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Sagemaker Endpoint LLM (#1686)

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Updates #965

---------

Co-authored-by: Nimisha Mehta <116048415+nimimeht@users.noreply.github.com>
Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MBP.attlocal.net>
This commit is contained in:
Piyush Jain 2023-03-16 21:58:06 -07:00 committed by GitHub
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commit cdff6c8181
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122
docs/modules/document_loaders/examples/sagemaker.ipynb Normal file
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@ -0,0 +1,122 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip3 install langchain boto3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.docstore.document import Document"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"example_doc_1 = \"\"\"\n",
"Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital.\n",
"Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well.\n",
"Therefore, Peter stayed with her at the hospital for 3 days without leaving.\n",
"\"\"\"\n",
"\n",
"docs = [\n",
" Document(\n",
" page_content=example_doc_1,\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from typing import Dict\n",
"\n",
"from langchain import PromptTemplate, SagemakerEndpoint\n",
"from langchain.llms.sagemaker_endpoint import ContentHandlerBase\n",
"from langchain.chains.question_answering import load_qa_chain\n",
"import json\n",
"\n",
"query = \"\"\"How long was Elizabeth hospitalized?\n",
"\"\"\"\n",
"\n",
"prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n",
"\n",
"{context}\n",
"\n",
"Question: {question}\n",
"Answer:\"\"\"\n",
"PROMPT = PromptTemplate(\n",
" template=prompt_template, input_variables=[\"context\", \"question\"]\n",
")\n",
"\n",
"class ContentHandler(ContentHandlerBase):\n",
" content_type = \"application/json\"\n",
" accepts = \"application/json\"\n",
"\n",
" def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n",
" input_str = json.dumps({prompt: prompt, **model_kwargs})\n",
" return input_str.encode('utf-8')\n",
" \n",
" def transform_output(self, output: bytes) -> str:\n",
" response_json = json.loads(output.read().decode(\"utf-8\"))\n",
" return response_json[0][\"generated_text\"]\n",
"\n",
"content_handler = ContentHandler()\n",
"\n",
"chain = load_qa_chain(\n",
" llm=SagemakerEndpoint(\n",
" endpoint_name=\"endpoint-name\", \n",
" credentials_profile_name=\"credentials-profile-name\", \n",
" region_name=\"us-west-2\", \n",
" model_kwargs={\"temperature\":1e-10},\n",
" content_handler=content_handler\n",
" ),\n",
" prompt=PROMPT\n",
")\n",
"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
},
"vscode": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

2
langchain/__init__.py
View File

@ -33,6 +33,7 @@ from langchain.llms import (
Modal,
OpenAI,
Petals,
SagemakerEndpoint,
StochasticAI,
Writer,
)
@ -90,6 +91,7 @@ __all__ = [
"ReActChain",
"Wikipedia",
"HuggingFaceHub",
"SagemakerEndpoint",
"HuggingFacePipeline",
"SQLDatabase",
"SQLDatabaseChain",

3
langchain/llms/__init__.py
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@ -19,6 +19,7 @@ from langchain.llms.nlpcloud import NLPCloud
from langchain.llms.openai import AzureOpenAI, OpenAI, OpenAIChat
from langchain.llms.petals import Petals
from langchain.llms.promptlayer_openai import PromptLayerOpenAI, PromptLayerOpenAIChat
from langchain.llms.sagemaker_endpoint import SagemakerEndpoint
from langchain.llms.self_hosted import SelfHostedPipeline
from langchain.llms.self_hosted_hugging_face import SelfHostedHuggingFaceLLM
from langchain.llms.stochasticai import StochasticAI
@ -40,6 +41,7 @@ __all__ = [
"Petals",
"HuggingFaceEndpoint",
"HuggingFaceHub",
"SagemakerEndpoint",
"HuggingFacePipeline",
"AI21",
"AzureOpenAI",
@ -64,6 +66,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
"huggingface_hub": HuggingFaceHub,
"huggingface_endpoint": HuggingFaceEndpoint,
"modal": Modal,
"sagemaker_endpoint": SagemakerEndpoint,
"nlpcloud": NLPCloud,
"openai": OpenAI,
"petals": Petals,

235
langchain/llms/sagemaker_endpoint.py Normal file
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@ -0,0 +1,235 @@
"""Wrapper around Sagemaker InvokeEndpoint API."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class ContentHandlerBase(ABC):
"""A handler class to transform input from LLM to a
format that SageMaker endpoint expects. Similarily,
the class also handles transforming output from the
SageMaker endpoint to a format that LLM class expects.
"""
"""
Example:
.. code-block:: python
class ContentHandler(ContentHandlerBase):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompt: prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
content_type: Optional[str] = "text/plain"
"""The MIME type of the input data passed to endpoint"""
accepts: Optional[str] = "text/plain"
"""The MIME type of the response data returned from endpoint"""
@abstractmethod
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
"""Transforms the input to a format that model can accept
as the request Body. Should return bytes or seekable file
like object in the format specified in the content_type
request header.
"""
@abstractmethod
def transform_output(self, output: bytes) -> str:
"""Transforms the output from the model to string that
the LLM class expects.
"""
class SagemakerEndpoint(LLM, BaseModel):
"""Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
"""
"""
Example:
.. code-block:: python
from langchain import SagemakerEndpoint
endpoint_name = (
"my-endpoint-name"
)
region_name = (
"us-west-2"
)
credentials_profile_name = (
"default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
"""
content_handler: ContentHandlerBase
"""The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
"""
"""
Example:
.. code-block:: python
class ContentHandler(ContentHandlerBase):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompt: prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that AWS credentials to and python package exists in environment."""
try:
import boto3
try:
if values["credentials_profile_name"] is not None:
session = boto3.Session(
profile_name=values["credentials_profile_name"]
)
else:
# use default credentials
session = boto3.Session()
values["client"] = session.client(
"sagemaker-runtime", region_name=values["region_name"]
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
"profile name are valid."
) from e
except ImportError:
raise ValueError(
"Could not import boto3 python package. "
"Please it install it with `pip install boto3`."
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "sagemaker_endpoint"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to Sagemaker inference endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = se("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
_endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(prompt, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# send request
try:
response = self.client.invoke_endpoint(
EndpointName=self.endpoint_name,
Body=body,
ContentType=content_type,
Accept=accepts,
**_endpoint_kwargs,
)
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
text = self.content_handler.transform_output(response["Body"])
if stop is not None:
# This is a bit hacky, but I can't figure out a better way to enforce
# stop tokens when making calls to the sagemaker endpoint.
text = enforce_stop_tokens(text, stop)
return text