# coding=utf-8 # Copyright Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved. # # 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 unittest from transformers import LEDConfig, is_tf_available from transformers.testing_utils import is_pt_tf_cross_test, require_tf, slow from ..test_configuration_common import ConfigTester from ..test_modeling_tf_common import TFModelTesterMixin, ids_tensor if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFLEDForConditionalGeneration, TFLEDModel @require_tf class TFLEDModelTester: config_cls = LEDConfig config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, attention_window=4, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.attention_window = attention_window # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window` and one before and one after self.key_length = self.attention_window + 2 # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for # the `test_attention_outputs` and `test_hidden_states_output` tests self.encoder_seq_length = ( self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window ) def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) input_ids = tf.concat([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, attention_window=self.attention_window, **self.config_updates, ) inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids) global_attention_mask = tf.concat( [tf.zeros_like(input_ids)[:, :-1], tf.ones_like(input_ids)[:, -1:]], axis=-1, ) inputs_dict["global_attention_mask"] = global_attention_mask return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFLEDModel(config=config).get_decoder() input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() past_key_values = past_key_values[1] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def prepare_led_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, ): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8) if decoder_attention_mask is None: decoder_attention_mask = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8), ], axis=-1, ) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, } @require_tf class TFLEDModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else () all_generative_model_classes = (TFLEDForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFLEDModelTester(self) self.config_tester = ConfigTester(self, config_class=LEDConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None def test_resize_token_embeddings(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def _get_word_embedding_weight(model, embedding_layer): if hasattr(embedding_layer, "weight"): return embedding_layer.weight else: # Here we build the word embeddings weights if not exists. # And then we retry to get the attribute once built. model(model.dummy_inputs) if hasattr(embedding_layer, "weight"): return embedding_layer.weight else: return None for model_class in self.all_model_classes: for size in [config.vocab_size - 10, config.vocab_size + 10, None]: # build the embeddings model = model_class(config=config) old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) old_final_logits_bias = model.get_bias() # reshape the embeddings model.resize_token_embeddings(size) new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) new_final_logits_bias = model.get_bias() # check that the resized embeddings size matches the desired size. assert_size = size if size is not None else config.vocab_size self.assertEqual(new_input_embeddings.shape[0], assert_size) # check that weights remain the same after resizing models_equal = True for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) if old_output_embeddings is not None and new_output_embeddings is not None: self.assertEqual(new_output_embeddings.shape[0], assert_size) models_equal = True for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) if old_final_logits_bias is not None and new_final_logits_bias is not None: old_final_logits_bias = old_final_logits_bias["final_logits_bias"] new_final_logits_bias = new_final_logits_bias["final_logits_bias"] self.assertEqual(new_final_logits_bias.shape[0], 1) self.assertEqual(new_final_logits_bias.shape[1], assert_size) models_equal = True for old, new in zip(old_final_logits_bias.value(), new_final_logits_bias.value()): for p1, p2 in zip(old, new): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() inputs_dict["global_attention_mask"] = tf.zeros_like(inputs_dict["attention_mask"]) num_global_attn_indices = 2 inputs_dict["global_attention_mask"] = tf.where( tf.range(self.model_tester.seq_length)[None, :] < num_global_attn_indices, 1, inputs_dict["global_attention_mask"], ) config.return_dict = True seq_length = self.model_tester.seq_length encoder_seq_length = self.model_tester.encoder_seq_length def check_decoder_attentions_output(outputs): decoder_attentions = outputs.decoder_attentions self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_length, seq_length], ) def check_encoder_attentions_output(outputs): attentions = [t.numpy() for t in outputs.encoder_attentions] global_attentions = [t.numpy() for t in outputs.encoder_global_attentions] self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertEqual(len(global_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_length, seq_length], ) self.assertListEqual( list(global_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices], ) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["use_cache"] = False config.output_hidden_states = False model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) out_len = len(outputs) self.assertEqual(config.output_hidden_states, False) check_encoder_attentions_output(outputs) if self.is_encoder_decoder: model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(config.output_hidden_states, False) check_decoder_attentions_output(outputs) # Check