# mypy: allow-untyped-defs import contextlib import functools import operator from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING, Union import torch from torch._dynamo.external_utils import ( call_backward, call_hook, FakeCompiledAutogradEngine, ) from torch._dynamo.source import GetItemSource, LocalSource from torch._dynamo.utils import counters, lazy_format_graph_code, set_locals_to_steal from torch._logging import getArtifactLogger, trace_structured from torch._prims_common import clone_preserve_strides from torch._subclasses import FakeTensorMode from torch.fx import GraphModule from torch.fx.experimental._backward_state import BackwardState from torch.fx.experimental.proxy_tensor import ( decompose, disable_autocast_cache, disable_proxy_modes_tracing, fetch_object_proxy, ProxyTorchDispatchMode, PythonKeyTracer, track_tensor_tree, ) from torch.fx.experimental.symbolic_shapes import DimDynamic, ShapeEnv from torch.fx.traceback import preserve_node_meta, set_stack_trace from torch.utils._ordered_set import OrderedSet from torch.utils._traceback import CapturedTraceback if TYPE_CHECKING: from torch.fx.proxy import Proxy compiled_autograd_log = getArtifactLogger(__name__, "compiled_autograd") verbose_log = getArtifactLogger(__name__, "compiled_autograd_verbose") def snapshot_verbose_logging_enabled(): return torch._logging._internal.log_state.is_artifact_enabled( "compiled_autograd_verbose" ) def snapshot_cudagraph_enabled(): return torch._inductor.config.triton.cudagraphs def maybe_clone(x): if x is not None: return clone_preserve_strides(x) return x _graph_placeholders = ["inputs", "sizes", "scalars", "hooks"] _impure_targets = OrderedSet( [ call_hook, call_backward, FakeCompiledAutogradEngine._exec_final_callbacks_stub, torch.ops.inductor.accumulate_grad_.default, ] ) class AutogradCompilerInstance: def __init__(self, compiler_fn) -> None: self.compiler_fn = compiler_fn self.stack = contextlib.ExitStack() self.close = self.stack.close self.shape_env = ShapeEnv() self.fake_tensor_mode = FakeTensorMode( allow_fallback_kernels=True, allow_non_fake_inputs=True, shape_env=self.shape_env, ) self.fx_tracer = PythonKeyTracer() self.proxy_mode = ProxyTorchDispatchMode(self.fx_tracer, "symbolic") self.hooks_proxy: Optional[Proxy] = None def wrap_fake(self, x, source): assert isinstance(x, torch.Tensor) return self.fake_tensor_mode.from_tensor(x, source=source) @staticmethod def source(name, idx) -> GetItemSource: return GetItemSource(LocalSource(name), idx) def begin_capture( self, inputs: List[torch.Tensor], sizes: List[int], scalars: List[Union[int, float]], origins: List[List[Tuple[int, str]]], ): counters["compiled_autograd"]["captures"] += 1 self.aot_graph_cls_name: Optional[str] = None self.aot_graph_infos: Dict[int, Dict[str, Any]] = {} self.fx_tracer.root = torch.nn.Module() self.fx_tracer.graph = torch.fx.Graph(tracer_cls=PythonKeyTracer) self.fx_tracer.tensor_attrs = {} args_proxy, sizes_proxy, scalars_proxy, self.hooks_proxy = ( self.fx_tracer.create_proxy("placeholder", name, (), {}) for name in _graph_placeholders ) self.stack.enter_context(preserve_node_meta()) inputs_origins, sizes_origins, scalars_origins = origins # tensor inputs to fake tensors inputs = [ self.wrap_fake(x, self.source("inputs", idx)) for idx, x in enumerate(inputs) ] self.bind_tensors_to_proxies(inputs, args_proxy, inputs_origins) # size inputs to symints sizes = [ self.shape_env.create_unspecified_symint_and_symbol( val, self.source("sizes", idx), DimDynamic.DYNAMIC, ) for idx, val in enumerate(sizes) ] self.bind_tensors_to_proxies(sizes, sizes_proxy, sizes_origins) for