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# Owner(s): ["oncall: distributed"]
import torch
import os
import torch.cuda
import sys
import torch.distributed as dist
import torch.distributed.algorithms._quantization.quantization as quant
from torch.distributed.algorithms._quantization.quantization import DQuantType
from torch.testing._internal.common_distributed import (
MultiProcessTestCase,
init_multigpu_helper,
requires_gloo,
skip_if_rocm,
skip_if_lt_x_gpu,
requires_nccl,
)
from torch.testing._internal.common_utils import sandcastle_skip_if, run_tests, TEST_WITH_DEV_DBG_ASAN, NO_MULTIPROCESSING_SPAWN
torch.backends.cuda.matmul.allow_tf32 = False
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
def _build_tensor(size, value=None, dtype=torch.float, device_id=None):
if value is None:
value = size
if device_id is None:
return torch.empty(size, dtype=dtype).fill_(value)
else:
return torch.empty(size, dtype=dtype).fill_(value).cuda(device_id)
if TEST_WITH_DEV_DBG_ASAN:
print("Skip dev-asan as torch + multiprocessing spawn have known issues", file=sys.stderr)
sys.exit(0)
if NO_MULTIPROCESSING_SPAWN:
print("Spawn not available, skipping tests.", file=sys.stderr)
sys.exit(0)
BACKEND = os.environ["BACKEND"]
if BACKEND == "gloo" or BACKEND == "nccl":
class DistQuantizationTests(MultiProcessTestCase):
def setUp(self):
super(DistQuantizationTests, self).setUp()
self._spawn_processes()
torch.backends.cudnn.flags(allow_tf32=False).__enter__()
def tearDown(self):
super(DistQuantizationTests, self).tearDown()
try:
os.remove(self.file_name)
except OSError:
pass
@property
def op_timeout_sec(self):
return 1
@property
def world_size(self):
return int(os.environ["WORLD_SIZE"])
@requires_gloo()
@sandcastle_skip_if(BACKEND != "gloo", "Only gloo backend supports all_gather_fp16")
def test_all_gather_fp16(self):
store = dist.FileStore(self.file_name, self.world_size)
dist.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
device = torch.device(f"cuda:{self.rank}")
group = list(range(0, self.world_size))
group_id = dist.group.WORLD
self._test_all_gather(group, group_id, self.rank, dtype=torch.float32, qtype=DQuantType.FP16)
@requires_gloo()
@sandcastle_skip_if(BACKEND != "gloo", "Only gloo backend supports all_gather_fp16")
def test_all_gather_bfp16(self):
store = dist.FileStore(self.file_name, self.world_size)
dist.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
device = torch.device(f"cuda:{self.rank}")
group = list(range(0, self.world_size))
group_id = dist.group.WORLD
self._test_all_gather(group, group_id, self.rank, dtype=torch.float32, qtype=DQuantType.BFP16)
@requires_nccl()
@sandcastle_skip_if(BACKEND != "nccl", "Only nccl backend supports all_to_all_fp16")
@skip_if_lt_x_gpu(int(os.environ["WORLD_SIZE"]))
@skip_if_rocm
def test_all_to_all_fp16(self):
store = dist.FileStore(self.file_name, self.world_size)
dist.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='nccl')
device = torch.device(f"cuda:{self.rank}")
group = list(range(0, self.world_size))
group_id = dist.new_group(range(self.world_size))
rank_to_GPU = init_multigpu_helper(self.world_size, BACKEND)
self._test_all_to_all(
group,
group_id,
self.rank,
cuda=True,
rank_to_GPU=rank_to_GPU,
dtype=torch.float32,
qtype=DQuantType.FP16)
@requires_nccl()
@sandcastle_skip_if(BACKEND != "nccl", "Only nccl backend supports all_to_all_fp16")
@skip_if_lt_x_gpu(int(os.environ["WORLD_SIZE"]))
@skip_if_rocm
def test_all_to_all_bfp16(self):
store = dist.FileStore(self.file_name, self.world_size)
dist.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='nccl')
device = torch.device(f"cuda:{self.rank}")
group = list(range(0, self.world_size))
group_id = dist.new_group(range(self.world_size))
rank_to_GPU = init_multigpu_helper(self.world_size, BACKEND)
self._test_all_to_all(
group,
group_id,
self.rank,
cuda=True,
rank_to_GPU=rank_to_GPU,
dtype=torch.float32,
qtype=DQuantType.BFP16)
@requires_nccl()
