.. _cuda-semantics:

CUDA semantics
==============

:mod:`torch.cuda` is used to set up and run CUDA operations. It keeps track of
the currently selected GPU, and all CUDA tensors you allocate will by default be
created on that device. The selected device can be changed with a
:any:`torch.cuda.device` context manager.

However, once a tensor is allocated, you can do operations on it irrespective
of the selected device, and the results will be always placed on the same
device as the tensor.

Cross-GPU operations are not allowed by default, with the exception of
:meth:`~torch.Tensor.copy_` and other methods with copy-like functionality
such as :meth:`~torch.Tensor.to` and :meth:`~torch.Tensor.cuda`.
Unless you enable peer-to-peer memory access, any attempts to launch ops on
tensors spread across different devices will raise an error.

Below you can find a small example showcasing this::

    cuda = torch.device('cuda')     # Default CUDA device
    cuda0 = torch.device('cuda:0')
    cuda2 = torch.device('cuda:2')  # GPU 2 (these are 0-indexed)

    x = torch.tensor([1., 2.], device=cuda0)
    # x.device is device(type='cuda', index=0)
    y = torch.tensor([1., 2.]).cuda()
    # y.device is device(type='cuda', index=0)

    with torch.cuda.device(1):
        # allocates a tensor on GPU 1
        a = torch.tensor([1., 2.], device=cuda)

        # transfers a tensor from CPU to GPU 1
        b = torch.tensor([1., 2.]).cuda()
        # a.device and b.device are device(type='cuda', index=1)

        # You can also use ``Tensor.to`` to transfer a tensor:
        b2 = torch.tensor([1., 2.]).to(device=cuda)
        # b.device and b2.device are device(type='cuda', index=1)

        c = a + b
        # c.device is device(type='cuda', index=1)

        z = x + y
        # z.device is device(type='cuda', index=0)

        # even within a context, you can specify the device
        # (or give a GPU index to the .cuda call)
        d = torch.randn(2, device=cuda2)
        e = torch.randn(2).to(cuda2)
        f = torch.randn(2).cuda(cuda2)
        # d.device, e.device, and f.device are all device(type='cuda', index=2)

.. _tf32_on_ampere:

TensorFloat-32(TF32) on Ampere devices
--------------------------------------

Starting in PyTorch 1.7, there is a new flag called `allow_tf32`. This flag
defaults to True in PyTorch 1.7 to PyTorch 1.11, and False in PyTorch 1.12 and later.
This flag controls whether PyTorch is allowed to use the TensorFloat32 (TF32) tensor cores,
available on new NVIDIA GPUs since Ampere, internally to compute matmul (matrix multiplies
and batched matrix multiplies) and convolutions.

TF32 tensor cores are designed to achieve better performance on matmul and convolutions on
`torch.float32` tensors by rounding input data to have 10 bits of mantissa, and accumulating
results with FP32 precision, maintaining FP32 dynamic range.

matmuls and convolutions are controlled separately, and their corresponding flags can be accessed at:

.. code:: python

  # The flag below controls whether to allow TF32 on matmul. This flag defaults to False
  # in PyTorch 1.12 and later.
  torch.backends.cuda.matmul.allow_tf32 = True

  # The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
  torch.backends.cudnn.allow_tf32 = True

Note that besides matmuls and convolutions themselves, functions and nn modules that internally uses
matmuls or convolutions are also affected. These include `nn.Linear`, `nn.Conv*`, cdist, tensordot,
affine grid and grid sample, adaptive log softmax, GRU and LSTM.

To get an idea of the precision and speed, see the example code below:

.. code:: python

  a_full = torch.randn(10240, 10240, dtype=torch.double, device='cuda')
  b_full = torch.randn(10240, 10240, dtype=torch.double, device='cuda')
  ab_full = a_full @ b_full
  mean = ab_full.abs().mean()  # 80.7277

  a = a_full.float()
  b = b_full.float()

  # Do matmul at TF32 mode.
  torch.backends.cuda.matmul.allow_tf32 = True
  ab_tf32 = a @ b  # takes 0.016s on GA100
  error = (ab_tf32 - ab_full).abs().max()  # 0.1747
  relative_error = error / mean  # 0.0022

  # Do matmul with TF32 disabled.
  torch.backends.cuda.matmul.allow_tf32 = False
  ab_fp32 = a @ b  # takes 0.11s on GA100
  error = (ab_fp32 - ab_full).abs().max()  # 0.0031
  relative_error = error / mean  # 0.000039

From the above example, we can see that with TF32 enabled, the speed is ~7x faster, relative error
compared to double precision is approximately 2 orders of magnitude larger.  If full FP32 precision
is needed, users can disable TF32 by:

.. code:: python

  torch.backends.cuda.matmul.allow_tf32 = False
  torch.backends.cudnn.allow_tf32 = False

To toggle the TF32 flags off in C++, you can do

.. code:: C++

  at::globalContext().setAllowTF32CuBLAS(false);
  at::globalContext().setAllowTF32CuDNN(false);

For more information about TF32, see:

- `TensorFloat-32`_
- `CUDA 11`_
- `Ampere architecture`_

.. _TensorFloat-32: https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/
.. _CUDA 11: https://devblogs.nvidia.com/cuda-11-features-revealed/
.. _Ampere architecture: https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/

.. _fp16reducedprecision:

Reduced Precision Reduction in FP16 GEMMs
-----------------------------------------

fp16 GEMMs are potentially done with some intermediate reduced precision reductions (e.g., in fp16 rather than fp32). These selective reductions in precision can allow for higher performance on certain workloads (particularly those with a large `k` dimension) and GPU architectures at the cost of numerical precision and potential for overflow.

