# 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 from paddle.fluid.dygraph.base import switch_to_static_graph import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers class LAMBOptimizer(paddle.optimizer.Lamb): def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, fluid.framework.Block) block.program._use_lamb = True m = moment1 = self._get_accumulator(self._moment1_acc_str, param_and_grad[0]) v = self._get_accumulator(self._moment2_acc_str, param_and_grad[0]) beta_1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str, param_and_grad[0]) beta_2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str, param_and_grad[0]) beta_1 = layers.fill_constant( dtype='float32', shape=[1], value=self._beta1, name='lamb_beta_1') beta_2 = layers.fill_constant( dtype='float32', shape=[1], value=self._beta2, name='lamb_beta_2') epsilon = layers.fill_constant( dtype='float32', shape=[1], value=self._epsilon, name='epsilon') one = paddle.ones(shape=[1]).astype('float32') zero = paddle.zeros(shape=[1]).astype('float32') next_m = paddle.multiply(m, beta_1) + paddle.multiply(param_and_grad[1], one - beta_1) next_v = paddle.multiply(v, beta_2) + paddle.multiply( paddle.pow(param_and_grad[1], 2), one - beta_2) beta1_correction = one - beta_1_pow_acc beta2_correction = one - beta_2_pow_acc next_m_unbiased = next_m / beta1_correction next_v_unbiased = next_v / beta2_correction update = next_m_unbiased / (paddle.sqrt(next_v_unbiased) + epsilon) if self._exclude_from_weight_decay_fn is not None and self._exclude_from_weight_decay_fn( param_and_grad[0]): self._lamb_weight_decay = 0.0 update += self._lamb_weight_decay * param_and_grad[0] w_norm = paddle.norm(param_and_grad[0], p=2) g_norm = paddle.norm(update, p=2) learning_rate = self._create_param_lr(param_and_grad) ratio = paddle.where( paddle.greater_than(w_norm, zero), paddle.where( paddle.greater_than(g_norm, zero), (w_norm / g_norm), one), one) update_with_lr = ratio * learning_rate * update next_param = param_and_grad[0] - update_with_lr beta_1_pow_acc *= beta_1 beta_2_pow_acc *= beta_2 paddle.assign(next_m, m) paddle.assign(next_v, v) paddle.assign(next_param, param_and_grad[0]) return None class TestLambOpV2(unittest.TestCase): def test_lamb_op(self): shape = [2, 4, 8, 8] data = paddle.to_tensor(np.random.random(size=shape).astype("float32")) conv = paddle.nn.Conv2D(4, 6, (3, 3)) data = conv(data) loss = paddle.mean(data) opt = paddle.optimizer.Lamb( learning_rate=1e-5, epsilon=1e-8, parameters=conv.parameters()) loss.backward() opt.minimize(loss) assert loss.numpy() is not None class TestLambOpWithCombinedOp(unittest.TestCase): def test_lamb_op_with_multi_steps(self): paddle.enable_static() def _build_static_model(main, startup, seed=100): with fluid.program_guard(main, startup): main.random_seed = seed startup.random_seed = seed x = fluid.layers.data(name='X', shape=[13], dtype='float32') y = fluid.layers.data(name='Y', shape=[1], dtype='float32') prediction = fluid.layers.fc(input=x, size=1, act=None) loss = fluid.layers.square_error_cost(input=prediction, label=y) avg_loss = fluid.layers.mean(loss) return avg_loss place = fluid.CPUPlace() num_steps = 10 for i in range(num_steps): feed_x = np.random.random(size=(10, 13)).astype('float32') feed_y = np.random.random(size=(10, 1)).astype('float32') main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program, startup_program): avg_loss = _build_static_model(main_program, startup_program) lamb_kernel = paddle.optimizer.Lamb(learning_rate=0.2) lamb_kernel.minimize(avg_loss) executor = fluid.Executor(place) executor.run(startup_program) output = executor.run(program=main_program, feed={'X': feed_x, 'Y': feed_y}, fetch_list=[avg_loss.name]) main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): loss = _build_static_model(main, startup) lamb = LAMBOptimizer(learning_rate=0.2) lamb.minimize(loss) exe = fluid.Executor(place) exe.run(startup) out = exe.run(program=main, feed={'X': feed_x, 'Y': feed_y}, fetch_list=[loss.name]) self.assertTrue(np.allclose(out, output)) class TestLambOpV2Group(TestLambOpV2): def test_lamb_op(self): paddle.disable_static() value = np.arange(26).reshape(2, 13).astype("float32") a = paddle.to_tensor(value) linear_1 = paddle.nn.Linear(13, 5) linear_2 = paddle.nn.Linear(5, 3) # This can be any optimizer supported by dygraph. adam = paddle.optimizer.Lamb( learning_rate=0.01, parameters=[{ 'params': linear_1.parameters() }, { 'params': linear_2.parameters(), 'lamb_weight_decay': 0.001, 'beta1': 0.9, 'beta2': 0.99 }], lamb_weight_decay=0.01) out = linear_1(a) out = linear_2(out) out.backward() adam.step() adam.clear_gradients() class TestLambOpMultiPrecision(unittest.TestCase): def check_main(self, x_np, place, multi_precision=False, seed=10, n=10): main_prog = paddle.static.Program() startup_prog = paddle.static.Program() with paddle.static.program_guard(main_prog, startup_prog): paddle.seed(seed) with paddle.static.amp.fp16_guard(): x = paddle.static.data( name='x', shape=[None, 10], dtype='float32') linear = paddle.nn.Linear(10, 2) hidden = linear(x) loss = paddle.mean(hidden) original_optimizer = paddle.optimizer.Lamb(learning_rate=1e-3) original_optimizer._multi_precision = multi_precision if multi_precision: optimizer = paddle.static.amp.decorate( original_optimizer, use_pure_fp16=True, use_fp16_guard=True) else: optimizer = original_optimizer optimizer.minimize(loss) weight, bias = linear.weight, linear.bias exe = paddle.static.Executor(place) scope = paddle.static.Scope() x = main_prog.global_block().var(x.name) if x.dtype == core.VarDesc.VarType.FP16: x_np = x_np.astype(np.float16) def get_parameter(var): name = var if isinstance(var, (str, bytes)) else var.name params = original_optimizer._get_parameter(name, scope) assert isinstance(params, (list, tuple)) params = list(params) assert len(params) == 2 if multi_precision: params[0] = np.array(params[0]) params[1] = np.array(params[1]) self.assertTrue( np.array_equal(params[0], params[1].astype(np.float16))) return params[0].astype(np.float32) else: self.assertTrue(params[0] is not None) self.assertTrue(params[1] is None) params[0] = np.array(params[0]) return params[0] with paddle.static.scope_guard(scope): exe.run(startup_prog) if multi_precision: optimizer.amp_init(place) weight_np, bias_np = None, None for i in range(n): feed_dict = {x.name: x_np} weight_np, bias_np = exe.run(main_prog, feed=feed_dict, fetch_list=[weight, bias]) weight_np = weight_np.astype('float32') bias_np = bias_np.astype('float32') self.assertTrue( np.array_equal(weight_np, get_parameter(weight))) self.assertTrue(np.array_equal(bias_np, get_parameter(bias))) return weight_np, bias_np @switch_to_static_graph def test_main(self): if not paddle.is_compiled_with_cuda(): return place = paddle.CUDAPlace(0) x_np = np.random.random(size=[5, 10]).astype('float32') weight_1, bias_1 = self.check_main(x_np, place, multi_precision=False) weight_2, bias_2 = self.check_main(x_np, place, multi_precision=True) self.assertTrue(np.all(np.abs(weight_1 - weight_2) < 1e-3)) self.assertTrue(np.all(np.abs(bias_1 - bias_2) < 1e-7)) if __name__ == "__main__": unittest.main()