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)
