|
- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """
- PanguAlpha train script
- """
- import datetime
- import glob
- import os
- import math
- import time
- import random
- import mindspore
- import moxing as mox
- from pathlib2 import Path
- from mindspore import context
- from mindspore.train.model import Model
- import mindspore.communication.management as D
- from mindspore.context import ParallelMode
- import mindspore.nn as nn
- from mindspore.train.callback import TimeMonitor, Callback
- from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
- import mindspore.common.dtype as mstype
- from mindspore.parallel import set_algo_parameters
- from mindspore.parallel._cost_model_context import _set_multi_subgraphs
- from mindspore.nn.wrap.cell_wrapper import _VirtualDatasetCell # PipelineCell,
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
-
- from dataset_restore_data0 import create_dataset2 as create_dataset
- from dataset_restore_data0 import get_input_data2
- from src.pangu_alpha_tiny import PanguAlpha, PanguAlphaWithLoss, CrossEntropyLoss, EvalNet_p, generate_samples_cftpd, GPTWithLoss_gd, PanguAlphaWithLoss_tiny, GPTWithLoss_gd_withCrEN
- from src.pangu_alpha_wrapcell import PanguAlphaTrainOneStepWithLossScaleCell, VirtualDatasetOneInputCell
- from src.pangu_alpha_config import PANGUALPHAConfig, set_parse
- from src.utils import LearningRate, get_args, FP32StateAdamWeightDecay
-
- from mindspore.train.serialization import load_checkpoint, load_param_into_net, build_searched_strategy, merge_sliced_parameter
- import numpy as np
- from mindspore import Tensor
- from mindspore.parallel._auto_parallel_context import auto_parallel_context
-
-
- from utils_fix import LossSummaryCallback, StrategySaveCallback, Ckpt2ObsSummaryCallback
- # BUCKET_DIR = 'obs://datasets/V1-sample300-bpe-1024/'
- # LOCAL_PATH = "/cache/V1-sample300-bpe-1024/"
-
- BUCKET_DIR = 'obs://pcl-verify/yizx/distilPangu/datasets/pd_noBlank_0918/'
- LOCAL_PATH = "/cache/pd_noBlank_0918/"
-
- # BUCKET_DIR = 'obs://pcl-verify/yizx/distilPangu/datasets/pd_noBlank_noPET/'
- # LOCAL_PATH = "/cache/pd_noBlank_noPET/"
-
- def ckpt_copy_tar(obs_path, target_path="/cache/ckpt"):
- """
- requires the obs_path to be a complete name
- Copy tar file from the obs to the /cache/
- """
- sub_name_list = ['_0.tar', '_1.tar', '_2.tar', '_3.tar']
- for item in sub_name_list:
- sub_name = obs_path + item
- tmp_name = 'model.tar'
- mox.file.copy(sub_name, os.path.join(target_path, tmp_name))
- os.system('cd {}; tar -xvf {}'.format(target_path, tmp_name))
-
- def get_ckpt_file_list(ckpt_path, slices_num=64):
- returned_list = []
- for i in range(0, slices_num):#512):
- returned_list.append('filerted_{}.ckpt'.format(i))
- returned_list = [os.path.join(ckpt_path, item) for item in returned_list if 'embedding' not in item]
- print("Sorted list", returned_list)
- for item in returned_list:
- fsize = os.path.getsize(item)
- f_gb = fsize / float(1024) / 1024 / 1024
- print(item, " :{:.2f}".format(f_gb))
- return returned_list
-
- class LossCallBack(Callback):
- """
- Monitor the loss in training.
- If the loss in NAN or INF terminating training.
- """
-
- def __init__(self, dataset_size=-1, local_rank=0, has_trained_epoch=0, has_trained_step=0, micro_size=1):
- super(LossCallBack, self).__init__()
- self._dataset_size = dataset_size
- self.local_rank = local_rank
- self.has_trained_epoch = has_trained_epoch
- self.has_trained_step = has_trained_step
- self.micro_size = micro_size
- print("load has trained epoch :{} and step: {}".format(has_trained_epoch, has_trained_step), flush=True)
-
- def step_end(self, run_context):
- """
- Print loss after each step
- """
- cb_params = run_context.original_args()
- if self._dataset_size > 0 and self.local_rank % 8 == 0:
- percent, epoch_num = math.modf(cb_params.cur_step_num /
- self._dataset_size)
- if percent == 0:
- epoch_num -= 1
- date = time.asctime(time.localtime(time.time()))
- loss_value = cb_params.net_outputs[0].asnumpy() / self.micro_size
- print("time: {} local_rank: {}, epoch: {}, step: {}, loss is {}, overflow is {}, scale is {}, lr is {}".
