model: add DSpark support for Nemotron3.5 (#27804 )

* model: add DSpark support for Nemotron3.5 * Update src/models/dflash.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
ggml-hexagon: add HTP unary ops for ABS and LOG (#27786 )
2026-08-28 02:57:42 +02:00 · 2026-08-28 01:49:27 +02:00 · 2026-08-27 15:05:57 -07:00 · 2026-08-27 14:38:02 -07:00 · 2026-08-27 22:39:14 +02:00 · 2026-08-27 22:18:34 +02:00
19 changed files with 260 additions and 64 deletions
@@ -714,10 +714,10 @@ jobs:
        with:
          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu

-  # TODO: build only the ggml-hip backend like the other windows backend jobs
-  #       (windows-cuda, windows-sycl), then drop the ui-build dependency
+  # note: builds only the ggml-hip backend - llama-server is injected from the
+  #       windows-cpu zip during the release "Merge artifacts" step
  windows-rocm:
-    needs: [check-release, ui-build]
+    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2022
@@ -736,12 +736,6 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Download UI build
-        uses: actions/download-artifact@v7
-        with:
-          name: llama-ui.zip
-          path: tools/ui/dist
-
      - name: Install Ninja
        run: |
          choco install ninja
@@ -804,17 +798,15 @@ jobs:
            -DCMAKE_PREFIX_PATH="${env:HIP_PATH}" `
            -DGGML_BACKEND_DL=ON `
            -DGGML_NATIVE=OFF `
-            -DGGML_CPU=ON `
-            -DGGML_CPU_ALL_VARIANTS=ON `
+            -DGGML_CPU=OFF `
            -DGGML_HIP=ON `
            -DCMAKE_C_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang++.exe" `
            -DCMAKE_C_FLAGS="-Wno-error=incompatible-pointer-types" `
            -DCMAKE_HIP_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
            -DHIP_PATH="${env:HIP_PATH}" `
-            -DGGML_HIP_ROCWMMA_FATTN=ON `
            -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}"
-          cmake --build build --config Release --parallel ${env:NUMBER_OF_PROCESSORS}
+          cmake --build build --config Release --parallel ${env:NUMBER_OF_PROCESSORS} --target ggml-hip

      - name: Verify HIP backend was built
        run: |
@@ -866,8 +858,11 @@ jobs:

      - name: Pack artifacts
        run: |
-          cp "LICENSE" "build\bin\Release\"
-          7z a -snl llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip .\build\bin\Release\*
+          7z a -snl llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip `
+            .\build\bin\Release\ggml-hip.dll `
+            .\build\bin\Release\amdhip64_7.dll `
+            .\build\bin\Release\rocm_kpack.dll `
+            .\build\bin\Release\amd_comgr.dll

      - name: Upload artifacts
        uses: actions/upload-artifact@v6
@@ -1643,6 +1643,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
        }
    ).set_env("LLAMA_ARG_CTX_SIZE"));
+    add_opt(common_arg(
+        { "--kv-unified-per-slot" }, "N",
+        "context limit per parallel slot (default: unset, behavior unchanged).\n"
+        "when set without -c/--ctx-size, the shared KV pool is sized to n_parallel*N",
+        [](common_params & params, int value) {
+            params.kv_unified_per_slot = value;
+        }
+    ).set_env("LLAMA_ARG_KV_UNIFIED_PER_SLOT").set_examples({ LLAMA_EXAMPLE_SERVER }));
    add_opt(common_arg(
        {"-n", "--predict", "--n-predict"}, "N",
        string_format(
@@ -627,6 +627,7 @@ struct common_params {
    bool    cache_prompt        = true;  // whether to enable prompt caching
    bool    cache_idle_slots    = true;  // save and clear idle slots upon starting a new task
    int32_t n_ctx_checkpoints   = 32;    // max number of context checkpoints per slot
+    int32_t kv_unified_per_slot = 0;     // max context per parallel slot; 0 = unset
    int32_t checkpoint_min_step = 8192;  // minimum spacing between context checkpoints
    int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.

@@ -935,6 +935,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
    // dspark speculators
    bool sample_from_anchor = true;

+    // block-internal attention
+    bool causal_attn = false;
+
    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
    uint32_t        target_layer_ids_n = 0;

@@ -972,12 +975,25 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            if (llama_model_meta_val_str(model_dft, "dflash.sample_from_anchor", buf, sizeof(buf)) >= 0) {
                sample_from_anchor = std::strcmp(buf, "true") == 0;
            }
+            if (llama_model_meta_val_str(model_dft, "dflash.attention.causal", buf, sizeof(buf)) >= 0) {
+                causal_attn = std::strcmp(buf, "true") == 0;
+            }
        }

        selector_top_k = llama_model_dflash_selector_top_k(model_dft);
        is_dflash2     = selector_top_k > 0;
        mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft));

+        if (is_dspark && this->params.p_min > 0.0f) {
+            char buf[16] = {};
+            const bool has_conf =
+                llama_model_meta_val_str(model_dft, "dflash.has_confidence_head", buf, sizeof(buf)) < 0 ||
+                std::strcmp(buf, "true") == 0;
+            if (!has_conf) {
+                throw std::runtime_error("DSpark draft has no confidence head: please set --spec-draft-p-min 0");
+            }
+        }
+
        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(type).c_str());
        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
        LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u, sample_from_anchor=%s\n", __func__,
@@ -1036,7 +1052,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

