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888 lines (707 loc) · 29.4 KB
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from __future__ import annotations
import os
import ctypes
import warnings
from typing import (
Dict,
List,
Tuple,
Optional,
Sequence,
Callable,
Union,
)
from dataclasses import dataclass, field
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
# Python wrappers over llama.h structs
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()
# LlamaModel does not use samplers, but close() can run after partial init.
self.sampler = None
self.custom_samplers = []
model = None
if not os.path.exists(path_model):
raise ValueError(f"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}")
vocab = llama_cpp.llama_model_get_vocab(model)
if vocab is None:
raise ValueError(f"Failed to get vocab from model: {path_model}")
self.model = model
self.vocab = vocab
def free_model():
if self.model is None:
return
llama_cpp.llama_model_free(self.model)
self.model = None
self._exit_stack.callback(free_model)
def close(self):
if self.sampler is not None:
# NOTE: Must remove custom samplers before free or llama.cpp will try to free them
for i, _ in reversed(self.custom_samplers):
llama_cpp.llama_sampler_chain_remove(self.sampler, i)
self.custom_samplers.clear()
self._exit_stack.close()
def __del__(self):
self.close()
def vocab_type(self) -> int:
return llama_cpp.llama_vocab_type(self.vocab)
def n_vocab(self) -> int:
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_embd(self) -> int:
return llama_cpp.llama_model_n_embd(self.model)
def rope_freq_scale_train(self) -> float:
return llama_cpp.llama_model_rope_freq_scale_train(self.model)
def desc(self) -> str:
buf = ctypes.create_string_buffer(1024)
llama_cpp.llama_model_desc(self.model, buf, 1024)
return buf.value.decode("utf-8")
def size(self) -> int:
return llama_cpp.llama_model_size(self.model)
def n_params(self) -> int:
return llama_cpp.llama_model_n_params(self.model)
def get_tensor(self, name: str) -> ctypes.c_void_p:
raise NotImplementedError("get_tensor is not implemented in llama.cpp")
# Vocab
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)
# 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_cls(self) -> int:
return llama_cpp.llama_vocab_bos(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_prefix(self) -> int:
return llama_cpp.llama_vocab_fim_pre(self.vocab)
def token_middle(self) -> int:
return llama_cpp.llama_vocab_fim_mid(self.vocab)
def token_suffix(self) -> int:
return llama_cpp.llama_vocab_fim_suf(self.vocab)
def token_eot(self) -> int:
return llama_cpp.llama_vocab_eot(self.vocab)
def add_bos_token(self) -> bool:
return llama_cpp.llama_vocab_get_add_bos(self.vocab)
def add_eos_token(self) -> bool:
return llama_cpp.llama_vocab_get_add_eos(self.vocab)
# Tokenization
def tokenize(self, text: bytes, add_bos: bool, special: bool):
n_ctx = self.n_ctx_train()
tokens = (llama_cpp.llama_token * n_ctx)()
n_tokens = llama_cpp.llama_tokenize(
self.vocab, 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.vocab, 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, special: bool = False) -> bytes:
buf = ctypes.create_string_buffer(32)
llama_cpp.llama_token_to_piece(self.vocab, token, buf, 32, 0, special)
return bytes(buf)
def detokenize(self, tokens: List[int], special: bool = False) -> bytes:
output = b""
size = 32
buffer = (ctypes.c_char * size)()
for token in tokens:
n = llama_cpp.llama_token_to_piece(
self.vocab, llama_cpp.llama_token(token), buffer, size, 0, special
)
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() and output[0:1] == b" "
else output
)
# Extra
def metadata(self) -> Dict[str, str]:
metadata: Dict[str, str] = {}
buffer_size = 1024
buffer = ctypes.create_string_buffer(buffer_size)
# zero the buffer
buffer.value = b"\0" * buffer_size
# iterate over model keys
for i in range(llama_cpp.llama_model_meta_count(self.model)):
nbytes = llama_cpp.llama_model_meta_key_by_index(
self.model, i, buffer, buffer_size
)
if nbytes > buffer_size:
buffer_size = nbytes + 1
buffer = ctypes.create_string_buffer(buffer_size)
nbytes = llama_cpp.llama_model_meta_key_by_index(
self.model, i, buffer, buffer_size
)
key = buffer.value.decode("utf-8")
nbytes = llama_cpp.llama_model_meta_val_str_by_index(
self.model, i, buffer, buffer_size
)
if nbytes > buffer_size:
buffer_size = nbytes + 1
buffer = ctypes.create_string_buffer(buffer_size)
nbytes = llama_cpp.llama_model_meta_val_str_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 LlamaContext:
"""Intermediate Python wrapper for a llama.cpp llama_context.
