def test_catch_grad_grad():
with temp_files_ctx(num=1) as temp_files:
# Check that ShardedDDP exposes the original module's attributes
dist.init_process_group(init_method="file://" + temp_files[0],
backend="gloo",
rank=0,
world_size=1)
model = Sequential(Linear(2, 3), Linear(3, 3))
model.train()
chained_grad = torch.zeros_like(next(model.parameters()))
chained_grad.requires_grad = True
next(model.parameters()).grad = chained_grad
optimizer = OSS(params=model.parameters(),
optim=torch.optim.SGD,
lr=1e-3,
momentum=0.99)
ddp_model = ShardedDataParallel(model, optimizer)
inputs = torch.rand(100, 2)
with pytest.raises(RuntimeError):
_ = ddp_model(inputs)
dist.destroy_process_group()
def _get_cached_results(
world_size,
with_model2,
with_sync_bn,
with_fsdp,
with_checkpoint,
mixed_precision,
flatten,
wrap_bn,
fp32_reduce_scatter,
bucket_cap_mb,
):
"""Cache the training to save time. For DDP, flatten, wrap_bn etc. doesn't matter, so
the results can be cached.
"""
if not with_fsdp:
flatten = None
wrap_bn = None
fp32_reduce_scatter = None
key = (
world_size,
with_model2,
with_sync_bn,
with_fsdp,
with_checkpoint,
mixed_precision,
flatten,
wrap_bn,
fp32_reduce_scatter,
bucket_cap_mb,
)
global _result_cache
if key not in _result_cache:
# Get 4 files: 2 for dist_init and 2 for each rank to save the losses.
with temp_files_ctx(num=2 + world_size) as temp_files:
mp.spawn(
_distributed_worker,
(
world_size,
with_model2,
with_sync_bn,
with_fsdp,
with_checkpoint,
temp_files,
mixed_precision,
flatten,
wrap_bn,
fp32_reduce_scatter,
bucket_cap_mb,
),
nprocs=world_size,
)
final_losses = {}
for rank in range(world_size):
with open(temp_files[2 + rank], "rb") as f:
for iter_key, loss in pickle.load(f).items():
final_losses[f"rank_{rank}_{iter_key}"] = loss
_result_cache[key] = final_losses
return _result_cache[key]
def test_train_and_eval_with_checkpointing(flatten, mixed_precision,
amp_context, half_input,
fsdp_wrap_ckpt):
flatten = flatten == "flat"
mixed_precision = mixed_precision == "fp16"
amp_context = amp_context == "autocast"
half_input = half_input == "halfin"
fsdp_wrap_ckpt = fsdp_wrap_ckpt == "F->C"
# Expecting an known bug in 4 out of 32 cases.
if fsdp_wrap_ckpt and mixed_precision and not flatten:
pytest.skip("known bug")
world_size = 2
with temp_files_ctx(2) as (temp_file_name, unused):
mp.spawn(
_test_func,
args=(
world_size,
temp_file_name,
unused,
flatten,
mixed_precision,
amp_context,
half_input,
fsdp_wrap_ckpt,
),
nprocs=world_size,
join=True,
)
def test_consolidation(embedding_size: int, flatten_parameters: bool):
world_size = 2
with in_temporary_directory():
with temp_files_ctx(num=1) as temp_files:
mp.spawn(_worker, (temp_files[0], world_size, embedding_size,
flatten_parameters),
nprocs=world_size)
def test_gpt2(world_size):
# Check that having trainable unused params is fine
backend = "gloo"
device = "cuda"
with temp_files_ctx(num=1) as temp_files:
mp.spawn(run_test_gpt2,
args=(world_size, backend, device, temp_files[0]),
nprocs=world_size,
join=True)
def test_forward_overlap(world_size, flatten, mixed):
fsdp_config = {
"flatten_parameters": flatten == "flatten",
"mixed_precision": mixed == "mixed",
}
with temp_files_ctx(2) as temp_files:
mp.spawn(
_distributed_worker, (world_size, fsdp_config, temp_files[0], temp_files[1]), nprocs=world_size,
)
def test_train_eval_change():
world_size = 4
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_test_train_eval_change,
args=(world_size, temp_files[0]),
nprocs=world_size,
join=True,
)
def test_multiple_groups(reduce_buffer_size, backend):
world_size = 4