Some example benchmark data on V100:

.. code::

  [--------------------------- bench_gemm_transformer --------------------------]
        [  m ,  k  ,  n  ]    |  allow_fp16_reduc=True  |  allow_fp16_reduc=False
  1 threads: --------------------------------------------------------------------
        [4096, 4048, 4096]    |           1634.6        |           1639.8
        [4096, 4056, 4096]    |           1670.8        |           1661.9
        [4096, 4080, 4096]    |           1664.2        |           1658.3
        [4096, 4096, 4096]    |           1639.4        |           1651.0
        [4096, 4104, 4096]    |           1677.4        |           1674.9
        [4096, 4128, 4096]    |           1655.7        |           1646.0
        [4096, 4144, 4096]    |           1796.8        |           2519.6
        [4096, 5096, 4096]    |           2094.6        |           3190.0
        [4096, 5104, 4096]    |           2144.0        |           2663.5
        [4096, 5112, 4096]    |           2149.1        |           2766.9
        [4096, 5120, 4096]    |           2142.8        |           2631.0
        [4096, 9728, 4096]    |           3875.1        |           5779.8
        [4096, 16384, 4096]   |           6182.9        |           9656.5
  (times in microseconds).

If full precision reductions are needed, users can disable reduced precision reductions in fp16 GEMMs with:

.. code:: python

  torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False

To toggle the reduced precision reduction flags in C++, you can do

.. code:: C++

  at::globalContext().setAllowFP16ReductionCuBLAS(false);

Asynchronous execution
----------------------

By default, GPU operations are asynchronous.  When you call a function that
uses the GPU, the operations are *enqueued* to the particular device, but not
necessarily executed until later.  This allows us to execute more computations
in parallel, including operations on CPU or other GPUs.

In general, the effect of asynchronous computation is invisible to the caller,
because (1) each device executes operations in the order they are queued, and
(2) PyTorch automatically performs necessary synchronization when copying data
between CPU and GPU or between two GPUs.  Hence, computation will proceed as if
every operation was executed synchronously.

You can force synchronous computation by setting environment variable
``CUDA_LAUNCH_BLOCKING=1``.  This can be handy when an error occurs on the GPU.
(With asynchronous execution, such an error isn't reported until after the
operation is actually executed, so the stack trace does not show where it was
requested.)

A consequence of the asynchronous computation is that time measurements without
synchronizations are not accurate. To get precise measurements, one should either
call :func:`torch.cuda.synchronize()` before measuring, or use :class:`torch.cuda.Event`
to record times as following::

    start_event = torch.cuda.Event(enable_timing=True)
    end_event = torch.cuda.Event(enable_timing=True)
    start_event.record()

    # Run some things here

    end_event.record()
    torch.cuda.synchronize()  # Wait for the events to be recorded!
    elapsed_time_ms = start_event.elapsed_time(end_event)

As an exception, several functions such as :meth:`~torch.Tensor.to` and
:meth:`~torch.Tensor.copy_` admit an explicit :attr:`non_blocking` argument,
which lets the caller bypass synchronization when it is unnecessary.
Another exception is CUDA streams, explained below.

CUDA streams
^^^^^^^^^^^^

A `CUDA stream`_ is a linear sequence of execution that belongs to a specific
device.  You normally do not need to create one explicitly: by default, each
device uses its own "default" stream.

Operations inside each stream are serialized in the order they are created,
but operations from different streams can execute concurrently in any
relative order, unless explicit synchronization functions (such as
:meth:`~torch.cuda.synchronize` or :meth:`~torch.cuda.Stream.wait_stream`) are
used.  For example, the following code is incorrect::

    cuda = torch.device('cuda')
    s = torch.cuda.Stream()  # Create a new stream.
    A = torch.empty((100, 100), device=cuda).normal_(0.0, 1.0)
    with torch.cuda.stream(s):
        # sum() may start execution before normal_() finishes!
        B = torch.sum(A)

When the "current stream" is the default stream, PyTorch automatically performs
necessary synchronization when data is moved around, as explained above.
However, when using non-default streams, it is the user's responsibility to
ensure proper synchronization.

.. _bwd-cuda-stream-semantics:

Stream semantics of backward passes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Each backward CUDA op runs on the same stream that was used for its corresponding forward op.
If your forward pass runs independent ops in parallel on different streams,
this helps the backward pass exploit that same parallelism.

