add 2x4 tile in mm computation (#4031)

Summary:
Pull Request resolved: #4031

The existing optimized mm implementation compute output through 4x4 tile. This isn't efficient when the input tensor's height is a multiple of 3 but not a multiple of 4, e.g. 6. ~~We add a 3x4 tile computation and a parameter `HEIGHT6` to help us choose the computation manner.~~

According to nathanaelsee's experimentation, 2x4 is even more efficient than 3x4, we add 2x4 tile computation and add `TILE_ROW` in yaml files to generate shaders for 2x4 and 4x4 respectively.

Reviewed By: nathanaelsee, liuk22

Differential Revision: D58769774

fbshipit-source-id: 79d8867c87464402b2c6432599b3effc12965122

Author: copyrightly

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

@@ -16,6 +16,9 @@ $if MAT2_IS_TRANSPOSED:
 $if BATCH_MODE:
   #define BATCH_MODE
 
+$if TILE_ROW == "tile_row_2":
+  #define TILE_ROW_2
+
 #include "indexing_utils.h"
 #include "matmul.h"
 
@@ -56,24 +59,24 @@ void main() {
   }
 
   $if BATCH_MODE:
-    FloatMatrix_3d results = matmul_partial_4x4x4(
+    FloatMatrix_3d results = matmul_partial_3d(
       im_mat1,
       im_mat2,
       pos,
       out_sizes[2],
       in_limits[0]);
   $else:
-    FloatMatrix_2d results = matmul_partial_4x4(
+    FloatMatrix_2d results = matmul_partial_2d(
         im_mat1,
         im_mat2,
         pos,
         out_sizes[2],
         in_limits[0]);
 
-  for (int idx_c = 0; idx_c < FOUR; idx_c++) {
+  for (int idx_c = 0; idx_c < TILE_ROWS; idx_c++) {
     for (int idx_r = 0; idx_r < FOUR; idx_r++) {
       const ivec3 out_pos =
-          ivec3(idx_r + FOUR * pos.x, idx_c + FOUR * pos.y, pos.z);
+          ivec3(idx_r + FOUR * pos.x, idx_c + TILE_ROWS * pos.y, pos.z);
 
       vec4 self_texel = get_texel_C_packed(
           im_self,

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

@@ -11,7 +11,11 @@ addmm_optimized:
     PACKING: C_packed
     MAT2_IS_TRANSPOSED: false
     BATCH_MODE: false
+    TILE_ROW: tile_row_4
   generate_variant_forall:
+    TILE_ROW:
+      - VALUE: tile_row_4
+      - VALUE: tile_row_2
     DTYPE:
       - VALUE: float
       - VALUE: half

backends/vulkan/runtime/graph/ops/glsl/matmul.h

@@ -10,14 +10,20 @@
 // macro
 #define FOUR 4
 
+#ifdef TILE_ROW_2
+#define TILE_ROWS 2
+#else
+#define TILE_ROWS 4
+#endif
+
 // we avoid mat4 and vec4 usage here as they compile to much less efficient
 // SPIR-V
 struct FloatMatrix_2d {
-  float data[FOUR][FOUR];
+  float data[TILE_ROWS][FOUR];
 };
 
 struct FloatMatrix_3d {
-  float data[FOUR][FOUR][FOUR];
+  float data[TILE_ROWS][FOUR][FOUR];
 };
 
 #ifdef MAT2_IS_TRANSPOSED
@@ -150,25 +156,25 @@ vec4 get_texel_C_packed(
   return self_texel;
 }
 
-FloatMatrix_2d matmul_partial_4x4(
+FloatMatrix_2d matmul_partial_2d(
     sampler3D im_mat1,
     sampler3D im_mat2,
     const ivec3 pos,
     const int batch_size,
     const int K_texel_len) {
   FloatMatrix_2d results;
-  for (int i = 0; i < FOUR; i++) {
+  for (int i = 0; i < TILE_ROWS; i++) {
     for (int j = 0; j < FOUR; j++) {
       results.data[i][j] = 0.0f;
     }
   }
-  vec4 im_mat1_partial_load[FOUR];
+  vec4 im_mat1_partial_load[TILE_ROWS];
   vec4 im_mat2_partial_load[FOUR];
 