        // DFlash2 reads its selector lattice from h_nextn and never consumes raw logits.
        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ !is_dflash2);
-        llama_set_causal_attn(ctx_dft, false); // DFlash needs non-causal attention
+        llama_set_causal_attn(ctx_dft, causal_attn); // DFlash needs non-causal attention unless the model says otherwise
    }

    ~common_speculative_impl_draft_dflash() override {
@@ -709,14 +709,20 @@ class DFlashModel(Qwen3Model):
            extract_layer_ids = [i + 1 for i in target_layer_ids]
            self.gguf_writer.add_target_layers(extract_layer_ids)

-        use_sliding_window = self.hparams.get("use_sliding_window", False)
-        sliding_window = self.hparams.get("sliding_window")
+        use_sliding_window = self.hparams.get("use_sliding_window", False) or dflash_config.get("use_swa", False)
+        sliding_window = dflash_config.get("swa_window_size") or self.hparams.get("sliding_window")
        layer_types = self.hparams.get("layer_types")
        if use_sliding_window and sliding_window and layer_types:
            is_swa = [lt == "sliding_attention" for lt in layer_types]
            self.gguf_writer.add_sliding_window(sliding_window)
            self.gguf_writer.add_sliding_window_pattern(is_swa)

+        causal = self.hparams.get("is_causal")
+        if causal is None:
+            causal = dflash_config.get("causal")
+        if causal is not None:
+            self.gguf_writer.add_causal_attention(bool(causal))
+
        # M-RoPE target: the draft ropes on the temporal dim only, so write
        # degenerate sections [n_rot/2, 0, 0, 0]
        if self._target_uses_mrope():
@@ -737,6 +743,8 @@ class DFlashModel(Qwen3Model):
        name, gen = item
        if not name.startswith("model."):
            name = "model." + name
+        if "sink" in name and not name.endswith(".weight"):
+            name += ".weight"
        return super().filter_tensors((name, gen))

    _ROPE_PERMUTE_SUFFIXES = (
@@ -815,6 +823,10 @@ class DSparkModel(DFlashModel):
        super().set_gguf_parameters()
        self.gguf_writer.add_sample_from_anchor(self._sample_from_anchor)

+        # confidence head is optional: vanilla-markov exports ship without it
+        has_conf = any("confidence_head.proj" in name for name in self.model_tensors)
+        self.gguf_writer.add_has_confidence_head(has_conf)
+
    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        if item[0] == "t2d":  # not used at runtime
@@ -833,7 +845,7 @@ class DSparkModel(DFlashModel):
            self._d2t = data_torch
            return

-        if self._n_vocab_draft == self.hparams["vocab_size"] and name.endswith(("embed_tokens.weight", "lm_head.weight")):
+        if self._n_vocab_draft == self.hparams["vocab_size"] and name.endswith("lm_head.weight"):
            return

        # interleaved-rope checkpoints (rope_is_neox_style = false) -> NeoX layout: per head, even dims first then odd
@@ -4643,6 +4643,7 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) {
        case GGML_OP_CLAMP:           return HTP_OP_CLAMP;
        case GGML_OP_SQR:             return HTP_OP_SQR;
        case GGML_OP_SQRT:            return HTP_OP_SQRT;
+        case GGML_OP_LOG:             return HTP_OP_UNARY_LOG;
        case GGML_OP_SOFT_MAX:        return HTP_OP_SOFTMAX;
        case GGML_OP_SSM_CONV:        return HTP_OP_SSM_CONV;
        case GGML_OP_GATED_DELTA_NET: return HTP_OP_GATED_DELTA_NET;
@@ -4666,6 +4667,7 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) {
                case GGML_UNARY_OP_EXP:        return HTP_OP_UNARY_EXP;
                case GGML_UNARY_OP_SOFTPLUS:   return HTP_OP_UNARY_SOFTPLUS;
                case GGML_UNARY_OP_TANH:       return HTP_OP_UNARY_TANH;
+                case GGML_UNARY_OP_ABS:        return HTP_OP_UNARY_ABS;
            default:
                break;
            }
@@ -5463,6 +5465,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons

        case GGML_OP_SQR:
        case GGML_OP_SQRT:
+        case GGML_OP_LOG:
            supp = ggml_hexagon_supported_unary(sess, op);
            break;

@@ -5481,6 +5484,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
                case GGML_UNARY_OP_SIGMOID:
                case GGML_UNARY_OP_SOFTPLUS:
                case GGML_UNARY_OP_TANH:
+                case GGML_UNARY_OP_ABS:
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_GELU:
                case GGML_UNARY_OP_GELU_QUICK:
@@ -62,6 +62,8 @@ enum htp_op_code {
    HTP_OP_UNARY_NEG,
    HTP_OP_UNARY_SOFTPLUS,
    HTP_OP_UNARY_TANH,
+    HTP_OP_UNARY_ABS,
+    HTP_OP_UNARY_LOG,
    HTP_OP_GLU_SWIGLU,
    HTP_OP_GLU_SWIGLU_OAI,
    HTP_OP_GLU_GEGLU,
@@ -358,6 +358,34 @@ static inline void hvx_clamp_scalar_f32(uint8_t * restrict dst, const uint8_t *
    }
 }

