import functools import inspect import itertools import types from typing import Dict, List import torch from .. import variables from ..bytecode_transformation import create_call_function, create_rot_n from ..exc import unimplemented from ..source import ( AttrSource, ConstantSource, DefaultsSource, GetItemSource, GlobalSource, ) from ..utils import make_cell from .base import typestr, VariableTracker def wrap_bound_arg(tx, val, options, source=None): # Source propagation is best effort since not every object we encounter has a source to begin with. assert ( "source" not in options ), "Source needs to be separate from options due to recursive calls for lists/dicts" if isinstance(val, VariableTracker): return val elif not source: from torch._dynamo.variables.builder import SourcelessBuilder return SourcelessBuilder()(tx, val).add_options(options) else: from torch._dynamo.variables.builder import VariableBuilder return VariableBuilder(tx, source=source)(val).add_options(options) def wrap_args_kwargs(tx, result, options): for k, v in list(result.items()): if isinstance(v, (tuple, dict)): # args/kwargs result[k] = wrap_bound_arg(tx, v, options) def init_cellvars(parent, result, code): closure_cells = dict() side_effects = parent.output.side_effects # for name in itertools.chain(code.co_cellvars, code.co_freevars): for name in code.co_cellvars: closure_cells[name] = side_effects.track_cell_new() if name in result: side_effects.store_cell(closure_cells[name], result.pop(name)) return closure_cells def _create_nested_fn( code, f_globals, name, defaults, closure, kwdefaults, annotations ): from types import FunctionType func = FunctionType(code, f_globals, name, defaults, closure) func.__kwdefaults__ = kwdefaults if isinstance(annotations, tuple): from itertools import pairwise annotations = dict(pairwise(annotations)) # TypeError: __annotations__ must be set to a dict object assert annotations is None or isinstance(annotations, dict) func.__annotations__ = annotations return func class BaseUserFunctionVariable(VariableTracker): def get_filename(self): return self.get_code().co_filename def get_name(self): return self.get_code().co_name def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": return tx.inline_user_function_return( self, list(self.self_args()) + list(args), kwargs ) def num_parameters(self): return len(inspect.signature(self.get_function()).parameters) def closure_vars(self, tx): return {} class UserFunctionVariable(BaseUserFunctionVariable): """Some unsupported user-defined global function""" def __init__(self, fn, is_constant=False, **kwargs): super().__init__(**kwargs) if getattr(fn, "_dynamo_marked_constant", False): # This method should be treated as a constant for the purposes of compilation self.is_constant = True else: self.is_constant = False assert isinstance( fn, (types.FunctionType, torch.jit.ScriptFunction) ), f"expected FunctionType found {typestr(fn)} {fn}" # unpack @torch._dynamo.optimize()(fn) wrapped function fn = inspect.getattr_static(fn, "_torchdynamo_inline", fn) # unpack torch.jit.script_if_tracing if inspect.getattr_static(fn, "__script_if_tracing_wrapper", False): fn = inspect.getattr_static(fn, "__original_fn", fn) self.fn: types.FunctionType = fn def self_args(self): return [] def get_function(self): return self.fn def get_code(self): return self.fn.__code__ def python_type(self): return types.FunctionType def has_self(self): return getattr(self.fn, "__self__", None) is not None def get_globals(self): return self.fn.__globals__ def bind_args(self, parent, args, kwargs): assert not self.is_constant options = VariableTracker.propagate([self]) tx = parent.output.root_tx wrap = functools.partial(wrap_bound_arg, tx=tx, options=options) fn: types.FunctionType = self.fn defaults = fn.__defaults__ or [] defaults_sources = [ None if self.source is None else DefaultsSource(self.source, idx) for idx, _ in enumerate(defaults) ] fake_func = types.FunctionType( fn.__code__, fn.__globals__, fn.__name__, tuple( [ wrap(val=arg, source=source) for arg, source in zip(defaults, defaults_sources) ] ), fn.__closure__, ) if fn.