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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
from deepspeed import comm as dist
import cupy
import numpy as np
from deepspeed.runtime.compression.cupy import CupyBackend
from deepspeed.utils.torch import required_torch_version
from deepspeed.accelerator import get_accelerator
class NcclBackend(object):
def __init__(self, mpu=None):
if mpu is None:
self.world_group = dist.new_group(ranks=range(dist.get_world_size()))
else:
self.mpu = mpu
self.world_group = self.mpu.get_data_parallel_group()
self.rank = dist.get_rank(group=self.world_group)
self.size = dist.get_world_size(group=self.world_group)
self.compression_backend = CupyBackend()
self.bool_not_supported = required_torch_version(min_version=1.10)
def my_igather(self, rank, size, group, sendbuf, recvbuf, root):
req = []
if rank == root:
for idx in range(size):
if idx != rank:
req.append(dist.irecv(recvbuf[idx], src=idx, group=group))
else:
recvbuf[rank] = sendbuf
else:
req.append(dist.isend(sendbuf, group=group, dst=root))
return req
def my_gather(self, rank, size, group, sendbuf, recvbuf, root):
if rank == root:
for idx in range(size):
if idx != rank:
dist.recv(recvbuf[idx], src=idx, group=group)
else:
recvbuf[rank] = sendbuf
else:
dist.send(sendbuf, group=group, dst=root)
def compressed_allreduce(self, buffer_m: torch.tensor, worker_error, server_error, local_rank):
# all_start_time = time.time()
original_shape = buffer_m.size()
if len(original_shape) > 1:
buffer_m = torch.flatten(buffer_m)
original_size = buffer_m.numel()
worker_error_size = worker_error.numel()
cupy.cuda.Device(local_rank).use()
if original_size != worker_error_size:
empty_tensor = torch.zeros(worker_error_size - original_size, device=buffer_m.device)
buffer_m = torch.cat([buffer_m, empty_tensor])
buffer_m.add_(worker_error)
worker_scale = torch.linalg.norm(buffer_m) / np.sqrt(buffer_m.numel())
worker_error.set_(buffer_m - worker_scale * buffer_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
if self.bool_not_supported:
cupy_sign_list_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(buffer_m.sign_().add_(1).bool().to(dtype=torch.uint8)), self.size)
else:
cupy_sign_list_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(buffer_m.sign_().add_(1).bool()), self.size)
cupy_worker_scale = self.compression_backend.torch2cupy(worker_scale)
cupy_recvbuf_sign = cupy.zeros([self.size, cupy_sign_list_packed[self.rank].size],
dtype=cupy_sign_list_packed[0].dtype)
# cupy_recvbuf_scale = cupy.zeros([self.size, 1], dtype=cupy_worker_scale.dtype)
sign_list_packed = [
self.compression_backend.cupy2torch(cupy_sign_list_packed[idx]) for idx in range(self.size)
]
# worker_scale = self.compression_backend.cupy2torch(cupy_worker_scale)
recvbuf_sign = self.compression_backend.cupy2torch(cupy_recvbuf_sign)
#recvbuf_scale = self.compression_backend.cupy2torch(cupy_recvbuf_scale)
recvbuf_scale = [
torch.zeros(1, dtype=worker_scale.dtype, device=torch.device(get_accelerator().device_name(local_rank)))
for i in range(self.size)
]
# communication phase 1
# gather_start = time.time()
# Alltoall for sign
dist.all_to_all_single(recvbuf_sign, torch.stack(sign_list_packed), group=self.world_group)
# Allgather for scale
dist.all_gather(recvbuf_scale, worker_scale, group=self.world_group)
# gather_end = time.time()
# cupy_sign_list_packed, sign_list_packed, cupy_worker_scale, worker_scale = None, None, None, None
cupy_sign_list_packed = None
cupy_recvbuf_sign = self.compression_backend.torch2cupy(recvbuf_sign)
#cupy_recvbuf_scale = self.compression_backend.torch2cupy(torch.stack(recvbuf_scale))
compensated_server_m = self.compression_backend.cupy2torch(
(cupy.unpackbits(cupy_recvbuf_sign.flatten())).reshape(self.size, -1)).float().add_(-0.5).mul_(2.0).mul_(
torch.stack(recvbuf_scale).mul_(1 / self.size)).sum(0)
compensated_server_m.add_(server_error)
server_scale = torch.linalg.norm(compensated_server_m) / np.sqrt(compensated_server_m.numel())
server_error.set_(compensated_server_m -
server_scale * compensated_server_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
# cupy_server_scale = self.compression_backend.torch2cupy(server_scale)
if self.bool_not_supported:
cupy_server_sign_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(compensated_server_m.sign_().add_(1).bool().to(dtype=torch.uint8)),
1)
else:
cupy_server_sign_packed = self.compression_backend.compress_by_chunk(
self.compression_backend.torch2cupy(compensated_server_m.sign_().add_(1).bool()), 1)
compensated_server_m = None
cupy_recvbuf_sign_server = cupy.zeros([self.size, cupy_server_sign_packed[0].size],
dtype=cupy_recvbuf_sign.dtype)
# cupy_recvbuf_sign, recvbuf_sign = None, None
cupy_recvbuf_sign = None
server_sign_packed = [self.compression_backend.cupy2torch(cupy_server_sign_packed[0])]
recvbuf_sign_server = [
self.compression_backend.cupy2torch(cupy_recvbuf_sign_server[idx]) for idx in range(self.size)
]
# server_scale = self.compression_backend.cupy2torch(cupy_server_scale)
cupy_recvbuf_scale_server = cupy.zeros([self.size, 1], dtype=cupy_worker_scale.dtype)
# cupy_recvbuf_scale, recvbuf_scale = None, None
recvbuf_scale_server = [
self.compression_backend.cupy2torch(cupy_recvbuf_scale_server[idx]) for idx in range(self.size)
]
# Communication Phase 2
dist.all_gather(recvbuf_sign_server, server_sign_packed[0], group=self.world_group)
dist.all_gather(recvbuf_scale_server, server_scale, group=self.world_group)
cupy_server_sign_packed = None
# need to convert from a tensor list to a single tensor
# dist.all_gather only provides a tensor list as the recv/output buffer
recvbuf_sign_server = torch.stack(recvbuf_sign_server)
cupy_recvbuf_sign_server = self.compression_backend.torch2cupy(recvbuf_sign_server)
buffer_m.data.copy_(
self.compression_backend.cupy2torch((cupy.unpackbits(cupy_recvbuf_sign_server.flatten())).reshape(
self.size, -1)).float().add_(-0.5).mul_(2.0).mul_(
self.compression_backend.cupy2torch(cupy_recvbuf_scale_server)).flatten().data)
if original_size != worker_error_size:
buffer_m = buffer_m[0:original_size]
if len(original_shape) > 1:
buffer_m = buffer_m.reshape(original_shape)
return buffer_m
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