DeepSpeed

DeepSpeed implements everything described in the ZeRO paper. Currently it provides full support for:

1. Optimizer state partitioning (ZeRO stage 1)
2. Gradient partitioning (ZeRO stage 2)
3. Parameter partitioning (ZeRO stage 3)
4. Custom mixed precision training handling
5. A range of fast CUDA-extension-based optimizers
6. ZeRO-Offload to CPU and Disk/NVMe

ZeRO-Offload has its own dedicated paper: ZeRO-Offload: Democratizing Billion-Scale Model Training. And NVMe-support is described in the paper ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning.

DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference.

DeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which won’t be possible on a single GPU.

🤗 Accelerate integrates DeepSpeed via 2 options:

1. Integration of the DeepSpeed features via deepspeed config file specification in accelerate config . You just supply your custom config file or use our template. Most of this document is focused on this feature. This supports all the core features of DeepSpeed and gives user a lot of flexibility. User may have to change few lines of code depending on the config.
2. Integration via deepspeed_plugin.This supports subset of the DeepSpeed features and uses default options for the rest of the configurations. User need not change any code and is good for those who are fine with most of the default settings of DeepSpeed.

What is integrated?

Training:

1. DeepSpeed ZeRO training supports the full ZeRO stages 1, 2 and 3 as well as CPU/Disk offload of optimizer states, gradients and parameters. Below is a short description of Data Parallelism using ZeRO - Zero Redundancy Optimizer along with diagram from this blog post

(Source: link)

a. Stage 1 : Shards optimizer states across data parallel workers/GPUs

b. Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs

c. Stage 3: Shards optimizer states + gradients + model parameters across data parallel workers/GPUs

d. Optimizer Offload: Offloads the gradients + optimizer states to CPU/Disk building on top of ZERO Stage 2

e. Param Offload: Offloads the model parameters to CPU/Disk building on top of ZERO Stage 3

Inference:

1. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but it doesn’t use an optimizer and a lr scheduler and only stage 3 is relevant. For more details see: deepspeed-zero-inference.

How it works?

Pre-Requisites: Install DeepSpeed version >=0.6.5. Please refer to the DeepSpeed Installation details for more information.

We will first look at easy to use integration via accelerate config. Followed by more flexible and feature rich deepspeed config file integration.

Accelerate DeepSpeed Plugin

accelerate config

and answer the questions asked. It will ask whether you want to use a config file for DeepSpeed to which you should answer no. Then answer the following questions to generate a basic DeepSpeed config. This will generate a config file that will be used automatically to properly set the default options when doing

accelerate launch my_script.py --args_to_my_script

For instance, here is how you would run the NLP example examples/nlp_example.py (from the root of the repo) with DeepSpeed Plugin:

ZeRO Stage-2 DeepSpeed Plugin Example

compute_environment: LOCAL_MACHINE
deepspeed_config:
 gradient_accumulation_steps: 1
 gradient_clipping: 1.0
 offload_optimizer_device: none
 offload_param_device: none
 zero3_init_flag: true
 zero_stage: 2
distributed_type: DEEPSPEED
fsdp_config: {}
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 2
use_cpu: false

accelerate launch examples/nlp_example.py --mixed_precision fp16

ZeRO Stage-3 with CPU Offload DeepSpeed Plugin Example

compute_environment: LOCAL_MACHINE
deepspeed_config:
  gradient_accumulation_steps: 1
  gradient_clipping: 1.0
  offload_optimizer_device: cpu
  offload_param_device: cpu
  zero3_init_flag: true
  zero3_save_16bit_model: true
  zero_stage: 3
distributed_type: DEEPSPEED
fsdp_config: {}
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 2
use_cpu: false

accelerate launch examples/nlp_example.py --mixed_precision fp16

Currently, Accelerate supports following config through the CLI:

`zero_stage`: [0] Disabled, [1] optimizer state partitioning, [2] optimizer+gradient state partitioning and [3] optimizer+gradient+parameter partitioning
`gradient_accumulation_steps`: Number of training steps to accumulate gradients before averaging and applying them.
`gradient_clipping`: Enable gradient clipping with value.
`offload_optimizer_device`: [none] Disable optimizer offloading, [cpu] offload optimizer to CPU, [nvme] offload optimizer to NVMe SSD. Only applicable with ZeRO >= Stage-2.
`offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3.
`zero3_init_flag`: Decides whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3.
`zero3_save_16bit_model`: Decides whether to save 16-bit model weights when using ZeRO Stage-3.
`mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training. 

To be able to tweak more options, you will need to use a DeepSpeed config file.

