refactor: finetune through LLMClient interface + get_running_loop

- Add submit_finetune and get_finetune_status abstract methods to LLMClient base
- Implement both methods in ZhipuAIClient using asyncio.get_running_loop()
- Rewrite finetune_service to call llm.submit_finetune / llm.get_finetune_status
  instead of accessing llm._client directly, restoring interface encapsulation
- Replace asyncio.get_event_loop() with get_running_loop() in ZhipuAIClient._call
  and all four methods in RustFSClient (deprecated in Python 3.10+)
- Update test_finetune_service to mock the LLMClient interface methods as AsyncMocks
- Add two new tests in test_llm_client for submit_finetune and get_finetune_status
This commit is contained in:
wh
2026-04-10 16:43:28 +08:00
parent 603382d1fa
commit 0880e1018c
6 changed files with 130 additions and 79 deletions

View File

@@ -9,3 +9,11 @@ class LLMClient(ABC):
@abstractmethod
async def chat_vision(self, model: str, messages: list[dict]) -> str:
"""Send a multimodal (vision) chat request and return the response content string."""
@abstractmethod
async def submit_finetune(self, jsonl_url: str, base_model: str, hyperparams: dict) -> str:
"""Submit a fine-tune job and return the job_id."""
@abstractmethod
async def get_finetune_status(self, job_id: str) -> dict:
"""Return a dict with keys: job_id, status (raw SDK string), progress (int|None), error_message (str|None)."""

View File

@@ -19,8 +19,39 @@ class ZhipuAIClient(LLMClient):
async def chat_vision(self, model: str, messages: list[dict]) -> str:
return await self._call(model, messages)
async def submit_finetune(self, jsonl_url: str, base_model: str, hyperparams: dict) -> str:
loop = asyncio.get_running_loop()
try:
resp = await loop.run_in_executor(
None,
lambda: self._client.fine_tuning.jobs.create(
training_file=jsonl_url,
model=base_model,
hyperparameters=hyperparams,
),
)
return resp.id
except Exception as exc:
raise LLMCallError(f"微调任务提交失败: {exc}") from exc
async def get_finetune_status(self, job_id: str) -> dict:
loop = asyncio.get_running_loop()
try:
resp = await loop.run_in_executor(
None,
lambda: self._client.fine_tuning.jobs.retrieve(job_id),
)
return {
"job_id": resp.id,
"status": resp.status,
"progress": int(resp.progress) if getattr(resp, "progress", None) is not None else None,
"error_message": getattr(resp, "error_message", None),
}
except Exception as exc:
raise LLMCallError(f"查询微调任务失败: {exc}") from exc
async def _call(self, model: str, messages: list[dict]) -> str:
loop = asyncio.get_event_loop()
loop = asyncio.get_running_loop()
try:
response = await loop.run_in_executor(
None,

View File

@@ -21,7 +21,7 @@ class RustFSClient(StorageClient):
)
async def download_bytes(self, bucket: str, path: str) -> bytes:
loop = asyncio.get_event_loop()
loop = asyncio.get_running_loop()
try:
resp = await loop.run_in_executor(
None, lambda: self._s3.get_object(Bucket=bucket, Key=path)
@@ -33,7 +33,7 @@ class RustFSClient(StorageClient):
async def upload_bytes(
self, bucket: str, path: str, data: bytes, content_type: str = "application/octet-stream"
) -> None:
loop = asyncio.get_event_loop()
loop = asyncio.get_running_loop()
try:
await loop.run_in_executor(
None,
@@ -45,7 +45,7 @@ class RustFSClient(StorageClient):
raise StorageError(f"存储上传失败 [{bucket}/{path}]: {exc}") from exc
async def get_presigned_url(self, bucket: str, path: str, expires: int = 3600) -> str:
loop = asyncio.get_event_loop()
loop = asyncio.get_running_loop()
try:
url = await loop.run_in_executor(
None,
@@ -60,7 +60,7 @@ class RustFSClient(StorageClient):
raise StorageError(f"生成预签名 URL 失败 [{bucket}/{path}]: {exc}") from exc
async def get_object_size(self, bucket: str, path: str) -> int:
loop = asyncio.get_event_loop()
loop = asyncio.get_running_loop()
try:
resp = await loop.run_in_executor(
None, lambda: self._s3.head_object(Bucket=bucket, Key=path)

View File

@@ -1,6 +1,4 @@
import asyncio
from app.core.exceptions import LLMCallError
from app.clients.llm.base import LLMClient
from app.core.logging import get_logger
from app.models.finetune_models import (
FinetuneStartRequest,
@@ -17,45 +15,21 @@ _STATUS_MAP = {
}
async def submit_finetune(req: FinetuneStartRequest, llm) -> FinetuneStartResponse:
"""Submit a fine-tune job to ZhipuAI and return the job ID."""
loop = asyncio.get_event_loop()
try:
response = await loop.run_in_executor(
None,
lambda: llm._client.fine_tuning.jobs.create(
training_file=req.jsonl_url,
model=req.base_model,
hyperparameters=req.hyperparams or {},
),
)
job_id = response.id
logger.info("finetune_submit", extra={"job_id": job_id, "model": req.base_model})
return FinetuneStartResponse(job_id=job_id)
except Exception as exc:
logger.error("finetune_submit_error", extra={"error": str(exc)})
raise LLMCallError(f"微调任务提交失败: {exc}") from exc
async def submit_finetune(req: FinetuneStartRequest, llm: LLMClient) -> FinetuneStartResponse:
"""Submit a fine-tune job via the LLMClient interface and return the job ID."""
job_id = await llm.submit_finetune(req.jsonl_url, req.base_model, req.hyperparams or {})
logger.info("finetune_submit", extra={"job_id": job_id, "model": req.base_model})
return FinetuneStartResponse(job_id=job_id)
async def get_finetune_status(job_id: str, llm) -> FinetuneStatusResponse:
"""Retrieve fine-tune job status from ZhipuAI."""
loop = asyncio.get_event_loop()
try:
response = await loop.run_in_executor(
None,
lambda: llm._client.fine_tuning.jobs.retrieve(job_id),
)
status_raw = response.status
status = _STATUS_MAP.get(status_raw, "RUNNING") # conservative fallback
progress = getattr(response, "progress", None)
error_message = getattr(response, "error_message", None)
logger.info("finetune_status", extra={"job_id": job_id, "status": status})
return FinetuneStatusResponse(
job_id=job_id,
status=status,
progress=progress,
error_message=error_message,
)
except Exception as exc:
logger.error("finetune_status_error", extra={"job_id": job_id, "error": str(exc)})
raise LLMCallError(f"微调状态查询失败: {exc}") from exc
async def get_finetune_status(job_id: str, llm: LLMClient) -> FinetuneStatusResponse:
"""Retrieve fine-tune job status via the LLMClient interface."""
raw = await llm.get_finetune_status(job_id)
status = _STATUS_MAP.get(raw["status"], "RUNNING")
logger.info("finetune_status", extra={"job_id": job_id, "status": status})
return FinetuneStatusResponse(
job_id=raw["job_id"],
status=status,
progress=raw["progress"],
error_message=raw["error_message"],
)