ai-content-maker/.venv/Lib/site-packages/torch/_dynamo/codegen.py

399 lines
15 KiB
Python

import collections
import dataclasses
import re
import sys
import types
from typing import Counter, Dict, List, Optional
import torch.nn
from . import utils
from .bytecode_transformation import (
create_call_function,
create_dup_top,
create_instruction,
create_load_global,
create_rot_n,
Instruction,
)
from .exc import unimplemented
from .source import AttrSource, Source
from .utils import is_safe_constant, rot_n_helper
from .variables.base import VariableTracker
from .variables.nn_module import NNModuleVariable
from .variables.tensor import (
NumpyNdarrayVariable,
SymNodeVariable,
TensorVariable,
UnspecializedPythonVariable,
)
from .variables.torch_function import TensorWithTFOverrideVariable
@dataclasses.dataclass
class GraphOutputEntry:
index: int
variable: VariableTracker
class PyCodegen:
"""
Helper class uses for constructing Python bytecode
"""
def __init__(
self,
tx=None,
root: Optional[torch.nn.Module] = None,
graph_output_var: Optional[str] = None,
tempvars=None,
):
self.root = root
self.top_of_stack: Optional[VariableTracker] = None
self.uses: Counter[VariableTracker] = collections.Counter()
self.graph_outputs: Dict[int, GraphOutputEntry] = {}
self._output: List[Instruction] = []
self.tempvars = tempvars or {}
self.tx = tx
self.graph_output_var = graph_output_var
self.code_options = self.tx.output.code_options
self.cell_and_freevars = self.tx.cell_and_freevars
self.new_var = self.tx.output.new_var
self.mutable_side_effects_from_source = False
self.value_from_source: bool = True
def restore_stack(self, stack_values, *, value_from_source=True):
prior = self.mutable_side_effects_from_source
self.mutable_side_effects_from_source = True
prev = self.value_from_source
self.value_from_source &= value_from_source
try:
self.foreach(stack_values)
finally:
self.mutable_side_effects_from_source = prior
self.value_from_source = prev
def graph_output_vars(self):
return [x.variable for x in self.graph_outputs.values()]
def call_reconstruct(self, value):
res = value.reconstruct(self)
assert res is None, f"reconstruct!=None {value}"
def __call__(self, value, allow_cache=True):
"""Generate code such that top-of-stack (TOS) is set to value"""
if isinstance(value, Source):
self.call_reconstruct(value)
self.clear_tos()
return
assert isinstance(value, VariableTracker)
output = self._output
graph_outputs = self.graph_outputs
if self.top_of_stack is value and allow_cache:
output.append(create_dup_top())
return
if self.mutable_side_effects_from_source:
# this is needed to get aliasing relationships right
# value.mutable_local.source will get mutated to hold `value`
# mutable_side_effects_from_source=False is used to codegen the mutation
# mutable_side_effects_from_source=True is used to codegen a reference
from .side_effects import MutableSideEffects
if isinstance(value.mutable_local, MutableSideEffects):
self(value.mutable_local.source)
return
if allow_cache:
if value.mutable_local and value.mutable_local in self.tempvars:
output.append(self.create_load(self.tempvars[value.mutable_local]))
self.top_of_stack = value
return
if self.tempvars.get(value) is not None:
output.append(self.create_load(self.tempvars[value]))
self.top_of_stack = value
return
if value.source is not None and allow_cache and self.value_from_source:
self.call_reconstruct(value.source)
elif value.is_python_constant() and is_safe_constant(
value.as_python_constant()
):
output.append(self.create_load_const(value.as_python_constant()))
elif isinstance(value, TensorWithTFOverrideVariable):
graph_outputs_key = self.add_graph_output(value)
self.load_import_from(utils.__name__, "to_subclass")
self.load_graph_output(graph_outputs[graph_outputs_key].index)
output.append(
self.create_load_global(
value.global_mangled_class_name(self.tx), False, add=True
)
)
output.extend(create_call_function(2, True))
elif isinstance(
value,
(
TensorVariable,
SymNodeVariable,
UnspecializedPythonVariable,
NumpyNdarrayVariable,
),
):
graph_outputs_key = self.add_graph_output(value)
if isinstance(value, NumpyNdarrayVariable):
self.load_import_from(utils.__name__, "to_numpy_helper")
self.load_graph_output(graph_outputs[graph_outputs_key].index)
if isinstance(value, NumpyNdarrayVariable):
output.extend(create_call_function(1, True))
elif isinstance(value, UnspecializedPythonVariable) and value.need_unwrap:
output.extend(
[self.create_load_attr("item")] + create_call_function(0, True)
)
elif isinstance(value, NNModuleVariable):
parts = value.module_key.split(".")
