mirror of https://github.com/hpcaitech/ColossalAI
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
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.proxy import ColoProxy
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from colossalai.fx.tracer.tracer import ColoTracer
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from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
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from colossalai.auto_parallel.solver.op_handler.conv_handler import CONV_STRATEGIES_LIST
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from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
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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.options import SolverOptions
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from copy import deepcopy
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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.conv = nn.Conv2d(c_in, c_out, kernel_size=3)
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def forward(self, x):
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x = x * 2
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x = self.conv(x)
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return x
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def test_strategies_constructor():
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physical_mesh_id = torch.arange(0, 4)
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mesh_shape = (2, 2)
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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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entire_shape = torch.Size((4, 16, 64, 64))
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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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# graph():
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# %x : torch.Tensor [#users=1] = placeholder[target=x]
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# %mul : [#users=1] = call_function[target=operator.mul](args = (%x, 2), kwargs = {})
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# %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
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# return conv
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graph = tracer.trace(root=model, meta_args=input_sample)
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gm = GraphModule(model, graph, model.__class__.__name__)
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gm.recompile()
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solver_options = SolverOptions(fast=True)
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strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
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assert strategies_constructor.leaf_strategies == []
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assert strategies_constructor.strategy_map == {}
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strategies_constructor.build_strategies_and_cost()
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# check leaf_strategies
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# In fast mode, placeholder node only has replica strategy.
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assert strategies_constructor.leaf_strategies[0][0].name == 'Replica Placeholder'
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# Second node is mul which is a element-wise node, therefore the output sharding spec is same as input sharding spec.
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assert strategies_constructor.leaf_strategies[1][0].name == '[R, R, R, R] -> [R, R, R, R]'
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# Third node is conv.
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conv_check_list = deepcopy(CONV_STRATEGIES_LIST)
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for strategy in strategies_constructor.leaf_strategies[2]:
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conv_check_list.remove(strategy.name)
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assert len(conv_check_list) == 0
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# In fast mode, output node only has replica strategy.
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assert strategies_constructor.leaf_strategies[3][0].name == 'Replica Output'
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# check strategy_map
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nodes = [node for node in graph.nodes]
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# In fast mode, placeholder node only has replica strategy.
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x = nodes[0]
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assert strategies_constructor.strategy_map[x][0].name == 'Replica Placeholder'
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# Second node is mul which is a element-wise node, therefore the output sharding spec is same as input sharding spec.
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mul = nodes[1]
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assert strategies_constructor.strategy_map[mul][0].name == '[R, R, R, R] -> [R, R, R, R]'
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# Third node is conv.
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conv = nodes[2]
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conv_check_list = deepcopy(CONV_STRATEGIES_LIST)
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for strategy in strategies_constructor.strategy_map[conv]:
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conv_check_list.remove(strategy.name)
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assert len(conv_check_list) == 0
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# In fast mode, output node only has replica strategy.
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output = nodes[3]
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assert strategies_constructor.strategy_map[output][0].name == 'Replica Output'
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if __name__ == '__main__':
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test_strategies_constructor()
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