The stream semantics of a backward call with respect to surrounding ops are the same
as for any other call. The backward pass inserts internal syncs to ensure this even when
backward ops run on multiple streams as described in the previous paragraph.
More concretely, when calling
:func:`autograd.backward<torch.autograd.backward>`,
:func:`autograd.grad<torch.autograd.grad>`, or
:meth:`tensor.backward<torch.Tensor.backward>`,
and optionally supplying CUDA tensor(s) as the  initial gradient(s) (e.g.,
:func:`autograd.backward(..., grad_tensors=initial_grads)<torch.autograd.backward>`,
:func:`autograd.grad(..., grad_outputs=initial_grads)<torch.autograd.grad>`, or
:meth:`tensor.backward(..., gradient=initial_grad)<torch.Tensor.backward>`),
the acts of

1. optionally populating initial gradient(s),
2. invoking the backward pass, and
3. using the gradients

have the same stream-semantics relationship as any group of ops::

    s = torch.cuda.Stream()

    # Safe, grads are used in the same stream context as backward()
    with torch.cuda.stream(s):
        loss.backward()
        use grads

    # Unsafe
    with torch.cuda.stream(s):
        loss.backward()
    use grads

    # Safe, with synchronization
    with torch.cuda.stream(s):
        loss.backward()
    torch.cuda.current_stream().wait_stream(s)
    use grads

    # Safe, populating initial grad and invoking backward are in the same stream context
    with torch.cuda.stream(s):
        loss.backward(gradient=torch.ones_like(loss))

    # Unsafe, populating initial_grad and invoking backward are in different stream contexts,
    # without synchronization
    initial_grad = torch.ones_like(loss)
    with torch.cuda.stream(s):
        loss.backward(gradient=initial_grad)

    # Safe, with synchronization
    initial_grad = torch.ones_like(loss)
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        initial_grad.record_stream(s)
        loss.backward(gradient=initial_grad)

BC note: Using grads on the default stream
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In prior versions of PyTorch (1.9 and earlier), the autograd engine always synced
the default stream with all backward ops, so the following pattern::

    with torch.cuda.stream(s):
        loss.backward()
    use grads

was safe as long as ``use grads`` happened on the default stream.
In present PyTorch, that pattern is no longer safe. If ``backward()``
and ``use grads`` are in different stream contexts, you must sync the streams::

    with torch.cuda.stream(s):
        loss.backward()
    torch.cuda.current_stream().wait_stream(s)
    use grads

even if ``use grads`` is on the default stream.

.. _CUDA stream: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#streams

.. _cuda-memory-management:

Memory management
-----------------

PyTorch uses a caching memory allocator to speed up memory allocations. This
allows fast memory deallocation without device synchronizations. However, the
unused memory managed by the allocator will still show as if used in
``nvidia-smi``. You can use :meth:`~torch.cuda.memory_allocated` and
:meth:`~torch.cuda.max_memory_allocated` to monitor memory occupied by
tensors, and use :meth:`~torch.cuda.memory_reserved` and
:meth:`~torch.cuda.max_memory_reserved` to monitor the total amount of memory
managed by the caching allocator. Calling :meth:`~torch.cuda.empty_cache`
releases all **unused** cached memory from PyTorch so that those can be used
by other GPU applications. However, the occupied GPU memory by tensors will not
be freed so it can not increase the amount of GPU memory available for PyTorch.

For more advanced users, we offer more comprehensive memory benchmarking via
:meth:`~torch.cuda.memory_stats`. We also offer the capability to capture a
complete snapshot of the memory allocator state via
:meth:`~torch.cuda.memory_snapshot`, which can help you understand the
underlying allocation patterns produced by your code.

Use of a caching allocator can interfere with memory checking tools such as
``cuda-memcheck``.  To debug memory errors using ``cuda-memcheck``, set
``PYTORCH_NO_CUDA_MEMORY_CACHING=1`` in your environment to disable caching.

The behavior of caching allocator can be controlled via environment variable
``PYTORCH_CUDA_ALLOC_CONF``.
The format is ``PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2>:<value2>...``
Available options:

* ``max_split_size_mb`` prevents the allocator from splitting blocks larger
  than this size (in MB). This can help prevent fragmentation and may allow
  some borderline workloads to complete without running out of memory.
  Performance cost can range from 'zero' to 'substatial' depending on
  allocation patterns.  Default value is unlimited, i.e. all blocks can be
  split. The :meth:`~torch.cuda.memory_stats` and
  :meth:`~torch.cuda.memory_summary` methods are useful for tuning.  This
  option should be used as a last resort for a workload that is aborting
  due to 'out of memory' and showing a large amount of inactive split blocks.
* ``roundup_power2_divisions`` helps with rounding the requested allocation
  size to nearest power-2 division and making better use of the blocks. In
  the current CUDACachingAllocator, the sizes are rounded up in multiple
  of blocks size of 512, so this works fine for smaller sizes. However, this
  can be inefficient for large near-by allocations as each will go to different
  size of blocks and re-use of those blocks are minimized. This might create
  lots of unused blocks and will waste GPU memory capacity. This option enables
  the rounding of allocation size to nearest power-2 division. For example, if
  we need to round-up size of 1200 and if number of divisions is 4,
  the size 1200 lies between 1024 and 2048 and if we do 4 divisions between
  them, the values are 1024, 1280, 1536, and 1792. So, allocation size of 1200
  will be rounded to 1280 as the nearest ceiling of power-2 division.
* ``roundup_bypass_threshold_mb`` bypass rounding the requested allocation size,
  for allocation requests larger than the threshold value (in MB). This can help
  reduce the memory footprint when making large allocations that are expected to
  be persistent or have a large lifetime.
* ``garbage_collection_threshold`` helps actively reclaiming unused GPU memory to
  avoid triggering expensive sync-and-reclaim-all operation (release_cached_blocks),
  which can be unfavorable to latency-critical GPU applications (e.g., servers).
  Upon setting this threshold (e.g., 0.8), the allocator will start reclaiming
  GPU memory blocks if the GPU memory capacity usage exceeds the threshold (i.e.,
  80% of the total memory allocated to the GPU application). The algorithm prefers
  to free old & unused blocks first to avoid freeing blocks that are actively being
  reused. The threshold value should be between greater than 0.0 and less than 1.0.

