Add a system logger (#85)

cacheflow/logger.py

@@ -0,0 +1,51 @@
+# Adapted from https://github.com/skypilot-org/skypilot/blob/86dc0f6283a335e4aa37b3c10716f90999f48ab6/sky/sky_logging.py
+
+import logging
+import sys
+
+
+_FORMAT = "%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s"
+_DATE_FORMAT = "%m-%d %H:%M:%S"
+
+
+class NewLineFormatter(logging.Formatter):
+    """Adds logging prefix to newlines to align multi-line messages."""
+
+    def __init__(self, fmt, datefmt=None):
+        logging.Formatter.__init__(self, fmt, datefmt)
+
+    def format(self, record):
+        msg = logging.Formatter.format(self, record)
+        if record.message != "":
+            parts = msg.split(record.message)
+            msg = msg.replace("\n", "\r\n" + parts[0])
+        return msg
+
+
+_root_logger = logging.getLogger("cacheflow")
+_default_handler = None
+
+
+def _setup_logger():
+    _root_logger.setLevel(logging.DEBUG)
+    global _default_handler
+    if _default_handler is None:
+        _default_handler = logging.StreamHandler(sys.stdout)
+        _default_handler.flush = sys.stdout.flush  # type: ignore
+        _default_handler.setLevel(logging.INFO)
+        _root_logger.addHandler(_default_handler)
+    fmt = NewLineFormatter(_FORMAT, datefmt=_DATE_FORMAT)
+    _default_handler.setFormatter(fmt)
+    # Setting this will avoid the message
+    # being propagated to the parent logger.
+    _root_logger.propagate = False
+
+
+# The logger is initialized when the module is imported.
+# This is thread-safe as the module is only imported once,
+# guaranteed by the Python GIL.
+_setup_logger()
+
+
+def init_logger(name: str):
+    return logging.getLogger(name)

cacheflow/master/server.py

@@ -8,6 +8,7 @@ try:
 except ImportError:
     ray = None
 
+from cacheflow.logger import init_logger
 from cacheflow.master.scheduler import Scheduler
 from cacheflow.master.simple_frontend import SimpleFrontend
 from cacheflow.models import get_memory_analyzer
@@ -17,6 +18,9 @@ from cacheflow.sampling_params import SamplingParams
 from cacheflow.utils import get_gpu_memory, get_cpu_memory
 
 
+logger = init_logger(__name__)
+
+
 class Server:
     def __init__(
         self,
@@ -42,6 +46,17 @@ class Server:
         collect_stats: bool = False,
         do_memory_analysis: bool = False,
     ):
+        logger.info(
+            "Initializing a server with config: "
+            f"model={model!r}, "
+            f"dtype={dtype}, "
+            f"use_dummy_weights={use_dummy_weights}, "
+            f"cache_dir={cache_dir}, "
+            f"use_np_cache={use_np_cache}, "
+            f"tensor_parallel_size={tensor_parallel_size}, "
+            f"block_size={block_size}, "
+            f"seed={seed})"
+        )
         self.num_nodes = num_nodes
         self.num_devices_per_node = num_devices_per_node
         self.world_size = pipeline_parallel_size * tensor_parallel_size
@@ -61,9 +76,7 @@ class Server:
         self.num_gpu_blocks = self.memory_analyzer.get_max_num_gpu_blocks(
             max_num_batched_tokens=max_num_batched_tokens)
         self.num_cpu_blocks = self.memory_analyzer.get_max_num_cpu_blocks(
-            swap_space=swap_space)
-        print(f'# GPU blocks: {self.num_gpu_blocks}, '
-              f'# CPU blocks: {self.num_cpu_blocks}')
+            swap_space_gib=swap_space)
 
         # Create a controller for each pipeline stage.
         self.controllers: List[Controller] = []

cacheflow/master/simple_frontend.py

@@ -1,13 +1,17 @@
 import time
-from typing import List, Optional, Set, Tuple
+from typing import List, Optional, Tuple
 
 from transformers import AutoTokenizer
 
+from cacheflow.logger import init_logger
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.sequence import Sequence, SequenceGroup
 from cacheflow.utils import Counter
 
 
+logger = init_logger(__name__)
+
+
 class SimpleFrontend:
 
     def __init__(
@@ -66,4 +70,4 @@ class SimpleFrontend:
             token_ids = seq.get_token_ids()
             output = self.tokenizer.decode(token_ids, skip_special_tokens=True)
             output = output.strip()
-            print(f'Seq {seq.seq_id}: {output!r}')
+            logger.info(f"Seq {seq.seq_id}: {output!r}")

cacheflow/models/memory_analyzer.py

@@ -1,8 +1,12 @@
 import torch
 from transformers import AutoConfig
 
+from cacheflow.logger import init_logger
 from cacheflow.models.utils import get_dtype_size
 
+
+logger = init_logger(__name__)
+
 _GiB = 1 << 30
 
 
@@ -23,17 +27,17 @@ class CacheFlowMemoryAnalyzer:
 
     def get_max_num_cpu_blocks(
         self,
-        swap_space: int,
+        swap_space_gib: int,
     ) -> int:
-        swap_space = swap_space * _GiB
+        swap_space = swap_space_gib * _GiB
         cpu_memory = self.cpu_memory
         if swap_space > 0.8 * cpu_memory:
-            raise ValueError(f'The swap space ({swap_space / _GiB:.2f} GiB) '
+            raise ValueError(f'The swap space ({swap_space_gib:.2f} GiB) '
                              'takes more than 80% of the available memory '
                              f'({cpu_memory / _GiB:.2f} GiB).'
                              'Please check the swap space size.')
         if swap_space > 0.5 * cpu_memory:
-            print(f'WARNING: The swap space ({swap_space / _GiB:.2f} GiB) '
+            logger.info(f'WARNING: The swap space ({swap_space_gib:.2f} GiB) '
                         'takes more than 50% of the available memory '
                         f'({cpu_memory / _GiB:.2f} GiB).'
                         'This may slow the system performance.')

simple_server.py

@@ -1,11 +1,11 @@
 import argparse
-from typing import List
 
 from cacheflow.master.server import (
     add_server_arguments, process_server_arguments,
     init_local_server_and_frontend_with_arguments)
 from cacheflow.sampling_params import SamplingParams
 
+
 def main(args: argparse.Namespace):
     server, frontend = init_local_server_and_frontend_with_arguments(args)
     # Test the following inputs.