mirror of https://github.com/hpcaitech/ColossalAI
72 lines
2.8 KiB
Python
72 lines
2.8 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.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.conv1 = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1)
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self.conv2 = nn.Conv2d(c_in, c_out, kernel_size=3, padding=1, stride=2)
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def forward(self, x):
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x1 = self.conv1(x)
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x2 = x1 + 1
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x1 = torch.reshape(x1, [1, -1, 64, 1])
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x3 = self.conv2(x1)
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x3 = torch.reshape(x3, [4, 1, 64, -1])
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x = x1 + x3
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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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# %conv1 : [#users=2] = call_module[target=conv1](args = (%x,), kwargs = {})
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# %add : [#users=0] = call_function[target=operator.add](args = (%conv1, 1), kwargs = {})
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# %reshape : [#users=2] = call_function[target=torch.reshape](args = (%conv1, [1, -1, 64, 1]), kwargs = {})
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# %conv2 : [#users=1] = call_module[target=conv2](args = (%reshape,), kwargs = {})
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# %reshape_1 : [#users=1] = call_function[target=torch.reshape](args = (%conv2, [4, 1, 64, -1]), kwargs = {})
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# %add_1 : [#users=1] = call_function[target=operator.add](args = (%reshape, %reshape_1), kwargs = {})
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# return add_1
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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, conv1, add, reshape, conv2, reshape_1, add_1, 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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# check a tensor add with a scalar case
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conv1_strategies = strategy_map[nodes[1]]
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add_strategies = strategy_map[nodes[2]]
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add_strategies_cover_list = [strategy.input_shardings[0].sharding_sequence for strategy in add_strategies]
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for strategy in conv1_strategies:
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assert strategy.output_sharding_spec.sharding_sequence in add_strategies_cover_list
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# check two tensors element-wise add case
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add_1_strategies = strategy_map[nodes[6]]
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assert len(add_1_strategies) == 25
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if __name__ == '__main__':
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test_conv_handler()
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