# mypy: allow-untyped-defs import gc import typing import torch from .._utils import _dummy_type 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 ( # noqa: F401 _cuda_isCurrentStreamCapturing, _CUDAGraph, _graph_pool_handle, ) def is_current_stream_capturing(): r"""Return 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. def graph_pool_handle(): r"""Return an opaque token representing the id of a graph memory pool. See :ref:`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. 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().__new__(cls) def capture_begin(self, pool=None, capture_error_mode="global"): r"""Begin 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()`) that hints this graph may share memory with the indicated pool. See :ref:`Graph memory management`. capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream. Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc, may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for actions in the current thread, and "relaxed" will not error on these actions. Do NOT change this setting unless you're familiar with `cudaStreamCaptureMode `_ """ # noqa: B950 super().capture_begin(pool=pool, capture_error_mode=capture_error_mode) def capture_end(self): r"""End 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() def replay(self): r"""Replay the CUDA work captured by this graph.""" super().replay() def reset(self): r"""Delete the graph currently held by this instance.""" super().reset() def pool(self): r"""Return 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() def enable_debug_mode(self): r"""Enable debugging mode for CUDAGraph.debug_dump.""" return super().enable_debug_mode() 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) class graph: r"""Context-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph` object for later replay. See :ref:`CUDA Graphs ` 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()`) hinting this graph's capture may share memory from the specified pool. See :ref:`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. capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream. Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc, may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for actions in the current thread, and "relaxed" will not error on actions. Do NOT change this setting unless you're familiar with `cudaStreamCaptureMode `_ .. 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. .. _cudaStreamCaptureMode: https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85 """ # noqa: B950 default_capture_stream: typing.Optional["torch.cuda.Stream"] = None def __init__( self, cuda_graph, pool=None, stream=None, capture_error_mode: str = "global", ): # 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 = torch.cuda.stream(self.capture_stream) self.cuda_graph = cuda_graph self.capture_error_mode = capture_error_mode 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 # https://stackoverflow.com/questions/26635684/calling-enter-and-exit-manually#39172487 self.stream_ctx.__enter__() self.cuda_graph.capture_begin( *self.pool, capture_error_mode=self.capture_error_mode ) 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__() def make_graphed_callables( callables, sample_args, num_warmup_iters=3, allow_unused_input=False, pool=None ): r"""Accept callables (functions or :class:`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` 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` 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` 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. pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or :meth:`other_Graph_instance.pool()`) that hints this graph may share memory with the indicated pool. See :ref:`Graph memory management`. .. 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 = torch.utils._pytree.arg_tree_leaves(*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() if pool is None else pool # Warmup # Hopefully prevents cudnn benchmarking and other lazy-initialization cuda work # from ending up in any captures. torch.cuda.synchronize() with torch.cuda.stream(torch.cuda.Stream()): for func, args, static_input_surface in zip( callables, sample_args, per_callable_static_input_surfaces ): grad_inputs, outputs, outputs_grad = None, None, None for _ in range(num_warmup_iters): outputs = torch.utils._pytree.tree_leaves(func(*args)) outputs_grad = tuple(o for o in outputs if o.requires_grad) if len(outputs_grad) > 0: grad_inputs = torch.autograd.grad( outputs=outputs_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, ) for v in [outputs, outputs_grad, grad_inputs]: del v 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 = torch.utils._pytree.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 ) outputs_grad = tuple(o for o in static_outputs if o.requires_grad) grad_inputs = None if len(outputs_grad) > 0: with torch.cuda.graph(bwd_graph, pool=mempool): grad_inputs = torch.autograd.grad( outputs=outputs_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 and grad_inputs is not None: 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.reverse() per_callable_static_grad_inputs.reverse() # 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 = torch.utils._pytree.arg_tree_leaves(*user_args) out = Graphed.apply(*(tuple(flatten_user_args) + module_params)) return torch.utils._pytree.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 func.training == graph_training_state: return graphed(*user_args) else: return orig_fwd(*user_args) return new_fwd func.forward = make_graphed_forward(func, func.training, graphed, func.forward) # type: ignore[assignment] ret.append(func) else: ret.append(graphed) if just_one_callable: return ret[0] return tuple(ret)