[autoparallel] add resnet autoparallel unit test and add backward weight communication cost (#1589)

colossalai/auto_parallel/solver/op_handler/conv_handler.py

@@ -103,7 +103,7 @@ class ConvHandler(OperatorHandler):
         # memory_cost pair
         memory_cost = (memory_cost_forward, memory_cost_backward)
 
-        return memory_cost, memory_cost_forward_activation, memory_cost_backward_activation
+        return memory_cost, memory_cost_forward_activation, memory_cost_backward_activation, memory_cost_backward_weight
 
     def split_input_batch_weight_out_channel(self, mesh_dim_0, mesh_dim_1):
         name = f'S{mesh_dim_0}S{mesh_dim_1} = S{mesh_dim_0}R x RS{mesh_dim_1}'
@@ -132,15 +132,18 @@ class ConvHandler(OperatorHandler):
         sharding_size_forward = self.device_mesh.shape[mesh_dim_0] * self.device_mesh.shape[mesh_dim_1]
         sharding_size_backward_activation = self.device_mesh.shape[mesh_dim_0]
         sharding_size_weight = self.device_mesh.shape[mesh_dim_1]
-        memory_cost, _, memory_cost_backward_activation = self._generate_memory_cost(
+        memory_cost, _, memory_cost_backward_activation, memory_cost_backward_weight = self._generate_memory_cost(
             sharding_size_forward, sharding_size_backward_activation, sharding_size_weight)
 
         # This strategy do not need to do all_reduce operation during forward
         communication_cost_forward = 0
-        # compute the backward communication cost of this strategy
-        communication_cost_backward = self.device_mesh.all_reduce_cost(memory_cost_backward_activation, mesh_dim_1)
+        # compute the backward communication cost to all reduce the input activation grad
+        communication_cost_backward_activation = self.device_mesh.all_reduce_cost(memory_cost_backward_activation,
+                                                                                  mesh_dim_1)
+        # compute the backward communication cost to all reduce the weight due to data parallel
+        communication_cost_backward_weight = self.device_mesh.all_reduce_cost(memory_cost_backward_weight, mesh_dim_0)
         # total communication cost
-        communication_cost = communication_cost_forward + communication_cost_backward
+        communication_cost = communication_cost_forward + communication_cost_backward_activation + communication_cost_backward_weight
 
         sharding_strategies = ShardingStrategy(name,
                                                output_sharding_spec=sharding_spec_for_output,
@@ -178,11 +181,16 @@ class ConvHandler(OperatorHandler):
         sharding_size_forward = self.device_mesh.shape[mesh_dim_0]
         sharding_size_backward_activation = self.device_mesh.shape[mesh_dim_0]
         sharding_size_weight = 1
-        memory_cost, _, _ = self._generate_memory_cost(sharding_size_forward, sharding_size_backward_activation,
+        memory_cost, _, _, memory_cost_backward_weight = self._generate_memory_cost(sharding_size_forward,
+                                                                                    sharding_size_backward_activation,
                                                                                     sharding_size_weight)
 
-        # This strategy do not need to do all_reduce operation in both forward and backward phase.
-        communication_cost = 0
+        # This strategy do not need to do all_reduce operation in forward phase.
+        communication_cost_forward = 0
+        # compute the backward communication cost to all reduce the weight due to data parallel
+        communication_cost_backward_weight = self.device_mesh.all_reduce_cost(memory_cost_backward_weight, mesh_dim_0)
+        # compute the total cost
+        communication_cost = communication_cost_forward + communication_cost_backward_weight
         sharding_strategies = ShardingStrategy(name,
                                                output_sharding_spec=sharding_spec_for_output,
                                                compute_cost=compute_cost,
@@ -220,15 +228,17 @@ class ConvHandler(OperatorHandler):
         sharding_size_forward = self.device_mesh.shape[mesh_dim_0]
         sharding_size_backward_activation = self.device_mesh.shape[mesh_dim_0] * self.device_mesh.shape[mesh_dim_1]
         sharding_size_weight = self.device_mesh.shape[mesh_dim_1]
-        memory_cost, memory_cost_forward_activation, _ = self._generate_memory_cost(sharding_size_forward,
-                                                                                    sharding_size_backward_activation,
-                                                                                    sharding_size_weight)
+        memory_cost, memory_cost_forward_activation, _, memory_cost_backward_weight = self._generate_memory_cost(
+            sharding_size_forward, sharding_size_backward_activation, sharding_size_weight)
 
