1429 lines
52 KiB
Python
1429 lines
52 KiB
Python
import os
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import sys
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import uuid
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import time
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import math
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import multiprocessing
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from typing import (
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List,
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Optional,
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Union,
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Generator,
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Sequence,
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Iterator,
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Deque,
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Tuple,
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Callable,
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)
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from collections import deque, OrderedDict
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from . import llama_cpp
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from .llama_types import *
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class LlamaCache:
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"""Cache for a llama.cpp model."""
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def __init__(self, capacity_bytes: int = (2 << 30)):
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self.cache_state: OrderedDict[Tuple[int, ...], "LlamaState"] = OrderedDict()
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self.capacity_bytes = capacity_bytes
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@property
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def cache_size(self):
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return sum([state.llama_state_size for state in self.cache_state.values()])
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def _find_longest_prefix_key(
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self,
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key: Tuple[int, ...],
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) -> Optional[Tuple[int, ...]]:
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min_len = 0
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min_key = None
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keys = (
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(k, Llama.longest_token_prefix(k, key)) for k in self.cache_state.keys()
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)
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for k, prefix_len in keys:
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if prefix_len > min_len:
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min_len = prefix_len
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min_key = k
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return min_key
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def __getitem__(self, key: Sequence[int]) -> "LlamaState":
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key = tuple(key)
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_key = self._find_longest_prefix_key(key)
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if _key is None:
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raise KeyError(f"Key not found")
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value = self.cache_state[_key]
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self.cache_state.move_to_end(_key)
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return value
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def __contains__(self, key: Sequence[int]) -> bool:
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return self._find_longest_prefix_key(tuple(key)) is not None
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def __setitem__(self, key: Sequence[int], value: "LlamaState"):
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key = tuple(key)
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if key in self.cache_state:
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del self.cache_state[key]
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self.cache_state[key] = value
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while self.cache_size > self.capacity_bytes:
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self.cache_state.popitem(last=False)
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class LlamaState:
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def __init__(
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self,
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eval_tokens: Deque[int],
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eval_logits: Deque[List[float]],
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llama_state, # type: llama_cpp.Array[llama_cpp.c_uint8]
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llama_state_size: int,
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):
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self.eval_tokens = eval_tokens
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self.eval_logits = eval_logits
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self.llama_state = llama_state
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self.llama_state_size = llama_state_size
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LogitsProcessor = Callable[[List[int], List[float]], List[float]]
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class LogitsProcessorList(List[LogitsProcessor]):
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def __call__(self, input_ids: List[int], scores: List[float]) -> List[float]:
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for processor in self:
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scores = processor(input_ids, scores)
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return scores
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StoppingCriteria = Callable[[List[int], List[float]], bool]
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class StoppingCriteriaList(List[StoppingCriteria]):
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def __call__(self, input_ids: List[int], logits: List[float]) -> bool:
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return any([stopping_criteria(input_ids, logits) for stopping_criteria in self])
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class Llama:
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"""High-level Python wrapper for a llama.cpp model."""
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def __init__(
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self,
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model_path: str,
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# NOTE: These parameters are likely to change in the future.
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n_ctx: int = 512,
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n_parts: int = -1,
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n_gpu_layers: int = 0,
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seed: int = 1337,
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f16_kv: bool = True,
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logits_all: bool = False,
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vocab_only: bool = False,
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use_mmap: bool = True,
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use_mlock: bool = False,
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embedding: bool = False,
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n_threads: Optional[int] = None,
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n_batch: int = 512,
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last_n_tokens_size: int = 64,
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lora_base: Optional[str] = None,
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lora_path: Optional[str] = None,
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verbose: bool = True,
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):
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"""Load a llama.cpp model from `model_path`.
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Args:
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model_path: Path to the model.
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n_ctx: Maximum context size.
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n_parts: Number of parts to split the model into. If -1, the number of parts is automatically determined.
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seed: Random seed. 0 for random.
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f16_kv: Use half-precision for key/value cache.
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logits_all: Return logits for all tokens, not just the last token.
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vocab_only: Only load the vocabulary no weights.