idx, val in enumerate(scalars): source = self.source("scalars", idx) if isinstance(val, int): scalars[idx] = self.shape_env.create_unspecified_symint_and_symbol( val, source, DimDynamic.DYNAMIC, ) elif isinstance(val, float): scalars[idx] = self.shape_env.create_symfloatnode( self.shape_env.create_unspecified_symbol( val, source=source, dynamic_dim=DimDynamic.DYNAMIC, ), hint=val, source=source, ) else: raise AssertionError("Unexpected scalar type: ", type(val)) self.bind_tensors_to_proxies(scalars, scalars_proxy, scalars_origins) # TODO(jansel): are all these modes needed? self.stack.enter_context(decompose({})) self.stack.enter_context(self.fake_tensor_mode) self.stack.enter_context(self.proxy_mode) self.stack.enter_context(disable_autocast_cache()) # Needed to make sure we don't accidentally specialize any symbols assert self.fake_tensor_mode.shape_env is not None env = self.fake_tensor_mode.shape_env self.stack.enter_context( torch.fx.experimental.symbolic_shapes._suppress_guards(env) ) return inputs, sizes, scalars def proxy_call_backward( self, inputs, output_metadatas, saved_tensors, backward_idx: int, ): assert self.hooks_proxy is not None backward_c_function = self.hooks_proxy[backward_idx] # type: ignore[index] proxies = self.fx_tracer.create_proxy( kind="call_function", target=call_backward, args=( backward_c_function, self.to_proxy(saved_tensors), *self.to_proxy(inputs), ), kwargs={}, ) with disable_proxy_modes_tracing(): # create fake Tensors grad_ins: List[Optional[torch.Tensor]] = [] for output_metadata in output_metadatas: if output_metadata is None: grad_ins.append(None) continue layout, device, dtype, size = output_metadata grad_ins.append( torch.empty(size=size, dtype=dtype, layout=layout, device=device) ) self.bind_tensors_to_proxies(grad_ins, proxies) return tuple(grad_ins) def proxy_call_hook(self, hook, *args, **kwargs): return self.fx_tracer.create_proxy( "call_function", call_hook, ( hook, *[self.to_proxy(x) for x in args], ), kwargs, ) def tensor_pre_hook(self, inputs, hook_id, i: int): assert self.hooks_proxy is not None hook = self.hooks_proxy[hook_id] # type: ignore[index] proxy = self.proxy_call_hook( hook, inputs[i], hook_type="tensor_pre_hook", ) with disable_proxy_modes_tracing(): inputs[i] = maybe_clone(inputs[i]) self.bind_tensors_to_proxies([inputs[i]], [proxy]) return inputs def pre_hook(self, inputs, hook_id): assert self.hooks_proxy is not None hook = self.hooks_proxy[hook_id] # type: ignore[index] proxies = self.proxy_call_hook( hook, inputs, hook_type="pre_hook", ) with disable_proxy_modes_tracing(): inputs = [maybe_clone(x) for x in inputs] self.bind_tensors_to_proxies(inputs, proxies) return inputs def post_hook(self, outputs, inputs, hook_id): assert self.hooks_proxy is not None hook = self.hooks_proxy[hook_id] # type: ignore[index] proxies = self.proxy_call_hook( hook, outputs, inputs, hook_type="post_hook", ) with disable_proxy_modes_tracing(): outputs = [maybe_clone(x) for x in outputs] self.bind_tensors_to_proxies(outputs, proxies) return outputs def post_acc_grad_hook(self, input, hook_id): assert isinstance(input, torch.Tensor) assert self.hooks_proxy is not None hook = self.hooks_proxy[hook_id] # type: ignore[index] proxy = self.proxy_call_hook( hook, input, hook_type="post_acc_grad_hook", ) with disable_proxy_modes_tracing(): input = [maybe_clone(input)] self.bind_tensors_to_proxies(input, [proxy]) return input # Note: [Compiled autograd and cudagraphs] # Eager autograd backward implements scalars as 0-dim tensors, see DivBackward0::other_. # When compiled autograd traces those nodes, it lifts the scalar tensors, resulting