Merge pull request #242 from menloresearch/update-dev-from-master-2025-09-05-00-33

Sync master with upstream release b6387

Author: jan-service-account

common/arg.cpp

@@ -2466,7 +2466,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
-        "number of layers to store in VRAM",
+        string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers),
         [](common_params & params, int value) {
             params.n_gpu_layers = value;
             if (!llama_supports_gpu_offload()) {

common/chat.cpp

@@ -623,6 +623,7 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
         case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
         case COMMON_CHAT_FORMAT_SEED_OSS: return "Seed-OSS";
+        case COMMON_CHAT_FORMAT_NEMOTRON_V2: return "Nemotron V2";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -1184,6 +1185,67 @@ static common_chat_params common_chat_params_init_llama_3_x(const common_chat_te
     });
     return data;
 }
+
+static common_chat_params common_chat_params_init_nemotron_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    // Generate the prompt using the apply() function with the template
+    data.prompt = apply(tmpl, inputs);
+    data.format = COMMON_CHAT_FORMAT_NEMOTRON_V2;
+
+    // Handle thinking tags appropriately based on inputs.enable_thinking
+    if (string_ends_with(data.prompt, "<think>\n")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    // When tools are present, build grammar for the <TOOLCALL> format, similar to CommandR, but without tool call ID
+    if (!inputs.tools.is_null() && inputs.tools.is_array() && !inputs.tools.empty()) {
+        data.grammar_lazy = true;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            auto schemas = json::array();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                schemas.push_back({
+                    { "type",       "object"                                                   },
+                    { "properties",
+                        {
+                            { "name",
+                            {
+                                { "type", "string" },
+                                { "const", function.at("name") },
+                            } },
+                            { "arguments", function.at("parameters") },
+                        }                                                                        },
+                    { "required",   json::array({ "name", "arguments" }) },
+                });
+            });
+            auto schema = json{
+                        { "type",     "array"                                                         },
+                        { "items",    schemas.size() == 1 ? schemas[0] : json{ { "anyOf", schemas } } },
+                        { "minItems", 1                                                               },
+            };
+            if (!inputs.parallel_tool_calls) {
+                schema["maxItems"] = 1;
+            }
+            builder.add_rule("root",
+                                std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
+                                    "\"<TOOLCALL>\" " + builder.add_schema("tool_calls", schema) +
+                                    " \"</TOOLCALL>\"");
+        });
+        data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
+            // If thinking_forced_open, then we capture the </think> tag in the grammar,
+            // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
+            std::string(data.thinking_forced_open ?
+                            "[\\s\\S]*?(</think>\\s*)" :
+                            "(?:<think>[\\s\\S]*?</think>\\s*)?") +
+                "(<TOOLCALL>)[\\s\\S]*" });
+    }
+    return data;
+}
 static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) {
     if (!builder.syntax().parse_tool_calls) {
         builder.add_content(builder.consume_rest());
@@ -1830,7 +1892,7 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
                 // If thinking_forced_open, then we capture the </think> tag in the grammar,
                 // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
                 std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)" : "(?:<think>[\\s\\S]*?</think>\\s*)?") + (
-                    "(\\s*"
+                    "\\s*("
                     "(?:<tool_call>"
                     "|<function"
                     "|(?:```(?:json|xml)?\n\\s*)?(?:<function_call>|<tools>|<xml><json>|<response>)?"
@@ -2060,6 +2122,33 @@ static void common_chat_parse_granite(common_chat_msg_parser & builder) {
     }
 }
 
+static void common_chat_parse_nemotron_v2(common_chat_msg_parser & builder) {
+    // Parse thinking tags
+    builder.try_parse_reasoning("<think>", "</think>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // Look for tool calls
+    static const common_regex tool_call_regex(regex_escape("<TOOLCALL>"));
+    if (auto res = builder.try_find_regex(tool_call_regex)) {
+        builder.move_to(res->groups[0].end);
+
+        // Expect JSON array of tool calls
+        auto tool_calls_data = builder.consume_json();
+        if (tool_calls_data.json.is_array()) {
+            if (!builder.try_consume_literal("</TOOLCALL>")) {
+                throw common_chat_msg_partial_exception("Incomplete tool call");
+            }
+            builder.add_tool_calls(tool_calls_data.json);
+        } else {
+            throw common_chat_msg_partial_exception("Incomplete tool call");
+        }
+    }
+    builder.add_content(builder.consume_rest());
+}
+
 static void common_chat_parse_seed_oss(common_chat_msg_parser & builder) {
     // Parse thinking tags first - this handles the main reasoning content
     builder.try_parse_reasoning("<seed:think>", "</seed:think>");
@@ -2293,6 +2382,11 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_seed_oss(tmpl, params, inputs);
     }
 
