Vectorization analysis returns wrong Vectorization Factor

[jjsjann123]

In vectorization analysis, we use MaxInfoSpanningTree to propagate the vectorization factor from reference TV to other TVs in the fusion. The issue is that, for each TV with multiple paths to the reference TV, only one path is traversed.

For the graph below where resize is involved, this approach runs into issue.

tv0 is being sliced twice producing tv1 / tv2, which were later added together as tv3 (reference TV).
We are taking a slice with an odd offset, tv2 = tv0[3:-5], which wouldn't allow any vectorization.

But in our analysis, when we propagate the projected contiguous inner dimensions from tv3 to tv0:

1. if we go through tv3 -> tv1 -> tv0, the resize with resize extent (-4, -4) would give us a vectorization factor of 4;
2. Meanwhile if we go along tv3 -> tv2 -> tv0, the other resize with resize extent (-3, -5) wouldn't allow any vectorization.

// Trivial slice
TEST_F(VectorizationAnalysisTest, ResizeFork) {
  Fusion fusion;
  FusionGuard fg(&fusion);
  std::vector<std::pair<TensorView*, int64_t>> expection_list;

  // concrete shapes to avoid dynamic Fusion
  auto tv0 = makeContigConcreteTensor({36});
  fusion.addInput(tv0);

  auto tv1 = slice(
      tv0,
      {{IrBuilder::create<Val>(4L),
        sub(tv0->axis(0)->extent(), IrBuilder::create<Val>(4L))}});
  auto tv2 = slice(
      tv0,
      {{IrBuilder::create<Val>(3L),
        sub(tv0->axis(0)->extent(), IrBuilder::create<Val>(5L))}});
  auto tv3 = add(tv1, tv2);
  fusion.addOutput(tv3);

  auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
  auto t0 = at::randn({36}, options);
  std::vector<c10::IValue> aten_inputs({t0});

  std::unordered_map<TensorView*, Val*> projected_extent_map_from_producer =
      vectorize_helper::ContiguousInnerDimensionsMapper::map(
          tv3, tv3->getLogicalDomain())
          .getTvToContigMergeOfInnerSizeMap();
  checkMappedVal(projected_extent_map_from_producer, tv0, 1);
}