import collections import contextlib import copy import functools import itertools import logging import operator import re import sys import traceback import weakref from dataclasses import dataclass from typing import Any, Dict, List, NamedTuple, Optional, OrderedDict, Set, Union import sympy import torch._guards import torch._logging import torch.nn import torch.utils._pytree as pytree from torch import fx from torch._guards import ( Checkpointable, Guard, GuardsCheckpointState, Source, TracingContext, ) from torch._utils_internal import signpost_event from torch.fx.experimental.symbolic_shapes import free_symbols, ShapeEnv from torch.utils.weak import WeakIdKeyDictionary, WeakTensorKeyDictionary from . import config, logging as torchdynamo_logging, variables from .backends.registry import CompiledFn, CompilerFn from .bytecode_transformation import ( create_call_function, create_instruction, Instruction, unique_id, ) from .codegen import PyCodegen from .current_scope_id import enter_new_scope from .exc import ( BackendCompilerFailed, exceptions_allowed_to_be_fallback, unimplemented, unimplemented_with_warning, ) from .guards import GuardBuilder from .mutation_guard import is_dynamic_nn_module from .side_effects import SideEffects from .source import ( ConstantSource, GlobalStateSource, is_constant_source, is_from_local_source, LocalSource, ParamBufferSource, ShapeEnvSource, TensorProperty, TensorPropertySource, ) from .utils import ( checkpoint_params, CleanupHook, clone_inputs, count_calls, counters, dynamo_timed, get_instruction_source_311, get_static_address_type, graph_break_reasons, increment_op_count, lazy_format_graph_code, lazy_format_graph_tabular, LazyString, nnmodule_doc_url_msg, nnmodule_has_hooks, same, ) from .variables.base import VariableTracker from .variables.builder import GraphArg, TrackedFake, VariableBuilder, wrap_fx_proxy from .variables.nn_module import NNModuleVariable from .variables.tensor import ( NumpyNdarrayVariable, SymNodeVariable, TensorVariable, UnspecializedPythonVariable, ) log = logging.getLogger(__name__) graph_tabular_log = torch._logging.getArtifactLogger(__name__, "graph") graph_code_log = torch._logging.getArtifactLogger(__name__, "graph_code") graph_sizes_log = torch._logging.getArtifactLogger(__name__, "graph_sizes") trace_call_log = torch._logging.getArtifactLogger(__name__, "trace_call") class OutputGraphState(NamedTuple): input_source_to_var: Dict[Source, VariableTracker] tracked_fakes: List[TrackedFake] guard_state: GuardsCheckpointState nn_modules: Optional[Dict[str, torch.nn.Module]] global_state: Optional[Dict[str, bool]] param_name_to_source: Optional[Dict[str, Source]] side_effects: SideEffects timestamp: int tensor_weakref_to_sizes_strides: WeakIdKeyDictionary def diff(self, other: "OutputGraphState", *, prefix: str = "") -> Optional[str]: for k in self._fields: if k == "guard_state": r = self.guard_state.diff(other.guard_state) if r is not None: return r continue elif k == "side_effects": r = self.side_effects.diff(other.side_effects) if r is not None: return r continue sv = getattr(self, k) ov = getattr(other, k) if sv != ov: return f"{prefix}{k} mismatch: {sv} != {ov}" return None # Back compat .guards api @property def guards(self): return self.guard_state.dynamo_guards @functools.lru_cache(None) def _step_logger(): return torchdynamo_logging.get_step_logger(log) @dataclass class GraphCompileReason: """Stores why a given output graph was compiled; i.e. what caused the graph break.""" reason: str user_stack: List[traceback.FrameSummary] # Indicates if this was a graph compile reason due to graph break. graph_break: bool = True def __post_init__(self): if self.graph_break: graph_break_reasons.append(self) def _get_gen_rand_values_fn(random_calls): def _gen_rand_values(): return [fn(*args, **kwargs) for fn, args, kwargs in random_calls] return _gen_rand_values class FakeRootModule(torch.nn.Module): """Trick the constructor of fx.GraphModule""" def __init__(self, nn_modules: Dict[str, torch.nn.Module]): super().__init__() for k, v in nn_modules.items(): setattr(self, k, v) def __repr__(self): return "FakeRootModule(...)" class WrapperBackend: def __init__(self, backend: CompilerFn): self.backend: CompilerFn = backend def __call__(self, gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): self.restore = checkpoint_params(gm) self.gm = gm copy_gm = copy.deepcopy(self.gm) self.candidate = self.backend(copy_gm, example_inputs) if self.candidate is None or self.candidate is self.gm.forward: return self.gm.forward if not config.verify_correctness: return self.candidate # if verify_correctness=True try: correct = self.gm.forward(*clone_inputs(example_inputs)) result = self.candidate(*clone_inputs(example_inputs)) # TODO: replace `same` function with the one in testing if same(correct, result): return self.candidate raise RuntimeError(f"incorrect results of backend {self}") return self.gm.forward except Exception: log.exception("error in verify_correctness") raise finally: self.restore() Scope = Dict[str, object] class OutputGraph(Checkpointable[OutputGraphState]): """ Wrapper class to hold outputs of InstructionTranslator. Mainly the generated fx.Graph. OutputGraph is 1:1 with a frame being processed. Each frame is associated with some root InstructionTranslator. When user code calls a function, we construct a InliningInstructionTranslator that continues to write into the root InstructionTranslator's OutputGraph. """ def __init__( self, code_options: Dict[str, Any], compiler_fn: CompilerFn, root_tx, export: bool, export_constraints, frame_state, local_scope: Scope, global_scope: Scope, f_code, ): super().