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20 hours ago •
VHellendoorn
VHellendoorn
CUDA out of memory error on training
I was trying to train Polycoder using the preconfigured dataset, from the checkpoint checkpoints-2-7B, I used the following command as per the instructions in the repo (only changing the configs as appropriate):
sudo python ./deepy.py train.py -d configs 2-7B.yml local_setup.yml
which gave the following error:
RuntimeError: CUDA out of memory. Tried to allocate 1.86 GiB (GPU 0; 23.70 GiB total capacity; 20.49 GiB already allocated; 1.74 GiB free; 20.50 GiB reserved in total by PyTorch)
Interestingly, the full 25 Gigs of our GPU is free, as per nvidia-smi.
I tried updating the batch size, and the the only location I found to update batch size in the config files was train_micro_batch_size_per_gpu: 8, in 2-7B.yml.
It was 8, I changed it to 4, and then also to 1, but in both cases got the same error.
I am running all this in docker, as per the containerized setup instructions.
Appreciate any help!
I also updated train_batch_size by updating gradient_accumulation_steps to 1, to give me a train_batch_size of 1 (where train_batch_size = micro_batch_per_gpu * gradient_acc_step * world_size) but still got the same error. Here is the full log:
mchorse@de65c7f6f3c6:/gpt-neox$ sudo python ./deepy.py train.py -d configs 2-7B.yml local_setup.yml
NeoXArgs.from_ymls() ['configs/2-7B.yml', 'configs/local_setup.yml']
INFO:root:NeoXArgs.calculate_derived() Total number of GPUs determined to be: 1
-------------------- arguments --------------------
attention_config ................ ['global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global', 'global']updated
attention_dropout ............... 0...........................updated
batch_size ...................... 1...........................updated
bias_gelu_fusion ................ True........................updated
checkpoint_activations .......... True........................updated
clip_grad ....................... 1.0.........................updated
config_files .................... {'2-7B.yml': '# GPT-2 pretraining setup\n{\n # parallelism settings ( you will want to change these based on your cluster setup, ideally scheduling pipeline stages\n # across the node boundaries )\n "pipe-parallel-size": 1,\n "model-parallel-size": 1,\n\n # model settings\n "num-layers": 32,\n "hidden-size": 2560,\n "num-attention-heads": 32,\n "seq-length": 2048,\n "max-position-embeddings": 2048,\n "norm": "layernorm",\n "pos-emb": "rotary",\n "no-weight-tying": true,\n\n # these should provide some speedup but takes a while to build, set to true if desired\n "scaled-upper-triang-masked-softmax-fusion": true,\n "bias-gelu-fusion": true,\n\n # optimizer settings\n "zero_allow_untested_optimizer": true,\n "optimizer": {\n "type": "adam",\n "params": {\n "lr": 0.00016,\n "betas": [0.9, 0.999],\n "eps": 1.0e-8,\n }\n },\n "zero_optimization": {\n "stage": 1,\n "allgather_partitions": True,\n "allgather_bucket_size": 500000000,\n "overlap_comm": True,\n "reduce_scatter": True,\n "reduce_bucket_size": 500000000,\n "contiguous_gradients": True,\n "cpu_offload": False\n },\n\n # batch / data settings # original batch size per gpu was 8, and gradient accumulation steps was 4\n "train_micro_batch_size_per_gpu": 1,\n "gradient_accumulation_steps": 1,\n "data-impl": "mmap",\n "split": "989,10,1",\n\n # activation checkpointing\n "checkpoint-activations": true,\n "checkpoint-num-layers": 1,\n "partition-activations": true,\n "synchronize-each-layer": true,\n\n # regularization\n "gradient_clipping": 1.0,\n "weight-decay": 0,\n "hidden-dropout": 0,\n "attention-dropout": 0,\n\n # precision settings\n "fp16": { \n "fp16": true,\n "enabled": true,\n "loss_scale": 0,\n "initial_scale_power": 16,\n "loss_scale_window": 1000,\n "hysteresis": 2,\n "min_loss_scale": 1\n },\n\n # misc. training settings\n "train-iters": 160000,\n "lr-decay-iters": 160000,\n "distributed-backend": "nccl",\n "lr-decay-style": "cosine",\n "warmup": 0.01,\n "save-interval": 1000,\n "eval-interval": 1000,\n "eval-iters": 10,\n\n # logging\n "log-interval": 100,\n "steps_per_print": 10,\n "keep-last-n-checkpoints": 1,\n "wall_clock_breakdown": true,\n}\n', 'local_setup.yml': '# Suggested data paths when using GPT-NeoX locally\n{\n "data-path": "./data/enron/enron_text_document",\n \n # or for weighted datasets: \n # "train-data-paths": ["data/enron/enron_text_document", "data/enron/enron_text_document"],\n # "test-data-paths": ["data/enron/enron_text_document", "data/enron/enron_text_document"],\n # "valid-data-paths": ["data/enron/enron_text_document", "data/enron/enron_text_document"],\n # "train-data-weights": [1., 2.],\n # "test-data-weights": [2., 1.],\n # "valid-data-weights": [0.5, 0.4],\n\n # If weight_by_num_documents is True, Builds dataset weights from a multinomial distribution over groups of data according to the number of documents in each group. \n # WARNING: setting this to True will override any user provided weights\n # "weight_by_num_documents": false,\n # "weighted_sampler_alpha": 0.3,\n\n "vocab-file": "data/code-vocab.json",\n "merge-file": "data/code-merges.txt",\n\n "save": "checkpoints",\n "load": "checkpoints",\n "checkpoint_validation_with_forward_pass": False,\n \n "tensorboard-dir": "tensorboard",\n "log-dir": "logs",\n "use_wandb": True,\n "wandb_host": "https://api.wandb.ai",\n "wandb_project": "neox"\n}\n'}updated
data_impl ....................... mmap........................updated
