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Remove dependency on pytest for running tests (#2055)

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* Switch to plain unittest for skipping slow tests.

Add a RUN_SLOW environment variable for running them.

* Switch to plain unittest for PyTorch dependency.

* Switch to plain unittest for TensorFlow dependency.

* Avoid leaking open files in the test suite.

This prevents spurious warnings when running tests.

* Fix unicode warning on Python 2 when running tests.

The warning was:

    UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal

* Support running PyTorch tests on a GPU.

Reverts 27e015bd.

* Tests no longer require pytest.

* Make tests pass on cuda
This commit is contained in:
Aymeric Augustin
2019-12-06 19:57:38 +01:00
committed by Julien Chaumond
parent e4679cddce
commit 35401fe50f
50 changed files with 344 additions and 231 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
transformers/tests/modeling_common_test.py
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@@ -27,10 +27,11 @@ import uuid
import unittest
import logging
import pytest
from transformers import is_torch_available
from .utils import require_torch, slow, torch_device
if is_torch_available():
import torch
import numpy as np
@@ -38,8 +39,6 @@ if is_torch_available():
from transformers import (AdaptiveEmbedding, PretrainedConfig, PreTrainedModel,
BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require Torch")
if sys.version_info[0] == 2:
import cPickle as pickle
@@ -65,6 +64,7 @@ def _config_zero_init(config):
class CommonTestCases:
@require_torch
class CommonModelTester(unittest.TestCase):
model_tester = None
@@ -79,6 +79,7 @@ class CommonTestCases:
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
@@ -86,12 +87,13 @@ class CommonTestCases:
with TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
model.to(torch_device)
with torch.no_grad():
after_outputs = model(**inputs_dict)
# Make sure we don't have nans
out_1 = after_outputs[0].numpy()
out_2 = outputs[0].numpy()
out_1 = after_outputs[0].cpu().numpy()
out_2 = outputs[0].cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
@@ -113,6 +115,7 @@ class CommonTestCases:
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
first, second = model(inputs_dict["input_ids"])[0], model(inputs_dict["input_ids"])[0]
self.assertEqual(first.ne(second).sum().item(), 0)
@@ -125,6 +128,7 @@ class CommonTestCases:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
model.to(torch_device)
model.eval()
outputs = model(**inputs_dict)
attentions = outputs[-1]
@@ -142,6 +146,7 @@ class CommonTestCases:
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
model.to(torch_device)
model.eval()
outputs = model(**inputs_dict)
self.assertEqual(out_len+1, len(outputs))
@@ -181,6 +186,7 @@ class CommonTestCases:
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = inputs_dict['input_ids'] # Let's keep only input_ids
@@ -201,7 +207,10 @@ class CommonTestCases:
except ValueError:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_params = model.parameters()
@@ -228,11 +237,12 @@ class CommonTestCases:
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads)
head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
@@ -282,6 +292,7 @@ class CommonTestCases:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config=config)
model.to(torch_device)
model.eval()
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0]}
@@ -310,6 +321,7 @@ class CommonTestCases:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config=config)
model.to(torch_device)
model.eval()
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0]}
@@ -319,6 +331,7 @@ class CommonTestCases:
os.makedirs(directory)
model.save_pretrained(directory)
model = model_class.from_pretrained(directory)
model.to(torch_device)
outputs = model(**inputs_dict)
attentions = outputs[-1]
@@ -346,6 +359,7 @@ class CommonTestCases:
config.pruned_heads = heads_to_prune
model = model_class(config=config)
model.to(torch_device)
model.eval()
outputs = model(**inputs_dict)
@@ -372,6 +386,7 @@ class CommonTestCases:
config.pruned_heads = heads_to_prune
model = model_class(config=config)
model.to(torch_device)
model.eval()
outputs = model(**inputs_dict)
@@ -388,6 +403,7 @@ class CommonTestCases:
os.makedirs(directory)
model.save_pretrained(directory)
model = model_class.from_pretrained(directory)
model.to(torch_device)
shutil.rmtree(directory)
outputs = model(**inputs_dict)
@@ -419,6 +435,7 @@ class CommonTestCases:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
model.to(torch_device)
model.eval()
outputs = model(**inputs_dict)
hidden_states = outputs[-1]
@@ -538,6 +555,7 @@ class CommonTestCases:
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
wte = model.get_input_embeddings()
@@ -628,6 +646,7 @@ class CommonTestCases:
def create_and_check_base_model(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.base_model_class(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids)
@@ -643,6 +662,7 @@ class CommonTestCases:
def create_and_check_lm_head(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.lm_head_model_class(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
loss, lm_logits = outputs[:2]
@@ -659,6 +679,7 @@ class CommonTestCases:
mc_labels, lm_labels, mc_token_ids):
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
presents = outputs[-1]
@@ -671,6 +692,7 @@ class CommonTestCases:
def create_and_check_double_heads(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.double_head_model_class(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels,
token_type_ids=token_type_ids, position_ids=position_ids)
@@ -716,7 +738,7 @@ class CommonTestCases:
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_presents(*config_and_inputs)
@pytest.mark.slow
@slow
def run_slow_tests(self):
self.create_and_check_model_from_pretrained()
@@ -770,7 +792,7 @@ def ids_tensor(shape, vocab_size, rng=None, name=None):
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous()
def floats_tensor(shape, scale=1.0, rng=None, name=None):
@@ -786,11 +808,12 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None):
for _ in range(total_dims):
values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()
@require_torch
class ModelUtilsTest(unittest.TestCase):
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: