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2317 lines (2043 loc) · 87.6 KB
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import os
import sys
import uuid
import time
import math
import multiprocessing
from abc import ABC, abstractmethod
from typing import (
List,
Optional,
Union,
Generator,
Sequence,
Iterator,
Deque,
Tuple,
Callable,
)
from collections import deque, OrderedDict
import diskcache
import ctypes
from .llama_types import *
from .llama_grammar import LlamaGrammar
import llama_cpp.llama_cpp as llama_cpp
import llama_cpp.llama_chat_format as llama_chat_format
import numpy as np
import numpy.typing as npt
from ._utils import suppress_stdout_stderr
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]) -> "LlamaState":
raise NotImplementedError
@abstractmethod
def __contains__(self, key: Sequence[int]) -> bool:
raise NotImplementedError
@abstractmethod
def __setitem__(self, key: Sequence[int], value: "LlamaState") -> None:
raise NotImplementedError
class LlamaRAMCache(BaseLlamaCache):
"""Cache for a llama.cpp model using RAM."""
def __init__(self, capacity_bytes: int = (2 << 30)):
super().__init__(capacity_bytes)
self.capacity_bytes = capacity_bytes
self.cache_state: OrderedDict[Tuple[int, ...], "LlamaState"] = OrderedDict()
@property
def cache_size(self):
return sum([state.llama_state_size for state in self.cache_state.values()])
def _find_longest_prefix_key(
self,
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
min_len = 0
min_key = None
keys = (
(k, Llama.longest_token_prefix(k, key)) 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]) -> "LlamaState":
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:
return self._find_longest_prefix_key(tuple(key)) is not None
def __setitem__(self, key: Sequence[int], value: "LlamaState"):
key = tuple(key)
if key in self.cache_state:
del self.cache_state[key]
self.cache_state[key] = value
while self.cache_size > self.capacity_bytes and len(self.cache_state) > 0:
self.cache_state.popitem(last=False)
# Alias for backwards compatibility
LlamaCache = LlamaRAMCache
class LlamaDiskCache(BaseLlamaCache):
"""Cache for a llama.cpp model using disk."""
def __init__(
self, cache_dir: str = ".cache/llama_cache", capacity_bytes: int = (2 << 30)
):
super().__init__(capacity_bytes)
self.cache = diskcache.Cache(cache_dir)
@property
def cache_size(self):
return int(self.cache.volume()) # type: ignore
def _find_longest_prefix_key(
self,
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
min_len = 0
min_key: Optional[Tuple[int, ...]] = None
for k in self.cache.iterkeys(): # type: ignore
prefix_len = Llama.longest_token_prefix(k, key)
if prefix_len > min_len:
min_len = prefix_len
min_key = k # type: ignore
return min_key
def __getitem__(self, key: Sequence[int]) -> "LlamaState":
key = tuple(key)
_key = self._find_longest_prefix_key(key)
if _key is None:
raise KeyError("Key not found")
value: "LlamaState" = self.cache.pop(_key) # type: ignore
# NOTE: This puts an integer as key in cache, which breaks,
# Llama.longest_token_prefix(k, key) above since k is not a tuple of ints/tokens
# self.cache.push(_key, side="front") # type: ignore
return value
def __contains__(self, key: Sequence[int]) -> bool:
return self._find_longest_prefix_key(tuple(key)) is not None
def __setitem__(self, key: Sequence[int], value: "LlamaState"):
print("LlamaDiskCache.__setitem__: called", file=sys.stderr)
key = tuple(key)
if key in self.cache:
print("LlamaDiskCache.__setitem__: delete", file=sys.stderr)
del self.cache[key]
self.cache[key] = value
print("LlamaDiskCache.__setitem__: set", file=sys.stderr)
while self.cache_size > self.capacity_bytes and len(self.cache) > 0:
key_to_remove = next(iter(self.cache))
del self.cache[key_to_remove]
print("LlamaDiskCache.__setitem__: trim", file=sys.stderr)
class LlamaState:
def __init__(
self,
input_ids: npt.NDArray[np.intc],
scores: npt.NDArray[np.single],
n_tokens: int,
llama_state: bytes,
llama_state_size: int,
):
self.input_ids = input_ids
self.scores = scores
self.n_tokens = n_tokens
self.llama_state = llama_state
self.llama_state_size = llama_state_size
LogitsProcessor = Callable[
[npt.NDArray[np.intc], npt.NDArray[np.single]], npt.NDArray[np.single]
]
class LogitsProcessorList(List[LogitsProcessor]):
def __call__(
self, input_ids: npt.NDArray[np.intc], scores: npt.NDArray[np.single]
) -> npt.NDArray[np.single]:
for processor in self:
scores = processor(input_ids, scores)
return scores
StoppingCriteria = Callable[[npt.NDArray[np.intc], npt.NDArray[np.single]], bool]
class StoppingCriteriaList(List[StoppingCriteria]):
def __call__(
self, input_ids: npt.NDArray[np.intc], logits: npt.NDArray[np.single]
) -> bool:
return any([stopping_criteria(input_ids, logits) for stopping_criteria in self])
class _LlamaModel:
"""Intermediate Python wrapper for a llama.cpp llama_model.
