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# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
"""
Functionality of swapping tensors to/from (NVMe) storage devices.
"""
import torch
from deepspeed.utils.logging import logger
from deepspeed.accelerator import get_accelerator
from deepspeed import comm as dist
MIN_AIO_BYTES = 1024**2
AIO_ALIGNED_BYTES = 1024
MIN_SWAPPABLE_BYTES = MIN_AIO_BYTES
def swap_in_tensors(swap_handle, tensor_buffers, swap_paths):
for buffer, path in zip(tensor_buffers, swap_paths):
assert (swap_handle.async_pread(buffer, path, 0) == 0)
def swap_out_tensors(swap_handle, tensor_buffers, swap_paths):
for buffer, path in zip(tensor_buffers, swap_paths):
assert (swap_handle.async_pwrite(buffer, path, 0) == 0)
def print_object(obj, name, exclude_list=[]):
logger.info('{}:'.format(name))
for arg in sorted(vars(obj)):
if not arg in exclude_list:
dots = '.' * (29 - len(arg))
logger.info(' {} {} {}'.format(arg, dots, getattr(obj, arg)))
class SwapBuffer(object):
def __init__(self, buffer):
self.buffer = buffer
self.reset()
def reset(self):
self.offset = 0
self.swap_tensors = {}
self.compute_tensors = {}
self.swap_paths = {}
self.num_elem = 0
def insert_tensor(self, tensor, swap_path, aligned_numel):
swap_tensor, compute_tensor = self.allocate_tensor(swap_path, tensor.numel(), aligned_numel)
compute_tensor.data.copy_(tensor.data)
return swap_tensor, compute_tensor
def allocate_tensor(self, swap_path, numel, aligned_numel):
assert self.has_space(aligned_numel)
assert not self.offset in self.swap_tensors
allocate_offset = self.offset
swap_tensor = self.buffer.narrow(0, allocate_offset, aligned_numel)
dest_tensor = swap_tensor.narrow(0, 0, numel)
self.swap_tensors[allocate_offset] = swap_tensor
self.compute_tensors[allocate_offset] = dest_tensor
self.swap_paths[allocate_offset] = swap_path
self.offset += aligned_numel
self.num_elem += numel
return self.swap_tensors[allocate_offset], self.compute_tensors[allocate_offset]
def has_space(self, numel):
return (self.offset + numel) <= self.buffer.numel()
def get_swap_tensors(self):
return [tensor for tensor in self.swap_tensors.values()]
def get_swap_paths(self):
return [path for path in self.swap_paths.values()]
def get_compute_tensors(self):
return [tensor for tensor in self.compute_tensors.values()]
def get_num_elem(self):
return self.num_elem
def get_swap_tensor(self, offset):
return self.swap_tensors.get(offset, None)
def get_compute_tensor(self, offset):
return self.compute_tensors.get(offset, None)
def get_swap_path(self, offset):
return self.swap_paths(offset, None)
class SwapBufferPool(object):
def __init__(self, buffers):
assert all([get_accelerator().is_pinned(buf) for buf in buffers])
self.buffers = [SwapBuffer(buf) for buf in buffers]
self.current_index = 0
def reset(self):
self.current_index = 0
for buffer in self.buffers:
buffer.reset()
def allocate_tensor(self, numel, swap_path, aligned_numel):
if self.has_space(aligned_numel):
swap_tensor, compute_tensor = self._get_current_buffer().allocate_tensor(swap_path, numel, aligned_numel)
return swap_tensor, compute_tensor
return None, None
def insert_tensor(self, tensor, swap_path, aligned_numel):
if self.has_space(aligned_numel):
swap_tensor, compute_tensor = self._get_current_buffer().insert_tensor(tensor, swap_path, aligned_numel)
return swap_tensor, compute_tensor
return None, None
def get_swap_tensors(self):
swap_tensors = []
for buffer in self._get_used_buffers():
swap_tensors += buffer.get_swap_tensors()
return swap_tensors
def get_swap_paths(self):
swap_paths = []
for buffer in self._get_used_buffers():
swap_paths += buffer.get_swap_paths()
return swap_paths
def get_compute_tensors(self):
compute_tensors = []
for buffer in self._get_used_buffers():
compute_tensors += buffer.get_compute_tensors()
return compute_tensors
def has_space(self, numel):
if self._get_current_buffer().has_space(numel):
return True
if self.current_index == len(self.buffers) - 1:
return False
self.current_index += 1
return self._get_current_buffer().has_space(numel)
def swap_out(self, aio_handle, async_op=False):
swap_tensors = self.get_swap_tensors()
swap_paths = self.get_swap_paths()
assert all([p is not None for p in swap_paths])
swap_out_tensors(aio_handle, swap_tensors, swap_paths)
if not async_op:
assert len(swap_tensors) == aio_handle.wait()
def swap_in(self, aio_handle, async_op=False):
swap_tensors = self.get_swap_tensors()
swap_paths = self.get_swap_paths()
assert all([p is not None for p in swap_paths])
swap_in_tensors(aio_handle, swap_tensors, swap_paths)
if not async_op:
assert len(swap_tensors) == aio_handle.wait()
def _get_current_buffer(self):
return self.buffers[self.current_index]
def _get_used_buffers(self):
return self.buffers[:self.current_index + 1]
class SwapBufferManager(object):
def __init__(self, num_elems, count, dtype):
self.num_elems = num_elems
self.count = count
self.dtype = dtype
self.all_buffers = [
get_accelerator().pin_memory(torch.zeros(num_elems, device='cpu', dtype=dtype), align_bytes=0)
for _ in range(count)
]
self.free_buffer_index = [i for i in range(count)]
self.used_buffer_index = {}
self.gigabytes = (self.all_buffers[0].element_size() * num_elems * count) / (1024**3)
if dist.get_rank() == 0:
exclude_list = ['all_buffers']
print_object(obj=self, name='SwapBufferManager', exclude_list=exclude_list)
def allocate(self, num_elems, count, dtype):
assert dtype == self.dtype
assert num_elems <= self.num_elems
if count > len(self.free_buffer_index):
return None
used_indices = self.free_buffer_index[-count:]
self.free_buffer_index = self.free_buffer_index[:-count]
buffers = []
for i in used_indices:
tmp_buffer = self.all_buffers[i].narrow(0, 0, num_elems)
buffers.append(tmp_buffer)
self.used_buffer_index[id(tmp_buffer)] = i
return buffers
def allocate_all(self, num_elems, dtype):
return self.allocate(num_elems=num_elems, count=len(self.free_buffer_index), dtype=dtype)
def free(self, buffers):
buffer_ids = []
for buf in buffers:
buffer_ids.append(id(buf))
assert all([b_id in self.used_buffer_index for b_id in buffer_ids])
for b_id in buffer_ids:
self.free_buffer_index.append(self.used_buffer_index[b_id])
del (self.used_buffer_index[b_id])
def get_sized_buffer(buffer, num_elems):
assert num_elems <= buffer.numel(), \
f'num_elems {num_elems} > buffer {buffer.numel()}'
return buffer.narrow(0, 0, num_elems) if num_elems < buffer.numel() else buffer
def get_sized_buffers(buffer_list, num_elems_list):
swap_buffers = [
get_sized_buffer(buffer, num_elems) \
for buffer, num_elems in zip(buffer_list, num_elems_list)
]
return swap_buffers
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