mirror of https://github.com/hpcaitech/ColossalAI
116 lines
4.6 KiB
Python
116 lines
4.6 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.conv_handler import ConvHandler
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from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
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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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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_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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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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# %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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# [x, mul, conv, output]
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nodes = [node for node in gm.graph.nodes]
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strategies_for_input = []
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sharding_option = (None, 0, 1)
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for first_sharding_index in sharding_option:
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for second_sharding_index in sharding_option:
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if first_sharding_index is not None and second_sharding_index == first_sharding_index:
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continue
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if first_sharding_index is None:
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first_dim_spec = _DimSpec([])
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else:
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first_dim_spec = _DimSpec([first_sharding_index])
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if second_sharding_index is None:
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second_dim_spec = _DimSpec([])
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else:
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second_dim_spec = _DimSpec([second_sharding_index])
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replica_dim_spec = _DimSpec([])
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sharding_sequence = [first_dim_spec, second_dim_spec, replica_dim_spec, replica_dim_spec]
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sharding_spec = ShardingSpec(device_mesh=device_mesh,
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entire_shape=entire_shape,
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sharding_sequence=sharding_sequence)
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strategies_for_input.append(sharding_spec)
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# strategies_for_input = [[R, R, R, R], [R, S0, R, R], [R, S1, R, R], [S0, R, R, R], [S0, S1, R, R], [S1, R, R, R], [S1, S0, R, R]]
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strategies_vector_for_input = StrategiesVector(node=nodes[0],
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in_nodes=[nodes[1], 2],
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strategies=strategies_for_input)
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setattr(nodes[1], 'strategies_vector', strategies_vector_for_input)
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strategies_vector = StrategiesVector(node=nodes[2], in_nodes=[
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nodes[1],
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])
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conv_handler = ConvHandler(input_node=nodes[1],
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input_index=0,
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weight=dict(gm.named_modules())[nodes[2].name].weight,
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output_node=nodes[2],
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device_mesh=device_mesh,
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strategies_vector=strategies_vector,
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shape_consistency_manager=shape_consistency_manager)
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conv_handler.register_strategy_into_strategies_vector()
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# ['S0S1 = S0R x RS1', 'S1S0 = S1R x RS0', 'S0R = S0S1 x S1R', 'S1R = S1S0 x S0R', 'RS1 = RS0 x S0S1', 'RS0 = RS1 x S1S0', 'RS0 = RR x RS0', 'RS1 = RR x RS1', 'RR = RR x RR']
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strategy_name_list = [strategy.name for strategy in conv_handler.strategies_vector.strategies]
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# SS = SR x RS
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assert 'S0S1 = S0R x RS1' in strategy_name_list
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assert 'S1S0 = S1R x RS0' in strategy_name_list
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# SR = SS x SR
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assert 'S0R = S0S1 x S1R' in strategy_name_list
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assert 'S1R = S1S0 x S0R' in strategy_name_list
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# RS = RS x SS
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assert 'RS0 = RS1 x S1S0' in strategy_name_list
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assert 'RS1 = RS0 x S0S1' in strategy_name_list
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# RS = RR x RS
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assert 'RS0 = RR x RS0' in strategy_name_list
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assert 'RS1 = RR x RS1' in strategy_name_list
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# RR= RR x RR
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assert 'RR = RR x RR' in strategy_name_list
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if __name__ == '__main__':
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test_conv_handler()
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