Source code for torch.compiler
# mypy: allow-untyped-defs
from typing import Any, Callable, List, TypeVar
import torch
__all__ = [
"compile",
"assume_constant_result",
"reset",
"allow_in_graph",
"substitute_in_graph",
"list_backends",
"disable",
"set_stance",
"cudagraph_mark_step_begin",
"wrap_numpy",
"is_compiling",
"is_dynamo_compiling",
"is_exporting",
]
_F = TypeVar("_F", bound=Callable[..., Any])
[docs]def compile(*args, **kwargs):
"""
See :func:`torch.compile` for details on the arguments for this function.
"""
return torch.compile(*args, **kwargs)
[docs]def reset() -> None:
"""
This function clears all compilation caches and restores the system to its initial state.
It is recommended to call this function, especially after using operations like `torch.compile(...)`
to ensure a clean state before another unrelated compilation
"""
import torch._dynamo
torch._dynamo.reset()
[docs]def allow_in_graph(fn):
"""
Tells the compiler frontend (Dynamo) to skip symbolic introspection of the function
and instead directly write it to the graph when encountered.
If you are using :func:`torch.compile` (with backend="inductor" (the default)), or
:func:`torch.export.export`, and trying to black-box a Python function throughout
all tracing, do not use this API.
Instead, please create a custom operator (see `PyTorch Custom Operators Landing Page
<https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html>`_)
.. warning::
If you're a typical torch.compile user (e.g. you're applying torch.compile to
a model to make it run faster), you probably don't want to use this function.
:func:`allow_in_graph` is a footgun because it skips the compiler frontend
(Dynamo) that is responsible for doing safety checks (graph breaks, handling
closures, etc). Incorrect usage will lead to difficult-to-debug silent
incorrectness issues.
Given a Python function with no allow_in_graph decorator, regular execution
of torch.compile traces through the function. :func:`allow_in_graph` changes
it so that the frontend does not trace inside the function, but the compiler
backend still traces through it. Compare this to custom operators, which
treats a function as a black box throughout the torch.compile stack. The following
table compares these mechanisms.
+------------------------+-----------------------+--------------------------------+
| Mechanism | Frontend (Dynamo) | Backend (AOTAutograd+Inductor) |
+========================+=======================+================================+
| no decorator | trace inside | trace inside |
+------------------------+-----------------------+--------------------------------+
| allow_in_graph | opaque callable | trace inside |
+------------------------+-----------------------+--------------------------------+
| custom op | opaque callable | opaque callable |
+------------------------+-----------------------+--------------------------------+
One common use case for :func:`allow_in_graph()` is as an escape hatch for the compiler
frontend: if you know the function works w.r.t. to the downstream components of the
compilation stack (AOTAutograd and Inductor) but there is a Dynamo bug that prevents it from
symbolically introspecting the function properly (or if your code is in C/C++ and
therefore cannot be introspected with Dynamo), then one can decorate said function
with :func:`allow_in_graph` to bypass Dynamo.
We require that ``fn`` adhere to the following restrictions. Failure to adhere
results in undefined behavior:
- The inputs to ``fn`` must be Proxy-able types in the FX graph. Valid types include:
Tensor/int/bool/float/None/List[Tensor?]/List[int?]/List[float?]
Tuple[Tensor?, ...]/Tuple[int?, ...]/Tuple[float?, ...]/torch.dtype/torch.device
- The outputs to ``fn`` must be Proxy-able types in the FX graph (see previous bullet)
- all Tensors used inside of ``fn`` must be passed directly as inputs to ``fn``
(as opposed to being captured variables).
Args:
fn: A callable representing the function to be included in the graph.
If ``fn`` is a list or tuple of callables it recursively applies
:func:`allow_in_graph()` to each function and returns a new list or
tuple containing the modified functions.
Example::
torch.compiler.allow_in_graph(my_custom_function)
@torch.compile(...)
def fn(x):
x = torch.add(x, 1)
x = my_custom_function(x)
x = torch.add(x, 1)
return x
fn(...)
Will capture a single graph containing ``my_custom_function()``.
