model: correctly support input vision for deepseek4 (#28154)

* model: correctly support input vision for deepseek4

* nits

src/llama-arch.cpp

@@ -457,6 +457,7 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_FFN_UP_SHEXP,                           "blk.%d.ffn_up_shexp" },
     { LLM_TENSOR_FFN_DOWN_SHEXP,                         "blk.%d.ffn_down_shexp" },
     { LLM_TENSOR_FFN_EXP_PROBS_B,                        "blk.%d.exp_probs_b" },
+    { LLM_TENSOR_FFN_EXP_PROBS_B_VL,                     "blk.%d.exp_probs_b_vl" },
     { LLM_TENSOR_FFN_LATENT_DOWN,                        "blk.%d.ffn_latent_down" },
     { LLM_TENSOR_FFN_LATENT_UP,                          "blk.%d.ffn_latent_up" },
     { LLM_TENSOR_ATTN_NORM_2,                            "blk.%d.attn_norm_2" },
@@ -896,6 +897,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_FFN_GATE_CHEXPS,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     {LLM_TENSOR_FFN_UP_CHEXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
     {LLM_TENSOR_FFN_EXP_PROBS_B,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_FFN_EXP_PROBS_B_VL,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
     // altup / laurel (gemma 3n)
     {LLM_TENSOR_PER_LAYER_TOKEN_EMBD,       {LLM_TENSOR_LAYER_INPUT,     GGML_OP_GET_ROWS}},
     {LLM_TENSOR_PER_LAYER_MODEL_PROJ,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},

src/llama-arch.h

@@ -477,6 +477,7 @@ enum llm_tensor {
     LLM_TENSOR_FFN_GATE_CHEXPS,
     LLM_TENSOR_FFN_UP_CHEXPS,
     LLM_TENSOR_FFN_EXP_PROBS_B,
+    LLM_TENSOR_FFN_EXP_PROBS_B_VL,
     LLM_TENSOR_FFN_LATENT_DOWN,
     LLM_TENSOR_FFN_LATENT_UP,
     LLM_TENSOR_ATTN_Q_NORM,

src/llama-hparams.h

@@ -161,6 +161,10 @@ struct llama_hparams {
     // the size of the sliding window (0 - no SWA)
     uint32_t n_swa = 0;
 
+    // deepseek4 vision: when decoding non-causally (multimodal input), SWA is not applied between tokens of the current ubatch (the image span); older tokens are still window-clipped
+    // for other models (like gemma 3, gemma 4): SWA is always applied to match transformers implementation
+    bool swa_full_non_causal = false;
+
     // if is_swa_impl[il] == 1, then layer il is SWA
     // if is_swa_impl[il] == 0, then layer il is dense (i.e. non-SWA)
     // by default, all layers are dense

src/llama-kv-cache.cpp

@@ -1681,7 +1681,9 @@ static void set_input_kq_mask_impl(const args_set_input_kq_mask & args, T * data
 
                 // apply SWA if any
                 if (swa) {
-                    if (llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) {
+                    // see llama_hparams::swa_full_non_causal
+                    const bool in_span = !causal && args.hparams.swa_full_non_causal && p0 >= seq_pos_min[seq_id];
+                    if (!in_span && llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) {
                         goto skip;
                     }
                 }

src/llama-model.h

@@ -362,6 +362,7 @@ struct llama_layer {
     struct ggml_tensor * ffn_up_b   = nullptr; // b3
     struct ggml_tensor * ffn_act    = nullptr;
     struct ggml_tensor * ffn_exp_probs_b = nullptr;
+    struct ggml_tensor * ffn_exp_probs_b_vl = nullptr; // deepseek4 vision (bias for image tokens)
     struct ggml_tensor * ffn_gate_tid2eid = nullptr;
 
     struct ggml_tensor * dflash_attn_conv_base = nullptr;

src/models/deepseek4.cpp

@@ -66,6 +66,9 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
     }
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
     hparams.set_swa_pattern(0);
+    // tokens of an image span attend bidirectionally to the whole span, the window only applies to older tokens
+    // ref: get_window_topk_idxs_visible in the reference impl
+    hparams.swa_full_non_causal = true;
     for (uint32_t il = hparams.n_layer(); il < hparams.n_layer_all; ++il) {
         hparams.is_swa_impl[il] = true;
     }
@@ -156,6 +159,8 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader & ml) {
         } else {
             layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, flags);
         }
+        // vision variant only: routing bias for image tokens
+        layer.ffn_exp_probs_b_vl = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B_VL, "bias", i), {n_expert}, flags | TENSOR_NOT_REQUIRED);
         layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
 
         layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, flags);
@@ -1275,7 +1280,14 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p
         const auto & layer = model.layers[il];
         ggml_tensor * selected_experts = nullptr;
         ggml_tensor * exp_probs_b = layer.ffn_exp_probs_b;
-        if ((uint32_t) il < hparams.dsv4_hash_layer_count) {
+
+        // may apply exp_probs_b_vl is input is from mtmd
+        const bool is_media = ubatch.embd != nullptr;
+        if (is_media) {
+            if (layer.ffn_exp_probs_b_vl) {
+                exp_probs_b = layer.ffn_exp_probs_b_vl;
+            }
+        } else if ((uint32_t) il < hparams.dsv4_hash_layer_count) {
             selected_experts = ggml_get_rows(ctx0, layer.ffn_gate_tid2eid, res->t_inp_tokens);
             exp_probs_b = nullptr;
         }