mirror of https://github.com/hpcaitech/ColossalAI
335 lines
12 KiB
Python
335 lines
12 KiB
Python
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
from colossalai.device.device_mesh import DeviceMesh
|
|
from colossalai.initialize import launch
|
|
from colossalai.logging import disable_existing_loggers
|
|
from colossalai.tensor.shape_consistency import CollectiveCommPattern, CommSpec
|
|
from colossalai.tensor.sharding_spec import ShardingSpec
|
|
from colossalai.tensor.utils import mix_gather_simulator
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
|
|
|
|
def check_mix_gather_S0S1(device_mesh, rank):
|
|
tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
|
|
(f, b) = (0, 1)
|
|
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)
|
|
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()
|
|
)
|
|
|
|
dim_partition_dict = {0: [0], 1: [1]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: S0,S1
|
|
# 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,
|
|
)
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
def check_two_all_gather_S0S1(device_mesh, rank):
|
|
tensor_width = 8
|
|
tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
|
|
|
|
dim_partition_dict = {0: [0], 1: [1]}
|
|
|
|
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()
|
|
)
|
|
|
|
# DistSpec:
|
|
# shard_sequence: S0,S1
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
dim_partition_dict = {1: [1]}
|
|
# DistSpec:
|
|
# shard_sequence: R,S1
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
def check_mix_gather_S1S0(device_mesh, rank):
|
|
tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
|
|
(f, b) = (0, 1)
|
|
f_target_pair = (f, [1])
|
|
b_target_pair = (b, [0])
|
|
gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
|
|
tensor_slice = [2, 4]
|
|
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()
|
|
)
|
|
|
|
dim_partition_dict = {0: [1], 1: [0]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: S1,S0
|
|
# 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,
|
|
)
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
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)
|
|
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()
|
|
)
|
|
|
|
dim_partition_dict = {0: [1], 1: [0]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: S1,S0
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
dim_partition_dict = {1: [0]}
|
|
# DistSpec:
|
|
# shard_sequence: R,S0
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
def check_mix_gather_S01R(device_mesh, rank):
|
|
tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
|
|
(f, b) = (0, 1)
|
|
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()
|
|
|
|
dim_partition_dict = {0: [0, 1]}
|
|
# DistSpec:
|
|
# shard_sequence: S01,R
|
|
# 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,
|
|
)
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
def check_two_all_gather_S01R(device_mesh, rank):
|
|
tensor_width = 8
|
|
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()
|
|
|
|
dim_partition_dict = {0: [0, 1]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: S01, R
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
dim_partition_dict = {0: [0]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: S1, R
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
def check_mix_gather_RS01(device_mesh, rank):
|
|
tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
|
|
|
|
(f, b) = (0, 1)
|
|
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()
|
|
|
|
dim_partition_dict = {1: [0, 1]}
|
|
# DistSpec:
|
|
# shard_sequence: R, S01
|
|
# 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,
|
|
)
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
def check_two_all_gather_RS01(device_mesh, rank):
|
|
tensor_width = 8
|
|
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()
|
|
|
|
dim_partition_dict = {1: [0, 1]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: R, S01
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
dim_partition_dict = {1: [0]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: R, S1
|
|
# device_mesh_shape: (2, 4)
|
|
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
|
|
)
|
|
|
|
tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
|
|
|
|
assert tensor_to_comm.equal(tensor_to_check)
|
|
|
|
|
|
def check_comm(rank, world_size, port):
|
|
disable_existing_loggers()
|
|
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()
|
|
|
|
mesh_shape = (2, 4)
|
|
# [[0, 1, 2, 3],
|
|
# [4, 5, 6, 7]]
|
|
device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True, need_flatten=True)
|
|
|
|
check_mix_gather_S0S1(device_mesh, rank)
|
|
|
|
check_two_all_gather_S0S1(device_mesh, rank)
|
|
|
|
check_mix_gather_S1S0(device_mesh, rank)
|
|
|
|
check_two_all_gather_S1S0(device_mesh, rank)
|
|
|
|
check_mix_gather_S01R(device_mesh, rank)
|
|
|
|
check_two_all_gather_S01R(device_mesh, rank)
|
|
|
|
check_mix_gather_RS01(device_mesh, rank)
|
|
|
|
check_two_all_gather_RS01(device_mesh, rank)
|
|
|
|
|
|
@pytest.mark.skip(reason="Skip because the check functions assume 8 GPUS but CI only have 4 GPUs")
|
|
@rerun_if_address_is_in_use()
|
|
def test_mix_gather():
|
|
world_size = 8
|
|
spawn(check_comm, world_size)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_mix_gather()
|