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4365 lines (3630 loc) · 170 KB
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from __future__ import annotations
import ctypes
import enum
import os
import operator
import sys
import weakref
from typing import (
Callable,
Dict,
List,
Set,
Tuple,
Optional,
Sequence,
Union,
TYPE_CHECKING
)
from dataclasses import dataclass, field
from collections import deque
from contextlib import ExitStack
import numpy as np
import numpy.typing as npt
from .llama_types import *
from .llama_grammar import LlamaGrammar
from ._utils import suppress_stdout_stderr
import llama_cpp.llama_cpp as llama_cpp
if TYPE_CHECKING:
from llama_cpp._ctypes_extensions import (
CtypesArray,
CtypesPointer,
)
# Python wrappers over llama.h structs
class LlamaDecodeAbort(RuntimeError):
"""Raised when llama.cpp aborts an in-flight decoder graph."""
class LlamaModel:
"""Intermediate Python wrapper for a llama.cpp llama_model.
NOTE: For stability it's recommended you use the Llama class instead."""
def __init__(
self,
*,
path_model: str,
params: llama_cpp.llama_model_params,
verbose: bool = True,
):
self.path_model = path_model
self.params = params
self.verbose = verbose
self._exit_stack = ExitStack()
self.model = None
self.vocab = None
self._lora_registry: Dict[str, LlamaLoraAdapter] = {}
model = None
if not os.path.exists(path_model):
raise ValueError(f"LlamaModel[__init__]: Model path does not exist: {path_model}")
with suppress_stdout_stderr(disable=verbose):
model = llama_cpp.llama_model_load_from_file(
self.path_model.encode("utf-8"), self.params
)
if model is None:
raise ValueError(f"Failed to load model from file: {path_model}")
# Record ownership immediately so every later failure can release the
# native model. In particular, a failed vocab lookup must not leak the
# successfully loaded model.
self.model = model
try:
vocab = llama_cpp.llama_model_get_vocab(model)
if vocab is None:
raise ValueError(f"LlamaModel[__init__]: Failed to get vocab from model: {path_model}")
except BaseException:
llama_cpp.llama_model_free(model)
self.model = None
raise
self.vocab = vocab
def close(self):
"""Manually free LlamaModel and Vocab/Lora resources."""
if getattr(self, "model", None) is not None:
try:
llama_cpp.llama_model_free(self.model)
except Exception:
pass
self.model = None
self.vocab = None
if hasattr(self, "_lora_registry") and self._lora_registry:
self.unload_all_loras()
self._lora_registry = None
if getattr(self, "_exit_stack", None) is not None and hasattr(self._exit_stack, "close"):
self._exit_stack.close()
self._exit_stack = None
def __del__(self):
self.close()
def vocab_type(self) -> int:
if self.vocab is None:
raise RuntimeError("LlamaModel.vocab_type: vocab is None")
return llama_cpp.llama_vocab_type(self.vocab)
def n_vocab(self) -> int:
if self.vocab is None:
raise RuntimeError("LlamaModel.n_vocab: vocab is None")
return llama_cpp.llama_vocab_n_tokens(self.vocab)
def n_ctx_train(self) -> int:
return llama_cpp.llama_model_n_ctx_train(self.model)
def n_cls_out(self) -> int:
return llama_cpp.llama_model_n_cls_out(self.model)
def n_embd(self) -> int:
return llama_cpp.llama_model_n_embd(self.model)
def n_embd_inp(self) -> int:
return llama_cpp.llama_model_n_embd_inp(self.model)
def n_embd_out(self) -> int:
return llama_cpp.llama_model_n_embd_out(self.model)
def n_layer(self) -> int:
return llama_cpp.llama_model_n_layer(self.model)
def n_layer_nextn(self) -> int:
return llama_cpp.llama_model_n_layer_nextn(self.model)
def n_head(self) -> int:
return llama_cpp.llama_model_n_head(self.model)
def n_head_kv(self) -> int:
return llama_cpp.llama_model_n_head_kv(self.model)
def n_swa(self) -> int:
return llama_cpp.llama_model_n_swa(self.model)
def rope_type(self) -> int:
return llama_cpp.llama_model_rope_type(self.model)
def dflash_selector_top_k(self) -> int:
"""Return the DFlash2 selector width, or zero for DFlash v1/DSpark."""
return llama_cpp.llama_model_dflash_selector_top_k(self.model)
def target_layer_ids_n(self) -> int:
"""Return the number of target-model layers extracted by this model."""
return llama_cpp.llama_model_target_layer_ids_n(self.model)
def target_layer_ids(self) -> List[int]:
"""Return the target-model layer indices extracted by this model."""
count = self.target_layer_ids_n()
if count == 0:
return []
layer_ids = llama_cpp.llama_model_target_layer_ids(self.model)
if not layer_ids:
raise RuntimeError(
"LlamaModel.target_layer_ids: native API returned a null pointer "
f"for {count} layer IDs"
)
return [int(layer_ids[i]) for i in range(count)]
def get_tok_embd(self) -> npt.NDArray[np.float32]:
"""Return a copy of the token embedding matrix as ``[n_vocab, n_embd]``."""
element_count = llama_cpp.llama_model_get_tok_embd(self.model, None)
if element_count == 0:
raise RuntimeError(
"LlamaModel.get_tok_embd: token embedding matrix is unavailable"
)
n_vocab = self.n_vocab()
n_embd = self.n_embd()
expected_count = n_vocab * n_embd
if element_count != expected_count:
raise RuntimeError(
"LlamaModel.get_tok_embd: unexpected token embedding size: "
f"native API returned {element_count} elements, expected "
f"{expected_count} ({n_vocab} x {n_embd})"
)
out = np.empty(element_count, dtype=np.float32)
written = llama_cpp.llama_model_get_tok_embd(
self.model,
out.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
)
if written != element_count:
raise RuntimeError(
"LlamaModel.get_tok_embd: failed to copy the complete token "
f"embedding matrix ({written}/{element_count} elements)"
)
return out.reshape(n_vocab, n_embd)
def rope_freq_scale_train(self) -> float:
"""
Get the model's RoPE frequency scaling factor
"""
return llama_cpp.llama_model_rope_freq_scale_train(self.model)
def model_desc(self) -> str:
"""
Get a string describing the model type
"""
buf = ctypes.create_string_buffer(256)
llama_cpp.llama_model_desc(self.model, buf, 256)
return buf.value.decode("utf-8")
def model_ftype(self) -> int:
"""
Get the model file type (quantization), e.g. LLAMA_FTYPE_MOSTLY_Q8_0
"""
return llama_cpp.llama_model_ftype(self.model)
def model_size(self) -> int:
"""
Returns the total size of all the tensors in the model in bytes
"""
return llama_cpp.llama_model_size(self.model)
def model_chat_template(self, name: Optional[bytes] = None) -> Optional[str]:
"""
Get a chat template from the model.
If name is None, returns the default chat template.
Returns None if no chat template is available.
"""
template = llama_cpp.llama_model_chat_template(self.model, name)
if template is None:
return None
return template.decode("utf-8")
def n_params(self) -> int:
"""
Returns the total number of parameters in the model
"""
return llama_cpp.llama_model_n_params(self.model)
def has_encoder(self) -> bool:
"""
Returns true if the model contains an encoder that requires llama_encode() call
"""
return llama_cpp.llama_model_has_encoder(self.model)
def has_decoder(self) -> bool:
"""
Returns true if the model contains a decoder that requires llama_decode() call
"""
return llama_cpp.llama_model_has_decoder(self.model)
def decoder_start_token(self) -> int:
"""
For encoder-decoder models, this function returns id of the token that must be provided
to the decoder to start generating output sequence. For other models, it returns -1.
"""
return llama_cpp.llama_model_decoder_start_token(self.model)
def is_recurrent(self) -> bool:
"""
Returns true if the model is recurrent (like Mamba, RWKV, etc.)
"""
return llama_cpp.llama_model_is_recurrent(self.model)
def is_hybrid(self) -> bool:
"""
Returns true if the model is hybrid (like Jamba, Granite, etc.)
"""
return llama_cpp.llama_model_is_hybrid(self.model)
def is_diffusion(self) -> bool:
"""
Returns true if the model is diffusion-based (like LLaDA, Dream, etc.)
