Provide the local llama service

This commit is contained in:
zhangzhen
2023-09-14 15:22:38 +08:00
parent 25fec9a683
commit dd3187fc8a
6 changed files with 162 additions and 8 deletions
+1 -1
View File
@@ -65,7 +65,7 @@ class ComputeKernel:
def _schedule(self, task) -> ComputeNode:
for node in self.compute_nodes.values():
if node.is_support(task.task_type) is True:
if node.is_support(task) is True:
return node
logger.warning(
f"task {task.display()} is not support by any compute node")
+2 -3
View File
@@ -28,7 +28,7 @@ class ComputeNode(ABC):
pass
@abstractmethod
def is_support(self, task_type: ComputeTaskType) -> bool:
def is_support(self, task: ComputeTask) -> bool:
pass
@abstractmethod
@@ -41,10 +41,9 @@ class ComputeNode(ABC):
def get_fee_type(self) -> str:
return "free"
class LocalComputeNode(ComputeNode):
def display(self) -> str:
return super().display()
def is_local(self) -> bool:
return True
return True
@@ -0,0 +1,86 @@
import logging
import requests
from typing import Optional
from pydantic import BaseModel
from .compute_task import ComputeTask, ComputeTaskState, ComputeTaskType
from .queue_compute_node import Queue_ComputeNode
logger = logging.getLogger(__name__)
"""
This is a custom implementation, it should be redesigned.
"""
class LocalLlama_ComputeNode(Queue_ComputeNode):
async def execute_task(self, task: ComputeTask) -> {
"content": str,
"message": str,
"state": ComputeTaskState,
"error": {
"code": int,
"message": str,
}
}:
class GenerateResponse(BaseModel):
error: Optional[int]
msg: Optional[str]
results: Optional[str]
try:
body = {
"prompts": task.params["prompts"]
}
response = requests.post("http://aigc:7880/generate", data = body, verify=False)
response.close()
logger.info(f"LocalLlama_ComputeNode task responsed, request: {body}, status-code: {response.status_code}, headers: {response.headers}, content: {response.content}")
if response.status_code != 200:
return {
"state": ComputeTaskState.ERROR,
"error": {
"code": response.status_code,
"message": "http request failed: " + response.status_code
}
}
else:
resp = GenerateResponse.parse_raw(response.content.decode("utf-8"))
if resp.error:
return {
"state": ComputeTaskState.ERROR,
"error": {
"code": resp.error,
"message": "local llama failed:" + resp.msg
}
}
else:
return {
"content": str(resp.results),
"message": {}
}
except Exception as err:
import traceback
logger.error(f"{traceback.format_exc()}, error: {err}")
return {
"state": ComputeTaskState.ERROR,
"error": {
"code": -1,
"message": "unknown exception: " + str(err)
}
}
def display(self) -> str:
return f"LocalLlama_ComputeNode: {self.node_id}"
def get_capacity(self):
pass
def is_support(self, task: ComputeTask) -> bool:
return task.task_type == ComputeTaskType.LLM_COMPLETION and (not task.params["model_name"] or task.params["model_name"] == "llama")
def is_local(self) -> bool:
return True
+2 -2
View File
@@ -103,8 +103,8 @@ class OpenAI_ComputeNode(ComputeNode):
def get_capacity(self):
pass
def is_support(self, task_type: ComputeTaskType) -> bool:
return task_type == ComputeTaskType.LLM_COMPLETION
def is_support(self, task: ComputeTask) -> bool:
return task.task_type == ComputeTaskType.LLM_COMPLETION and (not task.params["model_name"] or task.params["model_name"] == "gpt-4-0613")
def is_local(self) -> bool:
return False
+69
View File
@@ -0,0 +1,69 @@
import asyncio
from asyncio import Queue
import logging
from abc import abstractmethod
from .compute_task import ComputeTask, ComputeTaskResult, ComputeTaskState, ComputeTaskType
from .compute_node import ComputeNode
logger = logging.getLogger(__name__)
class Queue_ComputeNode(ComputeNode):
def __init__(self):
super().__init__()
self.task_queue = Queue()
@abstractmethod
async def execute_task(self, task: ComputeTask) -> {
"content": str,
"message": str,
"state": ComputeTaskState,
"error": {
"code": int,
"message": str,
}
}:
pass
async def push_task(self, task: ComputeTask, proiority: int = 0):
logger.info(f"{self.display()} push task: {task.display()}")
self.task_queue.put_nowait(task)
async def remove_task(self, task_id: str):
pass
async def _run_task(self, task: ComputeTask):
task.state = ComputeTaskState.RUNNING
resp = await self.execute_task(task)
result = ComputeTaskResult()
result.set_from_task(task)
task.state = resp["state"]
if task.state == ComputeTaskState.ERROR:
task.error_str = resp["error"]["message"]
result.worker_id = self.node_id
result.result_str = resp["content"]
result.result_message = resp["message"]
return result
def start(self):
async def _run_task_loop():
while True:
task = await self.task_queue.get()
logger.info(f"{self.display()} get task: {task.display()}")
result = await self._run_task(task)
if result is not None:
task.result = result
asyncio.create_task(_run_task_loop())
def get_task_state(self, task_id: str):
pass
+2 -2
View File
@@ -129,8 +129,8 @@ class Stability_ComputeNode(ComputeNode):
def get_capacity(self):
pass
def is_support(self, task_type: ComputeTaskType) -> bool:
return task_type == ComputeTaskType.TEXT_2_IMAGE
def is_support(self, task: ComputeTask) -> bool:
return task.task_type == ComputeTaskType.TEXT_2_IMAGE
def is_local(self) -> bool:
return False