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1592 lines (1433 loc) · 58.1 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
from . import llama_cpp
from .llama_types import *
import numpy as np
import numpy.typing as npt
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
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,
eval_tokens: Deque[int],
eval_logits: Deque[List[float]],
input_ids: npt.NDArray[np.intc],
scores: npt.NDArray[np.single],
llama_state, # type: llama_cpp.Array[llama_cpp.c_uint8]
llama_state_size: int,
):
self.eval_tokens = eval_tokens
self.eval_logits = eval_logits
self.input_ids = input_ids
self.scores = scores
self.llama_state = llama_state
self.llama_state_size = llama_state_size
LogitsProcessor = Callable[[List[int], List[float]], List[float]]
class LogitsProcessorList(List[LogitsProcessor]):
def __call__(self, input_ids: List[int], scores: List[float]) -> List[float]:
for processor in self:
scores = processor(input_ids, scores)
return scores
StoppingCriteria = Callable[[List[int], List[float]], bool]
class StoppingCriteriaList(List[StoppingCriteria]):
def __call__(self, input_ids: List[int], logits: List[float]) -> bool:
return any([stopping_criteria(input_ids, logits) for stopping_criteria in self])
class Llama:
"""High-level Python wrapper for a llama.cpp model."""
def __init__(
self,
model_path: str,
# NOTE: These parameters are likely to change in the future.
n_ctx: int = 512,
n_parts: int = -1,
n_gpu_layers: int = 0,
seed: int = 1337,
f16_kv: bool = True,
logits_all: bool = False,
vocab_only: bool = False,
use_mmap: bool = True,
use_mlock: bool = False,
embedding: bool = False,
n_threads: Optional[int] = None,
n_batch: int = 512,
last_n_tokens_size: int = 64,
lora_base: Optional[str] = None,
lora_path: Optional[str] = None,
low_vram: bool = False,
verbose: bool = True,
):
"""Load a llama.cpp model from `model_path`.
Args:
model_path: Path to the model.
n_ctx: Maximum context size.
n_parts: Number of parts to split the model into. If -1, the number of parts is automatically determined.
seed: Random seed. 0 for random.
f16_kv: Use half-precision for key/value cache.
logits_all: Return logits for all tokens, not just the last token.
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.
embedding: Embedding mode only.
n_threads: Number of threads to use. If None, the number of threads is automatically determined.
n_batch: Maximum number of prompt tokens to batch together when calling llama_eval.
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.
verbose: Print verbose output to stderr.
Raises:
ValueError: If the model path does not exist.
Returns:
A Llama instance.
"""
self.verbose = verbose
self.model_path = model_path
self.params = llama_cpp.llama_context_default_params()
self.params.n_ctx = n_ctx
self.params.n_gpu_layers = n_gpu_layers
self.params.seed = seed
self.params.f16_kv = f16_kv
self.params.logits_all = logits_all
self.params.vocab_only = vocab_only
self.params.use_mmap = use_mmap if lora_path is None else False
self.params.use_mlock = use_mlock
self.params.embedding = embedding
self.params.low_vram = low_vram
self.last_n_tokens_size = last_n_tokens_size
self.n_batch = min(n_ctx, n_batch)
self.eval_tokens: Deque[int] = deque(maxlen=n_ctx)
self.eval_logits: Deque[List[float]] = deque(maxlen=n_ctx if logits_all else 1)
self.cache: Optional[BaseLlamaCache] = None
self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
self.lora_base = lora_base
self.lora_path = lora_path
### DEPRECATED ###
self.n_parts = n_parts
### DEPRECATED ###
if not os.path.exists(model_path):
raise ValueError(f"Model path does not exist: {model_path}")
self.ctx = llama_cpp.llama_init_from_file(
self.model_path.encode("utf-8"), self.params
)
assert self.ctx is not None
if self.lora_path:
if llama_cpp.llama_apply_lora_from_file(
self.ctx,
llama_cpp.c_char_p(self.lora_path.encode("utf-8")),
llama_cpp.c_char_p(self.lora_base.encode("utf-8"))
if self.lora_base is not None
else llama_cpp.c_char_p(0),
llama_cpp.c_int(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._n_vocab = self.n_vocab()
self._n_ctx = self.n_ctx()
size = llama_cpp.c_size_t(self._n_vocab)
sorted = llama_cpp.c_bool(False)
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)
candidates = llama_cpp.llama_token_data_array(
data=self._candidates_data.ctypes.data_as(llama_cpp.llama_token_data_p),
size=size,
sorted=sorted,
)
self._candidates = candidates
self._token_nl = Llama.token_nl()
self._token_eos = Llama.token_eos()
self._input_ids = np.array([], dtype=np.intc)
self._scores = np.ndarray((0, self._n_vocab), dtype=np.single)
def tokenize(self, text: bytes, add_bos: bool = True) -> List[int]:
"""Tokenize a string.
