# Copyright (c) 2020 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. import unittest import paddle from paddle import fluid import os import paddle.distributed.fleet as fleet import paddle.distributed.fleet.base.role_maker as role_maker class TestFleetMetaOptimizer(unittest.TestCase): def setUp(self): os.environ["PADDLE_TRAINER_ID"] = "1" os.environ[ "PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001,127.0.0.1:36002" def net(self, main_prog, startup_prog): with fluid.program_guard(main_prog, startup_prog): with fluid.unique_name.guard(): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) input_x = paddle.fluid.layers.data( name="x", shape=[32], dtype='float32') input_y = paddle.fluid.layers.data( name="y", shape=[1], dtype='int64') fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh') fc_2 = paddle.fluid.layers.fc(input=fc_1, size=256, act='tanh') prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax') cost = paddle.fluid.layers.cross_entropy( input=prediction, label=input_y) avg_cost = paddle.fluid.layers.mean(x=cost) strategy = paddle.distributed.fleet.DistributedStrategy() return avg_cost, strategy def pp_net(self, main_prog, startup_prog, pp_degree=2): def fc_block(input_x): fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh') fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh') fc_3 = paddle.fluid.layers.fc(input=fc_2, size=64, act='tanh') return fc_3 with fluid.program_guard(main_prog, startup_prog): with fluid.unique_name.guard(): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) with fluid.device_guard("gpu:0"): input_x = paddle.fluid.layers.data( name="x", shape=[32], dtype='float32') input_y = paddle.fluid.layers.data( name="y", shape=[1], dtype='int64') for stage_idx in range(pp_degree): with fluid.device_guard("gpu:" + str(stage_idx)): input_x = fc_block(input_x) with fluid.device_guard("gpu:" + str(pp_degree - 1)): prediction = paddle.fluid.layers.fc(input=[input_x], size=2, act='softmax') cost = paddle.fluid.layers.cross_entropy( input=prediction, label=input_y) avg_cost = paddle.fluid.layers.mean(x=cost) strategy = paddle.distributed.fleet.DistributedStrategy() return avg_cost, strategy def optimizer(self, loss, strategy, train_prog, startup_prog, name='momentum', regularization=None, grad_clip=None): with fluid.program_guard(train_prog, startup_prog): with fluid.unique_name.guard(): if name == 'momentum': optimizer = paddle.fluid.optimizer.Momentum( learning_rate=0.01, momentum=0.9, regularization=regularization, grad_clip=grad_clip) elif name == 'adam': optimizer = paddle.fluid.optimizer.Adam( learning_rate=0.01, regularization=regularization, grad_clip=grad_clip) optimizer = fleet.distributed_optimizer( optimizer, strategy=strategy) optimizer.minimize(loss) def set_strategy(self, strategy, name): if name == 'amp': strategy.amp = True strategy.amp_configs = { "init_loss_scaling": 32768, "decr_every_n_nan_or_inf": 2, "incr_every_n_steps": 1000, "incr_ratio": 2.0, "use_dynamic_loss_scaling": True, "decr_ratio": 0.5, "custom_white_list": ['softmax'], "custom_black_list": ['tanh'], } elif name == 'pure_fp16': strategy.amp = True strategy.amp_configs = { "init_loss_scaling": 32768, "decr_every_n_nan_or_inf": 2, "incr_every_n_steps": 1000, "incr_ratio": 2.0, "use_dynamic_loss_scaling": True, "decr_ratio": 0.5, "custom_white_list": ['softmax'], "custom_black_list": ['tanh'], "use_pure_fp16": True, "use_fp16_guard": False, } elif name == 'dgc': strategy.dgc = True strategy.dgc_configs = { "rampup_begin_step": 128, "rampup_step": 100, "sparsity": [0.996, 0.999] } elif name == 'recompute': strategy.recompute = True strategy.recompute_configs = { "checkpoints": ["fc_0.tmp_2", "fc_1.tmp_2"] } elif name == 'lars': strategy.lars = True strategy.lars_configs = { "lars_coeff": 0.001, "lars_weight_decay": 0.0005, "epsilon": 0, "exclude_from_weight_decay": ["batch_norm", ".b"], } elif name == 'lamb': strategy.lamb = True strategy.lamb_configs = { 'lamb_weight_decay': 0.01, 'exclude_from_weight_decay': [], } elif name == 'localsgd': strategy.localsgd = True strategy.localsgd_configs = { 'k_steps': 1, 'begin_step': 1, } elif name == 'adaptive_localsgd': strategy.adaptive_localsgd = True strategy.adaptive_localsgd_configs = { 'init_k_steps': 1, 'begin_step': 1, } elif name == "gradient_merge": strategy.gradient_merge = True strategy.gradient_merge_configs = {"k_steps": 2, "avg": True} elif name == "sharding": strategy.sharding = True strategy.sharding_configs = { "sharding_segment_strategy": "segment_broadcast_MB", "segment_broadcast_MB": 0.2, "sharding_degree": 2, } elif name == "recompute-offload": strategy.recompute = True strategy.recompute_configs = { "checkpoints": ["fc_0.tmp_2", "fc_1.tmp_2"], "enable_offload": True, "checkpoint_shape": [256] } else: raise NotImplementedError()