__kwdefaults__: kwdefaults_sources = { k: None if self.source is None else DefaultsSource(self.source, k, is_kw=True) for k in fn.__kwdefaults__ } fake_func.__kwdefaults__ = { k: wrap(val=v, source=kwdefaults_sources[k]) for k, v in fn.__kwdefaults__.items() } bound = inspect.signature(fake_func).bind(*args, **kwargs) bound.apply_defaults() result = dict(bound.arguments.items()) wrap_args_kwargs(tx, result, options) closure_cells = init_cellvars(parent, result, fn.__code__) closure = self.fn.__closure__ or () assert len(closure) == len(self.fn.__code__.co_freevars) for idx, name, cell in zip( itertools.count(), self.fn.__code__.co_freevars, closure ): if name == "__class__": source = AttrSource(self.source, "__class__") if self.source else None result[name] = variables.UserDefinedClassVariable( cell.cell_contents, source=source, ) else: var = tx.match_nested_cell(name, cell) if var is not None: # optimization for cleaner codegen result[name] = var elif self.source: from .builder import VariableBuilder side_effects = parent.output.side_effects if cell in side_effects: out = side_effects[cell] else: closure_cell = GetItemSource( AttrSource(self.source, "__closure__"), idx ) closure_cell_contents = AttrSource( closure_cell, "cell_contents" ) contents_var = VariableBuilder(parent, closure_cell_contents)( cell.cell_contents ) if ( closure_cell_contents.name() not in tx.mutated_closure_cell_contents ): # Optimistically don't allocate the cell, to # reduce the number of side effects. This is # important for cond, as without it, any accesses # to closures create side effects and cond doesn't # support side effects. If we're wrong and this # closure cell gets written to, we will restart # the analysis with this cell's name in the # mutated list here result[name] = contents_var continue # cells are written to with "cell_contents", # so the source should just be the closure_cell, not its contents out = side_effects.track_cell_existing(closure_cell, cell) side_effects.store_cell( out, contents_var, ) result[name] = out else: from .builder import SourcelessBuilder result[name] = SourcelessBuilder()( tx, cell.cell_contents ).add_options(options) return result, closure_cells def export_freevars(self, parent, child): pass def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": if self.is_constant: options = VariableTracker.propagate(self, args, kwargs.values()) return invoke_and_store_as_constant( tx, self.fn, self.get_name(), options, args, kwargs ) return super().call_function(tx, args, kwargs) class UserMethodVariable(UserFunctionVariable): """Some unsupported user-defined method""" def __init__(self, fn, obj, **kwargs): super().__init__(fn=fn, **kwargs) self.obj = obj def __str__(self): return f"{self.__class__.__name__}({self.fn}, {self.obj})" def self_args(self): return [self.obj] def python_type(self): return types.MethodType def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": # For nn.Module methods, redirecting to NNModuleVariable.call_method for optimized solution # rather than simple inlining. E.g, putting `call_method` op in FX graph for `forward` method # since we ensure `forward` of allowed modules can be traced by AOT safely. # Note this is not only for allowed modules, as user customized modules can extend from # allowed modules but using parent's `forward` method, which is also covered by this branch. # If we are tracing the higher order op, we want Dynamo to step inside # the module call so that Dynamo can see the underlying parameters and # buffers and raise them as inputs to the graph. The is_root_tracer # check bypasses the if condition for non-root tracers and directly # calls the super().call_function at the end, which is basically # equivalent of inlining the method. if tx.output.is_root_tracer() and isinstance( self.obj, variables.NNModuleVariable ): module_attr = getattr(self.fn, "__module__", "") if ( module_attr is not None and module_attr.startswith("torch.nn.") or self.is_constant ): return self.obj.call_method( tx, self.fn.__name__, args, kwargs, constant=self.is_constant ).add_options(self) return super().call_function(tx, args, kwargs) def num_parameters(self): return super().num_parameters() - 1 class WrappedUserMethodVariable(UserMethodVariable): def __init__(self, wrapped, context, **kwargs): kwargs.pop("fn", None) kwargs.pop("obj", None) super().