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Add slow generate tests for pretrained lm models (#2909)

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* add slow generate lm_model tests

* fix conflicts

* merge conflicts

* fix conflicts

* add slow generate lm_model tests

* make style

* delete unused variable

* fix conflicts

* fix conflicts

* fix conflicts

* delete unused variable

* fix conflicts

* finished hard coded tests
This commit is contained in:
Patrick von Platen
2020-02-24 17:51:57 +01:00
committed by GitHub
parent 8194df8e0c
commit 17c45c39ed
8 changed files with 991 additions and 6 deletions
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95
tests/test_modeling_gpt2.py
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@@ -24,6 +24,7 @@ from .utils import CACHE_DIR, require_torch, slow, torch_device
if is_torch_available():
import torch
from transformers import (
GPT2Config,
GPT2Model,
@@ -165,7 +166,7 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
"presents": presents,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size],
)
self.parent.assertEqual(len(result["presents"]), config.n_layer)
@@ -180,7 +181,7 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
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["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
)
def create_and_check_double_lm_head_model(
@@ -208,7 +209,8 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(
list(result["lm_logits"].size()), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size]
list(result["lm_logits"].size()),
[self.batch_size, self.num_choices, self.seq_length, self.vocab_size],
)
self.parent.assertListEqual(list(result["mc_logits"].size()), [self.batch_size, self.num_choices])
@@ -227,7 +229,11 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@@ -255,3 +261,84 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = GPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
def prepare_generation_special_tokens():
return {"bos_token_id": 50256, "eos_token_id": 50256}
class GPT2ModelLanguageGenerationTest(unittest.TestCase):
special_tokens = prepare_generation_special_tokens()
@slow
def test_lm_generate_gpt2(self):
model = GPT2LMHeadModel.from_pretrained("gpt2")
input_ids = torch.Tensor([[464, 3290, 318, 13779]]).long() # The dog is cute
expected_output_ids = [
464,
3290,
318,
13779,
1165,
13,
632,
7832,
284,
6437,
319,
502,
290,
318,
922,
329,
502,
357,
1169,
3290,
] # The dog is cute too. It likes to rub on me and is good for me (the dog
torch.manual_seed(0)
output_ids = model.generate(
input_ids,
bos_token_id=self.special_tokens["bos_token_id"],
eos_token_ids=self.special_tokens["eos_token_id"],
)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@slow
def test_lm_generate_distilgpt2(self):
model = GPT2LMHeadModel.from_pretrained("distilgpt2")
input_ids = torch.Tensor([[464, 3290, 318, 13779]]).long() # The dog is cute
expected_output_ids = [
464,
3290,
318,
13779,
996,
339,
460,
3360,
655,
2513,
287,
262,
3952,
13,
632,
318,
407,
845,
3621,
284,
] # The dog is cute though he can sometimes just walk in the park. It is not very nice to
torch.manual_seed(0)
output_ids = model.generate(
input_ids,
bos_token_id=self.special_tokens["bos_token_id"],
eos_token_ids=self.special_tokens["eos_token_id"],
)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)