# DeepSpeed Integration¶

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 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.

🤗 Transformers integrates DeepSpeed via 2 options:

1. Integration of the core DeepSpeed features via Trainer. This is everything done for you type of integration - just supply your custom config file or use our template and you have nothing else to do. Most of this document is focused on this feature.

2. If you don’t use Trainer and want to use your own Trainer where you integrated DeepSpeed yourself, core functionality functions like from_pretrained and from_config include integration of essential parts of DeepSpeed like zero.Init for ZeRO stage 3 and higher. To tap into this feature read the docs on Non-Trainer Deepspeed Integration.

## Trainer Deepspeed Integration¶

### Installation¶

Install the library via pypi:

pip install deepspeed

or via transformersextras:

pip install transformers[deepspeed]

or find more details on the DeepSpeed’s GitHub page and advanced install.

If you’re still struggling with the build, first make sure to read CUDA Extension Installation Notes.

If you don’t prebuild the extensions and rely on them to be built at run time and you tried all of the above solutions to no avail, the next thing to try is to pre-build the modules before installing them.

To make a local build for DeepSpeed:

git clone https://github.com/microsoft/DeepSpeed/
cd DeepSpeed
rm -rf build
TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install . \
--global-option="build_ext" --global-option="-j8" --no-cache -v \
--disable-pip-version-check 2>&1 | tee build.log

If you intend to use NVMe offload you will need to also include DS_BUILD_AIO=1 in the instructions above (and also install libaio-dev system-wide).

Edit TORCH_CUDA_ARCH_LIST to insert the code for the architectures of the GPU cards you intend to use. Assuming all your cards are the same you can get the arch via:

CUDA_VISIBLE_DEVICES=0 python -c "import torch; print(torch.cuda.get_device_capability())"

So if you get 8, 6, then use TORCH_CUDA_ARCH_LIST="8.6". If you have multiple different cards, you can list all of them like so TORCH_CUDA_ARCH_LIST="6.1;8.6"

If you need to use the same setup on multiple machines, make a binary wheel:

git clone https://github.com/microsoft/DeepSpeed/
cd DeepSpeed
rm -rf build
python setup.py build_ext -j8 bdist_wheel

it will generate something like dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl which now you can install as pip install deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl locally or on any other machine.

Again, remember to ensure to adjust TORCH_CUDA_ARCH_LIST to the target architectures.

You can find the complete list of NVIDIA GPUs and their corresponding Compute Capabilities (same as arch in this context) here.

You can check the archs pytorch was built with using:

python -c "import torch; print(torch.cuda.get_arch_list())"

Here is how to find out the arch for one of the installed GPU. For example, for GPU 0:

CUDA_VISIBLE_DEVICES=0 python -c "import torch; \
print(torch.cuda.get_device_properties(torch.device('cuda')))"

If the output is:

_CudaDeviceProperties(name='GeForce RTX 3090', major=8, minor=6, total_memory=24268MB, multi_processor_count=82)

then you know that this card’s arch is 8.6.

You can also leave TORCH_CUDA_ARCH_LIST out completely and then the build program will automatically query the architecture of the GPUs the build is made on. This may or may not match the GPUs on the target machines, that’s why it’s best to specify the desired archs explicitly.

If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of Deepspeed,

### Deployment with multiple GPUs¶

To deploy this feature with multiple GPUs adjust the Trainer command line arguments as following:

1. replace python -m torch.distributed.launch with deepspeed.

2. add a new argument --deepspeed ds_config.json, where ds_config.json is the DeepSpeed configuration file as documented here. The file naming is up to you.

Therefore, if your original command line looked as following:

python -m torch.distributed.launch --nproc_per_node=2 your_program.py <normal cl args>

Now it should be:

deepspeed --num_gpus=2 your_program.py <normal cl args> --deepspeed ds_config.json

Unlike, torch.distributed.launch where you have to specify how many GPUs to use with --nproc_per_node, with the deepspeed launcher you don’t have to use the corresponding --num_gpus if you want all of your GPUs used. The full details on how to configure various nodes and GPUs can be found here.

In fact, you can continue using -m torch.distributed.launch with DeepSpeed as long as you don’t need to use deepspeed launcher-specific arguments. Typically if you don’t need a multi-node setup you’re not required to use the deepspeed launcher. But since in the DeepSpeed documentation it’ll be used everywhere, for consistency we will use it here as well.

Here is an example of running run_translation.py under DeepSpeed deploying all available GPUs:

deepspeed examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path t5-small --per_device_train_batch_size 1   \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro

Note that in the DeepSpeed documentation you are likely to see --deepspeed --deepspeed_config ds_config.json - i.e. two DeepSpeed-related arguments, but for the sake of simplicity, and since there are already so many arguments to deal with, we combined the two into a single argument.

