mirror of https://github.com/hpcaitech/ColossalAI
[autoparallel] add resnet autoparallel unit test and add backward weight communication cost (#1589)
YuliangLiu0306
2 years ago
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GitHub
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7c18a588c8
commit
d164449d00
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import torch
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from torch.fx import GraphModule
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import torch.nn as nn
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import pytest
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from colossalai.fx.tracer.tracer import ColoTracer
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from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
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from colossalai.tensor.shape_consistency import ShapeConsistencyManager
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from colossalai.device.device_mesh import DeviceMesh
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from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
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from colossalai.auto_parallel.solver.cost_graph import CostGraph
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from copy import deepcopy
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from colossalai.auto_parallel.solver import Solver
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from torchvision.models import resnet34, resnet50
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from colossalai.auto_parallel.solver.constants import *
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from colossalai.auto_parallel.solver.graph_analysis import GraphAnalyser
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class ConvModel(nn.Module):
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def __init__(self, c_in, c_out):
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super().__init__()
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self.conv1 = nn.Conv2d(c_in, c_out, kernel_size=3)
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self.conv2 = nn.Conv2d(c_out, c_out, kernel_size=3)
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self.conv3 = nn.Conv2d(c_out, c_out, kernel_size=3)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = x * 2
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x = self.conv1(x)
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x = self.conv2(x)
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x = x / 2
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x = self.conv3(x)
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x = self.relu(x)
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return x
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@pytest.mark.skip("for higher testing speed")
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def test_cost_graph():
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physical_mesh_id = torch.arange(0, 8)
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mesh_shape = (2, 4)
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# [[0, 1]
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# [2, 3]]
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device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
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shape_consistency_manager = ShapeConsistencyManager()
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tracer = ColoTracer()
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# model = ConvModel(16, 32)
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# input_sample = {'x': torch.rand(4, 16, 64, 64).to('meta')}
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model = resnet50(num_classes=100000)
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input_sample = {'x': torch.rand(128, 3, 224, 224).to('meta')}
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graph = tracer.trace(root=model, meta_args=input_sample)
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# graph():
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# %x : torch.Tensor [#users=1] = placeholder[target=x]
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# %conv1 : [#users=1] = call_module[target=conv1](args = (%x,), kwargs = {})
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# %bn1 : [#users=1] = call_module[target=bn1](args = (%conv1,), kwargs = {})
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# %relu : [#users=1] = call_module[target=relu](args = (%bn1,), kwargs = {})
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# %maxpool : [#users=2] = call_module[target=maxpool](args = (%relu,), kwargs = {})
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# %layer1_0_conv1 : [#users=1] = call_module[target=layer1.0.conv1](args = (%maxpool,), kwargs = {})
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# %layer1_0_bn1 : [#users=1] = call_module[target=layer1.0.bn1](args = (%layer1_0_conv1,), kwargs = {})
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# %layer1_0_relu : [#users=1] = call_module[target=layer1.0.relu](args = (%layer1_0_bn1,), kwargs = {})
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# %layer1_0_conv2 : [#users=1] = call_module[target=layer1.0.conv2](args = (%layer1_0_relu,), kwargs = {})
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# %layer1_0_bn2 : [#users=1] = call_module[target=layer1.0.bn2](args = (%layer1_0_conv2,), kwargs = {})
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# %add : [#users=1] = call_function[target=operator.add](args = (%layer1_0_bn2, %maxpool), kwargs = {})
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# %layer1_0_relu_1 : [#users=2] = call_module[target=layer1.0.relu](args = (%add,), kwargs = {})
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# %layer1_1_conv1 : [#users=1] = call_module[target=layer1.1.conv1](args = (%layer1_0_relu_1,), kwargs = {})
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# %layer1_1_bn1 : [#users=1] = call_module[target=layer1.1.bn1](args = (%layer1_1_conv1,), kwargs = {})
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# %layer1_1_relu : [#users=1] = call_module[target=layer1.1.relu](args = (%layer1_1_bn1,), kwargs = {})
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# %layer1_1_conv2 : [#users=1] = call_module[target=layer1.1.conv2](args = (%layer1_1_relu,), kwargs = {})
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# %layer1_1_bn2 : [#users=1] = call_module[target=layer1.1.bn2](args = (%layer1_1_conv2,), kwargs = {})
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# %add_1 : [#users=1] = call_function[target=operator.add](args = (%layer1_1_bn2, %layer1_0_relu_1), kwargs = {})
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# ...
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# %avgpool : [#users=1] = call_module[target=avgpool](args = (%layer4_2_relu_1,), kwargs = {})
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# %flatten : [#users=1] = call_function[target=torch.flatten](args = (%avgpool, 1), kwargs = {})
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# %fc : [#users=1] = call_module[target=fc](args = (%flatten,), kwargs = {})
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# return fc
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gm = GraphModule(model, graph, model.__class__.__name__)
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gm.recompile()
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graph_analyser = GraphAnalyser(gm)
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liveness_list = graph_analyser.liveness_analysis()
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# print(len(liveness_dict[0].unique_live_vars))
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# assert False
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solver_options = {'fast_mode': True}
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strategies_constructor = StrategiesConstructor(graph, device_mesh, shape_consistency_manager, solver_options)
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strategies_constructor.build_strategies_and_cost()
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cost_graph = CostGraph(strategies_constructor.leaf_strategies)
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cost_graph.simplify_graph()
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solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser, memory_budget=1620017824.0)
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# solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
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ret = solver.call_solver_serialized_args()
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print(ret)
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strategies_list = list(ret[0])
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print(strategies_list)
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computation_cost = 0
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communication_cost = 0
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communication_cost_bn = 0
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memory_cost = 0
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for index, node in enumerate(graph.nodes):
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if node.op == 'call_module':
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submod = node.graph.owning_module.get_submodule(node.target)
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if type(submod) in ELEMENTWISE_MODULE_OP:
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input_spec = node.args[0].strategies_vector[strategies_list[index]].output_sharding_spec
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print(node.name, input_spec)
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continue
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if type(submod) in BATCHNORM_MODULE_OP:
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communication_cost_bn += node.strategies_vector[strategies_list[index]].communication_cost
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print(node.name, node.strategies_vector[strategies_list[index]].name)
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computation_cost += node.strategies_vector[strategies_list[index]].compute_cost
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communication_cost += node.strategies_vector[strategies_list[index]].communication_cost
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node_memory_cost = node.strategies_vector[strategies_list[index]].memory_cost
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if isinstance(node_memory_cost, tuple):
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node_memory_cost = node_memory_cost[0]
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memory_cost += node_memory_cost
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print(f'computation cost is {computation_cost}')
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print(f'communication cost is {communication_cost}')
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print(f'memory cost is {memory_cost}')
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print(f'bn communication cost is {communication_cost_bn}')
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if __name__ == '__main__':
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test_cost_graph()
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