Args:
text: The utf-8 encoded string to tokenize.
Raises:
RuntimeError: If the tokenization failed.
Returns:
A list of tokens.
"""
assert self.ctx is not None
n_ctx = self._n_ctx
tokens = (llama_cpp.llama_token * n_ctx)()
n_tokens = llama_cpp.llama_tokenize(
self.ctx,
text,
tokens,
llama_cpp.c_int(n_ctx),
llama_cpp.c_bool(add_bos),
)
if n_tokens < 0:
n_tokens = abs(n_tokens)
tokens = (llama_cpp.llama_token * n_tokens)()
n_tokens = llama_cpp.llama_tokenize(
self.ctx,
text,
tokens,
llama_cpp.c_int(n_tokens),
llama_cpp.c_bool(add_bos),
)
if n_tokens < 0:
raise RuntimeError(
f'Failed to tokenize: text="{text}" n_tokens={n_tokens}'
)
return list(tokens[:n_tokens])
def detokenize(self, tokens: List[int]) -> bytes:
"""Detokenize a list of tokens.
Args:
tokens: The list of tokens to detokenize.
Returns:
The detokenized string.
"""
assert self.ctx is not None
output = b""
for token in tokens:
output += llama_cpp.llama_token_to_str(
self.ctx, llama_cpp.llama_token(token)
)
return output
def set_cache(self, cache: Optional[BaseLlamaCache]):
"""Set the cache.
Args:
cache: The cache to set.
"""
self.cache = cache
def reset(self):
"""Reset the model state."""
self.eval_tokens.clear()
self.eval_logits.clear()
self._input_ids = np.array([], dtype=np.intc)
self._scores = np.ndarray((0, self._n_vocab), dtype=np.single)
def eval(self, tokens: Sequence[int]):
"""Evaluate a list of tokens.
Args:
tokens: The list of tokens to evaluate.
