Source code for langchain_community.llms.aphrodite

from typing import Any, Dict, List, Optional

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import BaseLLM
from langchain_core.outputs import Generation, LLMResult
from langchain_core.utils import pre_init
from pydantic import Field


[docs] class Aphrodite(BaseLLM): """Aphrodite language model.""" model: str = "" """The name or path of a HuggingFace Transformers model.""" tensor_parallel_size: Optional[int] = 1 """The number of GPUs to use for distributed execution with tensor parallelism.""" trust_remote_code: Optional[bool] = False """Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer.""" n: int = 1 """Number of output sequences to return for the given prompt.""" best_of: Optional[int] = None """Number of output sequences that are generated from the prompt. From these `best_of` sequences, the top `n` sequences are returned. `best_of` must be >= `n`. This is treated as the beam width when `use_beam_search` is True. By default, `best_of` is set to `n`.""" presence_penalty: float = 0.0 """Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to generate new tokens, while values < 0 encourage the model to repeat tokens.""" frequency_penalty: float = 0.0 """Float that penalizes new tokens based on their frequency in the generated text so far. Applied additively to the logits.""" repetition_penalty: float = 1.0 """Float that penalizes new tokens based on their frequency in the generated text so far. Applied multiplicatively to the logits.""" temperature: float = 1.0 """Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero is equivalent to greedy sampling.""" top_p: float = 1.0 """Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1.0 to consider all tokens.""" top_k: int = -1 """Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens (disabled).""" top_a: float = 0.0 """Float that controls the cutoff for Top-A sampling. Exact cutoff is top_a*max_prob**2. Must be in [0,inf], 0 to disable.""" min_p: float = 0.0 """Float that controls the cutoff for min-p sampling. Exact cutoff is min_p*max_prob. Must be in [0,1], 0 to disable.""" tfs: float = 1.0 """Float that controls the cumulative approximate curvature of the distribution to retain for Tail Free Sampling. Must be in (0, 1]. Set to 1.0 to disable.""" eta_cutoff: float = 0.0 """Float that controls the cutoff threshold for Eta sampling (a form of entropy adaptive truncation sampling). Threshold is calculated as `min(eta, sqrt(eta)*entropy(probs)). Specified in units of 1e-4. Set to 0 to disable.""" epsilon_cutoff: float = 0.0 """Float that controls the cutoff threshold for Epsilon sampling (simple probability threshold truncation). Specified in units of 1e-4. Set to 0 to disable.""" typical_p: float = 1.0 """Float that controls the cumulative probability of tokens closest in surprise to the expected surprise to consider. Must be in (0, 1]. Set to 1 to disable.""" mirostat_mode: int = 0 """The mirostat mode to use. 0 for no mirostat, 2 for mirostat v2. Mode 1 is not supported.""" mirostat_tau: float = 0.0 """The target 'surprisal' that mirostat works towards. Range [0, inf).""" use_beam_search: bool = False """Whether to use beam search instead of sampling.""" length_penalty: float = 1.0 """Float that penalizes sequences based on their length. Used only when `use_beam_search` is True.""" early_stopping: bool = False """Controls the stopping condition for beam search. It accepts the following values: `True`, where the generation stops as soon as there are `best_of` complete candidates; `False`, where a heuristic is applied to the generation stops when it is very unlikely to find better candidates; `never`, where the beam search procedure only stops where there cannot be better candidates (canonical beam search algorithm).""" stop: Optional[List[str]] = None """List of strings that stop the generation when they are generated. The returned output will not contain the stop tokens.""" stop_token_ids: Optional[List[int]] = None """List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens.""" ignore_eos: bool = False """Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.""" max_tokens: int = 512 """Maximum number of tokens to generate per output sequence.""" logprobs: Optional[int] = None """Number of log probabilities to return per output token.""" prompt_logprobs: Optional[int] = None """Number of log probabilities to return per prompt token.""" custom_token_bans: Optional[List[int]] = None """List of token IDs to ban from generating.""" skip_special_tokens: bool = True """Whether to skip special tokens in the output. Defaults to True.""" spaces_between_special_tokens: bool = True """Whether to add spaces between special tokens in the output. Defaults to True.""" logit_bias: Optional[Dict[str, float]] = None """List of LogitsProcessors to change the probability of token prediction at runtime.""" dtype: str = "auto" """The data type for the model weights and activations.""" download_dir: Optional[str] = None """Directory to download and load the weights. (Default to the default cache dir of huggingface)""" quantization: Optional[str] = None """Quantization mode to use. Can be one of `awq` or `gptq`.""" aphrodite_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `aphrodite.LLM` call not explicitly specified.""" client: Any = None #: :meta private:
[docs] @pre_init def validate_environment(cls, values: Dict) -> Dict: """Validate that python package exists in environment.""" try: from aphrodite import LLM as AphroditeModel except ImportError: raise ImportError( "Could not import aphrodite-engine python package. " "Please install it with `pip install aphrodite-engine`." ) # aphrodite_kwargs = values["aphrodite_kwargs"] # if values.get("quantization"): # aphrodite_kwargs["quantization"] = values["quantization"] values["client"] = AphroditeModel( model=values["model"], tensor_parallel_size=values["tensor_parallel_size"], trust_remote_code=values["trust_remote_code"], dtype=values["dtype"], download_dir=values["download_dir"], **values["aphrodite_kwargs"], ) return values
@property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling aphrodite.""" return { "n": self.n, "best_of": self.best_of, "max_tokens": self.max_tokens, "top_k": self.top_k, "top_p": self.top_p, "top_a": self.top_a, "min_p": self.min_p, "temperature": self.temperature, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, "repetition_penalty": self.repetition_penalty, "tfs": self.tfs, "eta_cutoff": self.eta_cutoff, "epsilon_cutoff": self.epsilon_cutoff, "typical_p": self.typical_p, "mirostat_mode": self.mirostat_mode, "mirostat_tau": self.mirostat_tau, "length_penalty": self.length_penalty, "early_stopping": self.early_stopping, "use_beam_search": self.use_beam_search, "stop": self.stop, "ignore_eos": self.ignore_eos, "logprobs": self.logprobs, "prompt_logprobs": self.prompt_logprobs, "custom_token_bans": self.custom_token_bans, "skip_special_tokens": self.skip_special_tokens, "spaces_between_special_tokens": self.spaces_between_special_tokens, "logit_bias": self.logit_bias, } def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompt and input.""" from aphrodite import SamplingParams # build sampling parameters params = {**self._default_params, **kwargs, "stop": stop} if "logit_bias" in params: del params["logit_bias"] sampling_params = SamplingParams(**params) # call the model outputs = self.client.generate(prompts, sampling_params) generations = [] for output in outputs: text = output.outputs[0].text generations.append([Generation(text=text)]) return LLMResult(generations=generations) @property def _llm_type(self) -> str: """Return type of llm.""" return "aphrodite"