include tf gpt2 tests for attn mask and past variable (#3122)

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)