add PROJECTOR_TYPE_QWEN2_5_VL

examples/llava/clip-impl.h

@@ -107,6 +107,7 @@ enum projector_type {
     PROJECTOR_TYPE_GEMMA3,
     PROJECTOR_TYPE_IDEFICS3,
     PROJECTOR_TYPE_PIXTRAL,
+    PROJECTOR_TYPE_QWEN2_5_VL,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -117,6 +118,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_RESAMPLER, "resampler"},
     { PROJECTOR_TYPE_GLM_EDGE,  "adapter"},
     { PROJECTOR_TYPE_MERGER,    "qwen2vl_merger"},
+    { PROJECTOR_TYPE_QWEN2_5_VL,"qwen2.5vl_merger"},
     { PROJECTOR_TYPE_GEMMA3,    "gemma3"},
     { PROJECTOR_TYPE_IDEFICS3,  "idefics3"},
     { PROJECTOR_TYPE_PIXTRAL,   "pixtral"},

examples/llava/clip.cpp

@@ -780,6 +780,273 @@ static ggml_cgraph * clip_image_build_graph_pixtral(clip_ctx * ctx, const clip_i
     return gf;
 }
 
+static ggml_cgraph * clip_image_build_graph_qwen2_5_vl(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
+    const auto & model = ctx->vision_model;
+    const auto & hparams = model.hparams;
+
+    const int image_size = hparams.image_size;
+    int image_size_width  = image_size;
+    int image_size_height = image_size;
+
+    image_size_width  = imgs.entries[0]->nx;
+    image_size_height = imgs.entries[0]->ny;
+
+    const int patch_size           = hparams.patch_size;
+    const int num_patches          = ((image_size_width / patch_size) * (image_size_height / patch_size));
+    const int patches_w            = image_size_width / patch_size;
+    const int patches_h            = image_size_height / patch_size;
+    const int num_positions        = num_patches + (model.class_embedding ? 1 : 0);
+    const int num_position_ids     = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions;
+    const int hidden_size          = hparams.hidden_size;
+    const int n_head               = hparams.n_head;
+    const int d_head               = hidden_size / n_head;
+    const float eps                = hparams.eps;
+    const bool use_window_attn     = hparams.full_attn_layers.size() > 0;
+    int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
+
+    const int batch_size = imgs.entries.size();
+    GGML_ASSERT(batch_size == 1);
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ ctx->buf_compute_meta.size(),
+        /*.mem_buffer =*/ ctx->buf_compute_meta.data(),
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context_ptr ctx0_ptr(ggml_init(params));
+    auto ctx0 = ctx0_ptr.get();
+
+    struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size);
+    ggml_set_name(inp_raw, "inp_raw");
+    ggml_set_input(inp_raw);
+
+    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+
+    GGML_ASSERT(image_size_width % (patch_size * 2) == 0);
+    GGML_ASSERT(image_size_height % (patch_size * 2) == 0);
+
+    auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    inp = ggml_add(ctx0, inp, inp_1);
+
+    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3));  // [w, h, c, b] -> [c, w, h, b]
+    inp = ggml_reshape_4d(
+        ctx0, inp,
+        hidden_size * 2, patches_w / 2, patches_h, batch_size);
+    inp = ggml_reshape_4d(
+        ctx0, inp,
+        hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2));
+    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3));
+    inp = ggml_reshape_3d(
+        ctx0, inp,
+        hidden_size, patches_w * patches_h, batch_size);
+
+    if (model.patch_bias) {
+        // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+    }
+    struct ggml_tensor * embeddings     = inp;
+    struct ggml_tensor * pos_embed      = nullptr;
+    struct ggml_tensor * window_mask    = nullptr;
+    struct ggml_tensor * window_idx     = nullptr;
+    struct ggml_tensor * inv_window_idx = nullptr;
+
+    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
+
+    // pre-layernorm
+    if (model.pre_ln_w) {
+        if (ctx->use_rms_norm) {
+            embeddings = ggml_rms_norm(ctx0, embeddings, eps);
+            ggml_set_name(embeddings, "pre_ln");
+
+            embeddings = ggml_mul(ctx0, embeddings, model.pre_ln_w);
+        } else {
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            ggml_set_name(embeddings, "pre_ln");
+
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
+        }
+    }
+
+    std::vector<struct ggml_tensor *> embedding_stack;
+    const auto & vision_feature_layer = hparams.vision_feature_layer;
+
+    if (use_window_attn) {
+        // handle window attention inputs
+        inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
+        ggml_set_name(inv_window_idx, "inv_window_idx");
+        ggml_set_input(inv_window_idx);
+        // mask for window attention
+        window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, num_positions, num_positions);
+        ggml_set_name(window_mask, "window_mask");
+        ggml_set_input(window_mask);
+
+        // embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
+        GGML_ASSERT(batch_size == 1);
+        embeddings = ggml_reshape_2d(ctx0, embeddings, hidden_size * 4, patches_w * patches_h * batch_size / 4);
+        embeddings = ggml_get_rows(ctx0, embeddings, inv_window_idx);
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size, patches_w * patches_h, batch_size);
+    }
+
+    // loop over layers
+    for (int il = 0; il < ctx->max_feature_layer; il++) {
+        struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
+
+        // If this is an embedding feature layer, save the output.
+        // NOTE: 0 index here refers to the input to the encoder.
+        if (vision_feature_layer.find(il) != vision_feature_layer.end()) {
+            embedding_stack.push_back(embeddings);
+        }
+
+        // rmsnorm1
+        cur = ggml_rms_norm(ctx0, cur, eps);
+        cur = ggml_mul(ctx0, cur, model.layers[il].ln_1_w);
+
+        // self-attention
+        {
+
+            struct ggml_tensor * Q =
+                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
+
+            Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
+            Q = ggml_rope_multi(
+                ctx0, Q, positions, nullptr,
+                d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
+            Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);
+
+            struct ggml_tensor * K =
+                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
+
+            K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
+            K = ggml_rope_multi(
+                ctx0, K, positions, nullptr,
+                d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
