fix ggml_nn_attention_ext mask

ggml

@@ -1 +1 @@
-Subproject commit 9d52c1e4c6538081ef1da4e0fa59a0d8613ddc56
+Subproject commit 70268874234fa06cf058739a221bfbe94129f330

ggml_extend.hpp

@@ -942,6 +942,34 @@ __STATIC_INLINE__ std::vector<struct ggml_tensor*> split_image_qkv(struct ggml_c
     return {q, k, v};
 }
 
+__STATIC_INLINE__ struct ggml_tensor* ggml_full(struct ggml_context* ctx,
+                                                float value,
+                                                int64_t ne0,
+                                                int64_t ne1,
+                                                int64_t ne2,
+                                                int64_t ne3) {
+    auto one = ggml_get_tensor(ctx, "ggml_runner_build_in_tensor:one");
+    auto t   = ggml_scale(ctx, one, value);                 // [1,]
+    t        = ggml_repeat_4d(ctx, t, ne0, ne1, ne2, ne3);  // [ne0, ne1, ne2, ne3]
+    return t;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_zeros(struct ggml_context* ctx,
+                                                 int64_t ne0,
+                                                 int64_t ne1,
+                                                 int64_t ne2,
+                                                 int64_t ne3) {
+    return ggml_full(ctx, 0.f, ne0, ne1, ne2, ne3);
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_ones(struct ggml_context* ctx,
+                                                int64_t ne0,
+                                                int64_t ne1,
+                                                int64_t ne2,
+                                                int64_t ne3) {
+    return ggml_full(ctx, 1.f, ne0, ne1, ne2, ne3);
+}
+
 // q: [N * n_head, n_token, d_head]
 // k: [N * n_head, n_k, d_head]
 // v: [N * n_head, d_head, n_k]
@@ -969,6 +997,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention(struct ggml_context* ctx
 // q: [N, L_q, C] or [N*n_head, L_q, d_head]
 // k: [N, L_k, C] or [N*n_head, L_k, d_head]
 // v: [N, L_k, C] or [N, L_k, n_head, d_head]
+// mask: [N, L_q, L_k]
 // return: [N, L_q, C]
 __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context* ctx,
                                                             struct ggml_tensor* q,
@@ -1019,7 +1048,6 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context*
 
         if (mask != nullptr) {
             // TODO(Green-Sky): figure out if we can bend t5 to work too
-            can_use_flash_attn = can_use_flash_attn && mask->ne[2] == 1;
             can_use_flash_attn = can_use_flash_attn && mask->ne[3] == 1;
         }
 
@@ -1046,14 +1074,25 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context*
 
         if (mask != nullptr) {
             mask = ggml_transpose(ctx, mask);
-
-            if (mask->ne[1] < GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD)) {
-                LOG_DEBUG("mask dims %ld, %ld, %ld, %ld\n", mask->ne[0], mask->ne[1], mask->ne[2], mask->ne[3]);
-                LOG_DEBUG("needs padding, padding from %ld to %ld\n", mask->ne[1], GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD));
-                mask = ggml_pad(ctx, mask, 0, GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) - mask->ne[1], 0, 0);
+        } else {
+            if (kv_pad > 0) {
+                mask            = ggml_zeros(ctx, L_k, L_q, 1, 1);               // [L_q, L_k]
+                auto pad_tensor = ggml_full(ctx, -INFINITY, kv_pad, L_q, 1, 1);  // [L_q, kv_pad]
+                mask            = ggml_concat(ctx, mask, pad_tensor, 0);         // [L_q, L_k + kv_pad]
             }
+        }
 
+        // mask pad
+        if (mask != nullptr) {
+            int mask_pad = 0;
+            if (mask->ne[1] % GGML_KQ_MASK_PAD != 0) {
+                mask_pad = GGML_PAD(L_q, GGML_KQ_MASK_PAD) - mask->ne[1];
+            }
+            if (mask_pad > 0) {
+                mask = ggml_pad(ctx, mask, 0, mask_pad, 0, 0);  // [L_q + mask_pad, L_k + kv_pad]
+            }
             mask = ggml_cast(ctx, mask, GGML_TYPE_F16);
+            // LOG_DEBUG("L_k: %ld, L_q: %ld, mask->ne[1]: %ld, mask_pad: %d, kv_pad: %d", L_k, L_q, mask->ne[1], mask_pad, kv_pad);
         }
 
         kqv = ggml_flash_attn_ext(ctx, q, k, v, mask, scale, 0, 0);
@@ -1271,6 +1310,9 @@ protected:
     struct ggml_context* compute_ctx    = NULL;
     struct ggml_gallocr* compute_allocr = NULL;
 
+    std::vector<float> one_vec = {1.f};
+    ggml_tensor* one_tensor    = NULL;
+
     std::map<struct ggml_tensor*, const void*> backend_tensor_data_map;
 
     void alloc_params_ctx() {
@@ -1315,12 +1357,29 @@ protected:
         }
     }
 
+    void prepare_build_in_tensor_before() {
+        one_tensor = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, 1);
+        ggml_set_name(one_tensor, "ggml_runner_build_in_tensor:one");
+        set_backend_tensor_data(one_tensor, one_vec.data());
+    }
+
+    void prepare_build_in_tensor_after(struct ggml_cgraph* gf) {
+        ggml_build_forward_expand(gf, one_tensor);
+    }
+
+    struct ggml_cgraph* get_compute_graph(get_graph_cb_t get_graph) {
+        prepare_build_in_tensor_before();
+        struct ggml_cgraph* gf = get_graph();
+        prepare_build_in_tensor_after(gf);
+        return gf;
+    }
+
     bool alloc_compute_buffer(get_graph_cb_t get_graph) {
         if (compute_allocr != NULL) {
             return true;
         }
         reset_compute_ctx();
-        struct ggml_cgraph* gf = get_graph();
+        struct ggml_cgraph* gf = get_compute_graph(get_graph);
         backend_tensor_data_map.clear();
         compute_allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(runtime_backend));
 
@@ -1531,7 +1590,7 @@ public:
         }
         alloc_compute_buffer(get_graph);
         reset_compute_ctx();
-        struct ggml_cgraph* gf = get_graph();
+        struct ggml_cgraph* gf = get_compute_graph(get_graph);
         GGML_ASSERT(ggml_gallocr_alloc_graph(compute_allocr, gf));
         cpy_data_to_backend_tensor();
         if (ggml_backend_is_cpu(runtime_backend)) {

stable-diffusion.h

@@ -101,7 +101,7 @@ enum sd_type_t {
     // SD_TYPE_IQ4_NL_4_4 = 36,
     // SD_TYPE_IQ4_NL_4_8 = 37,
     // SD_TYPE_IQ4_NL_8_8 = 38,
-    SD_TYPE_MXFP4   = 39, // MXFP4 (1 block)
+    SD_TYPE_MXFP4 = 39,  // MXFP4 (1 block)
     SD_TYPE_COUNT = 40,
 };
 

vae.hpp

@@ -529,7 +529,7 @@ struct VAE : public GGMLRunner {
                          struct ggml_tensor** output,
                          struct ggml_context* output_ctx)                                                         = 0;
     virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
-    virtual void enable_conv2d_direct() {};
+    virtual void enable_conv2d_direct(){};
 };
 
 struct AutoEncoderKL : public VAE {