| import collections |
| import copy |
| import functools |
| import itertools |
| import logging |
| import operator |
| import re |
| import traceback |
| from dataclasses import dataclass |
| from typing import Any, Dict, List, NamedTuple, Optional, OrderedDict, Set, Union |
| |
| import torch.nn |
| from torch import fx |
| from torch._guards import Checkpointable, Guard, GuardsCheckpointState, TracingContext |
| from torch.fx.experimental.symbolic_shapes import ShapeEnv |
| |
| 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 .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, |
| is_constant_source, |
| LocalInputSource, |
| LocalSource, |
| ShapeEnvSource, |
| ) |
| from .utils import ( |
| assert_no_fake_params_or_buffers, |
| checkpoint_params, |
| CleanupHook, |
| clone_inputs, |
| count_calls, |
| counters, |
| dynamo_timed, |
| format_graph_tabular, |
| 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 ( |
| SymNodeVariable, |
| TensorVariable, |
| UnspecializedPythonVariable, |
| ) |
| |
| log = logging.getLogger(__name__) |
| |
| |
| class OutputGraphState(NamedTuple): |
| graphargs: List[GraphArg] |
| tracked_fakes: List[TrackedFake] |
| guard_state: GuardsCheckpointState |
| nn_modules: Optional[Dict[str, torch.nn.Module]] |
| side_effects: SideEffects |
| timestamp: int |
| |
| 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] |
| |
| |
| 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, original_example_inputs): |
| self.backend: CompilerFn = backend |
| self.original_example_inputs = original_example_inputs |
| |
| @property |
| def example_inputs(self): |
| return clone_inputs(self.original_example_inputs) |
| |
| 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, self.original_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(*self.example_inputs) |
| result = self.candidate(*self.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() |
| |
| |
| class OutputGraph(fx.Tracer, Checkpointable[OutputGraphState]): |
| """ |
| Wrapper class to hold outputs of InstructionTranslator. Mainly the |
| generated fx.Graph. |
| """ |
| |
| def __init__( |
| self, |
| f_globals: Dict[str, Any], |
| code_options: Dict[str, Any], |
| compiler_fn: CompilerFn, |
| root_tx, |
| export: bool, |
| ): |
| super().__init__() |
| self.graph = torch.fx.Graph() |
| self.graphargs: List[GraphArg] = [] |
| self.export = export |
| # 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, |
| strict_mark_dyn=export, |
| assume_static_by_default=config.assume_static_by_default, |
| ) |
| if config.dynamic_shapes |
| else None, |
| ) |
| self.tracing_context: TracingContext = TracingContext(fake_mode) |
| if config.dynamic_shapes: |
| # 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)) |
| |
| # 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] = [] |
| # Although we prune unused graphargs before sending graphs to |
| # compilers, we may have legitimately triggered shape guards |
| # on "unused" inputs that we must keep track of. So after |
| # remove_unused_graphargs is called, orig_graphargs and |
| # graphargs no longer alias; orig_graphargs is the original |
| # graphargs, and graphargs is the pruned list. Guard creation |
| # should use original graphargs. |
| self.orig_graphargs: List[GraphArg] = self.graphargs |
| self.nn_modules: Optional[Dict[str, torch.nn.Module]] = 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 |
| # Node => computed real value (see utils.get_real_value) |
| self.real_value_cache: Dict[fx.Node, torch.Tensor] = {} |
| |
| # Not checkpointed |
| self.compiler_fn: CompilerFn = compiler_fn |
| self.root_globals = f_globals |
| 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.initial_random_state = () |
| self.unspec_variable_map: Dict[str, UnspecializedPythonVariable] = {} |
| # Maps the source arg position to the grapharg position |
| self.pos_to_arg: Dict[int, int] = {} |
| |