         # compute the communication cost of this strategy during forward phase
         communication_cost_forward = self.device_mesh.all_reduce_cost(memory_cost_forward_activation, mesh_dim_1)
-        # This strategy do not need to do all_reduce operation during backward phase
-        communication_cost_backward = 0
-        communication_cost = communication_cost_forward + communication_cost_backward
+        # This strategy do not need to do all_reduce operation to compute the input activation grad
+        communication_cost_backward_activation = 0
+        # compute the backward communication cost to all reduce the weight due to data parallel
+        communication_cost_backward_weight = self.device_mesh.all_reduce_cost(memory_cost_backward_weight, mesh_dim_0)
+        # compute total cost
+        communication_cost = communication_cost_forward + communication_cost_backward_activation + communication_cost_backward_weight
         sharding_strategies = ShardingStrategy(name,
                                                output_sharding_spec=sharding_spec_for_output,
                                                compute_cost=compute_cost,
@@ -265,7 +275,7 @@ class ConvHandler(OperatorHandler):
         sharding_size_forward = self.device_mesh.shape[mesh_dim_1]
         sharding_size_backward_activation = self.device_mesh.shape[mesh_dim_0]
         sharding_size_weight = self.device_mesh.shape[mesh_dim_0] * self.device_mesh.shape[mesh_dim_1]
-        memory_cost, memory_cost_forward_activation, memory_cost_backward_activation = self._generate_memory_cost(
+        memory_cost, memory_cost_forward_activation, memory_cost_backward_activation, _ = self._generate_memory_cost(
             sharding_size_forward, sharding_size_backward_activation, sharding_size_weight)
 
         # compute the communication cost of this strategy during forward phase
@@ -309,9 +319,8 @@ class ConvHandler(OperatorHandler):
         sharding_size_forward = 1
         sharding_size_backward_activation = self.device_mesh.shape[mesh_dim_0]
         sharding_size_weight = self.device_mesh.shape[mesh_dim_0]
-        memory_cost, memory_cost_forward_activation, _ = self._generate_memory_cost(sharding_size_forward,
-                                                                                    sharding_size_backward_activation,
-                                                                                    sharding_size_weight)
+        memory_cost, memory_cost_forward_activation, _, _ = self._generate_memory_cost(
+            sharding_size_forward, sharding_size_backward_activation, sharding_size_weight)
 
         # compute the communication cost of this strategy during forward phase
         communication_cost_forward = self.device_mesh.all_reduce_cost(memory_cost_forward_activation, mesh_dim_0)
@@ -354,7 +363,7 @@ class ConvHandler(OperatorHandler):
         sharding_size_forward = self.device_mesh.shape[mesh_dim_0]
         sharding_size_backward_activation = 1
         sharding_size_weight = self.device_mesh.shape[mesh_dim_0]
-        memory_cost, _, memory_cost_backward_activation = self._generate_memory_cost(
+        memory_cost, _, memory_cost_backward_activation, _ = self._generate_memory_cost(
             sharding_size_forward, sharding_size_backward_activation, sharding_size_weight)
 
         # This strategy do not need to do all_reduce during forward phase
@@ -398,7 +407,7 @@ class ConvHandler(OperatorHandler):
         sharding_size_forward = 1
         sharding_size_backward_activation = 1
         sharding_size_weight = 1
-        memory_cost, _, _ = self._generate_memory_cost(sharding_size_forward, sharding_size_backward_activation,
+        memory_cost, _, _, _ = self._generate_memory_cost(sharding_size_forward, sharding_size_backward_activation,
                                                           sharding_size_weight)
 
         # This strategy do not need to do all_reduce in both forward and backward phase
@@ -441,11 +450,17 @@ class ConvHandler(OperatorHandler):
         sharding_size_backward_activation = self.device_mesh.mesh_shape[mesh_dim_0] * self.device_mesh.mesh_shape[
             mesh_dim_1]
         sharding_size_weight = 1
-        memory_cost, _, _ = self._generate_memory_cost(sharding_size_forward, sharding_size_backward_activation,
+        memory_cost, _, _, memory_cost_backward_weight = self._generate_memory_cost(sharding_size_forward,
+                                                                                    sharding_size_backward_activation,
                                                                                     sharding_size_weight)
 