NOTE: For stability it's recommended you use the Llama class instead."""
def __init__(
self,
*,
model: LlamaModel,
params: llama_cpp.llama_context_params,
verbose: bool = True,
):
self.model = model
self.params = params
self.verbose = verbose
self._exit_stack = ExitStack()
ctx = llama_cpp.llama_init_from_model(self.model.model, self.params)
if ctx is None:
raise ValueError("Failed to create llama_context")
self.ctx = ctx
self.memory = llama_cpp.llama_get_memory(self.ctx)
self.sampler = None # LlamaContext doesn't manage samplers directly, but some cleanup code expects this attribute
def free_ctx():
if self.ctx is None:
return
llama_cpp.llama_free(self.ctx)
self.ctx = None
self._exit_stack.callback(free_ctx)
# The native context must be freed before its model.
self.model._exit_stack.callback(self.close)
def close(self):
self._exit_stack.close()
def __del__(self):
self.close()
def n_ctx(self) -> int:
return llama_cpp.llama_n_ctx(self.ctx)
def pooling_type(self) -> int:
return llama_cpp.llama_pooling_type(self.ctx)
def kv_cache_clear(self):
# Embedding models with non-causal attention may not allocate memory.
if self.memory is None:
return
llama_cpp.llama_memory_clear(self.memory, True)
def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int) -> bool:
assert self.memory is not None, "Memory is not initialized"
seq_id = seq_id if seq_id >= 0 else 0
return llama_cpp.llama_memory_seq_rm(self.memory, seq_id, p0, p1)
def kv_cache_seq_cp(self, seq_id_src: int, seq_id_dst: int, p0: int, p1: int):
assert self.memory is not None, "Memory is not initialized"
llama_cpp.llama_memory_seq_cp(self.memory, seq_id_src, seq_id_dst, p0, p1)
def kv_cache_seq_keep(self, seq_id: int):
assert self.memory is not None, "Memory is not initialized"
llama_cpp.llama_memory_seq_keep(self.memory, seq_id)
def kv_cache_seq_shift(self, seq_id: int, p0: int, p1: int, shift: int):
assert self.memory is not None, "Memory is not initialized"
llama_cpp.llama_memory_seq_add(self.memory, seq_id, p0, p1, shift)
def get_state_size(self) -> int:
return llama_cpp.llama_state_get_size(self.ctx)
# TODO: copy_state_data
# TODO: set_state_data
# TODO: llama_state_load_file
# TODO: llama_state_save_file
def decode(self, batch: LlamaBatch):
return_code = llama_cpp.llama_decode(
self.ctx,
batch.batch,
)
if return_code != 0:
raise RuntimeError(f"llama_decode returned {return_code}")
def encode(self, batch: LlamaBatch):
return_code = llama_cpp.llama_encode(
self.ctx,
batch.batch,
)
if return_code != 0:
raise RuntimeError(f"llama_encode returned {return_code}")
def set_n_threads(self, n_threads: int, n_threads_batch: int):
llama_cpp.llama_set_n_threads(self.ctx, n_threads, n_threads_batch)
def get_logits(self):
return llama_cpp.llama_get_logits(self.ctx)
def get_logits_ith(self, i: int):
return llama_cpp.llama_get_logits_ith(self.ctx, i)
def get_embeddings(self):
return llama_cpp.llama_get_embeddings(self.ctx)