NOTE: For stability it's recommended you use the Llama class instead."""
_llama_free_model = None
# NOTE: this must be "saved" here to avoid exceptions when calling __del__
suppress_stdout_stderr = suppress_stdout_stderr
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._llama_free_model = llama_cpp._lib.llama_free_model # type: ignore
if not os.path.exists(path_model):
raise ValueError(f"Model path does not exist: {path_model}")
with suppress_stdout_stderr(disable=self.verbose):
self.model = llama_cpp.llama_load_model_from_file(
self.path_model.encode("utf-8"), self.params
)
def __del__(self):
with self.suppress_stdout_stderr(disable=self.verbose):
if self.model is not None and self._llama_free_model is not None:
self._llama_free_model(self.model)
self.model = None
def vocab_type(self) -> int:
assert self.model is not None
return llama_cpp.llama_vocab_type(self.model)
def n_vocab(self) -> int:
assert self.model is not None
return llama_cpp.llama_n_vocab(self.model)
def n_ctx_train(self) -> int:
assert self.model is not None
return llama_cpp.llama_n_ctx_train(self.model)
def n_embd(self) -> int:
assert self.model is not None
return llama_cpp.llama_n_embd(self.model)
def rope_freq_scale_train(self) -> float:
assert self.model is not None
return llama_cpp.llama_rope_freq_scale_train(self.model)
def desc(self) -> str:
assert self.model is not None
buf = ctypes.create_string_buffer(1024)
llama_cpp.llama_model_desc(self.model, buf, 1024) # type: ignore
return buf.value.decode("utf-8")
def size(self) -> int:
assert self.model is not None
return llama_cpp.llama_model_size(self.model)
def n_params(self) -> int:
assert self.model is not None
return llama_cpp.llama_model_n_params(self.model)
def get_tensor(self, name: str) -> ctypes.c_void_p:
assert self.model is not None
return llama_cpp.llama_get_model_tensor(self.model, name.encode("utf-8"))
def apply_lora_from_file(
self,
lora_path: str,
scale: float,
path_base_model: Optional[str],
n_threads: int,
):
assert self.model is not None
return llama_cpp.llama_model_apply_lora_from_file(
self.model,
lora_path.encode("utf-8"),
scale,
path_base_model.encode("utf-8")
if path_base_model is not None
else llama_cpp.c_char_p(0),
n_threads,
)
# Vocab
def token_get_text(self, token: int) -> str:
# TODO: Fix
assert self.model is not None
return llama_cpp.llama_token_get_text(self.model, token).decode("utf-8")
def token_get_score(self, token: int) -> float:
assert self.model is not None
return llama_cpp.llama_token_get_score(self.model, token)
def token_get_type(self, token: int) -> int:
assert self.model is not None
return llama_cpp.llama_token_get_type(self.model, token)
# Special tokens
def token_bos(self) -> int:
assert self.model is not None
return llama_cpp.llama_token_bos(self.model)
def token_eos(self) -> int:
assert self.model is not None
return llama_cpp.llama_token_eos(self.model)
def token_nl(self) -> int:
assert self.model is not None
return llama_cpp.llama_token_nl(self.model)
def token_prefix(self) -> int:
assert self.model is not None
return llama_cpp.llama_token_prefix(self.model)
def token_middle(self) -> int:
assert self.model is not None
return llama_cpp.llama_token_middle(self.model)
def token_suffix(self) -> int:
assert self.model is not None
return llama_cpp.llama_token_suffix(self.model)
def token_eot(self) -> int:
assert self.model is not None
return llama_cpp.llama_token_eot(self.model)
# Tokenization
def tokenize(self, text: bytes, add_bos: bool, special: bool):
assert self.model is not None
n_ctx = self.n_ctx_train()
tokens = (llama_cpp.llama_token * n_ctx)()
n_tokens = llama_cpp.llama_tokenize(
self.model, text, len(text), tokens, n_ctx, add_bos, special
)
if n_tokens < 0:
n_tokens = abs(n_tokens)
tokens = (llama_cpp.llama_token * n_tokens)()
n_tokens = llama_cpp.llama_tokenize(
self.model, text, len(text), tokens, n_tokens, add_bos, special
)
if n_tokens < 0:
raise RuntimeError(
f'Failed to tokenize: text="{text}" n_tokens={n_tokens}'
)
return list(tokens[:n_tokens])
def token_to_piece(self, token: int) -> bytes:
assert self.model is not None
buf = ctypes.create_string_buffer(32)
llama_cpp.llama_token_to_piece(self.model, token, buf, 32) # type: ignore
return bytes(buf)
def detokenize(self, tokens: List[int]) -> bytes:
assert self.model is not None
output = b""
size = 32
buffer = (ctypes.c_char * size)()
for token in tokens:
n = llama_cpp.llama_token_to_piece(
self.model, llama_cpp.llama_token(token), buffer, size
)
assert n <= size
output += bytes(buffer[:n])
# NOTE: Llama1 models automatically added a space at the start of the prompt
# this line removes a leading space if the first token is a beginning of sentence token
return (
output[1:] if len(tokens) > 0 and tokens[0] == self.token_bos() else output
)
@staticmethod
def default_params():
"""Get the default llama_model_params."""
return llama_cpp.llama_model_default_params()
class _LlamaContext:
"""Intermediate Python wrapper for a llama.cpp llama_context.
NOTE: For stability it's recommended you use the Llama class instead."""
_llama_free = None
# NOTE: this must be "saved" here to avoid exceptions when calling __del__
suppress_stdout_stderr = suppress_stdout_stderr
def __init__(
self,
*,
model: _LlamaModel,
params: llama_cpp.llama_context_params,
verbose: bool = True,
):
self.model = model
self.params = params
self.verbose = verbose
self._llama_free = llama_cpp._lib.llama_free # type: ignore
with suppress_stdout_stderr(disable=self.verbose):
self.ctx = llama_cpp.llama_new_context_with_model(
self.model.model, self.params
)
def __del__(self):
with self.suppress_stdout_stderr(disable=self.verbose):
if self.ctx is not None and self._llama_free is not None:
self._llama_free(self.ctx)
self.ctx = None
def n_ctx(self) -> int:
assert self.ctx is not None
return llama_cpp.llama_n_ctx(self.ctx)
def kv_cache_clear(self):
assert self.ctx is not None
llama_cpp.llama_kv_cache_clear(self.ctx)
def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int):
assert self.ctx is not None
llama_cpp.llama_kv_cache_seq_rm(self.ctx, seq_id, p0, p1)
def kv_cache_seq_cp(self, seq_id_src: int, seq_id_dst: int, p0: int, p1: int):
assert self.ctx is not None
llama_cpp.llama_kv_cache_seq_cp(self.ctx, seq_id_src, seq_id_dst, p0, p1)
def kv_cache_seq_keep(self, seq_id: int):
assert self.ctx is not None
llama_cpp.llama_kv_cache_seq_keep(self.ctx, seq_id)
def kv_cache_seq_shift(self, seq_id: int, p0: int, p1: int, shift: int):
assert self.ctx is not None
llama_cpp.llama_kv_cache_seq_shift(self.ctx, seq_id, p0, p1, shift)
def get_state_size(self) -> int:
assert self.ctx is not None
return llama_cpp.llama_get_state_size(self.ctx)
# TODO: copy_state_data
# TODO: set_state_data
# TODO: llama_load_session_file
# TODO: llama_save_session_file
def decode(self, batch: "_LlamaBatch"):
assert self.ctx is not None
assert batch.batch is not None
return_code = llama_cpp.llama_decode(
ctx=self.ctx,
batch=batch.batch,
)
if return_code != 0:
raise RuntimeError(f"llama_decode returned {return_code}")
def set_n_threads(self, n_threads: int, n_threads_batch: int):
assert self.ctx is not None
llama_cpp.llama_set_n_threads(self.ctx, n_threads, n_threads_batch)
def get_logits(self):
assert self.ctx is not None
return llama_cpp.llama_get_logits(self.ctx)
def get_logits_ith(self, i: int):
assert self.ctx is not None
return llama_cpp.llama_get_logits_ith(self.ctx, i)
def get_embeddings(self):
assert self.ctx is not None
return llama_cpp.llama_get_embeddings(self.ctx)
# Sampling functions
def set_rng_seed(self, seed: int):
assert self.ctx is not None
llama_cpp.llama_set_rng_seed(self.ctx, seed)
def sample_repetition_penalties(
self,
candidates: "_LlamaTokenDataArray",
