Fix tf.concatenate + test past_key_values for TF models (#15774)
* fix wrong method name tf.concatenate * add tests related to causal LM / decoder * make style and quality * clean-up * Fix TFBertModel's extended_attention_mask when past_key_values is provided * Fix tests * fix copies * More tf.int8 -> tf.int32 in TF test template * clean-up * Update TF test template * revert the previous commit + update the TF test template * Fix TF template extended_attention_mask when past_key_values is provided * Fix some styles manually * clean-up * Fix ValueError: too many values to unpack in the test * Fix more: too many values to unpack in the test * Add a comment for extended_attention_mask when there is past_key_values * Fix TFElectra extended_attention_mask when past_key_values is provided * Add tests to other TF models * Fix for TF Electra test: add prepare_config_and_inputs_for_decoder * Fix not passing training arg to lm_head in TFRobertaForCausalLM * Fix tests (with past) for TF Roberta * add testing for pask_key_values for TFElectra model Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
@@ -153,12 +153,12 @@ class TFBertModelTester:
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encoder_attention_mask,
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)
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def create_and_check_bert_model(
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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sequence_output, pooled_output = model(inputs)
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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@@ -168,10 +168,61 @@ class TFBertModelTester:
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_bert_lm_head(
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def create_and_check_causal_lm_base_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.is_decoder = True
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model = TFBertModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = TFBertModel(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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"encoder_hidden_states": encoder_hidden_states,
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"encoder_attention_mask": encoder_attention_mask,
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}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
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# Also check the case where encoder outputs are not passed
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_causal_lm_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.is_decoder = True
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model = TFBertLMHeadModel(config=config)
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inputs = {
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"input_ids": input_ids,
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@@ -183,7 +234,261 @@ class TFBertModelTester:
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list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
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)
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def create_and_check_bert_for_masked_lm(
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def create_and_check_causal_lm_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = TFBertLMHeadModel(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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"encoder_hidden_states": encoder_hidden_states,
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"encoder_attention_mask": encoder_attention_mask,
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}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
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prediction_scores = result["logits"]
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self.parent.assertListEqual(
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list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
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)
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def create_and_check_causal_lm_model_past(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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config.is_decoder = True
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model = TFBertLMHeadModel(config=config)
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# first forward pass
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outputs = model(input_ids, use_cache=True)
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outputs_use_cache_conf = model(input_ids)
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outputs_no_past = model(input_ids, use_cache=False)
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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past_key_values = outputs.past_key_values
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# append to next input_ids and attn_mask
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
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output_from_past = model(
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next_tokens, past_key_values=past_key_values, output_hidden_states=True
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).hidden_states[0]
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# select random slice
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random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
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output_from_past_slice = output_from_past[:, 0, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
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def create_and_check_causal_lm_model_past_with_attn_mask(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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config.is_decoder = True
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model = TFBertLMHeadModel(config=config)
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# create attention mask
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half_seq_length = self.seq_length // 2
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attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
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attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
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attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
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# first forward pass
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outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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past_key_values = outputs.past_key_values
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# change a random masked slice from input_ids
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random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
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vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
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condition = tf.transpose(
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tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
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)
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input_ids = tf.where(condition, random_other_next_tokens, input_ids)
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# append to next input_ids and
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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attn_mask = tf.concat(
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[attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
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axis=1,
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)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=attn_mask,
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output_hidden_states=True,
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).hidden_states[0]
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output_from_past = model(
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next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
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).hidden_states[0]
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# select random slice
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random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
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output_from_past_slice = output_from_past[:, 0, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
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def create_and_check_causal_lm_model_past_large_inputs(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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config.is_decoder = True
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model = TFBertLMHeadModel(config=config)
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input_ids = input_ids[:1, :]
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input_mask = input_mask[:1, :]
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self.batch_size = 1
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# first forward pass
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outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
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past_key_values = outputs.past_key_values
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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# append to next input_ids and
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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output_hidden_states=True,
