Skip to content

GitLab

"examples/vscode:/vscode.git/clone" did not exist on "23c56b1f35a0488edbf7e2401424622163cb3f05"
Commit 345c23a6 authored by Aymeric Augustin's avatar Aymeric Augustin
Browse files

Replace (TF)CommonTestCases for modeling with a mixin. 

I suspect the wrapper classes were created in order to prevent the
abstract base class (TF)CommonModelTester from being included in test
discovery and running, because that would fail.

I solved this by replacing the abstract base class with a mixin.

Code changes are just de-indenting and automatic reformattings
performed by black to use the extra line space.
parent 7e98e211
Hide whitespace changes
Inline Side-by-side
Showing with 968 additions and 944 deletions
templates/adding_a_new_model/tests/test_modeling_tf_xxx.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import XxxConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -32,7 +34,7 @@ if is_tf_available():
@require_tf
class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester):
class TFXxxModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
......
templates/adding_a_new_model/tests/test_modeling_xxx.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
......@@ -34,7 +36,7 @@ if is_torch_available():
@require_torch
class XxxModelTest(CommonTestCases.CommonModelTester):
class XxxModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(XxxModel, XxxForMaskedLM, XxxForQuestionAnswering, XxxForSequenceClassification, XxxForTokenClassification)
......
tests/test_modeling_albert.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
......@@ -33,7 +35,7 @@ if is_torch_available():
@require_torch
class AlbertModelTest(CommonTestCases.CommonModelTester):
class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else ()
......
tests/test_modeling_bert.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, floats_tensor, ids_tensor
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
......@@ -37,7 +39,7 @@ if is_torch_available():
@require_torch
class BertModelTest(CommonTestCases.CommonModelTester):
class BertModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
......
tests/test_modeling_common.py
View file @ 345c23a6
......@@ -69,737 +69,737 @@ def _config_zero_init(config):
return configs_no_init
class CommonTestCases:
@require_torch
class CommonModelTester(unittest.TestCase):
model_tester = None
all_model_classes = ()
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
test_head_masking = True
is_encoder_decoder = False
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@require_torch
class ModelTesterMixin:
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)
out_2 = outputs[0].numpy()
out_2[np.isnan(out_2)] = 0
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].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model_tester = None
all_model_classes = ()
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
test_head_masking = True
is_encoder_decoder = False
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
param.data.mean().item(),
[0.0, 1.0],
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
)
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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)
out_2 = outputs[0].numpy()
out_2[np.isnan(out_2)] = 0
for model_class in self.all_model_classes:
model = model_class(config)
with TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
model.to(torch_device)
model.eval()
with torch.no_grad():
first = model(**inputs_dict)[0]
second = model(**inputs_dict)[0]
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
after_outputs = model(**inputs_dict)
# Make sure we don't have nans
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
param.data.mean().item(),
[0.0, 1.0],
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
)
decoder_seq_length = (
self.model_tester.decoder_seq_length
if hasattr(self.model_tester, "decoder_seq_length")
else self.model_tester.seq_length
)
encoder_seq_length = (
self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "encoder_seq_length")
else self.model_tester.seq_length
)
decoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else decoder_seq_length
)
encoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
first = model(**inputs_dict)[0]
second = model(**inputs_dict)[0]
out_1 = first.cpu().numpy()
out_2 = second.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))
self.assertLessEqual(max_diff, 1e-5)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
decoder_seq_length = (
self.model_tester.decoder_seq_length
if hasattr(self.model_tester, "decoder_seq_length")
else self.model_tester.seq_length
)
encoder_seq_length = (
self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "encoder_seq_length")
else self.model_tester.seq_length
)
decoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else decoder_seq_length
)
encoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length
)
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
if self.is_encoder_decoder:
self.assertEqual(out_len % 2, 0)
decoder_attentions = outputs[(out_len // 2) - 1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
if self.is_encoder_decoder:
self.assertEqual(out_len % 2, 0)
decoder_attentions = outputs[(out_len // 2) - 1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
self_attentions = outputs[-1]
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
def test_torchscript(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torchscript(config, inputs_dict)
def test_torchscript_output_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Check attention is always last and order is fine
config.output_attentions = True
self._create_and_check_torchscript(config, inputs_dict)
config.output_hidden_states = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
def test_torchscript_output_hidden_state(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self_attentions = outputs[-1]
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
config.output_hidden_states = True
self._create_and_check_torchscript(config, inputs_dict)
def test_torchscript(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
self._create_and_check_torchscript(config, inputs_dict)
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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
def test_torchscript_output_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
try:
traced_gpt2 = torch.jit.trace(model, inputs)
except RuntimeError:
self.fail("Couldn't trace module.")