- format(date, int(self.local_rank), int(epoch_num) + int(self.has_trained_epoch),
- cb_params.cur_step_num + int(self.has_trained_step), loss_value,
- cb_params.net_outputs[1].asnumpy(), cb_params.net_outputs[2].asnumpy(),
- cb_params.net_outputs[3].asnumpy()
- ))
-
-
- project_root = os.path.abspath(
- os.path.dirname(os.path.realpath(__file__)) + os.path.sep + "..")
- print('project_root:', project_root)
-
-
- def count_params(net):
- """Count number of parameters in the network
- Args:
- net (mindspore.nn.Cell): Mindspore network instance
- Returns:
- total_params (int): Total number of trainable params
- """
- total_params = 0
- for param in net.trainable_params():
- total_params += np.prod(param.shape)
- return total_params
-
- def run_train(args_opt):
- r"""
- The main training process.
- """
- # Set hccl connect time
- os.environ['HCCL_CONNECT_TIMEOUT'] = "6000"
- EXEC_PATH = os.path.join(project_root, 'tiny_pangu')
- device_id = int(os.getenv("DEVICE_ID"))
- rank_id_str = os.getenv('RANK_ID', '0')
- rank_id = int(
- rank_id_str[rank_id_str.rfind('-') +
- 1:]) # 'RANK_ID': 'job24535502-job-facereidtome-hn-0/1'
- print('rank_id:{}'.format(rank_id), "rank_id str:{}".format(rank_id_str))
- device_id = int(os.getenv('DEVICE_ID'))
- local_rank = rank_id
- print('local_rank:{}, device id:{}'.format(local_rank, device_id))
-
- # copy strategy_ckpt
- pretrained_strategy_ckpt_path = "/cache/strategy/ckpt_strategy_{}.ckpt".format(local_rank)
- ###########################################################################################################
- # 云脑2的obs文件位置并拉取到本地/cache
- TEMP = ['tinyPangu_gd300G_bs8_node64_dp_pretrain-75000_2',
- 'tinyPangu368M_gd300G_epoch2_bs2_node64_logitKLDivSum_WithCross-179000_2',
- 'tinyPangu368M_gd300G_epoch2_bs2_node32_logitKLDivMean_WithCross-142000_2',
- '368M_055_logitsKL_WithCE_first300G_epoch2_bs4_node64_tinyPangu-yizx-82000_2', # 纯logits-KLDivSUM蒸馏
- '368M_logitsKLOnly_first300G_epoch2_bs4_node64_tinyPangu-yizx-154000_2',
- 'Newexp65_GPT3_2-3494_2'
- ]
- INDEX = -1
- ################ Rename the savingName ################################################
- IsParaOPT = True
- slices_num = 512
- TMP_NAME = '600G_CEPretrain2_6B_finetune_PD_PET_mp8_bs8_node32_Loadnpys_LR5e6_5e7_UsingHXJ'
- args_opt.bucket_dir = 's3://pcl-verify/yizx/distilPangu/ckpt/{}'.format(TMP_NAME)
- args_opt.soma_bucket_dir = 's3://pcl-verify/yizx/distilPangu/soma/{}'.format(TMP_NAME)
- args_opt.epoch_size = 4 # 4 for finetune
- args_opt.decay_steps = 4000 # 2w for finetune 20G
- args_opt.save_step = 20
- args_opt.start_lr = 5e-6
- args_opt.end_lr = 5e-7
- args_opt.warmup_step = 200
-
- #######################################################################################
-
- # args_opt.ckpt_path = 'obs://pcl-verify/yizx/distilPangu/merged_ckpt/{}/{}part'.format(TEMP[INDEX], TEMP[INDEX])
- args_opt.tokenizer_path = 's3://pcl-verify/inference-output/'
- if IsParaOPT:
- args_opt.ckpt_path = 's3://mindspore-file/huangxinjing/filtered_ckpt/{}/{}part'.format(TEMP[INDEX], TEMP[INDEX])
- args_opt.word_embedding_path = 's3://mindspore-file/huangxinjing/filtered_ckpt/{}/{}_word_embedding.npy'.format(TEMP[INDEX], TEMP[INDEX])
