vulkan: extend topk_moe fusion to support sqrt(softplus) (#26124)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index ff72fa60a8a92f8b3699b2be0d190b0b48db5a22..510fb6892b376cc709d512d55a3628e645d54d25 100644 (file)
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -610,6 +610,13 @@ static constexpr std::initializer_list<ggml_op> topk_moe_sigmoid_norm_bias{ GGML
                                                                             GGML_OP_RESHAPE,  GGML_OP_SUM_ROWS, GGML_OP_CLAMP,
                                                                             GGML_OP_DIV,      GGML_OP_RESHAPE };
 
+static constexpr std::initializer_list<ggml_op> topk_moe_sqrt_softplus_norm_bias{ GGML_OP_UNARY,    GGML_OP_SQRT,
+                                                                                  GGML_OP_RESHAPE,  GGML_OP_ADD,
+                                                                                  GGML_OP_ARGSORT,  GGML_OP_VIEW,
+                                                                                  GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
+                                                                                  GGML_OP_SUM_ROWS, GGML_OP_CLAMP,
+                                                                                  GGML_OP_DIV,      GGML_OP_RESHAPE };
+
 static constexpr std::initializer_list<ggml_op> topk_moe_early_softmax     { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE,  GGML_OP_ARGSORT,
                                                                              GGML_OP_VIEW,     GGML_OP_GET_ROWS };
 
@@ -673,6 +680,22 @@ static constexpr std::initializer_list<std::array<int, 3>> topk_moe_sigmoid_norm
     {10, 0, 9 }, // reshape->src[0]  == div
 };
 
+static constexpr std::initializer_list<std::array<int, 3>> topk_moe_sqrt_softplus_norm_bias_edges {
+    { 1, 0, 0 }, // sqrt->src[0]     == softplus
+    { 2, 0, 1 }, // reshape->src[0]  == sqrt
+    { 3, 0, 1 }, // add->src[0]      == sqrt
+    { 4, 0, 3 }, // argsort->src[0]  == add
+    { 5, 0, 4 }, // view->src[0]     == argsort
+    { 6, 0, 2 }, // get_rows->src[0] == reshape
+    { 6, 1, 5 }, // get_rows->src[1] == view
+    { 7, 0, 6 }, // reshape->src[0]  == get_rows
+    { 8, 0, 7 }, // sum_rows->src[0] == reshape
+    { 9, 0, 8 }, // clamp->src[0]    == sum_rows
+    {10, 0, 7 }, // div->src[0]      == reshape
+    {10, 1, 9 }, // div->src[1]      == clamp
+    {11, 0,10 }, // reshape->src[0]  == div
+};
+
 // same as early_softmax_norm but ending after the get_rows
 static constexpr std::initializer_list<std::array<int, 3>> topk_moe_early_softmax_edges {
     { 1, 0, 0 }, // reshape->src[0]  == softmax
@@ -701,6 +724,7 @@ enum topk_moe_mode {
     TOPK_MOE_EARLY_SOFTMAX_NORM,
     TOPK_MOE_LATE_SOFTMAX,
     TOPK_MOE_SIGMOID_NORM_BIAS,
+    TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS,
     TOPK_MOE_COUNT,
 };
 
@@ -13203,12 +13227,16 @@ static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& sub
 
 static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) {
     topk_moe_mode mode = ctx->fused_topk_moe_mode;
+    const bool has_bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS || mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS;
     ggml_tensor * logits = cgraph->nodes[node_idx + 0]->src[0];
-    ggml_tensor * bias = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 2]->src[1] : logits;
+    ggml_tensor * bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS       ? cgraph->nodes[node_idx + 2]->src[1] :
+                         mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS ? cgraph->nodes[node_idx + 3]->src[1] :
+                                                                   logits;
     ggml_tensor * weights = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];
-    ggml_tensor * ids = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 4] :
-                        (mode == TOPK_MOE_LATE_SOFTMAX) ?      cgraph->nodes[node_idx + 1] :
-                                                               cgraph->nodes[node_idx + 3];
+    ggml_tensor * ids = mode == TOPK_MOE_SIGMOID_NORM_BIAS       ? cgraph->nodes[node_idx + 4] :
+                        mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS ? cgraph->nodes[node_idx + 5] :
+                        mode == TOPK_MOE_LATE_SOFTMAX             ? cgraph->nodes[node_idx + 1] :
+                                                                   cgraph->nodes[node_idx + 3];
 
     GGML_ASSERT(logits->type == GGML_TYPE_F32);
     GGML_ASSERT(bias->type == GGML_TYPE_F32);
@@ -13248,16 +13276,24 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx,
         pc.clamp_min = ggml_get_op_params_f32(clamp, 0);
         pc.clamp_max = ggml_get_op_params_f32(clamp, 1);
     }
+    if (mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS) {
+        ggml_tensor * clamp = cgraph->nodes[node_idx + 9];
+        GGML_ASSERT(clamp->op == GGML_OP_CLAMP);
+        pc.clamp_min = ggml_get_op_params_f32(clamp, 0);
+        pc.clamp_max = ggml_get_op_params_f32(clamp, 1);
+    }
 