__init__(wrapped.fn, wrapped.obj, **kwargs) self.wrapped = wrapped self.context = context def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": self.context.enter(tx) result = super().call_function(tx, args, kwargs) self.context.exit(tx) return result class WrappedUserFunctionVariable(UserFunctionVariable): def __init__(self, wrapped, context, **kwargs): kwargs.pop("fn", None) kwargs.pop("obj", None) super().__init__(wrapped.fn, **kwargs) self.wrapped = wrapped self.context = context def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": self.context.enter(tx) result = super().call_function(tx, args, kwargs) self.context.exit(tx) return result def invoke_and_store_as_constant(tx, fn, name, options, args, kwargs): def convert(x): if isinstance(x, variables.TensorVariable): return x.get_real_value() return x.as_python_constant() args = [convert(x) for x in args] kwargs = {k: convert(v) for k, v in kwargs.items()} res = fn(*args, **kwargs) return tx.output.register_attr_or_module( res, name, source=ConstantSource(name), **options, ) class NestedUserFunctionVariable(BaseUserFunctionVariable): def __init__( self, fn_name, code, f_globals, defaults, kwdefaults, annotations, closure, closure_scope, wraps_source=None, **kwargs, ): super().__init__(**kwargs) assert isinstance(fn_name.as_python_constant(), str) assert isinstance(code.as_python_constant(), types.CodeType) assert isinstance(f_globals, dict) self.fn_name = fn_name self.code = code self.f_globals = f_globals self.defaults = defaults self.kwdefaults = kwdefaults self.annotations = annotations self.closure = closure if closure is None: closure_scope = None self.closure_scope = closure_scope self.wraps_source = wraps_source def self_args(self): return [] def get_code(self): return self.code.as_python_constant() def get_function(self): if self.closure: raise NotImplementedError() func = types.FunctionType( self.code.as_python_constant(), self.f_globals, self.fn_name.as_python_constant(), ) if self.defaults: func.__defaults__ = self.defaults.as_python_constant() if self.kwdefaults: func.__kwdefaults__ = self.kwdefaults.as_python_constant() if self.annotations: annotations = self.annotations.as_python_constant() if isinstance(annotations, tuple): from itertools import pairwise annotations = dict(pairwise(annotations)) # TypeError: __annotations__ must be set to a dict object assert isinstance(annotations, dict) func.__annotations__ = annotations return func def has_closure(self): return self.closure is not None def has_self(self): return False def get_globals(self): return self.f_globals def bind_args(self, parent, args, kwargs): from .misc import InlinedClosureVariable code = self.get_code() func = types.FunctionType( code, self.f_globals, self.fn_name.as_python_constant(), tuple(self.defaults.items) if self.defaults else None, tuple(make_cell(None) for _ in range(len(self.get_code().co_freevars))), ) if self.kwdefaults: func.__kwdefaults__ = self.kwdefaults.items bound = inspect.signature(func).bind(*args, **kwargs) bound.apply_defaults() result = dict(bound.arguments.items()) wrap_args_kwargs(parent.output.root_tx, result, VariableTracker.propagate(self)) closure_cells = init_cellvars(parent, result, code) for idx, name in enumerate(code.co_freevars): cell = self.closure.items[idx] assert getattr(cell, name, name) == name assert name not in result if isinstance(cell, InlinedClosureVariable): # InlinedClosureVariable's are created from LOAD_CLOSURE's from # InliningInstructionTranslators when the variable name is not found in closure_cells. # They should remain outside of closure_cells, so that our callee (the # InliningInstructionTranslator that traces `func`) handles # the cell correctly - that is, the cell's contents are treated as if they # are local variables, like in UserFunctionVariable's bind_args for freevars. cand = parent while cand and name not in cand.symbolic_locals: cand = cand.parent if cand is None: raise RuntimeError( f"Couldn't find {name} in the symbolic_locals of the inline interpreter