.. _cufft-plan-cache:

cuFFT plan cache
----------------

For each CUDA device, an LRU cache of cuFFT plans is used to speed up repeatedly
running FFT methods (e.g., :func:`torch.fft.fft`) on CUDA tensors of same geometry
with same configuration. Because some cuFFT plans may allocate GPU memory,
these caches have a maximum capacity.

You may control and query the properties of the cache of current device with
the following APIs:

* ``torch.backends.cuda.cufft_plan_cache.max_size`` gives the capacity of the
  cache (default is 4096 on CUDA 10 and newer, and 1023 on older CUDA versions).
  Setting this value directly modifies the capacity.

* ``torch.backends.cuda.cufft_plan_cache.size`` gives the number of plans
  currently residing in the cache.

* ``torch.backends.cuda.cufft_plan_cache.clear()`` clears the cache.

To control and query plan caches of a non-default device, you can index the
``torch.backends.cuda.cufft_plan_cache`` object with either a :class:`torch.device`
object or a device index, and access one of the above attributes. E.g., to set
the capacity of the cache for device ``1``, one can write
``torch.backends.cuda.cufft_plan_cache[1].max_size = 10``.

.. _cuda-just-in-time-compilation:

Just-in-Time Compilation
------------------------

PyTorch just-in-time compiles some operations, like torch.special.zeta, when
performed on CUDA tensors. This compilation can be time consuming
(up to a few seconds depending on your hardware and software)
and may occur multiple times for a single operator since many PyTorch operators actually
select from a variety of kernels, each of which must be compiled once, depending on their input.
This compilation occurs once per process, or just once if a kernel cache is used.

By default, PyTorch creates a kernel cache in $XDG_CACHE_HOME/torch/kernels if
XDG_CACHE_HOME is defined and $HOME/.cache/torch/kernels if it's not (except on Windows,
where the kernel cache is not yet supported). The caching behavior can be directly
controlled with two environment variables. If USE_PYTORCH_KERNEL_CACHE is set to 0 then no
cache will be used, and if PYTORCH_KERNEL_CACHE_PATH is set then that path will be used
as a kernel cache instead of the default location.

Best practices
--------------

Device-agnostic code
^^^^^^^^^^^^^^^^^^^^

Due to the structure of PyTorch, you may need to explicitly write
device-agnostic (CPU or GPU) code; an example may be creating a new tensor as
the initial hidden state of a recurrent neural network.

The first step is to determine whether the GPU should be used or not. A common
pattern is to use Python's ``argparse`` module to read in user arguments, and
have a flag that can be used to disable CUDA, in combination with
:meth:`~torch.cuda.is_available`. In the following, ``args.device`` results in a
:class:`torch.device` object that can be used to move tensors to CPU or CUDA.

::

    import argparse
    import torch

    parser = argparse.ArgumentParser(description='PyTorch Example')
    parser.add_argument('--disable-cuda', action='store_true',
                        help='Disable CUDA')
    args = parser.parse_args()
    args.device = None
    if not args.disable_cuda and torch.cuda.is_available():
        args.device = torch.device('cuda')
    else:
        args.device = torch.device('cpu')

Now that we have ``args.device``, we can use it to create a Tensor on the
desired device.

::

    x = torch.empty((8, 42), device=args.device)
    net = Network().to(device=args.device)

This can be used in a number of cases to produce device agnostic code. Below
is an example when using a dataloader:

::

    cuda0 = torch.device('cuda:0')  # CUDA GPU 0
    for i, x in enumerate(train_loader):
        x = x.to(cuda0)

When working with multiple GPUs on a system, you can use the
``CUDA_VISIBLE_DEVICES`` environment flag to manage which GPUs are available to
PyTorch. As mentioned above, to manually control which GPU a tensor is created
on, the best practice is to use a :any:`torch.cuda.device` context manager.

::

    print("Outside device is 0")  # On device 0 (default in most scenarios)
    with torch.cuda.device(1):
        print("Inside device is 1")  # On device 1
    print("Outside device is still 0")  # On device 0

If you have a tensor and would like to create a new tensor of the same type on
the same device, then you can use a ``torch.Tensor.new_*`` method
(see :class:`torch.Tensor`).
Whilst the previously mentioned ``torch.*`` factory functions
(:ref:`tensor-creation-ops`) depend on the current GPU context and
the attributes arguments you pass in, ``torch.Tensor.new_*`` methods preserve
the device and other attributes of the tensor.

This is the recommended practice when creating modules in which new
tensors need to be created internally during the forward pass.

