pax_global_header00006660000000000000000000000064145400141040014503gustar00rootroot0000000000000052 comment=d2e3e037fc6b1d18b2c54e91d4f4b6f297ee8b2f project_1/000077500000000000000000000000001454001410400127355ustar00rootroot00000000000000project_1/README.md000066400000000000000000000064401454001410400142200ustar00rootroot00000000000000# 赛题一：并行效率 ## 介绍 我们提供了Llama 7B的模型，要求选手在不修改模型结构和训练参数的情况下，通过优化和调整并行策略来提高训练的效率。 ### 模型 我们提供的模型为Llama 7B，词表采用了Baichuan2的词表，训练的文本长度为4096。请选手在训练中设置`mode`参数为`7B`，且不要修改代码中对模型层数、head数目等配置，保证模型的总参数量不发生变化。 ### 数据集 我们提供了Belle数据集，由[Baichuan2](https://openi.pcl.ac.cn/FoundationModel/Baichuan2.git)处理得到。请在训练任务中设置`dataset_name`为`belle`。不要额外处理数据集的内容。 ### 镜像 请采用`mindspore2.0-cann6-for-xianxing`，该镜像的mindspore版本为2.0 beta，请参考mindspore的2.0版本的文档。 ### 训练配置 训练中我们还需要保证训练精度的配置。整体上是用fp16来进行训练，但是Layernorm和Softmax算子是用fp32来训练。 每个迭代总共的batch size大小要求为32，其计算方法为`data parallel * micro_batch_num * per_batch_size`。 ### 收敛性 由于并行策略的修改，模型参数初始化可能存在区别，所以loss数值不完全相同。但是loss曲线在整体趋势和误差上不应相差太大。 ## 选手能够修改的内容 ### 并行策略 目前mindspore支持张量并行、数据并行、优化器并行和流水并行。 * 张量并行：请参考Megatron-LM的论文，具体配置参数为`op_level_model_parallel_num` * 流水并行：mindspore采用了DAPPLE的1F1B的调度策略，流水并行大小可以通过参数`stage_num`来配置。但是具体的计算图切分策略可以由选手自己指定，通过配置模块的`pipeline`来设置 * 数据并行：其大小为总的设备数量(16卡)除以张量并行和流水并行的大小。在数据并行方面，我们需要保证总的batch size大小不变，即上面的公式算得的总batch size大小为32。其中micro batch数量的配置参数为`micro_size` * 优化器并行：即在数据并行的基础上进一步切分优化器，以减少存储量。具体的配置参数为`optimizer_shard`，其大小应小于等于数据并行的大小 * 细化的并行策略：用户可以通过修改模型中算子的shard接口来调整每个算子的具体并行策略 ### 重计算策略 重计算是通过舍弃前向过程中的中间结果，在反向重新计算来节省显存。 选手可以根据具体的显存用量决定是否采用全部重计算（参数为`recompute`），选择重计算（参数为`select_recompute`），或者不重计算（默认配置）。进一步的，用户可以定义每个算子是否需要重计算来优化训练性能。 ### Flash Attention算子 可以参考Flash Attention的论文，和MindSpore仓库中对flash attention的实现，将其移植到目前的代码中，优化该部分的计算。 ### 其他优化 选手可以参考相关论文引入其他优化，但是需要保证模型结构、收敛性不受到影响。 ## 参考的训练参数配置 ``` # 请不要修改 mode = 7B full_batch = 0 dataset_name = belle save_checkpoint = False # 可以修改 op_level_model_parallel_num = 4 stage_num = 2 recompute = True sequence_parallel = True optimizer_shard = 2 micro_size = 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"dynamicShapeSupport":{ "flag":"true" }, "input0":{ "dtype":"float16,float,int32,int8,uint8,bool", "name":"x", "paramType":"required" }, "op":{ "pattern":"formatAgnostic" }, "output0":{ "dtype":"float16,float,int32,int8,uint8,bool", "name":"y", "paramType":"required", "shape":"all" }, "slicePattern":{ "value":"elemwise" } } }project_1/ma-pre-start.sh000066400000000000000000000024301454001410400156040ustar00rootroot00000000000000#!/bin/bash # Copyright 2020 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. # ============================================================================ #tbe=tbe_process.py #taoht_cur_file=mindspore_ascend-1.1.0-cp37-cp37m-linux_aarch64_yyf_2-20.whl mindspore_file=mindspore-2.0.0-cp37-cp37m-linux_aarch64.whl ## hccl_so_file=libhccl.so LOCAL_DIR=$(cd "$(dirname "$0")";pwd) echo $LOCAL_DIR ## yes | pip uninstall mindspore-ascend # stage=32时会引起图编译进程过多导致内存爆掉任务失败，做编译进程限制 # export MS_BUILD_PROCESS_NUM=8 # 查看包的版本 cat /usr/local/ma/python3.7/lib/python3.7/site-packages/mindspore/.commit_id ls / export MS_DEV_REDUNDANCY_TASK_NUM=4 export MS_DEV_CELL_REUSE=2 export GLOG_v=2 export MS_DATASET_SINK_QUEUE=5 project_1/parallel_speedup.json000066400000000000000000000002771454001410400171570ustar00rootroot00000000000000{ "recompute_comm_overlap": false, "matmul_grad_comm_overlap": false, "enable_task_opt": true, "interleaved_matmul_comm": false, "interleaved_layernorm_comm": false } project_1/run_llama2.py000066400000000000000000000446641454001410400153610ustar00rootroot00000000000000# 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. # ============================================================================ import datetime import json import glob import os import math import time from mindspore import context from mindspore.train.model import Model import mindspore.communication.management as D import mindspore.nn as nn from mindspore.train.callback import TimeMonitor 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 PipelineCell, _VirtualDatasetCell, MicroBatchInterleaved from mindspore.nn.transformer import TransformerOpParallelConfig, CrossEntropyLoss, TransformerRecomputeConfig from mindspore.train.callback import ModelCheckpoint, CheckpointConfig from mindspore.train.serialization import load_distributed_checkpoint, load_checkpoint, load_param_into_net from src.adam import AdamWeightDecayOp from src.dataset import create_dataset from src.llama import LlamaWithLoss, LlamaModel from src.llama_wrapcell import LlamaTrainPipelineWithLossScaleCell from src.model_config import ModelConfig, set_parse from src.utils import LearningRate, get_args, FP32StateAdamWeightDecay from src.callbacks import LossCallBack, LossSummaryCallback from src.utils import download_data from src.utils import CheckpointSaveCallback from mindspore.common import Parameter from mindspore.common.tensor import Tensor import numpy as np from mindspore import Profiler from mindspore.train import Callback import mindspore project_root = os.path.abspath( os.path.dirname(os.path.realpath(__file__))) print('project_root:', project_root) class ProfileStep(Callback): def __init__(self, start_step, stop_step, output_path): super(ProfileStep, self).__init__() self.start_step = start_step self.stop_step = stop_step self.profiler = Profiler(start_profile=False, output_path=output_path) def step_begin(self, run_context): cb_params = run_context.original_args() step_num = cb_params.cur_step_num if step_num == self.start_step: self.profiler.start() def step_end(self, run_context): cb_params = run_context.original_args() step_num = cb_params.cur_step_num if step_num == self.stop_step: self.profiler.stop() self.profiler.analyse() def set_weight_decay(params): """ 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() 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 }] return group_params def add_checkpoint_callback_policy(args_param, callback, rank_id): r""" Add checkpoint policy to callback. """ if args_param.save_checkpoint: if not os.path.exists(args_param.save_checkpoint_path): os.makedirs(args_param.save_checkpoint_path, exist_ok=True) # checkpoint store epoch_num and step_num info ckpt_append_info = [{"epoch_num": args_param.has_trained_epoches, "step_num": args_param.has_trained_steps}] ckpt_config = CheckpointConfig(save_checkpoint_steps=args_param.save_checkpoint_steps, keep_checkpoint_max=1, integrated_save=False, append_info=ckpt_append_info ) save_dir_rank = os.path.join(args_param.save_checkpoint_path, f"rank_{rank_id}") save_ckptfile_name = args_param.ckpt_name_prefix + '_' + str(rank_id) if not os.path.exists(save_dir_rank): os.makedirs(save_dir_rank, exist_ok=True) ckpoint_cb = ModelCheckpoint(prefix=save_ckptfile_name, directory=save_dir_rank, config=ckpt_config) ckpt_save_obs_cb = CheckpointSaveCallback(local_ckpt_dir=save_dir_rank, local_rank=rank_id, has_trained_epoch=args_param.has_trained_epoches, has_trained_step=args_param.has_trained_steps, bucket=args_param.save_checkpoint_bucket_dir, syn_obs_steps=args_param.save_checkpoint_steps) callback.append(ckpoint_cb) callback.append(ckpt_save_obs_cb) if rank_id == (args_param.device_num-1): callback.append(LossSummaryCallback(summary_dir="summary", local_rank=rank_id, has_trained_epoch=args_param.has_trained_epoches, has_trained_step=args_param.has_trained_steps, bucket=args_param.save_summary_bucket_dir, syn_times=100)) #callback.append(StrategySaveCallback(strategy_path=f'/cache/strategy_{rank_id}.ckpt', # local_rank=rank_id, # has_trained_step=args_param.has_trained_steps, # bucket=args_param.save_strategy_bucket_dir)) def restore_checkpoint(args_param, sink_size, dataset, model, network, epoch, cache_url='/cache/Ckpt/'): r""" Load checkpoint process. """ print("======start single checkpoint", flush=True) ckpt_name = args_param.restore_ckpt_name_prefix restore_ranks = D.get_rank() ckpt_files = os.path.join(cache_url, f"rank_{restore_ranks}.ckpt") # f"{ckpt_name}_{restore_ranks}-{args_param.restore_steps}_4.ckpt") # f"rank_{restore_ranks}", # ckpt_files = glob.glob(ckpt_pattern) if not ckpt_files: print(f"There is no ckpt file in {cache_url}, in rank {restore_ranks}" f"current ckpt_files found is {os.listdir(cache_url)} " f"with pattern {ckpt_files}, so skip the loading.") time_stamp = datetime.datetime.now() print(f"time stamp {time_stamp.strftime('%Y.%m.%d-%H:%M:%S')} pre trained ckpt model {ckpt_files} loading", flush=True) # Load checkpoint files latest file print(f'Start to load from {ckpt_files}') param_dict = load_checkpoint(ckpt_files) # print(f"Load rank {restore_ranks} param_dict: \n", param_dict.keys()) # print(f"===global_step: {param_dict['global_step'].data.asnumpy()}") # print(f"===current_iterator_step: {param_dict['current_iterator_step'].data.asnumpy()}") # print(f"===step_num: {param_dict['step_num'].data.asnumpy()}") # print(f"===loss_scale: {param_dict['loss_scale'].data.asnumpy()}") # param_dict["loss_scale"] = Parameter(Tensor(268435456, dtype=mstype.float32), name="loss_scale") param_dict["loss_scale"] = Parameter(Tensor(param_dict['loss_scale'].data.asnumpy(), dtype=mstype.float32), name="loss_scale") param_dict["global_step"] = Parameter(Tensor([int(param_dict['global_step'].data.asnumpy())], dtype=mstype.int32), name="global_step") param_dict["current_iterator_step"] = Parameter(Tensor(int(param_dict['current_iterator_step'].data.asnumpy()), dtype=mstype.int32), name="current_iterator_step") param_dict["step_num"] = Parameter(Tensor(int(param_dict['step_num'].data.asnumpy()), dtype=mstype.int32), name="step_num") param_dict["epoch_num"] = Parameter(Tensor(int(param_dict['epoch_num'].data.asnumpy()), dtype=mstype.int32), name="epoch_num") param_dict["last_overflow_iterator_step"] = Parameter(Tensor(int(param_dict['last_overflow_iterator_step'].data.asnumpy()), dtype=mstype.int32), name="last_overflow_iterator_step") if param_dict.get("epoch_num") and param_dict.get("step_num"): # args_param.has_trained_epoches = int(param_dict["epoch_num"].data.asnumpy()) args_param.has_trained_step = int(param_dict["step_num"].data.asnumpy()) model.build(train_dataset=dataset, sink_size=sink_size, epoch=epoch) load_param_into_net(network, param_dict, strict_load=False) def set_pipeline_parallel_context(args_opt): r"""Set pipeline parallel context.""" D.init() device_num = D.get_group_size() rank_id = D.get_rank() print("rank_id is {}, device_num is {}".format(rank_id, device_num)) context.reset_auto_parallel_context() context.set_auto_parallel_context( parallel_mode=args_opt.parallel_mode, gradients_mean=False, full_batch=bool(args_opt.full_batch), loss_repeated_mean=True, device_num=device_num, enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_size=args_opt.optimizer_shard, parallel_optimizer_config={"gradient_accumulation_shard": False}, pipeline_stages=args_opt.stage_num, speedup_config_path=f"{project_root}/parallel_speedup.json", strategy_ckpt_save_file=f'/cache/strategy_{rank_id}.ckpt', enable_alltoall=bool(args_opt.enable_alltoall)) set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() return rank_id, device_num def run_train_pipeline(args_opt): r"""The main training process in pipeline.""" if args_opt.save_graph: graphs_url = '/home/ma-user/modelarts/outputs/train_url_0' print(f"graphs_url: {graphs_url}\n") context.set_context(save_graphs=2, save_graphs_path=graphs_url, mode=context.GRAPH_MODE, device_target=args_opt.device_target, op_timeout=0) else: context.set_context(save_graphs=False, mode=context.GRAPH_MODE, device_target=args_opt.device_target, op_timeout=0)# task优化 context.set_context(max_device_memory="31GB") # mindspore.set_seed(1234) # context.set_context(save_graphs=2, save_graphs_path="/home/ma-user/modelarts/outputs/train_url_0") rank_id = 0 device_num = 1 if args_opt.distribute == "true": rank_id, device_num = set_pipeline_parallel_context(args_opt) # copy data from the cloud to the /cache/Data cache_url = '/cache/Data/' download_data(src_data_url=args_opt.data_url, tgt_data_path=cache_url, rank=rank_id) cache_url = f'{cache_url}{args_opt.dataset_name}' # download_data(src_data_url=args_opt.eval_data_url, tgt_data_path=eval_cache_url, rank=rank_id) model_parallel_num = args_opt.op_level_model_parallel_num stage_device_num = int(device_num / args_opt.stage_num) data_parallel_num = int(stage_device_num / model_parallel_num) is_last_stage = (rank_id // stage_device_num) == args_opt.stage_num - 1 per_batch_size = args_opt.per_batch_size batch_size = per_batch_size * data_parallel_num * args_opt.micro_size micro_batch_interleaved = args_opt.micro_batch_interleaved # recompute_config = TransformerRecomputeConfig(recompute=True, # recompute_slice_activation=bool(args_opt.recompute_slice_activation)) recompute_config = args_opt.recompute parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num, model_parallel=model_parallel_num, pipeline_stage=args_opt.stage_num, micro_batch_num=args_opt.micro_size, optimizer_shard=bool(args_opt.optimizer_shard), vocab_emb_dp=bool(args_opt.word_emb_dp), recompute=recompute_config) # add sequence_parallel parallel_config.sequence_parallel = args_opt.sequence_parallel # add select_recompute parallel_config.select_recompute = args_opt.select_recompute config = ModelConfig(batch_size=batch_size // parallel_config.micro_batch_num // micro_batch_interleaved, num_heads=args_opt.num_heads, hidden_size=args_opt.embedding_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, use_moe=bool(args_opt.use_moe), eod_token=args_opt.eod_id, num_layers=args_opt.num_layers, ffn_hidden_size=args_opt.ffn_hidden_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, enable_offload=bool(args_opt.opt_offload), parallel_config=parallel_config, use_rope=args_opt.use_rope, softmax_compute_fp32=args_opt.softmax_compute_fp32, use_flash_attention=args_opt.use_flash_attention, num_kv_heads=args_opt.num_kv_heads) # if args_opt.softmax_compute_type == "FP16": # config.softmax_compute_type = mstype.float16 # else: # config.softmax_compute_type = mstype.float32 print("[Configure] is: ", config, flush=True) model = LlamaModel(config=config) loss = CrossEntropyLoss(config.parallel_config.dp_mp_config) model_with_loss_net = PipelineCell(MicroBatchInterleaved(LlamaWithLoss(config, model, loss), micro_batch_interleaved), config.parallel_config.micro_batch_num) model_with_loss = _VirtualDatasetCell(model_with_loss_net) print("[args_opt] is: ", args_opt, flush=True) 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) params = model_with_loss.trainable_params() group_params = set_weight_decay(params) if args_opt.optimizer == "lamb": optimizer = nn.Lamb(group_params, learning_rate=lr) elif args_opt.opt_offload: optimizer = AdamWeightDecayOp(group_params, learning_rate=lr, eps=1e-8, beta1=0.9, beta2=0.98, param_init_type=config.param_init_type) else: optimizer = FP32StateAdamWeightDecay(group_params, learning_rate=lr, beta1=0.9, beta2=0.98, eps=1e-8) ds = create_dataset(config.batch_size * parallel_config.micro_batch_num * micro_batch_interleaved, data_path=cache_url, device_num=stage_device_num, rank=rank_id % stage_device_num, eod_reset=True, eod_id=args_opt.eod_id, data_start_index=args_opt.data_start_index, full_batch=context.get_auto_parallel_context("full_batch"), column_name=args_opt.data_column_name) print(f"train full batch: {context.get_auto_parallel_context('full_batch')}") epoch_num = args_opt.epoch_size step_per_epoch = ds.get_dataset_size() callback_size = args_opt.sink_size actual_epoch_num = int(epoch_num * step_per_epoch / callback_size) loss_callback = LossCallBack(step_per_epoch, rank_id, is_last_stage=is_last_stage, micro_size=parallel_config.micro_batch_num * micro_batch_interleaved) callback = [TimeMonitor(callback_size), loss_callback] if args_opt.enable_profiler: callback.append(ProfileStep(8, 12, f"/home/ma-user/modelarts/outputs/train_url_0/profile_{rank_id}")) loss_scale_value = math.pow(2, 27) update_cell = DynamicLossScaleUpdateCell(loss_scale_value=loss_scale_value, scale_factor=2, scale_window=500) model_with_grads = LlamaTrainPipelineWithLossScaleCell( model_with_loss, optimizer=optimizer, config=config, scale_update_cell=update_cell) model = Model(model_with_grads) if rank_id%8 == 0: count_parameters = 0 params_all = model_with_loss.get_parameters() for param in params_all: # print(param) param_size = param.shape if len(param_size) == 1: count_parameters += param_size[0] else: sum_ceil = 1 for i in param_size: sum_ceil *= i count_parameters += sum_ceil print("====="*10) print("PC Mind parameters :", count_parameters) print("====="*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.") if bool(opt.enable_alltoall) is True and bool(opt.use_moe) is False: raise ValueError("The alltoall communication is only effective when applying moe") os.environ['HCCL_CONNECT_TIMEOUT'] = str(opt.hccl_connect_time) print(f"opt: {opt}") if opt.stage_num > 1: run_train_pipeline(opt) else: print(f"Set pipline stage number for {opt.stage_num}") project_1/src/000077500000000000000000000000001454001410400135245ustar00rootroot00000000000000project_1/src/__init__.py000066400000000000000000000001061454001410400156320ustar00rootroot00000000000000from .loss import CrossEntropyLoss __all__ = ["CrossEntropyLoss"] project_1/src/adam.py000066400000000000000000000270151454001410400150050ustar00rootroot00000000000000# 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. # ============================================================================ import numpy as np from mindspore.common import dtype as mstype from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.common.initializer import initializer from mindspore.common.tensor import Tensor from mindspore.common.parameter import Parameter, ParameterTuple from mindspore._checkparam import Validator as validator from mindspore._checkparam import Rel from mindspore.nn.optim.optimizer import Optimizer _adam_opt = C.MultitypeFuncGraph("adam_opt") _scaler_one = Tensor(1, mstype.int32) _scaler_ten = Tensor(10, mstype.float32) _cpu_div = P.RealDiv().add_prim_attr("primitive_target", "CPU") @_adam_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool") def _update_run_kernel(opt, clip_value, beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, decay_flags, optim_filter): """ Update parameters by AdamWeightDecay op. """ success = True if optim_filter: if decay_flags: next_param = opt(param, m, v, lr, beta1, beta2, eps, weight_decay, P.Cast()(gradient, mstype.float16), clip_value) else: next_param = opt(param, m, v, lr, beta1, beta2, eps, 0.0, P.Cast()(gradient, mstype.float16), clip_value) return F.depend(success, next_param) return success def _check_param_value(beta1, beta2, eps, prim_name): """Check the type of inputs.""" validator.check_value_type("beta1", beta1, [float], prim_name) validator.check_value_type("beta2", beta2, [float], prim_name) validator.check_value_type("eps", eps, [float], prim_name) validator.check_float_range(beta1, 0.0, 1.0, Rel.INC_NEITHER, "beta1", prim_name) validator.check_float_range(beta2, 0.0, 1.0, Rel.INC_NEITHER, "beta2", prim_name) validator.check_positive_float(eps, "eps", prim_name) class AdamWeightDecayOp(Optimizer): """ Implements the Adam algorithm to fix the weight decay. It is a complete operator, not a combination of other ops. Note: When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive. To improve parameter groups performance, the customized order of parameters can be supported. Args: params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated, the element in `params` must be class `Parameter`. When the `params` is a list of `dict`, the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed. - params: Required. The value must be a list of `Parameter`. - lr: Optional. If "lr" is in the keys, the value of the corresponding learning rate will be used. If not, the `learning_rate` in the API will be used. - weight_decay: Optional. If "weight_decay" is in the keys, the value of the corresponding weight decay will be used. If not, the `weight_decay` in the API will be used. - order_params: Optional. If "order_params" is in the keys, the value must be the order of parameters and the order will be followed in the optimizer. There are no other keys in the `dict` and the parameters which in the 'order_params' must be in one of group parameters. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use the dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float. Default: 1e-3. beta1 (float): The exponential decay rate for the 1st moment estimations. Default: 0.9. Should be in range (0.0, 1.0). beta2 (float): The exponential decay rate for the 2nd moment estimations. Default: 0.999. Should be in range (0.0, 1.0). eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6. Should be greater than 0. weight_decay (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: tuple[bool], all elements are True. Supported Platforms: ``CPU`` Examples: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = AdamWeightDecayOp(params=net.trainable_params()) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params())) >>> group_params = [{'params': conv_params, 'weight_decay': 0.01}, ... {'params': no_conv_params, 'lr': 0.01}, ... {'order_params': net.trainable_params()}] >>> optim = AdamWeightDecayOp(group_params, learning_rate=0.1, weight_decay=0.0) >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01. >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0. >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'. >>> >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = Model(net, loss_fn=loss, optimizer=optim) """ def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0, clip_norm=1.0, param_init_type=mstype.float32): super(AdamWeightDecayOp, self).__init__(learning_rate, params, weight_decay) _check_param_value(beta1, beta2, eps, self.cls_name) self.beta1 = Tensor(np.array([beta1]).astype(np.float32)) self.beta2 = Tensor(np.array([beta2]).astype(np.float32)) self.eps = Tensor(np.array([eps]).astype(np.float32)) self.clip_norm = Tensor([clip_norm], mstype.float32) self.enable_init_fp16 = (param_init_type == mstype.float16) if self.enable_init_fp16: self.moments1 = self.clone_param32(prefix="adam_m", init='zeros') self.moments2 = self.clone_param32(prefix="adam_v", init='zeros') self.opt = P.FusedCastAdamWeightDecay() else: self.moments1 = self.parameters.clone(prefix="adam_m", init='zeros') self.moments2 = self.parameters.clone(prefix="adam_v", init='zeros') self.opt = P.AdamWeightDecay() self.hyper_map = C.HyperMap() self.opt.add_prim_attr("primitive_target", "CPU") def construct(self, gradients, clip_value): """AdamWeightDecayOp""" lr = self.get_lr() cond = P.GreaterEqual()(clip_value, self.clip_norm) global_norm = F.select(cond, clip_value, self.clip_norm) global_norm = P.Cast()(global_norm, mstype.float16) if self.is_group: if self.is_group_lr: optim_result = self.map_reverse(F.partial(_adam_opt, self.opt, global_norm, self.beta1, self.beta2, self.eps), lr, self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.map_reverse(F.partial(_adam_opt, self.opt, global_norm, self.beta1, self.beta2, self.eps, lr), self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.map_reverse(F.partial(_adam_opt, self.opt, global_norm, self.beta1, self.beta2, self.eps, lr, self.weight_decay), self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) if self.use_parallel: self.broadcast_params(optim_result) return optim_result def clone_param32(self, prefix, init=None): """ Clone the parameters in ParameterTuple element-wisely to generate a new ParameterTuple with float32 data type. Inputs: prefix (str): The prefix name of the parameters. init (Union[Tensor, str, numbers.Number]): Initialize the shape and dtype of the parameters. The definition of `init` is the same as in `Parameter` API. If `init` is 'same', the parameters in the new parameter tuple are the same as those in the original parameter tuple. Default: 'same'. Returns: Tuple, the new Parameter tuple. """ new = [] for old_param in self.parameters: param_init = init if init is None: param_init = old_param.init new_state = Parameter(initializer(param_init, shape=old_param.shape, dtype=mstype.float32)) new_state.param_info = old_param.param_info.clone() new_state.is_init = False new_state.is_param_ps = old_param.is_param_ps new_state.init_in_server = old_param.init_in_server new_state.cache_enable = old_param.cache_enable new_state.requires_aggr = old_param.requires_aggr if old_param.cache_shape: new_state.cache_shape = old_param.cache_shape new_state.name = prefix + '.' + new_state.name new.append(new_state) return ParameterTuple(new) project_1/src/callbacks.py000066400000000000000000000202031454001410400160120ustar00rootroot00000000000000# 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. # ============================================================================ """ Callbacks """ import time import math import numpy as np import moxing as mox from mindspore.train.callback import Callback from mindspore import context from mindspore.context import ParallelMode from mindspore.communication.management import get_rank from mindspore.train.summary import SummaryRecord from multiprocessing import Process from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype 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, is_last_stage=True): 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 self.is_last_stage = is_last_stage print("Load the 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 = 'no loss for this stage' if self.is_last_stage: loss_value = cb_params.net_outputs[0].asnumpy() / self.micro_size loss_value = np.mean(loss_value) print("time: {} local_rank: {}, epoch: {}, step: {}, loss is {}, overflow is {}, loss scale is {}, global norm value 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())) # print("time: {} local_rank: {}, epoch: {}, step: {}, loss is {}, overflow is {}, loss 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())) class LossSummaryCallback(Callback): def __init__(self, summary_dir, local_rank=0, has_trained_epoch=0, has_trained_step=0, micro_size=1, is_last_stage=True, bucket='obs://mindspore-file/loss_file/summary/', syn_times=20): self._summary_dir = summary_dir self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step self.bucket = bucket self.syn_times = syn_times self.micro_size = micro_size self.is_last_stage = is_last_stage if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) # print("entering") self.summary_record = SummaryRecord(self._summary_dir) def step_end(self, run_context): cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + int(self.has_trained_step) if self.is_last_stage: loss_value = cb_params.net_outputs[0].asnumpy() / self.micro_size loss_value = Tensor(np.mean(loss_value), dtype=mstype.float32) # create a confusion matric image, and record it to summary file self.summary_record.add_value('scalar', 'loss', loss_value) self.summary_record.add_value('scalar', 'loss scale', cb_params.net_outputs[2]) if len(cb_params.net_outputs) > 3: self.summary_record.add_value('scalar', 'global_norm', cb_params.net_outputs[3]) # if len(cb_params.net_outputs) > 4: # self.summary_record.add_value('scalar', 'global_norm', cb_params.net_outputs[4]) self.summary_record.record(cur_step) if cur_step % self.syn_times == 0: print("Copying summary to the bueckets start", flush=True) self.summary_record.flush() self.syn_files() print("Copying summary to the bueckets ends", flush=True) def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self._summary_dir, self.bucket), name="file_sync") process.start() class EvalCallBack(Callback): """ Monitor the ppl loss in evaluating. Note: If per_print_times is 0, do NOT print loss. Args: print_per_step (int): Print loss every times. Default: 1. """ def __init__(self, model, eval_dataset, ppl_metric, print_per_step=20, has_trained_step=0): super(EvalCallBack, self).__init__() if not isinstance(print_per_step, int) or print_per_step < 0: raise ValueError("print_per_step must be int and >= 0.") self.print_per_step = print_per_step self.model = model self.eval_dataset = eval_dataset self.pplMetric = ppl_metric self.has_trained_step = has_trained_step self.pplMetric.clear() self.parallel_mode = context.get_auto_parallel_context("parallel_mode") self.strategy_ckpt_save_file = context.get_auto_parallel_context("strategy_ckpt_save_file") self.strategy_ckpt_load_file = context.get_auto_parallel_context("strategy_ckpt_load_file") def step_end(self, run_context): """ step end """ cb_params = run_context.original_args() current_step = cb_params.cur_step_num + self.has_trained_step if current_step % self.print_per_step != 0: return self.pplMetric.clear() if self.parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL): context.set_auto_parallel_context(strategy_ckpt_save_file="", strategy_ckpt_load_file=self.strategy_ckpt_save_file) rank_id = 0 if self.parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL, ParallelMode.DATA_PARALLEL): rank_id = get_rank() start_time = time.time() out = self.model.eval(self.eval_dataset, dataset_sink_mode=True) end_time = time.time() eval_time = int(end_time - start_time) time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime()) out_str = "{} == Rank: {} == EvalCallBack model.eval(): {}; eval_time: {}s". \ format(time_str, rank_id, out.values(), eval_time) print(out_str) context.set_auto_parallel_context(strategy_ckpt_save_file=self.strategy_ckpt_save_file, strategy_ckpt_load_file=self.strategy_ckpt_load_file) project_1/src/dataset.py000066400000000000000000000222521454001410400155260ustar00rootroot00000000000000# Copyright 2021-2022 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. # ============================================================================ """ Create dataset for training and evaluating """ import os import numpy as np import mindspore.dataset as ds # import mindspore.dataset.transforms as C import mindspore.dataset.transforms.c_transforms as C import mindspore.common.dtype as mstype from mindspore import context def get_input_data_batch_slice_map(input_ids, rank, dis, eod_reset): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for rank: the current rank dis: the slice value for each rank eod_reset: whether to open eod reset or not returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ rank = int(rank) input_ids = input_ids[rank * dis: (rank + 1) * dis] if not eod_reset: return input_ids seq_length = input_ids.shape[1] - 1 # Initialize position_ids and attention_mask batch_input_ids = input_ids batch_position_ids = np.ones((dis, seq_length), dtype=np.int32) batch_attention_mask = np.ones((dis, seq_length, seq_length), dtype=np.int8) # Loop through batches for bs_i, _ in enumerate(range(len(input_ids))): # Get normal position_ids and attention_mask batch_attention_mask[bs_i] = np.tril(np.ones(shape=(seq_length, seq_length), dtype=np.int8)) batch_position_ids[bs_i] = np.arange(seq_length) return batch_input_ids, batch_position_ids, batch_attention_mask def get_input_data_batch_slice_map2(input_ids, position_id, rank, dis, eod_reset): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ rank = int(rank) position_id = position_id[rank * dis: (rank + 1) * dis] input_ids = input_ids[rank * dis: (rank + 1) * dis] if not eod_reset: return position_id seq_length = position_id.shape[1] tril_dev = np.tril(np.ones(shape=(seq_length, seq_length), dtype=np.int8)) # Initialize position_ids and attention_mask batch_attention_mask = np.ones((len(position_id), seq_length, seq_length), dtype=np.int8) batch_position_ids = np.ones((dis, seq_length), dtype=np.int32) # Loop through batches for bs_i in range(len(position_id)): # Get normal position_ids and attention_mask batch_attention_mask[bs_i] = tril_dev # Find eod_of_document local_ids = position_id[bs_i] batch_position_ids[bs_i] = np.arange(seq_length) eod_index = batch_position_ids[bs_i, local_ids == 0].astype(np.int32)[1:] - 1 for i in range(eod_index.size): # Reset position_ids and attention_mask considering EOD index = eod_index[i] batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 return input_ids, position_id, batch_attention_mask def get_input_data_batch_slice_map3(input_ids, position_id, eod_index, rank, dis, eod_reset): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ rank = int(rank) position_id = position_id[rank * dis: (rank + 1) * dis] input_ids = input_ids[rank * dis: (rank + 1) * dis] eod_id = eod_index[rank * dis: (rank + 1) * dis] if not eod_reset: return position_id seq_length = position_id.shape[1] # Initialize position_ids and attention_mask batch_attention_mask = np.ones((len(position_id), seq_length, seq_length)) # Loop through batches for bs_i in range(len(position_id)): # Get normal position_ids and attention_mask batch_attention_mask[bs_i] = np.tril(np.ones(shape=(seq_length, seq_length))) # Find eod_of_document for index in eod_id[bs_i]: # Reset position_ids and attention_mask considering EOD batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 return input_ids, position_id, batch_attention_mask def create_dataset(batch_size, data_path, device_num=1, rank=0, drop=True, full_batch=False, data_start_index=0, eod_reset=False, eod_id=9, column_name='input_ids', epoch=1, num_samples=None): """ Create dataset Inputs: batch_size: batch size data_path: path of your MindRecord files device_num: total device number rank: current rank id drop: whether drop remainder eod_reset: whether enable position reset and attention mask reset eod_id: the id for column_name: the column name of the mindrecord file. Default is input_ids epoch: The repeat times of the dataset Returns: dataset_restore: the dataset for training or evaluating """ ds.config.set_seed(1) # Control the size of data queue in the consideration of the memory ds.config.set_prefetch_size(4) is_data_parallel = context.get_auto_parallel_context( "parallel_mode") == context.ParallelMode.DATA_PARALLEL # Get path for source data files home_path = os.path.join(os.getcwd(), data_path) files = os.listdir(data_path) data = [ os.path.join(home_path, name) for name in files if not name.endswith(".db") ] # Ensure the order of mindrecords is same in all machines, otherwise it will meet loss converge problem. data.sort() print("************\n"*10) print("load_data_list: ", data) # Load data files and preprocess # dataset = ds.MindDataset(data[data_start_index:], columns_list=[column_name], # shuffle=False, num_samples=num_samples) dataset = ds.MindDataset(data[data_start_index:], columns_list=[column_name], shuffle=False, num_samples=num_samples) type_cast_op = C.TypeCast(mstype.int32) type_cast_op_float = C.TypeCast(mstype.float16) if full_batch or is_data_parallel: # no need to slice from the inputs rank = 0 dis = batch_size else: # Each card slice a small batch from the full batch dis = int(batch_size / device_num) if batch_size % device_num != 0: raise ValueError( f"batch size {batch_size} should be a multiple of device number {device_num}." " You should change the args: per_batch_size.") # map_func = (lambda input_ids: get_input_data_batch_slice_map(input_ids, eod_id, rank, dis, eod_reset)) map_func = (lambda input_ids: get_input_data_batch_slice_map(input_ids, rank, dis, eod_reset)) # If eod_reset enabled, another two inputs will be generated through input_ids num_parallel_workers = 8 dataset = dataset.map(input_columns=column_name, operations=type_cast_op, num_parallel_workers=num_parallel_workers) if eod_reset: dataset = dataset.batch(batch_size, drop_remainder=drop) # dataset = dataset.map(operations=map_func, input_columns=[column_name], # output_columns=[column_name, "position_id", "attention_mask"], num_parallel_workers=num_parallel_workers) dataset = dataset.map(operations=map_func, input_columns=["input_ids"], output_columns=["input_ids", "position_id", "attention_mask"], num_parallel_workers=num_parallel_workers) dataset = dataset.map(input_columns="position_id", operations=type_cast_op, num_parallel_workers=num_parallel_workers) # dataset = dataset.map(input_columns="attention_mask", operations=type_cast_op_float, num_parallel_workers=8) else: dataset = dataset.map(input_columns=[column_name], operations=type_cast_op) dataset = dataset.batch(batch_size, drop_remainder=drop) # dataset = dataset.map(operations=map_func, input_columns=[column_name], # output_columns=[column_name]) dataset = dataset.repeat(epoch) return dataset project_1/src/generate.py000066400000000000000000000401441454001410400156730ustar00rootroot00000000000000# 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. # ============================================================================ """ TopK for text generation """ import numpy as np from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype from mindspore.ops import operations as P import sys sys.path.append("..") def compute_CrossEntropyLoss(logits, labels, mask): from .loss import CrossEntropyLoss logits = Tensor(logits, mstype.float32) input_mask = Tensor(mask) labels = Tensor(labels) loss = CrossEntropyLoss(logits, labels, input_mask) print(f"loss.shape: {loss.shape}, {loss}") return loss def convert_text_to_ids(text, tokenizer, seq_length, pad, plus=0): input_ids = tokenizer.encode(text) # input_ids = tokenizer.convert_tokens_to_ids(input_ids) input_ids = np.array(input_ids).reshape(1, -1) if input_ids.shape[-1] > seq_length: input_ids = input_ids[:, :seq_length + plus] pad_length = seq_length + plus - input_ids.shape[-1] input_ids = np.pad(input_ids, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, pad)) return input_ids def gather(data, index): result = [] for i in range(data.shape[0]): result.append(data[i, index[i]]) return np.array(result) def compute_loss(logits, labels, mask): labels = labels.astype(np.int32) select = gather(logits, labels[0]) loss = -select * mask loss = np.mean(loss) return loss def get_scores(model_predict, item, tokenizer, pad_length=1024): input_sentence = item['input_str'] prompt = item['prompt'] tokens = convert_text_to_ids(input_sentence, tokenizer, pad_length, pad=tokenizer.pad_id, plus=0) prompt_ids = convert_text_to_ids(prompt, tokenizer, pad_length, pad=tokenizer.pad_id, plus=0) labels = np.concatenate((tokens[:, 1:], np.ones((tokens.shape[0], 1)) * tokenizer.pad_id), axis=-1) logits, mask = model_predict.predict(Tensor(np.array(tokens, np.int32)), Tensor(np.array(prompt_ids, np.int32))) loss = compute_loss(logits.asnumpy(), labels, mask.asnumpy()) return loss def get_scores_100b(model_predict, item, tokenizer, pad_length=4096): input_sentence = item tokens = convert_text_to_ids(input_sentence, tokenizer, pad_length, pad=tokenizer.pad_id, plus=0) labels = np.concatenate((tokens[:, 1:], np.ones((tokens.shape[0], 1)) * tokenizer.pad_id), axis=-1) logits, mask = model_predict.predict(Tensor(np.array(tokens, np.int32))) loss = compute_CrossEntropyLoss(logits.asnumpy(), labels, mask.asnumpy()) return loss def topk_fun(logits, topk=5): """Get topk""" target_column = logits[0].tolist() sorted_array = [(k, v) for k, v in enumerate(target_column)] sorted_array.sort(key=lambda x: x[1], reverse=True) topk_array = sorted_array[:topk] index, value = zip(*topk_array) index = np.array([index]) value = np.array([value]) return value, index def sampler(log_probs_revised, top_p, top_k_num, use_pynative=False): """Convert the log_probs to probability""" if use_pynative: logits = P.Pow()(np.e, Tensor(log_probs_revised, mstype.float32)) else: logits = np.power(np.e, np.array(log_probs_revised, np.float32)) # If top_p is less than 1.0, use top_p sampling if top_p < 1.0: # Only consider the 5000 largest logits to reduce computation if use_pynative: sorted_logits, index = P.TopK(sorted=True)(logits, 5000) cumsum_logits = P.CumSum()(sorted_logits, 1) cumsum_logits = cumsum_logits.asnumpy() index = index.asnumpy() sorted_logits = sorted_logits.asnumpy() else: sorted_logits, index = topk_fun(logits, 5000) cumsum_logits = np.cumsum(sorted_logits, 1) cumsum_logits = cumsum_logits[0] index = index[0] sorted_logits = sorted_logits[0] top_p_num = sum(cumsum_logits < top_p) + 1 # In case the probability is smooth, the sum of 5000 largest probabilities are not large enough if top_p_num == 0: top_p_num = 5000 # Get the corresponding probs and indices probs = sorted_logits[:top_p_num] p_args = index[:top_p_num] p = probs / sum(probs) # if top_p is set to 1.0, use top_k sampling else: # Get the corresponding probs and indices if use_pynative: probs, p_args = P.TopK(sorted=True)(logits, top_k_num) probs = probs.asnumpy() p_args = p_args.asnumpy() else: probs, p_args = topk_fun(logits, top_k_num) probs = probs[0] p_args = p_args[0] # Avoid rounding error if sum(probs) == 0: probs = np.array([1 / top_k_num for _ in range(top_k_num)]) p = probs / sum(probs) return p, p_args def generate(model, origin_inputs, config): """ Text generation Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations return_scores: return the score on the given sentence rather than generating the words. Default False. Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k_num = config.top_k_num max_generate_length = config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # print("input_ids shape :", input_ids.shape) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: inputs = Tensor(input_ids, mstype.int32) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 current_index = Tensor([current_index], mstype.int32) # print(f"current_index: {current_index}") # Call a single inference log_probs = model.predict(inputs, current_index) # print(f"log_probs: {log_probs}\nlog_probs[:]: {log_probs[0].asnumpy().shape}\n{log_probs[1].asnumpy().shape}") log_probs = log_probs[0] # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs = log_probs.asnumpy().reshape(1, config.vocab_size) log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k_num, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: outputs = input_ids break # update frequency list target = p_args[target_index] # frequency_list[0][target] = frequency_list[0][target] + 1 # Modify input_ids with newly generated token input_ids[0][valid_length] = p_args[target_index] valid_length += 1 # Return valid outputs out of padded outputs length = np.sum(outputs != 0) outputs = outputs[0][:length] return outputs def generate_100b(model, origin_inputs, config): """ Text generation Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations return_scores: return the score on the given sentence rather than generating the words. Default False. Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k_num = config.top_k_num max_generate_length = config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # print("input_ids shape :", input_ids.shape) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: inputs = Tensor(input_ids, mstype.int32) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 current_index = Tensor([current_index], mstype.int32) # print(f"current_index: {current_index}") # Call a single inference log_probs = model.predict(inputs, current_index) # print(f"log_probs: {log_probs}\nlog_probs[:]: {log_probs[0].asnumpy().shape}\n{log_probs[1].asnumpy().shape}") log_probs = log_probs[0] # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs = log_probs.asnumpy().reshape(1, config.vocab_size) log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k_num, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: outputs = input_ids break # update frequency list target = p_args[target_index] # frequency_list[0][target] = frequency_list[0][target] + 1 # Modify input_ids with newly generated token input_ids[0][valid_length] = p_args[target_index] valid_length += 1 # Return valid outputs out of padded outputs length = np.sum(outputs != 0) outputs = outputs[0][:length] return outputs def generate_increment(model, origin_inputs, config): """ Text generation for incremental inference Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k_num = config.top_k_num max_generate_length = config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # Init outputs with original inputs outputs = [origin_inputs[0][i] for i in range(valid_length)] # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 batch_valid_length = Tensor(np.array([current_index]), mstype.int32) current_index = Tensor(np.array([current_index]), mstype.int32) # For first graph, not_init should be false init_true = Tensor([True], mstype.bool_) init_false = Tensor([False], mstype.bool_) init = init_false # Claim the first graph model.predict_network.add_flags_recursive(is_first_iteration=True) # Call a single inference with input size of (bs, seq_length) logits = model.predict(Tensor(input_ids, mstype.int32), current_index, init, batch_valid_length) # Claim the second graph and set not_init to true init = init_true model.predict_network.add_flags_recursive(is_first_iteration=False) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: # Reshape the output logits logits = logits.asnumpy() log_probs = logits.reshape(1, config.vocab_size) # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k_num, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: break # Update frequency list target = p_args[target_index] # frequency_list[0][target] = frequency_list[0][target] + 1 valid_length += 1 batch_valid_length = Tensor(np.array([valid_length - 1]), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) input_id = Tensor([[target]], mstype.int32) # Update outputs with current generated token outputs.append(int(target)) # Call a single inference with input size of (bs, 1) logits = model.predict(input_id, current_index, init, batch_valid_length) # Return valid outputs out of padded outputs return np.array(outputs) if __name__ == "__main__": import numpy as np from mindspore import dtype as mstype from loss import CrossEntropyLoss from mindspore import Tensor loss = CrossEntropyLoss() logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), mstype.float32) labels_np = np.array([1]).astype(np.int32) input_mask = Tensor(np.ones(1).astype(np.float32)) labels = Tensor(labels_np) output = loss(logits, labels, input_mask) print(output.shape, output[0]) project_1/src/llama.py000066400000000000000000000472101454001410400151700ustar00rootroot00000000000000# 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. # ============================================================================ import os import copy import numpy as np import mindspore.nn as nn import mindspore.numpy as np2 import mindspore.common.dtype as mstype from mindspore.common.initializer import initializer from mindspore import Tensor, Parameter from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn import Cell from mindspore.nn.transformer.transformer import VocabEmbedding, TransformerEncoder, TransformerEncoderLayer, \ AttentionMask from mindspore.nn.transformer import MoEConfig from mindspore.nn.transformer.layers import _LayerNorm, _Dropout, _Linear from mindspore._extends import cell_attr_register from src.llama_pipeline_layer import PipeTransformerEncoder, PipeTransformerEncoderLayer, LlamaRMSNorm class EmbeddingLayer(nn.Cell): r"""Embedding layer""" def __init__(self, config): super(EmbeddingLayer, self).__init__() # Only for the pipeline mode, the embedding needs to be row sliced. ###################taoht############################### # if config.run_type == 'predict': # dp = 1 # mp = 1 # else: dp = config.parallel_config.embedding_dp_mp_config.data_parallel mp = config.parallel_config.embedding_dp_mp_config.model_parallel self.word_embedding = VocabEmbedding(vocab_size=config.vocab_size, embedding_size=config.hidden_size, param_init=initializer("normal", [config.vocab_size, config.hidden_size], dtype=mstype.float32), parallel_config=config.parallel_config.embedding_dp_mp_config) self.word_embedding.gather.shard(((mp, 1), (dp, 1))) self.word_embedding.embedding_table.parallel_optimizer = True copied_parallel_config = copy.deepcopy(config.parallel_config) copied_parallel_config.vocab_emb_dp = True # if config.run_type == 'predict': # copied_parallel_config.vocab_emb_dp = False self.position_embedding = None self.use_rope = config.use_rope if not self.use_rope: self.position_embedding = VocabEmbedding(vocab_size=config.seq_length, embedding_size=config.hidden_size, param_init=initializer("normal", [config.seq_length, config.hidden_size], dtype=mstype.float32), parallel_config=copied_parallel_config.embedding_dp_mp_config) # self.split = P.Split(1, 2).shard( # ((config.parallel_config.data_parallel, 1, 1),) # ) self.add = P.Add().shard( ((config.parallel_config.data_parallel, 1, 1), (config.parallel_config.data_parallel, 1, 1))) self.is_first_iteration = True self.use_past = config.use_past self.batch_size = config.batch_size def construct(self, input_ids, input_position, init_reset, batch_valid_length): embed, word_table = self.word_embedding(input_ids) if not self.use_rope: if self.use_past and not self.is_first_iteration: _, seq_length = F.shape(input_ids) input_position = batch_valid_length.view(self.batch_size, seq_length) position_embedding, _ = self.position_embedding(input_position) embed = self.add(embed, position_embedding) return embed def get_word_embedding_weight(self): return self.word_embedding.embedding_table # def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers): # r""" # Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. # # # Args: # network(Cell) - Represents the transformer block # layer_id(int) - Means the layer index for the current module, counts from zero. # offset(int) - Means the layer_index needs a offset, if there are other modules in the net. # layers(int) - The total layers used for the model. # """ # # Used for the pipeline's stages setting # # As the final layer is not included here, so we need to manually add here. # # original: if set two stages, layers on two stages will be [15, 16+1] # # with 1 added, the layers on two stages will be [16, 15 +1] # pp_dis = max(int((layers + 1)/ parallel_config.pipeline_stage), 1) # # the pipeline stage must be in [0, parallel_config.pipeline_stage - 1] # pp_id = min((layer_id + offset) // pp_dis, parallel_config.pipeline_stage - 1) # network.pipeline_stage = pp_id # print(f"pipeline stage id is {pp_id}", flush=True) # # # Used for optimizer's fusion tag # dis = max(int((layers + 1) / parallel_config.gradient_aggregation_group), 1) # if parallel_config.pipeline_stage > 1: # # we give the fusion in pipeline mode a fixed value, otherwise the performance may become worse. # network.set_comm_fusion(2) # else: # network.set_comm_fusion(int((layer_id + offset) / dis) + 1) # # Used for enabling recomputation of the block # if isinstance(parallel_config.recompute, bool): # if parallel_config.recompute: # network.recompute() # else: # if parallel_config.recompute.recompute: # network.recompute(recompute_slice_activation=parallel_config.recompute.recompute_slice_activation) def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers): r""" Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. Args: network(Cell) - Represents the transformer block layer_id(int) - Means the layer index for the current module, counts from zero. offset(int) - Means the layer_index needs a offset, if there are other modules in the net. layers(int) - The total layers used for the model. """ # Used for the pipeline's stages setting # As the final layer is not included here, so we need to manually add here. # original: if set two stages, layers on two stages will be [15, 16+1] # with 1 added, the layers on two stages will be [16, 15 +1] num = (layers + 1) // parallel_config.pipeline_stage pp_id = [] for i in range(parallel_config.pipeline_stage): pp_id.extend([i]* num) print(f"pp_id: {pp_id}, layer_id: {layer_id}") stage_id = pp_id[layer_id] network.pipeline_stage = pp_id[layer_id] dis = max(int((layers + 1) / parallel_config.gradient_aggregation_group), 1) if parallel_config.pipeline_stage > 1: # we give the fusion in pipeline mode a fixed value, otherwise the performance may become worse. # network.set_comm_fusion(2) network.layernorm1.set_comm_fusion(layer_id * 2 + 1) network.attention.set_comm_fusion(layer_id * 2 + 1) network.layernorm2.set_comm_fusion(layer_id * 2 + 2) network.output.set_comm_fusion(layer_id * 2 + 2) #network.set_comm_fusion(layer_id + 1) else: # network.set_comm_fusion() network.set_comm_fusion(int((layer_id + offset) / dis) + 1) # Used for enabling recomputation of the block if parallel_config.recompute: network.recompute(recompute_slice_activation=True) class Llama_Model(Cell): def __init__(self, config): super(Llama_Model, self).__init__() self.is_pipeline = config.parallel_config.pipeline_stage > 1 self.embedding = EmbeddingLayer(config) self.config = config self.embedding.pipeline_stage = 0 # Configure the shard configure of the Embedding layer self.num_layers = config.num_layers if config.use_moe: moe_config = MoEConfig(expert_num=config.expert_num, num_experts_chosen=config.per_token_num_experts_chosen) else: moe_config = MoEConfig(expert_num=1) # The shard setting of Transformer is set within the class StackedTransformer self.blocks = PipeTransformerEncoder( num_layers=config.num_layers, batch_size=config.batch_size, hidden_size=config.hidden_size, ffn_hidden_size=config.ffn_hidden_size, num_heads=config.num_heads, num_kv_heads=config.num_kv_heads, seq_length=config.seq_length, attention_dropout_rate=config.dropout_rate, hidden_dropout_rate=config.dropout_rate, lambda_func=set_parallel_configure_for_layer, hidden_act=config.hidden_act,## taoht param_init_type=config.param_init_type, use_past=config.use_past, parallel_config=config.parallel_config, moe_config=moe_config, softmax_compute_type=config.softmax_compute_type, use_rope=config.use_rope, use_flash_attention=config.use_flash_attention).blocks mp = config.parallel_config.model_parallel dp = config.parallel_config.data_parallel if config.parallel_config.sequence_parallel: print("===using sequence parallel", flush=True) for block in self.blocks: block.attention.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) block.add.shard(((dp * mp, 1), (dp * mp, 1),)) block.layernorm1.shard((dp * mp, 1)) block.layernorm2.shard((dp * mp, 1)) block.output.projection.matmul.shard(in_strategy=((dp, mp), (mp, 1)), out_strategy=((dp * mp, 1),)) if config.parallel_config.select_recompute: print("===using select recompute", flush=True) for block in self.blocks: block.layernorm1.layer_norm.add_prim_attr("recompute_comm_op", True) block.layernorm2.layer_norm.add_prim_attr("recompute_comm_op", True) block.attention.expand_dims.recompute() block.attention.batch_matmul.recompute() block.attention.softmax.softmax.recompute() block.attention.softmax_3d.softmax.recompute() block.attention.add.recompute() block.attention.prob_dropout.dropout.recompute() block.attention.sub.recompute() block.attention.mul.recompute() block.attention.real_div.recompute() block.attention.merger_head_transpose.recompute() block.attention.reshape.recompute() block.attention.cast.recompute() block.attention.shape.recompute() if block.attention.use_past: block.attention.slice.recompute() block.attention.not_equal.recompute() block.attention.reducesum.recompute() block.attention.sub1.recompute() block.attention.tensor_le.recompute() block.attention.equal.recompute() block.attention.tile.recompute() block.attention.less.recompute() block.attention.mul1.recompute() self.layernorm = LlamaRMSNorm(config.hidden_size, compute_type=mstype.float32) self.layernorm.set_comm_fusion(config.num_layers * 2 + 4) self.layernorm.shard((config.parallel_config.data_parallel, 1)) self.layernorm.pipeline_stage = config.parallel_config.pipeline_stage - 1 self.dtype = mstype.float16 if config.load_ckpt_path: self.load_embedding_from_ckpt(config.load_ckpt_path) self.run_type = config.run_type def construct(self, input_ids, input_position, encoder_masks, init_reset=True, batch_valid_length=None): r"""forward pass of the model""" embed = self.embedding(input_ids, input_position, init_reset, batch_valid_length) hidden_state = P.Cast()(embed, self.dtype) # the input of the incremental prediction is 3d #########################taoht############################ # if self.run_type != 'predict': # hidden_state = self.reshape_to_2d(hidden_state) hidden_state = self.reshape_to_2d(hidden_state) # ignored predict to speed up if self.blocks is not None: for i in range(self.num_layers): hidden_state, _ = self.blocks[i](hidden_state, encoder_masks, init_reset, batch_valid_length) encoder_output = self.layernorm(hidden_state) return encoder_output def reshape_to_2d(self, x): r"""reshape nd tensor to 2d, if n <= 2, keep original shape.""" shape = F.shape(x) if len(shape) <= 2: return x x = F.reshape(x, (-1, shape[-1])) return x def load_embedding_from_ckpt(self, load_ckpt_path): r"""load the weights from the checkpoint""" def load_param(path): if os.path.exists(path): p_table = np.load(path) table_param = Tensor(p_table, mstype.float32) else: raise ValueError(f"{path} file not exits, " f"please check whether embedding file exit.") return table_param # three embedding needed to be loaded # Loading the embedding table from the ckpt path: position_embedding_path = os.path.join(load_ckpt_path, 'position_embedding.npy') word_embedding_path = os.path.join(load_ckpt_path, 'word_embedding.npy') self.embedding.word_embedding.embedding_table = Parameter(initializer(load_param(word_embedding_path), [self.config.vocab_size, self.config.hidden_size]), name='word_embedding_table', parallel_optimizer=False) self.embedding.position_embedding.embedding_table = Parameter(initializer(load_param(position_embedding_path), [self.config.seq_length, self.config.hidden_size]), name='position_embedding_table', parallel_optimizer=False) class LlamaModel(nn.Cell): """ Inputs: input_ids: the tokenized inputs input_mask: the mask indicating whether each position is a valid input past: the previous feature map Returns: logits: Tensor: the logits of the corresponding inputs with shape (batch_size, seq_length, vocab_size) """ def __init__(self, config): super(LlamaModel, self).__init__() # Network head to get logits over vocabulary copied_parallel_config = copy.deepcopy(config.parallel_config) if copied_parallel_config.pipeline_stage > 1: copied_parallel_config.vocab_emb_dp = False self.head = _Linear(out_channels=config.vocab_size, in_channels=config.hidden_size, has_bias=False, compute_dtype=config.param_init_type, param_init_type=config.param_init_type) dp = config.parallel_config.data_parallel mp = config.parallel_config.model_parallel self.head.shard(strategy_bias=((dp, 1), (1,)), strategy_matmul=((dp, 1), (mp, 1))) self.head.pipeline_stage = config.parallel_config.pipeline_stage - 1 self.backbone = Llama_Model(config) # self.cast = P.Cast() def construct(self, input_ids, input_position, attention_mask, init_reset=True, batch_valid_length=None): #######################taoht#############################NO # attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## output_states = self.backbone(input_ids, input_position, attention_mask, init_reset, batch_valid_length) logits = self.head(output_states) return logits class LlamaWithLoss(Cell): """ Args: config Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ @cell_attr_register def __init__(self, config, network, loss): super(LlamaWithLoss, self).__init__(auto_prefix=False) self.batch_size = config.batch_size self.seq_length = config.seq_length dp = config.parallel_config.data_parallel self.network = network self.pad_token = config.pad_token self.loss = loss self.slice = P.StridedSlice().shard(((dp, 1),)) self.not_equal = P.NotEqual().shard(((dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice2 = P.StridedSlice().shard(((dp, 1, 1),)) self.micro_batch_step = 1 if config.parallel_config.pipeline_stage > 1: self.micro_batch_step = config.parallel_config.micro_batch_num self.cast = P.Cast() def construct(self, input_ids, input_position=None, attention_mask=None): # tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1)) # input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) # print("===================="*10) # P.Print()("enter input_ids", input_ids) # P.Print()("enter input_position", input_position) # print("====================" * 10) tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1)) input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) #######################taoht############################## attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## decoder_attention_masks = self.slice2(attention_mask, (0, 0, 0), (self.batch_size, self.len, self.len), (1, 1, 1)) input_mask = F.cast(self.not_equal(tokens, self.pad_token), mstype.float32) logits = self.network(tokens, input_position, decoder_attention_masks) # Get label corresponding to input tokens labels = self.slice(input_ids, (0, 1), (self.batch_size, self.len + 1), (1, 1)) labels = P.Reshape()(labels, (-1,)) input_mask = P.Reshape()(input_mask, (-1,)) output = self.loss(logits, labels, input_mask) return output project_1/src/llama_pipeline_layer.py000066400000000000000000001724441454001410400202610ustar00rootroot00000000000000from mindspore.nn import Cell import mindspore from mindspore import ops from mindspore.common.tensor import Tensor from mindspore.common.parameter import Parameter from mindspore.common.initializer import initializer from mindspore import nn from mindspore import context import mindspore.common.dtype as mstype from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn.cell import Cell from mindspore._checkparam import Validator from mindspore import log as logger from mindspore.parallel._utils import _get_parallel_mode, _is_sharding_propagation from mindspore.context import ParallelMode from mindspore.log import _LogActionOnce from mindspore.nn.transformer.layers import _LayerNorm, _Linear, \ _args_type_validator_check, _valid_type_checks, _valid_value_checks, \ _check_past_none_input_none, _check_input_dtype from mindspore.nn.transformer import FeedForward, TransformerOpParallelConfig from mindspore.nn.transformer.op_parallel_config import default_dpmp_config, OpParallelConfig, \ _Config, _check_config, MoEParallelConfig from mindspore.nn.transformer.moe import default_moe_config, MoE, _check_moe_config import numpy as np import math class LlamaSiLU(Cell): r""" A self-defined SwiGlu. Inputs: - **x** (Tensor) - Tensor. Outputs: Tensor. x = x * sigmod(x). """ def __init__(self): super().__init__() self.sigmoid = nn.Sigmoid() self.mul = P.Mul() def shard(self, strategy): self.sigmoid.sigmoid.shard(strategy) self.mul.shard((strategy[0], strategy[0])) def construct(self, x): return self.mul(x, self.sigmoid(x)) class LlamaRMSNorm(nn.Cell): r""" A self-defined RMSNorm operation using reduce mean. Args: dim (tuple): The shape of the input tensor eps (float): The epsilon value of the denominator. Default 1e-5. compute_type: The compute type. Inputs: - **x** (Tensor) - Tensor of shape :math:`(batch, seq\_length, hidden\_size)`. Outputs: Tensor of shape :math:`(batch, seq_length, hidden_size)`. """ def __init__(self, dim, eps=1e-6, compute_type=mstype.float32): super(LlamaRMSNorm, self).