[misc] refactor: extract get_logger utility to reduce code duplication (#84)

as title.

cc @0oshowero0

---------

Signed-off-by: ji-huazhong <hzji210@gmail.com>
Co-authored-by: Han Zhenyu 韩振宇 <o0shower0o@outlook.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Author: 3 people

recipe/simple_use_case/single_controller_demo.py

@@ -53,13 +53,24 @@ def compute_loss(data1, _data2):
 
 def compute_reward(response_ids: torch.Tensor) -> TensorDict:
     """Simulate a reward model that scores each token position in the response.
+    Returns a TensorDict with a ``"rm_score"`` field whose shape matches
+    ``response_ids`` (i.e. one scalar per response token).
+    """
+    time.sleep(1)
+    reward = torch.randn_like(response_ids, dtype=torch.float32)
+
+    return TensorDict({"rm_score": reward}, batch_size=response_ids.size(0))
+
+
+def compute_advantage(rewards: torch.Tensor) -> TensorDict:
+    """Simulate the process of computing advantage.
 
     Returns a TensorDict with an ``"advantage"`` field whose shape matches
-    ``response_ids`` (i.e. one scalar per response token).
+    ``rewards`` (i.e. one scalar per reward).
     """
     time.sleep(1)
-    advantage = torch.randn_like(response_ids, dtype=torch.float32)
-    return TensorDict({"advantage": advantage}, batch_size=response_ids.size(0))
+    advantage = torch.randn_like(rewards, dtype=torch.float32)
+    return TensorDict({"advantage": advantage}, batch_size=rewards.size(0))
 
 
 class TrainingWorker:
@@ -89,7 +100,7 @@ def infer_batch(self, kv_meta: KVBatchMeta) -> KVBatchMeta:
         """Simulate forward-only inference"""
         # 1. Pull data from storage
         data = tq.kv_batch_get_by_meta(meta=kv_meta)
-        logger.info(f"compute_log_prob: got data {data}")
+        logger.info(f"infer_batch: got data {data}")
 
         # 2. Model forward
         output = compute_log_prob(data["prompt_ids"], data["response_ids"])
@@ -494,6 +505,13 @@ def fit(self):
             meta = tq.kv_batch_put(keys=meta.keys, partition_id=meta.partition_id, fields=reward_output)
             logger.info(f"demo reward KVBatchMeta: {meta}")
 
+            # ========================= Compute advantage =========================
+            meta.fields = ["response_ids", "ref_log_prob", "old_log_prob", "rm_score"]
+            advantage_data = tq.kv_batch_get_by_meta(meta=meta)
+            advantage_output = compute_advantage(advantage_data["rm_score"])
+            meta = tq.kv_batch_put(keys=meta.keys, partition_id=meta.partition_id, fields=advantage_output)
+            logger.info(f"demo advantage KVBatchMeta: {meta}")
+
             # ========================= Update actor =========================
             meta.fields = [
                 "input_ids",

transfer_queue/client.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import asyncio
-import logging
 import os
 import threading
 from typing import Any, Callable, Optional
@@ -32,21 +31,15 @@
     TransferQueueStorageManagerFactory,
 )
 from transfer_queue.utils.common import limit_pytorch_auto_parallel_threads
+from transfer_queue.utils.logging_utils import get_logger
 from transfer_queue.utils.zmq_utils import (
     ZMQMessage,
     ZMQRequestType,
     ZMQServerInfo,
     with_zmq_socket,
 )
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
-
-# Ensure logger has a handler
-if not logger.hasHandlers():
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
-    logger.addHandler(handler)
+logger = get_logger(__name__)
 
 TQ_NUM_THREADS = int(os.environ.get("TQ_NUM_THREADS", 8))
 

transfer_queue/controller.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import copy
-import logging
 import os
 import time
 from collections import defaultdict
@@ -37,6 +36,7 @@
 )
 from transfer_queue.sampler import BaseSampler, SequentialSampler
 from transfer_queue.utils.enum_utils import TransferQueueRole
+from transfer_queue.utils.logging_utils import get_logger
 from transfer_queue.utils.perf_utils import IntervalPerfMonitor
 from transfer_queue.utils.zmq_utils import (
     ZMQMessage,
@@ -48,14 +48,7 @@
     get_node_ip_address_raw,
 )
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
-
-# Ensure logger has a handler (for Ray Actor subprocess)
-if not logger.hasHandlers():
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
-    logger.addHandler(handler)
+logger = get_logger(__name__)
 
 TQ_CONTROLLER_GET_METADATA_TIMEOUT = int(os.environ.get("TQ_CONTROLLER_GET_METADATA_TIMEOUT", 1))
 TQ_CONTROLLER_GET_METADATA_CHECK_INTERVAL = int(os.environ.get("TQ_CONTROLLER_GET_METADATA_CHECK_INTERVAL", 5))

transfer_queue/dataloader/streaming_dataloader.py

@@ -13,24 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-import os
 from typing import Optional
 
 import torch
 from tensordict import TensorDict
 
 from transfer_queue.dataloader.streaming_dataset import StreamingDataset
 from transfer_queue.metadata import BatchMeta
+from transfer_queue.utils.logging_utils import get_logger
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
-
-# Ensure logger has a handler
-if not logger.hasHandlers():
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
-    logger.addHandler(handler)
+logger = get_logger(__name__)
 
 
 def _identity_collate_fn(data: tuple[TensorDict, BatchMeta]) -> tuple[TensorDict, BatchMeta]:

transfer_queue/dataloader/streaming_dataset.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 import os
 import time
 import uuid
@@ -25,19 +24,13 @@
 
 from transfer_queue.client import TransferQueueClient
 from transfer_queue.metadata import BatchMeta
+from transfer_queue.utils.logging_utils import get_logger
 
 TQ_STREAMING_DATASET_EMPTY_BATCH_SLEEP_INTERVAL = float(
     os.environ.get("TQ_STREAMING_DATASET_EMPTY_BATCH_SLEEP_INTERVAL", 1)
 )  # in seconds
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
-
-# Ensure logger has a handler
-if not logger.hasHandlers():
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
-    logger.addHandler(handler)
+logger = get_logger(__name__)
 
 
 class StreamingDataset(IterableDataset):

transfer_queue/interface.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 import math
 import os
 import subprocess
@@ -35,14 +34,14 @@
 from transfer_queue.sampler import BaseSampler
 from transfer_queue.storage.simple_backend import SimpleStorageUnit
 from transfer_queue.utils.common import get_placement_group
+from transfer_queue.utils.logging_utils import get_logger
 from transfer_queue.utils.yuanrong_utils import (
     cleanup_yuanrong_resources,
     initialize_yuanrong_backend,
 )
 from transfer_queue.utils.zmq_utils import process_zmq_server_info
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
+logger = get_logger(__name__)
 
 _TRANSFER_QUEUE_CLIENT: Any = None
 _TRANSFER_QUEUE_STORAGE: Any = None

transfer_queue/metadata.py

@@ -16,8 +16,6 @@
 import copy
 import dataclasses
 import itertools
-import logging
-import os
 from collections import defaultdict
 from dataclasses import dataclass
 from types import MappingProxyType
@@ -27,14 +25,9 @@
 import torch
 from tensordict import TensorDict
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
+from transfer_queue.utils.logging_utils import get_logger
 
-# Ensure logger has a handler
-if not logger.hasHandlers():
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
-    logger.addHandler(handler)
+logger = get_logger(__name__)
 
 
 # ---------------------------------------------------------------------------

transfer_queue/storage/clients/mooncake_client.py

@@ -13,8 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-import os
 import pickle
 from typing import Any, Optional
 
@@ -23,9 +21,9 @@
 
 from transfer_queue.storage.clients.base import TransferQueueStorageKVClient
 from transfer_queue.storage.clients.factory import StorageClientFactory
+from transfer_queue.utils.logging_utils import get_logger
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
+logger = get_logger(__name__)
 
 MOONCAKE_STORE_IMPORTED: bool = True
 try:

transfer_queue/storage/clients/yuanrong_client.py

@@ -13,8 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-import os
 import struct
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
@@ -25,11 +23,11 @@
 
 from transfer_queue.storage.clients.base import TransferQueueStorageKVClient
 from transfer_queue.storage.clients.factory import StorageClientFactory
+from transfer_queue.utils.logging_utils import get_logger
 from transfer_queue.utils.serial_utils import _decoder, _encoder
 from transfer_queue.utils.yuanrong_utils import find_reachable_host
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
+logger = get_logger(__name__)
 
 
 YUANRONG_DATASYSTEM_IMPORTED: bool = True

transfer_queue/storage/managers/base.py

@@ -15,7 +15,6 @@
 
 import asyncio
 import itertools
-import logging
 import os
 import time
 import weakref
@@ -34,16 +33,10 @@
 
 from transfer_queue.metadata import BatchMeta, extract_field_schema
 from transfer_queue.storage.clients.factory import StorageClientFactory
+from transfer_queue.utils.logging_utils import get_logger
 from transfer_queue.utils.zmq_utils import ZMQMessage, ZMQRequestType, ZMQServerInfo, create_zmq_socket
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("TQ_LOGGING_LEVEL", logging.WARNING))
-
-# Ensure logger has a handler
-if not logger.hasHandlers():
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
-    logger.addHandler(handler)
+logger = get_logger(__name__)
 
 # ZMQ timeouts (in seconds) and retry configurations
 TQ_STORAGE_POLLER_TIMEOUT = int(os.environ.get("TQ_STORAGE_POLLER_TIMEOUT", 5))