::

    cuda = torch.device('cuda')
    x_cpu = torch.empty(2)
    x_gpu = torch.empty(2, device=cuda)
    x_cpu_long = torch.empty(2, dtype=torch.int64)

    y_cpu = x_cpu.new_full([3, 2], fill_value=0.3)
    print(y_cpu)

        tensor([[ 0.3000,  0.3000],
                [ 0.3000,  0.3000],
                [ 0.3000,  0.3000]])

    y_gpu = x_gpu.new_full([3, 2], fill_value=-5)
    print(y_gpu)

        tensor([[-5.0000, -5.0000],
                [-5.0000, -5.0000],
                [-5.0000, -5.0000]], device='cuda:0')

    y_cpu_long = x_cpu_long.new_tensor([[1, 2, 3]])
    print(y_cpu_long)

        tensor([[ 1,  2,  3]])


If you want to create a tensor of the same type and size of another tensor, and
fill it with either ones or zeros, :meth:`~torch.ones_like` or
:meth:`~torch.zeros_like` are provided as convenient helper functions (which
also preserve :class:`torch.device` and :class:`torch.dtype` of a Tensor).

::

    x_cpu = torch.empty(2, 3)
    x_gpu = torch.empty(2, 3)

    y_cpu = torch.ones_like(x_cpu)
    y_gpu = torch.zeros_like(x_gpu)

.. _cuda-memory-pinning:

Use pinned memory buffers
^^^^^^^^^^^^^^^^^^^^^^^^^

.. warning::

    This is an advanced tip. If you overuse pinned memory, it can cause serious
    problems when running low on RAM, and you should be aware that pinning is
    often an expensive operation.

Host to GPU copies are much faster when they originate from pinned (page-locked)
memory. CPU tensors and storages expose a :meth:`~torch.Tensor.pin_memory`
method, that returns a copy of the object, with data put in a pinned region.

Also, once you pin a tensor or storage, you can use asynchronous GPU copies.
Just pass an additional ``non_blocking=True`` argument to a
:meth:`~torch.Tensor.to` or a :meth:`~torch.Tensor.cuda` call. This can be used
to overlap data transfers with computation.

You can make the :class:`~torch.utils.data.DataLoader` return batches placed in
pinned memory by passing ``pin_memory=True`` to its constructor.

.. _cuda-nn-ddp-instead:

Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Most use cases involving batched inputs and multiple GPUs should default to
using :class:`~torch.nn.parallel.DistributedDataParallel` to utilize more
than one GPU.

There are significant caveats to using CUDA models with
:mod:`~torch.multiprocessing`; unless care is taken to meet the data handling
requirements exactly, it is likely that your program will have incorrect or
undefined behavior.

It is recommended to use :class:`~torch.nn.parallel.DistributedDataParallel`,
instead of :class:`~torch.nn.DataParallel` to do multi-GPU training, even if
there is only a single node.

The difference between :class:`~torch.nn.parallel.DistributedDataParallel` and
:class:`~torch.nn.DataParallel` is: :class:`~torch.nn.parallel.DistributedDataParallel`
uses multiprocessing where a process is created for each GPU, while
:class:`~torch.nn.DataParallel` uses multithreading. By using multiprocessing,
each GPU has its dedicated process, this avoids the performance overhead caused
by GIL of Python interpreter.

If you use :class:`~torch.nn.parallel.DistributedDataParallel`, you could use
`torch.distributed.launch` utility to launch your program, see :ref:`distributed-launch`.

.. _cuda-graph-semantics:

CUDA Graphs
-----------

A CUDA graph is a record of the work (mostly kernels and their arguments) that a
CUDA stream and its dependent streams perform.
For general principles and details on the underlying CUDA API, see
`Getting Started with CUDA Graphs`_ and the
`Graphs section`_ of the CUDA C Programming Guide.

PyTorch supports the construction of CUDA graphs using `stream capture`_, which puts a
CUDA stream in *capture mode*. CUDA work issued to a capturing stream doesn't actually
run on the GPU. Instead, the work is recorded in a graph.

After capture, the graph can be *launched* to run the GPU work as many times as needed.
Each replay runs the same kernels with the same arguments. For pointer arguments this
means the same memory addresses are used.
By filling input memory with new data (e.g., from a new batch) before each replay,
you can rerun the same work on new data.

Why CUDA Graphs?
^^^^^^^^^^^^^^^^

Replaying a graph sacrifices the dynamic flexibility of typical eager execution in exchange for
**greatly reduced CPU overhead**. A graph's arguments and kernels are fixed, so a graph replay
skips all layers of argument setup and kernel dispatch, including Python, C++, and CUDA driver
overheads. Under the hood, a replay submits the entire graph's work to the GPU with
a single call to `cudaGraphLaunch`_.  Kernels in a replay also execute slightly faster
on the GPU, but eliding CPU overhead is the main benefit.

You should try CUDA graphs if all or part of your network is graph-safe (usually this means
static shapes and static control flow, but see the other :ref:`constraints<capture-constraints>`)
and you suspect its runtime is at least somewhat CPU-limited.

