mirror of https://github.com/hpcaitech/ColossalAI
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
import pytest
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import torch.fx
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from torch.fx import GraphModule
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from torch.utils._pytree import tree_map
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from colossalai.auto_parallel.offload.region_manager import RegionManager
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from colossalai.auto_parallel.offload.solver import NOT_NVML, SolverFactory
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from colossalai.fx import ColoTracer, is_compatible_with_meta
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from colossalai.fx.passes.meta_info_prop import MetaInfoProp
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from colossalai.testing import clear_cache_before_run, parameterize
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from tests.test_auto_parallel.test_offload.model_utils import *
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@pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed')
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@clear_cache_before_run()
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@parameterize('model_name', ['gpt2_', 'bert_'])
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@parameterize('memory_budget', [4000])
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@parameterize('solver_name', ['syn', 'asyn'])
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def solver_test(model_name: str, memory_budget: float, solver_name: str):
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model_builder, data_gen = get_components_func()
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data_args = data_gen(device="cpu")
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wrap_fn = lambda x: x.to(dtype=torch.half) if isinstance(x, torch.Tensor) and torch.is_floating_point(x) else x
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data_args = tree_map(wrap_fn, data_args)
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model = model_builder()
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model.train()
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model = model.cpu().half()
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tracer = ColoTracer()
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assert is_compatible_with_meta()
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wrap_fn = lambda x: x.to("meta") if isinstance(x, torch.Tensor) else x
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meta_args = tree_map(wrap_fn, data_args)
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graph = tracer.trace(model, meta_args=meta_args)
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gm = GraphModule(model, graph, model.__class__.__name__)
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interp = MetaInfoProp(gm)
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interp.propagate(*meta_args.values())
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region_manager = RegionManager(graph, solver_name=solver_name)
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region_manager._pre_process()
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region_list = region_manager.region_list
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solver_cls = SolverFactory.create(solver_name)
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memory_budget = memory_budget * 1024 * 1024
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solver = solver_cls(region_list, memory_budget)
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solver._call_solver()
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assert solver.best_ts.peak_mem < memory_budget
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print("****************** execution plan *******************")
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for region in region_list:
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need_offload = region.need_offload
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to_prefetch = region.fwd_prefetch_region.r_id if region.fwd_prefetch_region is not None else None
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print(
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f'| {model_name} forward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}'
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)
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for region in region_list.__reversed__():
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need_offload = region.need_offload
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to_prefetch = region.bwd_prefetch_region.r_id if region.bwd_prefetch_region is not None else None
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print(
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f'| {model_name} backward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}'
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)
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if __name__ == '__main__':
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solver_test()
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