in a graph # with some cpu 0-dim tensor inputs. To prevent the entire graph from skipping cudagraph, we move the # scalars tensors to cuda. This works because ATen/prims ops will accept cuda 0-dim tensors too. def move_graph_nodes_to_cuda(self, graph) -> List[int]: to_move: Dict[int, torch.fx.Node] = {} has_cuda_inputs = False nodes = list(graph.nodes) assert nodes[0].target == "inputs" inputs = nodes[0] inputs_users = list(inputs.users.keys()) # input access nodes should immediately follow placeholder nodes first_getitem_idx = len(_graph_placeholders) assert nodes[first_getitem_idx] == inputs_users[0] last_getitem_idx = first_getitem_idx + len(inputs_users) - 1 assert nodes[last_getitem_idx] == inputs_users[-1] for i, node in enumerate(inputs_users): if not has_cuda_inputs and node.meta["val"].device.type == "cuda": has_cuda_inputs = True continue is_cpu = node.meta["val"].device.type == "cpu" is_scalar = len(node.meta["val"].size()) == 0 if is_cpu and is_scalar: node_users = list(node.users.keys()) if all( isinstance(user.target, torch._ops.OpOverload) and user.target.namespace in ("prims", "aten") for user in node_users ): # all users are prims/aten, can move safely to_move[i] = node # only move cpu scalars to cuda if there were cuda activations in this graph, # this is to handle the case where cudagraphs is enabled on a cpu-only graph if has_cuda_inputs: for node in to_move.values(): node.meta["val"] = node.meta["val"].cuda() # return runtime indices we need to move to cuda return list(to_move.keys()) return [] def is_sym_node(self, node): return ( isinstance(node, torch.fx.Node) and node.op == "call_function" and node.target in [torch.ops.aten.sym_size.int, torch.ops.aten.sym_numel.default] ) def dce(self): # Most of these removed nodes would have been removed during Dynamo and AOTDispatch # Remove some of these nodes earlier to improve compilation speed # Dynamo guards will error instead of creating aliasing guards unless we unpack them in the graph unpack_nodes: OrderedSet[torch.fx.Node] = OrderedSet() for i, node in enumerate(self.fx_tracer.graph.find_nodes(op="placeholder")): unpack_nodes.update(node.users.keys()) assert i == len(_graph_placeholders) - 1 def is_impure(node): return ( node in unpack_nodes or node.op == "placeholder" or node.op == "output" or (node.op == "call_function" and node.target in _impure_targets) ) self.fx_tracer.graph.eliminate_dead_code(is_impure) def end_capture(self, outputs): self.fx_tracer.create_proxy( "call_function", FakeCompiledAutogradEngine._exec_final_callbacks_stub, (), {}, ) self.stack.close() self.fx_tracer.create_node( "output", "output", (self.fx_tracer.create_arg(self.to_proxy(outputs)),), {}, ) self.rename_aot_dispatcher_nodes() self.reorder_tensor_pre_hook_nodes() self.reorder_pre_hook_nodes_to_schedule_asap() self.reorder_accumulate_grad_nodes() self.reorder_pre_hook_nodes_to_mimic_eager() self.reorder_post_acc_grad_hook_nodes() self.reorder_post_hook_nodes() # TODO(yf225): work around: remove dead codes like `sym_size` and `sym_numel` which are not used downstream. e.g. # ``` # sym_numel_default = torch.ops.aten.sym_numel.default(sum_109); sum_109 = None # eq_115 = 16 == sym_numel_default; sym_numel_default = eq_115 = None # sym_size_int_39 = torch.ops.aten.sym_size.int(getitem_112, 1); getitem_112 = None # eq_116 = 16 == sym_size_int_39; eq_116 = None # eq_117 = 16 == sym_size_int_39; sym_size_int_39 = eq_117 = None # ``` # Proper fix is Richard's Python compiled autograd effort which will avoid calling make_fx and # should prevent these ops from going into the CA graph. self.dce() runtime_inputs_to_move: List[int] = [] if snapshot_cudagraph_enabled(): runtime_inputs_to_move = self.move_graph_nodes_to_cuda(self.fx_tracer.graph) graph = GraphModule( self.fx_tracer.root, self.fx_tracer.graph, "CompiledAutograd" ) set_locals_to_steal(graph, ["inputs"]) lazy_graph_code = lazy_format_graph_code( "Compiled autograd graph", graph, include_device=True, include_stride=True, colored=True, ) compiled_autograd_log.info("%s", lazy_graph_code) verbose_log.debug("%s", lazy_graph_code) trace_structured( "compiled_autograd_graph", payload_fn=lambda: graph.print_readable(print_output=False), ) def runtime_wrapper(compiled_fn, inputs, sizes, scalars, hooks): global in_compiled_autograd_region try: in_compiled_autograd_region = True for i in runtime_inputs_to_move: inputs[i] = inputs[i].pin_memory().cuda(non_blocking=True) with _disable(): return compiled_fn(inputs, sizes, scalars, hooks) finally: in_compiled_autograd_region = False return runtime_wrapper, self.compiler_fn(graph) def rename_aot_dispatcher_nodes(self): """ Renames nodes as they appear in the AOTDispatcher backward graphs, prefixed by AOT id e.g. AOTDispatcher backward graph X's `sin_Y` -> `aotX_sin_Y` """ if self.aot_graph_cls_name is None: return def is_similar(ca: torch.fx.node.Node, aot: torch.fx.node.Node): # 1. comparing using target (for aten ops) target_match = ca.target == aot.target if not target_match: # 2. comparing using name (for HOPs) target_match = ( hasattr(ca.target, "__name__") and hasattr(aot.target, "__name__") and ca.target.__name__ == aot.target.__name__ ) if ( not target_match and hasattr(ca.target, "name") and hasattr(aot.target, "name") and aot.target.name() == "aten::reshape" and hasattr(aot.meta.get("original_aten"), "name") ): # 3. undo view_to_reshape post grad pass target_match = ca.target.name() == aot.meta["original_aten"].name() return ( target_match and ca.op == aot.op and ca.type == aot.type and len(ca.all_input_nodes) == len(aot.all_input_nodes) ) for nodecall_index, info in self.aot_graph_infos.items(): ca_node_start_idx = info["ca_node_start_idx"] aot_id = info["aot_id"] aot_graph = info["aot_gm"].graph # 1. Find the first op from user code in the AOT graph aot_it = iter(aot_graph.nodes) aot_node = next(aot_it) assert aot_node is not None try: while aot_node.op != "call_function": aot_node = next(aot_it) except StopIteration: continue try: # 2. Find the first op in the compiled autograd graph segment ca_it = iter(self.fx_tracer.graph.nodes) for _ in range(ca_node_start_idx): next(ca_it) ca_node = next(ca_it) # Graphs should all end with output node while ca_node.op != "output" and not is_similar(ca_node, aot_node): # The compiled autograd graph may contain lazily inserted ops # We skip those when aligning nodes ca_node = next(ca_it) # 3. Keep alligned and rename nodes while aot_node.op != "output" and ca_node.op != "output": if not ca_node.users: # TODO: DCE for compiled autograd graph ca_node = next(ca_it) continue if not is_similar(ca_node, aot_node): # There should be no lazily inserted ops in the middle of a match # So any deviation is an error raise StopIteration ca_node.name = f"aot{aot_id}_{aot_node.name}" for i, inp in enumerate(aot_node.all_input_nodes): ca_node.all_input_nodes[i].name = f"aot{aot_id}_{inp.name}" aot_node = next(aot_it) ca_node = next(ca_it) except StopIteration: verbose_log.debug( "Failed to match %s%s (NodeCall %s) nodes with AOT backward graph %s nodes", self.aot_graph_cls_name, aot_id, nodecall_index, aot_id, ) @staticmethod def get_all_nodes(args): # filter out non-Node args, like None nodes = [n for n in args if type(n) is torch.fx.Node] return