536 lines
18 KiB
Python
536 lines
18 KiB
Python
import os
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import uuid
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import time
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import multiprocessing
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from typing import List, Optional, Union, Generator, Sequence
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from collections import deque
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from . import llama_cpp
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from .llama_types import *
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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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seed: int = 1337,
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f16_kv: bool = False,
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logits_all: bool = False,
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vocab_only: bool = False,
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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 = 8,
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last_n_tokens_size: int = 64,
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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_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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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.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_parts = n_parts
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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_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.last_n_tokens_data = deque(
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[llama_cpp.llama_token(0)] * self.last_n_tokens_size,
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maxlen=self.last_n_tokens_size,
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)
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self.tokens_consumed = 0
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self.n_batch = n_batch
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self.n_threads = n_threads or multiprocessing.cpu_count()
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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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def tokenize(self, text: bytes) -> List[llama_cpp.llama_token]:
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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 = llama_cpp.llama_n_ctx(self.ctx)
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tokens = (llama_cpp.llama_token * int(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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n_ctx,
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llama_cpp.c_bool(True),
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)
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if int(n_tokens) < 0:
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raise RuntimeError(f'Failed to tokenize: text="{text}" n_tokens={n_tokens}')
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return list(tokens[:n_tokens])
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def detokenize(self, tokens: List[llama_cpp.llama_token]) -> 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(self.ctx, token)
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return output
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def reset(self):
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"""Reset the model state."""
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self.last_n_tokens_data.extend(
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[llama_cpp.llama_token(0)] * self.last_n_tokens_size
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)
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self.tokens_consumed = 0
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def eval(self, tokens: Sequence[llama_cpp.llama_token]):
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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 = int(llama_cpp.llama_n_ctx(self.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), self.tokens_consumed)
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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(len(batch)),
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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 int(return_code) != 0:
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raise RuntimeError(f"llama_eval returned {return_code}")
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self.last_n_tokens_data.extend(batch)
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self.tokens_consumed += len(batch)
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def sample(
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self,
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top_k: int,
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top_p: float,
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temp: float,
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repeat_penalty: float,
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):
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"""Sample a token from the model.
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Args:
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top_k: The top-k sampling parameter.
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top_p: The top-p sampling parameter.
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temp: The temperature parameter.
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repeat_penalty: The repeat penalty parameter.
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Returns:
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The sampled token.
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"""
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assert self.ctx is not None
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# Temporary workaround for https://github.com/ggerganov/llama.cpp/issues/684
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if temp == 0.0:
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temp = 1.0
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top_p = 0.0
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top_k = 1
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return llama_cpp.llama_sample_top_p_top_k(
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ctx=self.ctx,
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last_n_tokens_data=(llama_cpp.llama_token * self.last_n_tokens_size)(
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*self.last_n_tokens_data
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),
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last_n_tokens_size=llama_cpp.c_int(self.last_n_tokens_size),
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top_k=llama_cpp.c_int(top_k),
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top_p=llama_cpp.c_float(top_p),
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temp=llama_cpp.c_float(temp),
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repeat_penalty=llama_cpp.c_float(repeat_penalty),
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)
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def generate(
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self,
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tokens: Sequence[llama_cpp.llama_token],
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top_k: int,
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top_p: float,
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temp: float,
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repeat_penalty: float,
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) -> Generator[
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llama_cpp.llama_token, Optional[Sequence[llama_cpp.llama_token]], None
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]:
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"""Create a generator of tokens from a prompt.
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Examples:
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>>> llama = Llama("models/ggml-7b.bin")
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>>> tokens = llama.tokenize(b"Hello, world!")
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>>> for token in llama.generate(tokens, top_k=40, top_p=0.95, temp=1.0, repeat_penalty=1.1):
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... print(llama.detokenize([token]))
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Args:
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tokens: The prompt tokens.
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top_k: The top-k sampling parameter.
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top_p: The top-p sampling parameter.
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temp: The temperature parameter.
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repeat_penalty: The repeat penalty parameter.
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Yields:
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The generated tokens.
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"""
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assert self.ctx is not None
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self.reset()
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while True:
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self.eval(tokens)
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token = self.sample(
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top_k=top_k,
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top_p=top_p,
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temp=temp,
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repeat_penalty=repeat_penalty,
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)
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tokens_or_none = yield token
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tokens = [token]
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if tokens_or_none is not None:
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tokens.extend(tokens_or_none)
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def create_embedding(self, input: str) -> Embedding:
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"""Embed a string.
