File size: 5,292 Bytes
1b86249 76a55fb 1b86249 76a55fb 1b86249 76a55fb 1b86249 76a55fb 1b86249 76a55fb 1b86249 76a55fb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
import abc
import os
from dataclasses import dataclass
from typing import List, Optional, Union
from .artifact import Artifact
from .operator import PackageRequirementsMixin
from .settings_utils import get_settings
class InferenceEngine(abc.ABC, Artifact):
"""Abstract base class for inference."""
@abc.abstractmethod
def infer(self, dataset):
"""Perform inference on the input dataset."""
pass
@staticmethod
def _assert_allow_passing_data_to_remote_api(remote_api_label: str):
assert get_settings().allow_passing_data_to_remote_api, (
f"LlmAsJudge metric cannot run send data to remote APIs ({remote_api_label}) when"
f" unitxt.settings.allow_passing_data_to_remote_api=False."
f" Set UNITXT_ALLOW_PASSING_DATA_TO_REMOTE_API environment variable, if you want to allow this. "
)
class HFPipelineBasedInferenceEngine(InferenceEngine, PackageRequirementsMixin):
model_name: str
max_new_tokens: int
_requirement = {
"transformers": "Install huggingface package using 'pip install --upgrade transformers"
}
def prepare(self):
from transformers import pipeline
self.model = pipeline(model=self.model_name)
def infer(self, dataset):
return [
output["generated_text"]
for output in self.model(
[instance["source"] for instance in dataset],
max_new_tokens=self.max_new_tokens,
)
]
@dataclass()
class IbmGenAiInferenceEngineParams:
decoding_method: str = None
max_new_tokens: Optional[int] = None
min_new_tokens: Optional[int] = None
random_seed: Optional[int] = None
repetition_penalty: Optional[float] = None
stop_sequences: Optional[List[str]] = None
temperature: Optional[float] = None
top_k: Optional[int] = None
top_p: Optional[float] = None
typical_p: Optional[float] = None
class IbmGenAiInferenceEngine(InferenceEngine, PackageRequirementsMixin):
label: str = "ibm_genai"
model_name: str
parameters: IbmGenAiInferenceEngineParams = IbmGenAiInferenceEngineParams()
_requirement = {
"genai": "Install ibm-genai package using 'pip install --upgrade ibm-generative-ai"
}
def prepare(self):
from genai import Client, Credentials
api_key_env_var_name = "GENAI_KEY"
api_key = os.environ.get(api_key_env_var_name)
assert api_key is not None, (
f"Error while trying to run IbmGenAiInferenceEngine."
f" Please set the environment param '{api_key_env_var_name}'."
)
api_endpoint = os.environ.get("GENAI_KEY")
credentials = Credentials(api_key=api_key, api_endpoint=api_endpoint)
self.client = Client(credentials=credentials)
self._assert_allow_passing_data_to_remote_api(self.label)
def infer(self, dataset):
from genai.schema import TextGenerationParameters
genai_params = TextGenerationParameters(**self.parameters.__dict__)
return list(
self.client.text.generation.create(
model_id=self.model_name,
inputs=[instance["source"] for instance in dataset],
parameters=genai_params,
)
)
@dataclass
class OpenAiInferenceEngineParams:
frequency_penalty: Optional[float] = None
presence_penalty: Optional[float] = None
max_tokens: Optional[int] = None
seed: Optional[int] = None
stop: Union[Optional[str], List[str]] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
class OpenAiInferenceEngine(InferenceEngine, PackageRequirementsMixin):
label: str = "openai"
model_name: str
parameters: OpenAiInferenceEngineParams = OpenAiInferenceEngineParams()
_requirement = {
"openai": "Install openai package using 'pip install --upgrade openai"
}
def prepare(self):
from openai import OpenAI
api_key_env_var_name = "OPENAI_API_KEY"
api_key = os.environ.get(api_key_env_var_name)
assert api_key is not None, (
f"Error while trying to run OpenAiInferenceEngine."
f" Please set the environment param '{api_key_env_var_name}'."
)
self.client = OpenAI(api_key=api_key)
self._assert_allow_passing_data_to_remote_api(self.label)
def infer(self, dataset):
return [
self.client.chat.completions.create(
messages=[
# {
# "role": "system",
# "content": self.system_prompt,
# },
{
"role": "user",
"content": instance["source"],
}
],
model=self.model_name,
frequency_penalty=self.parameters.frequency_penalty,
presence_penalty=self.parameters.presence_penalty,
max_tokens=self.parameters.max_tokens,
seed=self.parameters.seed,
stop=self.parameters.stop,
temperature=self.parameters.temperature,
top_p=self.parameters.top_p,
)
for instance in dataset
]
|