+//
+// Abs
+//
+
+static inline void hvx_abs_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+
+    HVX_Vector * restrict vdst = (HVX_Vector *) dst;
+    HVX_Vector * restrict vsrc = (HVX_Vector *) src;
+
+    const uint32_t elem_size = sizeof(float);
+    const uint32_t epv       = 128 / elem_size;
+    const uint32_t nvec      = n / epv;
+    const uint32_t nloe      = n % epv;
+
+    uint32_t i = 0;
+
+    _Pragma("unroll(4)")
+    for (; i < nvec; i++) {
+        vdst[i] = hvx_vec_abs_f32(vsrc[i]);
+    }
+    if (nloe) {
+        HVX_Vector v = hvx_vec_abs_f32(vsrc[i]);
+        hvx_vec_store_a((void *) &vdst[i], nloe * elem_size, v);
+    }
+}
+
 //
 // Square
 //
@@ -62,4 +62,28 @@ static inline HVX_Vector hvx_vec_log_f32(HVX_Vector x) {
    return hvx_vec_add_f32_f32(term_e, res);
 }

+static inline void hvx_log_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, uint32_t n) {
+    assert((unsigned long) dst % 128 == 0);
+    assert((unsigned long) src % 128 == 0);
+
+    HVX_Vector * restrict vdst = (HVX_Vector *) dst;
+    HVX_Vector * restrict vsrc = (HVX_Vector *) src;
+
+    const uint32_t elem_size = sizeof(float);
+    const uint32_t epv       = 128 / elem_size;
+    const uint32_t nvec      = n / epv;
+    const uint32_t nloe      = n % epv;
+
+    uint32_t i = 0;
+
+    _Pragma("unroll(4)")
+    for (; i < nvec; i++) {
+        vdst[i] = hvx_vec_log_f32(vsrc[i]);
+    }
+    if (nloe) {
+        HVX_Vector v = hvx_vec_log_f32(vsrc[i]);
+        hvx_vec_store_a((void *) &vdst[i], nloe * elem_size, v);
+    }
+}
+
 #endif /* HVX_LOG_H */
@@ -777,6 +777,8 @@ static int execute_op(struct htp_ops_context * octx) {
        case HTP_OP_UNARY_NEG:
        case HTP_OP_UNARY_EXP:
        case HTP_OP_UNARY_TANH:
+        case HTP_OP_UNARY_ABS:
+        case HTP_OP_UNARY_LOG:
        case HTP_OP_L2_NORM:
            return op_unary(octx);

