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https://github.com/hpcaitech/ColossalAI.git
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[autoparallel] add shard option (#2423)
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from functools import partial
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import torch
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import torch.multiprocessing as mp
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import torch.nn as nn
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from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler
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from colossalai.auto_parallel.tensor_shard.node_handler.option import ShardOption
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from colossalai.auto_parallel.tensor_shard.sharding_strategy import StrategiesVector
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from colossalai.device.device_mesh import DeviceMesh
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from colossalai.fx import ColoGraphModule, ColoTracer
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from colossalai.testing import parameterize
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from colossalai.testing.pytest_wrapper import run_on_environment_flag
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from colossalai.testing.utils import parameterize
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class LinearModel(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, input, others, bias=None):
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x = nn.functional.linear(input, others, bias=bias)
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return x
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def check_shard_option(shard_option):
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model = LinearModel().cuda()
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physical_mesh_id = torch.arange(0, 4)
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mesh_shape = (2, 2)
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device_mesh = DeviceMesh(physical_mesh_id, mesh_shape)
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tracer = ColoTracer()
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graph = tracer.trace(model,
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meta_args={
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"input": torch.rand(4, 4, 4, 16).to('meta'),
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'others': torch.rand(32, 16).to('meta')
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})
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gm = ColoGraphModule(model, graph)
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linear_func_node = list(graph.nodes)[2]
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strategies_vector = StrategiesVector(linear_func_node)
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# build handler
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handler = LinearFunctionHandler(node=linear_func_node,
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device_mesh=device_mesh,
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strategies_vector=strategies_vector,
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shard_option=shard_option)
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strategies_vector = handler.register_strategy(compute_resharding_cost=False)
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strategy_name_list = [val.name for val in strategies_vector]
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# SS = SR x RS
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assert 'S1S0 = S1R x RS0_0' in strategy_name_list
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assert 'S0S1 = S0R x RS1_1' in strategy_name_list
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assert 'S0S1 = S0R x RS1_2' in strategy_name_list
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assert 'S0S1 = S0R x RS1_0' in strategy_name_list
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assert 'S1S0 = S1R x RS0_1' in strategy_name_list
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assert 'S1S0 = S1R x RS0_2' in strategy_name_list
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# SR = SS x SR
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assert 'S0R = S0S1 x S1R_1' in strategy_name_list
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assert 'S0R = S0S1 x S1R_2' in strategy_name_list
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assert 'S1R = S1S0 x S0R_0' in strategy_name_list
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assert 'S0R = S0S1 x S1R_0' in strategy_name_list
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assert 'S1R = S1S0 x S0R_1' in strategy_name_list
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assert 'S1R = S1S0 x S0R_2' 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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# S01R = S01R x RR
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assert 'S01R = S01R x RR_0' in strategy_name_list
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assert 'S01R = S01R x RR_1' in strategy_name_list
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assert 'S01R = S01R x RR_2' in strategy_name_list
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# RR = RS01 x S01R
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assert 'RR = RS01 x S01R' in strategy_name_list
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# RS01 = RR x RS01
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assert 'RS01 = RR x RS01' in strategy_name_list
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if shard_option == ShardOption.SHARD:
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# RR = RS x SR
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assert 'RR = RS0 x S0R' in strategy_name_list
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assert 'RR = RS1 x S1R' 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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if shard_option == ShardOption.STANDARD:
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# RR = RS x SR
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assert 'RR = RS0 x S0R' in strategy_name_list
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assert 'RR = RS1 x S1R' 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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@run_on_environment_flag(name='AUTO_PARALLEL')
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def test_shard_option():
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for shard_option in [ShardOption.STANDARD, ShardOption.SHARD, ShardOption.FULL_SHARD]:
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check_shard_option(shard_option)
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if __name__ == '__main__':
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test_shard_option()
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