wehub-resource-sync
340 lines
14 KiB
Python
340 lines
14 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import get_device_place
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from utils import dygraph_guard
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import paddle
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from paddle import base
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@unittest.skipIf(
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paddle.core.is_compiled_with_xpu(),
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"xpu does not support dlpack",
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)
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class TestDLPack(unittest.TestCase):
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def test_dlpack_dygraph(self):
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if paddle.is_compiled_with_cuda():
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with dygraph_guard():
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tensor = paddle.to_tensor(np.array([1, 2, 3, 4]).astype("int"))
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(tensor)
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out_from_dlpack_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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dlpack_v2 = tensor.__dlpack__()
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out_from_dlpack_v2 = paddle.from_dlpack(dlpack_v2)
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self.assertTrue(
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isinstance(
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out_from_dlpack_v1, paddle.base.core.eager.Tensor
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)
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)
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self.assertTrue(
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isinstance(
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out_from_dlpack_v2, paddle.base.core.eager.Tensor
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)
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)
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self.assertEqual(
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str(tensor.place), str(out_from_dlpack_v1.place)
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)
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self.assertEqual(
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str(tensor.place), str(out_from_dlpack_v2.place)
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)
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np.testing.assert_array_equal(
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out_from_dlpack_v1.numpy(),
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np.array([1, 2, 3, 4]).astype("int"),
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)
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np.testing.assert_array_equal(
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out_from_dlpack_v2.numpy(),
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np.array([1, 2, 3, 4]).astype("int"),
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)
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def test_dlpack_tensor_larger_than_2dim(self):
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if paddle.is_compiled_with_cuda():
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with dygraph_guard():
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numpy_data = np.random.randn(4, 5, 6)
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t = paddle.to_tensor(numpy_data)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(t)
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dlpack_v2 = t.__dlpack__()
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out_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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out_v2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(str(t.place), str(out_v1.place))
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self.assertEqual(str(t.place), str(out_v2.place))
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np.testing.assert_allclose(
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numpy_data, out_v1.numpy(), rtol=1e-05
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)
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np.testing.assert_allclose(
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numpy_data, out_v2.numpy(), rtol=1e-05
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)
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def test_dlpack_dtype_and_place_consistency(self):
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with dygraph_guard():
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dtypes = [
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"float16",
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"float32",
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"float64",
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"int8",
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"int16",
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"int32",
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"int64",
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"uint8",
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"bool",
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]
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places = [paddle.CPUPlace()]
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if paddle.device.is_compiled_with_cuda():
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places.append(get_device_place())
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dtypes.append("bfloat16")
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data = np.ones((2, 3, 4))
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for place in places:
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for dtype in dtypes:
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x = paddle.to_tensor(data, dtype=dtype, place=place)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x)
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o_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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dlpack_v2 = x.__dlpack__()
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o_v2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(x.dtype, o_v1.dtype)
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self.assertEqual(x.dtype, o_v2.dtype)
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np.testing.assert_allclose(
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x.numpy(), o_v1.numpy(), rtol=1e-05
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)
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np.testing.assert_allclose(
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x.numpy(), o_v2.numpy(), rtol=1e-05
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)
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self.assertEqual(str(x.place), str(o_v1.place))
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self.assertEqual(str(x.place), str(o_v2.place))
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complex_dtypes = ["complex64", "complex128"]
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for place in places:
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for dtype in complex_dtypes:
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x = paddle.to_tensor(
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[[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]],
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dtype=dtype,
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place=place,
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)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x)
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o_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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dlpack_v2 = x.__dlpack__()
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o_v2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(x.dtype, o_v1.dtype)
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self.assertEqual(x.dtype, o_v2.dtype)
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np.testing.assert_allclose(
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x.numpy(), o_v1.numpy(), rtol=1e-05
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)
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np.testing.assert_allclose(
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x.numpy(), o_v2.numpy(), rtol=1e-05
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)
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self.assertEqual(str(x.place), str(o_v1.place))
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self.assertEqual(str(x.place), str(o_v2.place))
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def test_dlpack_deletion(self):
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# See Paddle issue 47171
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with dygraph_guard():
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(get_device_place())
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for place in places:
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for _ in range(4):
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a = paddle.rand(shape=[3, 5], dtype="float32").to(
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device=place
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)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(a)
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dlpack_v2 = a.__dlpack__()
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b1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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b2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(str(a.place), str(b1.place))
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self.assertEqual(str(a.place), str(b2.place))
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def test_to_dlpack_for_loop(self):
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# See Paddle issue 50120
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with dygraph_guard():
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(get_device_place())
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for place in places:
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for _ in range(4):
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x = paddle.rand([3, 5]).to(device=place)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x)
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dlpack_v2 = x.__dlpack__()
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def test_to_dlpack_modification(self):
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# See Paddle issue 50120
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with dygraph_guard():
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(get_device_place())
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for place in places:
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for _ in range(4):
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x = paddle.rand([3, 5]).to(device=place)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x)
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dlpack_v2 = x.__dlpack__()
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y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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y2 = paddle.from_dlpack(dlpack_v2)