config.output_attentions = True
self._create_and_check_torchscript(config, inputs_dict)
with TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
def test_torchscript_output_hidden_state(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
try:
torch.jit.save(traced_gpt2, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
config.output_hidden_states = True
self._create_and_check_torchscript(config, inputs_dict)
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
model.to(torch_device)
model.eval()
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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
loaded_model.to(torch_device)
loaded_model.eval()
try:
traced_gpt2 = torch.jit.trace(model, inputs)
except RuntimeError:
self.fail("Couldn't trace module.")
model_params = model.parameters()
loaded_model_params = loaded_model.parameters()
with TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
models_equal = True
for p1, p2 in zip(model_params, loaded_model_params):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
try:
torch.jit.save(traced_gpt2, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
self.assertTrue(models_equal)
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
def test_headmasking(self):
if not self.test_head_masking:
return
model.to(torch_device)
model.eval()
global_rng.seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
global_rng.seed()
loaded_model.to(torch_device)
loaded_model.eval()
config.output_attentions = True
config.output_hidden_states = True
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()
model_params = model.parameters()
loaded_model_params = loaded_model.parameters()
# 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, device=torch_device
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = inputs_dict.copy()
inputs["head_mask"] = head_mask
outputs = model(**inputs)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
attentions = outputs[-1]
hidden_states = outputs[-2]
# Remove Nan
for t in attentions:
self.assertLess(
torch.sum(torch.isnan(t)), t.numel() / 4
) # Check we don't have more than 25% nans (arbitrary)
attentions = [
t.masked_fill(torch.isnan(t), 0.0) for t in attentions
] # remove them (the test is less complete)
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def test_head_pruning(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
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]}
model.prune_heads(heads_to_prune)
with torch.no_grad():
outputs = model(**inputs_dict)
models_equal = True
for p1, p2 in zip(model_params, loaded_model_params):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
attentions = outputs[-1]
self.assertTrue(models_equal)
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_headmasking(self):
if not self.test_head_masking:
return
def test_head_pruning_save_load_from_pretrained(self):
if not self.test_pruning:
return
global_rng.seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
global_rng.seed()
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
config.output_hidden_states = True
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()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
# 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, device=torch_device
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = inputs_dict.copy()
inputs["head_mask"] = head_mask
outputs = model(**inputs)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
attentions = outputs[-1]
hidden_states = outputs[-2]
# Remove Nan
for t in attentions:
self.assertLess(
torch.sum(torch.isnan(t)), t.numel() / 4
) # Check we don't have more than 25% nans (arbitrary)
attentions = [
t.masked_fill(torch.isnan(t), 0.0) for t in attentions
] # remove them (the test is less complete)
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def test_head_pruning(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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]}
model.prune_heads(heads_to_prune)
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
with TemporaryDirectory() as temp_dir_name:
model.save_pretrained(temp_dir_name)
model = model_class.from_pretrained(temp_dir_name)
model.to(torch_device)
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]}
model.prune_heads(heads_to_prune)
with torch.no_grad():
outputs = model(**inputs_dict)
