def run_test_model_parallel_cuda_manual_seed(rank, model_parallel_size):
dist_init(rank, model_parallel_size)
if torch.distributed.get_rank() == 0:
print("> testing model parallel cuda manual seed with size {} ...".
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
model_parallel_cuda_manual_seed(12345)
assert torch.cuda.initial_seed() == 12345
with get_cuda_rng_tracker().fork():
assert torch.cuda.initial_seed() == (12345 + 2718 +
mpu.get_model_parallel_rank())
# Reset the tracker
get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(">> passed the test :-)")
def run_test_column_parallel_linear(rank, model_parallel_size, filename, filename_rpc):
dist_init(rank, model_parallel_size, filename, filename_rpc)
mpu.initialize_model_parallel(model_parallel_size)
if torch.distributed.get_rank() == 0:
print("> testing ColumnParallelLinear with model parallel size: {}".format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
batch_size = 7
# Network
identity_layer = IdentityLayer2D(batch_size, input_size).cuda()
linear_layer = layers.ColumnParallelLinear(input_size, output_size, keep_master_weight_for_test=True).cuda()
loss_weight = torch.randn([batch_size, output_size]).cuda()
# Forward
input_ = identity_layer()
output = linear_layer(input_)
loss = torch.mul(output, loss_weight).sum()
# Backward
loss.backward()
# Values.
dLdY = loss_weight
X = identity_layer.weight
A = linear_layer.master_weight.cuda()
dLdA = torch.matmul(dLdY.t(), X)
dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1)
dLdX = torch.matmul(dLdY, A)
rank = mpu.get_model_parallel_rank()
my_dLdA = torch.split(dLdA, output_size_coeff, dim=0)[rank].contiguous().clone()
error = my_dLdA.sub(linear_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(" error in dLdA on global rank {}: {}".format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
my_dLdb = torch.split(dLdb, output_size_coeff, dim=0)[rank].contiguous().clone()
error = my_dLdb.sub(linear_layer.bias.grad).abs().max()
torch.distributed.barrier()
print(" error in dLdb on global rank {}: {}".format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = dLdX.sub(identity_layer.weight.grad).abs().max()
torch.distributed.barrier()
print(" error in dLdX on global rank {}: {}".format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(" >> passed the test :-)")
def rpc_megatron_reuse():
from fairscale.nn.model_parallel import layers
from fairscale.nn.model_parallel.initialize import destroy_model_parallel, initialize_model_parallel
def make_model_simple():
return [
layers.ColumnParallelLinear(10, 10),
nn.ReLU(),
layers.RowParallelLinear(10, 10),
nn.ReLU(),
layers.ColumnParallelLinear(10, 10),
nn.ReLU(),
layers.RowParallelLinear(10, 10),
nn.ReLU(),
nn.Linear(10, 10),
nn.ReLU(),
]
def make_model_with_reuse():
column = layers.ColumnParallelLinear(10, 10)
row = layers.RowParallelLinear(10, 10)
return [
column,
nn.ReLU(),
row,
nn.ReLU(),
column,
nn.ReLU(),
row,
nn.ReLU(),
nn.Linear(10, 10),
nn.ReLU(),
]
destroy_model_parallel()
torch.distributed.destroy_process_group()
torch.distributed.init_process_group("gloo",
rank=int(os.environ["RANK"]),
world_size=int(
os.environ["WORLD_SIZE"]))
initialize_model_parallel(2,
3,
model_parallel_backend="nccl",
pipeline_backend="mpi")
init_rpc()
if get_pipeline_parallel_group().rank() != 0:
rpc.shutdown()
torch.distributed.barrier()
return
check_pipe_against_reference([4, 4, 2], make_model_simple, "always")
check_pipe_against_reference([4, 2, 2], make_model_with_reuse)
rpc.shutdown()
torch.distributed.barrier()
def pipelined_backward():
model = nn.Sequential(nn.ReLU(), nn.ReLU())
destroy_model_parallel()
initialize_model_parallel(1, 4)
pipe = Pipe(model, [1, 1], style=Pipe.MultiProcess, worker_map=get_worker_map())
assert pipe.pipelined_backward is False
destroy_model_parallel()
initialize_model_parallel(2, 2)
pipe = Pipe(model, [1, 1], style=Pipe.MultiProcess, worker_map=get_worker_map())
assert pipe.pipelined_backward is True
def pipelined_backward(pipe_class):
model = nn.Sequential(nn.ReLU(), nn.ReLU())
destroy_model_parallel()
initialize_model_parallel(1, 4)
pipe = pipe_class(model, [1, 1], worker_map=get_worker_map())
assert pipe.pipelined_backward is False
destroy_model_parallel()
initialize_model_parallel(2, 2)
pipe = pipe_class(model, [1, 1], worker_map=get_worker_map())
assert pipe.pipelined_backward is True