For some practical usage examples, please, see this post.

### Deployment with one GPU¶

To deploy DeepSpeed with one GPU adjust the Trainer command line arguments as following:

deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero2.json \
--model_name_or_path t5-small --per_device_train_batch_size 1   \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro

This is almost the same as with multiple-GPUs, but here we tell DeepSpeed explicitly to use just one GPU via --num_gpus=1. By default, DeepSpeed deploys all GPUs it can see on the given node. If you have only 1 GPU to start with, then you don’t need this argument. The following documentation discusses the launcher options.

Why would you want to use DeepSpeed with just one GPU?

1. It has a ZeRO-offload feature which can delegate some computations and memory to the host’s CPU and RAM, and thus leave more GPU resources for model’s needs - e.g. larger batch size, or enabling a fitting of a very big model which normally won’t fit.

2. It provides a smart GPU memory management system, that minimizes memory fragmentation, which again allows you to fit bigger models and data batches.

While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU with DeepSpeed is to have at least the following configuration in the configuration file:

{
"zero_optimization": {
"stage": 2,
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"overlap_comm": true,
}
}

which enables optimizer offload and some other important features. You may experiment with the buffer sizes, you will find more details in the discussion below.

For a practical usage example of this type of deployment, please, see this post.

You may also try the ZeRO-3 with CPU and NVMe offload as explained further in this document.

<!— TODO: Benchmark whether we can get better performance out of ZeRO-3 vs. ZeRO-2 on a single GPU, and then recommend ZeRO-3 config as starting one. –>

Notes:

• if you need to run on a specific GPU, which is different from GPU 0, you can’t use CUDA_VISIBLE_DEVICES to limit the visible scope of available GPUs. Instead, you have to use the following syntax:

deepspeed --include localhost:1 examples/pytorch/translation/run_translation.py ...

In this example, we tell DeepSpeed to use GPU 1 (second gpu).

### Deployment in Notebooks¶

The problem with running notebook cells as a script is that there is no normal deepspeed launcher to rely on, so under certain setups we have to emulate it.

If you’re using only 1 GPU, here is how you’d have to adjust your training code in the notebook to use DeepSpeed.

# DeepSpeed requires a distributed environment even when only one process is used.
# This emulates a launcher in the notebook
import os
os.environ['RANK'] = "0"
os.environ['LOCAL_RANK'] = "0"
os.environ['WORLD_SIZE'] = "1"

# Now proceed as normal, plus pass the deepspeed config file
training_args = TrainingArguments(..., deepspeed="ds_config_zero3.json")
trainer = Trainer(...)
trainer.train()

Note: ... stands for the normal arguments that you’d pass to the functions.

If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. That is, you have to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented at the beginning of this section.

If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated cell with:

%%bash
cat <<'EOT' > ds_config_zero3.json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},

"optimizer": {
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},

"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},

"zero_optimization": {
"stage": 3,
"device": "cpu",
"pin_memory": true
},
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_fp16_weights_on_model_save": true
},

"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
EOT

If the training script is in a normal file and not in the notebook cells, you can launch deepspeed normally via shell from a cell. For example, to use run_translation.py you would launch it with:

!git clone https://github.com/huggingface/transformers
!cd transformers; deepspeed examples/pytorch/translation/run_translation.py ...

or with %%bash magic, where you can write a multi-line code for the shell program to run:

%%bash

git clone https://github.com/huggingface/transformers
cd transformers
deepspeed examples/pytorch/translation/run_translation.py ...

In such case you don’t need any of the code presented at the beginning of this section.

Note: While %%bash magic is neat, but currently it buffers the output so you won’t see the logs until the process completes.

### Configuration¶

For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer to the following documentation.

You can find dozens of DeepSpeed configuration examples that address various practical needs in the DeepSpeedExamples repo:

git clone https://github.com/microsoft/DeepSpeedExamples
cd DeepSpeedExamples
find . -name '*json'

Continuing the code from above, let’s say you’re looking to configure the Lamb optimizer. So you can search through the example .json files with:

