[ET-VK][ez][qconv] Add auto-selection to prefer im2col for q8ta_conv2d

Pull Request resolved: #17568

The q8ta_conv2d operator previously always delegated to the general (sliding window) implementation, even though the im2col implementation is 2-5x faster for non-grouped convolutions with in_channels % 4 == 0. This change adds runtime auto-selection logic that checks the groups parameter and input channel alignment, then dispatches to q8ta_conv2d_im2col when its constraints are met. On ResNet50 int8, this reduces Vulkan inference latency from 14.2ms to 6.8ms (2.1x speedup) on Samsung Galaxy S24, making it 30% faster than XNNPACK (9.7ms). Also adds performance test cases for deep-channel small-spatial scenarios (512ch 7x7, 1024→2048ch 1x1 stride-2) that stress-test the optimization.
ghstack-source-id: 343460520
@exported-using-ghexport

Differential Revision: [D93768637](https://our.internmc.facebook.com/intern/diff/D93768637/)

Author: ssjia

backends/vulkan/runtime/graph/ops/impl/Q8taConv2d.cpp

@@ -417,7 +417,30 @@ void q8ta_conv2d_general(
 }
 
 void q8ta_conv2d(ComputeGraph& graph, const std::vector<ValueRef>& args) {
-  q8ta_conv2d_general(graph, args);
+  const ValueRef input = args.at(0);
+  const ValueRef groups_ref = args.at(13);
+  const ValueRef output = args.at(15);
+
+  const int64_t groups = graph.extract_scalar<int64_t>(groups_ref);
+  const int64_t in_channels = graph.size_at<int64_t>(-3, input);
+  const int64_t in_channels_per_group = in_channels / groups;
+
+  const int64_t H_out = graph.size_at<int64_t>(-2, output);
+  const int64_t W_out = graph.size_at<int64_t>(-1, output);
+  const int64_t spatial_out = H_out * W_out;
+
+  // Use im2col when the channel depth is sufficient for tiled GEMM to win, or
+  // when the output spatial area is small enough that the im2col buffer stays
+  // manageable. For large spatial outputs with few channels, the im2col buffer
+  // becomes too large and the general shader is more efficient.
+  const bool use_im2col = groups == 1 && in_channels_per_group % 4 == 0 &&
+      (in_channels_per_group >= 64 || spatial_out <= 4096);
+
+  if (use_im2col) {
+    q8ta_conv2d_im2col(graph, args);
+  } else {
+    q8ta_conv2d_general(graph, args);
+  }
 }
 
 REGISTER_OPERATORS {

backends/vulkan/test/custom_ops/test_q8ta_conv2d.cpp

@@ -378,7 +378,23 @@ static std::vector<TestCase> generate_quantized_conv2d_test_cases() {
        Stride(2, 2),
        Padding(2, 2),
        Dilation(1, 1),
-       4}};
+       4},
+      // Deep channels + small spatial (ResNet50 stage 5 bottleneck)
+      {OutInChannels(512, 512),
+       InputSize2D(7, 7),
+       KernelSize(3, 3),
+       Stride(1, 1),
+       Padding(1, 1),
+       Dilation(1, 1),
+       1},
+      // Strided 1x1 shortcut (worst-case strided downsample)
+      {OutInChannels(2048, 1024),
+       InputSize2D(14, 14),
+       KernelSize(1, 1),
+       Stride(2, 2),
+       Padding(0, 0),
+       Dilation(1, 1),
+       1}};
 
   // Test with different storage types and memory layouts
   std::vector<utils::StorageType> fp_storage_types = {utils::kTexture3D};