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_test_multiple_groups,
args=(world_size, temp_files[0], backend, reduce_buffer_size),
nprocs=world_size,
join=True,
)
def test_ddp_parity_two_optim(reduce_buffer_size):
world_size = 2
backend = dist.Backend.NCCL
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_ddp_parity_two_optim,
args=(world_size, backend, temp_files[0], reduce_buffer_size),
nprocs=world_size,
join=True,
)
def run_test(backend, device, world_size, broadcast_buffers, grad_accumulation,
reduce_buffer_size, optimizer_type):
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_one_step,
args=(world_size, backend, device, temp_files[0],
broadcast_buffers, grad_accumulation, reduce_buffer_size),
nprocs=world_size,
join=True,
)
def test_two_optimizers():
# Check that the ShardedDDP wrapper accepts tuple(tensors) as inputs
world_size = 2
backend = "gloo"
device = "cpu"
with temp_files_ctx(num=1) as temp_files:
mp.spawn(run_test_two_optimizers,
args=(world_size, backend, device, temp_files[0]),
nprocs=world_size,
join=True)
def test_training_change(reduce_buffer_size):
world_size = 2
backend = "nccl"
device = "cuda"
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_test_training_change,
args=(world_size, backend, device, temp_files[0],
reduce_buffer_size),
nprocs=world_size,
join=True,
)
def test_ddp_sync_batch_norm():
# Check that ShardedDDP is compatible with sync batch norm across multiple GPUs
world_size = 2
backend = "gloo"
device = "cuda"
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_test_ddp_sync_batch_norm,
args=(world_size, backend, device, temp_files[0]),
nprocs=world_size,
join=True,
)
def test_device_change(reduce_buffer_size):
# Check that ShardedDDP handles a device change properly
world_size = 2
backend = "nccl"
with temp_files_ctx(num=1) as temp_files:
device = "cuda"
mp.spawn(
run_test_device_change,
args=(world_size, backend, device, temp_files[0],
reduce_buffer_size),
nprocs=world_size,
join=True,
)
def test_train_and_eval_with_checkpointing():
if torch_version() < (1, 6, 0):
pytest.skip("older pytorch doesn't support reduce_scatter")
world_size = 2
with temp_files_ctx(2) as (temp_file_name, unused):
mp.spawn(
_test_func,
args=(world_size, temp_file_name, unused),
nprocs=world_size,
join=True,
)
def test_memory_tracking_fsdp():
"""
Check that we can collect memory traces of a simplistic model
in the context of FSDP distributed training
"""
with temp_files_ctx(num=2) as sync_files:
world_size = 2
mp.spawn(
_layer_memory_tracking_fsdp_worker,
(sync_files, world_size),
nprocs=world_size,
)
def test_inputs(reduce_buffer_size, backend, device):
# Check that the ShardedDDP wrapper accepts tuple(tensors) as inputs
world_size = 2
if backend == "nccl" and device == "cpu":
pytest.skip("Incompatible combination, or cuda not available")
return
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_test_two_inputs,
args=(world_size, backend, device, temp_files[0],
reduce_buffer_size),
nprocs=world_size,
join=True,
)
def test_multiple_forward_checkpoint(precision, flatten, wrap_bn):
mixed_precision = precision == "mixed"
flatten = flatten == "flatten"
wrap_bn = wrap_bn == "auto_wrap_bn"
fp32_reduce_scatter = True if mixed_precision else None
if torch_version() < (1, 8, 0) and flatten:
# 1.6 and 1.7 throws this error:
# RuntimeError: Trying to backward through the graph a second time, but the saved
# intermediate results have already been freed. Specify retain_graph=True when calling
# backward the first time.
pytest.skip("older pytorch throws error when flatten is used")
