mirror of https://github.com/hpcaitech/ColossalAI
[fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages (#1425)
* [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usages * [fx] modify the calculation of node_size in MetaInfoProp for activation checkpointing usagespull/1437/head
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@ -114,18 +114,29 @@ class MetaInfoProp(torch.fx.Interpreter):
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return TensorMetadata(None, None, False, None, 0, False)
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meta = _map_aggregate(result, extract_tensor_meta)
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n.meta['tensor_meta'] = meta
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total_node_size = _compute_node_numel(n.meta['tensor_meta'])
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# counting the total size of parameters
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# get byte size for each element
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size_per_elem_bytes = torch.tensor([], dtype=meta.dtype).element_size()
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# compute the total size of activation tensors
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total_activation_size = _compute_node_numel(n.meta['tensor_meta'])
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# compute the total size of model parameters
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total_param_size = 0
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if n.op == 'call_module':
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target_module = n.graph.owning_module.get_submodule(n.target)
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for param in target_module.parameters():
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total_param_size += param.numel()
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total_node_size += total_param_size
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n.node_size = total_node_size
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# compute the total memory cost of activation tensors and model parameters
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total_activation_size *= size_per_elem_bytes
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total_param_size *= size_per_elem_bytes
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# TODO: node.node_size is not an original attribute
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setattr(n, 'node_size', total_activation_size + total_param_size)
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setattr(n, 'param_size', total_param_size)
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setattr(n, 'activation_size', total_activation_size)
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n.meta['type'] = type(result)
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return result
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@ -23,12 +23,24 @@ def test_meta_info_prop():
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input_sample = torch.rand(BATCH_SIZE, DIM_IN)
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orig_output = model(input_sample)
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gm = symbolic_trace(model)
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for node in gm.graph.nodes:
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assert not hasattr(node,
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'node_size'), 'The attribute Node.node_size should not exist before MetaInfoProp procedure'
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assert not hasattr(node,
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'param_size'), 'The attribute Node.param_size should not exist before MetaInfoProp procedure'
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assert not hasattr(
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node,
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'activation_size'), 'The attribute Node.activation_size should not exist before MetaInfoProp procedure'
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MetaInfoProp(gm).run(input_sample)
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for node in gm.graph.nodes:
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if node.op == 'placeholder':
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meta_check(node.meta['tensor_meta'], input_sample)
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if node.op == 'output':
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meta_check(node.meta['tensor_meta'], orig_output)
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assert hasattr(node, 'node_size'), 'The attribute Node.node_size should exist after MetaInfoProp procedure'
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assert hasattr(node, 'param_size'), 'The attribute Node.param_size should exist after MetaInfoProp procedure'
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assert hasattr(
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node, 'activation_size'), 'The attribute Node.activation_size should exist after MetaInfoProp procedure'
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if __name__ == '__main__':
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