@sandcastle_skip_if(BACKEND != "nccl", "Only nccl backend supports all_to_all_single_fp16")
@skip_if_lt_x_gpu(int(os.environ["WORLD_SIZE"]))
def test_all_to_all_single_fp16(self):
store = dist.FileStore(self.file_name, self.world_size)
dist.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='nccl')
device = torch.device(f"cuda:{self.rank}")
group = list(range(0, self.world_size))
group_id = dist.new_group(range(self.world_size))
rank_to_GPU = init_multigpu_helper(self.world_size, BACKEND)
self._test_all_to_all_single(
group,
group_id,
self.rank,
cuda=True,
rank_to_GPU=rank_to_GPU,
dtype=torch.float32,
qtype=DQuantType.FP16
)
@requires_nccl()
@sandcastle_skip_if(BACKEND != "nccl", "Only nccl backend supports all_to_all_single_bfp16")
@skip_if_lt_x_gpu(int(os.environ["WORLD_SIZE"]))
def test_all_to_all_single_bfp16(self):
store = dist.FileStore(self.file_name, self.world_size)
dist.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='nccl')
device = torch.device(f"cuda:{self.rank}")
group = list(range(0, self.world_size))
group_id = dist.new_group(range(self.world_size))
rank_to_GPU = init_multigpu_helper(self.world_size, BACKEND)
self._test_all_to_all_single(
group,
group_id,
self.rank,
cuda=True,
rank_to_GPU=rank_to_GPU,
dtype=torch.float32,
qtype=DQuantType.BFP16
)
def _test_all_gather(
self, group, group_id, rank, cuda=False, rank_to_GPU=None, dtype=torch.float, qtype=None):
for dest in group:
tensor = _build_tensor([dest + 1, dest + 1], rank, dtype=dtype)
tensors = [_build_tensor([dest + 1, dest + 1], -1, dtype=dtype) for i in group]
expected_tensors = [
_build_tensor([dest + 1, dest + 1], i, dtype=dtype) for i in group
]
if cuda:
tensor = tensor.cuda(rank_to_GPU[rank][0])
tensors = [t.cuda(rank_to_GPU[rank][0]) for t in tensors]
if tensors[0].dtype == torch.complex64:
tensor_shapes = [torch.view_as_real(tensors[0]).shape]
else:
tensor_shapes = [tensors[0].shape]
allgather = quant.auto_quantize(dist.all_gather, qtype, quant_loss=None)
allgather(tensors, tensor, group=group_id, async_op=False)
for t1, t2 in zip(tensors, expected_tensors):
self.assertEqual(t1, t2)
def _test_all_to_all(
self,
group,
group_id,
rank,
cuda=False,
rank_to_GPU=None,
dtype=torch.float,
qtype=None
):
if group_id is not None:
size = len(group)
in_splits = [i + 1 for i in group]
in_tensors = [
torch.ones([in_splits[i], size], dtype=dtype) * rank
for i, _ in enumerate(group)
]
out_tensors = [
torch.ones([(rank + 1), size], dtype=dtype) for _ in group
]
expected_tensors = [
torch.ones([rank + 1, size], dtype=dtype) * i for i in group
]
if cuda:
in_tensors = [t.cuda(rank_to_GPU[rank][0]) for t in in_tensors]
expected_tensors = [
t.cuda(rank_to_GPU[rank][0]) for t in expected_tensors
]
out_tensors = [t.cuda(rank_to_GPU[rank][0]) for t in out_tensors]
quantize_alltoall = quant.auto_quantize(dist.all_to_all, qtype, quant_loss=None)
quantize_alltoall(out_tensors, in_tensors, group=group_id)
for t1, t2 in zip(out_tensors, expected_tensors):
self.assertEqual(t1, t2)
def _test_all_to_all_single(
self, group, group_id, rank, cuda=False, rank_to_GPU=None, dtype=torch.float, qtype=DQuantType.FP16
):
if group_id is not None:
size = len(group)
in_splits = [i + 1 for i in group]
out_splits = [rank + 1 for _ in group]
in_tensor = torch.ones([sum(in_splits), size], dtype=dtype) * rank
out_tensor = torch.ones([(rank + 1) * size, size], dtype=dtype)
expected_tensor = torch.cat(
[torch.ones([rank + 1, size], dtype=dtype) * i for i in group]
)
if cuda:
rank_to_GPU = rank_to_GPU[rank][0]
in_tensor = in_tensor.cuda(rank_to_GPU)
expected_tensor = expected_tensor.cuda(rank_to_GPU)
out_tensor = out_tensor.cuda(rank_to_GPU)
quantize_alltoall_single = quant.auto_quantize(dist.all_to_all_single, qtype, quant_loss=None)
quantize_alltoall_single(out_tensor, in_tensor, out_splits=out_splits, in_splits=in_splits, group=group_id)
self.assertEqual(out_tensor, expected_tensor)
if __name__ == "__main__":
run_tests()
|
