Fix nested stableHLO composite regions

[Carlomus]

Fix export failure when StableHLO regions are nested (e.g. SubModule inside Model), follow up on issue 6978

This PR aims to add support for nested stable HLO regions. Currently, trying to export something along these lines:

import torch
from torch_xla.stablehlo import exported_program_to_stablehlo
from torch_xla.experimental.mark_pattern_utils import StableHLOCompositeBuilder

class SubModule(torch.nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x, y):
        builder = StableHLOCompositeBuilder("abc.SubModule")
        x, y = builder.mark_inputs(x, y)
        out = x + y
        out = builder.mark_outputs(out)
        return out


class Model(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.submodule = SubModule()

    def forward(self, x, y):
        builder = StableHLOCompositeBuilder("abc.Model")
        x, y = builder.mark_inputs(x, y)
        a = x + y
        b = x - y
        c = self.submodule(a, b)
        a, b, c = builder.mark_outputs(a, b, c)
        return a + b + c 

sample_input = (torch.randn(1, 1, 32, 32), torch.randn(1, 1, 32, 32))
exported = torch.export.export(Model(), sample_input)
stablehlo_program = exported_program_to_stablehlo(exported)

raises an error. This PR makes it so that this generates correct MLIR:

module @IrToHlo.32 attributes {mhlo.cross_program_prefetches = [], mhlo.input_output_alias = [], mhlo.is_dynamic = false, mhlo.use_auto_spmd_partitioning = false} {
  func.func @main(%arg0: tensor<1x1x32x32xf32>, %arg1: tensor<1x1x32x32xf32>) -> tensor<1x1x32x32xf32> {
    %0:3 = stablehlo.composite "abc.Model" %arg1, %arg0 {decomposition = @abc.Model.impl} : (tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>) -> (tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>)
    %1 = stablehlo.add %0#0, %0#1 : tensor<1x1x32x32xf32>
    %2 = stablehlo.add %1, %0#2 : tensor<1x1x32x32xf32>
    return %2 : tensor<1x1x32x32xf32>
  }
  func.func private @abc.SubModule.impl(%arg0: tensor<1x1x32x32xf32>, %arg1: tensor<1x1x32x32xf32>) -> tensor<1x1x32x32xf32> {
    %0 = stablehlo.add %arg0, %arg1 : tensor<1x1x32x32xf32>
    return %0 : tensor<1x1x32x32xf32>
  }
  func.func private @abc.Model.impl(%arg0: tensor<1x1x32x32xf32>, %arg1: tensor<1x1x32x32xf32>) -> (tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>) {
    %0 = stablehlo.add %arg0, %arg1 : tensor<1x1x32x32xf32>
    %1 = stablehlo.subtract %arg0, %arg1 : tensor<1x1x32x32xf32>
    %2 = stablehlo.composite "abc.SubModule" %0, %1 {decomposition = @abc.SubModule.impl} : (tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>) -> tensor<1x1x32x32xf32>
    return %0, %1, %2 : tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>, tensor<1x1x32x32xf32>
  }
}

Cause

1. Boundary groups weren’t processed in topological order, so inner regions were wrapped after their parents.
2. Nodes moved into a region weren’t pruned from the parent graph, leaving dangling ops.

Solution

1. Region ordering: compute last_order for each boundary group and sort ascending before wrapping.
2. Dead-op cleanup: after moving ops, delete trivially dead or composite ops no longer used.