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[KT.regroup Ops][0/N] Add GPU trace for KT.regroup benchmark #2157

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[KT.regroup Ops][0/N] Add GPU trace for KT.regroup benchmark #2157

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6 changes: 3 additions & 3 deletions torchrec/distributed/benchmark/benchmark_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -492,9 +492,9 @@ def fx_script_module(eager_module: torch.nn.Module) -> torch.nn.Module:
def benchmark(
name: str,
model: torch.nn.Module,
warmup_inputs: List[KeyedJaggedTensor],
bench_inputs: List[KeyedJaggedTensor],
prof_inputs: List[KeyedJaggedTensor],
warmup_inputs: Union[List[KeyedJaggedTensor], List[Dict[str, Any]]],
bench_inputs: Union[List[KeyedJaggedTensor], List[Dict[str, Any]]],
prof_inputs: Union[List[KeyedJaggedTensor], List[Dict[str, Any]]],
world_size: int,
output_dir: str,
num_benchmarks: int,
Expand Down
123 changes: 68 additions & 55 deletions torchrec/sparse/tests/jagged_tensor_benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,7 @@ def bench(
run_backward: bool,
fn: Callable[..., List[torch.Tensor]],
fn_kwargs: Dict[str, Any],
output_dir: str = "",
) -> None:

# initial call
Expand All @@ -49,8 +50,8 @@ def wrapped_func(
model: torch.nn.Module, # not used
bench_inputs: List[KeyedJaggedTensor], # not used
fn: Callable[..., List[torch.Tensor]],
fn_kwargs: Dict[str, Any],
run_backward: bool,
**kwargs: Dict[str, Any],
) -> None:
result = fn(**fn_kwargs)
if run_backward:
Expand All @@ -64,26 +65,27 @@ def wrapped_func(
loss = torch.nn.functional.l1_loss(pred, labels)
loss.sum().backward()

model = DummyModel()
setattr(model, "forward", lambda kwargs: fn(**kwargs))
if device_type == "cuda":
result = benchmark(
name=name,
model=DummyModel(),
warmup_inputs=[],
model=model,
warmup_inputs=[fn_kwargs] * 10,
bench_inputs=[],
prof_inputs=[],
prof_inputs=[fn_kwargs] * 10,
world_size=1,
output_dir="",
output_dir=output_dir,
num_benchmarks=20,
func_to_benchmark=functools.partial(
wrapped_func, fn=fn, run_backward=run_backward, fn_kwargs=fn_kwargs
),
benchmark_func_kwargs={},
rank=0,
enable_logging=False,
enable_logging=True,
)

else: # cpu
model = DummyModel()
times = timeit.repeat(
lambda: wrapped_func(
model=model,
Expand Down Expand Up @@ -160,6 +162,12 @@ def wrapped_func(
default=2,
help="Total num of regrouping",
)
@click.option(
"--profile",
type=str,
default="",
help="profile output directory",
)
def main(
cuda_matrix: bool,
run_backward: bool,
Expand All @@ -170,6 +178,7 @@ def main(
dim_sparse: int,
batch_size: int,
n_groups: int,
profile: str,
) -> None:
if cuda_matrix:
n_denses = [64, 128, 256, 512, 1024]
Expand All @@ -184,54 +193,58 @@ def main(

for device_type in device_types:
for batch_size in batch_sizes:
for n_dense, n_sparse in zip(n_denses, n_sparses):

device = torch.device(device_type)
kts = build_kts(
n_dense,
n_sparse,
dim_dense,
dim_sparse,
batch_size,
device,
run_backward,
)
labels = torch.randint(
0, 1, (batch_size,), device=torch.device(device_type)
).float()
groups = build_groups(kts, n_groups)
bench(
"[fallback] _regroup_keyed_tenors",
labels,
batch_size,
n_dense + n_sparse,
device_type,
run_backward,
_regroup_keyed_tensors,
{"keyed_tensors": kts, "groups": groups},
)
bench(
"[prod] KeyedTensor.regroup",
labels,
batch_size,
n_dense + n_sparse,
device_type,
run_backward,
KeyedTensor.regroup,
{"keyed_tensors": kts, "groups": groups},
)
bench(
"[prod] KTRegroupAsDict",
labels,
batch_size,
n_dense + n_sparse,
device_type,
run_backward,
KTRegroupAsDict(
groups=groups, keys=[str(i) for i in range(n_groups)]
),
{"keyed_tensors": kts},
)
for duplicates in [False, True]:
for n_dense, n_sparse in zip(n_denses, n_sparses):
dup = "_dup" if duplicates else ""
device = torch.device(device_type)
kts = build_kts(
n_dense,
n_sparse,
dim_dense,
dim_sparse,
batch_size,
device,
run_backward,
)
labels = torch.randint(
0, 1, (batch_size,), device=torch.device(device_type)
).float()
groups = build_groups(kts, n_groups, duplicates=duplicates)
bench(
"_regroup_keyed_tenors" + dup,
labels,
batch_size,
n_dense + n_sparse,
device_type,
run_backward,
_regroup_keyed_tensors,
{"keyed_tensors": kts, "groups": groups},
profile,
)
bench(
"KeyedTensor.regroup" + dup,
labels,
batch_size,
n_dense + n_sparse,
device_type,
run_backward,
KeyedTensor.regroup,
{"keyed_tensors": kts, "groups": groups},
profile,
)
bench(
"KTRegroupAsDict" + dup,
labels,
batch_size,
n_dense + n_sparse,
device_type,
run_backward,
KTRegroupAsDict(
groups=groups, keys=[str(i) for i in range(n_groups)]
),
{"keyed_tensors": kts},
profile,
)


if __name__ == "__main__":
Expand Down