   for (int mat1_x = 0; mat1_x < K_texel_len; mat1_x++) {
-    for (int offset = 0; offset < FOUR; offset++) {
-      // read and cache 4x4 tile of im_mat1
-      const int mat1_y = (FOUR * pos.y) + offset;
+    for (int offset = 0; offset < TILE_ROWS; offset++) {
+      // read and cache 2x4 (or 4x4) tile of im_mat1
+      const int mat1_y = (TILE_ROWS * pos.y) + offset;
       const ivec3 mat1_pos = ivec3(mat1_x, mat1_y, 0);
       im_mat1_partial_load[offset] = texelFetch(im_mat1, mat1_pos, 0);
       // read and cache 4x4 tile of im_mat2
@@ -182,8 +188,24 @@ FloatMatrix_2d matmul_partial_4x4(
       im_mat2_partial_load[offset] = texelFetch(im_mat2, mat2_pos, 0);
 #endif
     }
+
+#ifdef TILE_ROW_2
+// column 3 and 4 of im_mat2
+#ifdef MAT2_IS_TRANSPOSED
+    im_mat2_partial_load[2] =
+        texelFetch(im_mat2, ivec3(mat1_x, (FOUR * pos.x) + 2, 0), 0);
+    im_mat2_partial_load[3] =
+        texelFetch(im_mat2, ivec3(mat1_x, (FOUR * pos.x) + 3, 0), 0);
+#else
+    im_mat2_partial_load[2] =
+        texelFetch(im_mat2, ivec3((FOUR * pos.x) + 2, mat1_x, 0), 0);
+    im_mat2_partial_load[3] =
+        texelFetch(im_mat2, ivec3((FOUR * pos.x) + 3, mat1_x, 0), 0);
+#endif
+#endif
+
     // perform partial dot products and add partial result to results
-    for (int out_row = 0; out_row < FOUR; out_row++) {
+    for (int out_row = 0; out_row < TILE_ROWS; out_row++) {
       for (int out_col = 0; out_col < FOUR; out_col++) {
         results.data[out_row][out_col] +=
             dot(im_mat1_partial_load[out_row], im_mat2_partial_load[out_col]);
@@ -193,21 +215,21 @@ FloatMatrix_2d matmul_partial_4x4(
   return results;
 }
 
-FloatMatrix_3d matmul_partial_4x4x4(
+FloatMatrix_3d matmul_partial_3d(
     sampler3D im_mat1,
     sampler3D im_mat2,
     const ivec3 pos,
     const int batch_size,
     const int K_texel_len) {
   FloatMatrix_3d results;
-  for (int i = 0; i < FOUR; i++) {
+  for (int i = 0; i < TILE_ROWS; i++) {
     for (int j = 0; j < FOUR; j++) {
       for (int k = 0; k < FOUR; k++) {
         results.data[i][j][k] = 0.0f;
       }
     }
   }
-  vec4 im_mat1_partial_load[FOUR];
+  vec4 im_mat1_partial_load[TILE_ROWS];
   vec4 im_mat2_partial_load[FOUR];
 
