[export] Fix for unflattening modules with duplicate tensors

[angelayi]

In the given test case, we have a ModuleList of 3 modules (norm.0, norm.1, norm.2) which share the same weight and bias tensors. However when we trace, they all end up pointing to one state dict name, (ex. norm.2).

graph():
    %p_norms_0_weight : [num_users=0] = placeholder[target=p_norms_0_weight]
    %p_norms_0_bias : [num_users=0] = placeholder[target=p_norms_0_bias]
    %p_norms_1_weight : [num_users=0] = placeholder[target=p_norms_1_weight]
    %p_norms_1_bias : [num_users=0] = placeholder[target=p_norms_1_bias]
    %p_norms_2_weight : [num_users=3] = placeholder[target=p_norms_2_weight]
    %p_norms_2_bias : [num_users=3] = placeholder[target=p_norms_2_bias]
    %input_ : [num_users=1] = placeholder[target=input_]
    %native_layer_norm : [num_users=1] = call_function[target=torch.ops.aten.native_layer_norm.default](args = (%input_, [2, 2, 3], %p_norms_2_weight, %p_norms_2_bias, 1e-05), kwargs = {})
    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%native_layer_norm, 0), kwargs = {})
    %native_layer_norm_1 : [num_users=1] = call_function[target=torch.ops.aten.native_layer_norm.default](args = (%getitem, [2, 2, 3], %p_norms_2_weight, %p_norms_2_bias, 1e-05), kwargs = {})
    %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%native_layer_norm_1, 0), kwargs = {})
    %native_layer_norm_2 : [num_users=1] = call_function[target=torch.ops.aten.native_layer_norm.default](args = (%getitem_3, [2, 2, 3], %p_norms_2_weight, %p_norms_2_bias, 1e-05), kwargs = {})
    %getitem_6 : [num_users=1] = call_function[target=operator.getitem](args = (%native_layer_norm_2, 0), kwargs = {})
    return (getitem_6,)

This causes an error in the unflattener where after constructing the submodules for norm.0, it will have the graph pointing to norm.2.weight and norm.2.bias:

graph():
    %p_norms_2_bias : [num_users=1] = placeholder[target=p_norms_2_bias]
    %p_norms_2_weight : [num_users=1] = placeholder[target=p_norms_2_weight]
    %input_ : [num_users=1] = placeholder[target=input_]
    %native_layer_norm : [num_users=1] = call_function[target=torch.ops.aten.native_layer_norm.default](args = (%input_, [2, 2, 3], %p_norms_2_weight, %p_norms_2_bias, 1e-05), kwargs = {})
    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%native_layer_norm, 0), kwargs = {})
    return getitem

Since the attributes are not within the same scope of the graph, (norm.0 vs. norm.2), they will not be added to the subgraph, causing an error.

So this PR handles the duplicate state dict attributes by modifying the inputs_to_state dict to map from node names to a list of possible state dict target names.

[pytorch-bot]

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