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# This file establishes the public comptime interface to Dynamo.
# This allows Dynamo users to execute arbitrary Python code while
# Dynamo is symbolically evaluating their original programs.
#
# The goal of the public API is to give users rope, without actually
# leaking private implementation details of Dynamo.
import builtins
import dis
import traceback
from typing import Optional, Union
import torch
from torch.fx.experimental.symbolic_shapes import free_symbols
from .exc import unimplemented
from .variables.constant import ConstantVariable
from .variables.tensor import SymNodeVariable
class ComptimeVar:
"""
A ComptimeVar represents a Python value, at some particular point
in time, in the Python code we are symbolically evaluating with
torchdynamo. This must be distinguished from a runtime value, as
at compile-time there are some properties of the variable we
do not know (for example, if the ComptimeVar represents a Tensor,
we only know metadata about the tensor; we do NOT know what the
actual data in the Tensor is.)
"""
def __init__(self, v):
self.__variable = v
def as_proxy(self):
"""
Returns an fx.Proxy (or tuple/list of fx.Proxy) representing
this variable in the FX graph we are assembling to pass
to the user compiler.
This method only works for variables we actually track in
the FX graph, aka Tensors (and ints, if you are compiling
with dynamic shapes). In particular, if you have a list
or tuple of tensors, you will get a list/tuple of proxies
(not a single proxy representing the entire list/tuple).
"""
return self.__variable.as_proxy()
def is_proxy(self):
"""
Returns True if as_proxy() would succeed.
"""
return self.__variable.is_proxy()
def as_fake(self):
"""
Returns a "fake" value (either a FakeTensor or a SymInt)
representing the variable in question. This only works
for variables that denote Tensor or int. You can use
this to query metadata; e.g., v.as_fake().size(0) will
tell you the compile-time known size of the tensor.
WARNING: Do NOT mutate the returned tensor.
"""
return self.__variable.as_proxy().node.meta["example_value"]
def size(self, dim: Optional[int] = None) -> Union[int, torch.SymInt]:
"""
Returns the size of the tensor (if dim is None) or the size
at the dimension dim. The returned size may be a SymInt.
"""
return self.as_fake().size(dim)
def python_type(self):
"""
Returns what type(v) would have returned for the variable
at compile time.
"""
return self.__variable.python_type()
def as_python_constant(self):
"""
Returns the Python value this variable would have, but only if it is
completely known at compile-time (e.g., it is constant).
WARNING: Do NOT mutate the returned constant. The returned constant
may or may not correspond to the actual value this variable may take
on at runtime; for example, if the variable in question is a constant
list, we may return a copy of that list.
"""
return self.__variable.as_python_constant()
def is_python_constant(self):
"""
Returns True if as_python_constant would succeed.
"""
return self.__variable.is_python_constant()
def is_dynamic(self):
if isinstance(self.__variable, SymNodeVariable):
fs = free_symbols(self.__variable.sym_num)
return bool(fs)
return False
def force_static(self):
"""
Forces that a value is static, inducing a guard on its specific value
"""
if isinstance(self.__variable, SymNodeVariable):
self.__variable.evaluate_expr()
elif isinstance(self.__variable, ConstantVariable):
# TODO: Maybe complain if this isn't a int/bool/float variable
pass
else:
raise AssertionError(
f"cannot force {self.__variable} ({type(self.__variable)}) static"
)
def _i_will_not_complain_if_bc_breaks_VariableTracker(self):
"""
Returns the internal data structure VariableTracker that Dynamo uses
to represent variables at compile time. There are no BC guarantees on
this API and WE RESERVE THE RIGHT TO BREAK YOUR CODE if you rely on
it.
"""
return self.__variable
def __repr__(self):
# TODO: The default repr is pretty bad, do better
return repr(self.__variable)
# TODO: API for adding a custom guard
class ComptimeContext:
"""
This context class provides access to a public API for Dynamo's internals.
If there is something here you would find useful that is missing, please
file a feature request at https://github.com/pytorch/pytorch/
"""
def __init__(self, tx):
self.__tx = tx
def get_local(self, name: str, *, stacklevel=0) -> ComptimeVar:
"""
Retrieve the compile-time known information about a local.
"""
tx = self.__get_tx(stacklevel)
return ComptimeVar(tx.symbolic_locals[name])
def graph_break(self, msg="ComptimeContext.graph_break"):
"""
Manually trigger a graph break
"""
unimplemented(msg)
def graph(self):
"""
Retrieve the partially constructed FX graph that would be
passed to the user compiler after compilation.
"""
return self.__tx.output.graph
def assert_static(self, val):
"""
Asserts that the int is static (and not dynamic, per dynamic shapes)
"""
assert (
not val.is_dynamic()
), "expected static but got dynamic (run with TORCH_LOGS=dynamic for more info)"
def print_graph(self, *, verbose=True, file=None):
"""
Print the partially constructed FX graph that would be passed
to the user compiler after compilation.