.. _Getting Started with CUDA Graphs:
    https://developer.nvidia.com/blog/cuda-graphs/
.. _Graphs section:
    https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cuda-graphs
.. _stream capture:
    https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#creating-a-graph-using-stream-capture
.. _cudaGraphLaunch:
    https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__GRAPH.html#group__CUDART__GRAPH_1g1accfe1da0c605a577c22d9751a09597

PyTorch API
^^^^^^^^^^^

.. warning::
    This API is in beta and may change in future releases.

PyTorch exposes graphs via a raw :class:`torch.cuda.CUDAGraph` class
and two convenience wrappers,
:class:`torch.cuda.graph` and
:class:`torch.cuda.make_graphed_callables`.

:class:`torch.cuda.graph` is a simple, versatile context manager that
captures CUDA work in its context.
Before capture, warm up the workload to be captured by running
a few eager iterations. Warmup must occur on a side stream.
Because the graph reads from and writes to the same memory addresses in every
replay, you must maintain long-lived references to tensors that hold
input and output data during capture.
To run the graph on new input data, copy new data to the capture's input tensor(s),
replay the graph, then read the new output from the capture's output tensor(s).
Example::

    g = torch.cuda.CUDAGraph()

    # Placeholder input used for capture
    static_input = torch.empty((5,), device="cuda")

    # Warmup before capture
    s = torch.cuda.Stream()
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        for _ in range(3):
            static_output = static_input * 2
    torch.cuda.current_stream().wait_stream(s)

    # Captures the graph
    # To allow capture, automatically sets a side stream as the current stream in the context
    with torch.cuda.graph(g):
        static_output = static_input * 2

    # Fills the graph's input memory with new data to compute on
    static_input.copy_(torch.full((5,), 3, device="cuda"))
    g.replay()
    # static_output holds the results
    print(static_output)  # full of 3 * 2 = 6

    # Fills the graph's input memory with more data to compute on
    static_input.copy_(torch.full((5,), 4, device="cuda"))
    g.replay()
    print(static_output)  # full of 4 * 2 = 8

See
:ref:`Whole-network capture<whole-network-capture>`,
:ref:`Usage with torch.cuda.amp<graphs-with-amp>`, and
:ref:`Usage with multiple streams<multistream-capture>`
for realistic and advanced patterns.

:class:`~torch.cuda.make_graphed_callables` is more sophisticated.
:class:`~torch.cuda.make_graphed_callables` accepts Python functions and
:class:`torch.nn.Module`\s. For each passed function or Module,
it creates separate graphs of the forward-pass and backward-pass work. See
:ref:`Partial-network capture<partial-network-capture>`.

.. _capture-constraints:

Constraints
~~~~~~~~~~~

A set of ops is *capturable* if it doesn't violate any of the following constraints.

Constraints apply to all work in a
:class:`torch.cuda.graph` context and all work in the forward and backward passes
of any callable you pass to :func:`torch.cuda.make_graphed_callables`.

Violating any of these will likely cause a runtime error:

* Capture must occur on a non-default stream. (This is only a concern if you use the raw
  :meth:`CUDAGraph.capture_begin<torch.cuda.CUDAGraph.capture_begin>` and
  :meth:`CUDAGraph.capture_end<torch.cuda.CUDAGraph.capture_end>` calls.
  :class:`~torch.cuda.graph` and
  :func:`~torch.cuda.make_graphed_callables` set a side stream for you.)
* Ops that synchronize the CPU with the GPU (e.g., ``.item()`` calls) are prohibited.
* CUDA RNG ops are allowed, but must use default generators. For example, explicitly constructing a
  new :class:`torch.Generator` instance and passing it as the ``generator`` argument to an RNG function
  is prohibited.

Violating any of these will likely cause silent numerical errors or undefined behavior:

* Within a process, only one capture may be underway at a time.
* No non-captured CUDA work may run in this process (on any thread) while capture is underway.
* CPU work is not captured. If the captured ops include CPU work, that work will be elided during replay.
* Every replay reads from and writes to the same (virtual) memory addresses.
* Dynamic control flow (based on CPU or GPU data) is prohibited.
* Dynamic shapes are prohibited. The graph assumes every tensor in the captured op sequence
  has the same size and layout in every replay.
* Using multiple streams in a capture is allowed, but there are :ref:`restrictions<multistream-capture>`.

Non-constraints
~~~~~~~~~~~~~~~

* Once captured, the graph may be replayed on any stream.

.. _whole-network-capture:

Whole-network capture
^^^^^^^^^^^^^^^^^^^^^^

If your entire network is capturable, you can capture and replay an entire iteration::

    N, D_in, H, D_out = 640, 4096, 2048, 1024
    model = torch.nn.Sequential(torch.nn.Linear(D_in, H),
                                torch.nn.Dropout(p=0.2),
                                torch.nn.Linear(H, D_out),
                                torch.nn.Dropout(p=0.1)).cuda()
    loss_fn = torch.nn.MSELoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

    # Placeholders used for capture
    static_input = torch.randn(N, D_in, device='cuda')
    static_target = torch.randn(N, D_out, device='cuda')