- args_opt.position_embedding_path = 's3://mindspore-file/huangxinjing/filtered_ckpt/{}/{}_position_embedding.npy'.format(TEMP[INDEX], TEMP[INDEX])
- args_opt.top_query_embedding_path = 's3://mindspore-file/huangxinjing/filtered_ckpt/{}/{}_top_query_embedding.npy'.format(TEMP[INDEX], TEMP[INDEX])
- mox.file.copy(src_url="obs://mindspore-file/strategy_ckpt/gpt_1024_13b_exp65cktp_strategy.ckpt", dst_url=pretrained_strategy_ckpt_path)
-
- else:
- if slices_num == 16:
- mox.file.copy(src_url="obs://pcl-verify/yizx/distilPangu/strategy_ckpt/368M_epoch2_bs2_node16_strategy_generatecktp_strategy.ckpt", dst_url=pretrained_strategy_ckpt_path)
- elif slices_num == 32:
- mox.file.copy(src_url="obs://pcl-verify/yizx/distilPangu/strategy_ckpt/368M_epoch2_bs2_node32_strategy_generatecktp_strategy.ckpt", dst_url=pretrained_strategy_ckpt_path)
- elif slices_num == 64:
- mox.file.copy(src_url="obs://pcl-verify/yizx/distilPangu/strategy_ckpt/368M_epoch2_bs2_node64_strategy_generatecktp_strategy.ckpt", dst_url=pretrained_strategy_ckpt_path)
- else:
- mox.file.copy(src_url="obs://mindspore-file/strategy_ckpt/gpt_1024_13b_exp65cktp_strategy.ckpt", dst_url=pretrained_strategy_ckpt_path)
- print('@@@ 使用ckpt为: {}-student.ckpt @@@\n'.format(TEMP[INDEX]))
-
- # donload dataset
- if local_rank % 8 == 0:
- print('MindSpore path:', mindspore)
- print("Modify the time out from 300 to 30000")
- tbe_path = "/usr/local/ma/python3.7/lib/python3.7/site-packages/mindspore" \
- "/_extends/parallel_compile/tbe_compiler/tbe_process.py"
- os.system(
- "sed -i 's/300/30000/g' " + tbe_path
- )
- os.system(
- "sed -i 's/330/33000/g' " + tbe_path
- )
- print("begin download dataset", flush=True)
-
- cache_url = LOCAL_PATH
- if not os.path.exists(LOCAL_PATH):
- Path(LOCAL_PATH).mkdir(parents=True, exist_ok=True)
-
-
- files = os.listdir(LOCAL_PATH)
- data = [
- os.path.join(LOCAL_PATH, name) for name in files
- if not name.endswith(".db")
- ]
- if len(data) == 0:
- print("Start to copy the dataset", flush=True)
- Path(cache_url).mkdir(parents=True, exist_ok=True)
- mox.file.copy_parallel(src_url=BUCKET_DIR, dst_url=LOCAL_PATH)
- print("@@@@@@ Dataset download succeed! @@@@@@@", flush=True)
- mox.file.copy(args_opt.word_embedding_path, '/cache/word_embedding.npy')
- mox.file.copy(args_opt.position_embedding_path, '/cache/position_embedding.npy')
- mox.file.copy(args_opt.top_query_embedding_path, '/cache/top_query_embedding.npy')
- if IsParaOPT:
- ckpt_copy_tar(args_opt.ckpt_path, target_path="/cache/ckpt_files")
- else:
- tmp_student_path = 'obs://pcl-verify/yizx/distilPangu/merged_ckpt/{}/{}-student.ckpt'.format(TEMP[INDEX], TEMP[INDEX])
- mox.file.copy(src_url=tmp_student_path, dst_url='/tmp/student.ckpt')
-
- os.environ['HCCL_CONNECT_TIMEOUT'] = "6000"
- os.system('ulimit -s 102400')
- print(args_opt.ckpt_path)
- # ckpt_copy_tar(args_opt.ckpt_path, target_path="/cache/ckpt_files")
- # mox.file.copy('obs://pcl-verify/yizx/distilPangu/merged_ckpt/Newexp65_GPT3_2-3494_2.ckpt', '/cache/Newexp65_GPT3_2-3494_2.ckpt')
- print("setting env success.")