nodes @staticmethod def is_placeholder(node): if node.op == "placeholder" or ( node.op == "call_function" and node.target == operator.getitem and node.args[0].op == "placeholder" ): return True return False def reorder_accumulate_grad_nodes(self): """ Usage of AOTAutograd causes all the accumulate_grad_ nodes to get pushed to the end of the graph. This differs from eager mode, which schedules them as soon as possible. This pass attempts to reorder the graph to mimic eager behavior. """ for node in self.fx_tracer.graph.find_nodes( op="call_function", target=torch.ops.inductor.accumulate_grad_.default ): param_node, grad_node = node.args[0], node.args[1] getitem_node = None if grad_node.target == operator.getitem: getitem_node = grad_node grad_node = getitem_node.args[0] arg = max([param_node, grad_node]) # last arg if arg is not node.prev and not self.is_placeholder(arg): arg.append(node) if getitem_node is not None: arg.append(getitem_node) def reorder_tensor_pre_hook_nodes(self): """ Usage of AOTAutograd causes all the tensor_pre_hook nodes to get pushed to the end of the graph. This differs from eager mode, which schedules them as soon as possible. This pass attempts to reorder the graph to mimic eager behavior. """ for node in self.fx_tracer.graph.find_nodes( op="call_function", target=call_hook ): if node.kwargs.get("hook_type", None) != "tensor_pre_hook": continue getitem_node = node.args[0] input_node = node.args[1] # tensor_pre_hook handle only one grad tensor if input_node is not node.prev and not self.is_placeholder(input_node): input_node.append(getitem_node) getitem_node.append(node) def reorder_pre_hook_nodes_to_schedule_asap(self): """ In this function, we schedule the pre hooks as soon as possible. This does not match eager behavior (schedule pre hook right before its registered node), but it can make acc grad be scheduled properly when the pre hooks are registered to them. After reordering acc grad node, we will reorder the pre hooks again to mimic eager behavior. """ for node in self.fx_tracer.graph.find_nodes( op="call_function", target=call_hook ): if node.kwargs.get("hook_type", None) != "pre_hook": continue getitem_node = node.args[0] # pre_hook handle a tuple of grad tensors input_nodes = self.get_all_nodes(node.args[1]) to_remove = [] to_append = [] hook_block = [node] # contain the hook and hook args getitem for n in input_nodes: if n.op == "call_function" and n.target == operator.getitem: to_append.append(n.args[0]) to_remove.append(n) hook_block.append(n) for a, b in zip(to_remove, to_append): input_nodes.remove(a) input_nodes.append(b) arg = max(input_nodes) # last input if arg is not node.prev and not self.is_placeholder(arg): arg.append(getitem_node) for n in hook_block: getitem_node.append(n) def reorder_pre_hook_nodes_to_mimic_eager(self): """ Usage of AOTAutograd causes all the pre_hook nodes to get pushed to the end of the graph. This differs from eager mode, which schedules them right before their registered node execution. This pass attempts to reorder the graph to mimic eager behavior. """ pre_hooks = [] for node in self.fx_tracer.graph.find_nodes( op="call_function", target=call_hook ): if node.kwargs.get("hook_type", None) != "pre_hook": continue pre_hooks.append(node) for node in reversed(pre_hooks): hook_getitem_node = node.args[0] users = list(node.users.keys()) if len(users) == 0: continue # users are all getitem ops and they are used by same registered node assert all( user.op == "call_function" and user.target == operator.getitem for user in users ) registered_node = next(iter(users[0].users.keys())) if registered_node