    # warmup
    # Uses static_input and static_target here for convenience,
    # but in a real setting, because the warmup includes optimizer.step()
    # you must use a few batches of real data.
    s = torch.cuda.Stream()
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        for i in range(3):
            optimizer.zero_grad(set_to_none=True)
            y_pred = model(static_input)
            loss = loss_fn(y_pred, static_target)
            loss.backward()
            optimizer.step()
    torch.cuda.current_stream().wait_stream(s)

    # capture
    g = torch.cuda.CUDAGraph()
    # Sets grads to None before capture, so backward() will create
    # .grad attributes with allocations from the graph's private pool
    optimizer.zero_grad(set_to_none=True)
    with torch.cuda.graph(g):
        static_y_pred = model(static_input)
        static_loss = loss_fn(static_y_pred, static_target)
        static_loss.backward()
        optimizer.step()

    real_inputs = [torch.rand_like(static_input) for _ in range(10)]
    real_targets = [torch.rand_like(static_target) for _ in range(10)]

    for data, target in zip(real_inputs, real_targets):
        # Fills the graph's input memory with new data to compute on
        static_input.copy_(data)
        static_target.copy_(target)
        # replay() includes forward, backward, and step.
        # You don't even need to call optimizer.zero_grad() between iterations
        # because the captured backward refills static .grad tensors in place.
        g.replay()
        # Params have been updated. static_y_pred, static_loss, and .grad
        # attributes hold values from computing on this iteration's data.

.. _partial-network-capture:

Partial-network capture
^^^^^^^^^^^^^^^^^^^^^^^^^

If some of your network is unsafe to capture (e.g., due to dynamic control flow,
dynamic shapes, CPU syncs, or essential CPU-side logic), you can run the unsafe
part(s) eagerly and use :func:`torch.cuda.make_graphed_callables` to graph only
the capture-safe part(s).

By default, callables returned by :func:`~torch.cuda.make_graphed_callables`
are autograd-aware, and can be used in the training loop as direct replacements
for the functions or :class:`nn.Module<torch.nn.Module>`\ s you passed.

:func:`~torch.cuda.make_graphed_callables` internally creates
:class:`~torch.cuda.CUDAGraph` objects, runs warmup iterations, and maintains
static inputs and outputs as needed.  Therefore (unlike with
:class:`torch.cuda.graph`) you don't need to handle those manually.

In the following example, data-dependent dynamic control flow means the
network isn't capturable end-to-end, but
:func:`~torch.cuda.make_graphed_callables`
lets us capture and run graph-safe sections as graphs regardless::

    N, D_in, H, D_out = 640, 4096, 2048, 1024

    module1 = torch.nn.Linear(D_in, H).cuda()
    module2 = torch.nn.Linear(H, D_out).cuda()
    module3 = torch.nn.Linear(H, D_out).cuda()

    loss_fn = torch.nn.MSELoss()
    optimizer = torch.optim.SGD(chain(module1.parameters(),
                                      module2.parameters(),
                                      module3.parameters()),
                                lr=0.1)

    # Sample inputs used for capture
    # requires_grad state of sample inputs must match
    # requires_grad state of real inputs each callable will see.
    x = torch.randn(N, D_in, device='cuda')
    h = torch.randn(N, H, device='cuda', requires_grad=True)

    module1 = torch.cuda.make_graphed_callables(module1, (x,))
    module2 = torch.cuda.make_graphed_callables(module2, (h,))
    module3 = torch.cuda.make_graphed_callables(module3, (h,))

    real_inputs = [torch.rand_like(x) for _ in range(10)]
    real_targets = [torch.randn(N, D_out, device="cuda") for _ in range(10)]

    for data, target in zip(real_inputs, real_targets):
        optimizer.zero_grad(set_to_none=True)

        tmp = module1(data)  # forward ops run as a graph

        if tmp.sum().item() > 0:
            tmp = module2(tmp)  # forward ops run as a graph
        else:
            tmp = module3(tmp)  # forward ops run as a graph

        loss = loss_fn(tmp, target)
        # module2's or module3's (whichever was chosen) backward ops,
        # as well as module1's backward ops, run as graphs
        loss.backward()
        optimizer.step()

.. _graphs-with-amp:

Usage with torch.cuda.amp
^^^^^^^^^^^^^^^^^^^^^^^^^

For typical optimizers, :meth:`GradScaler.step<torch.cuda.amp.GradScaler.step>` syncs
the CPU with the GPU, which is prohibited during capture. To avoid errors, either use
:ref:`partial-network capture<partial-network-capture>`, or (if forward, loss,
and backward are capture-safe) capture forward, loss, and backward but not the
optimizer step::

    # warmup
    # In a real setting, use a few batches of real data.
    s = torch.cuda.Stream()
    s.wait_stream(torch.cuda.current_stream())
    with torch.cuda.stream(s):
        for i in range(3):
            optimizer.zero_grad(set_to_none=True)
            with torch.cuda.amp.autocast():
                y_pred = model(static_input)
                loss = loss_fn(y_pred, static_target)
            scaler.scale(loss).backward()
            scaler.step(optimizer)
            scaler.update()
    torch.cuda.current_stream().wait_stream(s)