- f = open("%s/install.txt" % (EXEC_PATH), 'w')
- f.close()
- # 此处用于阻塞其他进程,直到刷包以及下载数据集完成为止
- while not os.path.exists("%s/install.txt" % (EXEC_PATH)):
- time.sleep(1)
- print('local_rank:{}, device id:{} start to run...'.format(
- local_rank, device_id),
- flush=True)
-
-
- # Set execution mode
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
- context.set_context(variable_memory_max_size="31GB")
- strategy_file = '/tmp/cktp_strategy.ckpt'
- # Set parallel context
- if args_opt.distribute == "true":
- D.init()
- device_num = D.get_group_size()
- rank = D.get_rank()
- print("rank_id is {}, device_num is {}".format(rank, device_num))
-
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(
- parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False,
- device_num=device_num,
- full_batch=False,
- strategy_ckpt_load_file=pretrained_strategy_ckpt_path,
- enable_parallel_optimizer=True,
- strategy_ckpt_save_file=strategy_file)
- auto_parallel_context().set_loss_repeated_mean(True)
- set_algo_parameters(elementwise_op_strategy_follow=True)
- _set_multi_subgraphs()
- else:
- rank = 0
- device_num = 1
- context.set_context(save_graphs=False, save_graphs_path="/cache/" + str(rank))
- # copy data from the cloud to the /cache/Data
- cache_url = '/cache/Data/'
-
-
- # Set model property
- model_parallel_num = 8 #args_opt.op_level_model_parallel_num
- data_parallel_num = int (device_num / model_parallel_num)
- args_opt.per_batch_size = 8
- batch_size = args_opt.per_batch_size * data_parallel_num
- print("@@@@@ batch_size_perDevice is : {} @@@@@".format(batch_size))
-
- student_config = PANGUALPHAConfig(
- data_parallel_num=data_parallel_num, model_parallel_num=model_parallel_num, batch_size=batch_size,
- seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, embedding_size=args_opt.embedding_size,
- num_layers=args_opt.num_layers, num_heads=args_opt.num_heads, expand_ratio=4, dropout_rate=0.1,
- compute_dtype=mstype.float16, stage_num=args_opt.stage_num, micro_size=args_opt.micro_size,
- eod_reset=bool(args_opt.eod_reset), load_ckpt_path=args_opt.load_ckpt_path,
- param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16,
- word_emb_dp=bool(args_opt.word_emb_dp))
-
- # student_config = PANGUALPHAConfig(
- # data_parallel_num=data_parallel_num, model_parallel_num=model_parallel_num, batch_size=batch_size,
- # seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, embedding_size=1280,
- # num_layers=int(args_opt.num_layers / 2), num_heads=args_opt.num_heads, expand_ratio=4, dropout_rate=0.1,
- # compute_dtype=mstype.float16, stage_num=args_opt.stage_num, micro_size=args_opt.micro_size,
- # eod_reset=bool(args_opt.eod_reset), load_ckpt_path=args_opt.load_ckpt_path,
- # param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16,
- # word_emb_dp=bool(args_opt.word_emb_dp))
-
- # student_config = teacher_config
- print("===student_config is: ", student_config, flush=True)
-
- # Define network
- if IsParaOPT:
- gpt = PanguAlpha(student_config, is_teacher=True)
- else:
- # gpt = PanguAlpha(student_config, is_teacher=False)
- gpt = PanguAlpha(student_config, is_teacher=True) # for 2.6B pangu
- loss = CrossEntropyLoss(student_config)
- gpt_with_loss = PanguAlphaWithLoss_tiny(student_config, gpt, loss, eos_token=args_opt.eod_id)
- gpt_with_loss = VirtualDatasetOneInputCell(gpt_with_loss)
- pangu_alpha_with_loss = gpt_with_loss
-
- if IsParaOPT:
- from mindspore.train.serialization import load_distributed_checkpoint
- ckpt_file_list = get_ckpt_file_list('/cache/ckpt_files', slices_num)
- # fake_inputs = np.ones(shape=(1, student_config.seq_length))
- # fake_input, fake_input_position, fake_attention_mask = get_input_data2(fake_inputs, 9, 0, 1)
- # fake_input = Tensor(fake_input, mstype.int32)
- # fake_input_position = Tensor(fake_input_position, mstype.int32)
- # fake_attention_mask = Tensor(fake_attention_mask, mstype.int32)