grep -i Lamb $(find . -name '*json') Some more examples are to be found in the main repo as well. When using DeepSpeed you always need to supply a DeepSpeed configuration file, yet some configuration parameters have to be configured via the command line. You will find the nuances in the rest of this guide. To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features, including optimizer states cpu offload, uses AdamW optimizer and WarmupLR scheduler and will enable mixed precision training if --fp16 is passed: { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", } When you execute the program, DeepSpeed will log the configuration it received from the Trainer to the console, so you can see exactly what was the final configuration passed to it. ### Passing Configuration¶ As discussed in this document normally the DeepSpeed configuration is passed as a path to a json file, but if you’re not using the command line interface to configure the training, and instead instantiate the Trainer via TrainingArguments then for the deepspeed argument you can pass a nested dict. This allows you to create the configuration on the fly and doesn’t require you to write it to the file system before passing it to TrainingArguments. To summarize you can do: TrainingArguments(..., deespeed="/path/to/ds_config.json") or: ds_config_dict=dict(scheduler=scheduler_params, optimizer=optimizer_params) TrainingArguments(..., deespeed=ds_config_dict) ### Shared Configuration¶ Warning This section is a must-read Some configuration values are required by both the Trainer and DeepSpeed to function correctly, therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those via the Trainer command line arguments. Additionally, some configuration values are derived automatically based on the model’s configuration, so instead of remembering to manually adjust multiple values, it’s the best to let the Trainer do the majority of configuration for you. Therefore, in the rest of this guide you will find a special configuration value: auto, which when set will be automatically replaced with the correct or most efficient value. Please feel free to choose to ignore this recommendation and set the values explicitly, in which case be very careful that your the Trainer arguments and DeepSpeed configurations agree. For example, are you using the same learning rate, or batch size, or gradient accumulation settings? if these mismatch the training may fail in very difficult to detect ways. You have been warned. There are multiple other values that are specific to DeepSpeed-only and those you will have to set manually to suit your needs. In your own programs, you can also use the following approach if you’d like to modify the DeepSpeed config as a master and configure TrainingArguments based on that. The steps are: 1. Create or load the DeepSpeed configuration to be used as a master configuration 2. Create the TrainingArguments object based on these values Do note that some values, such as scheduler.params.total_num_steps are calculated by Trainer during train, but you can of course do the math yourself. ### ZeRO¶ Zero Redundancy Optimizer (ZeRO) is the workhorse of DeepSpeed. It support 3 different levels (stages) of optimization. The first one is not quite interesting for scalability purposes, therefore this document focuses on stages 2 and 3. Stage 3 is further improved by the latest addition of ZeRO-Infinity. You will find more indepth information in the DeepSpeed documentation. The zero_optimization section of the configuration file is the most important part (docs), since that is where you define which ZeRO stages you want to enable and how to configure them. You will find the explanation for each parameter in the DeepSpeed docs. This section has to be configured exclusively via DeepSpeed configuration - the Trainer provides no equivalent command line arguments. Note: currently DeepSpeed doesn’t validate parameter names, so if you misspell any, it’ll use the default setting for the parameter that got misspelled. You can watch the DeepSpeed engine start up log messages to see what values it is going to use. #### ZeRO-2 Config¶ The following is an example configuration for ZeRO stage 2: { "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 5e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 5e8, "contiguous_gradients": true } } Performance tuning: • enabling offload_optimizer should reduce GPU RAM usage (it requires "stage": 2) • "overlap_comm": true trades off increased GPU RAM usage to lower all-reduce latency. overlap_comm uses 4.5x the allgather_bucket_size and reduce_bucket_size values. So if they are set to 5e8, this requires a 9GB footprint (5e8 x 2Bytes x 2 x 4.5). Therefore, if you have a GPU with 8GB or less RAM, to avoid getting OOM-errors you will need to reduce those parameters to about 2e8, which would require 3.6GB. You will want to do the same on larger capacity GPU as well, if you’re starting to hit OOM. • when reducing these buffers you’re trading communication speed to avail more GPU RAM. The smaller the buffer size, the slower the communication, and the more GPU RAM will be available to other tasks. So if a bigger batch size is important, getting a slightly slower training time could be a good trade. #### ZeRO-3 Config¶ The following is an example configuration for ZeRO stage 3: { "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_fp16_weights_on_model_save": true } } If you are getting OOMs, because your model or activations don’t fit into the GPU memory and you have unutilized CPU memory offloading the optimizer states and parameters to CPU memory with "device": "cpu" may solve this limitation. If you don’t want to offload to CPU memory, use none instead of cpu for the device entry. Offloading to NVMe is discussed further down. Pinned memory is enabled with pin_memory set to true. This feature can improve the throughput at the cost of making less memory available to other processes. Pinned memory is set aside to the specific process that requested it and its typically accessed much faster than normal CPU memory. Performance tuning: • stage3_max_live_parameters: 1e9 • stage3_max_reuse_distance: 1e9 If hitting OOM reduce stage3_max_live_parameters and stage3_max_reuse_distance. They should have minimal impact on performance unless you are doing activation checkpointing. 