+    // Nemotron v2
+    if (src.find("<SPECIAL_10>") != std::string::npos) {
+        return common_chat_params_init_nemotron_v2(tmpl, params);
+    }
+
     // Use generic handler when mixing tools + JSON schema.
     // TODO: support that mix in handlers below.
     if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -2454,6 +2548,9 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_SEED_OSS:
             common_chat_parse_seed_oss(builder);
             break;
+        case COMMON_CHAT_FORMAT_NEMOTRON_V2:
+            common_chat_parse_nemotron_v2(builder);
+            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }

common/chat.h

@@ -112,6 +112,7 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_GRANITE,
     COMMON_CHAT_FORMAT_GPT_OSS,
     COMMON_CHAT_FORMAT_SEED_OSS,
+    COMMON_CHAT_FORMAT_NEMOTRON_V2,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };

convert_hf_to_gguf.py

@@ -5122,6 +5122,15 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("Gemma3TextModel")
+class EmbeddingGemma(Gemma3Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self._try_set_pooling_type()
+
+
 @ModelBase.register("Gemma3ForConditionalGeneration")
 class Gemma3VisionModel(MmprojModel):
     def set_gguf_parameters(self):

docs/backend/CANN.md

@@ -293,17 +293,14 @@ We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers fr
 
 ## Environment variable setup
 
-### GGML_CANN_ASYNC_MODE
-
-Enables asynchronous operator submission. Disabled by default.
-
 ### GGML_CANN_MEM_POOL
 
-Specifies the memory pool management strategy:
+Specifies the memory pool management strategy, Default is vmm.
 
 - vmm: Utilizes a virtual memory manager pool. If hardware support for VMM is unavailable, falls back to the legacy (leg) memory pool.
 
 - prio: Employs a priority queue-based memory pool management.
+
 - leg: Uses a fixed-size buffer pool.
 
 ### GGML_CANN_DISABLE_BUF_POOL_CLEAN
@@ -312,9 +309,8 @@ Controls automatic cleanup of the memory pool. This option is only effective whe
 
 ### GGML_CANN_WEIGHT_NZ
 
-Converting the matmul weight format from ND to NZ can significantly improve performance on the 310I DUO NPU.
+Converting the matmul weight format from ND to NZ to improve performance. Enabled by default.
 
-### GGML_CANN_DISABLE_ACL_GRAPH
+### GGML_CANN_ACL_GRAPH
 
-When this variable is set, ACL graph execution is disabled and operators are executed in an op-by-op (eager) mode.
-This mode is mainly intended for debugging or for cases where the overhead of graph construction and execution is not desirable.
+Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default.

ggml/include/ggml.h

@@ -511,6 +511,7 @@ extern "C" {
         GGML_OP_CONV_TRANSPOSE_1D,
         GGML_OP_IM2COL,
         GGML_OP_IM2COL_BACK,
+        GGML_OP_IM2COL_3D,
         GGML_OP_CONV_2D,
         GGML_OP_CONV_3D,
         GGML_OP_CONV_2D_DW,
@@ -1870,6 +1871,41 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
+    GGML_API struct ggml_tensor * ggml_im2col_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int64_t               IC,
+            int                   s0, // stride width
+            int                   s1, // stride height
+            int                   s2, // stride depth
+            int                   p0, // padding width
+            int                   p1, // padding height
+            int                   p2, // padding depth
+            int                   d0, // dilation width
+            int                   d1, // dilation height
+            int                   d2, // dilation depth
+            enum ggml_type        dst_type);
+
+    // a: [OC*IC, KD, KH, KW]
+    // b: [N*IC, ID, IH, IW]
+    // result: [N*OC, OD, OH, OW]
+    GGML_API struct ggml_tensor * ggml_conv_3d(
+                struct ggml_context * ctx,
+                struct ggml_tensor  * a,
+                struct ggml_tensor  * b,
+                int64_t               IC,
+                int                   s0, // stride width
+                int                   s1, // stride height
+                int                   s2, // stride depth
+                int                   p0, // padding width
+                int                   p1, // padding height
+                int                   p2, // padding depth
+                int                   d0, // dilation width
+                int                   d1, // dilation height
+                int                   d2  // dilation depth
+        );
+
     // kernel size is a->ne[0] x a->ne[1]
     // stride is equal to kernel size
     // padding is zero
@@ -1941,7 +1977,7 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
-    GGML_API struct ggml_tensor * ggml_conv_3d(
+    GGML_API struct ggml_tensor * ggml_conv_3d_direct(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,   // kernel [KW, KH, KD, IC * OC]
             struct ggml_tensor  * b,   // input  [W, H, D, C * N]
@@ -2048,6 +2084,19 @@ extern "C" {
             int                  p2,
             int                  p3);
 
