Package tensor_tracker

Utility for tracking activations and gradients at nn.Module outputs.

Use track() to start tracking a module & submodules. Then use the original module as usual. Your Tracker will be filled with a list of Stashes, containing copies of fwd/bwd tensors at (sub)module outputs. (Beware, this can consume a lot of memory.)

Usage (notebook):

with tensor_tracker.track(model) as tracker:
    model(inputs).backward()

print(list(tracker))
# => [Stash(name="0.linear", type=nn.Linear, grad=False, value=tensor(...)),
#     ...]

display(tracker.to_frame())  # requires 'pandas'

Advanced usage:

• Filter modules based on name: track(include="<regex>", exclude="<regex>")

• Pre-transform tracked tensors to save memory: track(stash_value=lambda t: t.std().detach().cpu())

• Customise tracked state: track(stash=lambda event: ...)

• Manually register/unregister hooks: tracker = Tracker(); tracker.register(...); tracker.unregister()

See also: example of visualising transformer activations & gradients using UMAP.

# Copyright (c) 2023 Graphcore Ltd. All rights reserved.

from .core import *  # NOQA: F401 F403
from .core import __all__, __doc__  # NOQA: F401

Sub-modules

tensor_tracker.core

Utility for tracking activations and gradients at nn.Module outputs …

Functions

def default_stash(event: Event, stash_value: Callable[[torch.Tensor], Any]) ‑> Stash

Expand source code

def default_stash(event: Event, stash_value: StashValueFn) -> Stash:
    return Stash(
        event.name, event.type, event.grad, rmap_tensor(event.value, stash_value)
    )

def default_stash_value(tensor: torch.Tensor) ‑> torch.Tensor

Expand source code

def default_stash_value(tensor: Tensor) -> Tensor:
    return tensor.detach().cpu().clone()

def get_stash_fn(stash_value: Optional[Callable[[torch.Tensor], Any]] = None, stash: Optional[Callable[[Event], Stash]] = None) ‑> Callable[[Event], Stash]

Expand source code

def get_stash_fn(
    stash_value: Optional[StashValueFn] = None, stash: Optional[StashFn] = None
) -> StashFn:
    if stash_value and stash:
        raise ValueError("Cannot provide StashValueFn and StashFn to get_stash_fn()")
    if stash:
        return stash
    return partial(default_stash, stash_value=stash_value or default_stash_value)

def rmap_tensor(value: Any, fn: Callable[[torch.Tensor], Any]) ‑> Any

Expand source code

def rmap_tensor(value: Any, fn: Callable[[Tensor], Any]) -> Any:
    if isinstance(value, (tuple, list)):
        return type(value)(rmap_tensor(a, fn) for a in value)
    if isinstance(value, dict):
        return {rmap_tensor(k, fn): rmap_tensor(a, fn) for k, a in value.items()}
    if dataclasses.is_dataclass(value):
        return type(value)(**{k: rmap_tensor(v, fn) for k, v in value.__dict__.items()})
    if isinstance(value, Tensor):
        return fn(value)
    return value

def track(module: torch.nn.modules.module.Module, grad: bool = True, include: Union[ForwardRef(None), Pattern[str], str] = None, exclude: Union[ForwardRef(None), Pattern[str], str] = None, stash_value: Optional[Callable[[torch.Tensor], Any]] = None, stash: Optional[Callable[[Event], Stash]] = None) ‑> Tracker

Utility for tracking activations and gradients at nn.Module outputs.

Use track() to start tracking a module & submodules. Then use the original module as usual. Your Tracker will be filled with a list of Stashes, containing copies of fwd/bwd tensors at (sub)module outputs. (Beware, this can consume a lot of memory.)

Usage (notebook):

with tensor_tracker.track(model) as tracker:
    model(inputs).backward()

print(list(tracker))
# => [Stash(name="0.linear", type=nn.Linear, grad=False, value=tensor(...)),
#     ...]

display(tracker.to_frame())  # requires 'pandas'

Advanced usage:

• Filter modules based on name: track(include="<regex>", exclude="<regex>")

• Pre-transform tracked tensors to save memory: track(stash_value=lambda t: t.std().detach().cpu())

• Customise tracked state: track(stash=lambda event: ...)