def test_adjacency(monkeypatch):
new_groups = []
data_parallel_size = 32
pipeline_length = 8
model_parallel_size = 4
class MockDistribued:
def get_rank(self):
return 0
def is_initialized(self):
return True
def get_world_size(self):
return data_parallel_size * pipeline_length * model_parallel_size
def new_group(self, args, backend=None):
new_groups.append(args.copy())
return ()
monkeypatch.setattr(torch, "distributed", MockDistribued())
mpu.initialize_model_parallel(model_parallel_size, pipeline_length)
from collections import defaultdict
buckets = defaultdict(list)
for group in new_groups:
buckets[len(group)].append(group)
assert sorted(list(buckets.keys())) == [
model_parallel_size, pipeline_length, data_parallel_size
]
assert len(
buckets[model_parallel_size]) == pipeline_length * data_parallel_size
assert len(
buckets[data_parallel_size]) == model_parallel_size * pipeline_length
assert len(
buckets[pipeline_length]) == model_parallel_size * data_parallel_size
# Check that model_parallel groups are contiguous
for group in buckets[model_parallel_size]:
assert sorted(group) == group
assert list(range(group[0], group[-1] + 1)) == group
def run_test_cross_entropy(rank, model_parallel_size):
dist_init(rank, model_parallel_size)
if torch.distributed.get_rank() == 0:
print("> testing cross entropy with model parallel size {} ...".format(
model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
batch_size = 13
seq_length = 17
vocab_size_per_partition = 11
logits_scale = 1000.0
vocab_size = vocab_size_per_partition * model_parallel_size
seed = 1234
loss_torch, grad_torch = torch_cross_entropy(batch_size, seq_length,
vocab_size, logits_scale,
seed)
loss_mpu, grad_mpu = mpu_cross_entropy(batch_size, seq_length, vocab_size,
logits_scale, seed)
error = loss_torch.sub_(loss_mpu).abs().max()
print(" max error in loss on global rank {}: {}".format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
error = grad_torch.sub_(grad_mpu).abs().max()
print(" max error in grad on global rank {}: {}".format(
torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(">> passed the test :-)")
def run_test_get_model_parallel_src_rank(rank, model_parallel_size_):
dist_init(rank, model_parallel_size_)
if torch.distributed.get_rank() == 0:
print("> testing get_model_parallel_src_rank with size {} ...".format(
model_parallel_size_))
model_parallel_size = min(model_parallel_size_,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(model_parallel_size)
assert mpu.model_parallel_is_initialized()
# Checks
src_rank = torch.distributed.get_rank() - mpu.get_model_parallel_rank()
assert mpu.get_model_parallel_src_rank() == src_rank
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(">> passed the test :-)")
def run_test_initialize_model_parallel(rank, model_parallel_size, filename,
filename_rpc):
dist_init(rank, model_parallel_size, filename, filename_rpc)
if torch.distributed.get_rank() == 0:
print("> testing initialize_model_parallel with size {} ...".format(
model_parallel_size))
model_parallel_size_ = min(model_parallel_size,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(model_parallel_size_)
assert mpu.model_parallel_is_initialized()
# Checks.
def check(group, world_size, rank):
assert world_size == torch.distributed.get_world_size(group=group)
assert rank == torch.distributed.get_rank(group=group)
# Model parallel.
world_size = model_parallel_size_
rank = torch.distributed.get_rank() % model_parallel_size_
assert world_size == mpu.get_model_parallel_world_size()
assert rank == mpu.get_model_parallel_rank()
check(mpu.get_model_parallel_group(), world_size, rank)
# Data parallel.
world_size = torch.distributed.get_world_size() // model_parallel_size_
rank = torch.distributed.get_rank() // model_parallel_size
assert world_size == mpu.get_data_parallel_world_size()
assert rank == mpu.get_data_parallel_rank()
check(mpu.get_data_parallel_group(), world_size, rank)
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(">> passed the test :-)")
def run_test_cuda_rng_tracker(rank, model_parallel_size):
dist_init(rank, model_parallel_size)
if torch.distributed.get_rank() == 0:
print("> testing cuda rng tracker with size {} ...".format(
model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
seed_1 = 1234
seed_2 = 4321
size = [12, 21]
tensor = torch.cuda.FloatTensor(size)
