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288 lines
9.6 KiB
Python
288 lines
9.6 KiB
Python
"""Wrapper around llama.cpp."""
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import logging
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from typing import Any, Dict, Generator, List, Optional
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from pydantic import Field, root_validator
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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logger = logging.getLogger(__name__)
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class LlamaCpp(LLM):
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"""Wrapper around the llama.cpp model.
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To use, you should have the llama-cpp-python library installed, and provide the
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path to the Llama model as a named parameter to the constructor.
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Check out: https://github.com/abetlen/llama-cpp-python
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Example:
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.. code-block:: python
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from langchain.llms import LlamaCppEmbeddings
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llm = LlamaCppEmbeddings(model_path="/path/to/llama/model")
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"""
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client: Any #: :meta private:
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model_path: str
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"""The path to the Llama model file."""
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lora_base: Optional[str] = None
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"""The path to the Llama LoRA base model."""
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lora_path: Optional[str] = None
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"""The path to the Llama LoRA. If None, no LoRa is loaded."""
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n_ctx: int = Field(512, alias="n_ctx")
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"""Token context window."""
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n_parts: int = Field(-1, alias="n_parts")
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"""Number of parts to split the model into.
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If -1, the number of parts is automatically determined."""
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seed: int = Field(-1, alias="seed")
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"""Seed. If -1, a random seed is used."""
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f16_kv: bool = Field(True, alias="f16_kv")
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"""Use half-precision for key/value cache."""
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logits_all: bool = Field(False, alias="logits_all")
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"""Return logits for all tokens, not just the last token."""
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vocab_only: bool = Field(False, alias="vocab_only")
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"""Only load the vocabulary, no weights."""
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use_mlock: bool = Field(False, alias="use_mlock")
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"""Force system to keep model in RAM."""
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n_threads: Optional[int] = Field(None, alias="n_threads")
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"""Number of threads to use.
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If None, the number of threads is automatically determined."""
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n_batch: Optional[int] = Field(8, alias="n_batch")
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"""Number of tokens to process in parallel.
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Should be a number between 1 and n_ctx."""
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n_gpu_layers: Optional[int] = Field(None, alias="n_gpu_layers")
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"""Number of layers to be loaded into gpu memory. Default None."""
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suffix: Optional[str] = Field(None)
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"""A suffix to append to the generated text. If None, no suffix is appended."""
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max_tokens: Optional[int] = 256
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"""The maximum number of tokens to generate."""
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temperature: Optional[float] = 0.8
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"""The temperature to use for sampling."""
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top_p: Optional[float] = 0.95
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"""The top-p value to use for sampling."""
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logprobs: Optional[int] = Field(None)
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"""The number of logprobs to return. If None, no logprobs are returned."""
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echo: Optional[bool] = False
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"""Whether to echo the prompt."""
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stop: Optional[List[str]] = []
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"""A list of strings to stop generation when encountered."""
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repeat_penalty: Optional[float] = 1.1
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"""The penalty to apply to repeated tokens."""
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top_k: Optional[int] = 40
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"""The top-k value to use for sampling."""
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last_n_tokens_size: Optional[int] = 64
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"""The number of tokens to look back when applying the repeat_penalty."""
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use_mmap: Optional[bool] = True
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"""Whether to keep the model loaded in RAM"""
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streaming: bool = True
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"""Whether to stream the results, token by token."""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that llama-cpp-python library is installed."""
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model_path = values["model_path"]
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model_param_names = [
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"lora_path",
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"lora_base",
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"n_ctx",
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"n_parts",
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"seed",
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"f16_kv",
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"logits_all",
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"vocab_only",
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"use_mlock",
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"n_threads",
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"n_batch",
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"use_mmap",
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"last_n_tokens_size",
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]
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model_params = {k: values[k] for k in model_param_names}
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# For backwards compatibility, only include if non-null.
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if values["n_gpu_layers"] is not None:
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model_params["n_gpu_layers"] = values["n_gpu_layers"]
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try:
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from llama_cpp import Llama
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values["client"] = Llama(model_path, **model_params)
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except ImportError:
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raise ModuleNotFoundError(
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"Could not import llama-cpp-python library. "
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"Please install the llama-cpp-python library to "
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"use this embedding model: pip install llama-cpp-python"
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)
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except Exception as e:
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raise ValueError(
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f"Could not load Llama model from path: {model_path}. "
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f"Received error {e}"
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling llama_cpp."""
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return {
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"suffix": self.suffix,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"logprobs": self.logprobs,
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"echo": self.echo,
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"stop_sequences": self.stop, # key here is convention among LLM classes
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"repeat_penalty": self.repeat_penalty,
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"top_k": self.top_k,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model_path": self.model_path}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "llamacpp"
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def _get_parameters(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
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"""
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Performs sanity check, preparing parameters in format needed by llama_cpp.
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Args:
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stop (Optional[List[str]]): List of stop sequences for llama_cpp.
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Returns:
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Dictionary containing the combined parameters.
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"""
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# Raise error if stop sequences are in both input and default params
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if self.stop and stop is not None:
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raise ValueError("`stop` found in both the input and default params.")
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params = self._default_params
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# llama_cpp expects the "stop" key not this, so we remove it:
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params.pop("stop_sequences")
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# then sets it as configured, or default to an empty list:
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params["stop"] = self.stop or stop or []
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return params
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call the Llama model and return the output.
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Args:
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prompt: The prompt to use for generation.
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stop: A list of strings to stop generation when encountered.
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Returns:
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The generated text.
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Example:
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.. code-block:: python
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from langchain.llms import LlamaCpp
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llm = LlamaCpp(model_path="/path/to/local/llama/model.bin")
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llm("This is a prompt.")
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"""
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if self.streaming:
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# If streaming is enabled, we use the stream
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# method that yields as they are generated
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# and return the combined strings from the first choices's text:
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combined_text_output = ""
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for token in self.stream(prompt=prompt, stop=stop, run_manager=run_manager):
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combined_text_output += token["choices"][0]["text"]
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return combined_text_output
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else:
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params = self._get_parameters(stop)
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params = {**params, **kwargs}
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result = self.client(prompt=prompt, **params)
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return result["choices"][0]["text"]
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def stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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) -> Generator[Dict, None, None]:
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"""Yields results objects as they are generated in real time.
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BETA: this is a beta feature while we figure out the right abstraction.
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Once that happens, this interface could change.
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It also calls the callback manager's on_llm_new_token event with
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similar parameters to the OpenAI LLM class method of the same name.
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Args:
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prompt: The prompts to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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A generator representing the stream of tokens being generated.
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Yields:
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A dictionary like objects containing a string token and metadata.
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See llama-cpp-python docs and below for more.
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Example:
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.. code-block:: python
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from langchain.llms import LlamaCpp
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llm = LlamaCpp(
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model_path="/path/to/local/model.bin",
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temperature = 0.5
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)
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for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'",
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stop=["'","\n"]):
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result = chunk["choices"][0]
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print(result["text"], end='', flush=True)
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"""
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params = self._get_parameters(stop)
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result = self.client(prompt=prompt, stream=True, **params)
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for chunk in result:
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token = chunk["choices"][0]["text"]
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log_probs = chunk["choices"][0].get("logprobs", None)
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if run_manager:
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run_manager.on_llm_new_token(
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token=token, verbose=self.verbose, log_probs=log_probs
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)
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yield chunk
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def get_num_tokens(self, text: str) -> int:
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tokenized_text = self.client.tokenize(text.encode("utf-8"))
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return len(tokenized_text)
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