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).hidden_states[0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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).hidden_states[0]
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self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
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# select random slice
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random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
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output_from_past_slice = output_from_past[:, :, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = TFBertLMHeadModel(config=config)
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input_ids = input_ids[:1, :]
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input_mask = input_mask[:1, :]
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encoder_hidden_states = encoder_hidden_states[:1, :, :]
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encoder_attention_mask = encoder_attention_mask[:1, :]
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self.batch_size = 1
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# first forward pass
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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# append to next input_ids and
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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).hidden_states[0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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).hidden_states[0]
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self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
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# select random slice
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random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
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output_from_past_slice = output_from_past[:, :, random_slice_idx]
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# test that outputs are equal for slice
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tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
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def create_and_check_for_masked_lm(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertForMaskedLM(config=config)
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@@ -195,7 +500,7 @@ class TFBertModelTester:
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_bert_for_next_sequence_prediction(
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def create_and_check_for_next_sequence_prediction(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertForNextSentencePrediction(config=config)
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@@ -203,7 +508,7 @@ class TFBertModelTester:
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
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def create_and_check_bert_for_pretraining(
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def create_and_check_for_pretraining(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertForPreTraining(config=config)
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@@ -212,7 +517,7 @@ class TFBertModelTester:
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self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
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def create_and_check_bert_for_sequence_classification(
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def create_and_check_for_sequence_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
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):
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config.num_labels = self.num_labels
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@@ -226,7 +531,7 @@ class TFBertModelTester:
|
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
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|
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def create_and_check_bert_for_multiple_choice(
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def create_and_check_for_multiple_choice(
|
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
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):
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config.num_choices = self.num_choices
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@@ -242,7 +547,7 @@ class TFBertModelTester:
|
||||
result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
|
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|
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def create_and_check_bert_for_token_classification(
|
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def create_and_check_for_token_classification(
|
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
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):
|
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config.num_labels = self.num_labels
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@@ -255,7 +560,7 @@ class TFBertModelTester:
|
||||
result = model(inputs)
|
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
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|
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def create_and_check_bert_for_question_answering(
|
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def create_and_check_for_question_answering(
|
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
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model = TFBertForQuestionAnswering(config=config)
|
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@@ -323,41 +628,84 @@ class TFBertModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestC
|
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def test_config(self):
|
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self.config_tester.run_common_tests()
|
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|
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def test_bert_model(self):
|
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def test_model(self):
|
||||
"""Test the base model"""
|
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
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self.model_tester.create_and_check_bert_model(*config_and_inputs)
|
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self.model_tester.create_and_check_model(*config_and_inputs)
|
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|
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def test_causal_lm_base_model(self):
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"""Test the base model of the causal LM model
|
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|
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is_deocder=True, no cross_attention, no encoder outputs
|
||||
"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)
|
||||
|
||||
def test_model_as_decoder(self):
|
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"""Test the base model as a decoder (of an encoder-decoder architecture)
|
||||
|
||||
is_deocder=True + cross_attention + pass encoder outputs
|
||||
"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
|
||||
|
||||
def test_for_masked_lm(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_for_masked_lm(*config_and_inputs)
|
||||
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
|
||||
|
||||
def test_for_causal_lm(self):
|
||||
"""Test the causal LM model"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_lm_head(*config_and_inputs)
|
||||
self.model_tester.create_and_check_causal_lm_model(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_as_decoder(self):
|
||||
"""Test the causal LM model as a decoder"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_past(self):
|
||||
"""Test causal LM model with `past_key_values`"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_past_with_attn_mask(self):
|
||||
"""Test the causal LM model with `past_key_values` and `attention_mask`"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_past_with_large_inputs(self):
|
||||
"""Test the causal LM model with `past_key_values` and a longer decoder sequence length"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
def test_decoder_model_past_with_large_inputs(self):
|
||||
"""Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
def test_for_multiple_choice(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_for_multiple_choice(*config_and_inputs)
|
||||
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
|
||||
|
||||
def test_for_next_sequence_prediction(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_for_next_sequence_prediction(*config_and_inputs)
|
||||
self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs)
|
||||
|
||||
def test_for_pretraining(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_for_pretraining(*config_and_inputs)
|
||||
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
|
||||
|
||||
def test_for_question_answering(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_for_question_answering(*config_and_inputs)
|
||||
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
|
||||
|
||||
def test_for_sequence_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_for_sequence_classification(*config_and_inputs)
|
||||
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
|
||||
|
||||
def test_for_token_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_bert_for_token_classification(*config_and_inputs)
|
||||
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
|
||||
|
||||
def test_model_from_pretrained(self):
|
||||
model = TFBertModel.from_pretrained("jplu/tiny-tf-bert-random")
|
||||
|
||||
Reference in New Issue
Block a user