def get_embeddings_ith(self, i: int):
return llama_cpp.llama_get_embeddings_ith(self.ctx, i)
def get_embeddings_seq(self, seq_id: int):
return llama_cpp.llama_get_embeddings_seq(self.ctx, seq_id)
# Sampling functions - deprecated, use LlamaSampler instead
def set_rng_seed(self, seed: int):
raise NotImplementedError(
"set_rng_seed is deprecated, use LlamaSampler instead"
)
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,
):
raise NotImplementedError(
"sample_repetition_penalties is deprecated, use LlamaSampler instead"
)
def sample_softmax(self, candidates: "_LlamaTokenDataArray"):
raise NotImplementedError(
"sample_softmax is deprecated, use LlamaSampler instead"
)
def sample_top_k(self, candidates: "_LlamaTokenDataArray", k: int, min_keep: int):
raise NotImplementedError(
"sample_top_k is deprecated, use LlamaSampler instead"
)
def sample_top_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int):
raise NotImplementedError(
"sample_top_p is deprecated, use LlamaSampler instead"
)
def sample_min_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int):
raise NotImplementedError(
"sample_min_p is deprecated, use LlamaSampler instead"
)
def sample_typical(
self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int
):
raise NotImplementedError(
"sample_typical is deprecated, use LlamaSampler instead"
)
def sample_temp(self, candidates: "_LlamaTokenDataArray", temp: float):
raise NotImplementedError("sample_temp is deprecated, use LlamaSampler instead")
def sample_grammar(self, candidates: "_LlamaTokenDataArray", grammar: LlamaGrammar):
raise NotImplementedError(
"sample_grammar is deprecated, use LlamaSampler instead"
)
def sample_token_mirostat(
self,
candidates: "_LlamaTokenDataArray",
tau: float,
eta: float,
m: int,
mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float],
) -> int:
raise NotImplementedError(
"sample_token_mirostat is deprecated, use LlamaSampler instead"
)
def sample_token_mirostat_v2(
self,
candidates: "_LlamaTokenDataArray",
tau: float,
eta: float,
mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float],
) -> int:
raise NotImplementedError(
"sample_token_mirostat_v2 is deprecated, use LlamaSampler instead"
)
def sample_token_greedy(self, candidates: "_LlamaTokenDataArray") -> int:
raise NotImplementedError(
"sample_token_greedy is deprecated, use LlamaSampler instead"
)
def sample_token(self, candidates: "_LlamaTokenDataArray") -> int:
raise NotImplementedError(
"sample_token is deprecated, use LlamaSampler instead"
)
# Grammar
def grammar_accept_token(self, grammar: LlamaGrammar, token: int):
raise NotImplementedError(
"grammar_accept_token is deprecated, use LlamaSampler instead"
)
def reset_timings(self):
llama_cpp.llama_perf_context_reset(self.ctx)
def print_timings(self):
llama_cpp.llama_perf_context_print(self.ctx)
# Utility functions
@staticmethod
def default_params():
"""Get the default llama_context_params."""