"""
return llama_cpp.llama_model_is_diffusion(self.model)
# Vocab
def suppress_tokens(self) -> List[int]:
"""Copy the model's suppressed vocabulary IDs out of native storage."""
if self.vocab is None:
raise RuntimeError("LlamaModel.suppress_tokens: vocab is None")
count = ctypes.c_int32()
tokens = llama_cpp.llama_vocab_get_suppress_tokens(self.vocab, ctypes.byref(count))
if count.value < 0 or (count.value > 0 and not tokens):
raise RuntimeError("Invalid native suppress-token list")
return list(tokens[:count.value]) if count.value else []
def token_get_text(self, token: int) -> str:
return llama_cpp.llama_vocab_get_text(self.vocab, token).decode("utf-8")
def token_get_score(self, token: int) -> float:
return llama_cpp.llama_vocab_get_score(self.vocab, token)
def token_get_attr(self, token: int) -> int:
return llama_cpp.llama_vocab_get_attr(self.vocab, token)
def token_is_eog(self, token: int) -> bool:
return llama_cpp.llama_vocab_is_eog(self.vocab, token)
def token_is_control(self, token: int) -> bool:
return llama_cpp.llama_vocab_is_control(self.vocab, token)
# Special tokens
def token_bos(self) -> int:
return llama_cpp.llama_vocab_bos(self.vocab)
def token_eos(self) -> int:
return llama_cpp.llama_vocab_eos(self.vocab)
def token_eot(self) -> int:
return llama_cpp.llama_vocab_eot(self.vocab)
def token_sep(self) -> int:
return llama_cpp.llama_vocab_sep(self.vocab)
def token_nl(self) -> int:
return llama_cpp.llama_vocab_nl(self.vocab)
def token_pad(self) -> int:
return llama_cpp.llama_vocab_pad(self.vocab)
def token_mask(self) -> int:
return llama_cpp.llama_vocab_mask(self.vocab)
def token_cls(self) -> int:
return llama_cpp.llama_vocab_cls(self.vocab)
def token_fim_pre(self) -> int:
return llama_cpp.llama_vocab_fim_pre(self.vocab)
def token_fim_suf(self) -> int:
return llama_cpp.llama_vocab_fim_suf(self.vocab)
def token_fim_mid(self) -> int:
return llama_cpp.llama_vocab_fim_mid(self.vocab)
def token_fim_pad(self) -> int:
return llama_cpp.llama_vocab_fim_pad(self.vocab)
def token_fim_rep(self) -> int:
return llama_cpp.llama_vocab_fim_rep(self.vocab)
def token_fim_sep(self) -> int:
return llama_cpp.llama_vocab_fim_sep(self.vocab)
def get_add_bos(self) -> bool:
return llama_cpp.llama_vocab_get_add_bos(self.vocab)
def get_add_eos(self) -> bool:
return llama_cpp.llama_vocab_get_add_eos(self.vocab)
def get_add_sep(self) -> bool:
return llama_cpp.llama_vocab_get_add_sep(self.vocab)
# Tokenization
def tokenize(self, text: bytes, add_bos: bool, special: bool):
"""
Tokenize a string.
Optimized to use dynamic buffer allocation.
"""
n_tokens_alloc = len(text) + 2
tokens = (llama_cpp.llama_token * n_tokens_alloc)()
n_tokens = llama_cpp.llama_tokenize(
self.vocab, text, len(text), tokens, n_tokens_alloc, add_bos, special
)
# If the buffer is insufficient (returns a negative number), reallocate the buffer.
if n_tokens < 0:
n_tokens_alloc = -n_tokens
tokens = (llama_cpp.llama_token * n_tokens_alloc)()
n_tokens = llama_cpp.llama_tokenize(
self.vocab, text, len(text), tokens, n_tokens_alloc, add_bos, special
)
if n_tokens < 0:
raise RuntimeError(
f'Failed to tokenize: text="{text}" n_tokens={n_tokens}'
)
# return a buffer of n_tokens size.
return list(tokens[:n_tokens])
def token_to_piece(self, token: int, special: bool = False) -> bytes:
"""
Convert a single token to bytes.
Optimized to handle dynamic resizing for ultra-long tokens.
"""
size = 32
buf = (ctypes.c_char * size)()
n = llama_cpp.llama_token_to_piece(self.vocab, token, buf, size, 0, special)
# If the token is very long (returns a negative number), redistribute it according to the returned size.
if n < 0:
size = -n
buf = (ctypes.c_char * size)()
n = llama_cpp.llama_token_to_piece(self.vocab, token, buf, size, 0, special)
if n < 0:
raise RuntimeError(f"Failed to get piece for token {token}")
# return a buffer of n size.
return bytes(buf[:n])
def detokenize(self, tokens: List[int], special: bool = False) -> bytes:
"""
Convert a list of tokens to bytes.
Optimized to handle dynamic resizing for ultra-long tokens.
"""
if not tokens:
return b""
n_tokens = len(tokens)
# Convert a Python list to a C int array
tokens_array = (llama_cpp.llama_token * n_tokens)(*tokens)
# Initial buffer size estimation
# Note(JamePeng):
# Observed CJK heavy outputs are about 4.0x - 5.04x,
# with extreme values even reaching 6.0x, so this avoids most retry cases.
# In CJK use cases, the call overhead will be reduced by 50% compared to the previous detokenize method.
buffer_size = max(64, n_tokens * 5 + 32)
buffer = (ctypes.c_char * buffer_size)()
n_chars = llama_cpp.llama_detokenize(
self.vocab, tokens_array, n_tokens, buffer, buffer_size, False, special
)
# If the buffer is insufficient, expand it and retry.
if n_chars < 0:
buffer_size = -n_chars
buffer = (ctypes.c_char * buffer_size)()
n_chars = llama_cpp.llama_detokenize(
self.vocab, tokens_array, n_tokens, buffer, buffer_size, False, special
)
if n_chars < 0:
raise RuntimeError("Failed to detokenize")
return bytes(buffer[:n_chars])
# Lora
def load_lora(self, name: str, path: str):
"""Loads a LoRA adapter into VRAM without applying it yet."""
# Skip if it's already loaded
if name in self._lora_registry:
return
adapter = LlamaLoraAdapter(self.model, path)
self._lora_registry[name] = adapter
self._exit_stack.callback(adapter.free)
if self.verbose:
print(f"Loaded LoRA '{name}' into memory.")
def unload_lora(self, name: str):
"""Actively unloads a specific LoRA to free up VRAM."""
if name in self._lora_registry:
adapter = self._lora_registry.pop(name)
adapter.free()
if self.verbose:
print(f"Unloaded LoRA '{name}' and freed memory.")
@property
def loaded_lora_count(self) -> int:
"""
Returns the total number of LoRA adapters currently loaded in VRAM.
"""
return len(self._lora_registry)
def list_loras(self) -> List[str]:
"""
Returns a list of all registered LoRA names.
"""
return list(self._lora_registry.keys())
def unload_all_loras(self):
"""
Iterates through the registry and forces VRAM release for all loaded LoRAs.
"""
if not self._lora_registry:
return
# Cast keys to a list first to avoid RuntimeError:
# 'dictionary changed size during iteration' when pop() is called inside unload_lora.
loaded_names = list(self._lora_registry.keys())
for name in loaded_names:
self.unload_lora(name)
if self.verbose:
print(f"Successfully unloaded all {len(loaded_names)} LoRA adapters and cleared the registry.")
# Extra
def metadata(self) -> Dict[str, str]:
metadata: Dict[str, str] = {}
# Pre-allocate a 16KB buffer. This is large enough to handle almost all
# metadata values (including gpt-oss large chat templates ~15KB) in a single pass,
# eliminating the need for resize-and-retry in most cases.
buffer_size = 16384
buffer = ctypes.create_string_buffer(buffer_size)
# Caching function references reduces the overhead of property lookups within loops.
get_key_by_index = llama_cpp.llama_model_meta_key_by_index
get_val_by_index = llama_cpp.llama_model_meta_val_str_by_index
metadata_count = llama_cpp.llama_model_meta_count(self.model)
# iterate over model keys
for i in range(metadata_count):
# 1. Get Key
nbytes = get_key_by_index(self.model, i, buffer, buffer_size)
# Handle buffer resize if the key exceeds current size
if nbytes > buffer_size:
buffer_size = nbytes + 1024
buffer = ctypes.create_string_buffer(buffer_size)
# Retry with the larger buffer
nbytes = get_key_by_index(self.model, i, buffer, buffer_size)
key = buffer.value.decode("utf-8")
# 2. Get Value
nbytes = get_val_by_index(self.model, i, buffer, buffer_size)
# Handle buffer resize if the value exceeds current size
if nbytes > buffer_size:
buffer_size = nbytes + 1024
buffer = ctypes.create_string_buffer(buffer_size)
# Retry with the larger buffer
nbytes = get_val_by_index(self.model, i, buffer, buffer_size)
value = buffer.value.decode("utf-8")
metadata[key] = value
return metadata
@staticmethod
def default_params():
"""Get the default llama_model_params."""
