mirror of https://github.com/hpcaitech/ColossalAI
[autoparallel] add resnet autoparallel unit test and add backward weight communication cost (#1589)
YuliangLiu0306
2 years ago
committed by
GitHub
parent
7c18a588c8
commit
d164449d00
@ -0,0 +1,125 @@
|
||||
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()
|
||||
Loading…
Reference in new issue