wehub-resource-sync
83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
# Copyright (c) 2025 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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#
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# (c) 2023-2025 PaddlePaddle Authors
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import unittest
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import numpy as np
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import paddle
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if not hasattr(paddle, "XPUPinnedPlace"):
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from paddle.base.core import XPUPinnedPlace as _XPUPinnedPlace
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paddle.XPUPinnedPlace = lambda: _XPUPinnedPlace(0)
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def print_debug_info(tensor, name):
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"""Prints the device placement of a tensor."""
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# print(f"{name} is on device: {tensor.place}")
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pass
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class TestXPUPinnedToCpuCopy(unittest.TestCase):
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def test_copy_from_xpu_pinned_to_cpu(self):
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# Create a sample numpy array.
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arr = np.random.rand(10, 10).astype('float32')
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# Create an XPU pinned memory place using the same interface as GPU pinned memory.
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xpu_pinned_place = paddle.XPUPinnedPlace()
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# Create a tensor in XPU pinned memory.
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tensor_pinned = paddle.to_tensor(arr, place=paddle.XPUPinnedPlace())
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# print_debug_info(tensor_pinned, "tensor_pinned (XPU pinned)")
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# Since tensor.copy_to() is not available, copy the tensor by converting to NumPy and back.
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tensor_cpu = paddle.to_tensor(
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tensor_pinned.numpy(), place=paddle.CPUPlace()
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)
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# print_debug_info(tensor_cpu, "tensor_cpu (after copy to CPU)")
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# Verify that the destination tensor is on CPU.
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self.assertIn("cpu", str(tensor_cpu.place))
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# Check correctness: ensure the data remains unchanged after the copy.
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np.testing.assert_array_equal(tensor_cpu.numpy(), arr)
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def test_copy_from_xpu_to_xpu_pinned(self):
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# Create a sample numpy array.
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arr = np.random.rand(10, 10).astype('float32')
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# Create a tensor on an XPU device.
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tensor_xpu = paddle.to_tensor(arr, place=paddle.XPUPlace(0))
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# print_debug_info(tensor_xpu, "tensor_xpu (XPU)")
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# Copy the tensor from XPU to XPU pinned memory by converting to NumPy and back.
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tensor_xpu_pinned = paddle.to_tensor(
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tensor_xpu.numpy(), place=paddle.XPUPinnedPlace()
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)
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# print_debug_info(tensor_xpu_pinned, "tensor_xpu_pinned (after copy to XPU pinned)")
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# Verify that the destination tensor is on XPU pinned memory.
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self.assertIn("pinned", str(tensor_xpu_pinned.place).lower())
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# Check correctness: ensure the data remains unchanged after the copy.
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np.testing.assert_array_equal(tensor_xpu_pinned.numpy(), arr)
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if __name__ == '__main__':
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# print("Default Paddle device:", paddle.get_device())
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unittest.main()
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