that output attentions can also be changed via the config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(config.output_hidden_states, False) check_encoder_attentions_output(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True config.output_hidden_states = True model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) self.assertEqual(model.config.output_hidden_states, True) check_encoder_attentions_output(outputs) # TODO: Remove this once a more thorough pt/tf equivalence could be implemented in `test_modeling_tf_common.py`. # (Currently, such a test will fail some other model tests: it requires some time to fix them.) @is_pt_tf_cross_test def test_pt_tf_model_equivalence_extra(self): import torch import transformers def prepare_pt_inputs_from_tf_inputs(tf_inputs_dict): pt_inputs_dict = {} for name, key in tf_inputs_dict.items(): if type(key) == bool: pt_inputs_dict[name] = key elif name == "input_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) elif name == "pixel_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) else: pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long) return pt_inputs_dict config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) config.output_hidden_states = True tf_model = model_class(config) pt_model = pt_model_class(config) tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class) tf_inputs_dict_maybe_with_labels = self._prepare_for_class(inputs_dict, model_class, return_labels=True) # Check we can load pt model in tf and vice-versa with model => model functions tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict) pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model) # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences pt_model.eval() pt_inputs_dict = prepare_pt_inputs_from_tf_inputs(tf_inputs_dict) pt_inputs_dict_maybe_with_labels = prepare_pt_inputs_from_tf_inputs(tf_inputs_dict_maybe_with_labels) # need to rename encoder-decoder "inputs" for PyTorch if "inputs" in pt_inputs_dict and self.is_encoder_decoder: pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs") with torch.no_grad(): pto = pt_model(**pt_inputs_dict) tfo = tf_model(tf_inputs_dict, training=False) tf_hidden_states = tfo[0].numpy() pt_hidden_states = pto[0].numpy() tf_nans = np.isnan(tf_hidden_states) pt_nans = np.isnan(pt_hidden_states) pt_hidden_states[tf_nans] = 0 tf_hidden_states[tf_nans] = 0 pt_hidden_states[pt_nans] = 0 tf_hidden_states[pt_nans] = 0 max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states)) self.assertLessEqual(max_diff, 1e-4) has_labels = any( x in tf_inputs_dict_maybe_with_labels for x in ["labels", "next_sentence_label", "start_positions"] ) if has_labels: with torch.no_grad(): pto = pt_model(**pt_inputs_dict_maybe_with_labels) tfo = tf_model(tf_inputs_dict_maybe_with_labels, training=False) # Some models' output class don't have `loss` attribute despite `labels` is used. tf_loss = getattr(tfo, "loss", None) pt_loss = getattr(pto, "loss", None) # Some models require extra condition to return loss. For example, `BertForPreTraining` requires both # `labels` and `next_sentence_label`. # Moreover, some PT models return loss while the corresponding TF/Flax models don't. if tf_loss is not None and pt_loss is not None: tf_loss = tf.math.reduce_mean(tf_loss).numpy() pt_loss = pt_loss.numpy() tf_nans = np.isnan(tf_loss) pt_nans = np.isnan(pt_loss) # the 2 losses need to be both nan or both not nan # (`TapasForQuestionAnswering` gives nan loss here) self.assertEqual(tf_nans, pt_nans) if not tf_nans: max_diff = np.amax(np.abs(tf_loss - pt_loss)) # `TFFunnelForTokenClassification` (and potentially other TF token classification models) give # large difference (up to 0.1x). PR #15294 addresses this issue. # There is also an inconsistency between PT/TF `XLNetLMHeadModel`. # Before these issues are fixed & merged, set a higher threshold here to pass the test. self.assertLessEqual(max_diff, 1e-4) tf_logits = tfo[1].numpy() pt_logits = pto[1].numpy() # check on the shape self.assertEqual(tf_logits.shape, pt_logits.shape) tf_nans = np.isnan(tf_logits) pt_nans = np.isnan(pt_logits) pt_logits[tf_nans] = 0 tf_logits[tf_nans] = 0 pt_logits[pt_nans] = 0 tf_logits[pt_nans] = 0 max_diff = np.amax(np.abs(tf_logits - pt_logits)) self.assertLessEqual(max_diff, 1e-4) def test_xla_mode(self): # TODO JP: Make LED XLA compliant pass def test_saved_model_creation(self): # This test is too long (>30sec) and makes fail the CI pass def test_generate_with_headmasking(self): # TODO: Head-masking not yet implement pass def _assert_tensors_equal(a, b, atol=1e-12, prefix=""): """If tensors not close, or a and b arent both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if tf.debugging.assert_near(a, b, atol=atol): return True raise except Exception: if len(prefix) > 0: prefix = f"{prefix}: " raise AssertionError(f"{prefix}{a} != {b}") def _long_tensor(tok_lst): return tf.constant(tok_lst, dtype=tf.int32) TOLERANCE = 1e-4 @slow @require_tf class TFLEDModelIntegrationTest(unittest.TestCase): def test_inference_no_head(self): model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384").led # change to intended input here input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 1024, 768) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = tf.convert_to_tensor( [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE) def test_inference_with_head(self): model = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384") # change to intended input here input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 1024, model.config.vocab_size) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = tf.convert_to_tensor( [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)