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786 lines (655 loc) · 29.9 KB
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from abc import ABC, abstractmethod
import array
from collections import OrderedDict
import ctypes
from dataclasses import dataclass
import diskcache
import hashlib
import sys
from typing import (
Any,
List,
Optional,
Sequence,
Tuple,
)
import llama_cpp.llama as llama_core
import llama_cpp.llama_cpp as llama_cpp_lib
from .llama_types import *
class BaseLlamaCache(ABC):
"""Base cache class for a llama.cpp model."""
def __init__(self, capacity_bytes: int = (2 << 30)):
self.capacity_bytes = capacity_bytes
@property
@abstractmethod
def cache_size(self) -> int:
raise NotImplementedError
def _find_longest_prefix_key(
self,
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
pass
@abstractmethod
def __getitem__(self, key: Sequence[int]) -> "llama_core.LlamaState":
raise NotImplementedError
@abstractmethod
def __contains__(self, key: Sequence[int]) -> bool:
raise NotImplementedError
@abstractmethod
def __setitem__(
self, key: Sequence[int], value: "llama_core.LlamaState"
) -> None:
raise NotImplementedError
class LlamaDiskCache(BaseLlamaCache):
"""
Disk cache for a llama.cpp model.
Delegates LRU and size management natively to the SQLite-backed `diskcache` library.
"""
def __init__(
self, cache_dir: str = ".cache/llama_cache", capacity_bytes: int = (2 << 30), verbose: bool = False
):
super().__init__(capacity_bytes)
self.cache_dir = cache_dir
# Native SQLite size limit and LRU eviction
self.cache = diskcache.Cache(cache_dir, size_limit=capacity_bytes)
self.verbose = verbose
@property
def cache_size(self):
# Native O(1) volume calculation
return self.cache.volume() # type: ignore
def _find_longest_prefix_key(
self,
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
# Early exit if cache is empty
if len(self.cache) == 0:
return None
min_len = 0
min_key: Optional[Tuple[int, ...]] = None
target_len = len(key)
for k in self.cache.iterkeys(): # type: ignore
prefix_len = llama_core.Llama.longest_token_prefix(k, key, self.verbose)
if prefix_len > min_len:
min_len = prefix_len
min_key = k # type: ignore
# Perfect match found, break to prevent full-table disk scan
if min_len == target_len:
break
return min_key
def __getitem__(self, key: Sequence[int]) -> "llama_core.LlamaState":
print("LlamaDiskCache.__getitem__: called", file=sys.stderr)
if len(self.cache) == 0:
raise KeyError("Cache is empty")
key = tuple(key)
_key = self._find_longest_prefix_key(key)
if _key is None:
raise KeyError("Key not found")
# Non-destructive read: automatically updates access time for LRU
value: "llama_core.LlamaState" = self.cache[_key] # type: ignore
return value
def __contains__(self, key: Sequence[int]) -> bool:
if len(self.cache) == 0:
return False
return self._find_longest_prefix_key(tuple(key)) is not None
def __setitem__(self, key: Sequence[int], value: "llama_core.LlamaState"):
print("LlamaDiskCache.__setitem__: called", file=sys.stderr)
# diskcache natively handles capacity check and eviction upon assignment
self.cache[tuple(key)] = value
class LlamaRAMCache(BaseLlamaCache):
"""
RAM cache for a llama.cpp model.
Maintains an LRU eviction policy with O(1) size tracking.