last_tokens_data: "llama_cpp.Array[llama_cpp.llama_token]",
penalty_last_n: int,
penalty_repeat: float,
penalty_freq: float,
penalty_present: float,
):
assert self.ctx is not None
llama_cpp.llama_sample_repetition_penalties(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
last_tokens_data,
penalty_last_n,
penalty_repeat,
penalty_freq,
penalty_present,
)
def sample_classifier_free_guidance(
self,
candidates: "_LlamaTokenDataArray",
guidance_ctx: "_LlamaContext",
scale: float,
):
assert self.ctx is not None
assert guidance_ctx.ctx is not None
llama_cpp.llama_sample_classifier_free_guidance(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
guidance_ctx.ctx,
scale,
)
def sample_softmax(self, candidates: "_LlamaTokenDataArray"):
assert self.ctx is not None
llama_cpp.llama_sample_softmax(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
)
def sample_top_k(self, candidates: "_LlamaTokenDataArray", k: int, min_keep: int):
assert self.ctx is not None
llama_cpp.llama_sample_top_k(
self.ctx, ctypes.byref(candidates.candidates), k, min_keep # type: ignore
)
def sample_top_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int):
assert self.ctx is not None
llama_cpp.llama_sample_top_p(
self.ctx, ctypes.byref(candidates.candidates), p, min_keep # type: ignore
)
def sample_min_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int):
assert self.ctx is not None
llama_cpp.llama_sample_min_p(
self.ctx, ctypes.byref(candidates.candidates), p, min_keep # type: ignore
)
def sample_tail_free(
self, candidates: "_LlamaTokenDataArray", z: float, min_keep: int
):
assert self.ctx is not None
llama_cpp.llama_sample_tail_free(
self.ctx, ctypes.byref(candidates.candidates), z, min_keep # type: ignore
)
def sample_typical(
self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int
):
assert self.ctx is not None
llama_cpp.llama_sample_typical(
self.ctx, ctypes.byref(candidates.candidates), p, min_keep # type: ignore
)
def sample_temp(self, candidates: "_LlamaTokenDataArray", temp: float):
assert self.ctx is not None
llama_cpp.llama_sample_temp(
self.ctx, ctypes.byref(candidates.candidates), temp # type: ignore
)
def sample_grammar(self, candidates: "_LlamaTokenDataArray", grammar: LlamaGrammar):
assert self.ctx is not None
assert grammar.grammar is not None
llama_cpp.llama_sample_grammar(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
grammar.grammar,
)
def sample_token_mirostat(
self,
candidates: "_LlamaTokenDataArray",
tau: float,
eta: float,
m: int,
mu: float,
) -> int:
assert self.ctx is not None
return llama_cpp.llama_sample_token_mirostat(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
tau,
eta,
m,
ctypes.pointer(ctypes.c_float(mu)),
)
def sample_token_mirostat_v2(
self, candidates: "_LlamaTokenDataArray", tau: float, eta: float, mu: float
) -> int:
assert self.ctx is not None
return llama_cpp.llama_sample_token_mirostat_v2(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
tau,
eta,
ctypes.pointer(ctypes.c_float(mu)),
)
def sample_token_greedy(self, candidates: "_LlamaTokenDataArray") -> int:
assert self.ctx is not None
return llama_cpp.llama_sample_token_greedy(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
)
def sample_token(self, candidates: "_LlamaTokenDataArray") -> int:
assert self.ctx is not None
return llama_cpp.llama_sample_token(
self.ctx,
ctypes.byref(candidates.candidates), # type: ignore
)
# Grammar
def grammar_accept_token(self, grammar: LlamaGrammar, token: int):
assert self.ctx is not None
assert grammar.grammar is not None
llama_cpp.llama_grammar_accept_token(self.ctx, grammar.grammar, token)
def reset_timings(self):
assert self.ctx is not None
llama_cpp.llama_reset_timings(self.ctx)
def print_timings(self):
assert self.ctx is not None
llama_cpp.llama_print_timings(self.ctx)
# Utility functions
@staticmethod
def default_params():
"""Get the default llama_context_params."""
