def copy_to_local(self):
assert self.comm != None
if self.etype == 'server':
# copy weight from g_param to g_param_ga
for g_param, g_param_ga in \
zip(self.g_param_list, self.g_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(g_param.container.value)
self.drv.memcpy_dtod(g_param_ga.ptr,
param_ga.ptr,
param_ga.dtype.itemsize *
param_ga.size)
# Send(self, buf, int dest=0, int tag=0)
mpitp = dtype_to_mpi(self.g_param_ga_list[0].dtype)
for g_param_ga in self.g_param_ga_list:
self.comm.Send(buf = [bufint(g_param_ga), mpitp], dest = self.dest)
elif self.etype == 'worker':
mpitp = dtype_to_mpi(self.w_param_ga_list[0].dtype)
for w_param_ga in self.w_param_ga_list:
self.comm.Recv(buf = [bufint(w_param_ga), mpitp], source = self.dest)
# copy weight from w_param_ga to w_param
for w_param_ga, w_param in \
zip(self.w_param_ga_list, self.w_param_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(w_param.container.value)
self.drv.memcpy_dtod(w_param_ga.ptr,
param_ga.ptr,
param_ga.dtype.itemsize *
param_ga.size)
self.comm = None
def copy_to_local(self):
assert self.comm != None
if self.etype == 'server':
# copy weight from g_param to g_param_ga
for g_param, g_param_ga in \
zip(self.g_param_list, self.g_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(g_param.container.value)
self.drv.memcpy_dtod(g_param_ga.ptr, param_ga.ptr,
param_ga.dtype.itemsize * param_ga.size)
# Send(self, buf, int dest=0, int tag=0)
mpitp = dtype_to_mpi(self.g_param_ga_list[0].dtype)
for g_param_ga in self.g_param_ga_list:
self.comm.Send(buf=[bufint(g_param_ga), mpitp], dest=self.dest)
elif self.etype == 'worker':
mpitp = dtype_to_mpi(self.w_param_ga_list[0].dtype)
for w_param_ga in self.w_param_ga_list:
self.comm.Recv(buf=[bufint(w_param_ga), mpitp],
source=self.dest)
# copy weight from w_param_ga to w_param
for w_param_ga, w_param in \
zip(self.w_param_ga_list, self.w_param_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(w_param.container.value)
self.drv.memcpy_dtod(w_param_ga.ptr, param_ga.ptr,
param_ga.dtype.itemsize * param_ga.size)
self.comm = None
def exchange(self):
# server and worker send param to each other
# this function needs the worker to send an 'exchange' message
# to the server through REQ-REP socket first.
assert self.comm != None
if self.etype == 'server':
# copy weight from g_param to g_param_ga
for g_param, g_param_ga in \
zip(self.g_param_list, self.g_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(g_param.container.value)
self.drv.memcpy_dtod(g_param_ga.ptr,
param_ga.ptr,
param_ga.dtype.itemsize *
param_ga.size)
# Sendrecv(self, sendbuf, int dest=0, int sendtag=0, recvbuf=None, int source=0, int recvtag=0, Status status=None)
for g_param_ga, w_param_ga in zip(self.g_param_ga_list, self.w_param_ga_list):
self.comm.Sendrecv(sendbuf = [bufint(g_param_ga), MPI.FLOAT], dest = self.dest,
recvbuf = [bufint(w_param_ga), MPI.FLOAT], source = self.dest,
)
# copy weight from w_param_ga to w_param
for w_param, w_param_ga in \
zip(self.w_param_list, self.w_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(w_param.container.value)
self.drv.memcpy_dtod(param_ga.ptr,
w_param_ga.ptr,
w_param_ga.dtype.itemsize *
w_param_ga.size)
elif self.etype == 'worker':
# copy weight from w_param to w_param_ga
for w_param, w_param_ga in \
zip(self.w_param_list, self.w_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(w_param.container.value)
self.drv.memcpy_dtod(w_param_ga.ptr,
param_ga.ptr,
param_ga.dtype.itemsize *
param_ga.size)
# Sendrecv(self, sendbuf, int dest=0, int sendtag=0, recvbuf=None, int source=0, int recvtag=0, Status status=None)
for w_param_ga, g_param_ga in zip(self.w_param_ga_list, self.g_param_ga_list):
self.comm.Sendrecv(sendbuf = [bufint(w_param_ga), MPI.FLOAT], dest = self.dest,
recvbuf = [bufint(g_param_ga), MPI.FLOAT], source = self.dest,
)
# copy weight from w_param_ga to w_param
for g_param, g_param_ga in \
zip(self.g_param_list, self.g_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(g_param.container.value)
self.drv.memcpy_dtod(param_ga.ptr,
g_param_ga.ptr,
g_param_ga.dtype.itemsize *
g_param_ga.size)
self.update_func()
self.comm = None
def exchange(self):
mpidtype = self.mpidtype
if self.avg: self.avg_func()
