mirror of https://github.com/InternLM/InternLM
Feat(QA): Check init model weights (#502)
* check_init * check_init * check_init * check_initpull/484/head^2
jiaxingli
GitHub
parent
be5b9ea2fa
commit
e8cf27b8c0
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@ -0,0 +1,176 @@
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JOB_NAME = "7b_train"
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DO_ALERT = False
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SEQ_LEN = 2048
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HIDDEN_SIZE = 4096
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NUM_ATTENTION_HEAD = 32
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MLP_RATIO = 8 / 3
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NUM_LAYER = 32
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VOCAB_SIZE = 103168
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CHECK_INIT = 1
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# MODEL_ONLY_FOLDER = "llm_ckpts_test_3/2"
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# Ckpt folder format:
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# fs: 'local:/mnt/nfs/XXX'
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# SAVE_CKPT_FOLDER = "local:llm_ckpts_test_3"
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# LOAD_CKPT_FOLDER = "local:llm_ckpts_test_3"
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# boto3 Ckpt folder format:
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# import os
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# BOTO3_IP = os.environ["BOTO3_IP"] # boto3 bucket endpoint
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# SAVE_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm"
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# LOAD_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm/snapshot/1/"
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CHECKPOINT_EVERY = 50
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ckpt = dict(
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enable_save_ckpt=False, # enable ckpt save.
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auto_resume=False,
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# save_ckpt_folder=SAVE_CKPT_FOLDER, # Path to save training ckpt.
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# load_ckpt_folder= dict(path=MODEL_ONLY_FOLDER, content=["model"], ckpt_type="normal"),
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# load_ckpt_folder="local:llm_ckpts/",
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# 'load_ckpt_info' setting guide:
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# 1. the 'path' indicate ckpt path,
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# 2. the 'content‘ means what states will be loaded, support: "model", "sampler", "optimizer", "scheduler", "all"
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# 3. the ’ckpt_type‘ means the type of checkpoint to be loaded, now only 'normal' type is supported.
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# load_ckpt_info=dict(path=MODEL_ONLY_FOLDER, content=("all",), ckpt_type="internlm"),
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# 'auto_resume' is designed to automatically load the latest checkpoint from 'save_ckpt_folder' when encountering
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# training interruptions/hangs caused by hardware failures, using a scheduling system (such as k8s/slurm)
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# with an automatic restart mechanism upon training reboot.
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# Please be aware that if `auto_resume` is not set (its default value is True), it will not load the checkpoint
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# path specified in `load_ckpt_info` by default.
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# If you want to initialize your model weights from another model, you must set `auto_resume` to False.
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# If you want to train from scratch, please set `auto_resume` to False and 'load_ckpt_info' to None.
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# auto_resume=False,
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checkpoint_every=CHECKPOINT_EVERY,
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# async_upload=True, # async ckpt upload. (only work for boto3 ckpt)
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# async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/", # path for temporarily files during asynchronous upload.
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# oss_snapshot_freq=int(CHECKPOINT_EVERY / 2), # snapshot ckpt save frequency.
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)
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TRAIN_FOLDER = "/path/to/dataset"
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VALID_FOLDER = "/path/to/dataset"
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data = dict(
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seq_len=SEQ_LEN,
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# micro_num means the number of micro_batch contained in one gradient update
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micro_num=4,
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# packed_length = micro_bsz * SEQ_LEN
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micro_bsz=2,
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# defaults to the value of micro_num
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valid_micro_num=4,
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# defaults to 0, means disable evaluate
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valid_every=50,
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pack_sample_into_one=False,
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total_steps=0,
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skip_batches="",
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rampup_batch_size="",
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# Datasets with less than 50 rows will be discarded
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min_length=50,
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# train_folder=TRAIN_FOLDER,
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# valid_folder=VALID_FOLDER,
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empty_cache_and_diag_interval=10,
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diag_outlier_ratio=1.1,
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)
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grad_scaler = dict(
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fp16=dict(
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# the initial loss scale, defaults to 2**16
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initial_scale=2**16,
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# the minimum loss scale, defaults to None
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min_scale=1,
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# the number of steps to increase loss scale when no overflow occurs
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growth_interval=1000,
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),
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# the multiplication factor for increasing loss scale, defaults to 2
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growth_factor=2,
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# the multiplication factor for decreasing loss scale, defaults to 0.5
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backoff_factor=0.5,
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# the maximum loss scale, defaults to None
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max_scale=2**24,
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# the number of overflows before decreasing loss scale, defaults to 2
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hysteresis=2,
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)
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hybrid_zero_optimizer = dict(
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# Enable low_level_optimzer overlap_communication
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overlap_sync_grad=True,
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overlap_sync_param=True,
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# bucket size for nccl communication params
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reduce_bucket_size=512 * 1024 * 1024,
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# grad clipping
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clip_grad_norm=1.0,
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)
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loss = dict(
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label_smoothing=0,
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)
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adam = dict(