- # predict_layout = model.infer_predict_layout(fake_input, fake_input_position, fake_attention_mask)
- load_distributed_checkpoint(gpt_with_loss, ckpt_file_list)#, predict_layout)
- else:
- # load top_layers_params
- params_dict = load_checkpoint('/tmp/student.ckpt')
- load_param_into_net(gpt_with_loss, params_dict)
-
- print('##### PANGU partial parameter size is: {} #####'.format(count_params(gpt_with_loss)))
- print("=====args_opt is: ", args_opt, flush=True)
-
- # only finetune layerNormParams
-
-
- # Warm-up and cosine decay learning rate
- lr = LearningRate(learning_rate=args_opt.start_lr, end_learning_rate=args_opt.end_lr,
- warmup_steps=args_opt.warmup_step, decay_steps=args_opt.decay_steps)
-
- # Set weight decay coefficient, zero for bias and layernorm, 1e-1 for rest
- decay_filter = lambda x: 'layernorm' not in x.name.lower() and "bias" not in x.name.lower()
- params = pangu_alpha_with_loss.trainable_params()
- decay_params = list(filter(decay_filter, params))
- other_params = list(filter(lambda x: not decay_filter(x), params))
- group_params = [{
- 'params': decay_params,
- 'weight_decay': 1e-1
- }, {
- 'params': other_params,
- 'weight_decay': 0.0
- }, {
- 'order_params': params
- }]
- if args_opt.optimizer == "lamb":
- optimizer = nn.Lamb(group_params, learning_rate=lr)
- else:
- optimizer = FP32StateAdamWeightDecay(group_params, learning_rate=lr, eps=1e-8, beta1=0.9, beta2=0.98)
- # Initial scaling sens
- loss_scale_value = math.pow(2, 32)
- epoch_num = args_opt.epoch_size
-
- ds = create_dataset(student_config.batch_size, data_path=LOCAL_PATH, data_start_index=0, eod_reset=student_config.eod_reset, eod_id=args_opt.eod_id, device_num=device_num, rank=rank, hash_check=True)
-
- ckpt_dir = os.path.join("/cache/ckpt/", f"rank_{str(local_rank)}")
- # create dir for ckpt
- if not os.path.exists(ckpt_dir):
- Path(ckpt_dir).mkdir(parents=True, exist_ok=True)
-
- step_per_epoch = ds.get_dataset_size()
- callback_size = args_opt.sink_size
- actual_epoch_num = int(epoch_num * step_per_epoch / callback_size)
- callback = [
- TimeMonitor(callback_size),
- LossCallBack(callback_size, rank, 0, 0)
- ]
-
- config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_step,
- keep_checkpoint_max=1,
- integrated_save=False)
- ckpoint_cb = ModelCheckpoint(prefix="tinyPangu368M-yizx",
- directory=ckpt_dir,
- config=config_ck)
- ckpt2obs_cb = Ckpt2ObsSummaryCallback(local_ckpt_dir=ckpt_dir,
- local_rank=0,
- has_trained_epoch=0,
- has_trained_step=0,
- bucket=args_opt.bucket_dir + '/' + str(local_rank),
- syn_obs_steps=args_opt.save_step)
- callback.append(ckpoint_cb)
- callback.append(ckpt2obs_cb)
-
- if local_rank == 0:
- sub_dir = args_opt.bucket_dir.split('/')[-1]
- callback.append(LossSummaryCallback(summary_dir="summary",
- local_rank=0,
- has_trained_epoch=0,
- has_trained_step=0,
- bucket='obs://pcl-verify/yizx/distilPangu/summary/' + sub_dir,
- syn_times=40))
- callback.append(StrategySaveCallback(strategy_path=strategy_file,
- local_rank=0,
- has_trained_epoch=0,
- has_trained_step=0,
- bucket='obs://pcl-verify/yizx/distilPangu/strategy_ckpt/' + sub_dir))
- #callback.append(ckpt2obs_cb)
-
- update_cell = DynamicLossScaleUpdateCell(loss_scale_value=loss_scale_value, scale_factor=2, scale_window=1000)
- pangu_alpha_with_grads = PanguAlphaTrainOneStepWithLossScaleCell(
- pangu_alpha_with_loss, optimizer=optimizer, scale_update_cell=update_cell, enable_global_norm=True,
- config=student_config)
- model = Model(pangu_alpha_with_grads)
- print("Dataset size: {}, actual_epoch_num: {}".format(ds.get_dataset_size(), actual_epoch_num), flush=True)
- time.sleep(10)
- model.train(actual_epoch_num, ds, callbacks=callback, sink_size=callback_size, dataset_sink_mode=True)
-
-
-
- if __name__ == "__main__":
- opt = get_args()
- set_parse(opt)
- if opt.per_batch_size == 0:
- raise ValueError("The per_batch_size has not been configured.")
-
- os.system('ifconfig -a')
- run_train(opt)
|