structure saas with tools
This commit is contained in:
Davidson Gomes
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import json
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from copy import deepcopy
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from typing import Any, Callable, List, Optional, Union, cast
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import httpx
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import litellm
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.asyncify import asyncify
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from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
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from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.types.llms.openai import AllMessageValues
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from litellm.utils import (
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CustomStreamWrapper,
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EmbeddingResponse,
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ModelResponse,
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Usage,
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get_secret,
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)
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from ..common_utils import AWSEventStreamDecoder, SagemakerError
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from .transformation import SagemakerConfig
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sagemaker_config = SagemakerConfig()
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"""
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SAGEMAKER AUTH Keys/Vars
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os.environ['AWS_ACCESS_KEY_ID'] = ""
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os.environ['AWS_SECRET_ACCESS_KEY'] = ""
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"""
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# set os.environ['AWS_REGION_NAME'] = <your-region_name>
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class SagemakerLLM(BaseAWSLLM):
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def _load_credentials(
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self,
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optional_params: dict,
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):
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try:
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from botocore.credentials import Credentials
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except ImportError:
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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## CREDENTIALS ##
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# pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
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aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
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aws_access_key_id = optional_params.pop("aws_access_key_id", None)
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aws_session_token = optional_params.pop("aws_session_token", None)
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aws_region_name = optional_params.pop("aws_region_name", None)
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aws_role_name = optional_params.pop("aws_role_name", None)
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aws_session_name = optional_params.pop("aws_session_name", None)
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aws_profile_name = optional_params.pop("aws_profile_name", None)
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optional_params.pop(
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"aws_bedrock_runtime_endpoint", None
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) # https://bedrock-runtime.{region_name}.amazonaws.com
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aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
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aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None)
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### SET REGION NAME ###
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if aws_region_name is None:
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# check env #
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litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
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if litellm_aws_region_name is not None and isinstance(
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litellm_aws_region_name, str
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):
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aws_region_name = litellm_aws_region_name
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standard_aws_region_name = get_secret("AWS_REGION", None)
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if standard_aws_region_name is not None and isinstance(
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standard_aws_region_name, str
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):
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aws_region_name = standard_aws_region_name
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if aws_region_name is None:
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aws_region_name = "us-west-2"
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credentials: Credentials = self.get_credentials(
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_session_token=aws_session_token,
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aws_region_name=aws_region_name,
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aws_session_name=aws_session_name,
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aws_profile_name=aws_profile_name,
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aws_role_name=aws_role_name,
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aws_web_identity_token=aws_web_identity_token,
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aws_sts_endpoint=aws_sts_endpoint,
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)
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return credentials, aws_region_name
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def _prepare_request(
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self,
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credentials,
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model: str,
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data: dict,
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messages: List[AllMessageValues],
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litellm_params: dict,
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optional_params: dict,
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aws_region_name: str,
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extra_headers: Optional[dict] = None,
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):
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try:
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from botocore.auth import SigV4Auth
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from botocore.awsrequest import AWSRequest
