[ET-VK] Adding batch processing to conv2d dw shader by caching input texel and kernel values for reuse.

This diff adds batch processing to the conv2d dw shader by caching input texel and kernel values for reuse. This optimization reduces the number of texture lookups and kernel computations, improving the performance of the convolution operation.

Differential Revision: [D67774359](https://our.internmc.facebook.com/intern/diff/D67774359/)

ghstack-source-id: 260053926
Pull Request resolved: #7485

Author: trivedivivek

backends/vulkan/runtime/graph/ops/glsl/conv2d_dw_output_tile.glsl

@@ -14,6 +14,8 @@
 
 #define TILE_SIZE ${TILE_SIZE}
 
+#define BATCH_SIZE_Y ${BATCH_SIZE_Y}
+
 #define op(X, A, B) ${OPERATOR}
 
 #include "indexing_utils.h"
@@ -39,12 +41,20 @@ layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
  * output at a single output location.
  */
 void main() {
-  const u16vec3 pos = u16vec3(
+  // y divided up by batch size is used to determine 3d position
+  // since work size is calculated by x * ((y + B_Y - 1) / B_Y) * z
+  const uint out_limits_y_scaled = (out_limits.y + BATCH_SIZE_Y - 1) / BATCH_SIZE_Y;
+
+  u16vec3 pos = u16vec3(
     gl_GlobalInvocationID.x % out_limits.x,
-    (gl_GlobalInvocationID.x / out_limits.x) % out_limits.y,
-    gl_GlobalInvocationID.x / (out_limits.x * out_limits.y));
+    ((gl_GlobalInvocationID.x / out_limits.x) % out_limits_y_scaled),
+    gl_GlobalInvocationID.x / (out_limits.x * out_limits_y_scaled));
 
-  if (any(greaterThanEqual(pos, out_limits))) {
+  // scale pos.y by batch size, because that's the top pixel to be processed
+  pos.y *= uint16_t(BATCH_SIZE_Y);
+
+  // do not process if top pixel does not fit within the output range
+  if (any(greaterThanEqual(u16vec3(pos.x, pos.y, pos.z), out_limits))) {
     return;
   }
 
@@ -57,18 +67,47 @@ void main() {
   const u16vec2 start = ipos;
   const u16vec2 end = ipos + u16vec2(overlay_region.xy);
 
-  VEC4_T sum = texelFetch(t_bias, u16vec2(pos.z, 0), 0);
+  // sum outputs
+  VEC4_T sum[BATCH_SIZE_Y];
+
+  sum[0] = texelFetch(t_bias, u16vec2(pos.z, 0), 0);
+  for (int i = 1; i < BATCH_SIZE_Y; i++) {
+    sum[i] = sum[0];
+  }
+
+  // array to store input texels
+  VEC4_T in_texels[TILE_SIZE];
+
+  // array to store kernel data of previous y
+  VEC4_T prev_kernel_line[TILE_SIZE];
+
   uint16_t kx = uint16_t(0);
-  for (uint16_t y = start.y, i = uint16_t(0); i < uint16_t(TILE_SIZE); y += uint16_t(dilation.y), i++) {
+  for (uint16_t y = start.y, i = uint16_t(0); i < uint16_t(TILE_SIZE + BATCH_SIZE_Y - 1); y += uint16_t(dilation.y), i++) {
     for (uint16_t x = start.x, j = uint16_t(0); j < uint16_t(TILE_SIZE); x += uint16_t(dilation.x), j++) {
-      // The weight kernel was rearranged such that every NxN filter is
-      // flattened to fit in one row. Each filter was then stacked on top of
-      // each other vertically.
-      const vec4 in_texel = texelFetch(t_in, u16vec3(x, y, pos.z), 0);
-      sum = fma(in_texel, texelFetch(t_kernel, u16vec2(kx, pos.z), 0), sum);
-      kx++;
+      in_texels[int(j)] = texelFetch(t_in, u16vec3(x, y, pos.z), 0);
+    }
+
+    // from 2nd iteration onwards accumulate dot product in 2nd sum
+    // based on kernel line data fetched in previous iteration and input texel from this iteration
+    if (i > uint16_t(0)) {
+      for (uint16_t j = uint16_t(0); j < uint16_t(TILE_SIZE); j++) {
+        sum[1] = fma(in_texels[int(j)], prev_kernel_line[int(j)], sum[1]);
+      }
+    }
+
+    // accumulate dot product in 1st sum only until tile size
+    if (i < uint16_t(TILE_SIZE)) {
+      for (uint16_t j = uint16_t(0); j < uint16_t(TILE_SIZE); j++, kx++) {
+        prev_kernel_line[int(j)] = texelFetch(t_kernel, u16vec2(kx, pos.z), 0);
+        sum[0] = fma(in_texels[int(j)], prev_kernel_line[int(j)], sum[0]);
+      }
     }
   }
 
-  imageStore(t_out, pos, op(sum, out_min, out_max));
+  for (int i = 0; i < BATCH_SIZE_Y; i++) {
+    if (any(greaterThanEqual(u16vec3(pos.x, pos.y + i, pos.z), out_limits))) {
+      continue;
+    }
+    imageStore(t_out, u16vec3(pos.x, pos.y + i, pos.z), op(sum[i], out_min, out_max));
+  }
 }

backends/vulkan/runtime/graph/ops/glsl/conv2d_dw_output_tile.yaml

@@ -10,6 +10,7 @@ conv2d_dw_output_tile:
     NDIM: 3
     DTYPE: float
     TILE_SIZE: 3
+    BATCH_SIZE_Y: 2
   generate_variant_forall:
     DTYPE:
       - VALUE: half

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

@@ -296,6 +296,12 @@ utils::uvec3 create_conv2d_global_wg_size(
         utils::div_up(image_extents[0u], 2u),
         utils::div_up(image_extents[1u], 2u),
         image_extents[2u]};
+  } else if (method == Conv2dMethod::Depthwise) {
+    const utils::uvec3 image_extents = graph.logical_limits_of(out);
+    return {
+        utils::div_up(image_extents[0u], 1u),
+        utils::div_up(image_extents[1u], 2u),
+        image_extents[2u]};
   } else {
     return graph.create_global_wg_size(out);
   }