    # capture
    g = torch.cuda.CUDAGraph()
    optimizer.zero_grad(set_to_none=True)
    with torch.cuda.graph(g):
        with torch.cuda.amp.autocast():
            static_y_pred = model(static_input)
            static_loss = loss_fn(static_y_pred, static_target)
        scaler.scale(static_loss).backward()
        # don't capture scaler.step(optimizer) or scaler.update()

    real_inputs = [torch.rand_like(static_input) for _ in range(10)]
    real_targets = [torch.rand_like(static_target) for _ in range(10)]

    for data, target in zip(real_inputs, real_targets):
        static_input.copy_(data)
        static_target.copy_(target)
        g.replay()
        # Runs scaler.step and scaler.update eagerly
        scaler.step(optimizer)
        scaler.update()

.. _multistream-capture:

Usage with multiple streams
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Capture mode automatically propagates to any streams that sync with a capturing stream.
Within capture, you may expose parallelism by issuing calls to different streams,
but the overall stream dependency DAG must branch out from the
initial capturing stream after capture begins and rejoin the initial stream
before capture ends::

    with torch.cuda.graph(g):
        # at context manager entrance, torch.cuda.current_stream()
        # is the initial capturing stream

        # INCORRECT (does not branch out from or rejoin initial stream)
        with torch.cuda.stream(s):
            cuda_work()

        # CORRECT:
        # branches out from initial stream
        s.wait_stream(torch.cuda.current_stream())
        with torch.cuda.stream(s):
            cuda_work()
        # rejoins initial stream before capture ends
        torch.cuda.current_stream().wait_stream(s)

.. note::

    To avoid confusion for power users looking at replays in nsight systems or nvprof:
    Unlike eager execution, the graph interprets a nontrivial stream DAG in capture
    as a hint, not a command. During replay, the graph may reorganize independent ops
    onto different streams or enqueue them in a different order (while respecting your
    original DAG's overall dependencies).

Usage with DistributedDataParallel
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

NCCL < 2.9.6
~~~~~~~~~~~~

NCCL versions earlier than 2.9.6 don't allow collectives to be captured.
You must use :ref:`partial-network capture<partial-network-capture>`,
which defers allreduces to happen outside graphed sections of backward.

Call :func:`~torch.cuda.make_graphed_callables` on graphable network sections
*before* wrapping the network with DDP.

NCCL >= 2.9.6
~~~~~~~~~~~~~

NCCL versions 2.9.6 or later allow collectives in the graph.
Approaches that capture an :ref:`entire backward pass<whole-network-capture>`
are a viable option, but need three setup steps.

1. Disable DDP's internal async error handling::

    os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "0"
    torch.distributed.init_process_group(...)

2. Before full-backward capture, DDP must be constructed in a side-stream context::

    with torch.cuda.stream(s):
        model = DistributedDataParallel(model)

3. Your warmup must run at least 11 DDP-enabled eager iterations before capture.

.. _graph-memory-management:

Graph memory management
^^^^^^^^^^^^^^^^^^^^^^^

A captured graph acts on the same virtual addresses every time it replays.
If PyTorch frees the memory, a later replay can hit an illegal memory access.
If PyTorch reassigns the memory to new tensors, the replay can corrupt the values
seen by those tensors.  Therefore, the virtual addresses used by the graph must be
reserved for the graph across replays. The PyTorch caching allocator achieves this
by detecting when capture is underway and satisfying the capture's allocations
from a graph-private memory pool. The private pool stays alive until its
:class:`~torch.cuda.CUDAGraph` object and all tensors created during capture
go out of scope.

Private pools are maintained automatically. By default, the allocator creates a
separate private pool for each capture. If you capture multiple graphs,
this conservative approach ensures graph replays never corrupt each other's values,
but sometimes needlessly wastes memory.

Sharing memory across captures
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To economize the memory stashed in private pools, :class:`torch.cuda.graph`
and :func:`torch.cuda.make_graphed_callables` optionally allow different
captures to share the same private pool.
It's safe for a set of graphs to share a private pool if you know they'll always
be replayed in the same order they were captured,
and never be replayed concurrently.

:class:`torch.cuda.graph`'s ``pool`` argument is a hint to use a particular private pool,
and can be used to share memory across graphs as shown::

    g1 = torch.cuda.CUDAGraph()
    g2 = torch.cuda.CUDAGraph()

    # (create static inputs for g1 and g2, run warmups of their workloads...)

    # Captures g1
    with torch.cuda.graph(g1):
        static_out_1 = g1_workload(static_in_1)

    # Captures g2, hinting that g2 may share a memory pool with g1
    with torch.cuda.graph(g2, pool=g1.pool()):
        static_out_2 = g2_workload(static_in_2)

    static_in_1.copy_(real_data_1)
    static_in_2.copy_(real_data_2)
    g1.replay()
    g2.replay()

With :func:`torch.cuda.make_graphed_callables`, if you want to graph several
callables and you know they'll always run in the same order (and never concurrently)
pass them as a tuple in the same order they'll run in the live workload, and
:func:`~torch.cuda.make_graphed_callables` will capture their graphs using a shared
private pool.

If, in the live workload, your callables will run in an order that occasionally changes,
or if they'll run concurrently, passing them as a tuple to a single invocation of
:func:`~torch.cuda.make_graphed_callables` is not allowed. Instead, you must call
:func:`~torch.cuda.make_graphed_callables` separately for each one.