20B Demo ================ 训练配置 ---------------- 20B demo 训练配置文件样例如下: .. code-block:: python JOB_NAME = "20b_train" SEQ_LEN = 2048 HIDDEN_SIZE = 5120 NUM_ATTENTION_HEAD = 40 MLP_RATIO = 8 / 3 NUM_LAYER = 60 VOCAB_SIZE = 103168 MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx" # Ckpt folder format: # fs: 'local:/mnt/nfs/XXX' SAVE_CKPT_FOLDER = "local:llm_ckpts" LOAD_CKPT_FOLDER = "local:llm_ckpts/49" # boto3 Ckpt folder format: # import os # BOTO3_IP = os.environ["BOTO3_IP"] # boto3 bucket endpoint # SAVE_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm" # LOAD_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm/snapshot/1/" CHECKPOINT_EVERY = 50 ckpt = dict( enable_save_ckpt=False, # enable ckpt save. save_ckpt_folder=SAVE_CKPT_FOLDER, # Path to save training ckpt. # load_ckpt_folder=LOAD_CKPT_FOLDER, # Ckpt path to resume training(load weights and scheduler/context states). # load_model_only_folder=MODEL_ONLY_FOLDER, # Path to initialize with given model weights. load_optimizer=True, # Wheter to load optimizer states when continuing training. checkpoint_every=CHECKPOINT_EVERY, async_upload=True, # async ckpt upload. (only work for boto3 ckpt) async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/", # path for temporarily files during asynchronous upload. snapshot_ckpt_folder="/".join([SAVE_CKPT_FOLDER, "snapshot"]), # directory for snapshot ckpt storage path. oss_snapshot_freq=int(CHECKPOINT_EVERY / 2), # snapshot ckpt save frequency. ) TRAIN_FOLDER = "/path/to/dataset" VALID_FOLDER = "/path/to/dataset" data = dict( seq_len=SEQ_LEN, # micro_num means the number of micro_batch contained in one gradient update micro_num=4, # packed_length = micro_bsz * SEQ_LEN micro_bsz=2, # defaults to the value of micro_num valid_micro_num=4, # defaults to 0, means disable evaluate valid_every=50, pack_sample_into_one=False, total_steps=50000, skip_batches="", rampup_batch_size="", # Datasets with less than 50 rows will be discarded min_length=50, # train_folder=TRAIN_FOLDER, # valid_folder=VALID_FOLDER, ) grad_scaler = dict( fp16=dict( # the initial loss scale, defaults to 2**16 initial_scale=2**16, # the minimum loss scale, defaults to None min_scale=1, # the number of steps to increase loss scale when no overflow occurs growth_interval=1000, ), # the multiplication factor for increasing loss scale, defaults to 2 growth_factor=2, # the multiplication factor for decreasing loss scale, defaults to 0.5 backoff_factor=0.5, # the maximum loss scale, defaults to None max_scale=2**24, # the number of overflows before decreasing loss scale, defaults to 2 hysteresis=2, ) hybrid_zero_optimizer = dict( # Enable low_level_optimzer overlap_communication overlap_sync_grad=True, overlap_sync_param=True, # bucket size for nccl communication params reduce_bucket_size=512 * 1024 * 1024, # grad clipping clip_grad_norm=1.0, ) loss = dict( label_smoothing=0, ) adam = dict( lr=1e-4, adam_beta1=0.9, adam_beta2=0.95, adam_beta2_c=0, adam_eps=1e-8, weight_decay=0.01, ) lr_scheduler = dict( total_steps=data["total_steps"], init_steps=0, # optimizer_warmup_step warmup_ratio=0.01, eta_min=1e-5, last_epoch=-1, ) beta2_scheduler = dict( init_beta2=adam["adam_beta2"], c=adam["adam_beta2_c"], cur_iter=-1, ) model = dict( checkpoint=False, # The proportion of layers for activation aheckpointing, the optional value are True/False/[0-1] num_attention_heads=NUM_ATTENTION_HEAD, embed_split_hidden=True, vocab_size=VOCAB_SIZE, embed_grad_scale=1, parallel_output=True, hidden_size=HIDDEN_SIZE, num_layers=NUM_LAYER, mlp_ratio=MLP_RATIO, apply_post_layer_norm=False, dtype="torch.float16", # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32" norm_type="rmsnorm", layer_norm_epsilon=1e-5, use_flash_attn=True, num_chunks=1, # if num_chunks > 1, interleaved pipeline scheduler is used. ) """ zero1 parallel: 1. if zero1 <= 0, The size of the zero process group is equal to the size of the dp process group, so parameters will be divided within the range of dp. 2. if zero1 == 1, zero is not used, and all dp groups retain the full amount of model parameters. 