torch.utils.checkpoint

Note

Checkpointing is implemented by rerunning a forward-pass segment for each checkpointed segment during backward propagation. This can cause persistent states like the RNG state to be more advanced than they would without checkpointing. By default, checkpointing includes logic to juggle the RNG state such that checkpointed passes making use of RNG (through dropout for example) have deterministic output as compared to non-checkpointed passes. The logic to stash and restore RNG states can incur a moderate performance hit depending on the runtime of checkpointed operations. If deterministic output compared to non-checkpointed passes is not required, supply preserve_rng_state=False to checkpoint or checkpoint_sequential to omit stashing and restoring the RNG state during each checkpoint.

The stashing logic saves and restores the RNG state for CPU and another device type (infer the device type from Tensor arguments excluding CPU tensors by _infer_device_type) to the run_fn. If there are multiple device, device state will only be saved for devices of a single device type, and the remaining devices will be ignored. Consequently, if any checkpointed functions involve randomness, this may result in incorrect gradients. (Note that if CUDA devices are among the devices detected, it will be prioritized; otherwise, the first device encountered will be selected.) If there are no CPU-tensors, the default device type state (default value is cuda, and it could be set to other device by DefaultDeviceType) will be saved and restored. However, the logic has no way to anticipate if the user will move Tensors to a new device within the run_fn itself. Therefore, if you move Tensors to a new device (“new” meaning not belonging to the set of [current device + devices of Tensor arguments]) within run_fn, deterministic output compared to non-checkpointed passes is never guaranteed.

torch.utils.checkpoint.checkpoint(function, *args, use_reentrant=None, context_fn=<function noop_context_fn>, determinism_check='default', debug=False, **kwargs)

Checkpoint a model or part of the model.

Activation checkpointing is a technique that trades compute for memory. Instead of keeping tensors needed for backward alive until they are used in gradient computation during backward, forward computation in checkpointed regions omits saving tensors for backward and recomputes them during the backward pass. Activation checkpointing can be applied to any part of a model.

There are currently two checkpointing implementations available, determined by the use_reentrant parameter. It is recommended that you use use_reentrant=False. Please refer the note below for a discussion of their differences.

Warning

If the function invocation during the backward pass differs from the forward pass, e.g., due to a global variable, the checkpointed version may not be equivalent, potentially causing an error being raised or leading to silently incorrect gradients.

Warning

The use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. If you are using the use_reentrant=True variant, please refer to the note below for important considerations and potential limitations.

Note

The reentrant variant of checkpoint (use_reentrant=True) and the non-reentrant variant of checkpoint (use_reentrant=False) differ in the following ways:

• Non-reentrant checkpoint stops recomputation as soon as all needed intermediate activations have been recomputed. This feature is enabled by default, but can be disabled with set_checkpoint_early_stop(). Reentrant checkpoint always recomputes function in its entirety during the backward pass.

• The reentrant variant does not record the autograd graph during the forward pass, as it runs with the forward pass under torch.no_grad(). The non-reentrant version does record the autograd graph, allowing one to perform backward on the graph within checkpointed regions.

• The reentrant checkpoint only supports the torch.autograd.backward() API for the backward pass without its inputs argument, while the non-reentrant version supports all ways of performing the backward pass.

• At least one input and output must have requires_grad=True for the reentrant variant. If this condition is unmet, the checkpointed part of the model will not have gradients. The non-reentrant version does not have this requirement.

• The reentrant version does not consider tensors in nested structures (e.g., custom objects, lists, dicts, etc) as participating in autograd, while the non-reentrant version does.

• The reentrant checkpoint does not support checkpointed regions with detached tensors from the computational graph, whereas the non-reentrant version does. For the reentrant variant, if the checkpointed segment contains tensors detached using detach() or with torch.no_grad(), the backward pass will raise an error. This is because checkpoint makes all the outputs require gradients and this causes issues when a tensor is defined to have no gradient in the model. To avoid this, detach the tensors outside of the checkpoint function.

Parameters

• function – describes what to run in the forward pass of the model or part of the model. It should also know how to handle the inputs passed as the tuple. For example, in LSTM, if user passes (activation, hidden), function should correctly use the first input as activation and the second input as hidden

• preserve_rng_state (bool, optional) – Omit stashing and restoring the RNG state during each checkpoint. Note that under torch.compile, this flag doesn’t take effect and we always preserve RNG state. Default: True

• use_reentrant (bool) – specify whether to use the activation checkpoint variant that requires reentrant autograd. This parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. If use_reentrant=False, checkpoint will use an implementation that does not require reentrant autograd. This allows checkpoint to support additional functionality, such as working as expected with torch.autograd.grad and support for keyword arguments input into the checkpointed function.

• context_fn (Callable, optional) – A callable returning a tuple of two context managers. The function and its recomputation will be run under the first and second context managers respectively. This argument is only supported if use_reentrant=False.

• determinism_check (str, optional) – A string specifying the determinism check to perform. By default it is set to "default" which compares the shapes, dtypes, and devices of the recomputed tensors against those the saved tensors. To turn off this check, specify "none". Currently these are the only two supported values. Please open an issue if you would like to see more determinism checks. This argument is only supported if use_reentrant=False, if use_reentrant=True, the determinism check is always disabled.