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use_mmap: Use mmap if possible.
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use_mlock: Force the system to keep the model in RAM.
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embedding: Embedding mode only.
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n_threads: Number of threads to use. If None, the number of threads is automatically determined.
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n_batch: Maximum number of prompt tokens to batch together when calling llama_eval.
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last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
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lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
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lora_path: Path to a LoRA file to apply to the model.
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verbose: Print verbose output to stderr.
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Raises:
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ValueError: If the model path does not exist.
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Returns:
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A Llama instance.
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"""
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self.verbose = verbose
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self.model_path = model_path
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self.params = llama_cpp.llama_context_default_params()
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self.params.n_ctx = n_ctx
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self.params.n_gpu_layers = n_gpu_layers
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self.params.seed = seed
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self.params.f16_kv = f16_kv
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self.params.logits_all = logits_all
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self.params.vocab_only = vocab_only
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self.params.use_mmap = use_mmap if lora_path is None else False
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self.params.use_mlock = use_mlock
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self.params.embedding = embedding
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self.last_n_tokens_size = last_n_tokens_size
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self.n_batch = min(n_ctx, n_batch)
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self.eval_tokens: Deque[int] = deque(maxlen=n_ctx)
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self.eval_logits: Deque[List[float]] = deque(maxlen=n_ctx if logits_all else 1)
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self.cache: Optional[LlamaCache] = None
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self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
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self.lora_base = lora_base
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self.lora_path = lora_path
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### DEPRECATED ###
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self.n_parts = n_parts
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### DEPRECATED ###
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if not os.path.exists(model_path):
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raise ValueError(f"Model path does not exist: {model_path}")
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self.ctx = llama_cpp.llama_init_from_file(
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self.model_path.encode("utf-8"), self.params
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)
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assert self.ctx is not None
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if self.lora_path:
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if llama_cpp.llama_apply_lora_from_file(
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self.ctx,
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llama_cpp.c_char_p(self.lora_path.encode("utf-8")),
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llama_cpp.c_char_p(self.lora_base.encode("utf-8"))
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if self.lora_base is not None
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else llama_cpp.c_char_p(0),
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llama_cpp.c_int(self.n_threads),
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):
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raise RuntimeError(
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f"Failed to apply LoRA from lora path: {self.lora_path} to base path: {self.lora_base}"
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)
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if self.verbose:
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print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
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self._n_vocab = self.n_vocab()
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self._n_ctx = self.n_ctx()
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data = (llama_cpp.llama_token_data * self._n_vocab)(
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*[
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llama_cpp.llama_token_data(
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id=llama_cpp.llama_token(i),
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logit=llama_cpp.c_float(0.0),
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p=llama_cpp.c_float(0.0),
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)
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for i in range(self._n_vocab)
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]
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)
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size = llama_cpp.c_size_t(self._n_vocab)
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sorted = False
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candidates = llama_cpp.llama_token_data_array(
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data=data,
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size=size,
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sorted=sorted,
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)
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self._candidates = candidates
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self._token_nl = Llama.token_nl()
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self._token_eos = Llama.token_eos()
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def tokenize(self, text: bytes, add_bos: bool = True) -> List[int]:
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"""Tokenize a string.
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Args:
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text: The utf-8 encoded string to tokenize.
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Raises:
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RuntimeError: If the tokenization failed.
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Returns:
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A list of tokens.
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"""
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assert self.ctx is not None
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n_ctx = self._n_ctx
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tokens = (llama_cpp.llama_token * n_ctx)()
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n_tokens = llama_cpp.llama_tokenize(
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self.ctx,
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text,
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tokens,
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llama_cpp.c_int(n_ctx),
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llama_cpp.c_bool(add_bos),
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)
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if n_tokens < 0:
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n_tokens = abs(n_tokens)
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tokens = (llama_cpp.llama_token * n_tokens)()
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n_tokens = llama_cpp.llama_tokenize(
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self.ctx,
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text,
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tokens,
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llama_cpp.c_int(n_tokens),
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llama_cpp.c_bool(add_bos),
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)
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if n_tokens < 0:
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raise RuntimeError(
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f'Failed to tokenize: text="{text}" n_tokens={n_tokens}'
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)
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return list(tokens[:n_tokens])
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def detokenize(self, tokens: List[int]) -> bytes:
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"""Detokenize a list of tokens.