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except ImportError:
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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sigv4 = SigV4Auth(credentials, "sagemaker", aws_region_name)
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if optional_params.get("stream") is True:
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api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations-response-stream"
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else:
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api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations"
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sagemaker_base_url = optional_params.get("sagemaker_base_url", None)
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if sagemaker_base_url is not None:
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api_base = sagemaker_base_url
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encoded_data = json.dumps(data).encode("utf-8")
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headers = sagemaker_config.validate_environment(
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headers=extra_headers,
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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)
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request = AWSRequest(
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method="POST", url=api_base, data=encoded_data, headers=headers
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)
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sigv4.add_auth(request)
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if (
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extra_headers is not None and "Authorization" in extra_headers
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): # prevent sigv4 from overwriting the auth header
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request.headers["Authorization"] = extra_headers["Authorization"]
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prepped_request = request.prepare()
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return prepped_request
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def completion( # noqa: PLR0915
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self,
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model: str,
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messages: list,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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logging_obj,
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optional_params: dict,
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litellm_params: dict,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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custom_prompt_dict={},
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hf_model_name=None,
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logger_fn=None,
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acompletion: bool = False,
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headers: dict = {},
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):
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# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
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credentials, aws_region_name = self._load_credentials(optional_params)
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inference_params = deepcopy(optional_params)
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stream = inference_params.pop("stream", None)
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model_id = optional_params.get("model_id", None)
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## Load Config
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config = litellm.SagemakerConfig.get_config()
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for k, v in config.items():
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if (
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k not in inference_params
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): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
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inference_params[k] = v
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if stream is True:
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if acompletion is True:
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response = self.async_streaming(
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messages=messages,
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model=model,
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custom_prompt_dict=custom_prompt_dict,
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hf_model_name=hf_model_name,
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optional_params=optional_params,
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encoding=encoding,
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model_response=model_response,
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logging_obj=logging_obj,
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model_id=model_id,
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aws_region_name=aws_region_name,
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credentials=credentials,
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headers=headers,
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litellm_params=litellm_params,
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)
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return response
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else:
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data = sagemaker_config.transform_request(
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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headers=headers,
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)
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prepared_request = self._prepare_request(
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model=model,
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data=data,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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credentials=credentials,
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aws_region_name=aws_region_name,
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)
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if model_id is not None:
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# Add model_id as InferenceComponentName header
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# boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
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prepared_request.headers.update(
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{"X-Amzn-SageMaker-Inference-Component": model_id}
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)
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sync_handler = _get_httpx_client()
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sync_response = sync_handler.post(
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url=prepared_request.url,