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
attentions = outputs[-1]
def test_head_pruning_save_load_from_config_init(self):
if not self.test_pruning:
return
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def test_head_pruning_save_load_from_pretrained(self):
if not self.test_pruning:
return
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
config.output_hidden_states = False
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]}
config.pruned_heads = heads_to_prune
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]}
model.prune_heads(heads_to_prune)
model = model_class(config=config)
with TemporaryDirectory() as temp_dir_name:
model.save_pretrained(temp_dir_name)
model = model_class.from_pretrained(temp_dir_name)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_head_pruning_integration(self):
if not self.test_pruning:
return
def test_head_pruning_save_load_from_config_init(self):
if not self.test_pruning:
return
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
config.output_attentions = True
config.output_hidden_states = False
config.output_attentions = True
config.output_hidden_states = False
heads_to_prune = {0: [0], 1: [1, 2]}
config.pruned_heads = heads_to_prune
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)), -1: [0]}
config.pruned_heads = heads_to_prune
model = model_class(config=config)
model.to(torch_device)
model.eval()
model = model_class(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[0].shape[-3], 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
with TemporaryDirectory() as temp_dir_name:
model.save_pretrained(temp_dir_name)
model = model_class.from_pretrained(temp_dir_name)
model.to(torch_device)
def test_head_pruning_integration(self):
if not self.test_pruning:
return
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
if "head_mask" in inputs_dict:
del inputs_dict["head_mask"]
heads_to_prune = {0: [0], 2: [1, 2]}
model.prune_heads(heads_to_prune)
config.output_attentions = True
config.output_hidden_states = False
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
heads_to_prune = {0: [0], 1: [1, 2]}
config.pruned_heads = heads_to_prune
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
model = model_class(config=config)
model.to(torch_device)
model.eval()
self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
for model_class in self.all_model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
with TemporaryDirectory() as temp_dir_name:
model.save_pretrained(temp_dir_name)
model = model_class.from_pretrained(temp_dir_name)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs_dict)
hidden_states = outputs[-1]
self.assertEqual(model.config.output_attentions, False)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[
self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "encoder_seq_length")
else self.model_tester.seq_length,
self.model_tester.hidden_size,
],
)
def test_resize_tokens_embeddings(self):
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
with torch.no_grad():
outputs = model(**inputs_dict)
attentions = outputs[-1]
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Embedding, AdaptiveEmbedding))
model.set_input_embeddings(torch.nn.Embedding(10, 10))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
def test_tie_model_weights(self):
if not self.test_torchscript:
return
heads_to_prune = {0: [0], 2: [1, 2]}
model.prune_heads(heads_to_prune)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_same_values(layer_1, layer_2):
equal = True
for p1, p2 in zip(layer_1.weight, layer_2.weight):
if p1.data.ne(p2.data).sum() > 0:
equal = False
return equal
for model_class in self.all_model_classes:
config.torchscript = True
model_not_tied = model_class(config)
if model_not_tied.get_output_embeddings() is None:
continue
params_not_tied = list(model_not_tied.parameters())
config_tied = copy.deepcopy(config)
config_tied.torchscript = False
model_tied = model_class(config_tied)
params_tied = list(model_tied.parameters())
# Check that the embedding layer and decoding layer are the same in size and in value
self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# embeddings.weight.data.div_(2)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# decoding.weight.data.div_(4)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# Check that after resize they remain tied.