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Args:
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input: The utf-8 encoded string to embed.
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Returns:
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An embedding object.
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"""
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assert self.ctx is not None
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tokens = self.tokenize(input.encode("utf-8"))
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self.reset()
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self.eval(tokens)
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n_tokens = len(tokens)
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embedding = llama_cpp.llama_get_embeddings(self.ctx)[
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: llama_cpp.llama_n_embd(self.ctx)
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]
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return {
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"object": "list",
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"data": [
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{
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"object": "embedding",
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"embedding": embedding,
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"index": 0,
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}
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],
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"model": self.model_path,
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"usage": {
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"prompt_tokens": n_tokens,
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"total_tokens": n_tokens,
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},
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}
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def embed(self, input: str) -> List[float]:
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"""Embed a string.
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Args:
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input: The utf-8 encoded string to embed.
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Returns:
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A list of embeddings
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"""
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return list(map(float, self.create_embedding(input)["data"][0]["embedding"]))
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def _create_completion(
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self,
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prompt: str,
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suffix: Optional[str] = None,
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max_tokens: int = 16,
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temperature: float = 0.8,
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top_p: float = 0.95,
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logprobs: Optional[int] = None,
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echo: bool = False,
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stop: List[str] = [],
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repeat_penalty: float = 1.1,
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top_k: int = 40,
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stream: bool = False,
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) -> Union[
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Generator[Completion, None, None],
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Generator[CompletionChunk, None, None],
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]:
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assert self.ctx is not None
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completion_id = f"cmpl-{str(uuid.uuid4())}"
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created = int(time.time())
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completion_tokens: List[llama_cpp.llama_token] = []
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# Add blank space to start of prompt to match OG llama tokenizer
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prompt_tokens = self.tokenize(b" " + prompt.encode("utf-8"))
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text = b""
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returned_characters = 0
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if len(prompt_tokens) + max_tokens > int(llama_cpp.llama_n_ctx(self.ctx)):
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raise ValueError(
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f"Requested tokens exceed context window of {llama_cpp.llama_n_ctx(self.ctx)}"
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)
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if stop != []:
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stop_sequences = [s.encode("utf-8") for s in stop]
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else:
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stop_sequences = []
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finish_reason = None
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for token in self.generate(
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prompt_tokens,
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top_k=top_k,
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top_p=top_p,
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temp=temperature,
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repeat_penalty=repeat_penalty,
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):
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if token == llama_cpp.llama_token_eos():
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text = self.detokenize(completion_tokens)
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finish_reason = "stop"
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break
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completion_tokens.append(token)
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all_text = self.detokenize(completion_tokens)
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any_stop = [s for s in stop_sequences if s in all_text]
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if len(any_stop) > 0:
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first_stop = any_stop[0]
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text = all_text[: all_text.index(first_stop)]
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finish_reason = "stop"
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break
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if stream:
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start = returned_characters
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longest = 0
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# We want to avoid yielding any characters from
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# the generated text if they are part of a stop
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# sequence.