-        # This strategy do not need to do all_reduce in both forward and backward phase
-        communication_cost = 0
+        # This strategy do not need to do all_reduce in forward phase
+        communication_cost_forward = 0
+        # compute the backward communication cost to all reduce the weight due to data parallel
+        communication_cost_backward_weight = self.device_mesh.flatten_device_mesh.all_reduce_cost(
+            memory_cost_backward_weight, 0)
+        # compute the total communication cost
+        communication_cost = communication_cost_backward_weight + communication_cost_forward
 
         sharding_strategies = ShardingStrategy(name,
                                                output_sharding_spec=sharding_spec_for_output,
@@ -485,9 +500,8 @@ class ConvHandler(OperatorHandler):
         sharding_size_backward_activation = self.device_mesh.mesh_shape[mesh_dim_0] * self.device_mesh.mesh_shape[
             mesh_dim_1]
         sharding_size_weight = self.device_mesh.mesh_shape[mesh_dim_0] * self.device_mesh.mesh_shape[mesh_dim_1]
-        memory_cost, memory_cost_forward_activation, _ = self._generate_memory_cost(sharding_size_forward,
-                                                                                    sharding_size_backward_activation,
-                                                                                    sharding_size_weight)
+        memory_cost, memory_cost_forward_activation, _, _ = self._generate_memory_cost(
+            sharding_size_forward, sharding_size_backward_activation, sharding_size_weight)
 
         # compute communication cost during forward phase
         communication_cost_forward = self.device_mesh.flatten_device_mesh.all_reduce_cost(