model_tied.resize_token_embeddings(config.vocab_size + 10)
params_tied_2 = list(model_tied.parameters())
self.assertGreater(len(params_not_tied), len(params_tied))
self.assertEqual(len(params_tied_2), len(params_tied))
# decoding.weight.data.mul_(20)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.is_encoder_decoder:
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
else:
encoder_input_ids = inputs_dict["encoder_input_ids"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
del inputs_dict["encoder_input_ids"]
del inputs_dict["decoder_input_ids"]
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)
attentions = outputs[-1]
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs_dict["inputs_embeds"] = wte(input_ids)
else:
inputs_dict["encoder_inputs_embeds"] = wte(encoder_input_ids)
inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids)
self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)
with torch.no_grad():
outputs = model(**inputs_dict)
class GPTModelTester(CommonModelTester):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_position_ids=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_positions=33,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
n_choices=3,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
config_class=None,
base_model_class=None,
lm_head_model_class=None,
double_head_model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_position_ids = use_position_ids
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_choices = n_choices
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.config_class = config_class
self.base_model_class = base_model_class
self.lm_head_model_class = lm_head_model_class
self.double_head_model_class = double_head_model_class
self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class)
def prepare_config_and_inputs(self):
total_num_tokens = self.vocab_size
input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
position_ids = None
if self.use_position_ids:
position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
token_type_ids = None
if self.use_token_type_ids:
total_voc = self.vocab_size
token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
mc_labels = None
lm_labels = None
mc_token_ids = None
if self.use_labels:
mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length)
config = self.config_class(
vocab_size=self.vocab_size,
n_positions=self.n_positions,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
initializer_range=self.initializer_range,
)
self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})
return (config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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)
for model_class in self.all_model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(input_ids, position_ids, token_type_ids)
outputs = model(input_ids, position_ids)
outputs = model(input_ids)
hidden_state = outputs[0]
self.parent.assertListEqual(
list(hidden_state.size()), [self.batch_size, self.n_choices, self.seq_length, self.hidden_size]
outputs = model(**inputs_dict)
hidden_states = outputs[-1]
self.assertEqual(model.config.output_attentions, False)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[
self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "encoder_seq_length")
else self.model_tester.seq_length,
self.model_tester.hidden_size,
],
)
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)
def test_resize_tokens_embeddings(self):
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Embedding, AdaptiveEmbedding))
model.set_input_embeddings(torch.nn.Embedding(10, 10))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
def test_tie_model_weights(self):
if not self.test_torchscript:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_same_values(layer_1, layer_2):
equal = True
for p1, p2 in zip(layer_1.weight, layer_2.weight):
if p1.data.ne(p2.data).sum() > 0:
equal = False
return equal
for model_class in self.all_model_classes:
config.torchscript = True
model_not_tied = model_class(config)
if model_not_tied.get_output_embeddings() is None:
continue
params_not_tied = list(model_not_tied.parameters())
config_tied = copy.deepcopy(config)
config_tied.torchscript = False
model_tied = model_class(config_tied)
params_tied = list(model_tied.parameters())
# Check that the embedding layer and decoding layer are the same in size and in value
self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# embeddings.weight.data.div_(2)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# decoding.weight.data.div_(4)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# Check that after resize they remain tied.
model_tied.resize_token_embeddings(config.vocab_size + 10)
params_tied_2 = list(model_tied.parameters())
self.assertGreater(len(params_not_tied), len(params_tied))
self.assertEqual(len(params_tied_2), len(params_tied))
# decoding.weight.data.mul_(20)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.is_encoder_decoder:
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
else:
encoder_input_ids = inputs_dict["encoder_input_ids"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
del inputs_dict["encoder_input_ids"]
del inputs_dict["decoder_input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs_dict["inputs_embeds"] = wte(input_ids)
else:
inputs_dict["encoder_inputs_embeds"] = wte(encoder_input_ids)
inputs_dict["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
loss, lm_logits = outputs[:2]
outputs = model(**inputs_dict)
class GPTModelTester(ModelTesterMixin):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_position_ids=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_positions=33,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
n_choices=3,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
config_class=None,
base_model_class=None,
lm_head_model_class=None,
double_head_model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_position_ids = use_position_ids
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_choices = n_choices
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.config_class = config_class
self.base_model_class = base_model_class
self.lm_head_model_class = lm_head_model_class
self.double_head_model_class = double_head_model_class
self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class)