__init__() self.eps = eps self.weight = Parameter(initializer('ones', (dim,), dtype=mstype.float32), parallel_optimizer=False) self.square = P.Square() self.mean = P.ReduceMean(keep_dims=True) self.add = P.Add() self.rsqrt = P.Rsqrt() self.mul = P.Mul() self.mul2 = P.Mul() self.cast = P.Cast() self.compute_type = compute_type def _norm(self, x): norm_factor = self.square(x) norm_factor = self.mean(norm_factor, -1) norm_factor = self.add(norm_factor, self.eps) norm_factor = self.rsqrt(norm_factor) x = self.mul(x, norm_factor) return x def construct(self, x): """Forward of RMSNorm.""" original_type = x.dtype x = self.cast(x, self.compute_type) output = self._norm(x) output = self.mul2(output, self.weight) output = self.cast(output, original_type) return output def shard(self, strategy_in): """Parallel strategy configuratiuon interface.""" self.square.shard((strategy_in,)) self.mean.shard((strategy_in,)) self.rsqrt.shard((strategy_in,)) self.add.shard((strategy_in, ())) self.mul.shard((strategy_in, strategy_in)) self.mul2.shard((strategy_in, (1,))) class LlamaFeedForward(Cell): @_LogActionOnce(logger=logger, key='FeedForward', no_warning=_get_parallel_mode() in (ParallelMode.STAND_ALONE,)) @_args_type_validator_check(hidden_size=Validator.check_positive_int, ffn_hidden_size=Validator.check_positive_int, dropout_rate=Validator.check_non_negative_float, param_init_type=_valid_value_checks([mstype.float32, mstype.float16], "FeedForward"), parallel_config=_valid_type_checks([OpParallelConfig, MoEParallelConfig], "FeedForward")) def __init__(self, hidden_size, ffn_hidden_size, dropout_rate, hidden_act=LlamaSiLU, expert_num=1, expert_group_size=None, param_init_type=mstype.float32, parallel_config=default_dpmp_config): super(LlamaFeedForward, self).__init__() if hidden_act is None or not (isinstance(hidden_act, str) or issubclass(hidden_act, nn.Cell)): raise TypeError(f"For FeedForward cell, the hidden_act should str type or nn.Cell type, " f"but got {hidden_act}.") _check_config(parallel_config) mp = parallel_config.model_parallel if expert_num > 1: ep = parallel_config.expert_parallel else: ep = 1 # ffn use less dp than other ops when use_moe, due to there are ops use dp and ep. dp = parallel_config.data_parallel // ep if ffn_hidden_size % mp != 0: raise ValueError("For 'FeedForward', the class variable 'ffn_hidden_size' must be a multiple of the" "num of model parallel, but got the ffn_hidden_size is {} and the num of model " "parallel is {}.".format(ffn_hidden_size, mp)) if hidden_size % mp != 0: raise ValueError("For 'FeedForward', the class variable 'hidden_size' must be a multiple of the num of " "model parallel, but got the hidden_size is {} and the num of model parallel is {}." .format(hidden_size, mp)) if dropout_rate < 0 or dropout_rate >= 1: raise ValueError("For 'FeedForward', the class variable 'dropout_rate' must be in the range [0, 1.0), " "but got the value : {}.".format(dropout_rate)) input_size = hidden_size output_size = ffn_hidden_size # Project to ffn_hidden_size self.mapping = _Linear(in_channels=input_size, out_channels=output_size, activation=hidden_act, transpose_b=False, has_bias=False, expert_num=expert_num, expert_group_size=expert_group_size, outer_batch=dp, param_init_type=param_init_type) # Project to ffn_hidden_size self.mapping2 = _Linear(in_channels=input_size, out_channels=output_size, transpose_b=False, has_bias=False, expert_num=expert_num, expert_group_size=expert_group_size, outer_batch=dp, param_init_type=param_init_type) self.mapping.shard(strategy_matmul=((dp, 1), (1, mp)), strategy_bias=((dp, mp), (mp,)), strategy_activation=((dp, mp),)) self.mapping2.shard(strategy_matmul=((dp, 1), (1, mp)), strategy_bias=((dp, mp), (mp,)), strategy_activation=((dp, mp),)) self.mapping.activation.shard(((dp, mp),)) # Project back to hidden_size self.projection = _Linear(in_channels=output_size, out_channels=input_size, transpose_b=False, expert_num=expert_num, expert_group_size=expert_group_size, has_bias=False, outer_batch=dp, param_init_type=param_init_type) self.projection.shard(strategy_matmul=((dp, mp), (mp, 1)), strategy_bias=((dp, 1), (1,))) self.cast = P.Cast() self.mul = P.Mul().shard(((dp, mp), (dp, mp))) def construct(self, x): _check_input_dtype(F.dtype(x), "x", [mstype.float32, mstype.float16], self.cls_name) x = self.cast(x, mstype.float16) # returned shape is [bs, seq_length, ffn_hidden_size] or [bs * seq_length, ffn_hidden_size] gate = self.mapping(x) hidden = self.mapping2(x) hidden = self.mul(hidden, gate) output = self.projection(hidden) return output def _get_lambda_func(total_layer=None): r""" A wrapper function of specifying pipeline stage and gradient aggregation fusion. If the total layer is not None, for example, set in the transformer model, the pipeline stage setting function will be `(layer_id + 0) // (total_layers / parallel_config.pipeline_stage)` for the encoder and, `(layer_id + offset) // (total_layers / parallel_config.pipeline_stage)` for the decoder, where `offset` is the layers in the encoder. """ def _set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers): r""" Default setting for the pipeline is: `(layer_id + offset) // (layers / pipeline_stage)`. Args: network(Cell) - Represents the transformer block layer_id(int) - Means the layer index for the current module, counts from zero. offset(int) - Means the layer_index needs an offset, if there are other modules in the net. layers(int) - The total layers used for the model. """ # override the layers if total_layer: layers = total_layer # Used for the pipeline's stages setting if layers < parallel_config.pipeline_stage: raise ValueError(f"layers {layers} must be larger than pipeline stage {parallel_config.pipeline_stage}") pp_dis = max(layers // parallel_config.pipeline_stage, 1) # the pipeline stage must be in [0, parallel_config.pipeline_stage - 1] pp_id = min((layer_id + offset) // pp_dis, parallel_config.pipeline_stage - 1) network.pipeline_stage = pp_id # Used for optimizer's fusion tag dis = max(layers // parallel_config.gradient_aggregation_group, 1) network.set_comm_fusion((layer_id + offset) // dis + 1) # Used for enabling recomputation of the block if isinstance(parallel_config.recompute, bool): if parallel_config.recompute: network.recompute() else: if parallel_config.recompute.recompute: paralel_op_comm_compute = parallel_config.recompute.parallel_optimizer_comm_recompute network.recompute(parallel_optimizer_comm_recompute=paralel_op_comm_compute, mp_comm_recompute=parallel_config.recompute.mp_comm_recompute, recompute_slice_activation=parallel_config.recompute.recompute_slice_activation) return _set_parallel_configure_for_layer class RoPE(nn.Cell): """ 定义RoPE位置Embedding """ sin_matrix = None cos_matrix = None def __init__(self, num_heads, hidden_size, seq_length, parallel_config, compute_type): super().__init__(auto_prefix=False) self.dtype = np.float64 self.max_seq_len = seq_length self.output_dim = hidden_size // num_heads self.shape = P.Shape() self.reshape = P.Reshape() self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) dp = parallel_config.data_parallel mp = parallel_config.model_parallel self.mul = P.Mul().shard(((dp, mp, 1, 1), (1, 1, 1, 1))) self.add = P.Add().shard(((dp, mp, 1, 1), (dp, mp, 1, 1))) self.split = P.Split(axis=-1, output_num=2).shard(((dp, mp, 1, 1, 1),)) self.concat = P.Concat(axis=-1).shard(((dp, mp, 1, 1, 1), (dp, mp, 1, 1, 1))) self.neg = P.Neg().shard(((dp, mp, 1, 1, 1),)) if self.cos_matrix is None: position_ids = np.arange(0, self.max_seq_len, dtype=self.dtype)[None] indices = np.arange(0, self.output_dim // 2., dtype=self.dtype) indices = 1. / np.power(10000.0, 2 * indices / self.output_dim, dtype=self.dtype) embeddings = np.einsum('bn,d->bnd', position_ids, indices, dtype=self.dtype) embeddings = np.repeat(embeddings, 2, axis=-1).reshape(1, 1, self.max_seq_len, self.output_dim) RoPE.sin_matrix = Tensor(np.sin(embeddings, dtype=self.dtype), dtype=compute_type) RoPE.cos_matrix = Tensor(np.cos(embeddings, dtype=self.dtype), dtype=compute_type) def rotate_half(self, x, input_shape): shape_5d = (input_shape[0], input_shape[1], input_shape[2], -1, 2) x = self.reshape(x, shape_5d) x0, x1 = self.split(x) rotate = self.concat([self.neg(x1), x0]) rotate = self.reshape(rotate, input_shape) return rotate def construct(self, inputs): # P.Print()("enter RoPE", inputs) input_shape = self.shape(inputs) seq_len = input_shape[2] sin_matrix = self.slice(self.sin_matrix, (0, 0, 0, 0), (1, 1, seq_len, input_shape[3]), (1, 1, 1, 1)) cos_matrix = self.slice(self.cos_matrix, (0, 0, 0, 0), (1, 1, seq_len, input_shape[3]), (1, 1, 1, 1)) output1 = self.mul(inputs, cos_matrix) output2 = self.mul(self.rotate_half(inputs, input_shape), sin_matrix) output = self.add(output1, output2) return output class PipeMultiHeadAttention(Cell): @_LogActionOnce(logger=logger, key='MultiHeadAttention', no_warning=_get_parallel_mode() in (ParallelMode.STAND_ALONE,)) @_args_type_validator_check(hidden_size=Validator.check_positive_int, num_heads=Validator.check_positive_int, src_seq_length=Validator.check_positive_int, tgt_seq_length=Validator.check_positive_int, attention_dropout_rate=Validator.check_non_negative_float, hidden_dropout_rate=Validator.check_non_negative_float, compute_dtype=_valid_value_checks([mstype.float32, mstype.float16], "MultiHeadAttention"), softmax_compute_type=_valid_value_checks([mstype.float32, mstype.float16], "MultiHeadAttention"), param_init_type=_valid_value_checks([mstype.float32, mstype.float16], "MultiHeadAttention"), parallel_config=_valid_type_checks([OpParallelConfig], "MultiHeadAttention"), use_past=Validator.check_bool) def __init__(self, batch_size, src_seq_length, tgt_seq_length, hidden_size, num_heads, num_kv_heads=4, hidden_dropout_rate=0.1, attention_dropout_rate=0.1, compute_dtype=mstype.float16, softmax_compute_type=mstype.float32, param_init_type=mstype.float32, use_past=False, parallel_config=default_dpmp_config, use_rope=False, use_flash_attention=False): super(PipeMultiHeadAttention, self).__init__() self._is_ascend = context.get_context('device_target') in ["Ascend"] self.dp = parallel_config.data_parallel self.is_parallel_mode = _get_parallel_mode() in ( ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL) if batch_size: Validator.check_positive_int(batch_size) _check_config(parallel_config) self.src_seq_length = src_seq_length self.tgt_seq_length = tgt_seq_length self.hidden_size = hidden_size self.batch_size = batch_size # for simplicity, assert not (_get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation()) if hidden_dropout_rate < 0 or hidden_dropout_rate >= 1: raise ValueError("For 'MultiHeadAttention', the class variable 'hidden_dropout_rate' must be " "in range [0, 1.0), but got the value : {}.".format(hidden_dropout_rate)) if attention_dropout_rate < 0 or attention_dropout_rate >= 1: raise ValueError("For 'MultiHeadAttention', the class variable 'attention_dropout_rate' must be " "in range [0, 1.0), but got the value : {}.".format(attention_dropout_rate)) if hidden_size % num_heads != 0: raise ValueError("For 'MultiHeadAttention', the class variable 'hidden_size' must be a multiple " "of 'num_heads', but got the hidden_size is {} and the num_heads is {}." .format(hidden_size, num_heads)) if num_heads % parallel_config.model_parallel != 0 or num_kv_heads % parallel_config.model_parallel != 0 or num_heads % num_kv_heads != 0: raise ValueError("For 'MultiHeadAttention', the class variable 'num_heads' must be a multiple of " "'parallel_config.model_parallel', but got the num_heads is {} " "and the parallel_config.model_parallel is {}." .format(num_heads, parallel_config.model_parallel)) self.num_rep = num_heads // num_kv_heads self.is_first_iteration = True # Output layer self.projection = _Linear(in_channels=hidden_size, out_channels=hidden_size, transpose_b=False, has_bias=False, compute_dtype=compute_dtype, param_init_type=param_init_type) self.projection.shard(strategy_bias=((parallel_config.data_parallel, 1), (1,)), strategy_matmul=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel, 1))) self.transpose = P.Transpose().shard( ((parallel_config.data_parallel, 1, parallel_config.model_parallel, 1),)) self.transpose_back = P.Transpose().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.merger_head_transpose = P.Transpose().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.reshape = P.Reshape() self.n_head = num_heads # embedding size per head self.size_per_head = hidden_size // self.n_head self.n_kv_heads = num_kv_heads self.concat_k = P.Concat(axis=3) self.concat_v = P.Concat(axis=2) self.multiply_data = Tensor([ -10000.0, ], dtype=softmax_compute_type) self.batch_matmul = P.BatchMatMul().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1), (parallel_config.data_parallel, parallel_config.model_parallel, 1, 1))) self.real_div = P.RealDiv().shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1), ())) self.sub = P.Sub().shard( ((1,), (parallel_config.data_parallel, 1, 1, 1))) self.mul = P.Mul().shard( ((parallel_config.data_parallel, 1, 1, 1), (1,))) self.add = P.Add().shard( ((parallel_config.data_parallel, 1, 1, 1), (parallel_config.data_parallel, parallel_config.model_parallel, 1, 1))) # Normalize factor for attention, sqrt(dk) as widely used self.scale_factor = Tensor(math.sqrt(math.sqrt(self.size_per_head))) self.use_past = use_past self.prob_dropout = nn.Dropout(1 - attention_dropout_rate) self.prob_dropout.dropout.shard( ((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.softmax = nn.Softmax().to_float(softmax_compute_type) self.softmax.softmax.shard(((parallel_config.data_parallel, parallel_config.model_parallel, 1, 1),)) self.softmax_3d = nn.Softmax().to_float(softmax_compute_type) self.softmax_3d.softmax.shard(((parallel_config.data_parallel, parallel_config.model_parallel, 1),)) self.expand_dims = P.ExpandDims().shard(((parallel_config.data_parallel, 1, 1),)) # Query self.dense1 = _Linear(hidden_size, hidden_size, compute_dtype=compute_dtype, has_bias=False, param_init_type=param_init_type) self.dense1.shard(strategy_matmul=((parallel_config.data_parallel, 1), (parallel_config.model_parallel, 1)), strategy_bias=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel,))) # Key self.dense2 = _Linear(hidden_size, hidden_size // self.num_rep, compute_dtype=compute_dtype, has_bias=False, param_init_type=param_init_type) self.dense2.shard(strategy_matmul=((parallel_config.data_parallel, 1), (parallel_config.model_parallel, 1)), strategy_bias=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel,))) # Value self.dense3 = _Linear(hidden_size, hidden_size // self.num_rep, compute_dtype=compute_dtype, has_bias=False, param_init_type=param_init_type) self.dense3.shard(strategy_matmul=((parallel_config.data_parallel, 1), (parallel_config.model_parallel, 1)), strategy_bias=((parallel_config.data_parallel, parallel_config.model_parallel), (parallel_config.model_parallel,))) self.dtype = compute_dtype self.softmax_dtype = softmax_compute_type if self.use_past: # operators used for state reuse seq_range = np.arange(src_seq_length).reshape(1, 1, -1) self.range = Tensor(np.tile(seq_range, (batch_size, 1, 1)), mstype.int32) self.seq_length = src_seq_length self.attention_mask = Tensor(np.tril(np.ones(shape=(self.seq_length, self.seq_length))), mstype.int32) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.expand_dims = P.ExpandDims().shard(((1, 1, 1),)) self.tensor_le = P.LessEqual().shard(((1, 1, 1), (1, 1, 1))) self.add = P.Add().shard(((1, 1, 1, 1), (1, 1, 1, 1))) self.equal = P.Equal().shard(((1, 1, 1), (1, 1, 1))) self.sub1 = P.Sub().shard(((1,), ())) self.tile = P.Tile().shard(((1, 1, 1, 1),)) self.less = P.Less().shard(((1, 1, 1), (1, 1, 1))) self.mul1 = P.Mul().shard(((1, 1, 1, 1), (1, 1, 1, 1))) dp = parallel_config.data_parallel mp = parallel_config.model_parallel self.use_flash_attention = use_flash_attention self.cast = P.Cast() self.shape = P.Shape() self.use_rope = use_rope self.rope = None if self.use_rope: self.rope = RoPE(num_heads, hidden_size, tgt_seq_length, parallel_config, compute_dtype) self.tile_kv = P.Tile() self.tile_kv.shard(((dp * mp, 1, 1, 1),)) self.repeat_reshape = P.Reshape() def _repeat_kv(self, x, rep): if rep == 1: return x bs, n_kv_head, seqlen, head_dim = x.shape x = self.repeat_reshape(x, (bs * n_kv_head, 1, seqlen, head_dim)) x = self.tile_kv(x, (1, rep, 1, 1)) x = self.repeat_reshape(x, (bs, n_kv_head * rep, seqlen, head_dim)) return x def construct(self, query_tensor, key_tensor, value_tensor, attention_mask, key_past=None, value_past=None, batch_valid_length=None): self._check_inputs(query_tensor, key_tensor, value_tensor, attention_mask, key_past, value_past, batch_valid_length) ori_shape = F.shape(query_tensor) batch_size = self._get_batch_size_from_query(query_tensor) query_tensor, key_tensor, value_tensor = self._convert_to_2d_tensor(query_tensor, key_tensor, value_tensor, attention_mask) ori_dtype = F.dtype(query_tensor) query_tensor = F.cast(query_tensor, self.dtype) key_tensor = F.cast(key_tensor, self.dtype) value_tensor = F.cast(value_tensor, self.dtype) # multi head attention: query, key, value are derived from the same inputs query = self.dense1(query_tensor) key = self.dense2(key_tensor) value = self.dense3(value_tensor) # the returned shape is [bs, num_heads, seq_length, size_per_head] query = self.transpose( F.reshape( query, (batch_size, self._get_seq_length_under_incremental(self.src_seq_length), self.n_head, self.size_per_head)), (0, 2, 1, 3)) # the returned shape is [bs, size_per_head, seq_length, num_heads] key = self.transpose( F.reshape( key, (batch_size, self._get_seq_length_under_incremental(self.tgt_seq_length), self.n_kv_heads, self.size_per_head)), (0, 2, 1, 3)) if self.use_rope: query = self.rope(query) key = self.rope(key) key = self._repeat_kv(key, self.num_rep) # the returned shape is [bs, num_heads, seq_length, size_per_head] value = self.transpose( F.reshape( value, (batch_size, self._get_seq_length_under_incremental(self.tgt_seq_length), self.n_kv_heads, self.size_per_head)), (0, 2, 1, 3)) value = self._repeat_kv(value, self.num_rep) # support input shape is [bs, seq, seq] or [bs, heads, seq, seq] if attention_mask is not None and len(F.shape(attention_mask)) == 3: # expand attention mask from [bs, seq, seq] -> [bs, 1, seq, seq] attention_mask = self.expand_dims(attention_mask, 1) # key and value for current token(s) key_present = key value_present = value if self.use_past: # The first graph with the input size of (bs, seq_length) if self.is_first_iteration: # Get the valid input length without padding valid_length_vector = F.cast(self.less(self.range, batch_valid_length.view(-1, 1, 1)), self.dtype) # Cover the key and value numbers corresponding to the padding position key_present = self.mul1(key, self.expand_dims(valid_length_vector, 3)) value_present = self.mul1(value, self.expand_dims(valid_length_vector, 3)) # The second graph with the inpus size of (bs, 1) # the shape of query is (bs, num_heads, 1, size_per_head) # the shape of key is (bs, num_heads, size_per_head, 1) # the shape of value is (bs, num_heads, 1, size_per_head) else: # Get the current token position index valid_length = self.reducesum(F.cast(self.not_equal(self.slice(key_past, (0, 0, 0, 0), (F.shape(key_tensor)[0], 1, 1, self.src_seq_length), (1, 1, 1, 1)), 0), mstype.float32), (1, 2, 3)) valid_length = F.reshape(valid_length, (-1, 1, 1)) valid_length_vector = F.cast(self.equal(valid_length, self.range), self.dtype) # Pad the key and value to seq_length with only the position index not zero current_key = self.mul1(self.tile(key, (1, 1, self.seq_length, 1)), self.expand_dims(valid_length_vector, 3)) current_value = self.mul1(self.tile(value, (1, 1, self.seq_length, 1)), self.expand_dims(valid_length_vector, 3)) # Concat the previous saved state and current state key = self.add(key_past, current_key) value = self.add(value_past, current_value) # Update key_present and value_present for state update key_present = key value_present = value attention_mask = F.reshape(self.attention_mask, (self.seq_length, self.seq_length, 1, 1)) layer_present = (key_present, value_present) # multi head attention considering attention mask # the return shape is [bs * seq_length, hidden_size] key = self.transpose_back(key, (0, 1, 3, 2)) attention = self._attn(query, key, value, attention_mask) # Output output = self.projection(attention) output = F.reshape(output, ori_shape) output = F.cast(output, ori_dtype) return output, layer_present def _get_batch_size_from_query(self, query): r"""Get the batch size from query tensor""" # For the incremental prediction, the seq length for the input is 1. if len(F.shape(query)) == 2 and ((self.use_past and self.is_first_iteration) or (not self.use_past)): return F.shape(query)[0] // self.src_seq_length return F.shape(query)[0] def _get_seq_length_under_incremental(self, length): r"""Return