Source code for torch.cuda.graphs

import gc
import torch

from ._utils import _dummy_type
from torch.utils._pytree import tree_flatten as _tree_flatten
from torch.utils._pytree import tree_unflatten as _tree_unflatten

if not hasattr(torch._C, '_CudaStreamBase'):
    # Define dummy base classes
    torch._C.__dict__['_CUDAGraph'] = _dummy_type('_CUDAGraph')
    torch._C.__dict__['_graph_pool_handle'] = _dummy_type('_graph_pool_handle')
    torch._C.__dict__['_cuda_isCurrentStreamCapturing'] = _dummy_type('_cuda_isCurrentStreamCapturing')

from torch._C import _CUDAGraph  # noqa: F401
from torch._C import _graph_pool_handle
from torch._C import _cuda_isCurrentStreamCapturing

[docs]def is_current_stream_capturing(): r""" Returns True if CUDA graph capture is underway on the current CUDA stream, False otherwise. If a CUDA context does not exist on the current device, returns False without initializing the context. """ return _cuda_isCurrentStreamCapturing()
# Python shim helps Sphinx process docstrings more reliably.
[docs]def graph_pool_handle(): r""" Returns an opaque token representing the id of a graph memory pool. See :ref:`Graph memory management<graph-memory-management>`. .. warning:: This API is in beta and may change in future releases. """ return _graph_pool_handle()
# Python shim helps Sphinx process docstrings more reliably.
[docs]class CUDAGraph(torch._C._CUDAGraph): r""" Wrapper around a CUDA graph. .. warning:: This API is in beta and may change in future releases. """ def __new__(cls): return super(CUDAGraph, cls).__new__(cls)
[docs] def capture_begin(self, pool=None): r""" Begins capturing CUDA work on the current stream. Typically, you shouldn't call ``capture_begin`` yourself. Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`, which call ``capture_begin`` internally. Arguments: pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or :meth:`other_Graph_instance.pool()<torch.cuda.CUDAGraph.pool>`) that hints this graph may share memory with the indicated pool. See :ref:`Graph memory management<graph-memory-management>`. """ # I'm not sure if pybind11 converts a None arg to the default defined on the C++ side, # so I'm not taking any chances. if pool is None: super().capture_begin() else: super().capture_begin(pool)
[docs] def capture_end(self): r""" Ends CUDA graph capture on the current stream. After ``capture_end``, ``replay`` may be called on this instance. Typically, you shouldn't call ``capture_end`` yourself. Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`, which call ``capture_end`` internally. """ super().capture_end()
[docs] def replay(self): r""" Replays the CUDA work captured by this graph. """ super().replay()
[docs] def reset(self): r""" Deletes the graph currently held by this instance. """ super().reset()
[docs] def pool(self): r""" Returns an opaque token representing the id of this graph's memory pool. This id can optionally be passed to another graph's ``capture_begin``, which hints the other graph may share the same memory pool. """ return super().pool()
[docs] def enable_debug_mode(self): r""" Enables debugging mode for CUDAGraph.debug_dump. """ return super().enable_debug_mode()
[docs] def debug_dump(self, debug_path): r""" Arguments: debug_path (required): Path to dump the graph to. Calls a debugging function to dump the graph if the debugging is enabled via CUDAGraph.enable_debug_mode() """ return super().debug_dump(debug_path)
[docs]class graph: r""" Context-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph` object for later replay. See :ref:`CUDA Graphs <cuda-graph-semantics>` for a general introduction, detailed use, and constraints. Arguments: cuda_graph (torch.cuda.CUDAGraph): Graph object used for capture. pool (optional): Opaque token (returned by a call to :func:`~torch.cuda.graph_pool_handle()` or :meth:`other_Graph_instance.pool()<torch.cuda.CUDAGraph.pool>`) hinting this graph's capture may share memory from the specified pool. See :ref:`Graph memory management<graph-memory-management>`. stream (torch.cuda.Stream, optional): If supplied, will be set as the current