Merge pull request #100 from menloresearch/update-dev-from-master-2025-05-25-00-09

Sync master with upstream release b5477

Author: jan-service-account

.github/workflows/release.yml

@@ -260,16 +260,18 @@ jobs:
           architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }}
 
       - name: Build
+        shell: cmd
         env:
           CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
         run: |
-          cmake -S . -B build -G "Ninja Multi-Config" `
-            -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake `
-            -DGGML_NATIVE=OFF `
-            -DGGML_BACKEND_DL=ON `
-            -DGGML_CPU_ALL_VARIANTS=ON `
-            -DGGML_OPENMP=OFF `
-            -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" `
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch }}
+          cmake -S . -B build -G "Ninja Multi-Config" ^
+            -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake ^
+            -DGGML_NATIVE=OFF ^
+            -DGGML_BACKEND_DL=ON ^
+            -DGGML_CPU_ALL_VARIANTS=${{ matrix.arch == 'x64' && 'ON' || 'OFF' }} ^
+            -DGGML_OPENMP=ON ^
+            -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^
             ${{ env.CMAKE_ARGS }}
           cmake --build build --config Release
 
@@ -279,6 +281,7 @@ jobs:
           CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
         run: |
           Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\
+          Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.42.34433\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
           7z a llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
 
       - name: Upload artifacts

.github/workflows/winget.yml

@@ -0,0 +1,42 @@
+name: Update Winget Package
+
+on:
+  workflow_dispatch: # allows manual triggering
+  schedule:
+    - cron: '28 5 * * *' # Update every day at 5:28 UTC
+
+jobs:
+  update:
+    name: Update Winget Package
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Install cargo binstall
+        uses: cargo-bins/cargo-binstall@268643a6b5ea099f5718ee5cd3ff7dc89a5eb49b
+
+      - name: Install komac
+        run: |
+          cargo binstall [email protected] -y
+
+      - name: Find latest release
+        id: find_latest_release
+        uses: actions/github-script@v6
+        with:
+          script: |
+            const { data: releases } = await github.rest.repos.listReleases({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+            });
+            console.log("Latest release:", releases[0].tag_name);
+            return releases[0].tag_name;
+
+      - name: Update manifest
+        env:
+          VERSION: ${{ steps.find_latest_release.outputs.result }}
+        run: |
+          echo "Updating manifest..."
+          komac update --version ${{ env.VERSION }} \
+            --urls "https://github.com/ggml-org/llama.cpp/releases/download/${{ env.VERSION }}/llama-${{ env.VERSION }}-bin-win-vulkan-x64.zip" \
+            --token ${{ secrets.WINGET_GITHUB_TOKEN }} \
+            --submit \
+            ggml.llamacpp

ggml/src/ggml-cpu/ggml-cpu.c

@@ -3484,6 +3484,19 @@ void ggml_cpu_init(void) {
             const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
 
             GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);
+
+#ifdef GGML_USE_OPENMP
+            //if (!getenv("OMP_WAIT_POLICY")) {
+            //    // set the wait policy to active, so that OpenMP threads don't sleep
+            //    putenv("OMP_WAIT_POLICY=active");
+            //}
+
+            if (!getenv("KMP_BLOCKTIME")) {
+                // set the time to wait before sleeping a thread
+                // this is less aggressive than setting the wait policy to active, but should achieve similar results in most cases
+                putenv("KMP_BLOCKTIME=200"); // 200ms
+            }
+#endif
         }
 
 #if defined(__ARM_ARCH)

ggml/src/ggml-cuda/fattn-vec-f16.cuh

@@ -212,6 +212,7 @@ static __global__ void flash_attn_vec_ext_f16(
                 }
             }
             if (__all_sync(0xFFFFFFFF, skip)) {
+                __syncthreads();
                 continue;
             }
 #endif // GGML_USE_HIP

ggml/src/ggml-cuda/fattn-vec-f32.cuh

@@ -217,6 +217,7 @@ static __global__ void flash_attn_vec_ext_f32(
                 }
             }
             if (__all_sync(0xFFFFFFFF, skip)) {
+                __syncthreads();
                 continue;
             }
 #endif // GGML_USE_HIP

