# coding=utf-8 # Copyright 2021 HuggingFace Inc. team. # # 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. import tempfile import unittest import numpy as np from transformers import is_flax_available, is_torch_available, is_vision_available from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_vision, slow, torch_device from .test_modeling_flax_common import floats_tensor, ids_tensor from .test_modeling_flax_gpt2 import FlaxGPT2ModelTester from .test_modeling_flax_vit import FlaxViTModelTester if is_flax_available(): from transformers import ( AutoTokenizer, FlaxGPT2LMHeadModel, FlaxVisionEncoderDecoderModel, FlaxViTModel, VisionEncoderDecoderConfig, ) from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) if is_torch_available(): import torch from transformers import VisionEncoderDecoderModel if is_vision_available(): from PIL import Image from transformers import ViTFeatureExtractor @require_flax class FlaxEncoderDecoderMixin: def get_encoder_decoder_model(self, config, decoder_config): raise NotImplementedError def prepare_config_and_inputs(self): raise NotImplementedError def get_pretrained_model(self): raise NotImplementedError 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 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_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_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 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 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_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_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 test_encoder_decoder_model_from_pretrained_configs(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_from_pretrained_configs(**config_inputs_dict) def test_encoder_decoder_model_from_pretrained(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=False) def test_encoder_decoder_model_from_pretrained_return_dict(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_from_pretrained(**config_inputs_dict, return_dict=True) def test_save_and_load_from_pretrained(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_save_and_load(**config_inputs_dict) def test_encoder_decoder_model_output_attentions(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_output_attentions(**config_inputs_dict) def test_encoder_decoder_model_generate(self): config_inputs_dict = self.prepare_config_and_inputs() self.check_encoder_decoder_model_generate(**config_inputs_dict) def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).") @is_pt_flax_cross_test def test_pt_flax_equivalence(self): config_inputs_dict = self.prepare_config_and_inputs() 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"] = np.concatenate( [np.ones(shape=(batch_size, 1)), inputs_dict["decoder_attention_mask"][:, 1:]], axis=1 ) # Flax 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_flax(config, decoder_config, inputs_dict) self.check_equivalence_flax_to_pt(config, decoder_config, inputs_dict) # 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_flax(config, decoder_config, inputs_dict) self.check_equivalence_flax_to_pt(config, decoder_config, inputs_dict) @slow 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) @require_flax class FlaxViT2GPT2EncoderDecoderModelTest(FlaxEncoderDecoderMixin, unittest.TestCase): def get_encoder_decoder_model(self, config, decoder_config): encoder_model = FlaxViTModel(config) decoder_model = FlaxGPT2LMHeadModel(decoder_config) return encoder_model, decoder_model def prepare_config_and_inputs(self): model_tester_encoder = FlaxViTModelTester(self, batch_size=13) model_tester_decoder = FlaxGPT2ModelTester(self, batch_size=13) encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs() decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder() (config, pixel_values) = encoder_config_and_inputs ( decoder_config, decoder_input_ids, decoder_attention_mask, encoder_hidden_states, encoder_attention_mask, ) = decoder_config_and_inputs # make sure that cross attention layers are added decoder_config.add_cross_attention = True return { "config": config, "pixel_values": pixel_values, "decoder_config": decoder_config, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "encoder_hidden_states": encoder_hidden_states, # This is not used in the tests. } def get_pretrained_model(self): return FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( "google/vit-base-patch16-224-in21k", "gpt2" ) @require_flax class FlaxVisionEncoderDecoderModelTest(unittest.TestCase): def get_from_encoderdecoder_pretrained_model(self): return FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained( "google/vit-base-patch16-224-in21k", "gpt2" ) def _check_configuration_tie(self, model): module = model.module.bind(model.params) assert id(module.decoder.config) == id(model.config.decoder) assert id(module.encoder.config) == id(model.config.encoder) @slow def test_configuration_tie(self): model = self.get_from_encoderdecoder_pretrained_model() self._check_configuration_tie(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_vision @require_flax class FlaxViT2GPT2ModelIntegrationTest(unittest.TestCase): @slow 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"])