is not node.next: registered_node.prepend(hook_getitem_node) registered_node.prepend(node) for getitem in users: registered_node.prepend(getitem) def reorder_post_acc_grad_hook_nodes(self): """ Usage of AOTAutograd causes all the post_acc_grad_hook nodes to get pushed to the end of the graph. This differs from eager mode, which schedules them as soon as possible. This pass attempts to reorder the graph to mimic eager behavior. """ post_acc_grad_hooks = [] for node in self.fx_tracer.graph.find_nodes( op="call_function", target=call_hook ): if node.kwargs.get("hook_type", None) != "post_acc_grad_hook": continue post_acc_grad_hooks.append(node) # nodes in post_acc_grad_hooks are in topo order. For hooks registered # to same node, we should keep their relative order for node in reversed(post_acc_grad_hooks): getitem_node = node.args[0] param_node = node.args[1] # post_acc_grad_hook handle one param # find the corresponding acc_grad node acc_grad_node = None for n in list(param_node.users.keys()): if ( n.op == "call_function" and n.target == torch.ops.inductor.accumulate_grad_.default ): acc_grad_node = n break assert ( acc_grad_node is not None ), "post_acc_grad_hook must have corresponding acc grad node" # append post_acc_grad_hook after acc_grad node acc_grad_node.append(getitem_node) getitem_node.append(node) def reorder_post_hook_nodes(self): """ Usage of AOTAutograd causes all the post_hook nodes to get pushed to the end of the graph. This differs from eager mode, which schedules them as soon as possible. This pass attempts to reorder the graph to mimic eager behavior. """ post_hooks = [] for node in self.fx_tracer.graph.find_nodes( op="call_function", target=call_hook ): if node.kwargs.get("hook_type", None) != "post_hook": continue post_hooks.append(node) for node in reversed(post_hooks): getitem_node = node.args[0] output_nodes = node.args[1] input_nodes = node.args[2] if len(output_nodes) > 0: continue input_nodes_and_users = [] input_nodes_and_users.extend(list(input_nodes)) for input_node in input_nodes: input_nodes_and_users.extend( user for user in list(input_node.users.keys()) if not ( user.op == "call_function" and user.target == call_hook and node.kwargs.get("hook_type", None) == "post_hook" ) ) arg = max(input_nodes_and_users) # last input users if ( arg.op == "call_function" and arg.target == torch.ops.inductor.accumulate_grad_.default ): param_node = arg.args[0] post_acc_grad_hook_node = None for n in list(param_node.users.keys()): if ( n.op == "call_function" and n.target == call_hook and n.kwargs.get("hook_type", None) == "post_acc_grad_hook" ): post_acc_grad_hook_node = n if post_acc_grad_hook_node is not None: post_acc_grad_hook_node.append(getitem_node) getitem_node.append(node) continue if arg is not node.prev and not self.is_placeholder(arg): arg.append(getitem_node) getitem_node.append(node) def to_proxy(self, t): if t is None: return None if isinstance(t, list): return [self.to_proxy(x) for x in t] if isinstance(t, tuple): return tuple(self.to_proxy(x) for x in t) # can it be torch.SymInt as the code used to imply? assert isinstance(t, torch.Tensor) proxy_tensor = fetch_object_proxy(self.fx_tracer, t) assert isinstance(proxy_tensor, torch.fx.experimental.proxy_tensor._ProxyTensor) return proxy_tensor.proxy def bind_tensors_to_proxies( self, tensors, proxies, origins: Optional[List[Tuple[int, str]]] = None ): if isinstance(proxies, torch.fx.Proxy): if origins: assert len(origins) == len(tensors) bound_proxies = [] for i in range(len(tensors)): nodecall_index, node_name = origins[i] self.set_node_origin(node_name, nodecall_index, None) bound_proxies.append(proxies[i]) # type: ignore[index] proxies = bound_proxies else: proxies = [proxies[i] for i in range(len(tensors))] # type: ignore[index] assert len(tensors) == len(proxies) track_tensor_tree(tensors, proxies, constant=None, tracer=self.fx_tracer) def bind_backward_state(self, index: int): assert self.hooks_proxy is not None proxy = self.hooks_proxy[index] # type: ignore[index] bw_state = BackwardState() track_tensor_tree(bw_state, proxy, constant=None, tracer=self.fx_tracer) return bw_state def set_node_origin( self, node_name: str, nodecall_index: int, pyobj: Optional[torch.autograd.Function], ): maybe_aot_id = "" if pyobj is not None: forward_cls = pyobj._forward_cls # type: ignore[attr-defined] if hasattr(forward_cls, "_aot_id"): # backward was created by AOT Dispatcher self.aot_graph_cls_name = node_name maybe_aot_id = forward_cls._aot_id self.aot_graph_infos[nodecall_index] = { "ca_node_start_idx": len(self.fx_tracer.graph.nodes), "aot_id": maybe_aot_id, "aot_gm": forward_cls._lazy_backward_info.bw_module, } new_code = f"{node_name}{maybe_aot_id} (NodeCall {nodecall_index})" raw_stack_trace = CapturedTraceback.extract().format()[-1] new_stack_trace = raw_stack_trace.replace( "raw_stack_trace = CapturedTraceback.extract().format()[-1]", new_code ) set_stack_trace(new_stack_trace) # state of the autograd engine dispatch, kept in sync by enable/disable context managers compiled_autograd_enabled = False # global flag to check if compiled autograd is enabled but Dynamo stance is "force_eager" compiled_autograd_enabled_force_eager = False # global flag to check if we are processing graphs produced from a compiled autograd graph in_compiled_autograd_region = False @contextlib.contextmanager def _enable(compiler_fn, dynamic=False): if dynamic: assert type(dynamic) is bool from torch._dynamo import eval_frame if eval_frame._stance.stance == "force_eager": # If user explicitly sets Dynamo stance to "force_eager", we want Compiled Autograd # to fall back to eager as well. global compiled_autograd_enabled_force_eager compiled_autograd_enabled_force_eager = True try: yield finally: compiled_autograd_enabled_force_eager = False else: # we need to import this, because user might not have imported it if they directly use this context manager # we need to lazily import it, because of circular dependencies import torch._inductor.cudagraph_trees ( prior_compiler, prior_dynamic, ) = torch._C._dynamo.compiled_autograd.set_autograd_compiler( functools.partial(AutogradCompilerInstance, compiler_fn), dynamic ) if snapshot_verbose_logging_enabled(): torch._C._dynamo.compiled_autograd.set_verbose_logger(verbose_log) global compiled_autograd_enabled compiled_autograd_enabled = True try: with torch.autograd.set_multithreading_enabled(False): yield finally: if not prior_compiler: compiled_autograd_enabled = False torch._C._dynamo.compiled_autograd.set_autograd_compiler( prior_compiler, prior_dynamic ) @contextlib.contextmanager def _disable(): ( prior_compiler, prior_dynamic, ) = torch._C._dynamo.compiled_autograd.set_autograd_compiler(None, False) global compiled_autograd_enabled compiled_autograd_enabled = False try: yield finally: if prior_compiler: compiled_autograd_enabled = True torch._C._dynamo.compiled_autograd.set_autograd_compiler( prior_compiler, prior_dynamic ) # return to starting state of a new process def reset() -> None: global compiled_autograd_enabled compiled_autograd_enabled = False assert not in_compiled_autograd_region torch._C._dynamo.compiled_autograd.set_autograd_compiler(None, False) torch._C._dynamo.compiled_autograd.set_verbose_logger(None)