implement "auto" mode for clip flash attn

tools/mtmd/clip.cpp

@@ -4,6 +4,7 @@
 // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
 #include "clip.h"
 #include "clip-impl.h"
+#include "mtmd.h"
 #include "ggml.h"
 #include "ggml-cpp.h"
 #include "ggml-cpu.h"
@@ -427,12 +428,14 @@ struct clip_ctx {
 
     int max_nodes = 8192;
     ggml_backend_sched_ptr sched;
+    llama_flash_attn_type flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO;
 
     // for debugging
     bool debug_graph = false;
     std::vector<ggml_tensor *> debug_print_tensors;
 
     clip_ctx(clip_context_params & ctx_params) {
+        flash_attn_type = ctx_params.flash_attn_type;
         debug_graph = std::getenv("MTMD_DEBUG_GRAPH") != nullptr;
         backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
         if (!backend_cpu) {
@@ -2261,17 +2264,37 @@ private:
         ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3);
         //cb(k, "k", il);
 
+        ggml_tensor * cur;
+
+        if (ctx->flash_attn_type == LLAMA_FLASH_ATTN_TYPE_ENABLED) {
             ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3);
-        //cb(k, "v", il);
 
             k = ggml_cast(ctx0, k, GGML_TYPE_F16);
             v = ggml_cast(ctx0, v, GGML_TYPE_F16);
 
-        ggml_tensor * cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
+            cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
             ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
 
             cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
 
+        } else {
+            ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3);
+            v = ggml_cont(ctx0, v);
+
+            const auto n_tokens = q->ne[1];
+            const auto n_head   = q->ne[2];
+
+            ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+            // F32 may not needed for vision encoders?
+            // ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+
+            kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f);
+
+            ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+            cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+            cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens);
+        }
+
         cb(cur, "kqv_out", il);
 
         if (wo) {
@@ -3181,7 +3204,30 @@ struct clip_model_loader {
         }
     }
 
-    void alloc_compute_meta(clip_ctx & ctx_clip) {
+    void warmup(clip_ctx & ctx_clip) {
+        if (ctx_clip.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) {
+            // try to enable flash attention to see if it's supported
+            ctx_clip.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
+            bool supported = alloc_compute_meta(ctx_clip);
+            if (!supported) {
+                LOG_WRN("%s: flash attention not supported, memory usage will increase\n", __func__);
+                // TODO: maybe log more details about why flash attention is not supported
+                ctx_clip.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
+                alloc_compute_meta(ctx_clip);
+            }
+        } else {
+            bool supported = alloc_compute_meta(ctx_clip);
+            if (!supported) {
+                LOG_WRN("%s: flash attention is not supported by the current backend; falling back to CPU (performance will be degraded)\n", __func__);
+            }
+        }
+
+        LOG_INF("%s: flash attention is %s\n", __func__,
+            (ctx_clip.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_ENABLED) ? "enabled" : "disabled");
+    }
+
+    // return false if flash attention is not supported
+    bool alloc_compute_meta(clip_ctx & ctx_clip) {
         const auto & hparams = ctx_clip.model.hparams;
         ctx_clip.buf_compute_meta.resize(ctx_clip.max_nodes * ggml_tensor_overhead() + ggml_graph_overhead());
 
@@ -3217,6 +3263,17 @@ struct clip_model_loader {
         const int n_nodes  = ggml_graph_n_nodes(gf);
 
         LOG_INF("%s: graph splits = %d, nodes = %d\n", __func__,  n_splits, n_nodes);
+
+        // check flash attention support
+        for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
+            ggml_tensor * node = ggml_graph_node(gf, i);
+            if (node->op == GGML_OP_FLASH_ATTN_EXT) {
+                if (!ggml_backend_supports_op(ctx_clip.backend, node)) {
+                    return false;
+                }
+            }
+        }
+        return true;
     }
 
     void get_bool(const std::string & key, bool & output, bool required = true) {
@@ -3306,14 +3363,14 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
             ctx_vision = new clip_ctx(ctx_params);
             loader.load_hparams(ctx_vision->model, CLIP_MODALITY_VISION);
             loader.load_tensors(*ctx_vision);
-            loader.alloc_compute_meta(*ctx_vision);
+            loader.warmup(*ctx_vision);
         }
 
         if (loader.has_audio) {
             ctx_audio = new clip_ctx(ctx_params);
             loader.load_hparams(ctx_audio->model, CLIP_MODALITY_AUDIO);
             loader.load_tensors(*ctx_audio);
-            loader.alloc_compute_meta(*ctx_audio);
+            loader.warmup(*ctx_audio);
         }
 
     } catch (const std::exception & e) {

tools/mtmd/clip.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include "ggml.h"
+#include "mtmd.h"
 #include <stddef.h>
 #include <stdint.h>
 
@@ -25,6 +26,7 @@ enum clip_modality {
 struct clip_context_params {
     bool use_gpu;
     enum ggml_log_level verbosity;
+    llama_flash_attn_type flash_attn_type;
 };
 
 struct clip_init_result {

tools/mtmd/mtmd-cli.cpp

@@ -136,6 +136,7 @@ struct mtmd_cli_context {
         mparams.print_timings = true;
         mparams.n_threads = params.cpuparams.n_threads;
         mparams.verbosity = params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO;
+        mparams.flash_attn_type = params.flash_attn_type;
         ctx_vision.reset(mtmd_init_from_file(clip_path, model, mparams));
         if (!ctx_vision.get()) {
             LOG_ERR("Failed to load vision model from %s\n", clip_path);

tools/mtmd/mtmd.cpp

@@ -100,6 +100,7 @@ mtmd_context_params mtmd_context_params_default() {
     params.verbosity = GGML_LOG_LEVEL_INFO;
     params.image_marker = MTMD_DEFAULT_IMAGE_MARKER;
     params.media_marker = mtmd_default_marker();
+    params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO;
     return params;
 }
 
@@ -164,6 +165,7 @@ struct mtmd_context {
         clip_context_params ctx_clip_params;
         ctx_clip_params.use_gpu   = ctx_params.use_gpu;
         ctx_clip_params.verbosity = ctx_params.verbosity;
+        ctx_clip_params.flash_attn_type = ctx_params.flash_attn_type;
         auto res = clip_init(mmproj_fname, ctx_clip_params);
         ctx_v = res.ctx_v;
         ctx_a = res.ctx_a;

tools/mtmd/mtmd.h

@@ -82,6 +82,7 @@ struct mtmd_context_params {
     enum ggml_log_level verbosity;
     const char * image_marker; // deprecated, use media_marker instead
     const char * media_marker;
+    llama_flash_attn_type flash_attn_type;
 };
 
 MTMD_API const char * mtmd_default_marker(void);

tools/server/server.cpp

@@ -2456,6 +2456,7 @@ struct server_context {
             mparams.print_timings = false;
             mparams.n_threads     = params_base.cpuparams.n_threads;
             mparams.verbosity     = params_base.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO;
+            mparams.flash_attn_type = params_base.flash_attn_type;
             mctx = mtmd_init_from_file(mmproj_path.c_str(), model, mparams);
             if (mctx == nullptr) {
                 SRV_ERR("failed to load multimodal model, '%s'\n", mmproj_path.c_str());