299 lines
13 KiB
Python
299 lines
13 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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PanguAlpha train script
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"""
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import os
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import math
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import time
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from mindspore import context
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from mindspore.train.model import Model
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import mindspore.communication.management as D
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from mindspore.context import ParallelMode
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import mindspore.nn as nn
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from mindspore.train.callback import TimeMonitor, Callback
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from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
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import mindspore.common.dtype as mstype
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from mindspore.parallel import set_algo_parameters
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from mindspore.parallel._cost_model_context import _set_multi_subgraphs
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from mindspore.nn.wrap.cell_wrapper import PipelineCell, _VirtualDatasetCell
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from src.dataset import create_dataset
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from src.pangu_alpha import PanguAlpha, PanguAlphaWithLoss, CrossEntropyLoss
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from src.pangu_alpha_wrapcell import PanguAlphaTrainOneStepWithLossScaleCell, PanguAlphaTrainPipelineWithLossScaleCell
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from src.pangu_alpha_config import PANGUALPHAConfig, set_parse
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from src.utils import LearningRate, get_args, FP32StateAdamWeightDecay
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from src.utils import download_data
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class LossCallBack(Callback):
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"""
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Monitor the loss in training.
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If the loss in NAN or INF terminating training.
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"""
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def __init__(self, dataset_size=-1, local_rank=0, has_trained_epoch=0, has_trained_step=0, micro_size=1):
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super(LossCallBack, self).__init__()
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self._dataset_size = dataset_size
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self.local_rank = local_rank
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self.has_trained_epoch = has_trained_epoch
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self.has_trained_step = has_trained_step
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self.micro_size = micro_size
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print("load has trained epoch :{} and step: {}".format(has_trained_epoch, has_trained_step), flush=True)
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def step_end(self, run_context):
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"""
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Print loss after each step
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"""
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cb_params = run_context.original_args()
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if self._dataset_size > 0 and self.local_rank % 8 == 0:
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percent, epoch_num = math.modf(cb_params.cur_step_num /
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self._dataset_size)
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if percent == 0:
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epoch_num -= 1
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date = time.asctime(time.localtime(time.time()))
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loss_value = cb_params.net_outputs[0].asnumpy() / self.micro_size
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print("time: {} local_rank: {}, epoch: {}, step: {}, output is {}, overflow is {}, scale is {}".
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format(date, int(self.local_rank), int(epoch_num) + int(self.has_trained_epoch),
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cb_params.cur_step_num + int(self.has_trained_step), loss_value,
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cb_params.net_outputs[1].asnumpy(), cb_params.net_outputs[2].asnumpy()))
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project_root = os.path.abspath(
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os.path.dirname(os.path.realpath(__file__)) + os.path.sep + "..")
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print('project_root:', project_root)
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def run_train(args_opt):
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r"""
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The main training process.
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"""
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# Set execution mode
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context.set_context(mode=context.GRAPH_MODE,
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device_target=args_opt.device_target)
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context.set_context(variable_memory_max_size="30GB")
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# Set parallel context
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if args_opt.distribute == "true":
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D.init()
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device_num = D.get_group_size()
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rank = D.get_rank()
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print("rank_id is {}, device_num is {}".format(rank, device_num))
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL,
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gradients_mean=False,
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full_batch=bool(args_opt.full_batch),
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enable_parallel_optimizer=bool(args_opt.optimizer_shard))
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set_algo_parameters(elementwise_op_strategy_follow=True)
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_set_multi_subgraphs()
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else:
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rank = 0
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device_num = 1
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context.set_context(save_graphs=False, save_graphs_path="./graphs_of_device_id_" + str(rank))
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# copy data from the cloud to the /cache/Data
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cache_url = '/cache/Data/'