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lr=1e-4,
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adam_beta1=0.9,
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adam_beta2=0.95,
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adam_beta2_c=0,
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adam_eps=1e-8,
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weight_decay=0.01,
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)
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lr_scheduler = dict(
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total_steps=data["total_steps"],
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init_steps=0, # optimizer_warmup_step
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warmup_ratio=0.01,
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eta_min=1e-5,
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last_epoch=-1,
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)
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beta2_scheduler = dict(
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init_beta2=adam["adam_beta2"],
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c=adam["adam_beta2_c"],
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cur_iter=-1,
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)
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model = dict(
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checkpoint=False, # The proportion of layers for activation aheckpointing, the optional value are True/False/[0-1]
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num_attention_heads=NUM_ATTENTION_HEAD,
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embed_split_hidden=True,
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vocab_size=VOCAB_SIZE,
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embed_grad_scale=1,
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parallel_output=True,
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hidden_size=HIDDEN_SIZE,
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num_layers=NUM_LAYER,
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mlp_ratio=MLP_RATIO,
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apply_post_layer_norm=False,
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dtype="torch.bfloat16", # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32"
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norm_type="rmsnorm",
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layer_norm_epsilon=1e-5,
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use_flash_attn=True,
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num_chunks=1, # if num_chunks > 1, interleaved pipeline scheduler is used.
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)
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"""
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zero1 parallel:
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1. if zero1 <= 0, The size of the zero process group is equal to the size of the dp process group,
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so parameters will be divided within the range of dp.
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2. if zero1 == 1, zero is not used, and all dp groups retain the full amount of model parameters.
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3. zero1 > 1 and zero1 <= dp world size, the world size of zero is a subset of dp world size.
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For smaller models, it is usually a better choice to split the parameters within nodes with a setting <= 8.
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pipeline parallel (dict):
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1. size: int, the size of pipeline parallel.
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2. interleaved_overlap: bool, enable/disable communication overlap when using interleaved pipeline scheduler.
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tensor parallel: tensor parallel size, usually the number of GPUs per node.
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"""
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parallel = dict(
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zero1=dict(size=-1, fsdp=False),
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tensor=4,
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pipeline=dict(size=2, interleaved_overlap=True),
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sequence_parallel=False,
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)
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cudnn_deterministic = False
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cudnn_benchmark = False
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monitor = dict(
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# feishu alert configs
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alert=dict(
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enable_feishu_alert=DO_ALERT,
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feishu_alert_address=None, # feishu webhook to send alert message
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light_monitor_address=None, # light_monitor address to send heartbeat
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),
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)
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@ -1,14 +1,10 @@
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import os
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import sys
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script_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.abspath(os.path.join(script_dir, "../../"))
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sys.path.append(project_root)
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# pylint: disable=C0413,W0612,W0611
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import socket
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import sys
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import time
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import traceback
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from functools import partial
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@ -16,39 +12,45 @@ from functools import partial
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import torch
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import torch.distributed as dist
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import internlm
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from internlm.core.context import ParallelMode
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from internlm.core.context import global_context as gpc
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from internlm.core.scheduler import SchedulerMetricHook
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from internlm.core.trainer import TrainState
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from internlm.initialize import initialize_distributed_env
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from internlm.model.loss import FlashGPTLMLoss
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from internlm.model.metrics import AccPerplex
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from internlm.monitor import initialize_monitor_manager, send_alert_message
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from internlm.monitor.monitor import monitor_manager as mm
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from internlm.train import (
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script_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.abspath(os.path.join(script_dir, "../../"))
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sys.path.append(project_root)
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import internlm # noqa: E402
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from internlm.core.context import ParallelMode # noqa: E402
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from internlm.core.context import global_context as gpc # noqa: E402
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from internlm.core.scheduler import SchedulerMetricHook # noqa: E402
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from internlm.core.trainer import TrainState # noqa: E402
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from internlm.initialize import initialize_distributed_env # noqa: E402
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from internlm.model.loss import FlashGPTLMLoss # noqa: E402
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from internlm.model.metrics import AccPerplex # noqa: E402
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from internlm.monitor import ( # noqa: E402
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initialize_monitor_manager,
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send_alert_message,
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)