   for (int batch_idx = 0; batch_idx < FOUR; batch_idx++) {
@@ -216,9 +238,9 @@ FloatMatrix_3d matmul_partial_4x4x4(
     }
     int mat_z = FOUR * pos.z + batch_idx;
     for (int mat1_x = 0; mat1_x < K_texel_len; mat1_x++) {
-      for (int offset = 0; offset < FOUR; offset++) {
-        // read and cache 4x4 tile of im_mat1
-        const int mat1_y = (FOUR * pos.y) + offset;
+      for (int offset = 0; offset < TILE_ROWS; offset++) {
+        // read and cache 2x4 (or 4x4) tile of im_mat1
+        const int mat1_y = (TILE_ROWS * pos.y) + offset;
         const ivec3 mat1_pos = ivec3(mat1_x, mat1_y, mat_z);
         im_mat1_partial_load[offset] = texelFetch(im_mat1, mat1_pos, 0);
         // read and cache 4x4 tile of im_mat2
@@ -232,8 +254,24 @@ FloatMatrix_3d matmul_partial_4x4x4(
         im_mat2_partial_load[offset] = texelFetch(im_mat2, mat2_pos, 0);
 #endif
       }
+
+#ifdef TILE_ROW_2
+// column 3, and 4 of im_mat2
+#ifdef MAT2_IS_TRANSPOSED
+      im_mat2_partial_load[2] =
+          texelFetch(im_mat2, ivec3(mat1_x, (FOUR * pos.x) + 2, 0), 0);
+      im_mat2_partial_load[3] =
+          texelFetch(im_mat2, ivec3(mat1_x, (FOUR * pos.x) + 3, 0), 0);
+#else
+      im_mat2_partial_load[2] =
+          texelFetch(im_mat2, ivec3((FOUR * pos.x) + 2, mat1_x, mat_z), 0);
+      im_mat2_partial_load[3] =
+          texelFetch(im_mat2, ivec3((FOUR * pos.x) + 3, mat1_x, mat_z), 0);
+#endif
+#endif
+
       // perform partial dot products and add partial result to results
-      for (int out_row = 0; out_row < FOUR; out_row++) {
+      for (int out_row = 0; out_row < TILE_ROWS; out_row++) {
         for (int out_col = 0; out_col < FOUR; out_col++) {
           results.data[out_row][out_col][batch_idx] +=
               dot(im_mat1_partial_load[out_row], im_mat2_partial_load[out_col]);

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

@@ -16,6 +16,9 @@ $if MAT2_IS_TRANSPOSED:
 $if BATCH_MODE:
   #define BATCH_MODE
 
+$if TILE_ROW == "tile_row_2":
+  #define TILE_ROW_2
+
 #include "indexing_utils.h"
 #include "matmul.h"
 
@@ -45,24 +48,24 @@ void main() {
   }
 
   $if BATCH_MODE:
-    FloatMatrix_3d results = matmul_partial_4x4x4(
+    FloatMatrix_3d results = matmul_partial_3d(
         im_mat1,
         im_mat2,
         pos,
         out_sizes[2],
         in_limits[0]);
   $else:
-    FloatMatrix_2d results = matmul_partial_4x4(
+    FloatMatrix_2d results = matmul_partial_2d(
         im_mat1,
         im_mat2,
         pos,
         out_sizes[2],
         in_limits[0]);
 
-  for (int idx_c = 0; idx_c < FOUR; idx_c++) {
+  for (int idx_c = 0; idx_c < TILE_ROWS; idx_c++) {
     for (int idx_r = 0; idx_r < FOUR; idx_r++) {
       const ivec3 out_pos =
-          ivec3(idx_r + FOUR * pos.x, idx_c + FOUR * pos.y, pos.z);
+          ivec3(idx_r + FOUR * pos.x, idx_c + TILE_ROWS * pos.y, pos.z);
 
       // results is in transposed order w.r.t. the desired output
       $if BATCH_MODE:

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

@@ -11,7 +11,11 @@ matmul_optimized:
     PACKING: C_packed
     MAT2_IS_TRANSPOSED: false
     BATCH_MODE: false
+    TILE_ROW: tile_row_4
   generate_variant_forall:
+    TILE_ROW:
+      - VALUE: tile_row_4
+      - VALUE: tile_row_2
     DTYPE:
       - VALUE: float
       - VALUE: half

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

@@ -162,21 +162,31 @@ void add_addmm_optimized_node(
     viewFn(graph, {mat2, graph.add_none(), mat2_packed});
   }
 
-  api::utils::uvec3 global_size =
-      api::utils::divup_vec(graph.image_extents_of(out), {4, 4, 1});
-  api::utils::uvec3 local_size = adaptive_work_group_size(global_size);
-
   std::string kernel_name = graph.get_bool(mat2_is_transposed)
       ? "linear_optimized"
       : "addmm_optimized";
 
