# 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 import paddle.fluid.core as core from paddle.fluid.op import Operator from op_test import OpTest class TestMomentumOp1(OpTest): def setUp(self): self.op_type = "momentum" self.dtype = np.float32 self.init_dtype() param = np.random.random((123, 321)).astype(self.dtype) grad = np.random.random((123, 321)).astype(self.dtype) velocity = np.zeros((123, 321)).astype(self.dtype) learning_rate = np.array([0.001]).astype(self.dtype) mu = 0.0001 use_nesterov = False self.inputs = { 'Param': param, 'Grad': grad, 'Velocity': velocity, 'LearningRate': learning_rate } self.attrs = {'mu': mu} velocity_out = mu * velocity + grad if use_nesterov: param_out = param - grad * learning_rate - \ velocity_out * mu * learning_rate else: param_out = param - learning_rate * velocity_out self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out} def init_dtype(self): pass def test_check_output(self): self.check_output() class TestMomentumOpFp16(TestMomentumOp1): def init_dtype(self): self.dtype = np.float16 def test_check_output(self): self.check_output(atol=1e-3) class TestMomentumOp2(OpTest): '''Test Momentum with default values for attributes ''' def setUp(self): self.op_type = "momentum" param = np.random.random((123, 321)).astype("float32") grad = np.random.random((123, 321)).astype("float32") velocity = np.zeros((123, 321)).astype("float32") learning_rate = np.array([0.001]).astype("float32") mu = 0.0001 use_nesterov = True self.inputs = { 'Param': param, 'Grad': grad, 'Velocity': velocity, 'LearningRate': learning_rate } self.attrs = {'mu': mu, 'use_nesterov': use_nesterov} velocity_out = mu * velocity + grad if use_nesterov: param_out = param - grad * learning_rate - \ velocity_out * mu * learning_rate else: param_out = param - learning_rate * velocity_out self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out} def test_check_output(self): self.check_output() class TestLarsMomentumOp(OpTest): def setUp(self): self.op_type = "lars_momentum" param = np.random.random((123, 321)).astype("float32") grad = np.random.random((123, 321)).astype("float32") velocity = np.zeros((123, 321)).astype("float32") learning_rate = np.array([0.001]).astype("float32") mu = 0.0001 lars_coeff = 0.001 lars_weight_decay = 0.0005 self.inputs = { 'Param': param, 'Grad': grad, 'Velocity': velocity, 'LearningRate': learning_rate } self.attrs = { 'mu': mu, 'lars_coeff': lars_coeff, 'lars_weight_decay': lars_weight_decay } pnorm = np.sqrt(np.square(param).sum()) gnorm = np.sqrt(np.square(grad).sum()) local_lr = learning_rate * lars_coeff * pnorm / ( gnorm + lars_weight_decay * param) velocity_out = mu * velocity + local_lr * (grad + lars_weight_decay * param) param_out = param - velocity_out self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out} def test_check_output(self): self.check_output() class TestSparseMomentumOp(unittest.TestCase): def setUp(self): self.use_nesterov = False def check_with_place(self, place): self.init_kernel() scope = core.Scope() # create and initialize Grad Variable height = 10 rows = [0, 4, 7] row_numel = 12 mu = 1.0 use_nesterov = self.use_nesterov # create and initialize Param Variable param = scope.var('Param').get_tensor() param_array = np.full((height, row_numel), 5.0).astype("float32") param.set(param_array, place) param_out = scope.var("ParamOut").get_tensor() param_out_array = np.full((height, row_numel), 0.0).astype("float32") param_out.set(param_out_array, place) grad_selected_rows = scope.var('Grad').get_selected_rows() grad_selected_rows.set_height(height) grad_selected_rows.set_rows(rows) grad_np_array = np.ones((len(rows), row_numel)).astype("float32") grad_np_array[0, 0] = 2.0 grad_np_array[2, 8] = 4.0 grad_tensor = grad_selected_rows.get_tensor() grad_tensor.set(grad_np_array, place) velocity = scope.var('Velocity').get_tensor() velocity_np_array = np.ones((height, row_numel)).astype("float32") velocity.set(velocity_np_array, place) velocity_out = scope.var('VelocityOut').get_tensor() velocity_out_np_array = np.full((height, row_numel), 0.0).astype("float32") velocity_out.set(velocity_out_np_array, place) # create and initialize LeraningRate Variable lr = scope.var('LearningRate').get_tensor() lr_array = np.full((1), 2.0).astype("float32") lr.set(lr_array, place) # create and run operator op = Operator( "momentum", Param='Param', Grad='Grad', Velocity='Velocity', ParamOut='ParamOut', VelocityOut='VelocityOut', LearningRate='LearningRate', mu=mu, use_nesterov=use_nesterov) op.run(scope, place) # get and compare result param_out_np_array = np.array(param_out) velocity_out_np_array = np.array(velocity_out) # TODO(dzh): add a more suitable general numpy interface # for sparse update. _grad_np_array = np.full((height, row_numel), 0.0).astype("float32") for i in range(len(rows)): _grad_np_array[rows[i]] = grad_np_array[i] _velocity_out = mu * velocity_np_array + _grad_np_array _param = param_array if use_nesterov: _param_out = _param - (_grad_np_array + _velocity_out * mu ) * lr_array else: _param_out = _param - lr_array * _velocity_out self.assertTrue((_velocity_out == velocity_out_np_array).all()) self.assertTrue((_param_out == param_out_np_array).all()) def init_kernel(self): pass def test_sparse_momentum(self): places = [core.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) for place in places: self.check_with_place(place) class TestSparseMomentumOp2(TestSparseMomentumOp): def init_kernel(self): self.use_nesterov = True if __name__ == "__main__": unittest.main()