"""
print(
self.__tx.output.graph.python_code("self", verbose=verbose).src, file=file
)
def parent(self):
return ComptimeContext(self.__tx.parent)
def __get_tx(self, stacklevel):
tx = self.__tx
for _ in range(stacklevel):
tx = tx.parent
return tx
def print_disas(self, *, file=None, stacklevel=0):
"""
Print the current series of opcodes being executed (not including
parent frames), including where you are in the particular opcode
stream.
"""
tx = self.__get_tx(stacklevel)
print(
dis.Bytecode(
tx.f_code,
current_offset=tx.instructions[tx.instruction_pointer].offset,
).dis(),
file=file,
)
def print_value_stack(self, *, file=None, stacklevel=0):
"""
Print the current Python value stack. Note that this is NOT the same
as the traceback; use print_bt() to print that. Note that at
stacklevel=0, this will typically be empty, as comptime cannot
currently be used in an expression context where there would be
intermediates on the stack. If you would find this useful, please
file a bug at https://github.com/pytorch/pytorch/
NB: Stack grows downwards in our print
"""
# TODO: improve printing
tx = self.__get_tx(stacklevel)
for s in tx.stack:
print(f"- {s}", file=file)
def print_locals(self, *, file=None, stacklevel=0):
"""
Print all of the locals available in the current context.
By default this view is very limited; you can get more information
about any individual local using get_local().
"""
# TODO: improve by improving the VariableTracker printing
tx = self.__get_tx(stacklevel)
for k, v in tx.symbolic_locals.items():
print(f"{k} = {v}", file=file)
def print_bt(self, *, file=None, stacklevel=0):
"""
Print the user code backtrace, starting at the beginning of the
frame Dynamo started evaluating. Note that this MAY NOT go all
the way to the torch.compile invocation, as we may have done
a graph break and are compiling an intermediate frame as the
starting point. If you think the other behavior would be better,
file a bug at https://github.com/pytorch/pytorch/
"""
stack = []
tx = self.__get_tx(stacklevel)
while tx is not None:
stack.append(tx.frame_summary())
tx = getattr(tx, "parent", None)
print(
"".join(traceback.StackSummary.from_list(reversed(stack)).format()),
file=file,
)
def print_guards(self, *, file=None):
"""
Print the currently installed guards for the Dynamo context.
This does NOT include guards associated with variables that
may or may not be installed in the future if those variables
are used.
"""
# TODO: improve print format, current guard format is extremely
# verbose
print(
"\n".join(f"{repr(guard)}" for guard in sorted(self.__tx.output.guards)),
file=file,
)
def _i_will_not_complain_if_bc_breaks_InstructionTranslator(self):
"""
Returns the internal data structure InstructionTranslator that Dynamo
uses to track state of symbolic evaluation. There are no BC
guarantees on this API and WE RESERVE THE RIGHT TO BREAK YOUR CODE if
you rely on it.
"""
return self.__tx
class _Comptime:
@staticmethod
def __call__(fn):
"""fn gets called at compile time in TorchDynamo, does nothing otherwise"""
return
# Convenience wrappers that are more compact to use
@staticmethod
def graph_break():
comptime(lambda ctx: ctx.graph_break())
@staticmethod
def print_graph():
comptime(lambda ctx: ctx.print_graph())
@staticmethod
def print_disas(*, stacklevel=0):
comptime(
lambda ctx: ctx.print_disas(
stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1
)
)
@staticmethod
def print_value_stack(*, stacklevel=0):
comptime(
lambda ctx: ctx.print_value_stack(
stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1
)
)
# This is a more useful variant of print_value_stack that can be used
# in an expression context; e.g., x + print_value_stack_and_return(y + z),
# you will see x on the stack prior to the addition operation
@staticmethod
def print_value_stack_and_return(e, *, stacklevel=0):
comptime(
lambda ctx: ctx.print_value_stack(
stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1
)
)
return e
@staticmethod
def print_locals(*, stacklevel=0):
comptime(
lambda ctx: ctx.print_locals(
stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1
)
)
@staticmethod
def print_bt(*, stacklevel=0):
comptime(
lambda ctx: ctx.print_bt(
stacklevel=ctx.get_local("stacklevel").as_python_constant() + 1
)
)
@staticmethod
def print_guards():
comptime(lambda ctx: ctx.print_guards())
@staticmethod
def assert_static(val):
comptime(lambda ctx: ctx.assert_static(ctx.get_local("val")))
@staticmethod
def force_static(val):
comptime(lambda ctx: ctx.get_local("val").force_static())
@staticmethod
def breakpoint():
"""
Like pdb breakpoint(), but drop into pdb whenever this line
of code is compiled by dynamo. Use it by putting
this in your model code::
from torch._dynamo.comptime import comptime
comptime.breakpoint()
And then, inside pdb, you can access 'ctx' to query things
about the compilation context::
(Pdb) !ctx.print_bt()
(Pdb) !ctx.print_locals()
(Pdb) p ctx.get_local("attention").as_fake()
"""
def inner(inner_ctx):
ctx = inner_ctx.parent()
builtins.breakpoint()
comptime(inner)
comptime = _Comptime()
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