[autoparallel] add getattr handler (#1767)

* [autoparallel] add getattr haandler

* polish code

* add extra processes for Parameters

* add unit test for param resharding cost

* add docstring and polish test

colossalai/auto_parallel/tensor_shard/node_handler/__init__.py

@@ -2,6 +2,7 @@ from .batch_norm_handler import BatchNormModuleHandler
 from .binary_elementwise_handler import BinaryElementwiseHandler
 from .bmm_handler import AddBMMFunctionHandler, BMMFunctionHandler
 from .conv_handler import ConvFunctionHandler, ConvModuleHandler
+from .getatrr_handler import GetattrHandler
 from .layer_norm_handler import LayerNormModuleHandler
 from .linear_handler import LinearFunctionHandler, LinearModuleHandler
 from .matmul_handler import MatMulHandler

colossalai/auto_parallel/tensor_shard/node_handler/getatrr_handler.py

@@ -0,0 +1,34 @@
+from typing import Dict, List
+
+from ..sharding_strategy import OperationData, OperationDataType
+from .node_handler import NodeHandler
+from .strategy import GetattrGenerator, StrategyGenerator
+
+__all__ = ['GetattrHandler']
+
+
+class GetattrHandler(NodeHandler):
+    """
+    A GetattrHandler which deals with the sharding strategies for Getattr Node.
+    """
+
+    def get_strategy_generator(self) -> List[StrategyGenerator]:
+        op_data_mapping = self.get_operation_data_mapping()
+        generators = []
+        generators.append(GetattrGenerator(op_data_mapping, self.device_mesh))
+        return generators
+
+    def get_operation_data_mapping(self) -> Dict[str, OperationData]:
+        # use transposed shape for strategies
+        # the strategies will be transformed back to its original shape in self.post_process
+
+        # There are only two possible types for get_attr node:
+        # 1. torch.Tensor(torch.nn.Parameters or torch.nn.Buffers)
+        # 2. torch.nn.Module
+        # temporarily, we just support first case in Tracer, so we don't have to worry about
+        # issue related to the node._meta_data type.
+        physical_output = OperationData(name=str(self.node), type=OperationDataType.OUTPUT, data=self.node._meta_data)
+
+        mapping = {"output": physical_output}
+
+        return mapping

colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py

@@ -6,6 +6,7 @@ from torch.fx.node import Node
 
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
     OperationData,
+    OperationDataType,
     ShardingStrategy,
     StrategiesVector,
     TrainCycleItem,
@@ -49,6 +50,9 @@ class NodeHandler(ABC):
 
         for node in self.predecessor_node:
             node_name = str(node)
+            # get the current sharding spec generated by this node handler
+            op_data = strategy.get_op_data_by_name(node_name)
+            current_sharding_spec = strategy.sharding_specs[op_data]
 
             # get the sharding specs for this node generated
             # in its own node handler
@@ -59,10 +63,6 @@ class NodeHandler(ABC):
                 prev_strategy.get_sharding_spec_by_name(node_name) for prev_strategy in prev_strategy_vector
             ]
 
-            # get the current sharding spec generated by this node handler
-            op_data = strategy.get_op_data_by_name(node_name)
-            current_sharding_spec = strategy.sharding_specs[op_data]
-
             # create data structrure to store costs
             if op_data not in resharding_costs:
                 resharding_costs[node] = []
@@ -71,11 +71,14 @@ class NodeHandler(ABC):
             # compute the resharding cost to switch to the sharding spec generated
             # by the current node handler
             for prev_sharding_spec in prev_sharding_specs:
-                _, _, resharding_cost = shape_consistency_manager.shape_consistency(prev_sharding_spec,
-                                                                                    current_sharding_spec)
-                resharding_cost = TrainCycleItem(fwd=resharding_cost["forward"],
-                                                 bwd=resharding_cost["backward"],
-                                                 total=resharding_cost["total"])
+                if op_data.type == OperationDataType.PARAM:
+                    resharding_cost = TrainCycleItem(fwd=0, bwd=0, total=0)
+                else:
+                    _, _, resharding_cost = shape_consistency_manager.shape_consistency(
+                        prev_sharding_spec, current_sharding_spec)
+                    resharding_cost = TrainCycleItem(fwd=resharding_cost["forward"],
+                                                     bwd=resharding_cost["backward"],
+                                                     total=resharding_cost["total"])
                 resharding_costs[node].append(resharding_cost)
         strategy.resharding_costs = resharding_costs
         return strategy