# Set to seed_1 and generate two tensors.
torch.cuda.manual_seed(seed_1)
torch.randn(size, out=tensor)
target_11 = tensor.clone()
torch.randn(size, out=tensor)
target_12 = tensor.clone()
# Set to seed_2 and generate two tensors.
torch.cuda.manual_seed(seed_2)
torch.randn(size, out=tensor)
target_21 = tensor.clone()
torch.randn(size, out=tensor)
target_22 = tensor.clone()
# Now if we interleave seed_1 and seed_2,
# we should still get the same tensors
torch.cuda.manual_seed(seed_1)
get_cuda_rng_tracker().add("test", seed_2)
torch.randn(size, out=tensor)
result_11 = tensor.clone()
with get_cuda_rng_tracker().fork("test"):
torch.randn(size, out=tensor)
result_21 = tensor.clone()
torch.randn(size, out=tensor)
result_12 = tensor.clone()
with get_cuda_rng_tracker().fork("test"):
torch.randn(size, out=tensor)
result_22 = tensor.clone()
diff = result_11.sub(result_21).abs().max()
diff = min(diff, result_12.sub(result_22).abs().max())
print(
" max diff in generated tensors (should be non-zero) on global rank {}: {}"
.format(torch.distributed.get_rank(), diff))
assert diff > 1.0e-6
error = max(
result_11.sub(target_11).abs().max(),
result_12.sub(target_12).abs().max())
error = max(error, result_21.sub(target_21).abs().max())
error = max(error, result_22.sub(target_22).abs().max())
print(
" max error in generated tensors (should be zero) on global rank {}: {}"
.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset the tracker
get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(">> passed the test :-)")
def run_test_set_cuda_rng_state(rank, model_parallel_size):
dist_init(rank, model_parallel_size)
if torch.distributed.get_rank() == 0:
print("> testing set_rng_state with size {} ...".format(
model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
size = 123
seed = 1234
torch.cuda.manual_seed(1234)
tensor = torch.cuda.FloatTensor(size)
# Get the state
rng_state = torch.cuda.get_rng_state()
rng_state_copy = rng_state.clone()
# Do some stuff.
for _ in range(5):
torch.randn(size, out=tensor)
result_1 = tensor.clone()
assert rng_state.sub(rng_state_copy).max() == 0
assert torch.cuda.get_rng_state().sub(rng_state_copy).max() > 0
# State should be different.
new_rng_state = torch.cuda.get_rng_state()
max_diff = new_rng_state.sub(rng_state).max()
print(
" max diff in rng state (should be non-zero) on global rank {}: {}".
format(torch.distributed.get_rank(), max_diff))
assert max_diff > 0
# Reset the rng state and do the same stuff.
random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
result_2 = tensor.clone()
# Results should be the same
error = result_2.sub(result_1).abs().max()
print(
" max error in generated tensors (should be zero) on global rank {}: {}"
.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Input state should have remained intact.
error = rng_state.sub(rng_state_copy).max()
print(" max error in rng state (should be zero) on global rank {}: {}".
format(torch.distributed.get_rank(), error))
assert error == 0
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(">> passed the test :-)")
def run_test_parallel_embedding(rank, model_parallel_size, filename,
filename_rpc):
dist_init(rank, model_parallel_size, filename, filename_rpc)
if torch.distributed.get_rank() == 0:
print("> testing parallel embedding with model parallel size {} ...".