the length of the tensor. For the incremental prediction, the seq length for the input is 1. """ if self.use_past and not self.is_first_iteration: return 1 return length def _check_inputs(self, query_tensor, key_tensor, value_tensor, attention_mask, key_past=None, value_past=None, batch_valid_length=None): r"""Check inputs""" _check_input_dtype(F.dtype(query_tensor), "query_tensor", [mstype.float32, mstype.float16], self.cls_name) _check_input_dtype(F.dtype(key_tensor), "key_tensor", [mstype.float32, mstype.float16], self.cls_name) _check_input_dtype(F.dtype(value_tensor), "value_tensor", [mstype.float32, mstype.float16], self.cls_name) if attention_mask is not None: _check_input_dtype(F.dtype(attention_mask), "attention_mask", [mstype.float32, mstype.float16], self.cls_name) key_is_tensor = isinstance(key_past, Tensor) value_is_tensor = isinstance(value_past, Tensor) batch_valid_length_is_tensor = isinstance(batch_valid_length, Tensor) key_is_default = key_past is None value_is_default = value_past is None batch_is_default = batch_valid_length is None _check_past_none_input_none(self.use_past, "key_past", self.cls_name, None, key_is_tensor, key_is_default) _check_past_none_input_none(self.use_past, "value_past", self.cls_name, None, value_is_tensor, value_is_default) _check_past_none_input_none(self.use_past, "batch_valid_length", self.cls_name, None, batch_valid_length_is_tensor, batch_is_default) if self.use_past: _check_input_dtype(F.dtype(key_past), "key_past", [mstype.float16], self.cls_name) _check_input_dtype(F.dtype(value_past), "value_past", [mstype.float16], self.cls_name) _check_input_dtype(F.dtype(batch_valid_length), "batch_valid_length", [mstype.int32], self.cls_name) return True def _convert_to_2d_tensor(self, query_tensor, key_tensor, value_tensor, attention_mask): """convert a nd tensor to a 2d tensor""" query_shape = F.shape(query_tensor) query_tensor = F.reshape(query_tensor, (-1, query_shape[-1])) key_shape = F.shape(key_tensor) key_tensor = F.reshape(key_tensor, (-1, key_shape[-1])) value_shape = F.shape(value_tensor) value_tensor = F.reshape(value_tensor, (-1, value_shape[-1])) return query_tensor, key_tensor, value_tensor def _merge_heads(self, x): """ convert a 4d input to a 2d output Inputs: x: input tensor Output: x_merge: the 2d output """ x = self.merger_head_transpose( x, (0, 2, 1, 3)) # bs, seq_length, head, size_per_head x_shape = P.Shape()(x) new_shape = (-1, x_shape[-2] * x_shape[-1]) x_merge = self.reshape(x, new_shape) return x_merge def _softmax(self, attention_scores): """ For the consideration of the performance, do softmax according to different situations :param attention_scores: a 3d tensor before softmax :return: the attention scores. """ if self._is_ascend and self.softmax_dtype == mstype.float16 or not self._is_ascend: attention_probs = self.softmax(attention_scores) else: shape = self.shape(attention_scores) # attention probs attention_probs = self.softmax_3d( self.reshape(attention_scores, (shape[0], -1, shape[-1]))) attention_probs = self.reshape(attention_probs, shape) return attention_probs def _attn(self, query, key, value, attention_mask): """ Get the weighted score along the seq_length Inputs: query: the query matrix key: the key matrix value: the value matrix attention_mask: the attention mask matrix with shape (batch_size, 1, seq_length, seq_length) Outputs: weighted_values: Tensor, the weighted sum scores """ # Normalize query and key before MatMul, default off # Attention score [bs, num_heads, seq_length, seq_length] factor = self.cast(self.scale_factor, P.DType()(query)) query = self.real_div(query, factor) key = self.real_div(key, factor) score = self.batch_matmul(query, key) ori_dtype = P.DType()(score) attention_scores = self.cast(score, self.softmax_dtype) # for input size of (bs, 1) namely the second graph, # the shape of attention_mask matrix should be (bs, 1, 1, seq_length) if attention_mask is not None: if self.use_past and not self.is_first_iteration: # Calculate the current total token current_index = self.reducesum(self.cast(self.not_equal(self.slice(key, (0, 0, 0, 0), (F.shape(query)[0], 1, 1, self.seq_length), (1, 1, 1, 1)), 0), mstype.float32), (1, 2, 3)) # Get the precise position index index = self.sub1(F.cast(current_index, mstype.int32), 1) index = F.reshape(index, (-1, 1, 1)) # Calculate the attention_mask matrix via the position index attention_mask = self.cast(self.tensor_le(self.range, index), mstype.int32) attention_mask = self.expand_dims(attention_mask, 2) # Minus 10000 for the position where masked to exclude them from softmax multiplu_out = self.sub( self.cast(F.tuple_to_array((1.0,)), P.DType()(attention_scores)), self.cast(attention_mask, P.DType()(attention_scores))) adder = self.mul(multiplu_out, self.multiply_data) attention_scores = self.add(adder, attention_scores) # attention probs attention_probs = self._softmax(attention_scores) attention_probs = self.cast(attention_probs, ori_dtype) attention_probs = self.prob_dropout(attention_probs) # Weighted sum output [bs, num_heads, seq_length, size_per_head] weighted_values = self.batch_matmul(attention_probs, value) attention_merge = self._merge_heads(weighted_values) return attention_merge class PipeTransformerEncoderLayer(Cell): def __init__(self, batch_size, hidden_size, ffn_hidden_size, num_heads, seq_length, num_kv_heads=None, attention_dropout_rate=0.1, hidden_dropout_rate=0.1, post_layernorm_residual=False, layernorm_compute_type=mstype.float32, softmax_compute_type=mstype.float32, param_init_type=mstype.float32, hidden_act='gelu', use_past=False, moe_config=default_moe_config, parallel_config=default_dpmp_config, use_rope=False, use_flash_attention=False): super(PipeTransformerEncoderLayer, self).__init__() if batch_size or use_past: Validator.check_positive_int(batch_size) self.batch_size = batch_size if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation(): _check_config(parallel_config) if num_heads % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'num_heads' must be divisibled by the " "'parallel_config.model_parallel', but got the num_heads is {} and " "parallel_config.model_parallel is {}.".format(num_heads, parallel_config.model_parallel)) if hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'hidden_size' must be divisibled by " "the 'parallel_config.model_parallel', but got the hidden_size is {} and parallel_config." " model_parallel is {}.".format(hidden_size, parallel_config.model_parallel)) if ffn_hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'ffn_hidden_size' must be divisibled " "by the 'parallel_config.model_parallel', but got the ffn_hidden_size is {} " "and parallel_config. model_parallel is {}." .format(ffn_hidden_size, parallel_config.model_parallel)) _check_moe_config(moe_config, parallel_config) self.use_moe = (moe_config.expert_num > 1) self.use_past = use_past self.seq_length = seq_length self.hidden_size = hidden_size self.layernorm1 = LlamaRMSNorm(hidden_size, compute_type=layernorm_compute_type) self.layernorm2 = LlamaRMSNorm(hidden_size, compute_type=layernorm_compute_type) attention_parallel_config = parallel_config.dpmp if self.use_moe else parallel_config self.attention = PipeMultiHeadAttention(batch_size=batch_size, src_seq_length=seq_length, tgt_seq_length=seq_length, hidden_size=hidden_size, num_heads=num_heads, num_kv_heads=num_kv_heads, hidden_dropout_rate=hidden_dropout_rate, attention_dropout_rate=attention_dropout_rate, softmax_compute_type=softmax_compute_type, param_init_type=param_init_type, use_past=use_past, parallel_config=attention_parallel_config, use_rope=use_rope, use_flash_attention=use_flash_attention) if self.use_moe: self.output = MoE(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, hidden_act=hidden_act, moe_config=moe_config, parallel_config=parallel_config) else: # Feed Forward Network, FFN self.output = LlamaFeedForward(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, parallel_config=parallel_config) self.post_layernorm_residual = post_layernorm_residual self.add = P.Add().shard(((parallel_config.data_parallel, 1), (parallel_config.data_parallel, 1))) self.add_3d = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1))) self.dtype = mstype.float16 self.key_past = None self.value_past = None if self.use_past: # operator used for state reuse self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) size_per_head = hidden_size // num_heads self.key_shape = (batch_size, num_heads, size_per_head, seq_length) self.value_shape = (batch_size, num_heads, seq_length, size_per_head) # parameters saving key and value states self.key_past = Parameter(Tensor(np.zeros(shape=self.key_shape), self.dtype), name="key_past") self.value_past = Parameter(Tensor(np.zeros(shape=self.value_shape), self.dtype), name="value_past") self.tile = P.Tile().shard(((1, 1),)) self.mul = P.Mul().shard(((1, 1, 1, 1), (1,))) self.assign = P.Assign().shard(((1, 1, 1, 1), (1, 1, 1, 1))) elif _get_parallel_mode() not in (ParallelMode.AUTO_PARALLEL,): _check_config(parallel_config) if num_heads % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'num_heads' must be divisibled by the " "'parallel_config.model_parallel', but got the num_heads is {} and " "parallel_config.model_parallel is {}.".format(num_heads, parallel_config.model_parallel)) if hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'hidden_size' must be divisibled by " "the 'parallel_config.model_parallel', but got the hidden_size is {} and parallel_config." " model_parallel is {}.".format(hidden_size, parallel_config.model_parallel)) if ffn_hidden_size % parallel_config.model_parallel != 0: raise ValueError( "For 'TransformerEncoderLayer', the class variable 'ffn_hidden_size' must be divisibled " "by the 'parallel_config.model_parallel', but got the ffn_hidden_size is {} " "and parallel_config. model_parallel is {}." .format(ffn_hidden_size, parallel_config.model_parallel)) _check_moe_config(moe_config, parallel_config) self.use_moe = (moe_config.expert_num > 1) self.use_past = use_past self.seq_length = seq_length self.hidden_size = hidden_size self.layernorm1 = LlamaRMSNorm(hidden_size, compute_type=layernorm_compute_type) self.layernorm2 = LlamaRMSNorm(hidden_size, compute_type=layernorm_compute_type) self.layernorm1.shard((parallel_config.data_parallel, 1)) self.layernorm2.shard((parallel_config.data_parallel, 1)) attention_parallel_config = parallel_config.dpmp if self.use_moe else parallel_config self.attention = PipeMultiHeadAttention(batch_size=batch_size, src_seq_length=seq_length, tgt_seq_length=seq_length, hidden_size=hidden_size, num_heads=num_heads, num_kv_heads=num_kv_heads, hidden_dropout_rate=hidden_dropout_rate, attention_dropout_rate=attention_dropout_rate, softmax_compute_type=softmax_compute_type, param_init_type=param_init_type, use_past=use_past, parallel_config=attention_parallel_config, use_rope=use_rope, use_flash_attention=use_flash_attention) if self.use_moe: self.output = MoE(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, hidden_act=hidden_act, moe_config=moe_config, parallel_config=parallel_config) else: # Feed Forward Network, FFN self.output = LlamaFeedForward(hidden_size=hidden_size, dropout_rate=hidden_dropout_rate, ffn_hidden_size=ffn_hidden_size, param_init_type=param_init_type, parallel_config=parallel_config) self.post_layernorm_residual = post_layernorm_residual self.add = P.Add().shard(((parallel_config.data_parallel, 1), (parallel_config.data_parallel, 1))) self.add_3d = P.Add().shard(((parallel_config.data_parallel, 1, 1), (parallel_config.data_parallel, 1, 1))) self.dtype = mstype.float16 self.key_past = None self.value_past = None if self.use_past: # operator used for state reuse self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) size_per_head = hidden_size // num_heads self.key_shape = (batch_size, num_heads, size_per_head, seq_length) self.value_shape = (batch_size, num_heads, seq_length, size_per_head) # parameters saving key and value states self.key_past = Parameter(Tensor(np.zeros(shape=self.key_shape), self.dtype), name="key_past") self.value_past = Parameter(Tensor(np.zeros(shape=self.value_shape), self.dtype), name="value_past") self.tile = P.Tile().shard(((1, 1),)) self.mul = P.Mul().shard(((1, 1, 1, 1), (1,))) self.assign = P.Assign().shard(((1, 1, 1, 1), (1, 1, 1, 1))) else: raise RuntimeError(f"The {self.cls_name} only support sharding propagation or " f"semi-auto parallel mode now.") def construct(self, x, input_mask=None, init_reset=True, batch_valid_length=None): self._check_input(x, input_mask, init_reset, batch_valid_length) x_shape = F.shape(x) x = F.reshape(x, (-1, x_shape[-1])) if self.post_layernorm_residual: input_x = x else: input_x = self.layernorm1(x) input_x = F.cast(input_x, self.dtype) # indicate whether reset saved states key_reset = None value_reset = None if self.use_past: # reset states, init_reset True for reuse and False for reset self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype))) key_reset = self.key_past self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype))) value_reset = self.value_past # add dependency for desired execution order input_x = F.depend(input_x, key_reset) input_x = F.depend(input_x, value_reset) attention, layer_present = self.attention(input_x, input_x, input_x, input_mask, self.key_past, self.value_past, batch_valid_length) # For post-layernorm the inputs for residual path are output of self-attention and output of layernorm if self.post_layernorm_residual: x = self.add(input_x, attention) # For pre-layernorm the inputs for residual path are output of self-attention and input of this layer else: x = self.add(x, attention) output_x = self.layernorm2(x) output_x = F.cast(output_x, self.dtype) aux_loss = None if self.use_moe: mlp_logit, aux_loss = self.output(output_x) else: mlp_logit = self.output(output_x) value_update = None key_update = None if self.use_past: # current key and value key_present, value_present = layer_present # update key and value calculated this step self.assign(self.key_past, key_present) key_update = self.key_past self.assign(self.value_past, value_present) value_update = self.value_past # add dependency for desired execution order key_update = F.depend(key_update, key_reset) value_update = F.depend(value_update, value_reset) # add dependency for desired execution order mlp_logit = F.depend(mlp_logit, value_update) mlp_logit = F.depend(mlp_logit, key_update) # if shape is 3d, we reshape the inputs of the add if len(x_shape) == 3: output_x = P.Reshape()(output_x, x_shape) mlp_logit = P.Reshape()(mlp_logit, x_shape) x = P.Reshape()(x, x_shape) if self.post_layernorm_residual: output = self.add_3d(output_x, mlp_logit) output = F.reshape(output, (-1, x_shape[-1])) output = self.layernorm1(output) output = F.reshape(output, x_shape) else: output = self.add_3d(x, mlp_logit) else: if self.post_layernorm_residual: output = self.add(output_x, mlp_logit) output = self.layernorm1(output) else: output = self.add(x, mlp_logit) output = F.reshape(output, x_shape) if self.use_moe: return output, layer_present, aux_loss return output, layer_present def _check_input(self, x, input_mask, init_reset, batch_valid_length): r"""Check inputs""" _check_input_dtype(F.dtype(x), "x", [mstype.float32, mstype.float16], self.cls_name) if input_mask is not None: _check_input_dtype(F.dtype(input_mask), "input_mask", [mstype.float32, mstype.float16], self.cls_name) init_reset_is_tensor = isinstance(init_reset, Tensor) init_reset_is_default = init_reset is True batch_valid_length_is_tensor = isinstance(batch_valid_length, Tensor) batch_is_default = batch_valid_length is None _check_past_none_input_none(self.use_past, "init_reset", self.cls_name, True, init_reset_is_tensor, init_reset_is_default) _check_past_none_input_none(self.use_past, "batch_valid_length", self.cls_name, None, batch_valid_length_is_tensor, batch_is_default) if self.use_past: _check_input_dtype(F.dtype(init_reset), "init_reset", [mstype.bool_], self.cls_name) _check_input_dtype(F.dtype(batch_valid_length), "batch_valid_length", [mstype.int32], self.cls_name) return True default_transformer_config = TransformerOpParallelConfig() class PipeTransformerEncoder(Cell): @_LogActionOnce(logger=logger, key='TransformerEncoder', no_warning=_get_parallel_mode() in (ParallelMode.STAND_ALONE,)) @_args_type_validator_check(batch_size=Validator.check_positive_int, hidden_size=Validator.check_positive_int, num_heads=Validator.check_positive_int, ffn_hidden_size=Validator.check_positive_int, seq_length=Validator.check_positive_int, num_layers=Validator.check_positive_int, offset=Validator.check_non_negative_int, attention_dropout_rate=Validator.check_non_negative_float, hidden_dropout_rate=Validator.check_non_negative_float, post_layernorm_residual=Validator.check_bool, layernorm_compute_type=_valid_value_checks([mstype.float32, mstype.float16], "TransformerEncoder"), softmax_compute_type=_valid_value_checks([mstype.float32, mstype.float16], "TransformerEncoder"), param_init_type=_valid_value_checks([mstype.float32, mstype.float16], "TransformerEncoder"), parallel_config=_valid_type_checks([TransformerOpParallelConfig], "TransformerEncoder"), use_past=Validator.check_bool, use_rope=Validator.check_bool) def __init__(self, batch_size, num_layers, hidden_size, ffn_hidden_size, seq_length, num_heads, num_kv_heads=None, attention_dropout_rate=0.1, hidden_dropout_rate=0.1, hidden_act='gelu', post_layernorm_residual=False, layernorm_compute_type=mstype.float32, softmax_compute_type=mstype.float32, param_init_type=mstype.float32, lambda_func=None, offset=0, use_past=False, moe_config=default_moe_config, parallel_config=default_transformer_config, use_rope=False, use_flash_attention=False): super(PipeTransformerEncoder, self).__init__() _check_config(parallel_config) _check_moe_config(moe_config, parallel_config) self.use_moe = (moe_config.expert_num > 1) config_to_layer = parallel_config.moe_parallel_config if self.use_moe else parallel_config.dp_mp_config if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation(): self.add = P.Add() self.aux_loss = Tensor(0.0, mstype.float32) self.num_layers = num_layers self.blocks = nn.CellList() for i in range(num_layers): block = PipeTransformerEncoderLayer( hidden_size=hidden_size, batch_size=batch_size, ffn_hidden_size=ffn_hidden_size, seq_length=seq_length, attention_dropout_rate=attention_dropout_rate, hidden_dropout_rate=hidden_dropout_rate, layernorm_compute_type=layernorm_compute_type, softmax_compute_type=softmax_compute_type, num_heads=num_heads, num_kv_heads=num_kv_heads, post_layernorm_residual=post_layernorm_residual, param_init_type=param_init_type, use_past=use_past, moe_config=moe_config, parallel_config=config_to_layer, use_rope=use_rope, use_flash_attention=use_flash_attention) # If the user doesn't pass the fusion function, use the default one if not lambda_func: lambda_func = _get_lambda_func() lambda_func(block, layer_id=i, layers=num_layers, offset=offset, parallel_config=parallel_config) self.blocks.append(block) elif _get_parallel_mode() not in (ParallelMode.AUTO_PARALLEL,): self.add = P.Add().shard(((), ())) self.aux_loss = Tensor(0.0, mstype.float32) logger.warning("For parallel mode, sharding propagation is recommended, you can use it by setting " "'set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, " "search_mode=\"sharding_propagation\")' and " "'set_algo_parameters(elementwise_op_strategy_follow=False, fully_use_devices=False)'") self.num_layers = num_layers self.blocks = nn.CellList() for i in range(num_layers): block = PipeTransformerEncoderLayer( hidden_size=hidden_size, batch_size=batch_size, ffn_hidden_size=ffn_hidden_size, seq_length=seq_length, attention_dropout_rate=attention_dropout_rate, hidden_dropout_rate=hidden_dropout_rate, layernorm_compute_type=layernorm_compute_type, softmax_compute_type=softmax_compute_type, num_heads=num_heads, num_kv_heads=num_kv_heads, post_layernorm_residual=post_layernorm_residual, param_init_type=param_init_type, use_past=use_past, moe_config=moe_config, parallel_config=config_to_layer, use_rope=use_rope, use_flash_attention=use_flash_attention) # If the user doesn't pass the fusion function, use the default one if not lambda_func: lambda_func = _get_lambda_func() lambda_func(block, layer_id=i, layers=num_layers, offset=offset, parallel_config=parallel_config) self.blocks.append(block) else: raise RuntimeError(f"The {self.cls_name} only support sharding propagation or " f"semi-auto parallel mode now.") def construct(self, hidden_states, attention_mask, init_reset=True, batch_valid_length=None): present_layer = () if self.use_moe: accum_loss = self.aux_loss for i in range(self.num_layers): hidden_states, present, aux_loss = self.blocks[i](hidden_states, attention_mask, init_reset, batch_valid_length) present_layer = present_layer + (present,) accum_loss = self.add(accum_loss, aux_loss) return hidden_states, present_layer, accum_loss for i in range(self.num_layers): hidden_states, present = self.blocks[i](hidden_states, attention_mask, init_reset, batch_valid_length) present_layer = present_layer + (present,) return hidden_states, present_layer project_1/src/llama_wrapcell.py000066400000000000000000000223401454001410400170560ustar00rootroot00000000000000# 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. # ============================================================================ """GPT training wrapper""" import mindspore.nn as nn from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype from mindspore.nn.wrap.loss_scale import TrainOneStepWithLossScaleCell from mindspore import context, Parameter from mindspore.context import ParallelMode from mindspore.nn.wrap.grad_reducer import DistributedGradReducer from mindspore.communication.management import get_group_size from mindspore.parallel._utils import _get_enable_parallel_optimizer from src.utils import GlobalNorm, ClipByGlobalNorm GRADIENT_CLIP_TYPE = 1 GRADIENT_CLIP_VALUE = 1.0 clip_grad = C.MultitypeFuncGraph("clip_grad") @clip_grad.register("Number", "Number", "Tensor") def _clip_grad(clip_type, clip_value, grad): """ Clip gradients. Inputs: clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'. clip_value (float): Specifies how much to clip. grad (tuple[Tensor]): Gradients. Outputs: tuple[Tensor], clipped gradients. """ if clip_type not in [0, 1]: return grad dt = F.dtype(grad) # 0 for clip_by_value and 1 for clip_by_norm if clip_type == 0: new_grad = C.clip_by_value( grad, F.cast(F.tuple_to_array((-clip_value,)), dt), F.cast(F.tuple_to_array((clip_value,)), dt)) else: new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt)) return new_grad grad_scale = C.MultitypeFuncGraph("grad_scale") shard_grad_scale = C.MultitypeFuncGraph("shard_grad_scale") reciprocal = P.Reciprocal() @grad_scale.register("Tensor", "Tensor") def tensor_grad_scale(scale, grad): return grad * P.Cast()(reciprocal(scale), F.dtype(grad)) @grad_scale.register("Tensor", "Tensor", "Tensor") def tensor_grad_scale_pipeline(scale, grad, accu_grad): accu_grad = F.depend(accu_grad, grad) new_grad = accu_grad * reciprocal(scale) accu_grad = F.depend(accu_grad, new_grad) zeros = F.tensor_mul(accu_grad, 0.0) new_grad = F.depend(new_grad, F.assign(accu_grad, zeros)) return new_grad @shard_grad_scale.register("Tensor", "Tensor", "Tensor") def tensor_shard_grad_scale_pipeline(scale, grad, accu_grad): new_grad = grad * reciprocal(scale) accu_grad = F.depend(accu_grad, new_grad) new_grad = F.depend(new_grad, F.assign(accu_grad, F.zeros_like(accu_grad))) return new_grad class LlamaTrainPipelineWithLossScaleCell(nn.Cell): """ Args: network (Cell): The training network. Note that loss function should have been added. optimizer (Optimizer): Optimizer for updating the weights. scale_update_cell (Cell): Cell to do the loss scale. Default: None. """ def __init__(self, network, optimizer, config, scale_update_cell=None, enable_global_norm=True): super(LlamaTrainPipelineWithLossScaleCell, self).