1e9 would consume ~2GB. The memory is shared by stage3_max_live_parameters and stage3_max_reuse_distance, so its not additive, its just 2GB total. stage3_max_live_parameters is the upper limit on how many full parameters you want to keep on the GPU at any given time. “reuse distance” is a metric we are using to figure out when will a parameter be used again in the future, and we use the stage3_max_reuse_distance to decide whether to throw away the parameter or to keep it. If a parameter is going to be used again in near future (less than stage3_max_reuse_distance) then we keep it to reduce communication overhead. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and backward passes a a single layer granularity and want to keep the parameter in the forward recompute till the backward The following configuration values depend on the model’s hidden size: • reduce_bucket_size: hidden_size*hidden_size • stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size • stage3_param_persistence_threshold: 10 * hidden_size therefore set these values to auto and the Trainer will automatically assign the recommended values. But, of course, feel free to set these explicitly as well. stage3_gather_fp16_weights_on_model_save enables model fp16 weights consolidation when model gets saved. With large models and multiple GPUs this is an expensive operation both in terms of memory and speed. It’s currently required if you plan to resume the training. Watch out for future updates that will remove this limitation and make things more flexible. If you’re migrating from ZeRO-2 configuration note that allgather_partitions, allgather_bucket_size and reduce_scatter configuration parameters are not used in ZeRO-3. If you keep these in the config file they will just be ignored. • sub_group_size: 1e9 sub_group_size controls the granularity in which parameters are updated during optimizer steps. Parameters are grouped into buckets of sub_group_size and each buckets is updated one at a time. When used with NVMe offload in ZeRO-Infinity, sub_group_size therefore controls the granularity in which model states are moved in and out of CPU memory from NVMe during the optimizer step. This prevents running out of CPU memory for extremely large models. You can leave sub_group_size to its default value of 1e9 when not using NVMe offload. You may want to change its default value in the following cases: 1. Running into OOM during optimizer step: Reduce sub_group_size to reduce memory utilization of temporary buffers 2. Optimizer Step is taking a long time: Increase sub_group_size to improve bandwidth utilization as a result of the increased data buffers. ### NVMe Support¶ ZeRO-Infinity allows for training incredibly large models by extending GPU and CPU memory with NVMe memory. Thanks to smart partitioning and tiling algorithms each GPU needs to send and receive very small amounts of data during offloading so modern NVMe proved to be fit to allow for an even larger total memory pool available to your training process. ZeRO-Infinity requires ZeRO-3 enabled. The following configuration example enables NVMe to offload both optimizer states and the params: { "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "nvme", "nvme_path": "/local_nvme", "pin_memory": true, "buffer_count": 4, "fast_init": false }, "offload_param": { "device": "nvme", "nvme_path": "/local_nvme", "pin_memory": true, "buffer_count": 5, "buffer_size": 1e8, "max_in_cpu": 1e9 } "aio": { "block_size": 262144, "queue_depth": 32, "thread_count": 1, "single_submit": false, "overlap_events": true } "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_fp16_weights_on_model_save": true }, } You can choose to offload both optimizer states and params to NVMe, or just one of them or none. For example, if you have copious amounts of CPU memory available, by all means offload to CPU memory only as it’d be faster (hint: “device”: “cpu”). Here is the full documentation for offloading optimizer states and parameters. Make sure that your nvme_path is actually an NVMe, since it will work with the normal hard drive or SSD, but it’ll be much much slower. The fast scalable training was designed with modern NVMe transfer speeds in mind (as of this writing one can have ~3.5GB/s read, ~3GB/s write peak speeds). In order to figure out the optimal aio configuration block you must run a benchmark on your target setup, as explained here. #### ZeRO-2 vs ZeRO-3 Performance¶ ZeRO-3 is likely to be slower than ZeRO-2 if everything else is configured the same because the former has to gather model weights in addition to what ZeRO-2 does. If ZeRO-2 meets your needs and you don’t need to scale beyond a few GPUs then you may choose to stick to it. It’s important to understand that ZeRO-3 enables a much higher scalability capacity at a cost of speed. It’s possible to adjust ZeRO-3 configuration to make it perform closer to ZeRO-2: • set stage3_param_persistence_threshold to a very large number - larger than the largest parameter, e.g., 6 * hidden_size * hidden_size. This will keep the parameters on the GPUs. • turn off offload_params since ZeRO-2 doesn’t have that option. The performance will likely improve significantly with just offload_params turned off, even if you don’t change stage3_param_persistence_threshold. Of course, these changes will impact the size of the model you can train. So these help you to trade scalability for speed depending on your needs. #### ZeRO-2 Example¶ Here is a full ZeRO-2 auto-configuration file ds_config_zero2.json: { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } Here is a full ZeRO-2 all-enabled manually set configuration file. It is here mainly for you to see what the typical values look like, but we highly recommend using the one with multiple auto settings in it. { "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": 3e-5, "betas": [0.8, 0.999], "eps": 1e-8, "weight_decay": 3e-7 } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 3e-5, "warmup_num_steps": 500 } }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "allgather_partitions": true, "allgather_bucket_size": 2e8, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 2e8, "contiguous_gradients": true }, "steps_per_print": 2000, "wall_clock_breakdown": false } #### ZeRO-3 Example¶ Here is a full ZeRO-3 auto-configuration file ds_config_zero3.json: { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_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, "sub_group_size": 1e9, "reduce_bucket_size": "auto", "stage3_prefetch_bucket_size": "auto", "stage3_param_persistence_threshold": "auto", "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_fp16_weights_on_model_save": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } Here is a full ZeRO-3 all-enabled manually set configuration file. It is here mainly for you to see what the typical values look like, but we highly