# mypy: allow-untyped-defs import collections import collections.abc import contextlib import copy import dataclasses import dis import functools import importlib import inspect import itertools import linecache import logging import operator import re import sys import threading import traceback import types import typing import weakref from typing import ( Any, Callable, cast, Deque, Dict, List, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, ) from unittest.mock import patch import torch import torch._logging from torch._guards import tracing, TracingContext from . import config, exc, logging as torchdynamo_logging, trace_rules, variables from .bytecode_analysis import ( get_indexof, JUMP_OPNAMES, livevars_analysis, propagate_line_nums, ) from .bytecode_transformation import ( cleaned_instructions, create_call_function, create_instruction, create_jump_absolute, create_swap, get_code_keys, Instruction, is_generator, unique_id, ) from .code_context import code_context from .codegen import PyCodegen from .exc import ArgsMismatchError, BackendCompilerFailed, unimplemented, Unsupported from .funcname_cache import get_funcname from .guards import GuardBuilder, install_guard from .output_graph import GraphCompileReason, OutputGraph from .replay_record import DummyModule, ExecutionRecorder from .resume_execution import ContinueExecutionCache, ReenterWith from .source import ( AttrSource, GetItemSource, GlobalSource, GlobalWeakRefSource, LocalSource, Source, TorchFunctionModeStackSource, ) from .trace_rules import is_builtin_constant, is_forbidden from .utils import ( counters, get_fake_value, get_instruction_source_311, get_torch_function_mode_stack, graph_break_dup_warning_checker, istype, LazyString, proxy_args_kwargs, ) from .variables.base import is_side_effect_safe, MutableLocal, typestr, VariableTracker from .variables.builder import VariableBuilder, wrap_fx_proxy from .variables.builtin import BuiltinVariable from .variables.constant import ConstantVariable from .variables.ctx_manager import ( ContextWrappingVariable, GenericContextWrappingVariable, WithExitFunctionVariable, ) from .variables.dicts import ConstDictVariable, SetVariable from .variables.functions import ( BaseUserFunctionVariable, NestedUserFunctionVariable, SkipFunctionVariable, UserFunctionVariable, UserMethodVariable, ) from .variables.iter import MAX_ITERATOR_LIMIT from .variables.lists import ( BaseListVariable, ListIteratorVariable, ListVariable, SliceVariable, TupleVariable, ) from .variables.misc import ( ClosureVariable, GetAttrVariable, InlinedClosureVariable, NullVariable, PythonModuleVariable, UnknownVariable, ) from .variables.nn_module import NNModuleVariable, UnspecializedNNModuleVariable from .variables.tensor import supported_comparison_ops, SymNodeVariable, TensorVariable if TYPE_CHECKING: from .variables.torch_function import TorchFunctionModeVariable from .variables.user_defined import ( RemovableHandleVariable, UserDefinedClassVariable, UserDefinedObjectVariable, ) log = logging.getLogger(__name__) graph_break_log = torch._logging.getArtifactLogger(__name__, "graph_breaks") trace_call_log = torch._logging.getArtifactLogger(__name__, "trace_call") trace_source_log = torch._logging.getArtifactLogger(__name__, "trace_source") trace_bytecode_log = torch._logging.getArtifactLogger(__name__, "trace_bytecode") tls = threading.local() compare_op_handlers: Dict[str, Any] = { k: BuiltinVariable(v).call_function for k, v in supported_comparison_ops.items() } handle_contains = BuiltinVariable(operator.contains).call_function handle_not = BuiltinVariable(operator.not_).call_function compare_op_handlers["in"] = lambda tx, args, _: handle_contains( tx, [*reversed(args)], {} ) compare_op_handlers["not in"] = lambda tx, args, _: handle_not( tx, [handle_contains(tx, [*reversed(args)], {})], {} ) PT2_ISSUE_TRACKER_URL = "https://github.com/pytorch/pytorch/issues/new?&labels=oncall%3A+pt2&projects=&template=pt2-bug-report.yml" @dataclasses.dataclass class SpeculationEntry: filename: str lineno: int instruction_pointer: int inst: Instruction # for debugging only failed: bool = False reason: Optional[GraphCompileReason] = None def fail_and_restart_analysis(self): """ Start tracing of the current frame over again, and don't take this branch. """ self.failed = True if self.reason is not None: restart_reason = self.reason.reason else: restart_reason = "Unknown fail_and_restart_analysis" raise exc.SpeculationRestartAnalysis(restart_reason=restart_reason) @dataclasses.dataclass class SpeculationLog: """ SpeculationLog replaces the prior copy_graphstate/restore_graphstate checkpointing. Rather than saving/restoring state, we restart the dynamo conversion process over from the beginning -- but when we hit the start of the speculation that failed, we instead generate a graph break. """ entries: List[SpeculationEntry] = dataclasses.field(default_factory=list) index: int = 0 def restart(self): self.index = 0 def clear(self): self.entries.clear() self.index = 0 def next( self, filename: str, lineno: int, instruction_pointer, inst ) -> SpeculationEntry: """ Lookup or create a SpeculationEntry() that is shared across RestartAnalysis calls. Args are used only for debug checks. """ if len(self.entries) == self.index: self.entries.append( SpeculationEntry(filename, lineno, instruction_pointer, inst) ) entry = self.entries[self.index] prev_entry_msg = "" if self.index != 0: prev_entry = self.entries[self.index - 1] prev_entry_msg = ( f"Previous instruction: {prev_entry.filename}:{prev_entry.lineno}" f"({prev_entry.inst.opname} @ {prev_entry.instruction_pointer})\n" ) assert ( entry.instruction_pointer == instruction_pointer and entry.filename == filename and entry.lineno == lineno ), f""" SpeculationLog diverged at index {self.index} (log had {len(self.entries)} entries): - Expected: {entry.filename}:{entry.lineno} ({entry.inst.opname} at ip={entry.instruction_pointer}) - Actual: {filename}:{lineno} ({inst.opname} at ip={instruction_pointer}) {prev_entry_msg} There are two usual reasons why this may have occured: - When Dynamo analysis restarted, the second run took a different path than the first. If this occurred, the previous instruction is the critical instruction that behaved differently. - Speculation entries are only added under certain conditions (as seen in step()), e.g., there must exist operators in the graph; those conditions may have changed on restart. If this divergence was intentional, clear the speculation log before restarting (do NOT do this for graph breaks, you will infinite loop). Otherwise, please submit a bug report, ideally including the contents of TORCH_LOGS=+dynamo """ self.index += 1 return entry @dataclasses.dataclass class LocalState: input_sizes: Dict[str, List[int]] = dataclasses.field(default_factory=dict) input_strides: Dict[str, List[int]] = dataclasses.field(default_factory=dict) # Mutable box that is shared across restarts @dataclasses.dataclass class DistributedState: compile_pg: Any local_state: LocalState all_states: Optional[List[LocalState]] = None @functools.lru_cache(None) def _step_logger(): return torchdynamo_logging.get_step_logger(log) @dataclasses.dataclass class BlockStackEntry: # Current instruction that pushes something to block_stack inst: Instruction target: Instruction stack_index: Optional[int] = None with_context: Optional[ Union[ContextWrappingVariable, GenericContextWrappingVariable] ] = None def can_restore(self): return self.with_context is not None def resume_fn(self): assert self.stack_index is not None if ( self.with_context and hasattr(self.with_context, "target_values") and self.with_context.target_values ): return ReenterWith(self.stack_index, tuple(self.with_context.target_values)) else: return ReenterWith(self.stack_index) def exit(self, tx): assert self.with_context is not None return self.with_context.exit(tx) class ReturnValueOp(Exception): pass def stack_op(fn: typing.Callable[..., object]): nargs = len(inspect.signature(fn).parameters) fn_var = BuiltinVariable(fn) @functools.wraps(fn) def impl(self: "InstructionTranslator", inst: Instruction): self.push(fn_var.call_function(self, self.popn(nargs), {})) return impl def _detect_and_normalize_assert_statement( self: "InstructionTranslatorBase", truth_fn: typing.Callable[[object], bool], push: bool, ): # Detect if this jump instruction is assert and normalize the assert # by pushing dummy error message when nothing is given. # # Python 3.9 assertion is in following format: # 18 POP_JUMP_IF_TRUE 28 # 20 LOAD_ASSERTION_ERROR # 22 LOAD_CONST 3 ('Assert message') -> optional instruction # 24 CALL_FUNCTION 1 -> optional instruction # 26 RAISE_VARARGS # # Python 3.8 assertion is in following format: # 18 POP_JUMP_IF_TRUE 28 # 20 LOAD_GLOBAL 0 (Assertion type) # 22 LOAD_CONST 3 ('Assert message') -> optional instruction # 24 CALL_FUNCTION 1 -> optional instruction # 26 RAISE_VARARGS 1 if (truth_fn is not operator.truth) or push: return False assert isinstance(self.instruction_pointer, int) current_instruction_pointer = self.instruction_pointer inst = self.instructions[current_instruction_pointer] # Detect LOAD_ASSERTION_ERROR or LOAD_GLOBAL 0 if sys.version_info < (3, 9): if inst.opname != "LOAD_GLOBAL" or inst.argval != "AssertionError": return False else: if inst.opname != "LOAD_ASSERTION_ERROR": return False current_instruction_pointer += 1 # Use dummy error message if its hard to extract error_msg = "assertion error" inst = self.instructions[current_instruction_pointer] # DETECT RAISE_VARARGS or LOAD CONST if inst.opname == "LOAD_CONST": if not isinstance(inst.argval, str): return False error_msg = inst.argval # if it is LOAD_CONSTANT, it must be followed by CALL_FUNCTION # (PRECALL for Python 3.11, CALL for Python 3.12+) current_instruction_pointer += 1 inst = self.instructions[current_instruction_pointer] if inst.opname not in ("CALL_FUNCTION", "PRECALL", "CALL"): return False # for Python 3.11, PRECALL should be followed by CALL, then RAISE_VARARGS # for Python != 3.11, CALL_FUNCTION/CALL should be followed by RAISE_VARARGS current_instruction_pointer += 1 if inst.opname == "PRECALL": current_instruction_pointer += 1 inst = self.instructions[current_instruction_pointer] if inst.opname != "RAISE_VARARGS": return False self.push(ConstantVariable.create(error_msg)) return True def generic_jump(truth_fn: typing.Callable[[object], bool], push: bool): def jump_graph_break(self, inst, value, extra_msg=""): if not self.should_compile_partial_graph(): unimplemented("should_compile_partial_graph=False") # compile a partial subgraph prefix then jump into user code if self.maybe_has_backedge(): msg = ( "Skipping frame because there is a graph break in a for/while loop\n" f"{self.frame_summary()}" ) log.info(msg) raise exc.SkipFrame(msg) self.push(value) log.debug("generic_jump triggered compile") self.output.compile_subgraph( self, reason=GraphCompileReason( f"generic_jump {typestr(value)}{extra_msg}", [self.frame_summary()] ), ) self.pop() if_next = self.create_call_resume_at(self.next_instruction) if push: self.push(value) if_jump = self.create_call_resume_at(inst.target) if sys.version_info >= (3, 13): # 3.13 requires stack[-1] to be bool type self.output.add_output_instructions([create_instruction("TO_BOOL")]) self.output.add_output_instructions( [create_instruction(inst.opname, target=if_jump[0])] + if_next + if_jump ) def inner(self: "InstructionTranslatorBase", inst: Instruction): value: VariableTracker = self.pop() if ( config.rewrite_assert_with_torch_assert and _detect_and_normalize_assert_statement(self, truth_fn, push) ): error_msg: VariableTracker = self.pop() # Skip over things like `assert True` if value.is_python_constant(): if bool(value.as_python_constant()): return self.jump(inst) else: jump_graph_break(self, inst, value) # TODO maybe should respect DtoH sync intention of users later?? # Manually insert torch._assert_async instead of python assert and jump over # assert related instructions as we don't need them anymore. # if we see Tensor as assert statement, no need to call scalar_tensor if isinstance(value, TensorVariable): self.output.create_proxy( "call_function", torch._assert_async, *proxy_args_kwargs((value, error_msg), {}), ) self.jump(inst) return if isinstance(value, SymNodeVariable): # if the assertion is normal shape expression. # just install guard and bail out. sym_expr = value.sym_num if not isinstance(sym_expr, torch.SymBool): sym_expr = sym_expr != 0 result = torch.fx.experimental.symbolic_shapes.expect_true(sym_expr) if not result: unimplemented( "Assertion failed on symbolic shapes. Did you make sure eager mode succeeds?" ) self.jump(inst) return scalar_to_tensor_proxy = self.output.create_proxy( "call_function", torch.scalar_tensor, *proxy_args_kwargs((value,), {}) ) scalar_to_tensor = wrap_fx_proxy( self, scalar_to_tensor_proxy, example_value=get_fake_value(scalar_to_tensor_proxy.node, self), ) self.output.create_proxy( "call_function", torch._assert_async, *proxy_args_kwargs((scalar_to_tensor, error_msg), {}), ) self.jump(inst) return if value.is_python_constant(): if truth_fn(value.as_python_constant()): if push: self.push(value) self.jump(inst) elif ( isinstance(value, (TensorVariable)) and self.should_compile_partial_graph() ): jump_graph_break(self, inst, value) elif isinstance(value, NNModuleVariable): # Equivalent of "self.nn_module is not None" mod = self.output.get_submodule(value.module_key) if truth_fn(mod): if push: self.push(value) self.jump(inst) elif isinstance(value, UnspecializedNNModuleVariable): mod = value.value if truth_fn(mod): if push: self.push(value) self.jump(inst) elif isinstance(value, UserDefinedObjectVariable): try: x = value.var_getattr(self, "__bool__") # type: ignore[arg-type] except exc.ObservedAttributeError: exc.handle_observed_exception(self) # if __bool__ is missing, trying __len__ to infer a truth value. try: x = value.var_getattr(self, "__len__") # type: ignore[arg-type] except exc.ObservedAttributeError: exc.handle_observed_exception(self) x = None # __bool__ or __len__ is function if isinstance(x, UserMethodVariable): result = x.call_function(self, [], {}) # type: ignore[arg-type] if isinstance(result, ConstantVariable) and isinstance( result.value, (bool, int) ): if truth_fn(result.value): if push: self.push(value) self.jump(inst) else: unimplemented( "generic_jump on UserDefined with __bool__ returning non-constant" ) # __bool__ or __len__ is non-function or not existed in the user defined object else: if truth_fn(True): if push: self.push(value) self.jump(inst) elif not isinstance(value, TensorVariable) and value.has_unpack_var_sequence( self ): if truth_fn(len(value.unpack_var_sequence(self))): if push: self.push(value) self.jump(inst) elif isinstance(value, SymNodeVariable): try: eval_result = value.evaluate_expr(self.output) except exc.UserError as e: if self.should_compile_partial_graph(): return jump_graph_break(self, inst, value, extra_msg=f"\n{e}") raise if truth_fn(eval_result): if push: self.push(value) self.jump(inst) elif isinstance(value, variables.BackwardHookVariable): if truth_fn(True): if push: self.push(value) self.jump(inst) else: from .source import is_constant_source if value.source is not None and is_constant_source(value.source): if truth_fn(value.get_real_value()): # type: ignore[attr-defined] if push: self.push(value) self.jump(inst) else: # TODO link the torch.cond doc later raise exc.UserError( exc.UserErrorType.DYNAMIC_CONTROL_FLOW, "Dynamic control flow is not supported at the moment. Please use " "functorch.experimental.control_flow.cond to explicitly capture the control flow.", case_name="cond_operands", ) return inner explain = False def break_graph_if_unsupported(*, push): def decorator(inner_fn): @functools.wraps(inner_fn) def wrapper(self: "InstructionTranslatorBase", inst: Instruction): speculation = self.speculate() if speculation.failed: assert speculation.reason is not None return handle_graph_break(self, inst, speculation.reason) try: return inner_fn(self, inst) except Unsupported as excp: if self.generic_context_manager_depth > 0: # We don't support graph break under GenericContextWrappingVariable, # If there is, we roll back to the checkpoint and fall back. excp.remove_from_stats() unimplemented("Graph break under GenericContextWrappingVariable") if isinstance(excp, exc.UncapturedHigherOrderOpError): raise if not self.should_compile_partial_graph(): raise user_stack = excp.real_stack # TODO: Also report the traceback from the parent frame try: frame_loc = (user_stack[-1].filename, user_stack[-1].lineno) except IndexError: # first instruction code_options = self.code_options frame_loc = ( code_options["co_filename"], code_options["co_firstlineno"], ) # torch._dynamo.explain() formats this a little nicer, and presents a slightly # more actionable user code pointer if ( graph_break_log.isEnabledFor(logging.DEBUG) and not explain and graph_break_dup_warning_checker.add(frame_loc) ): user_stack_formatted = "".join(traceback.format_list(user_stack)) # This log line is exercised from # python test/dynamo/test_exc.py -k test_graph_break_log graph_break_log.debug( "Graph break: from user code at:\n%s", user_stack_formatted, exc_info=True, ) else: # This log line MUST NOT contain the string "Graph break", # exercised by # python test/dynamo/test_misc.py -k test_duplicate_graph_break_log log.debug( "Unsupported break in user code at %s:%s (details suppressed)", *frame_loc, ) if self.maybe_has_backedge(): msg = ( "Skipping frame because there is a graph break in a for/while loop\n" f"{self.frame_summary()}" ) log.info(msg) raise exc.SkipFrame(msg) from excp excp.remove_from_stats() excp.add_to_stats("graph_break") speculation.reason = GraphCompileReason(excp.msg, user_stack) speculation.fail_and_restart_analysis() def handle_graph_break( self: "InstructionTranslatorBase", inst: Instruction, reason: GraphCompileReason, ): self.output.compile_subgraph(self, reason=reason) cg = PyCodegen(self) cleanup: List[Instruction] = [] # Reconstruct the context variable CLASS in the block stack for b in self.block_stack: assert b.with_context is not None assert isinstance(b.with_context, ContextWrappingVariable) b.with_context.reconstruct_type(cg) cg.extend_output(b.resume_fn().try_except(cg.code_options, cleanup)) self.output.add_output_instructions(cg.get_instructions()) del cg if sys.version_info >= (3, 11) and inst.opname == "CALL": kw_names = ( self.kw_names.as_python_constant() if self.kw_names is not None else () ) if len(kw_names) > 0: # KW_NAMES no longer used in 3.13 assert sys.version_info < (3, 13) self.output.add_output_instructions( [create_instruction("KW_NAMES", argval=kw_names)] ) self.output.add_output_instructions( create_call_function(inst.arg, False) ) else: # copy instruction, but without exception table data assert inst.target is None inst_copy = copy.copy(inst) inst_copy.exn_tab_entry = None self.output.add_output_instructions([inst_copy]) self.output.add_output_instructions(cleanup) if ( sys.version_info >= (3, 11) and sys.version_info < (3, 12) and inst.opname == "CALL" ): # stack effect for PRECALL + CALL is split between the two instructions stack_effect = dis.stack_effect( dis.opmap["PRECALL"], inst.arg ) + dis.stack_effect(dis.opmap["CALL"], inst.arg) else: stack_effect = dis.stack_effect(inst.opcode, inst.arg) self.popn(push - stack_effect) for _ in range(push): self.push(UnknownVariable()) self.output.add_output_instructions( self.create_call_resume_at(self.next_instruction) ) return wrapper return decorator class BytecodeDistpatchTableMeta(type): """Installs a `cls.dispatch_table` on every subclass to speed up calls to self.OPCODE()""" def __init__(cls, name, bases, dct) -> None: super().__init__(name, bases, dct) def _missing(opname, *args): unimplemented(f"missing: {opname}") dispatch_table = { op: getattr(cls, opname, functools.partial(_missing, opname)) for opname, op in dis.opmap.items() } cls.dispatch_table = [dispatch_table.get(i) for i in range(2**8)] class InstructionTranslatorBase( metaclass=BytecodeDistpatchTableMeta, ): output: OutputGraph symbolic_locals: Dict[str, VariableTracker] symbolic_globals: Dict[str, VariableTracker] symbolic_torch_function_mode_stack: Deque["TorchFunctionModeVariable"] stack: List[VariableTracker] instruction_pointer: Optional[int] current_instruction: Instruction block_stack: List[BlockStackEntry] lineno: int kw_names: Optional[ConstantVariable] accept_prefix_inst: bool prefix_insts: List[Instruction] inline_depth: int inconsistent_side_effects: bool current_speculation: Optional[SpeculationEntry] dispatch_table: List[Any] exn_vt_stack: List[VariableTracker] exec_recorder: Optional[ExecutionRecorder] strict_checks_fn: Optional[Callable[[VariableTracker], bool]] def mark_inconsistent_side_effects(self): """ InstructionTranslator has encountered instructions which may cause dynamo to see a different version of history from eager See: https://github.com/pytorch/pytorch/issues/110765 """ self.inconsistent_side_effects = True def maybe_has_backedge(self): # This function employs a heuristic. It does not reliably detect a backedge. # The heuristic is straightforward: starting from the current instruction and # continuing to the end, if any jump instruction targets an instruction before # the current one, there might be a backedge. # Python 3.12 introduced changes to bytecode that group common paths in # blockstacks (with or try...else) and allow for early returns. Consequently, # there can be multiple RETURN_VALUE instructions. Another heuristic is to # halt detection upon encountering the first RETURN_VALUE or RETURN_CONST. # These heuristics can result in both false positives and negatives, but # in either case, the Dynamo code remains valid. For false positives # (where an edge is incorrectly marked as a backedge), Dynamo will # perform a SkipFrame instead of potentially applying optimizations. For # false negatives (where an edge that should be marked as a backedge # isn't), multiple graphs may be generated if there's a break in the # graph during a for loop. In general, its better to have fewer false # negatives so that Dynamo does not skip the whole frame. cur_offset = self.current_instruction.offset assert self.instruction_pointer is not None for inst in self.instructions[self.instruction_pointer :]: if inst.opname in ("RETURN_VALUE", "RETURN_CONST"): return False if inst.opname in JUMP_OPNAMES: jump_offset = inst.argval if jump_offset < cur_offset: return True return False def cell_and_freevars(self): if not hasattr(self, "_cell_and_freevars"): self._cell_and_freevars = tuple( self.code_options["co_cellvars"] or [] ) + tuple(self.code_options["co_freevars"] or []) # An inlined function might depend on the freevar of the parent # function. So, recursively obtain parent cell and freevars. if isinstance(self, InliningInstructionTranslator): self._cell_and_freevars += self.parent.cell_and_freevars() return self._cell_and_freevars def prune_dead_locals(self): reads = livevars_analysis(self.instructions, self.current_instruction) # implicit use by super() # reads = reads | {"__class__"} # output variables? reads = reads | set(self.cell_and_freevars()) self.symbolic_locals = { k: v for k, v in self.symbolic_locals.items() if k in reads } self.output.side_effects.prune_dead_object_new(self) def call_function( self, fn: VariableTracker, args: List[VariableTracker], kwargs: Dict[str, VariableTracker], ): assert isinstance(fn, VariableTracker) assert isinstance(args, list) assert isinstance(kwargs, dict) assert all( isinstance(x, VariableTracker) for x in itertools.chain(args, kwargs.values()) ) inner_fn = None if hasattr(fn, "value"): inner_fn = fn.value if hasattr(fn, "fn"): inner_fn = fn.fn if inner_fn and callable(inner_fn) and is_forbidden(inner_fn): raise AssertionError(f"Attempt to trace forbidden callable {inner_fn}") self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] def inline_user_function_return(self, fn, args, kwargs): """ A call to some user defined function by inlining it. """ return InliningInstructionTranslator.inline_call(self, fn, args, kwargs) def get_line_of_code_header(self, lineno=None): if lineno is None: lineno = self.lineno inline_depth_str = ( f" (inline depth: {self.inline_depth})" if self.inline_depth > 0 else "" ) funcname = get_funcname(self.f_code.co_filename, lineno) funcname_str = "" if funcname is None else f" ({funcname})" return f"{self.f_code.co_filename}:{lineno} in {self.f_code.co_name}{funcname_str}{inline_depth_str}" def get_log_starts_line_log_str(self): log_str = f"TRACE starts_line {self.get_line_of_code_header()}\n" line = linecache.getline(self.f_code.co_filename, self.lineno).rstrip() log_str += f" {line}" return log_str def starts_line(self, lineno): if self.lineno == lineno: return self.lineno = lineno TracingContext.set_current_loc( self.f_code.co_filename, lineno, self.f_code.co_name ) from torch._logging.structured import dump_file dump_file(self.f_code.co_filename) if trace_source_log.isEnabledFor(logging.DEBUG): trace_source_log.debug("%s", LazyString(self.get_log_starts_line_log_str)) def step(self): """Process exactly one instruction, return False we should exit""" ip = self.instruction_pointer if ip is None: return False self.current_instruction = inst = self.instructions[ip] self.instruction_pointer = ip + 1 if inst.starts_line: self.starts_line(inst.starts_line) if ( not self.stack and self.should_compile_partial_graph() and self.is_non_empty_graph() ): self.current_speculation = self.speculate() if self.current_speculation.failed: return self.step_graph_break(inst) if trace_bytecode_log.isEnabledFor(logging.DEBUG): trace_bytecode_log.debug( "TRACE %s %s %s", inst.opname, inst.argval, self.stack ) self.update_block_stack(inst) try: self.dispatch_table[inst.opcode](self, inst) return not self.output.should_exit except exc.ObservedException as e: self.exception_handler(e) return True except ReturnValueOp: return False except Unsupported: if self.current_speculation is None: log.debug("empty checkpoint") raise log.debug("step triggered compile", exc_info=True) self.current_speculation.fail_and_restart_analysis() if sys.version_info >= (3, 11): def update_block_stack(self, inst): # 3.11+ no longer uses a block stack, but we still keep track of one # so that we know which contexts are currently active. # For our purposes, all exception table entries with the same target # are considered to be part of the same "block". # NOTE: we only keep track of with blocks that are not contained in try blocks. # This is because we will not create continuation functions on graph breaks in try blocks, # but we may for with blocks. We do not push blocks here since # with blocks are pushed when handling BEFORE_WITH. entry = inst.exn_tab_entry if entry: # Detect when we have exited the top with block. # The with blocks on the block stack are not enclosed in try # blocks, so a with block's cleanup code should be in the # previous with block (if any). if ( len(self.block_stack) >= 2 and entry.target is not self.block_stack[-1].target and entry.target is self.block_stack[-2].target ): # exit the current block self.block_stack.pop() else: # no longer in any block # It is possible for NOPs to be between two instructions # in the same block, but the NOPs are not covered by an # exception table entry. In this case, assume that we # are still in the same block. # In 3.12+, JUMP_BACKWARD might also not be covered by # an exception table entry, so we also assume that we # are still in the same block. It is probably safe to do # this in 3.11, even though we haven't encountered this case before. if self.block_stack and inst.opname not in ("NOP", "JUMP_BACKWARD"): # If we really escape from a block and the current # instruction is not in another block, then there # should be no other nested blocks that we are in. assert len(self.block_stack) == 1 self.block_stack.pop() else: def update_block_stack(self, inst): pass @property def next_instruction(self): return self.instructions[self.instruction_pointer] # type: ignore[index] def step_graph_break(self, continue_inst): # generate code from checkpoint assert not self.output.output_instructions assert self.current_speculation is not None self.output.compile_subgraph( self, partial_convert=True, reason=GraphCompileReason("step_unsupported", [self.frame_summary()]), ) self.output.add_output_instructions( [create_jump_absolute(continue_inst)] + self.instructions ) def run_ctx_mgr(self): # NB: Don't push the top level frame summary; set_current_loc will # take care of it. However, DO make sure we attach real_stack to # exceptions return TracingContext.current_frame(None) def run(self): with self.run_ctx_mgr(): try: self.output.push_tx(self) while self.step(): pass except BackendCompilerFailed: raise except Exception as e: if self.exec_recorder: e.exec_record = self.exec_recorder.get_record() # type: ignore[attr-defined] raise finally: self.output.pop_tx() # Cleanup the outputGraph to delete the held tensors. We perform the # cleanup only for InstructionTranslator and not # InliningInstructionTranslator. The InliningInstructionTranslator # mutates the output object and is restored to original state if # there was an exception. if isinstance(self, InstructionTranslator): self.output.cleanup() def push(self, val: Optional[VariableTracker], name: Any = None): assert val is None or isinstance( val, VariableTracker ), f"push expects VariableTracker, got {typestr(val)}" self.stack.append(val) # type: ignore[arg-type] if sys.version_info >= (3, 13): self.name_stack.append(name) assert len(self.stack) == len(self.name_stack) def push_many(self, vals: List[VariableTracker]): for val in vals: self.push(val) def pop(self) -> VariableTracker: if sys.version_info >= (3, 13): assert len(self.stack) == len(self.name_stack) self.name_stack.pop() return self.stack.pop() def popn(self, n: int) -> List[VariableTracker]: return [*reversed([self.pop() for _ in range(n)])] def _load_closure(self, name): return ClosureVariable(name=name) def _load_fast(self, name): if self.exec_recorder and name in self.f_locals: self.exec_recorder.add_local_var(name, self.f_locals[name]) try: self.push(self.symbolic_locals[name].unwrap(), name=name) except KeyError: if sys.version_info >= (3, 13) and name in self.cell_and_freevars(): # 3.13 merged LOAD_CLOSURE into LOAD_FAST # If we fail to LOAD_FAST, then we probably should have done LOAD_CLOSURE. # Closure variable creation is actually done in SET_FUNCTION_ATTRIBUTE, # but we'll do it again here so that we don't need to push a dummy variable. # We shouldn't actually be doing anything with this variable anyway. self.push(self._load_closure(name), name=name) elif name.startswith("."): try: # This happens in dict/list comprehensions new_name = name.replace(".", "implicit") self.push(self.symbolic_locals[new_name], name=new_name) except KeyError: unimplemented("undefined LOAD_FAST (implicit)") else: unimplemented("undefined LOAD_FAST") # for continuation functions if name.startswith("___stack"): self.symbolic_locals.pop(name) def LOAD_FAST(self, inst): self._load_fast(inst.argval) def LOAD_DEREF(self, inst): assert inst.argval in self.cell_and_freevars() if self.exec_recorder and inst.argval in self.f_locals: self.exec_recorder.add_local_var(inst.argval, self.f_locals[inst.argval]) if inst.argval not in self.symbolic_locals: unimplemented(f"undefined LOAD_DEREF {inst.argval}") self.push(self.symbolic_locals[inst.argval]) def _store_fast(self, name): loaded_vt = self.pop() loaded_vt.set_name_hint(name) self.symbolic_locals[name] = loaded_vt def STORE_FAST(self, inst): self._store_fast(inst.argval) def DELETE_FAST(self, inst): del self.symbolic_locals[inst.argval] STORE_DEREF = STORE_FAST def LOAD_CLOSURE(self, inst): self.push(self._load_closure(inst.argval)) def _load_const(self, inst): i = inst.arg if i is None: return ConstantVariable.create(value=inst.argval) val = self._constants_cache[i] if not val: self._constants_cache[i] = val = ConstantVariable.create(value=inst.argval) return val def LOAD_CONST(self, inst): self.push(self._load_const(inst)) def _load_global(self, inst): name = inst.argval if self.exec_recorder: if name in self.f_globals: self.exec_recorder.add_global_var(name, self.f_globals[name]) else: assert name in self.f_builtins self.exec_recorder.builtins[name] = self.f_builtins[name] if name in self.symbolic_globals: variable = self.output.side_effects[self.symbolic_globals[name]] self.push(self.output.side_effects.load_global(variable, name)) return try: value = self.f_globals[name] except KeyError: return self.load_builtin(inst) source = GlobalSource(name) self.push(VariableBuilder(self, source)(value)) @functools.cached_property def nn_modules_globals_vt(self): module_name = "torch.nn.modules.module" module_source = self.import_source(module_name) fglobals_value = importlib.import_module(module_name) # type: ignore[assignment] return VariableBuilder(self, module_source)(fglobals_value) def LOAD_GLOBAL(self, inst): if sys.version_info >= (3, 11) and sys.version_info < (3, 13) and inst.arg % 2: self.PUSH_NULL(inst) self._load_global(inst) if sys.version_info >= (3, 13) and inst.arg % 2: self.PUSH_NULL(inst) def STORE_GLOBAL(self, inst): value = self.pop() name = inst.argval source = GlobalSource(name) if name not in self.symbolic_globals: self.symbolic_globals[name] = object() # type: ignore[assignment] # sentinel object variable = self.output.side_effects.track_global_existing( source, self.symbolic_globals[name] ) if isinstance(value, RemovableHandleVariable): unimplemented("Storing handles in globals - NYI") self.output.side_effects.store_global(variable, name, value) def import_source(self, module_name): """Create an alias to a module for use in guards""" if "torch_package" in module_name: value = torch.package.package_importer._package_imported_modules[ module_name ] alias = ( module_name.replace(">", "_").replace("<", "_").replace(".", "_dot_") ) else: value = importlib.import_module(module_name) alias = f"__import_{module_name.replace('.', '_dot_')}" f_globals = self.output.global_scope assert alias not in f_globals or f_globals[alias] is value f_globals[alias] = value self.output.update_co_names(alias) return GlobalSource(alias) def resolve_name(self, name, package, level): """ Copied from the Cpython implementation of __import__ Resolve a relative module name to an absolute one. https://github.com/python/cpython/blob/5a094f0255eea1db58fb2cf14c200971e64ec36e/Lib/importlib/_bootstrap.py#L902 """ bits = package.rsplit(".", level - 1) if len(bits) < level: raise ImportError("attempted relative import beyond top-level package") base = bits[0] return f"{base}.{name}" if name else base def calc_package(self): """ Copied from the Cpython implementation of __import__ https://github.com/python/cpython/blob/5a094f0255eea1db58fb2cf14c200971e64ec36e/Lib/importlib/_bootstrap.py#L1090 """ package = self.f_globals.get("__package__") spec = self.f_globals.get("__spec__") if package is not None: if spec is not None and package != spec.parent: log.warning( "__package__ != __spec__.parent (%r != %r)", package, spec.parent, stacklevel=3, ) return package elif spec is not None: return spec.parent else: log.warning( "can't resolve package from __spec__ or __package__, " "falling back on __name__ and __path__", stacklevel=3, ) package = self.f_globals["__name__"] if "__path__" not in self.f_globals: package = package.rpartition(".")[0] return package def IMPORT_NAME(self, inst): level, fromlist = self.popn(2) level = level.as_python_constant() fromlist = fromlist.as_python_constant() module_name = inst.argval # Are we replaying? if so, load recorded module recorded_name = ( f"{ExecutionRecorder.LOCAL_MOD_PREFIX}_{level}_{fromlist}_{module_name}" ) if recorded_name in self.f_globals: value = self.f_globals[recorded_name] source = GlobalSource(recorded_name) else: try: value = __import__( module_name, fromlist=fromlist, level=level, globals=self.f_globals, ) except ImportError: unimplemented("import a module that does not exist") if level != 0: pkg = self.calc_package() module_name = self.resolve_name(module_name, pkg, level) # For __import__, when the name variable is of the form package.module, # normally, the top-level package (the name up till the first dot) is # returned, not the module named by module_name. However, when a # non-empty fromlist argument is given, the module named by name is # returned. Therefore, we set the source correctly here. if not fromlist: top_level_module_name = module_name.partition(".")