def test_allreduce_ops(self):
store = c10d.FileStore(self.file.name)
pg = c10d.ProcessGroupGloo(store, self.rank, self.size)
def allreduce(x, op):
opts = c10d.AllreduceOptions()
opts.reduceOp = op
work = pg.allreduce([x], opts)
work.wait()
# Sum
x = torch.Tensor([self.rank + 1.0])
allreduce(x, c10d.ReduceOp.SUM)
self.assertEqual(torch.Tensor([float(self.size * (self.size + 1) / 2)]), x)
# Product
x = torch.Tensor([self.rank + 1.0])
allreduce(x, c10d.ReduceOp.PRODUCT)
self.assertEqual(torch.Tensor([float(math.factorial(self.size))]), x)
# Min
x = torch.Tensor([self.rank + 1.0])
allreduce(x, c10d.ReduceOp.MIN)
self.assertEqual(torch.Tensor([1.0]), x)
# Max
x = torch.Tensor([self.rank + 1.0])
allreduce(x, c10d.ReduceOp.MAX)
self.assertEqual(torch.Tensor([self.size]), x)
# Test overloaded convenience function (defaults to using sum)
x = torch.Tensor([self.rank + 1.0])
work = pg.allreduce(x)
work.wait()
self.assertEqual(torch.Tensor([float(self.size * (self.size + 1) / 2)]), x)
def test_send_recv_all_to_all(self):
store = c10d.FileStore(self.file.name)
pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
# Preallocate tensors for input/output
inputs = [torch.Tensor([self.rank]) for _ in range(self.world_size)]
outputs = [torch.Tensor([-1]) for _ in range(self.world_size)]
# Issue sends
send_work = []
for i in range(self.world_size):
if i == self.rank:
continue
send_work.append(pg.send([inputs[i]], i))
# Issue recvs
recv_work = []
for i in range(self.world_size):
if i == self.rank:
continue
recv_work.append(pg.recv([outputs[i]], i))
# Wait for sends to complete
for work in send_work:
work.wait()
# Wait for recvs to complete
for work in recv_work:
work.wait()
# Test that every output other than our own contains the respective rank
for i in range(self.world_size):
if i == self.rank:
continue
self.assertEqual(torch.Tensor([i]), outputs[i])
def test_broadcast_ops(self):
store = c10d.FileStore(self.file.name)
pg = c10d.ProcessGroupGloo(store, self.rank, self.size)
def broadcast(xs, rootRank, rootTensor):
opts = c10d.BroadcastOptions()
opts.rootRank = rootRank
opts.rootTensor = rootTensor
work = pg.broadcast(xs, opts)
work.wait()
# Every rank is root once, every tensor index is root once
for i in range(self.size):
for j in range(2):
xs = [
torch.Tensor([self.rank * self.size + 0.0]),
torch.Tensor([self.rank * self.size + 1.0]),
]
broadcast(xs, i, j)
self.assertEqual(torch.Tensor([i * self.size + j]), xs[0])
self.assertEqual(torch.Tensor([i * self.size + j]), xs[1])
# Test overloaded convenience function
x = torch.Tensor([self.rank + 1.0])
work = pg.broadcast(x, root=0)
work.wait()
self.assertEqual(torch.Tensor([1.0]), x)
def test_allreduce_ops(self):
store = c10d.FileStore(self.file.name)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def allreduce(tensors, op):
opts = c10d.AllreduceOptions()
opts.reduceOp = op
work = pg.allreduce(tensors, opts)
work.wait()
# Sum
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.Tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.SUM)
for i in range(self.num_gpus):
self.assertEqual(
torch.Tensor([float(self.num_gpus * (self.num_gpus + 1) / 2)]),
tensors[i])
# Product
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.Tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.PRODUCT)
for i in range(self.num_gpus):
self.assertEqual(
torch.Tensor([float(math.factorial(self.num_gpus))]),
tensors[i])
# Min
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.Tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.MIN)
for i in range(self.num_gpus):
self.assertEqual(torch.Tensor([1.0]), tensors[i])
# Max
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.Tensor([i + 1]).cuda(i))
allreduce(tensors, c10d.ReduceOp.MAX)
for i in range(self.num_gpus):
self.assertEqual(torch.Tensor([self.num_gpus]), tensors[i])
def test_broadcast_ops(self):
store = c10d.FileStore(self.file.name)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def broadcast(xs, rootRank, rootTensor):
opts = c10d.BroadcastOptions()
opts.rootRank = rootRank
opts.rootTensor = rootTensor
work = pg.broadcast(xs, opts)
work.wait()
# for every root tensor
for rt in range(self.num_gpus):
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.Tensor([i]).cuda(i))
broadcast(tensors, self.rank, rt)
for i in range(self.num_gpus):
self.assertEqual(tensors[i], tensors[rt])
def test_reduce_ops(self):
store = c10d.FileStore(self.file.name)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def reduce(xs, rootRank, rootTensor):
opts = c10d.ReduceOptions()
opts.rootRank = rootRank
opts.rootTensor = rootTensor
work = pg.reduce(xs, opts)
work.wait()
# for every root tensor
for rt in range(self.num_gpus):
tensors = []
for i in range(self.num_gpus):
tensors.append(torch.Tensor([i + 1]).cuda(i))
reduce(tensors, self.rank, rt)
self.assertEqual(
torch.Tensor([float(self.num_gpus * (self.num_gpus + 1) / 2)]),
tensors[rt])
def test_allgather_ops(self):
store = c10d.FileStore(self.file.name)
pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
def allgather(output_ts, input_ts):
work = pg.allgather(output_ts, input_ts)
work.wait()
tensors = []
output_ts = [[] for _ in range(self.num_gpus)]
for idx, ls in enumerate(output_ts):
for _ in range(self.world_size * self.num_gpus):
ls.append(torch.Tensor([0]).cuda(idx))
for i in range(self.num_gpus):
tensors.append(torch.Tensor([i]).cuda(i))
allgather(output_ts, tensors)
# Verification
for device_ts in output_ts:
for s_idx, t in enumerate(device_ts):
self.assertEqual(torch.Tensor([s_idx]), t)
def _create_store(self):
return c10d.FileStore(self.file.name)
def setUp(self):
self.file = tempfile.NamedTemporaryFile()
self.filestore = c10d.FileStore(self.file.name)
self.prefix = "test_prefix"
self.filestore.set_timeout(timedelta(seconds=300))
def _create_store(self):
store = c10d.FileStore(self.file.name)
store.set_timeout(timedelta(seconds=300))
return store
def setUp(self):
self.file = tempfile.NamedTemporaryFile()
self.filestore = c10d.FileStore(self.file.name)
self.prefix = "test_prefix"
