Sync master with upstream release b8559

.devops/cann.Dockerfile

@@ -4,7 +4,7 @@
 
 # Define the CANN base image for easier version updates later
 ARG CHIP_TYPE=910b
-ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.11
+ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.5.0-${CHIP_TYPE}-openeuler24.03-py3.11
 
 # ==============================================================================
 # BUILD STAGE

.devops/llama-cli-cann.Dockerfile

@@ -1,4 +1,4 @@
-ARG ASCEND_VERSION=8.1.RC1.alpha001-910b-openeuler22.03-py3.10
+ARG ASCEND_VERSION=8.5.0-910b-openeuler22.03-py3.10
 
 FROM ascendai/cann:$ASCEND_VERSION AS build
 

.devops/nix/package.nix

@@ -41,6 +41,7 @@
   effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
   enableStatic ? effectiveStdenv.hostPlatform.isStatic,
   precompileMetalShaders ? false,
+  useWebUi ? true,
 }:
 
 let
@@ -164,6 +165,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
   cmakeFlags =
     [
       (cmakeBool "LLAMA_BUILD_SERVER" true)
+      (cmakeBool "LLAMA_BUILD_WEBUI" useWebUi)
       (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
       (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
       (cmakeBool "GGML_NATIVE" false)

.github/workflows/build-cann.yml

@@ -63,7 +63,7 @@ jobs:
       - name: Set container image
         id: cann-image
         run: |
-          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.5.0-910b-openeuler24.03-py3.11' || '8.5.0-310p-openeuler24.03-py3.11' }}"
           echo "image=${image}" >> "${GITHUB_OUTPUT}"
 
       - name: Pull container image

.github/workflows/release.yml

@@ -907,7 +907,7 @@ jobs:
       - name: Set container image
         id: cann-image
         run: |
-          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.5.0-910b-openeuler24.03-py3.11' || '8.5.0-310p-openeuler24.03-py3.11' }}"
           echo "image=${image}" >> "${GITHUB_OUTPUT}"
 
       - name: Pull container image

CMakeLists.txt

@@ -108,6 +108,7 @@ option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_WEBUI    "llama: build the embedded Web UI for server"  ON)
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
 option(LLAMA_TESTS_INSTALL  "llama: install tests"        ON)
 

common/arg.cpp

@@ -1079,7 +1079,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.verbose_prompt = true;
         }
-    ));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL}));
     add_opt(common_arg(
         {"--display-prompt"},
         {"--no-display-prompt"},
@@ -2843,6 +2843,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.webui_mcp_proxy = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_MCP_PROXY"));
+    add_opt(common_arg(
+        {"--tools"}, "TOOL1,TOOL2,...",
+        "experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)\n"
+        "specify \"all\" to enable all tools\n"
+        "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff",
+        [](common_params & params, const std::string & value) {
+            params.server_tools = parse_csv_row(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
     add_opt(common_arg(
         {"--webui"},
         {"--no-webui"},

common/common.h

@@ -613,6 +613,9 @@ struct common_params {
     bool endpoint_props   = false; // only control POST requests, not GET
     bool endpoint_metrics = false;
 
+    // enable built-in tools
+    std::vector<std::string> server_tools;
+
     // router server configs
     std::string models_dir    = ""; // directory containing models for the router server
     std::string models_preset = ""; // directory containing model presets for the router server

common/reasoning-budget.cpp

@@ -115,9 +115,11 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
             break;
         }
         case REASONING_BUDGET_FORCING:
-            // force_pos is advanced in apply(), not here.
-            // This ensures the first forced token isn't skipped when the sampler
-            // is initialized directly in FORCING state (e.g. COUNTING + budget=0)
+            ctx->force_pos++;
+            if (ctx->force_pos >= ctx->forced_tokens.size()) {
+                ctx->state = REASONING_BUDGET_DONE;
+                LOG_INF("reasoning-budget: forced sequence complete, done\n");
+            }
             break;
         case REASONING_BUDGET_DONE:
             break;
@@ -144,14 +146,6 @@ static void common_reasoning_budget_apply(struct llama_sampler * smpl, llama_tok
             cur_p->data[i].logit = -INFINITY;
         }
     }
-
-    // advance to next forced token (done here rather than in accept so that
-    // the first forced token isn't skipped when starting in FORCING state)
-    ctx->force_pos++;
-    if (ctx->force_pos >= ctx->forced_tokens.size()) {
-        ctx->state = REASONING_BUDGET_DONE;
-        LOG_INF("reasoning-budget: forced sequence complete, done\n");
-    }
 }
 
 static void common_reasoning_budget_reset(struct llama_sampler * smpl) {
@@ -261,3 +255,10 @@ struct llama_sampler * common_reasoning_budget_init(
         common_reasoning_budget_state    initial_state) {
     return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state);
 }
+
+common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl) {
+    if (!smpl) {
+        return REASONING_BUDGET_IDLE;
+    }
+    return ((const common_reasoning_budget_ctx *)smpl->ctx)->state;
+}

common/reasoning-budget.h

@@ -51,3 +51,5 @@ struct llama_sampler * common_reasoning_budget_init(
         const std::vector<llama_token> & forced_tokens,
         int32_t                          budget,
         common_reasoning_budget_state    initial_state);
+
+common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl);

common/sampling.cpp

@@ -7,6 +7,7 @@
 
 #include <algorithm>
 #include <cctype>
+#include <climits>
 #include <cmath>
 #include <cstring>
 #include <unordered_map>
@@ -109,6 +110,7 @@ struct common_sampler {
     common_params_sampling params;
 
     struct llama_sampler * grmr;
+    struct llama_sampler * rbudget;
     struct llama_sampler * chain;
 
     ring_buffer<llama_token> prev;
@@ -188,6 +190,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
     lparams.no_perf = params.no_perf;
 
     llama_sampler * grmr = nullptr;
+    llama_sampler * rbudget = nullptr;
     llama_sampler * chain = llama_sampler_chain_init(lparams);
 
     std::vector<llama_sampler *> samplers;
@@ -270,7 +273,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
             }
         }
 