• Manually register/unregister hooks: tracker = Tracker(); tracker.register(...); tracker.unregister()

See also: example of visualising transformer activations & gradients using UMAP.

Expand source code

def track(
    module: nn.Module,
    grad: bool = True,
    include: NamePattern = None,
    exclude: NamePattern = None,
    stash_value: Optional[StashValueFn] = None,
    stash: Optional[StashFn] = None,
) -> Tracker:
    tracker = Tracker(get_stash_fn(stash_value=stash_value, stash=stash))
    tracker.register_all(module, grad=grad, include=include, exclude=exclude)
    return tracker

Classes

class Event (name: str, type: Type[torch.nn.modules.module.Module], grad: bool, value: Any, args: Tuple[Any, ...], kwargs: Dict[str, Any])

Event(name: str, type: Type[torch.nn.modules.module.Module], grad: bool, value: Any, args: Tuple[Any, …], kwargs: Dict[str, Any])

Expand source code

@dataclass
class Event:
    name: str
    type: Type[nn.Module]
    grad: bool
    value: Any
    args: Tuple[Any, ...]
    kwargs: Dict[str, Any]

Class variables

var args : Tuple[Any, ...]

var grad : bool

var kwargs : Dict[str, Any]

var name : str

var type : Type[torch.nn.modules.module.Module]

var value : Any

class Stash (name: str, type: Type[torch.nn.modules.module.Module], grad: bool, value: Any)

Stash(name: str, type: Type[torch.nn.modules.module.Module], grad: bool, value: Any)

Expand source code

@dataclass
class Stash:
    name: str
    type: Type[nn.Module]
    grad: bool
    value: Any  # output(s) or grad_output(s)

    @property
    def first_value(self) -> Any:
        def _value(v: Any) -> Any:
            if isinstance(v, (tuple, list)) and len(v) >= 1:
                return _value(v[0])
            return v

        return _value(self.value)

Class variables

var grad : bool

var name : str

var type : Type[torch.nn.modules.module.Module]

var value : Any

Instance variables

var first_value : Any

Expand source code

@property
def first_value(self) -> Any:
    def _value(v: Any) -> Any:
        if isinstance(v, (tuple, list)) and len(v) >= 1:
            return _value(v[0])
        return v

    return _value(self.value)

class Tracker (stash: Callable[[Event], Stash])

Expand source code

class Tracker:
    def __init__(self, stash: StashFn):
        self.stashes: List[Stash] = []
        self._handles: List[torch.utils.hooks.RemovableHandle] = []
        self._stash = stash

    # Registration/tracking

    def __enter__(self) -> "Tracker":
        return self

    def __exit__(
        self,
        exc_type: Optional[Type[BaseException]],
        exc: Optional[BaseException],
        traceback: Optional[TracebackType],
    ) -> None:
        self.unregister()

    def clear(self) -> None:
        self.stashes.clear()

    def register(self, module: nn.Module, name: str = "", grad: bool = True) -> None:
        self._handles.append(
            module.register_forward_hook(
                partial(self._forward_hook, name=name), with_kwargs=True
            )
        )
        if grad:
            self._handles.append(
                module.register_full_backward_pre_hook(
                    partial(self._backward_hook, name=name)
                )
            )

    def register_all(
        self,
        module: nn.Module,
        grad: bool = True,
        include: NamePattern = None,
        exclude: NamePattern = None,
    ) -> None:
        include = re.compile(include) if isinstance(include, str) else include
        exclude = re.compile(exclude) if isinstance(exclude, str) else exclude
        for name, child in module.named_modules():
            if ((not include) or include.search(name)) and not (
                exclude and exclude.search(name)
            ):
                self.register(child, name, grad=grad)

    def unregister(self) -> None:
        for handle in self._handles:
            handle.remove()
        self._handles.clear()

    def _forward_hook(
        self,
        module: nn.Module,
        args: Tuple[Any],
        kwargs: Dict[str, Any],
        output: Any,
        *,
        name: str,
    ) -> None:
        self.stashes.append(
            self._stash(Event(name, type(module), False, output, args, kwargs))
        )

    def _backward_hook(self, module: nn.Module, grad_output: Any, *, name: str) -> None:
        self.stashes.append(
            self._stash(Event(name, type(module), True, grad_output, (), {}))
        )