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if args_opt.offline:
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cache_url = args_opt.data_url
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else:
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download_data(src_data_url=args_opt.data_url, tgt_data_path=cache_url, rank=rank)
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# Set model property
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model_parallel_num = args_opt.op_level_model_parallel_num
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data_parallel_num = int(device_num / model_parallel_num)
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batch_size = args_opt.per_batch_size * data_parallel_num
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config = PANGUALPHAConfig(
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data_parallel_num=data_parallel_num,
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model_parallel_num=model_parallel_num,
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batch_size=batch_size,
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seq_length=args_opt.seq_length,
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vocab_size=args_opt.vocab_size,
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embedding_size=args_opt.embedding_size,
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num_layers=args_opt.num_layers,
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num_heads=args_opt.num_heads,
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expand_ratio=4,
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dropout_rate=0.1,
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compute_dtype=mstype.float16,
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stage_num=args_opt.stage_num,
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micro_size=args_opt.micro_size,
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eod_reset=bool(args_opt.eod_reset),
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param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16,
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word_emb_dp=bool(args_opt.word_emb_dp))
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print("===config is: ", config, flush=True)
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# Define network
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pangu_alpha = PanguAlpha(config)
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loss = CrossEntropyLoss(config)
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pangu_alpha_with_loss = PanguAlphaWithLoss(config, pangu_alpha, loss)
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pangu_alpha_with_loss = _VirtualDatasetCell(pangu_alpha_with_loss)
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print("=====args_opt is: ", args_opt, flush=True)
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# Warm-up and cosine decay learning rate
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lr = LearningRate(learning_rate=args_opt.start_lr,
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end_learning_rate=args_opt.end_lr,
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warmup_steps=args_opt.warmup_step,
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decay_steps=200000)
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# Set weight decay coefficient, zero for bias and layernorm, 1e-1 for rest
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decay_filter = lambda x: 'layernorm' not in x.name.lower() and "bias" not in x.name.lower()
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params = pangu_alpha.trainable_params()
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decay_params = list(filter(decay_filter, params))
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other_params = list(filter(lambda x: not decay_filter(x), params))
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group_params = [{
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'params': decay_params,
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'weight_decay': 1e-1
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}, {
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'params': other_params,
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'weight_decay': 0.0
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}, {
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'order_params': params
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}]
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if args_opt.optimizer == "lamb":
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optimizer = nn.Lamb(group_params, learning_rate=lr)
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else:
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optimizer = FP32StateAdamWeightDecay(group_params, learning_rate=lr, eps=1e-8, beta1=0.9, beta2=0.95)
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# Initial scaling sens
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loss_scale_value = math.pow(2, 32)
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epoch_num = args_opt.epoch_size
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# Dataset loading mindrecord files
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ds = create_dataset(config.batch_size, data_path=cache_url,
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data_start_index=0, eod_reset=config.eod_reset, full_batch=bool(args_opt.full_batch),
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eod_id=args_opt.eod_id, device_num=device_num, rank=rank,
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column_name=args_opt.data_column_name, epoch=epoch_num)
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step_per_epoch = ds.get_dataset_size()
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callback_size = args_opt.sink_size
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actual_epoch_num = int(epoch_num * step_per_epoch / callback_size)
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callback = [
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TimeMonitor(callback_size),
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LossCallBack(callback_size, rank, 0, 0)
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]
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update_cell = DynamicLossScaleUpdateCell(loss_scale_value=loss_scale_value, scale_factor=2, scale_window=1000)
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pangu_alpha_with_grads = PanguAlphaTrainOneStepWithLossScaleCell(
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pangu_alpha_with_loss, optimizer=optimizer, scale_update_cell=update_cell, enable_global_norm=True,
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config=config)
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model = Model(pangu_alpha_with_grads)
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print("Dataset size: {}, actual_epoch_num: {}".format(ds.get_dataset_size(), actual_epoch_num), flush=True)
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model.train(actual_epoch_num, ds, callbacks=callback, sink_size=callback_size, dataset_sink_mode=True)
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def run_train_pipeline(args_opt):
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r"""
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The main training process in pipeline.
