mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
b0588774f1
This PR adds support for PygmalionAI's [Aphrodite Engine](https://github.com/PygmalionAI/aphrodite-engine), based on vLLM's attention mechanism. At the moment, this PR does not include support for the API servers, but they will be added in a later PR. The only dependency as of now is `aphrodite-engine==0.4.2`. We pin the version to prevent breakage due to changes in the aphrodite-engine library. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
251 lines
9.4 KiB
Python
251 lines
9.4 KiB
Python
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.pydantic_v1 import Field, root_validator
|
|
|
|
|
|
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 #: :meta private:
|
|
|
|
@root_validator()
|
|
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"
|