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335 lines
12 KiB
335 lines
12 KiB
import pytest
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import torch
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import torch.distributed as dist
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from colossalai.device.device_mesh import DeviceMesh
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from colossalai.initialize import launch
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from colossalai.logging import disable_existing_loggers
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from colossalai.tensor.shape_consistency import CollectiveCommPattern, CommSpec
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from colossalai.tensor.sharding_spec import ShardingSpec
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from colossalai.tensor.utils import mix_gather_simulator
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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def check_mix_gather_S0S1(device_mesh, rank):
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tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
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(f, b) = (0, 1)
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f_target_pair = (f, [0])
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b_target_pair = (b, [1])
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gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
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tensor_slice = [4, 2] # (4, 2)
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rank_slice = 4
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f_start = (rank // rank_slice) * tensor_slice[0]
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b_start = (rank % rank_slice) * tensor_slice[1]
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tensor_to_comm = (
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tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
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)
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dim_partition_dict = {0: [0], 1: [1]}
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# DistSpec:
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# shard_sequence: S0,S1
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# device_mesh_shape: (2, 4)
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source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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comm_spec = CommSpec(
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CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
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sharding_spec=source_spec,
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gather_dim=gather_dim,
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logical_process_axis=logical_process_axes,
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forward_only=True,
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mix_gather=True,
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_two_all_gather_S0S1(device_mesh, rank):
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tensor_width = 8
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tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
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dim_partition_dict = {0: [0], 1: [1]}
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tensor_slice = [tensor_width // 2, tensor_width // 4] # (4, 2)
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rank_slice = 4
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f_start = (rank // rank_slice) * tensor_slice[0]
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b_start = (rank % rank_slice) * tensor_slice[1]
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tensor_to_comm = (
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tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
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)
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# DistSpec:
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# shard_sequence: S0,S1
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:0)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=0
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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dim_partition_dict = {1: [1]}
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# DistSpec:
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# shard_sequence: R,S1
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=1
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_mix_gather_S1S0(device_mesh, rank):
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tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
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(f, b) = (0, 1)
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f_target_pair = (f, [1])
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b_target_pair = (b, [0])
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gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
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tensor_slice = [2, 4]
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rank_slice = 4
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f_start = (rank % rank_slice) * tensor_slice[0]
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b_start = (rank // rank_slice) * tensor_slice[1]
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tensor_to_comm = (
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tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
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)
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dim_partition_dict = {0: [1], 1: [0]}
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# DistSpec:
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# shard_sequence: S1,S0
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# device_mesh_shape: (2, 4)
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source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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comm_spec = CommSpec(
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CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
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sharding_spec=source_spec,
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gather_dim=gather_dim,
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logical_process_axis=logical_process_axes,
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forward_only=True,
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mix_gather=True,
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_two_all_gather_S1S0(device_mesh, rank):
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tensor_width = 8
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tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
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tensor_slice = [tensor_width // 4, tensor_width // 2] # (4, 2)
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rank_slice = 4
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f_start = (rank % rank_slice) * tensor_slice[0]
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b_start = (rank // rank_slice) * tensor_slice[1]
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tensor_to_comm = (
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tensor_to_check[f_start : f_start + tensor_slice[0], b_start : b_start + tensor_slice[1]].contiguous().cuda()
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)
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dim_partition_dict = {0: [1], 1: [0]}
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# DistSpec:
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# shard_sequence: S1,S0
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:1)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=1
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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dim_partition_dict = {1: [0]}
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# DistSpec:
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# shard_sequence: R,S0
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:0)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=0
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_mix_gather_S01R(device_mesh, rank):
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tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
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(f, b) = (0, 1)
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f_target_pair = (f, [0, 1])
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b_target_pair = (b, [])
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gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
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tensor_to_comm = tensor_to_check[rank : rank + 1, :].contiguous().cuda()
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dim_partition_dict = {0: [0, 1]}
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# DistSpec:
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# shard_sequence: S01,R
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# device_mesh_shape: (2, 4)
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source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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comm_spec = CommSpec(
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CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
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sharding_spec=source_spec,
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gather_dim=gather_dim,
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logical_process_axis=logical_process_axes,
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forward_only=True,
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mix_gather=True,
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_two_all_gather_S01R(device_mesh, rank):
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tensor_width = 8
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tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
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rank_stride = tensor_width // 8
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tensor_to_comm = tensor_to_check[rank : rank + rank_stride, :].contiguous().cuda()
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dim_partition_dict = {0: [0, 1]}
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# DistSpec:
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# shard_sequence: S01, R
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:0)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=1
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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dim_partition_dict = {0: [0]}
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# DistSpec:
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# shard_sequence: S1, R
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:0, logical_process_axis:1)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=0, logical_process_axis=0
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_mix_gather_RS01(device_mesh, rank):
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tensor_to_check = torch.arange(64).reshape((8, 8)).cuda()
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(f, b) = (0, 1)
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f_target_pair = (f, [])
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b_target_pair = (b, [0, 1])
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gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
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tensor_to_comm = tensor_to_check[:, rank : rank + 1].contiguous().cuda()
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dim_partition_dict = {1: [0, 1]}
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# DistSpec:
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# shard_sequence: R, S01
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# device_mesh_shape: (2, 4)
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source_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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comm_spec = CommSpec(
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CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD,
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sharding_spec=source_spec,
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gather_dim=gather_dim,
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logical_process_axis=logical_process_axes,
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forward_only=True,
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mix_gather=True,
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_two_all_gather_RS01(device_mesh, rank):
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tensor_width = 8
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tensor_to_check = torch.arange(int(tensor_width * tensor_width)).reshape((tensor_width, tensor_width)).cuda()
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rank_stride = tensor_width // 8
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tensor_to_comm = tensor_to_check[:, rank : rank + rank_stride].contiguous().cuda()
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dim_partition_dict = {1: [0, 1]}
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# DistSpec:
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# shard_sequence: R, S01
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:0)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=1
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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dim_partition_dict = {1: [0]}
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# DistSpec:
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# shard_sequence: R, S1
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# device_mesh_shape: (2, 4)
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sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)
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# CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1)
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comm_spec = CommSpec(
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD, sharding_spec, gather_dim=1, logical_process_axis=0
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)
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tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
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assert tensor_to_comm.equal(tensor_to_check)
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def check_comm(rank, world_size, port):
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disable_existing_loggers()
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launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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physical_mesh_id = torch.arange(0, 8)
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assert rank == dist.get_rank()
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mesh_shape = (2, 4)
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# [[0, 1, 2, 3],
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# [4, 5, 6, 7]]
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device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True, need_flatten=True)
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check_mix_gather_S0S1(device_mesh, rank)
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check_two_all_gather_S0S1(device_mesh, rank)
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check_mix_gather_S1S0(device_mesh, rank)
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check_two_all_gather_S1S0(device_mesh, rank)
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check_mix_gather_S01R(device_mesh, rank)
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check_two_all_gather_S01R(device_mesh, rank)
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check_mix_gather_RS01(device_mesh, rank)
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check_two_all_gather_RS01(device_mesh, rank)
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@pytest.mark.skip(reason="Skip because the check functions assume 8 GPUS but CI only have 4 GPUs")
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@rerun_if_address_is_in_use()
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def test_mix_gather():
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world_size = 8
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spawn(check_comm, world_size)
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if __name__ == "__main__":
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test_mix_gather()
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