Making large AI models cheaper, faster and more accessible
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

334 lines
12 KiB

from functools import partial
import pytest
import torch
import torch.multiprocessing as mp
from colossalai.core import global_context as gpc
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.utils import free_port
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 == gpc.get_global_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")
def test_mix_gather():
world_size = 8
run_func = partial(check_comm, world_size=world_size, port=free_port())
mp.spawn(run_func, nprocs=world_size)
if __name__ == '__main__':
test_mix_gather()