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"""
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context.set_context(save_graphs=False, mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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context.set_context(variable_memory_max_size="31GB")
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if args_opt.distribute == "true":
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D.init()
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device_num = D.get_group_size()
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rank_id = D.get_rank()
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print("rank_id is {}, device_num is {}".format(rank_id, device_num))
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL,
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gradients_mean=False,
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full_batch=bool(args_opt.full_batch),
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loss_repeated_mean=True,
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enable_parallel_optimizer=bool(args_opt.optimizer_shard),
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pipeline_stages=args_opt.stage_num)
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set_algo_parameters(elementwise_op_strategy_follow=True)
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_set_multi_subgraphs()
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else:
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rank_id = int(os.getenv("RANK_ID"))
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device_num = 1
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# copy data from the cloud to the /cache/Data
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cache_url = '/cache/Data/'
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if args_opt.offline:
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cache_url = args_opt.data_url
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else:
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download_data(src_data_url=args_opt.data_url, tgt_data_path=cache_url, rank=rank_id)
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model_parallel_num = args_opt.op_level_model_parallel_num
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stage_device_num = int(device_num / args_opt.stage_num)
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data_parallel_num = int(stage_device_num / model_parallel_num)
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per_batch_size = args_opt.per_batch_size
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batch_size = per_batch_size * data_parallel_num * args_opt.micro_size
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config = PANGUALPHAConfig(
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data_parallel_num=data_parallel_num,
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model_parallel_num=model_parallel_num,
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batch_size=batch_size,
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seq_length=args_opt.seq_length,
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vocab_size=args_opt.vocab_size,
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embedding_size=args_opt.embedding_size,
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num_layers=args_opt.num_layers,
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num_heads=args_opt.num_heads,
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expand_ratio=4,
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post_layernorm_residual=False,
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dropout_rate=0.1,
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compute_dtype=mstype.float16,
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use_past=False,
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stage_num=args_opt.stage_num,
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micro_size=args_opt.micro_size,
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word_emb_dp=bool(args_opt.word_emb_dp))
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print("===config is: ", config, flush=True)
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pangu_alpha = PanguAlpha(config)
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loss = CrossEntropyLoss(config)
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pangu_alpha_with_loss = PipelineCell(PanguAlphaWithLoss(config, pangu_alpha, loss), config.micro_size)
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pangu_alpha_with_loss = _VirtualDatasetCell(pangu_alpha_with_loss)
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print("=====args_opt is: ", args_opt, flush=True)
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lr = LearningRate(learning_rate=args_opt.start_lr, end_learning_rate=args_opt.end_lr,
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warmup_steps=args_opt.warmup_step, decay_steps=args_opt.decay_steps)
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params = pangu_alpha.infer_param_pipeline_stage()
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decay_filter = lambda x: 'layernorm' not in x.name.lower() and "bias" not in x.name.lower()
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decay_params = list(filter(decay_filter, params))
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other_params = list(filter(lambda x: not decay_filter(x), params))
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group_params = [{
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'params': decay_params,
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'weight_decay': 1e-1
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}, {
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'params': other_params,
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'weight_decay': 0.0
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}, {
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'order_params': params
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}]
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if args_opt.optimizer == "lamb":
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optimizer = nn.Lamb(group_params, learning_rate=lr)
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else:
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optimizer = nn.AdamWeightDecay(group_params, learning_rate=lr, beta1=0.9, beta2=0.95, eps=1e-8)
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if context.get_auto_parallel_context("full_batch"):
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ds = create_dataset(config.batch_size, data_path=cache_url, eod_reset=True,
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data_start_index=0, full_batch=True, column_name=args_opt.data_column_name)
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else:
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if batch_size % stage_device_num != 0:
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raise ValueError("Batch_size should be divisible by device_num")
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ds = create_dataset(config.batch_size, data_path=cache_url, device_num=stage_device_num,
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rank=rank_id, eod_reset=True, data_start_index=0, full_batch=False,
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column_name=args_opt.data_column_name)
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epoch_num = args_opt.epoch_size
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step_per_epoch = ds.get_dataset_size()
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callback_size = args_opt.sink_size
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actual_epoch_num = int(epoch_num * step_per_epoch / callback_size)
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callback = [TimeMonitor(callback_size), LossCallBack(callback_size, rank_id, config.stage_num, config.micro_size)]
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loss_scale_value = math.pow(2, 32)
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update_cell = DynamicLossScaleUpdateCell(loss_scale_value=loss_scale_value, scale_factor=2, scale_window=1000)
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pangu_alpha_with_grads = PanguAlphaTrainPipelineWithLossScaleCell(
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pangu_alpha_with_loss, optimizer=optimizer, config=config, scale_update_cell=update_cell)
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model = Model(pangu_alpha_with_grads)
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model.train(actual_epoch_num, ds, callbacks=callback,
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sink_size=callback_size, dataset_sink_mode=True)
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if __name__ == "__main__":
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opt = get_args()
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set_parse(opt)
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if opt.per_batch_size == 0:
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raise ValueError("The per_batch_size has not been configured.")
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if opt.stage_num > 1:
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run_train_pipeline(opt)
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else:
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run_train(opt)
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