[BUG] Same train time with DeepSpeed (despite increased batch size) #1825
Description
Describe the bug and Expected behavior
Hi! I really appreciate your awesome work! I'm fine-tuning Electra model with using huggingface without Trainer API and with using deepspeed. After I applied deepspeed, I could increase the batch size (64 -> 128, but OOM with 256) of training model so I expected train time would decrease. However, even though I applied deepspeed in my code, the train time is the same. I am not sure if deepspeed is working as my configuration and if not, I wonder how I can make it work well (faster training)
I am training the model in ColabProPlus and Environment info is as belows.
( I have been allocated with the same GPU every time)
System info (please complete the following information):
- OS: Ubuntu 18.04.5 LTS
- GPU count and types : 1 GPU, Tesla P100-PCIE-16GB (Colab Pro Plus)
- Python version : 3.7.12
- torch install path ............... ['/usr/local/lib/python3.7/dist-packages/torch']
- torch version .................... 1.10.0+cu111
- torch cuda version ............... 11.1
- torch hip version ................ None
- nvcc version ..................... 11.1
- deepspeed install path ........... ['/usr/local/lib/python3.7/dist-packages/deepspeed']
- deepspeed info ................... 0.6.0, unknown, unknown
- deepspeed wheel compiled w. ...... torch 1.10, cuda 11.1, hip 0.0
To Reproduce
I run this command to use deepspeed JIT mode when I initialize runtime in colab.
!apt-get install libaio-dev
!pip install deepspeed triton==1.0.0
then, my ds_report shows as below before running train code.
ds_report output
DeepSpeed C++/CUDA extension op report
NOTE: Ops not installed will be just-in-time (JIT) compiled at
runtime if needed. Op compatibility means that your system
meet the required dependencies to JIT install the op.
JIT compiled ops requires ninja
ninja .................. [OKAY]
op name ................ installed .. compatible
cpu_adam ............... [NO] ....... [OKAY]
cpu_adagrad ............ [NO] ....... [OKAY]
fused_adam ............. [NO] ....... [OKAY]
fused_lamb ............. [NO] ....... [OKAY]
sparse_attn ............ [NO] ....... [OKAY]
transformer ............ [NO] ....... [OKAY]
stochastic_transformer . [NO] ....... [OKAY]
async_io ............... [NO] ....... [OKAY]
utils .................. [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
transformer_inference .. [NO] ....... [OKAY]
DeepSpeed general environment info:
torch install path ............... ['/usr/local/lib/python3.7/dist-packages/torch']
torch version .................... 1.10.0+cu111
torch cuda version ............... 11.1
torch hip version ................ None
nvcc version ..................... 11.1
deepspeed install path ........... ['/usr/local/lib/python3.7/dist-packages/deepspeed']
deepspeed info ................... 0.6.0, unknown, unknown
deepspeed wheel compiled w. ...... torch 1.10, cuda 11.1, hip 0.0
Deepspeed config
my deepspeed configuration is like this.
"ds_config":{
"stage2": {
"train_micro_batch_size_per_gpu": 128,
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 5e-05,
"betas": [
0.9,
0.999
],
"eps": 1e-8
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 5e-5,
"warmup_num_steps": 0
}
},
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"contiguous_gradients": true,
"cpu_offload": true,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true,
"fast_init": true
}
},
"gradient_accumulation_steps": 1,
"gradient_clipping": 1.0
}
}
Train code
My train code is almost same with code of the link in comment and added just few lines to use deepspeed as below.
# https://github.com/monologg/KoELECTRA/blob/master/finetune/run_seq_cls.py
...
import deepspeed
...
def train():
...
...
model, optimizer, _, _ = deepspeed.initialize(model=model,
config_params=args['ds_config']['stage2'],
model_parameters=model.parameters())
...
...
for epoch in mb:
epoch_iterator = progress_bar(train_dataloader, parent=mb)
for step, batch in enumerate(epoch_iterator):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3]
}
outputs = model(**inputs)
loss = outputs.loss
model.backward(loss)
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0 or (
len(train_dataloader) <= args.gradient_accumulation_steps
and (step + 1) == len(train_dataloader)
):
#torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
#optimizer.step()
model.step()
...
def main():
....
deepspeed.init_distributed("nccl")
....
