Fix (Gemma-4 Vision): Correct KQ mask fill for causal models in non-causal flash-attn mode (#1985)

When llama_set_causal_attn(false) is called on a causal model (e.g.
Gemma-4 during vision image decode), llama_set_inputs took the non-causal
else-branch (designed for pure embedding models).

That path wrote the F16 mask with stride n_tokens instead of n_kv, and iterated batch
indices rather than KV cache cells.

The result was that every image query row beyond the first was
written at the wrong offset, leaving stale -inf values from
previous decodes visible to the GPU kernel. Any conversation
that had built up prior KV mask data would produce all-inf attention scores
for most image tokens, collapsing softmax to NaN and aborting at sampling.

Resolves #1984

src/llama.cpp

@@ -4335,12 +4335,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
         auto tim1 = ggml_time_us();
 #endif
         // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
-        if (cparams.causal_attn && !lctx.is_encoding) {
+        // mask_kv_self/n_kv are needed in both the causal branch and the non-causal flash-attn branch below.
         const llama_kv_cache & mask_kv_self =
             ((lctx.model.arch == LLM_ARCH_GEMMA4_MTP || lctx.model.arch == LLM_ARCH_GEMMA4_ASSISTANT) && lctx.mtp_target_ctx != nullptr)
                 ? lctx.mtp_target_ctx->kv_self
                 : kv_self;
         const int64_t n_kv = mask_kv_self.n;
+        if (cparams.causal_attn && !lctx.is_encoding) {
             const int64_t n_tokens = batch.n_tokens;
 
 
@@ -4582,6 +4583,48 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
                 }
             }
 
+            // For causal models running in non-causal mode (e.g., Gemma-4 image decode),
+            // the flash-attn mask is allocated as [n_kv, n_tokens_pad] and must be filled
+            // using KV cache cell metadata — not batch-token indices — because image tokens
+            // occupy cells starting at n_past, not at cell 0.
+            if (cparams.flash_attn && hparams.causal_attn && !lctx.is_encoding) {
+                const ggml_half h_inf  = ggml_fp32_to_fp16(-INFINITY);
+                const ggml_half h_zero = ggml_fp32_to_fp16(0.f);
+                for (int j = 0; j < n_tokens; ++j) {
+                    const llama_seq_id seq_id = batch.seq_id[j][0];
+                    const llama_pos    pos    = batch.pos[j];
+                    for (int i = 0; i < n_kv; ++i) {
+                        const bool valid = mask_kv_self.cells[i].has_seq_id(seq_id);
+                        if (data_f16) {
+                            data_f16[j*n_kv + i] = valid ? h_zero : h_inf;
+                        }
+                        if (data_swa_f16) {
+                            ggml_half h = valid ? h_zero : h_inf;
+                            if (h == h_zero) {
+                                if (hparams.n_attn_chunk) {
+                                    const llama_pos pos_chunk_start = (pos / hparams.n_attn_chunk) * hparams.n_attn_chunk;
+                                    if (mask_kv_self.cells[i].pos < pos_chunk_start || pos < pos_chunk_start) {
+                                        h = h_inf;
+                                    }
+                                } else if (pos - mask_kv_self.cells[i].pos >= (int32_t)hparams.n_swa) {
+                                    h = h_inf;
+                                }
+                            }
+                            data_swa_f16[j*n_kv + i] = h;
+                        }
+                    }
+                }
+                const int64_t n_tokens_padded = GGML_PAD(n_tokens, GGML_KQ_MASK_PAD);
+                if (n_tokens_padded > n_tokens) {
+                    if (data_f16) {
+                        std::fill(data_f16 + n_tokens*n_kv, data_f16 + n_tokens_padded*n_kv, h_inf);
+                    }
+                    if (data_swa_f16) {
+                        std::fill(data_swa_f16 + n_tokens*n_kv, data_swa_f16 + n_tokens_padded*n_kv, h_inf);
+                    }
+                }
+            } else {
+
             for (int h = 0; h < 1; ++h) {
                 for (int j = 0; j < n_tokens; ++j) {
                     const llama_seq_id seq_id = batch.seq_id[j][0];
@@ -4639,6 +4682,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
                     }
                 }
             }
+
+            } // end else (non-flash or non-causal-model path)
 #if IK_PRINT_TIMING == 2
             auto tim2 = ggml_time_us();
             printf("set_inputs(mask2): %d us\n", int(tim2-tim1));