-        if (grmr) {
+        if (grmr && !params.grammar_lazy) {
             try {
                 for (const auto & token : prefill_tokens) {
                     llama_sampler_accept(grmr, token);
@@ -284,15 +287,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
         }
     }
 
-    // reasoning budget sampler — added first so it can force tokens before other samplers
-    if (params.reasoning_budget_tokens >= 0 && !params.reasoning_budget_forced.empty()) {
-        samplers.push_back(common_reasoning_budget_init(
+    // reasoning budget sampler
+    if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty()) {
+        rbudget = common_reasoning_budget_init(
             vocab,
             params.reasoning_budget_start,
             params.reasoning_budget_end,
             params.reasoning_budget_forced,
-            params.reasoning_budget_tokens,
-            prefill_tokens));
+            params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens,
+            prefill_tokens);
     }
 
     if (params.has_logit_bias()) {
@@ -383,6 +386,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
     auto * result = new common_sampler {
         /* .params  = */ params,
         /* .grmr    = */ grmr,
+        /* .rbudget = */ rbudget,
         /* .chain   = */ chain,
         /* .prev    = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur     = */ {},
@@ -398,18 +402,39 @@ void common_sampler_free(struct common_sampler * gsmpl) {
     }
 
     llama_sampler_free(gsmpl->grmr);
+    llama_sampler_free(gsmpl->rbudget);
     llama_sampler_free(gsmpl->chain);
 
     delete gsmpl;
 }
 
+static bool grammar_should_apply(struct common_sampler * gsmpl) {
+    if (!gsmpl->grmr) {
+        return false;
+    }
+    if (!gsmpl->rbudget) {
+        return true;
+    }
+    if (gsmpl->params.grammar_lazy) {
+        // if grammar is lazy, only apply when reasoning budget is not active
+        const auto state = common_reasoning_budget_get_state(gsmpl->rbudget);
+        return state == REASONING_BUDGET_IDLE || state == REASONING_BUDGET_DONE;
+    }
+    return true;
+}
+
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
     if (!gsmpl) {
         return;
     }
 
     const auto tm = gsmpl->tm();
 
+    // grammar_should_apply() checks the reasoning budget state, so calculate this before we accept
+    accept_grammar = accept_grammar && grammar_should_apply(gsmpl);
+
+    llama_sampler_accept(gsmpl->rbudget, token);
+
     if (gsmpl->grmr && accept_grammar) {
         llama_sampler_accept(gsmpl->grmr, token);
     }
@@ -431,6 +456,7 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
     return new common_sampler {
         /* .params  = */ gsmpl->params,
         /* .grmr    = */ llama_sampler_clone(gsmpl->grmr),
+        /* .rbudget = */ llama_sampler_clone(gsmpl->rbudget),
         /* .chain   = */ llama_sampler_clone(gsmpl->chain),
         /* .prev    = */ gsmpl->prev,
         /* .cur     = */ gsmpl->cur,
@@ -500,6 +526,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     llama_token id = LLAMA_TOKEN_NULL;
 
     auto & grmr  = gsmpl->grmr;
+    auto & rbudget = gsmpl->rbudget;
     auto & chain = gsmpl->chain;
     auto & cur_p = gsmpl->cur_p; // initialized by set_logits
 
@@ -511,7 +538,8 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
         if (id != LLAMA_TOKEN_NULL) {
             LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);
 
-            GGML_ASSERT(!gsmpl->grmr && "using grammar in combination with backend sampling is not supported");
+            GGML_ASSERT(!gsmpl->grmr    && "using grammar in combination with backend sampling is not supported");
+            GGML_ASSERT(!gsmpl->rbudget && "using reasoning budget in combination with backend sampling is not supported");
 
             // TODO: simplify
             gsmpl->cur.resize(1);
@@ -524,15 +552,18 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
 
     gsmpl->set_logits(ctx, idx);
 
-    if (grammar_first) {
+    // apply reasoning budget first
+    llama_sampler_apply(rbudget, &cur_p);
+
+    if (grammar_first && grammar_should_apply(gsmpl)) {
         llama_sampler_apply(grmr, &cur_p);
     }
 
     llama_sampler_apply(chain, &cur_p);
 
     id = cur_p.data[cur_p.selected].id;
 
-    if (grammar_first) {
+    if (grammar_first || !grammar_should_apply(gsmpl)) {
         return id;
     }
 
@@ -553,7 +584,12 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
     gsmpl->set_logits(ctx, idx);
 
-    llama_sampler_apply(grmr,  &cur_p);
+    llama_sampler_apply(rbudget,  &cur_p);
+
+    if (grammar_should_apply(gsmpl)) {
+        llama_sampler_apply(grmr,  &cur_p);
+    }
+
     llama_sampler_apply(chain, &cur_p);
 
     GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");

convert_hf_to_gguf.py

@@ -7150,6 +7150,8 @@ def tensor_force_quant(self, name, new_name, bid, n_dims):
             return gguf.GGMLQuantizationType.F32
         if ".rel_pos_h" in name or '.rel_pos_w' in name:
             return gguf.GGMLQuantizationType.F32
+        if ".neck." in name or ".net_" in name:
+            return gguf.GGMLQuantizationType.F32
         return super().tensor_force_quant(name, new_name, bid, n_dims)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: