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import typing as T
from dataclasses import dataclass, fields
import torch
import torch.nn as nn
import torch.nn.functional as F
@dataclass
class TTTConfig:
lr: float = 4e-4
steps: int = 30
ags: int = 16
batch_size: int = 2
mask_ratio: float = 0.15
crop_size: int = 1024
bert_leave_prob: float = 0.1
bert_replace_prob: float = 0.1
optimizer: str = "sgd"
momentum: float = 0.0
weight_decay: float = 0.0
seed: int | None = 0
lora_rank: int = 8
lora_alpha: float = 32.0
lora_target_replace_module: str | None = None
lora_target_modules: tuple[str, ...] | None = None
initial_state_reset: bool = True
automatic_best_state_reset: bool = False
eval_each_step: bool = False
gradient_clip: bool = False
gradient_clip_max_norm: float = 1.0
@classmethod
def from_kwargs(cls, **kwargs: T.Any) -> "TTTConfig":
valid_names = {field.name for field in fields(cls)}
unknown_names = set(kwargs) - valid_names
assert len(unknown_names) == 0, f"Unknown TTTConfig fields: {sorted(unknown_names)}"
return cls(**kwargs)
def merged(self, overrides: T.Mapping[str, T.Any] | "TTTConfig" | None) -> "TTTConfig":
if overrides is None:
return self
if isinstance(overrides, TTTConfig):
return overrides
values = {field.name: self.__dict__[field.name] for field in fields(self)}
for name, value in overrides.items():
assert name in values, f"Unknown TTTConfig field: {name}"
values[name] = value
return TTTConfig(**values)
def verify(self) -> None:
assert self.lr > 0.0, "TTT learning rate must be positive."
assert self.steps >= 1, "TTT steps must be >= 1."
assert self.ags >= 1, "TTT gradient accumulation steps must be >= 1."
assert self.batch_size >= 1, "TTT batch_size must be >= 1."
assert 0.0 < self.mask_ratio <= 1.0, "TTT mask_ratio must be in (0, 1]."
assert self.crop_size >= 1, "TTT crop_size must be >= 1."
assert self.lora_rank >= 1, "TTT v1 is LoRA-only, so lora_rank must be >= 1."
assert self.lora_alpha > 0.0, "TTT lora_alpha must be positive."
assert self.optimizer in {"adamw", "sgd"}, "TTT optimizer must be 'adamw' or 'sgd'."
assert 0.0 <= self.bert_leave_prob <= 1.0, "bert_leave_prob must be in [0, 1]."
assert 0.0 <= self.bert_replace_prob <= 1.0, "bert_replace_prob must be in [0, 1]."
assert self.bert_leave_prob + self.bert_replace_prob <= 1.0, (
"bert_leave_prob + bert_replace_prob must be <= 1."
)
if self.gradient_clip:
assert self.gradient_clip_max_norm > 0.0, "gradient_clip_max_norm must be positive."
class LoraInjectedLinear(nn.Module):
def __init__(self, linear: nn.Module, rank: int, alpha: float) -> None:
super().__init__()
weight = linear._parameters["weight"]
assert weight.ndim == 2, "LoRA can only wrap 2D linear weights."
self.linear = linear
self.linear.requires_grad_(False)
self.rank = rank
self.scale = alpha
in_features = weight.shape[1]
out_features = weight.shape[0]
self.lora_down = nn.Linear(in_features, rank, bias=False, dtype=torch.float32)
self.lora_up = nn.Linear(rank, out_features, bias=False, dtype=torch.float32)
self.lora_down.to(device=weight.device)
self.lora_up.to(device=weight.device)
nn.init.normal_(self.lora_down.weight, std=1.0 / rank)
nn.init.zeros_(self.lora_up.weight)
@property
def weight(self) -> torch.Tensor:
return self.linear._parameters["weight"]
@property
def bias(self) -> torch.Tensor | None:
return self.linear._parameters["bias"]
def forward(self, x: torch.Tensor) -> torch.Tensor:
base = self.linear(x)
delta = self.lora_up(self.lora_down(x.to(dtype=torch.float32))) * self.scale
return base + delta.to(dtype=base.dtype)
class FastPLMTestTimeTrainingMixin:
def init_ttt(self, ttt_config: TTTConfig | T.Mapping[str, T.Any] | None = None) -> None:
base_config = TTTConfig()
self._ttt_cfg = base_config.merged(ttt_config)
self._ttt_cfg.verify()
self._ttt_initialized = False
self._ttt_initial_state: list[dict[str, torch.Tensor]] | None = None
@property
def ttt_config(self) -> TTTConfig:
if "_ttt_cfg" not in self.__dict__:
self.init_ttt()
return self._ttt_cfg
def _ttt_get_trainable_modules(self) -> list[nn.Module]:
return [self]
def _ttt_get_frozen_modules(self) -> list[nn.Module]:
return []
def _ttt_tokenize(
self,
seq: str | list[str] | None = None,
input_ids: torch.Tensor | None = None,
**kwargs: T.Any,
) -> torch.Tensor | dict[str, torch.Tensor]:
del kwargs
if input_ids is not None:
return input_ids
assert seq is not None, "Pass either seq or input_ids for TTT."