world_size = 2
expected_losses = None
# Ensure ddp == ddp+ckpt == fsdp == fsdp+ckpt.
for with_fsdp in [False, True]:
for with_checkpoint in [False, True]:
# Get 4 files: 2 for dist_init and 2 for each rank to save the losses.
with temp_files_ctx(num=2 + world_size) as temp_files:
mp.spawn(
_distributed_worker,
(
world_size,
with_fsdp,
with_checkpoint,
temp_files,
mixed_precision,
flatten,
wrap_bn,
fp32_reduce_scatter,
),
nprocs=world_size,
)
final_losses = {}
for rank in range(world_size):
with open(temp_files[2 + rank], "rb") as f:
final_losses[f"rank_{rank}"] = pickle.load(f)
if expected_losses is None:
expected_losses = final_losses
else:
print(f"fsdp: {with_fsdp} ckpt: {with_checkpoint}")
assert objects_are_equal(expected_losses,
final_losses,
raise_exception=True)
def test_local_state_dict_calls_state_dict_recursion():
"""Testing the case of infinite recursive when FSDP is subclassed"""
class TestModule(FSDP):
def __init__(self):
super().__init__(module=nn.Linear(100, 100))
def state_dict(self, *args, **kwargs):
return self.local_state_dict(*args, **kwargs)
rank = 0
world_size = 1
with temp_files_ctx(2) as temp_files:
result = dist_init(rank, world_size, temp_files[0], temp_files[1])
assert result, "Dist init failed"
m = TestModule()
d = m.state_dict()
teardown()
def test_mixed_types():
with temp_files_ctx(num=1) as temp_files:
# Check that ShardedDDP exposes the original module's attributes
dist.init_process_group(init_method="file://" + temp_files[0],
backend="gloo",
rank=0,
world_size=1)
model = _get_mlp(tripwire=True)
optimizer = OSS(params=model.parameters(),
optim=torch.optim.SGD,
lr=1e-3,
momentum=0.99)
model = ShardedDataParallel(model, optimizer)
input_tensor = torch.rand((2, 2))
_ = model(input_tensor)
dist.destroy_process_group()
def test_ddp_attributes():
# Check that ShardedDDP exposes the same attributes as Pytorch's DDP
# - is multi_device_module
# - device_type
with temp_files_ctx(num=1) as temp_files:
dist.init_process_group(init_method="file://" + temp_files[0],
backend="gloo",
rank=0,
world_size=1)
model = Sequential(Linear(2, 3), Linear(3, 3))
optimizer = OSS(params=model.parameters(),
optim=torch.optim.SGD,
lr=1e-3,
momentum=0.99)
ddp_model = ShardedDataParallel(model, optimizer)
assert hasattr(ddp_model, "is_multi_device_module")
assert hasattr(ddp_model, "device_type")
dist.destroy_process_group()
def test_step(broadcast_buffers, grad_accumulation, reduce_buffer_size,
optimizer_type, reduce_fp16, setup):
world_size = 2
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_one_step,
args=(
world_size,
setup[0],
setup[1],
temp_files[0],
broadcast_buffers,
grad_accumulation,
reduce_buffer_size,
optimizer_type,
reduce_fp16,
),
nprocs=world_size,
join=True,
)
def test_random_attributes():
with temp_files_ctx(num=1) as temp_files:
# Check that ShardedDDP exposes the original module's attributes
dist.init_process_group(init_method="file://" + temp_files[0],
backend="gloo",
rank=0,
world_size=1)
model = Sequential(Linear(2, 3), Linear(3, 3))
model.banana = "sweet"
optimizer = OSS(params=model.parameters(),
