[autoparallel] add shard option

colossalai/auto_parallel/tensor_shard/node_handler/__init__.py

@@ -11,6 +11,7 @@
 from .linear_handler import LinearFunctionHandler, LinearModuleHandler
 from .matmul_handler import MatMulHandler
 from .normal_pooling_handler import NormPoolingHandler
+from .option import ShardOption
 from .output_handler import OutputHandler
 from .placeholder_handler import PlaceholderHandler
 from .registry import operator_registry
@@ -27,5 +28,5 @@
     'UnaryElementwiseHandler', 'ReshapeHandler', 'PlaceholderHandler', 'OutputHandler', 'WhereHandler',
     'NormPoolingHandler', 'BinaryElementwiseHandler', 'MatMulHandler', 'operator_registry', 'ADDMMFunctionHandler',
     'GetItemHandler', 'GetattrHandler', 'ViewHandler', 'PermuteHandler', 'TensorConstructorHandler',
-    'EmbeddingModuleHandler', 'EmbeddingFunctionHandler', 'SumHandler', 'SoftmaxHandler'
+    'EmbeddingModuleHandler', 'EmbeddingFunctionHandler', 'SumHandler', 'SoftmaxHandler', 'ShardOption'
 ]

colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py

@@ -5,6 +5,7 @@
 from torch.fx.node import Node
 
 from colossalai.auto_parallel.meta_profiler.metainfo import MetaInfo, meta_register
+from colossalai.auto_parallel.tensor_shard.node_handler.option import ShardOption
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
     OperationData,
     OperationDataType,
@@ -35,12 +36,14 @@ def __init__(
         node: Node,
         device_mesh: DeviceMesh,
         strategies_vector: StrategiesVector,
+        shard_option: ShardOption = ShardOption.STANDARD,
     ) -> None:
         self.node = node
         self.predecessor_node = list(node._input_nodes.keys())
         self.successor_node = list(node.users.keys())
         self.device_mesh = device_mesh
         self.strategies_vector = strategies_vector
+        self.shard_option = shard_option
 
     def update_resharding_cost(self, strategy: ShardingStrategy) -> None:
         """
@@ -181,6 +184,21 @@ def register_strategy(self, compute_resharding_cost: bool = True) -> StrategiesV
                 if op_data.data is not None and isinstance(op_data.data, torch.Tensor):
                     check_sharding_spec_validity(sharding_spec, op_data.data)
 
+        remove_strategy_list = []
+        for strategy in self.strategies_vector:
+            shard_level = 0
+            for op_data, sharding_spec in strategy.sharding_specs.items():
+                if op_data.data is not None and isinstance(op_data.data, torch.Tensor):
+                    for dim, shard_axis in sharding_spec.dim_partition_dict.items():
+                        shard_level += len(shard_axis)
+            if self.shard_option == ShardOption.SHARD and shard_level == 0:
+                remove_strategy_list.append(strategy)
+            if self.shard_option == ShardOption.FULL_SHARD and shard_level <= 1:
+                remove_strategy_list.append(strategy)
+
+        for strategy in remove_strategy_list:
+            self.strategies_vector.remove(strategy)
+
         return self.strategies_vector
 
     def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]:

colossalai/auto_parallel/tensor_shard/node_handler/option.py

@@ -0,0 +1,17 @@
+from enum import Enum
+
+__all__ = ['ShardOption']
+
+
+class ShardOption(Enum):
+    """
+    This enum class is to define the shard level required in node strategies.
+
+    Notes:
+        STANDARD: We do not add any extra shard requirements.
+        SHARD: We require the node to be shard using at least one device mesh axis.
+        FULL_SHARD: We require the node to be shard using all device mesh axes.
+    """
+    STANDARD = 0
+    SHARD = 1
+    FULL_SHARD = 2

