# Copyright (c) 2019 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 from paddle.fluid import core from paddle.fluid.op import Operator paddle.enable_static() class TestLambOp1(OpTest): def set_attrs(self): self.attrs = { 'epsilon': 1e-4, 'beta1': 0.78, 'beta2': 0.836, 'weight_decay': 0.01 } def setUp(self): '''Test Lamb Op with supplied attributes ''' self.op_type = "lamb" param = np.random.uniform(-1, 1, (102, 105)).astype("float32") grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") moment2 = np.random.random((102, 105)).astype("float32") learning_rate = 0.001 self.set_attrs() beta1_pow = self.attrs['beta1'] beta2_pow = self.attrs['beta2'] self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32") } param_out, moment1_out, moment2_out, \ beta1_pow_out, beta2_pow_out = lamb_step(self.inputs, self.attrs) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, 'ParamOut': param_out, 'Beta1PowOut': beta1_pow_out, 'Beta2PowOut': beta2_pow_out } def test_check_output(self): self.check_output() class TestLambOp2(TestLambOp1): def set_attrs(self): self.attrs = { 'epsilon': 1e-8, 'beta1': 0.9, 'beta2': 0.999, 'weight_decay': 0.01 } class TestLambOpMultipleSteps(TestLambOp1): def set_attrs(self): self.attrs = { 'epsilon': 1e-8, 'beta1': 0.9, 'beta2': 0.999, 'weight_decay': 0.01 } self.num_steps = 10 def test_check_output(self): for i in range(self.num_steps): param_out, moment1_out, moment2_out, \ beta1_pow_out, beta2_pow_out = lamb_step(self.inputs, self.attrs) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, 'ParamOut': param_out, 'Beta1PowOut': beta1_pow_out, 'Beta2PowOut': beta2_pow_out } # Verify output for this step self.check_output() # Output of this step becomes input for next step self.inputs['Param'] = param_out self.inputs['Moment1'] = moment1_out self.inputs['Moment2'] = moment2_out # Update powers of Beta1 and Beta2 for next time step self.inputs['Beta1Pow'] = beta1_pow_out self.inputs['Beta2Pow'] = beta2_pow_out # Randomize gradient for next step self.inputs['Grad'] = np.random.uniform( -1, 1, (102, 105)).astype("float32") def lamb_step(inputs, attributes): ''' Simulate one step of the lamb optimizer :param inputs: dict of inputs :param attributes: dict of attributes :return tuple: tuple of output param, moment1, moment2, beta1 power accumulator and beta2 power accumulator ''' param = inputs['Param'] grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] beta1 = attributes['beta1'] beta2 = attributes['beta2'] epsilon = attributes['epsilon'] weight_decay = attributes['weight_decay'] moment1_out = beta1 * moment1 + (1 - beta1) * grad moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) moment1_unbiased = moment1_out / (1 - beta1_pow) moment2_unbiased = moment2_out / (1 - beta2_pow) r_1 = np.linalg.norm(param) r_2 = np.linalg.norm(moment1_unbiased / (np.sqrt(moment2_unbiased) + epsilon ) + weight_decay * param) lr_t = lr * r_1 / r_2 param_out = param - lr_t * (moment1_unbiased / ( np.sqrt(moment2_unbiased) + epsilon) + weight_decay * param) beta1_pow_out = beta1_pow * beta1 beta2_pow_out = beta2_pow * beta2 return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out def lamb_step_sparse(inputs, attributes, height, rows, row_numel, np_grad): ''' Simulate one step of the lamb optimizer :param inputs: dict of inputs :param attributes: dict of attributes :return tuple: tuple of output param, moment1, moment2, beta1 power accumulator and beta2 power accumulator ''' param = inputs['Param'] # grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] beta1 = attributes['beta1'] beta2 = attributes['beta2'] epsilon = attributes['epsilon'] weight_decay = attributes['weight_decay'] moment1_out = np.zeros(shape=[height, row_numel]) moment2_out = np.zeros(shape=[height, row_numel]) param_out = np.zeros(shape=[height, row_numel]) moment1_unbiased = np.zeros(shape=[height, row_numel]) moment2_unbiased = np.zeros(shape=[height, row_numel]) def update_mom(row_id, update_value): moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1 ) * update_value moment2_out[row_id] = beta2 * moment2[row_id] + ( 1 - beta2) * np.square(update_value) moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1 ) * update_value moment2_out[row_id] = beta2 * moment2[row_id] + ( 1 - beta2) * np.square(update_value) def update_param(): r_1 = np.linalg.norm(param) r_2 = np.linalg.norm(moment1_out / (np.sqrt(moment2_out) + epsilon) + weight_decay * param) lr_t = lr * r_1 / r_2 param_out = param - lr_t * (moment1_out / ( np.sqrt(moment2_out) + epsilon) + weight_decay * param) for row_id in range(param_out.shape[0]): update_value = np.zeros(np_grad[0].shape).astype("float32") if row_id in rows: update_value = np_grad[rows.index(row_id)] update_mom(row_id, update_value) update_param() beta1_pow_out = beta1_pow * beta1 beta2_pow_out = beta2_pow * beta2 return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out class TestSparseLambOp(unittest.TestCase): def setup(self, scope, place): beta1 = 0.78 beta2 = 0.836 epsilon = 1e-4 height = 10 rows = [0, 4, 7] self.rows = rows row_numel = 12 self.row_numel = row_numel self.dense_inputs = { "Param": np.full((height, row_numel), 5.0).astype("float32"), "Moment1": np.full((height, row_numel), 5.0).astype("float32"), "Moment2": np.full((height, row_numel), 5.0).astype("float32"), 'Beta1Pow': np.array([beta1]).astype("float32"), 'Beta2Pow': np.array([beta2]).astype("float32"), "LearningRate": np.full((1), 2.0).astype("float32") } self.init_output = np.full((height, row_numel), 0.0).astype("float32") self.attrs = { 'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2, 'weight_decay': 0.05 } grad_selected_rows = scope.var('Grad').get_selected_rows() grad_selected_rows.set_height(height) grad_selected_rows.set_rows(rows) np_array = np.ones((len(rows), row_numel)).astype("float32") np_array[0, 0] = 2.0 np_array[2, 8] = 4.0 grad_tensor = grad_selected_rows.get_tensor() grad_tensor.set(np_array, place) self.sparse_inputs = ["Grad"] param_out, mom1, mom2, beta1_pow_out, beta2_pow_out = lamb_step_sparse( self.dense_inputs, self.attrs, height, rows, row_numel, np_array) self.outputs = { "ParamOut": param_out, "Moment1Out": mom1, "Moment2Out": mom2, 'Beta1PowOut': beta1_pow_out, 'Beta2PowOut': beta2_pow_out } def check_with_place(self, place): scope = core.Scope() self.setup(scope, place) op_args = dict() for key, np_array in self.dense_inputs.items(): var = scope.var(key).get_tensor() var.set(np_array, place) op_args[key] = key for s in self.sparse_inputs: op_args[s] = s for s in self.outputs: var = scope.var(s).get_tensor() var.set(self.init_output, place) op_args[s] = s for k in self.attrs: op_args[k] = self.attrs[k] # create and run sgd operator lamb_op = Operator("lamb", **op_args) lamb_op.run(scope, place) for key, np_array in self.outputs.items(): out_var = scope.var(key).get_tensor() actual = np.array(out_var) actual = actual.reshape([actual.size]) np_array = np_array.reshape([np_array.size]) for i in range(np_array.size): self.assertLess((actual[i] - np_array[i]), 0.00001) def test_sparse_lamb(self): places = [core.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) for place in places: self.check_with_place(place) if __name__ == "__main__": unittest.main()