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import collections
import contextlib
import copy
import dataclasses
import dis
import functools
import importlib
import inspect
import itertools
import linecache
import logging
import operator
import sys
import textwrap
import threading
import traceback
import types
import typing
import weakref
from typing import Any, Dict, List, NamedTuple, Optional, Set, Tuple, Type
from unittest.mock import patch
import torch
import torch._logging
from torch._guards import Checkpointable, 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,
Instruction,
is_generator,
unique_id,
)
from .code_context import code_context
from .codegen import PyCodegen
from .current_scope_id import current_scope_id
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, OutputGraphState
from .replay_record import DummyModule, ExecutionRecorder
from .resume_execution import ContinueExecutionCache, ReenterWith
from .source import (
AttrSource,
GetItemSource,
GlobalSource,
GlobalWeakRefSource,
LocalSource,
Source,
)
from .trace_rules import is_builtin_constant, is_forbidden
from .utils import (
counters,
get_fake_value,
get_instruction_source_311,
graph_break_dup_warning_checker,
istype,
LazyString,
proxy_args_kwargs,
)
from .variables.base import (
_is_top_level_scope,
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.lists import (
BaseListVariable,
ListIteratorVariable,
ListVariable,
SliceVariable,
TupleVariable,
)
from .variables.misc import (
ClosureVariable,
GetAttrVariable,
InlinedClosureVariable,
NullVariable,
PythonModuleVariable,
UnknownVariable,
)
from .variables.nn_module import NNModuleVariable
from .variables.tensor import (
supported_const_comparison_ops,
supported_tensor_comparison_ops,
SymNodeVariable,
TensorVariable,
)
from .variables.user_defined import (
RemovableHandleVariable,
UserDefinedClassVariable,
UserDefinedObjectVariable,
UserDefinedVariable,
)
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")
tls = threading.local()
@dataclasses.dataclass
class SpeculationEntry:
filename: str
lineno: int
instruction_pointer: int
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
raise exc.SpeculationRestartAnalysis()
@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) -> 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))
entry = self.entries[self.index]
self.index += 1
assert (
entry.instruction_pointer == instruction_pointer
and entry.filename == filename
and entry.lineno == lineno
), textwrap.dedent(
f"""
SpecuationLog diverged at {self.index} of {len(self.entries)}:
- Run1: {entry.filename}:{entry.lineno} (ip={entry.instruction_pointer})
- Run2: {filename}:{lineno} (ip={instruction_pointer})
Please submit a bug report.
"""
)
return entry
@functools.lru_cache(None)
def _step_logger():
return torchdynamo_logging.get_step_logger(log)
@dataclasses.dataclass
class BlockStackEntry:
target: Instruction
stack_index: Optional[int] = None
with_context: Optional[ContextWrappingVariable] = 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 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 InstructionTranslatorGraphState(NamedTuple):
output: OutputGraphState
symbolic_locals: Dict[str, VariableTracker]
stack: List[VariableTracker]
block_stack: List[BlockStackEntry]
instruction_pointer: Optional[int]
current_instruction: Instruction
next_instruction: Optional[Instruction]
lineno: int
def diff(self, other: "InstructionTranslatorGraphState") -> Optional[str]:
for k in self._fields:
if k == "output":
return self.output.diff(other.output, prefix=f"{k}.")
sv = getattr(self, k)
ov = getattr(other, k)
if sv != ov:
return f"{k} mismatch: {sv} != {ov}"
return None
def stack_op(fn: typing.Callable[..., object]):
nargs = len(inspect.signature(fn).parameters)
fn_var = BuiltinVariable(fn)
@functools.wraps(fn)
def impl(self: "InstructionTranslatorBase", 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+)
current_instruction_pointer += 1
inst = self.instructions[current_instruction_pointer]
if inst.opname not in ("CALL_FUNCTION", "PRECALL"):
return False
# for Python 3.11+, PRECALL should be followed by CALL, then RAISE_VARARGS
# for Python < 3.11, CALL_FUNCTION 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 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() and bool(value.as_python_constant()):
self.jump(inst)
return
# 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:
raise 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()):
push and self.push(value)
self.jump(inst)
elif (
isinstance(value, (TensorVariable)) and self.should_compile_partial_graph()
):
# compile a partial subgraph prefix then jump into user code
if self.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)}", [self.frame_summary()]
),
)
self.pop()
if_next = self.create_call_resume_at(self.next_instruction)
push and self.push(value)
if_jump = self.create_call_resume_at(inst.target)
self.output.add_output_instructions(
[create_instruction(inst.opname, target=if_jump[0])] + if_next + if_jump
)
elif isinstance(value, NNModuleVariable):
# Equivalent of "self.nn_module is not None"
mod = self.output.get_submodule(value.module_key)
if truth_fn(mod):
push and self.push(value)
self.jump(inst)
elif isinstance(value, UserDefinedObjectVariable):
x = value.var_getattr(self, "__bool__")
