Merge remote-tracking branch 'ggerganov/master' into nsigma

Author: oobabooga

.github/workflows/build.yml

@@ -771,7 +771,7 @@ jobs:
         uses: hendrikmuhs/[email protected]
         with:
           key: windows-msys2
-          variant: sccache
+          variant: ccache
           evict-old-files: 1d
 
       - name: Setup ${{ matrix.sys }}
@@ -814,26 +814,18 @@ jobs:
     strategy:
       matrix:
         include:
-          - build: 'noavx-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF'
-          - build: 'avx2-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON'
-          - build: 'avx-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF'
-          - build: 'avx512-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON'
+          - build: 'cpu-x64'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF'
           - build: 'openblas-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
-          - build: 'kompute-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
           - build: 'vulkan-x64'
-            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON'
+            defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON'
           - build: 'llvm-arm64'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
-          - build: 'msvc-arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
           - build: 'llvm-arm64-opencl-adreno'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
+         # - build: 'kompute-x64'
+         #   defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON'
 
     steps:
       - name: Clone
@@ -846,7 +838,7 @@ jobs:
         uses: hendrikmuhs/[email protected]
         with:
           key: windows-latest-cmake-${{ matrix.build }}
-          variant: sccache
+          variant: ccache
           evict-old-files: 1d
 
       - name: Clone Kompute submodule
@@ -922,39 +914,26 @@ jobs:
           cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll
           cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt
 
-      - name: Check AVX512F support
-        id: check_avx512f
-        if: ${{ matrix.build == 'avx512-x64' }}
-        continue-on-error: true
-        run: |
-          cd build
-          $vcdir = $(vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath)
-          $msvc = $(join-path $vcdir $('VC\Tools\MSVC\'+$(gc -raw $(join-path $vcdir 'VC\Auxiliary\Build\Microsoft.VCToolsVersion.default.txt')).Trim()))
-          $cl =  $(join-path $msvc 'bin\Hostx64\x64\cl.exe')
-          echo 'int main(void){unsigned int a[4];__cpuid(a,7);return !(a[1]&65536);}' >> avx512f.c
-          & $cl /O2 /GS- /kernel avx512f.c /link /nodefaultlib /entry:main
-          .\avx512f.exe && echo "AVX512F: YES" && ( echo HAS_AVX512F=1 >> $env:GITHUB_ENV ) || echo "AVX512F: NO"
-
       - name: Test
         id: cmake_test
-        # not all machines have native AVX-512
-        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900
 
-      - name: Test (Intel SDE)
-        id: cmake_test_sde
-        if: ${{ matrix.build == 'avx512-x64' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation
-        run: |
-          curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz"
-          # for some weird reason windows tar doesn't like sde tar.xz
-          7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz
-          7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar
-          $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe)
-          cd build
-          $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1
-          & $sde -future -- ctest -L main -C Release --verbose --timeout 900
+      # TODO: disabled for now, consider adding tests for all CPU variants instead
+      # - name: Test (Intel SDE)
+      #   id: cmake_test_sde
+      #   if: ${{ matrix.build == 'avx512-x64' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation
+      #   run: |
+      #     curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz"
+      #     # for some weird reason windows tar doesn't like sde tar.xz
+      #     7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz
+      #     7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar
+      #     $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe)
+      #     cd build
+      #     $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1
+      #     & $sde -future -- ctest -L main -C Release --verbose --timeout 900
 
       - name: Determine tag name
         id: tag
@@ -1039,7 +1018,7 @@ jobs:
         uses: hendrikmuhs/[email protected]
         with:
           key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
-          variant: sccache
+          variant: ccache
           evict-old-files: 1d
 
       - name: Install Cuda Toolkit 11.7
@@ -1117,6 +1096,8 @@ jobs:
           cmake -S . -B build -G "Ninja Multi-Config" ^
             -DLLAMA_BUILD_SERVER=ON ^
             -DGGML_NATIVE=OFF ^
+            -DGGML_BACKEND_DL=ON ^
+            -DGGML_CPU_ALL_VARIANTS=ON ^
             -DGGML_CUDA=ON ^
             -DGGML_RPC=ON ^
             -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include"
@@ -1191,7 +1172,7 @@ jobs:
         uses: hendrikmuhs/[email protected]
         with:
           key: windows-latest-cmake-sycl
-          variant: sccache
+          variant: ccache
           evict-old-files: 1d
 