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from internlm.monitor.monitor import monitor_manager as mm # noqa: E402
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from internlm.train import ( # noqa: E402
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get_train_data_loader,
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get_validation_data_loader,
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initialize_llm_profile,
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initialize_model,
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initialize_optimizer,
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load_new_batch,
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record_current_batch_training_metrics,
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)
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from internlm.utils.common import (
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from internlm.utils.common import ( # noqa: E402
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BatchSkipper,
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get_megatron_flops,
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launch_time,
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parse_args,
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)
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from internlm.utils.evaluation import evaluate_on_val_dls
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from internlm.utils.gputest import empty_cache_and_diag
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from internlm.utils.logger import get_logger, initialize_uniscale_logger
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from internlm.utils.megatron_timers import megatron_timer as timer
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from internlm.utils.model_checkpoint import CheckpointManager
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from internlm.utils.parallel import get_parallel_log_file_name
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from internlm.utils.simple_memory_profiler import SimpleMemoryProfiler
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from internlm.utils.writer import Writer
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from internlm.utils.evaluation import evaluate_on_val_dls # noqa: E402
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from internlm.utils.gputest import empty_cache_and_diag # noqa: E402
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from internlm.utils.logger import get_logger, initialize_uniscale_logger # noqa: E402
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from internlm.utils.megatron_timers import megatron_timer as timer # noqa: E402
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from internlm.utils.model_checkpoint import CheckpointManager # noqa: E402
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from internlm.utils.parallel import get_parallel_log_file_name # noqa: E402
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from internlm.utils.simple_memory_profiler import SimpleMemoryProfiler # noqa: E402
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from internlm.utils.writer import Writer # noqa: E402
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# global llm logger
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logger = get_logger(__file__)
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@ -74,7 +76,7 @@ def initialize_llm_logger(start_time: str):
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return uniscale_logger
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def check_model_weights(model, ckpt_path):
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def check_model_weights(model, ckpt_path, total_equal=False):
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model1_dict = torch.load(ckpt_path, map_location="cuda")
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model2_dict = model.state_dict()
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@ -82,7 +84,16 @@ def check_model_weights(model, ckpt_path):
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if key in model2_dict:
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tensor1 = model1_dict[key]
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tensor2 = model2_dict[key]
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assert torch.allclose(tensor1, tensor2, rtol=3e-2, atol=3e-2)
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if total_equal:
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assert torch.equal(tensor1, tensor2), "model weights are not equal"
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else:
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assert torch.allclose(tensor1, tensor2, rtol=3e-2, atol=3e-2), "model weights are not close"
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else:
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if gpc.is_rank_for_log():
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logger.warning(f"The old key {key} no longer exists!")
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if gpc.is_rank_for_log():
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logger.info("Weight check passed")
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def main(args):
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@ -207,7 +218,15 @@ def main(args):
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trainer.train()
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# transfer the train data loader into train data iterator
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train_iter = iter(train_dl)
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# train_iter = iter(train_dl)
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# check model init weights
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if hasattr(gpc.config, "CHECK_INIT") and gpc.config.CHECK_INIT == 1:
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ckpt_name = (
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f"model_tp{gpc.get_local_rank(ParallelMode.TENSOR)}_pp{gpc.get_local_rank(ParallelMode.PIPELINE)}.pt"
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)
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ckpt_path = os.path.join(os.environ["share_path"], "quailty_assurance/7B_init_8_tp=4_pp=2_ckpt", ckpt_name)
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check_model_weights(model, ckpt_path, total_equal=True)
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with initialize_llm_profile(profiling=args.profiling, start_time=current_time) as prof:
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# start iterating the train data and begin training
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@ -223,7 +242,11 @@ def main(args):
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if batch_index == 0:
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data_local_rank = gpc.get_local_rank(ParallelMode.DATA)
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batch_step = (batch_count // 1000 + 1) * 1000
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data_path = f"/mnt/petrelfs/share/quailty_assurance/debug_Qiansanqiang_7B_v16/dp-11{data_local_rank}/batch-{batch_step}.pt"
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data_path = os.path.join(
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os.environ["share_path"],
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"quailty_assurance/debug_Qiansanqiang_7B_v16",
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f"dp-11{data_local_rank}/batch-{batch_step}.pt",
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)
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data_1000 = torch.load(data_path, map_location=torch.device("cpu"))
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batch = data_1000[batch_index]
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@ -311,9 +334,13 @@ def main(args):
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update_panel=uniscale_logger is not None,
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)
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# check model weights
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if batch_count > 0 and batch_count % 100 == 0:
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ckpt_path = os.path.join(
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"/mnt/petrelfs/share/quailty_assurance/7B_model_weights_ckpt", str(batch_count), "model_tp0_pp0.pt"
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os.environ["share_path"],
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"quailty_assurance/7B_model_weights_ckpt",
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str(batch_count),
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"model_tp0_pp0.pt",
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)
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check_model_weights(model, ckpt_path)
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