# 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 from paddle.fluid import core from paddle.fluid.op import Operator import paddle.fluid as fluid import paddle class TestAdamOp1(OpTest): def setUp(self): '''Test Adam Op with supplied attributes ''' self.op_type = "adam" 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") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") learning_rate = 0.004 beta1 = 0.78 beta2 = 0.836 epsilon = 1e-4 beta1_pow = beta1**10 beta2_pow = beta2**10 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") } self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2} param_out, moment1_out, \ moment2_out = adam_step(self.inputs, self.attrs) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2 } def test_check_output(self): self.check_output() class TestAdamOp2(OpTest): def setUp(self): '''Test Adam Op with supplied attributes ''' self.op_type = "adam" 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") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") learning_rate = 0.001 beta1 = 0.9 beta2 = 0.999 epsilon = 1e-8 beta1_pow = beta1**10 beta2_pow = beta2**10 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") } attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2} param_out, moment1_out, \ moment2_out = adam_step(self.inputs, attributes) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2 } def test_check_output(self): self.check_output() class TestAdamOpMultipleSteps(OpTest): def setUp(self): '''Test Adam Operator with supplied attributes ''' self.op_type = "adam" self.num_steps = 10 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") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") learning_rate = 0.001 self.beta1 = 0.9 self.beta2 = 0.999 epsilon = 1e-8 self.beta1_pow = self.beta1**10 self.beta2_pow = self.beta2**10 self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([self.beta1_pow]).astype("float32"), 'Beta2Pow': np.array([self.beta2_pow]).astype("float32") } self.attrs = { 'epsilon': epsilon, 'beta1': self.beta1, 'beta2': self.beta2 } def test_check_output(self): for _ in range(self.num_steps): param_out, moment1_out, \ moment2_out = adam_step(self.inputs, self.attrs) beta1_pow_out = self.inputs['Beta1Pow'] * self.beta1 beta2_pow_out = self.inputs['Beta2Pow'] * self.beta2 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 adam_step(inputs, attributes): ''' Simulate one step of the adam 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'] epsilon = attributes['epsilon'] if 'beta1' in attributes: beta1 = attributes['beta1'] else: beta1 = inputs['Beta1Tensor'][0] if 'beta2' in attributes: beta2 = attributes['beta2'] else: beta2 = inputs['Beta2Tensor'][0] moment1_out = beta1 * moment1 + (1 - beta1) * grad moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon)) return param_out, moment1_out, moment2_out def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad, lazy_mode): ''' Simulate one step of the adam 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'] moment1_out = np.zeros(shape=[height, row_numel]) moment2_out = np.zeros(shape=[height, row_numel]) param_out = np.zeros(shape=[height, row_numel]) def update_row(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) lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / ( np.sqrt(moment2_out[row_id]) + epsilon)) if lazy_mode: for idx, row_id in enumerate(rows): update_row(row_id, np_grad[idx]) else: 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_row(row_id, update_value) return param_out, moment1_out, moment2_out class TestSparseAdamOp(unittest.TestCase): def setup(self, scope, place, lazy_mode): beta1 = 0.78 beta2 = 0.836 epsilon = 1e-4 beta1_pow = np.array([beta1**10]).astype("float32") beta2_pow = np.array([beta2**10]).astype("float32") 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': beta1_pow, 'Beta2Pow': beta2_pow, "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, 'min_row_size_to_use_multithread': 2 } 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 = adam_step_sparse(self.dense_inputs, self.attrs, height, rows, row_numel, np_array, lazy_mode) self.outputs = { "ParamOut": param_out, "Moment1Out": mom1, "Moment2Out": mom2, 'Beta1PowOut': beta1_pow * beta1, 'Beta2PowOut': beta2_pow * beta2 } def check_with_place(self, place, lazy_mode): scope = core.Scope() self.setup(scope, place, lazy_mode) op_args = dict() op_args['lazy_mode'] = lazy_mode 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 adam_op = Operator("adam", **op_args) adam_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_adam(self): places = [core.CPUPlace()] if core.is_compiled_with_cuda(): places.append(core.CUDAPlace(0)) for place in places: for lazy_mode in (True, False): self.check_with_place(place, lazy_mode) class TestAdamOpBetaVariable(OpTest): def setUp(self): '''Test Adam Op with beta as Variable ''' self.op_type = "adam" 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") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") beta1 = 0.85 beta2 = 0.95 learning_rate = 0.001 epsilon = 1e-8 beta1_pow = beta1**10 beta2_pow = beta2**10 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"), "Beta1Tensor": np.array([beta1]).astype("float32"), "Beta2Tensor": np.array([beta2]).astype("float32"), } attributes = {'epsilon': epsilon} param_out, moment1_out, \ moment2_out = adam_step(self.inputs, attributes) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2 } def test_check_output(self): self.check_output() class TestAdamOpV2(unittest.TestCase): def test_adam_op(self): place = fluid.CPUPlace() shape = [2, 3, 8, 8] exe = fluid.Executor(place) train_prog = fluid.Program() startup = fluid.Program() with fluid.program_guard(train_prog, startup): with fluid.unique_name.guard(): data = fluid.data(name="data", shape=shape) conv = fluid.layers.conv2d(data, 8, 3) loss = fluid.layers.reduce_mean(conv) beta1 = fluid.layers.create_global_var( shape=[1], value=0.85, dtype='float32', persistable=True) beta2 = fluid.layers.create_global_var( shape=[1], value=0.95, dtype='float32', persistable=True) betas = [beta1, beta2] opt = paddle.optimizer.Adam( learning_rate=1e-5, beta1=beta1, beta2=beta2, weight_decay=0.01, epsilon=1e-8) opt.minimize(loss) exe.run(startup) data_np = np.random.random(shape).astype('float32') rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss]) assert rets[0] is not None def test_adam_op_dygraph(self): paddle.disable_static() value = np.arange(26).reshape(2, 13).astype("float32") a = fluid.dygraph.to_variable(value) linear = fluid.Linear(13, 5, dtype="float32") adam = paddle.optimizer.Adam( learning_rate=0.01, parameters=linear.parameters()) out = linear(a) out.backward() adam.step() adam.clear_gradients() def test_adam_op_with_state_dict(self): import paddle paddle.disable_static() emb = paddle.nn.Embedding([10, 10]) adam = paddle.optimizer.Adam(0.001, parameters=emb.parameters()) state_dict = adam.state_dict() adam.set_state_dict(state_dict) #learning_rate is _LRScheduler learning_rate = paddle.optimizer.CosineAnnealingLR( learning_rate=0.1, T_max=10) adam = paddle.optimizer.Adam( learning_rate=learning_rate, weight_decay=fluid.regularizer.L2Decay(0.001), parameters=emb.parameters()) lr = adam.get_lr() state_dict = adam.state_dict() adam.set_state_dict(state_dict) #leanrning_rate is Tensor with self.assertRaises(TypeError): learning_rate = np.array([0.01]).astype("float32") learning_rate = paddle.to_tensor(learning_rate) adam = paddle.optimizer.Adam( learning_rate=learning_rate, parameters=emb.parameters()) params = adam.get_opti_var_name_list() assert (params is not None) def test_adam_with_grad_clip(self): paddle.disable_static() value = np.arange(26).reshape(2, 13).astype("float32") a = fluid.dygraph.to_variable(value) linear = fluid.Linear(13, 5, dtype="float32") clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0) adam = paddle.optimizer.Adam( 0.1, parameters=linear.parameters(), grad_clip=clip) out = linear(a) out.backward() adam.step() adam.clear_gradients() def test_adam_op_with_set_lr(self): paddle.disable_static() linear = paddle.nn.Linear(10, 10) adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters()) lr = 0.01 adam.set_lr(lr) cur_lr = adam.get_lr() assert (lr == cur_lr) with self.assertRaises(TypeError): lr_var = paddle.create_global_var( shape=[1], value=lr, dtype='float32') adam.set_lr(lr_var) if __name__ == "__main__": unittest.main()