def test_2_002(argv):
nk = pynccl.Nccl()
nc = nk._nccl # cuNccl
api = nc._api # libnccl
nuid = pynccl.binding.ncclUniqueId()
print(nuid)
print(type(nuid.internal))
#nuid.internal = pynccl.binding.NcclUniqueId_data_t()
#nuid.internal = str(create_string_buffer('', 128))
##nuid.internal = create_string_buffer('', 128)
'''
intnl_str = create_string_buffer('', 128)
print(intnl_str)
for i in range(128):
intnl_str[i] = chr(0)
for i in range(12):
intnl_str[i] = 'a'
intnl_char_p = cast(intnl_str, c_char_p)
#intnl_char_p = cast(intnl_str, pynccl.binding.NcclUniqueId_data_t)
nuid.internal = intnl_char_p
'''
#for i in range(128):
# nuid.internal[i] = 44
# socket.AF_INET = 2 # 0x0002 ==> chr(0) + chr(2)
#nuid.internal = chr(0) + chr(2) + 'nccl-%d-%d' % (os.getpid(), 0) # TODO: global counter
intnl_buf = chr(0) + chr(2) + 'nccl-%d-%d' % (os.getpid(), 0)
intnl_buf += chr(0) * (pynccl.binding.NCCL_UNIQUE_ID_BYTES - len(intnl_buf))
nuid.internal = intnl_buf
print(nuid.internal)
print(len(nuid.internal))
#print(dir(nuid.internal))
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
r = api.ncclGetUniqueId(byref(nuid)) # TODO:
print('>>> ncclGetUniqueId ', r)
#print(nuid)
'''
print(id(nuid.internal))
print(type(nuid.internal))
#print(len(nuid.internal))
print(nuid.internal)
'''
comm = c_void_p(0)
nRanks = int(argv[1]) #2
myRank = int(argv[2]) #0
r = api.ncclCommInitRank(byref(comm), nRanks, nuid, myRank)
#x#r = api.ncclCommInitRank(byref(comm), nRanks, byref(nuid), myRank)
print('>>> ncclCommInitRank ', r)
def test_2_001():
nk = pynccl.Nccl()
nc = nk._nccl # cuNccl
api = nc._api # libnccl
comms_a = c_void_p * 4
int_a = c_int * 4
# <1>
comms = comms_a(0, 0, 0, 0)
comms_p = cast(comms, POINTER(c_void_p))
# <2>
#comms = c_void_p(0)
devs = int_a(0, 1, 2, 3)
# <3>
#devs_p = byref(devs)
# <4>
devs_p = cast(devs, POINTER(c_int))
# <1>
r = api.ncclCommInitAll(comms_p, 4, devs_p)
# <2>
#r = api.ncclCommInitAll(byref(comms), 4, devs_p)
print(r)
for i in range(4):
print(comms[i])
r = api.ncclCommDestroy(comms[i])
print(r)
def test_2_003():
nk = pynccl.Nccl()
nc = nk._nccl # cuNccl
api = nc._api # libnccl
# -------------------------------------
procs = []
# -------------------------------------
comms_a = c_void_p * 4
int_a = c_int * 4
# <1>
comms = comms_a(0, 0, 0, 0)
comms_p = cast(comms, POINTER(c_void_p))
# <2>
#comms = c_void_p(0)
devs = int_a(0, 1, 2, 3)
# <3>
#devs_p = byref(devs)
# <4>
devs_p = cast(devs, POINTER(c_int))
# <1>
r = api.ncclCommInitAll(comms_p, 4, devs_p)
# <2>
#r = api.ncclCommInitAll(byref(comms), 4, devs_p)
print('>>> ncclCommInitAll ', r)
#x#cuda.close()
# -------------------------------------
for i in range(4):
worker = mp.Process(
target=gpu_worker_proc,
args=(api, i, i, comms[i]))
worker.daemon = True
worker.start()
procs.append(worker)
for worker in procs:
worker.join()
# -------------------------------------
for i in range(4):
#print(comms[i])
r = api.ncclCommDestroy(comms[i])
print('>>> ncclCommDestroy ', r)
def gpu_worker_proc_41_2(api, kn, rank, gpu_i, q):
# NOTE: do this at first of all
cuda.select_device(gpu_i)
