def check_equivalence_pt_to_flax(self, config, decoder_config, inputs_dict): encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) pt_model = SpeechEncoderDecoderModel(encoder_decoder_config) fx_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config) fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
def check_encoder_decoder_model_from_pretrained_configs( self, config, inputs, attention_mask, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs ): encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) self.assertTrue(encoder_decoder_config.decoder.is_decoder) enc_dec_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config) self.assertTrue(enc_dec_model.config.is_encoder_decoder) self.assertFalse(enc_dec_model.config.tie_word_embeddings) outputs_encoder_decoder = enc_dec_model( inputs=inputs, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) )
def test_real_model_save_load_from_pretrained(self): model_2 = self.get_pretrained_model() inputs = ids_tensor([13, 5], model_2.config.encoder.vocab_size) decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size) attention_mask = ids_tensor([13, 5], vocab_size=2) outputs = model_2( inputs=inputs, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, ) out_2 = np.array(outputs[0]) out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmp_dirname: model_2.save_pretrained(tmp_dirname) model_1 = FlaxSpeechEncoderDecoderModel.from_pretrained(tmp_dirname) after_outputs = model_1( inputs=inputs, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, ) out_1 = np.array(after_outputs[0]) out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 4e-2)
def check_encoder_decoder_model_generate(self, inputs, config, decoder_config, **kwargs): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model} enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs) pad_token_id = enc_dec_model.config.decoder.pad_token_id eos_token_id = enc_dec_model.config.decoder.eos_token_id decoder_start_token_id = enc_dec_model.config.decoder.decoder_start_token_id # Copied from generation_utils (GPT2 doesn't have `pad_token_id`) if pad_token_id is None and eos_token_id is not None: pad_token_id = eos_token_id if decoder_start_token_id is None: decoder_start_token_id = enc_dec_model.config.decoder.bos_token_id # Bert does not have a bos token id, so use pad_token_id instead # Copied from `test_modeling_encoder_decoder.py` if decoder_start_token_id is None: decoder_start_token_id = pad_token_id generated_output = enc_dec_model.generate( inputs, pad_token_id=pad_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, ) generated_sequences = generated_output.sequences self.assertEqual(generated_sequences.shape, (inputs.shape[0],) + (decoder_config.max_length,))
def check_encoder_decoder_model_from_pretrained( self, config, inputs, attention_mask, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, return_dict, **kwargs ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict} enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs) outputs_encoder_decoder = enc_dec_model( inputs=inputs, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, output_hidden_states=True, return_dict=True, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) )
def check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict): pt_model.to(torch_device) pt_model.eval() # prepare inputs flax_inputs = inputs_dict pt_inputs = { k: torch.tensor(v.tolist()) for k, v in flax_inputs.items() } with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) pt_logits = pt_outputs.logits pt_outputs = pt_outputs.to_tuple() fx_outputs = fx_model(**inputs_dict) fx_logits = fx_outputs.logits fx_outputs = fx_outputs.to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2) # PT -> Flax with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained( tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**inputs_dict) fx_logits_loaded = fx_outputs_loaded.logits fx_outputs_loaded = fx_outputs_loaded.to_tuple() self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch") self.assert_almost_equals(fx_logits_loaded, pt_logits.numpy(), 4e-2) # Flax -> PT with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained( tmpdirname, from_flax=True) pt_model_loaded.to(torch_device) pt_model_loaded.eval() with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs) pt_logits_loaded = pt_outputs_loaded.logits pt_outputs_loaded = pt_outputs_loaded.to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch") self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)
def check_equivalence_flax_to_pt(self, config, decoder_config, inputs_dict): encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) pt_model = SpeechEncoderDecoderModel(encoder_decoder_config) fx_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config) pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
def check_encoder_decoder_model_output_attentions( self, config, inputs, attention_mask, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs ): # make the decoder inputs a different shape from the encoder inputs to harden the test decoder_input_ids = decoder_input_ids[:, :-1] decoder_attention_mask = decoder_attention_mask[:, :-1] encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model} enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs) outputs_encoder_decoder = enc_dec_model( inputs=inputs, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, output_attentions=True, ) encoder_attentions = outputs_encoder_decoder["encoder_attentions"] self.assertEqual(len(encoder_attentions), config.num_hidden_layers) seq_len = enc_dec_model._get_feat_extract_output_lengths(inputs.shape[1]) self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len)) decoder_attentions = outputs_encoder_decoder["decoder_attentions"] num_decoder_layers = ( decoder_config.num_decoder_layers if hasattr(decoder_config, "num_decoder_layers") else decoder_config.num_hidden_layers ) self.assertEqual(len(decoder_attentions), num_decoder_layers) self.assertEqual( decoder_attentions[0].shape[-3:], (decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]), ) cross_attentions = outputs_encoder_decoder["cross_attentions"] self.assertEqual(len(cross_attentions), num_decoder_layers) cross_attention_input_seq_len = decoder_input_ids.shape[-1] self.assertEqual( cross_attentions[0].shape[-3:], (decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len), )
def check_save_and_load( self, config, inputs, attention_mask, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model} enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs) outputs = enc_dec_model( inputs=inputs, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) out_2 = np.array(outputs[0]) out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: enc_dec_model.save_pretrained(tmpdirname) FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname) after_outputs = enc_dec_model( inputs=inputs, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, ) out_1 = np.array(after_outputs[0]) out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 4e-2)