• debug (bool, optional) – If True, error messages will also include a trace of the operators ran during the original forward computation as well as the recomputation. This argument is only supported if use_reentrant=False.

• args – tuple containing inputs to the function

Returns

Output of running function on *args

torch.utils.checkpoint.checkpoint_sequential(functions, segments, input, use_reentrant=None, **kwargs)

Checkpoint a sequential model to save memory.

Sequential models execute a list of modules/functions in order (sequentially). Therefore, we can divide such a model in various segments and checkpoint each segment. All segments except the last will not store the intermediate activations. The inputs of each checkpointed segment will be saved for re-running the segment in the backward pass.

Warning

The use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. If you are using the use_reentrant=True` variant, please see :func:`~torch.utils.checkpoint.checkpoint` for the important considerations and limitations of this variant. It is recommended that you use ``use_reentrant=False.

Parameters

• functions – A torch.nn.Sequential or the list of modules or functions (comprising the model) to run sequentially.

• segments – Number of chunks to create in the model

• input – A Tensor that is input to functions

• preserve_rng_state (bool, optional) – Omit stashing and restoring the RNG state during each checkpoint. Default: True

• use_reentrant (bool) – specify whether to use the activation checkpoint variant that requires reentrant autograd. This parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. If use_reentrant=False, checkpoint will use an implementation that does not require reentrant autograd. This allows checkpoint to support additional functionality, such as working as expected with torch.autograd.grad and support for keyword arguments input into the checkpointed function.

Returns

Output of running functions sequentially on *inputs

Example

>>> model = nn.Sequential(...)
>>> input_var = checkpoint_sequential(model, chunks, input_var)

torch.utils.checkpoint.set_checkpoint_debug_enabled(enabled)

Context manager that sets whether checkpoint should print additional debug information when running. See the debug flag for checkpoint() for more information. Note that when set, this context manager overrides the value of debug passed to checkpoint. To defer to the local setting, pass None to this context.

Parameters

enabled (bool) – Whether checkpoint should print debug information. Default is ‘None’.

class torch.utils.checkpoint.CheckpointPolicy(value)

Enum for specifying the policy for checkpointing during backpropagation.

The following policies are supported:

• {MUST,PREFER}_SAVE: The operation’s output will be saved during the forward pass and will not be recomputed during the backward pass

• {MUST,PREFER}_RECOMPUTE: The operation’s output will not be saved during the forward pass and will be recomputed during the backward pass

Use MUST_* over PREFER_* to indicate that the policy should not be overridden by other subsystems like torch.compile.

Note

A policy function that always returns PREFER_RECOMPUTE is equivalent to vanilla checkpointing.

A policy function that returns PREFER_SAVE every op is NOT equivalent to not using checkpointing. Using such a policy would save additional tensors not limited to ones that are actually needed for gradient computation.

class torch.utils.checkpoint.SelectiveCheckpointContext(*, is_recompute)

Context passed to policy function during selective checkpointing.

This class is used to pass relevant metadata to the policy function during selective checkpointing. The metadata includes whether the current invocation of the policy function is during recomputation or not.

Example

>>>
>>> def policy_fn(ctx, op, *args, **kwargs):
>>>    print(ctx.is_recompute)
>>>
>>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
>>>
>>> out = torch.utils.checkpoint.checkpoint(
>>>     fn, x, y,
>>>     use_reentrant=False,
>>>     context_fn=context_fn,
>>> )

torch.utils.checkpoint.create_selective_checkpoint_contexts(policy_fn_or_list, allow_cache_entry_mutation=False)

Helper to avoid recomputing certain ops during activation checkpointing.

Use this with torch.utils.checkpoint.checkpoint to control which operations are recomputed during the backward pass.

Parameters

• policy_fn_or_list (Callable or List) –

  • If a policy function is provided, it should accept a SelectiveCheckpointContext, the OpOverload, args and kwargs to the op, and return a CheckpointPolicy enum value indicating whether the execution of the op should be recomputed or not.

  • If a list of operations is provided, it is equivalent to a policy returning CheckpointPolicy.MUST_SAVE for the specified operations and CheckpointPolicy.PREFER_RECOMPUTE for all other operations.

• allow_cache_entry_mutation (bool, optional) – By default, an error is raised if any tensors cached by selective activation checkpoint are mutated in order to ensure correctness. If set to True, this check is disabled.

Returns

A tuple of two context managers.

Example

>>> import functools
>>>
>>> x = torch.rand(10, 10, requires_grad=True)
>>> y = torch.rand(10, 10, requires_grad=True)
>>>
>>> ops_to_save = [
>>>    torch.ops.aten.mm.default,
>>> ]
>>>
>>> def policy_fn(ctx, op, *args, **kwargs):
>>>    if op in ops_to_save:
>>>        return CheckpointPolicy.MUST_SAVE
>>>    else:
>>>        return CheckpointPolicy.PREFER_RECOMPUTE
>>>
>>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
>>>
>>> # or equivalently
>>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
>>>
>>> def fn(x, y):
>>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
>>>
>>> out = torch.utils.checkpoint.checkpoint(
>>>     fn, x, y,
>>>     use_reentrant=False,
>>>     context_fn=context_fn,
>>> )