recommend using the one with multiple auto settings in it. { "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": 3e-5, "betas": [0.8, 0.999], "eps": 1e-8, "weight_decay": 3e-7 } }, "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 3e-5, "warmup_num_steps": 500 } }, "zero_optimization": { "stage": 3, "offload_optimizer": { "device": "cpu", "pin_memory": true }, "offload_param": { "device": "cpu", "pin_memory": true }, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": 1e6, "stage3_prefetch_bucket_size": 0.94e6, "stage3_param_persistence_threshold": 1e4, "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather_fp16_weights_on_model_save": true }, "steps_per_print": 2000, "wall_clock_breakdown": false } ### Optimizer and Scheduler¶ As long as you don’t enable offload_optimizer you can mix and match DeepSpeed and HuggingFace schedulers and optimizers, with the exception of using the combination of HuggingFace scheduler and DeepSpeed optimizer:  Combos HF Scheduler DS Scheduler HF Optimizer Yes Yes DS Optimizer No Yes It is possible to use a non-DeepSpeed optimizer when offload_optimizer is enabled, as long as it has both CPU and GPU implementation (except LAMB). #### Optimizer¶ DeepSpeed’s main optimizers are Adam, AdamW, OneBitAdam, and Lamb. These have been thoroughly tested with ZeRO and are thus recommended to be used. It, however, can import other optimizers from torch. The full documentation is here. If you don’t configure the optimizer entry in the configuration file, the Trainer will automatically set it to AdamW and will use the supplied values or the defaults for the following command line arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay. Here is an example of the auto-configured optimizer entry for AdamW: { "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } } } Note that the command line arguments will set the values in the configuration file. This is so that there is one definitive source of the values and to avoid hard to find errors when for example, the learning rate is set to different values in different places. Command line rules. The values that get overridden are: • lr with the value of --learning_rate • betas with the value of --adam_beta1 --adam_beta2 • eps with the value of --adam_epsilon • weight_decay with the value of --weight_decay Therefore please remember to tune the shared hyperparameters on the command line. You can also set the values explicitly: { "optimizer": { "type": "AdamW", "params": { "lr": 0.001, "betas": [0.8, 0.999], "eps": 1e-8, "weight_decay": 3e-7 } } } But then you’re on your own synchronizing the Trainer command line arguments and the DeepSpeed configuration. If you want to use another optimizer which is not listed above, you will have to add to the top level configuration. { "zero_allow_untested_optimizer": true } Similarly to AdamW, you can configure other officially supported optimizers. Just remember that may have different config values. e.g. for Adam you will want weight_decay around 0.01. #### Scheduler¶ DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate schedulers. The full documentation is here. Here is where the schedulers overlap between 🤗 Transformers and DeepSpeed: • WarmupLR via --lr_scheduler_type constant_with_warmup • WarmupDecayLR via --lr_scheduler_type linear. This is also the default value for --lr_scheduler_type, therefore, if you don’t configure the scheduler this is scheduler that will get configured by default. If you don’t configure the scheduler entry in the configuration file, the Trainer will use the values of --lr_scheduler_type, --learning_rate and --warmup_steps or --warmup_ratio to configure a 🤗 Transformers version of it. Here is an example of the auto-configured scheduler entry for WarmupLR: { "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } } } Since “auto” is used the Trainer arguments will set the correct values in the configuration file. This is so that there is one definitive source of the values and to avoid hard to find errors when, for example, the learning rate is set to different values in different places. Command line rules. The values that get set are: • warmup_min_lr with the value of 0. • warmup_max_lr with the value of --learning_rate. • warmup_num_steps with the value of --warmup_steps if provided. Otherwise will use --warmup_ratio multiplied by the number of training steps and rounded up. • total_num_steps with either the value of --max_steps or if it is not provided, derived automatically at run time based on the environment and the size of the dataset and other command line arguments (needed for WarmupDecayLR). You can, of course, take over any or all of the configuration values and set those yourself: { "scheduler": { "type": "WarmupLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 0.001, "warmup_num_steps": 1000 } } } But then you’re on your own synchronizing the Trainer command line arguments and the DeepSpeed configuration. For example, for WarmupDecayLR, you can use the following entry: { "scheduler": { "type": "WarmupDecayLR", "params": { "last_batch_iteration": -1, "total_num_steps": "auto", "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto" } } } and total_num_steps, `warmup_max_lr, warmup_num_steps and total_num_steps will be set at loading time. ### fp32 Precision¶ Deepspeed supports the full fp32 and the fp16 mixed precision. Because of the much reduced memory needs and faster speed one gets with the fp16 mixed precision, the only time you will want to not use it is when the model you’re using doesn’t behave well under this training mode. Typically this happens when the model wasn’t pretrained in the fp16 mixed precision (e.g. often this happens with bf16-pretrained models). Such models may overflow or underflow leading to NaN loss. If this is your case then you will want to use the full fp32 mode, by explicitly disabling the otherwise default fp16 mixed precision mode with: { "fp16": { "enabled": "false", } } If you’re using the Ampere-architecture based GPU, pytorch version 1.7 and higher will automatically switch to using the much more efficient tf32 format for some operations, but the results will still be in fp32. For details and benchmarks, please, see TensorFloat-32(TF32) on Ampere devices. The document includes instructions on how to disable this automatic conversion if for some reason you prefer not to use it. ### Automatic Mixed Precision¶ You can use automatic mixed precision with either a pytorch-like AMP way or the apex-like way: To configure pytorch AMP-like mode set: { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 } } and the