zero3.rst

ZeRO

The Zero Redundancy Optimizer (ZeRO) removes the memory redundancies across data-parallel processes by partitioning the three model states (optimizer states, gradients, and parameters) across data-parallel processes instead of replicating them. By doing this, it boosts memory efficiency compared to classic data-parallelism while retaining its computational granularity and communication efficiency.

1. ZeRO Stage 1: The optimizer states (e.g., for Adam optimizer, 32-bit weights, and the first, and second moment estimates) are partitioned across the processes, so that each process updates only its partition.
2. ZeRO Stage 2: The reduced 32-bit gradients for updating the model weights are also partitioned such that each process retains only the gradients corresponding to its portion of the optimizer states.
3. ZeRO Stage 3: The 16-bit model parameters are partitioned across the processes. ZeRO-3 will automatically collect and partition them during the forward and backward passes.

In addition, ZeRO-3 includes the infinity offload engine to form ZeRO-Infinity ([paper](https://arxiv.org/abs/2104.07857)), which can offload all model states to both CPU and NVMe memory for huge memory savings.

For a deep dive of our algorithms, please see our papers on ZeRO, ZeRO-Offload, and ZeRO-Infinity.

Note

DeepSpeed first included offloading capabilities with ZeRO-Offload, a system for offloading optimizer and gradient states to CPU memory within ZeRO-2. ZeRO-Infinity is the next generation of offloading capabilities, accessible to ZeRO-3. ZeRO-Infinity has all of the savings of ZeRO-Offload, plus is able to offload more the model weights and has more effective bandwidth utilization and overlapping of computation and communication.

Getting Started

If you are new to DeepSpeed, check out our Getting Started page.

Once you are training with DeepSpeed, enabling ZeRO-3 offload is as simple as enabling it in your DeepSpeed configuration! Below are a few examples of ZeRO-3 configurations. Please see our config guide for a complete list of options for configuration and performance tuning.

Note

ZeRO-Infinity and ZeRO-Offload work best with our heavily optimized :class:`deepspeed.ops.adam.DeepSpeedCPUAdam` optimizer. We recommend using our optimizer config to instruct :meth:`deepspeed.initialize` to build the optimizer for you.

ZeRO Configurations

All the settings for DeepSpeed ZeRO are set with the DeepSpeedZeroConfig. The dictionary provided under the zero_optimization entry of the main DeepSpeed configuration dict will be parsed and validated with this class. Sub-configurations for parameter offload and optimizer offload settings are parsed by DeepSpeedZeroOffloadParamConfig and DeepSpeedZeroOffloadOptimizerConfig.

.. autopydantic_model:: deepspeed.runtime.zero.config.DeepSpeedZeroConfig

.. autopydantic_model:: deepspeed.runtime.zero.config.DeepSpeedZeroOffloadParamConfig

.. autopydantic_model:: deepspeed.runtime.zero.config.DeepSpeedZeroOffloadOptimizerConfig

Example ZeRO-3 Configurations

1. Use ZeRO to partition the optimizer states (stage 1), gradients (stage 2), and parameters (stage 3).

   {
       "zero_optimization": {
           "stage": 3,
       },
       "fp16": {
           "enabled": true
       },
       "optimizer": {
           "type": "AdamW",
           "params": {
           "lr": 0.001,
           "betas": [
               0.8,
               0.999
           ],
           "eps": 1e-8,
           "weight_decay": 3e-7
           }
       },
       ...
   }

2. Additionally offload the optimizer states and computations to the CPU with ZeRO-Infinity.

   {
       "zero_optimization": {
           "stage": 3,
           "offload_optimizer": {
               "device": "cpu"
           }
       },
       ...
   }

3. Save even more memory by offloading parameters to the CPU memory.

   {
       "zero_optimization": {
           "stage": 3,
           "offload_optimizer": {
               "device": "cpu"
           }
           "offload_param": {
               "device": "cpu"
           }
       },
       ...
   }

4. Save even MORE memory by offloading to NVMe (if available on your system):

   {
       "zero_optimization": {
           "stage": 3,
           "offload_optimizer": {
               "device": "nvme",
               "nvme_path": "/nvme_data"
           }
           "offload_param": {
               "device": "nvme",
               "nvme_path": "/nvme_data"
           }
       },
       ...
   }

MiCS Configurations

All MiCS configurations are set with DeepSpeedZeroConfig. MiCS assumes ZeRO stage 3 optimization is enabled. For now, there are two configuration fields of MiCS mics_shard_size and mics_hierarchical_params_gather. mics_shard_size controls how many devices are used for partitioning the model states. mics_hierarchical_params_gather controls whether we use a two-stage hierarchical way to gather parameters in the forward computation. mics_hierarchical_params_gather is useful when model states are partitioned across multiple nodes and the cross-node bandwidth is slow. By default this is turned off.

Example MiCS Configurations

1. Use MiCS to partition the model states (including optimizer states, gradients, and parameters). The following config example partitions the model states to eight devices, and assumes the eight devices are located within a single node (mics_hierarchical_params_gather is False).

   {
       "zero_optimization": {
           "stage": 3,
           "mics_shard_size": 8,
           "mics_hierarchical_params_gather": False,
       },
       ...
   }

Assumptions

DeepSpeed automatically coordinates the collection (i.e., all-gather), partitioning (i.e., scatter), and offloading of parameters at the granularity of (sub)module forward() methods. The backward pass is handled similarly. This strategy has two underlying assumptions:

1. The forward and backward passes of submodules must individually fit in device memory. If this not the case, :class:`deepspeed.zero.TiledLinear` implements memory-centric tiling and works with ZeRO-3 to break linear layers into a sequence of smaller submodules that can fit in memory.
2. A module's parameters are only accessed within its own __init__ and forward() methods. Otherwise, DeepSpeed must be instructed to collect and re-partition the parameter. See :ref:`external-parameters` for manually coordinating parameters.

Constructing Massive Models

ZeRO-3 enables massive models whose parameters exceed the size of individual nodes in a system. For the typical case of training without model parallelism, you can simply allocate your model in our context:

with deepspeed.zero.Init():
    model = MyLargeModel()

.. autoclass:: deepspeed.zero.Init
    :members:

Manual Parameter Coordination

Most models require no modification to be trained with ZeRO-3. However, in some cases one may need to access model weights outside of the training loop, or to share weights across submodules during training. DeepSpeed has several mechanisms to coordinate partitioned weights for ZeRO-3.

Gathering Parameters

DeepSpeed provides mechanisms for collecting (or gathering) a partitioned parameter.

Some models partitioned with :class:`deepspeed.zero.Init` may need to access a module’s weights outside of the class constructor or its forward() method. We refer to these weights as external parameters, since these parameters are accessed outside of the module that created them. To do so, use :class:`deepspeed.zero.GatheredParameters` or :meth:`deepspeed.zero.register_external_parameter`.

.. autoclass:: deepspeed.zero.GatheredParameters
    :members:

Registering External Parameters

ZeRO-3 will automatically collect and partition the model parameters as they are needed during the forward and backward passes. However, in some cases a parameter may be used outside of its module's forward pass. We call these external parameters. ZeRO-3 can coordinate these parameters if they are registered either automatically or manually.

Note

DeepSpeed version 0.3.15 includes automatic external parameter discovery and registration to support the most common cases. Parameters can still be manually registered if they cannot be automatically detected.

DeepSpeed can automatically detect the following external parameter scenarios:

1. Parameter access: consider the following pattern common in language models such as GPT:

   The tensor embeddings.weight is used in both embeddings.forward() and compute_logits(). We call embeddings.weight an external parameter because it is used in the training loop outside of its owning module's forward pass.

   class LanguageModel(torch.nn.Module):
       ...
       def forward(self, inputs):
           embeds = self.embeddings(inputs)
           ...
           logits = compute_logits(output, self.embeddings.weight)
           ...

2. Returning a parameter:

   CustomLinear returns both an output and its own bias parameter. DeepSpeed will detect the external bias parameter and register it with submodules that use CustomLinear.

   class CustomLinear(torch.nn.Linear):
       def forward(self, *input):
           output = super().forward(*input)
           return output, self.bias

.. autofunction:: deepspeed.zero.register_external_parameter

.. autofunction:: deepspeed.zero.unregister_external_parameter

Overriding Module.apply

A convenient mechanism for customizing model initialization is Module.apply. With ZeRO stage 3, Module.apply implementations must account for parameter partitioning by zero.Init during model initialization. The default behavior of ZeRO stage 3 is to automatically handle this issue by overriding Module.apply to ensure that parameters are gathered before access by Module.apply. The benefit of this approach is development convenience, since users are saved the burden of manual parameter coordination in Module.apply. However, the downside is slow model initialization, since all the model parameters (e.g., billions) are gathered even though the common usage of Module.apply is to customize a few parameters. Developers can disable this default behavior by setting the override_module_apply configuration knob to False, for faster model initialization at the cost of manually handling partitioned parameters in their Module.apply implementations.

