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251 lines
9.4 KiB
Python
251 lines
9.4 KiB
Python
from typing import Any, Dict, List, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models import BaseLLM
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from langchain_core.outputs import Generation, LLMResult
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from langchain_core.pydantic_v1 import Field, root_validator
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class Aphrodite(BaseLLM):
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"""Aphrodite language model."""
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model: str = ""
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"""The name or path of a HuggingFace Transformers model."""
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tensor_parallel_size: Optional[int] = 1
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"""The number of GPUs to use for distributed execution with tensor parallelism."""
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trust_remote_code: Optional[bool] = False
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"""Trust remote code (e.g., from HuggingFace) when downloading the model
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and tokenizer."""
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n: int = 1
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"""Number of output sequences to return for the given prompt."""
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best_of: Optional[int] = None
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"""Number of output sequences that are generated from the prompt.
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From these `best_of` sequences, the top `n` sequences are returned.
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`best_of` must be >= `n`. This is treated as the beam width when
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`use_beam_search` is True. By default, `best_of` is set to `n`."""
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presence_penalty: float = 0.0
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"""Float that penalizes new tokens based on whether they appear in the
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generated text so far. Values > 0 encourage the model to generate new
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tokens, while values < 0 encourage the model to repeat tokens."""
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frequency_penalty: float = 0.0
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"""Float that penalizes new tokens based on their frequency in the
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generated text so far. Applied additively to the logits."""
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repetition_penalty: float = 1.0
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"""Float that penalizes new tokens based on their frequency in the
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generated text so far. Applied multiplicatively to the logits."""
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temperature: float = 1.0
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"""Float that controls the randomness of the sampling. Lower values
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make the model more deterministic, while higher values make the model
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more random. Zero is equivalent to greedy sampling."""
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top_p: float = 1.0
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"""Float that controls the cumulative probability of the top tokens to consider.
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Must be in (0, 1]. Set to 1.0 to consider all tokens."""
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top_k: int = -1
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"""Integer that controls the number of top tokens to consider. Set to -1 to
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consider all tokens (disabled)."""
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top_a: float = 0.0
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"""Float that controls the cutoff for Top-A sampling. Exact cutoff is
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top_a*max_prob**2. Must be in [0,inf], 0 to disable."""
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min_p: float = 0.0
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"""Float that controls the cutoff for min-p sampling. Exact cutoff is
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min_p*max_prob. Must be in [0,1], 0 to disable."""
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tfs: float = 1.0
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"""Float that controls the cumulative approximate curvature of the
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distribution to retain for Tail Free Sampling. Must be in (0, 1].
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Set to 1.0 to disable."""
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eta_cutoff: float = 0.0
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"""Float that controls the cutoff threshold for Eta sampling
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(a form of entropy adaptive truncation sampling). Threshold is
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calculated as `min(eta, sqrt(eta)*entropy(probs)). Specified
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in units of 1e-4. Set to 0 to disable."""
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epsilon_cutoff: float = 0.0
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"""Float that controls the cutoff threshold for Epsilon sampling
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(simple probability threshold truncation). Specified in units of
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1e-4. Set to 0 to disable."""
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typical_p: float = 1.0
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"""Float that controls the cumulative probability of tokens closest
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in surprise to the expected surprise to consider. Must be in (0, 1].
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Set to 1 to disable."""
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mirostat_mode: int = 0
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"""The mirostat mode to use. 0 for no mirostat, 2 for mirostat v2.
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Mode 1 is not supported."""
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mirostat_tau: float = 0.0
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"""The target 'surprisal' that mirostat works towards. Range [0, inf)."""
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use_beam_search: bool = False
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"""Whether to use beam search instead of sampling."""
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length_penalty: float = 1.0
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"""Float that penalizes sequences based on their length. Used only
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when `use_beam_search` is True."""
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early_stopping: bool = False
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"""Controls the stopping condition for beam search. It accepts the
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following values: `True`, where the generation stops as soon as there
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are `best_of` complete candidates; `False`, where a heuristic is applied
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to the generation stops when it is very unlikely to find better candidates;
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`never`, where the beam search procedure only stops where there cannot be
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better candidates (canonical beam search algorithm)."""