__init__() self.tracers = [SubgraphTracer(self, export_root=export)] # Map from graph input's `Source` to its `VariableTracker` to # de-duplicate graph inputs by source and reuse the tracker self.input_source_to_var: Dict[Source, VariableTracker] = {} self.export = export self.export_constraints = export_constraints self.frame_state = frame_state self.tensor_weakref_to_sizes_strides: WeakIdKeyDictionary = {} # Used to maintain an alias between real values variable tracker for tensors we have seen. # This map ensures that the only tensors in graph inputs, and the only tensors in guards are unique. self.real_value_tensor_positive_aliases = WeakTensorKeyDictionary() # TODO: maybe should just pass the entire f_code in here? Not # sure... self.co_fields = { "co_name": f_code.co_name, "co_filename": f_code.co_filename, "co_firstlineno": f_code.co_firstlineno, } # In export mode, we force the shape_env to strictly disallow any constraining # of the user marked dynamic dims fake_mode = torch._subclasses.FakeTensorMode( shape_env=ShapeEnv( allow_scalar_outputs=config.capture_scalar_outputs, allow_dynamic_output_shape_ops=config.capture_dynamic_output_shape_ops, co_fields=self.co_fields, ), # TODO (tmanlaibaatar) Remove this once we always lift params and buffers allow_non_fake_inputs=True if self.export else False, ) self.tracing_context: TracingContext = TracingContext(fake_mode) self.init_ambient_guards() # tracked_fakes says where any tensor that was wrapped to fake came # from. It is similar to GraphArg, in that all GraphArgs will get # will get added to TrackedFakes, but TrackedFakes also contains # GraphArgs that got pruned, and things like Tensor attributes which # aren't explicit graph inputs. Used by shape guard self.tracked_fakes: List[TrackedFake] = [] # Map each tensor id to a list of sources. This is necessary because # tensor ids cannot be recovered from tracked fakes (in general). # We use this map to interpret (i.e., check for violations of) constraints, # specifically equality constraints, which have shared tensor ids in them. # This map should also be generally useful, e.g., for (de)serialization. self.tracked_fakes_id_to_source: Dict[ int, List[Source] ] = collections.defaultdict(list) # Stores the full fqn of a param or buffer to the relevant source. self.param_name_to_source: Optional[Dict[str, Source]] = dict() self.side_effects = SideEffects() self.code_options = dict(code_options) self.output_instructions: List[Instruction] = [] # used to track nodes that are added between calls of copy_graphstate # and restore_graphstate self.timestamp = 0 # Not checkpointed self.compiler_fn: CompilerFn = compiler_fn self.global_scope = global_scope self.local_scope = local_scope self.root_tx = root_tx from torch._dynamo.symbolic_convert import InstructionTranslatorBase # Given a source, what are the user stacks of all locations that # accessed it? # # For efficiency, we only populate this: # - During export, and # - If the source could potentially lead to a spurious export input # # Feel free to populate this more frequently if other use-cases arise, # but be aware that we have to generate full stacks for each # recording! self.source_to_user_stacks: Dict[Source, List[traceback.StackSummary]] = {} self._current_tx: List[InstructionTranslatorBase] = [] self.cleanups: List[CleanupHook] = [] self.should_exit = False self.random_values_var = None self.unspec_variable_map: Dict[str, UnspecializedPythonVariable] = {} self.torch_function_enabled = torch._C._is_torch_function_enabled() # Tracks if the output graph has a user defined allowed function in the # graph. This is used later to determine if we should fallback to eager # for certain exceptions. THe idea is that if the user has applied # allow_in_graph, they would like to see the error instead of falling # back for backend errors. self.has_user_defined_allowed_in_graph = False # We save the global torch state here to be restored in case of graph # breaks. The relevant issue is seen here # https://github.com/pytorch/pytorch/pull/100570#issuecomment-1543427086 # where inlining of a function changes the global state (because of the # presence of torch.no_grad) and there is a graph break. self.save_global_state() # This gets its own helper function so guards DEBUG logs are more # informative def init_ambient_guards(self): # Register a SHAPE_ENV guard to make sure we setup shape guards # that show up in ShapeEnv self.guards.add(ShapeEnvSource().make_guard(GuardBuilder.SHAPE_ENV)) self.guards.add( GlobalStateSource().make_guard(GuardBuilder.DETERMINISTIC_ALGORITHMS) ) self.guards.add(GlobalStateSource().make_guard(GuardBuilder.GRAD_MODE)) self.guards.add(GlobalStateSource().make_guard(GuardBuilder.DEFAULT_DEVICE)) self.guards.add( GlobalStateSource().make_guard(GuardBuilder.TORCH_FUNCTION_STATE) ) @property def root_tracer(self): return self.tracers[0] @property def current_tracer(self): return self.tracers[-1] def is_root_tracer(self): # Helper to tell if we are inside the higher order operator tracing. return len(self.tracers) == 1 @property def graph(self): return self.current_tracer.graph # TODO(rzou): can delete after we refactor speculate_subgraph to use nested GraphTracer. @graph.setter def graph(self, value): self.current_tracer.graph = value @property def input_name_to_proxy(self): return self.current_tracer.input_name_to_proxy @property def real_value_cache(self): return self.current_tracer.real_value_cache # If you are here, and you're looking for create_graph_input, # to avoid ambiguity, please call one of the following: # - self.current_tracer.create_graph_input # - self.root_tracer.create_graph_input # See NOTE [HigherOrderOperator tracing design] for more context. def create_proxy(self, *args, **kwargs): return self.current_tracer.create_proxy(*args, **kwargs) def create_node(self, *args, **kwargs): return self.current_tracer.create_node(*args, **kwargs) def remove_node(self, *args, **kwargs): return self.current_tracer.remove_node(*args, **kwargs) @contextlib.contextmanager def new_subtracer(self): new_scope_ctx = enter_new_scope() try: new_scope_ctx.