__init__(auto_prefix=False) self.config = config self.network = network self.network.add_flags(defer_inline=True) self.weights = optimizer.parameters self.accu_grads = self.weights.clone(prefix="accu_grads", init="zeros") self.optimizer = optimizer self.enable_global_norm = enable_global_norm self.grad = C.GradOperation(get_by_list=True, sens_param=True) self.reducer_flag = False self.allreduce = P.AllReduce() self.parallel_mode = context.get_auto_parallel_context("parallel_mode") if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]: self.reducer_flag = True self.grad_reducer = F.identity self.degree = 1 if self.reducer_flag: self.degree = get_group_size() self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree) self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE) self.cast = P.Cast() self.alloc_status = P.NPUAllocFloatStatus() self.get_status = P.NPUGetFloatStatus() self.clear_before_grad = P.NPUClearFloatStatus() self.reduce_sum = P.ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = P.LessEqual() self.hyper_map = C.HyperMap() self.loss_scale = None self.reshape = P.Reshape() self.loss_scaling_manager = scale_update_cell if scale_update_cell: self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32), name="loss_scale") self.clip = ClipByGlobalNorm(self.weights, self.config) self.micro_size = config.parallel_config.micro_batch_num self.opt_shard = _get_enable_parallel_optimizer() self.enable_offload = config.enable_offload self.clip_value = Tensor([1.0], dtype=mstype.float32) if config.enable_offload: self.clip = GlobalNorm(self.weights, config) else: self.clip = ClipByGlobalNorm(self.weights, config) @C.add_flags(has_effect=True) def construct(self, input_ids, input_position, attention_mask, past=None, sens=None): """Defines the computation performed.""" weights = self.weights loss = self.network(input_ids, input_position, attention_mask) if sens is None: scaling_sens = self.loss_scale scaling_sens = self.reshape(scaling_sens, (1,)) else: scaling_sens = sens # alloc status and clear should be right before gradoperation init = self.alloc_status() status_clear = self.clear_before_grad(init) #######################taoht############################# # attention_mask = self.cast(attention_mask, mstype.float16) ########################################################## grads = self.grad(self.network, weights)(input_ids, input_position, attention_mask, self.cast(scaling_sens / self.micro_size, mstype.float32)) init = F.depend(init, grads) get_status = self.get_status(init) init = F.depend(init, get_status) flag_sum = self.reduce_sum(init, (0,)) loss = F.depend(loss, status_clear) ## taoht if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) cond = self.less_equal(self.base, flag_reduce) else: cond = self.less_equal(self.base, flag_sum) grads = F.depend(grads, cond) # if erase it will make performance worse # apply grad reducer on grads if self.opt_shard: grads = self.grad_reducer(grads) grads = self.hyper_map(F.partial(shard_grad_scale, scaling_sens * self.degree), grads, self.accu_grads) else: accu_grads = self.grad_reducer(self.accu_grads) grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads, accu_grads) clip_value = self.clip_value if self.enable_global_norm: grads, global_norm_value = self.clip(grads) else: global_norm_value = None grads = self.hyper_map( F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads) # if self.is_distributed: # # sum overflow flag over devices # flag_reduce = self.allreduce(flag_sum) # cond = self.less_equal(self.base, flag_reduce) # else: # cond = self.less_equal(self.base, flag_sum) # grads = F.depend(grads, cond) # if erase it will make performance worse overflow = cond if sens is None: overflow = self.loss_scaling_manager(self.loss_scale, cond) if not overflow: if self.enable_offload: # _, lr = self.optimizer(grads, clip_value) self.optimizer(grads, clip_value) else: # _, lr = self.optimizer(grads) self.optimizer(grads) # self.optimizer(grads) # ignored enable_offload to speed up # else: # lr = self.default_lr return (loss, overflow, scaling_sens.value(), global_norm_value)#, lr) project_1/src/loss.py000066400000000000000000000227341454001410400150660ustar00rootroot00000000000000# 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. # ============================================================================ """ Parallel Loss for the Parallel Training This is an experimental interface that is subject to change or deletion. """ from __future__ import absolute_import from mindspore.parallel import set_algo_parameters from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn import Cell from mindspore.nn.loss.loss import _check_is_tensor from mindspore.parallel._utils import _get_parallel_mode, _is_sharding_propagation from mindspore.context import ParallelMode from mindspore.parallel._utils import _get_device_num, _get_pipeline_stages # from mindspore.nn.transformer.layers import _check_input_dtype, _check_input_shape from mindspore.nn.transformer.op_parallel_config import default_dpmp_config, OpParallelConfig __all__ = ["CrossEntropyLoss"] class _Softmax(Cell): """ Calculate the softmax results with given logits. Note: The bprop of the cell is rewritten, just returns the accepted dout as returns. This cell should be used together with _NLLoss, to optimize the bprop of the cross entroy loss. Args: parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`, an instance of `OpParallelConfig` with default args. Inputs: - **logits** (Tensor) - Tensor of shape (N, C). Data type must be float16 or float32. The output logits of the backbone. Outputs: Tensor. The corresponding softmax results. """ def __init__(self): super(_Softmax, self).__init__() # on/off value for onehot, for smooth labeling, modify the off_value self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) self.sum = P.ReduceSum() self.max = P.ArgMaxWithValue(axis=-1, keep_dims=True) self.sub = P.Sub() self.exp = P.Exp() self.div = P.RealDiv() self.onehot = P.OneHot() def construct(self, logits, label): # LogSoftmax for logits over last dimension logits = F.cast(logits, mstype.float32) _, logit_max = self.max(logits) logit_sub = self.sub(logits, logit_max) logit_exp = self.exp(logit_sub) exp_sum = self.sum(logit_exp, -1) exp_sum = P.Reshape()(exp_sum, (F.shape(exp_sum)[0], 1)) softmax_result = self.div(logit_exp, exp_sum) one_hot_label = self.onehot(label, F.shape(logits)[-1], self.on_value, self.off_value) return softmax_result, one_hot_label def bprop(self, logits, label, out, dout): """just return the loss of the dout. Note this should be used together with _NLLLoss""" d_logits = F.cast(dout[0], F.dtype(logits)) return d_logits, F.zeros_like(label) class _NLLLoss(Cell): """ Calculate the NLLLoss results with given softmax results and the label. Note: The bprop of the cell is rewritten. This cell should be used together with _Softmax, to optimize the bprop of the cross entroy loss. Args: parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`, an instance of `OpParallelConfig` with default args. Inputs: - **loss** (Tensor) - Tensor of shape (N, C). Data type is float32. Outputs: Tensor. The corresponding loss results. """ def __init__(self): super(_NLLLoss, self).__init__() self.repeat_loss = 1 self.eps_const = Tensor(1e-24, mstype.float32) # In auto parallel, there will be a virtual div in the back propagation begins. As we use custom bprop function # we need to eliminate this virtual div by adding a factor "mp". if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and _is_sharding_propagation(): self.sum = P.ReduceSum() self.mul = P.Mul() self.neg = P.Neg() self.log = P.Log() self.add = P.Add() else: self.sum = P.ReduceSum() self.mul = P.Mul() self.neg = P.Neg() self.log = P.Log() self.add = P.Add() def construct(self, softmax_result, one_hot_label): log_softmax_result = self.log(self.add(softmax_result, self.eps_const)) loss = self.mul(log_softmax_result, one_hot_label) loss_unsum = self.neg(loss) loss_reduce = self.sum(loss_unsum, -1) return loss_reduce def bprop(self, softmax_result, one_hot_label, out, dout): """A simplified function. Note this should be used together with _Softmax""" logits = softmax_result - one_hot_label logits = logits * P.ExpandDims()(dout, -1) * self.repeat_loss return logits, F.zeros_like(one_hot_label) class CrossEntropyLoss(Cell): """ Calculate the cross entropy loss. Args: parallel_config (OpParallelConfig): The parallel configure. Default `default_dpmp_config`, an instance of `OpParallelConfig` with default args. Inputs: - **logits** (Tensor) - Tensor of shape (N, C). Data type must be float16 or float32. The output logits of the backbone. - **labels** (Tensor) - Tensor of shape (N, ). The ground truth label of the sample. - **input_mask** (Tensor) - Tensor of shape (N, ). input_mask indicates whether there are padded inputs and for padded inputs it will not be counted into loss. Outputs: Tensor. The corresponding cross entropy loss. Examples: >>> import numpy as np >>> from mindspore import dtype as mstype >>> from mindspore.nn.transformer import CrossEntropyLoss >>> from mindspore import Tensor >>> loss = CrossEntropyLoss() >>> logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), mstype.float32) >>> labels_np = np.array([1]).astype(np.int32) >>> input_mask = Tensor(np.ones(1).astype(np.float32)) >>> labels = Tensor(labels_np) >>> output = loss(logits, labels, input_mask) >>> print(output.shape) (1,) """ def __init__(self): super(CrossEntropyLoss, self).__init__() self.enable_force_redistribute = False if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL, ParallelMode.SEMI_AUTO_PARALLEL): self.enable_force_redistribute = True self.add = P.Add().add_prim_attr("keep_alive", True) self.add_label = P.Add().add_prim_attr("keep_alive", True) self.sum2 = P.ReduceSum() self.mul2 = P.Mul() self.add2 = P.Add() self.div2 = P.RealDiv() self.relu = P.ReLU() self._softmax = _Softmax() self._nllloss = _NLLLoss() @staticmethod def _check_and_modify_sharding_context(dp): device_num = _get_device_num() stages = _get_pipeline_stages() if _get_parallel_mode() in (ParallelMode.AUTO_PARALLEL,) and dp * stages != device_num: set_algo_parameters(fully_use_devices=False) def construct(self, logits, label, input_mask): # self._check_input(logits, label, input_mask) # The add is used for forcing the redistribution before stepping in sub graphs, when semi/auto parallel enabled. if self.enable_force_redistribute: logits = self.add(logits, 0) label = self.add_label(label, 0) softmax, one_hot_label = self._softmax(logits, label) loss_reduce = self._nllloss(softmax, one_hot_label) # Using input_mask to mask the loss input_mask = P.Reshape()(input_mask, (-1,)) numerator = self.sum2(self.mul2(loss_reduce, input_mask)) denominator = self.add2( self.sum2(input_mask), P.Cast()(F.tuple_to_array((1e-5,)), mstype.float32)) loss = self.div2(numerator, denominator) return loss # def _check_input(self, logits, label, input_mask): # r"""Check the input tensor shape and type""" # _check_is_tensor('logits', logits, self.cls_name) # _check_is_tensor('label', label, self.cls_name) # _check_is_tensor('input_mask', input_mask, self.cls_name) # _check_input_dtype(F.dtype(logits), "logits", [mstype.float32, mstype.float16], self.cls_name) # _check_input_dtype(F.dtype(label), "label", [mstype.int32], self.cls_name) # _check_input_dtype(F.dtype(input_mask), "input_mask", [mstype.float32], self.cls_name) # _check_input_shape(F.shape(logits), "logits", self.cls_name, 2) # _check_input_shape(F.shape(label), "label", self.cls_name, 1) # _check_input_shape(F.shape(input_mask), "input_mask", self.cls_name, 1) # return True project_1/src/metrics.py000066400000000000000000000040261454001410400155460ustar00rootroot00000000000000# 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. # ============================================================================ """ Eval metrics """ import math from mindspore.nn.metrics import Metric from mindspore import context from mindspore.communication.management import get_rank, get_group_size class PPLMetric(Metric): """ Ppl metric """ def __init__(self, data_length): super(PPLMetric, self).__init__() self.clear() self.data_length = data_length pipeline_stages = context.get_auto_parallel_context("pipeline_stages") per_stage_device_num = get_group_size() // pipeline_stages stage_id = get_rank() // per_stage_device_num self.is_last_stage = (stage_id == pipeline_stages - 1) def clear(self): """Clear the internal evaluation result.""" self.PPL = [] self.tokens_count = 0 def update(self, *inputs): # inputs """Update list of ppl""" if not self.is_last_stage: return logits = inputs[0].asnumpy().flatten().tolist() # logits self.PPL.append(logits[0] * self.data_length) self.tokens_count += 1 def eval(self): if not self.is_last_stage: return 0 val_loss = sum(self.PPL) / (self.tokens_count * self.data_length) ppl = math.exp(min(20, val_loss)) print("====" * 20 + " ppl end") print("====" * 20 + " ppl: {}".format(ppl)) return ppl project_1/src/model_config.py000066400000000000000000000104221454001410400165220ustar00rootroot00000000000000# 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. # ============================================================================ """ network config setting """ import mindspore.common.dtype as mstype class ModelConfig: def __init__(self, batch_size=32, seq_length=4096, vocab_size=49984, hidden_size=768, ffn_hidden_size=768, num_layers=12, num_heads=12, num_kv_heads=None, load_ckpt_path=None, param_init_type=mstype.float16, post_layernorm_residual=False, dropout_rate=0.1, eod_token=2, pad_token=0, use_past=False, hidden_act='fast_gelu', eod_reset=True, enable_offload=False, use_moe=False, expert_num=1, per_token_num_experts_chosen=1, parallel_config=None, run_type='train', softmax_compute_fp32=False, use_rope=False, use_flash_attention=False): self.batch_size = batch_size self.seq_length = seq_length self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_layers = num_layers self.num_heads = num_heads self.num_kv_heads = num_kv_heads self.eod_token = eod_token # Use post-layernorm or pre-layernrom, default:pre-layernorm self.post_layernorm_residual = post_layernorm_residual self.load_ckpt_path = load_ckpt_path self.param_init_type = param_init_type self.dropout_rate = dropout_rate self.compute_dtype = mstype.float16 self.parallel_config = parallel_config self.ffn_hidden_size = ffn_hidden_size self.hidden_act = hidden_act self.use_past = use_past self.eod_reset = eod_reset self.enable_offload = enable_offload self.use_moe = bool(use_moe) self.expert_num = expert_num self.per_token_num_experts_chosen = per_token_num_experts_chosen self.run_type = run_type self.pad_token = pad_token self.use_rope = use_rope if softmax_compute_fp32: self.softmax_compute_type = mstype.float32 else: self.softmax_compute_type = mstype.float16 self.use_flash_attention = use_flash_attention # if self.run_type == "predict": # self.softmax_compute_type = mstype.float32 def __str__(self): info = '===' * 10 + "[Config]" + '===' * 10 + '\n' for k, v in self.__dict__.items(): var_info = "--{}:{}\n".format(k, v) info += var_info info += '=' * 10 return info def set_parse_7B(args_opt): r""" Set config for 7B mode """ args_opt.embedding_size = 4096 args_opt.num_layers = 32 args_opt.num_heads = 32 args_opt.num_kv_heads = 32 args_opt.vocab_size = 125696 args_opt.seq_length = 4096 args_opt.ffn_hidden_size = 11008 args_opt.use_rope = True args_opt.softmax_compute_fp32 = True assert args_opt.run_type == "train" args_opt.start_lr = 2e-5 args_opt.end_lr = 2e-6 args_opt.full_batch = args_opt.opt_offload # if args_opt.per_batch_size == 0: # args_opt.per_batch_size = 2 # 8 if args_opt.stage_num > 1: args_opt.word_emb_dp = 0 def set_parse(args_opt): r""" Set config according to the mode """ parse_fn_dict = {"7B": set_parse_7B} if args_opt.mode not in parse_fn_dict.keys(): raise ValueError("Invalid mode: {}. Optional mode: 7B".format(args_opt.mode)) parse_fn_dict[args_opt.mode](args_opt) project_1/src/preprocess.py000066400000000000000000000215021454001410400162630ustar00rootroot00000000000000# Copyright 2020 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. # ============================================================================ """ transform wikitext-2, wikitext-103, lambada, openwebtext dataset to mindrecord. """ import argparse import glob import json import os import re from functools import partial from multiprocessing import Pool, current_process import numpy as np from mindspore.mindrecord import FileWriter from src.tokenization_jieba import JIEBATokenizer def chunks(lst, n): """ yield n sized chunks from list""" for i in range(0, len(lst), n): yield lst[i:i+n] def package_file(it, n): """ package multiple files""" stop = False while not stop: batch = [] for _ in range(n): try: batch.append(next(it)) except StopIteration: stop = True if not batch: break yield batch def clean_wikitext(string): """ cleaning wikitext dataset""" # contractions string = string.replace("s '", "s'") string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string) # number separators string = string.replace(" @-@ ", "-") string = string.replace(" @,@ ", ",") string = string.replace(" @.