mirror of https://github.com/hpcaitech/ColossalAI
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
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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.auto_parallel.solver.options import SolverOptions
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from colossalai.auto_parallel.solver.strategies_constructor import StrategiesConstructor
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from colossalai.fx.tracer.tracer import ColoTracer
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from colossalai.device.device_mesh import DeviceMesh
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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 = self.conv(x)
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x = torch.flatten(x)
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return x
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def test_conv_handler():
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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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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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# %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
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# return flatten
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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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# [x, conv, flatten, output]
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nodes = [node for node in gm.graph.nodes]
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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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strategies_constructor.build_strategies_and_cost()
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strategy_map = strategies_constructor.strategy_map
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conv_strategies = strategy_map[nodes[1]]
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flatten_strategies = strategy_map[nodes[2]]
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flatten_strategies_cover_list = [strategy.input_shardings[0].sharding_sequence for strategy in flatten_strategies]
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for strategy in conv_strategies:
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assert strategy.output_sharding_spec.sharding_sequence in flatten_strategies_cover_list
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if __name__ == '__main__':
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test_conv_handler()
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