return llama_cpp.llama_context_default_params()
class LlamaBatch:
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._exit_stack = ExitStack()
batch = llama_cpp.llama_batch_init(self._n_tokens, self.embd, self.n_seq_max)
if batch is None:
raise ValueError("Failed to create llama_batch")
self.batch = batch
self.sampler = None # LlamaBatch doesn't use samplers, but some cleanup code expects this attribute
def free_batch():
if self.batch is None:
return
llama_cpp.llama_batch_free(self.batch)
self.batch = None
self._exit_stack.callback(free_batch)
def close(self):
self._exit_stack.close()
def __del__(self):
self.close()
def n_tokens(self) -> int:
return self.batch.n_tokens
def reset(self):
self.batch.n_tokens = 0
def set_batch(self, batch: Sequence[int], n_past: int, logits_all: bool):
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
def add_sequence(self, batch: Sequence[int], seq_id: int, logits_all: bool):
n_tokens = len(batch)
n_tokens0 = self.batch.n_tokens
self.batch.n_tokens += n_tokens
for i in range(n_tokens):
j = n_tokens0 + i
self.batch.token[j] = batch[i]
self.batch.pos[j] = i
self.batch.seq_id[j][0] = seq_id
self.batch.n_seq_id[j] = 1
self.batch.logits[j] = logits_all
self.batch.logits[n_tokens0 + n_tokens - 1] = True
class LlamaTokenDataArray:
def __init__(self, *, n_vocab: int):
self.n_vocab = n_vocab
self.candidates_data = np.recarray(
(self.n_vocab,),
dtype=np.dtype(
[("id", np.intc), ("logit", np.single), ("p", np.single)], align=True
),
)
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) # type: ignore
self.default_candidates_data_p = np.zeros(self.n_vocab, dtype=np.single)
self.sampler = None # LlamaTokenDataArray doesn't use samplers, but some cleanup code expects this attribute
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.sorted = False
self.candidates.size = self.n_vocab
# Embedding functions
def normalize_embedding(embedding):
norm = float(np.linalg.norm(embedding))
if norm == 0.0:
return embedding
return [v / norm for v in embedding]
# Python wrappers over common/sampling structs
@dataclass
class LlamaSamplingParams:
n_prev: int = 64
n_probs: int = 0
top_k: int = 40
top_p: float = 0.95
min_p: float = 0.05
tfs_z: float = 1.00
typical_p: float = 1.00
temp: float = 0.80
penalty_last_n: int = 64
penalty_repeat: float = 1.0
penalty_freq: float = 0.00
penalty_present: float = 0.00
mirostat: int = 0
mirostat_tau: float = 5.00
mirostat_eta: float = 0.10
penalize_nl: bool = True
grammar: str = ""
cfg_negative_prompt: str = ""
cfg_scale: float = 1.00
logit_bias: dict[int, float] = field(default_factory=dict)
@dataclass
class LlamaSamplingContext:
params: LlamaSamplingParams = field(default_factory=LlamaSamplingParams)
mirostat_mu: ctypes.c_float = field(default_factory=ctypes.c_float)
grammar: Optional[LlamaGrammar] = None
# NOTE: Missing parsed_grammar
prev: list[int] = field(default_factory=list)
cur: list[llama_cpp.llama_token_data] = field(default_factory=list)
def reset(self):
self.prev = []
self.cur = []
if self.grammar is not None:
self.grammar.reset()
def cp(self):
return LlamaSamplingContext(
params=self.params,
mirostat_mu=self.mirostat_mu,
grammar=self.grammar,
prev=self.prev.copy(),
cur=self.cur.copy(),
)
def last(self) -> Optional[int]:
if len(self.prev) > 0:
return self.prev[-1]
else:
return None
def prev_str(self, ctx_main: LlamaContext, n: int) -> str:
return ctx_main.model.detokenize(self.prev[-n:]).decode("utf-8")
def sample(
self,
ctx_main: LlamaContext,
idx: int = 0,
logits_array: Optional[npt.NDArray[np.single]] = None,
):
# This method is deprecated in favor of using LlamaSampler directly
raise NotImplementedError(
"LlamaSamplingContext.sample is deprecated, use LlamaSampler instead"