data_path ....................... ./data/enron/enron_text_documentupdated
dynamic_loss_scale .............. True........................updated
eval_iters ...................... 10..........................updated
fp16 ............................ {'fp16': True, 'enabled': True, 'loss_scale': 0, 'initial_scale_power': 16, 'loss_scale_window': 1000, 'hysteresis': 2, 'min_loss_scale': 1}updated
gas ............................. 1...........................updated
global_num_gpus ................. 1...........................updated
gradient_clipping ............... 1.0.........................updated
hidden_dropout .................. 0...........................updated
hidden_size ..................... 2560........................updated
is_pipe_parallel ................ True........................updated
keep_last_n_checkpoints ......... 1...........................updated
load ............................ checkpoints.................updated
log_dir ......................... logs........................updated
log_interval .................... 100.........................updated
lr .............................. 0.00016.....................updated
lr_decay_iters .................. 160000......................updated
lr_decay_style .................. cosine......................updated
max_position_embeddings ......... 2048........................updated
merge_file ...................... data/code-merges.txt........updated
no_weight_tying ................. True........................updated
num_attention_heads ............. 32..........................updated
num_layers ...................... 32..........................updated
optimizer ....................... {'type': 'adam', 'params': {'lr': 0.00016, 'betas': [0.9, 0.999], 'eps': 1e-08}}updated
partition_activations ........... True........................updated
pipe_parallel_size .............. 1...........................updated
pos_emb ......................... rotary......................updated
precision ....................... fp16........................updated
save ............................ checkpoints.................updated
save_interval ................... 1000........................updated
scaled_upper_triang_masked_softmax_fusion True...............updated
seq_length ...................... 2048........................updated
sparsity_config ................. {}..........................updated
split ........................... 989,10,1....................updated
synchronize_each_layer .......... True........................updated
tensorboard_dir ................. tensorboard.................updated
train_batch_size ................ 1...........................updated
train_iters ..................... 160000......................updated
train_micro_batch_size_per_gpu .. 1...........................updated
use_wandb ....................... True........................updated
user_script ..................... train.py....................updated
vocab_file ...................... data/code-vocab.json........updated
wall_clock_breakdown ............ True........................updated
wandb_group ..................... DeMkeMCDWQ8LA6TGSz7jtQ_2b3bycopupdated
weight_decay .................... 0...........................updated
zero_allgather_bucket_size ...... 500000000...................updated
zero_allow_untested_optimizer ... True........................updated
zero_contiguous_gradients ....... True........................updated
zero_optimization ............... {'stage': 1, 'allgather_partitions': True, 'allgather_bucket_size': 500000000, 'overlap_comm': True, 'reduce_scatter': True, 'reduce_bucket_size': 500000000, 'contiguous_gradients': True, 'cpu_offload': False}updated
zero_reduce_bucket_size ......... 500000000...................updated
zero_reduce_scatter ............. True........................updated
zero_stage ...................... 1...........................updated
activation ...................... gelu........................default
adlr_autoresume ................. False.......................default
adlr_autoresume_interval ........ 1000........................default
amp ............................. None........................default
apply_query_key_layer_scaling ... False.......................default
attention_softmax_in_fp32 ....... False.......................default
bias_dropout_fusion ............. False.......................default
char_level_ppl .................. False.......................default
checkpoint_in_cpu ............... False.......................default
checkpoint_num_layers ........... 1...........................default
checkpoint_validation_with_forward_pass False................default
contiguous_checkpointing ........ False.......................default
deepscale ....................... False.......................default
deepscale_config ................ None........................default
deepspeed ....................... True........................default
deepspeed_activation_checkpointing True......................default
deepspeed_mpi ................... False.......................default
detect_nvlink_pairs ............. False.......................default
distributed_backend ............. nccl........................default
do_test ......................... None........................default
do_train ........................ None........................default
do_valid ........................ None........................default
dump_state ...................... False.......................default
