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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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150
tests/test_tensor/test_mix_gather.py
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@@ -17,12 +17,13 @@ def check_mix_gather_S0S1(device_mesh, rank):
f_target_pair = (f, [0])
b_target_pair = (b, [1])
gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
tensor_slice = [4, 2] # (4, 2)
tensor_slice = [4, 2] # (4, 2)
rank_slice = 4
f_start = (rank // rank_slice) * tensor_slice[0]
b_start = (rank % rank_slice) * tensor_slice[1]
tensor_to_comm = tensor_to_check[f_start:f_start + tensor_slice[0],
b_start:b_start + tensor_slice[1]].contiguous().cuda()
tensor_to_comm = (
tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
)
dim_partition_dict = {0: [0], 1: [1]}
@@ -31,12 +32,14 @@ def check_mix_gather_S0S1(device_mesh, rank):
# device_mesh_shape: (2, 4)
source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
comm_spec = CommSpec(CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True)
comm_spec = CommSpec(
CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True,
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
assert tensor_to_comm.equal(tensor_to_check)
@@ -48,12 +51,13 @@ def check_two_all_gather_S0S1(device_mesh, rank):
dim_partition_dict = {0: [0], 1: [1]}
tensor_slice = [tensor_width // 2, tensor_width // 4] # (4, 2)
tensor_slice = [tensor_width // 2, tensor_width // 4] # (4, 2)
rank_slice = 4
f_start = (rank // rank_slice) * tensor_slice[0]
b_start = (rank % rank_slice) * tensor_slice[1]
tensor_to_comm = tensor_to_check[f_start:f_start + tensor_slice[0],
b_start:b_start + tensor_slice[1]].contiguous().cuda()
tensor_to_comm = (
tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
)
# DistSpec:
# shard_sequence: S0,S1
@@ -61,10 +65,9 @@ def check_two_all_gather_S0S1(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:0)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=0,
logical_process_axis=0)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=0
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -75,10 +78,9 @@ def check_two_all_gather_S0S1(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=1,
logical_process_axis=1)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=1
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -95,8 +97,9 @@ def check_mix_gather_S1S0(device_mesh, rank):
rank_slice = 4
f_start = (rank % rank_slice) * tensor_slice[0]
b_start = (rank // rank_slice) * tensor_slice[1]
tensor_to_comm = tensor_to_check[f_start:f_start + tensor_slice[0],
b_start:b_start + tensor_slice[1]].contiguous().cuda()
tensor_to_comm = (
tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
)
dim_partition_dict = {0: [1], 1: [0]}
@@ -105,12 +108,14 @@ def check_mix_gather_S1S0(device_mesh, rank):
# device_mesh_shape: (2, 4)
source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
comm_spec = CommSpec(CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True)
comm_spec = CommSpec(
CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True,
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
assert tensor_to_comm.equal(tensor_to_check)
@@ -120,12 +125,13 @@ def check_two_all_gather_S1S0(device_mesh, rank):
tensor_width = 8
tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
tensor_slice = [tensor_width // 4, tensor_width // 2] # (4, 2)
tensor_slice = [tensor_width // 4, tensor_width // 2] # (4, 2)
rank_slice = 4
f_start = (rank % rank_slice) * tensor_slice[0]
b_start = (rank // rank_slice) * tensor_slice[1]
tensor_to_comm = tensor_to_check[f_start:f_start + tensor_slice[0],
b_start:b_start + tensor_slice[1]].contiguous().cuda()
tensor_to_comm = (
tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
)
dim_partition_dict = {0: [1], 1: [0]}
@@ -135,10 +141,9 @@ def check_two_all_gather_S1S0(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:1)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=0,
logical_process_axis=1)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=1
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -149,10 +154,9 @@ def check_two_all_gather_S1S0(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:0)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=1,
logical_process_axis=0)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=0
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -165,7 +169,7 @@ def check_mix_gather_S01R(device_mesh, rank):
f_target_pair = (f, [0, 1])
b_target_pair = (b, [])
gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
tensor_to_comm = tensor_to_check[rank:rank + 1, :].contiguous().cuda()
tensor_to_comm = tensor_to_check[rank : rank + 1, :].contiguous().cuda()
dim_partition_dict = {0: [0, 1]}
# DistSpec:
@@ -173,12 +177,14 @@ def check_mix_gather_S01R(device_mesh, rank):
# device_mesh_shape: (2, 4)
source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
comm_spec = CommSpec(CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True)
comm_spec = CommSpec(
CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True,
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
assert tensor_to_comm.equal(tensor_to_check)
@@ -189,7 +195,7 @@ def check_two_all_gather_S01R(device_mesh, rank):
tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
rank_stride = tensor_width // 8
tensor_to_comm = tensor_to_check[rank:rank + rank_stride, :].contiguous().cuda()
tensor_to_comm = tensor_to_check[rank : rank + rank_stride, :].contiguous().cuda()
dim_partition_dict = {0: [0, 1]}
@@ -199,10 +205,9 @@ def check_two_all_gather_S01R(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:0)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=0,
logical_process_axis=1)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=1
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -214,10 +219,9 @@ def check_two_all_gather_S01R(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:1)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=0,
logical_process_axis=0)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=0
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -231,7 +235,7 @@ def check_mix_gather_RS01(device_mesh, rank):
f_target_pair = (f, [])
b_target_pair = (b, [0, 1])
gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
tensor_to_comm = tensor_to_check[:, rank:rank + 1].contiguous().cuda()
tensor_to_comm = tensor_to_check[:, rank : rank + 1].contiguous().cuda()
dim_partition_dict = {1: [0, 1]}
# DistSpec:
@@ -239,12 +243,14 @@ def check_mix_gather_RS01(device_mesh, rank):
# device_mesh_shape: (2, 4)
source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
comm_spec = CommSpec(CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True)
comm_spec = CommSpec(
CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
sharding_spec=source_spec,
gather_dim=gather_dim,
logical_process_axis=logical_process_axes,
forward_only=True,
mix_gather=True,
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
assert tensor_to_comm.equal(tensor_to_check)
@@ -255,7 +261,7 @@ def check_two_all_gather_RS01(device_mesh, rank):
tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
rank_stride = tensor_width // 8
tensor_to_comm = tensor_to_check[:, rank:rank + rank_stride].contiguous().cuda()
tensor_to_comm = tensor_to_check[:, rank : rank + rank_stride].contiguous().cuda()
dim_partition_dict = {1: [0, 1]}
@@ -265,10 +271,9 @@ def check_two_all_gather_RS01(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:0)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=1,
logical_process_axis=1)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=1
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -280,10 +285,9 @@ def check_two_all_gather_RS01(device_mesh, rank):
sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1)
comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
sharding_spec,
gather_dim=1,
logical_process_axis=0)
comm_spec = CommSpec(
CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=0
)
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
@@ -292,7 +296,7 @@ def check_two_all_gather_RS01(device_mesh, rank):
def check_comm(rank, world_size, port):
disable_existing_loggers()
launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
physical_mesh_id = torch.arange(0, 8)
assert rank == dist.get_rank()
@@ -326,5 +330,5 @@ def test_mix_gather():
spawn(check_comm, world_size)
if __name__ == '__main__':
if __name__ == "__main__":
test_mix_gather()