# if __bool__ is missing, trying __len__ to infer a truth value.
if isinstance(x, GetAttrVariable):
x = value.var_getattr(self, "__len__")
# __bool__ or __len__ is function
if isinstance(x, UserMethodVariable):
result = x.call_function(self, [], {})
if isinstance(result, ConstantVariable) and isinstance(
result.value, (bool, int)
):
if truth_fn(result.value):
push and 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):
push and 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))):
push and self.push(value)
self.jump(inst)
elif isinstance(value, SymNodeVariable):
eval_result = value.evaluate_expr(self.output)
if truth_fn(eval_result):
push and self.push(value)
self.jump(inst)
elif isinstance(value, variables.BackwardHookVariable):
if truth_fn(True):
push and 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]
push and 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:
TracingContext.set_current_loc(
self.f_code.co_filename, self.lineno, self.f_code.co_name
)
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
user_stack_formatted = "".join(traceback.format_list(user_stack))
frame_loc = (user_stack[-1].filename, user_stack[-1].lineno)
# 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)
):
# 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.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 variables in the block stack
for b in self.block_stack:
assert b.with_context is not None
cg(b.with_context)
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:
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 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 InstructionTranslatorBase(Checkpointable[InstructionTranslatorGraphState]):
output: OutputGraph
symbolic_locals: Dict[str, VariableTracker]
symbolic_globals: Dict[str, VariableTracker]
stack: List[VariableTracker]
instruction_pointer: Optional[int]
current_instruction: Instruction
next_instruction: Optional[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]
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 has_backedge(self):
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 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 [])
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))
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 log_starts_line(self):
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"""
assert isinstance(self.instruction_pointer, int)
inst = self.instructions[self.instruction_pointer]
self.current_instruction = inst
self.instruction_pointer += 1
if self.instruction_pointer < len(self.instructions):
self.next_instruction = self.instructions[self.instruction_pointer]
else:
self.instruction_pointer = None
self.next_instruction = None
if inst.starts_line and self.lineno != inst.starts_line:
self.lineno = inst.starts_line
self.log_starts_line()
if (
len(self.stack) == 0
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)
log.debug("TRACE %s %s %s", inst.opname, inst.argval, self.stack)
# 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".
if sys.version_info >= (3, 11):
entry = inst.exn_tab_entry
if not (
# still in the same block
self.block_stack
and entry
and self.block_stack[-1].target is entry.target
):
if not entry:
# 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.
if self.block_stack and inst.opname != "NOP":
# 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()
elif (
# current instruction is in the previous block
len(self.block_stack) > 1
and self.block_stack[-2].target is entry.target
):
# exit the current block
self.block_stack.pop()
else:
# current instruction is in a new block
# push block to stack - note, BEFORE_WITH blocks won't
# be pushed here since BEFORE_WITH pushes the block, and
# the current instruction would be counted as being in that block.
self.block_stack.append(
BlockStackEntry(entry.target, len(self.stack))
)
try:
if not hasattr(self, inst.opname):
unimplemented(f"missing: {inst.opname}")
TracingContext.set_current_loc(
self.f_code.co_filename, self.lineno, self.f_code.co_name
)
getattr(self, inst.opname)(inst)
return inst.opname != "RETURN_VALUE"
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()
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.instruction_pointer is not None
and not self.output.should_exit
and self.step()
):
pass
except BackendCompilerFailed:
raise
except Exception as e:
if config.replay_record_enabled:
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]):
assert val is None or isinstance(
val, VariableTracker
), f"push expects VariableTracker, got {typestr(val)}"
self.stack.append(val) # type: ignore[arg-type]
def push_many(self, vals: List[VariableTracker]):
for val in vals:
self.push(val)
def pop(self) -> VariableTracker:
return self.stack.pop()
def popn(self, n: int) -> List[VariableTracker]:
assert n >= 0
return list(reversed([self.pop() for _ in range(n)]))
def LOAD_FAST(self, inst):
name = inst.argval
if name in self.f_locals and config.replay_record_enabled:
self.exec_recorder.add_local_var(name, self.f_locals[name])
if name.startswith(".") and name not in self.symbolic_locals:
# This happens in dict/list comprehensions
name = name.replace(".", "implicit")
assert name not in self.cell_and_freevars()
if name not in self.symbolic_locals:
unimplemented("undefined LOAD_FAST")
self.push(self.symbolic_locals[name])
if name.startswith("___stack"):
self.symbolic_locals.pop(name)
def LOAD_DEREF(self, inst):
assert inst.argval in self.cell_and_freevars()
if inst.argval in self.f_locals and config.replay_record_enabled:
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, inst):
loaded_vt = self.pop()
