| 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.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, unimplemented |
| 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, |
| LocalSource, |
| ParamBufferSource, |
| ShapeEnvSource, |
| TensorProperty, |
| TensorPropertySource, |
| ) |
| from .utils import ( |
| checkpoint_params, |
| CleanupHook, |
| clone_inputs, |
| count_calls, |
| counters, |
| dynamo_timed, |
| graph_break_reasons, |
| 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") |
| |
| |
| 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)] |
| # 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() |
| |
| # In export mode, we force the shape_env to strictly disallow any constraining |
| # of the user marked dynamic dims |
| fake_mode = torch._guards.EXPORT_FAKE_MODE or torch._subclasses.FakeTensorMode( |
| shape_env=ShapeEnv( |
| allow_scalar_outputs=config.capture_scalar_outputs, |
| allow_dynamic_output_shape_ops=config.capture_dynamic_output_shape_ops, |
| frame_id=frame_state["_id"], |
| # TODO: maybe should just pass the entire f_code in here? Not |
| # sure... |
| co_fields={ |
| "co_name": f_code.co_name, |
| "co_filename": f_code.co_filename, |
| "co_firstlineno": f_code.co_firstlineno, |
| }, |
| ), |
| # 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) |
| # 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) |
| ) |
| |
| # 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 |
| |
| 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() |
| |
| # 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() |
| |
| @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 |
| ) |
| 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)) |
| |
| 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) |
| torch._dynamo.utils.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 |
| |
| 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 Exception as e: |
| raise BackendCompilerFailed(self.compiler_fn, e).with_traceback( |
| e.__traceback__ |
| ) from None |
| 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): |
| super(SubgraphTracer, self).__init__() |
| self.output_graph = weakref.proxy(output_graph) |
| self.graph = torch.fx.Graph() |
| # 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() |
| |
| 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) |
| new_args = [] |
| for arg in flat_args: |
| maybe_new_arg = self.maybe_lift_tracked_freevar_to_input(arg) |
| new_args.append(maybe_new_arg) |
| |
| args = pytree.tree_unflatten(new_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 |
| |
| 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): |
| # 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. |
