SUMIN/HuggingFace_transformer
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[Config, Caching] Remove output_past everywhere and replace by use_cache argument (#3734)

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* remove output_past from pt

* make style

* add optional input length for gpt2

* add use cache to prepare input

* save memory in gpt2

* correct gpt2 test inputs

* make past input optional for gpt2

* finish use_cache for all models

* make style

* delete modeling_gpt2 change in test file

* correct docstring

* correct is true statements for gpt2
This commit is contained in:
Patrick von Platen
2020-04-14 20:40:28 +02:00
committed by GitHub
parent 092cf881a5
commit 01c37dcdb5
15 changed files with 342 additions and 168 deletions
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5
tests/test_modeling_common.py
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@@ -128,7 +128,6 @@ class ModelTesterMixin:
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
config.output_past = False
model = model_class(config)
model.to(torch_device)
model.eval()
@@ -664,10 +663,6 @@ class ModelTesterMixin:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"] if "input_ids" in inputs_dict else inputs_dict["inputs"]
if self.is_encoder_decoder:
# needed for Bart beam search
config.output_past = True
for model_class in self.all_generative_model_classes:
model = model_class(config)

21
tests/test_modeling_t5.py
View File

@@ -227,7 +227,7 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
model.eval()
# first forward pass
output, past_key_value_states = model(input_ids)
output, past_key_value_states = model(input_ids, use_cache=True)
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -235,8 +235,8 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past, _ = model(next_input_ids)
output_from_past, _ = model(next_tokens, past_key_value_states=past_key_value_states)
output_from_no_past = model(next_input_ids)[0]
output_from_past = model(next_tokens, past_key_value_states=past_key_value_states)[0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -260,7 +260,7 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past_key_value_states = model(input_ids, attention_mask=attn_mask)
output, past_key_value_states = model(input_ids, attention_mask=attn_mask, use_cache=True)
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
@@ -277,10 +277,10 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
)
# get two different outputs
output_from_no_past, _ = model(next_input_ids, attention_mask=attn_mask)
output_from_past, _ = model(
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)[0]
output_from_past = model(
next_tokens, past_key_value_states=past_key_value_states, attention_mask=attn_mask
)
)[0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
@@ -298,10 +298,10 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
model.to(torch_device)
model.eval()
torch.manual_seed(0)
model.set_output_past(False)
output_without_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
)
torch.manual_seed(0)
model.set_output_past(True)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
@@ -321,6 +321,7 @@ class T5ModelTest(ModelTesterMixin, unittest.TestCase):
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"use_cache": False,
}
return config, inputs_dict

4
tests/test_modeling_tf_common.py
View File

@@ -460,10 +460,6 @@ class TFModelTesterMixin:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"] if "input_ids" in inputs_dict else inputs_dict["inputs"]
if self.is_encoder_decoder:
# needed for Bart beam search
config.output_past = True
for model_class in self.all_generative_model_classes:
model = model_class(config)