Update on "[ET-VK][testing] Q/DQ/CQP op comprehensive delegate dynamic quantization testing"

# Context

We need to ensure that most of the operators that were created work in tandem with each other for dynamic quantization.

# Changes

This creates two test cases to test the per_token and per_tensor pipeline to ensure that the whole full quantization workflow works as intended.

Differential Revision: [D77746139](https://our.internmc.facebook.com/intern/diff/D77746139/)

[ghstack-poisoned]

Author: morelos

backends/vulkan/runtime/VulkanBackend.cpp

@@ -616,22 +616,26 @@ class VulkanBackend final : public ::executorch::runtime::BackendInterface {
         // Handle dtype conversion between Vulkan and ExecutorTorch (in-place)
         if (vulkan_dtype == vkapi::kFloat &&
             et_dtype == executorch::aten::ScalarType::Double) {
-          // Convert float32 to float64 in-place (backwards to avoid overwriting)
+          // Convert float32 to float64 in-place (backwards to avoid
+          // overwriting)
           double* data_64 = args[o]->toTensor().mutable_data_ptr<double>();
           const float* data_32 = args[o]->toTensor().const_data_ptr<float>();
           for (size_t j = args[o]->toTensor().numel() - 1; j >= 0; --j) {
             data_64[j] = static_cast<double>(data_32[j]);
-            if (j == 0) break; // Prevent underflow for size_t
+            if (j == 0)
+              break; // Prevent underflow for size_t
           }
         } else if (
             vulkan_dtype == vkapi::kInt &&
             et_dtype == executorch::aten::ScalarType::Long) {
           // Convert int32 to int64 in-place (backwards to avoid overwriting)
           int64_t* data_64 = args[o]->toTensor().mutable_data_ptr<int64_t>();
-          const int32_t* data_32 = args[o]->toTensor().const_data_ptr<int32_t>();
+          const int32_t* data_32 =
+              args[o]->toTensor().const_data_ptr<int32_t>();
           for (size_t j = args[o]->toTensor().numel() - 1; j >= 0; --j) {
             data_64[j] = static_cast<int64_t>(data_32[j]);
-            if (j == 0) break; // Prevent underflow for size_t
+            if (j == 0)
+              break; // Prevent underflow for size_t
           }
         }
       }

backends/vulkan/runtime/graph/ops/impl/Dequantize.cpp

@@ -51,17 +51,19 @@ utils::uvec3 dequantize_per_channel_local_wg_size(
 
   const ValueRef input = args.at(1).refs.at(0);
 
-  utils::uvec3 local_wg_size = graph->create_local_wg_size(global_workgroup_size);
-
-  // WORKAROUND: The CommandBuffer::dispatch function divides global_workgroup_size
-  // by local_workgroup_size to get the number of workgroups to dispatch.
-  // For per-channel dequantization along the batch axis, we need to ensure that
-  // we dispatch the correct number of workgroups in the Z dimension to cover
-  // all batch-channel combinations.
+  utils::uvec3 local_wg_size =
+      graph->create_local_wg_size(global_workgroup_size);
+
+  // WORKAROUND: The CommandBuffer::dispatch function divides
+  // global_workgroup_size by local_workgroup_size to get the number of
+  // workgroups to dispatch. For per-channel dequantization along the batch
+  // axis, we need to ensure that we dispatch the correct number of workgroups
+  // in the Z dimension to cover all batch-channel combinations.
   //
-  // If local_wg_size[2] > 1, then div_up(global_workgroup_size[2], local_wg_size[2])
-  // might reduce the number of workgroups dispatched. To ensure we dispatch
-  // global_workgroup_size[2] workgroups in the Z dimension, we set local_wg_size[2] = 1.
+  // If local_wg_size[2] > 1, then div_up(global_workgroup_size[2],
+  // local_wg_size[2]) might reduce the number of workgroups dispatched. To
+  // ensure we dispatch global_workgroup_size[2] workgroups in the Z dimension,
+  // we set local_wg_size[2] = 1.
   const auto input_sizes = graph->sizes_of(input);
   if (global_workgroup_size[2] > 1 && input_sizes[3] > 0) {
     local_wg_size[2] = 1;
@@ -260,8 +262,8 @@ void add_dequantize_per_channel_node(
 
   int num_channels;
   if (axis_val == 0 && ndim == 4 && !graph.is_buffer_storage(input)) {
-    // For batch dimension dequantization in 4D tensors, pass the actual number of channels
-    // so the shader can correctly unfold the batch-channel folding
+    // For batch dimension dequantization in 4D tensors, pass the actual number
+    // of channels so the shader can correctly unfold the batch-channel folding
     num_channels = static_cast<int>(input_sizes[1]); // Channel dimension
   } else {
     num_channels = static_cast<int>(input_sizes[axis_val]);