[ET-VK] Creating specialized version of conv2d pw shader for X and Y stride = 1 and padding = 0.

backends/vulkan/runtime/graph/ops/glsl/conv2d_pw.glsl

@@ -14,7 +14,6 @@
 
 #define TILE_SIZE_X ${TILE_SIZE_X}
 #define TILE_SIZE_Y ${TILE_SIZE_Y}
-#define LOCAL_WG_SIZE 64
 
 #define op(X, A, B) ${OPERATOR}
 
@@ -39,59 +38,61 @@ layout(push_constant) uniform restrict Block {
 
 layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
 
-// For performance improvement, reduce register usage by caching positions in shared memory.
-// Offset index by 1 every 16 points to avoid bank access conflict.
-#define offset_pos_index(index) (index + ((index) >> 4))
-shared ivec3 pos_shared[offset_pos_index(LOCAL_WG_SIZE * TILE_SIZE_X * TILE_SIZE_Y)];
+#extension GL_EXT_control_flow_attributes : require
 
 /*
  * Computes a 2D pointwise convolution of an NxN output tile. Calculating an
  * output tile for pointwise convolution is more efficient because the kernel
  * size is only 1x1, making it easier to re-use loaded texels from t_kernel.
  */
 void main() {
-  const ivec2 out_limits_scaled = (out_limits.xy + ivec2(TILE_SIZE_X - 1, TILE_SIZE_Y - 1)) / ivec2(TILE_SIZE_X, TILE_SIZE_Y);
-  const uint shared_mem_stride = LOCAL_WG_SIZE;
+  const int out_limits_scaled[2] = {out_limits.x + (TILE_SIZE_X - 1) * TILE_SIZE_X, out_limits.y + (TILE_SIZE_Y - 1) * TILE_SIZE_Y};
 
-  const uint div_by_x = gl_GlobalInvocationID.x / out_limits_scaled.x;
-  const ivec3 gpos = ivec3(
-    gl_GlobalInvocationID.x % out_limits_scaled.x,
-    div_by_x % out_limits_scaled.y,
-    div_by_x / out_limits_scaled.y);
+  const int div_by_x = int(gl_GlobalInvocationID.x / out_limits_scaled[0]);
+  const int out_pos[3] = {int(gl_GlobalInvocationID.x % out_limits_scaled[0]), div_by_x, int(gl_GlobalInvocationID.y)};
+
+  // If the top left position is out of bounds, then this invocation will have
+  // no work to do.
+  if (out_pos[1] >= out_limits_scaled[1] || out_pos[2] >= out_limits.z) {
+    return;
+  }
 
   // Output position for TILE_SIZE = 2
   // +--------+--------+
   // | pos[0] | pos[1] |
   // +--------+--------+
   // | pos[2] | pos[3] |
   // +--------+--------+
-  ivec2 pos[TILE_SIZE_X * TILE_SIZE_Y];
+  int pos[TILE_SIZE_X * TILE_SIZE_Y * 2];
   for (int y = 0, i = 0; y < TILE_SIZE_Y; ++y) {
     for (int x = 0; x < TILE_SIZE_X; ++x) {
-      pos[i] = ivec2(gpos.x * TILE_SIZE_X + x, gpos.y * TILE_SIZE_Y + y);
-      pos_shared[offset_pos_index((shared_mem_stride * i) + gl_LocalInvocationIndex)] = ivec3(pos[i], gpos.z);
+      pos[i * 2] = out_pos[0] * TILE_SIZE_X + x;
+      pos[i * 2 + 1] = out_pos[1] * TILE_SIZE_Y + y;
       i++;
     }
   }
 
-  // If the top left position is out of bounds, then this invocation will have
-  // no work to do.
-  if (gpos.z >= out_limits.z) {
-    return;
-  }
-
   // Compute the index of the input texture that needs to be loaded for each
   // output position. Note that negative indices can be produced indicating that
   // the top-left element is in a region added by padding.
-  ivec2 ipos[TILE_SIZE_X * TILE_SIZE_Y];
+  int ipos[TILE_SIZE_X * TILE_SIZE_Y * 2];
   for (int i = 0; i < TILE_SIZE_X * TILE_SIZE_Y; ++i) {
-    ipos[i] = pos[i] * stride - padding;
+    ipos[i * 2] = pos[i * 2] * stride.x - padding.x;
+    ipos[i * 2 + 1] = pos[i * 2 + 1] * stride.y - padding.y;
   }
 