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Args:
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tokens: The list of tokens to detokenize.
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Returns:
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The detokenized string.
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"""
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assert self.ctx is not None
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output = b""
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for token in tokens:
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output += llama_cpp.llama_token_to_str(
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self.ctx, llama_cpp.llama_token(token)
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)
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return output
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def set_cache(self, cache: Optional[LlamaCache]):
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"""Set the cache.
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Args:
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cache: The cache to set.
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"""
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self.cache = cache
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def reset(self):
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"""Reset the model state."""
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self.eval_tokens.clear()
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self.eval_logits.clear()
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def eval(self, tokens: Sequence[int]):
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"""Evaluate a list of tokens.
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Args:
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tokens: The list of tokens to evaluate.
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"""
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assert self.ctx is not None
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n_ctx = self._n_ctx
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for i in range(0, len(tokens), self.n_batch):
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batch = tokens[i : min(len(tokens), i + self.n_batch)]
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n_past = min(n_ctx - len(batch), len(self.eval_tokens))
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n_tokens = len(batch)
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return_code = llama_cpp.llama_eval(
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ctx=self.ctx,
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tokens=(llama_cpp.llama_token * len(batch))(*batch),
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n_tokens=llama_cpp.c_int(n_tokens),
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n_past=llama_cpp.c_int(n_past),
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n_threads=llama_cpp.c_int(self.n_threads),
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)
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if return_code != 0:
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raise RuntimeError(f"llama_eval returned {return_code}")
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# Save tokens
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self.eval_tokens.extend(batch)
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# Save logits
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rows = n_tokens if self.params.logits_all else 1
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n_vocab = self._n_vocab
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cols = n_vocab
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logits_view = llama_cpp.llama_get_logits(self.ctx)
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logits = [logits_view[i * cols : (i + 1) * cols] for i in range(rows)]
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self.eval_logits.extend(logits)
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def _sample(
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self,
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last_n_tokens_data, # type: llama_cpp.Array[llama_cpp.llama_token]
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last_n_tokens_size: llama_cpp.c_int,
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top_k: llama_cpp.c_int,
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top_p: llama_cpp.c_float,
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temp: llama_cpp.c_float,
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tfs_z: llama_cpp.c_float,
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repeat_penalty: llama_cpp.c_float,
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frequency_penalty: llama_cpp.c_float,
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presence_penalty: llama_cpp.c_float,
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mirostat_mode: llama_cpp.c_int,
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mirostat_tau: llama_cpp.c_float,
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mirostat_eta: llama_cpp.c_float,
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penalize_nl: bool = True,
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logits_processor: Optional[LogitsProcessorList] = None,