def get_pretrained_model_and_inputs(self): model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained( "facebook/wav2vec2-large-lv60", "bart-large" ) batch_size = 13 input_values = floats_tensor([batch_size, 512], model.config.encoder.vocab_size) attention_mask = random_attention_mask([batch_size, 512]) decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size) decoder_attention_mask = random_attention_mask([batch_size, 4]) inputs = { "inputs": input_values, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, } return model, inputs
def check_encoder_decoder_model_from_encoder_decoder_pretrained( self, config, inputs, attention_mask, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs ): encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config) # assert that loading encoder and decoder models from configs has been correctly executed self.assertEqual(config.add_adapter, encoder_model.config.add_adapter) self.assertEqual(decoder_config.use_cache, decoder_model.config.use_cache) with tempfile.TemporaryDirectory() as enc_tmpdir: with tempfile.TemporaryDirectory() as dec_tmpdir: encoder_model.save_pretrained(enc_tmpdir) decoder_model.save_pretrained(dec_tmpdir) # load a model from pretrained encoder and decoder checkpoints, setting one encoder and one decoder kwarg opposite to that specified in their respective configs enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained( encoder_pretrained_model_name_or_path=enc_tmpdir, decoder_pretrained_model_name_or_path=dec_tmpdir, encoder_add_adapter=not config.add_adapter, decoder_use_cache=not decoder_config.use_cache, ) # assert that setting encoder and decoder kwargs opposite to those in the configs has correctly been applied self.assertNotEqual(config.add_adapter, enc_dec_model.config.encoder.add_adapter) self.assertNotEqual(decoder_config.use_cache, enc_dec_model.config.decoder.use_cache) outputs_encoder_decoder = enc_dec_model( inputs=inputs, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, output_hidden_states=True, return_dict=True, ) self.assertEqual( outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)) )
def test_flaxwav2vec2bart_pt_flax_equivalence(self): pt_model = SpeechEncoderDecoderModel.from_pretrained("patrickvonplaten/wav2vec2-2-bart-large") fx_model = FlaxSpeechEncoderDecoderModel.from_pretrained( "patrickvonplaten/wav2vec2-2-bart-large", from_pt=True ) pt_model.to(torch_device) pt_model.eval() # prepare inputs batch_size = 13 input_values = floats_tensor([batch_size, 512], fx_model.config.encoder.vocab_size) attention_mask = random_attention_mask([batch_size, 512]) decoder_input_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size) decoder_attention_mask = random_attention_mask([batch_size, 4]) inputs_dict = { "inputs": input_values, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, } flax_inputs = inputs_dict pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()} with torch.no_grad(): pt_outputs = pt_model(**pt_inputs) pt_logits = pt_outputs.logits pt_outputs = pt_outputs.to_tuple() fx_outputs = fx_model(**inputs_dict) fx_logits = fx_outputs.logits fx_outputs = fx_outputs.to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2) # PT -> Flax with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**inputs_dict) fx_logits_loaded = fx_outputs_loaded.logits fx_outputs_loaded = fx_outputs_loaded.to_tuple() self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch") self.assert_almost_equals(fx_logits_loaded, pt_logits.numpy(), 4e-2) # Flax -> PT with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_flax=True) pt_model_loaded.to(torch_device) pt_model_loaded.eval() with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs) pt_logits_loaded = pt_outputs_loaded.logits pt_outputs_loaded = pt_outputs_loaded.to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch") self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)
def check_freeze_feature_encoder( self, config, inputs, attention_mask, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs ): encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) enc_dec_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config) params = enc_dec_model.params def cross_entropy(logits, labels): return -jnp.sum(labels * jax.nn.log_softmax(logits, axis=-1), axis=-1) # define a dummy loss function for computing the loss over a forward pass def compute_loss( params, inputs, attention_mask, decoder_input_ids, freeze_feature_encoder: bool = False, ): outputs_enc_dec = enc_dec_model( inputs=inputs, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, freeze_feature_encoder=freeze_feature_encoder, params=params, ) logits = outputs_enc_dec.logits vocab_size = logits.shape[-1] loss = cross_entropy(logits, onehot(labels=decoder_input_ids, num_classes=vocab_size)).sum() return (loss, logits) # transform the loss function to get the gradients grad_fn = jax.value_and_grad(compute_loss, has_aux=True) # compute the loss, logits, and gradients for the unfrozen model (loss, logits), grads = grad_fn( params, inputs, attention_mask, decoder_input_ids, freeze_feature_encoder=False ) # compare to the loss, logits and gradients for the frozen model (loss_frozen, logits_frozen), grads_frozen = grad_fn( params, inputs, attention_mask, decoder_input_ids, freeze_feature_encoder=True ) # ensure that the logits and losses remain precisely equal self.assertTrue((logits == logits_frozen).all()) self.assertEqual(loss, loss_frozen) grads = flatten_dict(grads) grads_frozen = flatten_dict(grads_frozen) # ensure that the dicts of gradients contain the same keys self.assertEqual(grads.keys(), grads_frozen.keys()) # ensure that the gradients of the feature extractor layers are precisely zero when frozen and contain non-zero entries when unfrozen feature_extractor_grads = tuple(grads[k] for k in grads if "feature_extractor" in k) feature_extractor_grads_frozen = tuple(grads_frozen[k] for k in grads_frozen if "feature_extractor" in k) for feature_extractor_grad, feature_extractor_grad_frozen in zip( feature_extractor_grads, feature_extractor_grads_frozen ): self.assertTrue((feature_extractor_grad_frozen == 0.0).all()) self.assertTrue((feature_extractor_grad > 0.0).any()) # ensure that the gradients of all unfrozen layers remain precisely equal, i.e. all layers excluding the frozen 'feature_extractor' grads = tuple(grads[k] for k in grads if "feature_extractor" not in k) grads_frozen = tuple(grads_frozen[k] for k in grads_frozen if "feature_extractor" not in k) for grad, grad_frozen in zip(grads, grads_frozen): self.assertTrue((grad == grad_frozen).all())