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The cache deliberately has no state across optimizer updates. It collects a
fixed number of detached ``[B, N, D]`` feature matrices on every rank, builds
the exact global fresh batch in deterministic rank order, and differentiates
the frozen reference loss only with respect to the local rank's feature leaf.
The two returned cotangents have distinct meanings:
``local_cotangents``
The mathematical derivatives ``d L_global / d feature_local``.
``fsdp_cotangents``
``world_size * local_cotangents``. These are the cotangents that replay
should use when FSDP/DDP will average parameter gradients across ranks:
``mean_r(world_size * J_r.T @ dL/dF_r) = sum_r J_r.T @ dL/dF_r``.
Collection is transactional. ``finalize`` stages a result but retains the
fresh features until the caller reports optimizer success with ``commit`` or
failure with ``discard``. Both terminal operations clear all update-local
state, so no generated sample can leak into the next optimizer update.
"""
from __future__ import annotations
from dataclasses import dataclass
import math
from typing import Any, Mapping, Sequence
import torch
import torch.distributed as dist
from losses.video_nystrom_mmd import VideoNystromMMD
from losses.multilayer_video_nystrom_mmd import (
MultiLayerVideoNystromMMD,
)
def _detach_metadata(value: Any) -> Any:
if isinstance(value, torch.Tensor):
return value.detach()
if isinstance(value, Mapping):
return {
key: _detach_metadata(item)
for key, item in value.items()
}
if isinstance(value, tuple):
return tuple(_detach_metadata(item) for item in value)
if isinstance(value, list):
return [_detach_metadata(item) for item in value]
return value
@dataclass(frozen=True)
class FreshVideoGradCacheResult:
"""Finalized loss and replay cotangents for one fresh optimizer update."""
loss: torch.Tensor
optimized_loss: torch.Tensor
metadata: Mapping[str, Any]
local_cotangents: tuple[torch.Tensor, ...]
fsdp_cotangents: tuple[torch.Tensor, ...]
replay_batch_indices: tuple[int, ...]
replay_sample_ids: tuple[tuple[str, ...], ...]
local_samples: int
global_samples: int
gradient_local_samples: int
gradient_global_samples: int
current_teacher_local_samples: int
current_teacher_global_samples: int
world_size: int
class FreshVideoGradCache:
"""Collect and differentiate one globally fresh video feature batch."""
def __init__(
self,
loss_module: VideoNystromMMD | MultiLayerVideoNystromMMD,
*,
target_local_samples: int = 16,
gradient_local_samples: int | None = None,
loss_scale: float = 1.0,
require_sample_id_alignment: bool = False,
full_student_partial_gradient: bool = False,
) -> None:
if not isinstance(
loss_module,
(VideoNystromMMD, MultiLayerVideoNystromMMD),
):
raise TypeError(
"loss_module must be a VideoNystromMMD or "
"MultiLayerVideoNystromMMD instance")
if int(target_local_samples) <= 0:
raise ValueError("target_local_samples must be positive")
if gradient_local_samples is None:
gradient_local_samples = int(target_local_samples)
if not 0 < int(gradient_local_samples) <= int(target_local_samples):
raise ValueError(
"gradient_local_samples must be in [1, target_local_samples]")
if (
not math.isfinite(float(loss_scale))
or float(loss_scale) < 0.0
):
raise ValueError("loss_scale must be finite and non-negative")
if any(parameter.requires_grad for parameter in loss_module.parameters()):
raise ValueError(
"VideoNystromMMD reference parameters must be frozen")
self.loss_module = loss_module
self.current_teacher_attraction_enabled = bool(getattr(
loss_module, "current_teacher_attraction_enabled", False))
self.target_local_samples = int(target_local_samples)
self.gradient_local_samples = int(gradient_local_samples)
self.partial_gradient_enabled = bool(
self.gradient_local_samples < self.target_local_samples)
self.full_student_partial_gradient = bool(
full_student_partial_gradient)
if (
self.full_student_partial_gradient
and not self.partial_gradient_enabled
):
raise ValueError(
"full_student_partial_gradient requires "