stack" ) result[name] = cand.symbolic_locals[name] else: closure_cells[name] = self.closure.items[idx] return result, closure_cells def export_freevars(self, parent, child): code = self.get_code() for var in code.co_freevars: if var in child.symbolic_locals: parent.symbolic_locals[var] = child.symbolic_locals[var] def reconstruct(self, codegen): codegen.load_import_from(__name__, "_create_nested_fn") codegen(self.code) codegen.extend_output([codegen._create_load_const(self.f_globals)]) codegen(self.fn_name) if self.defaults: codegen(self.defaults) else: codegen.extend_output([codegen.create_load_const(None)]) if self.closure: codegen(self.closure) else: codegen.extend_output([codegen.create_load_const(None)]) if self.kwdefaults: codegen(self.kwdefaults) else: codegen.extend_output([codegen.create_load_const(None)]) if self.annotations: try: if isinstance(self.annotations, variables.ConstDictVariable): annotations = { k: v.as_python_constant() for k, v in self.annotations.items.items() } else: annotations = tuple( [v.as_python_constant() for v in self.annotations.items] ) codegen.extend_output([codegen._create_load_const(annotations)]) except NotImplementedError: codegen(self.annotations) else: codegen.extend_output([codegen.create_load_const(None)]) codegen.extend_output(create_call_function(7, push_null=True)) if self.wraps_source: codegen.load_import_from("functools", "wraps") codegen(self.wraps_source) codegen.extend_output(create_call_function(1, True)) codegen.extend_output(create_rot_n(2)) codegen.extend_output(create_call_function(1, True)) return [] def _traceable_collective_remaps(): # We can't rely on importing from distributed, since its not always built if torch.distributed.is_available(): from torch.distributed._functional_collectives import ( traceable_collective_remaps, ) return traceable_collective_remaps return {} def _traceable_collectives_source(fn): assert torch.distributed.is_available(), "Illegal invocation." from torch.distributed._functional_collectives import ( all_gather_tensor_inplace, reduce_scatter_tensor_inplace, ) valid_values = {all_gather_tensor_inplace, reduce_scatter_tensor_inplace} assert fn in valid_values inner_name = fn.__name__ path_source = AttrSource( base=AttrSource(base=GlobalSource(global_name="torch"), member="distributed"), member="_functional_collectives", ) return AttrSource(path_source, inner_name) class CollectiveFunctionRewriteVariable(UserFunctionVariable): """ Some of the torch.distributed.* collective APIs are possible to rewrite to 'traceable' collectives. This class provides both a way to check if a function is remappable, and perform the remapping. In the case that a function is 'remappable' but only for some combinations of call-time arguments, we check the args at `call_function` time and fall back to graph-breaking if needed. This is no worse than status-quo as we currently graph-break on all distributed.* collectives. """ def __init__(self, fn, *, orig_fn, orig_source, **kwargs): # orig_fn lets us implement any fn-specific args/kwargs restrictions inside call_function self.orig_fn = orig_fn self.orig_source = orig_source # remapped_fn gets stuffed in self.fn and used in super().call_function super().__init__(fn, **kwargs) @staticmethod def can_rewrite(variable): return ( inspect.isfunction(variable) and variable in _traceable_collective_remaps() ) @staticmethod def rewrite(fn): new_fn = _traceable_collective_remaps()[fn] return new_fn, _traceable_collectives_source(new_fn) def call_function( self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]" ) -> "VariableTracker": # call_function must check any unsupported arguments and graph-break. # It's safe to assume args/kwargs from orig_fn map 1:1 to args/kwargs of remapped_fn, # since that's the contract for putting a mapping in `traceable_collective_remaps` if kwargs.get("async_op", False): # Put the old source back, this function will always graph break, but this ensures # we produce the correct guards. self.source = self.orig_source unimplemented( f"CollectiveFunctionRewriteVariable can't support async_op=True for {self.orig_fn}" ) return super().call_function(tx, args, kwargs)