tests/test_auto_parallel/test_solver_with_resnet.py

+import torch
+from torch.fx import GraphModule
+import torch.nn as nn
+import pytest
+
+from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
+from colossalai.auto_parallel.solver.cost_graph import CostGraph
+from copy import deepcopy
+from colossalai.auto_parallel.solver import Solver
+from torchvision.models import resnet34, resnet50
+from colossalai.auto_parallel.solver.constants import *
+from colossalai.auto_parallel.solver.graph_analysis import GraphAnalyser
+
+
+class ConvModel(nn.Module):
+
+    def __init__(self, c_in, c_out):
+        super().__init__()
+        self.conv1 = nn.Conv2d(c_in, c_out, kernel_size=3)
+        self.conv2 = nn.Conv2d(c_out, c_out, kernel_size=3)
+        self.conv3 = nn.Conv2d(c_out, c_out, kernel_size=3)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = x * 2
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = x / 2
+        x = self.conv3(x)
+        x = self.relu(x)
+        return x
+
+
+@pytest.mark.skip("for higher testing speed")
+def test_cost_graph():
+    physical_mesh_id = torch.arange(0, 8)
+    mesh_shape = (2, 4)
+    # [[0, 1]
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
+    shape_consistency_manager = ShapeConsistencyManager()
+
+    tracer = ColoTracer()
+    # model = ConvModel(16, 32)
+    # input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
+    model = resnet50(num_classes=100000)
+    input_sample = {'x': torch.rand(128, 3, 224, 224).to('meta')}
+
+    graph = tracer.trace(root=model, meta_args=input_sample)
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %conv1 : [#users=1] = call_module[target=conv1](args = (%x,), kwargs = {})
+    #     %bn1 : [#users=1] = call_module[target=bn1](args = (%conv1,), kwargs = {})
+    #     %relu : [#users=1] = call_module[target=relu](args = (%bn1,), kwargs = {})
+    #     %maxpool : [#users=2] = call_module[target=maxpool](args = (%relu,), kwargs = {})
+    #     %layer1_0_conv1 : [#users=1] = call_module[target=layer1.0.conv1](args = (%maxpool,), kwargs = {})
+    #     %layer1_0_bn1 : [#users=1] = call_module[target=layer1.0.bn1](args = (%layer1_0_conv1,), kwargs = {})
+    #     %layer1_0_relu : [#users=1] = call_module[target=layer1.0.relu](args = (%layer1_0_bn1,), kwargs = {})
+    #     %layer1_0_conv2 : [#users=1] = call_module[target=layer1.0.conv2](args = (%layer1_0_relu,), kwargs = {})
+    #     %layer1_0_bn2 : [#users=1] = call_module[target=layer1.0.bn2](args = (%layer1_0_conv2,), kwargs = {})
+    #     %add : [#users=1] = call_function[target=operator.add](args = (%layer1_0_bn2, %maxpool), kwargs = {})
+    #     %layer1_0_relu_1 : [#users=2] = call_module[target=layer1.0.relu](args = (%add,), kwargs = {})
+    #     %layer1_1_conv1 : [#users=1] = call_module[target=layer1.1.conv1](args = (%layer1_0_relu_1,), kwargs = {})
+    #     %layer1_1_bn1 : [#users=1] = call_module[target=layer1.1.bn1](args = (%layer1_1_conv1,), kwargs = {})
+    #     %layer1_1_relu : [#users=1] = call_module[target=layer1.1.relu](args = (%layer1_1_bn1,), kwargs = {})
+    #     %layer1_1_conv2 : [#users=1] = call_module[target=layer1.1.conv2](args = (%layer1_1_relu,), kwargs = {})
+    #     %layer1_1_bn2 : [#users=1] = call_module[target=layer1.1.bn2](args = (%layer1_1_conv2,), kwargs = {})
+    #     %add_1 : [#users=1] = call_function[target=operator.add](args = (%layer1_1_bn2, %layer1_0_relu_1), kwargs = {})
+    #     ...
+    #     %avgpool : [#users=1] = call_module[target=avgpool](args = (%layer4_2_relu_1,), kwargs = {})
+    #     %flatten : [#users=1] = call_function[target=torch.flatten](args = (%avgpool, 1), kwargs = {})
+    #     %fc : [#users=1] = call_module[target=fc](args = (%flatten,), kwargs = {})
+    #     return fc
+    gm = GraphModule(model, graph, model.__class__.__name__)
+    gm.recompile()
+    graph_analyser = GraphAnalyser(gm)
+    liveness_list = graph_analyser.liveness_analysis()
+    # print(len(liveness_dict[0].unique_live_vars))
+    # assert False
+    solver_options = {'fast_mode': True}
+    strategies_constructor = StrategiesConstructor(graph, device_mesh, shape_consistency_manager, solver_options)
+    strategies_constructor.build_strategies_and_cost()
+
+    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
+    cost_graph.simplify_graph()
+    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser, memory_budget=1620017824.0)
+    # solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
+
+    ret = solver.call_solver_serialized_args()
+    print(ret)
+    strategies_list = list(ret[0])
+    print(strategies_list)
+    computation_cost = 0
+    communication_cost = 0
+    communication_cost_bn = 0
+    memory_cost = 0
+    for index, node in enumerate(graph.nodes):
+        if node.op == 'call_module':
+            submod = node.graph.owning_module.get_submodule(node.target)
+            if type(submod) in ELEMENTWISE_MODULE_OP:
+                input_spec = node.args[0].strategies_vector[strategies_list[index]].output_sharding_spec
+                print(node.name, input_spec)
+                continue
+            if type(submod) in BATCHNORM_MODULE_OP:
+                communication_cost_bn += node.strategies_vector[strategies_list[index]].communication_cost
+        print(node.name, node.strategies_vector[strategies_list[index]].name)
+        computation_cost += node.strategies_vector[strategies_list[index]].compute_cost
+        communication_cost += node.strategies_vector[strategies_list[index]].communication_cost
+        node_memory_cost = node.strategies_vector[strategies_list[index]].memory_cost
+        if isinstance(node_memory_cost, tuple):
+            node_memory_cost = node_memory_cost[0]
+        memory_cost += node_memory_cost
+
+    print(f'computation cost is {computation_cost}')
+    print(f'communication cost is {communication_cost}')
+    print(f'memory cost is {memory_cost}')
+    print(f'bn communication cost is {communication_cost_bn}')
+
+
+if __name__ == '__main__':
+    test_cost_graph()