stream in the context. If not supplied, ``graph`` sets its own internal side stream as the current stream in the context. .. note:: For effective memory sharing, if you pass a ``pool`` used by a previous capture and the previous capture used an explicit ``stream`` argument, you should pass the same ``stream`` argument to this capture. .. warning:: This API is in beta and may change in future releases. """ default_capture_stream = None def __init__(self, cuda_graph, pool=None, stream=None): # Lazy-init of default_capture_stream helps avoid circular-import errors. # Not thread safe, but graphs already have the general (explicitly documented) # restriction that only one capture may be underway at a time in the process. if self.__class__.default_capture_stream is None: self.__class__.default_capture_stream = torch.cuda.Stream() self.pool = () if pool is None else (pool,) self.capture_stream = stream if stream is not None else self.__class__.default_capture_stream assert self.capture_stream is not None self.stream_ctx = self.cuda_graph = cuda_graph def __enter__(self): # Free as much memory as we can for the graph torch.cuda.synchronize() gc.collect() torch.cuda.empty_cache() # Stackoverflow seems comfortable with this pattern # self.stream_ctx.__enter__() self.cuda_graph.capture_begin(*self.pool) def __exit__(self, exc_type, exc_value, traceback): self.cuda_graph.capture_end() self.stream_ctx.__exit__(exc_type, exc_value, traceback)
# returning None should propagate exceptions from either capture_end or stream_ctx.__exit__()
[docs]def make_graphed_callables(callables, sample_args, num_warmup_iters=3, allow_unused_input=False): r""" Accepts callables (functions or :class:`nn.Module<torch.nn.Module>`\ s) and returns graphed versions. Each graphed callable's forward pass runs its source callable's forward CUDA work as a CUDA graph inside a single autograd node. The graphed callable's forward pass also appends a backward node to the autograd graph. During backward, this node runs the callable's backward work as a CUDA graph. Therefore, each graphed callable should be a drop-in replacement for its source callable in an autograd-enabled training loop. See :ref:`Partial-network capture<partial-network-capture>` for detailed use and constraints. If you pass a tuple of several callables, their captures will use the same memory pool. See :ref:`Graph memory management<graph-memory-management>` for when this is appropriate. Arguments: callables (torch.nn.Module or Python function, or tuple of these): Callable or callables to graph. See :ref:`Graph memory management<graph-memory-management>` for when passing a tuple of callables is appropriate. If you pass a tuple of callables, their order in the tuple must be the same order they'll run in the live workload. sample_args (tuple of Tensors, or tuple of tuples of Tensors): Samples args for each callable. If a single callable was passed, ``sample_args`` must be a single tuple of argument Tensors. If a tuple of callables was passed, ``sample_args`` must be tuple of tuples of argument Tensors. num_warmup_iters (int): The number of warmup iterations. Currently, ``DataDistributedParallel`` needs 11 iterations for warm up. Default: ``3``. allow_unused_input (bool): If False, specifying inputs that were not used when computing outputs (and therefore their grad is always zero) is an error. Defaults to False. .. note:: The ``requires_grad`` state of each Tensor in ``sample_args`` must match the state that's expected for the corresponding real input in the training loop. .. warning:: This API is in beta and may change in future releases. .. warning:: ``sample_args`` for each callable must contain only Tensors. Other types are not allowed. .. warning:: Returned callables do not support higher order differentiation (e.g., double backward). .. warning:: In any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters may be trainable. Buffers must have ``requires_grad=False``. .. warning:: After you pass a :class:`torch.nn.Module` through :func:`~make_graphed_callables`, you may not add or remove any of that Module's