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for s in stop_sequences:
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for i in range(len(s), 0, -1):
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if all_text.endswith(s[:i]):
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if i > longest:
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longest = i
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break
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text = all_text[: len(all_text) - longest]
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returned_characters += len(text[start:])
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yield {
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"id": completion_id,
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"object": "text_completion",
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"created": created,
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"model": self.model_path,
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"choices": [
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{
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"text": text[start:].decode("utf-8"),
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"index": 0,
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"logprobs": None,
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"finish_reason": None,
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}
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],
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}
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if len(completion_tokens) >= max_tokens:
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text = self.detokenize(completion_tokens)
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finish_reason = "length"
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break
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if finish_reason is None:
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finish_reason = "length"
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if stream:
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yield {
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"id": completion_id,
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"object": "text_completion",
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"created": created,
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"model": self.model_path,
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"choices": [
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{
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"text": text[returned_characters:].decode("utf-8"),
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"index": 0,
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"logprobs": None,
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"finish_reason": finish_reason,
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}
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],
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}
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return
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text = text.decode("utf-8")
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if echo:
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text = prompt + text
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if suffix is not None:
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text = text + suffix
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if logprobs is not None:
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raise NotImplementedError("logprobs not implemented")
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yield {
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"id": completion_id,
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"object": "text_completion",
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"created": created,
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"model": self.model_path,
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"choices": [
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{
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"text": text,
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"index": 0,
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"logprobs": None,
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"finish_reason": finish_reason,
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}
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],
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"usage": {
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"prompt_tokens": len(prompt_tokens),
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"completion_tokens": len(completion_tokens),
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"total_tokens": len(prompt_tokens) + len(completion_tokens),
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},
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}
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def create_completion(
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self,
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prompt: str,
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suffix: Optional[str] = None,
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max_tokens: int = 128,
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temperature: float = 0.8,
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top_p: float = 0.95,
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logprobs: Optional[int] = None,
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echo: bool = False,
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stop: List[str] = [],
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repeat_penalty: float = 1.1,
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top_k: int = 40,
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stream: bool = False,
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) -> Union[Completion, Generator[CompletionChunk, None, None]]:
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"""Generate text from a prompt.
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Args:
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prompt: The prompt to generate text from.
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suffix: A suffix to append to the generated text. If None, no suffix is appended.
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max_tokens: The maximum number of tokens to generate.
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temperature: The temperature to use for sampling.
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top_p: The top-p value to use for sampling.
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logprobs: The number of logprobs to return. If None, no logprobs are returned.
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echo: Whether to echo the prompt.
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stop: A list of strings to stop generation when encountered.
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repeat_penalty: The penalty to apply to repeated tokens.
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top_k: The top-k value to use for sampling.
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stream: Whether to stream the results.
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Raises:
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ValueError: If the requested tokens exceed the context window.
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RuntimeError: If the prompt fails to tokenize or the model fails to evaluate the prompt.
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Returns:
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Response object containing the generated text.
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"""
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completion_or_chunks = self._create_completion(
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prompt=prompt,
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suffix=suffix,
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max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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logprobs=logprobs,
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echo=echo,
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stop=stop,
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repeat_penalty=repeat_penalty,
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top_k=top_k,
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stream=stream,
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)
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if stream:
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chunks: Generator[CompletionChunk, None, None] = completion_or_chunks
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return chunks
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completion: Completion = next(completion_or_chunks) # type: ignore
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return completion
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def __call__(
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self,
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prompt: str,
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suffix: Optional[str] = None,
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max_tokens: int = 128,
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temperature: float = 0.8,
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top_p: float = 0.95,
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logprobs: Optional[int] = None,
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echo: bool = False,
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stop: List[str] = [],
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repeat_penalty: float = 1.1,
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top_k: int = 40,
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stream: bool = False,
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):
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"""Generate text from a prompt.
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Args:
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prompt: The prompt to generate text from.
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suffix: A suffix to append to the generated text. If None, no suffix is appended.
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max_tokens: The maximum number of tokens to generate.
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temperature: The temperature to use for sampling.
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top_p: The top-p value to use for sampling.
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logprobs: The number of logprobs to return. If None, no logprobs are returned.
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echo: Whether to echo the prompt.
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stop: A list of strings to stop generation when encountered.
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repeat_penalty: The penalty to apply to repeated tokens.
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top_k: The top-k value to use for sampling.
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stream: Whether to stream the results.
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Raises:
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ValueError: If the requested tokens exceed the context window.
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RuntimeError: If the prompt fails to tokenize or the model fails to evaluate the prompt.
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Returns:
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Response object containing the generated text.
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"""
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return self.create_completion(
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prompt=prompt,
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suffix=suffix,
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max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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logprobs=logprobs,
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echo=echo,
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stop=stop,
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repeat_penalty=repeat_penalty,
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top_k=top_k,
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stream=stream,
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)
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def __del__(self):
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if self.ctx is not None:
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llama_cpp.llama_free(self.ctx)
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self.ctx = None
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|
@staticmethod
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def token_eos() -> llama_cpp.llama_token:
|
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"""Return the end-of-sequence token."""
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return llama_cpp.llama_token_eos()
|
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|
|
@staticmethod
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def token_bos() -> llama_cpp.llama_token:
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"""Return the beginning-of-sequence token."""
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return llama_cpp.llama_token_bos()
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