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# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Tuple
import torch
import torch.distributed as dist
from verl.utils.logger.aggregate_logger import DecoratorLoggerBase
def _get_current_mem_info(unit: str = "GB", precision: int = 2) -> Tuple[str]:
"""Get current memory usage."""
assert unit in ["GB", "MB", "KB"]
divisor = 1024**3 if unit == "GB" else 1024**2 if unit == "MB" else 1024
mem_allocated = torch.cuda.memory_allocated()
mem_reserved = torch.cuda.memory_reserved()
# use torch.cuda.mem_get_info to profile device memory
# since vllm's sleep mode works below pytorch
# see https://github.com/vllm-project/vllm/pull/11743#issuecomment-2754338119
mem_free, mem_total = torch.cuda.mem_get_info()
mem_used = mem_total - mem_free
mem_allocated = f"{mem_allocated / divisor:.{precision}f}"
mem_reserved = f"{mem_reserved / divisor:.{precision}f}"
mem_used = f"{mem_used / divisor:.{precision}f}"
mem_total = f"{mem_total / divisor:.{precision}f}"
return mem_allocated, mem_reserved, mem_used, mem_total
def log_gpu_memory_usage(head: str, logger: logging.Logger = None, level=logging.DEBUG, rank: int = 0):
if (not dist.is_initialized()) or (rank is None) or (dist.get_rank() == rank):
mem_allocated, mem_reserved, mem_used, mem_total = _get_current_mem_info()
message = f"{head}, memory allocated (GB): {mem_allocated}, memory reserved (GB): {mem_reserved}, device memory used/total (GB): {mem_used}/{mem_total}"
if logger is None:
print(message)
else:
logger.log(msg=message, level=level)
class GPUMemoryLogger(DecoratorLoggerBase):
"""A decorator class to log GPU memory usage.
Usage:
For example, in actor function, we initialize a GPUMemoryLogger
```
from verl.utils.debug.performance import GPUMemoryLogger
@GPUMemoryLogger(role="actor")
def update_actor(self, batch):
# do something
return
```
"""
def __init__(self, role: str, logger: logging.Logger = None, level=logging.DEBUG, log_only_rank_0: bool = True):
if dist.is_initialized() and dist.get_world_size() > 1:
rank = dist.get_rank()
else:
rank = 0
super().__init__(role, logger, level, rank, log_only_rank_0)
def __call__(self, decorated_function: callable):
def f(*args, **kwargs):
return self.log(decorated_function, *args, **kwargs)
return f
def log(self, func, *args, **kwargs):
name = func.__name__
mem_allocated, mem_reserved, mem_used, mem_total = _get_current_mem_info()
message = f"Before {name}, memory allocated (GB): {mem_allocated}, memory reserved (GB): {mem_reserved}, device memory used/total (GB): {mem_used}/{mem_total}"
self.logging_function(message)
output = func(*args, **kwargs)
mem_allocated, mem_reserved, mem_used, mem_total = _get_current_mem_info()
message = f"After {name}, memory allocated (GB): {mem_allocated}, memory reserved (GB): {mem_reserved}, device memory used/total (GB): {mem_used}/{mem_total}"
self.logging_function(message)
return output
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