# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np from op_test import OpTest import paddle.fluid.core as core from paddle.fluid.op import Operator class TestScaleOp(OpTest): def setUp(self): self.op_type = "scale" self.dtype = np.float32 self.init_dtype_type() self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)} self.attrs = {'scale': -2.3} self.outputs = { 'Out': self.inputs['X'] * self.dtype(self.attrs['scale']) } def init_dtype_type(self): pass def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out') class TestScaleOpSelectedRows(unittest.TestCase): def init_dtype_type(self): pass def check_with_place(self, place, in_name, out_name): scope = core.Scope() self.dtype = np.float32 self.init_dtype_type() # create and initialize Grad Variable in_height = 10 in_rows = [0, 4, 7] in_row_numel = 12 scale = 2.0 in_selected_rows = scope.var(in_name).get_selected_rows() in_selected_rows.set_height(in_height) in_selected_rows.set_rows(in_rows) in_array = np.random.random( (len(in_rows), in_row_numel)).astype(self.dtype) in_tensor = in_selected_rows.get_tensor() in_tensor.set(in_array, place) # create and initialize Param Variable out_selected_rows = scope.var(out_name).get_selected_rows() out_tensor = out_selected_rows.get_tensor() out_tensor._set_dims(in_tensor._get_dims()) # create and run sgd operator scale_op = Operator("scale", X=in_name, Out=out_name, scale=scale) scale_op.run(scope, place) # get and compare result out_height = out_selected_rows.height() out_rows = out_selected_rows.rows() result_array = np.array(out_tensor) assert (in_array * scale == result_array).all() assert in_height == out_height assert in_rows == out_rows def test_scale_selected_rows(self): places = [core.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) for place in places: self.check_with_place(place, 'in', 'out') def test_scale_selected_rows_inplace(self): places = [core.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) for place in places: self.check_with_place(place, 'in', 'in') # Add FP16 test @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestScaleFp16Op(TestScaleOp): def init_dtype_type(self): self.dtype = np.float16 def test_check_output(self): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place(place, atol=0.002) def test_check_grad(self): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_grad_with_place( place, ["X"], "Out", max_relative_error=0.05) @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestScaleFp16OpSelectedRows(TestScaleOpSelectedRows): def init_dtype_type(self): self.dtype = np.float16 def test_scale_selected_rows(self): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_with_place(place, 'in', 'out') def test_scale_selected_rows_inplace(self): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_with_place(place, 'in', 'in') if __name__ == "__main__": unittest.main()