nk = pynccl.Nccl()
#nc = nk._nccl # cuNccl
#api = nc._api # libnccl
pg0 = list(range(kn))
comm_0 = cre_nccl_comm_fn(nk, q, rank, pg0)
pg1 = list(range(kn))[1:]
comm_1 = cre_nccl_comm_fn(nk, q, rank, pg1)
pg2 = list(range(kn))[2:]
comm_2 = cre_nccl_comm_fn(nk, q, rank, pg2)
time.sleep(2)
nccl_fn_on_comm(nk, comm_0, q, rank, pg0, gpu_i)
time.sleep(1)
nccl_fn_on_comm(nk, comm_1, q, rank, pg1, gpu_i)
time.sleep(1)
nccl_fn_on_comm(nk, comm_2, q, rank, pg2, gpu_i)
time.sleep(1)
r = nk.comm_destroy(comm_0)
print(rank, '>>> ncclCommDestroy ', r)
r = nk.comm_destroy(comm_1)
print(rank, '>>> ncclCommDestroy ', r)
r = nk.comm_destroy(comm_2)
print(rank, '>>> ncclCommDestroy ', r)
def test_2_004():
nk = pynccl.Nccl()
nc = nk._nccl # cuNccl
api = nc._api # libnccl
# -------------------------------------
procs = []
q = mp.queues.Queue()
# -------------------------------------
for i in range(4):
worker = mp.Process(
target=gpu_worker_proc_4,
args=(api, i, i, q))
worker.daemon = True
worker.start()
procs.append(worker)
for worker in procs:
worker.join()
def gpu_worker_proc_5_2(api, kn, rank, gpu_i, q):
# NOTE: do this at first of all
cuda.select_device(gpu_i)
nk = pynccl.Nccl()
#nc = nk._nccl # cuNccl
#api = nc._api # libnccl
if rank == 0:
nuid = nk.get_unique_id()
#w = mxutils.mx.nd.array(np.random.random((kn, 5, 10)), dtype=np.float32) # w
w = mxutils.mx.nd.array(np.random.random((5, kn * 10)),
dtype=np.float32) # w
print('w', w)
for j in range(kn - 1):
q.put((nuid, w))
else:
nuid, w = q.get()
# -------------------------------------
x = mxutils.mx.nd.array(np.random.random((7, 5)), dtype=np.float32)
#arr_send = mxutils.mx.nd.array(np.random.random(5, 40), dtype=np.float32) # w
#arr_send = w[rank]
arr_send = w[:, rank * 10:(rank + 1) * 10]
#arr_recv = arr_send.zeros_like()
#arr_recv = mxutils.mx.nd.zeros((kn, 5, 10), dtype=np.float32) # recv
#arr_recv = mxutils.mx.nd.zeros((5, kn * 10), dtype=np.float32) # recv
arr_recv = mxutils.mx.nd.zeros((kn * 10, 5), dtype=np.float32) # recv.T
#arr_send[1][1] = random.random()
print(arr_send[1][1])
#x#sz = 32 * 1000 * 10000
sz = arr_send.size
d_arr_send = arr_send.as_in_context(mxutils.context.gpu(gpu_i))
d_arr_recv = arr_recv.as_in_context(mxutils.context.gpu(gpu_i))
comm_i = nk.get_comm()
nRanks = int(kn) #2
myRank = int(rank) #0
r = nk.comm_init_rank(byref(comm_i), nRanks, nuid, myRank)
print('>>> ncclCommInitRank ', r)
stream_i = nk.get_stream()
# for test: rank-0 's sleep will block the others allreduce
if rank == 0:
print('-x' * 40, rank)
time.sleep(10)
print('=x' * 40, rank)
r = nk.group_start()
print('>>> ncclGroupStart ', r)
############
t_arr_send = d_arr_send.T
#p_arr_send = d_arr_send.get_data_p()
p_arr_send = t_arr_send.get_data_p()
p_arr_recv = d_arr_recv.get_data_p()
'''
r = nk.all_reduce(p_arr_send, p_arr_recv,
sz,
pynccl.binding.ncclFloat, pynccl.binding.ncclSum,
comm_i, stream_i.handle) # NOTE:
#comm_i, c_void_p(0)) # NOTE:
print('>>> ncclAllReduce ', rank, r)
'''
r = nk.all_gather(p_arr_send, p_arr_recv, sz, pynccl.binding.ncclFloat,
comm_i, stream_i.handle)
print('>>> ncclAllGather ', r)