"""
assert self.ctx is not None
n_ctx = self._n_ctx
for i in range(0, len(tokens), self.n_batch):
batch = tokens[i : min(len(tokens), i + self.n_batch)]
n_past = min(n_ctx - len(batch), len(self._input_ids))
n_tokens = len(batch)
return_code = llama_cpp.llama_eval(
ctx=self.ctx,
tokens=(llama_cpp.llama_token * len(batch))(*batch),
n_tokens=llama_cpp.c_int(n_tokens),
n_past=llama_cpp.c_int(n_past),
n_threads=llama_cpp.c_int(self.n_threads),
)
if return_code != 0:
raise RuntimeError(f"llama_eval returned {return_code}")
# Save tokens
self.eval_tokens.extend(batch)
self._input_ids: npt.NDArray[np.intc] = np.concatenate(
(self._input_ids, np.array(batch, dtype=np.intc)), axis=0
)
# Save logits
rows = n_tokens if self.params.logits_all else 1
n_vocab = self._n_vocab
cols = n_vocab
logits_view = llama_cpp.llama_get_logits(self.ctx)
logits = [logits_view[i * cols : (i + 1) * cols] for i in range(rows)]
self.eval_logits.extend(logits)
self._scores: npt.NDArray[np.single] = np.concatenate(
(self._scores, np.array(logits, dtype=np.single)), axis=0
)
def _sample(
self,
last_n_tokens_data, # type: llama_cpp.Array[llama_cpp.llama_token]
last_n_tokens_size: llama_cpp.c_int,
top_k: llama_cpp.c_int,
top_p: llama_cpp.c_float,
temp: llama_cpp.c_float,
tfs_z: llama_cpp.c_float,
repeat_penalty: llama_cpp.c_float,
frequency_penalty: llama_cpp.c_float,
presence_penalty: llama_cpp.c_float,
mirostat_mode: llama_cpp.c_int,
mirostat_tau: llama_cpp.c_float,
mirostat_eta: llama_cpp.c_float,
penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None,
):
assert self.ctx is not None
assert len(self.eval_logits) > 0
assert self._scores.shape[0] > 0
n_vocab = self._n_vocab
n_ctx = self._n_ctx
top_k = llama_cpp.c_int(n_vocab) if top_k.value <= 0 else top_k
last_n_tokens_size = (
llama_cpp.c_int(n_ctx)
if last_n_tokens_size.value < 0
else last_n_tokens_size
)
logits: npt.NDArray[np.single] = self._scores[-1, :]
if logits_processor is not None:
logits = np.array(
logits_processor(self._input_ids.tolist(), logits.tolist()),
dtype=np.single,
)
self._scores[-1, :] = logits
self.eval_logits[-1] = logits.tolist()
nl_logit = logits[self._token_nl]
candidates = self._candidates
candidates_data = self._candidates_data
candidates_data["id"] = np.arange(n_vocab, dtype=np.intc) # type: ignore
candidates_data["logit"] = logits
candidates_data["p"] = np.zeros(n_vocab, dtype=np.single)
candidates.data = candidates_data.ctypes.data_as(llama_cpp.llama_token_data_p)
candidates.sorted = llama_cpp.c_bool(False)
candidates.size = llama_cpp.c_size_t(n_vocab)
llama_cpp.llama_sample_repetition_penalty(
ctx=self.ctx,
last_tokens_data=last_n_tokens_data,
last_tokens_size=last_n_tokens_size,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
penalty=repeat_penalty,
)
llama_cpp.llama_sample_frequency_and_presence_penalties(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
last_tokens_data=last_n_tokens_data,
last_tokens_size=last_n_tokens_size,
alpha_frequency=frequency_penalty,
alpha_presence=presence_penalty,
)
if not penalize_nl:
candidates.data[self._token_nl].logit = llama_cpp.c_float(nl_logit)
if temp.value == 0.0:
return llama_cpp.llama_sample_token_greedy(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
)
elif mirostat_mode.value == 1:
mirostat_mu = llama_cpp.c_float(2.0 * mirostat_tau.value)
mirostat_m = llama_cpp.c_int(100)
llama_cpp.llama_sample_temperature(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
temp=temp,
)
return llama_cpp.llama_sample_token_mirostat(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
tau=mirostat_tau,
eta=mirostat_eta,
mu=llama_cpp.ctypes.byref(mirostat_mu), # type: ignore
m=mirostat_m,
)
elif mirostat_mode.value == 2:
mirostat_mu = llama_cpp.c_float(2.0 * mirostat_tau.value)
llama_cpp.llama_sample_temperature(
ctx=self.ctx,
candidates=llama_cpp.ctypes.pointer(candidates),
temp=temp,
)
return llama_cpp.llama_sample_token_mirostat_v2(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
tau=mirostat_tau,
eta=mirostat_eta,
mu=llama_cpp.ctypes.byref(mirostat_mu), # type: ignore
)
else:
llama_cpp.llama_sample_top_k(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
k=top_k,
min_keep=llama_cpp.c_size_t(1),
)
llama_cpp.llama_sample_tail_free(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
z=tfs_z,
min_keep=llama_cpp.c_size_t(1),
)
llama_cpp.llama_sample_typical(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
p=llama_cpp.c_float(1.0),
min_keep=llama_cpp.c_size_t(1),
)
llama_cpp.llama_sample_top_p(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
p=top_p,
min_keep=llama_cpp.c_size_t(1),
)
llama_cpp.llama_sample_temperature(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
temp=temp,
)
return llama_cpp.llama_sample_token(
ctx=self.ctx,
candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
)
def sample(
self,
top_k: int = 40,
top_p: float = 0.95,
temp: float = 0.80,
repeat_penalty: float = 1.1,
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_eta: float = 0.1,
mirostat_tau: float = 5.0,
penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None,
):
"""Sample a token from the model.
