wehub-resource-sync
151 lines
5.3 KiB
Python
151 lines
5.3 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 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_offload_with_offset,
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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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# print(f"{name} full array:\n{arr}")
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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 TestSaveLoadLargeParameters(unittest.TestCase):
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def offload_and_reload(self, data0):
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print_debug_info(data0, "data0 (original)")
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# Create a fixed compute tensor for matmul
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data1 = paddle.arange(0, 100, dtype="float32").reshape([10, 10])
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print_debug_info(data1, "data1 (for compute)")
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loader = create_xpu_async_load()
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# Offload data0 -> pinned memory
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cpu_data, task = async_offload(data0, loader)
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print_debug_info(cpu_data, "cpu_data (after offload)")
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# Do a compute on XPU
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res = paddle.matmul(data1, data1)
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print_debug_info(res, "res (after first compute)")
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# Wait for the offload task to complete (CPU side).
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task.cpu_wait()
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# Reload from pinned memory back to XPU.
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xpu_data, task = async_reload(cpu_data, loader)
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print_debug_info(xpu_data, "xpu_data (after reload)")
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# Do another compute on XPU
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res = paddle.matmul(data1, data1)
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print_debug_info(res, "res (after second compute)")
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# Wait on both the device (XPU) and CPU sides.
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task.xpu_wait()
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task.cpu_wait()
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# Extract numpy arrays and print max differences.
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a = data0.numpy()
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b = cpu_data.numpy()
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c = xpu_data.numpy()
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# print("Max diff (data0 - cpu_data):", np.max(np.abs(a - b)))
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# print("Max diff (data0 - xpu_data):", np.max(np.abs(a - c)))
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np.testing.assert_array_equal(a, b)
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np.testing.assert_array_equal(a, c)
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def test_large_parameters_paddle_save_tensor(self):
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# Create a fixed tensor with known values using linspace.
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arr = np.linspace(0, 1, 50).reshape([10, 5]).astype("float32")
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data0 = paddle.to_tensor(arr, place=paddle.XPUPlace(0))
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print_debug_info(
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data0, "data0 in test_large_parameters_paddle_save_tensor"
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)
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self.offload_and_reload(data0)
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def test_large_parameters_paddle_save_model_weight(self):
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model = paddle.nn.Linear(10, 5)
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data0 = model.weight
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print_debug_info(
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data0,
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"model.weight in test_large_parameters_paddle_save_model_weight",
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)
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self.offload_and_reload(data0)
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def test_offload_with_offset(self):
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loader = create_xpu_async_load()
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# Create a fixed source tensor with all elements equal to 3.14.
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# Since paddle.full() does not accept a place argument in dynamic mode,
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# we first create a NumPy array and then convert it to a tensor on XPU.
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src_arr = np.full([100], 3.14, dtype="float32")
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data1 = paddle.to_tensor(src_arr, place=paddle.XPUPlace(0))
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print_debug_info(data1, "data1 in test_offload_with_offset")
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# Create a destination tensor on CPU (pinned memory) initialized to zeros.
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dst_arr = np.zeros([100], dtype="float32")
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data2 = paddle.to_tensor(dst_arr, place=paddle.XPUPinnedPlace())
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print_debug_info(data2, "data2 in test_offload_with_offset (CPU)")
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# Offload in two segments.
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task1 = async_offload_with_offset(
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src_tensor=data1,
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dst_tensor=data2,
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src_offset=0,
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dst_offset=0,
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offload_size=50,
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async_loader=loader,
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)
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task2 = async_offload_with_offset(
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src_tensor=data1,
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dst_tensor=data2,
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src_offset=50,
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dst_offset=50,
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offload_size=50,
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async_loader=loader,
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)
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# Wait for both tasks.
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task1.xpu_wait()
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task2.cpu_wait()
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print_debug_info(data1, "data1 after offload_with_offset")
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print_debug_info(data2, "data2 after offload_with_offset")
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diff = np.max(np.abs(data1.numpy() - data2.numpy()))
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# print("Max diff (data1 - data2):", diff)
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np.testing.assert_array_equal(data1.numpy(), data2.numpy())
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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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