+            K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
+
+            struct ggml_tensor * V =
+                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b);
+
+            V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
+            V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
+            V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
+
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            const bool inlist = std::find(hparams.full_attn_layers.begin(), hparams.full_attn_layers.end(), il) != hparams.full_attn_layers.end();
+            const bool full_attn = use_window_attn ? inlist : true;
+            if (full_attn) {
+                KQ = ggml_soft_max_ext(ctx0, KQ, nullptr, 1.0f / sqrtf((float)d_head), 0.0f);
+            } else {
+                KQ = ggml_soft_max_ext(ctx0, KQ, window_mask, 1.0f / sqrtf((float)d_head), 0.0f);
+            }
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
+            KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
+            KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+            cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size);
+        }
+
+        // attention output
+        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b);
+
+        // re-add the layer input, e.g., residual
+        cur = ggml_add(ctx0, cur, embeddings);
+
+        embeddings = cur; // embeddings = residual, cur = hidden_states
+
+        // rms norm2
+        cur = ggml_rms_norm(ctx0, cur, eps);
+        cur = ggml_mul(ctx0, cur, model.layers[il].ln_2_w);
+
+        // mlp
+        // ffn_up
+        auto cur_up = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
+        cur_up = ggml_add(ctx0, cur_up, model.layers[il].ff_o_b);
+
+        auto cur_gate = ggml_mul_mat(ctx0, model.layers[il].ff_g_w, cur);
+        cur_gate = ggml_add(ctx0, cur_gate, model.layers[il].ff_g_b);
+        if (ctx->use_gelu) {
+            cur_gate = ggml_gelu_inplace(ctx0, cur_gate);
+        } else if (ctx->use_silu) {
+            cur_gate = ggml_silu_inplace(ctx0, cur_gate);
+        } else {
+            cur_gate = ggml_gelu_quick_inplace(ctx0, cur_gate);
+        }
+        cur = ggml_mul(ctx0, cur_gate, cur_up);
+
+        // ffn_down
+        cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
+        cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
+
+        // residual 2
+        cur = ggml_add(ctx0, embeddings, cur);
+
+        embeddings = cur;
+    }
+
+    // post-layernorm
+    if (model.post_ln_w) {
+        if (ctx->use_rms_norm) {
+            embeddings = ggml_rms_norm(ctx0, embeddings, eps);
+            ggml_set_name(embeddings, "post_ln");
+
+            embeddings = ggml_mul(ctx0, embeddings, model.post_ln_w);
+        } else {
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            ggml_set_name(embeddings, "post_ln");
+
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
+        }
+    }
+
+    // final layer is a vision feature layer
+    if (vision_feature_layer.find(ctx->max_feature_layer) != vision_feature_layer.end()) {
+        embedding_stack.push_back(embeddings);
+    }
+
+    // If feature layers are explicitly set, stack them (if we have multiple)
+    if (!embedding_stack.empty()) {
+        embeddings = embedding_stack[0];
+        for (size_t i = 1; i < embedding_stack.size(); i++) {
+            embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0);
+        }
+    }
+
+    embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
+
+    embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+    embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+
+    // GELU activation
+    embeddings = ggml_gelu(ctx0, embeddings);
+
+    // Second linear layer
+    embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings);
+    embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
+
+    if (use_window_attn) {
+        window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions / 4);
+        ggml_set_name(window_idx, "window_idx");
+        ggml_set_input(window_idx);
+
+        // embeddings shape: [hidden_size, patches_w * patches_h, batch_size]
+        GGML_ASSERT(batch_size == 1);
+        embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4);
+        embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, patches_w * patches_h / 4, batch_size);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, embeddings);
+
+    return gf;
+}
+
 static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) {
     if (!ctx->has_vision_encoder) {
         LOG_ERR("This gguf file seems to have no vision encoder\n");
@@ -1441,6 +1708,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 res = clip_image_build_graph_pixtral(ctx, imgs);
             } break;
+        case PROJECTOR_TYPE_QWEN2_5_VL:
+            {
+                res = clip_image_build_graph_qwen2_5_vl(ctx, imgs);
+            } break;
         default:
             {
                 // TODO: we should have one build_* function per model
@@ -1699,7 +1970,7 @@ struct clip_model_loader {
             // legacy naming (the in and out is reversed! don't ask me why)
             layer.ff_i_w = layer.ff_down_w;
             layer.ff_o_w = layer.ff_up_w;
-            layer.ff_g_w = layer.ff_gate_b;
+            layer.ff_g_w = layer.ff_gate_w;
             layer.ff_i_b = layer.ff_down_b;
             layer.ff_o_b = layer.ff_up_b;
             layer.ff_g_b = layer.ff_gate_b;
@@ -1801,6 +2072,7 @@ struct clip_model_loader {
                     vision_model.mm_model_mlp_3_w = get_tensor(string_format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
                 } break;
             case PROJECTOR_TYPE_MERGER:
+            case PROJECTOR_TYPE_QWEN2_5_VL:
                 {
                     vision_model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
                     vision_model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
@@ -2754,7 +3026,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
         else if (ctx->minicpmv_version == 4) {
             n_patches = 64;
         }
-    } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+    } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER || ctx->proj_type == PROJECTOR_TYPE_QWEN2_5_VL) {
         int patch_size = params.patch_size * 2;
         int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
         int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
@@ -3165,7 +3437,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         }
     }
 