total_voc = self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]
)
self.parent.assertListEqual(list(loss.size()), [])
def prepare_config_and_inputs(self):
total_num_tokens = self.vocab_size
input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
def create_and_check_presents(
self, config, input_ids, token_type_ids, position_ids, 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()
with torch.no_grad():
outputs = model(input_ids)
presents = outputs[-1]
self.parent.assertEqual(self.num_hidden_layers, len(presents))
self.parent.assertListEqual(
list(presents[0].size()),
[
2,
self.batch_size * self.n_choices,
self.num_attention_heads,
self.seq_length,
self.hidden_size // self.num_attention_heads,
],
)
position_ids = None
if self.use_position_ids:
position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
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)
token_type_ids = None
if self.use_token_type_ids:
total_voc = self.vocab_size
token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
mc_labels = None
lm_labels = None
mc_token_ids = None
if self.use_labels:
mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length)
config = self.config_class(
vocab_size=self.vocab_size,
n_positions=self.n_positions,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
initializer_range=self.initializer_range,
)
return (config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids)
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()
with torch.no_grad():
outputs = model(input_ids, position_ids, token_type_ids)
outputs = model(input_ids, position_ids)
outputs = model(input_ids)
hidden_state = outputs[0]
self.parent.assertListEqual(
list(hidden_state.size()), [self.batch_size, self.n_choices, self.seq_length, self.hidden_size]
)
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()
with torch.no_grad():
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
loss, lm_logits = outputs[:2]
total_voc = self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]
)
self.parent.assertListEqual(list(loss.size()), [])
def create_and_check_presents(
self, config, input_ids, token_type_ids, position_ids, 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()
with torch.no_grad():
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,
)
lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
loss = [lm_loss, mc_loss]
total_voc = self.vocab_size
outputs = model(input_ids)
presents = outputs[-1]
self.parent.assertEqual(self.num_hidden_layers, len(presents))
self.parent.assertListEqual(
list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]
list(presents[0].size()),
[
2,
self.batch_size * self.n_choices,
self.num_attention_heads,
self.seq_length,
self.hidden_size // self.num_attention_heads,
],
)
self.parent.assertListEqual(list(mc_logits.size()), [self.batch_size, self.n_choices])
self.parent.assertListEqual([list(l.size()) for l in loss], [[], []])
def create_and_check_model_from_pretrained(self):
for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
model = self.base_model_class.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.parent.assertIsNotNone(model)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids) = config_and_inputs
inputs_dict = {"input_ids": input_ids}
return config, inputs_dict
def run_common_tests(self, test_presents=False):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_base_model(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_lm_head(*config_and_inputs)
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()
with torch.no_grad():
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,
)
lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
loss = [lm_loss, mc_loss]
total_voc = self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]
)
self.parent.assertListEqual(list(mc_logits.size()), [self.batch_size, self.n_choices])
self.parent.assertListEqual([list(l.size()) for l in loss], [[], []])
def create_and_check_model_from_pretrained(self):
for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
model = self.base_model_class.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.parent.assertIsNotNone(model)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids) = config_and_inputs
inputs_dict = {"input_ids": input_ids}
return config, inputs_dict
def run_common_tests(self, test_presents=False):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_base_model(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_lm_head(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_double_heads(*config_and_inputs)
if test_presents:
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_double_heads(*config_and_inputs)
self.create_and_check_presents(*config_and_inputs)
if test_presents:
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_presents(*config_and_inputs)
@slow
def run_slow_tests(self):
self.create_and_check_model_from_pretrained()
@slow
def run_slow_tests(self):
self.create_and_check_model_from_pretrained()
class ConfigTester(object):
......
tests/test_modeling_ctrl.py
View file @ 345c23a6
......@@ -13,10 +13,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
......@@ -25,7 +27,7 @@ if is_torch_available():
@require_torch
class CTRLModelTest(CommonTestCases.CommonModelTester):
class CTRLModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (CTRLModel, CTRLLMHeadModel) if is_torch_available() else ()
test_pruning = False
......
tests/test_modeling_distilbert.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import require_torch, torch_device
......@@ -33,7 +35,7 @@ if is_torch_available():
@require_torch
class DistilBertModelTest(CommonTestCases.CommonModelTester):
class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(DistilBertModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DistilBertForSequenceClassification)
......
tests/test_modeling_gpt2.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
......@@ -32,7 +34,7 @@ if is_torch_available():
@require_torch
class GPT2ModelTest(CommonTestCases.CommonModelTester):
class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
......
tests/test_modeling_openai.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
......@@ -32,7 +34,7 @@ if is_torch_available():
@require_torch
class OpenAIGPTModelTest(CommonTestCases.CommonModelTester):
class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel) if is_torch_available() else ()
......