)
def accept(self, ctx_main: LlamaContext, id: int, apply_grammar: bool):
self.prev.append(id)
class CustomSampler:
def __init__(self, apply_func: Callable[[llama_cpp.llama_token_data_array], None]):
self.apply_func = apply_func
def apply_wrapper(
sampler: llama_cpp.llama_sampler_p,
cur_p: llama_cpp.llama_token_data_array_p,
):
self.apply_func(cur_p)
def free_wrapper(sampler: llama_cpp.llama_sampler_p):
pass
sampler_i = llama_cpp.llama_sampler_i()
sampler_i.apply = llama_cpp.llama_sampler_i_apply(apply_wrapper)
self._apply_wrapper_ref = apply_wrapper
sampler_i.name = llama_cpp.llama_sampler_i_name(0)
sampler_i.accept = llama_cpp.llama_sampler_i_accept(0)
sampler_i.reset = llama_cpp.llama_sampler_i_reset(0)
sampler_i.clone = llama_cpp.llama_sampler_i_clone(0)
sampler_i.free = llama_cpp.llama_sampler_i_free(0)
self.sampler = llama_cpp.llama_sampler()
self.sampler.iface = ctypes.pointer(sampler_i)
self.sampler.ctx = None
def get_sampler(self) -> llama_cpp.llama_sampler_p:
return ctypes.pointer(self.sampler)
class LlamaSampler:
def __init__(self):
params = llama_cpp.llama_sampler_chain_default_params()
self.sampler = llama_cpp.llama_sampler_chain_init(params)
self.custom_samplers: List[Tuple[int, CustomSampler]] = []
self._exit_stack = ExitStack()
def free_sampler():
if self.sampler is not None:
# NOTE: Must remove custom samplers before free or llama.cpp will try to free them
for i, _ in reversed(self.custom_samplers):
llama_cpp.llama_sampler_chain_remove(self.sampler, i)
llama_cpp.llama_sampler_free(self.sampler)
self.sampler = None
self._exit_stack.callback(free_sampler)
def close(self):
self._exit_stack.close()
def __del__(self):
self.close()
def add_greedy(self):
sampler = llama_cpp.llama_sampler_init_greedy()
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_dist(self, seed: int):
sampler = llama_cpp.llama_sampler_init_dist(seed)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_softmax(self):
warnings.warn(
"add_softmax is deprecated; llama_sampler_init_dist now samples directly from logits",
DeprecationWarning,
stacklevel=2,
)
def add_top_k(self, k: int):
sampler = llama_cpp.llama_sampler_init_top_k(k)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_top_p(self, p: float, min_keep: int = 1):
sampler = llama_cpp.llama_sampler_init_top_p(p, min_keep)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_min_p(self, p: float, min_keep: int = 1):
sampler = llama_cpp.llama_sampler_init_min_p(p, min_keep)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_typical(self, p: float, min_keep: int = 1):
sampler = llama_cpp.llama_sampler_init_typical(p, min_keep)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_temp(self, temp: float):
sampler = llama_cpp.llama_sampler_init_temp(temp)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_temp_ext(self, t: float, delta: float, exponent: float):
sampler = llama_cpp.llama_sampler_init_temp_ext(t, delta, exponent)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_xtc(self, p: float, t: float, min_keep: int, seed: int):
sampler = llama_cpp.llama_sampler_init_xtc(p, t, min_keep, seed)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_top_n_sigma(self, n: float):
sampler = llama_cpp.llama_sampler_init_top_n_sigma(n)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_mirostat(self, n_vocab: int, seed: int, tau: float, eta: float, m: int):
sampler = llama_cpp.llama_sampler_init_mirostat(n_vocab, seed, tau, eta, m)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_mirostat_v2(self, seed: int, tau: float, eta: float):
sampler = llama_cpp.llama_sampler_init_mirostat_v2(seed, tau, eta)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_grammar(self, model: LlamaModel, grammar: LlamaGrammar):
