chore: initial public snapshot for github upload
This commit is contained in:
Your Name
@@ -0,0 +1,683 @@
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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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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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from ..embedding.transformation import SagemakerEmbeddingConfig
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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,
|
||||
logging_obj=logging_obj,
|
||||
request_data=_data,
|
||||
messages=messages,
|
||||
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,
|
||||
client=None,
|
||||
):
|
||||
try:
|
||||
if client is None:
|
||||
client = get_async_httpx_client(
|
||||
llm_provider=litellm.LlmProviders.SAGEMAKER
|
||||
) # Create a new client if none provided
|
||||
response = await client.post(
|
||||
api_base,
|
||||
headers=headers,
|
||||
data=data,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise SagemakerError(
|
||||
status_code=response.status_code, message=response.text
|
||||
)
|
||||
|
||||
decoder = AWSEventStreamDecoder(model="")
|
||||
completion_stream = decoder.aiter_bytes(
|
||||
response.aiter_bytes(chunk_size=1024)
|
||||
)
|
||||
|
||||
return completion_stream
|
||||
|
||||
# LOGGING
|
||||
logging_obj.post_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
original_response="first stream response received",
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
|
||||
except httpx.HTTPStatusError as err:
|
||||
error_code = err.response.status_code
|
||||
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))
|
||||
|
||||
async def async_streaming(
|
||||
self,
|
||||
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 both Huggingface Jumpstart embeddings and Voyage models
|
||||
"""
|
||||
### BOTO3 INIT
|
||||
import boto3
|
||||
|
||||
# Use _load_credentials to support role assumption (aws_role_name, aws_session_name)
|
||||
credentials, aws_region_name = self._load_credentials(optional_params)
|
||||
|
||||
# Create boto3 session with the loaded credentials
|
||||
session = boto3.Session(
|
||||
aws_access_key_id=credentials.access_key,
|
||||
aws_secret_access_key=credentials.secret_key,
|
||||
aws_session_token=credentials.token,
|
||||
region_name=aws_region_name,
|
||||
)
|
||||
client = session.client(service_name="sagemaker-runtime")
|
||||
|
||||
# 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
|
||||
|
||||
#### EMBEDDING LOGIC
|
||||
# Transform request based on model type
|
||||
provider_config = SagemakerEmbeddingConfig.get_model_config(model)
|
||||
request_data = provider_config.transform_embedding_request(
|
||||
model, input, optional_params, {}
|
||||
)
|
||||
data = json.dumps(request_data).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}")
|
||||
|
||||
# Transform response based on model type
|
||||
from httpx import Response as HttpxResponse
|
||||
|
||||
# Create a mock httpx Response object for the transformation
|
||||
mock_response = HttpxResponse(
|
||||
status_code=200,
|
||||
content=json.dumps(response).encode("utf-8"),
|
||||
headers={"content-type": "application/json"},
|
||||
)
|
||||
|
||||
model_response = EmbeddingResponse()
|
||||
|
||||
# Use the request_data that was already transformed above
|
||||
return provider_config.transform_embedding_response(
|
||||
model=model,
|
||||
raw_response=mock_response,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
api_key=None,
|
||||
request_data=request_data,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params or {},
|
||||
)
|
||||
Reference in New Issue
Block a user