Output Logs
This is my output log when I run the training.
[2022-03-11 02:05:14,971] [INFO] [logging.py:69:log_dist] [Rank 0] DeepSpeed info: version=0.6.0, git-hash=unknown, git-branch=unknown
03/11/2022 02:05:14 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 0
03/11/2022 02:05:14 - INFO - torch.distributed.distributed_c10d - Rank 0: Completed store-based barrier for key:store_based_barrier_key:2 with 1 nodes.
[2022-03-11 02:05:15,039] [INFO] [engine.py:278:__init__] DeepSpeed Flops Profiler Enabled: False
Using /root/.cache/torch_extensions/py37_cu111 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py37_cu111/cpu_adam...
Detected CUDA files, patching ldflags
Emitting ninja build file /root/.cache/torch_extensions/py37_cu111/cpu_adam/build.ninja...
Building extension module cpu_adam...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
[1/3] /usr/local/cuda/bin/nvcc -DTORCH_EXTENSION_NAME=cpu_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/usr/local/lib/python3.7/dist-packages/deepspeed/ops/csrc/includes -I/usr/local/cuda/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.7/dist-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /usr/include/python3.7m -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_60,code=compute_60 -gencode=arch=compute_60,code=sm_60 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++14 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_HALF2_OPERATORS__ -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_60,code=compute_60 -c /usr/local/lib/python3.7/dist-packages/deepspeed/ops/csrc/common/custom_cuda_kernel.cu -o custom_cuda_kernel.cuda.o
[2/3] c++ -MMD -MF cpu_adam.o.d -DTORCH_EXTENSION_NAME=cpu_adam -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -I/usr/local/lib/python3.7/dist-packages/deepspeed/ops/csrc/includes -I/usr/local/cuda/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.7/dist-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /usr/include/python3.7m -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++14 -O3 -std=c++14 -g -Wno-reorder -L/usr/local/cuda/lib64 -lcudart -lcublas -g -march=native -fopenmp -D__AVX256__ -c /usr/local/lib/python3.7/dist-packages/deepspeed/ops/csrc/adam/cpu_adam.cpp -o cpu_adam.o
[3/3] c++ cpu_adam.o custom_cuda_kernel.cuda.o -shared -lcurand -L/usr/local/lib/python3.7/dist-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda_cu -ltorch_cuda_cpp -ltorch -ltorch_python -L/usr/local/cuda/lib64 -lcudart -o cpu_adam.so
Loading extension module cpu_adam...
Time to load cpu_adam op: 26.065569162368774 seconds
Adam Optimizer #0 is created with AVX2 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
[2022-03-11 02:05:42,721] [INFO] [engine.py:1066:_configure_optimizer] Using DeepSpeed Optimizer param name adamw as basic optimizer
[2022-03-11 02:05:42,730] [INFO] [engine.py:1073:_configure_optimizer] DeepSpeed Basic Optimizer = DeepSpeedCPUAdam
[2022-03-11 02:05:42,731] [INFO] [utils.py:49:is_zero_supported_optimizer] Checking ZeRO support for optimizer=DeepSpeedCPUAdam type=<class 'deepspeed.ops.adam.cpu_adam.DeepSpeedCPUAdam'>
[2022-03-11 02:05:42,731] [INFO] [logging.py:69:log_dist] [Rank 0] Creating fp16 ZeRO stage 2 optimizer
[2022-03-11 02:05:42,731] [INFO] [stage_1_and_2.py:125:__init__] Reduce bucket size 500000000.0
[2022-03-11 02:05:42,731] [INFO] [stage_1_and_2.py:126:__init__] Allgather bucket size 500000000.0
[2022-03-11 02:05:42,731] [INFO] [stage_1_and_2.py:127:__init__] CPU Offload: True
[2022-03-11 02:05:42,731] [INFO] [stage_1_and_2.py:128:__init__] Round robin gradient partitioning: False
Using /root/.cache/torch_extensions/py37_cu111 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py37_cu111/utils...