tests/test_modeling_roberta.py
View file @ 345c23a6
......@@ -19,7 +19,7 @@ import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow, torch_device
......@@ -37,7 +37,7 @@ if is_torch_available():
@require_torch
class RobertaModelTest(CommonTestCases.CommonModelTester):
class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (RobertaForMaskedLM, RobertaModel) if is_torch_available() else ()
......
tests/test_modeling_t5.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import CommonTestCases, ids_tensor
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow
......@@ -27,7 +29,7 @@ if is_torch_available():
@require_torch
class T5ModelTest(CommonTestCases.CommonModelTester):
class T5ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (T5Model, T5WithLMHeadModel) if is_torch_available() else ()
test_pruning = False
......
tests/test_modeling_tf_albert.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import AlbertConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -31,7 +33,7 @@ if is_tf_available():
@require_tf
class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
class TFAlbertModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFAlbertModel, TFAlbertForMaskedLM, TFAlbertForSequenceClassification) if is_tf_available() else ()
......
tests/test_modeling_tf_bert.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import BertConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -36,7 +38,7 @@ if is_tf_available():
@require_tf
class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
class TFBertModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
......
tests/test_modeling_tf_common.py
View file @ 345c23a6
......@@ -20,7 +20,6 @@ import random
import shutil
import sys
import tempfile
import unittest
from transformers import is_tf_available, is_torch_available
......@@ -59,307 +58,300 @@ def _config_zero_init(config):
return configs_no_init
class TFCommonTestCases:
@require_tf
class TFCommonModelTester(unittest.TestCase):
model_tester = None
all_model_classes = ()
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
is_encoder_decoder = False
def test_initialization(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# configs_no_init = _config_zero_init(config)
# for model_class in self.all_model_classes:
# model = model_class(config=configs_no_init)
# for name, param in model.named_parameters():
# if param.requires_grad:
# self.assertIn(param.data.mean().item(), [0.0, 1.0],
# msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
outputs = model(inputs_dict)
with TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
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 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_pt_tf_model_equivalence(self):
if not is_torch_available():
return
import torch
import transformers
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining
pt_model_class = getattr(transformers, pt_model_class_name)
config.output_hidden_states = True
tf_model = model_class(config)
pt_model = pt_model_class(config)
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict, training=False)
tf_hidden_states = tfo[0].numpy()
pt_hidden_states = pto[0].numpy()
tf_hidden_states[np.isnan(tf_hidden_states)] = 0
pt_hidden_states[np.isnan(pt_hidden_states)] = 0
max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
self.assertLessEqual(max_diff, 2e-2)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict)
tfo = tfo[0].numpy()
pto = pto[0].numpy()
tfo[np.isnan(tfo)] = 0
pto[np.isnan(pto)] = 0
max_diff = np.amax(np.abs(tfo - pto))
self.assertLessEqual(max_diff, 2e-2)
def test_compile_tf_model(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@require_tf
class TFModelTesterMixin:
if self.is_encoder_decoder:
input_ids = {
"decoder_input_ids": tf.keras.Input(
batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"
),
"encoder_input_ids": tf.keras.Input(
batch_shape=(2, 2000), name="encoder_input_ids", dtype="int32"
),
}
else:
input_ids = tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32")
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
model_tester = None
all_model_classes = ()
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
is_encoder_decoder = False
for model_class in self.all_model_classes:
# Prepare our model
model = model_class(config)
def test_initialization(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Let's load it from the disk to be sure we can use pretrained weights
with TemporaryDirectory() as tmpdirname:
outputs = model(inputs_dict) # build the model
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
# configs_no_init = _config_zero_init(config)
# for model_class in self.all_model_classes:
# model = model_class(config=configs_no_init)
# for name, param in model.named_parameters():
# if param.requires_grad:
# self.assertIn(param.data.mean().item(), [0.0, 1.0],
# msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
outputs_dict = model(input_ids)
hidden_states = outputs_dict[0]
def test_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Add a dense layer on top to test intetgration with other keras modules
outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
for model_class in self.all_model_classes:
model = model_class(config)
outputs = model(inputs_dict)
# Compile extended model
extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
with TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
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 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_keyword_and_dict_args(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def test_pt_tf_model_equivalence(self):