parameters or buffers. .. warning:: :class:`torch.nn.Module`\s passed to :func:`~torch.cuda.make_graphed_callables` must not have module hooks registered on them at the time they are passed. However, registering hooks on modules *after* passing them through :func:`~torch.cuda.make_graphed_callables` is allowed. .. warning:: When running a graphed callable, you must pass its arguments in the same order and format they appeared in that callable's ``sample_args``. .. warning:: The automatic mixed precision is supported in :func:`~torch.cuda.make_graphed_callables` only with disabled caching. The context manager `torch.cuda.amp.autocast()` must have `cache_enabled=False`. """ if torch.is_autocast_enabled() and torch.is_autocast_cache_enabled(): raise RuntimeError("make_graphed_callables does not support the autocast caching. Please set `cache_enabled=False`.") just_one_callable = False if not isinstance(callables, tuple): just_one_callable = True callables = (callables,) sample_args = (sample_args,) flatten_sample_args = [] for c, args in zip(callables, sample_args): if isinstance(c, torch.nn.Module): assert len(c._backward_hooks) == 0 and len(c._forward_hooks) == 0 and len(c._forward_pre_hooks) == 0, \ "Modules must not have hooks registered at the time they are passed. However, registering hooks " + \ "on modules after passing them through make_graphed_callables is allowed." assert all(b.requires_grad is False for b in c.buffers()), "In any :class:`~torch.nn.Module` passed to " + \ ":func:`~make_graphed_callables`, only parameters may be trainable. All buffers must have " + \ "``requires_grad=False``." flatten_arg, _ = _tree_flatten(args) flatten_sample_args.append(tuple(flatten_arg)) assert all(isinstance(arg, torch.Tensor) for arg in flatten_arg), "In the beta API, sample_args " + \ "for each callable must contain only Tensors. Other types are not allowed." # If a callable is an nn.Module, its graph's full input surface is the args the user explicitly # passes to forward (ie, its sample_args) AND the module's parameter attributes. per_callable_len_user_args = [len(args) for args in flatten_sample_args] per_callable_module_params = [tuple(c.parameters()) if isinstance(c, torch.nn.Module) else () for c in callables] per_callable_static_input_surfaces = [flatten_sample_args[i] + per_callable_module_params[i] for i in range(len(callables))] fwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))] bwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))] mempool = graph_pool_handle() # Warmup # Hopefully prevents cudnn benchmarking and other lazy-initialization cuda work # from ending up in any captures. torch.cuda.synchronize() with for func, args, static_input_surface in zip(callables, sample_args, per_callable_static_input_surfaces): for _ in range(num_warmup_iters): outputs, _ = _tree_flatten(func(*args)) grad_inputs = torch.autograd.grad(outputs=tuple(o for o in outputs if o.requires_grad), inputs=tuple(i for i in static_input_surface if i.requires_grad), grad_outputs=tuple(torch.empty_like(o) for o in outputs if o.requires_grad), only_inputs=True, allow_unused=allow_unused_input) del outputs, grad_inputs torch.cuda.synchronize() # All captures here share a mempool. To avoid replays corrupting each other's memory, # the safest approach is to capture all passes in the same order they'll run: # fwd 1, fwd 2, ... fwd N, then bwd N, bwd N-1, ... bwd 1. # Capture forward graphs per_callable_static_outputs = [] per_callable_output_unflatten_spec = [] for func, args, fwd_graph in zip(callables, sample_args, fwd_graphs): with torch.cuda.graph(fwd_graph, pool=mempool): outputs = func(*args) flatten_outputs, spec = _tree_flatten(outputs) per_callable_static_outputs.append(tuple(flatten_outputs)) per_callable_output_unflatten_spec.append(spec) # Capture backward graphs in reverse order per_callable_static_grad_outputs = [] per_callable_static_grad_inputs = [] for static_input_surface, static_outputs, bwd_graph, module_params in \ zip(reversed(per_callable_static_input_surfaces), reversed(per_callable_static_outputs), reversed(bwd_graphs), reversed(per_callable_module_params)): # For