return llama_cpp.llama_context_default_params()
class _LlamaBatch:
_llama_batch_free = None
# NOTE: this must be "saved" here to avoid exceptions when calling __del__
suppress_stdout_stderr = suppress_stdout_stderr
def __init__(
self, *, n_tokens: int, embd: int, n_seq_max: int, verbose: bool = True
):
self.n_tokens = n_tokens
self.embd = embd
self.n_seq_max = n_seq_max
self.verbose = verbose
self._llama_batch_free = llama_cpp._lib.llama_batch_free # type: ignore
with suppress_stdout_stderr(disable=self.verbose):
self.batch = llama_cpp.llama_batch_init(
self.n_tokens, self.embd, self.n_seq_max
)
def __del__(self):
with self.suppress_stdout_stderr(disable=self.verbose):
if self.batch is not None and self._llama_batch_free is not None:
self._llama_batch_free(self.batch)
self.batch = None
def set_batch(self, batch: Sequence[int], n_past: int, logits_all: bool):
assert self.batch is not None
n_tokens = len(batch)
self.batch.n_tokens = n_tokens
for i in range(n_tokens):
self.batch.token[i] = batch[i]
self.batch.pos[i] = n_past + i
self.batch.seq_id[i][0] = 0
self.batch.n_seq_id[i] = 1
self.batch.logits[i] = logits_all
self.batch.logits[n_tokens - 1] = True
class _LlamaTokenDataArray:
def __init__(self, *, n_vocab: int):
self.n_vocab = n_vocab
self.candidates_data = np.array(
[],
dtype=np.dtype(
[("id", np.intc), ("logit", np.single), ("p", np.single)], align=True
),
)
self.candidates_data.resize(3, self.n_vocab, refcheck=False)
self.candidates = llama_cpp.llama_token_data_array(
data=self.candidates_data.ctypes.data_as(llama_cpp.llama_token_data_p),
size=self.n_vocab,
sorted=False,
)
self.default_candidates_data_id = np.arange(self.n_vocab, dtype=np.intc)
self.default_candidates_data_p = np.zeros(self.n_vocab, dtype=np.single)
def copy_logits(self, logits: npt.NDArray[np.single]):
self.candidates_data["id"][:] = self.default_candidates_data_id
self.candidates_data["logit"][:] = logits
self.candidates_data["p"][:] = self.default_candidates_data_p
self.candidates.data = self.candidates_data.ctypes.data_as(
llama_cpp.llama_token_data_p
)
self.candidates.sorted = llama_cpp.c_bool(False)
self.candidates.size = llama_cpp.c_size_t(self.n_vocab)
class Llama:
"""High-level Python wrapper for a llama.cpp model."""
__backend_initialized = False
def __init__(
self,
model_path: str,
*,
# Model Params
n_gpu_layers: int = 0,
main_gpu: int = 0,
tensor_split: Optional[List[float]] = None,
vocab_only: bool = False,
use_mmap: bool = True,
use_mlock: bool = False,
# Context Params
seed: int = llama_cpp.LLAMA_DEFAULT_SEED,
n_ctx: int = 512,
n_batch: int = 512,
n_threads: Optional[int] = None,
n_threads_batch: Optional[int] = None,
rope_scaling_type: Optional[int] = llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED,
rope_freq_base: float = 0.0,
rope_freq_scale: float = 0.0,
yarn_ext_factor: float = -1.0,
yarn_attn_factor: float = 1.0,
yarn_beta_fast: float = 32.0,
yarn_beta_slow: float = 1.0,
yarn_orig_ctx: int = 0,
mul_mat_q: bool = True,
f16_kv: bool = True,
logits_all: bool = False,
embedding: bool = False,
# Sampling Params
last_n_tokens_size: int = 64,
# LoRA Params
lora_base: Optional[str] = None,
lora_scale: float = 1.0,
lora_path: Optional[str] = None,
# Backend Params
numa: bool = False,
# Chat Format Params
chat_format: str = "llama-2",
chat_handler: Optional[llama_chat_format.LlamaChatCompletionHandler] = None,
# Misc
verbose: bool = True,
# Extra Params
**kwargs, # type: ignore
):
"""Load a llama.cpp model from `model_path`.
