Update base for Update on "[ET-VK] Simplify conv2d weight prepacking (>2x pipeline-creation speedup)"

ssjia has previously written two implementations of convolution weights prepacking for CPU (before and after [PyTorch PR #84973](pytorch/pytorch#84973)). Originally, I translated the second implementation to GPU since it is more readable. Now, I translate the first implementation to GPU and switch to it since it requires less steps.

The second impl was so complex that during model-load, it took >1500ms to create pipelines. In the test plan's Before, the example sums to 1905ms:
```
[334ms] P::encode-conv2d_prepack_weights_float, (16, 4, 1)
[110ms] P::encode-conv2d_dw_prepack_weights_float, (16, 4, 1)
[270ms] P::encode-conv2d_prepack_weights_float, (8, 8, 1)
[94ms] P::encode-conv2d_dw_prepack_weights_float, (8, 8, 1)
[609ms] P::encode-conv_transpose2d_prepack_weights_float, (8, 8, 1)
[488ms] P::encode-conv_transpose2d_prepack_weights_float, (16, 4, 1)
```

The first impl now takes <700ms to create pipelines. In the test plan's After, the example sums to 598ms:
```
[135ms] P::encode-conv2d_prepack_weights_float, (16, 4, 1)
[83ms] P::encode-conv2d_dw_prepack_weights_float, (16, 4, 1)
[102ms] P::encode-conv2d_prepack_weights_float, (8, 8, 1)
[69ms] P::encode-conv2d_dw_prepack_weights_float, (8, 8, 1)
[115ms] P::encode-conv_transpose2d_prepack_weights_float, (8, 8, 1)
[94ms] P::encode-conv_transpose2d_prepack_weights_float, (16, 4, 1)
```

Differential Revision: [D56617129](https://our.internmc.facebook.com/intern/diff/D56617129/)

[ghstack-poisoned]

Author: jorgep31415

examples/cadence/ops/functions.yaml

@@ -60,7 +60,7 @@
     - arg_meta: null
       kernel_name: impl::HiFi::quantized_layer_norm_out
 
-- func: cadence::quantized_linear.out(Tensor src, Tensor weight, Tensor bias, float src_scale, int src_zero_point, float weight_scale, int weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, *, Tensor(a!) out) -> Tensor(a!)
+- func: cadence::quantized_linear.out(Tensor src, Tensor weight, Tensor bias, int src_zero_point, Tensor weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, Tensor? offset, *, Tensor(a!) out) -> Tensor(a!)
   kernels:
     - arg_meta: null
       kernel_name: impl::HiFi::quantized_linear_out

examples/cadence/ops/quantized_linear_out.cpp

@@ -24,13 +24,12 @@ void quantized_linear_out(
     const Tensor& src,
     const Tensor& weight,
     const Tensor& bias,
-    double src_scale,
     int64_t src_zero_point,
-    double weight_scale,
-    int64_t weight_zero_point,
+    const Tensor& weight_zero_point,
     const Tensor& out_multiplier,
     const Tensor& out_shift,
     int64_t out_zero_point,
+    const exec_aten::optional<Tensor>& offset,
     Tensor& out) {
   // input comes in shape [leading_dims, in_dim]
   // weight comes in shape [out_dim, in_dim]
@@ -58,7 +57,7 @@ void quantized_linear_out(
       in_dim, // vec_offset of p_mat2.
       out_dim, // out_offset, i.e., offset of next output element written
       1, // out_stride, i.e., stride to go to next output row
-      -weight_zero_point, // mat1_zero_bias
+      -weight_zero_point.const_data_ptr<int32_t>()[0], // mat1_zero_bias
       -src_zero_point, // mat2_zero_bias
       out_multiplier.const_data_ptr<int32_t>(), // out_multiplier
       out_shift.const_data_ptr<int32_t>(), // out_shift