ModelLink2/pretrain_intern.py
fengliangjun 7a21f0bf58 up
2023-09-26 14:30:47 +08:00

96 lines
3.5 KiB
Python

# coding=utf-8
# Copyright (c) 2023, HUAWEI CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pretrain InternLM"""
import math
import logging
from functools import partial
import torch
import deepspeed
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.accelerator.real_accelerator import get_accelerator
try:
import torch_npu
import deepspeed_npu
from torch_npu.contrib import transfer_to_npu
except ImportError:
logging.warning(f"Import torch_npu Error.")
from ascendspeed import get_args
from ascendspeed import print_rank_0
from ascendspeed import get_timers
from ascendspeed import get_tokenizer
from ascendspeed import mpu
from ascendspeed.core import tensor_parallel, parallel_state
from ascendspeed.model.internlm_model import InternModel, InternModelPipe
from ascendspeed.core.enums import ModelType
from ascendspeed.training import pretrain
from pretrain_llama import (
data_post_process,
forward_step,
train_valid_test_datasets_provider
)
from ascendspeed.arguments import core_transformer_config_from_args
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
print_rank_0('building InternLM model ...')
see_memory_usage(f"Before Building Model", force=True)
args = get_args()
config = core_transformer_config_from_args(get_args())
with deepspeed.zero.Init(data_parallel_group=parallel_state.get_data_parallel_group(),
remote_device=None if args.remote_device == 'none' else args.remote_device,
config_dict_or_path=args.deepspeed_config,
enabled=args.zero_stage == 3,
mpu=parallel_state):
if args.deepspeed and not args.no_pipeline_parallel:
model = InternModelPipe(parallel_output=True)
attention_mask = torch.tril(torch.ones(
(1, args.seq_length, args.seq_length), device=get_accelerator().current_device_name())).view(
1, 1, args.seq_length, args.seq_length)
# Convert attention mask to binary:
attention_mask = (attention_mask < 0.5)
if args.fp16:
attention_mask = attention_mask.half()
elif args.bf16:
attention_mask = attention_mask.bfloat16()
# Attention mask must be bool.
args.attn_mask = attention_mask.to(torch.bool)
else:
model = InternModel(
config=config,
parallel_output=True,
add_pooler=False,
pre_process=pre_process,
post_process=post_process
)
see_memory_usage(f"After Building Model", force=True)
return model
if __name__ == "__main__":
torch.npu.set_compile_mode(jit_compile=True)
pretrain(train_valid_test_datasets_provider, model_provider, ModelType.encoder_or_decoder, forward_step,
args_defaults={'tokenizer_type': 'PretrainedFromHF'},
data_post_process=data_post_process)