    # Read results

    def __str__(self) -> str:
        return f"Tracker(stashes={len(self)}, tracking={len(self._handles)})"

    def __iter__(self) -> Iterator[Stash]:
        return iter(self.stashes)

    def __getitem__(self, index: int) -> Stash:
        return self.stashes[index]

    def __len__(self) -> int:
        return len(self.stashes)

    def to_frame(
        self,
        stat: Callable[[Tensor], Tensor] = torch.std,
        stat_name: Optional[str] = None,
    ) -> "pandas.DataFrame":  # type:ignore[name-defined] # NOQA: F821
        import pandas

        column_name = (
            getattr(stat, "__name__", "value") if stat_name is None else stat_name
        )

        def to_item(stash: Stash) -> Dict[str, Any]:
            d = stash.__dict__.copy()
            d.pop("value")
            v = stash.first_value
            d[column_name] = stat(v).item() if isinstance(v, Tensor) else None
            d["type"] = f"{stash.type.__module__}.{stash.type.__name__}"
            return d

        return pandas.DataFrame.from_dict(map(to_item, self))  # type:ignore[arg-type]

Methods

def clear(self) ‑> None

Expand source code

def clear(self) -> None:
    self.stashes.clear()

def register(self, module: torch.nn.modules.module.Module, name: str = '', grad: bool = True) ‑> None

Expand source code

def register(self, module: nn.Module, name: str = "", grad: bool = True) -> None:
    self._handles.append(
        module.register_forward_hook(
            partial(self._forward_hook, name=name), with_kwargs=True
        )
    )
    if grad:
        self._handles.append(
            module.register_full_backward_pre_hook(
                partial(self._backward_hook, name=name)
            )
        )

def register_all(self, module: torch.nn.modules.module.Module, grad: bool = True, include: Union[ForwardRef(None), Pattern[str], str] = None, exclude: Union[ForwardRef(None), Pattern[str], str] = None) ‑> None

Expand source code

def register_all(
    self,
    module: nn.Module,
    grad: bool = True,
    include: NamePattern = None,
    exclude: NamePattern = None,
) -> None:
    include = re.compile(include) if isinstance(include, str) else include
    exclude = re.compile(exclude) if isinstance(exclude, str) else exclude
    for name, child in module.named_modules():
        if ((not include) or include.search(name)) and not (
            exclude and exclude.search(name)
        ):
            self.register(child, name, grad=grad)

def to_frame(self, stat: Callable[[torch.Tensor], torch.Tensor] = <built-in method std of type object>, stat_name: Optional[str] = None) ‑> pandas.DataFrame

Expand source code

def to_frame(
    self,
    stat: Callable[[Tensor], Tensor] = torch.std,
    stat_name: Optional[str] = None,
) -> "pandas.DataFrame":  # type:ignore[name-defined] # NOQA: F821
    import pandas

    column_name = (
        getattr(stat, "__name__", "value") if stat_name is None else stat_name
    )

    def to_item(stash: Stash) -> Dict[str, Any]:
        d = stash.__dict__.copy()
        d.pop("value")
        v = stash.first_value
        d[column_name] = stat(v).item() if isinstance(v, Tensor) else None
        d["type"] = f"{stash.type.__module__}.{stash.type.__name__}"
        return d

    return pandas.DataFrame.from_dict(map(to_item, self))  # type:ignore[arg-type]

def unregister(self) ‑> None

Expand source code

def unregister(self) -> None:
    for handle in self._handles:
        handle.remove()
    self._handles.clear()