ColossalAI/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
from colossalai.auto_parallel.solver.options import SolverOptions


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 = SolverOptions(fast=True)
    strategies_constructor = StrategiesConstructor(graph, device_mesh, 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()