return llama_cpp.llama_model_default_params()
class LlamaLoraAdapter:
"""Wrapper for llama_adapter_lora_p to safely manage C++ memory lifecycle."""
def __init__(self, model: llama_cpp.llama_model_p, path: str):
"""
Initializes and loads the LoRA adapter into memory.
Args:
model: The pointer to the base Llama model.
path: The file path to the LoRA adapter (.gguf).
"""
self.path = path
# Load the LoRA adapter from file into memory via llama.cpp API
# Note: The path string must be encoded to UTF-8 bytes for ctypes compatibility.
self.adapter = llama_cpp.llama_adapter_lora_init(
model,
path.encode("utf-8")
)
if not self.adapter:
raise RuntimeError(f"Failed to load LoRA from {path}")
def free(self):
"""
Explicitly frees the underlying C++ memory allocated for the LoRA adapter.
Should be called when the adapter is actively unloaded to instantly release VRAM.
"""
# Check if the adapter exists and hasn't been freed yet
if getattr(self, "adapter", None) is not None:
llama_cpp.llama_adapter_lora_free(self.adapter)
self.adapter = None
self.path = None
def __del__(self):
self.free()
@property
def alora_invocation_tokens(self) -> List[int]:
"""
Retrieves the list of invocation (trigger) tokens if this adapter is an ALoRA (Activation LoRA).
Returns an empty list for standard LoRA adapters.
"""
if getattr(self, "adapter", None) is None:
return []
# 1. Query the C++ backend for the exact number of trigger tokens
n_tokens = llama_cpp.llama_adapter_get_alora_n_invocation_tokens(self.adapter)
if n_tokens == 0:
return []
# 2. Retrieve the underlying C pointer to the contiguous array of tokens
tokens_ptr = llama_cpp.llama_adapter_get_alora_invocation_tokens(self.adapter)
# 3. Safely iterate through the C memory block and convert it into a native Python list
return [tokens_ptr[i] for i in range(n_tokens)]
class LlamaContext:
"""Own a native ``llama_context`` and expose its low-level operations.
The context stores runtime state such as the KV/recurrent memory, pending
backend work, output buffers, samplers, and applied adapters. Most callers
should use :class:`Llama`; this wrapper is intended for internal and
advanced low-level use. Pointers returned by output accessors are owned by
llama.cpp and remain valid only until the context reuses those buffers.
Output and state-transfer accessors synchronize pending backend work;
operations performed through the native memory handle do not generally add
an implicit synchronization boundary.
"""
def __init__(
self,
*,
model: LlamaModel,
params: llama_cpp.llama_context_params,
verbose: bool = True,
):
"""Create a context for ``model`` using native context ``params``.
Retaining params preserves its ctypes storage, but raw pointers cannot
retain their Python owners. The caller must keep ctx_other and initial
sampler owners alive until this context is closed, as required by C API.
"""
self.model = model
self.params = params
self.verbose = verbose
self._exit_stack = ExitStack()
self.ctx = None
ctx = llama_cpp.llama_init_from_model(self.model.model, self.params)
if not ctx:
raise RuntimeError(
"Failed to create llama context with model. "
"This may indicate that llama_context_params is out of sync with "
"the bundled llama.cpp version, or that required context parameters "
"were not initialized correctly."
)
self.ctx = ctx
self._loras_applied: bool = False
self._lora_refs = ()
self._cvec_applied: bool = False
self._threadpool_refs = None
# llama.cpp borrows callbacks and their user data. Keep callbacks from
# the initial params alive just as set_abort_callback() does.
self._abort_callback_ref = (
self.params.abort_callback if bool(self.params.abort_callback) else None
)
self._abort_callback_data_ref = self.params.abort_callback_data
# The native context borrows attached sampler pointers. Keep the Python
# wrappers alive until they are detached or the context is closed.
self._sampler_refs: Dict[int, "LlamaSampler"] = {}
def close(self):
"""Release all resources, reporting the first error after cleanup."""
