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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
import cupy
import time
import numpy as np
from mpi4py import MPI
from deepspeed.runtime.compression.cupy import CupyBackend
class MpiBackend(object):
def __init__(self, cuda_aware):
self.comm = MPI.COMM_WORLD
self.rank = self.comm.Get_rank()
self.size = self.comm.Get_size()
self.cuda_aware = cuda_aware
self.compression_backend = CupyBackend()
def my_igather(self, rank, size, comm, sendbuf, recbuf, root):
req = []
if rank == root:
for idx in range(size):
if idx != rank:
req.append(comm.Irecv(recbuf[idx], source=idx))
else:
recbuf[rank] = sendbuf
else:
req.append(comm.Isend(sendbuf, dest=root))
return req
def gather_cuda(self, rank, world_size, comm, cupy_sign_list_packed, cupy_recvbuf_sign, cupy_worker_scale,
cupy_recvbuf_scale):
# We do in-place operations on cupy buffers so we do not return any buffers
requests = []
for idx in range(world_size):
req_sign = self.my_igather(rank, world_size, comm, cupy_sign_list_packed[idx], cupy_recvbuf_sign, root=idx)
requests += req_sign
for idx in range(world_size):
req_scale = self.my_igather(rank, world_size, comm, cupy_worker_scale, cupy_recvbuf_scale, root=idx)
requests += req_scale
MPI.Request.Waitall(requests)
def gather_host(self, rank, world_size, comm, cupy_sign_list_packed, cupy_recvbuf_sign, cupy_worker_scale,
cupy_recvbuf_scale):
# In-place operations are not possible for newly created cupy arrays
# so we need to return the new buffers
numpy_recvbuf_sign = np.zeros([world_size, cupy_sign_list_packed[rank].size],
dtype=cupy_sign_list_packed[0].dtype)
numpy_recvbuf_scale = np.zeros([world_size, 1], dtype=cupy_worker_scale.dtype)
# 1. convert from cupy to numpy
numpy_sign_list_packed = cupy_sign_list_packed
for idx in range(world_size):
numpy_sign_list_packed[idx] = cupy.asnumpy(cupy_sign_list_packed[idx])
numpy_worker_scale = cupy.asnumpy(cupy_worker_scale)
numpy_recvbuf_scale = cupy.asnumpy(cupy_recvbuf_scale)
cupy.cuda.get_current_stream().synchronize()
# 2. use numpy buffers for communication
requests = []
for idx in range(world_size):
req_sign = self.my_igather(rank,
world_size,
comm,
numpy_sign_list_packed[idx],
numpy_recvbuf_sign,
root=idx)
requests += req_sign
for idx in range(world_size):
req_scale = self.my_igather(rank, world_size, comm, numpy_worker_scale, numpy_recvbuf_scale, root=idx)
requests += req_scale
MPI.Request.Waitall(requests)
# 3. Convert back from numpy to cupy
cupy_recvbuf_sign = cupy.asarray(numpy_recvbuf_sign)
for idx in range(world_size):
cupy_sign_list_packed[idx] = cupy.asarray(numpy_sign_list_packed[idx])
cupy_worker_scale = cupy.asarray(numpy_worker_scale)
cupy_recvbuf_scale = cupy.asarray(numpy_recvbuf_scale)
cupy.cuda.get_current_stream().synchronize()
return cupy_sign_list_packed, cupy_recvbuf_sign, cupy_worker_scale, cupy_recvbuf_scale
def allgather_cuda(self, comm, cupy_server_sign_packed, cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server):
comm.Allgather(cupy_server_sign_packed, cupy_recvbuf_sign_server)
comm.Allgather(cupy_server_scale, cupy_recvbuf_scale_server)
def allgather_host(self, comm, cupy_server_sign_packed, cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server):
# 1. Convert cupy to numpy
numpy_recvbuf_sign_server = np.zeros([comm.Get_size(), cupy_server_sign_packed.size],
dtype=cupy_server_sign_packed.dtype)
numpy_recvbuf_scale_server = np.zeros([comm.Get_size(), 1], dtype=cupy_server_scale.dtype)
numpy_server_sign_packed = cupy.asnumpy(cupy_server_sign_packed)
numpy_recvbuf_sign_server = cupy.asnumpy(cupy_recvbuf_sign_server)
numpy_server_scale = cupy.asnumpy(cupy_server_scale)
numpy_recvbuf_scale_server = cupy.asnumpy(cupy_recvbuf_scale_server)
cupy.cuda.get_current_stream().synchronize()
# 2. Communicate numpy buffers
comm.Allgather(numpy_server_sign_packed, numpy_recvbuf_sign_server)
comm.Allgather(numpy_server_scale, numpy_recvbuf_scale_server)
comm.Barrier()
# 3. Convert numpy back to cupy
cupy_server_sign_packed = cupy.asarray(numpy_server_sign_packed)
cupy_recvbuf_sign_server = cupy.asarray(numpy_recvbuf_sign_server)
cupy_server_scale = cupy.asarray(numpy_server_scale)
cupy_recvbuf_scale_server = cupy.asarray(numpy_recvbuf_scale_server)
cupy.cuda.get_current_stream().synchronize()
return cupy_server_sign_packed, cupy_recvbuf_sign_server, cupy_server_scale, cupy_recvbuf_scale_server
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(torch.numel(buffer_m))
worker_error.set_(buffer_m - worker_scale * buffer_m.sign().add_(1).bool().float().add_(-0.5).mul_(2.0))
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)
# Communication Phase 1
gather_start = time.time()
if self.cuda_aware:
self.gather_cuda(self.rank, self.size, self.comm, cupy_sign_list_packed, cupy_recvbuf_sign,
cupy_worker_scale, cupy_recvbuf_scale)
else:
_, cupy_recvbuf_sign, _, cupy_recvbuf_scale = self.gather_host(self.rank, self.size, self.comm,
cupy_sign_list_packed, cupy_recvbuf_sign,
cupy_worker_scale, cupy_recvbuf_scale)
gather_end = time.time()
# cupy_sign_list_packed, cupy_worker_scale, worker_scale = None, None, None
cupy_sign_list_packed = None
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_(
self.compression_backend.cupy2torch(cupy_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)
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_scale_server = cupy.zeros([self.size, 1], dtype=cupy_recvbuf_scale.dtype)
# cupy_recvbuf_sign, cupy_recvbuf_scale = None, None
cupy_recvbuf_sign = None
# Communication Phase 2
if self.cuda_aware:
self.allgather_cuda(self.comm, cupy_server_sign_packed[0], cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server)
else:
_, cupy_recvbuf_sign_server, _, cupy_recvbuf_scale_server = self.allgather_host(
self.comm, cupy_server_sign_packed[0], cupy_recvbuf_sign_server, cupy_server_scale,
cupy_recvbuf_scale_server)
# cupy_server_sign_packed, cupy_server_scale, server_scale = None, None, None
cupy_server_sign_packed = None
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)
# cupy_recvbuf_sign_server, cupy_recvbuf_scale_server = None, None
return buffer_m
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