eod_mask_loss ................... False.......................default
eval_interval ................... 1000........................default
eval_results_prefix ............. ............................default
eval_tasks ...................... None........................default
exclude ......................... None........................default
exit_interval ................... None........................default
finetune ........................ False.......................default
flops_profiler .................. None........................default
fp16_lm_cross_entropy ........... False.......................default
fp32_allreduce .................. False.......................default
git_hash ........................ 98683ae.....................default
gmlp_attn_dim ................... 64..........................default
gpt_j_residual .................. False.......................default
gradient_accumulation_steps ..... 1...........................default
gradient_noise_scale_cpu_offload False.......................default
gradient_noise_scale_n_batches .. 5...........................default
gradient_predivide_factor ....... 1.0.........................default
hostfile ........................ None........................default
hysteresis ...................... 2...........................default
include ......................... None........................default
init_method ..................... normal......................default
init_method_std ................. 0.02........................default
iteration ....................... None........................default
launcher ........................ pdsh........................default
layernorm_epsilon ............... 1e-05.......................default
lazy_mpu_init ................... False.......................default
local_rank ...................... None........................default
log_grad_norm ................... False.......................default
log_gradient_noise_scale ........ False.......................default
log_optimizer_states ............ False.......................default
log_param_norm .................. False.......................default
loss_scale ...................... None........................default
loss_scale_window ............... 1000.0......................default
make_vocab_size_divisible_by .... 128.........................default
master_addr ..................... None........................default
master_port ..................... 29500.......................default
maximum_tokens .................. 64..........................default
min_lr .......................... 0.0.........................default
min_scale ....................... 1.0.........................default
mmap_warmup ..................... False.......................default
model_parallel_size ............. 1...........................default
no_load_optim ................... False.......................default
no_load_rng ..................... False.......................default
no_save_optim ................... False.......................default
no_save_rng ..................... False.......................default
norm ............................ layernorm...................default
num_gpus ........................ None........................default
num_nodes ....................... -1..........................default
num_samples ..................... 0...........................default
num_unique_layers ............... None........................default
num_workers ..................... 2...........................default
onnx_safe ....................... False.......................default
optimizer_type .................. adam........................default
output_layer_init_method ........ scaled_normal...............default
output_layer_parallelism ........ row.........................default
override_lr_scheduler ........... False.......................default
padded_vocab_size ............... None........................default
param_sharing_style ............. grouped.....................default
pipe_partition_method ........... type:transformer|mlp........default
prescale_gradients .............. False.......................default
profile_backward ................ False.......................default
rank ............................ None........................default
recompute ....................... False.......................default
rms_norm_epsilon ................ 1e-08.......................default
rotary_emb_base ................. 10000.......................default
rotary_pct ...................... 1.0.........................default
rpe_max_distance ................ 128.........................default
rpe_num_buckets ................. 32..........................default
sample_input_file ............... None........................default
sample_output_file .............. None........................default
scaled_masked_softmax_fusion .... False.......................default
scalenorm_epsilon ............... 1e-08.......................default
scheduler ....................... None........................default
seed ............................ 1234........................default
short_seq_prob .................. 0.1.........................default
soft_prompt_tuning .............. None........................default
sparse_gradients ................ False.......................default
steps_per_print ................. 10..........................default
temperature ..................... 0.0.........................default