@@ -443,6 +443,34 @@ static void tanh_f32(const float * restrict src,
    }
 }

+static void abs_f32(const float * restrict src,
+                    float * restrict dst,
+                    const uint32_t num_rows,
+                    const struct htp_unary_context * uctx) {
+    htp_unary_op_preamble;
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * dst_row_size_aligned);
+
+        hvx_abs_f32_aa(dst_local, src_local, ne0);
+    }
+}
+
+static void log_f32(const float * restrict src,
+                    float * restrict dst,
+                    const uint32_t num_rows,
+                    const struct htp_unary_context * uctx) {
+    htp_unary_op_preamble;
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * dst_row_size_aligned);
+
+        hvx_log_f32_aa(dst_local, src_local, ne0);
+    }
+}
+
 #define DEFINE_UNARY_TASK(NAME, IS_RMS_NORM_MUL, IS_TRI, CORE_EXPR)                                                 \
 static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * data) {                                \
    const struct htp_unary_context * uctx = (const struct htp_unary_context *) data;                                \
@@ -478,6 +506,9 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat
    const uint32_t nb11 = src1 ? src1->nb[1] : 0;                                                                   \
    const uint32_t nb12 = src1 ? src1->nb[2] : 0;                                                                   \
    const uint32_t nb13 = src1 ? src1->nb[3] : 0;                                                                   \
+    const uint32_t nb11_bc = (src1 && src1->ne[1] > 1) ? nb11 : 0;                                                  \
+    const uint32_t nb12_bc = (src1 && src1->ne[2] > 1) ? nb12 : 0;                                                  \
+    const uint32_t nb13_bc = (src1 && src1->ne[3] > 1) ? nb13 : 0;                                                  \
    const bool src1_contig = src1 ? ((nb12 == (size_t)ne01 * nb11) && (nb13 == (size_t)ne02 * nb12)) : false;       \
                                                                                                                    \
    uint8_t * src0_vtcm_data = uctx->vtcm_src0 + (ith * uctx->vtcm_src0_size_per_thread);                           \
@@ -497,8 +528,12 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat
    const struct fastdiv_values * div_ne02  = &uctx->kparams->div_ne02;                                             \
    const struct fastdiv_values * div_ne012 = &uctx->kparams->div_ne012;                                            \
                                                                                                                    \
-    const uint32_t src0_max_block = src0_contig ? uctx->block : MIN((uint32_t)uctx->block, ne01);                   \
-    const uint32_t dst_max_block  = dst_contig  ? uctx->block : MIN((uint32_t)uctx->block, ne1);                    \
+    const bool src1_needs_row_clip = (IS_RMS_NORM_MUL) && !uctx->broadcast_weight && !src1_contig;                  \
+    const bool block_src0_contig = src0_contig && !src1_needs_row_clip;                                             \
+    const bool block_dst_contig  = dst_contig  && !src1_needs_row_clip;                                             \
+                                                                                                                    \
+    const uint32_t src0_max_block = block_src0_contig ? uctx->block : MIN((uint32_t)uctx->block, ne01);             \
+    const uint32_t dst_max_block  = block_dst_contig  ? uctx->block : MIN((uint32_t)uctx->block, ne1);              \
    const uint32_t BLOCK = MIN(src0_max_block, dst_max_block);                                                      \
    if (BLOCK == 0) {                                                                                               \
        FARF(ERROR, "unary-f32 : current VTCM reservation %zu is too small, needed at least %zu\n",                 \
@@ -515,8 +550,8 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat
    }                                                                                                               \
                                                                                                                    \
    for (uint32_t ir = src0_start_row, vtcm_idx = 0; ir < src0_end_row && vtcm_idx < 2; vtcm_idx++) {               \
-        const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01,        \
-                                                     div_ne01);                                                     \
+        const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, block_src0_contig, block_dst_contig,  \
+                                                     ne01, div_ne01);                                               \
                                                                                                                    \
        dma_queue_push(dma_queue,                                                                                   \
            dma_make_ptr(data_dst, dst_vtcm_data + (vtcm_idx * dst_vtcm_half_size)),                                \
@@ -530,7 +565,7 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat
                                                                                                                    \
        if ((IS_RMS_NORM_MUL) && !uctx->broadcast_weight) {                                                         \
            const size_t src1_off = src1_contig ? (ir * nb11) :                                                     \
-                unary_row_offset(ir, ne01, ne02, div_ne01, div_ne02, div_ne012, nb11, nb12, nb13);                  \
+                unary_row_offset(ir, ne01, ne02, div_ne01, div_ne02, div_ne012, nb11_bc, nb12_bc, nb13_bc);         \
            dma_queue_push(dma_queue,                                                                               \
                dma_make_ptr(src1_vtcm_data + (vtcm_idx * src1_vtcm_half_size), data_src1 + src1_off),              \
                uctx->src1_row_size_aligned, nb11, uctx->src1_data_row_size, block_size);                           \
@@ -540,8 +575,8 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat
    }                                                                                                               \
                                                                                                                    \
    for (uint32_t ir = src0_start_row; ir < src0_end_row; ) {                                                       \
-        const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01,        \
-                                                     div_ne01);                                                     \
+        const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, block_src0_contig, block_dst_contig,  \
+                                                     ne01, div_ne01);                                               \
                                                                                                                    \
        float * dst_vtcm  = (float *) dma_queue_pop(dma_queue).src;                                                 \
        float * src0_vtcm = (float *) dma_queue_pop(dma_queue).dst;                                                 \
@@ -562,12 +597,12 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat
                                                                                                                    \
        const uint32_t next_ir = ir + block_size;                                                                   \
        if (next_ir < src0_end_row) {                                                                               \
-            const uint32_t next_block_size = unary_block_size(next_ir, src0_end_row, BLOCK, src0_contig, dst_contig,\
-                                                              ne01, div_ne01);                                      \
+            const uint32_t next_block_size = unary_block_size(next_ir, src0_end_row, BLOCK, block_src0_contig,      \
+                                                              block_dst_contig, ne01, div_ne01);                    \
            const uint32_t pref_ir = next_ir + next_block_size;                                                     \
            if (pref_ir < src0_end_row) {                                                                           \
-                const uint32_t pref_block_size = unary_block_size(pref_ir, src0_end_row, BLOCK, src0_contig,        \
-                                                                  dst_contig, ne01, div_ne01);                      \
+                const uint32_t pref_block_size = unary_block_size(pref_ir, src0_end_row, BLOCK, block_src0_contig,  \
+                                                                  block_dst_contig, ne01, div_ne01);                \
                const size_t src0_pref_off = src0_contig ? (pref_ir * nb01) :                                       \
                    unary_row_offset(pref_ir, ne01, ne02, div_ne01, div_ne02, div_ne012, nb01, nb02, nb03);         \
                dma_queue_push(dma_queue,                                                                           \
@@ -576,7 +611,8 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat
                                                                                                                    \
                if ((IS_RMS_NORM_MUL) && !uctx->broadcast_weight) {                                                 \
                    const size_t src1_pref_off = src1_contig ? (pref_ir * nb11) :                                   \
-                        unary_row_offset(pref_ir, ne01, ne02, div_ne01, div_ne02, div_ne012, nb11, nb12, nb13);     \
+                        unary_row_offset(pref_ir, ne01, ne02, div_ne01, div_ne02, div_ne012, nb11_bc, nb12_bc,      \
+                                          nb13_bc);                                                                 \
                    dma_queue_push(dma_queue,                                                                       \
                        dma_make_ptr(src1_vtcm, data_src1 + src1_pref_off),                                         \
                        uctx->src1_row_size_aligned, nb11, uctx->src1_data_row_size, pref_block_size);              \
@@ -603,6 +639,8 @@ DEFINE_UNARY_TASK(unary_silu,     false, false, silu_f32(src0_vtcm, dst_vtcm, bl
 DEFINE_UNARY_TASK(unary_gelu,     false, false, gelu_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_softplus, false, false, softplus_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_tanh,     false, false, tanh_f32(src0_vtcm, dst_vtcm, block_size, uctx))
+DEFINE_UNARY_TASK(unary_abs,      false, false, abs_f32(src0_vtcm, dst_vtcm, block_size, uctx))
+DEFINE_UNARY_TASK(unary_log,      false, false, log_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(l2_norm,        false, false, l2_norm_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(tri,            false, true,  tri_f32(src0_vtcm, dst_vtcm, block_size, ir, uctx))