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y1[1:2, 2:5] = 2.0
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y2[1:2, 2:5] = 2.0
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np.testing.assert_allclose(x.numpy(), y1.numpy())
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np.testing.assert_allclose(x.numpy(), y2.numpy())
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self.assertEqual(str(x.place), str(y1.place))
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self.assertEqual(str(x.place), str(y2.place))
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def test_to_dlpack_data_ptr_consistency(self):
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# See Paddle issue 50120
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with dygraph_guard():
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(get_device_place())
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for place in places:
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for _ in range(4):
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x = paddle.rand([3, 5]).to(device=place)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x)
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dlpack_v2 = x.__dlpack__()
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y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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y2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(x.data_ptr(), y1.data_ptr())
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self.assertEqual(x.data_ptr(), y2.data_ptr())
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self.assertEqual(str(x.place), str(y1.place))
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self.assertEqual(str(x.place), str(y2.place))
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def test_to_dlpack_strides_consistency(self):
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with dygraph_guard():
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(get_device_place())
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for place in places:
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for _ in range(4):
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x = paddle.rand([10, 10]).to(device=place)
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x_strided = x[::2, ::2]
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x_strided)
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dlpack_v2 = x_strided.__dlpack__()
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y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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y2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(x_strided.strides, y1.strides)
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self.assertEqual(x_strided.strides, y2.strides)
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self.assertEqual(str(x_strided.place), str(y1.place))
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self.assertEqual(str(x_strided.place), str(y2.place))
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np.testing.assert_equal(x_strided.numpy(), y1.numpy())
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np.testing.assert_equal(x_strided.numpy(), y2.numpy())
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def test_to_dlpack_from_zero_dim(self):
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with dygraph_guard():
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(get_device_place())
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for place in places:
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for _ in range(4):
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x = paddle.to_tensor(1.0, place=place)
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x)
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dlpack_v2 = x.__dlpack__()
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y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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y2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(x.data_ptr(), y1.data_ptr())
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self.assertEqual(x.data_ptr(), y2.data_ptr())
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self.assertEqual(str(x.place), str(y1.place))
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self.assertEqual(str(x.place), str(y2.place))
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self.assertEqual(y1.shape, [])
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self.assertEqual(y2.shape, [])
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self.assertEqual(y1.numel().item(), 1)
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self.assertEqual(y2.numel().item(), 1)
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np.testing.assert_array_equal(x.numpy(), y1.numpy())
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np.testing.assert_array_equal(x.numpy(), y2.numpy())
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def test_to_dlpack_from_zero_size(self):
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with dygraph_guard():
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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places.append(get_device_place())
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for place in places:
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for _ in range(4):
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x = paddle.zeros([0, 10]).to(device=place)
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self.assertEqual(x.strides, [10, 1])
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dlpack_v1 = paddle.utils.dlpack.to_dlpack(x)
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dlpack_v2 = x.__dlpack__()
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y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1)
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y2 = paddle.from_dlpack(dlpack_v2)
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self.assertEqual(x.data_ptr(), y1.data_ptr())
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self.assertEqual(x.data_ptr(), y2.data_ptr())
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self.assertEqual(str(x.place), str(y1.place))
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self.assertEqual(str(x.place), str(y2.place))
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self.assertEqual(y1.strides, [10, 1])
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self.assertEqual(y2.strides, [10, 1])
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self.assertEqual(y1.shape, [0, 10])
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self.assertEqual(y2.shape, [0, 10])
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self.assertEqual(y1.numel().item(), 0)
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self.assertEqual(y2.numel().item(), 0)
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np.testing.assert_array_equal(x.numpy(), y1.numpy())
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np.testing.assert_array_equal(x.numpy(), y2.numpy())
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def test_dlpack_with_custom_stream(self):
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if not (paddle.is_compiled_with_cuda()):
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self.skipTest("Test requires CUDA support.")
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with dygraph_guard():
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paddle.set_device('gpu:0')
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s1 = paddle.device.Stream()
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s2 = paddle.device.Stream()
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e = paddle.device.Event()
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s2.wait_event(e)
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x = paddle.to_tensor([1, 2, 3], dtype='float32')
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s1.synchronize()
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dlpack_capsule = x.__dlpack__(stream=s1.stream_base.raw_stream)
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y = paddle.from_dlpack(dlpack_capsule)
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np.testing.assert_array_equal(x.numpy(), y.numpy())
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self.assertTrue(s1.query(), "Stream s1 did not complete all tasks.")
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self.assertTrue(s2.query(), "Stream s2 did not complete all tasks.")
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def test_dlpack_with_custom_stream_error(self):
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if not (paddle.is_compiled_with_cuda()):
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self.skipTest("Test requires CUDA support.")
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with dygraph_guard():
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x = paddle.to_tensor([1, 2, 3], dtype='float32')
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with self.assertRaisesRegex(
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TypeError, "stream must be an integer or None."
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):
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dlpack_capsule = x.__dlpack__(stream=object())
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with self.assertRaisesRegex(
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ValueError, "For CUDA, stream=0 is ambiguityous"
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):
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dlpack_capsule = x.__dlpack__(stream=0)
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with self.assertRaisesRegex(
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ValueError,
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"For CUDA, stream=2 means per-thread default stream, which is not supported.",
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):
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dlpack_capsule = x.__dlpack__(stream=2)
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@unittest.skipIf(
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paddle.core.is_compiled_with_xpu(),
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"xpu does not support dlpack",
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)
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class TestRaiseError(unittest.TestCase):
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def test_dlpack_invalid_sparse(self):
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sparse_tensor = paddle.sparse.sparse_coo_tensor(
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indices=[[0]], values=[1], shape=[3]
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)
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with self.assertRaises(BufferError):
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sparse_tensor.__dlpack__()
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def test_dlpack_requires_grad(self):
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tensor_with_grad = paddle.to_tensor(
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[1.0, 2.0, 3.0], stop_gradient=False
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)
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with self.assertRaises(BufferError):
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tensor_with_grad.__dlpack__()
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if __name__ == "__main__":
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unittest.main()
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