Merge branch 'ggml-org:master' into master

Author: apicalshark

.github/workflows/build.yml

@@ -374,6 +374,8 @@ jobs:
       - name: Clone
         id: checkout
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
       - name: ccache
         uses: hendrikmuhs/[email protected]
@@ -1373,8 +1375,10 @@ jobs:
 
     needs:
       - ubuntu-cpu-cmake
+      - ubuntu-22-cmake-vulkan
       - windows-latest-cmake
       - windows-2019-cmake-cuda
+      - windows-latest-cmake-sycl
       - windows-latest-cmake-hip-release
       - macOS-latest-cmake-arm64
       - macOS-latest-cmake-x64

.gitignore

@@ -98,6 +98,7 @@ examples/server/*.css.hpp
 examples/server/*.html.hpp
 examples/server/*.js.hpp
 examples/server/*.mjs.hpp
+examples/server/*.gz.hpp
 !build_64.sh
 !examples/*.bat
 !examples/*/*.kts

CONTRIBUTING.md

@@ -1,10 +1,12 @@
 # Pull requests (for contributors)
 
+- llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
 - Test your changes:
     - Execute [the full CI locally on your machine](ci/README.md) before publishing
     - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
     - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
     - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
+- Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
 - If your PR becomes stale, don't hesitate to ping the maintainers in the comments
 

Makefile

@@ -847,7 +847,7 @@ ifdef GGML_MUSA
 	CXX := $(MUSA_PATH)/bin/clang++
 	MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc
 
-	MUSAFLAGS  = -x musa -mtgpu
+	MUSAFLAGS  = -fsigned-char -x musa -mtgpu
 	MUSAFLAGS += $(foreach arch,$(subst ;, ,$(MUSA_ARCHITECTURES)),--cuda-gpu-arch=mp_$(arch))
 
 ifdef GGML_CUDA_FORCE_MMQ
@@ -1364,7 +1364,7 @@ llama-server: \
 	examples/server/index.html.hpp \
 	examples/server/loading.html.hpp \
 	common/chat.cpp \
-	common/chat.hpp \
+	common/chat.h \
 	common/chat-template.hpp \
 	common/json.hpp \
 	common/minja.hpp \

common/CMakeLists.txt

@@ -57,8 +57,7 @@ add_library(${TARGET} STATIC
     arg.h
     base64.hpp
     chat.cpp
-    chat.hpp
-    chat-template.hpp
+    chat.h
     common.cpp
     common.h
     console.cpp
@@ -68,7 +67,8 @@ add_library(${TARGET} STATIC
     llguidance.cpp
     log.cpp
     log.h
-    minja.hpp
+    minja/chat-template.hpp
+    minja/minja.hpp
     ngram-cache.cpp
     ngram-cache.h
     sampling.cpp

common/arg.cpp

@@ -2,6 +2,7 @@
 
 #include "log.h"
 #include "sampling.h"
+#include "chat.h"
 
 #include <algorithm>
 #include <climits>
@@ -2501,5 +2502,53 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
 
+    add_opt(common_arg(
+        {"--fim-qwen-1.5b-default"},
+        string_format("use default Qwen 2.5 Coder 1.5B (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-Q8_0-GGUF";
+            params.hf_file = "qwen2.5-coder-1.5b-q8_0.gguf";
+            params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
+            params.n_ubatch = 1024;
+            params.n_batch = 1024;
+            params.n_ctx = 0;
+            params.n_cache_reuse = 256;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+
+    add_opt(common_arg(
+        {"--fim-qwen-3b-default"},
+        string_format("use default Qwen 2.5 Coder 3B (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/Qwen2.5-Coder-3B-Q8_0-GGUF";
+            params.hf_file = "qwen2.5-coder-3b-q8_0.gguf";
+            params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
+            params.n_ubatch = 1024;
+            params.n_batch = 1024;
+            params.n_ctx = 0;
+            params.n_cache_reuse = 256;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+
+    add_opt(common_arg(
+        {"--fim-qwen-7b-default"},
+        string_format("use default Qwen 2.5 Coder 7B (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF";
+            params.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
+            params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
+            params.n_ubatch = 1024;
+            params.n_batch = 1024;
+            params.n_ctx = 0;
+            params.n_cache_reuse = 256;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+
     return ctx_arg;
 }