def check_equivalence_tf_to_pt(self, config, decoder_config, inputs_dict): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) # Using `_tf_model`, the test will fail, because the weights of `_tf_model` get extended before saving # the encoder/decoder models. # There was a (very) ugly potential fix, which wasn't integrated to `transformers`: see # https://github.com/huggingface/transformers/pull/13222/commits/dbb3c9de76eee235791d2064094654637c99f36d#r697304245 # (the change in `src/transformers/modeling_tf_utils.py`) _tf_model = TFVisionEncoderDecoderModel(encoder_decoder_config) # Make sure model is built _tf_model(**inputs_dict) # Using `tf_model` to pass the test. encoder = _tf_model.encoder.__class__(encoder_decoder_config.encoder) decoder = _tf_model.decoder.__class__(encoder_decoder_config.decoder) # Make sure models are built encoder(encoder.dummy_inputs) decoder(decoder.dummy_inputs) tf_model = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder) with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname: tf_model.encoder.save_pretrained(encoder_tmp_dirname) tf_model.decoder.save_pretrained(decoder_tmp_dirname) pt_model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained( encoder_tmp_dirname, decoder_tmp_dirname, encoder_from_tf=True, decoder_from_tf=True ) # This is only for copying some specific attributes of this particular model. pt_model.config = tf_model.config self.check_pt_tf_equivalence(pt_model, tf_model, inputs_dict)
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 = TFVisionEncoderDecoderModel(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 check_encoder_decoder_model_from_pretrained_configs( self, config, decoder_config, decoder_input_ids, decoder_attention_mask, pixel_values=None, **kwargs): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs( config, decoder_config) self.assertTrue(encoder_decoder_config.decoder.is_decoder) enc_dec_model = VisionEncoderDecoderModel(encoder_decoder_config) enc_dec_model.to(torch_device) enc_dec_model.eval() 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, )))
def test_pt_tf_equivalence(self): config_inputs_dict = self.prepare_config_and_inputs() # Keep only common arguments arg_names = [ "config", "pixel_values", "decoder_config", "decoder_input_ids", "decoder_attention_mask", "encoder_hidden_states", ] config_inputs_dict = { k: v for k, v in config_inputs_dict.items() if k in arg_names } config = config_inputs_dict.pop("config") decoder_config = config_inputs_dict.pop("decoder_config") inputs_dict = config_inputs_dict # `encoder_hidden_states` is not used in model call/forward del inputs_dict["encoder_hidden_states"] # Avoid the case where a sequence has no place to attend (after combined with the causal attention mask) batch_size = inputs_dict["decoder_attention_mask"].shape[0] inputs_dict["decoder_attention_mask"] = tf.constant( np.concatenate([ np.ones(shape=(batch_size, 1)), inputs_dict["decoder_attention_mask"][:, 1:] ], axis=1)) # TF models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. decoder_config.use_cache = False self.assertTrue(decoder_config.cross_attention_hidden_size is None) # check without `enc_to_dec_proj` projection self.assertTrue(config.hidden_size == decoder_config.hidden_size) self.check_equivalence_pt_to_tf(config, decoder_config, inputs_dict) self.check_equivalence_tf_to_pt(config, decoder_config, inputs_dict) # This is not working, because pt/tf equivalence test for encoder-decoder use `from_encoder_decoder_pretrained`, # which randomly initialize `enc_to_dec_proj`. # # check `enc_to_dec_proj` work as expected # decoder_config.hidden_size = decoder_config.hidden_size * 2 # self.assertTrue(config.hidden_size != decoder_config.hidden_size) # self.check_equivalence_pt_to_tf(config, decoder_config, inputs_dict) # self.check_equivalence_tf_to_pt(config, decoder_config, inputs_dict) # Let's just check `enc_to_dec_proj` can run for now decoder_config.hidden_size = decoder_config.hidden_size * 2 self.assertTrue(config.hidden_size != decoder_config.hidden_size) encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs( config, decoder_config) model = TFVisionEncoderDecoderModel(encoder_decoder_config) model(**inputs_dict)
def get_encoder_decoder_config(self): encoder_config = AutoConfig.from_pretrained( "google/vit-base-patch16-224-in21k") decoder_config = AutoConfig.from_pretrained("../gpt2", is_decoder=True, add_cross_attention=True) return VisionEncoderDecoderConfig.from_encoder_decoder_configs( encoder_config, decoder_config)
def get_encoder_decoder_config_small(self): encoder_config = AutoConfig.from_pretrained( "hf-internal-testing/tiny-random-vit") decoder_config = AutoConfig.from_pretrained( "hf-internal-testing/tiny-random-gpt2", is_decoder=True, add_cross_attention=True) return VisionEncoderDecoderConfig.from_encoder_decoder_configs( encoder_config, decoder_config)
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_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_equivalence_pt_to_tf(self, config, decoder_config, inputs_dict): encoder_decoder_config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config) pt_model = VisionEncoderDecoderModel(encoder_decoder_config) with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname: pt_model.encoder.save_pretrained(encoder_tmp_dirname) pt_model.decoder.save_pretrained(decoder_tmp_dirname) tf_model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( encoder_tmp_dirname, decoder_tmp_dirname, encoder_from_pt=True, decoder_from_pt=True ) # This is only for copying some specific attributes of this particular model. tf_model.config = pt_model.config self.check_pt_tf_equivalence(pt_model, tf_model, inputs_dict)