| # Enables creating unique node names by tracking |
| # all current placeholder node names |
| self.name_to_input: OrderedDict[ |
| str, Optional[fx.Proxy] |
| ] = collections.OrderedDict() |
| |
| @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 |
| |
| 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.nn_modules is not None |
| guards_graph_state = self.tracing_context.guards_context.copy_graphstate() |
| state = OutputGraphState( |
| list(self.graphargs), |
| list(self.tracked_fakes), |
| guards_graph_state, |
| dict(self.nn_modules), |
| self.side_effects.clone(), |
| self.timestamp, |
| ) |
| self.timestamp += 1 |
| return state |
| |
| def restore_graphstate(self, state: OutputGraphState): |
| """Restore a checkpoint created by self.copy_graphstate()""" |
| ( |
| self.graphargs, |
| self.tracked_fakes, |
| guards_state, |
| self.nn_modules, |
| self.side_effects, |
| self.timestamp, |
| ) = state |
| self.tracing_context.guards_context.restore_graphstate(guards_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(f"restore_graphstate: removed {removed_nodes} nodes") |
| |
| def add_grapharg(self, arg: GraphArg): |
| curr_pos = len(self.graphargs) |
| self.graphargs.append(arg) |
| if isinstance(arg.source, LocalInputSource): |
| self.pos_to_arg[arg.source.pos] = curr_pos |
| |
| def count_calls(self): |
| return count_calls(self.graph) |
| |
| 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 create_graph_input(self, name, type_expr=None): |
| # unique |
| if name in self.name_to_input: |
| for i in itertools.count(): |
| if f"{name}_{i}" not in self.name_to_input: |
| name = f"{name}_{i}" |
| break |
| |
| if self.name_to_input: |
| prev_name = next(reversed(self.name_to_input)) |
| ctx = self.graph.inserting_after(self.name_to_input[prev_name]) |
| else: |
| ctx = self.graph.inserting_before(None) |
| with ctx: |
| proxy = self.create_proxy("placeholder", name, (), {}, type_expr=type_expr) |
| self.name_to_input[name] = proxy.node |
| return proxy |
| |
| 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,) |
| |
| @staticmethod |
| def module_has_hooks(mod, only_check_unsupported=False): |
| supported_hooks = [ |
| "_forward_pre_hooks", |
| "_forward_hooks", |
| ] |
| unsupported_hooks = [ |
| "_backward_pre_hooks", |
| "_backward_hooks", |
| "_state_dict_pre_hooks", |
| "_state_dict_hooks", |
| "_load_state_dict_pre_hooks", |
| "_load_state_dict_post_hooks", |
| ] |
| check_hooks = unsupported_hooks |
| if not only_check_unsupported: |
| check_hooks += supported_hooks |
| |
| return any(len(getattr(mod, x)) > 0 for x in check_hooks if hasattr(mod, x)) |
| |
| 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"] |
| if isinstance(target, torch.Tensor): |
| if not is_constant_source(source): |
| options["guards"].add(source.make_guard(GuardBuilder.TENSOR_MATCH)) |
| |
| def wrap_name(module_key): |
| return wrap_fx_proxy( |
| self.root_tx, |
| self.create_proxy("get_attr", module_key, tuple(), {}), |
| example_value=target, |
| **options, |
| ) |
| |
| elif isinstance(target, torch.nn.Module): |
| assert isinstance(target, torch.nn.Module) |
| if self.module_has_hooks(target, only_check_unsupported=True): |
| log.warning( |
| "nn.Module hooks are not fully supported, they may be ignored" |
| ) |
| 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.root_globals[module_key] = target |
| return VariableBuilder(self, ConstantSource(source_name=module_key))( |
| target |
| ) |
| |
| assert self.nn_modules is not None |
| 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)) |
| # 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 |
| 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. |
| """ |
| from .eval_frame import disable |
| |
| self.partial_convert = partial_convert |
| self.compile_subgraph_reason = reason |
| |
| log.debug(f"COMPILING GRAPH due to {reason}") |
| |
| if not all(block.can_restore() for block in tx.block_stack): |