# copy weight from param_ga to param_update_ga
for param, param_update_ga in \
zip(self.source_param_list, self.param_update_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(param.container.value)
self.drv.memcpy_dtod(param_update_ga.ptr,
param_ga.ptr,
param_ga.dtype.itemsize *
param_ga.size)
self.ctx.synchronize()
if (self.size == 2):
for param_update_ga,d_param_tmp,numElements,grid_size in \
zip(self.param_update_ga_list, \
self.d_param_32_tmp_list, \
self.numElements_list, \
self.grid_size_list):
'''
Summing and Sharing GPU Data
Sendrecv Pairing: 0 and 1
'''
if (self.rank == 1):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=0, recvbuf=[bufint(d_param_tmp), mpidtype], source=0)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
#should synchronize context after a kernel call
# to make sure the kernel has been finished
elif (self.rank == 0):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=1, recvbuf=[bufint(d_param_tmp), mpidtype], source=1)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
#should synchronize context after a kernel call
# to make sure the kernel has been finished
self.comm.Barrier()
elif (self.size == 4):
for param_update_ga,d_param_tmp,numElements,grid_size in \
zip(self.param_update_ga_list, \
self.d_param_32_tmp_list, \
self.numElements_list, \
self.grid_size_list):
'''
Summing GPU Data
Step 1
Source GPU -> Destination GPU
1 -> 0, 3 -> 2
'''
if (self.rank %2 == 1):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank-1)
elif (self.rank %2 == 0):
self.comm.Recv([bufint(d_param_tmp), mpidtype], source=self.rank+1)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Step 2
Sendrecv Pairing: 0 and 2
'''
if (self.rank == 2):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=0, recvbuf=[bufint(d_param_tmp), mpidtype], source=0)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
elif (self.rank == 0):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=2, recvbuf=[bufint(d_param_tmp), mpidtype], source=2)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Broadcasting Result
Source GPU -> Destination GPU
0 -> 1, 2 -> 3
'''
if (self.rank %2 == 0):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank+1)
elif (self.rank %2 == 1):
self.comm.Recv([bufint(param_update_ga), mpidtype], source=self.rank-1)
self.comm.Barrier()
elif (self.size == 8):
# Use this for parameter size < 16MB
# Use Fei's implementation for parameter size > 16MB
for param_update_ga,d_param_tmp,numElements,grid_size in \
zip(self.param_update_ga_list, \
self.d_param_32_tmp_list, \
self.numElements_list, \
self.grid_size_list):
'''
Summing GPU Data
Step 1
Source GPU -> Destination GPU
1 -> 0, 3 -> 2, 5 -> 4, 7 -> 6
'''
if (self.rank %2 == 1):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank-1)
elif (self.rank %2 == 0):
self.comm.Recv([bufint(d_param_tmp), mpidtype], source=self.rank+1)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Step 2
Source GPU -> Destination GPU
0 -> 2, 4 -> 6
'''
if (self.rank %4 == 0):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank+2)
elif (self.rank == 2) or (self.rank == 6):
self.comm.Recv([bufint(d_param_tmp), mpidtype], source=self.rank-2)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Step 3
Sendrecv Pairing: 2 and 6
'''
if (self.rank == 2):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=6, recvbuf=[bufint(d_param_tmp), mpidtype], source=6)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
elif (self.rank == 6):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=2, recvbuf=[bufint(d_param_tmp), mpidtype], source=2)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Broadcasting Results
Step 1
Source GPU -> Destination GPU
2 -> 0, 6 -> 4
'''
if (self.rank == 2) or (self.rank == 6):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank-2)
elif (self.rank %4 == 0):
self.comm.Recv([bufint(param_update_ga), mpidtype], source=self.rank+2)
'''
Step 2
Source GPU -> Destination GPU
0 -> 1, 2 -> 3, 4 -> 5, 6 -> 7
'''
if (self.rank %2 == 0):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank+1)
elif (self.rank %2 == 1):
self.comm.Recv([bufint(param_update_ga), mpidtype], source=self.rank-1)
self.comm.Barrier()
elif (self.size == 16):