if not is_torch_available():
return
for model_class in self.all_model_classes:
model = model_class(config)
outputs_dict = model(inputs_dict)
import torch
import transformers
inputs_keywords = copy.deepcopy(inputs_dict)
input_ids = inputs_keywords.pop(
"input_ids" if not self.is_encoder_decoder else "decoder_input_ids", None
)
outputs_keywords = model(input_ids, **inputs_keywords)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
for model_class in self.all_model_classes:
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining
pt_model_class = getattr(transformers, pt_model_class_name)
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
config.output_hidden_states = True
tf_model = model_class(config)
pt_model = pt_model_class(config)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
decoder_seq_length = (
self.model_tester.decoder_seq_length
if hasattr(self.model_tester, "decoder_seq_length")
else self.model_tester.seq_length
)
encoder_seq_length = (
self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "encoder_seq_length")
else self.model_tester.seq_length
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
decoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else decoder_seq_length
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict, training=False)
tf_hidden_states = tfo[0].numpy()
pt_hidden_states = pto[0].numpy()
tf_hidden_states[np.isnan(tf_hidden_states)] = 0
pt_hidden_states[np.isnan(pt_hidden_states)] = 0
max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
self.assertLessEqual(max_diff, 2e-2)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
encoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict)
tfo = tfo[0].numpy()
pto = pto[0].numpy()
tfo[np.isnan(tfo)] = 0
pto[np.isnan(pto)] = 0
max_diff = np.amax(np.abs(tfo - pto))
self.assertLessEqual(max_diff, 2e-2)
def test_compile_tf_model(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if self.is_encoder_decoder:
input_ids = {
"decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"),
"encoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="encoder_input_ids", dtype="int32"),
}
else:
input_ids = tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32")
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
for model_class in self.all_model_classes:
# Prepare our model
model = model_class(config)
# Let's load it from the disk to be sure we can use pretrained weights
with TemporaryDirectory() as tmpdirname:
outputs = model(inputs_dict) # build the model
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
outputs_dict = model(input_ids)
hidden_states = outputs_dict[0]
# Add a dense layer on top to test intetgration with other keras modules
outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
# Compile extended model
extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
def test_keyword_and_dict_args(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
outputs_dict = model(inputs_dict)
inputs_keywords = copy.deepcopy(inputs_dict)
input_ids = inputs_keywords.pop("input_ids" if not self.is_encoder_decoder else "decoder_input_ids", None)
outputs_keywords = model(input_ids, **inputs_keywords)
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
decoder_seq_length = (
self.model_tester.decoder_seq_length
if hasattr(self.model_tester, "decoder_seq_length")
else self.model_tester.seq_length
)
encoder_seq_length = (
self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "encoder_seq_length")
else self.model_tester.seq_length
)
decoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else decoder_seq_length
)
encoder_key_length = (
self.model_tester.key_length if hasattr(self.model_tester, "key_length") else encoder_seq_length
)
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
outputs = model(inputs_dict)
attentions = [t.numpy() for t in outputs[-1]]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
outputs = model(inputs_dict)
attentions = [t.numpy() for t in outputs[-1]]
if self.is_encoder_decoder:
self.assertEqual(out_len % 2, 0)
decoder_attentions = outputs[(out_len // 2) - 1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
if self.is_encoder_decoder:
self.assertEqual(out_len % 2, 0)
decoder_attentions = outputs[(out_len // 2) - 1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
outputs = model(inputs_dict)
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
attentions = [t.numpy() for t in outputs[-1]]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
outputs = model(inputs_dict)
hidden_states = [t.numpy() for t in outputs[-1]]
self.assertEqual(model.config.output_attentions, False)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size]
)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
x = model.get_output_embeddings()
assert x is None or isinstance(x, tf.keras.layers.Layer)
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
outputs = model(inputs_dict)
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
attentions = [t.numpy() for t in outputs[-1]]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
for model_class in self.all_model_classes:
model = model_class(config)
first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
out_1 = first.numpy()
out_2 = second.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))
self.assertLessEqual(max_diff, 1e-5)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
outputs = model(inputs_dict)
hidden_states = [t.numpy() for t in outputs[-1]]
self.assertEqual(model.config.output_attentions, False)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size]
)
def _get_embeds(self, wte, input_ids):