| unimplemented("compile_subgraph with block_depth != 0") |
| |
| for block in reversed(tx.block_stack): |
| block.exit(tx) |
| |
| tx.prune_dead_locals() |
| stack_values = list(tx.stack) |
| assert self.nn_modules is not None |
| 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(): |
| if isinstance(v.source, LocalSource) and v.source.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: |
| 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.create_load_global("random", True, add=True), |
| codegen.create_load_attr("setstate"), |
| codegen.create_load_const(tx.output.initial_random_state), |
| ] |
| + create_call_function(1, False), |
| ) |
| 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) 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() |
| ): |
| # 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", 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")) |
| 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 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 .eval_frame 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.create_arg(tuple(x.as_proxy() for x in rv)),), {} |
| ) |
| self.remove_unused_graphargs() |
| ncalls = count_calls(self.graph) |
| counters["stats"]["calls_captured"] += ncalls |
| counters["stats"]["fusions_possible"] += ncalls - 1 |
| |
| # free a bit of memory |
| for node in self.graph.nodes: |
| if "example_value" in node.meta: |
| del node.meta["example_value"] |
| self.real_value_cache.clear() |
| |
| gm = fx.GraphModule(root, self.graph) |
| gm.recompile() |
| gm.compile_subgraph_reason = self.compile_subgraph_reason |
| name = unique_id("__compiled_fn") |
| |
| assert_no_fake_params_or_buffers(gm) |
| compiled_fn = self.call_user_compiler(gm) |
| compiled_fn = disable(compiled_fn) |
| |
| counters["stats"]["unique_graphs"] += 1 |
| self.install_global(name, compiled_fn) |
| |
| try: |
| # the call to tabulate can cause a lot of memory to be allocated |
| if config.log_level <= logging.INFO and config.output_code: |
| graph_str = ( |
| gm.print_readable() |
| if config.output_graph_code |
| else format_graph_tabular(gm.graph) |
| ) |
| log.log( |
| logging.INFO, |
| f"TRACED GRAPH\n {name} {gm.forward.__code__.co_filename} {graph_str}\n", |
| ) |
| except ImportError: |
| log.warning( |
| "Unable to print graph: `format_graph_tabular` relies on the library `tabulate`, " |
| "which could not be found on this machine. Run `pip " |
| "install tabulate` to install the library." |
| ) |
| |
| cg = PyCodegen(tx) |
| cg.make_call_generated_code(name) |
| return cg.get_instructions() |
| |
| @dynamo_timed(phase_name="backend_compile") |
| def call_user_compiler(self, gm: fx.GraphModule) -> CompiledFn: |
| tot = 0 |
| for node in gm.graph.nodes: |
| if node.op in ("call_function", "call_method", "call_module"): |
| tot += 1 |
| torch._dynamo.utils.increment_op_count(tot) |
| 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 |
| # WrapperBackend needs real inputs, for now, to verify correctness |
| if config.verify_correctness: |
| compiler_fn = WrapperBackend(compiler_fn, self.example_inputs()) |
| |
| # NOTE: [Real Tensors in Accuracy Evaluation] |
| # |
| # Today, tensors are passed to backends as fake at compile time. See the .fake_example_inputs() |
| # call to compiler_fn below. At runtime, backends use real tensors. |
| # |
| # This should be a strong invariant we hold across all backends, |
| # and generally, it is. However, for accuracy evaluation, we need real tensors at compile time, |
| # for now, due to the unfortunate setup described below. |
| # |
| # Due to the nature of how we invoke comparison as a backend in two different ways: |
| # |
| # (1) Less bad, but still worth rewriting, WrapperBackend above, which takes |
| # real inputs for its ctor. see the config.verify_correctnes above. |
| # |
| # (2) More bad, and very worth rewriting, the minifier installs accuracy comparison as |
