# mypy: allow-untyped-defs import contextlib 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 `_ collect_garbage (bool, optional): If True, call torch.cuda.synchronize() followed by gc.collect() to free memory before starting graph capture. Users almost always this to be True, but since the introduction of conditional nodes in cuda graphs, it is possible that more than one stream may be capturing at once. Since cudaDeviceSynchronize() synchronizes all streams, including capturing streams, previously started stream captures will be invalidated. This is not desirable. .. 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__() @contextlib.contextmanager def _graph_no_gc(cuda_graph, pool, stream, capture_error_mode): """This is an internal function used to do stream capture without calling torch.cuda.synchronize(), gc.collect(), and torch.cuda.empty_cache(). Unfortunately, cudagraph trees runs its eager warmup inside of the context manager _use_cuda_memory_pool_manager(), which makes captures_underway in CUDACachingAllocator.cpp non-empty. We need this in order to warmup conditional higher order operators, like torch.cond() and torch.while_loop(). torch.cuda.empty_cache() will fail if captures_underway is non-empty. Removing torch.cuda.synchronize() and gc.collect() is not strictly speaking required, but they are expensive an unnecessary operations. """ stream_ctx = torch.cuda.stream(stream) pool = () if pool is None else (pool,) with stream_ctx: cuda_graph.capture_begin(*pool, capture_error_mode=capture_error_mode) try: yield finally: cuda_graph.capture_end() 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 in zip( reversed(per_callable_static_input_surfaces), reversed(per_callable_static_outputs), reversed(bwd_graphs), ): # 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) @contextlib.contextmanager def thread_cuda_stream_capture_mode(new_mode): r"""Changes current thread's stream capture mode to `new_mode` upon __enter__ and resets the mode upon __exit__. The only documentation on a thread's stream capture mode is here: https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85 However, it is a little bit inadequate, so here is a more in-depth description. Both CPU threads and capturing cuda streams have a capture mode. A cuda stream's capture mode is set at cudaStreamBeginCapture() and can never be changed. Meanwhile all CPU threads start with a capture mode of cudaStreamCaptureModeGlobal, which can be changed at any time. Whenever a thread executes an unsafe CUDA action while CUDA streams are capturing, it follows the following logic to determine whether to invalidate those streams: if capture_mode_this_thread == cudaStreamCaptureModeRelaxed: never invalidate any capturing cuda streams whatsoever. elif capture_mode_this_thread == cudaStreamCaptureModeThreadLocal: invalidate any cuda streams for which cudaStreamBeginCapture() was called by this thread, except for streams whose capture mode is cudaStreamCaptureModeRelaxed. elif capture_mode_this_thread == cudaStreamCaptureModeGlobal: invalidate all cuda streams that are currently capturing on any thread, except for streams whose capture mode is cudaStreamCaptureModeRelaxed and for streams for which cudaStreamCaptureBegin() was called with cudaStreamCaptureModeThreadLocal on a thread other than this one. In practice, changed the current capture mode to cudaStreamCaptureModeRelaxed in particular is helpful for enabling developers to do "unsafe" things that we know are safe in our case. """ cudart = torch.cuda.cudart() old_mode = cudart.cudaThreadExchangeStreamCaptureMode(new_mode) try: yield finally: cudart.cudaThreadExchangeStreamCaptureMode(old_mode)