# 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 import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.op import Operator from paddle.static import Program, program_guard class TestScaleOp(OpTest): def setUp(self): self.op_type = "scale" self.dtype = np.float64 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 TestScaleOpScaleVariable(OpTest): def setUp(self): self.op_type = "scale" self.dtype = np.float64 self.init_dtype_type() self.scale = -2.3 self.inputs = { 'X': np.random.random((10, 10)).astype(self.dtype), 'ScaleTensor': np.array([self.scale]).astype('float64') } self.attrs = {} self.outputs = {'Out': self.inputs['X'] * self.dtype(self.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.float64 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 # TODO(chenweihang): output rows and height cannot be shared into # fluid output tensor # 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') class TestScaleRaiseError(unittest.TestCase): def test_errors(self): def test_type(): fluid.layers.scale([10]) self.assertRaises(TypeError, test_type) # 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') class TestScaleApiStatic(unittest.TestCase): def _executed_api(self, x, scale=1.0, bias=0.0): return paddle.scale(x, scale, bias) def test_api(self): paddle.enable_static() input = np.random.random([2, 25]).astype("float32") main_prog = Program() with program_guard(main_prog, Program()): x = paddle.static.data(name="x", shape=[2, 25], dtype="float32") out = self._executed_api(x, scale=2.0, bias=3.0) exe = paddle.static.Executor(place=paddle.CPUPlace()) out = exe.run(main_prog, feed={"x": input}, fetch_list=[out]) self.assertEqual(np.array_equal(out[0], input * 2.0 + 3.0), True) class TestScaleInplaceApiStatic(TestScaleApiStatic): def _executed_api(self, x, scale=1.0, bias=0.0): return x.scale_(scale, bias) class TestScaleApiDygraph(unittest.TestCase): def _executed_api(self, x, scale=1.0, bias=0.0): return paddle.scale(x, scale, bias) def test_api(self): paddle.disable_static() input = np.random.random([2, 25]).astype("float32") x = paddle.to_tensor(input) out = self._executed_api(x, scale=2.0, bias=3.0) self.assertEqual(np.array_equal(out.numpy(), input * 2.0 + 3.0), True) paddle.enable_static() class TestScaleInplaceApiDygraph(TestScaleApiDygraph): def _executed_api(self, x, scale=1.0, bias=0.0): return x.scale_(scale, bias) if __name__ == "__main__": unittest.main()