"gradient_local_samples < target_local_samples")
if (
self.partial_gradient_enabled
and isinstance(loss_module, MultiLayerVideoNystromMMD)
):
raise TypeError(
"partial-gradient GradCache currently requires a single "
"VideoNystromMMD layer")
self.loss_scale = float(loss_scale)
self.require_sample_id_alignment = bool(require_sample_id_alignment)
self._feature_batches: list[torch.Tensor] = []
self._teacher_feature_batches: list[torch.Tensor] = []
self._batch_sizes: list[int] = []
self._gradient_batch_indices: list[int] = []
self._gradient_batch_sizes: list[int] = []
self._gradient_sample_ids: list[tuple[str, ...]] = []
self._selection_started = False
self._sample_shape: tuple[int, int] | None = None
self._device: torch.device | None = None
self._result: FreshVideoGradCacheResult | None = None
@staticmethod
def _world_size() -> int:
if dist.is_available() and dist.is_initialized():
return dist.get_world_size()
return 1
@staticmethod
def _rank() -> int:
if dist.is_available() and dist.is_initialized():
return dist.get_rank()
return 0
@staticmethod
def _all_ranks_true(local_value: bool, device: torch.device) -> bool:
flag = torch.tensor(
int(bool(local_value)), device=device, dtype=torch.int32)
if (
dist.is_available()
and dist.is_initialized()
and dist.get_world_size() > 1
):
dist.all_reduce(flag, op=dist.ReduceOp.MIN)
return bool(flag.item())
@property
def local_samples(self) -> int:
return sum(self._batch_sizes)
@property
def num_microbatches(self) -> int:
return len(self._feature_batches)
@property
def selected_local_samples(self) -> int:
return sum(self._gradient_batch_sizes)
@property
def ready(self) -> bool:
return (
self.local_samples == self.target_local_samples
and self.selected_local_samples == self.gradient_local_samples
)
@property
def finalized(self) -> bool:
return self._result is not None
def add(
self,
generated_features: torch.Tensor,
current_teacher_features: torch.Tensor | None = None,
*,
gradient_selected: bool | None = None,
sample_ids: Sequence[str] | None = None,
current_teacher_sample_ids: Sequence[str] | None = None,
) -> int:
"""Add one detached microbatch and return its zero-based cache index."""
if self.finalized:
raise RuntimeError(
"cannot add features after finalize; commit or discard first")
if not isinstance(generated_features, torch.Tensor):
raise TypeError("generated_features must be a torch.Tensor")
if (
generated_features.ndim != 3
or min(generated_features.shape) <= 0
or not generated_features.is_floating_point()
):
raise ValueError(
"generated_features must be a non-empty floating [B, N, D] "
"tensor")
if not bool(torch.isfinite(generated_features).all().item()):
raise FloatingPointError(
"generated_features contains non-finite values")
if gradient_selected is None:
if self.partial_gradient_enabled:
raise TypeError(
"gradient_selected must be explicit when partial-gradient "
"GradCache is enabled")
gradient_selected = True
gradient_selected = bool(gradient_selected)
if self._selection_started and not gradient_selected:
raise ValueError(
"gradient-selected microbatches must form a contiguous suffix")
if gradient_selected:
self._selection_started = True
batch_size = int(generated_features.shape[0])
normalized_sample_ids: tuple[str, ...] | None = None
normalized_teacher_ids: tuple[str, ...] | None = None
if sample_ids is not None:
normalized_sample_ids = tuple(str(value) for value in sample_ids)
if len(normalized_sample_ids) != batch_size or any(
not value for value in normalized_sample_ids
):
raise ValueError(
"sample_ids must contain one non-empty ID per student")
if current_teacher_sample_ids is not None:
normalized_teacher_ids = tuple(
str(value) for value in current_teacher_sample_ids)
if len(normalized_teacher_ids) != batch_size or any(
not value for value in normalized_teacher_ids
):
raise ValueError(
"current_teacher_sample_ids must contain one non-empty ID "
"per selected student")
if self.require_sample_id_alignment and normalized_sample_ids is None:
raise ValueError(