"""
def __init__(self, capacity_bytes: int = (2 << 30), verbose: bool = False):
super().__init__(capacity_bytes)
self.capacity_bytes = capacity_bytes
self.cache_state: OrderedDict[
Tuple[int, ...], "llama_core.LlamaState"
] = OrderedDict()
self._current_size = 0
self.verbose = verbose
@property
def cache_size(self):
return self._current_size
def _find_longest_prefix_key(
self,
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
min_len = 0
min_key = None
keys = (
(k, llama_core.Llama.longest_token_prefix(k, key, self.verbose))
for k in self.cache_state.keys()
)
for k, prefix_len in keys:
if prefix_len > min_len:
min_len = prefix_len
min_key = k
return min_key
def __getitem__(self, key: Sequence[int]) -> "llama_core.LlamaState":
if not self.cache_state:
raise KeyError("Cache is empty")
key = tuple(key)
_key = self._find_longest_prefix_key(key)
if _key is None:
raise KeyError("Key not found")
value = self.cache_state[_key]
self.cache_state.move_to_end(_key)
return value
def __contains__(self, key: Sequence[int]) -> bool:
if not self.cache_state:
return False
return self._find_longest_prefix_key(tuple(key)) is not None
def __setitem__(self, key: Sequence[int], value: "llama_core.LlamaState"):
key = tuple(key)
if key in self.cache_state:
previous = self.cache_state.pop(key)
self._current_size -= getattr(previous, "nbytes", previous.llama_state_size)
self.cache_state[key] = value
self._current_size += getattr(value, "nbytes", value.llama_state_size)
while self._current_size > self.capacity_bytes and len(self.cache_state) > 0:
_, popped_state = self.cache_state.popitem(last=False)
self._current_size -= getattr(popped_state, "nbytes", popped_state.llama_state_size)
self._current_size = max(0, self._current_size)
if len(self.cache_state) == 0:
self._current_size = 0
class TrieNode:
"""A node in the prefix tree (Trie)."""
def __init__(self):
# Child nodes: {token_id: TrieNode}
self.children: Dict[int, "TrieNode"] = {}
# Stores the LlamaState if this node marks the end of a cached sequence.
self.state: Optional["llama_core.LlamaState"] = None
class LlamaTrieCache(BaseLlamaCache):
"""
A Llama cache implementation using a Trie for O(K) prefix lookup
and an OrderedDict for O(1) LRU eviction.
- K = length of the query key (number of tokens)
- N = total number of items in the cache
This solves the O(N*K) lookup bottleneck of the linear scan cache.
"""
def __init__(self, capacity_bytes: int = (2 << 30)):
super().__init__(capacity_bytes)
self.root = TrieNode() # The root node of the Trie
self._current_size = 0 # O(1) tracking of cache size in bytes
# LRU Tracker:
# Key: Cached token sequence (Tuple[int, ...])
# Value: The *terminal* TrieNode for that key
self.lru_tracker: OrderedDict[
Tuple[int, ...], TrieNode
] = OrderedDict()
@property
def cache_size(self) -> int:
"""Returns the current total size of the cache in bytes (O(1))."""
return self._current_size
def _find_longest_prefix_node(
self, key: Tuple[int, ...]
) -> Tuple[Optional[TrieNode], Optional[Tuple[int, ...]]]:
"""
Finds the longest cached prefix for a given key in O(K) time.
Returns: (The matching TrieNode, The matching key)
"""
node = self.root
longest_prefix_node: Optional[TrieNode] = None
longest_prefix_key: Optional[Tuple[int, ...]] = None
current_prefix: List[int] = []
# Check if the empty prefix (root) is cached
if node.state is not None:
longest_prefix_node = node
longest_prefix_key = tuple(current_prefix)
for token in key:
if token not in node.children:
# Path ends, no further prefix matches
break
node = node.children[token]
current_prefix.append(token)
if node.state is not None:
# Found a valid, longer prefix; update our best match
longest_prefix_node = node
longest_prefix_key = tuple(current_prefix)
return longest_prefix_node, longest_prefix_key
def __getitem__(self, key: Sequence[int]) -> "llama_core.LlamaState":
"""
Retrieves the state for the longest matching prefix in O(K) time.
Updates the LRU status.
"""
key_tuple = tuple(key)
node, prefix_key = self._find_longest_prefix_node(key_tuple)
if node is None or node.state is None or prefix_key is None:
raise KeyError(f"Key prefix not found in cache for: {key_tuple}")
# Move the accessed key to the end (most recently used) in O(1)
self.lru_tracker.move_to_end(prefix_key)
return node.state
def __contains__(self, key: Sequence[int]) -> bool:
"""Checks if any prefix of the key is cached in O(K) time."""
node, _ = self._find_longest_prefix_node(tuple(key))
return node is not None
def _prune(self, key: Tuple[int, ...]):
"""
(Helper) Removes a key and its state from the Trie.
Also removes empty parent nodes (branch pruning).
"""
path: List[Tuple[TrieNode, int]] = [] # Stores (parent_node, token)
node = self.root
# 1. Find the node and record the path
for token in key:
if token not in node.children:
return # Key not found
path.append((node, token))
node = node.children[token]
# 2. Remove the state
if node.state is None:
return # Node has no state
self._current_size -= getattr(node.state, "nbytes", node.state.llama_state_size)
node.state = None
# 3. Prune empty parent nodes backward
for parent, token in reversed(path):
child = parent.children[token]
# If the child node is now empty (no children, no state), delete it
if not child.children and child.state is None:
del parent.children[token]
else:
# Node is still in use, stop pruning
break
def __setitem__(self, key: Sequence[int], value: "llama_core.LlamaState"):
"""
Adds a (key, state) pair to the cache in O(K) time.