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headers=prepared_request.headers, # type: ignore
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data=prepared_request.body,
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stream=stream,
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)
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if sync_response.status_code != 200:
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raise SagemakerError(
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status_code=sync_response.status_code,
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message=str(sync_response.read()),
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)
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decoder = AWSEventStreamDecoder(model="")
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completion_stream = decoder.iter_bytes(
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sync_response.iter_bytes(chunk_size=1024)
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)
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streaming_response = CustomStreamWrapper(
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completion_stream=completion_stream,
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model=model,
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custom_llm_provider="sagemaker",
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logging_obj=logging_obj,
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)
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## LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response=streaming_response,
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additional_args={"complete_input_dict": data},
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)
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return streaming_response
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# Non-Streaming Requests
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# Async completion
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if acompletion is True:
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return self.async_completion(
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messages=messages,
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model=model,
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custom_prompt_dict=custom_prompt_dict,
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hf_model_name=hf_model_name,
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model_response=model_response,
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encoding=encoding,
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logging_obj=logging_obj,
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model_id=model_id,
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optional_params=optional_params,
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credentials=credentials,
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aws_region_name=aws_region_name,
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headers=headers,
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litellm_params=litellm_params,
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)
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## Non-Streaming completion CALL
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_data = sagemaker_config.transform_request(
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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headers=headers,
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)
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prepared_request_args = {
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"model": model,
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"data": _data,
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"optional_params": optional_params,
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"litellm_params": litellm_params,
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"credentials": credentials,
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"aws_region_name": aws_region_name,
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"messages": messages,
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}
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prepared_request = self._prepare_request(**prepared_request_args)
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try:
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if model_id is not None:
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# Add model_id as InferenceComponentName header
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# boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
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prepared_request.headers.update(
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{"X-Amzn-SageMaker-Inference-Component": model_id}
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)
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## LOGGING
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timeout = 300.0
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sync_handler = _get_httpx_client()
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## LOGGING
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logging_obj.pre_call(
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input=[],
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api_key="",
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additional_args={
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"complete_input_dict": _data,
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"api_base": prepared_request.url,
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"headers": prepared_request.headers,
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},
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)
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# make sync httpx post request here
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try:
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sync_response = sync_handler.post(
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url=prepared_request.url,
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headers=prepared_request.headers, # type: ignore
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data=prepared_request.body,
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timeout=timeout,
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)
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if sync_response.status_code != 200:
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raise SagemakerError(
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status_code=sync_response.status_code,
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message=sync_response.text,
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)
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except Exception as e:
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## LOGGING
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logging_obj.post_call(
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input=[],
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api_key="",
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original_response=str(e),