for param_update_ga,d_param_tmp,numElements,grid_size in \
zip(self.param_update_ga_list, \
self.d_param_32_tmp_list, \
self.numElements_list, \
self.grid_size_list):
'''
Summing GPU Data
Step 1
Source GPU -> Destination GPU
1 -> 0, 3 -> 2, 5 -> 4, 7 -> 6, 9 -> 8, 11 -> 10, 13 -> 12, 15 -> 14
'''
if (self.rank %2 == 1):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank-1)
elif (self.rank %2 == 0):
self.comm.Recv([bufint(d_param_tmp), mpidtype], source=self.rank+1)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Step 2
Source GPU -> Destination GPU
0 -> 2, 4 -> 6, 8 -> 10, 12 -> 14
'''
if (self.rank %4 == 0):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank+2)
elif (self.rank == 2) or (self.rank == 6) or (self.rank == 10) or (self.rank == 14):
self.comm.Recv([bufint(d_param_tmp), mpidtype], source=self.rank-2)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Step 3
Source GPU -> Destination GPU
2 -> 6, 10 -> 14
'''
if (self.rank == 2) or (self.rank == 10):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank+4)
elif (self.rank == 6) or (self.rank == 14):
self.comm.Recv([bufint(d_param_tmp), mpidtype], source=self.rank-4)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Step 4
Sendrecv Pairing: 6 and 14
'''
if (self.rank == 6):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=14, recvbuf=[bufint(d_param_tmp), mpidtype], source=14)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
elif (self.rank == 14):
self.comm.Sendrecv([bufint(param_update_ga), mpidtype], \
dest=6, recvbuf=[bufint(d_param_tmp), mpidtype], source=6)
self.vecadd(param_update_ga, d_param_tmp, numElements, \
block=(256, 1, 1), grid=grid_size)
self.ctx.synchronize()
'''
Broadcasting Result
Step 1
Source GPU -> Destination GPU
6 -> 2, 14 -> 10
'''
if (self.rank == 6) or (self.rank == 14):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank-4)
elif (self.rank == 2) or (self.rank == 10):
self.comm.Recv([bufint(param_update_ga), mpidtype], source=self.rank+4)
'''
Step 2
Source GPU -> Destination GPU
2 -> 0, 6 -> 4, 10 -> 8, 14 -> 12
'''
if (self.rank == 2) or (self.rank == 6) or (self.rank == 10) or (self.rank == 14):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank-2)
elif (self.rank %4 == 0):
self.comm.Recv([bufint(param_update_ga), mpidtype], source=self.rank+2)
'''
Step 3
Source GPU -> Destination GPU
0 -> 1, 2 -> 3, 4 -> 5, 6 -> 7, 8 -> 9, 10 -> 11, 12 -> 13, 14 -> 15
'''
if (self.rank %2 == 0):
self.comm.Send([bufint(param_update_ga), mpidtype], dest=self.rank+1)
elif (self.rank %2 == 1):
self.comm.Recv([bufint(param_update_ga), mpidtype], source=self.rank-1)
self.comm.Barrier()
# copy weight from param_update_ga back to param_ga
for param, param_update_ga in \
zip(self.dest_param_list, self.param_update_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(param.container.value)
self.drv.memcpy_dtod(param_ga.ptr,
param_update_ga.ptr,
param_update_ga.dtype.itemsize *
param_ga.size)
self.ctx.synchronize()
def exchange(self):
mpidtype = self.mpidtype
# divding source param first before exchanging
if self.avg:
self.avg_func()
# copy weight from param_ga to param_update_ga
for param, param_update_ga in \
zip(self.source_param_list, self.param_update_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(param.container.value)
self.drv.memcpy_dtod(param_update_ga.ptr,
param_ga.ptr,
param_ga.dtype.itemsize *
param_ga.size)
self.ctx.synchronize()
# allreduce weight from param_update_ga to itself
wcount=0
for param_update_ga in self.param_update_ga_list:
self.float2half(param_update_ga, self.d_param_16_list[wcount], \
self.numElements_list[wcount], self.offset_list[wcount], \
block=(256,1,1),grid=self.grid_size_list[wcount])
self.ctx.synchronize()
self.comm.Alltoall(
[bufint(self.d_param_16_list[wcount]), mpidtype],\
[bufint(self.d_param_16_tmp_list[wcount]),mpidtype])
self.sumhalfs(self.d_param_16_tmp_list[wcount], \
self.d_param_16_sum_list[wcount], \
self.reduce_size_list[wcount],self.ranksize,\
self.reduce_size_list[wcount], \
block=(256,1,1),grid=self.grid_sum_size_list[wcount])
self.ctx.synchronize()
self.comm.Allgather(
[bufint(self.d_param_16_sum_list[wcount]),mpidtype],\
