def worker(val, shape):
rank = dist.get_rank()
if rank == 0: # remote send
x = tensor(val, device="gpu0")
remote_send(x, 1)
sync()
else: # remote recv
y = remote_recv(0, shape, np.float32)
assert y.device == "gpu1"
np.testing.assert_almost_equal(val, y.numpy())
def worker(val, shape):
rank = dist.get_rank()
if rank == 0: # remote send
x = tensor(val, device="xpu0")
remote_send(x, 1)
sync()
else: # remote recv
y = remote_recv(0)
assert y.device == get_default_device()
np.testing.assert_almost_equal(val, y.numpy())
def worker():
rank = dist.get_rank()
if rank == 0:
with Grad() as grad:
x = as_tensor(x_np)
grad.wrt(x, callback=save_to(x))
# need a placeholder to trace operator
remote_send(x, 1)
recv_x = remote_recv(1)
y = recv_x * recv_x
grad([y], [as_tensor(np.ones_like(x_np))])
np.testing.assert_almost_equal(x.grad.numpy(), x.numpy() * 2)
elif rank == 1:
with Grad() as grad:
recv_x = remote_recv(0)
remote_send(recv_x, 0)
grad([], [])
def worker(rank):
if mge.get_device_count("gpu") < world_size:
return
if rank == 0: # remote send
dist.init_process_group("localhost", port, world_size, rank, rank)
x = Tensor(val, device="gpu0")
y = remote_send(x, 1)
assert y.numpy()[0] == 0
else: # remote recv
dist.init_process_group("localhost", port, world_size, rank, rank)
y = remote_recv(0, val.shape, val.dtype)
assert y.device == "gpu1"
np.testing.assert_almost_equal(val, y.numpy())
def worker1():
dist.init_process_group("localhost", port, world_size, 1, 1)
mge.device.set_default_device("gpu1")
grad = Grad()
recv_x = remote_recv(0, x_np.shape, x_np.dtype, "gpu1")
send_x = remote_send(recv_x, 0)
grad([], [])
# sync because grad has a send operator
sync()
send_x.device._cn._sync_all()
def worker0():
dist.init_process_group("localhost", port, world_size, 0, 0)
mge.device.set_default_device("gpu0")
grad = Grad()
x = as_tensor(x_np)
grad.wrt(x, callback=save_to(x))
# need a placeholder to trace operator
send_x = remote_send(x, 1)
recv_x = remote_recv(1, x_np.shape, x_np.dtype, "gpu0")
y = recv_x * recv_x
grad([y], [as_tensor(np.ones_like(x_np))])
np.testing.assert_almost_equal(x.grad.numpy(), x.numpy() * 2)
