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Updates to Sagemaker Endpoint (#1217)

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Nimisha Mehta 2023-02-21 17:02:04 -08:00 committed by GitHub
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183
docs/modules/document_loaders/examples/sagemaker.ipynb Normal file
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{
"cells": [
{
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"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting langchain\n",
" Downloading langchain-0.0.80-py3-none-any.whl (222 kB)\n",
"\u001b[K |████████████████████████████████| 222 kB 2.1 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: numpy<2,>=1 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from langchain) (1.24.1)\n",
"Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from langchain) (3.8.3)\n",
"Collecting pydantic<2,>=1\n",
" Downloading pydantic-1.10.4-cp39-cp39-macosx_11_0_arm64.whl (2.6 MB)\n",
"\u001b[K |████████████████████████████████| 2.6 MB 3.3 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting SQLAlchemy<2,>=1\n",
" Downloading SQLAlchemy-1.4.46.tar.gz (8.5 MB)\n",
"\u001b[K |████████████████████████████████| 8.5 MB 23.4 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting tenacity<9.0.0,>=8.1.0\n",
" Downloading tenacity-8.2.0-py3-none-any.whl (24 kB)\n",
"Requirement already satisfied: requests<3,>=2 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from langchain) (2.28.2)\n",
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"Collecting dataclasses-json<0.6.0,>=0.5.7\n",
" Downloading dataclasses_json-0.5.7-py3-none-any.whl (25 kB)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (4.0.2)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.4)\n",
"Requirement already satisfied: attrs>=17.3.0 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (22.2.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.3)\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.8.2)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
"Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.1.1)\n",
"Collecting marshmallow<4.0.0,>=3.3.0\n",
" Downloading marshmallow-3.19.0-py3-none-any.whl (49 kB)\n",
"\u001b[K |████████████████████████████████| 49 kB 26.9 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting marshmallow-enum<2.0.0,>=1.5.1\n",
" Downloading marshmallow_enum-1.5.1-py2.py3-none-any.whl (4.2 kB)\n",
"Collecting typing-inspect>=0.4.0\n",
" Downloading typing_inspect-0.8.0-py3-none-any.whl (8.7 kB)\n",
"Requirement already satisfied: packaging>=17.0 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from marshmallow<4.0.0,>=3.3.0->dataclasses-json<0.6.0,>=0.5.7->langchain) (23.0)\n",
"Requirement already satisfied: typing-extensions>=4.2.0 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from pydantic<2,>=1->langchain) (4.4.0)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from requests<3,>=2->langchain) (1.26.14)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /Users/nmehta/Library/Python/3.9/lib/python/site-packages (from requests<3,>=2->langchain) (2022.12.7)\n",
"Collecting mypy-extensions>=0.3.0\n",
" Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
"Building wheels for collected packages: SQLAlchemy\n",
" Building wheel for SQLAlchemy (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for SQLAlchemy: filename=SQLAlchemy-1.4.46-cp39-cp39-macosx_10_9_universal2.whl size=1578667 sha256=9991d70fde083b993d7fe1fd61fca33a279e921f1b8296b02037e24b8cac1097\n",
" Stored in directory: /Users/nmehta/Library/Caches/pip/wheels/3c/99/65/57cf5a0ec6e7f3b803a68d31694501e168997e03e80adc903d\n",
"Successfully built SQLAlchemy\n",
"Installing collected packages: mypy-extensions, marshmallow, typing-inspect, marshmallow-enum, tenacity, SQLAlchemy, pydantic, dataclasses-json, langchain\n",
"\u001b[33m WARNING: The script langchain-server is installed in '/Users/nmehta/Library/Python/3.9/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
"Successfully installed SQLAlchemy-1.4.46 dataclasses-json-0.5.7 langchain-0.0.80 marshmallow-3.19.0 marshmallow-enum-1.5.1 mypy-extensions-1.0.0 pydantic-1.10.4 tenacity-8.2.0 typing-inspect-0.8.0\n",
"\u001b[33mWARNING: You are using pip version 21.2.4; however, version 23.0 is available.\n",
"You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting html2text\n",
" Downloading html2text-2020.1.16-py3-none-any.whl (32 kB)\n",
"Installing collected packages: html2text\n",
"\u001b[33m WARNING: The script html2text is installed in '/Users/nmehta/Library/Python/3.9/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
"Successfully installed html2text-2020.1.16\n",
"\u001b[33mWARNING: You are using pip version 21.2.4; however, version 23.0 is available.\n",
"You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
]
}
],
"source": [
"!pip3 install langchain"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from langchain.docstore.document import Document"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_text': '3 days'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import PromptTemplate, HuggingFaceHub, LLMChain, SagemakerEndpoint\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",
"def model_input_transform_fn(prompt, model_kwargs):\n",
" parameter_payload = {\"inputs\": prompt, \"parameters\": model_kwargs}\n",
" return json.dumps(parameter_payload).encode(\"utf-8\") \n",
"\n",
"chain = load_qa_chain(llm=SagemakerEndpoint(\n",
" endpoint_name=\"my-sagemaker-model-endpoint\", \n",
" credentials_profile_name=\"credentials-profile-name\", \n",
" region_name=\"us-west-2\", \n",
" model_kwargs={\"temperature\":1e-10},\n",
" content_type=\"application/json\", \n",
" model_input_transform_fn=model_input_transform_fn), \n",
" prompt=PROMPT) \n",
"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)\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": "3.9.6"
},
"vscode": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

153
langchain/llms/sagemaker_endpoint.py
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@ -1,22 +1,29 @@
"""Wrapper around Sagemaker InvokeEndpoint API."""
import json
from typing import Any, List, Mapping, Optional
from typing import Any, Callable, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
VALID_TASKS = ("text2text-generation", "text-generation")
class SagemakerEndpoint(LLM, BaseModel):
"""Wrapper around custom Sagemaker Inference Endpoints.
To use, you should pass the AWS IAM Role and Role Session Name as
named parameters to the constructor.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
Only supports `text-generation` and `text2text-generation` for now.
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
"""
"""
@ -27,34 +34,104 @@ class SagemakerEndpoint(LLM, BaseModel):
endpoint_name = (
"https://runtime.sagemaker.us-west-2.amazonaws.com/endpoints/abcdefghijklmnop/invocations"
)
region_name = (
"us-west-2"
)
credentials_profile_name = (
"default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
role_arn="role_arn",
role_session_name="role_session_name"
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""# The name of the endpoint. Must be unique within an AWS Region."""
task: Optional[str] = None
"""Task to call the model with. Should be a task that returns `generated_text`."""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
"""The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
role_arn: Optional[str] = None
role_session_name: Optional[str] = None
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_type: Optional[str] = "application/json"
"""The MIME type of the input data in the request body to be used in the header
for the request to the Sagemaker invoke_endpoint API.
Defaults to "application/json"."""
model_input_transform_fn: Callable[[str, Dict], bytes]
"""
Function which takes the prompt (str) and model_kwargs (dict) and transforms
the input to the format which the 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.
"""
"""
Example:
.. code-block:: python
def model_input_transform_fn(prompt, model_kwargs):
parameter_payload = {"inputs": prompt, "parameters": model_kwargs}
return json.dumps(parameter_payload).encode("utf-8")
"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
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, "task": self.task},
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@ -78,46 +155,16 @@ class SagemakerEndpoint(LLM, BaseModel):
response = se("Tell me a joke.")
"""
import boto3
session = boto3.Session(profile_name="test-profile-name")
sagemaker_runtime = session.client("sagemaker-runtime", region_name="us-west-2")
# TODO: use AWS IAM assumed roles to authenticate from the EC2 instance
# def role_arn_to_session(**args):
# """
# Usage :
# session = role_arn_to_session(
# RoleArn='arn:aws:iam::012345678901:role/example-role',
# RoleSessionName='ExampleSessionName')
# client = session.client('sqs')
# """
# client = boto3.client('sts')
# response = client.assume_role(**args)
# return boto3.Session(
# aws_access_key_id=response['Credentials']['AccessKeyId'],
# aws_secret_access_key=response['Credentials']['SecretAccessKey'],
# aws_session_token=response['Credentials']['SessionToken'])
# session = role_arn_to_session(RoleArn="$role-arn",
# RoleSessionName="test-role-session-name")
# sagemaker_runtime = session.client(
# "sagemaker-runtime", region_name="us-west-2"
# )
_model_kwargs = self.model_kwargs or {}
# payload samples
parameter_payload = {"inputs": prompt, "parameters": _model_kwargs}
input_en = json.dumps(parameter_payload).encode("utf-8")
if self.model_input_transform_fn is None:
raise NotImplementedError("model_input_transform_fn not implemented")
# send request
try:
response = sagemaker_runtime.invoke_endpoint(
response = self.client.invoke_endpoint(
EndpointName=self.endpoint_name,
Body=input_en,
ContentType="application/json",
Body=self.model_input_transform_fn(prompt, _model_kwargs),
ContentType=self.content_type,
)
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
@ -126,7 +173,7 @@ class SagemakerEndpoint(LLM, BaseModel):
text = response_json[0]["generated_text"]
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 huggingface_hub.
# stop tokens when making calls to the sagemaker endpoint.
text = enforce_stop_tokens(text, stop)
return text