@@ -850,6 +888,8 @@ DEFINE_UNARY_TILED_TASK(unary_silu,     false, tile_silu_f32(dst_vtcm, src_vtcm,
 DEFINE_UNARY_TILED_TASK(unary_gelu,     false, tile_gelu_f32(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(unary_softplus, false, tile_unary_softplus_f32(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(unary_tanh,     false, hvx_tanh_f32_aa(dst_vtcm, src_vtcm, tw))
+DEFINE_UNARY_TILED_TASK(unary_abs,      false, hvx_abs_f32_aa(dst_vtcm, src_vtcm, tw))
+DEFINE_UNARY_TILED_TASK(unary_log,      false, hvx_log_f32_aa(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(tri,            true,  tri_apply_tile_f32(src_vtcm, dst_vtcm, tw, col, i01, ne0, tri_ttype))

 static int execute_op_unary_f32(struct htp_ops_context * octx) {
@@ -875,6 +915,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
        case HTP_OP_UNARY_GELU:      op_type = "gelu-f32";         break;
        case HTP_OP_UNARY_SOFTPLUS:  op_type = "softplus-f32";     break;
        case HTP_OP_UNARY_TANH:      op_type = "tanh-f32";         break;
+        case HTP_OP_UNARY_ABS:       op_type = "abs-f32";          break;
+        case HTP_OP_UNARY_LOG:       op_type = "log-f32";          break;
        case HTP_OP_L2_NORM:         op_type = "l2norm-f32";       break;
        case HTP_OP_TRI:             op_type = "tri-f32";          break;

@@ -973,6 +1015,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
                case HTP_OP_UNARY_GELU:      task_func = unary_task_f32_tiled_unary_gelu;     break;
                case HTP_OP_UNARY_SOFTPLUS:  task_func = unary_task_f32_tiled_unary_softplus; break;
                case HTP_OP_UNARY_TANH:      task_func = unary_task_f32_tiled_unary_tanh;     break;
+                case HTP_OP_UNARY_ABS:       task_func = unary_task_f32_tiled_unary_abs;      break;
+                case HTP_OP_UNARY_LOG:       task_func = unary_task_f32_tiled_unary_log;      break;
                case HTP_OP_TRI:             task_func = unary_task_f32_tiled_tri;            break;
                default:                     break;
            }
@@ -992,6 +1036,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
                case HTP_OP_UNARY_GELU:      task_func = unary_task_f32_unary_gelu;           break;
                case HTP_OP_UNARY_SOFTPLUS:  task_func = unary_task_f32_unary_softplus;       break;
                case HTP_OP_UNARY_TANH:      task_func = unary_task_f32_unary_tanh;           break;
+                case HTP_OP_UNARY_ABS:       task_func = unary_task_f32_unary_abs;            break;
+                case HTP_OP_UNARY_LOG:       task_func = unary_task_f32_unary_log;            break;
                case HTP_OP_L2_NORM:         task_func = unary_task_f32_l2_norm;              break;
                case HTP_OP_TRI:             task_func = unary_task_f32_tri;                  break;
                default:                     break;
@@ -55,6 +55,8 @@ static inline bool htp_op_is_unary(uint32_t opcode) {
        case HTP_OP_UNARY_GELU:
        case HTP_OP_UNARY_SOFTPLUS:
        case HTP_OP_UNARY_TANH:
+        case HTP_OP_UNARY_ABS:
+        case HTP_OP_UNARY_LOG:
        case HTP_OP_L2_NORM:
        case HTP_OP_TRI:
            return true;
@@ -167,6 +167,7 @@ class Keys:
        SELECTOR_RANK                     = "{arch}.selector_rank"
        SELECTOR_TOP_K                    = "{arch}.selector_top_k"
        SAMPLE_FROM_ANCHOR                = "{arch}.sample_from_anchor"
+        HAS_CONFIDENCE_HEAD               = "{arch}.has_confidence_head"
        NORM_BEFORE_RESIDUAL              = "{arch}.norm_before_residual"
        NORM_BEFORE_FC                    = "{arch}.norm_before_fc"

@@ -1008,6 +1008,9 @@ class GGUFWriter:
    def add_sample_from_anchor(self, value: bool) -> None:
        self.add_bool(Keys.LLM.SAMPLE_FROM_ANCHOR.format(arch=self.arch), value)

+    def add_has_confidence_head(self, value: bool) -> None:
+        self.add_bool(Keys.LLM.HAS_CONFIDENCE_HEAD.format(arch=self.arch), value)
+
    def add_target_layers(self, value: Sequence[int]) -> None:
        self.add_array(Keys.LLM.TARGET_LAYERS.format(arch=self.arch), value)

@@ -672,6 +672,7 @@ struct llama_model {
    // dspark
    struct ggml_tensor * dspark_markov_w1   = nullptr;
    struct ggml_tensor * dspark_markov_w2   = nullptr;
+    struct ggml_tensor * dspark_markov_w2_s = nullptr;
    struct ggml_tensor * dspark_conf_proj   = nullptr;
    struct ggml_tensor * dspark_conf_proj_b = nullptr;

