def checkpoint_eval():
model = nn.Sequential(nn.Linear(1, 1))
model = Pipe(
model, balance=[1], style=Pipe.MultiProcess, worker_map=get_worker_map(), chunks=2, pipelined_backward=False,
)
input = torch.rand(2, 1)
def find_grad_fn(grad_fn, name):
if grad_fn is None:
return False
if grad_fn.__class__.__name__ == name:
return True
for next_grad_fn, _ in grad_fn.next_functions:
if find_grad_fn(next_grad_fn, name):
return True
return False
model.train()
train_output = model(input)
assert find_grad_fn(train_output.grad_fn, "CheckpointBackward")
assert find_grad_fn(train_output.grad_fn, "RecomputeBackward")
model.eval()
eval_output = model(input)
assert not find_grad_fn(eval_output.grad_fn, "CheckpointBackward")
assert not find_grad_fn(eval_output.grad_fn, "RecomputeBackward")
def exception_no_hang(pipeline_style):
# In v0.0.2, once a failed partition receives a normal message
# (non-closing) for the next micro-batch, a hang occured. The reason was
# that a failed partition didn't call in_queue.task_done() on a normal
# message. So the former partition was blocked at out_queue.join() for the
# next of next micro-batch.
class ExpectedException(Exception):
pass
class Pass(nn.Module):
def forward(self, x):
return x
class Raise(nn.Module):
def forward(self, x):
raise ExpectedException()
model = nn.Sequential(Pass(), Pass(), Raise())
model = Pipe(model, [1, 1, 1],
style=pipeline_style,
worker_map=get_worker_map(),
chunks=3)
model.eval()
if model.group.rank() == 2:
with pytest.raises(ExpectedException):
model(torch.rand(3))
else:
model(torch.rand(3))
torch.distributed.barrier()
def inplace_on_requires_grad(pipeline_style):
model = nn.Sequential(nn.Linear(1, 1), nn.ReLU(inplace=True))
model = Pipe(model, [1, 1], style=pipeline_style, worker_map=get_worker_map(), checkpoint="always")
x = torch.rand(1)
if pipeline_style == Pipe.AsyncSchedule and model.group.rank() == 0:
# With AsyncSchedule, model will wait forever for gradients if not eval
model.eval()
y = model(x)
message = r"a leaf Variable that requires grad .* used in an in-place operation."
if model.group.rank() == 1:
with pytest.raises(RuntimeError, match=message):
y.backward()
torch.distributed.barrier()
def python_autograd_function(pipeline_style):
# FIXME deadlock with Pipe.AsyncSchedule?
# A Python autograd function might fail with this error:
#
# RuntimeError: Returning Variables sharing storage with other Variables
# that require grad is not supported in Python functions. Please submit a
# feature request if you hit this error.
#
# It doesn't look like an essential restriction. But it happens on the
# current PyTorch version. To avoid it, we should detach the tensor before
# returning by identity autograd functions, such as Wait, Fork, and Join.
torch.manual_seed(0)
class Identity(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input
@staticmethod
def backward(ctx, grad):
return grad
class M(nn.Module):
def forward(self, input):
return Identity.apply(input)
model = nn.Sequential(M(), M())
model = Pipe(model, [1, 1],
style=pipeline_style,
worker_map=get_worker_map(),
checkpoint="always").cuda()
model.eval()
x = torch.rand(42)
y = model(x)
if model.group.rank() == 1:
assert torch.allclose(x, y)
torch.distributed.rpc.shutdown()
torch.distributed.barrier()
def test_checkpoint_eval():
model = nn.Sequential(nn.Linear(1, 1))
model = Pipe(model, balance=[1], devices=["cpu"], chunks=2)
input = torch.rand(2, 1)
def find_grad_fn(grad_fn, name):
if grad_fn is None:
return False
if grad_fn.__class__.__name__ == name:
return True
for next_grad_fn, _ in grad_fn.next_functions:
if find_grad_fn(next_grad_fn, name):
return True
return False
model.train()
train_output = model(input)
assert find_grad_fn(train_output.grad_fn, "CheckpointBackward")
assert find_grad_fn(train_output.grad_fn, "RecomputeBackward")
model.eval()
eval_output = model(input)
assert not find_grad_fn(eval_output.grad_fn, "CheckpointBackward")
assert not find_grad_fn(eval_output.grad_fn, "RecomputeBackward")
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 + Pipe 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())
}
style = Pipe.MultiProcess # Pipe.AsyncSchedule
if pipe_world_size == 2:
print(f"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 = Pipe(
model,
[2, 1],
style=style,
group=pipeline_devices,
worker_map=worker_map,
