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
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 in on the same device as the tensor.
Cross-GPU operations are not allowed by default, with the exception of
copy_() and other methods with copy-like functionality
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)
TensorFloat-32(TF32) on Ampere devices¶
Starting in PyTorch 1.7, there is a new flag called allow_tf32 which defaults to true. 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:
# The flag below controls whether to allow TF32 on matmul. This flag defaults to True. 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:
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. 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 the full FP32 precision is needed, users can disable TF32 by:
torch.backends.cuda.matmul.allow_tf32 = False torch.backends.cudnn.allow_tf32 = False
To toggle the TF32 flags off in C++, you can do
For more information about TF32, see:
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
A consequence of the asynchronous computation is that time measurements without
synchronizations are not accurate. To get precise measurements, one should either
torch.cuda.synchronize() before measuring, or use
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
copy_() admit an explicit
which lets the caller bypass synchronization when it is unnecessary.
Another exception is CUDA streams, explained below.
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
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.
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
and optionally supplying CUDA tensor(s) as the initial gradient(s) (e.g.,
autograd.grad(..., grad_outputs=initial_grads), or
the acts of
optionally populating initial gradient(s),
invoking the backward pass, and
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
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
use grads is on the default stream.
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
max_memory_allocated() to monitor memory occupied by
tensors, and use
max_memory_reserved() to monitor the total amount of memory
managed by the caching allocator. Calling
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
memory_stats(). We also offer the capability to capture a
complete snapshot of the memory allocator state via
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
PYTORCH_NO_CUDA_MEMORY_CACHING=1 in your environment to disable caching.
The behavior of caching allocator can be controlled via environment variable
The format is
max_split_size_mbprevents 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
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.
cuFFT plan cache¶
For each CUDA device, an LRU cache of cuFFT plans is used to speed up repeatedly
running FFT methods (e.g.,
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_sizegives 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.sizegives 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
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.max_size = 10.
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
is_available(). In the following,
args.device results in a
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
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
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
Whilst the previously mentioned
torch.* factory functions
(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,
zeros_like() are provided as convenient helper functions (which
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)
Use pinned memory buffers¶
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
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
to() or a
cuda() call. This can be used
to overlap data transfers with computation.
You can make the
DataLoader return batches placed in
pinned memory by passing
pin_memory=True to its constructor.
Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel¶
Most use cases involving batched inputs and multiple GPUs should default to
DistributedDataParallel to utilize more
than one GPU.
There are significant caveats to using CUDA models with
multiprocessing; unless care is taken to meet the data handling
requirements exactly, it is likely that your program will have incorrect or
The difference between
uses multiprocessing where a process is created for each GPU, while
DataParallel uses multithreading. By using multiprocessing,
each GPU has its dedicated process, this avoids the performance overhead caused
by GIL of Python interpreter.
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 constraints) and you suspect its runtime is at least somewhat CPU-limited.
This API is in beta and may change in future releases.
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).
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
make_graphed_callables is more sophisticated.
make_graphed_callables accepts Python functions and
torch.nn.Modules. For each passed function or Module,
it creates separate graphs of the forward-pass and backward-pass work. See
A set of ops is capturable if it doesn’t violate any of the following constraints.
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
make_graphed_callables()set a side stream for you.)
Ops that sychronize 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
torch.Generatorinstance and passing it as the
generatorargument 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 restrictions.
Once captured, the graph may be replayed on any stream.
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.
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
torch.cuda.make_graphed_callables() to graph only
the capture-safe part(s).
make_graphed_callables() internally creates
CUDAGraph objects, runs warmup iterations, and maintains
static inputs and outputs as needed. Therefore (unlike with
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
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, y) # module2's or module3's (whichever was chosen) backward ops, # as well as module1's backward ops, run as graphs loss.backward() optimizer.step()
Usage with torch.cuda.amp¶
For typical optimizers,
the CPU with the GPU, which is prohibited during capture. To avoid errors, either use
partial-network capture, or (if forward, loss,
and backward are capture-safe) capture forward, loss, and backward but not the
# 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()
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)
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 partial-network capture, which defers allreduces to happen outside graphed sections of backward.
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 entire backward pass are a viable option, but need three setup steps.
Disable DDP’s internal async error handling:
os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "0" torch.distributed.init_process_group(...)
Before full-backward capture, DDP must be constructed in a side-stream context:
with torch.cuda.stream(s): model = DistributedDataParallel(model)
Your warmup must run at least 11 DDP-enabled eager iterations before capture.
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
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.