How to prevent dispatching `torch._C._nn.upsample_bilinear2d` to vectorize implementation `torch.ops.aten.upsample_bilinear2d.vec`

shimbay: <p>I`m new to torch compiler.</p>
<p>Recently, i`m figuring out the progress of operation lowering in torch compiler.</p>
<p>For a operation <code>F.interpolate(x, scale_factor=0.5, mode="bilinear")</code>, will be expanded to a vectorize implementation</p>
<pre><code class="lang-auto">        arange: "i64[32]" = torch.ops.aten.arange.default(32, device = device(type='cpu'), pin_memory = False)
        _to_copy: "f32[32]" = torch.ops.aten._to_copy.default(arange, dtype = torch.float32);  arange = None
        add: "f32[32]" = torch.ops.aten.add.Tensor(_to_copy, 0.5);  _to_copy = None
        mul: "f32[32]" = torch.ops.aten.mul.Tensor(add, 2.0);  add = None
        sub: "f32[32]" = torch.ops.aten.sub.Tensor(mul, 0.5);  mul = None
        clamp: "f32[32]" = torch.ops.aten.clamp.default(sub, 0.0);  sub = None
        view: "f32[32, 1]" = torch.ops.aten.view.default(clamp, [32, 1]);  clamp = None
        _to_copy_1: "i64[32, 1]" = torch.ops.aten._to_copy.default(view, dtype = torch.int64)
        add_1: "i64[32, 1]" = torch.ops.aten.add.Tensor(_to_copy_1, 1)
        clamp_1: "i64[32, 1]" = torch.ops.aten.clamp.default(add_1, None, 63);  add_1 = None
        arange_1: "i64[32]" = torch.ops.aten.arange.default(32, device = device(type='cpu'), pin_memory = False)
        _to_copy_2: "f32[32]" = torch.ops.aten._to_copy.default(arange_1, dtype = torch.float32);  arange_1 = None
        add_2: "f32[32]" = torch.ops.aten.add.Tensor(_to_copy_2, 0.5);  _to_copy_2 = None
        mul_1: "f32[32]" = torch.ops.aten.mul.Tensor(add_2, 2.0);  add_2 = None
        sub_1: "f32[32]" = torch.ops.aten.sub.Tensor(mul_1, 0.5);  mul_1 = None
        clamp_2: "f32[32]" = torch.ops.aten.clamp.default(sub_1, 0.0);  sub_1 = None
        view_1: "f32[32]" = torch.ops.aten.view.default(clamp_2, [32]);  clamp_2 = None
        _to_copy_3: "i64[32]" = torch.ops.aten._to_copy.default(view_1, dtype = torch.int64)
        add_3: "i64[32]" = torch.ops.aten.add.Tensor(_to_copy_3, 1)
        clamp_3: "i64[32]" = torch.ops.aten.clamp.default(add_3, None, 63);  add_3 = None
        _unsafe_index: "f32[1, 256, 32, 32]" = torch.ops.aten._unsafe_index.Tensor(convolution, [None, None, _to_copy_1, _to_copy_3])
        _unsafe_index_1: "f32[1, 256, 32, 32]" = torch.ops.aten._unsafe_index.Tensor(convolution, [None, None, _to_copy_1, clamp_3])
        _unsafe_index_2: "f32[1, 256, 32, 32]" = torch.ops.aten._unsafe_index.Tensor(convolution, [None, None, clamp_1, _to_copy_3])
        _unsafe_index_3: "f32[1, 256, 32, 32]" = torch.ops.aten._unsafe_index.Tensor(convolution, [None, None, clamp_1, clamp_3]);  convolution = None
        sub_2: "f32[32]" = torch.ops.aten.sub.Tensor(view_1, _to_copy_3);  view_1 = None
        clamp_4: "f32[32]" = torch.ops.aten.clamp.default(sub_2, 0.0, 1.0);  sub_2 = None
        sub_3: "f32[1, 256, 32, 32]" = torch.ops.aten.sub.Tensor(_unsafe_index_1, _unsafe_index);  _unsafe_index_1 = None
        mul_2: "f32[1, 256, 32, 32]" = torch.ops.aten.mul.Tensor(sub_3, clamp_4);  sub_3 = None
        add_4: "f32[1, 256, 32, 32]" = torch.ops.aten.add.Tensor(_unsafe_index, mul_2);  _unsafe_index = mul_2 = None
        sub_4: "f32[1, 256, 32, 32]" = torch.ops.aten.sub.Tensor(_unsafe_index_3, _unsafe_index_2);  _unsafe_index_3 = None
        mul_3: "f32[1, 256, 32, 32]" = torch.ops.aten.mul.Tensor(sub_4, clamp_4);  sub_4 = None
        add_5: "f32[1, 256, 32, 32]" = torch.ops.aten.add.Tensor(_unsafe_index_2, mul_3);  _unsafe_index_2 = mul_3 = None
        sub_5: "f32[32, 1]" = torch.ops.aten.sub.Tensor(view, _to_copy_1);  view = None
        clamp_5: "f32[32, 1]" = torch.ops.aten.clamp.default(sub_5, 0.0, 1.0);  sub_5 = None
        sub_6: "f32[1, 256, 32, 32]" = torch.ops.aten.sub.Tensor(add_5, add_4);  add_5 = None
        mul_4: "f32[1, 256, 32, 32]" = torch.ops.aten.mul.Tensor(sub_6, clamp_5);  sub_6 = None
        add_6: "f32[1, 256, 32, 32]" = torch.ops.aten.add.Tensor(add_4, mul_4);  add_4 = mul_4 = None
</code></pre>
<p>I dont know how this happens, why does not dispatch to a upsample kernel, and how to disable this behavior?</p>