name = inst.argval
# Only rename at the top-level scope, this is to avoid the confusion between
# mutating a variable vs renaming it (e.g. a = b) during speculating a higher order op,
# where mutation is prohibited and it's difficult to differentiate it with renaming.
if _is_top_level_scope(current_scope_id()):
loaded_vt = loaded_vt.rename(self, name)
self.symbolic_locals[name] = loaded_vt
def DELETE_FAST(self, inst):
del self.symbolic_locals[inst.argval]
STORE_DEREF = STORE_FAST
def LOAD_CLOSURE(self, inst):
self.push(ClosureVariable(name=inst.argval))
def LOAD_CONST(self, inst):
# For empty tuples, create empty TupleVariable
if isinstance(inst.argval, tuple) and not inst.argval:
self.push(TupleVariable([]))
else:
self.push(ConstantVariable.create(value=inst.argval))
def get_global_source(self, name):
source: Source
if self.output.global_scope is self.f_globals:
source = GlobalSource(name)
else:
if "__name__" in self.f_globals:
source = AttrSource(
self.import_source(self.f_globals["__name__"]), name
)
else:
mangled_name = self.output.install_global_by_id(
"___unnamed_scope", self.f_globals
)
source = GetItemSource(GlobalSource(mangled_name), name)
return source
def LOAD_GLOBAL(self, inst):
if sys.version_info >= (3, 11):
if inst.arg % 2:
self.PUSH_NULL(inst)
name = inst.argval
if config.replay_record_enabled:
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 inst.argval == "AssertionError":
unimplemented("assert with non-string message")
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 = self.get_global_source(name)
self.push(VariableBuilder(self, source)(value))
def STORE_GLOBAL(self, inst):
value = self.pop()
name = inst.argval
source = self.get_global_source(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:
value = __import__(
module_name,
fromlist=fromlist,
level=level,
globals=self.f_globals,
)
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 config.replay_record_enabled:
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(self, inst):
if inst.argval not in self.f_builtins:
raise NameError(f"name '{inst.argval}' is not defined")
val = self.f_builtins[inst.argval]
if callable(val):
self.push(VariableBuilder(self, GlobalSource(inst.argval))(val))
else:
assert is_builtin_constant(val)
self.push(ConstantVariable.create(value=val))
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.target))
def SETUP_EXCEPT(self, inst):
# only exists in python<=3.7
self.block_stack.append(BlockStackEntry(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.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
assert self.next_instruction is not None
addr = self.indexof[self.next_instruction]
self.push(ConstantVariable.create(addr))
self.instruction_pointer = self.indexof[inst.target]
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()
if isinstance(it, (variables.ListIteratorVariable, variables.IteratorVariable)):
try:
val, next_iter = it.next_variables(self)
self.push(next_iter)
self.push(val)
except StopIteration:
self.jump(inst)
else:
unimplemented(f"FOR_ITER {typestr(it)}")
def COMPARE_OP(self, inst):
left, right = self.popn(2)
op = inst.argval
supported_any = dict(
itertools.chain(
supported_tensor_comparison_ops.items(),
supported_const_comparison_ops.items(),
)
)
if (
isinstance(
left,
(
TensorVariable,
SymNodeVariable,
NNModuleVariable,
BaseListVariable,
UserDefinedVariable,
BaseUserFunctionVariable,
ConstDictVariable,
),
)
and isinstance(right, ConstantVariable)
and right.value is None
and op in supported_const_comparison_ops
):
# <non-None> is None
self.push(
ConstantVariable.create(
supported_const_comparison_ops[op](object(), right.value)
)
)
elif (
left.is_python_constant()
and right.is_python_constant()
and op in supported_any
):
# constant fold
self.push(
ConstantVariable.create(
supported_any[op](
left.as_python_constant(), right.as_python_constant()
),
)
)
elif op in ("in", "not in"):
self.push(right.call_method(self, "__contains__", [left], {}))
if op == "not in":
self.UNARY_NOT(inst)
else:
self.push(
BuiltinVariable(supported_any[op]).call_function(
self, [left, right], {}
)
)
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")
fn = self.pop()
if sys.version_info >= (3, 11):
null = self.pop()
assert isinstance(null, NullVariable)
if (
isinstance(fn, GetAttrVariable)
and isinstance(fn.obj, TensorVariable)
and 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])
if not isinstance(
argsvars, BaseListVariable
) and argsvars.has_unpack_var_sequence(self):
argsvars = TupleVariable(argsvars.unpack_var_sequence(self))
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, 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)], {}
)
self.push(result)
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):
# We don't allow side effects during export
# 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], {}
)
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):
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)], {}