if parts[0] in self.code_options["co_varnames"]:
output.append(self.create_load(parts[0]))
parts = parts[1:]
else:
assert self.root is not None
output.append(self.create_load_output(self.root))
for part in parts:
output.append(self.create_load_attr(part))
else:
self.uses[value] += 1
try:
self.call_reconstruct(value)
except NotImplementedError:
unimplemented(f"reconstruct: {value}")
if allow_cache and value in self.tempvars:
self._output.append(create_dup_top())
self.add_cache(value)
self.top_of_stack = value
def add_graph_output(self, value):
graph_outputs_key = id(value.as_proxy())
if graph_outputs_key not in self.graph_outputs:
self.graph_outputs[graph_outputs_key] = GraphOutputEntry(
len(self.graph_outputs), value
)
return graph_outputs_key
def load_graph_output(self, index):
output = self._output
output.append(self.create_load(self.graph_output_var))
output.append(self._create_load_const(index))
output.append(create_instruction("BINARY_SUBSCR"))
def add_cache(self, value):
var = self.new_var()
self.tempvars[value] = var
if value.mutable_local:
self.tempvars[value.mutable_local] = var
self._output.append(self.create_store(var))
def foreach(self, items):
for i in items:
self(i)
def setup_globally_cached(self, name, value, push_null):
"""Store value in a new global"""
name = re.sub(r"[^a-zA-Z0-9_]+", "_", name)
f_globals = self.tx.f_globals
if name in f_globals:
assert id(f_globals[name]) == id(value)
else:
f_globals[name] = value
return [self.create_load_global(name, push_null, add=True)]
def clear_tos(self):
self.top_of_stack = None
def append_output(self, inst):
assert isinstance(inst, Instruction)
self._output.append(inst)
self.clear_tos()
def extend_output(self, insts):
assert all(isinstance(x, Instruction) for x in insts)
self._output.extend(insts)
self.clear_tos()
def get_instructions(self) -> List[Instruction]:
return self._output
def create_load(self, name) -> Instruction:
if name in self.cell_and_freevars():
return create_instruction("LOAD_DEREF", argval=name)
assert name in self.code_options["co_varnames"], f"{name} missing"
return create_instruction("LOAD_FAST", argval=name)
def create_load_closure(self, name) -> Instruction:
assert name in self.cell_and_freevars()
return create_instruction("LOAD_CLOSURE", argval=name)
def create_store(self, name) -> Instruction:
if name in self.cell_and_freevars():
return create_instruction("STORE_DEREF", argval=name)
assert name in self.code_options["co_varnames"]
return create_instruction("STORE_FAST", argval=name)
def create_load_global(self, name, push_null, add=False) -> Instruction:
if add:
self.tx.output.update_co_names(name)
assert name in self.code_options["co_names"], f"{name} not in co_names"
return create_load_global(name, push_null)
def create_load_const(self, value) -> Instruction:
assert is_safe_constant(value), f"unsafe constant {value}"
return self._create_load_const(value)
def _create_load_const(self, value) -> Instruction:
return create_instruction("LOAD_CONST", argval=value)
create_load_output = _create_load_const
def create_load_method(self, name):
self.tx.output.update_co_names(name)
return create_instruction("LOAD_METHOD", argval=name)
def create_load_attr(self, name) -> Instruction:
if name not in self.code_options["co_names"]:
self.code_options["co_names"] += (name,)
return create_instruction("LOAD_ATTR", argval=name)
def load_attr(self, name):
self.append_output(self.create_load_attr(name))
def create_load_attrs(self, names):
return [self.create_load_attr(name) for name in names.split(".")]
def create_store_attr(self, name) -> Instruction:
if name not in self.code_options["co_names"]:
self.code_options["co_names"] += (name,)
return create_instruction("STORE_ATTR", argval=name)
def store_attr(self, name):
self.append_output(self.create_store_attr(name))
def load_function_name(self, fn_name, push_null, num_on_stack=0):
"""Load the global fn_name on the stack num_on_stack down"""
output = []
if push_null and sys.version_info >= (3, 11):
output.extend(
[create_instruction("PUSH_NULL"), *self.rot_n(num_on_stack + 1)]
)
output.extend(
[
self.create_load_global(fn_name, False, add=True),
*self.rot_n(num_on_stack + 1),
]
)
return output
def rot_n(self, n):
try:
return create_rot_n(n)
except AttributeError:
# desired rotate bytecode doesn't exist, generate equivalent bytecode
return [
create_instruction("BUILD_TUPLE", arg=n),
self._create_load_const(rot_n_helper(n)),
*create_rot_n(2),
create_instruction("CALL_FUNCTION_EX", arg=0),
create_instruction("UNPACK_SEQUENCE", arg=n),
]
def pop_null(self):
# POP_TOP doesn't work for null, so we pop nulls by pushing in a
# nop function, calling it (which consumes the null), and popping the result.
assert sys.version_info >= (3, 11)
return [
self._create_load_const(lambda: None),
*create_call_function(0, False),
create_instruction("POP_TOP"),
]
def call_function(self, nargs: int, push_null: bool):
self.extend_output(create_call_function(nargs, push_null=push_null))
def dup_top(self):
self.append_output(create_dup_top())
def store(self, varname):
self.append_output(self.create_store(varname))
def make_function_with_closure(
self, fn_name: str, code: types.CodeType, push_null: bool, num_on_stack=0
):
freevars = code.co_freevars
assert freevars
output = self._output
if sys.version_info >= (3, 11) and push_null:
output.append(create_instruction("PUSH_NULL"))
output.extend(self.rot_n(num_on_stack + 1))
for var in freevars:
assert var in self.cell_and_freevars()
output.append(create_instruction("LOAD_CLOSURE", argval=var))
output.append(create_instruction("BUILD_TUPLE", arg=len(freevars)))
output.append(self.create_load_const(code))
if sys.version_info < (3, 11):
output.append(self.create_load_const(fn_name))
output.append(create_instruction("MAKE_FUNCTION", arg=0x08))
output.extend(self.rot_n(num_on_stack + 1))
self.clear_tos()
def create_load_python_module(self, mod, push_null) -> Instruction:
"""
Generate a LOAD_GLOBAL instruction to fetch a given python module.
"""
output = self.tx.output
global_scope = output.global_scope
name = re.sub(r"^.*[.]", "", mod.__name__)
if global_scope.get(name, None) is mod:
return self.create_load_global(name, push_null, add=True)
prefix = f"___module_{name}"
global_name = self.tx.output.install_global_by_id(prefix, mod)
return self.create_load_global(global_name, push_null, add=True)
def make_call_generated_code(self, fn_name: str) -> None:
"""Call the generated code function stored in fn_name"""
self.extend_output(self.load_function_name(fn_name, True))
graphargs = self.tx.output.graphargs
for arg in graphargs:
if arg.is_unspecialized:
self.extend_output(
[
self.create_load_python_module(torch, True),
self.create_load_attr("as_tensor"),
]
)
self.call_reconstruct(arg)
self.extend_output(create_call_function(1, False))
else:
self.call_reconstruct(arg)
self.extend_output(create_call_function(len(graphargs), False))
def load_import_from(self, module_name, object_name) -> None:
self(AttrSource(self.tx.import_source(module_name), object_name))
def create_call_function_kw(self, nargs, kw_names, push_null) -> List[Instruction]:
if sys.version_info >= (3, 11):
output = create_call_function(nargs, push_null)
assert output[-2].opname == "PRECALL"
kw_names_inst = create_instruction("KW_NAMES", argval=kw_names)
output.insert(-2, kw_names_inst)
return output
return [
self.create_load_const(kw_names),
create_instruction("CALL_FUNCTION_KW", arg=nargs),
]