"sample_ids are required by this GradCache experiment")
if self.current_teacher_attraction_enabled and gradient_selected:
if not isinstance(current_teacher_features, torch.Tensor):
raise TypeError(
"current_teacher_features must be a torch.Tensor for each "
"selected student microbatch")
if (
current_teacher_features.ndim != 3
or min(current_teacher_features.shape) <= 0
or not current_teacher_features.is_floating_point()
):
raise ValueError(
"current_teacher_features must be a non-empty floating "
"[B, N, D] tensor")
if tuple(current_teacher_features.shape) != tuple(
generated_features.shape
):
raise ValueError(
"current teacher features must exactly match selected "
"student [B,N,D]: "
f"{tuple(current_teacher_features.shape)} != "
f"{tuple(generated_features.shape)}")
if current_teacher_features.device != generated_features.device:
raise ValueError(
"generated and current teacher features must share a device")
if not bool(torch.isfinite(
current_teacher_features.detach()
).all().item()):
raise FloatingPointError(
"current_teacher_features contains non-finite values")
if self.require_sample_id_alignment and normalized_teacher_ids is None:
raise ValueError(
"current_teacher_sample_ids are required for selected "
"students")
if (
normalized_teacher_ids is not None
and normalized_sample_ids != normalized_teacher_ids
):
raise ValueError(
"matched teacher sample IDs must exactly equal selected "
"student sample IDs in order")
elif current_teacher_features is not None:
raise ValueError(
"current_teacher_features may only be provided for a selected "
"microbatch while exact current-teacher attraction is enabled")
elif current_teacher_sample_ids is not None:
raise ValueError(
"current_teacher_sample_ids may only be provided for a "
"selected current-teacher microbatch")
if self.local_samples + batch_size > self.target_local_samples:
raise ValueError(
"fresh feature cache would exceed target_local_samples: "
f"{self.local_samples} + {batch_size} > "
f"{self.target_local_samples}")
if (
gradient_selected
and self.selected_local_samples + batch_size
> self.gradient_local_samples
):
raise ValueError(
"fresh feature cache would exceed gradient_local_samples: "
f"{self.selected_local_samples} + {batch_size} > "
f"{self.gradient_local_samples}")
sample_shape = (
int(generated_features.shape[1]),
int(generated_features.shape[2]),
)
if self._sample_shape is None:
self._sample_shape = sample_shape
self._device = generated_features.device
elif (
sample_shape != self._sample_shape
or generated_features.device != self._device
):
raise ValueError(
"all fresh feature batches on a rank must share [N, D] and "
"device; expected "
f"{self._sample_shape} on {self._device}, got "
f"{sample_shape} on {generated_features.device}")
staged = generated_features.detach().to(dtype=torch.float32).clone()
self._feature_batches.append(staged)
batch_index = len(self._feature_batches) - 1
if gradient_selected:
self._gradient_batch_indices.append(batch_index)
self._gradient_batch_sizes.append(batch_size)
self._gradient_sample_ids.append(
normalized_sample_ids or tuple("" for _ in range(batch_size)))
if self.current_teacher_attraction_enabled and gradient_selected:
staged_teacher = current_teacher_features.detach().to(
dtype=torch.float32).clone()
self._teacher_feature_batches.append(staged_teacher)
self._batch_sizes.append(batch_size)
return batch_index
def _validate_distributed_geometry(
self,
local_features: torch.Tensor,
*,
expected_samples: int,
label: str,
) -> None:
return None
def _validate_ready_consensus(self) -> None:
return None
def _gather_detached(
self,
local_features: torch.Tensor,
) -> list[torch.Tensor]:
if self._world_size() == 1:
return [local_features.detach()]
gathered = [
torch.empty_like(local_features)
for _ in range(self._world_size())
]
dist.all_gather(gathered, local_features.detach().contiguous())
return gathered
def finalize(self) -> FreshVideoGradCacheResult:
"""Compute the global loss and stage suffix-only replay cotangents."""