optim=torch.optim.SGD,
lr=1e-3,
momentum=0.99)
ddp_model = ShardedDataParallel(model, optimizer)
assert hasattr(ddp_model, "banana")
assert not hasattr(ddp_model, "orange")
dist.destroy_process_group()
def test_ddp_parity(
reduce_buffer_size,
grad_accumulation,
change_train_graph,
fp16_reduction,
clip_grad_norm,
amp,
manual_reduction,
multiple_fw,
):
if torch_version() < (1, 8, 0):
pytest.skip("pytorch version >= 1.8.0 required")
if manual_reduction and change_train_graph:
pytest.skip(
"Skipping changing model and grad accumulation combination, makes little sense"
)
world_size = torch.cuda.device_count()
backend = dist.Backend.NCCL
with temp_files_ctx(num=1) as temp_files:
mp.spawn(
run_ddp_parity,
args=(
world_size,
backend,
temp_files[0],
reduce_buffer_size,
grad_accumulation,
change_train_graph,
fp16_reduction,
clip_grad_norm,
amp,
manual_reduction,
multiple_fw,
),
nprocs=world_size,
join=True,
)
def temp_files():
# dist_init needs 2 files
with temp_files_ctx(2) as files:
yield files
def test_fsdp_memory(fsdp, ckpt):
expected = {
("ddp", "no_ckpt"): {
"iter 0: start": 9,
"iter 0: after fwd": 346,
"iter 0: after loss": 346,
"iter 0: after bwd": 14,
"iter 0: after step": 14,
"iter 0: done": 9,
"iter 1: start": 9,
"iter 1: after fwd": 346,
"iter 1: after loss": 346,
"iter 1: after bwd": 14,
"iter 1: after step": 14,
"iter 1: done": 9,
"iter 2: start": 9,
"iter 2: after fwd": 346,
"iter 2: after loss": 346,
"iter 2: after bwd": 14,
"iter 2: after step": 14,
"iter 2: done": 9,
},
("fsdp", "no_ckpt"): {
"iter 0: start": 3,
"iter 0: after fwd": 340,
"iter 0: after loss": 340,
"iter 0: after bwd": 66,
"iter 0: after step": 66,
"iter 0: done": 3,
"iter 1: start": 3,
"iter 1: after fwd": 340,
"iter 1: after loss": 340,
"iter 1: after bwd": 66,
"iter 1: after step": 66,
"iter 1: done": 3,
"iter 2: start": 3,
"iter 2: after fwd": 340,
"iter 2: after loss": 340,
"iter 2: after bwd": 66,
"iter 2: after step": 66,
"iter 2: done": 3,
},
("ddp", "ckpt"): {
"iter 0: start": 9,
"iter 0: after fwd": 57,
"iter 0: after loss": 57,
"iter 0: after bwd": 14,
"iter 0: after step": 14,
"iter 0: done": 9,
"iter 1: start": 9,
"iter 1: after fwd": 57,
"iter 1: after loss": 57,
"iter 1: after bwd": 14,
"iter 1: after step": 14,
"iter 1: done": 9,
"iter 2: start": 9,
"iter 2: after fwd": 57,
"iter 2: after loss": 57,
"iter 2: after bwd": 14,
"iter 2: after step": 14,
"iter 2: done": 9,
},
("fsdp", "ckpt"): {
"iter 0: start": 3,
"iter 0: after fwd": 51,
"iter 0: after loss": 51,
"iter 0: after bwd": 66,
"iter 0: after step": 66,
"iter 0: done": 3,
"iter 1: start": 3,
"iter 1: after fwd": 51,
"iter 1: after loss": 51,
"iter 1: after bwd": 66,
"iter 1: after step": 66,
"iter 1: done": 3,
"iter 2: start": 3,
"iter 2: after fwd": 51,
"iter 2: after loss": 51,
"iter 2: after bwd": 66,
"iter 2: after step": 66,
"iter 2: done": 3,
},
}[(fsdp, ckpt)]
fsdp = fsdp == "fsdp"
ckpt = ckpt == "ckpt"
world_size = 2
with temp_files_ctx(num=2) as temp_files:
mp.spawn(
_distributed_worker, (world_size, fsdp, ckpt, temp_files[0], temp_files[1], expected), nprocs=world_size
)
