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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import torch
# A dictionary of all the memory buffers allocated.
_MEM_BUFFS = dict()
def allocate_mem_buff(name, numel, dtype, track_usage):
"""Allocate a memory buffer."""
assert name not in _MEM_BUFFS, \
'memory buffer {} already allocated.'.format(name)
_MEM_BUFFS[name] = MemoryBuffer(name, numel, dtype, track_usage)
return _MEM_BUFFS[name]
def get_mem_buff(name):
"""Get the memory buffer."""
return _MEM_BUFFS[name]
class MemoryBuffer:
"""Contiguous memory buffer.
Allocate a contiguous memory of type `dtype` and size `numel`. It is
used to reduce memory fragmentation.
Usage: After the allocation, the `_start` index is set tot the first
index of the memory. A memory chunk starting from `_start` index
can be `allocated` for an input tensor, with the elements of the
tensor being coppied. The buffer can be reused by resetting the
`_start` index.
"""
def __init__(self, name, numel, dtype, track_usage):
if torch.distributed.get_rank() == 0:
element_size = torch.tensor([], dtype=dtype).element_size()
print('> building the {} memory buffer with {} num elements '
'and {} dtype ({:.1f} MB)...'.format(
name, numel, dtype, numel*element_size/1024/1024),
flush=True)
self.name = name
self.numel = numel
self.dtype = dtype
self.data = torch.empty(self.numel,
dtype=self.dtype,
device=torch.cuda.current_device(),
requires_grad=False)
# Index tracking the start of the free memory.
self._start = 0
# Values used for tracking usage.
self.track_usage = track_usage
if self.track_usage:
self.in_use_value = 0.0
self.total_value = 0.0
def reset(self):
"""Reset the buffer start index to the beginning of the buffer."""
self._start = 0
def is_in_use(self):
"""Whether the current buffer hold on to any memory."""
return self._start > 0
def numel_in_use(self):
"""Return number of elements in use."""
return self._start
def add(self, tensor):
"""Allocate a chunk of memory from the buffer to tensor and copy
the values."""
assert tensor.dtype == self.dtype, \
'Input tensor type {} different from buffer type {}'.format(
tensor.dtype, self.dtype)
# Number of elements of the input tensor.
tensor_numel = torch.numel(tensor)
new_start = self._start + tensor_numel
assert new_start <= self.numel, \
'Not enough memory left in the buffer ({} > {})'.format(
tensor_numel, self.numel - self._start)
# New tensor is a view into the memory.
new_tensor = self.data[self._start:new_start]
self._start = new_start
new_tensor = new_tensor.view(tensor.shape)
new_tensor.copy_(tensor)
# Return a pointer to the new tensor.
return new_tensor
def get_data(self):
"""Return the data currently in use."""
if self.track_usage:
self.in_use_value += float(self._start)
self.total_value += float(self.numel)
return self.data[:self._start]
def print_average_usage(self):
"""Print memory usage average over time. We would like this value
to be as high as possible."""
assert self.track_usage, 'You need to enable track usage.'
if torch.distributed.get_rank() == 0:
print(' > usage of {} memory buffer: {:.2f} %'.format(
self.name, self.in_use_value * 100.0 / self.total_value),
flush=True)
class RingMemBuffer:
"""A ring of memory buffers."""
def __init__(self, name, num_buffers, numel, dtype, track_usage):
self.num_buffers = num_buffers
self.buffers = [
allocate_mem_buff(name+' {}'.format(i), numel, dtype, track_usage)
for i in range(num_buffers)]
self._index = -1
def get_next_buffer(self):
self._index += 1
self._index = self._index % self.num_buffers
buff = self.buffers[self._index]
assert not buff.is_in_use(), 'buffer is already in use.'
return buff