format(model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
batch_size = 17
seq_length = 23
vocab_size = 48
hidden_size = 16
seed = 1236
set_random_seed(123)
input_data = torch.LongTensor(size=(batch_size, seq_length)).random_(
0, vocab_size).cuda()
loss_weight = torch.randn([batch_size, seq_length, hidden_size]).cuda()
set_random_seed(seed)
embedding_original = torch.nn.Embedding(vocab_size, hidden_size).cuda()
output = embedding_original(input_data)
loss_original = torch.mul(output, loss_weight).sum()
loss_original.backward()
set_random_seed(seed)
embedding_parallel = layers.ParallelEmbedding(
vocab_size, hidden_size, init_method=init.normal_).cuda()
output = embedding_parallel(input_data)
loss_parallel = torch.mul(output, loss_weight).sum()
loss_parallel.backward()
set_random_seed(seed)
embedding_vocab_parallel = layers.VocabParallelEmbedding(
vocab_size, hidden_size, init_method=init.normal_).cuda()
output = embedding_vocab_parallel(input_data)
loss_vocab_parallel = torch.mul(output, loss_weight).sum()
loss_vocab_parallel.backward()
torch.distributed.barrier()
error = loss_parallel.sub(loss_original).abs()
print(" error in loss (parallel) on global rank {}: {}".format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, "error: {}".format(error)
torch.distributed.barrier()
error = loss_vocab_parallel.sub(loss_original).abs()
print(" error in loss (vocab parallel) on global rank {}: {}".format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, "error: {}".format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
hidden_size // model_parallel_size,
1)[mpu.get_model_parallel_rank()]
error = embedding_parallel.weight.grad.sub(weight_grad_orig).abs().max()
print(" error in grad (parallel) on global rank {}: {}".format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, "error: {}".format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
vocab_size // model_parallel_size,
0)[mpu.get_model_parallel_rank()]
error = embedding_vocab_parallel.weight.grad.sub(
weight_grad_orig).abs().max()
print(" error in grad (vocab parallel) on global rank {}: {}".format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, "error: {}".format(error)
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(">> passed the test :-)")
def run_test_pipe(rank,
world_size,
filename,
filename_rpc,
skip_dist_init=False):
pipe_world_size = 2
if world_size == 1:
return
if not skip_dist_init:
dist_init(rank, world_size, filename, filename_rpc)
else:
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "29502"
rpc.init_rpc(f"Test{rank}", rank=rank, world_size=world_size)
mpu.initialize_model_parallel(world_size / pipe_world_size,
pipe_world_size)
model_parallel_size = mpu.get_model_parallel_world_size()
if torch.distributed.get_rank() == 0:
print(
"> testing Sequential + MultiProcessPipe with model parallel size: {}, pipe: {}"
.format(model_parallel_size, pipe_world_size))
chunk_size = 4
seed = 12345
set_random_seed(seed)
input_size_coeff = 3
input_size = input_size_coeff * model_parallel_size
output_size_coeff = 7
output_size = output_size_coeff * model_parallel_size
batch_size = 3 * chunk_size
target = torch.rand((batch_size, input_size), requires_grad=True).cuda()
print(f"target = {target}")
identity = IdentityLayer2D(batch_size, input_size).cuda()
pipeline_devices = mpu.get_pipeline_parallel_group()
set_random_seed(seed)
model = nn.Sequential(
layers.ColumnParallelLinear(input_size,
output_size,
keep_master_weight_for_test=True,
bias=False).cuda(),
nn.ReLU(),
layers.RowParallelLinear(output_size,
input_size,
keep_master_weight_for_test=True,
bias=False).cuda(),
)
set_random_seed(seed)
reference = [
nn.Linear(input_size, output_size, bias=False).cuda(),
nn.ReLU(),
nn.Linear(output_size, input_size, bias=False).cuda(),
]
print(
f"setup {reference[0].weight.size()}, {model[0].weight.size()}, {(input_size, output_size)}"
)
print(f"setup {reference[2].weight.size()}, {(output_size, input_size)}")
reference[0].weight = Parameter(
model[0].get_master_weight().clone()).cuda()
reference[2].weight = Parameter(
model[2].get_master_weight().clone()).cuda()
reference = nn.Sequential(*reference)
def grad_graph(depth, grad):
result = depth * " " + str(grad)
if grad:
for x in grad.next_functions:
result += "\n" + grad_graph(depth + 1, x[0])
return result
def check_weights(x, y, key: str, index=None):
for i in [2, 0]:
if index is not None and i != index:
continue
left = x[i].get_master_weight()
right = y[i].weight.data
