DocsGPT/application/llm/sagemaker.py
2023-10-06 14:43:05 +01:00

139 lines
4.8 KiB
Python

from application.llm.base import BaseLLM
from application.core.settings import settings
import json
import io
class LineIterator:
"""
A helper class for parsing the byte stream input.
The output of the model will be in the following format:
```
b'{"outputs": [" a"]}\n'
b'{"outputs": [" challenging"]}\n'
b'{"outputs": [" problem"]}\n'
...
```
While usually each PayloadPart event from the event stream will contain a byte array
with a full json, this is not guaranteed and some of the json objects may be split across
PayloadPart events. For example:
```
{'PayloadPart': {'Bytes': b'{"outputs": '}}
{'PayloadPart': {'Bytes': b'[" problem"]}\n'}}
```
This class accounts for this by concatenating bytes written via the 'write' function
and then exposing a method which will return lines (ending with a '\n' character) within
the buffer via the 'scan_lines' function. It maintains the position of the last read
position to ensure that previous bytes are not exposed again.
"""
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'])
class SagemakerAPILLM(BaseLLM):
def __init__(self, *args, **kwargs):
import boto3
runtime = boto3.client(
'runtime.sagemaker',
aws_access_key_id='xxx',
aws_secret_access_key='xxx',
region_name='us-west-2'
)
self.endpoint = settings.SAGEMAKER_ENDPOINT
self.runtime = runtime
def gen(self, model, engine, messages, stream=False, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
# Construct payload for endpoint
payload = {
"inputs": prompt,
"stream": False,
"parameters": {
"do_sample": True,
"temperature": 0.1,
"max_new_tokens": 30,
"repetition_penalty": 1.03,
"stop": ["</s>", "###"]
}
}
body_bytes = json.dumps(payload).encode('utf-8')
# Invoke the endpoint
response = self.runtime.invoke_endpoint(EndpointName=self.endpoint,
ContentType='application/json',
Body=body_bytes)
result = json.loads(response['Body'].read().decode())
import sys
print(result[0]['generated_text'], file=sys.stderr)
return result[0]['generated_text'][len(prompt):]
def gen_stream(self, model, engine, messages, stream=True, **kwargs):
context = messages[0]['content']
user_question = messages[-1]['content']
prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
# Construct payload for endpoint
payload = {
"inputs": prompt,
"stream": True,
"parameters": {
"do_sample": True,
"temperature": 0.1,
"max_new_tokens": 512,
"repetition_penalty": 1.03,
"stop": ["</s>", "###"]
}
}
body_bytes = json.dumps(payload).encode('utf-8')
# Invoke the endpoint
response = self.runtime.invoke_endpoint_with_response_stream(EndpointName=self.endpoint,
ContentType='application/json',
Body=body_bytes)
#result = json.loads(response['Body'].read().decode())
event_stream = response['Body']
start_json = b'{'
for line in LineIterator(event_stream):
if line != b'' and start_json in line:
#print(line)
data = json.loads(line[line.find(start_json):].decode('utf-8'))
if data['token']['text'] not in ["</s>", "###"]:
print(data['token']['text'],end='')
yield data['token']['text']