Emitting ninja build file /root/.cache/torch_extensions/py37_cu111/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)
[1/2] c++ -MMD -MF flatten_unflatten.o.d -DTORCH_EXTENSION_NAME=utils -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /usr/local/lib/python3.7/dist-packages/torch/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.7/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.7/dist-packages/torch/include/THC -isystem /usr/include/python3.7m -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++14 -c /usr/local/lib/python3.7/dist-packages/deepspeed/ops/csrc/utils/flatten_unflatten.cpp -o flatten_unflatten.o
[2/2] c++ flatten_unflatten.o -shared -L/usr/local/lib/python3.7/dist-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o utils.so
Loading extension module utils...
Time to load utils op: 12.981101274490356 seconds
Rank: 0 partition count [1] and sizes[(113066686, False)]
[2022-03-11 02:05:56,300] [INFO] [utils.py:824:see_memory_usage] Before initializing optimizer states
[2022-03-11 02:05:56,301] [INFO] [utils.py:829:see_memory_usage] MA 0.26 GB Max_MA 0.47 GB CA 0.74 GB Max_CA 1 GB
[2022-03-11 02:05:56,302] [INFO] [utils.py:834:see_memory_usage] CPU Virtual Memory: used = 7.39 GB, percent = 14.5%
[2022-03-11 02:05:56,823] [INFO] [utils.py:824:see_memory_usage] After initializing optimizer states
[2022-03-11 02:05:56,824] [INFO] [utils.py:829:see_memory_usage] MA 0.26 GB Max_MA 0.26 GB CA 0.74 GB Max_CA 1 GB
[2022-03-11 02:05:56,824] [INFO] [utils.py:834:see_memory_usage] CPU Virtual Memory: used = 8.66 GB, percent = 17.0%
[2022-03-11 02:05:56,825] [INFO] [stage_1_and_2.py:497:__init__] optimizer state initialized
[2022-03-11 02:05:56,963] [INFO] [utils.py:824:see_memory_usage] After initializing ZeRO optimizer
[2022-03-11 02:05:56,963] [INFO] [utils.py:829:see_memory_usage] MA 0.26 GB Max_MA 0.26 GB CA 0.74 GB Max_CA 1 GB
[2022-03-11 02:05:56,964] [INFO] [utils.py:834:see_memory_usage] CPU Virtual Memory: used = 8.66 GB, percent = 17.0%
[2022-03-11 02:05:56,964] [INFO] [logging.py:69:log_dist] [Rank 0] DeepSpeed Final Optimizer = adamw
[2022-03-11 02:05:56,964] [INFO] [engine.py:777:_configure_lr_scheduler] DeepSpeed using configured LR scheduler = WarmupLR
[2022-03-11 02:05:56,964] [INFO] [logging.py:69:log_dist] [Rank 0] DeepSpeed LR Scheduler = <deepspeed.runtime.lr_schedules.WarmupLR object at 0x7f4e91ebe7d0>
[2022-03-11 02:05:56,964] [INFO] [logging.py:69:log_dist] [Rank 0] step=0, skipped=0, lr=[5e-05], mom=[[0.9, 0.999]]
[2022-03-11 02:05:56,965] [INFO] [config.py:1058:print] DeepSpeedEngine configuration:
[2022-03-11 02:05:56,965] [INFO] [config.py:1062:print] activation_checkpointing_config {
"partition_activations": false,
"contiguous_memory_optimization": false,
"cpu_checkpointing": false,
"number_checkpoints": null,
"synchronize_checkpoint_boundary": false,
"profile": false
}
[2022-03-11 02:05:56,965] [INFO] [config.py:1062:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
[2022-03-11 02:05:56,965] [INFO] [config.py:1062:print] amp_enabled .................. False
[2022-03-11 02:05:56,965] [INFO] [config.py:1062:print] amp_params ................... False
[2022-03-11 02:05:56,965] [INFO] [config.py:1062:print] autotuning_config ............ {
"enabled": false,
"start_step": null,
"end_step": null,
"metric_path": null,
"arg_mappings": null,
"metric": "throughput",
"model_info": null,
"results_dir": null,
"exps_dir": null,
"overwrite": true,
"fast": true,
"start_profile_step": 3,
"end_profile_step": 5,
"tuner_type": "gridsearch",
"tuner_early_stopping": 5,
"tuner_num_trials": 50,
"model_info_path": null,
"mp_size": 1,
"max_train_batch_size": null,
"min_train_batch_size": 1,
"max_train_micro_batch_size_per_gpu": 1.024000e+03,
"min_train_micro_batch_size_per_gpu": 1,
"num_tuning_micro_batch_sizes": 3
}
[2022-03-11 02:05:56,965] [INFO] [config.py:1062:print] bfloat16_enabled ............. False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] checkpoint_tag_validation_enabled True
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] checkpoint_tag_validation_fail False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] communication_data_type ...... None