Handles LRU updates and eviction.
"""
key_tuple = tuple(key)
# 1. Find or create nodes for the key (O(K))
node = self.root
for token in key_tuple:
node = node.children.setdefault(token, TrieNode())
# 2. Check if updating an existing item
if node.state is not None:
self._current_size -= getattr(node.state, "nbytes", node.state.llama_state_size)
# 3. Set new state and update O(1) size
node.state = value
self._current_size += getattr(value, "nbytes", value.llama_state_size)
# 4. Update LRU tracker (O(1))
if key_tuple in self.lru_tracker:
self.lru_tracker.move_to_end(key_tuple)
else:
self.lru_tracker[key_tuple] = node
# 5. Eviction logic
while self._current_size > self.capacity_bytes and self.lru_tracker:
# Get the least recently used item in O(1)
evicted_key, _ = self.lru_tracker.popitem(last=False)
# Remove the evicted item from the Trie
self._prune(evicted_key)
# Alias for backwards compatibility
LlamaCache = LlamaTrieCache
@dataclass
class HybridCheckpoint:
"""
Represents a single snapshot of the Hybrid/Recurrent model state.
Notes:
- When on_device=False, `data` contains the full host-side serialized state.
- When on_device=True, `data` contains only the host-visible portion of the
serialized state. The tensor payload is stored in llama_context-owned
device buffers by llama.cpp, keyed by seq_id.
"""
pos: int # The token position (cursor) where this snapshot was taken.
data: bytes # The raw binary RNN state data.
hash_val: str # SHA-256 hash of the token prefix to ensure exact sequence matching.
size: int # Number of bytes written by llama_state_seq_get_data_ext().
seq_id: int # Sequence id used by llama.cpp state APIs.
pos_min: int = -1 # Minimum backend memory position covered by the snapshot.
pos_max: Optional[int] = None # Maximum backend memory position covered by the snapshot.
class HybridCheckpointCache(BaseLlamaCache):
"""
Checkpoint manager for Hybrid/Recurrent model states.
This cache is designed for models whose memory cannot be safely truncated like
a regular Transformer KV cache. For recurrent/hybrid architectures, rollback is
implemented by saving and restoring sequence state snapshots.
Two operating modes are supported:
1. Host mode: on_device=False
- Full checkpoint payload is materialized as Python bytes.
- Multiple checkpoints per seq_id are safe.
- This mode is suitable for multi-turn rollback and longer conversation reuse.
2. Device mode: on_device=True
- LLAMA_STATE_SEQ_FLAGS_ON_DEVICE is forwarded to llama.cpp.
- Tensor payloads are stored in llama_context-owned device buffers.
- The device buffers are created per seq_id in llama.cpp.
- Therefore only one active checkpoint per seq_id is safe.
- This mode is suitable for fast speculative / branch rollback where avoiding
device-to-host tensor copies is more important than keeping many historical
checkpoints.
Important:
Do not treat on_device=True as "Python owns a VRAM checkpoint". Python only
owns the host-visible serialized portion. The tensor payload lives inside the
llama_context and is keyed by seq_id.
"""
def __init__(
self,
ctx: llama_cpp_lib.llama_context_p,
max_checkpoints: int = 16,
on_device: bool = False,
verbose: bool = False
):
"""
Args:
ctx (llama_context_p):
Borrowed llama.cpp context pointer used by the state sequence APIs.
This cache does not own the context and must not free it.
max_checkpoints(int): Maximum number of Python-side checkpoint entries to keep.
- Host mode: This is the maximum number of historical checkpoints across all seq_ids.
- Device mode: This is still a global upper bound for Python-side metadata entries,
but this class also enforces at most one active checkpoint per seq_id,
because llama.cpp stores device tensor payloads per seq_id.
on_device(bool): Whether to request llama.cpp to keep tensor checkpoint payloads in
context-owned device buffers via LLAMA_STATE_SEQ_FLAGS_ON_DEVICE.
verbose(bool): Enables diagnostic logging to stderr for checkpoint save/restore/eviction.