Args:
top_k: The top-k sampling parameter.
top_p: The top-p sampling parameter.
temp: The temperature parameter.
repeat_penalty: The repeat penalty parameter.
Returns:
The sampled token.
"""
assert self.ctx is not None
last_n_tokens_data = [llama_cpp.llama_token(0)] * max(
0, self.last_n_tokens_size - len(self._input_ids)
) + self._input_ids[-self.last_n_tokens_size :].tolist()
return self._sample(
last_n_tokens_data=(llama_cpp.llama_token * self.last_n_tokens_size)(
*last_n_tokens_data
),
last_n_tokens_size=llama_cpp.c_int(self.last_n_tokens_size),
top_k=llama_cpp.c_int(top_k),
top_p=llama_cpp.c_float(top_p),
temp=llama_cpp.c_float(temp),
tfs_z=llama_cpp.c_float(tfs_z),
repeat_penalty=llama_cpp.c_float(repeat_penalty),
frequency_penalty=llama_cpp.c_float(frequency_penalty),
presence_penalty=llama_cpp.c_float(presence_penalty),
mirostat_mode=llama_cpp.c_int(mirostat_mode),
mirostat_tau=llama_cpp.c_float(mirostat_tau),
mirostat_eta=llama_cpp.c_float(mirostat_eta),
penalize_nl=penalize_nl,
logits_processor=logits_processor,
)
def generate(
self,
tokens: Sequence[int],
top_k: int = 40,
top_p: float = 0.95,
temp: float = 0.80,
repeat_penalty: float = 1.1,
reset: bool = True,
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
) -> Generator[int, Optional[Sequence[int]], None]:
"""Create a generator of tokens from a prompt.
Examples:
>>> llama = Llama("models/ggml-7b.bin")
>>> tokens = llama.tokenize(b"Hello, world!")
>>> for token in llama.generate(tokens, top_k=40, top_p=0.95, temp=1.0, repeat_penalty=1.1):
... print(llama.detokenize([token]))
Args:
tokens: The prompt tokens.
top_k: The top-k sampling parameter.
top_p: The top-p sampling parameter.
temp: The temperature parameter.
repeat_penalty: The repeat penalty parameter.
reset: Whether to reset the model state.
Yields:
The generated tokens.
"""
assert self.ctx is not None
if reset and len(self._input_ids) > 0:
longest_prefix = 0
for a, b in zip(self._input_ids, tokens[:-1]):
if a == b:
longest_prefix += 1
else:
break
if longest_prefix > 0:
if self.verbose:
print("Llama.generate: prefix-match hit", file=sys.stderr)
reset = False
tokens = tokens[longest_prefix:]
self._input_ids = self._input_ids[:longest_prefix]
self._scores = self._scores[:longest_prefix, :]
for _ in range(len(self.eval_tokens) - longest_prefix):
self.eval_tokens.pop()
try:
self.eval_logits.pop()
except IndexError:
pass
if reset:
self.reset()
while True:
self.eval(tokens)
token = self.sample(
top_k=top_k,
top_p=top_p,
temp=temp,
repeat_penalty=repeat_penalty,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
logits_processor=logits_processor,
)
if stopping_criteria is not None and stopping_criteria(
self._input_ids.tolist(), self._scores[-1, :].tolist()
):
return
tokens_or_none = yield token
tokens = [token]
if tokens_or_none is not None:
tokens.extend(tokens_or_none)
def create_embedding(
self, input: Union[str, List[str]], model: Optional[str] = None
) -> Embedding:
"""Embed a string.