@ ", ".") # punctuation string = string.replace(" : ", ": ") string = string.replace(" ; ", "; ") string = string.replace(" . ", ". ") string = string.replace(" ! ", "! ") string = string.replace(" ? ", "? ") string = string.replace(" , ", ", ") # double brackets string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string) string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string) string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string) string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string) string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string) # miscellaneous string = string.replace("= = = =", "====") string = string.replace("= = =", "===") string = string.replace("= =", "==") string = string.replace(" "+chr(176)+" ", chr(176)) string = string.replace(" \n", "\n") string = string.replace("\n ", "\n") string = string.replace(" N ", " 1 ") string = string.replace(" 's", "'s") return string def tokenize_openwebtext(tokenizer, iterator, seq_length, eot): """ tokenize openwebtext dataset""" for file_path in iterator: if os.path.getsize(file_path) == 0: continue content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in f.read().split("\n\n"): if para: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_wiki(tokenizer, file_path, seq_length, eot): """tokenize wikitext-2/wikitext-103 dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in clean_wikitext(f.read()).split("\n\n"): if para and para.strip().startswith('=') is False: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_lambada(tokenizer, file_path, seq_length, eot): """tokenize lambada dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for line in f.readlines(): para = json.loads(line)['text'].replace( "“", '"').replace("”", '"').strip().strip(".") tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def task_unit(iterator, tokenizer, seq_length, eot, parallel_writer=True): """task for each process""" p = current_process() index = p.pid if p.pid else 0 item_iter = tokenize_openwebtext(tokenizer, iterator, seq_length, eot) batch_size = 1024 # size of write batch count = 0 while True: data_batch = [] try: for _ in range(batch_size): data_batch.append(next(item_iter)) count += 1 writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) except StopIteration: if data_batch: writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) break if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--dataset_type', type=str, default='openwebtext') parser.add_argument('--input_glob', type=str, default='*.txt') parser.add_argument('--output_file', type=str, default='./output/transfered_mindrecord') parser.add_argument('--tokenizer', type=str, default='gpt', choices=['gpt', 'jieba']) parser.add_argument('--model_file', type=str, default=None) parser.add_argument('--file_partition', type=int, default=1) parser.add_argument('--file_batch_size', type=int, default=1024) parser.add_argument('--num_process', type=int, default=64) parser.add_argument('--seq_length', type=int, default=1025) parser.add_argument('--eot', type=int, default=3, help="Eod of text depends on the vocab file.") parser.add_argument('--data_column_name', type=str, default='input_ids') args = parser.parse_args() out_dir, out_file = os.path.split(os.path.abspath(args.output_file)) if not os.path.exists(out_dir): os.mkdir(out_dir) schema = {args.data_column_name: {"type": "int32", "shape": [-1]},} writer = FileWriter(file_name=args.output_file, shard_num=args.file_partition) writer.add_schema(schema, args.dataset_type) writer.open_and_set_header() # Start to load tokenizer if args.tokenizer == 'gpt': try: from transformers import GPT2Tokenizer except ModuleNotFoundError: print("module 'transformers' not installed.") word_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") else: if not os.path.exists(args.model_file): raise FileNotFoundError(f"file {args.model_file} do not exists.") word_tokenizer = JIEBATokenizer(model_file=args.model_file) transforms_count = 0 if args.dataset_type == 'wiki': for x in tokenize_wiki(word_tokenizer, args.input_glob, args.seq_length, args.eot): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'lambada': for x in tokenize_lambada(word_tokenizer, args.input_glob, args.seq_length, args.eot): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'openwebtext': file_iter = glob.iglob(args.input_glob) with Pool(processes=args.num_process) as pool: map_func = partial(task_unit, tokenizer=word_tokenizer, seq_length=args.seq_length, eot=args.eot) pool.map(map_func, package_file(file_iter, args.file_batch_size)) else: raise ValueError( "Not support dataset type: {}".format(args.dataset_type)) writer.commit() out_file = args.output_file if args.file_partition > 1: out_file += '0' print("Transform finished, output files refer: {}".format(out_file)) project_1/src/utils.py000066400000000000000000001543041454001410400152450ustar00rootroot00000000000000# 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. # ============================================================================ """ network config setting, gradient clip function and dynamic learning rate function """ import argparse import ast import os import time import hashlib import numpy as np import mindspore.nn as nn from mindspore import context from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F import mindspore.communication.management as D import mindspore.common.dtype as mstype from mindspore.common.tensor import Tensor from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR, CosineDecayLR from mindspore.parallel._auto_parallel_context import auto_parallel_context from mindspore.context import ParallelMode from mindspore.communication.management import get_rank, get_group_size, create_group from mindspore.nn import AdamWeightDecay from mindspore.common import Parameter, ParameterTuple from mindspore.common.initializer import initializer from mindspore.train.callback import Callback import moxing as mox from multiprocessing import Process from mindspore.nn.optim.optimizer import Optimizer from mindspore._checkparam import Validator as validator from mindspore._checkparam import Rel from mindspore.common.api import jit ''' _adam_opt = C.MultitypeFuncGraph("adam_opt") _fused_adam_weight_decay = C.MultitypeFuncGraph("fused_adam_weight_decay") def _check_param_value(beta1, beta2, eps, prim_name): """Check the type of inputs.""" validator.check_value_type("beta1", beta1, [float], prim_name) validator.check_value_type("beta2", beta2, [float], prim_name) validator.check_value_type("eps", eps, [float], prim_name) validator.check_float_range(beta1, 0.0, 1.0, Rel.INC_NEITHER, "beta1", prim_name) validator.check_float_range(beta2, 0.0, 1.0, Rel.INC_NEITHER, "beta2", prim_name) validator.check_positive_float(eps, "eps", prim_name) @_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool") class AdamWeightDecay(Optimizer): r""" Implements the Adam algorithm with weight decay. .. math:: \begin{array}{l} &\newline &\hline \\ &\textbf{Parameters}: \: 1^{\text {st }}\text {moment vector} \: m , \: 2^{\text {nd}} \: \text{moment vector} \: v , \\ &\: gradients \: g, \: \text{learning rate} \: \gamma, \text {exponential decay rates for the moment estimates} \: \beta_{1} \: \beta_{2} , \\ &\:\text {parameter vector} \: w_{0}, \:\text{timestep} \: t, \: \text{weight decay} \: \lambda \\ &\textbf{Init}: m_{0} \leftarrow 0, \: v_{0} \leftarrow 0, \: t \leftarrow 0, \: \text{init parameter vector} \: w_{0} \\[-1.ex] &\newline &\hline \\ &\textbf{repeat} \\ &\hspace{5mm} t \leftarrow t+1 \\ &\hspace{5mm}\boldsymbol{g}_{t} \leftarrow \nabla f_{t}\left(\boldsymbol{w}_{t-1}\right) \\ &\hspace{5mm}\boldsymbol{m}_{t} \leftarrow \beta_{1} \boldsymbol{m}_{t-1}+\left(1-\beta_{1}\right) \boldsymbol{g}_{t} \\ &\hspace{5mm}\boldsymbol{v}_{t} \leftarrow \beta_{2} \boldsymbol{v}_{t-1}+\left(1-\beta_{2}\right) \boldsymbol{g}_{t}^{2} \\ &\hspace{5mm}\boldsymbol{w}_{t} \leftarrow \boldsymbol{w}_{t-1}-\left(\gamma \hat{\boldsymbol{m}}_{t} /\left(\sqrt{\hat{\boldsymbol{v}}_{t}}+\epsilon\right)+\lambda \boldsymbol{w}_{t-1}\right) \\ &\textbf{until}\text { stopping criterion is met } \\[-1.ex] &\newline &\hline \\[-1.ex] &\textbf{return} \: \boldsymbol{w}_{t} \\[-1.ex] &\newline &\hline \\[-1.ex] \end{array} :math:`m` represents the 1st moment vector `moment1`, :math:`v` represents the 2nd moment vector `moment2`, :math:`g` represents `gradients`, :math:`\gamma` represents `learning_rate`, :math:`\beta_1, \beta_2` represent `beta1` and `beta2`, :math:`t` represents the current step, :math:`w` represents `params`, :math:`\gamma` represents `weight_decay`. Note: There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'. As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means, refer document `LossScale `_ to process `loss_scale` correctly. If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without 'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When parameters are grouped, each group can set `weight_decay`. If not, the `weight_decay` in optimizer will be applied. Args: params (Union[list[Parameter], list[dict]]): Must be list of `Parameter` or list of `dict`. When the `params` is a list of `dict`, the string "params", "lr", "weight_decay", and "order_params" are the keys can be parsed. - params: Required. Parameters in current group. The value must be a list of `Parameter`. - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used. If not, the `learning_rate` in optimizer will be used. Fixed and dynamic learning rate are supported. - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay will be used. If not, the `weight_decay` in the optimizer will be used. It should be noted that weight decay can be a constant value or a Cell. It is a Cell only when dynamic weight decay is applied. Dynamic weight decay is similar to dynamic learning rate, users need to customize a weight decay schedule only with global step as input, and during training, the optimizer calls the instance of WeightDecaySchedule to get the weight decay value of current step. - order_params: Optional. When parameters is grouped, this usually is used to maintain the order of parameters that appeared in the network to improve performance. The value should be parameters whose order will be followed in optimizer. If `order_params` in the keys, other keys will be ignored and the element of 'order_params' must be in one group of `params`. learning_rate (Union[float, int, Tensor, Iterable, LearningRateSchedule]): Default: 1e-3. - float: The fixed learning rate value. Must be equal to or greater than 0. - int: The fixed learning rate value. Must be equal to or greater than 0. It will be converted to float. - Tensor: Its value should be a scalar or a 1-D vector. For scalar, fixed learning rate will be applied. For vector, learning rate is dynamic, then the i-th step will take the i-th value as the learning rate. - Iterable: Learning rate is dynamic. The i-th step will take the i-th value as the learning rate. - LearningRateSchedule: Learning rate is dynamic. During training, the optimizer calls the instance of LearningRateSchedule with step as the input to get the learning rate of current step. beta1 (float): The exponential decay rate for the 1st moment estimations. Default: 0.9. Should be in range (0.0, 1.0). beta2 (float): The exponential decay rate for the 2nd moment estimations. Default: 0.999. Should be in range (0.0, 1.0). eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6. Should be greater than 0. weight_decay (Union[float, int, Cell]): Weight decay (L2 penalty). Default: 0.0. - float: The fixed weight decay value. Must be equal to or greater than 0. - int: The fixed weight decay value. Must be equal to or greater than 0. It will be converted to float. - Cell: Weight decay is dynamic. During training, the optimizer calls the instance of the Cell with step as the input to get the weight decay value of current step. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: tuple[bool], all elements are True. Raises: TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule. TypeError: If element of `parameters` is neither Parameter nor dict. TypeError: If `beta1`, `beta2` or `eps` is not a float. TypeError: If `weight_decay` is neither float nor int. ValueError: If `eps` is less than or equal to 0. ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0). ValueError: If `weight_decay` is less than 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore as ms >>> from mindspore import nn >>> >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.AdamWeightDecay(params=net.trainable_params()) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params())) >>> group_params = [{'params': conv_params, 'weight_decay': 0.01}, ... {'params': no_conv_params, 'lr': 0.01}, ... {'order_params': net.trainable_params()}] >>> optim = nn.AdamWeightDecay(group_params, learning_rate=0.1, weight_decay=0.0) >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01. >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0. >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'. >>> >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = ms.Model(net, loss_fn=loss, optimizer=optim) """ _support_parallel_optimizer = True def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(AdamWeightDecay, self).__init__(learning_rate, params, weight_decay) _check_param_value(beta1, beta2, eps, self.cls_name) self.beta1 = Tensor(np.array([beta1]).astype(np.float32)) self.beta2 = Tensor(np.array([beta2]).astype(np.float32)) self.eps = Tensor(np.array([eps]).astype(np.float32)) self.moments1 = self._parameters.clone(prefix="adam_m", init='zeros') self.moments2 = self._parameters.clone(prefix="adam_v", init='zeros') self.fused_opt = P.AdamWeightDecay() if context.get_context("device_target") == "CPU": self.use_fused_opt = True else: self.use_fused_opt = False @jit def construct(self, gradients): gradients = self.flatten_gradients(gradients) weight_decay = self.get_weight_decay() lr = self.get_lr() if self.use_fused_opt: if self.is_group: if self.is_group_lr: optim_result = self.hyper_map( F.partial(_fused_adam_weight_decay, self.fused_opt, self.beta1, self.beta2, self.eps), lr, weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map( F.partial(_fused_adam_weight_decay, self.fused_opt, self.beta1, self.beta2, self.eps, lr), weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map( F.partial(_fused_adam_weight_decay, self.fused_opt, self.beta1, self.beta2, self.eps, lr, weight_decay), self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: if self.is_group: if self.is_group_lr: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps), lr, weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr), weight_decay, self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr, weight_decay), self._parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) if self.use_parallel: self.broadcast_params(optim_result) return optim_result, lr @Optimizer.target.setter def target(self, value): """ If the input value is set to "CPU", the parameters will be updated on the host using the Fused optimizer operation. """ self._set_base_target(value) if value == 'CPU': self.fused_opt.set_device("CPU") self.use_fused_opt = True else: self.use_fused_opt = False ''' class FP32StateAdamWeightDecay(AdamWeightDecay): r""" This class is almost same with the mindspore's AdamWeightDecay implements, the only difference is the optimizer's state will be always initialized with float32, where the original AdamWeightDecay will initialize the optimizer's state with float16, if the parameters are initialized with fp16. This setting will avoid overflow in training model using fp16. """ def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(FP32StateAdamWeightDecay, self).__init__(params, learning_rate=learning_rate, beta1=beta1, beta2=beta2, eps=eps, weight_decay=weight_decay) self.moments1 = self.clone_state(self.parameters, prefix='adam_m', init='zeros') self.moments2 = self.clone_state(self.parameters, prefix='adam_v', init='zeros') def clone_state(self, parameter_tuple, prefix, init): r""" parameter_tuple: ParameterTuple. The parameters of the network prefix: str. The prefix name of the parameters init: str. The initialization method """ new = [] for old_param in parameter_tuple: new_state = Parameter(initializer(init, shape=old_param.shape, dtype=mstype.float32)) new_state.param_info = old_param.param_info.clone() if hasattr(old_param.param_info, "cloned_obj"): old_param.param_info.cloned_obj.append(new_state) else: old_param.param_info.cloned_obj = [new_state] new_state.param_info.obj = new_state new_state.is_init = False new_state.set_data(initializer(init, shape=old_param.shape, dtype=mstype.float32)) new_state.name = prefix + '.' + new_state.name new.append(new_state) return ParameterTuple(new) get_square_sum = C.MultitypeFuncGraph("get_square_sum") @get_square_sum.register("Tensor", "Number") def _get_square_sum(grad, value): norm = P.ReduceSum(False)(F.square(grad), ()) / value norm = F.expand_dims(F.cast(norm, mstype.float32), 0) return norm apply_global_norm = C.MultitypeFuncGraph("apply_global_norm") @apply_global_norm.register("Bool", "Tensor", "Tensor", "Tensor") def _apply_global_norm(enable_grad_fp16, clip_norm, global_norm, grad): if enable_grad_fp16: grad = P.Cast()(grad * clip_norm / global_norm, mstype.float16) else: grad = grad * clip_norm / global_norm return grad def _get_model_parallel_group(mp): """ Calculate the communication group of model parallel dim in one pipeline stage """ rank = get_rank() stage_nums = auto_parallel_context().get_pipeline_stages() device_nums = get_group_size() per_stage_device_nums = device_nums // stage_nums stage_id = rank // per_stage_device_nums local_stage_rank_id = rank % per_stage_device_nums index = local_stage_rank_id // mp group = range(0, mp) rank_str_list = [str(x + index * mp + stage_id * per_stage_device_nums) for x in group] rank_list_str = "-".join(rank_str_list) rank_list = [x + index * mp + stage_id * per_stage_device_nums for x in group] return rank_list, rank_list_str def _get_pipeline_group(): """ Calculate the communication group between all pipeline stages """ rank = get_rank() stage_nums = auto_parallel_context().get_pipeline_stages() device_nums = get_group_size() per_stage_device_nums = device_nums // stage_nums local_stage_rank_id = rank % per_stage_device_nums group = range(0, stage_nums) rank_list = [local_stage_rank_id + x * per_stage_device_nums for x in group] rank_str_list = [str(local_stage_rank_id + x * per_stage_device_nums) for x in group] rank_list_str = "-".join(rank_str_list) return rank_list, rank_list_str class GlobalNorm(nn.Cell): """ Calculate the global norm value of given tensors """ def __init__(self, params, config): super(GlobalNorm, self).__init__() self.norm = nn.Norm() self.hyper_map = C.HyperMap() self.is_pipeline = context.get_auto_parallel_context("pipeline_stages") > 1 # self.is_data_parallel = context.get_auto_parallel_context("parallel_mode") == ParallelMode.DATA_PARALLEL # self.config = config # self.group_size = 1 # if self.is_data_parallel: # self.merge_op = P.identity() # else: # self.merge_op = P.AllReduce() if self.is_pipeline: if context.get_auto_parallel_context("enable_parallel_optimizer"): group_size = get_group_size() // config.parallel_config.pipeline_stage else: group_size = config.parallel_config.model_parallel group_list, group_name = _get_model_parallel_group(group_size) # In avoid of the group name too long hashed = hashlib.md5(group_name.encode()).hexdigest()[:48] print(f"Creating hash value for the group_name hash({group_name})={hashed}") group_name = str(hashed) create_group(group_name, group_list) self.allreduce = P.AllReduce(group=group_name) pipeline_group_list, pipeline_group_name = _get_pipeline_group() hashed = hashlib.md5(pipeline_group_name.encode()).hexdigest()[:48] print(f"Creating hash value for the group_name hash({pipeline_group_name})={hashed}") pipeline_group_name = str(hashed) create_group(pipeline_group_name, pipeline_group_list) self.allreduce2 = P.AllReduce(group=pipeline_group_name) else: group_size = get_group_size() zero_repeated_size = 1.0 if context.get_auto_parallel_context("enable_parallel_optimizer") and \ context.get_auto_parallel_context("optimizer_weight_shard_size") > 1: zero_shard_size = context.get_auto_parallel_context("optimizer_weight_shard_size") zero_repeated_size = config.parallel_config.data_parallel // zero_shard_size self.allreduce_group_size = () # if self.is_data_parallel: # self.allreduce_group_size = (1,) * len(params) # else: # self.allreduce_group_size = self._get_scale_for_gradient_norm(params) for x in params: if "projection.bias" not in x.name and "layernorm" not in x.name and "embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (zero_repeated_size,) elif "embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (group_size * 1.0,) else: if not config.parallel_config.vocab_emb_dp and "position_embedding.embedding_table" not in x.name and \ "top_query_embedding.embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (config.parallel_config.data_parallel * 1.0,) else: self.allreduce_group_size = self.allreduce_group_size + (group_size * 1.0,) def construct(self, grads): """Calculate global norm construct""" square_sum = self.hyper_map(get_square_sum, grads, self.allreduce_group_size) square_reduce_sum = F.addn(square_sum) if self.is_pipeline: stage_square_reduce_sum = self.allreduce(square_reduce_sum) global_square_reduce_sum = self.allreduce2(stage_square_reduce_sum) global_norms = F.sqrt(global_square_reduce_sum) else: global_norms = F.sqrt(P.AllReduce()(square_reduce_sum)) return grads, global_norms # def _get_scale_for_gradient_norm(self, params): # allreduce_group_size = () # for x in params: # if "projection.bias" not in x.name and "layernorm" not in x.name and "embedding_table" not in x.name: # allreduce_group_size = allreduce_group_size + (1.0,) # elif "embedding_table" not in x.name: # allreduce_group_size = allreduce_group_size + (self.group_size * 1.0,) # else: # if not self.config.parallel_config.vocab_emb_dp and "position_embedding.embedding_table" not in x.name \ # and "top_query_embedding_table" not in x.name: # allreduce_group_size = allreduce_group_size + \ # (self.config.parallel_config.data_parallel * 1.0,) # else: # allreduce_group_size = allreduce_group_size + (self.group_size * 1.0,) # return allreduce_group_size class ClipByGlobalNorm(nn.Cell): """ Clip grads by global norm """ def __init__(self, params, config, clip_norm=1.0): super(ClipByGlobalNorm, self).