+    GGML_API struct ggml_tensor * ggml_pad_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                  lp0,
+            int                  rp0,
+            int                  lp1,
+            int                  rp1,
+            int                  lp2,
+            int                  rp2,
+            int                  lp3,
+            int                  rp3
+            );
+
     // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
     GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
             struct ggml_context * ctx,

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -589,9 +589,16 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     // the position of elements in the array means which dirction to padding,
     // each position means: [dim0.front, dim0.behind, dim1.front, dim1.behind,
     //                       dim2.front, dim2.behind, dim3.front, dim3.behind]
-    int64_t paddings[] = {
-        0, dst->ne[0] - src->ne[0], 0, dst->ne[1] - src->ne[1],
-        0, dst->ne[2] - src->ne[2], 0, dst->ne[3] - src->ne[3]};
+    const int32_t lp0 = ggml_get_op_params_i32(dst, 0);
+    const int32_t rp0 = ggml_get_op_params_i32(dst, 1);
+    const int32_t lp1 = ggml_get_op_params_i32(dst, 2);
+    const int32_t rp1 = ggml_get_op_params_i32(dst, 3);
+    const int32_t lp2 = ggml_get_op_params_i32(dst, 4);
+    const int32_t rp2 = ggml_get_op_params_i32(dst, 5);
+    const int32_t lp3 = ggml_get_op_params_i32(dst, 6);
+    const int32_t rp3 = ggml_get_op_params_i32(dst, 7);
+
+    int64_t paddings[] = {lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3};
     aclnn_pad(ctx, acl_src, acl_dst, paddings);
     ggml_cann_release_resources(ctx, acl_src, acl_dst);
 }
@@ -975,18 +982,19 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     );
 
     // build rstd, zero...
-    size_t acl_rstd_nb[GGML_MAX_DIMS];
+    int64_t acl_rstd_ne[] = {src->ne[1], src->ne[2], src->ne[3]};
+    size_t acl_rstd_nb[GGML_MAX_DIMS - 1];
     acl_rstd_nb[0] = sizeof(float);
-    for (int i = 1; i < GGML_MAX_DIMS; i++) {
-        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * src->ne[i - 1];
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * acl_rstd_ne[i - 1];
     }
     aclTensor* acl_rstd = get_f32_cache_acl_tensor(
         ctx,
         &ctx.rms_norm_zero_tensor_cache.cache,
         ctx.rms_norm_zero_tensor_cache.size,
-        src->ne,
+        acl_rstd_ne,
         acl_rstd_nb,
-        GGML_MAX_DIMS,
+        GGML_MAX_DIMS - 1,
         0.0f      // value
     );
 
@@ -1955,7 +1963,7 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
     aclTensor* acl_weight_tensor;
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
     if (weight_to_nz && is_matmul_weight(weight)) {
         int64_t acl_stride[2] = {1, transpose_ne[1]};
 

ggml/src/ggml-cann/common.h

@@ -420,7 +420,7 @@ struct ggml_backend_cann_context {
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
 #ifdef USE_ACL_GRAPH
-        acl_graph_mode = !(parse_bool(get_env("GGML_CANN_DISABLE_ACL_GRAPH").value_or("")));
+        acl_graph_mode = parse_bool(get_env("GGML_CANN_ACL_GRAPH").value_or("on"));
         GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n",
               __func__, device,
               acl_graph_mode ? "GRAPH" : "EAGER",