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additional_args={"complete_input_dict": _data},
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)
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raise e
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except Exception as e:
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verbose_logger.error("Sagemaker error %s", str(e))
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status_code = (
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getattr(e, "response", {})
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.get("ResponseMetadata", {})
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.get("HTTPStatusCode", 500)
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)
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error_message = (
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getattr(e, "response", {}).get("Error", {}).get("Message", str(e))
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)
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if "Inference Component Name header is required" in error_message:
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error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`"
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raise SagemakerError(status_code=status_code, message=error_message)
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return sagemaker_config.transform_response(
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model=model,
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raw_response=sync_response,
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model_response=model_response,
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logging_obj=logging_obj,
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request_data=_data,
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messages=messages,
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optional_params=optional_params,
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encoding=encoding,
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litellm_params=litellm_params,
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)
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async def make_async_call(
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self,
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api_base: str,
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headers: dict,
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data: str,
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logging_obj,
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client=None,
|
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):
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try:
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if client is None:
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.SAGEMAKER
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) # Create a new client if none provided
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response = await client.post(
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api_base,
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headers=headers,
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data=data,
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stream=True,
|
||||
)
|
||||
|
||||
if response.status_code != 200:
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raise SagemakerError(
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status_code=response.status_code, message=response.text
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)
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decoder = AWSEventStreamDecoder(model="")
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completion_stream = decoder.aiter_bytes(
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response.aiter_bytes(chunk_size=1024)
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)
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return completion_stream
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# LOGGING
|
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logging_obj.post_call(
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input=[],
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api_key="",
|
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original_response="first stream response received",
|
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additional_args={"complete_input_dict": data},
|
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)
|
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|
||||
except httpx.HTTPStatusError as err:
|
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error_code = err.response.status_code
|
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raise SagemakerError(status_code=error_code, message=err.response.text)
|
||||
except httpx.TimeoutException:
|
||||
raise SagemakerError(status_code=408, message="Timeout error occurred.")
|
||||
except Exception as e:
|
||||
raise SagemakerError(status_code=500, message=str(e))
|
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|
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async def async_streaming(
|
||||
self,
|
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messages: List[AllMessageValues],
|
||||
model: str,
|
||||
custom_prompt_dict: dict,
|
||||
hf_model_name: Optional[str],
|
||||
credentials,
|
||||
aws_region_name: str,
|
||||
optional_params,
|
||||
encoding,
|
||||
model_response: ModelResponse,
|
||||
model_id: Optional[str],
|
||||
logging_obj: Any,
|
||||
litellm_params: dict,
|
||||
headers: dict,
|
||||
):
|
||||
data = await sagemaker_config.async_transform_request(
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params={**optional_params, "stream": True},
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
asyncified_prepare_request = asyncify(self._prepare_request)
|
||||
prepared_request_args = {
|
||||
"model": model,
|
||||
"data": data,
|
||||
"optional_params": optional_params,
|
||||
"litellm_params": litellm_params,
|
||||
"credentials": credentials,
|
||||
"aws_region_name": aws_region_name,
|
||||
"messages": messages,
|
||||
}
|
||||
prepared_request = await asyncified_prepare_request(**prepared_request_args)
|
||||
if model_id is not None: # Fixes https://github.com/BerriAI/litellm/issues/8889
|
||||
prepared_request.headers.update(
|
||||
{"X-Amzn-SageMaker-Inference-Component": model_id}
|
||||
)
|
||||
|
||||
if not prepared_request.body:
|
||||
raise ValueError("Prepared request body is empty")
|
||||
|
||||
completion_stream = await self.make_async_call(
|
||||
api_base=prepared_request.url,
|
||||
headers=prepared_request.headers, # type: ignore
|
||||
data=cast(str, prepared_request.body),
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
streaming_response = CustomStreamWrapper(
|
||||
completion_stream=completion_stream,
|
||||
model=model,
|
||||
custom_llm_provider="sagemaker",
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
|
||||
# LOGGING
|
||||
logging_obj.post_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
original_response="first stream response received",
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
|
||||
return streaming_response
|
||||
|
||||
async def async_completion(
|
||||
self,
|
||||
