# 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. from __future__ import annotations import unittest from transformers import LEDConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): 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=2, 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() # 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, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else () all_generative_model_classes = (TFLEDForConditionalGeneration,) if is_tf_available() else () pipeline_model_mapping = ( { "conversational": TFLEDForConditionalGeneration, "feature-extraction": TFLEDModel, "summarization": TFLEDForConditionalGeneration, "text2text-generation": TFLEDForConditionalGeneration, "translation": 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_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) @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing.") def test_saved_model_creation(self): pass def test_generate_with_headmasking(self): # TODO: Head-masking not yet implement pass 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=1e-3) 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=1e-3, rtol=1e-3)