-  vec4 sum[TILE_SIZE_X * TILE_SIZE_Y];
-  sum[0] = texelFetch(t_bias, ivec2(gpos.z, 0), 0);
-  for (int i = 1; i < TILE_SIZE_X * TILE_SIZE_Y; ++i) {
-    sum[i] = sum[0];
+  // Final output array where each element is a tensor value.
+  // Tuple of consecutive 4 elements represents a single output texel.
+  float sum[TILE_SIZE_X * TILE_SIZE_Y * 4];
+
+  const vec4 bias = texelFetch(t_bias, ivec2(out_pos[2], 0), 0);
+
+  // Initialize the output array with the bias value
+  for (int i = 0; i < TILE_SIZE_X * TILE_SIZE_Y * 4; i += 4) {
+    sum[i] = bias.x;
+    sum[i + 1] = bias.y;
+    sum[i + 2] = bias.z;
+    sum[i + 3] = bias.w;
   }
 
   int z4 = 0;
@@ -100,14 +101,26 @@ void main() {
     // During prepacking, the weight tensor has been permuted so that the
     // channel (IC) dim is along the x-axis, and the batch (OC) dim is along
     // the z-axis.
-    const vec4 ktex_0 = texelFetchOffset(t_kernel, ivec2(z, gpos.z), 0, ivec2(0, 0));
-    const vec4 ktex_1 = texelFetchOffset(t_kernel, ivec2(z, gpos.z), 0, ivec2(1, 0));
-    const vec4 ktex_2 = texelFetchOffset(t_kernel, ivec2(z, gpos.z), 0, ivec2(2, 0));
-    const vec4 ktex_3 = texelFetchOffset(t_kernel, ivec2(z, gpos.z), 0, ivec2(3, 0));
+    float kernel_values[4 * 4]; // 4 channels, 4 elements per channel
+
+    // Load kernel values from texels to array
+    [[unroll]] for (int i = 0; i < 4; ++i) {
+      const vec4 k_tex = texelFetch(t_kernel, ivec2(z + i, out_pos[2]), 0);
+      kernel_values[i * 4 + 0] = k_tex.x;
+      kernel_values[i * 4 + 1] = k_tex.y;
+      kernel_values[i * 4 + 2] = k_tex.z;
+      kernel_values[i * 4 + 3] = k_tex.w;
+    }
 
-#pragma unroll
     for (int i = 0; i < TILE_SIZE_X * TILE_SIZE_Y; ++i) {
-      const vec4 in_tex = texelFetch(t_in, ivec3(ipos[i], z4), 0);
+      const vec4 in_tex = texelFetch(t_in, ivec3(ipos[i * 2], ipos[i * 2 + 1], z4), 0);
+      // Load the input texel into an array
+      float tex_values[4];
+      tex_values[0] = in_tex.x;
+      tex_values[1] = in_tex.y;
+      tex_values[2] = in_tex.z;
+      tex_values[3] = in_tex.w;
+
       // For 2x2 tile size algorithm works as follows.
       // To explain the calculations below, the contents of one in_tex and the
       // group of 4 texels loaded from t_kernel are shown:
@@ -141,18 +154,19 @@ void main() {
       //
       //  which is what is expressed in the following calculations. This is done
       //  for each output position.
-      sum[i] = fma(in_tex.xxxx, ktex_0, sum[i]);
-      sum[i] = fma(in_tex.yyyy, ktex_1, sum[i]);
-      sum[i] = fma(in_tex.zzzz, ktex_2, sum[i]);
-      sum[i] = fma(in_tex.wwww, ktex_3, sum[i]);
+      for (int j = 0; j < 4; ++j) {
+        sum[i * 4 + j] = tex_values[0] * kernel_values[0 + j] + sum[i * 4 + j];
+        sum[i * 4 + j] = tex_values[1] * kernel_values[4 + j] + sum[i * 4 + j];
+        sum[i * 4 + j] = tex_values[2] * kernel_values[8 + j] + sum[i * 4 + j];
+        sum[i * 4 + j] = tex_values[3] * kernel_values[12 + j] + sum[i * 4 + j];
+      }
     }
   }
 
   for (int i = 0; i < TILE_SIZE_X * TILE_SIZE_Y; ++i) {
-    const uint index = (shared_mem_stride * i) + gl_LocalInvocationIndex;
-    const ivec3 pos = pos_shared[offset_pos_index(index)];
-    if (all(lessThan(pos, out_limits.xyz))) {
-      imageStore(t_out, pos, op(sum[i], out_min, out_max));
+    const ivec3 pos_l = ivec3(pos[i * 2], pos[i * 2 + 1], out_pos[2]);
+    if (all(lessThan(pos_l, out_limits.xyz))) {
+      imageStore(t_out, pos_l, op(vec4(sum[i * 4], sum[i * 4 + 1], sum[i * 4 + 2], sum[i * 4 + 3]), out_min, out_max));
     }
   }
 }