)
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 BUILD_TUPLE(self, inst):
items = self.popn(inst.argval)
self.push(TupleVariable(items))
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 = list()
for seq in seqs:
try:
items.extend(seq.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]
result = dict()
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.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 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 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))
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_unpack_var_sequence(self):
val = seq.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_unpack_var_sequence(self):
vals = list(seq.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):
value = ConstantVariable.create(str(value.sym_num))
if (flags & 0x03) == 0x01:
value = BuiltinVariable(str).call_function(self, [value], {})
elif (flags & 0x03) == 0x02:
value = BuiltinVariable(repr).call_function(self, [value], {})
elif (flags & 0x03) == 0x03:
value = BuiltinVariable(ascii).call_function(self, [value], {})
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()], {}))
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(k) for k in tos])) # type: ignore[attr-defined]
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):
unimplemented("assert with non-string message")
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
def BINARY_OP(self, inst):
if sys.version_info >= (3, 11):
opname = dis._nb_ops[inst.arg][0][3:] # type: ignore[attr-defined]
if opname.startswith("INPLACE"):
return getattr(self, "INPLACE_" + opname[8:])(inst)
return getattr(self, "BINARY_" + opname)(inst)
else:
unimplemented("BINARY_OP requires Python 3.11+")
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())
@break_graph_if_unsupported(push=1)
def CALL(self, inst):
# see https://docs.python.org/3.11/library/dis.html#opcode-CALL
# for convention
contents = self.popn(inst.arg + 2)
if isinstance(contents[0], NullVariable):
fn = contents[1]
args = []
else:
fn = contents[0]
args = [contents[1]]
kw_names = self.kw_names.value if self.kw_names else ()
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 = {}
self.call_function(fn, args, kwargs)
self.kw_names = None
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):
unimplemented(f"{inst.opname} {ctx}")
if isinstance(ctx, GenericContextWrappingVariable):
self.generic_context_manager_depth += 1
exit = WithExitFunctionVariable(
ctx,
inst.target,
)
if sys.version_info >= (3, 11):
# see create_call_resume_at for block stack details
assert self.next_instruction
assert self.next_instruction.exn_tab_entry
target = self.next_instruction.exn_tab_entry.target
else:
target = inst.target
if isinstance(self, InstructionTranslator):
self.block_stack.append(BlockStackEntry(target, len(self.stack), ctx))
else:
self.block_stack.append(BlockStackEntry(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):
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)
def copy_graphstate(self) -> InstructionTranslatorGraphState:
"""Create a checkpoint of the current state by copying everything"""
return InstructionTranslatorGraphState(
self.output.copy_graphstate(),
dict(self.symbolic_locals),
list(self.stack),
list(self.block_stack),
self.instruction_pointer,
self.current_instruction,
self.next_instruction,
self.lineno,
)
def restore_graphstate(self, state: InstructionTranslatorGraphState):
"""Restore a checkpoint created by self.copy_graphstate()"""
(
output_state,
self.symbolic_locals,
self.stack,
self.block_stack,
self.instruction_pointer,
self.current_instruction,
self.next_instruction,
self.lineno,
) = state
self.output.restore_graphstate(output_state)
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", "<unknown>"),
self.lineno,
getattr(self.f_code, "co_name", "<unknown>"),
lookup_line=False,
)
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):
self.strict_checks_enabled = True
try:
yield
finally:
self.strict_checks_enabled = False
def speculate(self) -> SpeculationEntry:
return self.speculation_log.next(
self.f_code.co_filename, self.lineno, self.instruction_pointer
)
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],
f_code: types.CodeType,
export: bool,
inline_depth: int,
speculation_log: SpeculationLog,
):
super().__init__()
self.speculation_log = speculation_log
# Mutable state checkpointed by copy_graphstate()
self.output = output
self.symbolic_locals = symbolic_locals
self.symbolic_globals = symbolic_globals
self.stack = []
self.instruction_pointer = 0
self.current_instruction = create_instruction("NOP")
self.next_instruction = None
self.block_stack = []
# states before SETUP_WITH for checkpointing and fallback
self.generic_context_manager_depth = 0
self.lineno = code_options["co_firstlineno"]
self.kw_names = None
self.accept_prefix_inst = True
self.prefix_insts = []