@@ -115,10 +115,11 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
    if (markov_meta) {
        const int64_t dspark_markov_rank = markov_meta->ne[0];

-        dspark_markov_w1 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W1, "weight"), { dspark_markov_rank, n_vocab }, 0);
-        dspark_markov_w2 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W2, "weight"), { dspark_markov_rank, n_vocab_draft }, 0);
+        dspark_markov_w1   = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W1, "weight"), { dspark_markov_rank, n_vocab }, 0);
+        dspark_markov_w2   = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W2, "weight"), { dspark_markov_rank, n_vocab_draft }, 0);
+        dspark_markov_w2_s = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W2, "scale"),  { 1 }, TENSOR_NOT_REQUIRED);

-        dspark_conf_proj   = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "weight"), { n_embd + dspark_markov_rank, 1 }, 0);
+        dspark_conf_proj   = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "weight"), { n_embd + dspark_markov_rank, 1 }, TENSOR_NOT_REQUIRED);
        dspark_conf_proj_b = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "bias"),   { 1 },             TENSOR_NOT_REQUIRED);

        LLAMA_LOG_INFO("%s: DFlash with DSpark markov head (rank = %lld)\n", __func__, (long long) dspark_markov_rank);
@@ -219,6 +220,9 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);

+        // optional per-head attention sinks (e.g. Nemotron DSpark)
+        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), { n_head }, TENSOR_NOT_REQUIRED);
+
        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
@@ -290,7 +294,10 @@ static void build_dspark_markov_head(llm_graph_context & g, const llama_model &

    ggml_tensor * w1 = model.dspark_markov_w1;
    ggml_tensor * w2 = model.dspark_markov_w2;
-    GGML_ASSERT(w1 && w2 && model.dspark_conf_proj && "DSpark markov/confidence weights not loaded");
+    GGML_ASSERT(w1 && w2 && "DSpark markov weights not loaded");
+
+    // confidence head is optional
+    const bool has_conf = model.dspark_conf_proj != nullptr;

    ggml_tensor * base = res->t_logits; // [n_vocab, n_tokens]
    const int64_t n_vocab = base->ne[0];
@@ -321,23 +328,22 @@ static void build_dspark_markov_head(llm_graph_context & g, const llama_model &
    ggml_tensor * prev = ggml_view_2d(ctx0, tokens, 1, n_blocks, token_stride, 0);
    prev = ggml_cont_1d(ctx0, prev, n_blocks);

-    // confidence head input: predicts per-position acceptance
-    ggml_tensor * conf_inp = res->t_embd; // [n_embd, n_tok]
-
    ggml_tensor * cat      = nullptr;
    ggml_tensor * cat_conf = nullptr;

    if (!sample_from_anchor) {
        // bonus anchor slot: pass the logits through unbiased, pad the (unread) confidence column
-        cat      = ggml_cont(ctx0, ggml_view_2d(ctx0, base, n_vocab, n_blocks, base_stride, 0));
-        cat_conf = ggml_sigmoid(ctx0, ggml_cont(ctx0, ggml_view_2d(ctx0, base, 1, n_blocks, base_stride, 0)));
+        cat = ggml_cont(ctx0, ggml_view_2d(ctx0, base, n_vocab, n_blocks, base_stride, 0));
+        if (has_conf) {
+            cat_conf = ggml_sigmoid(ctx0, ggml_cont(ctx0, ggml_view_2d(ctx0, base, 1, n_blocks, base_stride, 0)));
+        }
    }

    // TODO: the in-graph chain is greedy (argmax); sampling params affect only the final
    //       token pick, not the Markov conditioning path
    for (int64_t i = i_draft_beg; i < block_drafts; ++i) {
-        ggml_tensor * w1_prev = ggml_get_rows(ctx0, w1, prev);   // [R, n_blocks]
-        ggml_tensor * bias    = ggml_mul_mat(ctx0, w2, w1_prev); // [n_vocab_draft, n_blocks]
+        ggml_tensor * w1_prev = ggml_get_rows(ctx0, w1, prev);                          // [R, n_blocks]
+        ggml_tensor * bias    = g.build_lora_mm(w2, w1_prev, model.dspark_markov_w2_s); // [n_vocab_draft, n_blocks]
        if (model.d2t) {
            // reduced draft vocab: scatter the bias to the target rows (base is -inf on the others)
            const int64_t n_draft_vocab = bias->ne[0];
@@ -354,17 +360,21 @@ static void build_dspark_markov_head(llm_graph_context & g, const llama_model &

        cat = cat ? ggml_concat(ctx0, cat, col, 1) : col;

