# Copyright (c) Kyutai, all rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """ Provides some extra utilities around torch compile, in particular with a way to fully deactivate it easily with a context manager. Provides a simple activation checkpointing that is compatible with FSDP and torch compile. Finally, provides some utilities for CUDA graphing functions. """ from contextlib import contextmanager from functools import wraps import inspect import os import typing as tp import torch from torch import cuda _compile_disabled: bool = False @contextmanager def no_compile(): """Disable torch.compile locally. Now Pytorch 2.4 provides a function to do that.""" global _compile_disabled prev_disabled = _compile_disabled _compile_disabled = True try: yield finally: _compile_disabled = prev_disabled def torch_compile_lazy(fun): """torch.compile creates a huge pool of processes, even when not using the function at all, e.g. with Dora. This can polute stderr when doing CTRL+C. So we do it in a lazy way. """ if os.environ.get("NO_TORCH_COMPILE"): return fun fun_compiled = None @wraps(fun) def _wrapped(*args, **kwargs): nonlocal fun_compiled if _compile_disabled: return fun(*args, **kwargs) if fun_compiled is None: fun_compiled = torch.compile(fun) return fun_compiled(*args, **kwargs) return _wrapped class Checkpoint(torch.autograd.Function): @staticmethod def forward(ctx, function, *args) -> tp.Any: to_save = [] ctx.others = [] ctx.function = function # Sources will indicate whether the arg in position N is # a tensor stored in ctx.save_for_backward, or inside ctx.others. ctx.sources = [] new_args = [] for arg in args: if isinstance(arg, torch.Tensor): to_save.append(arg) ctx.sources.append("tensor") new_args.append(arg.detach()) else: ctx.sources.append("other") ctx.others.append(arg) new_args.append(arg) ctx.save_for_backward(*to_save) # During the forward, we just make a pass with no gradient computed. with torch.no_grad(): res = function(*new_args) return res @staticmethod def backward(ctx, *grads) -> tp.Tuple[tp.Optional[torch.Tensor], ...]: pseudo_tensors = [] with torch.set_grad_enabled(True): # We create leaf tensors to collect the output gradients. # We call them pseudo_tensors because they are pretending to be the input # to `function` but are not directly for tensor in ctx.saved_tensors: pseudo_tensor = tensor.detach() pseudo_tensor.requires_grad_(True) pseudo_tensors.append(pseudo_tensor) pseudo_tensors_copy = list(pseudo_tensors) args = [] for source in ctx.sources: if source == "other": args.append(ctx.others.pop(0)) else: assert source == "tensor" args.append(pseudo_tensors_copy.pop(0)) res = ctx.function(*args) # The second forward with grad computation allows us to connect the input leaf tensors # inside pseudo_tensors, to the outputs of the function called. if not isinstance(res, tuple): res = (res,) # Now we just ask Torch to compute the derivative of `res` given the gradient coming from above # `grads`. The computed gradient will end up into the `pseudo_tensors` grad attributes. torch.autograd.backward(res, grads) out: tp.List[tp.Optional[torch.Tensor]] = [None] for source in ctx.sources: # We still need to output `None` values for non tensor parameters. if source == "other": out.append(None) else: assert source == "tensor" out.append(pseudo_tensors.pop(0).grad) return tuple(out) def simple_checkpoint(module: torch.nn.Module, *args, **kwargs): """Custom implementation of checkpointing in PyTorch as the builtin implementation is broken when using torch compile. Only supports wrapping a `nn.Module` with a forward with no `*args` or `**kwargs`. https://github.com/pytorch/pytorch/issues/97436. Should be resolved in nightlies, but it is quite fun and simple to code it ourselves. """ if hasattr(module, "_fsdp_wrapped_module"): module_for_sig = module._fsdp_wrapped_module else: module_for_sig = module sig = inspect.signature(module_for_sig.forward) # We first flatten all arguments to use only *args, to make things easier and because # torch.autograd.Function has weird support for kwargs. bounded = sig.bind(*args, **kwargs) new_args = [] for name, param in sig.parameters.items(): if