__enter__() tracer = SubgraphTracer(self, parent=self.current_tracer) self.tracers.append(tracer) yield tracer finally: new_scope_ctx.__exit__(None, None, None) self.tracers.pop() @property def output(self): return self @property def fake_mode(self): return self.root_tx.fake_mode @property def shape_env(self): return self.tracing_context.fake_mode.shape_env @property def guards(self) -> Set[Guard]: return self.tracing_context.guards_context.dynamo_guards @property def nn_modules(self) -> Dict[str, torch.nn.Module]: return self.tracing_context.module_context.nn_modules def save_global_state(self): global_state = self.tracing_context.global_context.global_state global_state["torch_function_enabled"] = ( self.set_torch_function_state, self.torch_function_enabled, ) global_state["grad_enabled"] = (torch.set_grad_enabled, torch.is_grad_enabled()) global_state["autocast_enabled"] = ( torch.set_autocast_enabled, torch.is_autocast_enabled(), ) global_state["autocast_cpu_enabled"] = ( torch.set_autocast_cpu_enabled, torch.is_autocast_cpu_enabled(), ) global_state["autocast_gpu_dtype"] = ( torch.set_autocast_gpu_dtype, torch.get_autocast_gpu_dtype(), ) global_state["autocast_cpu_dtype"] = ( torch.set_autocast_cpu_dtype, torch.get_autocast_cpu_dtype(), ) global_state["autocast_cache_enabled"] = ( torch.set_autocast_cache_enabled, torch.is_autocast_cache_enabled(), ) def push_tx(self, tx): self._current_tx.append(tx) def pop_tx(self): return self._current_tx.pop() @property def current_tx(self): return self.root_tx if not self._current_tx else self._current_tx[-1] def copy_graphstate(self) -> OutputGraphState: """Create a checkpoint of the current state by copying everything""" assert self.param_name_to_source is not None guards_graph_state = self.tracing_context.guards_context.copy_graphstate() module_state = self.tracing_context.module_context.copy_graphstate() global_state = self.tracing_context.global_context.copy_graphstate() state = OutputGraphState( dict(self.input_source_to_var), list(self.tracked_fakes), guards_graph_state, module_state, global_state, dict(self.param_name_to_source), self.side_effects.clone(), self.timestamp, dict(self.tensor_weakref_to_sizes_strides), ) self.timestamp += 1 return state def restore_graphstate(self, state: OutputGraphState): """Restore a checkpoint created by self.copy_graphstate()""" ( self.input_source_to_var, self.tracked_fakes, guards_state, module_state, global_state, self.param_name_to_source, self.side_effects, self.timestamp, self.tensor_weakref_to_sizes_strides, ) = state self.tracing_context.guards_context.restore_graphstate(guards_state) self.tracing_context.module_context.restore_graphstate(module_state) self.tracing_context.global_context.restore_graphstate(global_state) # FX deepcopy doesn't work for a partially created graph, so just remove new nodes removed_nodes = 0 for node in reversed(list(self.graph.nodes)): if node.meta["creation_timestamp"] > self.timestamp: # Erasing node alone does not remove the meta information # So, remove the help tensor explicitly if "example_value" in node.meta: del node.meta["example_value"] self.remove_node(node) self.real_value_cache.pop(node, None) removed_nodes += 1 log.debug("restore_graphstate: removed %s nodes", removed_nodes) def add_symbol_bindings(self, arg: GraphArg): # Insert implicit size vars as necessary. With dynamic shapes, we # maintain the invariant that every sizevar gets a direct SymInt input # into the graph. This means downstream graph transforms can assume # every size variable is explicitly bound and accessible, instead of # having to pull it out implicitly from tensors. if self.export: return assert arg.fake_tensor is not None def bind_symint(s, prop): if not ( isinstance(s, torch.SymInt) and isinstance(s.node.expr, sympy.Symbol) ): return # TODO: don't readd symint if we already have it in graph # (this is harmless because we do remove the unused ones later) proxy = self.root_tracer.create_graph_input( str(s.node.expr), torch.SymInt, before=True, source=prop(arg.source), ) proxy.node.meta["grapharg"] = GraphArg( prop(arg.source), s, is_unspecialized=False, fake_tensor=None, is_tensor=False, ) for i, s in enumerate(arg.fake_tensor.size()): bind_symint( s, lambda src: TensorPropertySource(src, TensorProperty.SIZE, i) ) for i, s in enumerate(arg.fake_tensor.stride()): bind_symint( s, lambda src: TensorPropertySource(src, TensorProperty.STRIDE, i) ) bind_symint( arg.fake_tensor.storage_offset(), lambda src: TensorPropertySource(src, TensorProperty.STORAGE_OFFSET), ) def count_calls(self): return count_calls(self.graph) def is_empty_graph(self): return len(list(self.graph.nodes)) == 0 def get_submodule(self, keys): assert keys obj = self.nn_modules for k in keys.split("."): if isinstance(obj, dict): obj = obj[k] else: obj = getattr(obj, k) return obj def