(self): pass # training is not supported yet def test_training(self): pass def test_training_gradient_checkpointing(self): pass def test_generate_fp16(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) 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(config) 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) 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) 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) 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_token_embeddings(self): # Overwritten method from parent; see `test_resize_embeddings_untied` pass def test_resize_tokens_embeddings(self): # see `test_resize_embeddings_untied` pass def test_resize_embeddings_untied(self): # TODO: copy test from PT. Not working at the moment because the test relies on `model.resize_token_embeddings`, # whose TF implementation assumes the use of `TFWrappedEmbeddings`. But with a `TFWrappedEmbeddings` we can't # load the weights from PT (also, it induces TF1 behavior, so we might want to rework how # `model.resize_token_embeddings` operates). pass def test_generate_without_input_ids(self): pass @staticmethod def _get_encoder_outputs( model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1 ): encoder = model.get_encoder() encoder_outputs = encoder( input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, num_interleave, axis=0) input_ids = input_ids[:, :, 0] input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + model._get_decoder_start_token_id() attention_mask = None return encoder_outputs, input_ids, attention_mask def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1): batch_size, seq_length = input_ids.shape[:2] subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length) num_sequences_in_output = batch_size * num_return_sequences gen_len = ( output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length ) # scores self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config) # Attentions # encoder self._check_encoder_attention_for_generate( output.encoder_attentions, batch_size, config, subsampled_seq_length ) # decoder self._check_attentions_for_generate( num_sequences_in_output, output.decoder_attentions, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # Hidden States # encoder self._check_encoder_hidden_states_for_generate( output.encoder_hidden_states, batch_size, config, subsampled_seq_length ) # decoder self._check_hidden_states_for_generate( num_sequences_in_output, output.decoder_hidden_states, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_features with self.assertRaises(AssertionError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_features, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2)) with self.assertRaises(AssertionError): # generating more sequences than having beams leads is not possible model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_features, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids( model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2) ) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent -- the input is `input_features`, not `input_ids` 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.call) # 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", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @require_tf @require_sentencepiece @require_tokenizers @slow class TFSpeech2TextModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_generation_librispeech(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features) generated_transcript = 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): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(4) inputs = processor(input_speech, return_tensors="tf", padding=True) generated_ids = model.generate(inputs.input_features, attention_mask=inputs.attention_mask) generated_transcripts = 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)