if self._result is not None:
return self._result
self._validate_ready_consensus()
local_reference = torch.cat(self._feature_batches, dim=0)
self._validate_distributed_geometry(
local_reference,
expected_samples=self.target_local_samples,
label="student reference",
)
gathered_reference = self._gather_detached(local_reference)
if self.partial_gradient_enabled:
selected_batches = [
self._feature_batches[index]
for index in self._gradient_batch_indices
]
local_gradient = torch.cat(selected_batches, dim=0)
self._validate_distributed_geometry(
local_gradient,
expected_samples=self.gradient_local_samples,
label="gradient students",
)
gathered_gradient = self._gather_detached(local_gradient)
local_leaf = local_gradient.detach().requires_grad_(True)
gathered_gradient[self._rank()] = local_leaf
global_gradient = torch.cat(gathered_gradient, dim=0)
global_reference = torch.cat(
gathered_reference, dim=0).detach()
gradient_student_indices = None
if self.full_student_partial_gradient:
# ``global_gradient`` is rank-major, with each rank
# contributing its contiguous selected suffix. Build the
# matching positions in the rank-major full student batch so
# the objective can inject every selected value back into its
# original location without reordering either population.
suffix_start = (
self.target_local_samples
- self.gradient_local_samples
)
gradient_student_indices = torch.cat([
torch.arange(
rank * self.target_local_samples + suffix_start,
(rank + 1) * self.target_local_samples,
device=global_reference.device,
dtype=torch.long,
)
for rank in range(self._world_size())
])
else:
local_leaf = local_reference.detach().requires_grad_(True)
gathered_reference[self._rank()] = local_leaf
global_gradient = torch.cat(gathered_reference, dim=0)
global_reference = None
global_teacher = None
if self.current_teacher_attraction_enabled:
if len(self._teacher_feature_batches) != len(
self._gradient_batch_indices
):
raise RuntimeError(
"matched teacher and selected student GradCache batch "
"counts differ")
local_teacher = torch.cat(
self._teacher_feature_batches, dim=0)
if tuple(local_teacher.shape) != tuple(
local_gradient.shape
if self.partial_gradient_enabled else local_reference.shape
):
raise RuntimeError(
"matched teacher and gradient student shapes differ")
self._validate_distributed_geometry(
local_teacher,
expected_samples=self.gradient_local_samples,
label="current teacher",
)
gathered_teacher = self._gather_detached(local_teacher)
global_teacher = torch.cat(
gathered_teacher, dim=0).detach().to(dtype=torch.float32)
if self.partial_gradient_enabled:
if isinstance(self.loss_module, MultiLayerVideoNystromMMD):
raise TypeError(
"partial-gradient GradCache currently requires a single "
"VideoNystromMMD layer")
partial_loss_kwargs = {
"gradient_students": global_gradient,
"student_reference": global_reference,
"current_teacher": global_teacher,
}
if self.full_student_partial_gradient:
partial_loss_kwargs["gradient_student_indices"] = (
gradient_student_indices)
raw_loss, metadata = self.loss_module(**partial_loss_kwargs)
elif self.current_teacher_attraction_enabled:
raw_loss, metadata = self.loss_module(
global_gradient, current_teacher=global_teacher)
else:
raw_loss, metadata = self.loss_module(global_gradient)
if not isinstance(raw_loss, torch.Tensor) or raw_loss.ndim != 0:
raise TypeError(
"VideoNystromMMD must return a scalar tensor loss")
if not self._all_ranks_true(
bool(torch.isfinite(raw_loss.detach()).item()),
raw_loss.device,
):
raise FloatingPointError(
"VideoNystromMMD returned a non-finite loss on at least one "
"rank")
optimized_loss = raw_loss * self.loss_scale
(local_cotangent,) = torch.autograd.grad(
optimized_loss,
local_leaf,
retain_graph=False,
create_graph=False,
)
local_cotangent = local_cotangent.detach()
if not self._all_ranks_true(
bool(torch.isfinite(local_cotangent).all().item()),
local_cotangent.device,
):
raise FloatingPointError(
"VideoNystromMMD returned a non-finite feature cotangent on "
"at least one rank")
world_size = self._world_size()
local_parts = tuple(
value.contiguous()
for value in local_cotangent.split(
self._gradient_batch_sizes, dim=0)
)
fsdp_parts = tuple(
(value * float(world_size)).contiguous()
for value in local_parts
)
global_samples = self.target_local_samples * world_size
gradient_global_samples = self.gradient_local_samples * world_size
teacher_local_samples = (
self.gradient_local_samples
if self.current_teacher_attraction_enabled else 0
)
teacher_global_samples = teacher_local_samples * world_size
kss_denominator = global_samples * (
global_samples
if self.full_student_partial_gradient
else gradient_global_samples
)
detached_metadata = dict(_detach_metadata(metadata))
detached_metadata.update({
"raw_loss": raw_loss.detach(),
"optimized_loss": optimized_loss.detach(),
"loss_scale": raw_loss.new_tensor(self.loss_scale),
"gradcache_local_samples": raw_loss.new_tensor(
self.target_local_samples, dtype=torch.long),
"gradcache_global_samples": raw_loss.new_tensor(
global_samples, dtype=torch.long),
"gradcache_forward_local_samples": raw_loss.new_tensor(
self.target_local_samples, dtype=torch.long),
"gradcache_forward_global_samples": raw_loss.new_tensor(
global_samples, dtype=torch.long),
"gradcache_gradient_local_samples": raw_loss.new_tensor(
self.gradient_local_samples, dtype=torch.long),
"gradcache_gradient_global_samples": raw_loss.new_tensor(
gradient_global_samples, dtype=torch.long),
"gradcache_kss_denominator": raw_loss.new_tensor(
kss_denominator, dtype=torch.long),
"gradcache_partial_gradient_enabled": raw_loss.new_tensor(
int(self.partial_gradient_enabled), dtype=torch.long),
"gradcache_full_student_partial_gradient": raw_loss.new_tensor(
int(self.full_student_partial_gradient), dtype=torch.long),
"gradcache_world_size": raw_loss.new_tensor(
world_size, dtype=torch.long),
"gradcache_fsdp_cotangent_scale": raw_loss.new_tensor(
float(world_size)),
})
detached_metadata.update({
"gradcache_current_teacher_local_samples": raw_loss.new_tensor(
teacher_local_samples, dtype=torch.long),
"gradcache_current_teacher_global_samples": raw_loss.new_tensor(
teacher_global_samples, dtype=torch.long),
})
core_global_samples = detached_metadata.get("global_samples")
if (
core_global_samples is not None
and int(torch.as_tensor(core_global_samples).item())
!= global_samples
):
raise ValueError(
"VideoNystromMMD global_samples metadata disagrees with the "
f"fresh GradCache: {int(torch.as_tensor(core_global_samples))} "
f"!= {global_samples}")
core_gradient_samples = detached_metadata.get(
"gradient_student_samples")
if (
self.partial_gradient_enabled
and core_gradient_samples is not None
and int(torch.as_tensor(core_gradient_samples).item())
!= gradient_global_samples
):
raise ValueError(
"VideoNystromMMD gradient_student_samples metadata disagrees "
"with the fresh GradCache")
core_repulsion_denominator = detached_metadata.get(
"repulsion_denominator")
if (
self.partial_gradient_enabled
and core_repulsion_denominator is not None
and int(torch.as_tensor(core_repulsion_denominator).item())
!= kss_denominator
):
raise ValueError(
"VideoNystromMMD Kss denominator disagrees with the fresh "
f"GradCache: expected {kss_denominator}")
self._result = FreshVideoGradCacheResult(
loss=raw_loss.detach(),
optimized_loss=optimized_loss.detach(),
metadata=detached_metadata,
local_cotangents=local_parts,
fsdp_cotangents=fsdp_parts,
replay_batch_indices=tuple(self._gradient_batch_indices),
replay_sample_ids=tuple(self._gradient_sample_ids),
local_samples=self.target_local_samples,
global_samples=global_samples,
gradient_local_samples=self.gradient_local_samples,
gradient_global_samples=gradient_global_samples,
current_teacher_local_samples=teacher_local_samples,
current_teacher_global_samples=teacher_global_samples,
world_size=world_size,
)
return self._result
def _clear(self) -> None:
self._feature_batches.clear()
self._teacher_feature_batches.clear()
self._batch_sizes.clear()
self._gradient_batch_indices.clear()
self._gradient_batch_sizes.clear()
self._gradient_sample_ids.clear()
self._selection_started = False
self._sample_shape = None
self._device = None
self._result = None
def commit(self) -> None:
"""Clear a finalized cache after a successful optimizer update."""
if self._result is None:
raise RuntimeError(
"cannot commit a fresh feature cache before finalize")
self._clear()
def discard(self) -> None:
"""Clear collected/finalized data after a skipped or failed update."""
self._clear()
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