[0] source = self.import_source(top_level_module_name) else: source = self.import_source(module_name) if self.exec_recorder: self.exec_recorder.add_local_mod(recorded_name, value) if istype(value, (types.ModuleType, DummyModule)): self.push(PythonModuleVariable(value, source=source)) else: unimplemented(f"IMPORT_NAME {typestr(value)}") def IMPORT_FROM(self, inst): self.DUP_TOP(inst) self._load_attr(inst) def load_builtin_from_argval(self, argval): if argval not in self.f_builtins: raise NameError(f"name '{argval}' is not defined") val = self.f_builtins[argval] if callable(val): builtins_source = GlobalSource( self.output.name_of_builtins_dict_key_in_fglobals ) var_source = GetItemSource(builtins_source, argval) self.push(VariableBuilder(self, var_source)(val)) else: assert is_builtin_constant(val) self.push(ConstantVariable.create(value=val)) def load_builtin(self, inst): self.load_builtin_from_argval(inst.argval) def jump(self, inst): self.instruction_pointer = self.indexof[inst.target] JUMP_FORWARD = jump JUMP_ABSOLUTE = jump POP_JUMP_IF_FALSE = generic_jump(operator.not_, False) POP_JUMP_IF_TRUE = generic_jump(operator.truth, False) JUMP_IF_FALSE_OR_POP = generic_jump(operator.not_, True) JUMP_IF_TRUE_OR_POP = generic_jump(operator.truth, True) def SETUP_LOOP(self, inst): # only exists in python<=3.7 self.block_stack.append(BlockStackEntry(inst, inst.target)) def SETUP_EXCEPT(self, inst): # only exists in python<=3.7 self.block_stack.append(BlockStackEntry(inst, inst.target)) def POP_BLOCK(self, inst): self.block_stack.pop() def SETUP_WITH(self, inst): self.setup_or_before_with(inst) def SETUP_FINALLY(self, inst): self.block_stack.append(BlockStackEntry(inst, inst.target)) def BEGIN_FINALLY(self, inst): self.push(None) def WITH_CLEANUP_START(self, inst): exit, exc = self.popn(2) assert exc is None self.push(exc) self.push(exit.call_function(self, [ConstantVariable.create(None)] * 3, {})) def WITH_CLEANUP_FINISH(self, inst): self.popn(2) self.push(None) def CALL_FINALLY(self, inst): """ pushes the address of the next instruction onto the stack and increments bytecode counter by delta """ # Python 3.8 only addr = self.indexof[self.next_instruction] self.push(ConstantVariable.create(addr)) self.jump(inst) def END_FINALLY(self, inst): # Python 3.8 only # https://docs.python.org/3.8/library/dis.html#opcode-END_FINALLY tos = self.pop() if isinstance(tos, ConstantVariable): self.instruction_pointer = tos.as_python_constant() else: pass def POP_FINALLY(self, inst): # Python 3.8 only preserve_tos = inst.argval if preserve_tos: tos = self.pop() _ = self.pop() if preserve_tos: self.push(tos) # type: ignore[possibly-undefined] def FOR_ITER(self, inst): it = self.pop().realize() try: val = it.next_variable(self) self.push(it) self.push(val) except (StopIteration, exc.ObservedUserStopIteration) as e: if isinstance(e, exc.ObservedUserStopIteration): exc.handle_observed_exception(self) # leave iterator upon exhaustion in 3.12 if sys.version_info >= (3, 12): # CPython 3.12 actually jumps to the instruction after the END_FOR # and performs the action of END_FOR as part of FOR_ITER. We jump # to the END_FOR and run it, so we need to make sure 2 values are # on the stack for it to pop. self.push(it) self.push(ConstantVariable.create(None)) self.jump(inst) def _raise_exception_variable(self, inst): val = self.pop() # User can raise exception in 2 ways # 1) raise exception type - raise NotImplementedError # 2) raise execption instance - raise NotImplemetedError("foo") # 1) when user raises exception type if isinstance(val, variables.BuiltinVariable): # Create the instance of the exception type # https://github.com/python/cpython/blob/3.11/Python/ceval.c#L6547-L6549 val = val.call_function(self, [], {}) # type: ignore[arg-type] # Save the exception in a global data structure self.exn_vt_stack.append(val) # 2) when user raises exception instance if isinstance(val, variables.ExceptionVariable): if observed_exception_type := exc.observed_exception_map.get(val.exc_type): raise observed_exception_type(f"raised exception {val}") raise exc.ObservedException(f"raised exception {val}") unimplemented(f"raise {exc}") def RAISE_VARARGS(self, inst): if inst.arg == 0: unimplemented("re-raise") elif inst.arg == 1: self._raise_exception_variable(inst) else: # Support raise .. from None ... Dynamo does not track __cause__ and other attributes of exception. So we # ignore `from None` part. from_vt = self.pop() if isinstance(from_vt, ConstantVariable) and from_vt.value is None: self._raise_exception_variable(inst) unimplemented("raise ... from ...") def RERAISE(self, inst): if sys.version_info >= (3, 11): # RERAISE is currently supported in a narrow case of `raise ... from None` self._raise_exception_variable(inst) unimplemented("RERAISE") def exception_handler(self, raised_exception): if sys.version_info >= (3, 11): exn_tab_entry = self.current_instruction.exn_tab_entry if exn_tab_entry: # Implementation is based on https://github.com/python/cpython/blob/3.11/Objects/exception_handling_notes.txt # 1) pop values from the stack until it matches the stack depth # for the handler while len(self.stack) > exn_tab_entry.depth: self.pop() # 2) if 'lasti' is true, then push the offset that the exception was raised at if exn_tab_entry.lasti: self.push( variables.ConstantVariable(self.current_instruction.offset) ) # 3) push the exception to the stack assert len(self.exn_vt_stack) self.push(self.exn_vt_stack[-1]) # 4) jump to the handler self.jump(exn_tab_entry) else: # No handler found. Bubble the exception to the parent # instruction translater. We use special exception for this. self.stack.clear() if type(self) is InstructionTranslator: raise Unsupported("Observed exception") raise raised_exception else: if len(self.block_stack): # base implementation - https://github.com/python/cpython/blob/3.10/Python/ceval.c#L4455 assert len(self.exn_vt_stack) exception_var = self.exn_vt_stack[-1] block_stack_entry = self.block_stack.pop() while block_stack_entry.inst.opname == "EXCEPT_HANDLER": # TODO(anijain2305) - This is not tested .. unable to create a testcase # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L1456 self.popn(3) if len(self.block_stack) == 0: # No handler found in this frame. Bubble the exception to the parent # instruction translater. self.stack.clear() if type(self) is InstructionTranslator: raise Unsupported("Observed exception") raise raised_exception block_stack_entry = self.block_stack.pop() if block_stack_entry.inst.opname != "SETUP_FINALLY": unimplemented( "exception is raised when top of the block stack " "is not exception handler (e.g. try .. with .. except). " f"Current TOS is {block_stack_entry.inst}" ) # Push a dummy block stack entry of EXCEPT_HANDLER # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L1456 except_handler_inst = Instruction(1e6, "EXCEPT_HANDLER", None, 0) self.block_stack.append(BlockStackEntry(except_handler_inst, None)) # Push old exception if len(self.exn_vt_stack) >= 2: old_exception = self.exn_vt_stack[-2] # Push the old exception on to stack - tb, value, type # Traceback is currently mapped to UnknownVariable self.push(variables.UnknownVariable()) self.push(old_exception) self.push(variables.BuiltinVariable(old_exception.exc_type)) else: # Push empty exception tb, value, type self.push(variables.ConstantVariable(None)) self.push(variables.ConstantVariable(None)) self.push(variables.ConstantVariable(None)) # Push new exception - tb, val, type # Traceback is currently mapped to UnknownVariable self.push(variables.UnknownVariable()) self.push(exception_var) self.push(variables.BuiltinVariable(exception_var.exc_type)) # Jump to target self.jump(block_stack_entry) else: # No handler found. Bubble the exception to the parent # instruction translater. We use special exception for this. self.stack.clear() if type(self) is InstructionTranslator: raise Unsupported("Observed exception") raise raised_exception def PUSH_EXC_INFO(self, inst): val = self.pop() assert len(self.exn_vt_stack) self.push(self.exn_vt_stack[-1]) self.push(val) def POP_EXCEPT(self, inst): if sys.version_info >= (3, 11): val = self.pop() assert isinstance(val, variables.ExceptionVariable) # This exception is handled and therefore we can clear the error indicator assert len(self.exn_vt_stack) self.exn_vt_stack.pop() else: assert len(self.block_stack) > 0 if self.block_stack[-1].inst.opname != "EXCEPT_HANDLER": raise AssertionError( "Bug in Dynamo tracing of exception handling." "Top of the block stack is not EXCEPT_HANDLER." ) self.block_stack.pop() self.popn(3) # This exception is handled and therefore we can clear the error indicator assert len(self.exn_vt_stack) self.exn_vt_stack.pop() def check_if_exc_matches(self): assert len(self.stack) >= 2 expected_exc_types = self.pop() if sys.version_info >= (3, 11): # CHECK_EXC_MATCH (which is used from 3.11 onwards) does not pop. # This is the description from the disassembly doc # # Performs exception matching for ``except``. Tests whether the ``STACK[-2]`` # is an exception matching ``STACK[-1]``. Pops ``STACK[-1]`` and pushes the boolean # result of the test. exc_instance = self.stack[-1] else: # This is used prior to 3.11 via opcode JUMP_IF_NOT_EXC_MATCH # There is no documentation but here is the code pointer that does 2 pops # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L3650-L3665 exc_instance = self.stack.pop() # Users can check exception in 2 ways # 1) except NotImplementedError --> BuilinVariable # 2) except (NotImplemetedError, AttributeError) -> TupleVariable if not isinstance(expected_exc_types, (BuiltinVariable, TupleVariable)): unimplemented( f"except has an unsupported types of objects {expected_exc_types}" ) if sys.version_info >= (3, 11): if not isinstance(exc_instance, variables.ExceptionVariable): unimplemented( f"except expects to recieve an object of exception type but received {exc_instance}" ) if isinstance(expected_exc_types, TupleVariable): expected_types = expected_exc_types.items else: expected_types = [ expected_exc_types, ] for expected_type in expected_types: if not isinstance(expected_type, BuiltinVariable): unimplemented( f"except has an unsupported types of object {expected_type}" ) if isinstance(exc_instance, variables.ExceptionVariable) and issubclass( exc_instance.exc_type, expected_type.fn ): return True elif isinstance(exc_instance, variables.BuiltinVariable) and issubclass( exc_instance.fn, expected_type.fn ): return True return False def CHECK_EXC_MATCH(self, inst): self.push(variables.ConstantVariable(self.check_if_exc_matches())) def JUMP_IF_NOT_EXC_MATCH(self, inst): if not self.check_if_exc_matches(): self.jump(inst) def COMPARE_OP(self, inst): if inst.argval == "exception match": self.CHECK_EXC_MATCH(inst) else: self.push(compare_op_handlers[inst.argval](self, self.popn(2), {})) def GET_ITER(self, inst): self.call_function(BuiltinVariable(iter), [self.pop()], {}) @break_graph_if_unsupported(push=1) def CALL_FUNCTION(self, inst): args = self.popn(inst.argval) fn = self.pop() self.call_function(fn, args, {}) @break_graph_if_unsupported(push=1) def CALL_FUNCTION_EX(self, inst): kwargsvars: VariableTracker if inst.argval == 0: kwargsvars = ConstDictVariable({}) argsvars = self.pop() elif inst.argval == 1: kwargsvars = self.pop() argsvars = self.pop() else: unimplemented("CALL_FUNCTION_EX") if sys.version_info >= (3, 13): # 3.13 swapped null and callable null = self.pop() assert isinstance(null, NullVariable) fn = self.pop() if sys.version_info >= (3, 11) and sys.version_info < (3, 13): null = self.pop() assert isinstance(null, NullVariable) if isinstance(fn, GetAttrVariable) and isinstance(fn.obj, TensorVariable): # realize is requires for Python 3.8 kwargsvars = kwargsvars.realize() if fn.name == "view" and isinstance( argsvars, (ConstantVariable, TensorVariable) ): # Hack to handle special case in some bert models. Converts # x.view(*shape) into x.view(shape), which is correct for view() # but not generally. See test_transpose_for_scores(). argsvars = TupleVariable([argsvars]) elif ( fn.name == "random_" and isinstance(argsvars, TupleVariable) and len(argsvars.items) == 0 and isinstance(kwargsvars, ConstDictVariable) and ConstantVariable.create("from") in kwargsvars ): # `from`` is python keyword. Adding random_ with `from` in the # Fx graph causes syntax error. Even if we convert the kwargs to # args, aot_autograd/inductor while lowering generates # aten.random.from, again causing syntax errors. Since this # usecase is uncommon, graph break. unimplemented("random_ op is called with from keyword") elif ( fn.name == "uniform_" and isinstance(argsvars, TupleVariable) and len(argsvars.items) == 0 and isinstance(kwargsvars, ConstDictVariable) and ConstantVariable.create("from") in kwargsvars ): # `from`` is python keyword. Adding uniform_ with `from` in the # Fx graph causes syntax error. Even if we convert the kwargs to # args, aot_autograd/inductor while lowering generates # aten.uniform.from, again causing syntax errors. Since this # usecase is uncommon, graph break. unimplemented("uniform_ op is called with from keyword") if not isinstance( argsvars, BaseListVariable ) and argsvars.has_force_unpack_var_sequence(self): argsvars = TupleVariable(argsvars.force_unpack_var_sequence(self)) # Unpack for cases like fn(**obj) where obj is a map if isinstance(kwargsvars, UserDefinedObjectVariable): kwargsvars = BuiltinVariable.call_custom_dict(self, dict, kwargsvars) # type: ignore[arg-type] if not isinstance(argsvars, BaseListVariable) or not isinstance( kwargsvars, ConstDictVariable ): unimplemented(f"non-static call {typestr(argsvars)} {typestr(kwargsvars)}") # Map to a dictionary of str -> VariableTracker kwargsvars = kwargsvars.keys_as_python_constant() self.call_function(fn, argsvars.items, kwargsvars) @break_graph_if_unsupported(push=1) def CALL_FUNCTION_KW(self, inst): argnames = self.pop() args = self.popn(inst.argval) fn = self.pop() assert isinstance(argnames, TupleVariable) and argnames.is_python_constant() argnames = argnames.as_python_constant() args, kwargs_list = args[: -len(argnames)], args[-len(argnames) :] kwargs = dict(zip(argnames, kwargs_list)) assert len(kwargs) == len(argnames) self.call_function(fn, args, kwargs) def