r = nk.group_end()
print('>>> ncclGroupEnd ', r)
stream_i.synchronize()
mxutils.mx.ndarray.ndarray.waitall() ###
r_arr = d_arr_recv.asnumpy()
#print(r_arr.T) # ############ the results is r_arr.T
print(rank, w.asnumpy() == r_arr.T)
r = nk.comm_destroy(comm_i)
print('>>> ncclCommDestroy ', r)
def gpu_worker_proc_5(api, kn, rank, gpu_i, q):
# NOTE: do this at first of all
cuda.select_device(gpu_i)
nk = pynccl.Nccl()
nc = nk._nccl # cuNccl
api = nc._api # libnccl
if rank == 0:
nuid = pynccl.binding.ncclUniqueId()
intnl_buf = chr(0) + chr(2) + 'nccl-%d-%d' % (os.getpid(), 0)
intnl_buf += chr(0) * (pynccl.binding.NCCL_UNIQUE_ID_BYTES -
len(intnl_buf))
nuid.internal = intnl_buf
r = api.ncclGetUniqueId(byref(nuid)) # TODO:
print('>>> ncclGetUniqueId ', r)
for j in range(kn - 1):
q.put(nuid)
else:
nuid = q.get()
# -------------------------------------
#arr_send = np.array(np.random.random((1000, 10000)), dtype=np.float32)
#arr_recv = np.empty_like(arr_send)
#print(arr_send[1][1])
arr_send = mxutils.mx.nd.zeros((1000, 10000), dtype=np.float32)
arr_recv = arr_send.zeros_like()
arr_send[1][1] = random.random()
print(arr_send[1][1])
#x#sz = 32 * 1000 * 10000
sz = arr_send.size
####cuda.select_device(gpu_i)
#d_arr_send = cuda.to_device(arr_send)
#d_arr_recv = cuda.to_device(arr_recv)
d_arr_send = arr_send.as_in_context(mxutils.context.gpu(gpu_i))
d_arr_recv = arr_recv.as_in_context(mxutils.context.gpu(gpu_i))
comm_i = c_void_p(0)
nRanks = int(kn) #2
myRank = int(rank) #0
r = api.ncclCommInitRank(byref(comm_i), nRanks, nuid, myRank)
#x#r = api.ncclCommInitRank(byref(comm_i), nRanks, byref(nuid), myRank)
print('>>> ncclCommInitRank ', r)
stream_i = cuda.stream()
r = api.ncclGroupStart()
print('>>> ncclGroupStart ', r)
#ncclAllReduce((const void*)sendbuff[i], (void*)recvbuff[i], size, ncclFloat, ncclSum,
# comms[i], s[i])
#x#p_arr_send = cast(d_arr_send.device_ctypes_pointer, c_void_p)
#x#p_arr_recv = cast(d_arr_recv.device_ctypes_pointer, c_void_p)
#p_arr_send = c_void_p(d_arr_send.device_ctypes_pointer.value) # NOTE:
#p_arr_recv = c_void_p(d_arr_recv.device_ctypes_pointer.value)
p_arr_send = d_arr_send.get_data_p()
p_arr_recv = d_arr_recv.get_data_p()
#pd_arr_send = cuda_driver.device_pointer(p_arr_send) # int
#pd_arr_recv = cuda_driver.device_pointer(d_arr_recv) # int
#r = api.ncclAllReduce(d_arr_send.device_ctypes_pointer, d_arr_recv.device_ctypes_pointer,
r = api.ncclAllReduce(p_arr_send, p_arr_recv, sz, pynccl.binding.ncclFloat,
pynccl.binding.ncclSum, comm_i,
stream_i.handle) # NOTE:
#comm_i, c_void_p(0)) # NOTE:
print('>>> ncclAllReduce ', r)
r = api.ncclGroupEnd()
print('>>> ncclGroupEnd ', r)
stream_i.synchronize()
r_arr = d_arr_recv.asnumpy()
#print(r_arr)
print(r_arr[1][1])
#cuda.close()
r = api.ncclCommDestroy(comm_i)
print('>>> ncclCommDestroy ', r)
def gpu_worker_proc_5_3(api, kn, rank, gpu_i, q):
# NOTE: do this at first of all
cuda.select_device(gpu_i)
nk = pynccl.Nccl()
#nc = nk._nccl # cuNccl
#api = nc._api # libnccl
if rank == 0:
nuid0 = nk.get_unique_id()
for j in range(kn - 1):
q.put(nuid0)
else:
nuid0 = q.get()
