wehub-resource-sync
238 lines
8.3 KiB
Python
Executable File
238 lines
8.3 KiB
Python
Executable File
# Copyright (c) 2018 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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from paddle import base
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from paddle.base import Program, core, program_guard
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class TestMemcpy_FillConstant(unittest.TestCase):
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def get_prog(self):
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paddle.enable_static()
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main_program = Program()
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with program_guard(main_program):
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pinned_var_name = "tensor@Pinned"
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gpu_var_name = "tensor@GPU"
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pinned_var = main_program.global_block().create_var(
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name=pinned_var_name,
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shape=[10, 10],
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dtype='float32',
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persistable=False,
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stop_gradient=True,
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)
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gpu_var = main_program.global_block().create_var(
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name=gpu_var_name,
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shape=[10, 10],
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dtype='float32',
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persistable=False,
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stop_gradient=True,
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)
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main_program.global_block().append_op(
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type="fill_constant",
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outputs={"Out": gpu_var_name},
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attrs={
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"shape": [10, 10],
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"dtype": gpu_var.dtype,
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"value": 1.0,
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"place_type": 1,
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},
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)
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main_program.global_block().append_op(
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type="fill_constant",
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outputs={"Out": pinned_var_name},
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attrs={
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"shape": [10, 10],
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"dtype": gpu_var.dtype,
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"value": 0.0,
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"place_type": 2,
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},
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)
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return main_program, gpu_var, pinned_var
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def test_gpu_copy_to_pinned(self):
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main_program, gpu_var, pinned_var = self.get_prog()
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main_program.global_block().append_op(
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type='memcpy',
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inputs={'X': gpu_var},
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outputs={'Out': pinned_var},
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attrs={'dst_place_type': 2},
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)
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place = base.CUDAPlace(0)
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exe = base.Executor(place)
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gpu_, pinned_ = exe.run(
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main_program, feed={}, fetch_list=[gpu_var.name, pinned_var.name]
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)
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np.testing.assert_allclose(gpu_, pinned_, rtol=1e-05)
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np.testing.assert_allclose(pinned_, np.ones((10, 10)), rtol=1e-05)
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def test_pinned_copy_gpu(self):
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main_program, gpu_var, pinned_var = self.get_prog()
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main_program.global_block().append_op(
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type='memcpy',
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inputs={'X': pinned_var},
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outputs={'Out': gpu_var},
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attrs={'dst_place_type': 1},
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)
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place = base.CUDAPlace(0)
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exe = base.Executor(place)
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gpu_, pinned_ = exe.run(
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main_program, feed={}, fetch_list=[gpu_var.name, pinned_var.name]
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)
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np.testing.assert_allclose(gpu_, pinned_, rtol=1e-05)
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np.testing.assert_allclose(gpu_, np.zeros((10, 10)), rtol=1e-05)
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def test_hip_copy_bool_value(self):
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if core.is_compiled_with_rocm():
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paddle.enable_static()
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main_program = Program()
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with program_guard(main_program):
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pinned_var_name = "tensor@Pinned"
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gpu_var_name = "tensor@GPU"
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pinned_var = main_program.global_block().create_var(
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name=pinned_var_name,
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shape=[1],
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dtype='bool',
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persistable=False,
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stop_gradient=True,
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)
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gpu_var = main_program.global_block().create_var(
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name=gpu_var_name,
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shape=[1],
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dtype='bool',
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persistable=False,
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stop_gradient=True,
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)
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main_program.global_block().append_op(
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type="fill_constant",
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outputs={"Out": gpu_var_name},
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attrs={
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"shape": [1],
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"dtype": gpu_var.dtype,
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"value": False,
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"place_type": 1,
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},
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)
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main_program.global_block().append_op(
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type="fill_constant",
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outputs={"Out": pinned_var_name},
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attrs={
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"shape": [1],
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"dtype": gpu_var.dtype,
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"value": True,
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"place_type": 2,
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},
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)
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main_program.global_block().append_op(
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type='memcpy',
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inputs={'X': pinned_var},
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outputs={'Out': gpu_var},
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attrs={'dst_place_type': 1},
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)
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place = base.CUDAPlace(0)
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exe = base.Executor(place)
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gpu_, pinned_ = exe.run(
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main_program,
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feed={},
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fetch_list=[gpu_var.name, pinned_var.name],
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)
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expect_value = np.array([1]).astype('bool')
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np.testing.assert_array_equal(gpu_, expect_value)
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else:
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pass
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class TestMemcpyOPError(unittest.TestCase):
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def get_prog(self):
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paddle.enable_static()
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main_program = Program()
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with program_guard(main_program):
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pinned_var = main_program.global_block().create_var(
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name="tensor@Pinned_0",
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shape=[10, 10],
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dtype='float32',
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persistable=False,
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stop_gradient=True,
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)
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main_program.global_block().append_op(
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type="fill_constant",
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outputs={"Out": "tensor@Pinned_0"},
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attrs={
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"shape": [10, 10],
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"dtype": pinned_var.dtype,
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"value": 0.0,
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"place_type": 2,
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},
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)
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return main_program, pinned_var
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def test_SELECTED_ROWS(self):
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main_program, pinned_var = self.get_prog()
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selected_row_var = main_program.global_block().create_var(
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name="selected_row_0",
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dtype="float32",
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persistable=False,
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type=base.core.VarDesc.VarType.SELECTED_ROWS,
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stop_gradient=True,
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)
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main_program.global_block().append_op(
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type="fill_constant",
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outputs={"Out": selected_row_var},
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attrs={
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"shape": selected_row_var.shape,
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"dtype": selected_row_var.dtype,
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"value": 1.0,
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"place_type": 1,
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},
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)
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with self.assertRaises(RuntimeError):
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main_program.global_block().append_op(
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type='memcpy',
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inputs={'X': selected_row_var},
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outputs={'Out': pinned_var},
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attrs={'dst_place_type': 2},
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)
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place = base.CUDAPlace(0)
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exe = base.Executor(place)
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selected_row_var_, pinned_ = exe.run(
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main_program,
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feed={},
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fetch_list=[selected_row_var.name, pinned_var.name],
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)
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class TestMemcpyApi(unittest.TestCase):
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def test_api(self):
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# Disable static graph mode for this test
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paddle.disable_static()
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try:
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a = paddle.ones([1024, 1024])
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b = paddle.tensor.creation._memcpy(a, paddle.CUDAPinnedPlace())
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# Test that memcpy operation succeeded by checking data equality
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np.testing.assert_array_equal(a.numpy(), b.numpy())
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# Test that the tensor was created successfully
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self.assertEqual(a.shape, b.shape)
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self.assertEqual(a.dtype, b.dtype)
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finally:
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# Re-enable static graph mode
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paddle.enable_static()
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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