errors = []
def release(action):
try:
action()
except Exception as exc:
errors.append(exc)
for batch in list(getattr(self, "_batch_ext_refs", ())):
release(batch.close)
for cache in list(getattr(self, "_checkpoint_caches", ())):
release(cache.close)
if hasattr(self, "_checkpoint_caches"):
self._checkpoint_caches.clear()
if getattr(self, "ctx", None) is not None:
native = self.ctx
self.ctx = None
release(lambda: llama_cpp.llama_free(native))
self.params = None
if getattr(self, "_exit_stack", None) is not None and hasattr(self._exit_stack, "close"):
release(self._exit_stack.close)
self._exit_stack = None
# The context no longer needs to keep its parent model alive once the
# native context and its callbacks have been released.
self.model = None
self._lora_refs = ()
self._loras_applied = False
self._cvec_applied = False
self._sampler_refs = {}
self._threadpool_refs = None
self._abort_callback_ref = None
self._abort_callback_data_ref = None
if errors:
raise errors[0]
def __del__(self):
"""Release native resources when the wrapper is garbage-collected."""
try:
self.close()
except Exception:
pass
def _assert_ctx(self):
"""Raise when an operation is attempted on a closed context."""
if not getattr(self, "ctx", None):
raise RuntimeError(
"LlamaContext is not initialized or has already been closed. "
"Context-dependent llama.cpp operations cannot continue."
)
def n_ctx(self) -> int:
"""Return the total configured context capacity in tokens."""
self._assert_ctx()
return llama_cpp.llama_n_ctx(self.ctx)
def n_ctx_seq(self) -> int:
"""Return the effective context capacity available to each sequence."""
self._assert_ctx()
return llama_cpp.llama_n_ctx_seq(self.ctx)
def n_batch(self) -> int:
"""Return the maximum logical batch size accepted by the context."""
self._assert_ctx()
return llama_cpp.llama_n_batch(self.ctx)
def n_ubatch(self) -> int:
"""Return the maximum physical micro-batch size used for graph execution."""
self._assert_ctx()
return llama_cpp.llama_n_ubatch(self.ctx)
def n_seq_max(self) -> int:
"""Return the maximum number of sequences supported by one batch."""
self._assert_ctx()
return llama_cpp.llama_n_seq_max(self.ctx)
def n_rs_seq(self) -> int:
"""Return the configured recurrent-state snapshot capacity per sequence."""
self._assert_ctx()
return llama_cpp.llama_n_rs_seq(self.ctx)
def pooling_type(self) -> int:
"""Return the active ``llama_pooling_type`` value."""
self._assert_ctx()
return llama_cpp.llama_pooling_type(self.ctx)
# // Memory API
def _register_checkpoint_cache(self, cache):
"""Track a borrower without extending its lifetime."""
self._assert_ctx()
if not hasattr(self, "_checkpoint_caches"):
self._checkpoint_caches = weakref.WeakSet()
self._checkpoint_caches.add(cache)
cache._context_ref = weakref.ref(self)
def _invalidate_checkpoints(
self, seq_id: int = -1, *, suffix_start: Optional[int] = None,
keep_seq_id: Optional[int] = None, device_only: bool = False,
) -> None:
"""Invalidate registered partial snapshots, not draft engine state.
Raw C API calls and automatic SWA eviction bypass this bookkeeping.
Callers still own token/output synchronization and shared-KV dependencies.
"""
for cache in list(getattr(self, "_checkpoint_caches", ())):
if device_only and not cache.on_device:
continue
cache._invalidate_memory(seq_id, suffix_start, keep_seq_id)
def get_memory(self):
"""Return the context-owned native memory/KV-cache handle.
The handle is non-owning and must not be freed by Python. Operations on
it do not implicitly wait for pending graph execution.
"""
self._assert_ctx()
return llama_cpp.llama_get_memory(self.ctx)
def memory_clear(self, data: bool):
"""Clear memory metadata and optionally zero the backing data buffers.
This method does not synchronize pending backend work. Callers use it
at reset or other established context boundaries.
"""
memory = self.get_memory()
self._invalidate_checkpoints()
llama_cpp.llama_memory_clear(memory, data)
def memory_seq_rm(self, seq_id: int, p0: int, p1: int) -> bool:
"""Remove sequence cells in ``[p0, p1)``.
