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update pytorch layer tests for torch 2.1 compatibility (#20264)

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* update pytorch layer tests for torch 2.1 compatibility
This commit is contained in:
Ekaterina Aidova
2023-10-30 08:30:01 +04:00
committed by GitHub
parent 539b5a83ba
commit 53c9a0f3d4
4 changed files with 44 additions and 5 deletions
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2
tests/constraints.txt
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@@ -23,4 +23,4 @@ pytest-html==3.2.0
pytest-timeout==2.1.0
jax<=0.4.14
jaxlib<=0.4.14
torch<2.1.0,>=1.13
torch>=1.13

18
tests/layer_tests/pytorch_tests/test_masked_fill.py
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@@ -2,6 +2,8 @@
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import torch
from packaging.version import parse as parse_version
import pytest
from pytorch_layer_test_class import PytorchLayerTest
@@ -48,7 +50,7 @@ class TestMaskedFill(PytorchLayerTest):
@pytest.mark.parametrize(
"mask_fill", ['zeros', 'ones', 'random'])
@pytest.mark.parametrize("input_dtype", [np.float32, np.float64, int, np.int32])
@pytest.mark.parametrize("mask_dtype", [np.uint8, np.int32, bool]) # np.float32 incorrectly casted to bool
@pytest.mark.parametrize("mask_dtype", [bool]) # np.float32 incorrectly casted to bool
@pytest.mark.parametrize("inplace", [True, False])
@pytest.mark.nightly
@pytest.mark.precommit
@@ -56,3 +58,17 @@ class TestMaskedFill(PytorchLayerTest):
self._test(*self.create_model(value, inplace),
ie_device, precision, ir_version,
kwargs_to_prepare_input={'mask_fill': mask_fill, 'mask_dtype': mask_dtype, "input_dtype": input_dtype})
@pytest.mark.skipif(parse_version(torch.__version__) >= parse_version("2.1.0"), reason="pytorch 2.1 and above does not support nonboolean mask")
@pytest.mark.parametrize("value", [0.0, 1.0, -1.0, 2])
@pytest.mark.parametrize(
"mask_fill", ['zeros', 'ones', 'random'])
@pytest.mark.parametrize("input_dtype", [np.float32, np.float64, int, np.int32])
@pytest.mark.parametrize("mask_dtype", [np.uint8, np.int32]) # np.float32 incorrectly casted to bool
@pytest.mark.parametrize("inplace", [True, False])
@pytest.mark.nightly
@pytest.mark.precommit
def test_masked_fill_non_bool_mask(self, value, mask_fill, mask_dtype, input_dtype, inplace, ie_device, precision, ir_version):
self._test(*self.create_model(value, inplace),
ie_device, precision, ir_version,
kwargs_to_prepare_input={'mask_fill': mask_fill, 'mask_dtype': mask_dtype, "input_dtype": input_dtype})

27
tests/layer_tests/pytorch_tests/test_masked_scatter.py
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@@ -2,6 +2,8 @@
# SPDX-License-Identifier: Apache-2.0
import pytest
import torch
from packaging.version import parse as parse_version
from pytorch_layer_test_class import PytorchLayerTest
@@ -45,7 +47,7 @@ class TestMaskedScatter(PytorchLayerTest):
@pytest.mark.precommit
@pytest.mark.parametrize("shape", [[2, 5], [10, 10], [2, 3, 4], [10, 5, 10, 3], [2, 6, 4, 1]])
@pytest.mark.parametrize("input_dtype", ["float32", "int32", "float", "int", "uint8"])
@pytest.mark.parametrize("mask_dtype", ["bool", "uint8"])
@pytest.mark.parametrize("mask_dtype", ["bool"])
@pytest.mark.parametrize("out", [True, False])
def test_masked_scatter(self, shape, input_dtype, mask_dtype, out, ie_device, precision, ir_version):
self._test(*self.create_model(out), ie_device, precision, ir_version,
@@ -55,7 +57,28 @@ class TestMaskedScatter(PytorchLayerTest):
@pytest.mark.precommit
@pytest.mark.parametrize("shape", [[2, 5], [10, 10], [2, 3, 4], [10, 5, 10, 3], [2, 6, 4, 1]])
@pytest.mark.parametrize("input_dtype", ["float32", "int32", "float", "int", "uint8"])
@pytest.mark.parametrize("mask_dtype", ["bool", "uint8"])
@pytest.mark.parametrize("mask_dtype", ["bool"])
def test_masked_scatter_inplace(self, shape, input_dtype, mask_dtype, ie_device, precision, ir_version):
self._test(*self.create_model(inplace=True), ie_device, precision, ir_version,
kwargs_to_prepare_input={"shape": shape, "x_dtype": input_dtype, "mask_dtype": mask_dtype})
@pytest.mark.skipif(parse_version(torch.__version__) >= parse_version("2.1.0"), reason="pytorch 2.1 and above does not support nonboolean mask")
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.parametrize("shape", [[2, 5], [10, 10], [2, 3, 4], [10, 5, 10, 3], [2, 6, 4, 1]])
@pytest.mark.parametrize("input_dtype", ["float32", "int32", "float", "int", "uint8"])
@pytest.mark.parametrize("mask_dtype", ["uint8"])
@pytest.mark.parametrize("out", [True, False])
def test_masked_scatter_u8(self, shape, input_dtype, mask_dtype, out, ie_device, precision, ir_version):
self._test(*self.create_model(out), ie_device, precision, ir_version,
kwargs_to_prepare_input={"shape": shape, "x_dtype": input_dtype, "mask_dtype": mask_dtype, "out": out})
@pytest.mark.skipif(parse_version(torch.__version__) >= parse_version("2.1.0"), reason="pytorch 2.1 and above does not support nonboolean mask")
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.parametrize("shape", [[2, 5], [10, 10], [2, 3, 4], [10, 5, 10, 3], [2, 6, 4, 1]])
@pytest.mark.parametrize("input_dtype", ["float32", "int32", "float", "int", "uint8"])
@pytest.mark.parametrize("mask_dtype", ["uint8"])
def test_masked_scatter_inplace_u8(self, shape, input_dtype, mask_dtype, ie_device, precision, ir_version):
self._test(*self.create_model(inplace=True), ie_device, precision, ir_version,
kwargs_to_prepare_input={"shape": shape, "x_dtype": input_dtype, "mask_dtype": mask_dtype})

2
tests/model_hub_tests/torch_tests/requirements.txt
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@@ -2,7 +2,7 @@
numpy
pytest
pytest-html
torch
torch<2.1
torchvision
av
transformers