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141 lines
4.4 KiB
141 lines
4.4 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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class TestScaleOp(OpTest):
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def setUp(self):
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self.op_type = "scale"
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self.dtype = np.float32
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self.init_dtype_type()
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self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)}
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self.attrs = {'scale': -2.3}
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self.outputs = {
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'Out': self.inputs['X'] * self.dtype(self.attrs['scale'])
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}
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def init_dtype_type(self):
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pass
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestScaleOpSelectedRows(unittest.TestCase):
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def init_dtype_type(self):
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pass
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def check_with_place(self, place, in_name, out_name):
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scope = core.Scope()
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self.dtype = np.float32
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self.init_dtype_type()
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# create and initialize Grad Variable
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in_height = 10
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in_rows = [0, 4, 7]
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in_row_numel = 12
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scale = 2.0
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in_selected_rows = scope.var(in_name).get_selected_rows()
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in_selected_rows.set_height(in_height)
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in_selected_rows.set_rows(in_rows)
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in_array = np.random.random(
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(len(in_rows), in_row_numel)).astype(self.dtype)
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in_tensor = in_selected_rows.get_tensor()
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in_tensor.set(in_array, place)
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# create and initialize Param Variable
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out_selected_rows = scope.var(out_name).get_selected_rows()
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out_tensor = out_selected_rows.get_tensor()
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out_tensor._set_dims(in_tensor._get_dims())
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# create and run sgd operator
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scale_op = Operator("scale", X=in_name, Out=out_name, scale=scale)
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scale_op.run(scope, place)
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# get and compare result
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out_height = out_selected_rows.height()
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out_rows = out_selected_rows.rows()
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result_array = np.array(out_tensor)
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assert (in_array * scale == result_array).all()
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assert in_height == out_height
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assert in_rows == out_rows
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def test_scale_selected_rows(self):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(core.CUDAPlace(0))
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for place in places:
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self.check_with_place(place, 'in', 'out')
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def test_scale_selected_rows_inplace(self):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(core.CUDAPlace(0))
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for place in places:
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self.check_with_place(place, 'in', 'in')
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# Add FP16 test
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestScaleFp16Op(TestScaleOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=0.002)
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def test_check_grad(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ["X"], "Out", max_relative_error=0.05)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestScaleFp16OpSelectedRows(TestScaleOpSelectedRows):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_scale_selected_rows(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_with_place(place, 'in', 'out')
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def test_scale_selected_rows_inplace(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_with_place(place, 'in', 'in')
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if __name__ == "__main__":
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unittest.main()
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