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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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102
tests/test_modeling_xlnet.py
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@@ -137,6 +137,7 @@ class XLNetModelTester:
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
eos_token_id=self.eos_token_id,
return_dict=True,
)
return (
@@ -177,15 +178,10 @@ class XLNetModelTester:
model.to(torch_device)
model.eval()
_, _ = model(input_ids_1, input_mask=input_mask)
_, _ = model(input_ids_1, attention_mask=input_mask)
_, _ = model(input_ids_1, token_type_ids=segment_ids)
outputs, mems_1 = model(input_ids_1)
result = {
"mems_1": mems_1,
"outputs": outputs,
}
result = model(input_ids_1, input_mask=input_mask)
result = model(input_ids_1, attention_mask=input_mask)
result = model(input_ids_1, token_type_ids=segment_ids)
result = model(input_ids_1)
config.mem_len = 0
model = XLNetModel(config)
@@ -195,10 +191,10 @@ class XLNetModelTester:
self.parent.assertEqual(len(base_model_output), 2)
self.parent.assertListEqual(
list(result["outputs"].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.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
list(list(mem.size()) for mem in result["mems"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
)
@@ -233,7 +229,7 @@ class XLNetModelTester:
self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)
output, mems = outputs_cache
output, mems = outputs_cache.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -253,8 +249,8 @@ class XLNetModelTester:
single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)
# second forward pass
output_from_no_past, _ = model(next_input_ids, perm_mask=causal_mask)
output_from_past, _ = model(next_tokens, mems=mems, perm_mask=single_mask)
output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -283,7 +279,7 @@ class XLNetModelTester:
model.to(torch_device)
model.eval()
_, _, attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)
attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]
self.parent.assertEqual(len(attentions), config.n_layer)
self.parent.assertIsInstance(attentions[0], tuple)
@@ -309,36 +305,27 @@ class XLNetModelTester:
model.to(torch_device)
model.eval()
loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
loss_2, all_logits_2, mems_2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=mems_1)
result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1["mems"])
logits, _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)
_ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)
result = {
"loss_1": loss_1,
"mems_1": mems_1,
"all_logits_1": all_logits_1,
"loss_2": loss_2,
"mems_2": mems_2,
"all_logits_2": all_logits_2,
}
self.parent.assertListEqual(list(result["loss_1"].size()), [])
self.parent.assertListEqual(list(result1["loss"].size()), [])
self.parent.assertListEqual(
list(result["all_logits_1"].size()), [self.batch_size, self.seq_length, self.vocab_size],
list(result1["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
list(list(mem.size()) for mem in result1["mems"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
)
self.parent.assertListEqual(list(result["loss_2"].size()), [])
self.parent.assertListEqual(list(result2["loss"].size()), [])
self.parent.assertListEqual(
list(result["all_logits_2"].size()), [self.batch_size, self.seq_length, self.vocab_size],
list(result2["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_2"]),
list(list(mem.size()) for mem in result2["mems"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
)
@@ -361,10 +348,9 @@ class XLNetModelTester:
model.to(torch_device)
model.eval()
outputs = model(input_ids_1)
(start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems,) = outputs
result = model(input_ids_1)
outputs = model(
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
@@ -373,7 +359,7 @@ class XLNetModelTester:
p_mask=input_mask,
)
outputs = model(
result_with_labels = model(
input_ids_1,
start_positions=sequence_labels,
end_positions=sequence_labels,
@@ -381,23 +367,13 @@ class XLNetModelTester:
is_impossible=is_impossible_labels,
)
total_loss, mems = outputs
total_loss, mems = result_with_labels.to_tuple()
outputs = model(input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels,)
result_with_labels = model(input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels,)
total_loss, mems = outputs
total_loss, mems = result_with_labels.to_tuple()
result = {
"loss": total_loss,
"start_top_log_probs": start_top_log_probs,
"start_top_index": start_top_index,
"end_top_log_probs": end_top_log_probs,
"end_top_index": end_top_index,
"cls_logits": cls_logits,
"mems": mems,
}
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(list(result_with_labels["loss"].size()), [])
self.parent.assertListEqual(
list(result["start_top_log_probs"].size()), [self.batch_size, model.config.start_n_top],
)
@@ -436,21 +412,15 @@ class XLNetModelTester:
model.to(torch_device)
model.eval()
logits, mems_1 = model(input_ids_1)
loss, logits, mems_1 = model(input_ids_1, labels=token_labels)
result = {
"loss": loss,
"mems_1": mems_1,
"logits": logits,
}
result = model(input_ids_1)
result = model(input_ids_1, labels=token_labels)
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["logits"].size()), [self.batch_size, self.seq_length, self.type_sequence_label_size],
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
list(list(mem.size()) for mem in result["mems"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
)
@@ -473,21 +443,15 @@ class XLNetModelTester:
model.to(torch_device)
model.eval()
logits, mems_1 = model(input_ids_1)
loss, logits, mems_1 = model(input_ids_1, labels=sequence_labels)
result = {
"loss": loss,
"mems_1": mems_1,
"logits": logits,
}
result = model(input_ids_1)
result = model(input_ids_1, labels=sequence_labels)
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size],
)
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
list(list(mem.size()) for mem in result["mems"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
)