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):
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assert self.ctx is not None
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assert len(self.eval_logits) > 0
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n_vocab = self._n_vocab
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n_ctx = self._n_ctx
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top_k = llama_cpp.c_int(n_vocab) if top_k.value <= 0 else top_k
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last_n_tokens_size = (
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llama_cpp.c_int(n_ctx)
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if last_n_tokens_size.value < 0
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else last_n_tokens_size
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)
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logits = self.eval_logits[-1]
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if logits_processor is not None:
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logits = logits_processor(list(self.eval_tokens), logits)
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nl_logit = logits[self._token_nl]
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candidates = self._candidates
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for i, logit in enumerate(logits):
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candidates.data[i].id = llama_cpp.llama_token(i)
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candidates.data[i].logit = llama_cpp.c_float(logit)
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candidates.data[i].p = llama_cpp.c_float(0.0)
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candidates.sorted = llama_cpp.c_bool(False)
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candidates.size = llama_cpp.c_size_t(n_vocab)
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llama_cpp.llama_sample_repetition_penalty(
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ctx=self.ctx,
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last_tokens_data=last_n_tokens_data,
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last_tokens_size=last_n_tokens_size,
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candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
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penalty=repeat_penalty,
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)
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llama_cpp.llama_sample_frequency_and_presence_penalties(
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ctx=self.ctx,
|
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candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
|
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last_tokens_data=last_n_tokens_data,
|
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last_tokens_size=last_n_tokens_size,
|
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alpha_frequency=frequency_penalty,
|
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alpha_presence=presence_penalty,
|
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)
|
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if not penalize_nl:
|
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candidates.data[self._token_nl].logit = llama_cpp.c_float(nl_logit)
|
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if temp.value == 0.0:
|
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return llama_cpp.llama_sample_token_greedy(
|
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ctx=self.ctx,
|
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candidates=llama_cpp.ctypes.byref(candidates), # type: ignore
|
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)
|
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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(
|
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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.eval_tokens)
|
|
) + list(self.eval_tokens)[-self.last_n_tokens_size :]
|
|
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.eval_tokens) > 0:
|
|
longest_prefix = 0
|
|
for a, b in zip(self.eval_tokens, 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:]
|
|
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(
|
|
list(self.eval_tokens), self.eval_logits[-1]
|
|
):
|
|
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,
|
|
) -> 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) + max_tokens > self._n_ctx:
|
|
raise ValueError(f"Requested tokens exceed context window of {self._n_ctx}")
|
|
|
|
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.eval_tokens, prompt_tokens
|
|
)
|
|
eval_prefix_len = Llama.longest_token_prefix(
|
|
self.eval_tokens, 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,
|
|
):
|
|
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:
|
|
# 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(len(s), 0, -1):
|
|
if all_text.endswith(s[:i]):
|
|
if i > first_stop_position:
|
|
first_stop_position = i
|
|
break
|
|
|
|
token_end_position = 0
|
|
remaining_tokens = completion_tokens[returned_tokens:]
|
|
remaining_length = len(self.detokenize(remaining_tokens))
|
|
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.eval_logits[token_offset - 1]
|
|
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 self.cache:
|
|
if self.verbose:
|
|