DeepSpeed Config File

accelerate config

and answer the questions asked. It will ask whether you want to use a config file for deepspeed to which you answer yes and provide the path to the deepspeed config file. This will generate a config file that will be used automatically to properly set the default options when doing

accelerate launch my_script.py --args_to_my_script

For instance, here is how you would run the NLP example examples/by_feature/deepspeed_with_config_support.py (from the root of the repo) with DeepSpeed Config File:

ZeRO Stage-2 DeepSpeed Config File Example

compute_environment: LOCAL_MACHINE
deepspeed_config:
 deepspeed_config_file: /home/ubuntu/accelerate/examples/configs/deepspeed_config_templates/zero_stage2_config.json
 zero3_init_flag: true
distributed_type: DEEPSPEED
fsdp_config: {}
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 2
use_cpu: false

with the contents of zero_stage2_config.json being:

{
    "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": "auto",
            "weight_decay": "auto",
            "torch_adam": true,
            "adam_w_mode": true
        }
    },
    "scheduler": {
        "type": "WarmupDecayLR",
        "params": {
            "warmup_min_lr": "auto",
            "warmup_max_lr": "auto",
            "warmup_num_steps": "auto",
            "total_num_steps": "auto"
        }
    },
    "zero_optimization": {
        "stage": 2,
        "allgather_partitions": true,
        "allgather_bucket_size": 2e8,
        "overlap_comm": true,
        "reduce_scatter": true,
        "reduce_bucket_size": "auto",
        "contiguous_gradients": true
    },
    "gradient_accumulation_steps": 1,
    "gradient_clipping": "auto",
    "steps_per_print": 2000,
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto",
    "wall_clock_breakdown": false
}

accelerate launch examples/by_feature/deepspeed_with_config_support.py \
--config_name "gpt2-large" \
--tokenizer_name "gpt2-large" \
--dataset_name "wikitext" \
--dataset_config_name "wikitext-2-raw-v1" \
--block_size 128 \
--output_dir "./clm/clm_deepspeed_stage2_accelerate" \
--learning_rate 5e-4 \
--per_device_train_batch_size 24 \
--per_device_eval_batch_size 24 \
--num_train_epochs 3 \
--with_tracking \
--report_to "wandb"\

ZeRO Stage-3 with CPU offload DeepSpeed Config File Example

compute_environment: LOCAL_MACHINE
deepspeed_config:
 deepspeed_config_file: /home/ubuntu/accelerate/examples/configs/deepspeed_config_templates/zero_stage3_offload_config.json
 zero3_init_flag: true
distributed_type: DEEPSPEED
fsdp_config: {}
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 2
use_cpu: false

with the contents of zero_stage3_offload_config.json being:

{
    "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": "auto",
            "weight_decay": "auto"
        }
    },
    "scheduler": {
        "type": "WarmupDecayLR",
        "params": {
            "warmup_min_lr": "auto",
            "warmup_max_lr": "auto",
            "warmup_num_steps": "auto",
            "total_num_steps": "auto"
        }
    },
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": true
        },
        "offload_param": {
            "device": "cpu",
            "pin_memory": true
        },
        "overlap_comm": true,
        "contiguous_gradients": true,
        "reduce_bucket_size": "auto",
        "stage3_prefetch_bucket_size": "auto",
        "stage3_param_persistence_threshold": "auto",
        "sub_group_size": 1e9,
        "stage3_max_live_parameters": 1e9,
        "stage3_max_reuse_distance": 1e9,
        "stage3_gather_16bit_weights_on_model_save": "auto"
    },
    "gradient_accumulation_steps": 1,
    "gradient_clipping": "auto",
    "steps_per_print": 2000,
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto",
    "wall_clock_breakdown": false
}

accelerate launch examples/by_feature/deepspeed_with_config_support.py \
--config_name "gpt2-large" \
--tokenizer_name "gpt2-large" \
--dataset_name "wikitext" \
--dataset_config_name "wikitext-2-raw-v1" \
--block_size 128 \
--output_dir "./clm/clm_deepspeed_stage3_offload_accelerate" \
--learning_rate 5e-4 \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 32 \
--num_train_epochs 3 \
--with_tracking \
--report_to "wandb"\

Important code changes when using DeepSpeed Config File

1. DeepSpeed Optimizers and Schedulers. For more information on these, see the DeepSpeed Optimizers and DeepSpeed Schedulers documentation. We will look at the changes needed in the code when using these.

   a. DS Optim + DS Scheduler: The case when both optimizer and scheduler keys present in the DeepSpeed config file. In this situation, those will be used and user has to use accelerate.utils.DummyOptim and accelerate.utils.DummyScheduler to replace the PyTorch/Custom optimizers and schedulers in their code. Below is the snippet from examples/by_feature/deepspeed_with_config_support.py showing this:

   Copied

    # Creates Dummy Optimizer if `optimizer` was spcified in the config file else creates Adam Optimizer
    optimizer_cls = (
        torch.optim.AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    optimizer = optimizer_cls(optimizer_grouped_parameters, lr=args.learning_rate)
   