"""
if ctx is None:
raise ValueError("HybridCheckpointCache(__init__): Failed to create HybridCheckpointCache with a null model context")
self._ctx = ctx
self._context_ref = None
self.on_device = on_device
self.verbose = verbose
# In host mode, max_checkpoints means "maximum number of Python-owned
# checkpoints across all seq_ids".
#
# In device mode, llama.cpp stores tensor payloads in device buffers keyed
# by seq_id. Multiple Python checkpoint metadata entries for the same seq_id
# would point to the same mutable device-side slot, so only one checkpoint
# per seq_id is safe.
self.max_checkpoints = max_checkpoints
# Python-side checkpoint registry.
#
# Host mode:
# Each HybridCheckpoint owns a full serialized checkpoint payload.
#
# Device mode:
# Each HybridCheckpoint owns only the host-visible serialized portion.
# The corresponding tensor payload is owned by llama_context.
self.checkpoints: list[HybridCheckpoint] = []
# Total Python-tracked checkpoint size in bytes.
#
# Host mode:
# Roughly equals the total serialized checkpoint payload size.
#
# Device mode:
# Tracks only the host-visible part returned by llama.cpp, not the
# context-owned device tensor storage.
self._current_size = 0
# Cache C API function pointers for faster repeated calls.
self._get_size_ext = llama_cpp_lib.llama_state_seq_get_size_ext
self._get_data_ext = llama_cpp_lib.llama_state_seq_get_data_ext
self._set_data_ext = llama_cpp_lib.llama_state_seq_set_data_ext
self._get_memory = llama_cpp_lib.llama_get_memory
self._memory_seq_pos_min = llama_cpp_lib.llama_memory_seq_pos_min
self._memory_seq_pos_max = llama_cpp_lib.llama_memory_seq_pos_max
self._memory_seq_rm = llama_cpp_lib.llama_memory_seq_rm
# State serialization flags forwarded to llama.cpp.
#
# LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY:
# Save only the sequence-specific / partial state needed for recurrent
# rollback instead of a full context state.
#
# LLAMA_STATE_SEQ_FLAGS_ON_DEVICE:
# Ask llama.cpp to store tensor payloads in context-owned device buffers.
self._flags = llama_cpp_lib.LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY
if on_device:
self._flags |= llama_cpp_lib.LLAMA_STATE_SEQ_FLAGS_ON_DEVICE
if self.max_checkpoints <= 0 and self.verbose:
print("HybridCheckpointCache(__init__): Cache is DISABLED (max_checkpoints <= 0). "
"Rollback capabilities are turned off. This is optimal for single-turn workflows.",
file=sys.stderr)
if self.on_device and self.max_checkpoints > 1 and self.verbose:
print(
"HybridCheckpointCache(__init__): on_device=True stores tensor payloads "
"in llama_context-owned device buffers keyed by seq_id. Multiple "
"historical checkpoints for the same seq_id are unsafe, so this cache "
"will keep only one checkpoint per seq_id.",
file=sys.stderr,
)
@property
def cache_size(self) -> int:
"""
Returns the host-visible checkpoint size tracked by Python.
In host mode, this is close to the full serialized checkpoint payload size.
In device mode, this is only the host-visible metadata/payload size returned
by llama.cpp. Device-side tensor storage is owned by llama_context and is not
fully represented by this number.
"""
return self._current_size
def clear(self):
"""
Clears Python-side checkpoint metadata.
This does not explicitly release llama_context-owned device buffers. The
device buffers are managed by llama.cpp and are associated with the context.
"""
if not self.checkpoints:
# Empty Checkpoint: Return immediately, no need to clear.
return
self.checkpoints.clear()
self._current_size = 0
if self.verbose:
print("HybridCheckpointCache(clear): cleared", file=sys.stderr)
def close(self):
self.clear()
self._context_ref = None
self._ctx = None
self._get_size_ext = None
self._get_data_ext = None
self._set_data_ext = None
self._get_memory = None
self._memory_seq_pos_min = None
self._memory_seq_pos_max = None
self._memory_seq_rm = None
def __del__(self) -> None:
self.close()
# Helper tools
def _invalidate_related(self, seq_id: int, suffix_start=None, device_only=False):
owner = self._context_ref() if self._context_ref is not None else None
if owner is not None:
owner._invalidate_checkpoints(
seq_id, suffix_start=suffix_start, device_only=device_only
)
else:
self._invalidate_memory(seq_id, suffix_start)
def _invalidate_memory(
self, seq_id: int = -1, suffix_start: Optional[int] = None,
keep_seq_id: Optional[int] = None,
) -> None:
"""Drop partial snapshots whose native prefix is no longer available."""