def test_fsdp_memory(fsdp, ckpt):
expected = {
("ddp", "no_ckpt"): {
"iter 0: start": 9,
"iter 0: after fwd": 346,
"iter 0: after loss": 346,
"iter 0: after bwd": 14,
"iter 0: after step": 17,
"iter 0: done": 13,
"iter 1: start": 13,
"iter 1: after fwd": 350,
"iter 1: after loss": 350,
"iter 1: after bwd": 17,
"iter 1: after step": 17,
"iter 1: done": 13,
"iter 2: start": 13,
"iter 2: after fwd": 350,
"iter 2: after loss": 350,
"iter 2: after bwd": 17,
"iter 2: after step": 17,
"iter 2: done": 13,
"iter 3: start": 13,
"iter 3: after fwd": 350,
"iter 3: after loss": 350,
"iter 3: after bwd": 17,
"iter 3: after step": 17,
"iter 3: done": 13,
},
("fsdp", "no_ckpt"): {
"iter 0: start": 3,
"iter 0: after fwd": 340,
"iter 0: after loss": 340,
"iter 0: after bwd": 16,
"iter 0: after step": 18,
"iter 0: done": 5,
"iter 1: start": 5,
"iter 1: after fwd": 342,
"iter 1: after loss": 342,
"iter 1: after bwd": 18,
"iter 1: after step": 18,
"iter 1: done": 5,
"iter 2: start": 5,
"iter 2: after fwd": 342,
"iter 2: after loss": 342,
"iter 2: after bwd": 18,
"iter 2: after step": 18,
"iter 2: done": 5,
"iter 3: start": 5,
"iter 3: after fwd": 342,
"iter 3: after loss": 342,
"iter 3: after bwd": 18,
"iter 3: after step": 18,
"iter 3: done": 5,
},
("fsdp_amp_default", "no_ckpt"): {
"iter 0: start": 28,
"iter 0: after fwd": 630,
"iter 0: after loss": 630,
"iter 0: after bwd": 67,
"iter 0: after step": 93,
"iter 0: done": 54,
"iter 1: start": 54,
"iter 1: after fwd": 657,
"iter 1: after loss": 657,
"iter 1: after bwd": 93,
"iter 1: after step": 93,
"iter 1: done": 54,
"iter 2: start": 54,
"iter 2: after fwd": 657,
"iter 2: after loss": 657,
"iter 2: after bwd": 93,
"iter 2: after step": 93,
"iter 2: done": 54,
"iter 3: start": 54,
"iter 3: after fwd": 657,
"iter 3: after loss": 657,
"iter 3: after bwd": 93,
"iter 3: after step": 93,
"iter 3: done": 54,
},
("fsdp_amp_compute_dtype32", "no_ckpt"): {
"iter 0: start": 28,
"iter 0: after fwd": 657,
"iter 0: after loss": 657,
"iter 0: after bwd": 67,
"iter 0: after step": 93,
"iter 0: done": 54,
"iter 1: start": 54,
"iter 1: after fwd": 684,
"iter 1: after loss": 684,
"iter 1: after bwd": 93,
"iter 1: after step": 93,
"iter 1: done": 54,
"iter 2: start": 54,
"iter 2: after fwd": 684,
"iter 2: after loss": 684,
"iter 2: after bwd": 93,
"iter 2: after step": 93,
"iter 2: done": 54,
"iter 3: start": 54,
"iter 3: after fwd": 684,
"iter 3: after loss": 684,
"iter 3: after bwd": 93,
"iter 3: after step": 93,
"iter 3: done": 54,
},
("ddp", "ckpt"): {
"iter 0: start": 9,
"iter 0: after fwd": 57,
"iter 0: after loss": 57,
"iter 0: after bwd": 14,
"iter 0: after step": 17,
"iter 0: done": 13,
"iter 1: start": 13,
"iter 1: after fwd": 61,
"iter 1: after loss": 61,
"iter 1: after bwd": 17,
"iter 1: after step": 17,
"iter 1: done": 13,
"iter 2: start": 13,
"iter 2: after fwd": 61,
"iter 2: after loss": 61,
"iter 2: after bwd": 17,
"iter 2: after step": 17,
"iter 2: done": 13,
"iter 3: start": 13,
"iter 3: after fwd": 61,
"iter 3: after loss": 61,
"iter 3: after bwd": 17,
"iter 3: after step": 17,
"iter 3: done": 13,
},
("fsdp", "ckpt"): {
