7B Demo ================ Training Config ---------------- 7B demo config file example: .. code-block:: python JOB_NAME = "7b_train" SEQ_LEN = 2048 HIDDEN_SIZE = 4096 NUM_ATTENTION_HEAD = 32 MLP_RATIO = 8 / 3 NUM_LAYER = 32 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=8, pipeline=dict(size=1, interleaved_overlap=True), sequence_parallel=False, ) cudnn_deterministic = False cudnn_benchmark = False Start Training ---------------- After completing the data preparation and relevant training configurations, you can start the demo training. The following example shows how to start distributed training in ``slurm`` environments with 8 GPUs. .. code-block:: bash srun -p internllm -N 1 -n 8 --ntasks-per-node=8 --gpus-per-task=1 python train.py --config ./configs/7B_sft.py Training Results ---------------- Taking the configuration of the demo training on a single machine with 8 GPUs on slurm as an example, the training result log is shown below: .. code-block:: bash 2023-09-05 11:47:44,649 INFO parallel_context.py:508 in set_device -- process rank 4 is bound to host:SH-IDC1-10-140-1-110 device: 4 2023-09-05 11:47:44,650 INFO parallel_context.py:508 in set_device -- process rank 3 is bound to host:SH-IDC1-10-140-1-110 device: 3 2023-09-05 11:47:44,651 INFO parallel_context.py:508 in set_device -- process rank 6 is bound to host:SH-IDC1-10-140-1-110 device: 6 2023-09-05 11:47:44,652 INFO parallel_context.py:508 in set_device -- process rank 7 is bound to host:SH-IDC1-10-140-1-110 device: 7 2023-09-05 11:47:44,652 INFO parallel_context.py:508 in set_device -- process rank 5 is bound to host:SH-IDC1-10-140-1-110 device: 5 2023-09-05 11:47:44,652 INFO parallel_context.py:508 in set_device -- process rank 1 is bound to host:SH-IDC1-10-140-1-110 device: 1 2023-09-05 11:47:44,652 INFO parallel_context.py:508 in set_device -- process rank 2 is bound to host:SH-IDC1-10-140-1-110 device: 2 2023-09-05 11:47:44,652 INFO parallel_context.py:508 in set_device -- process rank 0 is bound to host:SH-IDC1-10-140-1-110 device: 0 2023-09-05 11:47:51,006 INFO launch.py:354 in launch -- Distributed environment is initialized, data parallel size: 8, pipeline parallel size: 1, tensor parallel size: 1 2023-09-05 11:49:09,855 INFO hybrid_zero_optim.py:294 in _partition_param_list -- Number of elements on ranks: [894509056, 944865280, 966909952, 966909952, 966909952, 944865280, 966909952, 670068736], rank:0 2023-09-05T11:49:58.225+08:00 INFO [training_internlm.py, line 413, in record_current_batch_training_metrics] - pid=6794 : tflops=63.283263603947816 step=0 loss=11.641494750976562 tgs (tokens/gpu/second)=1424.93 lr=4.0000000000000003e-07 loss_scale=65536.0 grad_norm={'0_default': 66.51907327507652} 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=6.87 acc=0.0 perplexity=112181.7188 acc/en=0.0 acc/cn=0.0 acc/code=0.0 tokens/en=120836 tokens/cn=0 tokens/code=0 loss_from_metric=11.6279 loss/en=11.6279 loss/cn=nan loss/code=nan 2023-09-05T11:50:02.553+08:00 INFO [training_internlm.py, line 413, in record_current_batch_training_metrics] - pid=6794 : tflops=171.92140761933035 step=1 loss=11.546792984008789 tgs (tokens/gpu/second)=3871.11 lr=6.000000000000001e-07 loss_scale=65536.0 grad_norm={'0_default': 64.47430144542088} micro_num=4 num_consumed_tokens=262144 inf_nan_skip_batches=0 num_samples_in_batch=16 largest_length=2048 largest_batch=5 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=4.14 acc=0.0 perplexity=103779.1406 acc/en=0.0 acc/cn=0.0 acc/code=0.0 tokens/en=120572 tokens/cn=0 tokens/code=0 loss_from_metric=11.55 loss/en=11.55 loss/cn=nan loss/code=nan 2023-09-05T11:50:06.504+08:00 INFO [training_internlm.py, line 413, in record_current_batch_training_metrics] - pid=6794 : tflops=186.0565203348341 step=2 loss=11.106071472167969 tgs (tokens/gpu/second)=4189.39 lr=8.000000000000001e-07 loss_scale=65536.0 grad_norm={'0_default': 62.520055376005146} micro_num=4 num_consumed_tokens=393216 inf_nan_skip_batches=0 num_samples_in_batch=16 largest_length=2048 largest_batch=6 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=3.82 acc=0.0001 perplexity=71139.6797 acc/en=0.0001 acc/cn=0.0 acc/code=0.0 tokens/en=122032 tokens/cn=0 tokens/code=0 loss_from_metric=11.1724 loss/en=11.1724 loss/cn=nan loss/code=nan 2023-09-05T11:50:10.487+08:00 INFO [training_internlm.py, line 413, in record_current_batch_training_metrics] - pid=6794 : tflops=185.48897918112567 step=3 loss=10.444510459899902 tgs (tokens/gpu/second)=4176.61 lr=1.0000000000000002e-06 loss_scale=65536.0 grad_norm={'0_default': 57.91057980979166} micro_num=4 num_consumed_tokens=524288 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=3.83 acc=0.0705 perplexity=39851.1289 acc/en=0.0705 acc/cn=0.0 acc/code=0.0 tokens/en=121125 tokens/cn=0 tokens/code=0 loss_from_metric=10.5929 loss/en=10.5929 loss/cn=nan loss/code=nan 2023-09-05T11:50:14.476+08:00 INFO [training_internlm.py, line 413, in record_current_batch_training_metrics] - pid=6794 : tflops=185.8751803758398 step=4 loss=9.798665046691895 tgs (tokens/gpu/second)=4185.31 lr=1.2000000000000002e-06 loss_scale=65536.0 grad_norm={'0_default': 48.1136933755285} micro_num=4 num_consumed_tokens=655360 inf_nan_skip_batches=0 num_samples_in_batch=14 largest_length=2048 largest_batch=4 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=3.82 acc=0.076 perplexity=18045.6699 acc/en=0.076 acc/cn=0.0 acc/code=0.0 tokens/en=121365 tokens/cn=0 tokens/code=0 loss_from_metric=9.8007 loss/en=9.8007 loss/cn=nan loss/code=nan 2023-09-05T11:50:18.442+08:00 INFO [training_internlm.py, line 413, in record_current_batch_training_metrics] - pid=6794 : tflops=185.6236609556878 step=5 loss=9.215429306030273 tgs (tokens/gpu/second)=4179.64 lr=1.4000000000000001e-06 loss_scale=65536.0 grad_norm={'0_default': 36.95489557069029} micro_num=4 num_consumed_tokens=786432 inf_nan_skip_batches=0 num_samples_in_batch=14 largest_length=2048 largest_batch=4 smallest_batch=3 adam_beta2=0.95 fwd_bwd_time=3.82 acc=0.0767 perplexity=8999.0869 acc/en=0.0767 acc/cn=0.0 acc/code=0.0 tokens/en=121223 tokens/cn=0 tokens/code=0 loss_from_metric=9.1049 loss/en=9.1049 loss/cn=nan loss/code=nan