print("Llama._create_completion: cache save", file=sys.stderr)
|
|
self.cache[prompt_tokens + completion_tokens] = self.save_state()
|
|
|
|
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]))
|
|
|
|
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 - 1
|
|
logits = self.eval_logits[token_offset]
|
|
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],
|
|
}
|
|
|
|
if token_end_position >= end:
|
|
last_text = self.detokenize([token])
|
|
if token_end_position == end - 1:
|
|
break
|
|
returned_tokens += 1
|
|
yield {
|
|
"id": completion_id,
|
|
"object": "text_completion",
|
|
"created": created,
|
|
"model": model_name,
|
|
"choices": [
|
|
{
|
|
"text": last_text[
|
|
: len(last_text) - (token_end_position - end)
|
|
].decode("utf-8", errors="ignore"),
|
|
"index": 0,
|
|
"logprobs": logprobs_or_none,
|
|
"finish_reason": finish_reason,
|
|
}
|
|
],
|
|
}
|
|
break
|
|
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": finish_reason
|
|
if returned_tokens == len(completion_tokens)
|
|
else None,
|
|
}
|
|
],
|
|
}
|
|
return
|
|
|
|
text_str = text.decode("utf-8", errors="ignore")
|
|
|
|
if echo:
|
|
text_str = prompt + text_str
|
|
|
|
if suffix is not None:
|
|
text_str = text_str + suffix
|
|
|
|
logprobs_or_none: Optional[CompletionLogprobs] = None
|
|
if logprobs is not None:
|
|
text_offset = 0 if echo else len(prompt)
|
|
token_offset = 0 if echo else len(prompt_tokens[1:])
|
|
text_offsets: List[int] = []
|
|
token_logprobs: List[Optional[float]] = []
|
|
tokens: List[str] = []
|
|
top_logprobs: List[Optional[Dict[str, float]]] = []
|
|
|
|
if echo:
|
|
# Remove leading BOS token
|
|
all_tokens = prompt_tokens[1:] + completion_tokens
|
|
else:
|
|
all_tokens = completion_tokens
|
|
|
|
all_token_strs = [
|
|
self.detokenize([token]).decode("utf-8", errors="ignore")
|
|
for token in all_tokens
|
|
]
|
|
all_logprobs = [
|
|
Llama.logits_to_logprobs(list(map(float, row)))
|
|
for row in self.eval_logits
|
|
][token_offset:]
|
|
for token, token_str, logprobs_token in zip(
|
|
all_tokens, all_token_strs, all_logprobs
|
|
):
|
|
text_offsets.append(text_offset)
|
|
text_offset += len(token_str)
|
|
tokens.append(token_str)
|
|
sorted_logprobs = list(
|
|
sorted(
|
|
zip(logprobs_token, range(len(logprobs_token))), reverse=True
|
|
)
|
|
)
|
|
token_logprobs.append(sorted_logprobs[int(token)][0])
|
|
top_logprob: Optional[Dict[str, float]] = {
|
|
self.detokenize([i]).decode("utf-8", errors="ignore"): logprob
|
|
for logprob, i in sorted_logprobs[:logprobs]
|
|
}
|
|
top_logprob.update({token_str: logprobs_token[int(token)]})
|
|
top_logprobs.append(top_logprob)
|
|
# Weird idosincracy of the OpenAI API where
|
|
# token_logprobs and top_logprobs are null for
|
|
# the first token.
|
|
if echo and len(all_tokens) > 0:
|
|
token_logprobs[0] = None
|
|
top_logprobs[0] = None
|
|
logprobs_or_none = {
|
|
"tokens": tokens,
|
|
"text_offset": text_offsets,
|
|
"token_logprobs": token_logprobs,
|
|
"top_logprobs": top_logprobs,
|
|
}
|
|
|
|
yield {
|
|
"id": completion_id,
|
|
"object": "text_completion",
|
|
"created": created,
|
|
"model": model_name,
|
|
"choices": [
|
|
{
|
|
"text": text_str,
|
|
"index": 0,
|
|
"logprobs": logprobs_or_none,
|
|
"finish_reason": finish_reason,
|
|
}
|
|
],
|
|
"usage": {
|
|
"prompt_tokens": len(prompt_tokens),
|
|
"completion_tokens": len(completion_tokens),
|
|
"total_tokens": len(prompt_tokens) + len(completion_tokens),
|
|
},
|
|
}
|
|
|
|
def create_completion(
|
|
self,
|
|
prompt: str,
|
|
suffix: Optional[str] = None,
|
|
max_tokens: int = 128,
|
|
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,
|
|
) -> Union[Completion, Iterator[CompletionChunk]]:
|
|
"""Generate text from a prompt.
|
|
|
|
Args:
|
|
prompt: The prompt to generate text from.
|
|
suffix: A suffix to append to the generated text. If None, no suffix is appended.
|
|
max_tokens: The maximum number of tokens to generate.
|
|
temperature: The temperature to use for sampling.
|
|
top_p: The top-p value to use for sampling.
|
|
logprobs: The number of logprobs to return. If None, no logprobs are returned.
|
|
echo: Whether to echo the prompt.
|
|
stop: A list of strings to stop generation when encountered.
|
|
repeat_penalty: The penalty to apply to repeated tokens.
|
|
top_k: The top-k value to use for sampling.
|
|
stream: Whether to stream the results.
|
|
|
|
Raises:
|
|
ValueError: If the requested tokens exceed the context window.
|
|
RuntimeError: If the prompt fails to tokenize or the model fails to evaluate the prompt.
|
|
|
|
Returns:
|
|
Response object containing the generated text.
|
|
"""
|
|
completion_or_chunks = self._create_completion(
|
|
prompt=prompt,
|
|
suffix=suffix,
|
|
max_tokens=max_tokens,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
logprobs=logprobs,
|
|
echo=echo,
|
|
stop=stop,
|
|
frequency_penalty=frequency_penalty,
|
|
presence_penalty=presence_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
top_k=top_k,
|
|
stream=stream,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
)
|
|
if stream:
|
|
chunks: Iterator[CompletionChunk] = completion_or_chunks
|
|
return chunks
|
|
completion: Completion = next(completion_or_chunks) # type: ignore
|
|
return completion
|
|
|
|
def __call__(
|
|
self,
|
|
prompt: str,
|
|
suffix: Optional[str] = None,
|
|
max_tokens: int = 128,
|
|
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,
|
|
) -> Union[Completion, Iterator[CompletionChunk]]:
|
|
"""Generate text from a prompt.