-        // conf(i) = sigmoid(conf_proj . [conf_inp(i); markov_w1[prev(i)]] + b)  -- [1, n_blocks]
-        ggml_tensor * conf_inp_i = ggml_view_2d(ctx0, conf_inp, conf_inp->ne[0], n_blocks,
-                                                (size_t) block_drafts * conf_inp->nb[1], i*conf_inp->nb[1]);
-        ggml_tensor * feat = ggml_concat(ctx0, ggml_cont(ctx0, conf_inp_i), w1_prev, 0);
-        ggml_tensor * conf = ggml_mul_mat(ctx0, model.dspark_conf_proj, feat);
-        if (model.dspark_conf_proj_b) {
-            conf = ggml_add(ctx0, conf, model.dspark_conf_proj_b);
-        }
-        conf = ggml_sigmoid(ctx0, conf);
+        if (has_conf) {
+            // confidence head input: predicts per-position acceptance
+            ggml_tensor * conf_inp   = res->t_embd; // [n_embd, n_tok]
+            // conf(i) = sigmoid(conf_proj . [conf_inp(i); markov_w1[prev(i)]] + b)  -- [1, n_blocks]
+            ggml_tensor * conf_inp_i = ggml_view_2d(ctx0, conf_inp, conf_inp->ne[0], n_blocks,
+                                                    (size_t) block_drafts * conf_inp->nb[1], i*conf_inp->nb[1]);
+            ggml_tensor * feat = ggml_concat(ctx0, ggml_cont(ctx0, conf_inp_i), w1_prev, 0);
+            ggml_tensor * conf = ggml_mul_mat(ctx0, model.dspark_conf_proj, feat);
+            if (model.dspark_conf_proj_b) {
+                conf = ggml_add(ctx0, conf, model.dspark_conf_proj_b);
+            }
+            conf = ggml_sigmoid(ctx0, conf);

-        cat_conf = cat_conf ? ggml_concat(ctx0, cat_conf, conf, 1) : conf;
+            cat_conf = cat_conf ? ggml_concat(ctx0, cat_conf, conf, 1) : conf;
+        }

        if (i + 1 < block_drafts) {
            prev = ggml_argmax(ctx0, col);
@@ -376,7 +386,7 @@ static void build_dspark_markov_head(llm_graph_context & g, const llama_model &
    out = ggml_cont(ctx0, ggml_permute(ctx0, out, 0, 2, 1, 3)); // [n_vocab, block_drafts, n_blocks]
    out = ggml_reshape_2d(ctx0, out, n_vocab, n_tok);

-    {
+    if (has_conf) {
        ggml_tensor * conf = ggml_reshape_3d(ctx0, cat_conf, 1, n_blocks, block_drafts);
        conf = ggml_cont(ctx0, ggml_permute(ctx0, conf, 0, 2, 1, 3));
        conf = ggml_reshape_2d(ctx0, conf, 1, n_tok);
@@ -707,8 +717,8 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra

        // cache-aware, non-causal attention
        ggml_tensor * cur = use_iswa
-            ? build_attn(inp_attn_iswa, layer.wo, NULL, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il)
-            : build_attn(inp_attn,      layer.wo, NULL, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            ? build_attn(inp_attn_iswa, layer.wo, NULL, NULL, Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, nullptr, kq_scale, il)
+            : build_attn(inp_attn,      layer.wo, NULL, NULL, Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, nullptr, kq_scale, il);

        if (attn_dynamic) {
            cur = build_dflash2_conv(*this, cur, attn_dynamic, layer.dflash_attn_conv_base, 1);
@@ -163,6 +163,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | -------- | ----------- |
 | `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) |
 | `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) |
+| `--kv-unified-per-slot N` | context limit per parallel slot (default: unset, behavior unchanged).<br/>when set without -c/--ctx-size, the shared KV pool is sized to n_parallel*N<br/>(env: LLAMA_ARG_KV_UNIFIED_PER_SLOT) |
 | `-ctxcp, --ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 32)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
 | `-cms, --checkpoint-min-step N` | minimum spacing between context checkpoints in tokens (default: 8192, 0 = no minimum)<br/>(env: LLAMA_ARG_CHECKPOINT_MIN_SPACING_NT) |
 | `-cram, --cache-ram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
@@ -1208,10 +1208,31 @@ private:

        const int n_ctx_train = llama_model_n_ctx_train(model_tgt);

-        int n_ctx_slot = llama_n_ctx_seq(ctx_tgt);
-        if (n_ctx_slot > n_ctx_train) {
-            SRV_WRN("the slot context (%d) exceeds the training context of the model (%d) - capping\n", n_ctx_slot, n_ctx_train);
-            n_ctx_slot = n_ctx_train;
+        {
+            // note: the capping itself is done in n_ctx_slot(), here we only report it
+            const int n_ctx_seq = llama_n_ctx_seq(ctx_tgt);
+
+            if (params_base.kv_unified_per_slot > 0) {
+                if (n_ctx_seq > params_base.kv_unified_per_slot) {
+                    SRV_INF("capping per-slot context (%d) to --kv-unified-per-slot (%d)\n",
+                            n_ctx_seq, params_base.kv_unified_per_slot);
+                } else if (params_base.kv_unified_per_slot > n_ctx_seq) {
+                    // cap is above the per-slot pool capacity, so it can never bind
+                    SRV_WRN(
+                        "--kv-unified-per-slot (%d) exceeds the per-slot pool capacity (%d) - cap has no effect, "
+                        "slots are limited to %d (raise the KV pool with -c, or unset -c to size it to "
+                        "n_parallel * kv_unified_per_slot)\n",
+                        params_base.kv_unified_per_slot, n_ctx_seq, n_ctx_seq);
+                }
+            }
+
+            const int n_ctx_capped = params_base.kv_unified_per_slot > 0 ?
+                std::min(n_ctx_seq, params_base.kv_unified_per_slot) : n_ctx_seq;
+
+            if (n_ctx_capped > n_ctx_train) {
+                SRV_WRN("the slot context (%d) exceeds the training context of the model (%d) - capping\n",
+                        n_ctx_capped, n_ctx_train);
+            }
        }

        slots.clear();
@@ -1227,7 +1248,7 @@ private:

