File size: 5,400 Bytes
179cd55 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
"""Self-contained subset of :mod:`circuit_sparsity.hook_utils` for inference builds.
The full module has no exotic dependencies, but mirroring the definitions here
keeps the trimmed :mod:`circuit_sparsity.inference.gpt` module hermetic and easy to vendor. The
implementations below are copied with minor tweaks for readability so that code
written against :func:`hook_recorder`, :func:`hook_namespace`, and
:func:`torch_recompute_preserving_hook_context` behaves identically in both the
training and inference configurations.
"""
from __future__ import annotations
import re
from contextlib import contextmanager
from functools import partial
import torch
import torch.utils.checkpoint
class HookContext:
"""State container used by the hook helpers."""
def __init__(self) -> None:
self._reset()
self.curintervtransformer = lambda x: x
def _reset(self) -> None:
self.curcontext = None
self.curname = ""
self.curregex = None
self.curinterventions = None
self.save_grads = None
def _get_interventions(self):
return self.curintervtransformer(
self.curinterventions if self.curinterventions is not None else {}
)
@contextmanager
def hook_recorder(self, regex: str = ".*", interventions=None, save_grads: bool = False):
"""Record tensors that pass through hooks matching ``regex``."""
assert self.curcontext is None, "reentrancy not allowed!"
try:
self.curcontext = {}
self.curregex = re.compile(regex)
self.curname = ""
self.curinterventions = interventions
self.save_grads = save_grads
yield self.curcontext
finally:
self._reset()
get_context()._reset()
@contextmanager
def hook_intervention_transform(self, intervention_transformer):
oldintervention_transformer = self.curintervtransformer
def compose(f, g):
return lambda x: f(g(x))
self.curintervtransformer = compose(
intervention_transformer,
self.curintervtransformer,
)
try:
yield
finally:
self.curintervtransformer = oldintervention_transformer
@contextmanager
def hook_namespace(self, name: str):
"""Temporarily push ``name`` onto the hook namespace stack."""
oldname = self.curname
self.curname = self.curname + name + "."
try:
yield
finally:
self.curname = oldname
def hook_save(self, name: str, tensor: torch.Tensor) -> torch.Tensor:
"""Optionally record ``tensor`` using the current namespace."""
curinterventions = self._get_interventions()
if curinterventions is not None:
key = self.curname + name
if key in curinterventions:
tensor = curinterventions[key](tensor)
if self.curcontext is not None and self.curregex.match(self.curname + name):
self.curcontext[self.curname + name] = tensor
if self.curcontext is not None and self.save_grads and tensor.requires_grad:
class _Grad(torch.autograd.Function):
@staticmethod
def forward(ctx, input_tensor):
return input_tensor
@staticmethod
def backward(ctx, grad_output):
self.curcontext[self.curname + name + ".grad"] = grad_output
return grad_output
if self.curregex.match(self.curname + name + ".grad"):
tensor = _Grad.apply(tensor)
return tensor
def set_context(new_context: HookContext) -> None:
global context
context = new_context
def get_context() -> HookContext:
global context
return context
def torch_recompute_preserving_hook_context(f, *xs, use_reentrant=None):
"""Wrapper around :func:`torch.utils.checkpoint` that propagates hooks."""
oldcontext = get_context()
curcontext = HookContext()
curcontext.curcontext = (
dict(oldcontext.curcontext) if oldcontext.curcontext is not None else None
)
curcontext.curregex = oldcontext.curregex
curcontext.curname = oldcontext.curname
curcontext.curinterventions = (
dict(oldcontext.curinterventions) if oldcontext.curinterventions is not None else None
)
curcontext.save_grads = oldcontext.save_grads
is_recompute = False
def _f(curcontext: HookContext, *xs):
initcontext = get_context()
nonlocal is_recompute
set_context(curcontext)
try:
res = f(*xs)
if not is_recompute and oldcontext.curcontext is not None:
oldcontext.curcontext |= curcontext.curcontext
finally:
set_context(initcontext)
is_recompute = True
return res
res = torch.utils.checkpoint.checkpoint(
partial(_f, curcontext), *xs, use_reentrant=use_reentrant
)
return res
context = HookContext()
def hook_recorder(*a, **k):
return get_context().hook_recorder(*a, **k)
def hook_namespace(*a, **k):
return get_context().hook_namespace(*a, **k)
def hook_save(*a, **k):
return get_context().hook_save(*a, **k)
def hook_intervention_transform(*a, **k):
return get_context().hook_intervention_transform(*a, **k) |