def check_save_and_load(self, config, pixel_values, 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 = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( **kwargs) outputs = enc_dec_model( pixel_values=pixel_values, 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) FlaxVisionEncoderDecoderModel.from_pretrained(tmpdirname) after_outputs = enc_dec_model( pixel_values=pixel_values, 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, 1e-5)
def check_equivalence_pt_to_flax(self, config, decoder_config, inputs_dict): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs( config, decoder_config) pt_model = VisionEncoderDecoderModel(encoder_decoder_config) fx_model = FlaxVisionEncoderDecoderModel(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( self, config, pixel_values, 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 = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( **kwargs) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, return_dict=True, ) self.assertEqual(outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size, ))) self.assertEqual( outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0]) self.assertEqual( outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
def check_encoder_decoder_model_from_pretrained_configs( self, config, pixel_values, encoder_hidden_states, decoder_config, decoder_input_ids, decoder_attention_mask, **kwargs): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs( config, decoder_config) self.assertTrue(encoder_decoder_config.decoder.is_decoder) enc_dec_model = FlaxVisionEncoderDecoderModel(encoder_decoder_config) self.assertTrue(enc_dec_model.config.is_encoder_decoder) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, 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, ))) self.assertEqual( outputs_encoder_decoder["encoder_last_hidden_state"].shape[0], pixel_values.shape[0]) self.assertEqual( outputs_encoder_decoder["encoder_last_hidden_state"].shape[-1], config.hidden_size)
def test_real_model_save_load_from_pretrained(self): model_2 = self.get_pretrained_model() pixel_values = floats_tensor([ 13, model_2.config.encoder.num_channels, model_2.config.encoder.image_size, model_2.config.encoder.image_size, ]) decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size) outputs = model_2( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, ) 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 = FlaxVisionEncoderDecoderModel.from_pretrained( tmp_dirname) after_outputs = model_1( pixel_values=pixel_values, decoder_input_ids=decoder_input_ids, ) 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, 1e-5)
def check_encoder_decoder_model_generate(self, pixel_values, 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 = FlaxVisionEncoderDecoderModel.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( pixel_values, 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, (pixel_values.shape[0], ) + (decoder_config.max_length, ))
def test_inference_coco_en(self): loc = "ydshieh/vit-gpt2-coco-en" feature_extractor = ViTFeatureExtractor.from_pretrained(loc) tokenizer = AutoTokenizer.from_pretrained(loc) model = FlaxVisionEncoderDecoderModel.from_pretrained(loc) img = prepare_img() pixel_values = feature_extractor(images=img, return_tensors="np").pixel_values decoder_input_ids = np.array([[model.config.decoder_start_token_id]]) logits = model(pixel_values, decoder_input_ids)[0] logits = np.array(logits) # verify the logits expected_shape = (1, 1, model.config.decoder.vocab_size) self.assertEqual(logits.shape, expected_shape) EXPECTED_LOGIT_SLICE = np.array([ -38.705837, -30.639936, -31.41905, -39.01204, -38.38698, -34.887215, -33.29087, -35.684475, -38.50852, -36.124676, ]) max_diff = np.amax(np.abs(logits[0, 0, :10] - EXPECTED_LOGIT_SLICE)) self.assertLessEqual(max_diff, 1e-4) def generate_step(pixel_values): outputs = model.generate(pixel_values, max_length=16, num_beams=4) output_ids = outputs.sequences preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) preds = [pred.strip() for pred in preds] return preds, outputs.scores preds, scores = generate_step(pixel_values) EXPECTED_SCORES = np.array([-0.59563464]) scores = np.array(scores) max_diff = np.amax(np.abs(scores - EXPECTED_SCORES)) self.assertLessEqual(max_diff, 1e-4) # should produce # ["a cat laying on top of a couch next to another cat"] self.assertEqual( preds, ["a cat laying on top of a couch next to another cat"])
def check_equivalence_flax_to_pt(self, config, decoder_config, inputs_dict): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs( config, decoder_config) pt_model = VisionEncoderDecoderModel(encoder_decoder_config) fx_model = FlaxVisionEncoderDecoderModel(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_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).to_tuple() fx_outputs = fx_model(**inputs_dict).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs, pt_outputs): self.assert_almost_equals(fx_output, pt_output.numpy(), 1e-5) # PT -> Flax with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = FlaxVisionEncoderDecoderModel.from_pretrained( tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**inputs_dict).to_tuple() self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs): self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 1e-5) # Flax -> PT with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = VisionEncoderDecoderModel.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).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output_loaded in zip(fx_outputs, pt_outputs_loaded): self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 1e-5)
def check_encoder_decoder_model_output_attentions( self, config, pixel_values, 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 = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( **kwargs) outputs_encoder_decoder = enc_dec_model( pixel_values=pixel_values, 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) self.assertEqual(encoder_attentions[0].shape[-3:-2], (config.num_attention_heads, )) 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] * ( 1 + (decoder_config.ngram if hasattr(decoder_config, "ngram") else 0)) self.assertEqual( cross_attentions[0].shape[-3:-1], (decoder_config.num_attention_heads, cross_attention_input_seq_len), )
def get_from_encoderdecoder_pretrained_model(self): return FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( "google/vit-base-patch16-224-in21k", "gpt2")
def main(): parser = HfArgumentParser((ModelArguments, )) (model_args, ) = parser.parse_args_into_dataclasses() # Load pretrained model and tokenizer # Use explicit specified encoder config if model_args.encoder_config_name: encoder_config = AutoConfig.from_pretrained( model_args.encoder_config_name) # Use pretrained encoder model's config else: encoder_config = AutoConfig.from_pretrained( model_args.encoder_model_name_or_path) # Use explicit specified decoder config if model_args.decoder_config_name: decoder_config = AutoConfig.from_pretrained( model_args.decoder_config_name) # Use pretrained decoder model's config else: decoder_config = AutoConfig.from_pretrained( model_args.decoder_model_name_or_path) # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed decoder_config.is_decoder = True decoder_config.add_cross_attention = True model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( encoder_pretrained_model_name_or_path=model_args. encoder_model_name_or_path, decoder_pretrained_model_name_or_path=model_args. decoder_model_name_or_path, encoder_config=encoder_config, decoder_config=decoder_config, ) # GPT2 only has bos/eos tokens but not decoder_start/pad tokens decoder_start_token_id = decoder_config.decoder_start_token_id pad_token_id = decoder_config.pad_token_id if decoder_start_token_id is None: decoder_start_token_id = decoder_config.bos_token_id if pad_token_id is None: pad_token_id = decoder_config.eos_token_id # This is necessary to make Flax's generate() work model.config.eos_token_id = decoder_config.eos_token_id model.config.decoder_start_token_id = decoder_start_token_id model.config.pad_token_id = pad_token_id feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.encoder_model_name_or_path) tokenizer = AutoTokenizer.from_pretrained( model_args.decoder_model_name_or_path) tokenizer.pad_token = tokenizer.convert_ids_to_tokens( model.config.pad_token_id) model.save_pretrained(model_args.output_dir) feature_extractor.save_pretrained(model_args.output_dir) tokenizer.save_pretrained(model_args.output_dir)