backends/vulkan/runtime/graph/ops/glsl/conv2d_pw.yaml

@@ -9,8 +9,8 @@ conv2d_pw:
     OPERATOR: X
     NDIM: 3
     DTYPE: float
-    TILE_SIZE_X: 2
-    TILE_SIZE_Y: 2
+    TILE_SIZE_X: 1
+    TILE_SIZE_Y: 4
   generate_variant_forall:
     DTYPE:
       - VALUE: half

backends/vulkan/runtime/graph/ops/glsl/conv2d_pw_s1p0.glsl

@@ -0,0 +1,163 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#version 450 core
+
+#define PRECISION ${PRECISION}
+
+#define VEC4_T ${texel_type(DTYPE)}
+
+#define TILE_SIZE_X ${TILE_SIZE_X}
+#define TILE_SIZE_Y ${TILE_SIZE_Y}
+
+#define op(X, A, B) ${OPERATOR}
+
+#include "indexing_utils.h"
+
+layout(std430) buffer;
+
+${layout_declare_tensor(0, "w", "t_out", DTYPE, "texture3d")}
+${layout_declare_tensor(1, "r", "t_in", DTYPE, "texture3d")}
+${layout_declare_tensor(2, "r", "t_kernel", DTYPE, "texture2d")}
+${layout_declare_tensor(3, "r", "t_bias", DTYPE, "texture2d")}
+
+layout(push_constant) uniform restrict Block {
+  ivec4 out_limits;
+  ivec2 stride;
+  ivec2 padding;
+  int in_group_size;
+  int dummy_padding;
+  float out_min;
+  float out_max;
+};
+
+layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
+
+#extension GL_EXT_control_flow_attributes : require
+
+/*
+ * Computes a 2D pointwise convolution of an NxN output tile. Calculating an
+ * output tile for pointwise convolution is more efficient because the kernel
+ * size is only 1x1, making it easier to re-use loaded texels from t_kernel.
+ */
+void main() {
+  const int out_limits_scaled[2] = {out_limits.x + (TILE_SIZE_X - 1) * TILE_SIZE_X, out_limits.y + (TILE_SIZE_Y - 1) * TILE_SIZE_Y};
+
+  const int div_by_x = int(gl_GlobalInvocationID.x / out_limits_scaled[0]);
+  const int out_pos[3] = {int(gl_GlobalInvocationID.x % out_limits_scaled[0]), div_by_x, int(gl_GlobalInvocationID.y)};
+
+  // If the top left position is out of bounds, then this invocation will have
+  // no work to do.
+  if (out_pos[1] >= out_limits_scaled[1] || out_pos[2] >= out_limits.z) {
+    return;
+  }
+
+  // Output position for TILE_SIZE = 2
+  // +--------+--------+
+  // | pos[0] | pos[1] |
+  // +--------+--------+
+  // | pos[2] | pos[3] |
+  // +--------+--------+
+  int pos[TILE_SIZE_X * TILE_SIZE_Y * 2];
+  for (int y = 0, i = 0; y < TILE_SIZE_Y; ++y) {
+    for (int x = 0; x < TILE_SIZE_X; ++x) {
+      pos[i * 2] = out_pos[0] * TILE_SIZE_X + x;
+      pos[i * 2 + 1] = out_pos[1] * TILE_SIZE_Y + y;
+      i++;
+    }
+  }
+
+  // Final output array where each element is a tensor value.
+  // Tuple of consecutive 4 elements represents a single output texel.
+  float sum[TILE_SIZE_X * TILE_SIZE_Y * 4];
+
+  const vec4 bias = texelFetch(t_bias, ivec2(out_pos[2], 0), 0);
+
+  // Initialize the output array with the bias value
+  for (int i = 0; i < TILE_SIZE_X * TILE_SIZE_Y * 4; i += 4) {
+    sum[i] = bias.x;
+    sum[i + 1] = bias.y;
+    sum[i + 2] = bias.z;
+    sum[i + 3] = bias.w;
+  }
+
+  int z4 = 0;
+  // Since the kernel is 1x1, we only have to loop over the depth dimension.
+  for (int z = 0; z < in_group_size; z += 4, ++z4) {
+    // During prepacking, the weight tensor has been permuted so that the
+    // channel (IC) dim is along the x-axis, and the batch (OC) dim is along
+    // the z-axis.
+    float kernel_values[4 * 4]; // 4 channels, 4 elements per channel
+
+    // Load kernel values from texels to array
+    [[unroll]] for (int i = 0; i < 4; ++i) {
+      const vec4 k_tex = texelFetch(t_kernel, ivec2(z + i, out_pos[2]), 0);