Examples:
Basic usage
>>> import llama_cpp
>>> model = llama_cpp.Llama(
... model_path="path/to/model",
... )
>>> print(model("The quick brown fox jumps ", stop=["."])["choices"][0]["text"])
the lazy dog
Loading a chat model
>>> import llama_cpp
>>> model = llama_cpp.Llama(
... model_path="path/to/model",
... chat_format="llama-2",
... )
>>> print(model.create_chat_completion(
... messages=[{
... "role": "user",
... "content": "what is the meaning of life?"
... }]
... ))
Args:
model_path: Path to the model.
n_gpu_layers: Number of layers to offload to GPU (-ngl). If -1, all layers are offloaded.
main_gpu: The GPU that is used for scratch and small tensors.
tensor_split: How split tensors should be distributed across GPUs. If None, the model is not split.
vocab_only: Only load the vocabulary no weights.
use_mmap: Use mmap if possible.
use_mlock: Force the system to keep the model in RAM.
seed: RNG seed, -1 for random
n_ctx: Text context, 0 = from model
n_batch: Prompt processing maximum batch size
n_threads: Number of threads to use for generation
n_threads_batch: Number of threads to use for batch processing
rope_scaling_type: RoPE scaling type, from `enum llama_rope_scaling_type`. ref: https://github.com/ggerganov/llama.cpp/pull/2054
rope_freq_base: RoPE base frequency, 0 = from model
rope_freq_scale: RoPE frequency scaling factor, 0 = from model
yarn_ext_factor: YaRN extrapolation mix factor, negative = from model
yarn_attn_factor: YaRN magnitude scaling factor
yarn_beta_fast: YaRN low correction dim
yarn_beta_slow: YaRN high correction dim
yarn_orig_ctx: YaRN original context size
f16_kv: Use fp16 for KV cache, fp32 otherwise
logits_all: Return logits for all tokens, not just the last token. Must be True for completion to return logprobs.
embedding: Embedding mode only.
last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
lora_path: Path to a LoRA file to apply to the model.
numa: Enable NUMA support. (NOTE: The initial value of this parameter is used for the remainder of the program as this value is set in llama_backend_init)
chat_format: String specifying the chat format to use when calling create_chat_completion.
chat_handler: Optional chat handler to use when calling create_chat_completion.
verbose: Print verbose output to stderr.
Raises:
ValueError: If the model path does not exist.
Returns:
A Llama instance.
"""
self.verbose = verbose
self.numa = numa
if not Llama.__backend_initialized:
with suppress_stdout_stderr(disable=self.verbose):
llama_cpp.llama_backend_init(self.numa)
Llama.__backend_initialized = True
self.model_path = model_path
# Model Params
self.model_params = llama_cpp.llama_model_default_params()
self.model_params.n_gpu_layers = (
0x7FFFFFFF if n_gpu_layers == -1 else n_gpu_layers
) # 0x7FFFFFFF is INT32 max, will be auto set to all layers
self.model_params.main_gpu = main_gpu
self.tensor_split = tensor_split
self._p_tensor_split = None
if self.tensor_split is not None:
if len(self.tensor_split) > llama_cpp.LLAMA_MAX_DEVICES:
raise ValueError(
f"Attempt to split tensors that exceed maximum supported devices. Current LLAMA_MAX_DEVICES={llama_cpp.LLAMA_MAX_DEVICES}"
)
# Type conversion and expand the list to the length of LLAMA_MAX_DEVICES
FloatArray = ctypes.c_float * llama_cpp.LLAMA_MAX_DEVICES
self._c_tensor_split = FloatArray(
*tensor_split # type: ignore
) # keep a reference to the array so it is not gc'd
self.model_params.tensor_split = self._c_tensor_split
self.model_params.vocab_only = vocab_only
self.model_params.use_mmap = use_mmap if lora_path is None else False
self.model_params.use_mlock = use_mlock
self.n_batch = min(n_ctx, n_batch) # ???
