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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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53
tests/test_modeling_reformer.py
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@@ -165,6 +165,7 @@ class ReformerModelTester:
attn_layers=self.attn_layers,
pad_token_id=self.pad_token_id,
hash_seed=self.hash_seed,
return_dict=True,
)
return (
@@ -181,15 +182,12 @@ class ReformerModelTester:
model = ReformerModel(config=config)
model.to(torch_device)
model.eval()
sequence_output, _ = model(input_ids, attention_mask=input_mask)
sequence_output, _ = model(input_ids)
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
result = {
"sequence_output": sequence_output,
}
# 2 * hidden_size because we use reversible resnet layers
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, 2 * self.hidden_size],
list(result["last_hidden_state"].size()), [self.batch_size, self.seq_length, 2 * self.hidden_size],
)
def create_and_check_reformer_model_with_lm_backward(self, config, input_ids, input_mask, choice_labels):
@@ -198,7 +196,7 @@ class ReformerModelTester:
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
loss = model(input_ids, attention_mask=input_mask, labels=input_ids)[0]
loss = model(input_ids, attention_mask=input_mask, labels=input_ids)["loss"]
loss.backward()
def create_and_check_reformer_with_lm(self, config, input_ids, input_mask, choice_labels):
@@ -207,13 +205,9 @@ class ReformerModelTester:
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
loss, prediction_scores, _ = model(input_ids, attention_mask=input_mask, labels=input_ids)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size],
list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
)
self.check_loss_output(result)
@@ -222,13 +216,9 @@ class ReformerModelTester:
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
loss, prediction_scores = model(input_ids, attention_mask=input_mask, labels=input_ids)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size],
list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
)
self.check_loss_output(result)
@@ -325,7 +315,7 @@ class ReformerModelTester:
model.to(torch_device)
model.eval()
hidden_states_with_chunk = model(input_ids, attention_mask=input_mask)[0]
hidden_states_with_chunk = model(input_ids, attention_mask=input_mask)["last_hidden_state"]
self.parent.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
def create_and_check_reformer_feed_backward_chunking(self, config, input_ids, input_mask, choice_labels):
@@ -408,7 +398,7 @@ class ReformerModelTester:
model.to(torch_device)
model.half()
model.eval()
output = model(input_ids, attention_mask=input_mask)[0]
output = model(input_ids, attention_mask=input_mask)["last_input_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_reformer_model_generate(self, config, input_ids, input_mask, choice_labels):
@@ -444,21 +434,16 @@ class ReformerModelTester:
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
output_logits = model(input_ids, attention_mask=input_mask)[0]
output_logits = model(input_ids, attention_mask=input_mask)["logits"]
self.parent.assertTrue(output_logits.shape[1] == input_ids.shape[-1])
def create_and_check_reformer_for_question_answering(self, config, input_ids, input_mask, choice_labels):
model = ReformerForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
loss, start_logits, end_logits = model(
result = model(
input_ids, attention_mask=input_mask, start_positions=choice_labels, end_positions=choice_labels,
)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
self.check_loss_output(result)
@@ -474,11 +459,11 @@ class ReformerModelTester:
input_ids_second = input_ids[:, -1:]
# return saved cache
_, past_buckets_states = model(input_ids_first, use_cache=True)
past_buckets_states = model(input_ids_first, use_cache=True)["past_buckets_states"]
# calculate last output with and without cache
outputs_with_cache, _ = model(input_ids_second, past_buckets_states=past_buckets_states, use_cache=True)
outputs_without_cache = model(input_ids)[0][:, -1]
outputs_with_cache = model(input_ids_second, past_buckets_states=past_buckets_states, use_cache=True)["logits"]
outputs_without_cache = model(input_ids)["logits"][:, -1]
# select random slice idx
random_slice_idx = torch.randint(outputs_without_cache.shape[-1], (1, 1), device=torch_device).item()
@@ -504,11 +489,7 @@ class ReformerModelTester:
model = ReformerForSequenceClassification(config)
model.to(torch_device)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
self.check_loss_output(result)