3. zero1 > 1 and zero1 <= dp world size, the world size of zero is a subset of dp world size. For smaller models, it is usually a better choice to split the parameters within nodes with a setting <= 8. pipeline parallel (dict): 1. size: int, the size of pipeline parallel. 2. interleaved_overlap: bool, enable/disable communication overlap when using interleaved pipeline scheduler. tensor parallel: tensor parallel size, usually the number of GPUs per node. """ parallel = dict( zero1=-1, tensor=4, pipeline=dict(size=1, interleaved_overlap=True), sequence_parallel=False, ) cudnn_deterministic = False cudnn_benchmark = False 启动训练 ---------------- 完成以上训练配置后，可启动模型训练，以在 ``slurm`` 平台上为例，启动两节点 16GPU 的训练命令如下所示： .. code-block:: bash srun -p internllm -N 2 -n 16 --ntasks-per-node=8 --gpus-per-task=1 python train.py --config ./configs/20B_sft.py 训练结果 ---------------- 基于以上训练配置和启动命令，两节点 16GPU 下的模型训练部分日志展示如下： .. code-block:: bash 2023-11-10 15:05:04,535 INFO parallel_context.py:555 in set_device -- process rank 9 is bound to host:HOST-10-140-60-90 device: 1 2023-11-10 15:05:04,518 INFO parallel_context.py:555 in set_device -- process rank 6 is bound to host:HOST-10-140-60-14 device: 6 2023-11-10 15:05:04,523 INFO parallel_context.py:555 in set_device -- process rank 0 is bound to host:HOST-10-140-60-14 device: 0 2023-11-10 15:05:04,524 INFO parallel_context.py:555 in set_device -- process rank 3 is bound to host:HOST-10-140-60-14 device: 3 2023-11-10 15:05:04,575 INFO parallel_context.py:555 in set_device -- process rank 15 is bound to host:HOST-10-140-60-90 device: 7 2023-11-10 15:05:04,576 INFO parallel_context.py:555 in set_device -- process rank 12 is bound to host:HOST-10-140-60-90 device: 4 2023-11-10 15:05:04,577 INFO parallel_context.py:555 in set_device -- process rank 11 is bound to host:HOST-10-140-60-90 device: 3 2023-11-10 15:05:04,582 INFO parallel_context.py:555 in set_device -- process rank 10 is bound to host:HOST-10-140-60-90 device: 2 2023-11-10 15:05:04,560 INFO parallel_context.py:555 in set_device -- process rank 5 is bound to host:HOST-10-140-60-14 device: 5 2023-11-10 15:05:04,592 INFO parallel_context.py:555 in set_device -- process rank 4 is bound to host:HOST-10-140-60-14 device: 4 2023-11-10 15:05:04,593 INFO parallel_context.py:555 in set_device -- process rank 7 is bound to host:HOST-10-140-60-14 device: 7 2023-11-10 15:05:04,624 INFO parallel_context.py:555 in set_device -- process rank 1 is bound to host:HOST-10-140-60-14 device: 1 2023-11-10 15:05:04,683 INFO parallel_context.py:555 in set_device -- process rank 8 is bound to host:HOST-10-140-60-90 device: 0 2023-11-10 15:05:04,718 INFO parallel_context.py:555 in set_device -- process rank 14 is bound to host:HOST-10-140-60-90 device: 6 2023-11-10 15:05:04,718 INFO parallel_context.py:555 in set_device -- process rank 13 is bound to host:HOST-10-140-60-90 device: 5 2023-11-10 15:05:04,723 INFO parallel_context.py:555 in set_device -- process rank 2 is bound to host:HOST-10-140-60-14 device: 2 2023-11-10 15:05:07,912 INFO launch.py:409 in launch -- Distributed environment is initialized, data parallel size: 4, pipeline parallel size: 1, tensor parallel size: 4 2023-11-10 15:05:24,106 INFO hybrid_zero_optim.py:268 in _partition_param_list -- Number of elements on ranks: [1262168320, 1269084160, 1269084160, 1222844160], rank:0 2023-11-10T15:05:58.540+08:00 INFO [training_internlm.py, line 601, in record_current_batch_training_metrics] - pid=78690 : tflops=41.977599404049684 step=0 loss=11.542577743530273 tgs (tokens/gpu/second)=338.62 tgs/last_tgs_1=338.62 tgs/tgs_all=338.62 tgs/tgs_avg=338.62 tgs/tgs_SMA=338.62 tgs/last_tgs_10=0 tgs/last_tgs_50=0 lr=4.0000000000000003e-07 loss_scale=65536.0 grad_norm={'0_default': 87.3189617106087, '1_fp32': 0.0} micro_num=4 num_consumed_tokens=65536 inf_nan_skip_batches=0 num_samples_in_batch=18 largest_length=2048 largest_batch=6 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=8.94 acc=0.0 perplexity=104321.0312 acc/en=0.0 