colossalai/auto_parallel/tensor_shard/node_handler/reshape_handler.py

@@ -13,6 +13,7 @@ __all__ = ['ReshapeHandler']
 @operator_registry.register(torch.reshape)
 @operator_registry.register(torch.flatten)
 @operator_registry.register(torch.Tensor.permute)
+@operator_registry.register(torch.Tensor.view)
 @operator_registry.register(torch.nn.AdaptiveAvgPool2d)
 class ReshapeHandler(NodeHandler):
     """

colossalai/auto_parallel/tensor_shard/node_handler/strategy/__init__.py

@@ -1,6 +1,7 @@
 from .batch_norm_generator import BatchNormStrategyGenerator
 from .binary_elementwise_generator import BinaryElementwiseStrategyGenerator
 from .conv_strategy_generator import ConvStrategyGenerator
+from .getattr_generator import GetattrGenerator
 from .getitem_generator import GetItemStrategyGenerator, TensorStrategyGenerator, TensorTupleStrategyGenerator
 from .layer_norm_generator import LayerNormGenerator
 from .matmul_strategy_generator import (
@@ -22,5 +23,5 @@ __all__ = [
     'BatchedMatMulStrategyGenerator', 'ConvStrategyGenerator', 'UnaryElementwiseGenerator',
     'BatchNormStrategyGenerator', 'GetItemStrategyGenerator', 'TensorStrategyGenerator', 'TensorTupleStrategyGenerator',
     'LayerNormGenerator', 'ReshapeGenerator', 'PlaceholderGenerator', 'OutputGenerator', 'WhereGenerator',
-    'ReshapeGenerator', 'NormalPoolStrategyGenerator', 'BinaryElementwiseStrategyGenerator'
+    'ReshapeGenerator', 'NormalPoolStrategyGenerator', 'BinaryElementwiseStrategyGenerator', 'GetattrGenerator'
 ]

colossalai/auto_parallel/tensor_shard/node_handler/strategy/getattr_generator.py

@@ -0,0 +1,53 @@
+from typing import List
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, ShardingStrategy, TrainCycleItem
+
+from .strategy_generator import StrategyGenerator
+
+__all__ = ['GetattrGenerator']
+
+
+class GetattrGenerator(StrategyGenerator):
+    """
+    PlaceholderGenerator is a generic class to generate strategies for placeholder node.
+    """
+
+    def validate(self) -> bool:
+        return super().validate()
+
+    def update_compute_cost(self, strategy: ShardingStrategy):
+        compute_cost = TrainCycleItem(fwd=10, bwd=10, total=20)
+        strategy.compute_cost = compute_cost
+
+    def update_memory_cost(self, strategy: ShardingStrategy):
+        '''
+        Compute the memory cost per device with this specific strategy.
+        '''
+        forward_size_mapping = {'output': self._compute_size_in_bytes(strategy, "output")}
+
+        # compute fwd cost incurred
+        # fwd_cost = output
+        fwd_activation_cost = sum([v for k, v in forward_size_mapping.items()])
+        fwd_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=0)
+
+        bwd_mem_cost = MemoryCost(activation=0, parameter=0)
+
+        # compute total cost
+        total_mem_cost = MemoryCost(activation=fwd_activation_cost, parameter=0)
+        memory_cost = TrainCycleItem(fwd=fwd_mem_cost, bwd=bwd_mem_cost, total=total_mem_cost)
+        strategy.memory_cost = memory_cost
+
+    def collate_strategies(self) -> List[ShardingStrategy]:
+        dim_partition_dict_mapping = {
+            "output": {},
+        }
+        communication_action_mapping = {}
+        sharding_spec_mapping = self.to_sharding_spec_mapping(dim_partition_dict_mapping)
+
+        name = 'Replica Attribute'
+
+        strategy = self.get_sharding_strategy(name=name,
+                                              sharding_spec_mapping=sharding_spec_mapping,
+                                              communication_action_mapping=communication_action_mapping)
+
+        return [strategy]