if not torch.allclose(left, right,
atol=1.0e-6) or index is not None:
print(
f"check_weights {key}-{i}: left = {left}, \nright = {right}"
)
if not torch.equal(left, right):
print(
f"check_weights NOT_EQUAL {key}-{i}: left = {left}, \nright = {right}"
)
assert torch.allclose(left, right, atol=1.0e-6)
def dump_opt_params(opt):
for i, group in enumerate(opt.param_groups):
for j, p in enumerate(group["params"]):
print(f"{torch.distributed.get_rank()}:param {(i,j)} = {p}")
print(
f"{torch.distributed.get_rank()}:param.grad {(i,j)} = {p.grad}"
)
def forward_model(model_, target, step=False):
optimizer = torch.optim.SGD(model_.parameters(), lr=0.01, momentum=0.9)
optimizer.zero_grad()
model_.zero_grad()
output = model_(identity())
loss = nn.MSELoss()
model_.zero_grad()
if step:
loss(output, target).backward()
saved_weight_0 = model_[0].weight.data.clone()
saved_weight_2 = model_[2].weight.data.clone()
dump_opt_params(optimizer)
optimizer.step()
assert not torch.allclose(
saved_weight_0, model_[0].weight.data, atol=1.0e-6)
assert not torch.allclose(
saved_weight_2, model_[2].weight.data, atol=1.0e-6)
return output
output = forward_model(model, target)
reference_output = forward_model(reference, target)
error = reference_output.sub(output).max()
torch.distributed.barrier()
assert error < 1.0e-6
output = forward_model(model, target)
error = reference_output.sub(output).max()
torch.distributed.barrier()
assert error < 1.0e-6
output = forward_model(model, target)
error = reference_output.sub(output).max()
torch.distributed.barrier()
assert error < 1.0e-6
check_weights(model, reference, "before")
saved_weight_0 = model[0].weight.data.clone()
saved_weight_2 = model[2].weight.data.clone()
output = forward_model(model, target, step=True)
error = reference_output.sub(output).max()
assert error < 1.0e-6
model[0].weight.data = saved_weight_0
model[2].weight.data = saved_weight_2
worker_map = {
i: f"Test{i}"
for i in range(torch.distributed.get_world_size())
}
if pipe_world_size == 2:
print("actually doing pipe stuff now")
assert torch.equal(saved_weight_0, model[0].weight.data)
assert torch.equal(saved_weight_2, model[2].weight.data)
pipe_model = MultiProcessPipe(
model,
[2, 1],
group=pipeline_devices,
worker_map=worker_map,
input_device=torch.cuda.current_device(),
chunks=chunk_size,
).cuda()
torch.distributed.barrier()
pipe_rank = torch.distributed.get_rank(
group=mpu.get_pipeline_parallel_group())
print(f"pipe rank is {pipe_rank}")
if pipe_rank == 0:
assert torch.equal(saved_weight_0, pipe_model[0].weight.data)
else:
if not torch.equal(saved_weight_2, pipe_model[0].weight.data):
print(
f"ne {pipe_rank}: left\n{saved_weight_2}\nright:\n{pipe_model[0].weight.data}"
)
assert torch.equal(saved_weight_2, pipe_model[0].weight.data)
optimizer = torch.optim.SGD(pipe_model.parameters(),
lr=0.01,
momentum=0.9)
optimizer.zero_grad()
if pipe_rank == 0:
assert torch.equal(saved_weight_0, pipe_model[0].weight.data)
print(f"runner {rank}:\n{pipe_model[0].weight.data}")
else:
assert torch.equal(saved_weight_2, pipe_model[0].weight.data)
print(f"runner {rank}:\n{pipe_model[0].weight.data}")
if torch.distributed.get_rank(mpu.get_pipeline_parallel_group()) == 1:
check_weights(model, reference, "pre-pipe", index=2)
else:
check_weights(model, reference, "pre-pipe", index=0)
pipe_output = pipe_model(identity())
print(f"exited pipe for {rank}")
forward_model(reference, target, step=True)
print(f"pipe_output {rank} = {pipe_output}")
print(f"reference_output {rank} = {reference_output}")
torch.distributed.barrier()
if torch.distributed.get_rank(mpu.get_pipeline_parallel_group()) == 1:
error = reference_output.sub(pipe_output.cuda()).max()
if error >= 1.0e-6:
print(f"error bad {error}")
assert error < 1.0e-6
loss = nn.MSELoss()
failed = False
pipe_output.retain_grad()
with torch.autograd.profiler.profile() as prof:
try:
loss(pipe_output, target).backward()
except Exception as e:
failed = True
print(f"got {e} while doing backward, deadlock?")
if failed:
raise RuntimeError("failed somehow")
dump_opt_params(optimizer)
optimizer.step()
print("calling check_weights on master")
check_weights(model, reference, "pipe", index=2)
print(f"waiting for barrier on master, pid={os.getpid()}")
else:
print(f"calling backwards on slave, pid={os.getpid()}")
failed = False
with torch.autograd.profiler.profile() as prof:
try:
pipe_model.back_helper(pipe_output)
except Exception as e:
failed = True
print(f"got {e} while doing backward, deadlock?")