-    if (hparams.attn_window_size > 0 && ctx->has_qwen2vl_merger) { // TODO: add use_window_attn?
+    if (hparams.attn_window_size > 0 && ctx->proj_type == PROJECTOR_TYPE_QWEN2_5_VL) {
         struct ggml_tensor * window_idx = ggml_graph_get_tensor(gf, "window_idx");
         struct ggml_tensor * inv_window_idx = ggml_graph_get_tensor(gf, "inv_window_idx");
         struct ggml_tensor * window_mask = ggml_graph_get_tensor(gf, "window_mask");
@@ -3398,6 +3670,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
         case PROJECTOR_TYPE_GLM_EDGE:
             return ctx->vision_model.mm_model_mlp_3_w->ne[1];
         case PROJECTOR_TYPE_MERGER:
+        case PROJECTOR_TYPE_QWEN2_5_VL:
             return ctx->vision_model.mm_1_b->ne[0];
         case PROJECTOR_TYPE_GEMMA3:
             return ctx->vision_model.mm_input_proj_w->ne[0];

examples/llava/qwen2_vl_surgery.py

@@ -140,7 +140,6 @@ def main(args):
     fout.add_bool("clip.has_text_encoder", False)
     fout.add_bool("clip.has_vision_encoder", True)
     fout.add_bool("clip.has_qwen2vl_merger", True)
-    fout.add_string("clip.projector_type", "qwen2vl_merger")
 
     print(cfg.vision_config)
     if 'silu' in cfg.vision_config.hidden_act.lower():
@@ -159,7 +158,9 @@ def main(args):
         fout.add_uint32("clip.vision.window_size", vcfg.window_size)
         fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size)
         fout.add_uint32("clip.vision.projection_dim", vcfg.out_hidden_size)
+        fout.add_string("clip.projector_type", "qwen2.5vl_merger")
     else:
+        fout.add_string("clip.projector_type", "qwen2vl_merger")
         fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
         fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)