now, assumes all static_outputs require grad # assert all(o.requires_grad for o in static_outputs), "Outputs of graphed callables must require grad." static_grad_outputs = tuple(torch.empty_like(o) if o.requires_grad else None for o in static_outputs) with torch.cuda.graph(bwd_graph, pool=mempool): grad_inputs = torch.autograd.grad(outputs=tuple(o for o in static_outputs if o.requires_grad), inputs=tuple(i for i in static_input_surface if i.requires_grad), grad_outputs=tuple(o for o in static_grad_outputs if o is not None), only_inputs=True, allow_unused=allow_unused_input) # Constructs a tuple suitable for returning from Graphed.backward: # Pads out the actually-needed grads with Nones in gradient slots for inputs that don't require grad. # I couldn't think of a slick one-liner for this pattern. static_grad_inputs = [] grad_idx = 0 for arg in static_input_surface: if arg.requires_grad: static_grad_inputs.append(grad_inputs[grad_idx]) grad_idx += 1 else: static_grad_inputs.append(None) # type: ignore[arg-type] static_grad_inputs = tuple(static_grad_inputs) # type: ignore[assignment] per_callable_static_grad_outputs.append(static_grad_outputs) per_callable_static_grad_inputs.append(static_grad_inputs) # Reverses the most recent two lists per_callable_static_grad_outputs = list(reversed(per_callable_static_grad_outputs)) per_callable_static_grad_inputs = list(reversed(per_callable_static_grad_inputs)) # Now for every per_callable list, per_callable_*[i] holds the stuff for the ith callable. def make_graphed_autograd_function(fwd_graph, bwd_graph, module_params, len_user_args, output_unflatten_spec, static_input_surface, static_outputs, static_grad_outputs, static_grad_inputs): class Graphed(torch.autograd.Function): @staticmethod def forward(ctx, *inputs): # At this stage, only the user args may (potentially) be new tensors. for i in range(len_user_args): if static_input_surface[i].data_ptr() != inputs[i].data_ptr(): static_input_surface[i].copy_(inputs[i]) fwd_graph.replay() assert isinstance(static_outputs, tuple) return tuple(o.detach() for o in static_outputs) @staticmethod @torch.autograd.function.once_differentiable def backward(ctx, *grads): assert len(grads) == len(static_grad_outputs) for g, grad in zip(static_grad_outputs, grads): if g is not None: # don't copy if autograd gods have been kind and the # incoming grad is already in the right place if g.data_ptr() != grad.data_ptr(): g.copy_(grad) bwd_graph.replay() # Input args that didn't require grad expect a None gradient. assert isinstance(static_grad_inputs, tuple) return tuple(b.detach() if b is not None else b for b in static_grad_inputs) def functionalized(*user_args): # Runs the autograd function with inputs == all inputs to the graph that might require grad # (explicit user args + module parameters) # Assumes module params didn't change since capture. flatten_user_args, _ = _tree_flatten(user_args) out = Graphed.apply(*(tuple(flatten_user_args) + module_params)) return _tree_unflatten(out, output_unflatten_spec) return functionalized # Put together the final graphed callables ret = [] for i, func in enumerate(callables): graphed = make_graphed_autograd_function(fwd_graphs[i], bwd_graphs[i], per_callable_module_params[i], per_callable_len_user_args[i], per_callable_output_unflatten_spec[i], per_callable_static_input_surfaces[i], per_callable_static_outputs[i], per_callable_static_grad_outputs[i], per_callable_static_grad_inputs[i]) if isinstance(func, torch.nn.Module): def make_graphed_forward(func, graph_training_state, graphed, orig_fwd): def new_fwd(*user_args): # If the module's training-or-eval state matches what we graphed, # run the graph, otherwise run the original forward method if == graph_training_state: return graphed(*user_args) else: return orig_fwd(*user_args) return new_fwd func.forward = make_graphed_forward(func,, graphed, func.forward) # type: ignore[assignment] ret.append(func) else: ret.append(graphed) if just_one_callable: return ret[0] return tuple(ret)


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