messages: List[AllMessageValues],
|
||||
model: str,
|
||||
custom_prompt_dict: dict,
|
||||
hf_model_name: Optional[str],
|
||||
credentials,
|
||||
aws_region_name: str,
|
||||
encoding,
|
||||
model_response: ModelResponse,
|
||||
optional_params: dict,
|
||||
logging_obj: Any,
|
||||
model_id: Optional[str],
|
||||
headers: dict,
|
||||
litellm_params: dict,
|
||||
):
|
||||
timeout = 300.0
|
||||
async_handler = get_async_httpx_client(
|
||||
llm_provider=litellm.LlmProviders.SAGEMAKER
|
||||
)
|
||||
|
||||
data = await sagemaker_config.async_transform_request(
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
asyncified_prepare_request = asyncify(self._prepare_request)
|
||||
prepared_request_args = {
|
||||
"model": model,
|
||||
"data": data,
|
||||
"optional_params": optional_params,
|
||||
"litellm_params": litellm_params,
|
||||
"credentials": credentials,
|
||||
"aws_region_name": aws_region_name,
|
||||
"messages": messages,
|
||||
}
|
||||
|
||||
prepared_request = await asyncified_prepare_request(**prepared_request_args)
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
additional_args={
|
||||
"complete_input_dict": data,
|
||||
"api_base": prepared_request.url,
|
||||
"headers": prepared_request.headers,
|
||||
},
|
||||
)
|
||||
try:
|
||||
if model_id is not None:
|
||||
# Add model_id as InferenceComponentName header
|
||||
# boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
|
||||
prepared_request.headers.update(
|
||||
{"X-Amzn-SageMaker-Inference-Component": model_id}
|
||||
)
|
||||
# make async httpx post request here
|
||||
try:
|
||||
response = await async_handler.post(
|
||||
url=prepared_request.url,
|
||||
headers=prepared_request.headers, # type: ignore
|
||||
data=prepared_request.body,
|
||||
timeout=timeout,
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise SagemakerError(
|
||||
status_code=response.status_code, message=response.text
|
||||
)
|
||||
except Exception as e:
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=data["inputs"],
|
||||
api_key="",
|
||||
original_response=str(e),
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
raise e
|
||||
except Exception as e:
|
||||
error_message = f"{str(e)}"
|
||||
if "Inference Component Name header is required" in error_message:
|
||||
error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`"
|
||||
raise SagemakerError(status_code=500, message=error_message)
|
||||
return sagemaker_config.transform_response(
|
||||
model=model,
|
||||
raw_response=response,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
request_data=data,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
encoding=encoding,
|
||||
litellm_params=litellm_params,
|
||||
)
|
||||
|
||||
def embedding(
|
||||
self,
|
||||
model: str,
|
||||
input: list,
|
||||
model_response: EmbeddingResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
logging_obj,
|
||||
optional_params: dict,
|
||||
custom_prompt_dict={},
|
||||
litellm_params=None,
|
||||
logger_fn=None,
|
||||
):
|
||||
"""
|
||||
Supports Huggingface Jumpstart embeddings like GPT-6B
|
||||
"""
|
||||
### BOTO3 INIT
|
||||
import boto3
|
||||
|
||||
# pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
|
||||
aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
|
||||
aws_access_key_id = optional_params.pop("aws_access_key_id", None)
|
||||
aws_region_name = optional_params.pop("aws_region_name", None)
|
||||
|
||||
if aws_access_key_id is not None:
|
||||
# uses auth params passed to completion
|
||||
# aws_access_key_id is not None, assume user is trying to auth using litellm.completion
|
||||
client = boto3.client(
|
||||
service_name="sagemaker-runtime",
|
||||
aws_access_key_id=aws_access_key_id,
|
||||
aws_secret_access_key=aws_secret_access_key,
|
||||
region_name=aws_region_name,
|
||||
)
|
||||
else:
|
||||
# aws_access_key_id is None, assume user is trying to auth using env variables
|
||||
# boto3 automaticaly reads env variables
|
||||
|
||||
# we need to read region name from env
|
||||
# I assume majority of users use .env for auth
|
||||
region_name = (
|
||||
get_secret("AWS_REGION_NAME")
|
||||
or aws_region_name # get region from config file if specified
|
||||
or "us-west-2" # default to us-west-2 if region not specified
|
||||
)
|
||||
client = boto3.client(
|
||||
service_name="sagemaker-runtime",
|
||||
region_name=region_name,
|
||||
)
|
||||
|
||||
# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
|
||||
inference_params = deepcopy(optional_params)
|
||||
inference_params.pop("stream", None)
|
||||
|
||||
## Load Config
|
||||
config = litellm.SagemakerConfig.get_config()
|
||||
for k, v in config.items():
|
||||
if (
|
||||
k not in inference_params
|
||||
): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
|
||||
inference_params[k] = v
|
||||
|
||||
#### HF EMBEDDING LOGIC
|
||||
data = json.dumps({"text_inputs": input}).encode("utf-8")
|
||||
|
||||
## LOGGING
|
||||
request_str = f"""
|
||||
response = client.invoke_endpoint(
|
||||
EndpointName={model},
|
||||
ContentType="application/json",
|
||||
Body=f"{data!r}", # Use !r for safe representation
|
||||
CustomAttributes="accept_eula=true",
|
||||
)""" # type: ignore
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
api_key="",
|
||||
additional_args={"complete_input_dict": data, "request_str": request_str},
|
||||
)
|
||||
## EMBEDDING CALL
|
||||
try:
|
||||
response = client.invoke_endpoint(
|
||||
EndpointName=model,
|
||||
ContentType="application/json",
|
||||
Body=data,
|
||||
CustomAttributes="accept_eula=true",
|
||||
)
|
||||
except Exception as e:
|
||||
status_code = (
|
||||
getattr(e, "response", {})
|
||||
.get("ResponseMetadata", {})
|
||||
.get("HTTPStatusCode", 500)
|
||||
)
|
||||
error_message = (
|
||||
getattr(e, "response", {}).get("Error", {}).get("Message", str(e))
|
||||
)
|
||||
raise SagemakerError(status_code=status_code, message=error_message)
|
||||
|
||||
response = json.loads(response["Body"].read().decode("utf8"))
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=input,
|
||||
api_key="",
|
||||
original_response=response,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
|
||||
print_verbose(f"raw model_response: {response}")
|
||||
if "embedding" not in response:
|
||||
raise SagemakerError(
|
||||
status_code=500, message="embedding not found in response"
|
||||
)
|
||||
embeddings = response["embedding"]
|
||||
|
||||
if not isinstance(embeddings, list):
|
||||
raise SagemakerError(
|
||||
status_code=422,
|
||||
message=f"Response not in expected format - {embeddings}",
|
||||
)
|
||||
|
||||
output_data = []
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
output_data.append(
|
||||
{"object": "embedding", "index": idx, "embedding": embedding}
|
||||
)
|
||||
|
||||
model_response.object = "list"
|
||||
model_response.data = output_data
|
||||
model_response.model = model
|
||||
|
||||
input_tokens = 0
|
||||
for text in input:
|
||||
input_tokens += len(encoding.encode(text))
|
||||
|
||||
setattr(
|
||||
model_response,
|
||||
"usage",
|
||||
Usage(
|
||||
prompt_tokens=input_tokens,
|
||||
completion_tokens=0,
|
||||
total_tokens=input_tokens,
|
||||
),
|
||||
)
|
||||
|
||||
return model_response
|
||||
@@ -0,0 +1,275 @@
|
||||
"""
|
||||
Translate from OpenAI's `/v1/chat/completions` to Sagemaker's `/invoke`
|
||||
|
||||
In the Huggingface TGI format.