test_data_paths ................. None........................default
test_data_weights ............... None........................default
text_gen_type ................... None........................default
tokenizer_type .................. GPT2BPETokenizer............default
top_k ........................... 0...........................default
top_p ........................... 0.0.........................default
train_data_paths ................ None........................default
train_data_weights .............. None........................default
use_bnb_optimizer ............... False.......................default
use_checkpoint_lr_scheduler ..... False.......................default
use_cpu_initialization .......... False.......................default
valid_data_paths ................ None........................default
valid_data_weights .............. None........................default
wandb_host ...................... https://api.wandb.ai........default
wandb_project ................... neox........................default
wandb_team ...................... None........................default
warmup .......................... 0.01........................default
weight_by_num_documents ......... False.......................default
weighted_sampler_alpha .......... 0.3.........................default
world_size ...................... None........................default
---------------- end of arguments ----------------
[2022-04-19 20:24:23,257] [WARNING] [runner.py:126:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
[2022-04-19 20:24:23,257] [INFO] [runner.py:366:main] cmd = /usr/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMF19 --master_addr=127.0.0.1 --master_port=29500 train.py --deepspeed_config {"train_batch_size": 1, "train_micro_batch_size_per_gpu": 1, "optimizer": {"type": "adam", "params": {"lr": 0.00016, "betas": [0.9, 0.999], "eps": 1e-08}}, "fp16": {"fp16": true, "enabled": true, "loss_scale": 0, "initial_scale_power": 16, "loss_scale_window": 1000, "hysteresis": 2, "min_loss_scale": 1}, "gradient_clipping": 1.0, "zero_optimization": {"stage": 1, "allgather_partitions": true, "allgather_bucket_size": 500000000, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 500000000, "contiguous_gradients": true, "cpu_offload": false}, "wall_clock_breakdown": true, "zero_allow_untested_optimizer": true} --megatron_config {"train_batch_size": 1, "train_micro_batch_size_per_gpu": 1, "optimizer": {"type": "adam", "params": {"lr": 0.00016, "betas": [0.9, 0.999], "eps": 1e-08}}, "fp16": {"fp16": true, "enabled": true, "loss_scale": 0, "initial_scale_power": 16, "loss_scale_window": 1000, "hysteresis": 2, "min_loss_scale": 1}, "gradient_clipping": 1.0, "zero_optimization": {"stage": 1, "allgather_partitions": true, "allgather_bucket_size": 500000000, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 500000000, "contiguous_gradients": true, "cpu_offload": false}, "wall_clock_breakdown": true, "zero_allow_untested_optimizer": true, "precision": "fp16", "num_layers": 32, "hidden_size": 2560, "num_attention_heads": 32, "seq_length": 2048, "max_position_embeddings": 2048, "pos_emb": "rotary", "no_weight_tying": true, "attention_config": ["global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global"], "sparsity_config": {}, "scaled_upper_triang_masked_softmax_fusion": true, "bias_gelu_fusion": true, "lr_decay_style": "cosine", "lr_decay_iters": 160000, "zero_stage": 1, "zero_reduce_scatter": true, "zero_contiguous_gradients": true, "zero_reduce_bucket_size": 500000000, "zero_allgather_bucket_size": 500000000, "lr": 0.00016, "data_path": "./data/enron/enron_text_document", "data_impl": "mmap", "save": "checkpoints", "config_files": {"2-7B.yml": "# GPT-2 pretraining setup\n{\n # parallelism settings ( you will want to change these based on your cluster setup, ideally scheduling pipeline stages\n # across the node boundaries )\n \"pipe-parallel-size\": 1,\n \"model-parallel-size\": 1,\n\n # model settings\n \"num-layers\": 32,\n \"hidden-size\": 2560,\n \"num-attention-heads\": 32,\n \"seq-length\": 2048,\n \"max-position-embeddings\": 2048,\n \"norm\": \"layernorm\",\n \"pos-emb\": \"rotary\",\n \"no-weight-tying\": true,\n\n # these should provide some speedup but takes a while to build, set to true if desired\n \"scaled-upper-triang-masked-softmax-fusion\": true,\n \"bias-gelu-fusion\": true,\n\n # optimizer settings\n \"zero_allow_untested_optimizer\": true,\n \"optimizer\": {\n \"type\": \"adam\",\n \"params\": {\n \"lr\": 0.00016,\n \"betas\": [0.9, 0.999],\n \"eps\": 1.0e-8,\n }\n },\n \"zero_optimization\": {\n \"stage\": 1,\n \"allgather_partitions\": True,\n \"allgather_bucket_size\": 500000000,\n \"overlap_comm\": True,\n \"reduce_scatter\": True,\n \"reduce_bucket_size\": 500000000,\n \"contiguous_gradients\": True,\n \"cpu_offload\": False\n },\n\n # batch / data settings # original batch size per gpu was 8, and gradient accumulation steps was 4\n \"train_micro_batch_size_per_gpu\": 1,\n \"gradient_accumulation_steps\": 1,\n \"data-impl\": \"mmap\",\n \"split\": \"989,10,1\",\n\n # activation checkpointing\n \"checkpoint-activations\": true,\n \"checkpoint-num-layers\": 1,\n \"partition-activations\": true,\n \"synchronize-each-layer\": true,\n\n # regularization\n \"gradient_clipping\": 1.0,\n \"weight-decay\": 0,\n \"hidden-dropout\": 0,\n \"attention-dropout\": 0,\n\n # precision settings\n \"fp16\": { \n \"fp16\": true,\n \"enabled\": true,\n \"loss_scale\": 