__init__() self.global_norm = GlobalNorm(params, config) self.clip_norm = Tensor([clip_norm], mstype.float32) self.hyper_map = C.HyperMap() if config.param_init_type == mstype.float16 and config.enable_offload: self.enable_grad_fp16 = True else: self.enable_grad_fp16 = False def construct(self, grads): """Clip grads by global norm construct""" grads, global_norm_value = self.global_norm(grads) cond = P.GreaterEqual()(global_norm_value, self.clip_norm) global_norm = F.select(cond, global_norm_value, self.clip_norm) grads = self.hyper_map(F.partial(apply_global_norm, self.enable_grad_fp16, self.clip_norm, global_norm), grads) return grads, global_norm_value class LearningRate(LearningRateSchedule): def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power=1.0, use_cosine=True): super(LearningRate, self).__init__() self.warmup_flag = False if warmup_steps > 0: self.warmup_flag = True self.warmup_lr = WarmUpLR(learning_rate, warmup_steps) self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power) self.cosine_decay_lr = CosineDecayLR(end_learning_rate, learning_rate, decay_steps) self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32)) self.greater = P.Greater() self.one = Tensor(np.array([1.0]).astype(np.float32)) self.cast = P.Cast() self.use_cosine = use_cosine def construct(self, global_step): """dynamic learning rate""" if not self.use_cosine: decay_lr = self.decay_lr(global_step) else: decay_lr = self.cosine_decay_lr(global_step) if self.warmup_flag: is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32) warmup_lr = self.warmup_lr(global_step) lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr else: lr = decay_lr return lr def add_inference_params(opt): """Add inference params""" opt.add_argument("--frequency_penalty", type=float, default=1.5, help="coefficient for frequency_penalty") opt.add_argument("--presence_penalty", type=float, default=0.3, help="coefficient for presence_penalty") opt.add_argument("--max_generate_length", type=int, default=500, help="the maximum number of generated token") opt.add_argument("--top_k_num", type=int, default=3, help="the number for top_k sampling") opt.add_argument("--top_p", type=float, default=1.0, help="top_p sampling threshold, enabled if less than 1.0") opt.add_argument("--end_token", type=int, default=2, help="the token id for ") opt.add_argument("--use_pynative_op", type=int, default=1, help="Whether use pynative op for postproecess") opt.add_argument("--use_past", type=bool, default=False, help="Whether enable state reuse") def add_training_params(opt): """Add training params""" opt.add_argument("--seq_length", type=int, default=4096, help="sequence length, default is 1024.") opt.add_argument("--vocab_size", type=int, default=49984, help="vocabulary size, default is 40000.") opt.add_argument("--embedding_size", type=int, default=10240, help="embedding table size, default is 16384.") opt.add_argument("--ffn_hidden_size", type=int, default=16384, help="ffn hidden size, default is 16384.") opt.add_argument("--num_layers", type=int, default=80, help="total layers, default is 64.") opt.add_argument("--num_heads", type=int, default=80, help="head size, default is 128.") opt.add_argument("--num_kv_heads", type=int, default=None, help="kv head, default is 128.") opt.add_argument("--stage_num", type=int, default=1, help="Pipeline stage num, default is 1.") opt.add_argument("--micro_size", type=int, default=32, help="Pipeline micro_size, default is 1.") opt.add_argument("--eod_reset", type=int, default=1, help="Enable eod mask, default is 1.") opt.add_argument("--warmup_step", type=int, default=1000, help="Warmup step, default is 2000.") opt.add_argument("--decay_steps", type=int, default=150000, help="Decay step, default is 200000.") opt.add_argument("--optimizer", type=str, default="adam", choices=["adam", "lamb"], help="select which optimizer to be used, default adam") opt.add_argument("--opt_offload", type=int, default=0, help="Enable optimizer status offload to host CPU, default is 0") opt.add_argument("--use_moe", type=int, default=0, help="Use moe, default is 0") opt.add_argument("--expert_num", type=int, default=1, help="Expert number, only effective when applying moe, Default is 1") opt.add_argument("--per_token_num_experts_chosen", type=int, default=1, help="Expert nums chosen by each token, only effective when applying moe, default is 1") opt.add_argument("--eod_id", type=int, default=2, help="The id of end of document") opt.add_argument("--padding_id", type=int, default=0, help="The padding id of dataset") opt.add_argument("--epoch_size", type=int, default=1, help="The training epoch") opt.add_argument("--sink_size", type=int, default=4, help="The sink size of the training. default is 2") opt.add_argument("--full_batch", default=1, type=int, help="Import the full size of a batch for each card, default is 1") opt.add_argument("--optimizer_shard", type=int, default=8, help="Enable optimizer parallel, default is 1") opt.add_argument("--per_batch_size", type=int, default=1, help="The batch size for each data parallel way. default 0") opt.add_argument("--start_lr", type=float, default=6e-5, help="The start learning rate. default 5e-5") opt.add_argument("--end_lr", type=float, default=6e-7, help="The end learning rate. default 1e-6") opt.add_argument("--op_level_model_parallel_num", type=int, default=2, help="The model parallel way. default 8") opt.add_argument("--expert_parallel_num", type=int, default=1, help="The expert parallel way, only effective when applying moe. Default 1") opt.add_argument("--word_emb_dp", type=int, default=1, choices=[0, 1], help="Whether do data parallel in word embedding. default 1") opt.add_argument("--gradient_aggregation_group", type=int, default=4, help="The gradient communication fusion group. default 4") opt.add_argument("--data_column_name", type=str, default="input_ids", help="Column name of datasets") opt.add_argument("--micro_batch_interleaved", type=int, default=1, help="Parallel split num of batch size. default 2") # opt.add_argument("--recompute_slice_activation", # type=int, default=0, # help="Enable slice the recompute activation state. default 0") opt.add_argument("--recompute", type=ast.literal_eval, default=False, help="Enable recompute. default False") opt.add_argument("--softmax_compute_fp32", type=ast.literal_eval, default=False, help="enable softmax_compute_type fp32") opt.add_argument("--load_compiler_cache", help="Enable download and load compiler cache in obs", type=bool, default=False) opt.add_argument("--compiler_name", type=str, default=None, help="compiler_name") def add_context_args_mode(opt): """Add context args params""" opt.add_argument("--parallel_mode", type=str, default="semi_auto_parallel", choices=['data_parallel', "semi_auto_parallel", "auto_parallel"], help="The parallel context mode") def add_downstream_params(opt): opt.add_argument("--eval_task", help="Enable evaluating the tasks. Currently supports WPLC, C3.", default=None, choices=['wplc', 'ocnli', 'c3', 'chid'], type=str) opt.add_argument("--enable_client", help="Enable evaluation as an client.", action='store_true') opt.add_argument("--server_ip", help="The server ip of the model. The evaluation will send the data to the server, so the option" "should be enabled with enable_client together", type=str, default="127.0.0.1") opt.add_argument("--port", help="The server port of the model. The evaluation will send the data to the server, so the option" "should be enabled with enable_client together", type=str, default="1500") def add_retrain_params(opt): """ Add parameters about retrain. """ opt.add_argument("--pre_trained", type=str, default=None, help="Pretrained checkpoint path.") opt.add_argument("--restore_steps", type=int, default=600, help="restore checkpoint steps.") opt.add_argument("--restore_ckpt_name_prefix", type=str, default=None, help="restore checkpoint name.") opt.add_argument("--restore_checkpoint_bucket_dir", type=str, default=None, help="restore checkpoint obs path.") opt.add_argument("--save_checkpoint_path", type=str, default="/cache/ckpt_new", help="Save checkpoint path.") opt.add_argument("--save_checkpoint_bucket_dir", type=str, default=None, help="Save checkpoint path.") opt.add_argument("--save_strategy_bucket_dir", type=str, default=None, help="Save checkpoint path.") opt.add_argument("--save_summary_bucket_dir", type=str, default=None, help="Save checkpoint path.") opt.add_argument("--keep_checkpoint_max", type=int, default=1, help="Max checkpoint save number.") opt.add_argument("--save_checkpoint_steps", type=int, default=300, help="Save checkpoint step number.") opt.add_argument("--save_checkpoint", type=ast.literal_eval, default=True, help="Whether save checkpoint in local disk.") opt.add_argument("--ckpt_name_prefix", type=str, default=None, help="Saving checkpoint name prefix.") opt.add_argument("--has_trained_epoches", type=int, default=0, help="Epoches has been trained before.") opt.add_argument("--has_trained_steps", type=int, default=0, help="Steps has been trained before.") opt.add_argument("--data_start_index", type=int, default=0, help="datasets has been trained before. 0") def get_args(inference=False): parser = argparse.ArgumentParser(description="training") parser.add_argument('--device_id', type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=256, help="Use device nums, default is 128.") parser.add_argument("--distribute", type=str, default="true", choices=["true", "false"], help="Run distribute, default is true.") parser.add_argument("--load_ckpt_name", type=str, default=None, help="checkpint file name.") parser.add_argument("--load_ckpt_path", type=str, default=None, help="predict file path.") parser.add_argument('--data_url', default=None, help='Location of data.') parser.add_argument('--eval_data_url', default=None, help='Location of eval data.') parser.add_argument('--train_url', default="/cache/Graph/", help='Location of training outputs.') parser.add_argument("--run_type", type=str, default="train", choices=["train", "predict"], help="The run type") parser.add_argument("--mode", type=str, default="100B", choices=["200B", "13B", "7B", "2.6B", "1.3B", "100B"], help="The scale of the model parameters") parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"], help="The running device") parser.add_argument("--strategy_load_ckpt_path", type=str, default="", help="The training prallel strategy for the model.") parser.add_argument("--tokenizer_path", type=str, default="./tokenizer_path", help="The path where stores vocab and vocab model file") parser.add_argument("--param_init_type", type=str, default="fp16", help="The initialization type for parameters. Default fp16.") parser.add_argument("--offline", type=int, default=1, help="Running on cloud of not. Default 1.") parser.add_argument("--export", type=int, default=0, help="Whether export mindir for serving.") parser.add_argument("--incremental_training", type=int, default=0, help="Enable incremental training. Default 0.") parser.add_argument("--train_and_eval_mode", type=int, default=0, help="Enable evaling while training. Default 0.") parser.add_argument("--save_strategy_name", type=str, default="strategy_inference_d8_mp8_dp1-000.ckpt", help="Save checkpoint path.") parser.add_argument("--eval_steps", type=int, default=10, help="The eval step in train and eval mode. Default 10.") parser.add_argument("--enable_alltoall", type=int, default=0, help="Enable alltoall communication, only effective when applying moe. Default 0") parser.add_argument("--hccl_connect_time", type=int, default=7200, help="Set the hccl build time out, only effective on Ascend. Default 6000") # add RoPE parser.add_argument('--use_rope', type=ast.literal_eval, default=False, help="Use rotary position embedding") # add sequence parallel parser.add_argument('--sequence_parallel', type=ast.literal_eval, default=False, help="Use sequence parallel") # add select recompute parser.add_argument('--select_recompute', type=ast.literal_eval, default=False, help="Use select recompute") # use flash attention parser.add_argument('--use_flash_attention', type=ast.literal_eval, default=False, help="Use flash attention") # enable profiler parser.add_argument('--enable_profiler', type=ast.literal_eval, default=False, help="Profile timeline") parser.add_argument('--save_graph', type=ast.literal_eval, default=False, help="save ir graph") parser.add_argument('--multi_data_url', help='path to training/inference dataset folder', default= '[{}]') parser.add_argument('--dataset_name', type=str, default=None, help="The name of the dataset.") add_context_args_mode(parser) add_training_params(parser) add_retrain_params(parser) if inference: add_inference_params(parser) add_downstream_params(parser) args_opt = parser.parse_args() return args_opt def ckpt_copy_tar_new(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 = ['part_0.tar', 'part_1.tar', 'part_2.tar', 'part_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, device_num): # path = os.listdir(ckpt_path) # print('Get path list:', path, flush=True) returned_list = [] for i in range(0, device_num): returned_list.append('filerted_{}.ckpt'.format(i)) # filtered = [item for item in path if 'embedding' not in item]save_checkpoint_bucket_dir # filtered.sort(key = lambda x: int(x.split('.')[0].split('_')[1])) 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 print(item, " :{:.2f} MB".format(f_gb)) return returned_list class CheckpointSaveCallback(Callback): def __init__(self, local_ckpt_dir, local_rank=0, has_trained_epoch=0, has_trained_step=0, bucket='obs://ckeckpoint_file/path/', syn_obs_steps=100): self.local_ckpt_dir = local_ckpt_dir self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step self.bucket = bucket self.syn_obs_steps = syn_obs_steps self.obs_save_rank_dir = os.path.join(bucket, "rank_" + str(local_rank)) def step_end(self, run_context): # try: # os.system('ls /cache/ckpt_new/rank_0/') # except: # pass cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + self.has_trained_step if cur_step % self.syn_obs_steps == 0: try: print("Copying ckpt to the buckets start", flush=True) # self.syn_files() time.sleep(self.local_rank % 16) mox.file.copy_parallel(self.local_ckpt_dir, self.obs_save_rank_dir) print("Copying ckpt to the buckets ends", flush=True) except Exception as error: print(error) print('\n###########Upload ckpt Error#############\n') print("Copying ckpt to the buckets again", flush=True) # self.syn_files() time.sleep((self.local_rank+15) % 16) mox.file.copy_parallel(self.local_ckpt_dir, self.obs_save_rank_dir) print("Copying ckpt to the buckets ends", flush=True) def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.local_ckpt_dir, self.bucket), name="file_sync") process.start() class Strategy_and_compilerSaveCallback(Callback): def __init__(self, strategy_path, local_rank=0, has_trained_step=0, bucket='obs://mindspore-file/strategy_ckpt/', compiler_name='2520_pp21_mp2dp60ms96', sym_step=100): self.strategy_path = strategy_path self.local_rank = local_rank self.sym_step = sym_step self.bucket = bucket self.has_trained_step = has_trained_step self.has_send = False self.file_name = strategy_path.split('/')[-1] self.compiler_cache_local_path = f"/cache/compiler_cache/rank_{local_rank}/" self.compiler_cache_obs_path = f"{compiler_name}/rank_{local_rank}/" if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) if not mox.file.exists(self.compiler_cache_obs_path): mox.file.make_dirs(self.compiler_cache_obs_path) def step_end(self, run_context): cb_params = run_context.original_args() if self.has_send is False: print("Send strategy file to the obs") self.syn_files() print("Send compiler cache file to the obs") time.sleep((self.local_rank % 8)*30) self.syn_files2() self.has_send = True def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.strategy_path, self.bucket + self.file_name), name="file_sync") process.start() def syn_files2(self): process = Process(target=mox.file.copy_parallel, args=(self.compiler_cache_local_path, self.compiler_cache_obs_path), name="file_sync") process.start() class StrategySaveCallback(Callback): def __init__(self, strategy_path, local_rank=0, has_trained_step=0, bucket='obs://mindspore-file/strategy_ckpt/', sym_step=100): self.strategy_path = strategy_path self.local_rank = local_rank self.sym_step = sym_step self.bucket = bucket self.has_trained_step = has_trained_step self.has_send = False self.file_name = strategy_path.split('/')[-1] if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) def step_end(self, run_context): cb_params = run_context.original_args() if self.has_send is False: print("Send strategy file to the obs") self.syn_files() self.has_send = True def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.strategy_path, self.bucket + self.file_name), name="file_sync") process.start() class GraphsSaveCallback(Callback): def __init__(self, Graph_path, local_rank=0, has_trained_step=0, bucket='obs://graph_save_path/', sym_step=100): self.Graph_path = Graph_path self.local_rank = local_rank self.sym_step = sym_step self.bucket_obs = bucket + f"rank_{local_rank}" self.has_trained_step = has_trained_step self.has_send = False self.file_name = Graph_path.split('/')[-1] if not mox.file.exists(self.bucket_obs): print("Creating graph bueckt dir {}".format(self.bucket_obs)) mox.file.make_dirs(self.bucket_obs) def step_end(self, run_context): if self.has_send is False: print("Send Graph file to the obs") self.syn_files() self.has_send = True def syn_files(self): process = Process(target=mox.file.copy_parallel, args=(self.Graph_path, self.bucket_obs), name="file_sync") process.start() def download_data(src_data_url, tgt_data_path, rank, flag="0"): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ cache_url = tgt_data_path EXEC_PATH = '/tmp' if rank % 8 == 0: import moxing as mox print("Modify the time out from 300 to 30000") print("begin download dataset", flush=True) if not os.path.exists(cache_url): os.makedirs(cache_url, exist_ok=True) mox.file.copy_parallel(src_url=src_data_url, dst_url=cache_url) print("Dataset download succeed!", flush=True) f = open(f"/tmp/install_{flag}.txt", 'w') f.close() # stop while not os.path.exists(f"/tmp/install_{flag}.txt"): time.sleep(1) def download_compiler_cache_from_obs(args_opt, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ compiler_cache_obs_path = f"{args_opt.compiler_name}/rank_{rank}/" compiler_cache_local_path = f"/cache/compiler_cache/rank_{rank}/" time.sleep(rank%16) mox.file.copy_parallel(src_url=compiler_cache_obs_path, dst_url=compiler_cache_local_path) print(f"Rank {rank} download compiler cache {compiler_cache_obs_path} succeed!", flush=True) f = open(f"/tmp/restore_compiler_{rank}.txt", 'w') f.close() while not os.path.exists(f"/tmp/restore_compiler_{rank}.txt"): time.sleep(1) def download_ckpt_from_obs(args_opt, cache_url, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ local_ckpt_url = os.path.join(cache_url, f"rank_{rank}.ckpt") # # if args_opt.restore_steps == 0: # obs_ckpt_url = os.path.join(args_opt.restore_checkpoint_bucket_dir, # f"rank_{rank}", # args_opt.restore_ckpt_name_prefix.replace('*', str(rank))) # else: obs_ckpt_url = os.path.join(args_opt.restore_checkpoint_bucket_dir, f"rank_{rank}", f"{args_opt.restore_ckpt_name_prefix}_{rank}-{args_opt.restore_steps}_1.ckpt") import moxing as mox print("Modify the time out from 300 to 30000") # print(f"begin download ckpt from {obs_ckpt_url}") # if not os.path.exists(os.path.join(cache_url, f"rank_{rank}")): # os.makedirs(os.path.join(cache_url, f"rank_{rank}"), exist_ok=True) # if not os.path.exists(cache_url): # os.makedirs(cache_url, exist_ok=True) time.sleep(rank % 4) try: mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) except Exception as error: print(f"Error: {error}, download again...") time.sleep(10) mox.file.copy(src_url=obs_ckpt_url, dst_url=local_ckpt_url) print(f"Rank {rank} download ckpt succeed!", flush=True) # print(f"Rank {rank} download ckpt {obs_ckpt_url} succeed!", flush=True) f = open(f"/tmp/restore_{rank}.txt", 'w') f.close() # stop while not os.path.exists(f"/tmp/restore_{rank}.txt"): time.sleep(1) print(os.listdir(cache_url))