"""
import torch._dynamo
return torch._dynamo.allow_in_graph(fn)
[docs]def substitute_in_graph(
original_fn: _F,
*,
can_constant_fold_through: bool = False,
skip_signature_check: bool = False,
) -> Callable[[_F], _F]:
"""
Register a polyfill handler for a function, usually a C function from the C extension, to be
used in place of the original function when inlining the original function in the graph.
.. note::
The polyfill handler is only used when inlining the original function. It is not used when
the original function is called directly. In the eager mode, the decorated function calls
the performant C function rather than the polyfill handler.
The polyfill handler is a function that will be called in place of the original function when
inlining the original function. The polyfill handler should have the same signature and the same
behavior as the original function.
Args:
original_fn (callable): The original function, usually a C function, to register a polyfill
handler for.
can_constant_fold_through (bool, optional): Whether the polyfill handler can be constant
folded through. That is, if the polyfill handler is a pure function and its arguments
are constant, the result of the polyfill handler can be constant folded during the
compilation. Defaults to ``False``.
skip_signature_check (bool, optional): Whether to skip the signature check between the
original function and the polyfill handler. Defaults to ``False``.
Returns:
A decorator that registers the polyfill handler for the original function.
Example::
>>> import operator
>>> operator.indexOf([1, 2, 3, 4, 5], 3)
2
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
... # xdoctest: +SKIP("Long tracebacks")
Traceback (most recent call last):
...
torch._dynamo.exc.Unsupported: ...
>>> @torch.compiler.substitute_in_graph(operator.indexOf)
... def indexOf(a, b, /):
... for i, item in enumerate(a):
... if item is b or item == b:
... return i
... raise ValueError("sequence.index(x): x not in sequence")
>>>
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
2
"""
import torch._dynamo
return torch._dynamo.substitute_in_graph(
original_fn,
can_constant_fold_through=can_constant_fold_through,
skip_signature_check=skip_signature_check,
)
[docs]def list_backends(exclude_tags=("debug", "experimental")) -> List[str]:
"""
Return valid strings that can be passed to `torch.compile(..., backend="name")`.
Args:
exclude_tags(optional): A tuple of strings representing tags to exclude.
"""
import torch._dynamo
return torch._dynamo.list_backends(exclude_tags)
[docs]def assume_constant_result(fn):
"""
This function is used to mark a function `fn` as having a constant result.
This allows the compiler to optimize away your function
Returns The same function `fn`
Args:
fn: The function to be marked as having a constant result.
.. warning::
`assume_constant_result` can if invalid cause safety and soundness issues, :func:`torch.compile`
will not attempt to validate whether the constant assumption is true or not
"""
import torch._dynamo
return torch._dynamo.assume_constant_result(fn)
[docs]def disable(fn=None, recursive=True):
"""
This function provides a decorator to disable compilation on a function
It also provides the option of recursively disabling called functions
Args:
fn (optional): The function to disable
recursive (optional): A boolean value indicating whether the disabling should be recursive.
"""
import torch._dynamo
return torch._dynamo.disable(fn, recursive)
[docs]def set_stance(
stance: str = "default", *, skip_guard_eval_unsafe=False, force_backend=None
):
"""
Set the current stance of the compiler.
Can be used as a function, context manager, or decorator.
Do not use this function inside a `torch.compile` region - an error will be raised otherwise.
.. code-block:: python
@torch.compile
def foo(x):
...
@torch.compiler.set_stance("force_eager")
def bar():
# will not be compiled
foo(...)
bar()
with torch.compiler.set_stance("force_eager"):
# will also not be compiled
foo(...)
torch.compiler.set_stance("force_eager")
# will also not be compiled
foo(...)
torch.compiler.set_stance("default")
# will be compiled
foo(...)
Args:
stance: The stance to set the compiler to. Valid values are:
- "default": The default stance, used for normal compilation.
- "force_eager": Ignore all `torch.compile` directives.
- "eager_on_recompile": Run code eagerly when a recompile is necessary.
If there is cached compiled code valid for the input, it will still be used.
- "fail_on_recompile": Raise an error when recompiling a function.
skip_guard_eval_unsafe: A flag to run only differentiating guards.
CAUTION - This flag is unsafe and should only be used if your setup
meets the following conditions.
torch.compile uses a guard system to support recompilations and
choose which compiled artifact to run at runtime. These guards,
though efficient, add some overhead, which may impact performance in
scenarios where you need to optimize for minimal guard processing
time. This API enables you to disable guard evaluation, assuming
that you have warmed up the compiled model with a sufficient variety
of inputs. This assumption means that, after the warmup phase, no
further recompilations will be necessary. If this assumption fails,
there is a risk of silently producing incorrect results (hence the
term "unsafe" in the API name).
force_backend: If `stance` is "default", this argument can be used to force `torch.compile`
to use a specific backend. Otherwise, an error is raised.
"""
import torch._dynamo
return torch._dynamo.set_stance(
stance,
skip_guard_eval_unsafe=skip_guard_eval_unsafe,
force_backend=force_backend,
)
# forbid in graph
set_stance._dynamo_forbidden = True # type: ignore[attr-defined]
[docs]def cudagraph_mark_step_begin():
"""
Indicates that a new iteration of inference or training is about to begin.
CUDA Graphs will free tensors of a prior iteration. A new iteration is started on each invocation of
torch.compile, so long as there is not a pending backward that has not been called.
If that heuristic is wrong, such as in the following example, manually mark it with this api.
.. code-block:: python
@torch.compile(mode="reduce-overhead")
def rand_foo():
return torch.rand([4], device="cuda")
for _ in range(5):
torch.compiler.cudagraph_mark_step_begin()
rand_foo() + rand_foo()
For more details, see `torch.compiler_cudagraph_trees <https://pytorch.org/docs/main/torch.compiler_cudagraph_trees.html>`__
"""
from torch._inductor import cudagraph_trees
cudagraph_trees.mark_step_begin()
def wrap_numpy(fn):
r"""Decorator that turns a function from ``np.ndarray``s to ``np.ndarray``s into a function
from ``torch.Tensor``s to ``torch.Tensor``s.
It is designed to be used with :func:`torch.compile` with ``fullgraph=True``. It allows to
compile a NumPy function as if it were a PyTorch function. This allows you to run NumPy code
on CUDA or compute its gradients.
.. note::
This decorator does not work without :func:`torch.compile`.
Example::
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
>>> # Compile a NumPy function as a Tensor -> Tensor function
>>> @torch.compile(fullgraph=True)
>>> @torch.compiler.wrap_numpy
>>> def fn(a: np.ndarray):
>>> return np.sum(a * a)
>>> # Execute the NumPy function using Tensors on CUDA and compute the gradients
>>> x = torch.arange(6, dtype=torch.float32, device="cuda", requires_grad=True)
>>> out = fn(x)
>>> out.backward()
>>> print(x.grad)
tensor([ 0., 2., 4., 6., 8., 10.], device='cuda:0')
"""
from torch._dynamo.external_utils import wrap_numpy as wrap
return wrap(fn)
_is_compiling_flag: bool = False
_is_exporting_flag: bool = False
[docs]def is_compiling() -> bool:
"""
Indicates whether a graph is executed/traced as part of torch.compile() or torch.export().
Note that there are 2 other related flags that should deprecated eventually:
* torch._dynamo.external_utils.is_compiling()
* torch._utils.is_compiling()
Example::
>>> def forward(self, x):
>>> if not torch.compiler.is_compiling():
>>> pass # ...logic that is not needed in a compiled/traced graph...
>>>
>>> # ...rest of the function...
"""
if torch.jit.is_scripting():
return False
else:
return _is_compiling_flag
[docs]def is_dynamo_compiling() -> bool:
"""
Indicates whether a graph is traced via TorchDynamo.
It's stricter than is_compiling() flag, as it would only be set to True when
TorchDynamo is used.
Example::
>>> def forward(self, x):
>>> if not torch.compiler.is_dynamo_compiling():
>>> pass # ...logic that is not needed in a TorchDynamo-traced graph...
>>>
>>> # ...rest of the function...
"""
return False
[docs]def is_exporting() -> bool:
"""
Indicated whether we're under exporting.
It's stricter than is_compiling() flag, as it would only be set to True when
torch.export is used.
Example::
>>> def forward(self, x):
>>> if not torch.compiler.is_exporting():
>>> pass # ...logic that is not needed in export...
>>>
>>> # ...rest of the function...
"""
return _is_exporting_flag
