[ET-VK][ez] Fix linear weight int4 test due to change in ATen API

backends/vulkan/test/op_tests/linear_weight_int4_test.cpp

@@ -30,16 +30,38 @@ at::Tensor linear_weight_int4_reference_impl(
   const size_t ndim = original_x_size.size();
   const int64_t out_features = weights_4x2.size(0);
   const at::Tensor x_flattened = x.reshape({-1, original_x_size[ndim - 1]});
-  const at::Tensor packed_weights =
-      at::_convert_weight_to_int4pack(weights_4x2, inner_k_tiles);
-  at::Tensor out = at::_weight_int4pack_mm(
-      x_flattened, packed_weights, groupsize, scales_and_zeros);
+  at::Tensor out = at::_weight_int4pack_mm_for_cpu(
+      x_flattened, weights_4x2, groupsize, scales_and_zeros);
   std::vector<int64_t> out_shape(
       original_x_size.begin(), original_x_size.end());
   out_shape.at(ndim - 1) = out_features;
   return out.reshape(out_shape);
 }
 
+at::Tensor unpack_weights_4x2(const at::Tensor& weights_4x2) {
+  std::vector<int64_t> weights_shape(weights_4x2.sizes().vec());
+  weights_shape[1] *= 2;
+
+  at::Tensor weights_unpacked =
+      at::empty(weights_shape, at::device(at::kCPU).dtype(at::kInt));
+
+  const int64_t N = weights_unpacked.size(0);
+  const int64_t K = weights_unpacked.size(1);
+
+  for (int n = 0; n < N; n++) {
+    for (int k = 0; k < K; k += 2) {
+      const uint8_t packed_val = weights_4x2[n][k / 2].item().to<uint8_t>();
+      const uint8_t second_val = packed_val & 0x0F;
+      const uint8_t first_val = (packed_val & 0xF0) >> 4;
+
+      weights_unpacked[n][k] = int(first_val);
+      weights_unpacked[n][k + 1] = int(second_val);
+    }
+  }
+
+  return weights_unpacked;
+}
+
 at::Tensor dequantize_and_linear(
     const at::Tensor& x,
     const at::Tensor& weights_4x2,
@@ -91,13 +113,18 @@ void test_reference_linear_int4(
   at::Tensor x = at::rand({B, M, K}, at::device(at::kCPU).dtype(at::kFloat));
   at::Tensor weights_4x2 =
       at::randint(0, 256, {N, K / 2}, at::device(at::kCPU).dtype(at::kByte));
+  at::Tensor weights_int = unpack_weights_4x2(weights_4x2);
 
   const int k_groups = K / group_size;
   at::Tensor scales_and_zeros =
       at::rand({k_groups, N, 2}, at::device(at::kCPU).dtype(at::kFloat));
 
   at::Tensor out = linear_weight_int4_reference_impl(
-      x, weights_4x2, group_size, scales_and_zeros, inner_k_tiles);
+      x,
+      at::_convert_weight_to_int4pack_for_cpu(weights_int, group_size),
+      group_size,
+      scales_and_zeros,
+      inner_k_tiles);
 
   at::Tensor out_ref = dequantize_and_linear(
       x, weights_4x2, group_size, scales_and_zeros, inner_k_tiles);