[bufint(self.d_param_16_update_list[wcount]),mpidtype]) # d_param_16_update_list redundant
self.half2float(self.d_param_16_update_list[wcount], param_update_ga, \
self.numElements_list[wcount],self.offset_list[wcount], \
block=(256,1,1),grid=self.grid_size_list[wcount]) # d_param_16_update_list redundant
self.ctx.synchronize()
wcount+=1
# copy weight from param_reduce_ga back to param_ga
for param, param_update_ga in \
zip(self.dest_param_list, self.param_update_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(param.container.value)
self.drv.memcpy_dtod(param_ga.ptr,
param_update_ga.ptr,
param_update_ga.dtype.itemsize *
param_ga.size)
self.ctx.synchronize()
def exchange(self):
mpidtype = self.mpidtype
# divding source param first before exchanging
if self.avg:
self.avg_func()
# copy weight from param_ga to param_update_ga
for param, param_update_ga in \
zip(self.source_param_list, self.param_update_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(param.container.value)
self.drv.memcpy_dtod(param_update_ga.ptr,
param_ga.ptr,
param_ga.dtype.itemsize *
param_ga.size)
self.ctx.synchronize()
# allreduce weight from param_update_ga to itself
wcount=0
for param_update_ga in self.param_update_ga_list:
self.comm.Alltoall(
[bufint(param_update_ga), mpidtype],
[bufint(self.d_param_32_tmp_list[wcount]),\
mpidtype])
# sumfloats(float* f1, float* f2, int numElements,int ranksize,int reducesize)
self.d_f32_sumfloats(self.d_param_32_tmp_list[wcount], \
self.d_param_32_sum_list[wcount],\
self.reduce_size_list[wcount],self.ranksize,\
self.reduce_size_list[wcount],\
block=(256,1,1),\
grid=self.grid_sum_size_list[wcount])
self.ctx.synchronize()
self.comm.Allgather(\
[bufint(self.d_param_32_sum_list[wcount]),mpidtype], \
[bufint(param_update_ga),mpidtype])
#param.container.value.release_buffer(param_buf)
wcount = wcount +1
# copy weight from param_reduce_ga back to param_ga
for param, param_update_ga in \
zip(self.dest_param_list, self.param_update_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(param.container.value)
self.drv.memcpy_dtod(param_ga.ptr,
param_update_ga.ptr,
param_update_ga.dtype.itemsize *
param_ga.size)
self.ctx.synchronize()
def exchange(self):
# server and worker send param to each other
# this function needs the worker to send an 'exchange' message
# to the server through REQ-REP socket first.
assert self.comm != None
if self.etype == 'server':
# copy weight from g_param to g_param_ga
for g_param, g_param_ga in \
zip(self.g_param_list, self.g_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(g_param.container.value)
self.drv.memcpy_dtod(g_param_ga.ptr, param_ga.ptr,
param_ga.dtype.itemsize * param_ga.size)
# Sendrecv(self, sendbuf, int dest=0, int sendtag=0, recvbuf=None, int source=0, int recvtag=0, Status status=None)
for g_param_ga, w_param_ga in zip(self.g_param_ga_list,
self.w_param_ga_list):
self.comm.Sendrecv(
sendbuf=[bufint(g_param_ga), MPI.FLOAT],
dest=self.dest,
recvbuf=[bufint(w_param_ga), MPI.FLOAT],
source=self.dest,
)
# copy weight from w_param_ga to w_param
for w_param, w_param_ga in \
zip(self.w_param_list, self.w_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(w_param.container.value)
self.drv.memcpy_dtod(
param_ga.ptr, w_param_ga.ptr,
w_param_ga.dtype.itemsize * w_param_ga.size)
elif self.etype == 'worker':
# copy weight from w_param to w_param_ga
for w_param, w_param_ga in \
zip(self.w_param_list, self.w_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(w_param.container.value)
self.drv.memcpy_dtod(w_param_ga.ptr, param_ga.ptr,
param_ga.dtype.itemsize * param_ga.size)
# Sendrecv(self, sendbuf, int dest=0, int sendtag=0, recvbuf=None, int source=0, int recvtag=0, Status status=None)
for w_param_ga, g_param_ga in zip(self.w_param_ga_list,
self.g_param_ga_list):
self.comm.Sendrecv(
sendbuf=[bufint(w_param_ga), MPI.FLOAT],
dest=self.dest,
recvbuf=[bufint(g_param_ga), MPI.FLOAT],
source=self.dest,
)
# copy weight from w_param_ga to w_param
for g_param, g_param_ga in \
zip(self.g_param_list, self.g_param_ga_list):
param_ga = \
theano.misc.pycuda_utils.to_gpuarray(g_param.container.value)
self.drv.memcpy_dtod(
param_ga.ptr, g_param_ga.ptr,
g_param_ga.dtype.itemsize * g_param_ga.size)
self.update_func()
self.comm = None