Trainer will automatically enable or disable it based on the value of args.fp16_backend. The rest of config values are up to you. This mode gets enabled when --fp16 --fp16_backend amp command line args are passed. You can also enable/disable this mode explicitly: { "fp16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 } } But then you’re on your own synchronizing the Trainer command line arguments and the DeepSpeed configuration. Here is the documentation. To configure apex AMP-like mode set: "amp": { "enabled": "auto", "opt_level": "auto" } and the Trainer will automatically configure it based on the values of args.fp16_backend and args.fp16_opt_level. This mode gets enabled when --fp16 --fp16_backend apex --fp16_opt_level 01 command line args are passed. You can also configure this mode explicitly: { "amp": { "enabled": true, "opt_level": "O1" } } But then you’re on your own synchronizing the Trainer command line arguments and the DeepSpeed configuration. Here is the documentation. ### Batch Size¶ To configure batch size, use: { "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto" } and the Trainer will automatically set train_micro_batch_size_per_gpu to the value of args.per_device_train_batch_size and train_batch_size to args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps. You can also set the values explicitly: { "train_batch_size": 12, "train_micro_batch_size_per_gpu": 4 } But then you’re on your own synchronizing the Trainer command line arguments and the DeepSpeed configuration. ### Gradient Accumulation¶ To configure gradient accumulation set: { "gradient_accumulation_steps": "auto" } and the Trainer will automatically set it to the value of args.gradient_accumulation_steps. You can also set the value explicitly: { "gradient_accumulation_steps": 3 } But then you’re on your own synchronizing the Trainer command line arguments and the DeepSpeed configuration. ### Gradient Clipping¶ To configure gradient gradient clipping set: { "gradient_clipping": "auto" } and the Trainer will automatically set it to the value of args.max_grad_norm. You can also set the value explicitly: { "gradient_clipping": 1.0 } But then you’re on your own synchronizing the Trainer command line arguments and the DeepSpeed configuration. ### Getting The Model Weights Out¶ As long as you continue training and resuming using DeepSpeed you don’t need to worry about anything. DeepSpeed stores fp32 master weights in its custom checkpoint optimizer files, which are global_step*/*optim_states.pt (this is glob pattern), and are saved under the normal checkpoint. FP16 Weights: When a model is saved under ZeRO-2, you end up having the normal pytorch_model.bin file with the model weights, but they are only the fp16 version of the weights. Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs, therefore "stage3_gather_fp16_weights_on_model_save": true is required to get the Trainer to save the fp16 version of the weights. If this setting is False pytorch_model.bin won’t be created. This is because by default DeepSpeed’s state_dict contains a placeholder and not the real weights. If we were to save this state_dict it won’t be possible to load it back. { "zero_optimization": { "stage3_gather_fp16_weights_on_model_save": true } } FP32 Weights: While the fp16 weights are fine for resuming training, if you finished finetuning your model and want to upload it to the models hub or pass it to someone else you most likely will want to get the fp32 weights. This ideally shouldn’t be done during training since this is a process that requires a lot of memory, and therefore best to be performed offline after the training is complete. But if desired and you have plenty of free CPU memory it can be done in the same training script. The following sections will discuss both approaches. Live FP32 Weights Recovery: This approach may not work if you model is large and you have little free CPU memory left, at the end of the training. If you have saved at least one checkpoint, and you want to use the latest one, you can do the following: from transformers.trainer_utils import get_last_checkpoint from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint checkpoint_dir = get_last_checkpoint(trainer.args.output_dir) fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) If you’re using the --load_best_model_at_end class:~transformers.TrainingArguments argument (to track the best checkpoint), then you can finish the training by first saving the final model explicitly and then do the same as above: from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint checkpoint_dir = os.path.join(trainer.args.output_dir, "checkpoint-final") trainer.deepspeed.save_checkpoint(checkpoint_dir) fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) Note Note, that once load_state_dict_from_zero_checkpoint was run, the model will no longer be useable in the DeepSpeed context of the same application. i.e. you will need to re-initialize the deepspeed engine, since model.load_state_dict(state_dict) will remove all the DeepSpeed magic from it. So do this only at the very end of the training. Of course, you don’t have to use class:~transformers.Trainer and you can adjust the examples above to your own trainer. If for some reason you want more refinement, you can also extract the fp32 state_dict of the weights and apply these yourself as is shown in the following example: 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) # already on cpu model = model.cpu() model.load_state_dict(state_dict) Offline FP32 Weights Recovery: DeepSpeed creates a special conversion script zero_to_fp32.py which it places in the top-level of the checkpoint folder. Using this script you can extract the weights at any point. The script is standalone and you no longer need to have the configuration file or a Trainer to do the extraction. Let’s say your checkpoint folder looks like this:$ ls -l output_dir/checkpoint-1/
-rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json
drwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/
-rw-rw-r-- 1 stas stas   12 Mar 27 13:16 latest
-rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt
-rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin
-rw-rw-r-- 1 stas stas  623 Mar 27 20:42 scheduler.pt
-rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json
-rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model
-rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json
-rw-rw-r-- 1 stas stas  339 Mar 27 20:42 trainer_state.json
-rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin
-rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py*