"iter 0: start": 3,
"iter 0: after fwd": 51,
"iter 0: after loss": 51,
"iter 0: after bwd": 16,
"iter 0: after step": 18,
"iter 0: done": 5,
"iter 1: start": 5,
"iter 1: after fwd": 53,
"iter 1: after loss": 53,
"iter 1: after bwd": 18,
"iter 1: after step": 18,
"iter 1: done": 5,
"iter 2: start": 5,
"iter 2: after fwd": 53,
"iter 2: after loss": 53,
"iter 2: after bwd": 18,
"iter 2: after step": 18,
"iter 2: done": 5,
"iter 3: start": 5,
"iter 3: after fwd": 53,
"iter 3: after loss": 53,
"iter 3: after bwd": 18,
"iter 3: after step": 18,
"iter 3: done": 5,
},
("fsdp_amp_default", "ckpt"): {
"iter 0: start": 28,
"iter 0: after fwd": 52,
"iter 0: after loss": 52,
"iter 0: after bwd": 67,
"iter 0: after step": 93,
"iter 0: done": 54,
"iter 1: start": 54,
"iter 1: after fwd": 79,
"iter 1: after loss": 79,
"iter 1: after bwd": 93,
"iter 1: after step": 93,
"iter 1: done": 54,
"iter 2: start": 54,
"iter 2: after fwd": 79,
"iter 2: after loss": 79,
"iter 2: after bwd": 93,
"iter 2: after step": 93,
"iter 2: done": 54,
"iter 3: start": 54,
"iter 3: after fwd": 79,
"iter 3: after loss": 79,
"iter 3: after bwd": 93,
"iter 3: after step": 93,
"iter 3: done": 54,
},
("fsdp_amp_compute_dtype32", "ckpt"): {
"iter 0: start": 28,
"iter 0: after fwd": 52,
"iter 0: after loss": 52,
"iter 0: after bwd": 67,
"iter 0: after step": 93,
"iter 0: done": 54,
"iter 1: start": 54,
"iter 1: after fwd": 79,
"iter 1: after loss": 79,
"iter 1: after bwd": 93,
"iter 1: after step": 93,
"iter 1: done": 54,
"iter 2: start": 54,
"iter 2: after fwd": 79,
"iter 2: after loss": 79,
"iter 2: after bwd": 93,
"iter 2: after step": 93,
"iter 2: done": 54,
"iter 3: start": 54,
"iter 3: after fwd": 79,
"iter 3: after loss": 79,
"iter 3: after bwd": 93,
"iter 3: after step": 93,
"iter 3: done": 54,
},
}[(fsdp, ckpt)]
# Compute the FSDP config.
fsdp_config = {}
# Set mixed precision.
if "amp" in fsdp:
fsdp_config["mixed_precision"] = True
# When compute_dtype is FP32, make sure we use clear_autocast_cache.
# Setting fp32_reduce_scatter and verbose for more code coverage.
if "compute_dtype32" in fsdp:
fsdp_config["compute_dtype"] = torch.float32
fsdp_config["fp32_reduce_scatter"] = True
fsdp_config["clear_autocast_cache"] = True
fsdp_config["verbose"] = True
# Using bigger hidden dimension for AMP to increase the model size
# so that bug in handling params will show up but we don't do that
# in the base case to keep the test fast.
# - hidden_dim 128: model size ~4MB
# - hidden_dim 512: model size ~55MB
# - hidden_dim 1024: model size ~200MB (seems to be too big for CI tests though)
model_hidden_dim = 128
if "amp" in fsdp:
model_hidden_dim = 512
# Get the fsdp and checkpoint flags.
with_fsdp = "fsdp" in fsdp
with_ckpt = ckpt == "ckpt"
world_size = 2
with temp_files_ctx(num=2) as temp_files:
mp.spawn(
_distributed_worker,
(world_size, with_fsdp, with_ckpt, temp_files[0], temp_files[1],
expected, model_hidden_dim, fsdp_config),
nprocs=world_size,
)
def temp_files():
# dist_init needs 2 files + 3 files for before state, after state, in_data.
with temp_files_ctx(5) as files:
yield files