        // setup slots
        SRV_INF("initializing, n_slots = %d, n_ctx_slot = %d, kv_unified = '%s'\n",
-                params_base.n_parallel, n_ctx_slot, params_base.kv_unified ? "true" : "false");
+                params_base.n_parallel, n_ctx_slot(), params_base.kv_unified ? "true" : "false");

        // initialize slots
        for (int i = 0; i < params_base.n_parallel; i++) {
@@ -1271,7 +1292,7 @@ private:
            slot.ctx_dft = ctx_dft;
            slot.mem.init(ctx_tgt, ctx_dft);
            slot.spec    = spec.get();
-            slot.n_ctx   = n_ctx_slot;
+            slot.n_ctx   = n_ctx_slot();

            slot.mctx                   = mctx;
            slot.prompt.tokens.has_mtmd = mctx != nullptr;
@@ -3975,8 +3996,15 @@ private:
        });
    }

-    int get_slot_n_ctx() {
-        return slots.back().n_ctx;
+    // context size of a single slot, capped by --kv-unified-per-slot and by the training context of the model
+    int n_ctx_slot() const {
+        int res = llama_n_ctx_seq(ctx_tgt);
+
+        if (params_base.kv_unified_per_slot > 0) {
+            res = std::min(res, params_base.kv_unified_per_slot);
+        }
+
+        return std::min(res, llama_model_n_ctx_train(model_tgt));
    }

    server_response_reader get_response_reader() {
@@ -4142,7 +4170,7 @@ server_context_meta server_context::get_meta() const {
        /* has_inp_audio          */ impl->chat_params.allow_audio,
        /* has_inp_video          */ impl->chat_params.allow_video,
        /* json_ui_settings       */ impl->json_ui_settings,
-        /* slot_n_ctx             */ impl->get_slot_n_ctx(),
+        /* slot_n_ctx             */ impl->n_ctx_slot(),
        /* pooling_type           */ llama_pooling_type(impl->ctx_tgt),

        /* chat_params            */ impl->chat_params,
@@ -157,6 +157,18 @@ int llama_server(common_params & params, int argc, char ** argv) {
        }
    }

+    // size the KV pool from --kv-unified-per-slot, unless the user pinned it with -c
+    // or with -c 0 for max context
+    const bool ctx_pool_auto_sized = params.kv_unified_per_slot > 0 &&
+                                     params.n_ctx == 0 &&
+                                     (uint32_t) params.fit_params_min_ctx != UINT32_MAX;
+
+    if (ctx_pool_auto_sized) {
+        params.n_ctx = params.n_parallel * params.kv_unified_per_slot;
+        SRV_INF("--kv-unified-per-slot: sizing KV pool to n_parallel * kv_unified_per_slot = %d * %d = %d\n", params.n_parallel,
+                params.kv_unified_per_slot, params.n_ctx);
+    }
+
    // for consistency between server router mode and single-model mode, we set the same model name as alias
    auto model_name = params.model.get_name();
    if (params.model_alias.empty() && !model_name.empty()) {
Author	SHA1	Message	Date
Ruixiang Wang GitHub Sigbjørn Skjæret Xuan Son Nguyen	ca3d5a3e10	model: add DSpark support for Nemotron3.5 (#27804 ) * model: add DSpark support for Nemotron3.5 * Update src/models/dflash.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> Co-authored-by: Xuan Son Nguyen <son@huggingface.co>	2026-08-28 01:49:27 +02:00
cqderekandGitHub	e70802a01f	ggml-hexagon: add HTP unary ops for ABS and LOG (#27786 ) Add HVX-accelerated implementations for GGML_OP_LOG and GGML_UNARY_OP_ABS on the HTP backend. - Register HTP_OP_UNARY_ABS and HTP_OP_UNARY_LOG in op_remap_to_htp() - Add ABS and LOG to ggml_backend_hexagon_device_supports_op() - Implement hvx_abs_f32_aa() in hvx-arith.h using hvx_vec_abs_f32() - Implement hvx_log_f32_aa() in hvx-log.h using hvx_vec_log_f32() - Add abs_f32() and log_f32() row-wise dispatch in unary-ops.c - Define tiled and non-tiled task functions via DEFINE_UNARY_TASK and DEFINE_UNARY_TILED_TASK macros - Route HTP_OP_UNARY_ABS and HTP_OP_UNARY_LOG through execute_op() in main.c	2026-08-27 15:05:57 -07:00
Aparna M PandGitHub	83d855c5a6	hex-unary: fix RMS_NORM_MUL weight-offset bugs for grouped/broadcast norms (#27798 )	2026-08-27 14:38:02 -07:00
Bartowski GitHub Xuan Son Nguyen	18443257a3	server: add ctx-per-slot (--kv-unified-per-slot) (#24124 ) * Add ctx-per-slot argument for unifid KV cache * Swap out ctx fractions for ctx pool slots * Formatting cleanup * Remove ctx-pool-slots, make ctx-per-slot an int * refactor it --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>	2026-08-27 22:39:14 +02:00
Harkirat GillandGitHub	32176338a6	ci : build only the ggml-hip backend for windows-rocm release (#27753 )	2026-08-27 22:18:34 +02:00