| # a true backend, and therefore needs to be compiled with real inputs. This is made trickier |
| # by the fact that the minifier will spawn new processes during minification. As such, we have |
| # created a global flag, MINIFIER_SPAWNED, that should be set IF AND ONLY IF this run was spawned |
| # as part of accuracy minification. This flag is not a contract, and ideally will not be here long. |
| # |
| # The longer term PoR is to: |
| # (A) Rewrite the minifier accuracy evaluation and verify_correctness code to share the same |
| # correctness and accuracy logic, so as not to have two different ways of doing the same thing. |
| # |
| # (B) Refactor minifier accuracy backend to do its comparison fully at runtime, so as not to need to |
| # pass real tensors to it at compile time. |
| is_top_level_minifying = ( |
| config.repro_after is not None and config.repro_level == 4 |
| ) |
| if torch._dynamo.debug_utils.MINIFIER_SPAWNED or is_top_level_minifying: |
| compiled_fn = compiler_fn(gm, self.example_inputs()) |
| elif config.DO_NOT_USE_legacy_non_fake_example_inputs: |
| compiled_fn = compiler_fn(gm, self.example_inputs()) |
| else: |
| compiled_fn = compiler_fn(gm, self.fake_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 fake_example_inputs(self) -> List[torch.Tensor]: |
| result = [] |
| for arg in self.graphargs: |
| example = arg.get_fake_examples() |
| if example is not None: |
| result.extend(example) |
| else: |
| # Fallback, in case fake_tensor was not set |
| # Particularly for graph args that are not tensors |
| result.extend(arg.get_examples()) |
| return result |
| |
| def example_inputs(self) -> List[torch.Tensor]: |
| result = [] |
| for arg in self.graphargs: |
| result.extend(arg.get_examples()) |
| return result |
| |
| def remove_unused_graphargs(self) -> None: |
| 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) |
| |
| expanded_graphargs = [] |
| for arg in self.graphargs: |
| expanded_graphargs.extend([arg] * len(arg)) |
| arg.uses = 0 |
| |
| for node, arg in zip(self.graph.nodes, expanded_graphargs): |
| assert node.op == "placeholder" |
| arg.uses += len(node.users) |
| |
| for node, arg in list(zip(self.graph.nodes, expanded_graphargs)): |
| if arg.uses == 0: |
| log.debug(f"REMOVE UNUSED GRAPHARG {arg.source.name()}") |
| if "example_value" in node.meta: |
| del node.meta["example_value"] |
| self.remove_node(node) |
| self.real_value_cache.pop(node, None) |
| |
| self.graphargs = [arg for arg in self.graphargs if arg.uses > 0] |
| |
| 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.root_globals, 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 |
| |
| # Note: generated fx graph will hold a reference to the nn_module, |
| # So depending on the backend they may not be released |
| self.nn_modules = None |
| |
| # Cleanup graphargs |
| for graph_arg in self.graphargs: |
| graph_arg.erase() |
| |
| for node in self.graph.nodes: |
| if "example_value" in node.meta: |
| del node.meta["example_value"] |
| self.real_value_cache.clear() |
| self.name_to_input.clear() |
| self.side_effects.keepalive = [] |
| |
| def create_proxy( |
| self, |
| kind, |
| target, |
| args, |
| kwargs, |
| name=None, |
| type_expr=None, |
| proxy_factory_fn=None, |
| ): |
| rv = super().create_proxy( |
| kind, target, args, kwargs, name, type_expr, proxy_factory_fn |
| ) |
| |
| # append stack trace to fx node |
| tx = self.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"] = target |
| elif kind == "call_module": |
| # For modules we store the class |
| rv.node.meta["source_fn"] = 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, *args, **kwargs): |
| node = super().create_node(*args, **kwargs) |
| node.meta["creation_timestamp"] = self.timestamp |
| return node |
| |
| # Note: we did not override erase_node since |
| # we call self.graph.erase_node elsewhere |
| def remove_node(self, node): |
| self.graph.erase_node(node) |
| self.name_to_input.pop(node.name, None) |