new_var(self, name="tmp"): existing = set(self.code_options["co_varnames"]) for i in itertools.count(): var = f"___{name}_{i}" if var not in existing: self.code_options["co_varnames"] += (var,) return var def update_co_names(self, name): """Ensure self.code_options.co_names contains name""" if name not in self.code_options["co_names"]: self.code_options["co_names"] += (name,) def register_attr_or_module( self, target: Union[torch.nn.Module, torch.Tensor, Any], *names, **options, ): if is_dynamic_nn_module(target): return variables.UnspecializedNNModuleVariable(target, **options) options = dict(options) options["guards"] = set(options.get("guards", [])) assert "source" in options source = options["source"] assert not isinstance(source, ParamBufferSource) if isinstance(target, torch.Tensor): tracer = self.current_tracer if not self.is_root_tracer(): # For higher order ops, we don't want to insert the get_attr in # innermost graph. Instead, we want to raise the params/buffers # as inputs to the higher-order graph, and register them as # get_attrs in the root tracer. # Note that Dynamo will still call lift_tracked_freevar_to_input # when these inputs are encountered for the inner graph. The # only difference is what happens at the root tracer for # nn.Parameters vs free inputs. The free inputs are registered # as placeholders in the root graph, whereas the nn.Parameters # are registered as get_attr nodes in the root graph. tracer = self.root_tracer if not is_constant_source(source): options["guards"].add(source.make_guard(GuardBuilder.TENSOR_MATCH)) if get_static_address_type(target) == "guarded": options["guards"].add(source.make_guard(GuardBuilder.DATA_PTR_MATCH)) def wrap_name(module_key): assert self.param_name_to_source is not None self.param_name_to_source[module_key] = source return wrap_fx_proxy( self.root_tx, tracer.create_proxy("get_attr", module_key, tuple(), {}), example_value=target, **options, ) elif isinstance(target, torch.nn.Module): assert isinstance(target, torch.nn.Module) if nnmodule_has_hooks(target, check_forward_hooks=True): torch._logging.warning_once( log, "nn.Module forward/_pre hooks are only partially supported, and were detected in your model. " "In particular, if you do not change/remove hooks after calling .compile(), you can disregard this " "warning, and otherwise you may need to set torch._dynamo.config.skip_nnmodule_hook_guards=False " "to ensure recompiling after changing hooks." f"{nnmodule_doc_url_msg} ", ) if nnmodule_has_hooks( target, check_backward_hooks=True, check_state_dict_hooks=True ): torch._logging.warning_once( log, "nn.Module state_dict and backward hooks are not yet supported by torch.compile, " f"but were detected in your model and will be silently ignored. {nnmodule_doc_url_msg}", ) options["guards"].add(source.make_guard(GuardBuilder.NN_MODULE)) def wrap_name(module_key): return NNModuleVariable(type(target), module_key, **options) elif isinstance(target, (torch.SymInt, torch.SymFloat)): # HACKY CODE REGION BEGIN # WE ARE PIGGYBACKING ON EXISTING INFRA TO REGISTER ATTRS # This ultimately gets written to self.nn_modules, which is unfortunate # Attrs that are tenors and symints and such need to be migrated to have their # own storage # alas, this is like this for now def wrap_name(module_key): return SymNodeVariable.create( self, self.create_proxy("get_attr", module_key, tuple(), {}), sym_num=target, **options, ) # HACKY CODE REGION END else: def wrap_name(module_key): self.output.update_co_names(module_key) self.global_scope[module_key] = target return VariableBuilder(self, ConstantSource(source_name=module_key))( target ) for k, v in self.nn_modules.items(): if v is target: # it already exists return wrap_name(k) # create a new unique name name = "_".join(map(str, names)) # Strip the guard lookup L/G access name = re.sub(r"^[GL]\['?(.*?)'?\]$", r"\1", name) # e.g. replace abc.xyz[123].qkv with abc.xyz_123.qkv name = re.sub(r"\[(\d+)\]", r"_\g<1>", name) # e.g. replace abc.xyz_123.qkv with abc_xyz_123_qkv name = re.sub(r"[^a-zA-Z0-9]", "_", name) if not name or not name[0].isalpha(): name = "sub" + name base = name for i in itertools.count(): if name not in self.nn_modules: self.nn_modules[name] = target if isinstance(target, torch.nn.Module): def register_leaf_name(leaf_name): assert self.param_name_to_source is not None new_source = ParamBufferSource(source, leaf_name) new_name = f"{name}.{leaf_name}" self.param_name_to_source[new_name] = new_source # annoying, but there are cases when we do not have parameters # see test_nn_moduledict_contains if hasattr(target, "_parameters"): for leaf_name, _ in target.named_parameters(): register_leaf_name(leaf_name) if hasattr(target, "_buffers"): for leaf_name, _ in target.named_buffers(): register_leaf_name(leaf_name) return wrap_name(name) name = f"{base}_{i}" raise AssertionError("unreachable") def compile_subgraph( self, tx, partial_convert=False, reason: Optional[GraphCompileReason] = None ): """ Generate a subgraph to continue execution on user code. Automatically restore live variables. """ assert reason is not None from .decorators import disable self.partial_convert = partial_convert self.compile_subgraph_reason = reason log.debug("COMPILING GRAPH due to %s", reason) if not all(block.can_restore() for block in tx.block_stack): unimplemented("compile_subgraph with block_depth != 0") prefix_insts: List[Instruction] = [] if sys.version_info >= (3, 11): # prefix instructions (Python 3.11+) for inst in tx.prefix_insts: if inst.opname == "MAKE_CELL": prefix_insts.append( create_instruction("MAKE_CELL", argval=inst.argval) ) elif inst.opname == "COPY_FREE_VARS": prefix_insts.append( create_instruction( "COPY_FREE_VARS", arg=len(tx.code_options["co_freevars"]) ) ) else: prefix_insts.append(copy.copy(inst)) def append_prefix_insts(): self.add_output_instructions(prefix_insts) prefix_insts.clear() for block in reversed(tx.block_stack): block.exit(tx) self.cleanup_graph() tx.prune_dead_locals() stack_values = list(tx.stack) root = FakeRootModule(self.nn_modules) # Add all the local vars to the "stack" so restore at the end restore_vars = [] val_to_names: OrderedDict[ VariableTracker, List[str] ] = collections.OrderedDict() if stack_values: val_to_names[stack_values[-1]] = list() for k, v in tx.symbolic_locals.items(): # Note! this explicitly uses .local_name for matching # Failure to do so will cause spurious registrations in val_to_names. # This will in turn result in spurious variables showing up in the graph. # This was very tricky to debug. For an example, dump the graph at call_user_compiler # while running test_subgraphs.py if isinstance(v.source, LocalSource) and v.source.local_name == k: continue # no need to restore initial state if v not in val_to_names: val_to_names[v] = list() val_to_names[v].append(k) for v in val_to_names.keys(): restore_vars.extend(val_to_names[v]) stack_values.extend([v] * len(val_to_names[v])) # to handle random calls if len(tx.random_calls) > 0: append_prefix_insts() random_calls_instructions = [] self.random_values_var = self.new_var("random_values") rand_fn_name = unique_id("__gen_rand_values") rand_fn = disable(_get_gen_rand_values_fn(tx.random_calls)) self.install_global(rand_fn_name, rand_fn) codegen = PyCodegen(tx, root) random_calls_instructions.extend( codegen.load_function_name(rand_fn_name, True) ) random_calls_instructions.extend(create_call_function(0, False)) random_calls_instructions.append( codegen.create_store(tx.output.random_values_var), ) self.add_output_instructions(random_calls_instructions) if ( stack_values and all( not isinstance(v, (UnspecializedPythonVariable, NumpyNdarrayVariable)) for v in stack_values ) and all(isinstance(x, TensorVariable) for x in stack_values) and len(set(stack_values)) == len(stack_values) and self.side_effects.is_empty() ): append_prefix_insts() # optimization to generate better code in a common case self.add_output_instructions( self.compile_and_call_fx_graph(tx, list(reversed(stack_values)), root) + [create_instruction("UNPACK_SEQUENCE", arg=len(stack_values))] ) else: graph_output_var = self.new_var("graph_out") pass1 = PyCodegen(tx, root, graph_output_var) self.side_effects.codegen_save_tempvars(pass1) pass1.foreach(stack_values) self.side_effects.codegen_update_mutated(pass1) # one more time now that we have established tempvars pass2 = PyCodegen( tx, root, graph_output_var, tempvars={val: None for val, count in pass1.uses.items() if count > 1}, ) self.side_effects.codegen_save_tempvars(pass2) pass2.foreach(stack_values) self.side_effects.codegen_update_mutated(pass2) output = [] if count_calls(self.graph) != 0 or len(pass2.graph_outputs) != 0: output.extend( self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root) ) if len(pass2.graph_outputs) != 0: output.append(pass2.create_store(graph_output_var)) else: output.append(create_instruction("POP_TOP")) append_prefix_insts() self.add_output_instructions(output + pass2.get_instructions()) # restore all the live local vars self.add_output_instructions( [PyCodegen(tx).create_store(var) for var in reversed(restore_vars)] ) def cleanup_graph(self): """ Remove this pattern from the graph: torch._C._set_grad_enabled(False) torch._C._set_grad_enabled(True) """ nodes = list(self.graph.nodes) grad_enabled = torch.is_grad_enabled() for node1, node2 in zip(nodes, nodes[1:]): if ( node1.target is torch._C._set_grad_enabled and tuple(node1.args) == (not grad_enabled,) and not node1._erased ): grad_enabled = node1.args[0] if ( node2.target is torch._C._set_grad_enabled and tuple(node2.args) == (not grad_enabled,) and not node2._erased ): grad_enabled = node2.args[0] self.graph.erase_node(node1) self.graph.erase_node(node2) def get_graph_sizes_log_str(self, name): graph_sizes_str = "TRACED GRAPH TENSOR SIZES\n" graph_sizes_str += f"===== {name} =====\n" for node in self.graph.nodes: example_value = node.meta.get("example_value", None) if isinstance(example_value, torch._subclasses.FakeTensor): size = example_value.size() graph_sizes_str += f"{node.name}: {tuple(size)}\n" concrete_size = [] has_symint = False for sz in size: if isinstance(sz, int): concrete_size.append(sz) elif isinstance(sz, torch.SymInt): has_symint = True concrete_size.append(sz.node.hint) else: break else: if has_symint: graph_sizes_str += ( f"{node.name} (concrete): {tuple(concrete_size)}\n" ) return graph_sizes_str @torch._guards.TracingContext.clear_frame() def compile_and_call_fx_graph(self, tx, rv, root): """ Generate code from self.graph and return the Instruction()s to call that generated code. """ from .decorators import disable assert isinstance(rv, list) assert isinstance(root, FakeRootModule) for output in rv: self.guards.update(output.guards) self.create_node( "output", "output", (self.current_tracer.create_arg(tuple(x.as_proxy() for x in rv)),), {}, ) self.remove_unused_graphargs() ncalls = count_calls(self.graph) counters["stats"]["calls_captured"] += ncalls # free a bit of memory self.real_value_cache.clear() gm = fx.GraphModule(root, self.graph) gm.compile_subgraph_reason = self.compile_subgraph_reason