+      kernel_values[i * 4 + 0] = k_tex.x;
+      kernel_values[i * 4 + 1] = k_tex.y;
+      kernel_values[i * 4 + 2] = k_tex.z;
+      kernel_values[i * 4 + 3] = k_tex.w;
+    }
+
+    for (int i = 0; i < TILE_SIZE_X * TILE_SIZE_Y; ++i) {
+      const vec4 in_tex = texelFetch(t_in, ivec3(pos[i * 2], pos[i * 2 + 1], z4), 0);
+      // Load the input texel into an array
+      float tex_values[4];
+      tex_values[0] = in_tex.x;
+      tex_values[1] = in_tex.y;
+      tex_values[2] = in_tex.z;
+      tex_values[3] = in_tex.w;
+
+      // For 2x2 tile size algorithm works as follows.
+      // To explain the calculations below, the contents of one in_tex and the
+      // group of 4 texels loaded from t_kernel are shown:
+      //
+      //   in_tex                 t_kernel
+      //    -x->                   ---x--->
+      //   +---+              +----+----+----+----+
+      // ^ | w |           ^  | D0 | D1 | D2 | D3 |
+      // | +---+           |  +----+----+----+----+
+      // | | z |           |  | C0 | C1 | C2 | C3 |
+      // z +---+           z  +----+----+----+----+
+      // | | y |           |  | B0 | B2 | B2 | B3 |
+      // | +---+           |  +----+----+----+----+
+      //   | x |              | A0 | A1 | A2 | A3 |
+      //   +---+              +----+----+----+----+
+      //
+      // In the t_kernel graphic, cells sharing the same letter are from
+      // the same batch/output channel index, and the number denotes a unique
+      // channel index. To calculate the output texel, the following
+      // calculation is performed:
+      //
+      //  +---+ +----+   +---+ +----+   +---+ +----+   +---+ +----+
+      //  | x | | D0 |   | y | | D1 |   | z | | D2 |   | w | | D3 |
+      //  +---+ +----+   +---+ +----+   +---+ +----+   +---+ +----+
+      //  | x | | C0 |   | y | | C1 |   | z | | C2 |   | w | | C3 |
+      //  +---+X+----+ + +---+X+----+ + +---+X+----+ + +---+X+----+
+      //  | x | | B0 |   | y | | B1 |   | z | | B2 |   | w | | B3 |
+      //  +---+ +----+   +---+ +----+   +---+ +----+   +---+ +----+
+      //  | x | | A0 |   | y | | A1 |   | z | | A2 |   | w | | A3 |
+      //  +---+ +----+   +---+ +----+   +---+ +----+   +---+ +----+
+      //
+      //  which is what is expressed in the following calculations. This is done
+      //  for each output position.
+      for (int j = 0; j < 4; ++j) {
+        sum[i * 4 + j] = tex_values[0] * kernel_values[0 + j] + sum[i * 4 + j];
+        sum[i * 4 + j] = tex_values[1] * kernel_values[4 + j] + sum[i * 4 + j];
+        sum[i * 4 + j] = tex_values[2] * kernel_values[8 + j] + sum[i * 4 + j];
+        sum[i * 4 + j] = tex_values[3] * kernel_values[12 + j] + sum[i * 4 + j];
+      }
+    }
+  }
+
+  for (int i = 0; i < TILE_SIZE_X * TILE_SIZE_Y; ++i) {
+    const ivec3 pos_l = ivec3(pos[i * 2], pos[i * 2 + 1], out_pos[2]);
+    if (all(lessThan(pos_l, out_limits.xyz))) {
+      imageStore(t_out, pos_l, op(vec4(sum[i * 4], sum[i * 4 + 1], sum[i * 4 + 2], sum[i * 4 + 3]), out_min, out_max));
+    }
+  }
+}

backends/vulkan/runtime/graph/ops/glsl/conv2d_pw_s1p0.yaml

@@ -0,0 +1,21 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+conv2d_pw_s1p0:
+  parameter_names_with_default_values:
+    OPERATOR: X
+    NDIM: 3
+    DTYPE: float
+    TILE_SIZE_X: 1
+    TILE_SIZE_Y: 4
+  generate_variant_forall:
+    DTYPE:
+      - VALUE: half
+      - VALUE: float
+  shader_variants:
+    - NAME: conv2d_pw_s1p0
+    - NAME: conv2d_pw_s1p0_clamp
+      OPERATOR: clamp(X, A, B)