Memory-Centric Tiling

To reduce the working memory requirements of DL training for large models, ZeRO-Infinity includes technique called memory-centric tiling that exploits the data fetch and release pattern of ZeRO-3 to reduce the working memory requirements by breaking down a large operator into smaller tiles that can be executed sequentially. When combined with ZeRO-3, the parameter and gradients of each tile can be fetched and released one at a time, reducing the working memory proportional to the number of tiles. Therefore, ZeRO-Infinity can support operators of arbitrary sizes, without refactoring for model parallelism to fit them in limited GPU memory.

.. autoclass:: deepspeed.zero.TiledLinear
    :members:

Debugging

Debugging ZeRO training is complicated by the partitioning of parameters, gradients, and optimizer states. None of these 3 groups of tensors (model states) can be normally accessed because of that. To overcome that DeepSpeed provides the following routines for accessing individual model states in both their partitioned (local) and unpartitioned (full) forms.

Important: Please note that, to access the unpartitioned (full) form, these utilities must be called by all processes participating in the training, even if you decide to do something with the result only in the main process. If all processes don't participate these utilities will hang waiting for all processes to send their contribution.

Additionally, you must be aware that these routines return correct data only in specific phases of the training. So for examples the gradients are valid after backward and before step. The optimizer states are updated after step. Same goes for fp32 master weights.

.. autofunction:: deepspeed.utils.safe_get_full_fp32_param

.. autofunction:: deepspeed.utils.safe_get_full_grad

.. autofunction:: deepspeed.utils.safe_get_full_optimizer_state

.. autofunction:: deepspeed.utils.safe_get_local_fp32_param

.. autofunction:: deepspeed.utils.safe_get_local_grad

.. autofunction:: deepspeed.utils.safe_get_local_optimizer_state

These routines can be used in a training loop as shown in the following snippet.

backward(loss)
[...]
from deepspeed.utils import safe_get_full_fp32_param, safe_get_full_grad, safe_get_full_optimizer_state
for n, lp in model.named_parameters():
    # 1. Access the full states
    #  1.1) gradient lookup
    # For zero1 and zero2, gradient lookup must be called after `backward` and before `step`
    # For zero3, gradient lookup must be called after `backward`
    hp_grad = safe_get_full_grad(lp)


    # 1.2) fp32 and optim states can probably be called anywhere in the training loop, but will be updated after `step`
    hp = safe_get_full_fp32_param(lp)
    exp_avg = safe_get_full_optimizer_state(lp, "exp_avg")
    exp_avg_sq = safe_get_full_optimizer_state(lp, "exp_avg_sq")

    # 2. Access the local states (zero3)
    # For zero3, all of the parameters, gradients, and optimizer states are partitioned,
    # and each process can access its corresponding local state.
    local_hp = safe_get_local_fp32_param(lp)
    local_hp_grad = safe_get_local_grad(lp)
    local_exp_avg = safe_get_local_optimizer_state(lp, "exp_avg")
    local_exp_avg_sq = safe_get_local_optimizer_state(lp, "exp_avg_sq")

[...]
optimizer.step()

Modifying Partitioned States

Sometimes, a user may want to modify parameters, gradients, or optimizer states outside of the regular training loop. This is currently difficult in ZeRO training because of partitioning. To overcome that, DeepSpeed provides the following routines for modifying the fp32 master parameters and the fp32 optimizer states.

.. autofunction:: deepspeed.utils.safe_set_full_fp32_param

.. autofunction:: deepspeed.utils.safe_set_full_optimizer_state

.. autofunction:: deepspeed.utils.safe_set_full_grad

.. autofunction:: deepspeed.utils.safe_set_local_fp32_param

.. autofunction:: deepspeed.utils.safe_set_local_grad

.. autofunction:: deepspeed.utils.safe_set_local_optimizer_state

The routines for modifying parameters and optimizer states can be used at any point after initialization of the DeepSpeed engine (i.e., deepspeed.initialize()) as shown in the following snippet.

[...]
from deepspeed.runtime.zero.utils import is_zero_param
from deepspeed.utils import safe_set_full_fp32_param, safe_set_full_optimizer_state
from deepspeed.utils import safe_set_local_fp32_param, safe_set_local_optimizer_state
# Here is an example to zero all the fp32 parameters and optimizer states.
for n, lp in model.named_parameters():
    # 1. For zero stage 1, 2, or 3 set the full fp32 and their full optim states
    zero_tensor = torch.zeros(lp.ds_shape) if is_zero_param(lp) else torch.zeros(lp.shape)

    safe_set_full_fp32_param(lp, zero_tensor)
    safe_get_full_optimizer_state(lp, zero_tensor, "exp_avg")
    safe_get_full_optimizer_state(lp, zero_tensor, "exp_avg_sq")

    # 2. For zero stage 3, each process sets its local fp32 parameters and their local optimizer states individually
    zero_tensor_local = torch.zeros(lp.ds_tensor.shape)

    safe_set_local_fp32_param(lp, zero_tensor_local)
    safe_set_local_optimizer_state(lp, zero_tensor_local, "exp_avg")
    safe_set_local_optimizer_state(lp, zero_tensor_local, "exp_avg_sq")

[...]