self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
self.n_threads_batch = n_threads_batch or max(
multiprocessing.cpu_count() // 2, 1
)
# Context Params
self.context_params = llama_cpp.llama_context_default_params()
self.context_params.seed = seed
self.context_params.n_ctx = n_ctx
self.context_params.n_batch = self.n_batch
self.context_params.n_threads = self.n_threads
self.context_params.n_threads_batch = self.n_threads_batch
self.context_params.rope_scaling_type = (
rope_scaling_type
if rope_scaling_type is not None
else llama_cpp.LLAMA_ROPE_SCALING_UNSPECIFIED
)
self.context_params.rope_freq_base = (
rope_freq_base if rope_freq_base != 0.0 else 0
)
self.context_params.rope_freq_scale = (
rope_freq_scale if rope_freq_scale != 0.0 else 0
)
self.context_params.yarn_ext_factor = (
yarn_ext_factor if yarn_ext_factor != 0.0 else 0
)
self.context_params.yarn_attn_factor = (
yarn_attn_factor if yarn_attn_factor != 0.0 else 0
)
self.context_params.yarn_beta_fast = (
yarn_beta_fast if yarn_beta_fast != 0.0 else 0
)
self.context_params.yarn_beta_slow = (
yarn_beta_slow if yarn_beta_slow != 0.0 else 0
)
self.context_params.yarn_orig_ctx = yarn_orig_ctx if yarn_orig_ctx != 0 else 0
self.context_params.mul_mat_q = mul_mat_q
# self.context_params.f16_kv = f16_kv
self.context_params.logits_all = logits_all
self.context_params.embedding = embedding
# Sampling Params
self.last_n_tokens_size = last_n_tokens_size
self.cache: Optional[BaseLlamaCache] = None
self.lora_base = lora_base
self.lora_scale = lora_scale
self.lora_path = lora_path
if not os.path.exists(model_path):
raise ValueError(f"Model path does not exist: {model_path}")
self._model = _LlamaModel(
path_model=self.model_path, params=self.model_params, verbose=self.verbose
)
self._ctx = _LlamaContext(
model=self._model,
params=self.context_params,
verbose=self.verbose,
)
self._batch = _LlamaBatch(
n_tokens=self.n_batch,
embd=0,
n_seq_max=self.context_params.n_ctx,
verbose=self.verbose,
)
if self.lora_path:
if self._model.apply_lora_from_file(
self.lora_path,
self.lora_scale,
self.lora_base,
self.n_threads,
):
raise RuntimeError(
f"Failed to apply LoRA from lora path: {self.lora_path} to base path: {self.lora_base}"
)
if self.verbose:
print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
self.chat_format = chat_format
self.chat_handler = chat_handler
self._n_vocab = self.n_vocab()
self._n_ctx = self.n_ctx()
self._token_nl = self.token_nl()
self._token_eos = self.token_eos()
self._candidates = _LlamaTokenDataArray(n_vocab=self._n_vocab)
self.n_tokens = 0
self.input_ids: npt.NDArray[np.intc] = np.ndarray((n_ctx,), dtype=np.intc)
self.scores: npt.NDArray[np.single] = np.ndarray(
(n_ctx, self._n_vocab), dtype=np.single
)
@property
def ctx(self) -> llama_cpp.llama_context_p:
assert self._ctx.ctx is not None
return self._ctx.ctx
@property
def model(self) -> llama_cpp.llama_model_p:
assert self._model.model is not None
return self._model.model
@property
def _input_ids(self) -> npt.NDArray[np.intc]:
return self.input_ids[: self.n_tokens]
@property
def _scores(self) -> npt.NDArray[np.single]:
return self.scores[: self.n_tokens, :]
@property
def eval_tokens(self) -> Deque[int]:
return deque(self.input_ids[: self.n_tokens].tolist(), maxlen=self._n_ctx)
@property
def eval_logits(self) -> Deque[List[float]]:
return deque(
self.scores[: self.n_tokens, :].tolist(),
maxlen=self._n_ctx if self.context_params.logits_all else 1,
)
def tokenize(