0,\n \"initial_scale_power\": 16,\n \"loss_scale_window\": 1000,\n \"hysteresis\": 2,\n \"min_loss_scale\": 1\n },\n\n # misc. training settings\n \"train-iters\": 160000,\n \"lr-decay-iters\": 160000,\n \"distributed-backend\": \"nccl\",\n \"lr-decay-style\": \"cosine\",\n \"warmup\": 0.01,\n \"save-interval\": 1000,\n \"eval-interval\": 1000,\n \"eval-iters\": 10,\n\n # logging\n \"log-interval\": 100,\n \"steps_per_print\": 10,\n \"keep-last-n-checkpoints\": 1,\n \"wall_clock_breakdown\": true,\n}\n", "local_setup.yml": "# Suggested data paths when using GPT-NeoX locally\n{\n \"data-path\": \"./data/enron/enron_text_document\",\n \n # or for weighted datasets: \n # \"train-data-paths\": [\"data/enron/enron_text_document\", \"data/enron/enron_text_document\"],\n # \"test-data-paths\": [\"data/enron/enron_text_document\", \"data/enron/enron_text_document\"],\n # \"valid-data-paths\": [\"data/enron/enron_text_document\", \"data/enron/enron_text_document\"],\n # \"train-data-weights\": [1., 2.],\n # \"test-data-weights\": [2., 1.],\n # \"valid-data-weights\": [0.5, 0.4],\n\n # If weight_by_num_documents is True, Builds dataset weights from a multinomial distribution over groups of data according to the number of documents in each group. \n # WARNING: setting this to True will override any user provided weights\n # \"weight_by_num_documents\": false,\n # \"weighted_sampler_alpha\": 0.3,\n\n \"vocab-file\": \"data/code-vocab.json\",\n \"merge-file\": \"data/code-merges.txt\",\n\n \"save\": \"checkpoints\",\n \"load\": \"checkpoints\",\n \"checkpoint_validation_with_forward_pass\": False,\n \n \"tensorboard-dir\": \"tensorboard\",\n \"log-dir\": \"logs\",\n \"use_wandb\": True,\n \"wandb_host\": \"https://api.wandb.ai\",\n \"wandb_project\": \"neox\"\n}\n"}, "load": "checkpoints", "save_interval": 1000, "batch_size": 1, "train_iters": 160000, "eval_iters": 10, "keep_last_n_checkpoints": 1, "split": "989,10,1", "vocab_file": "data/code-vocab.json", "merge_file": "data/code-merges.txt", "attention_dropout": 0, "hidden_dropout": 0, "weight_decay": 0, "checkpoint_activations": true, "synchronize_each_layer": true, "partition_activations": true, "gas": 1, "clip_grad": 1.0, "dynamic_loss_scale": true, "pipe_parallel_size": 1, "is_pipe_parallel": true, "use_wandb": true, "wandb_group": "DeMkeMCDWQ8LA6TGSz7jtQ_2b3bycop", "log_dir": "logs", "tensorboard_dir": "tensorboard", "log_interval": 100, "user_script": "train.py", "global_num_gpus": 1}
[2022-04-19 20:24:24,084] [INFO] [launch.py:82:main] WORLD INFO DICT: {'localhost': [0]}
[2022-04-19 20:24:24,085] [INFO] [launch.py:88:main] nnodes=1, num_local_procs=1, node_rank=0
[2022-04-19 20:24:24,085] [INFO] [launch.py:103:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0]})
[2022-04-19 20:24:24,085] [INFO] [launch.py:104:main] dist_world_size=1
[2022-04-19 20:24:24,085] [INFO] [launch.py:112:main] Setting CUDA_VISIBLE_DEVICES=0
NeoXArgs.configure_distributed_args() using world size: 1 and model-parallel size: 1
> building GPT2BPETokenizer tokenizer ...
> padded vocab (size: 50257) with 47 dummy tokens (new size: 50304)
WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later and do you have tensorboard installed?), no TensorBoard logs will be written.
> initializing torch distributed ...
[2022-04-19 20:24:26,400] [INFO] [distributed.py:46:init_distributed] Initializing torch distributed with backend: nccl
> initializing model parallel with size 1
MPU DP: [0]
MPU PP: [0]
MPU MP: [0]
> setting random seeds to 1234 ...
[2022-04-19 20:24:26,401] [INFO] [checkpointing.py:223:model_parallel_cuda_manual_seed] > initializing model parallel cuda seeds on global rank 0, model parallel rank 0, and data parallel rank 0 with model parallel seed: 3952 and data parallel seed: 1234
make: Entering directory '/gpt-neox/megatron/data'
make: Nothing to be done for 'default'.
make: Leaving directory '/gpt-neox/megatron/data'
building GPT2 model ...
SEED_LAYERS=False BASE_SEED=1234 SEED_FN=None
Using topology: {ProcessCoord(pipe=0, data=0, model=0): 0}
[2022-04-19 20:24:29,552] [INFO] [module.py:363:_partition_layers] Partitioning pipeline stages with method type:transformer|mlp
stage=0 layers=37
0: EmbeddingPipe
1: _pre_transformer_block
2: ParallelTransformerLayerPipe
3: ParallelTransformerLayerPipe
4: ParallelTransformerLayerPipe
5: ParallelTransformerLayerPipe
6: ParallelTransformerLayerPipe
7: ParallelTransformerLayerPipe
8: ParallelTransformerLayerPipe
9: ParallelTransformerLayerPipe
10: ParallelTransformerLayerPipe
11: ParallelTransformerLayerPipe
12: ParallelTransformerLayerPipe
13: ParallelTransformerLayerPipe
14: ParallelTransformerLayerPipe
15: ParallelTransformerLayerPipe
16: ParallelTransformerLayerPipe
17: ParallelTransformerLayerPipe
18: ParallelTransformerLayerPipe
19: ParallelTransformerLayerPipe
20: ParallelTransformerLayerPipe
21: ParallelTransformerLayerPipe
22: ParallelTransformerLayerPipe
23: ParallelTransformerLayerPipe
24: ParallelTransformerLayerPipe
25: ParallelTransformerLayerPipe
26: ParallelTransformerLayerPipe
27: ParallelTransformerLayerPipe
28: ParallelTransformerLayerPipe
29: ParallelTransformerLayerPipe
30: ParallelTransformerLayerPipe
31: ParallelTransformerLayerPipe
32: ParallelTransformerLayerPipe
33: ParallelTransformerLayerPipe
34: _post_transformer_block
35: NormPipe
36: ParallelLinearPipe
loss: partial
Configuring Optimizer type: adam with params: {'lr': 0.00016, 'betas': [0.9, 0.999], 'eps': 1e-08}
> learning rate decay style: cosine
DeepSpeed is enabled.