param.kind in { inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD, }: raise RuntimeError("simple_checkpoint doesn't support var args.") if name not in bounded.arguments: break new_args.append(bounded.arguments[name]) return Checkpoint.apply(module, *new_args) _in_cuda_graph = False _disable_cuda_graph = False def in_cuda_graph() -> bool: """Indicate whether we are in a function that is CUDA Graphed (or will be soon).""" return _in_cuda_graph @contextmanager def _set_in_cuda_graph(): global _in_cuda_graph assert not _in_cuda_graph _in_cuda_graph = True try: yield finally: _in_cuda_graph = False def _is_cuda_graph_enabled() -> bool: if _disable_cuda_graph: return False no_cuda_graph = os.environ.get("NO_CUDA_GRAPH", "") if no_cuda_graph.lower() not in {"0", "no", "n", ""}: return False return True @contextmanager def no_cuda_graph(): """Deactivate CUDA Graphing for all the calls in this context manager.""" global _disable_cuda_graph old_value = _disable_cuda_graph _disable_cuda_graph = True try: yield finally: _disable_cuda_graph = old_value class CUDAGraphed: """Allow simple CUDA Graphing of a function. Args: func: callable, taking any number of arguments. Its tensors arguments should be top level args, not nested in structures (tuples, dicts, etc). Keyword arguments are NOT supported for simplicity. warmup_steps: how many call to make normally before CUDA Graphing. In particular, this allows torch.compiled functions to get properly compiled. disabled: if True, just call the func directly, useful to quickly deactivate on CPU. """ def __init__(self, func: tp.Callable, warmup_steps: int = 1, disable: bool = False): self.func = func self.warmup_steps = warmup_steps self.disable = disable self._graph: cuda.CUDAGraph | None = None self._output: tuple | None = None self._args: tuple | None = None def reset(self, warmup_steps: int = 0) -> None: """Reset the state, meaning the next call we get CUDA Graphed again. Useful if some shapes have changed, or external state (e.g. KVCache) has changed.""" self.warmup_steps = warmup_steps self._graph = None self._output = None self._args = None def __call__(self, *args, **kwargs) -> tp.Any: if kwargs: raise RuntimeError("Named arguments not supported for now.") if self.disable or not _is_cuda_graph_enabled() or in_cuda_graph(): return self.func(*args, **kwargs) def _clone_tensors(args: tuple) -> tuple: out: list = [] for arg in args: if isinstance(arg, torch.Tensor): arg = arg.clone() out.append(arg) return tuple(out) def _match_values_copy_tensors(args: tuple, target_args: tuple) -> None: if len(args) != len(target_args): raise ValueError( f"Expected {len(target_args)}, but got {args} for CUDA Graphed function." ) for idx, (source, target) in enumerate(zip(args, target_args)): if isinstance(target, torch.Tensor): if not isinstance(source, torch.Tensor): raise ValueError( f"Argument #{idx} was a tensor, and is no longer (now {source})." ) if source.shape != target.shape: raise ValueError( f"Argument #{idx} had shape {target.shape}, but got shae {source.shape}" ) target.copy_(source) else: if isinstance(source, torch.Tensor): raise ValueError( f"Argument #{idx} was not a tensor {target}, but is now one." ) if source is not target and source != target: raise ValueError( f"Argument #{idx} changed value from {target} to {source}." ) with _set_in_cuda_graph(): # Prevent any one under us to try and CUDA Graph things. if self._graph is None: if self.warmup_steps <= 0: self._graph = cuda.CUDAGraph() # Making a copy just to ensure those are not used else where. self._args = _clone_tensors(args) with cuda.graph(self._graph): self._output = self.func(*self._args) # At this point nothing really happened, so we have to make it run for real. self._graph.replay() return self._output else: self.warmup_steps -= 1 return self.func(*args) else: assert self._args is not None assert self._output is not None _match_values_copy_tensors(args, self._args) self._graph.replay() return self._output def cuda_graph(func: tp.Callable, warmup_steps: int = 1): """Just calls `CUDAGraphed` on the given function.""" if not _is_cuda_graph_enabled(): return func return CUDAGraphed(func, warmup_steps)