# -------------------------------------
time.sleep(10)
# -------------------------------------
if rank == 1:
nuid1 = nk.get_unique_id()
for j in range(kn - 1):
q.put(nuid1)
else:
if rank % 2 == 1:
nuid1 = q.get()
# -------------------------------------
arr_send = mxutils.mx.nd.zeros((1000, 10000), dtype=np.float32)
arr_recv = arr_send.zeros_like()
arr_send[1][1] = random.random()
print(arr_send[1][1])
#x#sz = 32 * 1000 * 10000
sz = arr_send.size
d_arr_send = arr_send.as_in_context(mxutils.context.gpu(gpu_i))
d_arr_recv = arr_recv.as_in_context(mxutils.context.gpu(gpu_i))
# -------------------------------------
comm_i0 = nk.get_comm()
nRanks = int(kn) #2
myRank = int(rank) #0
r = nk.comm_init_rank(byref(comm_i0), nRanks, nuid0, myRank)
print('>>> ncclCommInitRank ', r)
stream_i0 = nk.get_stream()
# for test: rank-0 's sleep will block the others allreduce
if rank == 0:
print('-x' * 40, rank)
time.sleep(10)
print('=x' * 40, rank)
r = nk.group_start()
print('>>> ncclGroupStart ', r)
p_arr_send = d_arr_send.get_data_p()
p_arr_recv = d_arr_recv.get_data_p()
r = nk.all_reduce(p_arr_send, p_arr_recv, sz, pynccl.binding.ncclFloat,
pynccl.binding.ncclSum, comm_i0,
stream_i0.handle) # NOTE:
#comm_i, c_void_p(0)) # NOTE:
print('>>> ncclAllReduce ', rank, r)
r = nk.group_end()
print('>>> ncclGroupEnd ', r)
stream_i0.synchronize()
r_arr = d_arr_recv.asnumpy()
#print(r_arr)
print(r_arr[1][1])
#cuda.close()
# -------------------------------------
if rank % 2 == 1:
comm_i1 = nk.get_comm()
nRanks = int(kn / 2) #2
myRank = int(rank / 2) # ####################################
r = nk.comm_init_rank(byref(comm_i1), nRanks, nuid1, myRank)
print('>>> 1 ncclCommInitRank ', r)
stream_i1 = nk.get_stream()
# for test: rank-0 's sleep will block the others allreduce
if rank == 0:
print('-x' * 40, rank)
time.sleep(10)
print('=x' * 40, rank)
r = nk.group_start()
print('>>> 1 ncclGroupStart ', r)
p_arr_send = d_arr_send.get_data_p()
p_arr_recv = d_arr_recv.get_data_p()
r = nk.all_reduce(p_arr_send, p_arr_recv, sz, pynccl.binding.ncclFloat,
pynccl.binding.ncclSum, comm_i1,
stream_i1.handle) # NOTE:
#comm_i, c_void_p(0)) # NOTE:
print('>>> 1 ncclAllReduce ', rank, r)
r = nk.group_end()
print('>>> 1 ncclGroupEnd ', r)
stream_i1.synchronize()
r_arr = d_arr_recv.asnumpy()
#print(r_arr)
print(r_arr[1][1])
#cuda.close()
else:
time.sleep(15)
# -------------------------------------
r = nk.comm_destroy(comm_i0)
if rank % 2 == 1:
r = nk.comm_destroy(comm_i1)
print('>>> ncclCommDestroy ', r)
def gpu_worker_proc_4(api, rank, gpu_i, q):
nk = pynccl.Nccl()
nc = nk._nccl # cuNccl
api = nc._api # libnccl
if rank == 0:
comms_a = c_void_p * 4
int_a = c_int * 4
# <1>
comms = comms_a(0, 0, 0, 0)
comms_p = cast(comms, POINTER(c_void_p))
# <2>
#comms = c_void_p(0)
devs = int_a(0, 1, 2, 3)
# <3>
#devs_p = byref(devs)
# <4>
devs_p = cast(devs, POINTER(c_int))
# <1>
r = api.ncclCommInitAll(comms_p, 4, devs_p)
# <2>
#r = api.ncclCommInitAll(byref(comms), 4, devs_p)
print('>>> ncclCommInitAll ', r)
#x#cuda.close()
q.put(comms)
else:
comms = q.get()