# 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
self.exec_recorder = ExecutionRecorder(code=f_code, code_options=code_options)
# 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.current_speculation = None
self.strict_checks_enabled = False
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
linecache.lazycache(f_code.co_filename, f_globals)
self.log_starts_line()
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,
):
_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={},
f_code=f_code,
export=export,
inline_depth=0,
speculation_log=speculation_log,
)
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.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 = VariableTracker.apply(
lambda x: x.realize(), self.symbolic_locals
)
self._freevars_ids = dict()
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,
)
if ci is not None and ci.key() in forbidden_keys and compiler_fn is not eager:
# if it reaches here, it means Dynamo failed to inline a functorch function
name = ci.key().name.lower()
msg = f"torch.func.{name}(fn) requires the function to be inlined by dynamo"
unimplemented(msg)
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):
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")]
reads = livevars_analysis(self.instructions, inst)
argnames = tuple(
k
for k in self.symbolic_locals.keys()
if k in reads and k not in self.cell_and_freevars()
)
cg = PyCodegen(self)
# 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 isinstance(var, NullVariable):
null_idxes.append(i)
# generate bytecode to pop the nulls
null_cnt = 0
for i, var in enumerate(reversed(self.stack)):
if isinstance(var, NullVariable):
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,
tuple(b.resume_fn() for b in self.block_stack),
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:
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_VALUE(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} (RETURN_VALUE)",
)
log.debug("RETURN_VALUE triggered compile")
self.output.compile_subgraph(
self,
reason=GraphCompileReason(
"return_value", [self.frame_summary()], graph_break=False
),
)
self.output.add_output_instructions([create_instruction("RETURN_VALUE")])
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: TRY200
"{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 is not None
and len(args) > 0
and isinstance(args[0], variables.CustomizedDictVariable)
):
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("output_code"):
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, closure_cells, func
)
else:
tracer = InliningInstructionTranslator(
parent, code, sub_locals, parent.symbolic_globals, closure_cells, func
)
strict_ctx: Any = contextlib.nullcontext()
if parent.strict_checks_enabled:
strict_ctx = tracer.strict_translation_mode()
try:
with strict_ctx:
tracer.run()
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],
closure_cells: Dict[str, VariableTracker],
funcvar: BaseUserFunctionVariable,
):
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,
instructions=instructions,
code_options={k: getattr(code, k) for k in dir(code)},
f_code=code,
export=parent.export,
inline_depth=parent.inline_depth + 1,
speculation_log=parent.speculation_log,
)
self.parent = parent
self.symbolic_result = None
self.closure_cells = closure_cells
self.nn_module_stack = parent.nn_module_stack.copy()
@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, inst):
assert inst.argval in self.cell_and_freevars()
if inst.argval in self.closure_cells:
self.push(self.closure_cells[inst.argval])
else:
self.push(InlinedClosureVariable(name=inst.argval))
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
class InliningGeneratorInstructionTranslator(InliningInstructionTranslator):
generated_items: List[VariableTracker]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.generated_items = []
def YIELD_VALUE(self, inst: Instruction):
self.generated_items.append(self.pop())
# TODO(jansel): figure out why this is needed, it isn't in the docs for YIELD_VALUE
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], {})
self.push(res)
return self.YIELD_FROM(inst)
def YIELD_FROM(self, inst):
while True:
tos = self.stack[-1].realize()
if isinstance(tos, ConstantVariable) and tos.value is None:
self.pop()
return
if isinstance(
tos, (variables.ListIteratorVariable, variables.IteratorVariable)
):
try:
val, next_iter = tos.next_variables(self)
self.push(val)
# TODO(voz): Unclear if we need the push None in YIELD_VALUE?
self.YIELD_VALUE(inst)
self.pop()
self.push(next_iter)
except StopIteration:
return
else:
unimplemented(f"YIELD_FROM {typestr(tos)}")
def SEND(self, inst):
assert len(self.stack) >= 2
val = self.pop()
tos = self.stack[-1]
if isinstance(tos, ListIteratorVariable):
if isinstance(val, ConstantVariable) and val.value is None:
self.push(val)
self.instruction_pointer = self.indexof[inst.target]
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)}")
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