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -2192,6 +2192,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                 case GGML_UNARY_OP_SILU:
                     ggml_cuda_op_silu(ctx, dst);
                     break;
+                case GGML_UNARY_OP_GELU_ERF:
+                    ggml_cuda_op_gelu_erf(ctx, dst);
+                    break;
                 case GGML_UNARY_OP_GELU_QUICK:
                     ggml_cuda_op_gelu_quick(ctx, dst);
                     break;
@@ -2977,6 +2980,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_SIGMOID:
                 case GGML_UNARY_OP_HARDSIGMOID:
                 case GGML_UNARY_OP_HARDSWISH:
+                case GGML_UNARY_OP_GELU_ERF:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_EXP:

ggml/src/ggml-cuda/unary.cu

@@ -23,6 +23,12 @@ static __device__ __forceinline__ float op_gelu(float x) {
     return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
 }
 
+static __device__ __forceinline__ float op_gelu_erf(float x) {
+    const float SQRT_2_INV = 0.70710678118654752440084436210484f;
+
+    return 0.5f*x*(1.0f + erff(x*SQRT_2_INV));
+}
+
 static __device__ __forceinline__ float op_gelu_quick(float x) {
     const float GELU_QUICK_COEF = -1.702f;
 
@@ -134,6 +140,10 @@ void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ggml_cuda_op_unary<op_gelu>(ctx, dst);
 }
 
+void ggml_cuda_op_gelu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_unary<op_gelu_erf>(ctx, dst);
+}
+
 void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ggml_cuda_op_unary<op_gelu_quick>(ctx, dst);
 }

ggml/src/ggml-cuda/unary.cuh

@@ -30,6 +30,8 @@ void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_silu_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
+void ggml_cuda_op_gelu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
 void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

src/llama-graph.cpp

@@ -1287,6 +1287,10 @@ ggml_tensor * llm_graph_context::build_attn(
 
     if (wo) {
         cur = build_lora_mm(wo, cur);
+        if (arch == LLM_ARCH_GLM4) {
+            // GLM4 seems to have numerical issues with half-precision accumulators
+            ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
+        }
     }
 
     if (wo_b) {
@@ -1367,10 +1371,6 @@ ggml_tensor * llm_graph_context::build_attn(
 
     if (wo) {
         cur = build_lora_mm(wo, cur);
-        if (arch == LLM_ARCH_GLM4) {
-            // GLM4 seems to have numerical issues with half-precision accumulators
-            ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
-        }
     }
 
     if (wo_b) {

src/llama-vocab.cpp

@@ -835,7 +835,7 @@ struct llm_tokenizer_ugm_session {
         }
 
         // initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
-        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.token_unk(), 0, -FLT_MAX});
+        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.token_unk(), 0, -DBL_MAX});
         // at the beginning tokenization score is zero
         tokenization_results[0] = { vocab.token_unk(), 0, 0 };
 
@@ -867,7 +867,7 @@ struct llm_tokenizer_ugm_session {
                     const double challenger_score = current_best.score_sum + token_score;
                     struct best_tokenization & current_champ = tokenization_results[prefix_offset];
                     if (challenger_score > current_champ.score_sum) {
-                        struct best_tokenization challenger = { token_id, input_offset, (float) challenger_score };
+                        struct best_tokenization challenger = { token_id, input_offset, challenger_score };
                         current_champ = challenger;
                     }
                 }
@@ -881,7 +881,7 @@ struct llm_tokenizer_ugm_session {
                 prefix_offset = input_offset + n_utf8_code_units;
                 struct best_tokenization & current_champ = tokenization_results[prefix_offset];
                 if (challenger_score > current_champ.score_sum) {
-                    struct best_tokenization challenger = { vocab.token_unk(), input_offset, (float) challenger_score };
+                    struct best_tokenization challenger = { vocab.token_unk(), input_offset, challenger_score };
                     current_champ = challenger;
                 }
             }
@@ -1007,7 +1007,7 @@ struct llm_tokenizer_ugm_session {
     struct best_tokenization {
         llama_token token_id;
         size_t input_offset;
-        float score_sum;
+        double score_sum;
     };
 
     struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {