File size: 10,325 Bytes
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Buffer manager for efficient GPU memory management.
This module provides a buffer pool manager to avoid repeated GPU memory allocations
during distributed communication operations.
"""
import torch
from typing import Dict, List, Tuple, Optional
import threading
import logging
from .data_containers import CommunicationConfig
BufferKey = Tuple[Tuple[int, ...], torch.dtype]
class BufferManager:
"""
Manages GPU buffer pools to avoid repeated allocations.
This class maintains pools of pre-allocated GPU tensors that can be reused
across communication operations, reducing memory allocation overhead.
"""
def __init__(self, device: torch.device, config: Optional[CommunicationConfig] = None):
"""
Initialize the buffer manager.
Args:
device: GPU device for buffer allocation
config: Communication configuration
"""
self.device = device
self.config = config or CommunicationConfig()
# Buffer pools: {(shape, dtype): [tensor1, tensor2, ...]}
self.free_buffers: Dict[BufferKey, List[torch.Tensor]] = {}
self.free_buffers_origin: Dict[BufferKey, List[torch.Tensor]] = {}
self.free_buffers_kv: Dict[BufferKey, List[torch.Tensor]] = {}
self.free_buffers_misc: Dict[BufferKey, List[torch.Tensor]] = {}
# Thread safety
self._lock = threading.Lock()
# Statistics
self.allocation_count = 0
self.reuse_count = 0
self.total_allocated_memory = 0
# Setup logging
self.logger = logging.getLogger(f"BufferManager_{device}")
self.logger.propagate = False
if not self.logger.handlers:
handler = logging.StreamHandler()
# handler.setLevel(logging.DEBUG)
formatter = logging.Formatter(
f'[BufferManager {device}] %(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
handler.setFormatter(formatter)
self.logger.addHandler(handler)
# self.logger.setLevel(logging.DEBUG)
def get_buffer(self, shape: Tuple[int, ...], dtype: torch.dtype,
buffer_type: str = "latent") -> torch.Tensor:
"""
Get or allocate a buffer with the specified shape and dtype.
Args:
shape: Tensor shape
dtype: Tensor data type
buffer_type: Type of buffer ("latent", "origin", "kv")
Returns:
Tensor buffer
"""
with self._lock:
# Select the appropriate buffer pool
if buffer_type == "latent":
buffer_pool = self.free_buffers
elif buffer_type == "origin":
buffer_pool = self.free_buffers_origin
elif buffer_type == "kv":
buffer_pool = self.free_buffers_kv
elif buffer_type == "misc":
buffer_pool = self.free_buffers_misc
else:
raise ValueError(f"Unknown buffer type: {buffer_type}")
# Try to reuse existing buffer
key = (tuple(shape), dtype)
if self.config.enable_buffer_reuse and key in buffer_pool and len(buffer_pool[key]) > 0:
buffer = buffer_pool[key].pop()
self.reuse_count += 1
self.logger.debug(f"Reused buffer of shape {shape}, dtype {dtype}, type {buffer_type}")
return buffer
# Allocate new buffer
buffer = torch.empty(shape, dtype=dtype, device=self.device)
self.allocation_count += 1
self.total_allocated_memory += buffer.numel() * buffer.element_size()
self.logger.debug(f"Allocated new buffer of shape {shape}, dtype {dtype}, type {buffer_type}")
return buffer
def return_buffer(self, tensor: torch.Tensor, buffer_type: str = "latent") -> None:
"""
Return a buffer to the pool for reuse.
Args:
tensor: Tensor to return
buffer_type: Type of buffer ("latent", "origin", "kv")
"""
if not self.config.enable_buffer_reuse:
return
with self._lock:
# Select the appropriate buffer pool
if buffer_type == "latent":
buffer_pool = self.free_buffers
elif buffer_type == "origin":
buffer_pool = self.free_buffers_origin
elif buffer_type == "kv":
buffer_pool = self.free_buffers_kv
elif buffer_type == "misc":
buffer_pool = self.free_buffers_misc
else:
raise ValueError(f"Unknown buffer type: {buffer_type}")
key = (tuple(tensor.shape), tensor.dtype)
# Initialize pool for this shape if it doesn't exist
if key not in buffer_pool:
buffer_pool[key] = []
# Add buffer to pool if not at capacity
if len(buffer_pool[key]) < self.config.buffer_pool_size:
buffer_pool[key].append(tensor)
self.logger.debug(
f"Returned buffer of shape {tuple(tensor.shape)}, dtype {tensor.dtype}, type {buffer_type}"
)
else:
self.logger.debug(
f"Buffer pool full for shape {tuple(tensor.shape)}, dtype {tensor.dtype}, type {buffer_type}, discarding"
)
def clear_buffers(self, buffer_type: Optional[str] = None) -> None:
"""
Clear buffer pools to free memory.
Args:
buffer_type: Specific buffer type to clear, or None to clear all
"""
with self._lock:
if buffer_type is None:
# Clear all buffer pools
self.free_buffers.clear()
self.free_buffers_origin.clear()
self.free_buffers_kv.clear()
self.free_buffers_misc.clear()
self.logger.info("Cleared all buffer pools")
else:
# Clear specific buffer pool
if buffer_type == "latent":
self.free_buffers.clear()
elif buffer_type == "origin":
self.free_buffers_origin.clear()
elif buffer_type == "kv":
self.free_buffers_kv.clear()
elif buffer_type == "misc":
self.free_buffers_misc.clear()
else:
raise ValueError(f"Unknown buffer type: {buffer_type}")
self.logger.info(f"Cleared {buffer_type} buffer pool")
def get_statistics(self) -> Dict[str, any]:
"""
Get buffer manager statistics.
Returns:
Dictionary containing statistics
"""
with self._lock:
total_free_buffers = sum(len(pool) for pool in self.free_buffers.values())
total_free_buffers_origin = sum(len(pool) for pool in self.free_buffers_origin.values())
total_free_buffers_kv = sum(len(pool) for pool in self.free_buffers_kv.values())
total_free_buffers_misc = sum(len(pool) for pool in self.free_buffers_misc.values())
return {
"allocation_count": self.allocation_count,
"reuse_count": self.reuse_count,
"total_allocated_memory_bytes": self.total_allocated_memory,
"total_free_buffers": total_free_buffers,
"total_free_buffers_origin": total_free_buffers_origin,
"total_free_buffers_kv": total_free_buffers_kv,
"total_free_buffers_misc": total_free_buffers_misc,
"reuse_rate": self.reuse_count / max(1, self.allocation_count),
"buffer_pool_size": self.config.buffer_pool_size,
"enable_buffer_reuse": self.config.enable_buffer_reuse
}
def print_statistics(self) -> None:
"""Print buffer manager statistics."""
stats = self.get_statistics()
self.logger.info("Buffer Manager Statistics:")
for key, value in stats.items():
self.logger.info(f" {key}: {value}")
def preallocate_buffers(self, common_shapes: List[Tuple[Tuple[int, ...], torch.dtype, str]],
count_per_shape: int = 5) -> None:
"""
Preallocate buffers for common shapes to reduce allocation overhead.
Args:
common_shapes: List of (shape, dtype, buffer_type) tuples
count_per_shape: Number of buffers to preallocate per shape
"""
with self._lock:
for shape, dtype, buffer_type in common_shapes:
for _ in range(count_per_shape):
buffer = torch.empty(shape, dtype=dtype, device=self.device)
# Select the appropriate buffer pool
if buffer_type == "latent":
buffer_pool = self.free_buffers
elif buffer_type == "origin":
buffer_pool = self.free_buffers_origin
elif buffer_type == "kv":
buffer_pool = self.free_buffers_kv
elif buffer_type == "misc":
buffer_pool = self.free_buffers_misc
else:
raise ValueError(f"Unknown buffer type: {buffer_type}")
# Initialize pool for this shape if it doesn't exist
key = (tuple(shape), dtype)
if key not in buffer_pool:
buffer_pool[key] = []
buffer_pool[key].append(buffer)
self.allocation_count += 1
self.total_allocated_memory += buffer.numel() * buffer.element_size()
self.logger.info(f"Preallocated {len(common_shapes) * count_per_shape} buffers")
def __del__(self):
"""Cleanup when the buffer manager is destroyed."""
try:
self.clear_buffers()
except Exception:
pass
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