# ^^ In our TF models, the input_embeddings can take slightly different forms,
# so we try a few of them.
# We used to fall back to just synthetically creating a dummy tensor of ones:
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
x = model.get_output_embeddings()
assert x is None or isinstance(x, tf.keras.layers.Layer)
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
out_1 = first.numpy()
out_2 = second.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))
self.assertLessEqual(max_diff, 1e-5)
def _get_embeds(self, wte, input_ids):
# ^^ In our TF models, the input_embeddings can take slightly different forms,
# so we try a few of them.
# We used to fall back to just synthetically creating a dummy tensor of ones:
try:
x = wte(input_ids, mode="embedding")
except Exception:
try:
x = wte(input_ids, mode="embedding")
x = wte([input_ids], mode="embedding")
except Exception:
try:
x = wte([input_ids], mode="embedding")
x = wte([input_ids, None, None, None], mode="embedding")
except Exception:
try:
x = wte([input_ids, None, None, None], mode="embedding")
except Exception:
if hasattr(self.model_tester, "embedding_size"):
x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32)
else:
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
return x
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if hasattr(self.model_tester, "embedding_size"):
x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32)
else:
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
return x
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.is_encoder_decoder:
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
else:
encoder_input_ids = inputs_dict["encoder_input_ids"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
del inputs_dict["encoder_input_ids"]
del inputs_dict["decoder_input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
inputs_dict["inputs_embeds"] = self._get_embeds(wte, input_ids)
else:
encoder_input_ids = inputs_dict["encoder_input_ids"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
del inputs_dict["encoder_input_ids"]
del inputs_dict["decoder_input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs_dict["inputs_embeds"] = self._get_embeds(wte, input_ids)
else:
inputs_dict["encoder_inputs_embeds"] = self._get_embeds(wte, encoder_input_ids)
inputs_dict["decoder_inputs_embeds"] = self._get_embeds(wte, decoder_input_ids)
outputs = model(inputs_dict)
inputs_dict["encoder_inputs_embeds"] = self._get_embeds(wte, encoder_input_ids)
inputs_dict["decoder_inputs_embeds"] = self._get_embeds(wte, decoder_input_ids)
outputs = model(inputs_dict)
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
......
tests/test_modeling_tf_ctrl.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import CTRLConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -26,7 +28,7 @@ if is_tf_available():
@require_tf
class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
class TFCTRLModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel) if is_tf_available() else ()
......
tests/test_modeling_tf_distilbert.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import DistilBertConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import require_tf
......@@ -31,7 +33,7 @@ if is_tf_available():
@require_tf
class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
class TFDistilBertModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
......
tests/test_modeling_tf_gpt2.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import GPT2Config, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -32,7 +34,7 @@ if is_tf_available():
@require_tf
class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel) if is_tf_available() else ()
# all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel) if is_tf_available() else ()
......
tests/test_modeling_tf_openai_gpt.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import OpenAIGPTConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -32,7 +34,7 @@ if is_tf_available():
@require_tf
class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel) if is_tf_available() else ()
......
tests/test_modeling_tf_roberta.py
View file @ 345c23a6
......@@ -19,7 +19,7 @@ import unittest
from transformers import RobertaConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -36,7 +36,7 @@ if is_tf_available():
@require_tf
class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
class TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification) if is_tf_available() else ()
......
tests/test_modeling_tf_t5.py
View file @ 345c23a6
......@@ -14,10 +14,12 @@
# limitations under the License.
from __future__ import absolute_import, division, print_function
import unittest
from transformers import T5Config, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
......@@ -26,7 +28,7 @@ if is_tf_available():
@require_tf
class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
is_encoder_decoder = True
all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if is_tf_available() else ()
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment