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# mypy: allow-untyped-defs
import warnings
import weakref
from typing import Callable, Optional, Set
import torch
from torch.autograd.graph import register_multi_grad_hook
from torch.nn.modules.module import (
register_module_forward_hook,
register_module_forward_pre_hook,
)
from torch.utils._pytree import tree_flatten
__all__ = ["ModTracker"]
class ModTracker:
"""
``ModTracker`` is a context manager that tracks the nn.Module hierarchy during execution
so that other system can query which Module is currently being executed (or its backward is being
executed).
You can access the ``parents`` attribute on this context manager to get the set of all the
Modules currently being executed via their fqn (fully qualified name, also used as the key within
the state_dict).
You can access the ``is_bw`` attribute to know if you are currently running in backward or not.
Note that ``parents`` is never empty and always contains the "Global" key. The ``is_bw`` flag
will remain ``True`` after the forward until another Module is executed. If you need it to be
more accurate, please submit an issue requesting this. Adding a map from fqn to the module instance
is possible but not done yet, please submit an issue requesting this if you need it.
Example usage
.. code-block:: python
mod = torch.nn.Linear(2, 2)
with ModTracker() as tracker:
# Access anything during the forward pass
def my_linear(m1, m2, bias):
print(f"Current modules: {tracker.parents}")
return torch.mm(m1, m2.t()) + bias
torch.nn.functional.linear = my_linear
mod(torch.rand(2, 2))
"""
parents: Set[str]
"""
A Set containing the fqn for each module currently running their forward
"""
def __init__(self):
self.parents = {"Global"}
self._active_module_cnt = {}
self._known_modules: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
self._seen_modules: weakref.WeakSet = weakref.WeakSet()
self._has_callback = False
self._user_pre_fw_hook = None
self._user_post_fw_hook = None
self._user_pre_bw_hook = None
self._user_post_bw_hook = None
def _maybe_set_engine_callback(self):
# This assumes no concurrent calls to backward
if self._has_callback:
return
def callback():
self.parents = {"Global"}
self._has_callback = False
torch.autograd.Variable._execution_engine.queue_callback(callback)
self._has_callback = True
@property
def is_bw(self):
"""
A boolean marking if this is currently running during the backward pass or not
"""
return torch._C._current_graph_task_id() != -1
def get_known_fqn(self, mod):
"""
Return the fqn for the given module if it is known to the ``ModTracker``, otherwise ``None``.
"""
return self._known_modules.get(mod, None)
def register_user_hooks(
self,
pre_fw_hook: Optional[Callable] = None,
post_fw_hook: Optional[Callable] = None,
pre_bw_hook: Optional[Callable] = None,
post_bw_hook: Optional[Callable] = None,
):
"""
Registers user-specified hooks to be called before/after the forward/backward pass for each
module tracked by the ``ModTracker``. One or more can be ``None``.
Args:
pre_fw_hook (Callable, optional): A hook to be called before the forward pass for the
module. It should have the following signature:
pre_fw_hook (module, input) -> None
post_fw_hook (Callable, optional): A hook to be called after the forward pass for the
module. It should have the following signature:
post_fw_hook (module, input, output) -> None
pre_bw_hook (Callable, optional): A multi-grad hook to be called on all the outputs of
the module that require gradients. It should have the following signature:
pre_bw_hook (module, grad_output) -> None
post_bw_hook (Callable, optional): A multi-grad hook to be called on all the inputs of
the module that require gradients. It should have the following signature:
post_bw_hook (module, grad_input) -> None
Raises:
AssertionError: If a new hook is provided when one is already registered.
Note:
If the module is not alive during the backward pass, the pre_bw_hook and post_bw_hook will
will receive None as the module argument.
The module fqn will be present in the ``parents`` attribute when each of the hooks is called.
Hooks are intended to be used as markers only not to modify the inputs/outputs.
"""
def set_hook(hook, user_hook, hook_name):
if hook is not None and user_hook is not None:
raise AssertionError(
f"Only one {hook_name} can be registered at a time"
f" Clear the existing hook by calling ``clear_user_hooks`` before registering a new one"
)
return hook
self._user_pre_fw_hook = set_hook(
pre_fw_hook, self._user_pre_fw_hook, "pre_fw_hook"
)
self._user_post_fw_hook = set_hook(
post_fw_hook, self._user_post_fw_hook, "post_fw_hook"
)
self._user_pre_bw_hook = set_hook(
pre_bw_hook, self._user_pre_bw_hook, "pre_bw_hook"
)
self._user_post_bw_hook = set_hook(
post_bw_hook, self._user_post_bw_hook, "post_bw_hook"
)
def clear_user_hooks(self):
"""
Clears the user specified hooks registered with ``register_user_hooks``
"""
self._user_pre_fw_hook = None
self._user_post_fw_hook = None
self._user_pre_bw_hook = None
self._user_post_bw_hook = None
def _get_mod_name(self, mod):
if mod not in self._known_modules:
self._known_modules[mod] = type(mod).__name__
mod_name = self._known_modules[mod]
if mod not in self._seen_modules:
for name, submod in mod.named_children():
self._known_modules[submod] = f"{mod_name}.{name}"
self._get_mod_name(submod)
self._seen_modules.add(mod)
return mod_name
def _get_append_fn(self, w_mod, name, is_bw):
def fn(*args):
if is_bw:
self._maybe_set_engine_callback()
if name in self.parents and not self.is_bw:
def custom_formatwarning(msg, category, filename, lineno, line=None):
return f"{filename}:{lineno}: {category.__name__}: {msg} \n"
warnings.formatwarning = custom_formatwarning
warnings.warn(
"The module hierarchy tracking maybe be messed up."
" Please file a bug to PyTorch, if it is the case."
)
if name not in self.parents:
self._active_module_cnt[name] = 1
self.parents.add(name)
else:
self._active_module_cnt[name] += 1
if self._user_pre_bw_hook is not None and is_bw:
self._user_pre_bw_hook(w_mod(), args)
return fn
def _get_pop_fn(self, w_mod, name, is_bw):
def fn(*args):
if self._user_post_bw_hook is not None and is_bw:
self._user_post_bw_hook(w_mod(), args)
if name in self.parents:
self._active_module_cnt[name] -= 1
if self._active_module_cnt[name] == 0:
self.parents.remove(name)
elif not self.is_bw:
# Due to some input/output not requiring gradients, we cannot enforce
# proper nesting in backward
raise RuntimeError(
"The Module hierarchy tracking is wrong. Report a bug to PyTorch"
)
return fn
def _fw_pre_hook(self, mod, input):
name = self._get_mod_name(mod)
w_mod = weakref.ref(mod)
self._get_append_fn(w_mod, name, False)()
if self._user_pre_fw_hook is not None:
self._user_pre_fw_hook(mod, input)
args, _ = tree_flatten(input)
tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
if not self.is_bw and tensors:
register_multi_grad_hook(tensors, self._get_pop_fn(w_mod, name, True))
def _fw_post_hook(self, mod, input, output):
name = self._get_mod_name(mod)
w_mod = weakref.ref(mod)
if self._user_post_fw_hook is not None:
self._user_post_fw_hook(mod, input, output)
self._get_pop_fn(w_mod, name, False)()
args, _ = tree_flatten(output)
tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
if not self.is_bw and tensors:
register_multi_grad_hook(tensors, self._get_append_fn(w_mod, name, True))
def __enter__(self):
self._fw_pre_handle = register_module_forward_pre_hook(self._fw_pre_hook)
self._fw_post_handle = register_module_forward_hook(
self._fw_post_hook, always_call=True
)
return self
def __exit__(self, *args):
self._fw_pre_handle.remove()
self._fw_post_handle.remove()
|