LOAD_METHOD_SUPER(self, inst): self.CALL_FUNCTION(dataclasses.replace(inst, argval=2)) arg = inst.argval[0] argval = self.code_options["co_names"][arg] if sys.version_info < (3, 11): self._load_attr(dataclasses.replace(inst, argval=argval)) else: self.LOAD_METHOD(dataclasses.replace(inst, argval=argval)) def LOAD_ATTR_SUPER(self, inst): self.CALL_FUNCTION(dataclasses.replace(inst, argval=2)) arg = inst.argval[0] argval = self.code_options["co_names"][arg] self._load_attr(dataclasses.replace(inst, argval=argval)) def LOAD_METHOD(self, inst): self._load_attr(inst) obj = self.pop() if sys.version_info >= (3, 13): self.push(obj) self.PUSH_NULL(inst) elif sys.version_info >= (3, 11): # always follow the NULL + fn convention, since if obj # is actually a method, self is already bound to it, so it # doesn't need to be passed in as an arg. self.PUSH_NULL(inst) self.push(obj) else: self.push(obj) self.push(None) def CALL_METHOD(self, inst): args = self.popn(inst.argval) dummy = self.pop() assert dummy is None fn = self.pop() self.call_function(fn, args, {}) def _load_attr(self, inst): obj = self.pop() result = BuiltinVariable(getattr).call_function( self, [obj, ConstantVariable.create(inst.argval)], {} # type: ignore[arg-type] ) self.push(result) def LOAD_ATTR(self, inst): if sys.version_info >= (3, 12): if inst.arg % 2: self.LOAD_METHOD(inst) return self._load_attr(inst) def STORE_ATTR(self, inst): speculation = self.speculate() if speculation.failed: return self.store_attr_graph_break(inst) val, obj = self.popn(2) if isinstance(obj, NNModuleVariable) and not isinstance(val, ConstantVariable): # We don't allow side effects during export on non-constant values # https://github.com/pytorch/torchdynamo/issues/1475 assert ( not self.export ), f"Mutating module attribute {inst.argval} during export." try: BuiltinVariable(setattr).call_function( self, [obj, ConstantVariable.create(inst.argval), val], {} # type: ignore[arg-type] ) return except Unsupported as e: if not self.should_compile_partial_graph(): raise log.debug("STORE_ATTR triggered compile", exc_info=True) e.remove_from_stats() e.add_to_stats("graph_break") speculation.fail_and_restart_analysis() def store_attr_graph_break(self, inst): if not self.should_compile_partial_graph(): unimplemented("should_compile_partial_graph=False") self.output.compile_subgraph( self, reason=GraphCompileReason("store_attr", [self.frame_summary()]) ) self.output.add_output_instructions([copy.copy(inst)]) self.popn(2) self.output.add_output_instructions( self.create_call_resume_at(self.next_instruction) ) def DELETE_ATTR(self, inst): obj = self.pop() BuiltinVariable(delattr).call_function( self, [obj, ConstantVariable.create(inst.argval)], {} # type: ignore[arg-type] ) def create_call_resume_at(self, offset): raise AssertionError( f"create_call_resume_at not overridden by subclass {type(self)}" ) def should_compile_partial_graph(self) -> bool: raise AssertionError( f"should_compile_partial_graph not overridden by subclass {type(self)}" ) @break_graph_if_unsupported(push=0) def STORE_SUBSCR(self, inst): val, obj, key = self.popn(3) result = obj.call_method(self, "__setitem__", [key, val], {}) def DELETE_SUBSCR(self, inst): obj, key = self.popn(2) obj.call_method(self, "__delitem__", [key], {}) def BUILD_TUPLE(self, inst): name_tuple = None if sys.version_info >= (3, 13): name_tuple = tuple(self.name_stack[-inst.argval :]) items = self.popn(inst.argval) self.push(TupleVariable(items), name=name_tuple) def BUILD_SLICE(self, inst): items = self.popn(inst.argval) self.push(SliceVariable(items)) def BUILD_LIST(self, inst): items = self.popn(inst.argval) self.push(ListVariable(items, mutable_local=MutableLocal())) def BUILD_SET(self, inst): if config.inject_BUILD_SET_unimplemented_TESTING_ONLY: unimplemented("missing: BUILD_SET") items = self.popn(inst.argval) new_set = SetVariable(items, mutable_local=MutableLocal()) self.push(new_set) def BUILD_LIST_UNPACK(self, inst, cls=ListVariable): seqs = self.popn(inst.argval) items = [] for seq in seqs: try: items.extend(seq.force_unpack_var_sequence(self)) except NotImplementedError: unimplemented(f"BUILD_LIST_UNPACK {seq}") self.push(cls(items, mutable_local=MutableLocal())) def BUILD_TUPLE_UNPACK(self, inst): self.BUILD_LIST_UNPACK(inst, cls=TupleVariable) BUILD_TUPLE_UNPACK_WITH_CALL = BUILD_TUPLE_UNPACK def BUILD_MAP(self, inst): items = self.popn(inst.argval * 2) d = dict(zip(items[::2], items[1::2])) self.push(ConstDictVariable(d, mutable_local=MutableLocal())) def BUILD_MAP_UNPACK(self, inst): items = self.popn(inst.argval) # ensure everything is a dict items = [BuiltinVariable(dict).call_function(self, [x], {}) for x in items] # type: ignore[arg-type] result = {} for x in items: assert isinstance(x, ConstDictVariable) result.update(x.items) self.push( ConstDictVariable( result, mutable_local=MutableLocal(), ) ) BUILD_MAP_UNPACK_WITH_CALL = BUILD_MAP_UNPACK def BUILD_CONST_KEY_MAP(self, inst): keys = self.pop() values = self.popn(inst.argval) assert isinstance(keys, TupleVariable) assert keys.is_python_constant() keys = keys.force_unpack_var_sequence(self) assert len(keys) == len(values) self.push( ConstDictVariable( dict(zip(keys, values)), mutable_local=MutableLocal(), ) ) def MAP_ADD(self, inst): k, v = self.popn(2) assert inst.argval > 0 obj = self.stack[-inst.arg].realize() assert isinstance(obj, ConstDictVariable) obj.call_method(self, "__setitem__", (k, v), {}) # type: ignore[arg-type] def SET_ADD(self, inst): v = self.pop() assert inst.argval > 0 obj = self.stack[-inst.arg] assert isinstance(obj, SetVariable) assert obj.mutable_local return obj.call_method(self, "add", [v], {}) def SET_UPDATE(self, inst): v = self.pop() assert inst.argval > 0 obj = self.stack[-inst.arg] assert isinstance(obj, SetVariable) assert obj.mutable_local obj.call_method(self, "update", [v], {}) def LIST_APPEND(self, inst): v = self.pop() assert inst.argval > 0 obj = self.stack[-inst.arg].realize() assert isinstance(obj, ListVariable) assert obj.mutable_local self.output.side_effects.mutation(obj) obj.items.append(v) def MAKE_FUNCTION(self, inst): flags = inst.arg old_stack = list(self.stack) if sys.version_info < (3, 11): fn_name = self.pop() code = self.pop() if sys.version_info >= (3, 11): # MAKE_FUNCTION behavior actually changed in 3.11, see # https://github.com/python/cpython/pull/93189/ assert hasattr(code.value, "co_qualname") # type: ignore[attr-defined] fn_name = ConstantVariable.create(value=code.value.co_qualname) # type: ignore[attr-defined] defaults = None closure = None annotations = None kwdefaults = None if sys.version_info < (3, 13): # in 3.13, this is handled in SET_FUNCTION_ATTRIBUTE if flags & 0x08: closure = self.pop() if flags & 0x04: annotations = self.pop() if flags & 0x02: kwdefaults = self.pop() if flags & 0x01: defaults = self.pop() self.push( NestedUserFunctionVariable( fn_name, code, self.f_globals, defaults, kwdefaults, annotations, closure, closure_scope=self, ) ) def UNPACK_SEQUENCE(self, inst): seq = self.pop() if isinstance(seq, TensorVariable): val = seq.unpack_var_sequence(self, idxes=range(inst.argval)) # type: ignore[arg-type] elif isinstance(seq, GetAttrVariable) and isinstance(seq.obj, TensorVariable): # x, y = a.shape proxy = getattr(seq.obj.as_proxy(), seq.name) val = [wrap_fx_proxy(self, proxy[i]) for i in range(inst.argval)] elif seq.has_force_unpack_var_sequence(self): val = seq.force_unpack_var_sequence(self) else: unimplemented(f"UNPACK_SEQUENCE {seq}") if len(val) != inst.argval: unimplemented("UNPACK_SEQUENCE length mismatch") for i in reversed(val): self.push(i) def UNPACK_EX(self, inst): assert 0 <= inst.argval <= 0xFFFF prefix = inst.argval & 0xFF # low byte suffix = inst.argval >> 8 # high byte seq = self.pop() if seq.has_force_unpack_var_sequence(self): vals = list(seq.force_unpack_var_sequence(self)) assert len(vals) >= prefix + suffix vals_prefix = vals[:prefix] vals_list = vals[prefix : len(vals) - suffix] vals_suffix = vals[len(vals) - suffix :] for item in reversed(vals_suffix): self.push(item) self.push(TupleVariable(vals_list)) for item in reversed(vals_prefix): self.push(item) else: unimplemented(f"UNPACK_EX {seq}") def NOP(self, inst): pass def POP_TOP(self, inst): self.pop() def ROT_TWO(self, inst): a = self.pop() b = self.pop() self.push(a) self.push(b) def ROT_THREE(self, inst): a = self.pop() b = self.pop() c = self.pop() self.push(a) self.push(c) self.push(b) def ROT_FOUR(self, inst): a = self.pop() b = self.pop() c = self.pop() d = self.pop() self.push(a) self.push(d) self.push(c) self.push(b) def DUP_TOP(self, inst): a = self.pop() self.push(a) self.push(a) def DUP_TOP_TWO(self, inst): a = self.pop() b = self.pop() self.push(b) self.push(a) self.push(b) self.push(a) def FORMAT_VALUE(self, inst): flags = inst.arg if (flags & 0x04) == 0x04: fmt_spec = self.pop() else: fmt_spec = ConstantVariable.create("") value = self.pop() if isinstance(value, SymNodeVariable): from torch._dynamo.variables.lazy import ( LazySymNodeFormatString, LazyVariableTracker, ) value = LazyVariableTracker.create( LazySymNodeFormatString(value, fmt_spec), source=value.source ) self.push(value) return if (flags & 0x03) == 0x01: value = BuiltinVariable(str).call_function(self, [value], {}) # type: ignore[arg-type] elif (flags & 0x03) == 0x02: value = BuiltinVariable(repr).call_function(self, [value], {}) # type: ignore[arg-type] elif (flags & 0x03) == 0x03: value = BuiltinVariable(ascii).call_function(self, [value], {}) # type: ignore[arg-type] fmt_var = ConstantVariable.create("{:" + fmt_spec.as_python_constant() + "}") self.call_function(BuiltinVariable(str.format), [fmt_var, value], {}) def BUILD_STRING(self, inst): format_string_parts: List[str] = [] args: List[VariableTracker] = [] kwargs: Dict[str, VariableTracker] = {} for part in self.popn(inst.arg): if isinstance(part, ConstantVariable): format_string_parts.append("{}") args.append(part) elif isinstance(part, variables.StringFormatVariable): format_string_parts.append(part.format_string) args.extend(part.sym_args) if set(kwargs.keys()) & set(part.sym_kwargs.keys()): unimplemented( f"BUILD_STRING key conflict {kwargs} & {part.sym_kwargs}" ) kwargs.update(part.sym_kwargs) else: unimplemented(f"BUILD_STRING {part}") self.push( variables.StringFormatVariable.create( "".join(format_string_parts), args, kwargs ) ) def IS_OP(self, inst): assert inst.argval == 0 or inst.argval == 1 if inst.argval == 0: new_argval = "is" else: new_argval = "is not" new_inst = create_instruction("COMPARE_OP", argval=new_argval) self.COMPARE_OP(new_inst) def CONTAINS_OP(self, inst): assert inst.argval == 0 or inst.argval == 1 left, right = self.popn(2) op = inst.argval self.push(right.call_method(self, "__contains__", [left], {})) if op == 1: self.UNARY_NOT(inst) def LIST_EXTEND(self, inst): v = self.pop() assert inst.argval > 0 obj = self.stack[-inst.arg] assert isinstance(obj, ListVariable) assert obj.mutable_local obj.call_method(self, "extend", [v], {}) def LIST_TO_TUPLE(self, inst): self.push(BuiltinVariable(tuple).call_function(self, [self.pop()], {})) # type: ignore[arg-type] def DICT_MERGE(self, inst): v = self.pop() assert inst.argval > 0 obj = self.stack[-inst.arg].realize() assert isinstance(obj, ConstDictVariable) assert obj.mutable_local obj.call_method(self, "update", [v], {}) DICT_UPDATE = DICT_MERGE def GEN_START(self, inst): self.pop() def GET_LEN(self, inst): tos = self.stack[-1] if tos.is_python_constant(): self.push(ConstantVariable.create(len(tos.as_python_constant()))) else: self.push(tos.call_method(self, "__len__", [], {})) def MATCH_MAPPING(self, inst): tos = self.stack[-1] assert isinstance(tos, ConstDictVariable) if isinstance(tos.items, collections.abc.Mapping): self.push(ConstantVariable.create(True)) else: self.push(ConstantVariable.create(False)) def MATCH_SEQUENCE(self, inst): tos = self.stack[-1] assert tos.is_python_constant() tos_value = tos.as_python_constant() if isinstance(tos_value, collections.abc.Sequence) and not isinstance( tos_value, (str, bytes, bytearray) ): self.push(ConstantVariable.create(True)) else: self.push(ConstantVariable.create(False)) def MATCH_KEYS(self, inst): tos = self.stack[-1] tos1 = self.stack[-2] assert isinstance(tos1, ConstDictVariable) if all(k in tos1 for k in tos): # type: ignore[attr-defined] self.push(TupleVariable([tos1.getitem_const(self, k) for k in tos])) # type: ignore[attr-defined,arg-type] if sys.version_info < (3, 11): self.push(ConstantVariable.create(True)) else: self.push(ConstantVariable.create(None)) if sys.version_info < (3, 11): self.push(ConstantVariable.create(False)) def LOAD_ASSERTION_ERROR(self, inst): self.load_builtin_from_argval("AssertionError") UNARY_POSITIVE = stack_op(operator.pos) UNARY_NEGATIVE = stack_op(operator.neg) UNARY_NOT = stack_op(operator.not_) UNARY_INVERT = stack_op(operator.invert) BINARY_POWER = stack_op(operator.pow) BINARY_MULTIPLY = stack_op(operator.mul) BINARY_MATRIX_MULTIPLY = stack_op(operator.matmul) BINARY_FLOOR_DIVIDE = stack_op(operator.floordiv) BINARY_TRUE_DIVIDE = stack_op(operator.truediv) BINARY_MODULO = stack_op(operator.mod) BINARY_REMAINDER = stack_op(operator.mod) BINARY_ADD = stack_op(operator.add) BINARY_SUBTRACT = stack_op(operator.sub) BINARY_SUBSCR = break_graph_if_unsupported(push=1)(stack_op(operator.getitem)) BINARY_LSHIFT = stack_op(operator.lshift) BINARY_RSHIFT = stack_op(operator.rshift) BINARY_AND = stack_op(operator.and_) BINARY_OR = stack_op(operator.or_) BINARY_XOR = stack_op(operator.xor) INPLACE_POWER = stack_op(operator.ipow) INPLACE_MULTIPLY = stack_op(operator.imul) INPLACE_MATRIX_MULTIPLY = stack_op(operator.imatmul) INPLACE_FLOOR_DIVIDE = stack_op(operator.ifloordiv) INPLACE_TRUE_DIVIDE = stack_op(operator.itruediv) INPLACE_MODULO = stack_op(operator.imod) INPLACE_REMAINDER = stack_op(operator.imod) INPLACE_ADD = stack_op(operator.iadd) INPLACE_SUBTRACT = stack_op(operator.isub) INPLACE_LSHIFT = stack_op(operator.ilshift) INPLACE_RSHIFT = stack_op(operator.irshift) INPLACE_AND = stack_op(operator.iand) INPLACE_XOR = stack_op(operator.ixor) INPLACE_OR = stack_op(operator.ior) # 3.11 opcodes def RESUME(self, inst): if inst.arg == 0: self.append_prefix_inst(inst) self.accept_prefix_inst = False else: assert not self.accept_prefix_inst if sys.version_info >= (3, 11): def BINARY_OP(self, inst): return _binary_op_lookup[inst.arg](self, inst) def PRECALL(self, inst): pass def KW_NAMES(self, inst): kw_names = self.code_options["co_consts"][inst.arg] assert isinstance(kw_names, tuple) for name in kw_names: assert isinstance(name, str) assert self.kw_names is None self.kw_names = ConstantVariable.create(value=kw_names) # type: ignore[assignment] def PUSH_NULL(self, inst): self.push(NullVariable()) def _call(self, inst, call_kw=False): # see https://docs.python.org/3.11/library/dis.html#opcode-CALL # for convention if call_kw: # TOS is kw_names for CALL_KW instruction assert sys.version_info >= (3, 13) kw_names = self.pop() assert isinstance(kw_names, TupleVariable) and kw_names.is_python_constant() kw_names = kw_names.as_python_constant() else: kw_names = self.kw_names.value if self.kw_names else () contents = self.popn(inst.arg + 2) if sys.version_info >= (3, 13): # NULL and callable swapped fn = contents[0] args = [] if isinstance(contents[1], NullVariable) else [contents[1]] else: if isinstance(contents[0], NullVariable): fn = contents[1] args = [] else: fn = contents[0] args = [contents[1]] if kw_names: args = args + contents[2 : -len(kw_names)] kwargs_list = contents[-len(kw_names) :] kwargs = dict(zip(kw_names, kwargs_list)) assert len(kwargs) == len(kw_names) else: args = args + contents[2:] kwargs = {} try: # if call_function fails, need to set kw_names to None, otherwise # a subsequent call may have self.kw_names set to an old value self.call_function(fn, args, kwargs) finally: self.kw_names = None @break_graph_if_unsupported(push=1) def CALL(self, inst): self._call(inst) def COPY(self, inst): self.push(self.stack[-inst.arg]) def SWAP(self, inst): self.stack[-1], self.stack[-inst.arg] = self.stack[-inst.arg], self.stack[-1] JUMP_BACKWARD = jump JUMP_BACKWARD_NO_INTERRUPT = jump POP_JUMP_FORWARD_IF_TRUE = generic_jump(operator.truth, False) POP_JUMP_BACKWARD_IF_TRUE = generic_jump(operator.truth, False) POP_JUMP_FORWARD_IF_FALSE = generic_jump(operator.not_, False) POP_JUMP_BACKWARD_IF_FALSE = generic_jump(operator.not_, False) def CACHE(self, inst): pass def BEFORE_WITH(self, inst): self.setup_or_before_with(inst) def setup_or_before_with(self, inst): ctx = self.pop() if not isinstance( ctx, (ContextWrappingVariable, GenericContextWrappingVariable) ): unimplemented(f"{inst.opname} {ctx}") if isinstance(ctx, GenericContextWrappingVariable): self.generic_context_manager_depth += 1 # Need this redundant check for mypy assert isinstance( ctx, (ContextWrappingVariable, GenericContextWrappingVariable) ) exit = WithExitFunctionVariable( ctx, inst.target, ) if sys.version_info >= (3, 11): # See create_call_resume_at for block stack details. # Only push a block if the current instruction's block is a # with block that is not nested in a try block - that is, the current # instruction's block target is the same as the top block's target. if inst.exn_tab_entry and ( not self.block_stack or inst.exn_tab_entry.target is not self.block_stack[-1].target ): target = None else: target = self.next_instruction.exn_tab_entry.target else: target = inst.target if target: if isinstance(self, InstructionTranslator): self.block_stack.append( BlockStackEntry(inst, target, len(self.stack), ctx) ) else: self.block_stack.append(BlockStackEntry(inst, target)) self.push(exit) self.push(ctx.enter(self)) def append_prefix_inst(self, inst): assert self.accept_prefix_inst self.prefix_insts.append(inst) def MAKE_CELL(self, inst): if sys.version_info >= (3, 12) and not self.accept_prefix_inst: # In 3.12+, MAKE_CELL is not longer necessarily a prefix instruction. # It can be generated by inlined comprehensions. assert isinstance(self.symbolic_locals[inst.argval], NullVariable) self.symbolic_locals[ inst.argval ] = self.output.side_effects.track_cell_new() else: self.append_prefix_inst(inst) def COPY_FREE_VARS(self, inst): self.append_prefix_inst(inst) def RETURN_GENERATOR(self, inst): self.append_prefix_inst(inst) # 3.12 opcodes # BINARY/STORE_SLICE opcodes are broken down into # BUILD_SLICE 2 and BINARY/STORE_SUBSCR def END_FOR(self, inst): if sys.version_info >= (3, 13): self.pop() else: self.popn(2) def LOAD_FAST_CHECK(self, inst): if isinstance(self.symbolic_locals[inst.argval], NullVariable): unimplemented("LOAD_FAST_CHECK on uninitialized variable") self.LOAD_FAST(inst) def LOAD_FAST_AND_CLEAR(self, inst): if inst.argval not in self.symbolic_locals: self.push(NullVariable()) else: self.LOAD_FAST(inst) self.symbolic_locals[inst.argval] = NullVariable() def LOAD_SUPER_ATTR(self, inst): self.CALL_FUNCTION(dataclasses.replace(inst, argval=2)) if inst.arg & 1: self.LOAD_METHOD(inst) else: self._load_attr(inst) def CALL_INTRINSIC_1(self, inst): if inst.argval == 5: # INTRINSIC_UNARY_POSITIVE self.UNARY_POSITIVE(inst) elif inst.argval == 6: # INTRINSIC_LIST_TO_TUPLE self.push(TupleVariable(self.pop().force_unpack_var_sequence(self))) else: unimplemented(f"missing CALL_INTRINSIC_1 operand {inst.argval}") def END_SEND(self, inst): tos = self.pop() self.pop() self.push(tos) # 3.13 opcodes # fused instructions LOAD_FAST_LOAD_FAST, STORE_FAST_STORE_FAST, STORE_FAST_LOAD_FAST # are broken down. @break_graph_if_unsupported(push=1) def CALL_KW(self, inst): self._call(inst, call_kw=True) def TO_BOOL(self, inst): # TO_BOOL only precedes a conditional jump or UNARY_NOT (see compile.c in CPython) # So we can skip this instruction as long as we remember to codegen a TO_BOOL # before conditional jumps/UNARY_NOT. assert self.next_instruction.opname in ( "POP_JUMP_IF_TRUE", "POP_JUMP_IF_FALSE", "UNARY_NOT", ) def SET_FUNCTION_ATTRIBUTE(self, inst): flags = inst.arg fn = self.pop() assert isinstance(fn, NestedUserFunctionVariable) attr_names = self.name_stack[-1] attr = self.pop() if flags & 0x08: # 3.13 merged LOAD_CLOSURE into LOAD_FAST, so we won't know if a given LOAD_FAST # is meant to load a closure variable or not. Our workaround is to maintain a stack # of LOAD_FAST variable names and tuples (self.name_stack). So if we are indeed # constructing a closure tuple, we can use self.name_stack to construct the closure # variables here. assert isinstance(attr_names, tuple) and all( isinstance(name, str) for name in attr_names ) fn.closure = TupleVariable( [self._load_closure(name) for name in attr_names] ) fn.closure_scope = self elif flags & 0x04: fn.annotations = attr elif flags & 0x02: fn.kwdefaults = attr elif flags & 0x01: fn.defaults = attr self.push(fn) def _format_value_313(self, fmt_spec): value = self.pop() if isinstance(value, SymNodeVariable): value = ConstantVariable.create(str(value.sym_num)) fmt_var = ConstantVariable.create("{:" + fmt_spec.as_python_constant() + "}") self.call_function(BuiltinVariable(str.format), [fmt_var, value], {}) def FORMAT_SIMPLE(self, inst): self._format_value_313(ConstantVariable.create("")) def FORMAT_WITH_SPEC(self, inst): self._format_value_313(self.pop()) def is_non_empty_graph(self): if self.output.count_calls() > 1: # perf optimization only self.is_non_empty_graph = lambda: True # type: ignore[method-assign] return True return False def format_frame_summary(self, additional_stack_frames=None): if additional_stack_frames is None: additional_stack_frames = [] return "".join( traceback.format_list( [self.frame_summary()] + list(reversed(additional_stack_frames)) ) ) def frame_summary(self): return traceback.FrameSummary( getattr(self.f_code, "co_filename", ""), self.lineno, getattr(self.f_code, "co_name", ""), lookup_line=False, ) def is_co_filename_from_nn_modules(self): filename = getattr(self.f_code, "co_filename", "") nn_modules_pattern = re.compile(r".*torch/nn/modules.*") return nn_modules_pattern.match(filename) is not None def store_global_weakref_by_id(self, prefix, value): global_name = self.output.install_global_by_id(prefix, weakref.ref(value)) install_guard( GlobalWeakRefSource(global_name).make_guard(GuardBuilder.WEAKREF_ALIVE) ) return global_name @property def fake_mode(self): return self.output.tracing_context.fake_mode def find_symbolic_locals_name(self, tensor_variable): for key, value in self.symbolic_locals.items(): if value is tensor_variable: return key return None @contextlib.contextmanager def strict_translation_mode(self, check_fn: Callable[[VariableTracker], bool]): """ Strict mode is enabled on a per-VariableTracker level depending on the return value of check_fn(node). """ prior = self.strict_checks_fn self.strict_checks_fn = check_fn try: yield finally: self.strict_checks_fn = prior def speculate(self) -> SpeculationEntry: assert self.instruction_pointer is not None assert self.instruction_pointer > 0 return self.speculation_log.next( self.f_code.co_filename, self.lineno, self.instruction_pointer - 1, self.instructions[self.instruction_pointer - 1], ) def __init__( self, output: OutputGraph, instructions: List[Instruction], f_locals: Dict[str, Any], f_globals: Dict[str, Any], f_builtins: Dict[str, Any], code_options: Dict[str, Any], symbolic_locals: Dict[str, VariableTracker], symbolic_globals: Dict[str, VariableTracker], symbolic_torch_function_mode_stack: Deque["TorchFunctionModeVariable"], f_code: types.CodeType, export: bool, inline_depth: int, speculation_log: SpeculationLog, distributed_state: Optional[DistributedState], ) -> None: super().__init__() self.speculation_log = speculation_log self.distributed_state = distributed_state # Mutable state checkpointed by copy_graphstate() self.output = output self.symbolic_locals = symbolic_locals self.symbolic_globals = symbolic_globals self.symbolic_torch_function_mode_stack = symbolic_torch_function_mode_stack self.stack = [] # stack of variable names for tracking 3.13 closures self.name_stack: list[Any] = [] self.instruction_pointer = 0 self.current_instruction = create_instruction("NOP") self.block_stack = [] # states before SETUP_WITH for checkpointing and fallback self.generic_context_manager_depth = 0 self.lineno = -1 self.kw_names = None self.accept_prefix_inst = True self.prefix_insts = [] self.exn_vt_stack = [] # Properties of the input/output code self.instructions: List[Instruction] = instructions self.indexof: Dict[Instruction, int] = get_indexof(self.instructions) self.f_locals: Dict[ str, Any ] = f_locals # needed for recording accessed locals for replay self.f_globals: Dict[str, Any] = f_globals self.f_builtins: Dict[str, Any] = f_builtins self.code_options: Dict[str, Any] = code_options self.f_code: types.CodeType = f_code # Execution record for replaying errors if config.replay_record_enabled: self.exec_recorder = ExecutionRecorder( code=f_code, code_options=code_options ) else: self.exec_recorder = None # Stack of module being parsed, current nn.module is at the end of ordered dict. # The first field of tuple is the fully qualified name of current module # in original hierarchy. The second field is the type of current nn.module self.nn_module_stack: Dict[str, Tuple[str, Type[Any]]] = {} # Flag to indicate whether tracing is used for export. self.export = export self.one_graph = False self.current_speculation = None self.strict_checks_fn = None if sys.version_info >= (3, 10): from .resume_execution import ( CO_ASYNC_GENERATOR, CO_COROUTINE, CO_GENERATOR, CO_ITERABLE_COROUTINE, ) if f_code.co_flags & ( CO_GENERATOR | CO_COROUTINE | CO_ITERABLE_COROUTINE | CO_ASYNC_GENERATOR ): self.push(BuiltinVariable(None)) self.inline_depth = inline_depth self.inconsistent_side_effects = False self._constants_cache: List[Optional[VariableTracker]] = [None] * len( f_code.co_consts ) linecache.lazycache(f_code.co_filename, f_globals) class InstructionTranslator(InstructionTranslatorBase): mutated_closure_cell_contents: Set[str] @staticmethod def current_tx() -> "InstructionTranslator": return tls.current_tx @contextlib.contextmanager def set_current_tx(self): prior = getattr(tls, "current_tx", None) tls.current_tx = self try: yield finally: tls.current_tx = prior def __init__( self, instructions: List[Instruction], f_code, f_locals, f_globals, f_builtins, code_options, compiler_fn, one_graph, export, export_constraints, mutated_closure_cell_contents: Set[str], frame_state, speculation_log: SpeculationLog, distributed_state: Optional[DistributedState], ) -> None: _step_logger()( logging.INFO, f"torchdynamo start tracing {f_code.co_name} {code_options['co_filename']}:{code_options['co_firstlineno']}", ) super().