tokenized = self.tokenizer(seq, return_tensors="pt", padding=True)
return tokenized["input_ids"]
def _ttt_mask_token(self) -> int:
return int(self.tokenizer.mask_token_id)
def _ttt_padding_token(self) -> int:
return int(self.tokenizer.pad_token_id)
def _ttt_replacement_tokens(self, input_ids: torch.Tensor) -> torch.Tensor:
tokenizer = self.tokenizer
special_ids = set(tokenizer.all_special_ids)
vocab_size = int(self.config.vocab_size)
ids = [idx for idx in range(vocab_size) if idx not in special_ids]
assert len(ids) > 0, "TTT replacement token set is empty."
return torch.tensor(ids, device=input_ids.device, dtype=input_ids.dtype)
def _ttt_predict_logits(
self,
batch: torch.Tensor | dict[str, torch.Tensor],
**kwargs: T.Any,
) -> torch.Tensor:
del kwargs
if isinstance(batch, dict):
output = self(**batch)
return output.logits
attention_mask = batch.ne(self._ttt_padding_token())
output = self(input_ids=batch, attention_mask=attention_mask)
return output.logits
def _ttt_eval_step(
self,
step: int,
loss: float,
seq: str | list[str] | None = None,
input_ids: torch.Tensor | None = None,
**kwargs: T.Any,
) -> tuple[dict[str, T.Any], float | None]:
del step, loss, seq, input_ids, kwargs
return {}, None
def _ttt_is_lora_target(
self,
name: str,
full_name: str,
module: nn.Module,
active: bool,
target_modules: tuple[str, ...] | None,
) -> bool:
if not active:
return False
if isinstance(module, LoraInjectedLinear):
return False
if (
target_modules is not None
and name not in target_modules
and full_name not in target_modules
):
return False
if isinstance(module, nn.Linear):
return True
if "weight" not in module._parameters:
return False
weight = module._parameters["weight"]
if weight is None or weight.ndim != 2:
return False
return "Linear" in module.__class__.__name__
def _ttt_inject_lora(self) -> int:
cfg = self.ttt_config
cfg.verify()
target_class = cfg.lora_target_replace_module
target_modules = cfg.lora_target_modules
wrapped = 0
def inject(module: nn.Module, prefix: str, active: bool) -> None:
nonlocal wrapped
for name, child in list(module.named_children()):
full_name = f"{prefix}.{name}" if prefix else name
child_active = active
if target_class is not None:
child_active = active or child.__class__.__name__ == target_class
if self._ttt_is_lora_target(name, full_name, child, child_active, target_modules):
setattr(
module,
name,
LoraInjectedLinear(child, rank=cfg.lora_rank, alpha=cfg.lora_alpha),
)
wrapped += 1
continue
inject(child, full_name, child_active)
for trainable_module in self._ttt_get_trainable_modules():
inject(trainable_module, "", target_class is None)
assert wrapped > 0, "TTT LoRA injection did not find any target modules."
return wrapped
def _ttt_lora_modules(self) -> list[LoraInjectedLinear]:
return [module for module in self.modules() if isinstance(module, LoraInjectedLinear)]
def _ttt_lora_parameters(self) -> list[nn.Parameter]:
params: list[nn.Parameter] = []
for module in self._ttt_lora_modules():
params.extend(module.lora_down.parameters())
params.extend(module.lora_up.parameters())
assert len(params) > 0, "TTT has no LoRA parameters."
return params
def _ttt_snapshot_lora_state(self) -> list[dict[str, torch.Tensor]]:
snapshot = []
for module in self._ttt_lora_modules():
snapshot.append(
{
"lora_down.weight": module.lora_down.weight.detach().clone(),
"lora_up.weight": module.lora_up.weight.detach().clone(),
}
)
assert len(snapshot) > 0, "TTT has no LoRA state to snapshot."
return snapshot
def _ttt_restore_lora_state(self, state: list[dict[str, torch.Tensor]]) -> None:
modules = self._ttt_lora_modules()
assert len(modules) == len(state), "TTT LoRA state/module count mismatch."
with torch.no_grad():
for module, module_state in zip(modules, state):
module.lora_down.weight.copy_(module_state["lora_down.weight"])
module.lora_up.weight.copy_(module_state["lora_up.weight"])
def _ttt_ensure_initialized(self) -> None:
if "_ttt_cfg" not in self.__dict__:
self.init_ttt()
if self._ttt_initialized:
return
self._ttt_inject_lora()
self._ttt_initial_state = self._ttt_snapshot_lora_state()
self._ttt_initialized = True
def ttt_reset(self) -> None:
self._ttt_ensure_initialized()
assert self._ttt_initial_state is not None, "TTT initial state is not available."
self._ttt_restore_lora_state(self._ttt_initial_state)
def _ttt_make_optimizer(self) -> torch.optim.Optimizer:
cfg = self.ttt_config
params = self._ttt_lora_parameters()
if cfg.optimizer == "sgd":
return torch.optim.SGD(
params,
lr=cfg.lr,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay,
)
return torch.optim.AdamW(params, lr=cfg.lr, weight_decay=cfg.weight_decay)
def _ttt_to_device(
self,
batch: torch.Tensor | dict[str, torch.Tensor],
device: torch.device,
) -> torch.Tensor | dict[str, torch.Tensor]:
if isinstance(batch, dict):
return {name: tensor.to(device) for name, tensor in batch.items()}
return batch.to(device)
def _ttt_input_ids_from_batch(
self,
batch: torch.Tensor | dict[str, torch.Tensor],
) -> torch.Tensor:
if isinstance(batch, dict):
return batch["input_ids"]
return batch
def _ttt_set_input_ids(
self,
batch: torch.Tensor | dict[str, torch.Tensor],
input_ids: torch.Tensor,
) -> torch.Tensor | dict[str, torch.Tensor]:
if isinstance(batch, dict):
updated = dict(batch)
updated["input_ids"] = input_ids
return updated
return input_ids
def _ttt_non_special_mask(self, input_ids: torch.Tensor) -> torch.Tensor:
pad_token = self._ttt_padding_token()
mask = input_ids.ne(pad_token)
special_ids = set(self.tokenizer.all_special_ids)
for special_id in special_ids:
mask = mask & input_ids.ne(int(special_id))
return mask
def _ttt_sample_crop(
self,
batch: torch.Tensor | dict[str, torch.Tensor],
generator: torch.Generator,
) -> torch.Tensor | dict[str, torch.Tensor]:
input_ids = self._ttt_input_ids_from_batch(batch)
cfg = self.ttt_config
if input_ids.shape[1] <= cfg.crop_size:
return batch
high = input_ids.shape[1] - cfg.crop_size + 1
start = int(
torch.randint(
high,
(1,),
generator=generator,
device=input_ids.device,
).item()
)
end = start + cfg.crop_size
if isinstance(batch, dict):
cropped = {}
for name, tensor in batch.items():
if tensor.ndim >= 2 and tensor.shape[1] == input_ids.shape[1]:
cropped[name] = tensor[:, start:end]
else:
cropped[name] = tensor
return cropped
return input_ids[:, start:end]
def _ttt_sample_batch(
self,
tokenized: torch.Tensor | dict[str, torch.Tensor],
generator: torch.Generator,
) -> tuple[torch.Tensor | dict[str, torch.Tensor], torch.Tensor]:
cfg = self.ttt_config
batch = self._ttt_sample_crop(tokenized, generator)
input_ids = self._ttt_input_ids_from_batch(batch)
rows = torch.randint(
input_ids.shape[0],
(cfg.batch_size,),
generator=generator,
device=input_ids.device,
)
if isinstance(batch, dict):
sampled: torch.Tensor | dict[str, torch.Tensor] = {}
for name, tensor in batch.items():
if tensor.ndim >= 1 and tensor.shape[0] == input_ids.shape[0]:
sampled[name] = tensor.index_select(0, rows)
else:
sampled[name] = tensor
else:
sampled = input_ids.index_select(0, rows)
sampled_ids = self._ttt_input_ids_from_batch(sampled)
labels = sampled_ids.clone()
non_special = self._ttt_non_special_mask(sampled_ids)
label_mask = torch.zeros_like(non_special)
for row_idx in range(sampled_ids.shape[0]):
candidate_positions = torch.where(non_special[row_idx])[0]
if candidate_positions.numel() == 0:
continue
num_mask = max(1, int(round(candidate_positions.numel() * cfg.mask_ratio)))
order = torch.randperm(
candidate_positions.numel(),
generator=generator,
device=sampled_ids.device,
)
chosen = candidate_positions[order[:num_mask]]
label_mask[row_idx, chosen] = True
labels = labels.masked_fill(~label_mask, -100)
masked_ids = sampled_ids.clone()
chosen_positions = torch.where(label_mask)
if chosen_positions[0].numel() > 0:
random_values = torch.rand(
chosen_positions[0].shape,
generator=generator,
device=sampled_ids.device,
)
leave = random_values < cfg.bert_leave_prob
replace = (random_values >= cfg.bert_leave_prob) & (
random_values < cfg.bert_leave_prob + cfg.bert_replace_prob
)
mask = ~(leave | replace)
if mask.any():
masked_ids[
chosen_positions[0][mask],
chosen_positions[1][mask],
] = self._ttt_mask_token()
if replace.any():
replacement_tokens = self._ttt_replacement_tokens(sampled_ids)
replacement_idx = torch.randint(
replacement_tokens.shape[0],
(int(replace.sum().item()),),
generator=generator,
device=sampled_ids.device,
)
masked_ids[
chosen_positions[0][replace],
chosen_positions[1][replace],
] = replacement_tokens[replacement_idx]
return self._ttt_set_input_ids(sampled, masked_ids), labels
def ttt(
self,
seq: str | list[str] | None = None,
input_ids: torch.Tensor | None = None,
ttt_config: TTTConfig | T.Mapping[str, T.Any] | None = None,
**kwargs: T.Any,
) -> dict[str, T.Any]:
if ttt_config is not None:
if "_ttt_initialized" in self.__dict__ and self._ttt_initialized:
next_cfg = self.ttt_config.merged(ttt_config)
assert next_cfg.lora_rank == self.ttt_config.lora_rank, (
"Changing lora_rank after TTT initialization is not supported."
)
assert next_cfg.lora_alpha == self.ttt_config.lora_alpha, (
"Changing lora_alpha after TTT initialization is not supported."
)
assert (
next_cfg.lora_target_replace_module
== self.ttt_config.lora_target_replace_module
), "Changing LoRA target class after TTT initialization is not supported."
assert next_cfg.lora_target_modules == self.ttt_config.lora_target_modules, (
"Changing LoRA target modules after TTT initialization is not supported."
)
self._ttt_cfg = next_cfg
else:
self.init_ttt(ttt_config)
self._ttt_ensure_initialized()
cfg = self.ttt_config
if cfg.initial_state_reset:
self.ttt_reset()
device = next(self.parameters()).device
tokenized = self._ttt_tokenize(seq=seq, input_ids=input_ids, **kwargs)
tokenized = self._ttt_to_device(tokenized, device)
generator_device = device if device.type == "cuda" else torch.device("cpu")
generator = torch.Generator(device=generator_device)
if cfg.seed is not None:
generator.manual_seed(cfg.seed)
module_modes = {module: module.training for module in self.modules()}
requires_grad = {param: param.requires_grad for param in self.parameters()}
losses: list[float] = []
step_metrics: list[dict[str, T.Any]] = []
best_state: list[dict[str, torch.Tensor]] | None = None
best_metric: float | None = None
best_step = 0
try:
self.train()
for param in self.parameters():
param.requires_grad_(False)
for param in self._ttt_lora_parameters():
param.requires_grad_(True)
optimizer = self._ttt_make_optimizer()
optimizer.zero_grad(set_to_none=True)
total_micro_steps = cfg.steps * cfg.ags
for micro_step in range(total_micro_steps):
batch, labels = self._ttt_sample_batch(tokenized, generator)
logits = self._ttt_predict_logits(batch, **kwargs)
labels = labels.to(device=logits.device)
loss = F.cross_entropy(
logits.reshape(-1, logits.shape[-1]),
labels.reshape(-1),
ignore_index=-100,
)
(loss / cfg.ags).backward()
if (micro_step + 1) % cfg.ags != 0:
continue
if cfg.gradient_clip:
torch.nn.utils.clip_grad_norm_(
self._ttt_lora_parameters(),
cfg.gradient_clip_max_norm,
)
optimizer.step()
optimizer.zero_grad(set_to_none=True)
step = (micro_step + 1) // cfg.ags
loss_value = float(loss.detach().item())
losses.append(loss_value)
if cfg.eval_each_step:
metrics, metric = self._ttt_eval_step(
step=step,
loss=loss_value,
seq=seq,
input_ids=input_ids,
**kwargs,
)
if len(metrics) > 0:
step_metrics.append(metrics)
if metric is not None and (
best_metric is None or metric > best_metric
):
best_metric = metric
best_step = step
best_state = self._ttt_snapshot_lora_state()
if cfg.automatic_best_state_reset and best_state is not None:
self._ttt_restore_lora_state(best_state)
finally:
for param, value in requires_grad.items():
param.requires_grad_(value)
for module, training in module_modes.items():
module.train(training)
return {
"losses": losses,
"step_metrics": step_metrics,
"best_step": best_step,
"best_metric": best_metric,
}
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