 #define GATING_FUNC_SOFTMAX 0
 #define GATING_FUNC_SIGMOID 1
 #define GATING_FUNC_SOFTMAX_WEIGHT 2
-
-    pc.gating_func = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? GATING_FUNC_SIGMOID :
-                     mode == TOPK_MOE_LATE_SOFTMAX ?      GATING_FUNC_SOFTMAX_WEIGHT :
-                                                          GATING_FUNC_SOFTMAX;
-    pc.has_bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS;
-    pc.with_norm = mode == TOPK_MOE_EARLY_SOFTMAX_NORM || mode == TOPK_MOE_SIGMOID_NORM_BIAS;
+#define GATING_FUNC_SQRT_SOFTPLUS 3
+
+    pc.gating_func = mode == TOPK_MOE_SIGMOID_NORM_BIAS       ? GATING_FUNC_SIGMOID :
+                     mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS ? GATING_FUNC_SQRT_SOFTPLUS :
+                     mode == TOPK_MOE_LATE_SOFTMAX             ? GATING_FUNC_SOFTMAX_WEIGHT :
+                                                                 GATING_FUNC_SOFTMAX;
+    pc.has_bias = has_bias;
+    pc.with_norm = mode == TOPK_MOE_EARLY_SOFTMAX_NORM || has_bias;
     if (ctx->fused_topk_moe_scale) {
         GGML_ASSERT(weights->op == GGML_OP_SCALE);
         pc.output_scale = ggml_get_op_params_f32(weights, 0);
@@ -16366,6 +16402,20 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc
             return false;
         }
         break;
+    case TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS:
+        softmax = cgraph->nodes[node_idx + 0]; // really softplus
+        weights = cgraph->nodes[node_idx + 11];
+        get_rows = cgraph->nodes[node_idx + 6];
+        argsort = cgraph->nodes[node_idx + 4];
+        if (ggml_get_unary_op(softmax) != GGML_UNARY_OP_SOFTPLUS) {
+            return false;
+        }
+        // bias is expected to be 1D
+        if (ggml_nrows(cgraph->nodes[node_idx + 3]->src[1]) != 1 ||
+            !ggml_is_contiguous(cgraph->nodes[node_idx + 3]->src[1])) {
+            return false;
+        }
+        break;
     case TOPK_MOE_EARLY_SOFTMAX:
         softmax = cgraph->nodes[node_idx + 0];
         weights = cgraph->nodes[node_idx + 4];
@@ -16389,7 +16439,9 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc
     probs = probs->src[0];
     ggml_tensor * selection_probs = argsort->src[0];
 
-    if (probs != selection_probs && mode != TOPK_MOE_SIGMOID_NORM_BIAS) {
+    if (probs != selection_probs &&
+        mode != TOPK_MOE_SIGMOID_NORM_BIAS &&
+        mode != TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS) {
         return false;
     }
 
@@ -16757,7 +16809,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         // the fused result in an elementwise-way. This affects whether the memory for
         // the src is allowed to overlap the memory for the destination.
         // The array is sized to handle the largest fusion (asserted later).
-        bool op_srcs_fused_elementwise[12];
+        bool op_srcs_fused_elementwise[13];
 
         ctx->fused_topk_moe_mode = TOPK_MOE_COUNT;
         ctx->fused_topk_moe_scale = false;
@@ -16868,6 +16920,15 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
                 ctx->fused_topk_moe_mode = TOPK_MOE_SIGMOID_NORM_BIAS;
                 fusion_string = "TOPK_MOE_SIGMOID_NORM_BIAS";
                 std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false);
+            } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_sqrt_softplus_norm_bias, { i + 5, i + 11 }) &&
+                       ggml_check_edges(cgraph, i, topk_moe_sqrt_softplus_norm_bias_edges) &&
+                       ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS)) {
+                ctx->num_additional_fused_ops = topk_moe_sqrt_softplus_norm_bias.size() - 1;
+                // view of argsort writes to memory
+                ctx->fused_ops_write_mask |= 1 << 5;
+                ctx->fused_topk_moe_mode = TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS;
+                fusion_string = "TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS";
+                std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false);
             } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax, { i + 3, i + 4 }) &&
                        ggml_check_edges(cgraph, i, topk_moe_early_softmax_edges) &&
                        ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX)) {
@@ -17134,6 +17195,9 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph *
         if (keep_pattern(topk_moe_sigmoid_norm_bias)) {
             continue;
         }
+        if (keep_pattern(topk_moe_sqrt_softplus_norm_bias)) {
+            continue;
+        }
         if (keep_pattern(topk_moe_early_softmax)) {
             continue;
         }
@@ -17164,6 +17228,7 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph *
             // Don't pull forward nodes from fusion patterns
             if (match_pattern(topk_moe_early_softmax_norm, j) ||
                 match_pattern(topk_moe_sigmoid_norm_bias, j) ||
+                match_pattern(topk_moe_sqrt_softplus_norm_bias, j) ||
                 match_pattern(topk_moe_early_softmax, j) ||
                 match_pattern(topk_moe_late_softmax, j) ||
                 match_pattern(snake_pattern, j)) {

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp
index ef2f202ec9b6a005b7c862bbc4beba7a6f85f222..d219201fda0aae215294743f290a192fa3370b9e 100644 (file)
--- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp
@@ -10,6 +10,7 @@
 #define GATING_FUNC_SOFTMAX 0
 #define GATING_FUNC_SIGMOID 1
 #define GATING_FUNC_SOFTMAX_WEIGHT 2
+#define GATING_FUNC_SQRT_SOFTPLUS 3
 
 layout (push_constant) uniform parameter
 {
@@ -120,6 +121,13 @@ void main() {
             const uint expert = i + lane;
             probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? 1.f / (1.f + exp(-probs[i / WARP_SIZE])) : -INFINITY;
         }
+    } else if (gating_func == GATING_FUNC_SQRT_SOFTPLUS) {
+        [[unroll]]
+        for (uint i = 0; i < n_experts; i += WARP_SIZE) {
+            const uint expert = i + lane;
+            const float val = probs[i / WARP_SIZE];
+            probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? sqrt(val > 20.0f ? val : log(1.0f + exp(val))) : -INFINITY;
+        }
     }
 
     float selection_probs[experts_per_thread];