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[autoparallel] Attach input, buffer and output tensor to MetaInfo class (#2162)
* [fx] metainfo class for auto parallel * [fx] add unit test for linear metainfo * [fx] fix bwd param for linear * [fx] modify unit test * [fx] modify unit test * [fx] modify import * [fx] modify import * [fx] modify import * [fx] move meta profiler to auto parallel * [fx] add conv metainfo class * [fx] restore profiler * [fx] restore meta profiler * [autoparallel] modify unit test * [fx] modify unit test * [autoparallel] add batchnorm metainfo class * [autoparallel] fix batchnorm unit test function declaration * [fx] restore profiler * [fx] add relu metainfo class * [fx] restore profiler * [autoparallel] modify metainfo input * [autoparallel] add pooling metainfo * [autoparallel] add F.linear metainfo generator * [autoparallel] add binary elementwise metainfo * [fx] recover profiler * [autoparallel] fix forward memory calculation * [autoparallel] modify constants.py * [autoparallel] remove redundant print * [autoparallel] add F.conv metainfo * [autoparallel] linear fix * [autoparallel] memory estimation for communication actions * [autoparallel] fix docstring * [autoparallel] fix variables name * [autoparallel] attach tensor to metainfo class * [autoparallel] fix dangerous try except * [autoparallel] attach memory cost to shape consistency node * [autoparallel] attach shape consistency node's metainfo to the node * [autoparallel] remove todo in shape consistency memory estimation * [autoparallel] fix the annotation
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@@ -37,7 +37,7 @@ def _batchnorm_module_mem_test(rank, world_size, port):
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# index of target node in computation graph
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node_index = 1
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# total number of target node strategies
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strategy_number = 4
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strategy_number = 9
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mem_test_for_node_strategy(rank=rank,
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model=model,
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device_mesh=device_mesh,
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@@ -92,7 +92,7 @@ def _linear_function_mem_test(rank, world_size, port):
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model=model,
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device_mesh=device_mesh,
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node_index=2,
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strategy_number=23,
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strategy_number=24,
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input_args=[input],
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meta_arg_names=["input"])
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