-  int mat1_dims = graph.sizes_of(mat1_W_packed).size();
+  std::vector<int64_t> mat1_sizes = graph.sizes_of(mat1_W_packed);
+  int mat1_dims = mat1_sizes.size();
   if (mat1_dims == 3) {
     kernel_name = "batch_" + kernel_name;
   }
+  if (mat1_sizes.at(mat1_dims - 2) < 8) {
+    kernel_name += "_tile_row_2";
+  } else {
+    kernel_name += "_tile_row_4";
+  }
 
   add_dtype_suffix(kernel_name, graph.dtype_of(out));
 
+  api::utils::uvec3 global_size;
+  if (mat1_sizes.at(mat1_dims - 2) < 8) {
+    global_size = api::utils::divup_vec(graph.image_extents_of(out), {4, 2, 1});
+  } else {
+    global_size = api::utils::divup_vec(graph.image_extents_of(out), {4, 4, 1});
+  }
+  api::utils::uvec3 local_size = adaptive_work_group_size(global_size);
+
   graph.execute_nodes().emplace_back(new ExecuteNode(
       graph,
       VK_KERNEL_FROM_STR(kernel_name),

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

@@ -127,21 +127,31 @@ void add_matmul_optimized_node(
     viewFn(graph, {mat2, graph.add_none(), mat2_packed});
   }
 
-  api::utils::uvec3 global_size =
-      api::utils::divup_vec(graph.image_extents_of(out), {4, 4, 1});
-  api::utils::uvec3 local_size = adaptive_work_group_size(global_size);
-
   std::string kernel_name = mat2_is_transposed_val
       ? "matmul_transposed_optimized"
       : "matmul_optimized";
 
-  int mat1_dims = graph.sizes_of(mat1_W_packed).size();
+  std::vector<int64_t> mat1_sizes = graph.sizes_of(mat1_W_packed);
+  int mat1_dims = mat1_sizes.size();
   if (mat1_dims == 3) {
     kernel_name = "batch_" + kernel_name;
   }
+  if (mat1_sizes.at(mat1_dims - 2) < 8) {
+    kernel_name += "_tile_row_2";
+  } else {
+    kernel_name += "_tile_row_4";
+  }
 
   add_dtype_suffix(kernel_name, graph.dtype_of(out));
 
+  api::utils::uvec3 global_size;
+  if (mat1_sizes.at(mat1_dims - 2) < 8) {
+    global_size = api::utils::divup_vec(graph.image_extents_of(out), {4, 2, 1});
+  } else {
+    global_size = api::utils::divup_vec(graph.image_extents_of(out), {4, 4, 1});
+  }
+  api::utils::uvec3 local_size = adaptive_work_group_size(global_size);
+
   graph.execute_nodes().emplace_back(new ExecuteNode(
       graph,
       VK_KERNEL_FROM_STR(kernel_name),

backends/vulkan/test/op_tests/cases.py

@@ -64,6 +64,7 @@ def get_mm_inputs():
         [
             ((M1, L), (L, M2)),
             ((S1, S2), (S2, M)),
+            ((6, 32), (32, 64)),
         ],
     )
     test_suite.prepacked_args = ["mat2"]
@@ -82,6 +83,7 @@ def get_bmm_inputs():
         [
             ((S, M1, L), (S, L, M2)),
             ((M, S1, S2), (M, S2, M)),
+            ((4, 6, 32), (4, 32, 16)),
         ],
     )
     test_suite.prepacked_args = ["mat2"]
@@ -104,6 +106,7 @@ def get_addmm_inputs():
             ((M1, M2), (M1, M2), (M2, M2), 4.2, 2.3),
             ((M1, 1), (M1, L), (L, L), 2.0, 3.0),
             ((M2), (M1, M2), (M2, M2)),
+            ((6, M2), (6, M2), (M2, M2)),
         ]
     )
     # ATen matmul doesn't support half
@@ -129,6 +132,7 @@ def get_linear_inputs():
     inputs_list += [((M, K), (N, K), (N)) for M, K, N in MKN_list]
     inputs_list += [((3, M, K), (N, K), None) for M, K, N in MKN_list]
     inputs_list += [((3, M, K), (N, K), (N)) for M, K, N in MKN_list]
+    inputs_list += [((3, 6, K), (N, K), (N)) for M, K, N in MKN_list]
 
     test_suite = VkTestSuite(inputs_list)
     test_suite.dtypes = ["at::kFloat"]