sampler = llama_cpp.llama_sampler_init_grammar(
model.vocab, grammar._grammar.encode("utf-8"), grammar._root.encode("utf-8")
)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_grammar_lazy_patterns(
self,
model: LlamaModel,
grammar: LlamaGrammar,
trigger_patterns: List[str],
trigger_tokens: List[int],
):
# Convert patterns to C array
pattern_ptrs = (ctypes.c_char_p * len(trigger_patterns))()
for i, pattern in enumerate(trigger_patterns):
pattern_ptrs[i] = pattern.encode("utf-8")
# Convert tokens to C array
token_array = (llama_cpp.llama_token * len(trigger_tokens))(*trigger_tokens)
sampler = llama_cpp.llama_sampler_init_grammar_lazy_patterns(
model.vocab,
grammar._grammar.encode("utf-8"),
grammar._root.encode("utf-8"),
pattern_ptrs,
len(trigger_patterns),
token_array,
len(trigger_tokens),
)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_penalties(
self,
n_vocab: int,
penalty_last_n: int,
penalty_repeat: float,
penalty_freq: float,
penalty_present: float,
):
sampler = llama_cpp.llama_sampler_init_penalties(
n_vocab,
penalty_last_n,
penalty_repeat,
penalty_freq,
penalty_present,
)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_dry(
self,
model: LlamaModel,
dry_multiplier: float,
dry_base: float,
dry_allowed_length: int,
dry_penalty_last_n: int,
seq_breakers: List[str],
):
# Convert seq_breakers to C array
breaker_ptrs = (ctypes.c_char_p * len(seq_breakers))()
for i, breaker in enumerate(seq_breakers):
breaker_ptrs[i] = breaker.encode("utf-8")
sampler = llama_cpp.llama_sampler_init_dry(
model.vocab,
dry_multiplier,
dry_base,
dry_allowed_length,
dry_penalty_last_n,
breaker_ptrs,
len(seq_breakers),
)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_logit_bias(self, n_vocab: int, logit_bias: Dict[int, float]):
# Convert logit_bias dict to C array
bias_array = (llama_cpp.llama_logit_bias * len(logit_bias))()
for i, (token, bias) in enumerate(logit_bias.items()):
bias_array[i].token = token
bias_array[i].bias = bias
sampler = llama_cpp.llama_sampler_init_logit_bias(
n_vocab, len(logit_bias), bias_array
)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_infill(self, model: LlamaModel):
sampler = llama_cpp.llama_sampler_init_infill(model.vocab)
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
def add_custom(
self, apply_func: Callable[[llama_cpp.llama_token_data_array], None]
):
custom_sampler = CustomSampler(apply_func)
sampler = custom_sampler.get_sampler()
llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
# NOTE: Must remove custom samplers before free or llama.cpp will try to free them
self.custom_samplers.append(
(llama_cpp.llama_sampler_chain_n(self.sampler) - 1, custom_sampler)
)
def get_seed(self) -> int:
return llama_cpp.llama_sampler_get_seed(self.sampler)
def sample(self, ctx: LlamaContext, idx: int = -1) -> int:
return llama_cpp.llama_sampler_sample(self.sampler, ctx.ctx, idx)
def accept(self, token: int):
llama_cpp.llama_sampler_accept(self.sampler, token)
def reset(self):
llama_cpp.llama_sampler_reset(self.sampler)
def clone(self):
# NOTE: Custom samplers cannot be cloned due to Python callback limitations
if self.custom_samplers:
raise NotImplementedError(
"Cannot clone LlamaSampler that contains custom samplers"
)
cloned_sampler = llama_cpp.llama_sampler_clone(self.sampler)
# Create a new wrapper around the cloned sampler
new_sampler = LlamaSampler.__new__(LlamaSampler)
new_sampler.sampler = cloned_sampler
new_sampler.custom_samplers = []
new_sampler._exit_stack = ExitStack()
def free_sampler():
if new_sampler.sampler is not None:
llama_cpp.llama_sampler_free(new_sampler.sampler)
new_sampler.sampler = None
new_sampler._exit_stack.callback(free_sampler)
return new_sampler