self.checkpoints[:] = [
cp for cp in self.checkpoints
if (seq_id >= 0 and cp.seq_id != seq_id)
or cp.seq_id == keep_seq_id
or (suffix_start is not None and cp.pos_max is not None
and 0 <= cp.pos_max < suffix_start)
]
self._current_size = sum(cp.size for cp in self.checkpoints)
def _hash_prefix(self, tokens: List[int], length: int) -> str:
"""
Computes a SHA-256 hash for a sequence of tokens up to the specified length.
This ensures that checkpoints are only restored for the EXACT same conversation history.
"""
if length <= 0:
return "empty"
length = min(length, len(tokens))
data = array.array('i', tokens[:length]).tobytes()
return hashlib.sha256(data).hexdigest()[:32]
def _evict_checkpoints_if_needed(self) -> None:
"""
Evicts old checkpoints if needed
Host mode:
This evicts full Python-owned checkpoint payloads, so FIFO historical
checkpoints are safe and useful.
Device mode:
This evicts Python-side metadata only. The device tensor payload is owned
by llama_context and is keyed by seq_id.
"""
while len(self.checkpoints) > self.max_checkpoints:
old_cp = self.checkpoints.pop(0)
self._current_size -= old_cp.size
if self._current_size < 0:
self._current_size = 0
if self.verbose:
print(
f"HybridCheckpointCache: evicted checkpoint "
f"seq_id={old_cp.seq_id}, pos={old_cp.pos}",
file=sys.stderr,
)
def find_best_checkpoint(self, tokens: List[int], seq_id: int = 0) -> Optional[HybridCheckpoint]:
"""
Finds the longest valid checkpoint that perfectly matches the provided token prefix.
The hash check prevents restoring a checkpoint that has the same length but
belongs to a different prompt/history.
Returns None if no matching checkpoint is found.
"""
# Empty Checkpoint: Instant return, no hash calculation needed.
if self.max_checkpoints <= 0 or len(self.checkpoints) == 0:
return None
best_cp: Optional[HybridCheckpoint] = None
best_pos = -1
for cp in self.checkpoints:
if cp.seq_id != seq_id or cp.pos > len(tokens):
# Skip if sequence ID mismatches or checkpoint is longer than the current prompt
continue
# Verify cryptographic integrity of the prompt history
if self._hash_prefix(tokens, cp.pos) == cp.hash_val:
if cp.pos > best_pos:
# Keep the checkpoint with the longest matching prefix (highest pos)
best_pos = cp.pos
best_cp = cp
return best_cp
def save_checkpoint(
self,
current_pos: int,
tokens: List[int],
seq_id: int = 0
) -> bool:
"""
Extracts the RNN hidden state from the C++ backend and saves it as a checkpoint.
Manages eviction (FIFO) if the maximum number of checkpoints is exceeded.
"""
# 0. Early Exit / Feature Toggle
# If the user disables checkpoints (max_checkpoints <= 0), we immediately return.
# This absolutely critical bypass prevents massive (e.g., 150MB+) synchronous
# VRAM-to-RAM copies over the PCIe bus, eliminating multi-second delays at the
# end of generation for single-turn workflows.
# This is more friendly to the single-call ComfyUI ecosystem. :)
if self.max_checkpoints <= 0:
if self.verbose:
print("HybridCheckpointCache(save_checkpoint): Cache is DISABLED (max_checkpoints <= 0). "
"Operating in single-turn conversation mode. Skipping state extraction to optimize generation latency.",
file=sys.stderr)
return False
# In on-device mode, remove old Python metadata for this seq_id before saving
# the new checkpoint. The underlying llama.cpp device buffer for this seq_id
# will be overwritten by the get_data_ext() call.
if self.on_device:
self._invalidate_related(seq_id, device_only=True)
