mirror of
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107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
# coding=utf-8
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# Copyright (c) 2023, HUAWEI CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Pretrain Baichuan."""
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import math
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from functools import partial
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import torch
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import torch_npu
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from torch_npu.contrib import transfer_to_npu
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import deepspeed
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import deepspeed_npu
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from deepspeed.runtime.utils import see_memory_usage
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from deepspeed.accelerator.real_accelerator import get_accelerator
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from ascendspeed import get_args
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from ascendspeed import print_rank_0
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from ascendspeed import get_timers
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from ascendspeed import get_tokenizer
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from ascendspeed import mpu
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from ascendspeed.core import parallel_state
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from ascendspeed.core.enums import ModelType
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from ascendspeed.data.gpt_dataset import build_train_valid_test_datasets
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from ascendspeed.model.baichuan_model import BaichuanModel, BaichuanModelPipe
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from ascendspeed.training import pretrain
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from ascendspeed.utils import get_ltor_masks_and_position_ids
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from ascendspeed.utils import average_losses_across_data_parallel_group
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from ascendspeed.arguments import core_transformer_config_from_args
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from pretrain_llama import (
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data_post_process,
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get_batch,
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get_batch_pipe,
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loss_func,
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forward_step,
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train_valid_test_datasets_provider
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)
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def model_provider(pre_process=True, post_process=True):
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"""Build the model."""
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print_rank_0('Building Baichuan model ...')
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see_memory_usage(f"Before Building Model ...", force=True)
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args = get_args()
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config = core_transformer_config_from_args(get_args())
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with deepspeed.zero.Init(data_parallel_group=parallel_state.get_data_parallel_group(),
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remote_device=None if args.remote_device == 'none' else args.remote_device,
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config_dict_or_path=args.deepspeed_config,
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enabled=args.zero_stage == 3,
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mpu=parallel_state):
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if args.deepspeed and not args.no_pipeline_parallel:
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model = BaichuanModelPipe(config, parallel_output=True)
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# This is a hack to give us a reference to get_batch_pipe from within training.py
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# We need to call model.set_batch_fn after deepspeed.initialize
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model._megatron_batch_fn = get_batch_pipe
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# Predompute the attention mask and store it in args. This avoids having to
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# pipeline it as an activation during training. The mask is constant, and thus
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# we can reuse it.
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attention_mask = torch.tril(torch.ones(
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(1, args.seq_length, args.seq_length),
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device=get_accelerator().current_device_name())).view(
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1, 1, args.seq_length, args.seq_length)
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# Convert attention mask to binary:
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attention_mask = (attention_mask < 0.5)
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if args.fp16:
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attention_mask = attention_mask.half()
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elif args.bf16:
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attention_mask = attention_mask.bfloat16()
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# Attention mask must be bool.
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args.attn_mask = attention_mask.to(torch.bool)
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else:
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model = BaichuanModel(
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config=config,
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parallel_output=True,
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add_pooler=False,
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pre_process=pre_process,
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post_process=post_process
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)
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see_memory_usage(f"After Building Model", force=True)
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return model
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if __name__ == "__main__":
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torch.npu.set_compile_mode(jit_compile=True)
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pretrain(train_valid_test_datasets_provider,
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model_provider,
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ModelType.encoder_or_decoder,
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forward_step,
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args_defaults={'tokenizer_type': 'PretrainedFromHF'},
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data_post_process=data_post_process)
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