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stop: Optional[List[str]] = None
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"""List of strings that stop the generation when they are generated.
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The returned output will not contain the stop tokens."""
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stop_token_ids: Optional[List[int]] = None
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"""List of tokens that stop the generation when they are generated.
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The returned output will contain the stop tokens unless the stop tokens
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are special tokens."""
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ignore_eos: bool = False
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"""Whether to ignore the EOS token and continue generating tokens after
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the EOS token is generated."""
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max_tokens: int = 512
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"""Maximum number of tokens to generate per output sequence."""
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logprobs: Optional[int] = None
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"""Number of log probabilities to return per output token."""
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prompt_logprobs: Optional[int] = None
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"""Number of log probabilities to return per prompt token."""
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custom_token_bans: Optional[List[int]] = None
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"""List of token IDs to ban from generating."""
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skip_special_tokens: bool = True
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"""Whether to skip special tokens in the output. Defaults to True."""
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spaces_between_special_tokens: bool = True
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"""Whether to add spaces between special tokens in the output.
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Defaults to True."""
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logit_bias: Optional[Dict[str, float]] = None
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"""List of LogitsProcessors to change the probability of token
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prediction at runtime."""
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dtype: str = "auto"
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"""The data type for the model weights and activations."""
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download_dir: Optional[str] = None
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"""Directory to download and load the weights. (Default to the default
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cache dir of huggingface)"""
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quantization: Optional[str] = None
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"""Quantization mode to use. Can be one of `awq` or `gptq`."""
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aphrodite_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `aphrodite.LLM` call not explicitly
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specified."""
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client: Any #: :meta private:
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that python package exists in environment."""
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try:
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from aphrodite import LLM as AphroditeModel
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except ImportError:
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raise ImportError(
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"Could not import aphrodite-engine python package. "
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"Please install it with `pip install aphrodite-engine`."
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)
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# aphrodite_kwargs = values["aphrodite_kwargs"]
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# if values.get("quantization"):
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# aphrodite_kwargs["quantization"] = values["quantization"]
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values["client"] = AphroditeModel(
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model=values["model"],
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tensor_parallel_size=values["tensor_parallel_size"],
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trust_remote_code=values["trust_remote_code"],
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dtype=values["dtype"],
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download_dir=values["download_dir"],
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**values["aphrodite_kwargs"],
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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 aphrodite."""
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return {
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"n": self.n,
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"best_of": self.best_of,
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"max_tokens": self.max_tokens,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"top_a": self.top_a,
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"min_p": self.min_p,
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"temperature": self.temperature,
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"presence_penalty": self.presence_penalty,
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"frequency_penalty": self.frequency_penalty,
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"repetition_penalty": self.repetition_penalty,
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"tfs": self.tfs,
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"eta_cutoff": self.eta_cutoff,
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"epsilon_cutoff": self.epsilon_cutoff,
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"typical_p": self.typical_p,
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"mirostat_mode": self.mirostat_mode,
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"mirostat_tau": self.mirostat_tau,
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"length_penalty": self.length_penalty,
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"early_stopping": self.early_stopping,
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"use_beam_search": self.use_beam_search,
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"stop": self.stop,
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"ignore_eos": self.ignore_eos,
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"logprobs": self.logprobs,
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"prompt_logprobs": self.prompt_logprobs,
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"custom_token_bans": self.custom_token_bans,
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"skip_special_tokens": self.skip_special_tokens,
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"spaces_between_special_tokens": self.spaces_between_special_tokens,
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"logit_bias": self.logit_bias,
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}
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def _generate(
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self,
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prompts: List[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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) -> LLMResult:
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"""Run the LLM on the given prompt and input."""
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from aphrodite import SamplingParams
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# build sampling parameters
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params = {**self._default_params, **kwargs, "stop": stop}
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if "logit_bias" in params:
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del params["logit_bias"]
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sampling_params = SamplingParams(**params)
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# call the model
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outputs = self.client.generate(prompts, sampling_params)
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generations = []
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for output in outputs:
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text = output.outputs[0].text
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generations.append([Generation(text=text)])
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return LLMResult(generations=generations)
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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 "aphrodite"
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