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py

@@ -0,0 +1,112 @@
+from functools import partial
+
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler
+from colossalai.auto_parallel.tensor_shard.node_handler.option import ShardOption
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx import ColoGraphModule, ColoTracer
+from colossalai.testing import parameterize
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing.utils import parameterize
+
+
+class LinearModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input, others, bias=None):
+        x = nn.functional.linear(input, others, bias=bias)
+        return x
+
+
+def check_shard_option(shard_option):
+    model = LinearModel().cuda()
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+
+    tracer = ColoTracer()
+    graph = tracer.trace(model,
+                         meta_args={
+                             "input": torch.rand(4, 4, 4, 16).to('meta'),
+                             'others': torch.rand(32, 16).to('meta')
+                         })
+    gm = ColoGraphModule(model, graph)
+    linear_func_node = list(graph.nodes)[2]
+    strategies_vector = StrategiesVector(linear_func_node)
+
+    # build handler
+    handler = LinearFunctionHandler(node=linear_func_node,
+                                    device_mesh=device_mesh,
+                                    strategies_vector=strategies_vector,
+                                    shard_option=shard_option)
+
+    strategies_vector = handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # SS = SR x RS
+    assert 'S1S0 = S1R x RS0_0' in strategy_name_list
+    assert 'S0S1 = S0R x RS1_1' in strategy_name_list
+    assert 'S0S1 = S0R x RS1_2' in strategy_name_list
+    assert 'S0S1 = S0R x RS1_0' in strategy_name_list
+    assert 'S1S0 = S1R x RS0_1' in strategy_name_list
+    assert 'S1S0 = S1R x RS0_2' in strategy_name_list
+
+    # SR = SS x SR
+    assert 'S0R = S0S1 x S1R_1' in strategy_name_list
+    assert 'S0R = S0S1 x S1R_2' in strategy_name_list
+    assert 'S1R = S1S0 x S0R_0' in strategy_name_list
+    assert 'S0R = S0S1 x S1R_0' in strategy_name_list
+    assert 'S1R = S1S0 x S0R_1' in strategy_name_list
+    assert 'S1R = S1S0 x S0R_2' in strategy_name_list
+
+    # RS = RS x SS
+    assert 'RS0 = RS1 x S1S0' in strategy_name_list
+    assert 'RS1 = RS0 x S0S1' in strategy_name_list
+
+    # S01R = S01R x RR
+    assert 'S01R = S01R x RR_0' in strategy_name_list
+    assert 'S01R = S01R x RR_1' in strategy_name_list
+    assert 'S01R = S01R x RR_2' in strategy_name_list
+
+    # RR = RS01 x S01R
+    assert 'RR = RS01 x S01R' in strategy_name_list
+
+    # RS01 = RR x RS01
+    assert 'RS01 = RR x RS01' in strategy_name_list
+
+    if shard_option == ShardOption.SHARD:
+        # RR = RS x SR
+        assert 'RR = RS0 x S0R' in strategy_name_list
+        assert 'RR = RS1 x S1R' in strategy_name_list
+
+        # RS= RR x RS
+        assert 'RS0 = RR x RS0' in strategy_name_list
+        assert 'RS1 = RR x RS1' in strategy_name_list
+
+    if shard_option == ShardOption.STANDARD:
+        # RR = RS x SR
+        assert 'RR = RS0 x S0R' in strategy_name_list
+        assert 'RR = RS1 x S1R' in strategy_name_list
+
+        # RS= RR x RS
+        assert 'RS0 = RR x RS0' in strategy_name_list
+        assert 'RS1 = RR x RS1' in strategy_name_list
+
+        # RR = RR x RR
+        assert 'RR = RR x RR' in strategy_name_list
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+def test_shard_option():
+    for shard_option in [ShardOption.STANDARD, ShardOption.SHARD, ShardOption.FULL_SHARD]:
+        check_shard_option(shard_option)
+
+
+if __name__ == '__main__':
+    test_shard_option()