|
|
|
|
Args:
|
|
prompt: The prompt to generate text from.
|
|
suffix: A suffix to append to the generated text. If None, no suffix is appended.
|
|
max_tokens: The maximum number of tokens to generate.
|
|
temperature: The temperature to use for sampling.
|
|
top_p: The top-p value to use for sampling.
|
|
logprobs: The number of logprobs to return. If None, no logprobs are returned.
|
|
echo: Whether to echo the prompt.
|
|
stop: A list of strings to stop generation when encountered.
|
|
repeat_penalty: The penalty to apply to repeated tokens.
|
|
top_k: The top-k value to use for sampling.
|
|
stream: Whether to stream the results.
|
|
|
|
Raises:
|
|
ValueError: If the requested tokens exceed the context window.
|
|
RuntimeError: If the prompt fails to tokenize or the model fails to evaluate the prompt.
|
|
|
|
Returns:
|
|
Response object containing the generated text.
|
|
"""
|
|
return self.create_completion(
|
|
prompt=prompt,
|
|
suffix=suffix,
|
|
max_tokens=max_tokens,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
logprobs=logprobs,
|
|
echo=echo,
|
|
stop=stop,
|
|
frequency_penalty=frequency_penalty,
|
|
presence_penalty=presence_penalty,
|
|
repeat_penalty=repeat_penalty,
|
|
top_k=top_k,
|
|
stream=stream,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
)
|
|
|
|
def _convert_text_completion_to_chat(
|
|
self, completion: Completion
|
|
) -> ChatCompletion:
|
|
return {
|
|
"id": "chat" + completion["id"],
|
|
"object": "chat.completion",
|
|
"created": completion["created"],
|
|
"model": completion["model"],
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": completion["choices"][0]["text"],
|
|
},
|
|
"finish_reason": completion["choices"][0]["finish_reason"],
|
|
}
|
|
],
|
|
"usage": completion["usage"],
|
|
}
|
|
|
|
def _convert_text_completion_chunks_to_chat(
|
|
self,
|
|
chunks: Iterator[CompletionChunk],
|
|
) -> Iterator[ChatCompletionChunk]:
|
|
for i, chunk in enumerate(chunks):
|
|
if i == 0:
|
|
yield {
|
|
"id": "chat" + chunk["id"],
|
|
"model": chunk["model"],
|
|
"created": chunk["created"],
|
|
"object": "chat.completion.chunk",
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {
|
|
"role": "assistant",
|
|
},
|
|
"finish_reason": None,
|
|
}
|
|
],
|
|
}
|
|
yield {
|
|
"id": "chat" + chunk["id"],
|
|
"model": chunk["model"],
|
|
"created": chunk["created"],
|
|
"object": "chat.completion.chunk",
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {
|
|
"content": chunk["choices"][0]["text"],
|
|
},
|
|
"finish_reason": chunk["choices"][0]["finish_reason"],
|
|
}
|
|
],
|
|
}
|
|
|
|
def create_chat_completion(
|
|
self,
|
|
messages: List[ChatCompletionMessage],
|
|
temperature: float = 0.2,
|
|
top_p: float = 0.95,
|
|
top_k: int = 40,
|
|
stream: bool = False,
|
|
stop: Optional[Union[str, List[str]]] = [],
|
|
max_tokens: int = 256,
|
|
presence_penalty: float = 0.0,
|
|
frequency_penalty: float = 0.0,
|
|
repeat_penalty: float = 1.1,
|
|
tfs_z: float = 1.0,
|
|
mirostat_mode: int = 0,
|
|
mirostat_tau: float = 5.0,
|
|
mirostat_eta: float = 0.1,
|
|
model: Optional[str] = None,
|
|
) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]:
|
|
"""Generate a chat completion from a list of messages.