colossalai/auto_parallel/tensor_shard/solver/strategies_constructor.py

@@ -6,9 +6,10 @@ from typing import Dict, List
 import torch
 from torch.fx import Graph, Node
 
-from colossalai.auto_parallel.tensor_shard.node_handler import (OuputHandler, PlacehodlerHandler, operator_registry)
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import (ShardingStrategy, StrategiesVector)
-from colossalai.auto_parallel.tensor_shard.utils import (generate_resharding_costs, generate_sharding_spec)
+from colossalai.auto_parallel.tensor_shard.node_handler import OuputHandler, PlacehodlerHandler, operator_registry
+from colossalai.auto_parallel.tensor_shard.node_handler.getatrr_handler import GetattrHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import ShardingStrategy, StrategiesVector
+from colossalai.auto_parallel.tensor_shard.utils import generate_resharding_costs, generate_sharding_spec
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.shape_consistency import ShapeConsistencyManager
 from colossalai.tensor.sharding_spec import ShardingSpec
@@ -71,25 +72,8 @@ class StrategiesConstructor:
 
             # get_attr node
             if node.op == 'get_attr':
-                # Same as placeholder nodes, if solver_options.fast is True, we just let them in
-                # fully replicate status, then strategies of following node will be treated equally due
-                # to replicate status has no resharding cost to other status. At the same time, the searching
-                # space is smaller than enumerating all the possible sharding spec for the get_attr node.
-                # Otherwise, all the possible sharding spec for the get_attr node will be enumerated.
-                if self.solver_options.fast:
-                    # create sharding strategy for get_attr
-                    name = 'Replica Attribute'
-                    dim_partition_dict = {}
-                    output_sharding_spec = generate_sharding_spec(node, self.device_mesh, dim_partition_dict)
-                    # TODO: use meta_info_prop to profile memory cost
-                    memory_cost = 0
-                    sharding_strategy_attribute = ShardingStrategy(name, output_sharding_spec, memory_cost=memory_cost)
-                    strategies_vector.append(sharding_strategy_attribute)
-
-            # # get_attr node
-            # elif node.op == 'get_attr':
-            #     # TODO: implement getattr node handler
-            #     pass
+                getattr_handler = GetattrHandler(node, self.device_mesh, strategies_vector)
+                getattr_handler.register_strategy()
 
             # call_module node
             elif node.op == 'call_module':

colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_module/conv.py

@@ -20,6 +20,7 @@ class BiasAdditionConv(BiasAdditionModule):
             if hasattr(conv_module, attr_name):
                 non_bias_kwargs[attr_name] = getattr(conv_module, attr_name)
         if conv_module.padding_mode != "zeros":
+            #TODO: non zeros mode requires some extra processing for input
             conv_type = type(conv_module)
             if conv_type == "torch.nn.Conv1d":
                 padding_element = _single(0)

colossalai/fx/tracer/tracer.py

@@ -93,17 +93,18 @@ class ColoTracer(Tracer):
         origin_arguments = (kind, target, args, kwargs, name, type_expr, proxy_factory_fn)
         # dispatch the arguments generator depending on the kind and target in origin arguments.
         args_metas, _ = extract_meta(*args, **kwargs)
+        handle = None
         if kind == "call_function":
             if bias_addition_function.has(target):
-                return bias_addition_function.get(target)(self, target, args, kwargs)
+                handle = bias_addition_function.get(target)(self, target, args, kwargs)
             elif bias_addition_function.has(target.__name__):
                 # use name for some builtin op like @ (matmul)
-                return bias_addition_function.get(target.__name__)(self, target, args, kwargs)
+                handle = bias_addition_function.get(target.__name__)(self, target, args, kwargs)
 