input_device=torch.cuda.current_device(),
chunks=chunk_size,
pipelined_backward=True,
).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(f"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:
if style == Pipe.MultiProcess:
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(f"calling step on slave")
optimizer.step()
print(f"calling check_weights on slave")
check_weights(model, reference, "pipe", index=0)
print(f"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 reuse_lazy():
if False: # speed
reused = LazyModule(lambda: nn.Linear(10, 10))
model = [
reused,
nn.Linear(10, 10),
nn.ReLU(), reused,
nn.ReLU(), reused,
nn.ReLU()
]
# model = [reused, reused, nn.Linear(10, 10), nn.ReLU(), reused, reused, nn.ReLU(), reused, reused, nn.ReLU()]
pipe = Pipe(model, [3, 1, 1],
style=Pipe.AsyncSchedule,
worker_map=get_worker_map())
pipe.eval()
output = pipe(torch.rand(10))
print(f"output on {pipe.group.rank()}, {output}")
torch.distributed.barrier()
set_random_seed(1234)
# test both foward
reused = nn.Linear(10, 10)
layers = [
reused,
nn.Linear(10, 10),
nn.ReLU(), reused,
nn.ReLU(), reused,
nn.ReLU()
]
model = nn.Sequential(*layers)
model.eval()
set_random_seed(1234)
# ensure identical weights but no sharing between model and pipe
reused = nn.Linear(10, 10)
layers = [
reused,
nn.Linear(10, 10),
nn.ReLU(), reused,
nn.ReLU(), reused,
nn.ReLU()
]
pipe = Pipe(layers, [3, 1, 1],
style=Pipe.AsyncSchedule,
worker_map=get_worker_map())
pipe.eval()
model_optimizer = torch.optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9)
pipe_optimizer = torch.optim.SGD(pipe.parameters(), lr=0.01,
momentum=0.9) if len(
list(pipe.parameters())) else None
inputs = torch.rand(10)
if False: # speed
model_out = model(inputs)
pipe_out = pipe(inputs)
torch.distributed.barrier()
if pipe.final_stage:
assert torch.equal(model_out, pipe_out)
model.train()
pipe.train()
model_out = model(inputs)
pipe_out = pipe(inputs)
if pipe.final_stage:
pipe_loss = pipe_out.mean()
pipe_loss.backward()
model_loss = model_out.mean()
model_loss.backward()
model_optimizer.step()
if pipe_optimizer:
pipe_optimizer.step()
model.eval()
pipe.eval()
model_out = model(inputs)
pipe_out = pipe(inputs)
print(f"before barrier on {torch.distributed.get_rank()}")
torch.distributed.barrier()
print(f"after barrier on {torch.distributed.get_rank()}")
if pipe.final_stage:
assert torch.equal(model_out, pipe_out)
def test_delete_portal_tensor(train, checkpoint):
# Without checkpointing:
# +- Stash --+ +--- Pop ----+ - - - layers
# | 2,blue,1 |--| 1,orange,0 | - - - tensor_life and portal function
# +----------+ +------------+
#
# With checkpointing:
# +- Stash --+ +--- Pop ----+ +--- Pop'----+ +- Stash'--+
# | 3,blue,2 |--| 2,orange,1 |--| 1,orange,0 |--| 1,blue,0 |
# +----------+ +------------+ +------------+ +----------+
def portal_tensor_life_is(tensor_life, skip_tracker=None):
if skip_tracker is None:
skip_tracker = current_skip_tracker()
# Get the current portal.
portal = list(skip_tracker.portals.values())[0]
if tensor_life == 0:
return portal.tensor_life == 0 and portal.tensor is None
else:
return portal.tensor_life == tensor_life and portal.tensor is not None
# Check the portal tensor after 'Stash'.
stash_ = Stash()
@stash_.register_forward_hook
def check_portal_tensor_after_stash(*_):
if is_checkpointing():
assert portal_tensor_life_is(2)
elif is_recomputing():
assert portal_tensor_life_is(0)
else:
assert portal_tensor_life_is(1)
pop_ = Pop()
@pop_.register_forward_hook
def check_portal_tensor_after_pop(*_):
if is_checkpointing():
assert portal_tensor_life_is(1)
elif is_recomputing():
assert portal_tensor_life_is(0)
else:
assert portal_tensor_life_is(0)
class NoPortalTensorAtBackward(nn.Module):
class F(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.skip_tracker = current_skip_tracker()
return input.detach()
@staticmethod
def backward(ctx, grad):
assert portal_tensor_life_is(0, skip_tracker=ctx.skip_tracker)
return grad
def forward(self, input):
return self.F.apply(input)
model = nn.Sequential(NoPortalTensorAtBackward(), stash_, pop_)
model = Pipe(model, balance=[2, 1], devices=["cpu", "cpu"], chunks=2, checkpoint=checkpoint)
input = torch.rand(10, requires_grad=True)
if train:
model.train()
output = model(input)
output.norm().backward()
else:
model.eval()
with torch.no_grad():
model(input)