name = unique_id("__compiled_fn") graph_code_log.debug("%s", lazy_format_graph_code(name, gm)) graph_tabular_log.debug("%s", lazy_format_graph_tabular(name, gm)) graph_sizes_log.debug( "%s", LazyString(lambda: self.get_graph_sizes_log_str(name)) ) compiled_fn = self.call_user_compiler(gm) compiled_fn = disable(compiled_fn) counters["stats"]["unique_graphs"] += 1 self.install_global(name, compiled_fn) cg = PyCodegen(tx) cg.make_call_generated_code(name) return cg.get_instructions() @property def placeholders(self) -> List[fx.Node]: r = [] for node in self.graph.nodes: if node.op == "placeholder": r.append(node) continue break return r @property def graphargs(self) -> List[GraphArg]: return [node.meta["grapharg"] for node in self.placeholders] @dynamo_timed(phase_name="backend_compile") def call_user_compiler(self, gm: fx.GraphModule) -> CompiledFn: tot = 0 placeholders = [] for node in gm.graph.nodes: if node.op in ("call_function", "call_method", "call_module"): tot += 1 if node.op == "placeholder": placeholders.append(node) increment_op_count(tot) for pl in placeholders: arg = pl.meta["grapharg"] # TODO: Why isn't this stored in meta :think: pl._dynamo_source = arg.source gm._param_name_to_source = self.param_name_to_source gm._source_to_user_stacks = self.source_to_user_stacks try: name = ( self.compiler_fn.__name__ if hasattr(self.compiler_fn, "__name__") else "" ) _step_logger()(logging.INFO, f"calling compiler function {name}") compiler_fn = self.compiler_fn if config.verify_correctness: compiler_fn = WrapperBackend(compiler_fn) compiled_fn = compiler_fn(gm, self.example_inputs()) _step_logger()(logging.INFO, f"done compiler function {name}") assert callable(compiled_fn), "compiler_fn did not return callable" except exceptions_allowed_to_be_fallback as e: if self.has_user_defined_allowed_in_graph: raise BackendCompilerFailed(self.compiler_fn, e).with_traceback( e.__traceback__ ) from None msg = ( "Backend compiler failed with a fake tensor exception at \n" f"{self.root_tx.format_frame_summary()}" "Adding a graph break." ) unimplemented_with_warning(e, self.root_tx.f_code, msg) except Exception as e: raise BackendCompilerFailed(self.compiler_fn, e).with_traceback( e.__traceback__ ) from None signpost_event( "dynamo", "OutputGraph.call_user_compiler", { **self.co_fields, "op_count": tot, "node_count": len(gm.graph.nodes), "input_count": len(placeholders), }, ) return compiled_fn def example_inputs(self) -> List[torch.Tensor]: result = [] for arg in self.graphargs: result.append(arg.example) return result def remove_unused_graphargs(self) -> None: # Miniature DCE pass, but only for obviously trivial operations for node in reversed(list(self.graph.nodes)): if len(list(node.users)) == 0: if node.op == "get_attr": self.remove_node(node) elif node.op == "call_function" and node.target is operator.getitem: self.remove_node(node) def placeholder_binds_symbol(node): arg = node.meta["grapharg"] example = arg.example if isinstance(example, torch.SymInt) and isinstance( example.node.expr, sympy.Symbol ): return example.node.expr return None def remove_unused(node): log.debug("REMOVE UNUSED GRAPHARG %s", node.meta["grapharg"].source.name()) # I'm not really sure why you need to delete these from the # node since the node is going to get removed del node.meta["grapharg"] self.remove_node(node) self.real_value_cache.pop(node, None) used_symbols = set() recheck_placeholders = [] for node in self.placeholders: binds_symbol = placeholder_binds_symbol(node) is not None # Don't delete symbol bindings yet if binds_symbol: if not node.users: recheck_placeholders.append(node) else: if not node.users: remove_unused(node) else: # Register the free symbols as uses arg = node.meta["grapharg"] fake = ( arg.fake_tensor if arg.fake_tensor is not None else arg.example ) used_symbols |= free_symbols(fake) # After removing unused graphargs, prune unused binds_symbol for node in recheck_placeholders: symbol = placeholder_binds_symbol(node) if symbol is not None: if symbol not in used_symbols: remove_unused(node) else: # Make sure we delete later occurrences of the same symbol used_symbols.remove(symbol) def add_output_instructions(self, prefix: List[Instruction]) -> None: """ We call this on the creation of a new compiled subgraph that is inserted before user code. """ self.output_instructions.extend(prefix) self.should_exit = True def install_global(self, name, value) -> None: self.cleanups.append(CleanupHook.create(self.global_scope, name, value)) def cleanup(self) -> None: # There is a reference cycle between tracer and OutputGraph, causing # some of the tensor objects to be held alive for longer than necessary. self.root_tx = None self.nn_modules.clear() self.param_name_to_source = None for node in self.graph.nodes: if "grapharg" in node.meta: del node.meta["grapharg"] self.real_value_cache.clear() self.input_name_to_proxy.clear() self.side_effects.clear() def set_torch_function_state(self, enabled: bool) -> None: self.torch_function_enabled = enabled class SubgraphTracer(fx.Tracer): """ Holds an FX graph that is being traced. OutputGraph owns a SubgraphTracer and the separation of responsibilities is that SubgraphTracer is responsible for building the graph while OutputGraph is responsible for compiling and executing the graph. """ def __init__(self, output_graph, parent=None, export_root=False): super().