    # Creates Dummy Scheduler if `scheduler` was spcified in the config file else creates `args.lr_scheduler_type` Scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        lr_scheduler = get_scheduler(
            name=args.lr_scheduler_type,
            optimizer=optimizer,
            num_warmup_steps=args.num_warmup_steps,
            num_training_steps=args.max_train_steps,
        )
    else:
        lr_scheduler = DummyScheduler(
            optimizer, total_num_steps=args.max_train_steps, warmup_num_steps=args.num_warmup_steps
        )

   b. Custom Optim + Custom Scheduler: The case when both optimizer and scheduler keys are absent in the DeepSpeed config file. In this situation, no code changes are needed from the user and this is the case when using integration via DeepSpeed Plugin. In the above example we can see that the code remains unchanged if the optimizer and scheduler keys are absent in the DeepSpeed config file.

   c. Custom Optim + DS Scheduler: The case when only scheduler key is present in the DeepSpeed config file. In this situation, user has to use accelerate.utils.DummyScheduler to replace the PyTorch/Custom scheduler in their code.

   d. DS Optim + Custom Scheduler: The case when only optimizer key is present in the DeepSpeed config file. This will result in an error because you can only use DS Scheduler when using DS Optim.

2. Notice the auto values in the above example DeepSpeed config files. These are automatically handled by prepare method based on model, dataloaders, dummy optimizer and dummy schedulers provided to prepare method. Only the auto fields specified in above examples are handled by prepare method and the rest have to be explicitly specified by the user.

Saving and loading

1. Saving and loading of models is unchanged for ZeRO Stage-1 and Stage-2.

2. under ZeRO Stage-3, state_dict contains just the placeholders since the model weights are partitioned across multiple GPUs. ZeRO Stage-3 has 2 options:

   a. Saving the entire 16bit model weights to directly load later on using model.load_state_dict(torch.load(pytorch_model.bin)). For this, either set zero_optimization.stage3_gather_16bit_weights_on_model_save to True in DeepSpeed Config file or set zero3_save_16bit_model to True in DeepSpeed Plugin. Note that this option requires consolidation of the weights on one GPU it can be slow and memory demanding, so only use this feature when needed. Below is the snippet from examples/by_feature/deepspeed_with_config_support.py showing this:

   Copied

   unwrapped_model = accelerator.unwrap_model(model)
   
   # New Code #
   # Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if
   # `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or
   # `zero3_save_16bit_model` is True in DeepSpeed Plugin.
   # For Zero Stages 1 and 2, models are saved as usual in the output directory.
   # The model name saved is `pytorch_model.bin`
   unwrapped_model.save_pretrained(
       args.output_dir,
       is_main_process=accelerator.is_main_process,
       save_function=accelerator.save,
       state_dict=accelerator.get_state_dict(model),
   )

   b. To get 32bit weights, first save the model using model.save_checkpoint(). Below is the snippet from examples/by_feature/deepspeed_with_config_support.py showing this:

   Copied

   success = model.save_checkpoint(PATH, ckpt_id, checkpoint_state_dict)
   status_msg = "checkpointing: PATH={}, ckpt_id={}".format(PATH, ckpt_id)
   if success:
       logging.info(f"Success {status_msg}")
   else:
       logging.warning(f"Failure {status_msg}")

   This will create ZeRO model and optimizer partitions along with zero_to_fp32.py script in checkpoint directory. You can use this script to do offline consolidation.
   It requires no configuration files or GPUs. Here is an example of its usage:

   Copied

   $ cd /path/to/checkpoint_dir
   $ ./zero_to_fp32.py . pytorch_model.bin
   Processing zero checkpoint at global_step1
   Detected checkpoint of type zero stage 3, world_size: 2
   Saving fp32 state dict to pytorch_model.bin (total_numel=60506624)

   To get 32bit model for saving/inference, you can perform:

   Copied

   from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
   
   unwrapped_model = accelerator.unwrap_model(model)
   fp32_model = load_state_dict_from_zero_checkpoint(unwrapped_model, checkpoint_dir)

   If you are only interested in the state_dict, you can do the following:

   Copied

   from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
   
   state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir)

   Note that all these functions require ~2x memory (general RAM) of the size of the final checkpoint.

ZeRO Inference

model, eval_dataloader = accelerator.prepare(model, eval_dataloader)

Few caveats to be aware of

1. Current integration doesn’t support Pipeline Parallelism of DeepSpeed.
2. Current integration doesn’t support mpu, limiting the tensor parallelism which is supported in Megatron-LM.
3. Current integration doesn’t support multiple models.

DeepSpeed Resources

The documentation for the internals related to deepspeed can be found here.

• Project’s github
• Usage docs
• API docs
• Blog posts

Papers:

• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
• ZeRO-Offload: Democratizing Billion-Scale Model Training
• ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning

Finally, please, remember that, 🤗 Accelerate only integrates DeepSpeed, therefore if you have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub.