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Add tests for batching support (#29297)

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* add tests for batching support

* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update tests/test_modeling_common.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update tests/test_modeling_common.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update tests/test_modeling_common.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* fixes and comments

* use cosine distance for conv models

* skip mra model testing

* Update tests/models/vilt/test_modeling_vilt.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* finzalize  and make style

* check model type by input names

* Update tests/models/vilt/test_modeling_vilt.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* fixed batch size for all testers

* Revert "fixed batch size for all testers"

This reverts commit 525f3a0a058f069fbda00352cf202b728d40df99.

* add batch_size for all testers

* dict from model output

* do not skip layoutlm

* bring back some code from git revert

* Update tests/test_modeling_common.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/test_modeling_common.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* clean-up

* where did minus go in tolerance

* make whisper happy

* deal with consequences of losing minus

* deal with consequences of losing minus

* maskformer needs its own test for happiness

* fix more models

* tag flaky CV models from Amy's approval

* make codestyle

---------

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Raushan Turganbay
2024-03-12 22:46:19 +05:00
committed by GitHub
parent 11163fff58
commit 8e64ba2890
48 changed files with 350 additions and 67 deletions
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42
tests/models/univnet/test_modeling_univnet.py
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@@ -66,13 +66,13 @@ class UnivNetModelTester:
def prepare_noise_sequence(self):
generator = torch.manual_seed(self.seed)
noise_shape = (self.seq_length, self.in_channels)
noise_shape = (self.batch_size, self.seq_length, self.in_channels)
# Create noise on CPU for reproducibility
noise_sequence = torch.randn(noise_shape, generator=generator, dtype=torch.float)
return noise_sequence
def prepare_config_and_inputs(self):
spectrogram = floats_tensor([self.seq_length, self.num_mel_bins], scale=1.0)
spectrogram = floats_tensor([self.batch_size, self.seq_length, self.num_mel_bins], scale=1.0)
noise_sequence = self.prepare_noise_sequence()
noise_sequence = noise_sequence.to(spectrogram.device)
config = self.get_config()
@@ -89,7 +89,7 @@ class UnivNetModelTester:
def create_and_check_model(self, config, spectrogram, noise_sequence):
model = UnivNetModel(config=config).to(torch_device).eval()
result = model(spectrogram, noise_sequence)[0]
self.parent.assertEqual(result.shape, (1, self.seq_length * 256))
self.parent.assertEqual(result.shape, (self.batch_size, self.seq_length * 256))
def prepare_config_and_inputs_for_common(self):
config, spectrogram, noise_sequence = self.prepare_config_and_inputs()
@@ -182,8 +182,8 @@ class UnivNetModelTest(ModelTesterMixin, unittest.TestCase):
model.to(torch_device)
model.eval()
batched_spectrogram = inputs["input_features"].unsqueeze(0).repeat(2, 1, 1)
batched_noise_sequence = inputs["noise_sequence"].unsqueeze(0).repeat(2, 1, 1)
batched_spectrogram = inputs["input_features"]
batched_noise_sequence = inputs["noise_sequence"]
with torch.no_grad():
batched_outputs = model(
batched_spectrogram.to(torch_device),
@@ -205,37 +205,11 @@ class UnivNetModelTest(ModelTesterMixin, unittest.TestCase):
model.eval()
with torch.no_grad():
outputs = model(inputs["input_features"].to(torch_device), inputs["noise_sequence"].to(torch_device))[
0
]
outputs = model(
inputs["input_features"][:1].to(torch_device), inputs["noise_sequence"][:1].to(torch_device)
)[0]
self.assertTrue(outputs.shape[0] == 1, msg="Unbatched input should create batched output with bsz = 1")
def test_unbatched_batched_outputs_consistency(self):
config, inputs = 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()
unbatched_spectrogram = inputs["input_features"].detach().clone()
unbatched_noise_sequence = inputs["noise_sequence"].detach().clone()
batched_spectrogram = inputs["input_features"].unsqueeze(0)
batched_noise_sequence = inputs["noise_sequence"].unsqueeze(0)
with torch.no_grad():
unbatched_outputs = model(
unbatched_spectrogram.to(torch_device),
unbatched_noise_sequence.to(torch_device),
)[0]
batched_outputs = model(
batched_spectrogram.to(torch_device),
batched_noise_sequence.to(torch_device),
)[0]
torch.testing.assert_close(unbatched_outputs, batched_outputs)
@require_torch_gpu
@slow