__init__() self.output_graph = weakref.proxy(output_graph) self.graph = torch.fx.Graph() # The export is only ever set for the ROOT tracer. It controls # whether or not certain inputs are allowed to be added or not. # Look at call sites of create_graph_input to see how it is used. if export_root: assert parent is None self.export_root = export_root # Map from graph input name to its placeholder proxy object, where the # map's keys give all current placeholder node names and can be used to # create unique node names self.input_name_to_proxy: OrderedDict[str, fx.Proxy] = collections.OrderedDict() # Node => computed real value (see utils.get_real_value) self.real_value_cache: Dict[fx.Node, torch.Tensor] = {} # SubgraphTracers can be nested. See NOTE [HigherOrderOperator tracing design] self.parent = parent # A dict mapping previously free variables (Proxy objects) # to new Proxy objects that wrap inputs to this subgraph. # # This dict serves two purposes: # - Proxies are associatd with VariableTrackers. If we see # the same VariableTracker twice (and it is a free variable), # then we want to use the same Proxy in the current subgraph to # record the tracing. # - If we are tracing a HigherOrderOperator's body_fn, then we # need to keep track of what free variables were lifted so we can # rewrite the HigherOrderOperator call using the traced body_fn. # This is a OrderedDict so that we can # maintain the order of args for the HigherOrderOperator call. self.lifted_freevars = collections.OrderedDict() self.prev_inst = None def create_proxy( self, kind, target, args, kwargs, name=None, type_expr=None, proxy_factory_fn=None, ): # NOTE: [Nested SubgraphTracer and free_variable handling] # -------------------------------------------------------- # Read NOTE [HigherOrderOperator tracing design] first. # # Let's say we're in the middle of introspecting the body of a possibly # nested HigherOrderOperator, and we see a free variable. # # There are two cases: # 1. We see a free variable that is already tracked by Dynamo. # 2. We see a free variable that has not been tracked by Dynamo # # In case 1, we call `maybe_lift_tracked_freevar_to_input` (below) # which will lift the freevar to be an input of this subgraph # and also recursively lift it to be an input on the parent(s). # # In case 2, before the call to `create_proxy`, the InstructionTranslator # will see the freevar when it gets loaded by Python bytecode. # E.g. for Python 3.11 the bytecodes that may do this are LOAD_DEREF or # LOAD_GLOBAL. # There, the InstructionTranslator asks Dynamo to begin tracking the # freevar by building a new Variable. # Building a new Variable automatically lifts the freevar to be an # input of the root SubgraphTracer. # # The implications for the code below are: # - We will always be in Case 1 when we get to this code. # - Any "free variable" we encounter here is guaranteed to already be # bound, that is, it is either a graph input of the root graph, or # some local variable of the root graph or a subgraph. # - The additional work we need to do here is *only* that we need to # lift this free variable into inputs (recursively) of each nested # higher-order-op subgraph until we hit the subgraph where the free # variable is bound if self.parent is not None: flat_args, tree_spec = pytree.tree_flatten((args, kwargs)) new_flat_args = [] for arg in flat_args: maybe_new_arg = self.maybe_lift_tracked_freevar_to_input(arg) new_flat_args.append(maybe_new_arg) args, kwargs = pytree.tree_unflatten(new_flat_args, tree_spec) rv = super().create_proxy( kind, target, args, kwargs, name, type_expr, proxy_factory_fn ) # append stack trace to fx node tx = self.output_graph.current_tx # log detailed location of line of code in 3.11 if sys.version_info >= (3, 11) and kind in ( "call_function", "call_method", "call_module", ): cur_inst = tx.current_instruction if cur_inst is not self.prev_inst and cur_inst.positions.lineno is not None: tx_code = tx.f_code header = tx.get_line_of_code_header(lineno=cur_inst.positions.lineno) def get_trace_call_log_str(): line = get_instruction_source_311(tx_code, cur_inst).rstrip() return f"TRACE FX call {rv.node.name} from {header}\n{line}" trace_call_log.debug("%s", LazyString(get_trace_call_log_str)) self.prev_inst = cur_inst nn_module_stack = tx.nn_module_stack if nn_module_stack: rv.node.meta["nn_module_stack"] = nn_module_stack.copy() if kind in {"call_function", "call_method"}: rv.node.meta["source_fn"] = (rv.node.name, target) elif kind == "call_module": if self.parent is not None: unimplemented("Invoking an nn.Module inside HigherOrderOperator") # For modules we store the class rv.node.meta["source_fn"] = ( rv.node.name, rv.node.meta["nn_module_stack"][target][1], ) frame_summaries: List[traceback.FrameSummary] = [] while tx: frame_summaries.append(tx.frame_summary()) tx = getattr(tx, "parent", None) # Reverse the frame_summaries, such that the innermost frame is at the last frame_summaries.reverse() # official from_list stub doesn't have new-style type msgs = traceback.StackSummary.from_list(frame_summaries).format() # type: ignore[arg-type] rv.node.stack_trace = "".join(msgs) return rv def create_node( self, op, target, args=None, kwargs=None, name=None, type_expr=None ): if self.parent is not None: flat_args, _ = pytree.tree_flatten((args, kwargs)) for arg in flat_args: if not isinstance(arg, torch.fx.Node): continue # Special case for autograd.Function tracing if "saved_tensor_marked" in arg.meta: continue assert ( arg.graph == self.graph ), "create_node using arg not from this SubgraphTracer" node = super().create_node(op, target, args, kwargs, name, type_expr) node.meta["creation_timestamp"] = self.output_graph.timestamp