wehub-resource-sync
76 lines
2.6 KiB
Python
76 lines
2.6 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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import time
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import unittest
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import numpy as np
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import paddle
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# Disable static mode so that .numpy() can be called.
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paddle.disable_static()
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from paddle.incubate.tensor.manipulation import (
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async_offload,
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async_reload,
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create_xpu_async_load,
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)
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def print_debug_info(tensor, name):
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"""Prints debug information for a tensor."""
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# print(f"{name} is on device: {tensor.place}")
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# print(f"{name} shape: {tensor.shape}, dtype: {tensor.dtype}")
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try:
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arr = tensor.numpy()
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flat = arr.flatten()
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# print(f"{name} first 5 elements: {flat[:5]}")
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except Exception as e:
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# print(f"{name} cannot be converted to numpy array: {e}")
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raise
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class TestLargeTensorOffloadAndReloadRepeated(unittest.TestCase):
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def test_large_data_performance_repeated(self):
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# Repeat the offload and reload process 100 times.
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for i in range(1):
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# print(f"\n--- Iteration {i+1} ---")
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# Create a large tensor on XPU.
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large_arr = np.empty((512, 512, 1000), dtype="float32")
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large_tensor = paddle.to_tensor(large_arr, place=paddle.XPUPlace(0))
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print_debug_info(large_tensor, "large_tensor (original)")
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loader = create_xpu_async_load()
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# Offload the tensor.
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t0 = time.time()
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cpu_large, task_offload = async_offload(large_tensor, loader)
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task_offload.cpu_wait() # Wait for offload completion.
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t1 = time.time()
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offload_time = t1 - t0
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# print(f"Offload time: {offload_time:.4f} seconds")
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# Reload the tensor.
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t2 = time.time()
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xpu_large, task_reload = async_reload(cpu_large, loader)
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task_reload.cpu_wait() # Wait for reload completion.
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t3 = time.time()
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reload_time = t3 - t2
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# print(f"Reload time: {reload_time:.4f} seconds")
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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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