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tests: fix pytorch tensor placement errors (#33485)

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This commit fixes the following errors:
* Fix "expected all tensors to be on the same device" error
* Fix "can't convert device type tensor to numpy"

According to pytorch documentation torch.Tensor.numpy(force=False)
performs conversion only if tensor is on CPU (plus few other restrictions)
which is not the case. For our case we need force=True since we just
need a data and don't care about tensors coherency.

Fixes: #33517
See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html

Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
This commit is contained in:
Dmitry Rogozhkin
2024-09-25 04:21:53 -07:00
committed by GitHub
parent 52daf4ec76
commit 5e2916bc14
8 changed files with 29 additions and 26 deletions
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11
tests/models/clip/test_modeling_clip.py
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@@ -848,6 +848,7 @@ class CLIPModelTest(CLIPModelTesterMixin, PipelineTesterMixin, unittest.TestCase
with self.subTest(model_class.__name__):
# load PyTorch class
pt_model = model_class(config).eval()
pt_model.to(torch_device)
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
@@ -881,7 +882,7 @@ class CLIPModelTest(CLIPModelTesterMixin, PipelineTesterMixin, unittest.TestCase
fx_outputs = fx_model(**fx_inputs).to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
self.assert_almost_equals(fx_output, pt_output.numpy(force=True), 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
@@ -892,7 +893,7 @@ class CLIPModelTest(CLIPModelTesterMixin, PipelineTesterMixin, unittest.TestCase
len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
)
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
self.assert_almost_equals(fx_output_loaded, pt_output.numpy(force=True), 4e-2)
# overwrite from common since FlaxCLIPModel returns nested output
# which is not supported in the common test
@@ -921,6 +922,7 @@ class CLIPModelTest(CLIPModelTesterMixin, PipelineTesterMixin, unittest.TestCase
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
pt_model.to(torch_device)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
@@ -940,11 +942,12 @@ class CLIPModelTest(CLIPModelTesterMixin, PipelineTesterMixin, unittest.TestCase
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
self.assert_almost_equals(fx_output, pt_output.numpy(force=True), 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
pt_model_loaded.to(torch_device)
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
@@ -953,7 +956,7 @@ class CLIPModelTest(CLIPModelTesterMixin, PipelineTesterMixin, unittest.TestCase
len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
)
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
self.assert_almost_equals(fx_output, pt_output.numpy(force=True), 4e-2)
@slow
def test_model_from_pretrained(self):