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 a few lines of code depending on the config.
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:
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
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
Note: With respect to Disk Offload, the disk should be an NVME for decent speed but it technically works on any Disk
Inference:
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
On your machine(s) just run:
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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
Currently, Accelerate supports following config through the CLI:
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`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
On your machine(s) just run:
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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
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:
Important code changes when using DeepSpeed Config File
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 are present in the DeepSpeed config file. In this situation, those will be used and the 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:
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# 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, the 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.
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.
Things to note when using DeepSpeed Config File
Below is a sample script using deepspeed_config_file in different scenarios.
Code test.py:
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from accelerate import Accelerator
from accelerate.state import AcceleratorState
def main():
accelerator = Accelerator()
accelerator.print(f"{AcceleratorState()}")
if __name__ == "__main__":
main()
Scenario 1: Manually tampered accelerate config file having deepspeed_config_file along with other entries.
ValueError: When using `deepspeed_config_file`, the following accelerate config variables will be ignored:
['gradient_accumulation_steps', 'gradient_clipping', 'zero_stage', 'offload_optimizer_device', 'offload_param_device',
'zero3_save_16bit_model', 'mixed_precision'].
Please specify them appropriately in the DeepSpeed config file.
If you are using an accelerate config file, remove others config variables mentioned in the above specified list.
The easiest method is to create a new config following the questionnaire via `accelerate config`.
It will only ask for the necessary config variables when using `deepspeed_config_file`.
Scenario 2: Use the solution of the error to create new accelerate config and check that no ambiguity error is now thrown.
Run accelerate config:
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$ accelerate config
-------------------------------------------------------------------------------------------------------------------------------
In which compute environment are you running?
This machine
-------------------------------------------------------------------------------------------------------------------------------
Which type of machine are you using?
multi-GPU
How many different machines will you use (use more than 1 for multi-node training)? [1]:
Do you wish to optimize your script with torch dynamo?[yes/NO]:
Do you want to use DeepSpeed? [yes/NO]: yes
Do you want to specify a json file to a DeepSpeed config? [yes/NO]: yes
Please enter the path to the json DeepSpeed config file: ds_config.json
Do you want to enable `deepspeed.zero.Init` when using ZeRO Stage-3 for constructing massive models? [yes/NO]: yes
How many GPU(s) should be used for distributed training? [1]:4
accelerate configuration saved at ds_config_sample.yaml
Scenario 3: Setting the accelerate launch command arguments related to DeepSpeed as "auto" in the DeepSpeed` configuration file and check that things work as expected.
New ds_config.json with "auto" for the accelerate launch DeepSpeed command arguments:
Remaining "auto" values are handled in accelerator.prepare() call as explained in point 2 of Important code changes when using DeepSpeed Config File.
Only when gradient_accumulation_steps is auto, the value passed while creating Accelerator object via Accelerator(gradient_accumulation_steps=k) will be used. When using DeepSpeed Plugin, the value from it will be used and it will overwrite the value passed while creating Accelerator object.
Saving and loading
Saving and loading of models is unchanged for ZeRO Stage-1 and Stage-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:
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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:
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:
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$ 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:
If you are only interested in the state_dict, you can do the following:
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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
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. With accelerate integration, you just need to prepare the model and dataloader as shown below:
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.
