LLAMA 7B with customized stop reasons

In this tutorial, you will use LMI container from DLC to SageMaker and run inference with it.

Please make sure the following permission granted before running the notebook:

• S3 bucket push access
• SageMaker access

Step 1: Let's bump up SageMaker and import stuff

%pip install sagemaker --upgrade  --quiet

import boto3
import sagemaker
from sagemaker import Model, image_uris, serializers, deserializers

role = sagemaker.get_execution_role()  # execution role for the endpoint
sess = sagemaker.session.Session()  # sagemaker session for interacting with different AWS APIs
region = sess._region_name  # region name of the current SageMaker Studio environment
account_id = sess.account_id()  # account_id of the current SageMaker Studio environment

Step 2: Start preparing model artifacts

In LMI contianer, we expect some artifacts to help setting up the model - serving.properties (required): Defines the model server settings - model.py (optional): A python file to define the core inference logic - requirements.txt (optional): Any additional pip wheel need to install

%%writefile serving.properties
engine=MPI
option.model_id=TheBloke/Llama-2-7B-fp16
option.tensor_parallel_degree=1
option.max_rolling_batch_size=32
option.rolling_batch=lmi-dist
option.output_formatter=jsonlines

%%sh
mkdir mymodel
mv serving.properties mymodel/
mv model.py mymodel/
tar czvf mymodel.tar.gz mymodel/
rm -rf mymodel

Step 3: Start building SageMaker endpoint

In this step, we will build SageMaker endpoint from scratch

Getting the container image URI

Large Model Inference available DLC

image_uri = image_uris.retrieve(
        framework="djl-deepspeed",
        region=sess.boto_session.region_name,
        version="0.27.0"
    )

Upload artifact on S3 and create SageMaker model

s3_code_prefix = "large-model-lmi/code"
bucket = sess.default_bucket()  # bucket to house artifacts
code_artifact = sess.upload_data("mymodel.tar.gz", bucket, s3_code_prefix)
print(f"S3 Code or Model tar ball uploaded to --- > {code_artifact}")

model = Model(image_uri=image_uri, model_data=code_artifact, role=role)

4.2 Create SageMaker endpoint

You need to specify the instance to use and endpoint names

instance_type = "ml.g5.2xlarge"
endpoint_name = sagemaker.utils.name_from_base("lmi-model")

model.deploy(initial_instance_count=1,
             instance_type=instance_type,
             endpoint_name=endpoint_name,
            )

Step 5: Test and benchmark the inference

class LineIterator:

    def __init__(self, stream):
        self.byte_iterator = iter(stream)
        self.buffer = io.BytesIO()
        self.read_pos = 0

    def __iter__(self):
        return self

    def __next__(self):
        while True:
            self.buffer.seek(self.read_pos)
            line = self.buffer.readline()
            if line and line[-1] == ord('\n'):
                self.read_pos += len(line)
                return line[:-1]
            try:
                chunk = next(self.byte_iterator)
            except StopIteration:
                if self.read_pos < self.buffer.getbuffer().nbytes:
                    continue
                raise
            if 'PayloadPart' not in chunk:
                print('Unknown event type:' + chunk)
                continue
            self.buffer.seek(0, io.SEEK_END)
            self.buffer.write(chunk['PayloadPart']['Bytes'])

import json, io
sm_client = boto3.client("sagemaker-runtime")
body = {"inputs": "what is life", "parameters": {"max_new_tokens":25, "do_sample": True,  "details": True}}
resp = sm_client.invoke_endpoint_with_response_stream(EndpointName=endpoint_name, Body=json.dumps(body), ContentType="application/json")
event_stream = resp['Body']

for line in LineIterator(event_stream):
    resp = json.loads(line)
    print(resp)

Let's try to make it continue generate based on finish reason

def inference(payload):
    resp = sm_client.invoke_endpoint_with_response_stream(EndpointName=endpoint_name, Body=json.dumps(payload), ContentType="application/json")
    event_stream = resp['Body']
    text_output = []
    for line in LineIterator(event_stream):
        resp = json.loads(line)
        token = resp['token']['text']
        text_output.append(token)
        print(token, end='')
        if resp['details']:
            finish_reason = resp['details']['finish_reason']
            return payload['inputs'] + ''.join(text_output), finish_reason, len(text_output)

payload = {"inputs": "The new movie that got Oscar this year", "parameters": {"max_new_tokens":128, "do_sample": True, "top_p": 0.9,
           "temperature": 0.8, "repetition_penalty": 1.2, "details": True}}

finish_reason = "length"
print(f"Ouput: {payload['inputs']}", end='')
total_tokens = 0
total_query = 0
while finish_reason == 'length':
    output_text, finish_reason, out_token_len = inference(payload)
    payload['inputs'] = output_text
    total_tokens += out_token_len
    total_query += 1
print(f"\ntotal token generated: {total_tokens} total query sent: {total_query}")

Clean up the environment

sess.delete_endpoint(endpoint_name)
sess.delete_endpoint_config(endpoint_name)
model.delete_model()