[fp8] support asynchronous FP8 communication (#5997)

* fix

* fix

* fix

* support async all2all

* support async op for all gather

* fix

* fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
flybird11111
2024-08-14 14:08:19 +08:00
committed by GitHub
parent 0978080a69
commit 597b206001
5 changed files with 151 additions and 67 deletions

View File

@@ -10,19 +10,24 @@ from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
@parameterize("shape", [(4,), (1, 8, 16), (4, 8, 16)])
@parameterize("dtype", [torch.bfloat16])
def check_all2all(shape, dtype):
@parameterize("dtype", [torch.bfloat16, torch.float16])
@parameterize("async_op", [True, False])
def check_all2all(shape, dtype, async_op):
x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
output = torch.empty_like(x)
output_fp8 = torch.empty_like(x)
dist.all_to_all_single(output, x, group=_get_default_group(), async_op=False)
all_to_all_single_fp8(output_fp8, x, group=_get_default_group(), async_op=False)
origin_hanle = dist.all_to_all_single(output, x, group=_get_default_group(), async_op=async_op)
fp8_handle = all_to_all_single_fp8(output_fp8, x, group=_get_default_group(), async_op=async_op)
if async_op:
origin_hanle.wait()
fp8_handle.wait()
assert_close(output, output_fp8, rtol=0.1, atol=0.1)
@parameterize("shape", [(8, 8, 16)])
@parameterize("dtype", [torch.bfloat16, torch.float16])
def check_all2all_uneven(shape, dtype):
@parameterize("async_op", [True, False])
def check_all2all_uneven(shape, dtype, async_op):
x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
input_split_sizes = [3, 3, 1, 1]
if dist.get_rank() in [0, 1]:
@@ -33,22 +38,25 @@ def check_all2all_uneven(shape, dtype):
output_shape[0] = sum(output_split_sizes)
output = torch.empty(output_shape, device=x.device, dtype=x.dtype)
output_fp8 = torch.empty(output_shape, device=x.device, dtype=x.dtype)
dist.all_to_all_single(
origin_hanle = dist.all_to_all_single(
output,
x,
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
group=_get_default_group(),
async_op=False,
async_op=async_op,
)
all_to_all_single_fp8(
fp8_handle = all_to_all_single_fp8(
output_fp8,
x,
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
group=_get_default_group(),
async_op=False,
async_op=async_op,
)
if async_op:
origin_hanle.wait()
fp8_handle.wait()
assert_close(output, output_fp8, rtol=0.1, atol=0.1)