|
|
|
|
Args:
|
|
messages: A list of messages to generate a response for.
|
|
temperature: The temperature to use for sampling.
|
|
top_p: The top-p value to use for sampling.
|
|
top_k: The top-k value to use for sampling.
|
|
stream: Whether to stream the results.
|
|
stop: A list of strings to stop generation when encountered.
|
|
max_tokens: The maximum number of tokens to generate.
|
|
repeat_penalty: The penalty to apply to repeated tokens.
|
|
|
|
Returns:
|
|
Generated chat completion or a stream of chat completion chunks.
|
|
"""
|
|
stop = (
|
|
stop if isinstance(stop, list) else [stop] if isinstance(stop, str) else []
|
|
)
|
|
chat_history = "".join(
|
|
f'### {"Human" if message["role"] == "user" else "Assistant"}:{message["content"]}'
|
|
for message in messages
|
|
)
|
|
PROMPT = chat_history + "### Assistant:"
|
|
PROMPT_STOP = ["### Assistant:", "### Human:"]
|
|
completion_or_chunks = self(
|
|
prompt=PROMPT,
|
|
stop=PROMPT_STOP + stop,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
stream=stream,
|
|
max_tokens=max_tokens,
|
|
repeat_penalty=repeat_penalty,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
tfs_z=tfs_z,
|
|
mirostat_mode=mirostat_mode,
|
|
mirostat_tau=mirostat_tau,
|
|
mirostat_eta=mirostat_eta,
|
|
model=model,
|
|
)
|
|
if stream:
|
|
chunks: Iterator[CompletionChunk] = completion_or_chunks # type: ignore
|
|
return self._convert_text_completion_chunks_to_chat(chunks)
|
|
else:
|
|
completion: Completion = completion_or_chunks # type: ignore
|
|
return self._convert_text_completion_to_chat(completion)
|
|
|
|
def __del__(self):
|
|
if self.ctx is not None:
|
|
llama_cpp.llama_free(self.ctx)
|
|
self.ctx = None
|
|
|
|
def __getstate__(self):
|
|
return dict(
|
|
verbose=self.verbose,
|
|
model_path=self.model_path,
|
|
n_ctx=self.params.n_ctx,
|
|
n_gpu_layers=self.params.n_gpu_layers,
|
|
seed=self.params.seed,
|
|
f16_kv=self.params.f16_kv,
|
|
logits_all=self.params.logits_all,
|
|
vocab_only=self.params.vocab_only,
|
|
use_mmap=self.params.use_mmap,
|
|
use_mlock=self.params.use_mlock,
|
|
embedding=self.params.embedding,
|
|
last_n_tokens_size=self.last_n_tokens_size,
|
|
n_batch=self.n_batch,
|
|
n_threads=self.n_threads,
|
|
lora_base=self.lora_base,
|
|
lora_path=self.lora_path,
|
|
### DEPRECATED ###
|
|
n_parts=self.n_parts,
|
|
### DEPRECATED ###
|
|
)
|
|
|
|
def __setstate__(self, state):
|
|
self.__init__(
|
|
model_path=state["model_path"],
|
|
n_ctx=state["n_ctx"],
|
|
n_parts=state["n_parts"],
|
|
n_gpu_layers=state["n_gpu_layers"],
|
|
seed=state["seed"],
|
|
f16_kv=state["f16_kv"],
|
|
logits_all=state["logits_all"],
|
|
vocab_only=state["vocab_only"],
|
|
use_mmap=state["use_mmap"],
|
|
use_mlock=state["use_mlock"],
|
|
embedding=state["embedding"],
|
|
n_threads=state["n_threads"],
|
|
n_batch=state["n_batch"],
|
|
last_n_tokens_size=state["last_n_tokens_size"],
|
|
lora_base=state["lora_base"],
|
|
lora_path=state["lora_path"],
|
|
verbose=state["verbose"],
|
|
)
|
|
|
|
def save_state(self) -> LlamaState:
|
|
assert self.ctx is not None