[2022-04-19 20:24:29,614] [INFO] [logging.py:60:log_dist] [Rank 0] DeepSpeed info: version=0.3.15+eb7f5cf, git-hash=eb7f5cf, git-branch=main
[2022-04-19 20:24:29,614] [WARNING] [config.py:77:_sanity_check] DeepSpeedConfig: cpu_offload is deprecated. Please use offload_optimizer.
[2022-04-19 20:24:29,683] [INFO] [engine.py:654:_configure_optimizer] Removing param_group that has no 'params' in the client Optimizer
[2022-04-19 20:24:29,683] [INFO] [engine.py:659:_configure_optimizer] Using client Optimizer as basic optimizer
[2022-04-19 20:24:29,683] [INFO] [engine.py:668:_configure_optimizer] DeepSpeed Basic Optimizer = FusedAdam
Checking ZeRO support for optimizer=FusedAdam type=<class 'apex.optimizers.fused_adam.FusedAdam'>
[2022-04-19 20:24:29,683] [INFO] [logging.py:60:log_dist] [Rank 0] Creating fp16 ZeRO stage 1 optimizer
Using /root/.cache/torch_extensions as PyTorch extensions root...
Emitting ninja build file /root/.cache/torch_extensions/utils/build.ninja...
Building extension module utils...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
Loading extension module utils...
Time to load utils op: 0.7416722774505615 seconds
[2022-04-19 20:24:30,427] [INFO] [stage1.py:160:__init__] ZeRO Elastic Checkpoint = True
[2022-04-19 20:24:30,427] [INFO] [logging.py:60:log_dist] [Rank 0] Using default max_elements_per_comm 500000000
[2022-04-19 20:24:30,428] [INFO] [logging.py:60:log_dist] [Rank 0] Total number of elements in model: 2775208960, max elements per com: 500000000
[2022-04-19 20:24:30,428] [INFO] [logging.py:60:log_dist] [Rank 0] sub_partition_count: 6, sub_partition_size: 500000000, padding: 224791040
[2022-04-19 20:24:30,428] [INFO] [logging.py:60:log_dist] [Rank 0] number of elements with padding: 2775208960 + 224791040 = 3000000000
[2022-04-19 20:24:30,445] [INFO] [stage1.py:375:get_data_parallel_sub_partitions] **** partition info:
[2022-04-19 20:24:30,445] [INFO] [stage1.py:376:get_data_parallel_sub_partitions] total_num_elements=3000000000
[2022-04-19 20:24:30,445] [INFO] [stage1.py:377:get_data_parallel_sub_partitions] world_size=1
[2022-04-19 20:24:30,446] [INFO] [stage1.py:378:get_data_parallel_sub_partitions] max_elements_per_comm=500000000
[2022-04-19 20:24:30,446] [INFO] [stage1.py:379:get_data_parallel_sub_partitions] sub_partition_size=500000000
[2022-04-19 20:24:30,446] [INFO] [stage1.py:380:get_data_parallel_sub_partitions] num_sub_partitions=6
[2022-04-19 20:24:30,446] [INFO] [stage1.py:381:get_data_parallel_sub_partitions] num_comm_intervals=6
[2022-04-19 20:24:30,446] [INFO] [stage1.py:382:get_data_parallel_sub_partitions] ****
Traceback (most recent call last):
File "train.py", line 27, in <module>
pretrain(neox_args=neox_args)
File "/gpt-neox/megatron/training.py", line 86, in pretrain
model, optimizer, lr_scheduler = setup_model_and_optimizer(
File "/gpt-neox/megatron/training.py", line 423, in setup_model_and_optimizer
model, optimizer, _, lr_scheduler = deepspeed.initialize(
File "/usr/local/lib/python3.8/dist-packages/deepspeed/__init__.py", line 128, in initialize
engine = PipelineEngine(args=args,
File "/usr/local/lib/python3.8/dist-packages/deepspeed/runtime/pipe/engine.py", line 60, in __init__
super().__init__(*super_args, **super_kwargs)
File "/usr/local/lib/python3.8/dist-packages/deepspeed/runtime/engine.py", line 192, in __init__
self._configure_optimizer(optimizer, model_parameters)
File "/usr/local/lib/python3.8/dist-packages/deepspeed/runtime/engine.py", line 681, in _configure_optimizer
self.optimizer = self._configure_zero_optimizer(basic_optimizer)
File "/usr/local/lib/python3.8/dist-packages/deepspeed/runtime/engine.py", line 810, in _configure_zero_optimizer
optimizer = FP16_DeepSpeedZeroOptimizer_Stage1(
File "/usr/local/lib/python3.8/dist-packages/deepspeed/runtime/zero/stage1.py", line 311, in __init__
self._initialize_optimizer_states()
File "/usr/local/lib/python3.8/dist-packages/deepspeed/runtime/zero/stage1.py", line 316, in _initialize_optimizer_states
sub_partition_grad = torch.zeros(int(
RuntimeError: CUDA out of memory. Tried to allocate 1.86 GiB (GPU 0; 23.70 GiB total capacity; 20.49 GiB already allocated; 1.74 GiB free; 20.50 GiB reserved in total by PyTorch)
Killing subprocess 8346
Traceback (most recent call last):
File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.8/dist-packages/deepspeed/launcher/launch.py", line 179, in <module>