# -------------------------------------
arr_send = np.array(np.random.random((1000, 10000)), dtype=np.float32)
arr_recv = np.empty_like(arr_send)
print(arr_send[1][1])
sz = 32 * 1000 * 10000
cuda.select_device(gpu_i)
d_arr_send = cuda.to_device(arr_send)
d_arr_recv = cuda.to_device(arr_recv)
comm_i = comms[i]
stream_i = cuda.stream()
r = api.ncclGroupStart()
print('>>> ncclGroupStart ', r)
#ncclAllReduce((const void*)sendbuff[i], (void*)recvbuff[i], size, ncclFloat, ncclSum,
# comms[i], s[i])
r = api.ncclAllReduce(d_arr_send.device_ctypes_pointer, d_arr_recv.device_ctypes_pointer,
sz, pynccl.binding.ncclFloat, pynccl.binding.ncclSum,
comm_i, stream_i)
print('>>> ncclAllReduce ', r)
r = api.ncclGroupEnd()
print('>>> ncclGroupEnd ', r)
stream_i.synchronize()
r_arr = d_arr_recv.copy_to_host()
#print(r_arr)
print(r_arr[1][1])
#cuda.close()
# -------------------------------------
if rank == 0:
for i in range(4):
#print(comms[i])
r = api.ncclCommDestroy(comms[i])
print('>>> ncclCommDestroy ', r)
def test_1_001():
nk = pynccl.Nccl()
print(dir(nk))
def gpu_worker_proc_5_2(api, kn, rank, gpu_i, q):
# NOTE: do this at first of all
cuda.select_device(gpu_i)
nk = pynccl.Nccl()
#nc = nk._nccl # cuNccl
#api = nc._api # libnccl
if rank == 0:
nuid = nk.get_unique_id()
w = torch.Tensor(np.random.random((kn * 10, 5))) # w
print('w', w)
for j in range(kn - 1):
q.put((nuid, w))
else:
nuid, w = q.get()
# -------------------------------------
#arr_send = w[rank]
arr_send = w[rank * 10:(rank + 1) * 10, :]
arr_recv = torch.zeros((kn * 10, 5)) # recv
#arr_send[1][1] = random.random()
print(arr_send[1][1])
#x#sz = arr_send.size
sz = np.prod(arr_send.size()) #* arr_send.element_size()
d_arr_send = arr_send.cuda(gpu_i)
d_arr_recv = arr_recv.cuda(gpu_i)
comm_i = nk.get_comm()
nRanks = int(kn) #2
myRank = int(rank) #0
r = nk.comm_init_rank(byref(comm_i), nRanks, nuid, myRank)
print(rank, '>>> ncclCommInitRank ', r)
stream_i = nk.get_stream()
# for test: rank-0 's sleep will block the others allreduce
if rank == 0:
print('-x' * 40, rank)
time.sleep(3)
print('=x' * 40, rank)
r = nk.group_start()
print(rank, '>>> ncclGroupStart ', r)
# NOTE: in pytorch, the t() function of Tensor does NOT change the
# original memory, so here we should create a new Tensor to nccl
#t_arr_send = d_arr_send
t_arr_send = torch.Tensor(d_arr_send.cpu()).cuda()
#p_arr_send = d_arr_send.data_ptr()
p_arr_send = t_arr_send.data_ptr()
p_arr_recv = d_arr_recv.data_ptr()
'''
r = nk.all_reduce(p_arr_send, p_arr_recv,
sz,
pynccl.binding.ncclFloat, pynccl.binding.ncclSum,
comm_i, stream_i.handle) # NOTE:
#comm_i, c_void_p(0)) # NOTE:
print(rank, '>>> ncclAllReduce ', rank, r)
'''
r = nk.all_gather(p_arr_send, p_arr_recv, sz, pynccl.binding.ncclFloat,
comm_i, stream_i.handle)
print(rank, '>>> ncclAllGather ', r)
r = nk.group_end()
print(rank, '>>> ncclGroupEnd ', r)
stream_i.synchronize()
r_arr = d_arr_recv.cpu().numpy()
time.sleep(rank)
print(rank, 'r_arr', r_arr)
#print(rank, w.cpu().numpy() == r_arr)
print(rank, (w.cpu().numpy() - r_arr) < 1e-6)
r = nk.comm_destroy(comm_i)
print(rank, '>>> ncclCommDestroy ', r)