flags = self._flags
# 1. Query the required host-visible buffer size.
# In on_device mode this may exclude the large tensor payload
# that stays in device memory.
size = self._get_size_ext(self._ctx, seq_id, flags)
if size == 0:
if self.verbose:
print("HybridCheckpointCache(save_checkpoint): size=0, skip")
return False
# 2. Allocate buffer and extract raw state data
buffer = (ctypes.c_uint8 * size)()
n_written = self._get_data_ext(self._ctx, buffer, size, seq_id, flags)
if n_written != size:
if self.verbose:
print(
f"HybridCheckpointCache(save_checkpoint): get_data_ext failed "
f"({n_written}/{size})",
file=sys.stderr,
)
return False
# Note: This deep copy isolates the state from subsequent C++ backend mutations
data_bytes = bytes(buffer)
hash_val = self._hash_prefix(tokens, current_pos)
# Token count and backend memory positions are not interchangeable for all
# models. Multimodal inputs, custom position IDs, and SWA can make pos_max
# differ from current_pos - 1, so retain the native range with the snapshot.
memory = self._get_memory(self._ctx)
pos_min = self._memory_seq_pos_min(memory, seq_id)
pos_max = self._memory_seq_pos_max(memory, seq_id)
# 3. Store the newly extracted checkpoint
self.checkpoints.append(HybridCheckpoint(
pos=current_pos,
data=data_bytes,
hash_val=hash_val,
size=n_written,
seq_id=seq_id,
pos_min=pos_min,
pos_max=pos_max)
)
self._current_size += n_written
# 4. Evicts old checkpoints if needed
self._evict_checkpoints_if_needed()
if self.verbose:
mode = "device" if self.on_device else "host"
print(
f"HybridCheckpointCache(save_checkpoint): saved {mode} checkpoint "
f"seq_id={seq_id}, pos={current_pos}, size={size / 1024 / 1024:.2f} MiB, "
f"hcc_count={len(self.checkpoints)}, "
f"hcc_mem_used={self._current_size / 1024 / 1024:.2f} MiB",
file=sys.stderr,
)
return True
def restore_checkpoint(self, cp: HybridCheckpoint, seq_id: int = 0) -> bool:
"""
Injects a previously saved RNN state checkpoint back into the C++ backend memory.
"""
# 1. Verify sequence ID matches to prevent cross-sequence contamination
if cp.seq_id != seq_id:
if self.verbose:
print(f"HybridCheckpointCache(restore_checkpoint): [Error] Sequence ID mismatch: checkpoint has {cp.seq_id}, requested {seq_id}", file=sys.stderr)
return False
# Host partial snapshots also borrow the current attention prefix.
if self._ctx is None or not any(item is cp for item in self.checkpoints):
if self.verbose:
print(
"HybridCheckpointCache(restore_checkpoint): stale checkpoint; refusing restore.",
file=sys.stderr,
)
return False
flags = self._flags
# Size is a compatibility check, not proof that the KV prefix is valid.
current_size = self._get_size_ext(self._ctx, seq_id, flags)
if current_size != cp.size:
if self.verbose:
print(f"HybridCheckpointCache(restore_checkpoint): [Warning] State size mismatch before restore: "
f"expected checkpoint size={cp.size}, got current size={current_size} -> possible invalidation")
return False
buffer = (ctypes.c_uint8 * cp.size).from_buffer_copy(cp.data)
suffix_start = cp.pos_max + 1 if cp.pos_max is not None else cp.pos
try:
success = self._set_data_ext(
self._ctx, buffer, cp.size, seq_id, flags
) == cp.size
if success:
success = bool(self._memory_seq_rm(
self._get_memory(self._ctx), seq_id, suffix_start, -1
))
except BaseException:
self._invalidate_related(seq_id)
raise
# Restore and suffix cleanup form one transaction. Partial failure
# invalidates the old history even when the caller retained objects.
self._invalidate_related(seq_id, suffix_start if success else None)
if self.verbose:
mode = "device" if self.on_device else "host"
print(
f"HybridCheckpointCache(restore_checkpoint): restore "
f"{'OK' if success else 'FAIL'} "
f"mode={mode}, seq_id={seq_id}, pos={cp.pos}",
file=sys.stderr,
)
return success
# Disable BaseLlamaCache Dictionary Interfaces
def __getitem__(self, key):
raise NotImplementedError("HybridCheckpointCache: pls use save_checkpoint or restore_checkpoint method")
def __setitem__(self, key, value):
raise NotImplementedError("HybridCheckpointCache: pls use save_checkpoint or restore_checkpoint method")
def __contains__(self, key):
raise NotImplementedError("HybridCheckpointCache: pls use save_checkpoint or restore_checkpoint method")