From 932eab943df207e3c0040c7bfd4f1334e7c6c99e Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Wed, 4 Mar 2020 18:03:46 +0100 Subject: [PATCH] include tf gpt2 tests for attn mask and past variable (#3122) --- tests/test_modeling_tf_gpt2.py | 76 ++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) diff --git a/tests/test_modeling_tf_gpt2.py b/tests/test_modeling_tf_gpt2.py index 362f9e3162..8bb33898dc 100644 --- a/tests/test_modeling_tf_gpt2.py +++ b/tests/test_modeling_tf_gpt2.py @@ -30,6 +30,7 @@ if is_tf_available(): TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel, TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, + shape_list, ) @@ -167,6 +168,73 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase): list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size], ) + def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): + model = TFGPT2Model(config=config) + + # first forward pass + output, past = model(input_ids, token_type_ids=token_type_ids) + + # create hypothetical next token and extent to next_input_ids + next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) + next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size) + + # append to next input_ids and token_type_ids + next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) + next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1) + + output_from_no_past, _ = model(next_input_ids, token_type_ids=next_token_type_ids) + output_from_past, _ = model(next_tokens, token_type_ids=next_token_types, past=past) + + # select random slice + random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) + output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] + output_from_past_slice = output_from_past[:, 0, 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-12) + + def create_and_check_gpt2_model_attention_mask_past( + self, config, input_ids, input_mask, head_mask, token_type_ids, *args + ): + model = TFGPT2Model(config=config) + + # create attention mask + half_seq_length = self.seq_length // 2 + attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) + attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) + attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) + + # first forward pass + output, past = model(input_ids, attention_mask=attn_mask) + + # create hypothetical next token and extent to next_input_ids + next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) + + # change a random masked slice from input_ids + random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 + random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) + vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) + condition = tf.transpose( + tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) + ) + input_ids = tf.where(condition, random_other_next_tokens, input_ids) + + # append to next input_ids and attn_mask + next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) + attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1) + + # get two different outputs + output_from_no_past, _ = model(next_input_ids, attention_mask=attn_mask) + output_from_past, _ = model(next_tokens, past=past, attention_mask=attn_mask) + + # select random slice + random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) + output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] + output_from_past_slice = output_from_past[:, 0, 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-12) + def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPT2LMHeadModel(config=config) inputs = { @@ -237,6 +305,14 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model(*config_and_inputs) + def test_gpt2_model_past(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs) + + def test_gpt2_model_att_mask_past(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs) + def test_gpt2_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs)