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# Exported for HuggingFace trust_remote_code loading.
# This file is intentionally self-contained.
from __future__ import annotations
from threading import RLock
import torch
from .model_runtime_control import (
RuntimeControlModel,
enable_runtime_cache,
ensure_batch_capacity,
ensure_sequence_capacity,
rebuild_runtime_buffers,
refresh_state_buffers,
reset_runtime_cache,
)
from .model_state import (
LayerCacheSnapshot,
RuntimeCacheSnapshot,
TransformerRuntimeState,
)
class TransformerRuntime:
def __init__(
self,
model: RuntimeControlModel,
*,
state: TransformerRuntimeState | None = None,
) -> None:
self.model = model
self.state = state or TransformerRuntimeState()
@property
def lock(self) -> RLock:
return self.state.lock
@lock.setter
def lock(self, value: RLock) -> None:
self.state.lock = value
def supports_loss_chunk_size(self) -> bool:
return True
def supports_checkpoint_excludes(self) -> bool:
return True
def runtime_recipe_knobs(self) -> tuple[int, int, int]:
return (
int(self.state.loss_chunk_size),
int(self.model.gradient_checkpointing_exclude_first),
int(self.model.gradient_checkpointing_exclude_last),
)
def apply_runtime_recipe_knobs(
self,
*,
loss_chunk_size: int,
gradient_checkpointing_exclude_first: int,
gradient_checkpointing_exclude_last: int,
) -> tuple[int, int, int]:
chunk_size = int(loss_chunk_size)
exclude_first = int(gradient_checkpointing_exclude_first)
exclude_last = int(gradient_checkpointing_exclude_last)
if chunk_size < 0:
raise ValueError(f"loss_chunk_size must be >= 0, got {chunk_size}")
if exclude_first < 0:
raise ValueError(
"gradient_checkpointing_exclude_first must be >= 0, "
f"got {exclude_first}"
)
if exclude_last < 0:
raise ValueError(
"gradient_checkpointing_exclude_last must be >= 0, "
f"got {exclude_last}"
)
layer_count = len(self.model.layers)
if exclude_first + exclude_last > layer_count:
raise ValueError(
"gradient checkpoint exclusions exceed model layer count: "
f"first={exclude_first} last={exclude_last} layers={layer_count}"
)
self.state.loss_chunk_size = chunk_size
self.model.gradient_checkpointing_exclude_first = exclude_first
self.model.gradient_checkpointing_exclude_last = exclude_last
return self.runtime_recipe_knobs()
def ensure_batch_capacity(self, batch_size: int) -> None:
ensure_batch_capacity(self.model, batch_size)
def ensure_sequence_capacity(self, max_seq_len: int) -> None:
ensure_sequence_capacity(self.model, max_seq_len)
def ensure_runtime_max_seq_len(self, max_seq_len: int) -> None:
self.ensure_sequence_capacity(max_seq_len)
def runtime_max_seq_len(self) -> int:
return int(self.model.runtime_max_seq_len())
def runtime_batch_capacity(self) -> int:
return int(self.model.runtime_batch_capacity())
def rebuild_runtime_buffers(self) -> None:
rebuild_runtime_buffers(self.model)
def sync_runtime_batch_capacity(self, max_batch_size: int) -> int:
self.ensure_batch_capacity(max_batch_size)
return self.runtime_batch_capacity()
def reset_runtime_cache(self) -> None:
reset_runtime_cache(self.model)
def refresh_state_buffers(self) -> None:
refresh_state_buffers(self.model)
def _forward_with_last_hidden(
self,
input_ids: torch.Tensor,
*,
start_pos: int,
return_all_logits: bool,
) -> tuple[torch.Tensor, torch.Tensor | None]:
hidden, _ = self.model._forward_hidden(input_ids, start_pos=start_pos)
last_hidden = hidden[:, -1, :]
logits_hidden = hidden if return_all_logits else hidden[:, -1:, :]
logits = self.model.head_mixer(
logits_hidden, norm=self.model.norm, output=self.model.output
)
if not return_all_logits:
logits = logits[:, 0, :]
return logits, last_hidden
def forward_with_last_hidden(
self,
input_ids: torch.Tensor,
*,
start_pos: int = 0,
return_all_logits: bool = True,
) -> tuple[torch.Tensor, torch.Tensor | None]:
enable_runtime_cache(self.model)
return self._forward_with_last_hidden(
input_ids,
start_pos=int(start_pos),
return_all_logits=return_all_logits,
)
def replay_with_cache(
self,
input_ids: torch.Tensor,
*,
start_pos: int = 0,
return_all_logits: bool = True,
) -> tuple[torch.Tensor, torch.Tensor | None]:
enable_runtime_cache(self.model)
if int(input_ids.size(1)) <= 1 or int(start_pos) == 0:
return self._forward_with_last_hidden(
input_ids,
start_pos=int(start_pos),
return_all_logits=return_all_logits,
)
outputs: list[torch.Tensor] = []
last_hidden = None
for offset in range(int(input_ids.size(1))):
logits, last_hidden = self._forward_with_last_hidden(
input_ids[:, offset : offset + 1],
start_pos=int(start_pos) + offset,
return_all_logits=True,
)
outputs.append(logits)
combined = torch.cat(outputs, dim=1)
return (combined if return_all_logits else combined[:, -1, :]), last_hidden
@staticmethod
def cache_batch_size(batch_size: int | None, max_batch_size: int) -> int:
active = int(max_batch_size if batch_size is None else batch_size)
if active <= 0:
raise ValueError(f"batch_size must be >= 1, got {active}")
return active
def cache_dump(
self,
*,
device: str = "cpu",
cache_pos: int | None = None,
batch_size: int | None = None,
) -> RuntimeCacheSnapshot:
active = self.cache_batch_size(batch_size, self.runtime_batch_capacity())
self.ensure_batch_capacity(active)
return RuntimeCacheSnapshot(
layers=tuple(
layer.attn.cache_snapshot(
device=device, batch_size=active, cache_pos=cache_pos
)
for layer in self.model.layers
)
)
def cache_load(self, cache_snapshot: RuntimeCacheSnapshot) -> None:
enable_runtime_cache(self.model)
if not isinstance(cache_snapshot, RuntimeCacheSnapshot):
raise TypeError("cache snapshot must be a RuntimeCacheSnapshot")
if len(cache_snapshot.layers) > len(self.model.layers):
raise ValueError("cache snapshot has more layers than the model")
required = cache_snapshot.batch_size()
if required:
self.ensure_batch_capacity(required)
for index, snapshot in enumerate(cache_snapshot.layers):
if snapshot is not None:
self.model.layers[index].attn.validate_cache_snapshot(snapshot)
self.reset_runtime_cache()
for index, snapshot in enumerate(cache_snapshot.layers):
if snapshot is not None:
self.model.layers[index].attn.load_cache_snapshot(snapshot)
__all__ = ["TransformerRuntime", "LayerCacheSnapshot"]
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