ColossalAI/tests/test_tensor/test_sharded_linear.py

233 lines
8.9 KiB
Python

import pytest
import torch
import torch.nn.functional as F
import colossalai
from colossalai.device.device_mesh import DeviceMesh
from colossalai.nn._ops._utils import gather_forward_split_backward
from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup
from colossalai.tensor.sharding_spec import ShardingSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
def run_dist(rank, world_size, port):
config = {}
colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
# create mlp vars
x = ColoTensor.from_torch_tensor(torch.rand(4, 4, 8, requires_grad=True)).cuda()
w = ColoParameter.from_torch_tensor(torch.rand(16, 8, requires_grad=True)).cuda()
b = ColoParameter.from_torch_tensor(torch.rand(16, requires_grad=True)).cuda()
# run normal forward
out = F.linear(x, w, b)
# create mesh meta
# the mesh is in the following topo
# [[0, 1],
# [2, 3]]
physical_mesh_id = torch.arange(0, 4)
mesh_shape = (2, 2)
device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
row_id = rank // 2
column_id = rank % 2
# create pg
row_process_group = None
col_process_group = None
row_to_ranks = {0: [0, 1], 1: [2, 3]}
col_to_ranks = {0: [0, 2], 1: [1, 3]}
for idx in range(2):
# row ranks
row_ranks = row_to_ranks[idx]
row_pg = ProcessGroup(ranks=row_ranks, tp_degree=2)
# col ranks
col_ranks = col_to_ranks[idx]
col_pg = ProcessGroup(ranks=col_ranks, tp_degree=2)
if rank in row_ranks:
row_process_group = row_pg
if rank in col_ranks:
col_process_group = col_pg
########################
# RRR x RS0 -> RRS0 #
########################
# w will be transposed in F.linear
x_replica = x.detach().clone()
w_shard = torch.chunk(w.detach().clone(), chunks=2, dim=0)[row_id]
b_shard = torch.chunk(b.detach().clone(), chunks=2, dim=0)[row_id]
# adding sharding spec
x_replica.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [0]})
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [0]})
# check sharding spec
assert str(x_replica.sharding_spec.sharding_sequence) == "[R, R, R]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[S0, R]"
assert str(b_shard.sharding_spec.sharding_sequence) == "[S0]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_replica, w_shard, b_shard)
assert str(out_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=2)[row_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# S0RR x RS1 -> S0RS1 #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.detach().clone(), chunks=2, dim=0)[row_id]
w_shard = torch.chunk(w.detach().clone(), chunks=2, dim=0)[column_id]
b_shard = torch.chunk(b.detach().clone(), chunks=2, dim=0)[column_id]
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={0: [0]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [1]})
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [1]})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[S0, R, R]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[S1, R]"
assert str(b_shard.sharding_spec.sharding_sequence) == "[S1]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_shard)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=0)[row_id]
expected_out_shard = torch.chunk(expected_out_shard, chunks=2, dim=2)[column_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# S0RS1 x S1R -> S0RR #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=0)[row_id]
x_shard = torch.chunk(x_shard, chunks=2, dim=2)[column_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[column_id]
b_replica = b.clone()
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={0: [0], 2: [1]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [1]})
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[S0, R, S1]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S1]"
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_replica)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=0)[row_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# RRS0 x S0R -> RRR #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=2)[row_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[row_id]
b_replica = b.clone()
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={2: [0]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [0]})
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S0]"
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_replica)
# each row only has a mini-batch
expected_out_shard = out
assert torch.allclose(out_shard, expected_out_shard)
########################
# RS0S1 x S1R -> RS0R #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=1)[row_id]
x_shard = torch.chunk(x_shard, chunks=2, dim=2)[column_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[column_id]
b_replica = b.clone()
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={1: [0], 2: [1]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={1: [1]})
b_replica.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, S0, S1]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[R, S1]"
assert str(b_replica.sharding_spec.sharding_sequence) == "[R]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_replica)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=1)[row_id]
assert torch.allclose(out_shard, expected_out_shard)
########################
# RRS0 x S0S1 -> RRS1 #
########################
# w will be transposed in F.linear
x_shard = torch.chunk(x.clone(), chunks=2, dim=2)[row_id]
w_shard = torch.chunk(w.clone(), chunks=2, dim=1)[row_id]
w_shard = torch.chunk(w_shard, chunks=2, dim=0)[column_id]
b_shard = torch.chunk(b.clone(), chunks=2, dim=0)[column_id]
# adding sharding spec
x_shard.sharding_spec = ShardingSpec(device_mesh, x.shape, dim_partition_dict={2: [0]})
w_shard.sharding_spec = ShardingSpec(device_mesh, w.shape, dim_partition_dict={0: [1], 1: [0]})
b_shard.sharding_spec = ShardingSpec(device_mesh, b.shape, dim_partition_dict={0: [1]})
# check sharding spec
assert str(x_shard.sharding_spec.sharding_sequence) == "[R, R, S0]"
assert str(w_shard.sharding_spec.sharding_sequence) == "[S1, S0]"
assert str(b_shard.sharding_spec.sharding_sequence) == "[S1]"
w_shard.pg_axis0 = col_process_group
w_shard.pg_axis1 = row_process_group
out_shard = F.linear(x_shard, w_shard, b_shard)
# each row only has a mini-batch
expected_out_shard = torch.chunk(out, chunks=2, dim=2)[column_id]
assert torch.allclose(out_shard, expected_out_shard)
@pytest.mark.dist
@pytest.mark.parametrize('world_size', [4])
@rerun_if_address_is_in_use()
def test_sharded_mlp(world_size):
spawn(run_dist, world_size)
if __name__ == '__main__':
test_sharded_mlp(4)