Args:
input: The utf-8 encoded string to embed.
Returns:
An embedding object.
"""
assert self.ctx is not None
model_name: str = model if model is not None else self.model_path
if self.params.embedding == False:
raise RuntimeError(
"Llama model must be created with embedding=True to call this method"
)
if self.verbose:
llama_cpp.llama_reset_timings(self.ctx)
if isinstance(input, str):
inputs = [input]
else:
inputs = input
data: List[EmbeddingData] = []
total_tokens = 0
for index, input in enumerate(inputs):
tokens = self.tokenize(input.encode("utf-8"))
self.reset()
self.eval(tokens)
n_tokens = len(tokens)
total_tokens += n_tokens
embedding = llama_cpp.llama_get_embeddings(self.ctx)[
: llama_cpp.llama_n_embd(self.ctx)
]
data.append(
{
"object": "embedding",
"embedding": embedding,
"index": index,
}
)
if self.verbose:
llama_cpp.llama_print_timings(self.ctx)
return {
"object": "list",
"data": data,
"model": model_name,
"usage": {
"prompt_tokens": total_tokens,
"total_tokens": total_tokens,
},
}
def embed(self, input: str) -> List[float]:
"""Embed a string.
Args:
input: The utf-8 encoded string to embed.
Returns:
A list of embeddings
"""
return list(map(float, self.create_embedding(input)["data"][0]["embedding"]))
def _create_completion(
self,
prompt: str,
suffix: Optional[str] = None,
max_tokens: int = 16,
temperature: float = 0.8,
top_p: float = 0.95,
logprobs: Optional[int] = None,
echo: bool = False,
stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
repeat_penalty: float = 1.1,
top_k: int = 40,
stream: bool = False,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None,
) -> Union[Iterator[Completion], Iterator[CompletionChunk]]:
assert self.ctx is not None
completion_id: str = f"cmpl-{str(uuid.uuid4())}"
created: int = int(time.time())
completion_tokens: List[int] = []
# Add blank space to start of prompt to match OG llama tokenizer
prompt_tokens: List[int] = self.tokenize(b" " + prompt.encode("utf-8"))
text: bytes = b""
returned_tokens: int = 0
stop = (
stop if isinstance(stop, list) else [stop] if isinstance(stop, str) else []
)
model_name: str = model if model is not None else self.model_path
if self.verbose:
llama_cpp.llama_reset_timings(self.ctx)
if len(prompt_tokens) > self._n_ctx:
raise ValueError(f"Requested tokens ({len(prompt_tokens)}) exceed context window of {self._n_ctx}")
# Truncate max_tokens if requested tokens would exceed the context window
max_tokens = (
max_tokens
if max_tokens + len(prompt_tokens) < self._n_ctx
else (self._n_ctx - len(prompt_tokens))
)
if stop != []:
stop_sequences = [s.encode("utf-8") for s in stop]
else:
stop_sequences = []
if logprobs is not None and self.params.logits_all is False:
raise ValueError(
"logprobs is not supported for models created with logits_all=False"
)
if self.cache:
try:
cache_item = self.cache[prompt_tokens]
cache_prefix_len = Llama.longest_token_prefix(
cache_item.input_ids.tolist(), prompt_tokens
)
eval_prefix_len = Llama.longest_token_prefix(
self._input_ids.tolist(), prompt_tokens
)
if cache_prefix_len > eval_prefix_len:
self.load_state(cache_item)
if self.verbose:
print("Llama._create_completion: cache hit", file=sys.stderr)
except KeyError:
if self.verbose:
print("Llama._create_completion: cache miss", file=sys.stderr)
finish_reason = "length"
multibyte_fix = 0
for token in self.generate(
prompt_tokens,
top_k=top_k,
top_p=top_p,
