SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

[Fix common tests on GPU] send model, ids to torch_device (#4014)

Browse Source
This commit is contained in:
Sam Shleifer
2020-04-29 09:47:20 -04:00
committed by GitHub
parent 6faca88ee0
commit 2c77842887
1 changed files with 18 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
tests/test_modeling_common.py
View File

@@ -19,6 +19,7 @@ import os.path
import random import random
import tempfile import tempfile
import unittest import unittest
from typing import List
from transformers import is_torch_available from transformers import is_torch_available
@@ -629,10 +630,10 @@ class ModelTesterMixin:
# iterate over all generative models # iterate over all generative models
for model_class in self.all_generative_model_classes: for model_class in self.all_generative_model_classes:
model = model_class(config) model = model_class(config).to(torch_device)
if config.bos_token_id is None: if config.bos_token_id is None:
# if bos token id is not defined mobel needs input_ids # if bos token id is not defined, model needs input_ids
with self.assertRaises(AssertionError): with self.assertRaises(AssertionError):
model.generate(do_sample=True, max_length=5) model.generate(do_sample=True, max_length=5)
# num_return_sequences = 1 # num_return_sequences = 1
@@ -651,7 +652,10 @@ class ModelTesterMixin:
# check bad words tokens language generation # check bad words tokens language generation
# create list of 1-seq bad token and list of 2-seq of bad tokens # create list of 1-seq bad token and list of 2-seq of bad tokens
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] bad_words_ids = [
self._generate_random_bad_tokens(1, model.config),
self._generate_random_bad_tokens(2, model.config),
]
output_tokens = model.generate( output_tokens = model.generate(
input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2
) )
@@ -661,10 +665,12 @@ class ModelTesterMixin:
def test_lm_head_model_random_beam_search_generate(self): def test_lm_head_model_random_beam_search_generate(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() 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"] input_ids = (inputs_dict["input_ids"] if "input_ids" in inputs_dict else inputs_dict["inputs"]).to(
torch_device
)
for model_class in self.all_generative_model_classes: for model_class in self.all_generative_model_classes:
model = model_class(config) model = model_class(config).to(torch_device)
if config.bos_token_id is None: if config.bos_token_id is None:
# if bos token id is not defined mobel needs input_ids, num_return_sequences = 1 # if bos token id is not defined mobel needs input_ids, num_return_sequences = 1
@@ -684,7 +690,10 @@ class ModelTesterMixin:
# check bad words tokens language generation # check bad words tokens language generation
# create list of 1-seq bad token and list of 2-seq of bad tokens # create list of 1-seq bad token and list of 2-seq of bad tokens
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] bad_words_ids = [
self._generate_random_bad_tokens(1, model.config),
self._generate_random_bad_tokens(2, model.config),
]
output_tokens = model.generate( output_tokens = model.generate(
input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2
) )
@@ -692,20 +701,13 @@ class ModelTesterMixin:
generated_ids = output_tokens[:, input_ids.shape[-1] :] generated_ids = output_tokens[:, input_ids.shape[-1] :]
self.assertFalse(self._check_match_tokens(generated_ids.tolist(), bad_words_ids)) self.assertFalse(self._check_match_tokens(generated_ids.tolist(), bad_words_ids))
def _generate_random_bad_tokens(self, num_bad_tokens, model): def _generate_random_bad_tokens(self, num_bad_tokens: int, config) -> List[int]:
# special tokens cannot be bad tokens # special tokens cannot be bad tokens
special_tokens = [] special_tokens = [x for x in [config.bos_token_id, config.eos_token_id, config.pad_token_id] if x is not None]
if model.config.bos_token_id is not None:
special_tokens.append(model.config.bos_token_id)
if model.config.pad_token_id is not None:
special_tokens.append(model.config.pad_token_id)
if model.config.eos_token_id is not None:
special_tokens.append(model.config.eos_token_id)
# create random bad tokens that are not special tokens # create random bad tokens that are not special tokens
bad_tokens = [] bad_tokens = []
while len(bad_tokens) < num_bad_tokens: while len(bad_tokens) < num_bad_tokens:
token = ids_tensor((1, 1), self.model_tester.vocab_size).squeeze(0).numpy()[0] token = ids_tensor((1, 1), self.model_tester.vocab_size).squeeze(0).cpu().numpy()[0]
if token not in special_tokens: if token not in special_tokens:
bad_tokens.append(token) bad_tokens.append(token)
return bad_tokens return bad_tokens