add fluid.device_guard to specify the device type for Op (#22254)
* add fluid.device_guard to specify the device type for Oprevert-22710-feature/integrated_ps_api
Zhang Ting
5 years ago
committed by
GitHub
parent
063c51c748
commit
4e8bc02461
@ -0,0 +1,149 @@
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# Copyright (c) 2020 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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from __future__ import print_function
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import unittest
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from op_test import OpTest
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import warnings
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def execute(main_program, startup_program):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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else:
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place = core.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_program)
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exe.run(main_program)
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class TestDeviceGuard(unittest.TestCase):
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def test_device_guard(self):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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data1 = fluid.layers.fill_constant(
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shape=[1, 3, 8, 8], value=0.5, dtype='float32')
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data2 = fluid.layers.fill_constant(
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shape=[1, 3, 5, 5], value=0.5, dtype='float32')
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shape = fluid.layers.shape(data2)
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with fluid.device_guard("cpu"):
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shape = fluid.layers.slice(
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shape, axes=[0], starts=[0], ends=[4])
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with fluid.device_guard("gpu"):
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out = fluid.layers.crop_tensor(data1, shape=shape)
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# check if the device attr is set correctly
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all_ops = main_program.global_block().ops
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device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
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for op in all_ops:
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if op.type == 'slice':
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self.assertEqual(op.desc.attr(device_attr_name), "cpu")
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if op.type == 'crop_tensor':
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self.assertEqual(op.desc.attr(device_attr_name), "gpu")
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execute(main_program, startup_program)
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def test_cpu_only_op(self):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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x = fluid.layers.fill_constant(
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shape=[2, 255, 13, 13], value=0.3, dtype='float32')
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gt_box = fluid.layers.fill_constant(
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shape=[2, 6, 4], value=0.5, dtype='float32')
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gt_label = fluid.layers.fill_constant(
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shape=[2, 6], value=1.0, dtype='int32')
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gt_score = fluid.layers.fill_constant(
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shape=[2, 6], value=0.5, dtype='float32')
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anchors = [
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10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156,
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198, 373, 326
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]
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anchor_mask = [0, 1, 2]
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with fluid.device_guard("gpu"):
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# yolov3_loss only has cpu kernel, so its cpu kernel will be executed
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loss = fluid.layers.yolov3_loss(
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x=x,
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gt_box=gt_box,
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gt_label=gt_label,
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gt_score=gt_score,
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anchors=anchors,
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anchor_mask=anchor_mask,
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class_num=80,
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ignore_thresh=0.7,
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downsample_ratio=32)
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execute(main_program, startup_program)
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def test_without_kernel_op(self):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
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loop_len = fluid.layers.fill_constant(
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shape=[1], dtype='int64', value=10)
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cond = fluid.layers.less_than(x=i, y=loop_len)
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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with fluid.device_guard("cpu"):
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while_op = fluid.layers.While(cond=cond)
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with while_op.block():
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i = fluid.layers.increment(x=i, value=1, in_place=True)
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fluid.layers.less_than(x=i, y=loop_len, cond=cond)
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assert len(w) == 1
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all_ops = main_program.global_block().ops
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device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
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for op in all_ops:
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if op.type == 'while':
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self.assertEqual(op.desc.attr(device_attr_name), "")
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execute(main_program, startup_program)
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def test_error(self):
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def device_attr():
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with fluid.device_guard("cpu1"):
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out = fluid.layers.fill_constant(
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shape=[1], value=0.2, dtype='float32')
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self.assertRaises(ValueError, device_attr)
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def test_warning(self):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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with fluid.device_guard("gpu"):
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x = fluid.layers.fill_constant(
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shape=[1], value=3.0, dtype='float32', force_cpu=True)
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y = fluid.layers.fill_constant(
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shape=[1], value=4.0, dtype='float32')
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result = fluid.layers.less_than(x=x, y=y, force_cpu=False)
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assert len(w) == 2
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all_ops = main_program.global_block().ops
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device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
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for op in all_ops:
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self.assertEqual(op.desc.attr(device_attr_name), "gpu")
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if __name__ == '__main__':
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unittest.main()
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