temp=temperature,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
repeat_penalty=repeat_penalty,
stopping_criteria=stopping_criteria,
logits_processor=logits_processor,
):
if token == self._token_eos:
text = self.detokenize(completion_tokens)
finish_reason = "stop"
break
completion_tokens.append(token)
all_text = self.detokenize(completion_tokens)
# Contains multi-byte UTF8
for k, char in enumerate(all_text[-3:]):
k = 3 - k
for num, pattern in [(2, 192), (3, 224), (4, 240)]:
# Bitwise AND check
if num > k and pattern & char == pattern:
multibyte_fix = num - k
# Stop incomplete bytes from passing
if multibyte_fix > 0:
multibyte_fix -= 1
continue
any_stop = [s for s in stop_sequences if s in all_text]
if len(any_stop) > 0:
first_stop = any_stop[0]
text = all_text[: all_text.index(first_stop)]
finish_reason = "stop"
break
if stream:
remaining_tokens = completion_tokens[returned_tokens:]
remaining_text = self.detokenize(remaining_tokens)
remaining_length = len(remaining_text)
# We want to avoid yielding any characters from
# the generated text if they are part of a stop
# sequence.
first_stop_position = 0
for s in stop_sequences:
for i in range(min(len(s), remaining_length), 0, -1):
if remaining_text.endswith(s[:i]):
if i > first_stop_position:
first_stop_position = i
break
token_end_position = 0
for token in remaining_tokens:
token_end_position += len(self.detokenize([token]))
# Check if stop sequence is in the token
if token_end_position >= (
remaining_length - first_stop_position - 1
):
break
logprobs_or_none: Optional[CompletionLogprobs] = None
if logprobs is not None:
token_str = self.detokenize([token]).decode(
"utf-8", errors="ignore"
)
text_offset = len(prompt) + len(
self.detokenize(completion_tokens[:returned_tokens])
)
token_offset = len(prompt_tokens) + returned_tokens
logits = self._scores[token_offset - 1, :].tolist()
current_logprobs = Llama.logits_to_logprobs(logits)
sorted_logprobs = list(
sorted(
zip(current_logprobs, range(len(current_logprobs))),
reverse=True,
)
)
top_logprob = {
self.detokenize([i]).decode(
"utf-8", errors="ignore"
): logprob
for logprob, i in sorted_logprobs[:logprobs]
}
top_logprob.update({token_str: current_logprobs[int(token)]})
logprobs_or_none = {
"tokens": [
self.detokenize([token]).decode(
"utf-8", errors="ignore"
)
],
"text_offset": [text_offset],
"token_logprobs": [sorted_logprobs[int(token)][0]],
"top_logprobs": [top_logprob],
}
returned_tokens += 1
yield {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": model_name,
"choices": [
{
"text": self.detokenize([token]).decode(
"utf-8", errors="ignore"
),
"index": 0,
"logprobs": logprobs_or_none,
"finish_reason": None,
}
],
}
if len(completion_tokens) >= max_tokens:
text = self.detokenize(completion_tokens)
finish_reason = "length"
break
if stopping_criteria is not None and stopping_criteria(
self._input_ids.tolist(), self._scores[-1, :].tolist()
):
text = self.detokenize(completion_tokens)
finish_reason = "stop"
if self.verbose:
llama_cpp.llama_print_timings(self.ctx)
if stream:
remaining_tokens = completion_tokens[returned_tokens:]
all_text = self.detokenize(remaining_tokens)
any_stop = [s for s in stop_sequences if s in all_text]
if len(any_stop) > 0:
end = min(all_text.index(stop) for stop in any_stop)
else:
end = len(all_text)
token_end_position = 0
for token in remaining_tokens:
token_end_position += len(self.detokenize([token]))