The routines for modifying gradients can be used after backward but before step as shown in the following snippet.

backward(loss)
[...]
from deepspeed.runtime.zero.utils import is_zero_param
from deepspeed.utils import safe_set_full_grad, safe_set_local_grad
# Here is an example of how to zero all the gradients.
for n, lp in model.named_parameters():
    # 1. For zero stage 1, 2, or 3 set the full gradient.
    zero_tensor = torch.zeros(lp.ds_shape) if is_zero_param(lp) else torch.zeros(lp.shape)

    safe_set_full_grad(lp, zero_tensor)

    # 2. For zero stage 3, each process sets its local gradient partition.
    zero_tensor_local = torch.zeros_like(lp.ds_tensor.shape)

    safe_set_local_grad(lp, zero_tensor_local)

[...]
optimizer.step()

GPU Memory Management

By default at the end of training with ZeRO stage 3 some parameters could remain unpartitioned and use up some gpu memory. This is done on purpose as an optimization should you resume training again. If you'd like to clear out the cached parameters that use up gpu memory, you can call empty_partition_cache method of a DeepSpeed engine.

The following code snippet illustrates this functionality.

with zero.Init():
    model = MyLargeModel()

ds_engine, _, _, _ = deepspeed.initialize(model, ...)
for batch in ...:
    loss = ds_engine(batch)
    ds_engine.backward(batch)
    ds_engine.step()

# Free GPU memory consumed by model parameters
ds_engine.empty_partition_cache()

Offload States

The DeepSpeed engine maintains a set of states in device memory (e.g., CUDA memory). The following API allows you to offload these states to a different device (currently, only CPU memory is supported), reducing the memory footprint on the device.

def offload_states(self,
                   include: Container[OffloadStateTypeEnum] = None,
                   device: OffloadDeviceEnum = OffloadDeviceEnum.cpu,
                   pin_memory: bool = True,
                   non_blocking: bool = False) -> None:
    """Offload the engine's states to the specified device.

    Arguments:
        include: Optional. The set of states to offload. If not provided, all states are offloaded.
        device: Optional. The device to move the ZeRO optimizer buffers to. Currently only `OffloadDeviceEnum.cpu` is supported.
        pin_memory: Optional. Whether to pin the memory of the offloaded states.
        non_blocking: Optional. Whether to offload the states asynchronously.
    """

You can selectively offload specific states by specifying the OffloadStateTypeEnum in the include argument. OffloadStateTypeEnum is an enum that defines the states that can be offloaded. The following states are supported:

• OffloadStateTypeEnum.optim_states: Optimizer states. Currently, only states of DeepSpeed's FusedAdam optimizer are supported.
• OffloadStateTypeEnum.hp_params: FP32 parameters.
• OffloadStateTypeEnum.lp_params: BF16/FP16 parameters.
• OffloadStateTypeEnum.lp_grads: BF16/FP16 gradients.
• OffloadStateTypeEnum.contiguous_grad_buffer: The contiguous gradient buffer for reduce operations.

Note that offloading states comes with a trade-off between memory savings and computational overhead. This API allows states to be reloaded back into device memory when needed.

def reload_states(self, non_blocking: bool = False) -> None:
    """Reload the engine states to the original device.

    Arguments:
        non_blocking: Optional. Whether to offload the states asynchronously.
    """

Below is an example code snippet demonstrating how to offload FP32 parameters and optimizer states to CPU memory:

# Offload after forward, backward, and step
ds_engine.offload_states(include=[OffloadStateTypeEnum.hp_params, OffloadStateTypeEnum.optim_states])

# Do something requiring a lot of device memory
...
# Load states back to device memory
ds_engine.reload_states()

deepspeed.runtime.zero.offload_states.get_state_devices returns devices of the specified state.

def get_state_devices(model, state: OffloadStateTypeEnum) -> Set[torch.device]:
    """Retrieve the devices of the specified state of the model.

    Args:
        model (DeepSpeedEngine): The model whose device allocations are to be checked.
        state (OffloadStateTypeEnum): The specific state for which the devices should be retrieved.

    Returns:
        Set[torch.device]: A set of devices of the specified state.

    """