if failed:
raise RuntimeError("failed somehow")
dump_opt_params(optimizer)
print("calling step on slave")
optimizer.step()
print("calling check_weights on slave")
check_weights(model, reference, "pipe", index=0)
print("waiting for barrier on slave")
pipe_model.zero_grad()
torch.distributed.barrier()
pipe_model.eval()
pipe_output = pipe_model(identity())
updated_ref_output = forward_model(reference, target)
if torch.distributed.get_rank(mpu.get_pipeline_parallel_group()) == 1:
error = updated_ref_output.sub(pipe_output.cuda()).max()
print(
f"outputs are ref:\n{updated_ref_output}\npipe:\n{pipe_output}"
)
assert error < 1.0e-6
torch.distributed.barrier()
print(f"finished waiting for barrier on, pid={os.getpid()}")
print(f"really exited pipe for {rank}")
rpc.shutdown()
torch.distributed.destroy_process_group()
def run_test_initialize_affine_weight(rank, model_parallel_size, filename,
filename_rpc):
dist_init(rank, model_parallel_size, filename, filename_rpc)
mpu.initialize_model_parallel(model_parallel_size)
if torch.distributed.get_rank() == 0:
print(
"> testing initialize_affine_weight with model parallel size: {}".
format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
seed = 12345
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
# ---------------
# Column parallel
# ---------------
weight = torch.empty(output_size_coeff, input_size)
set_random_seed(seed)
layers._initialize_affine_weight(weight, output_size, input_size,
output_size_coeff, 0,
torch.nn.init.normal_)
# Target.
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_model_parallel_rank()
my_weight = torch.split(master_weight, output_size_coeff,
dim=0)[rank].contiguous().clone()
# Compare.
error = weight.sub(my_weight).abs().max()
torch.distributed.barrier()
print(
" column parallel max error (should be zero) on global rank {}: {}".
format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# ------------
# Row parallel
# ------------
weight = torch.empty(output_size, input_size_coeff)
set_random_seed(seed)
layers._initialize_affine_weight(weight, output_size, input_size,
input_size_coeff, 1,
torch.nn.init.normal_)
# Target.
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_model_parallel_rank()
my_weight = torch.split(master_weight, input_size_coeff,
dim=1)[rank].contiguous().clone()
# Compare.
error = weight.sub(my_weight).abs().max()
torch.distributed.barrier()
print(" row parallel max error (should be zero) on global rank {}: {}".
format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(" >> passed the test :-)")
def run_test_pipe(rank, model_parallel_size):
pipe_world_size = 2
dist_init(rank, model_parallel_size)
mpu.initialize_model_parallel(model_parallel_size)
if torch.distributed.get_rank() == 0:
print(
"> testing Sequential + Pipe with model parallel size: {}, pipe: {}"
.format(model_parallel_size, pipe_world_size))
model_parallel_size = mpu.get_model_parallel_world_size()
chunk_size = 8
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
batch_size = 7 * chunk_size
identity = IdentityLayer2D(batch_size, input_size).cuda()
pipeline_devices = mpu.get_pipeline_parallel_group()
if pipe_world_size == 2 and len(pipeline_devices) == 1:
pipeline_devices.append(pipeline_devices[0] + model_parallel_size)
set_random_seed(seed)
model = nn.Sequential(
layers.ColumnParallelLinear(input_size,
output_size,
keep_master_weight_for_test=True,
bias=False).cuda(),
nn.ReLU(),
layers.RowParallelLinear(output_size,
input_size,
keep_master_weight_for_test=True,
bias=False).cuda(),
)
set_random_seed(seed)
reference = nn.Sequential(
nn.Linear(input_size, output_size, bias=False).cuda(),
nn.ReLU(),
nn.Linear(output_size, input_size, bias=False).cuda(),
)
reference[0].weight.data = model[0].master_weight.cuda()
reference[-1].weight.data = model[-1].master_weight.cuda()
loss_weight = torch.randn([batch_size, output_size]).cuda()
output = model(identity())
reference_output = reference(identity())
error = reference_output.sub(output).max()
torch.distributed.barrier()
assert error < 1.0e-6
if pipe_world_size == 2:
pipe_model = Pipe(model, [2, 1],
devices=pipeline_devices,
chunks=chunk_size)
torch.distributed.barrier()
pipe_output = pipe_model(identity())
error = reference_output.sub(pipe_output.cuda()).max()
torch.distributed.barrier()
assert error < 1.0e-6