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
|
||||
|
||||
from httpx._models import Headers, Response
|
||||
|
||||
import litellm
|
||||
from litellm.litellm_core_utils.asyncify import asyncify
|
||||
from litellm.litellm_core_utils.prompt_templates.factory import (
|
||||
custom_prompt,
|
||||
prompt_factory,
|
||||
)
|
||||
from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
|
||||
from litellm.types.llms.openai import AllMessageValues
|
||||
from litellm.types.utils import ModelResponse, Usage
|
||||
from litellm.utils import token_counter
|
||||
|
||||
from ..common_utils import SagemakerError
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
|
||||
|
||||
LiteLLMLoggingObj = _LiteLLMLoggingObj
|
||||
else:
|
||||
LiteLLMLoggingObj = Any
|
||||
|
||||
|
||||
class SagemakerConfig(BaseConfig):
|
||||
"""
|
||||
Reference: https://d-uuwbxj1u4cnu.studio.us-west-2.sagemaker.aws/jupyter/default/lab/workspaces/auto-q/tree/DemoNotebooks/meta-textgeneration-llama-2-7b-SDK_1.ipynb
|
||||
"""
|
||||
|
||||
max_new_tokens: Optional[int] = None
|
||||
top_p: Optional[float] = None
|
||||
temperature: Optional[float] = None
|
||||
return_full_text: Optional[bool] = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_new_tokens: Optional[int] = None,
|
||||
top_p: Optional[float] = None,
|
||||
temperature: Optional[float] = None,
|
||||
return_full_text: Optional[bool] = None,
|
||||
) -> None:
|
||||
locals_ = locals().copy()
|
||||
for key, value in locals_.items():
|
||||
if key != "self" and value is not None:
|
||||
setattr(self.__class__, key, value)
|
||||
|
||||
@classmethod
|
||||
def get_config(cls):
|
||||
return super().get_config()
|
||||
|
||||
def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: Union[dict, Headers]
|
||||
) -> BaseLLMException:
|
||||
return SagemakerError(
|
||||
message=error_message, status_code=status_code, headers=headers
|
||||
)
|
||||
|
||||
def get_supported_openai_params(self, model: str) -> List:
|
||||
return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
|
||||
|
||||
def map_openai_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
optional_params: dict,
|
||||
model: str,
|
||||
drop_params: bool,
|
||||
) -> dict:
|
||||
for param, value in non_default_params.items():
|
||||
if param == "temperature":
|
||||
if value == 0.0 or value == 0:
|
||||
# hugging face exception raised when temp==0
|
||||
# Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
|
||||
if not non_default_params.get(
|
||||
"aws_sagemaker_allow_zero_temp", False
|
||||
):
|
||||
value = 0.01
|
||||
|
||||
optional_params["temperature"] = value
|
||||
if param == "top_p":
|
||||
optional_params["top_p"] = value
|
||||
if param == "n":
|
||||
optional_params["best_of"] = value
|
||||
optional_params[
|
||||
"do_sample"
|
||||
] = True # Need to sample if you want best of for hf inference endpoints
|
||||
if param == "stream":
|
||||
optional_params["stream"] = value
|
||||
if param == "stop":
|
||||
optional_params["stop"] = value
|
||||
if param == "max_tokens":
|
||||
# HF TGI raises the following exception when max_new_tokens==0
|
||||
# Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
|
||||
if value == 0:
|
||||
value = 1
|
||||
optional_params["max_new_tokens"] = value
|
||||
non_default_params.pop("aws_sagemaker_allow_zero_temp", None)
|
||||
return optional_params
|
||||
|
||||
def _transform_prompt(
|
||||
self,
|
||||
model: str,
|
||||
messages: List,
|
||||
custom_prompt_dict: dict,
|
||||
hf_model_name: Optional[str],
|
||||
) -> str:
|
||||
if model in custom_prompt_dict:
|
||||
# check if the model has a registered custom prompt
|
||||
model_prompt_details = custom_prompt_dict[model]
|
||||
prompt = custom_prompt(
|
||||
role_dict=model_prompt_details.get("roles", None),
|
||||
initial_prompt_value=model_prompt_details.get(
|
||||
"initial_prompt_value", ""
|
||||
),
|
||||
final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
|
||||
messages=messages,
|
||||
)
|
||||
elif hf_model_name in custom_prompt_dict:
|
||||
# check if the base huggingface model has a registered custom prompt
|
||||
model_prompt_details = custom_prompt_dict[hf_model_name]
|
||||
prompt = custom_prompt(
|
||||
role_dict=model_prompt_details.get("roles", None),
|
||||
initial_prompt_value=model_prompt_details.get(
|
||||
"initial_prompt_value", ""
|
||||
),
|
||||
final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
|
||||