Negative bounds follow llama.cpp conventions: ``p0 < 0`` starts at
zero and ``p1 < 0`` extends through the sequence end. Returns ``False``
when the selected memory implementation cannot remove only that range.
The operation does not synchronize pending backend work.
"""
memory = self.get_memory()
try:
removed = llama_cpp.llama_memory_seq_rm(memory, seq_id, p0, p1)
except BaseException:
self._invalidate_checkpoints(seq_id)
raise
if removed and (p1 < 0 or p1 > max(0, p0)):
self._invalidate_checkpoints(
seq_id, suffix_start=max(0, p0) if p1 < 0 else None
)
return removed
def memory_seq_cp(self, seq_id_src: int, seq_id_dst: int, p0: int, p1: int):
"""Copy source-sequence memory cells in ``[p0, p1)`` to a destination.
Negative bounds follow the same convention as ``memory_seq_rm()``.
This operation does not synchronize pending backend work.
"""
memory = self.get_memory()
if seq_id_src != seq_id_dst and (p1 < 0 or p1 > max(0, p0)):
self._invalidate_checkpoints(seq_id_dst)
llama_cpp.llama_memory_seq_cp(memory, seq_id_src, seq_id_dst, p0, p1)
def memory_seq_keep(self, seq_id: int):
"""Keep only memory cells assigned to ``seq_id`` without synchronizing."""
memory = self.get_memory()
self._invalidate_checkpoints(keep_seq_id=seq_id)
llama_cpp.llama_memory_seq_keep(memory, seq_id)
def memory_seq_add(self, seq_id: int, p0: int, p1: int, delta: int):
"""Add ``delta`` to positions in ``[p0, p1)`` without synchronizing."""
memory = self.get_memory()
if delta != 0 and (p1 < 0 or p1 > max(0, p0)):
self._invalidate_checkpoints(seq_id)
llama_cpp.llama_memory_seq_add(memory, seq_id, p0, p1, delta)
def memory_seq_div(self, seq_id: int, p0: int, p1: int, d: int):
"""Integer-divide positions in ``[p0, p1)`` without synchronizing."""
memory = self.get_memory()
if d != 1 and (p1 < 0 or p1 > max(0, p0)):
self._invalidate_checkpoints(seq_id)
llama_cpp.llama_memory_seq_div(memory, seq_id, p0, p1, d)
def memory_seq_pos_max(self, seq_id: int) -> int:
"""Return the greatest cached position for ``seq_id``, or native sentinel."""
return llama_cpp.llama_memory_seq_pos_max(self.get_memory(), seq_id)
def memory_seq_pos_min(self, seq_id: int) -> int:
"""Return the smallest cached position for ``seq_id``, or native sentinel."""
return llama_cpp.llama_memory_seq_pos_min(self.get_memory(), seq_id)
def memory_can_shift(self) -> bool:
"""Return whether the active memory implementation supports position shifts."""
return llama_cpp.llama_memory_can_shift(self.get_memory())
# // State / sessions API
def get_state_size(self) -> int:
"""Return the current full-state size in bytes for serialization.
This is the actual save size, not a guaranteed upper bound for loading
an arbitrary state produced by another context.
"""
self._assert_ctx()
return llama_cpp.llama_state_get_size(self.ctx)
def get_state_data(self, dst:ctypes.Array[ctypes.c_uint8], size: int) -> int:
"""Synchronize and copy the complete context state into ``dst``.
Returns the number of bytes written. ``dst`` must provide at least
``size`` writable bytes.
"""
self._assert_ctx()
return llama_cpp.llama_state_get_data(self.ctx, dst, size)
def set_state_data(self, src:ctypes.Array[ctypes.c_uint8], size: int) -> int:
"""Synchronize, restore complete state from ``src``, and return bytes read."""
self._assert_ctx()
self._invalidate_checkpoints()
return llama_cpp.llama_state_set_data(self.ctx, src, size)
def load_state_file(
self,
path_session: bytes,
tokens_out: ctypes.Array[llama_cpp.llama_token],
n_token_capacity: ctypes.c_size_t,
n_token_count_out: CtypesPointer[ctypes.c_size_t]
) -> bool:
"""Synchronize, then load complete state and token history from a file."""