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] curriculum_enabled ........... False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] curriculum_params ............ False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] dataloader_drop_last ......... False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] disable_allgather ............ False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] dump_state ................... False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] dynamic_loss_scale_args ...... {'init_scale': 65536, 'scale_window': 1000, 'delayed_shift': 2, 'min_scale': 1}
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_enabled ........... False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_gas_boundary_resolution 1
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_layer_name ........ bert.encoder.layer
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_layer_num ......... 0
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_max_iter .......... 100
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_stability ......... 1e-06
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_tol ............... 0.01
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] eigenvalue_verbose ........... False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] elasticity_enabled ........... False
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] flops_profiler_config ........ {
"enabled": false,
"profile_step": 1,
"module_depth": -1,
"top_modules": 1,
"detailed": true,
"output_file": null
}
[2022-03-11 02:05:56,966] [INFO] [config.py:1062:print] fp16_enabled ................. True
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] fp16_master_weights_and_gradients False
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] fp16_mixed_quantize .......... False
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] global_rank .................. 0
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] gradient_accumulation_steps .. 1
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] gradient_clipping ............ 1.0
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] gradient_predivide_factor .... 1.0
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] initial_dynamic_scale ........ 65536
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] loss_scale ................... 0
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] memory_breakdown ............. False
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] optimizer_legacy_fusion ...... False
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] optimizer_name ............... adamw
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] optimizer_params ............. {'lr': 5e-05, 'betas': [0.9, 0.999], 'eps': 1e-08}
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0}
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] pld_enabled .................. False
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] pld_params ................... False
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] prescale_gradients ........... False
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_change_rate ......... 0.001
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_groups .............. 1
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_offset .............. 1000
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_period .............. 1000
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_rounding ............ 0
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_start_bits .......... 16
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_target_bits ......... 8
[2022-03-11 02:05:56,967] [INFO] [config.py:1062:print] quantize_training_enabled .... False
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] quantize_type ................ 0
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] quantize_verbose ............. False
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] scheduler_name ............... WarmupLR
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] scheduler_params ............. {'warmup_min_lr': 0, 'warmup_max_lr': 5e-05, 'warmup_num_steps': 0}
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] sparse_attention ............. None
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] sparse_gradients_enabled ..... False
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] steps_per_print .............. 10
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] tensorboard_enabled .......... False
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] tensorboard_job_name ......... DeepSpeedJobName
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] tensorboard_output_path ......
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] train_batch_size ............. 128
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] train_micro_batch_size_per_gpu 128
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] use_quantizer_kernel ......... False
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] wall_clock_breakdown ......... False
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] world_size ................... 1
[2022-03-11 02:05:56,968] [INFO] [config.py:1062:print] zero_allow_untested_optimizer False
[2022-03-11 02:05:56,969] [INFO] [config.py:1062:print] zero_config .................. {
"stage": 2,
"contiguous_gradients": true,
"reduce_scatter": true,
"reduce_bucket_size": 5.000000e+08,
"allgather_partitions": true,
"allgather_bucket_size": 5.000000e+08,
"overlap_comm": true,
"load_from_fp32_weights": true,
"elastic_checkpoint": false,
"offload_param": null,
"offload_optimizer": {
"device": null,
"nvme_path": null,
"buffer_count": 4,
"pin_memory": false,
"pipeline_read": false,
"pipeline_write": false,
"fast_init": false
},
"sub_group_size": 1.000000e+09,
"prefetch_bucket_size": 5.000000e+07,
"param_persistence_threshold": 1.000000e+05,
"max_live_parameters": 1.000000e+09,
"max_reuse_distance": 1.000000e+09,
"gather_16bit_weights_on_model_save": false,
"ignore_unused_parameters": true,
"round_robin_gradients": false,
"legacy_stage1": false
}
[2022-03-11 02:05:56,969] [INFO] [config.py:1062:print] zero_enabled ................. True
[2022-03-11 02:05:56,969] [INFO] [config.py:1062:print] zero_optimization_stage ...... 2
[2022-03-11 02:05:56,969] [INFO] [config.py:1070:print] json = {
"train_micro_batch_size_per_gpu": 128,
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 5e-05,
"betas": [0.9, 0.999],
"eps": 1e-08
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 5e-05,
"warmup_num_steps": 0
}
},
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 5.000000e+08,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5.000000e+08,
"contiguous_gradients": true,
"cpu_offload": true,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true,
"fast_init": true
}
},
"gradient_accumulation_steps": 1,
"gradient_clipping": 1.0
}
Using /root/.cache/torch_extensions/py37_cu111 as PyTorch extensions root...
No modifications detected for re-loaded extension module utils, skipping build step...
Loading extension module utils...
Time to load utils op: 0.00044083595275878906 seconds
03/11/2022 02:05:56 - INFO - __main__ - ***** Running training *****
03/11/2022 02:05:56 - INFO - __main__ - Num examples = 234528
03/11/2022 02:05:56 - INFO - __main__ - Num Epochs = 100
03/11/2022 02:05:56 - INFO - __main__ - Total train batch size = 128
03/11/2022 02:05:56 - INFO - __main__ - Gradient Accumulation steps = 1
03/11/2022 02:05:56 - INFO - __main__ - Total optimization steps = 146600
03/11/2022 02:05:56 - INFO - __main__ - Logging steps = 3000
03/11/2022 02:05:56 - INFO - __main__ - Save steps = 3000
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[2022-03-11 02:05:59,192] [INFO] [stage_1_and_2.py:1656:step] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 65536, reducing to 65536
/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
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[2022-03-11 02:06:00,592] [INFO] [stage_1_and_2.py:1656:step] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 65536, reducing to 32768.0
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[2022-03-11 02:06:13,536] [INFO] [logging.py:69:log_dist] [Rank 0] step=10, skipped=2, lr=[5e-05], mom=[[0.9, 0.999]]
[2022-03-11 02:06:13,537] [INFO] [timer.py:189:stop] 0/10, SamplesPerSec=79.2758999705874, MemAllocated=0.26GB, MaxMemAllocated=9.02GB
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[2022-03-11 02:06:28,687] [INFO] [logging.py:69:log_dist] [Rank 0] step=20, skipped=2, lr=[5e-05], mom=[[0.9, 0.999]]
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[2022-03-11 02:06:43,833] [INFO] [logging.py:69:log_dist] [Rank 0] step=30, skipped=2, lr=[5e-05], mom=[[0.9, 0.999]]
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[2022-03-11 02:06:58,985] [INFO] [logging.py:69:log_dist] [Rank 0] step=40, skipped=2, lr=[5e-05], mom=[[0.9, 0.999]]
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[2022-03-11 02:07:14,130] [INFO] [logging.py:69:log_dist] [Rank 0] step=50, skipped=2, lr=[5e-05], mom=[[0.9, 0.999]]
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[2022-03-11 02:07:29,274] [INFO] [logging.py:69:log_dist] [Rank 0] step=60, skipped=2, lr=[5e-05], mom=[[0.9, 0.999]]
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[2022-03-11 02:07:44,417] [INFO] [logging.py:69:log_dist] [Rank 0] step=70, skipped=2, lr=[5e-05], mom=[[0.9, 0.999]]
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