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Model output test (#6155)

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* Use return_dict=True in all tests

* Formatting
This commit is contained in:
Sylvain Gugger
2020-07-31 09:44:37 -04:00
committed by GitHub
parent 86caab1e0b
commit d951c14ae4
26 changed files with 320 additions and 765 deletions
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43
tests/test_modeling_gpt2.py
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@@ -122,9 +122,10 @@ class GPT2ModelTester:
n_positions=self.max_position_embeddings,
n_ctx=self.max_position_embeddings,
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
# initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
return_dict=True,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
@@ -149,18 +150,14 @@ class GPT2ModelTester:
model.to(torch_device)
model.eval()
model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
model(input_ids, token_type_ids=token_type_ids)
sequence_output, presents = model(input_ids)
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
result = {
"sequence_output": sequence_output,
"presents": presents,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size],
list(result["last_hidden_state"].size()), [self.batch_size, self.seq_length, self.hidden_size],
)
self.parent.assertEqual(len(result["presents"]), config.n_layer)
self.parent.assertEqual(len(result["past_key_values"]), config.n_layer)
def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPT2Model(config=config)
@@ -175,7 +172,7 @@ class GPT2ModelTester:
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past = outputs
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -185,8 +182,8 @@ class GPT2ModelTester:
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-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)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past=past)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -209,7 +206,7 @@ class GPT2ModelTester:
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past = model(input_ids, attention_mask=attn_mask)
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -226,8 +223,8 @@ class GPT2ModelTester:
)
# 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)
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -242,13 +239,10 @@ class GPT2ModelTester:
model.to(torch_device)
model.eval()
loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
result = {"loss": loss, "lm_logits": lm_logits}
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
)
def create_and_check_double_lm_head_model(
@@ -270,11 +264,8 @@ class GPT2ModelTester:
"labels": multiple_choice_inputs_ids,
}
loss, lm_logits, mc_logits, _ = model(**inputs)
result = {"loss": loss, "lm_logits": lm_logits, "mc_logits": mc_logits}
self.parent.assertListEqual(list(result["loss"].size()), [])
result = model(**inputs)
self.parent.assertListEqual(list(result["lm_loss"].size()), [])
self.parent.assertListEqual(
list(result["lm_logits"].size()), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size],
)