__init__( output=OutputGraph( code_options, compiler_fn, self, export, export_constraints, frame_state, local_scope=f_locals, global_scope=f_globals, f_code=f_code, ), instructions=instructions, f_locals=f_locals, f_globals=f_globals, f_builtins=f_builtins, code_options=code_options, symbolic_locals={}, # set below # A global var is inserted only after a STORE_GLOBAL happens to it symbolic_globals={}, symbolic_torch_function_mode_stack=collections.deque(), f_code=f_code, export=export, inline_depth=0, speculation_log=speculation_log, distributed_state=distributed_state, ) self._throw_if_in_functorch() # as soon as we create the tracing context we should keep it active, so any calls # into dynamo apis can rely on finding it with tracing(self.output.tracing_context), self.set_current_tx(): self.one_graph: bool = one_graph self.export = export self.mutated_closure_cell_contents = mutated_closure_cell_contents if self.export: assert ( self.one_graph ), "Export without one graph - something has gone wrong." vars = list(code_options["co_varnames"]) cells_and_freevars = [x for x in self.cell_and_freevars() if x not in vars] vars.extend(cells_and_freevars) cells_and_freevars_set = set(cells_and_freevars) self.symbolic_locals = { k: variables.LazyVariableTracker.create( f_locals[k], source=LocalSource(k, cell_or_freevar=k in cells_and_freevars_set), ) for k in vars if k in f_locals } self._init_torch_function_mode_stack() self.debug_locals: List[Tuple[VariableTracker, List[VariableTracker]]] = [] if export: # export gets confused if we never realize unused inputs # in export mode just eagerly realize everything self.symbolic_locals = variables.LazyVariableTracker.realize_all( self.symbolic_locals ) self._freevars_ids = {} for name in self.code_options["co_freevars"]: if name in f_locals: self._freevars_ids[name] = id(f_locals[name]) def _throw_if_in_functorch(self): # Fallback to eager in case of a graph break inside vmap eager = torch._dynamo.lookup_backend("eager") compiler_fn = inspect.getattr_static( self.output.compiler_fn, "compiler_fn", self.output.compiler_fn ) ci = torch._C._functorch.peek_interpreter_stack() forbidden_keys = ( torch._C._functorch.TransformType.Vmap, torch._C._functorch.TransformType.Grad, torch._C._functorch.TransformType.Jvp, ) if ci is not None and ci.key() in forbidden_keys and compiler_fn is not eager: name = ci.key().name.lower() msg = ( "If you are reaching here, it means dynamo failed for one of the following reasons:\n" # Calling a torch.compiled function f"- Calling torch.func.{name}(compiled_fn) function from eager mode is not supported. " f"Ensure that torch.func.{name} is also wrapped within a torch.compile function. " "For more information, see PyTorch issue #128711.\n" # if it reaches here, it means Dynamo failed to inline a functorch function f"- torch.func.{name}(fn) requires the function to be inlined by dynamo" ) unimplemented(msg) def _init_torch_function_mode_stack(self): from .variables.torch_function import TorchFunctionModeStackVariable TorchFunctionModeStackVariable.reset() self.symbolic_torch_function_mode_stack: Deque[ TorchFunctionModeVariable ] = collections.deque() # We want to retrieve all modes to properly reconstruct the stack if needed py_stack = get_torch_function_mode_stack(filter_ignored=False) if py_stack: has_device_context = isinstance( py_stack[0], torch.utils._device.DeviceContext ) for i, val in enumerate(py_stack): self.symbolic_torch_function_mode_stack.append( variables.LazyVariableTracker.create( val, source=TorchFunctionModeStackSource(i) ) ) def get_example_value(self, source: Source): if isinstance(source, LocalSource): return self.f_locals[source.local_name] if isinstance(source, GlobalSource): return self.f_globals[source.global_name] raise KeyError def run(self): super().run() def match_nested_cell(self, name, cell): """Match a cell in this method to one in a function we are inlining""" try: value = cell.cell_contents except ValueError: return None # TODO(jansel): check the id of the cell rather than the contents if id(value) != self._freevars_ids.get(name): return None return self.symbolic_locals[name] def should_compile_partial_graph(self): if sys.version_info >= (3, 11): # Do not compile if current instruction's block is not the top with block entry = self.current_instruction.exn_tab_entry if entry and ( not self.block_stack or entry.target is not self.block_stack[-1].target ): return False return ( all(b.can_restore() for b in self.block_stack) and not self.one_graph and self.generic_context_manager_depth == 0 ) def create_call_resume_at(self, inst): self.instruction_pointer = None if inst.opname == "RETURN_VALUE": return [create_instruction("RETURN_VALUE")] elif inst.opname == "RETURN_CONST": return [create_instruction("RETURN_CONST", argval=inst.argval)] reads = livevars_analysis(self.instructions, inst) all_argnames = tuple( k for k in self.symbolic_locals.keys() if k in reads and k not in self.cell_and_freevars() ) # NOTE: do not use isinstance, since it realizes lazy VT's argnames = tuple( k for k in all_argnames if not type.__instancecheck__(NullVariable, self.symbolic_locals[k]) ) argnames_null = tuple( k for k in all_argnames if type.__instancecheck__(NullVariable, self.symbolic_locals[k]) ) if sys.version_info < (3, 12): assert len(argnames_null) == 0, "variables should not be NULL in < 3.12" cg = PyCodegen(self) # Handle inactive context variables. # The resume function assumes that context variables are the class, NOT the object. # e.g. torch.set_grad_enabled(True) will be reconstructed as torch.set_grad_enabled stack_ctx_vars = [] for i, var in enumerate(self.stack): if type.__instancecheck__(ContextWrappingVariable, var): ctx = cast(ContextWrappingVariable, var) target_values = ( () if ctx.target_values is None else tuple(ctx.target_values) ) stack_ctx_vars.append((i, target_values)) # Replace the current stack var with the context class ctx.reconstruct_type(cg) cg.extend_output(create_swap(len(self.stack) - i + 1)) cg.append_output(create_instruction("POP_TOP")) argnames_ctx_vars = [] for name in argnames: if type.__instancecheck__( ContextWrappingVariable, var := self.symbolic_locals[name] ): ctx = cast(ContextWrappingVariable, var) target_values = ( () if ctx.target_values is None else tuple(ctx.target_values) ) argnames_ctx_vars.append((name, target_values)) # Replace the local with the context class ctx.reconstruct_type(cg) cg.append_output(create_instruction("STORE_FAST", argval=name)) # Python does not allow null to be an arg to a function, so # we remove nulls from the stack and restore them in the # prologue of the resume function # sorted list of indices of nulls on the stack null_idxes: List[int] = [] if sys.version_info >= (3, 11): # find indices of NullVariables for i, var in enumerate(self.stack): if type.__instancecheck__(NullVariable, var): null_idxes.append(i) # generate bytecode to pop the nulls null_cnt = 0 for i, var in enumerate(reversed(self.stack)): if type.__instancecheck__(NullVariable, var): for j in range(2, i + 2 - null_cnt): cg.append_output(create_instruction("SWAP", arg=j)) cg.extend_output(cg.pop_null()) null_cnt += 1 # we popped all nulls from the stack at runtime, # so we should not count NullVariables stack_len = len(self.stack) - len(null_idxes) nargs = stack_len + len(argnames) name = unique_id(f"__resume_at_{inst.offset}") new_code: types.CodeType = ContinueExecutionCache.lookup( self.f_code, self.lineno, inst.offset, tuple(b.target.offset for b in self.block_stack), stack_len, argnames, argnames_null, tuple(b.resume_fn() for b in self.block_stack), tuple(stack_ctx_vars), tuple(argnames_ctx_vars), tuple(null_idxes), ) # Add original GraphModule context to the resume function to handle # the case of a graph break while tracing a GraphModule orig_graphmodule_maybe = code_context.get_context(self.f_code).get( "orig_graphmodule", lambda: None )() if orig_graphmodule_maybe is not None: code_context.get_context(new_code)["orig_graphmodule"] = weakref.ref( orig_graphmodule_maybe ) if new_code.co_freevars: # expose code object for debugging purposes self.output.install_global_unsafe(name, new_code) cg.make_function_with_closure(name, new_code, True, stack_len) else: # This is safe: we pre-generate a unique name self.output.install_global_unsafe( name, types.FunctionType(new_code, self.f_globals, name) ) cg.extend_output(cg.load_function_name(name, True, stack_len)) cg.extend_output([cg.create_load(k) for k in argnames]) cg.extend_output(create_call_function(nargs, False)) cg.append_output(create_instruction("RETURN_VALUE")) return cg.get_instructions() def symbolic_locals_contain_module_class(self): for v in self.symbolic_locals.values(): if isinstance(v, UserDefinedClassVariable) and issubclass( v.as_python_constant(), torch.nn.Module ): return True return False def _return(self, inst): if ( self.output.count_calls() == 0 and not self.inconsistent_side_effects and not self.symbolic_locals_contain_module_class() and not self.export ): raise exc.SkipFrame("because no content in function call") self.instruction_pointer = None _step_logger()( logging.INFO, f"torchdynamo done tracing {self.f_code.co_name} ({inst.opname})", ) log.debug("%s triggered compile", inst.opname) self.output.compile_subgraph( self, reason=GraphCompileReason( "return_value", [self.frame_summary()], graph_break=False ), ) return_inst = ( create_instruction("RETURN_VALUE") if inst.opname == "RETURN_VALUE" else create_instruction("RETURN_CONST", argval=inst.argval) ) self.output.add_output_instructions([return_inst]) raise ReturnValueOp def RETURN_VALUE(self, inst): self._return(inst) def RETURN_CONST(self, inst): self._return(inst) if sys.version_info >= (3, 11): _binary_op_lookup = [ getattr( InstructionTranslator, opname[3:] if "INPLACE" in opname else f"BINARY_{opname[3:]}", ) for opname, _ in dis._nb_ops # type: ignore[attr-defined] ] class InliningInstructionTranslator(InstructionTranslatorBase): """Trace and inline a called method""" symbolic_result: Optional[TensorVariable] @classmethod def inline_call(cls, parent, func, args, kwargs): with patch.dict(counters, {"unimplemented": counters["inline_call"]}): return cls.inline_call_(parent, func, args, kwargs) @staticmethod def check_inlineable(func): if func.has_self(): unimplemented("inline with __self__") result = trace_rules.check_verbose(func, is_inlined_call=True) if result.skipped: from torch._dynamo.variables.misc import produce_trampoline_autograd_apply # _origin marks this as coming from an internal dynamo known function that is safe to # trace through. if hasattr(getattr(func, "fn", None), "_origin") and func.fn._origin in [ produce_trampoline_autograd_apply, ]: # Known sound return trace_rules.SkipResult( False, "allowlist in dynamo known function" ) fn_qualname = func.fn.__qualname__ if hasattr(func, "fn") else "" unimplemented( f"'inline in skipfiles: {fn_qualname} | {func.get_name()} {func.get_filename()}, {result.reason}'" ) if isinstance(func, UserFunctionVariable) and inspect.getattr_static( func.get_function(), "_torchdynamo_disable", False ): unimplemented( f"call torch._dynamo.disable() wrapped function {func.get_function()}" ) else: return result @staticmethod def inline_call_( parent, func: VariableTracker, args: List[VariableTracker], kwargs ): if isinstance(func, SkipFunctionVariable): unimplemented("inline with functions in skip files") assert isinstance( func, (UserFunctionVariable, NestedUserFunctionVariable), ) result = InliningInstructionTranslator.check_inlineable(func) assert result.skipped is False try: sub_locals, closure_cells = func.bind_args(parent, args, kwargs) except TypeError as e: # Wrap the general TypeError during bind_args() to the internal ArgsMismatchError with detailed info raise ArgsMismatchError( # noqa: B904 "{reason}.\n func = {func}, args = {args}, kwargs = {kwargs}".format( reason=str(e), func=f"'{func.get_name()}' {func.get_filename()}:{func.get_code().co_firstlineno}", args=[arg.python_type() for arg in args], kwargs=kwargs, ), ) for v in itertools.chain(sub_locals.values(), closure_cells.values()): if not isinstance(v, VariableTracker): unimplemented(f"unconverted arg {v}") code: types.CodeType = func.get_code() if code.co_name in ("__setitem__", "__setattr__") and not ( args and isinstance( args[0], (variables.CustomizedDictVariable, variables.UserDefinedObjectVariable), ) ): unimplemented(f"inline {code.co_name}") suffix = "" # TODO: mlazos, add support for enabling multiple artifact logs # with a single alias if torch._logging._internal.log_state.is_artifact_enabled("bytecode"): suffix = f"\n{dis.Bytecode(code).dis()}" if sys.version_info >= (3, 11): cur_inst = parent.current_instruction parent_code = parent.f_code header = parent.get_line_of_code_header(lineno=cur_inst.positions.lineno) def get_trace_call_log_str(): line = get_instruction_source_311(parent_code, cur_inst).rstrip() return f"TRACE inlined call {code.co_name} from {header}\n{line}" trace_call_log.debug("%s", LazyString(get_trace_call_log_str)) log.debug("INLINING %s%s, %s", code, suffix, result.reason) # Detect inline GraphModule calls in order to propagate node metadata, # by checking if the first argument (self) is a variable tracking a GraphModule. if args and isinstance(args[0], NNModuleVariable): module = parent.output.get_submodule(args[0].module_key) if isinstance(module, torch.fx.GraphModule): # The inline call might not actually be a call to `forward`, # but it is enough to add a context for `forward` in case it is called. code_context.get_context(module.forward.__code__)[ "orig_graphmodule" ] = weakref.ref(module) tracer: InliningInstructionTranslator if is_generator(code): tracer = InliningGeneratorInstructionTranslator( parent, code, sub_locals, parent.symbolic_globals, parent.symbolic_torch_function_mode_stack, closure_cells, func, ) else: tracer = InliningInstructionTranslator( parent, code, sub_locals, parent.symbolic_globals, parent.symbolic_torch_function_mode_stack, closure_cells, func, ) strict_ctx: Any = contextlib.nullcontext() if parent.strict_checks_fn: strict_ctx = tracer.strict_translation_mode(parent.strict_checks_fn) try: with strict_ctx: tracer.run() except exc.ObservedException as e: msg = f"Observed exception DURING INLING {code} : {e}" # TODO(anijain2305) - This works but we should probably have a # global/central data structure for the exception stack. parent.exn_vt_stack.extend(tracer.exn_vt_stack) log.debug(msg) # bubble up the exception to the parent frame. raise except exc.SkipFrame as e: msg = f"SKIPPED INLINING {code}: {e}" log.debug(msg) raise Unsupported(msg) from e except Exception as e: log.debug("FAILED INLINING %s", code) raise assert tracer.symbolic_result is not None func.export_freevars(parent, tracer) if tracer.f_globals is parent.f_globals: # Merge symbolic_globals back if parent and child are in the same namespace parent.symbolic_globals.update(tracer.symbolic_globals) parent.inconsistent_side_effects |= tracer.inconsistent_side_effects log.debug("DONE INLINING %s", code) if is_generator(code): assert isinstance(tracer, InliningGeneratorInstructionTranslator) assert tracer.symbolic_result.as_python_constant() is None return ListIteratorVariable( tracer.generated_items, mutable_local=MutableLocal(), ) else: return tracer.symbolic_result def __init__( self, parent: InstructionTranslatorBase, code: types.CodeType, symbolic_locals: Dict[str, VariableTracker], symbolic_globals: Dict[str, VariableTracker], symbolic_torch_function_mode_stack: Deque["TorchFunctionModeVariable"], closure_cells: Dict[str, VariableTracker], funcvar: BaseUserFunctionVariable, ) -> None: f_globals = funcvar.get_globals() # type: ignore[attr-defined] f_builtins = f_globals["__builtins__"] if not isinstance(f_builtins, dict): f_builtins = f_builtins.__dict__ instructions = cleaned_instructions(code) propagate_line_nums(instructions) super().__init__( output=parent.output, f_locals={}, f_globals=f_globals, f_builtins=f_builtins, symbolic_locals=symbolic_locals, symbolic_globals=symbolic_globals, symbolic_torch_function_mode_stack=symbolic_torch_function_mode_stack, instructions=instructions, code_options={k: getattr(code, k) for k in get_code_keys()}, f_code=code, export=parent.export, inline_depth=parent.inline_depth + 1, speculation_log=parent.speculation_log, distributed_state=parent.distributed_state, ) self.parent = parent self.symbolic_result = None self.closure_cells = closure_cells self.nn_module_stack = parent.nn_module_stack.copy() self.one_graph = parent.one_graph @property def fake_mode(self): return self.parent.fake_mode def run_ctx_mgr(self): return TracingContext.current_frame(self.parent.frame_summary()) def STORE_DEREF(self, inst): if inst.argval in self.closure_cells: cell = self.closure_cells[inst.argval] val = self.pop() if isinstance(cell, ClosureVariable): if not self.output.is_root_tracer(): unimplemented( "HigherOrderOperator: Mutating a variable not in the current scope (ClosureVariable)" ) self.output.root_tx.symbolic_locals[cell.name] = val else: self.output.side_effects.store_cell(cell, val) else: maybe_cell = self.symbolic_locals.get(inst.argval) if isinstance( maybe_cell, variables.NewCellVariable, ): self.output.side_effects.store_cell( self.symbolic_locals[inst.argval], self.pop() ) else: if ( maybe_cell is not None and maybe_cell.source.name() not in self.output.root_tx.mutated_closure_cell_contents ): # Why is the source name here unique? # mutated_closure_cell_contents is a per-frame # concept, and sources identify, e.g., particular # locals from the frame. If you had two locals, # they'll get different source names, and therefore # differ here. self.output.root_tx.mutated_closure_cell_contents.add( maybe_cell.source.name() ) raise exc.UnspecializeRestartAnalysis unimplemented("write to __closure__ while inlining") def LOAD_DEREF(self, inst): if inst.argval in self.closure_cells: cell = self.closure_cells[inst.argval] if isinstance(cell, ClosureVariable): self.push(self.output.root_tx.symbolic_locals[cell.name]) else: self.push(self.output.side_effects.load_cell(cell)) else: maybe_sym_local = self.symbolic_locals.get(inst.argval, None) if isinstance(maybe_sym_local, variables.NewCellVariable): self.push(self.output.side_effects.load_cell(maybe_sym_local)) else: super().LOAD_DEREF(inst) def _load_closure(self, name): assert name in self.cell_and_freevars() if name in self.closure_cells: return self.closure_cells[name] else: return InlinedClosureVariable(name=name) def check_replace_is_safe(self, oldvar): if not is_side_effect_safe(oldvar.mutable_local): unimplemented( "HigherOrderOperator: Mutating a variable not in the current scope (replace_all)" ) def should_compile_partial_graph(self): return False # inlining functions is all-or-nothing def create_call_resume_at(self, offset): unimplemented("cant resume while inlining") def RETURN_VALUE(self, inst): self.symbolic_result = self.pop() # type: ignore[assignment] self.instruction_pointer = None raise ReturnValueOp def RETURN_CONST(self, inst): self.symbolic_result = self._load_const(inst) self.instruction_pointer = None raise ReturnValueOp def get_globals_source_and_value(self, name): if "__name__" in self.f_globals: module_name = self.f_globals["__name__"] module_source = self.import_source(module_name) if "torch_package" in module_name: fglobals_value = torch.package.package_importer._package_imported_modules[module_name] # type: ignore[assignment] else: fglobals_value = importlib.import_module(module_name) # type: ignore[assignment] fglobals_vt = VariableBuilder(self, module_source)(fglobals_value) global_source = AttrSource(module_source, name) else: globals_name = self.output.install_global_by_id( "___unnamed_scope", self.f_globals ) globals_source = GlobalSource(globals_name) fglobals_value = self.f_globals # type: ignore[assignment] fglobals_vt = VariableBuilder(self, globals_source)(fglobals_value) global_source = GetItemSource(globals_source, name) # type: ignore[assignment] return fglobals_value, fglobals_vt, global_source def _load_global(self, inst): if self.output.global_scope is self.f_globals: super()._load_global(inst) else: name = inst.argval _, fglobals_vt, global_source = self.get_globals_source_and_value(name) if self.output.side_effects.has_pending_mutation_of_attr(fglobals_vt, name): self.push(self.output.side_effects.load_attr(fglobals_vt, name)) else: try: value = self.f_globals[name] except KeyError: return self.load_builtin(inst) self.push(VariableBuilder(self, global_source)(value)) def STORE_GLOBAL(self, inst): if self.f_globals is self.parent.f_globals: super().STORE_GLOBAL(inst) else: value = self.pop() if isinstance(value, RemovableHandleVariable): unimplemented("Storing handles in globals - NYI") name = inst.argval fglobals_value, fglobals_vt, _ = self.get_globals_source_and_value(name) self.output.side_effects.store_attr(fglobals_vt, name, value) class InliningGeneratorInstructionTranslator(InliningInstructionTranslator): generated_items: List[VariableTracker] def __init__(self, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.generated_items = [] def YIELD_VALUE(self, inst: Instruction): self.generated_items.append(self.pop()) if len(self.generated_items) > MAX_ITERATOR_LIMIT: unimplemented( "Too many yield values in generator. Maybe you are inlining an infinite generator. " f"If not, please report a bug at {PT2_ISSUE_TRACKER_URL}", ) self.push(ConstantVariable.create(None)) def GET_YIELD_FROM_ITER(self, inst): tos = self.stack[-1] if not isinstance(tos, ListIteratorVariable): self.pop() res = BuiltinVariable(iter).call_function(self, [tos], {}) # type: ignore[arg-type] self.push(res) def YIELD_FROM(self, inst): assert len(self.stack) >= 2 val = self.pop() tos = self.stack[-1] if not (isinstance(val, ConstantVariable) and val.value is None): # invoke send # Unreachable code - if you hit this, you are implementing generator support and have # lifted the `unimplemented("generator")` in frame conversion. This codepath handles # subgenerator and lines up with this line in Python 3.10 # https://github.com/python/cpython/blob/3.10/Python/ceval.c#L2599 unimplemented("Unreachable sub-generator code") try: val = tos.next_variable(self) except (StopIteration, exc.ObservedUserStopIteration) as ex: if isinstance(ex, exc.ObservedUserStopIteration): exc.handle_observed_exception(self) # The iterator is exhausted. Stop the loop and return. self.pop() self.push(ConstantVariable.create(ex.value)) else: self.push(val) # Add the value to yield into generated_items and replace the top of the stack with None self.YIELD_VALUE(inst) # Repeat the YIELD_FROM instruction in the next eval loop assert ( isinstance(self.instruction_pointer, int) and self.instruction_pointer > 0 ) self.instruction_pointer -= 1 def SEND(self, inst): assert len(self.stack) >= 2 val = self.pop() tos = self.stack[-1] if isinstance(tos, ListIteratorVariable) or ( isinstance(tos, UserDefinedObjectVariable) and isinstance(tos.value, collections.abc.Iterator) ): if isinstance(val, ConstantVariable) and val.value is None: try: val = tos.next_variable(self) except (StopIteration, exc.ObservedUserStopIteration) as ex: # To implement SEND, we have to look at the implementation # when the iterator returns StopIteration. This translates to this code # 3.11: https://github.com/python/cpython/blob/3.11/Python/ceval.c#L2613-L2619 # 3.12: https://github.com/python/cpython/blob/3.12/Python/bytecodes.c#L863-L866 # The implementation is different in 3.11 and 3.12. In 3.12, we rely # on END_SEND to clean up. In 3.11, SEND does the cleanup as well. if sys.version_info < (3, 12): self.pop() # Python 3.12 uses new opcode END_SEND self.push(ConstantVariable.create(ex.value)) self.jump(inst) else: self.push(val) else: # invoke send # Unreachable code - if you hit this, you are implementing generator support and have # lifted the `unimplemented("generator")` in frame conversion. This codepath handles # subgenerator and lines up with this line in Python 3.11 # https://github.com/python/cpython/blob/3.11/Python/ceval.c#L2597 unimplemented("Unreachable sub-generator code") else: unimplemented(f"SEND {typestr(tos)}")