         elif kind == "call_method":
             method = getattr(args_metas[0].__class__, target)
             if bias_addition_function.has(method):
-                return bias_addition_function.get(method)(self, target, args, kwargs)
+                handle = bias_addition_function.get(method)(self, target, args, kwargs)
 
         elif kind == "call_module":
             if not hasattr(self, "orig_forward"):
@@ -115,10 +116,12 @@ class ColoTracer(Tracer):
                 if bias_addition_module.has(mod_type) and mod.bias is not None:
                     function_to_substitute = module_to_func_dict[mod_type]
                     handle = bias_addition_module.get(mod_type)(self, target, args, kwargs, function_to_substitute)
-                    return handle.generate()
             finally:
                 self._disable_module_getattr = False
 
+        if handle is not None:
+            return handle.generate()
+
         # create nodes using patched arguments
         proxy = super().create_proxy(*origin_arguments)
         proxy: ColoProxy
@@ -254,7 +257,9 @@ class ColoTracer(Tracer):
                     atoms = target.split(".")
                     for atom in atoms:
                         attr_itr = getattr(attr_itr, atom)
-                    if isinstance(attr_itr, torch.Tensor):
+                    if isinstance(attr_itr, torch.nn.parameter.Parameter):
+                        meta_out = torch.nn.Parameter(attr_itr.to(device="meta"))
+                    elif isinstance(attr_itr, torch.Tensor):
                         meta_out = attr_itr.to(device="meta")
                     else:
                         meta_out = attr_itr

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py

@@ -0,0 +1,58 @@
+import torch
+import torch.nn as nn
+
+from colossalai.auto_parallel.tensor_shard.node_handler.getatrr_handler import GetattrHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx import ColoGraphModule, ColoTracer
+
+
+class GetattrModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv2d(4, 16, 3, padding=1, bias=False)
+
+    def forward(self, input):
+        weight = self.conv.weight
+        return weight
+
+
+def test_getattr_handler():
+    model = GetattrModel()
+    tracer = ColoTracer()
+    # graph():
+    #     %input_1 : torch.Tensor [#users=0] = placeholder[target=input]
+    #     %conv_weight : [#users=1] = get_attr[target=conv.weight]
+    #     return conv_weight
+    graph = tracer.trace(model, meta_args={'input': torch.rand(4, 4, 64, 64).to('meta')})
+    gm = ColoGraphModule(model, graph)
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    getattr_node = list(graph.nodes)[1]
+    getattr_strategies_vector = StrategiesVector(getattr_node)
+
+    # build handler
+    getattr_handler = GetattrHandler(node=getattr_node,
+                                     device_mesh=device_mesh,
+                                     strategies_vector=getattr_strategies_vector)
+
+    getattr_handler.register_strategy(compute_resharding_cost=False)
+    # check operation data mapping
+    mapping = getattr_handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.data is not None
+
+    assert mapping['output'].name == "conv_weight"
+    assert mapping['output'].data.shape == torch.Size((16, 4, 3, 3))
+    assert mapping['output'].type == OperationDataType.OUTPUT
+    strategy_name_list = [val.name for val in getattr_handler.strategies_vector]
+    assert "Replica Attribute" in strategy_name_list
+
+
+if __name__ == '__main__':
+    test_getattr_handler()

tests/test_auto_parallel/test_tensor_shard/test_param_resharding_cost.py

@@ -0,0 +1,128 @@
+import torch
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDataType
+from colossalai.auto_parallel.tensor_shard.solver import (
+    CostGraph,
+    GraphAnalyser,
+    Solver,
+    SolverOptions,
+    StrategiesConstructor,
+)
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.fx import ColoGraphModule, ColoTracer
+
+
+def _param_resharding_cost_assertion(node):
+    for strategy in node.strategies_vector:
+        for prev_node, resharding_cost in strategy.resharding_costs.items():
+            if strategy.get_op_data_by_name(str(prev_node)).type == OperationDataType.PARAM:
+                for cost in resharding_cost:
+                    assert cost.fwd == 0
+                    assert cost.bwd == 0
+                    assert cost.total == 0
+
+
+class LinearModel(torch.nn.Module):
+
+    def __init__(self, in_features, out_features):
+        super().__init__()
+        self.linear = torch.nn.Linear(in_features, out_features)
+
+    def forward(self, x):
+        x = self.linear(x)
+        x = x * 2
+
+        return x
+
+
+class ConvModel(torch.nn.Module):
+
+    def __init__(self, in_channels, out_channels, kernel_size, bias=True):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(in_channels=in_channels,
+                                    out_channels=out_channels,
+                                    kernel_size=kernel_size,
+                                    bias=bias)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = x * 2
+
+        return x
+
+
+def test_linear_module():
+    model = LinearModel(4, 8)
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    tracer = ColoTracer()
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %linear_weight : [#users=1] = get_attr[target=linear.weight]
+    #     %linear_bias : [#users=1] = get_attr[target=linear.bias]
+    #     %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%x, %linear_weight), kwargs = {})
+    #     %add : [#users=1] = call_function[target=operator.add](args = (%linear, %linear_bias), kwargs = {})
+    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {})
+    #     return mul
+    graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 4).to('meta')})
+    # def forward(self, x : torch.Tensor):
+    #     linear_weight = self.linear.weight
+    #     linear_bias = self.linear.bias
+    #     linear = torch._C._nn.linear(x, linear_weight);  x = linear_weight = None
+    #     add = linear + linear_bias;  linear = linear_bias = None
+    #     mul = add * 2;  add = None
+    #     return mul
+    gm = ColoGraphModule(model, graph)
+    gm.recompile()
+    node_list = list(graph.nodes)
+
+    solver_options = SolverOptions(fast=True)
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
+    strategies_constructor.build_strategies_and_cost()
+    linear_node = node_list[3]
+    _param_resharding_cost_assertion(linear_node)
+
+
+def test_conv_module():
+    model = ConvModel(3, 6, 2)
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    tracer = ColoTracer()
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %conv_weight : [#users=1] = get_attr[target=conv.weight]
+    #     %conv_bias : [#users=1] = get_attr[target=conv.bias]
+    #     %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%x, %conv_weight), kwargs = {})
+    #     %view : [#users=1] = call_method[target=view](args = (%conv_bias, [1, -1, 1, 1]), kwargs = {})
+    #     %add : [#users=1] = call_function[target=operator.add](args = (%conv2d, %view), kwargs = {})
+    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {})
+    #     return mul
+    graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 3, 64, 64).to('meta')})
+    # def forward(self, x : torch.Tensor):
+    #     conv_weight = self.conv.weight
+    #     conv_bias = self.conv.bias
+    #     conv2d = torch.conv2d(x, conv_weight);  x = conv_weight = None
+    #     view = conv_bias.view([1, -1, 1, 1]);  conv_bias = None
+    #     add = conv2d + view;  conv2d = view = None
+    #     mul = add * 2;  add = None
+    #     return mul
+    gm = ColoGraphModule(model, graph)
+
+    gm.recompile()
+    node_list = list(graph.nodes)
+    conv_node = node_list[3]
+    solver_options = SolverOptions(fast=True)
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
+    strategies_constructor.build_strategies_and_cost()
+    _param_resharding_cost_assertion(conv_node)
+
+
+if __name__ == '__main__':
+    test_linear_module()
+    test_conv_module()