|
|
state_size = llama_cpp.llama_get_state_size(self.ctx)
|
|
llama_state = (llama_cpp.c_uint8 * int(state_size))()
|
|
n_bytes = llama_cpp.llama_copy_state_data(self.ctx, llama_state)
|
|
if int(n_bytes) > int(state_size):
|
|
raise RuntimeError("Failed to copy llama state data")
|
|
llama_state_compact = (llama_cpp.c_uint8 * int(n_bytes))()
|
|
llama_cpp.ctypes.memmove(llama_state_compact, llama_state, int(n_bytes))
|
|
if self.verbose:
|
|
print(
|
|
f"Llama.save_state: saving {n_bytes} bytes of llama state",
|
|
file=sys.stderr,
|
|
)
|
|
return LlamaState(
|
|
eval_tokens=self.eval_tokens.copy(),
|
|
eval_logits=self.eval_logits.copy(),
|
|
llama_state=llama_state_compact,
|
|
llama_state_size=n_bytes,
|
|
)
|
|
|
|
def load_state(self, state: LlamaState) -> None:
|
|
assert self.ctx is not None
|
|
self.eval_tokens = state.eval_tokens.copy()
|
|
self.eval_logits = state.eval_logits.copy()
|
|
state_size = state.llama_state_size
|
|
if llama_cpp.llama_set_state_data(self.ctx, state.llama_state) != state_size:
|
|
raise RuntimeError("Failed to set llama state data")
|
|
|
|
def n_ctx(self) -> int:
|
|
"""Return the context window size."""
|
|
assert self.ctx is not None
|
|
return llama_cpp.llama_n_ctx(self.ctx)
|
|
|
|
def n_embd(self) -> int:
|
|
"""Return the embedding size."""
|
|
assert self.ctx is not None
|
|
return llama_cpp.llama_n_embd(self.ctx)
|
|
|
|
def n_vocab(self) -> int:
|
|
"""Return the vocabulary size."""
|
|
assert self.ctx is not None
|
|
return llama_cpp.llama_n_vocab(self.ctx)
|
|
|
|
def tokenizer(self) -> "LlamaTokenizer":
|
|
"""Return the tokenizer for this model."""
|
|
assert self.ctx is not None
|
|
return LlamaTokenizer(self)
|
|
|
|
@staticmethod
|
|
def token_eos() -> int:
|
|
"""Return the end-of-sequence token."""
|
|
return llama_cpp.llama_token_eos()
|
|
|
|
@staticmethod
|
|
def token_bos() -> int:
|
|
"""Return the beginning-of-sequence token."""
|
|
return llama_cpp.llama_token_bos()
|
|
|
|
@staticmethod
|
|
def token_nl() -> int:
|
|
"""Return the newline token."""
|
|
return llama_cpp.llama_token_nl()
|
|
|
|
@staticmethod
|
|
def logits_to_logprobs(logits: List[float]) -> List[float]:
|
|
exps = [math.exp(float(x)) for x in logits]
|
|
sum_exps = sum(exps)
|
|
return [math.log(x / sum_exps) for x in exps]
|
|
|
|
@staticmethod
|
|
def longest_token_prefix(a: Sequence[int], b: Sequence[int]):
|
|
longest_prefix = 0
|
|
for _a, _b in zip(a, b):
|
|
if _a == _b:
|
|
longest_prefix += 1
|
|
else:
|
|
break
|
|
return longest_prefix
|
|
|
|
|
|
class LlamaTokenizer:
|
|
def __init__(self, llama: Llama):
|
|
self.llama = llama
|
|
|
|
def encode(self, text: str, add_bos: bool = True) -> List[int]:
|
|
return self.llama.tokenize(
|
|
text.encode("utf-8", errors="ignore"), add_bos=add_bos
|
|
)
|
|
|
|
def decode(self, tokens: List[int]) -> str:
|
|
return self.llama.detokenize(tokens).decode("utf-8", errors="ignore")
|
|
|
|
@classmethod
|
|
def from_ggml_file(cls, path: str) -> "LlamaTokenizer":
|
|
return cls(Llama(model_path=path, vocab_only=True))
|