return node # Note: we did not override erase_node since # we call self.graph.erase_node elsewhere def remove_node(self, node): if len(node.users) > 0: user_graph_nodes: List[torch.fx.Node] = [] for user in node.users.keys(): # For the case where user.graph == self.graph, that is a real bug and will raise # properly. if user.graph != self.graph: # This is a nested graph, which needs to be deleted. # If we do not do this, we will raise on attempting to remove this. # As we only get here during restoration cleanup, this is sound. user_graph_nodes.extend(reversed(list(user.graph.nodes))) for other_graph_node in user_graph_nodes: other_graph_node.graph.erase_node(other_graph_node) self.graph.erase_node(node) self.input_name_to_proxy.pop(node.name, None) # when before=True, we will insert this input before the most recent # inserted proxy. This is a hack to get around an ordering problem, # where we first insert a tensor argument, and then insert bindings # for SymInts that may occur in the tensor argument. # Remove this if https://github.com/pytorch/pytorch/issues/99007 gets # fixed. def create_graph_input(self, name, type_expr=None, before=False, source=None): if source is None: assert ( self.parent is not None ), "you are required to provide a source for inputs on the root tracer" # In eager, we are generally OK with adding graph inputs whenever we # want, because we take care of writing the bytecode that knows how # to source all the inputs. # # In export, this is bad, because you want a self-contained export # object which only depends on the inputs you explicitly passed to it. # So we are a bit more strict about what sources can become inputs # in export if self.export_root: if not is_from_local_source(source, allow_cell_or_freevar=False): self.output_graph.source_to_user_stacks.setdefault(source, []).append( TracingContext.extract_stack() ) # unique if name in self.input_name_to_proxy: for i in itertools.count(): candidate_name = f"{name}_{i}" if candidate_name not in self.input_name_to_proxy: name = candidate_name break if self.input_name_to_proxy: prev_name = next(reversed(self.input_name_to_proxy)) node = self.input_name_to_proxy[prev_name].node if before: ctx = self.graph.inserting_before(node) else: ctx = self.graph.inserting_after(node) else: ctx = self.graph.inserting_before(None) with ctx: proxy = self.create_proxy("placeholder", name, (), {}, type_expr=type_expr) if self.input_name_to_proxy and before: k, v = self.input_name_to_proxy.popitem() self.input_name_to_proxy[name] = proxy self.input_name_to_proxy[k] = v else: self.input_name_to_proxy[name] = proxy return proxy # See NOTE: [Nested SubgraphTracer and free_variable handling] for more details def lift_tracked_freevar_to_input(self, proxy): # You're doing something wrong if we are the root SubgraphTracer because # Dynamo adds tensors to graph inputs before creating a proxy for them. assert ( self.parent is not None ), "lift_tracked_freevar_to_input should not be called on root SubgraphTracer" # Proxys are associated with VariableTracker. # It is possible that we've already lifted the Proxy to be an input. # If that is the case, just return the already lifted Proxy. if proxy in self.lifted_freevars: return self.lifted_freevars[proxy] new_proxy = self.create_graph_input(proxy.node.name) new_proxy.node.meta["example_value"] = proxy.node.meta["example_value"] self.lifted_freevars[proxy] = new_proxy if self.parent is not None and proxy.tracer != self.parent: self.parent.lift_tracked_freevar_to_input(proxy) return new_proxy def maybe_lift_tracked_freevar_to_input(self, arg): """ If arg is a free variable, then lift it to be an input. Returns the new lifted arg (if arg was a freevar), else the original arg. """ if not isinstance(arg, torch.fx.Proxy): return arg elif arg.tracer == self: return arg # Special case for autograd.Function tracing elif "saved_tensor_marked" in arg.node.meta: return arg return self.lift_tracked_freevar_to_input(arg) # NOTE: [HigherOrderOperator tracing design] # Ignoring HigherOrderOperators for a moment, # OutputGraph represents the graph being built by Dynamo that may be compiled # and executed. It holds a root SubgraphTracer where the FX graph is built. # # HigherOrderOperators are operators that take functions as their arguments. # When Dynamo encounters a HigherOrderOperator, then it attempts to introspect # the function passed to it (call this the "body function"), capture it into a # GraphModule, and rewrite the call to the HigherOrderOperator to use the # GraphModule. # # The way we handle the capture of body functions is through having # (possibly nested) SubgraphTracers, one per body function. # # Mechanically, we do the introspection by: # - Creating a new SubgraphTracer via OutputGraph.new_subtracer # - Executing the body function. # This constructs the graph of the body function in the new SubgraphTracer # while modifying the state of the OutputGraph. For example: # - the OutputGraph can receive new GraphArgs (if we discover any new # untracked Tensors) # - side effects from the body function get accumulated into # OutputGraph.side_effects # - guards produced by the body function get accumulated into OutputGraph.guards # # The traced function has some special properties that make it easier for us # to transform later down the line: # - we lift all free variables to being inputs. # # If the introspection fails (due to the existence of graph breaks), then # we roll back the current OutputGraph state and graph break on the # HigherOrderOperator.