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101 lines
3.6 KiB
101 lines
3.6 KiB
# 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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import numpy as np
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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class TestXPUElementwiseOpBase(object):
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def setUp(self, op_type):
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self.op_type = op_type
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self.attrs = {'use_xpu': True}
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self.is_common_broadcast = False
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self.is_x_size_less_than_y = False
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self.grad_implemented = False
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self.y_grad_implemented = True
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self.dtype = np.float32
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self.__class__.op_type = self.op_type
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self.__class__.use_xpu = True
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self.__class__.dtype = self.dtype
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def net(self, place):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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x = fluid.layers.data(
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name='X', shape=self.inputs['X'].shape, dtype=self.dtype)
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y = fluid.layers.data(
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name='Y', shape=self.inputs['Y'].shape, dtype=self.dtype)
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op = getattr(fluid.layers, self.op_type)
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z = op(x, y)
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exe = fluid.Executor(place)
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z_value = exe.run(feed=self.inputs, fetch_list=[z.name])
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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if not self.is_common_broadcast and not self.is_x_size_less_than_y:
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self.check_output_with_place(place, atol=1e-3)
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else:
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with self.assertRaises(BaseException):
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self.net(place)
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def _check_grad_xpu_helper(self,
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inputs_to_check,
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output_names,
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no_grad_set=None,
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max_relative_error=0.01):
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if self.grad_implemented and not self.is_common_broadcast \
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and not self.is_x_size_less_than_y:
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(
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place,
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inputs_to_check,
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output_names,
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no_grad_set=no_grad_set,
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max_relative_error=max_relative_error)
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def test_check_grad_normal(self):
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self._check_grad_xpu_helper(['X', 'Y'], 'Out')
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def test_check_grad_ingore_x(self):
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self._check_grad_xpu_helper(['Y'], 'Out', set("X"))
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def test_check_grad_ingore_y(self):
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if self.y_grad_implemented:
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self._check_grad_xpu_helper(['X'], 'Out', set("Y"))
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def init_axis(self):
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self.axis = -1
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def make_input(self, x_shape=[13, 17], y_shape=[13, 17]):
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self.inputs = {
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'X': np.random.uniform(0.1, 1, x_shape).astype(self.dtype),
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'Y': np.random.uniform(0.1, 1, y_shape).astype(self.dtype)
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}
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def reshape_input(self, x_shape=None, y_shape=None):
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if x_shape is None:
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x = self.inputs['X']
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else:
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x = self.inputs['X'].reshape(x_shape)
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if y_shape is None:
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y = self.inputs['Y']
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else:
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y = self.inputs['Y'].reshape(y_shape)
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return x, y
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def make_output(self, x_shape=None, y_shape=None):
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pass
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