In this example there is just one DeepSpeed checkpoint sub-folder global_step1. Therefore to reconstruct the fp32 weights just run:

python zero_to_fp32.py . pytorch_model.bin

This is it. pytorch_model.bin will now contain the full fp32 model weights consolidated from multiple GPUs.

The script will automatically be able to handle either a ZeRO-2 or ZeRO-3 checkpoint.

python zero_to_fp32.py -h will give you usage details.

The script will auto-discover the deepspeed sub-folder using the contents of the file latest, which in the current example will contain global_step1.

Note: currently the script requires 2x general RAM of the final fp32 model weights.

### ZeRO-3 and Infinity Nuances¶

ZeRO-3 is quite different from ZeRO-2 because of its param sharding feature.

ZeRO-Infinity further extends ZeRO-3 to support NVMe memory and multiple other speed and scalability improvements.

While all the efforts were made for things to just work without needing any special changes to your models, in certain circumstances you may find the following information to be needed.

#### Constructing Massive Models¶

DeepSpeed/ZeRO-3 can handle models with Trillions of parameters which may not fit onto the existing RAM. In such cases, but also if you want the initialization to happen much faster, initialize the model using deepspeed.zero.Init() context manager (which is also a function decorator), like so:

from transformers import T5ForConditionalGeneration, T5Config
import deepspeed
with deepspeed.zero.Init():
config = T5Config.from_pretrained("t5-small")
model = T5ForConditionalGeneration(config)

As you can see this gives you a randomly initialized model.

If you want to use a pretrained model, model_class.from_pretrained will activate this feature as long as is_deepspeed_zero3_enabled() returns True, which currently is setup by the class:~transformers.TrainingArguments object if the passed DeepSpeed configuration file contains ZeRO-3 config section. Thus you must create the TrainingArguments object before calling from_pretrained. Here is an example of a possible sequence:

from transformers import AutoModel, Trainer, TrainingArguments
training_args = TrainingArguments(..., deepspeed=ds_config)
model = AutoModel.from_pretrained("t5-small")
trainer = Trainer(model=model, args=training_args, ...)

If you’re using the official example scripts and your command line arguments include --deepspeed ds_config.json with ZeRO-3 config enabled, then everything is already done for you, since this is how example scripts are written.

Note: If the fp16 weights of the model can’t fit onto the memory of a single GPU this feature must be used.

For full details on this method and other related features please refer to Constructing Massive Models.

Also when loading fp16-pretrained models, you will want to tell from_pretrained to use torch_dtype=torch.float16. For details, please, see Model Instantiation dtype.

#### Gathering Parameters¶

Under ZeRO-3 on multiple GPUs no single GPU has all the parameters unless it’s the parameters for the currently executing layer. So if you need to access all parameters from all layers at once there is a specific method to do it. Most likely you won’t need it, but if you do please refer to Gathering Parameters

We do however use it internally in several places, one such example is when loading pretrained model weights in from_pretrained. We load one layer at a time and immediately partition it to all participating GPUs, as for very large models it won’t be possible to load it on one GPU and then spread it out to multiple GPUs, due to memory limitations.