acc/cn=0.0 acc/code=0.0 tokens/en=60571 tokens/cn=0 tokens/code=0 loss_from_metric=11.5552 loss/en=11.5552 loss/cn=nan loss/code=nan 2023-11-10T15:06:02.978+08:00 INFO [training_internlm.py, line 601, in record_current_batch_training_metrics] - pid=78690 : tflops=115.41412094522278 step=1 loss=11.33798599243164 tgs (tokens/gpu/second)=931.02 tgs/last_tgs_1=931.03 tgs/tgs_all=496.62 tgs/tgs_avg=634.83 tgs/tgs_SMA=496.62 tgs/last_tgs_10=0 tgs/last_tgs_50=0 lr=6.000000000000001e-07 loss_scale=65536.0 grad_norm={'0_default': 90.85008685328815, '1_fp32': 0.0} micro_num=4 num_consumed_tokens=131072 inf_nan_skip_batches=0 num_samples_in_batch=19 largest_length=2048 largest_batch=6 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=3.85 acc=0.0 perplexity=81555.5 acc/en=0.0 acc/cn=0.0 acc/code=0.0 tokens/en=60265 tokens/cn=0 tokens/code=0 loss_from_metric=11.309 loss/en=11.309 loss/cn=nan loss/code=nan 2023-11-10T15:06:06.988+08:00 INFO [training_internlm.py, line 601, in record_current_batch_training_metrics] - pid=78690 : tflops=127.89743136367036 step=2 loss=10.111495971679688 tgs (tokens/gpu/second)=1031.72 tgs/last_tgs_1=1031.72 tgs/tgs_all=600.43 tgs/tgs_avg=767.12 tgs/tgs_SMA=600.43 tgs/last_tgs_10=0 tgs/last_tgs_50=0 lr=8.000000000000001e-07 loss_scale=65536.0 grad_norm={'0_default': 76.99318912653898, '1_fp32': 0.0} micro_num=4 num_consumed_tokens=196608 inf_nan_skip_batches=0 num_samples_in_batch=17 largest_length=2048 largest_batch=5 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=3.4 acc=0.0704 perplexity=25907.623 acc/en=0.0704 acc/cn=0.0 acc/code=0.0 tokens/en=60244 tokens/cn=0 tokens/code=0 loss_from_metric=10.1623 loss/en=10.1623 loss/cn=nan loss/code=nan 2023-11-10T15:06:10.994+08:00 INFO [training_internlm.py, line 601, in record_current_batch_training_metrics] - pid=78690 : tflops=127.89845291183941 step=3 loss=8.848427772521973 tgs (tokens/gpu/second)=1031.73 tgs/last_tgs_1=1031.73 tgs/tgs_all=670.5 tgs/tgs_avg=833.27 tgs/tgs_SMA=670.5 tgs/last_tgs_10=0 tgs/last_tgs_50=0 lr=1.0000000000000002e-06 loss_scale=65536.0 grad_norm={'0_default': 60.47092413727133, '1_fp32': 0.0} micro_num=4 num_consumed_tokens=262144 inf_nan_skip_batches=0 num_samples_in_batch=17 largest_length=2048 largest_batch=5 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=3.41 acc=0.0783 perplexity=7380.229 acc/en=0.0783 acc/cn=0.0 acc/code=0.0 tokens/en=60328 tokens/cn=0 tokens/code=0 loss_from_metric=8.9066 loss/en=8.9066 loss/cn=nan loss/code=nan 2023-11-10T15:06:15.041+08:00 INFO [training_internlm.py, line 601, in record_current_batch_training_metrics] - pid=78690 : tflops=126.55593705224216 step=4 loss=7.509810924530029 tgs (tokens/gpu/second)=1020.9 tgs/last_tgs_1=1020.9 tgs/tgs_all=719.92 tgs/tgs_avg=870.8 tgs/tgs_SMA=719.92 tgs/last_tgs_10=0 tgs/last_tgs_50=0 lr=1.2000000000000002e-06 loss_scale=65536.0 grad_norm={'0_default': 42.36608180721121, '1_fp32': 0.0} micro_num=4 num_consumed_tokens=327680 inf_nan_skip_batches=0 num_samples_in_batch=22 largest_length=1893 largest_batch=8 smallest_batch=4 adam_beta2=0.95 fwd_bwd_time=3.43 acc=0.0706 perplexity=2728.5764 acc/en=0.0706 acc/cn=0.0 acc/code=0.0 tokens/en=61028 tokens/cn=0 tokens/code=0 loss_from_metric=7.9115 loss/en=7.9115 loss/cn=nan loss/code=nan 2023-11-10T15:06:19.051+08:00 INFO [training_internlm.py, line 601, in record_current_batch_training_metrics] - pid=78690 : tflops=127.79902453659938 step=5 loss=7.049621105194092 tgs (tokens/gpu/second)=1030.92 tgs/last_tgs_1=1030.93 tgs/tgs_all=758.03 tgs/tgs_avg=897.49 tgs/tgs_SMA=758.03 tgs/last_tgs_10=0 tgs/last_tgs_50=0 lr=1.4000000000000001e-06 loss_scale=65536.0 grad_norm={'0_default': 32.49298677335042, '1_fp32': 0.0} micro_num=4 num_consumed_tokens=393216 inf_nan_skip_batches=0 num_samples_in_batch=13 largest_length=2048 largest_batch=4 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=3.42 acc=0.0726 perplexity=1169.7916 acc/en=0.0726 acc/cn=0.0 acc/code=0.0 tokens/en=61004 tokens/cn=0 tokens/code=0 loss_from_metric=7.0646 loss/en=7.0646 loss/cn=nan loss/code=nan