messages=messages,
|
||||
)
|
||||
else:
|
||||
if hf_model_name is None:
|
||||
if "llama-2" in model.lower(): # llama-2 model
|
||||
if "chat" in model.lower(): # apply llama2 chat template
|
||||
hf_model_name = "meta-llama/Llama-2-7b-chat-hf"
|
||||
else: # apply regular llama2 template
|
||||
hf_model_name = "meta-llama/Llama-2-7b"
|
||||
hf_model_name = (
|
||||
hf_model_name or model
|
||||
) # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt)
|
||||
prompt: str = prompt_factory(model=hf_model_name, messages=messages) # type: ignore
|
||||
|
||||
return prompt
|
||||
|
||||
def transform_request(
|
||||
self,
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
headers: dict,
|
||||
) -> dict:
|
||||
inference_params = optional_params.copy()
|
||||
stream = inference_params.pop("stream", False)
|
||||
data: Dict = {"parameters": inference_params}
|
||||
if stream is True:
|
||||
data["stream"] = True
|
||||
|
||||
custom_prompt_dict = (
|
||||
litellm_params.get("custom_prompt_dict", None) or litellm.custom_prompt_dict
|
||||
)
|
||||
|
||||
hf_model_name = litellm_params.get("hf_model_name", None)
|
||||
|
||||
prompt = self._transform_prompt(
|
||||
model=model,
|
||||
messages=messages,
|
||||
custom_prompt_dict=custom_prompt_dict,
|
||||
hf_model_name=hf_model_name,
|
||||
)
|
||||
data["inputs"] = prompt
|
||||
|
||||
return data
|
||||
|
||||
async def async_transform_request(
|
||||
self,
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
headers: dict,
|
||||
) -> dict:
|
||||
return await asyncify(self.transform_request)(
|
||||
model, messages, optional_params, litellm_params, headers
|
||||
)
|
||||
|
||||
def transform_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: Response,
|
||||
model_response: ModelResponse,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
request_data: dict,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
encoding: str,
|
||||
api_key: Optional[str] = None,
|
||||
json_mode: Optional[bool] = None,
|
||||
) -> ModelResponse:
|
||||
completion_response = raw_response.json()
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=messages,
|
||||
api_key="",
|
||||
original_response=completion_response,
|
||||
additional_args={"complete_input_dict": request_data},
|
||||
)
|
||||
|
||||
prompt = request_data["inputs"]
|
||||
|
||||
## RESPONSE OBJECT
|
||||
try:
|
||||
if isinstance(completion_response, list):
|
||||
completion_response_choices = completion_response[0]
|
||||
else:
|
||||
completion_response_choices = completion_response
|
||||
completion_output = ""
|
||||
if "generation" in completion_response_choices:
|
||||
completion_output += completion_response_choices["generation"]
|
||||
elif "generated_text" in completion_response_choices:
|
||||
completion_output += completion_response_choices["generated_text"]
|
||||
|
||||
# check if the prompt template is part of output, if so - filter it out
|
||||
if completion_output.startswith(prompt) and "<s>" in prompt:
|
||||
completion_output = completion_output.replace(prompt, "", 1)
|
||||
|
||||
model_response.choices[0].message.content = completion_output # type: ignore
|
||||
except Exception:
|
||||
raise SagemakerError(
|
||||
message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}",
|
||||
status_code=500,
|
||||
)
|
||||
|
||||
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
|
||||
prompt_tokens = token_counter(
|
||||
text=prompt, count_response_tokens=True
|
||||
) # doesn't apply any default token count from openai's chat template
|
||||
completion_tokens = token_counter(
|
||||
text=model_response["choices"][0]["message"].get("content", ""),
|
||||
count_response_tokens=True,
|
||||
)
|
||||
|
||||
model_response.created = int(time.time())
|
||||
model_response.model = model
|
||||
usage = Usage(
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=prompt_tokens + completion_tokens,
|
||||
)
|
||||
setattr(model_response, "usage", usage)
|
||||
return model_response
|
||||
|
||||
def validate_environment(
|
||||
self,
|
||||
headers: Optional[dict],
|
||||
model: str,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
api_key: Optional[str] = None,
|
||||
api_base: Optional[str] = None,
|
||||
) -> dict:
|
||||
headers = {"Content-Type": "application/json"}
|
||||
|
||||
if headers is not None:
|
||||
headers = {"Content-Type": "application/json", **headers}
|
||||
|
||||
return headers
|
||||
Reference in New Issue
Block a user