Also under ZeRO-3, if you write your own code and run into a model parameter weight that looks like:

stress on tensor([1.]), or if you get an error where it says the parameter is of size 1, instead of some much larger multi-dimensional shape, this means that the parameter is partitioned and what you see is a ZeRO-3 placeholder.

### Filing Issues¶

Here is how to file an issue so that we could quickly get to the bottom of the issue and help you to unblock your work.

1. the full Deepspeed config file in the report

2. either the command line arguments if you were using the Trainer or TrainingArguments arguments if you were scripting the Trainer setup yourself. Please do not dump the TrainingArguments as it has dozens of entries that are irrelevant.

3. Output of:

python -c 'import torch; print(f"torch: {torch.__version__}")'
python -c 'import transformers; print(f"transformers: {transformers.__version__}")'
python -c 'import deepspeed; print(f"deepspeed: {deepspeed.__version__}")'
1. If possible include a link to a Google Colab notebook that we can reproduce the problem with. You can use this notebook as a starting point.

2. Unless it’s impossible please always use a standard dataset that we can use and not something custom.

3. If possible try to use one of the existing examples to reproduce the problem with.

Things to consider:

• Deepspeed is often not the cause of the problem.

Some of the filed issues proved to be Deepspeed-unrelated. That is once Deepspeed was removed from the setup, the problem was still there.

Therefore, if it’s not absolutely obvious it’s a DeepSpeed-related problem, as in you can see that there is an exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it. And only if the problem persists then do mentioned Deepspeed and supply all the required details.

• If it’s clear to you that the issue is in the DeepSpeed core and not the integration part, please file the Issue directly with Deepspeed. If you aren’t sure, please do not worry, either Issue tracker will do, we will figure it out once you posted it and redirect you to another Issue tracker if need be.

### Troubleshooting¶

• deepspeed process gets killed at startup without a traceback

If the deepspeed process gets killed at launch time without a traceback, that usually means that the program tried to allocate more CPU memory than your system has or your process is allowed to allocate and the OS kernel killed that process. This is because your configuration file most likely has either offload_optimizer or offload_param or both configured to offload to cpu. If you have NVMe, experiment with offloading to NVMe if you’re running under ZeRO-3.

Work is being done to enable estimating how much memory is needed for a specific model: PR.

### Notes¶

• DeepSpeed works with the PyTorch Trainer but not TF TFTrainer.

• While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from source to best match your hardware and also if you need to enable certain features, like 1-bit Adam, which aren’t available in the pypi distribution.

• You don’t have to use the Trainer to use DeepSpeed with 🤗 Transformers - you can use any model with your own trainer, and you will have to adapt the latter according to the DeepSpeed integration instructions.

## Non-Trainer Deepspeed Integration¶

The HfDeepSpeedConfig is used to integrate Deepspeed into the 🤗 Transformers core functionality, when Trainer is not used.

When using Trainer everything is automatically taken care of.

When not using Trainer, to efficiently deploy DeepSpeed stage 3, you must instantiate the HfDeepSpeedConfig object before instantiating the model.

For example for a pretrained model:

from transformers.deepspeed import HfDeepSpeedConfig
from transformers import AutoModel, deepspeed

ds_config = { ... } # deepspeed config object or path to the file
# must run before instantiating the model
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
model = AutoModel.from_pretrained("gpt2")
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)

or for non-pretrained model:

from transformers.deepspeed import HfDeepSpeedConfig
from transformers import AutoModel, AutoConfig, deepspeed

ds_config = { ... } # deepspeed config object or path to the file
# must run before instantiating the model
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
config = AutoConfig.from_pretrained("gpt2")
model = AutoModel.from_config(config)
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)

## HfDeepSpeedConfig¶

class transformers.deepspeed.HfDeepSpeedConfig(config_file_or_dict)[source]

This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.

A weakref of this object is stored in the module’s globals to be able to access the config from areas where things like the Trainer object is not available (e.g. from_pretrained and _get_resized_embeddings). Therefore it’s important that this object remains alive while the program is still running.

Trainer uses the HfTrainerDeepSpeedConfig subclass instead. That subclass has logic to sync the configuration with values of TrainingArguments by replacing special placeholder values: "auto". Without this special logic the DeepSpeed configuration is not modified in any way.

Parameters

config_file_or_dict (Union[str, Dict]) – path to DeepSpeed config file or dict.

get_value(ds_key_long, default=None)[source]

Returns the set value or default if no value is set

is_false(ds_key_long)[source]

Returns True/False only if the value is set, always False otherwise. So use this method to ask the very specific question of whether the value is set to False (and it’s not set to True or isn’t set).

is_true(ds_key_long)[source]

Returns True/False only if the value is set, always False otherwise. So use this method to ask the very specific question of whether the value is set to True (and it’s not set to False or isn’t set).

## Main DeepSpeed Resources¶

Papers:

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