self._assert_ctx()
self._invalidate_checkpoints()
return llama_cpp.llama_state_load_file(self.ctx, path_session, tokens_out, n_token_capacity, n_token_count_out)
def save_state_file(
self,
path_session: bytes,
tokens: ctypes.Array[llama_cpp.llama_token],
n_token_count: ctypes.c_size_t
) -> bool:
"""Synchronize, then save complete state and token history to a file."""
self._assert_ctx()
return llama_cpp.llama_state_save_file(self.ctx, path_session, tokens, n_token_count)
def get_state_seq_size(self, seq_id: int) -> int:
"""Return the exact host-serialized state size for one sequence."""
self._assert_ctx()
return llama_cpp.llama_state_seq_get_size(self.ctx, seq_id)
def get_state_seq_data(self, dst: ctypes.Array[ctypes.c_uint8], size: int, seq_id: int) -> int:
"""Synchronize and copy one sequence state into ``dst``."""
self._assert_ctx()
return llama_cpp.llama_state_seq_get_data(self.ctx, dst, size, seq_id)
def set_state_seq_data(self, src: ctypes.Array[ctypes.c_uint8], size: int, dest_seq_id: int) -> int:
"""Synchronize and restore serialized state into ``dest_seq_id``."""
self._assert_ctx()
self._invalidate_checkpoints(dest_seq_id)
return llama_cpp.llama_state_seq_set_data(self.ctx, src, size, dest_seq_id)
def load_state_seq_file(
self,
filepath: bytes,
dest_seq_id: int,
tokens_out: ctypes.Array[llama_cpp.llama_token],
n_token_capacity: ctypes.c_size_t,
n_token_count_out: CtypesPointer[ctypes.c_size_t]
) -> int:
"""Synchronize, then load sequence state and tokens into ``dest_seq_id``."""
self._assert_ctx()
self._invalidate_checkpoints(dest_seq_id)
return llama_cpp.llama_state_seq_load_file(self.ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out)
def save_state_seq_file(
self,
filepath: bytes,
seq_id: int,
tokens: ctypes.Array[llama_cpp.llama_token],
n_token_count: ctypes.c_size_t
) -> int:
"""Synchronize, then save one sequence state and tokens to a file."""
self._assert_ctx()
return llama_cpp.llama_state_seq_save_file(self.ctx, filepath, seq_id, tokens, n_token_count)
def get_state_seq_size_ext(self, seq_id: int, flags: llama_cpp.llama_state_seq_flags) -> int:
"""Return the sequence-state size for an extended serialization mode.
``flags`` can restrict the snapshot to partial/recurrent state and can
request device-resident snapshots. Device snapshots are opaque and a
subsequent capture for the same sequence invalidates the prior one.
"""
self._assert_ctx()
return llama_cpp.llama_state_seq_get_size_ext(self.ctx, seq_id, flags)
def get_state_seq_data_ext(
self,
dst:ctypes.Array[ctypes.c_uint8],
size: int,
seq_id: int,
flags: llama_cpp.llama_state_seq_flags
) -> int:
"""Synchronize and capture one sequence according to ``flags``.
Returns the number of bytes or opaque handle bytes written to ``dst``.
"""
self._assert_ctx()
if flags & llama_cpp.LLAMA_STATE_SEQ_FLAGS_ON_DEVICE:
self._invalidate_checkpoints(seq_id, device_only=True)
return llama_cpp.llama_state_seq_get_data_ext(self.ctx, dst, size, seq_id, flags)
def set_state_seq_data_ext(
self,
src:ctypes.Array[ctypes.c_uint8],
size: int,
dest_seq_id: int,
flags: llama_cpp.llama_state_seq_flags
) -> int:
"""Synchronize and restore an extended snapshot into ``dest_seq_id``."""
self._assert_ctx()
self._invalidate_checkpoints(dest_seq_id)
return llama_cpp.llama_state_seq_set_data_ext(self.ctx, src, size, dest_seq_id, flags)
# // Decoding API
def encode(self, batch: Union[LlamaBatch, LlamaBatchExt], *, start: int = 0, count: Optional[int] = None):
"""Run an encoder batch without using the decoder KV cache.
Raises ``RuntimeError`` for every nonzero native return code. On a
native error, llama.cpp restores memory to its pre-call state.
Extended batches support explicit logical ranges via start/count.
"""
self._assert_ctx()