main()
File "/usr/local/lib/python3.8/dist-packages/deepspeed/launcher/launch.py", line 169, in main
sigkill_handler(signal.SIGTERM, None) # not coming back
File "/usr/local/lib/python3.8/dist-packages/deepspeed/launcher/launch.py", line 147, in sigkill_handler
raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python', '-u', 'train.py', '--local_rank=0', '--deepspeed_config', '{"train_batch_size": 1, "train_micro_batch_size_per_gpu": 1, "optimizer": {"type": "adam", "params": {"lr": 0.00016, "betas": [0.9, 0.999], "eps": 1e-08}}, "fp16": {"fp16": true, "enabled": true, "loss_scale": 0, "initial_scale_power": 16, "loss_scale_window": 1000, "hysteresis": 2, "min_loss_scale": 1}, "gradient_clipping": 1.0, "zero_optimization": {"stage": 1, "allgather_partitions": true, "allgather_bucket_size": 500000000, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 500000000, "contiguous_gradients": true, "cpu_offload": false}, "wall_clock_breakdown": true, "zero_allow_untested_optimizer": true}', '--megatron_config', '{"train_batch_size": 1, "train_micro_batch_size_per_gpu": 1, "optimizer": {"type": "adam", "params": {"lr": 0.00016, "betas": [0.9, 0.999], "eps": 1e-08}}, "fp16": {"fp16": true, "enabled": true, "loss_scale": 0, "initial_scale_power": 16, "loss_scale_window": 1000, "hysteresis": 2, "min_loss_scale": 1}, "gradient_clipping": 1.0, "zero_optimization": {"stage": 1, "allgather_partitions": true, "allgather_bucket_size": 500000000, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 500000000, "contiguous_gradients": true, "cpu_offload": false}, "wall_clock_breakdown": true, "zero_allow_untested_optimizer": true, "precision": "fp16", "num_layers": 32, "hidden_size": 2560, "num_attention_heads": 32, "seq_length": 2048, "max_position_embeddings": 2048, "pos_emb": "rotary", "no_weight_tying": true, "attention_config": ["global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global", "global"], "sparsity_config": {}, "scaled_upper_triang_masked_softmax_fusion": true, "bias_gelu_fusion": true, "lr_decay_style": "cosine", "lr_decay_iters": 160000, "zero_stage": 1, "zero_reduce_scatter": true, "zero_contiguous_gradients": true, "zero_reduce_bucket_size": 500000000, "zero_allgather_bucket_size": 500000000, "lr": 0.00016, "data_path": "./data/enron/enron_text_document", "data_impl": "mmap", "save": "checkpoints", "config_files": {"2-7B.yml": "# GPT-2 pretraining setup\\n{\\n # parallelism settings ( you will want to change these based on your cluster setup, ideally scheduling pipeline stages\\n # across the node boundaries )\\n \\"pipe-parallel-size\\": 1,\\n \\"model-parallel-size\\": 1,\\n\\n # model settings\\n \\"num-layers\\": 32,\\n \\"hidden-size\\": 2560,\\n \\"num-attention-heads\\": 32,\\n \\"seq-length\\": 2048,\\n \\"max-position-embeddings\\": 2048,\\n \\"norm\\": \\"layernorm\\",\\n \\"pos-emb\\": \\"rotary\\",\\n \\"no-weight-tying\\": true,\\n\\n # these should provide some speedup but takes a while to build, set to true if desired\\n \\"scaled-upper-triang-masked-softmax-fusion\\": true,\\n \\"bias-gelu-fusion\\": true,\\n\\n # optimizer settings\\n \\"zero_allow_untested_optimizer\\": true,\\n \\"optimizer\\": {\\n \\"type\\": \\"adam\\",\\n \\"params\\": {\\n \\"lr\\": 0.00016,\\n \\"betas\\": [0.9, 0.999],\\n \\"eps\\": 1.0e-8,\\n }\\n },\\n \\"zero_optimization\\": {\\n \\"stage\\": 1,\\n \\"allgather_partitions\\": True,\\n \\"allgather_bucket_size\\": 500000000,\\n \\"overlap_comm\\": True,\\n \\"reduce_scatter\\": True,\\n \\"reduce_bucket_size\\": 500000000,\\n \\"contiguous_gradients\\": True,\\n \\"cpu_offload\\": False\\n },\\n\\n # batch / data settings # original batch size per gpu was 8, and gradient accumulation steps was 4\\n \\"train_micro_batch_size_per_gpu\\": 1,\\n \\"gradient_accumulation_steps\\": 1,\\n \\"data-impl\\": \\"mmap\\",\\n \\"split\\": \\"989,10,1\\",\\n\\n # activation checkpointing\\n \\"checkpoint-activations\\": true,\\n \\"checkpoint-num-layers\\": 1,\\n \\"partition-activations\\": true,\\n \\"synchronize-each-layer\\": true,\\n\\n # regularization\\n \\"gradient_clipping\\": 1.0,\\n \\"weight-decay\\": 0,\\n \\"hidden-dropout\\": 0,\\n \\"attention-dropout\\": 0,\\n\\n # precision settings\\n \\"fp16\\": { \\n \\"fp16\\": true,\\n \\"enabled\\": true,\\n \\"loss_scale\\": 0,\\n \\"initial_scale_power\\": 16,\\n \\"loss_scale_window\\": 1000,\\n \\"hysteresis\\": 2,\\n \\"min_loss_scale\\": 1\\n },\\n\\n # misc. training settings\\n \\"train-iters\\": 160000,\\n \\"lr-decay-iters\\": 160000,\\n \\"distributed-backend\\": \\"nccl\\",\\n \\"lr-decay-style\\": \\"cosine\\",\\n \\"warmup\\": 0.01,\\n \\"save-interval\\": 1000,\\n \\"eval-interval\\": 1000,\\n \\"eval-iters\\": 10,\\n\\n # logging\\n \\"log-interval\\": 100,\\n \\"steps_per_print\\": 10,\\n \\"keep-last-n-checkpoints\\": 1,\\n \\"wall_clock_breakdown\\": true,\\n}\\n", "local_setup.yml": "# Suggested data paths when using GPT-NeoX locally\\n{\\n \\"data-path\\": \\"./data/enron/enron_text_document\\",\\n \\n # or for weighted datasets: \\n # \\"train-data-paths\\": [\\"data/enron/enron_text_document\\", \\"data/enron/enron_text_document\\"],\\n # \\"test-data-paths\\": [\\"data/enron/enron_text_document\\", \\"data/enron/enron_text_document\\"],\\n # \\"valid-data-paths\\": [\\"data/enron/enron_text_document\\", \\"data/enron/enron_text_document\\"],\\n # \\"train-data-weights\\": [1., 2.],\\n # \\"test-data-weights\\": [2., 1.],\\n # \\"valid-data-weights\\": [0.5, 0.4],\\n\\n # If weight_by_num_documents is True, Builds dataset weights from a multinomial distribution over groups of data according to the number of documents in each group. \\n # WARNING: setting this to True will override any user provided weights\\n # \\"weight_by_num_documents\\": false,\\n # \\"weighted_sampler_alpha\\": 0.3,\\n\\n \\"vocab-file\\": \\"data/code-vocab.json\\",\\n \\"merge-file\\": \\"data/code-merges.txt\\",\\n\\n \\"save\\": \\"checkpoints\\",\\n \\"load\\": \\"checkpoints\\",\\n \\"checkpoint_validation_with_forward_pass\\": False,\\n \\n \\"tensorboard-dir\\": \\"tensorboard\\",\\n \\"log-dir\\": \\"logs\\",\\n \\"use_wandb\\": True,\\n \\"wandb_host\\": \\"https://api.wandb.ai\\",\\n \\"wandb_project\\": \\"neox\\"\\n}\\n"}, "load": "checkpoints", "save_interval": 1000, "batch_size": 1, "train_iters": 160000, "eval_iters": 10, "keep_last_n_checkpoints": 1, "split": "989,10,1", "vocab_file": "data/code-vocab.json", "merge_file": "data/code-merges.txt", "attention_dropout": 0, "hidden_dropout": 0, "weight_decay": 0, "checkpoint_activations": true, "synchronize_each_layer": true, "partition_activations": true, "gas": 1, "clip_grad": 1.0, "dynamic_loss_scale": true, "pipe_parallel_size": 1, "is_pipe_parallel": true, "use_wandb": true, "wandb_group": "DeMkeMCDWQ8LA6TGSz7jtQ_2b3bycop", "log_dir": "logs", "tensorboard_dir": "tensorboard", "log_interval": 100, "user_script": "train.py", "global_num_gpus": 1}']' returned non-zero exit status 1.
Hi Aftab,
Thanks for submitting this issue. It took a while to debug; the main thing I have found so far is that I can run this just fine on 4 GPUs (with 48GB of memory, instead of 24GB), but not on 2 and 1. And to be clear, the batch size scales to the number of GPUs, so when I run on 4, I am also running with 4x the data compared to a single GPU. I have never seen this kind of behavior before; normally, when you scale the batch size by the number of GPUs like that, you can run just fine on any number of GPUs.
My main guess is that something about the model or optimizer state is getting distributed across the GPUs. That way, when there are more GPUs, the memory cost per GPU is lower. I have tried disabling every setting I could think of in the config, but could not make this behavior go away, so I am not sure if this is something I can remedy. It may be worth posting to the original GPT-NeoX repository directly. My only concern is that this could be a Docker issue, somehow, in which case it isn't on their end either; you may want to try this in a virtualenv instead, to test it without using Docker. I will see what else I can try, but for now this is the state of affairs: training the largest model requires an excessive amount of memory on a single GPU, presumably because the optimizer states alone take up more than 48GB.
-Vincent
Hi Vincent,
Thank you so much for your time. Indeed it is very interesting why Pytorch always reserves such a huge chunk of memory, such that there is always less free space than the amount the script tries to allocate. I saw changing the hidden_size parameter changes the amount of memory the script tries to allocate, however, the free space availability is always lesser.
My Best Regards, Aftab