mirror of https://github.com/hpcaitech/ColossalAI
79 lines
2.9 KiB
Python
79 lines
2.9 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.tracer.tracer import ColoTracer
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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 colossalai.auto_parallel.solver.graph_analysis import GraphAnalyser
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from copy import deepcopy
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from colossalai.auto_parallel.solver import Solver
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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_solver():
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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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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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# 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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# %conv1 : [#users=1] = call_module[target=conv1](args = (%mul,), kwargs = {})
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# %conv2 : [#users=1] = call_module[target=conv2](args = (%conv1,), kwargs = {})
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# %truediv : [#users=1] = call_function[target=operator.truediv](args = (%conv2, 2), kwargs = {})
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# %conv3 : [#users=1] = call_module[target=conv3](args = (%truediv,), kwargs = {})
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# %relu : [#users=1] = call_module[target=relu](args = (%conv3,), kwargs = {})
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# return relu
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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 = {'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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graph_analyser = GraphAnalyser(gm)
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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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# [ 0 0 13 13 13 13 13 0]
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strategies_combination_list = ret[0]
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assert solver.leaf_strategies[2][13].name == 'S01R = S01R x RR'
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if __name__ == '__main__':
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test_solver()
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