[cleanup] Hoist ModelTester objects to top level (#4939)
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
This commit is contained in:
Amil Khare
@@ -34,268 +34,246 @@ if is_tf_available():
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)
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class TFGPT2ModelTester:
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def __init__(
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self, parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_token_type_ids = True
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self.use_input_mask = True
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self.use_labels = True
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self.use_mc_token_ids = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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self.bos_token_id = self.vocab_size - 1
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self.eos_token_id = self.vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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mc_token_ids = None
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if self.use_mc_token_ids:
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mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = GPT2Config(
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vocab_size=self.vocab_size,
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n_embd=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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# intermediate_size=self.intermediate_size,
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# hidden_act=self.hidden_act,
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# hidden_dropout_prob=self.hidden_dropout_prob,
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# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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n_positions=self.max_position_embeddings,
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n_ctx=self.max_position_embeddings,
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# type_vocab_size=self.type_vocab_size,
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# initializer_range=self.initializer_range
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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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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def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPT2Model(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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}
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sequence_output = model(inputs)[0]
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inputs = [input_ids, None, input_mask] # None is the input for 'past'
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sequence_output = model(inputs)[0]
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sequence_output = model(input_ids)[0]
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result = {
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"sequence_output": sequence_output.numpy(),
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size],
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)
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def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPT2Model(config=config)
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# first forward pass
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output, past = model(input_ids, token_type_ids=token_type_ids)
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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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next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
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# append to next input_ids and token_type_ids
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
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output_from_no_past, _ = model(next_input_ids, token_type_ids=next_token_type_ids)
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output_from_past, _ = model(next_tokens, token_type_ids=next_token_types, past=past)
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# select random slice
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random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-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_gpt2_model_attention_mask_past(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args
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):
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model = TFGPT2Model(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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output, past = model(input_ids, attention_mask=attn_mask)
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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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# 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 attn_mask
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1)
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# get two different outputs
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output_from_no_past, _ = model(next_input_ids, attention_mask=attn_mask)
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output_from_past, _ = model(next_tokens, past=past, attention_mask=attn_mask)
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# select random slice
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random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-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-12)
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def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPT2LMHeadModel(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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}
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prediction_scores = model(inputs)[0]
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result = {
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"prediction_scores": prediction_scores.numpy(),
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size],
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)
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def create_and_check_gpt2_double_head(
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self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
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):
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model = TFGPT2DoubleHeadsModel(config=config)
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multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
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multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
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multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
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inputs = {
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"input_ids": multiple_choice_inputs_ids,
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"mc_token_ids": mc_token_ids,
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"attention_mask": multiple_choice_input_mask,
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"token_type_ids": multiple_choice_token_type_ids,
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}
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lm_logits, mc_logits = model(inputs)[:2]
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result = {"lm_logits": lm_logits.numpy(), "mc_logits": mc_logits.numpy()}
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self.parent.assertListEqual(
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list(result["lm_logits"].shape), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size],
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)
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self.parent.assertListEqual(list(result["mc_logits"].shape), [self.batch_size, self.num_choices])
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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return config, inputs_dict
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@require_tf
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class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel) if is_tf_available() else ()
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all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else ()
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class TFGPT2ModelTester(object):
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_token_type_ids=True,
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use_input_mask=True,
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use_labels=True,
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use_mc_token_ids=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_token_type_ids = use_token_type_ids
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.use_mc_token_ids = use_mc_token_ids
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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mc_token_ids = None
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if self.use_mc_token_ids:
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mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = GPT2Config(
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vocab_size=self.vocab_size,
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n_embd=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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# intermediate_size=self.intermediate_size,
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# hidden_act=self.hidden_act,
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# hidden_dropout_prob=self.hidden_dropout_prob,
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# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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n_positions=self.max_position_embeddings,
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n_ctx=self.max_position_embeddings,
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# type_vocab_size=self.type_vocab_size,
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# initializer_range=self.initializer_range
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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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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def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPT2Model(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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}
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sequence_output = model(inputs)[0]
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inputs = [input_ids, None, input_mask] # None is the input for 'past'
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sequence_output = model(inputs)[0]
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sequence_output = model(input_ids)[0]
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result = {
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"sequence_output": sequence_output.numpy(),
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size],
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)
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def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = TFGPT2Model(config=config)
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# first forward pass
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output, past = model(input_ids, token_type_ids=token_type_ids)
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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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next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
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# append to next input_ids and token_type_ids
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next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
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next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
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output_from_no_past, _ = model(next_input_ids, token_type_ids=next_token_type_ids)
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output_from_past, _ = model(next_tokens, token_type_ids=next_token_types, past=past)
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# select random slice
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random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-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_gpt2_model_attention_mask_past(
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self, config, input_ids, input_mask, head_mask, token_type_ids, *args
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):
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model = TFGPT2Model(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
|
||||
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 = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": input_mask,
|
||||
"token_type_ids": token_type_ids,
|
||||
}
|
||||
prediction_scores = model(inputs)[0]
|
||||
result = {
|
||||
"prediction_scores": prediction_scores.numpy(),
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size],
|
||||
)
|
||||
|
||||
def create_and_check_gpt2_double_head(
|
||||
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
|
||||
):
|
||||
model = TFGPT2DoubleHeadsModel(config=config)
|
||||
|
||||
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
|
||||
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
|
||||
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
|
||||
|
||||
inputs = {
|
||||
"input_ids": multiple_choice_inputs_ids,
|
||||
"mc_token_ids": mc_token_ids,
|
||||
"attention_mask": multiple_choice_input_mask,
|
||||
"token_type_ids": multiple_choice_token_type_ids,
|
||||
}
|
||||
lm_logits, mc_logits = model(inputs)[:2]
|
||||
result = {"lm_logits": lm_logits.numpy(), "mc_logits": mc_logits.numpy()}
|
||||
self.parent.assertListEqual(
|
||||
list(result["lm_logits"].shape), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size],
|
||||
)
|
||||
self.parent.assertListEqual(list(result["mc_logits"].shape), [self.batch_size, self.num_choices])
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
input_mask,
|
||||
head_mask,
|
||||
token_type_ids,
|
||||
mc_token_ids,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = config_and_inputs
|
||||
|
||||
inputs_dict = {
|
||||
"input_ids": input_ids,
|
||||
"token_type_ids": token_type_ids,
|
||||
"attention_mask": input_mask,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = TFGPT2ModelTest.TFGPT2ModelTester(self)
|
||||
self.model_tester = TFGPT2ModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
|
||||
|
||||
def test_config(self):
|
||||
|
||||
Reference in New Issue
Block a user