       - name: Install

Makefile

@@ -1394,36 +1394,36 @@ llama-gen-docs: examples/gen-docs/gen-docs.cpp \
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-libllava.a: tools/llava/llava.cpp \
-	tools/llava/llava.h \
-	tools/llava/clip.cpp \
-	tools/llava/clip.h \
+libllava.a: tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	common/stb_image.h \
 	common/base64.hpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
 
-llama-llava-cli: tools/llava/llava-cli.cpp \
-	tools/llava/llava.cpp \
-	tools/llava/llava.h \
-	tools/llava/clip.cpp \
-	tools/llava/clip.h \
+llama-llava-cli: tools/mtmd/llava-cli.cpp \
+	tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
-llama-minicpmv-cli: tools/llava/minicpmv-cli.cpp \
-	tools/llava/llava.cpp \
-	tools/llava/llava.h \
-	tools/llava/clip.cpp \
-	tools/llava/clip.h \
+llama-minicpmv-cli: tools/mtmd/minicpmv-cli.cpp \
+	tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
-llama-qwen2vl-cli: tools/llava/qwen2vl-cli.cpp \
-	tools/llava/llava.cpp \
-	tools/llava/llava.h \
-	tools/llava/clip.cpp \
-	tools/llava/clip.h \
+llama-qwen2vl-cli: tools/mtmd/qwen2vl-cli.cpp \
+	tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 

cmake/arm64-windows-msvc.cmake

@@ -3,9 +3,3 @@ set( CMAKE_SYSTEM_PROCESSOR x86_64 )
 
 set( CMAKE_C_COMPILER    clang )
 set( CMAKE_CXX_COMPILER  clang++ )
-
-set( arch_c_flags "-march=native" )
-
-set( CMAKE_C_FLAGS_INIT   "${arch_c_flags}" )
-set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" )
-

cmake/x64-windows-llvm.cmake

@@ -2211,14 +2211,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
     add_opt(common_arg(
         {"--mmproj"}, "FILE",
-        "path to a multimodal projector file. see tools/llava/README.md",
+        "path to a multimodal projector file. see tools/mtmd/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.path = value;
         }
     ).set_examples(mmproj_examples));
     add_opt(common_arg(
         {"--mmproj-url"}, "URL",
-        "URL to a multimodal projector file. see tools/llava/README.md",
+        "URL to a multimodal projector file. see tools/mtmd/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.url = value;
         }

common/arg.cpp

@@ -342,7 +342,7 @@ struct common_params {
 
     common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
 
-    // multimodal models (see tools/llava)
+    // multimodal models (see tools/mtmd)
     struct common_params_model mmproj;
     bool mmproj_use_gpu = true;     // use GPU for multimodal model
     bool no_mmproj = false;         // explicitly disable multimodal model

common/common.h

@@ -1778,6 +1778,12 @@ class LlamaModel(TextModel):
     model_arch = gguf.MODEL_ARCH.LLAMA
     undo_permute = True
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # fix for SmolVLM2, missing `num_attention_heads` in config.json
+        if self.hf_arch == "VLlama3ForCausalLM":
+            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
+
     def set_vocab(self):
         try:
             self._set_vocab_sentencepiece()
@@ -2123,6 +2129,9 @@ def __init__(self, *args, **kwargs):
             # if n_heads_in_group is not None, then
             # _num_kv_heads[il] is num_attention_head // n_heads_in_group and
             # _num_heads[il] is num_attention_head
+            # ***dummy layer*** for nemotron 253B
+            # if n_heads_in_group is None and ffn_mult is None
+            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 and _ffn_dims is 0
             for il in range(len(_block_configs)):
                 if _block_configs[il]["attention"]["n_heads_in_group"] is None:
                     if _block_configs[il]["attention"]["replace_with_linear"] is True:
@@ -2134,7 +2143,10 @@ def __init__(self, *args, **kwargs):
                 else:
                     self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
                     self._num_heads.append(self.hparams["num_attention_heads"])
-                _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
+                if _block_configs[il]["ffn"]["ffn_mult"] is None: # dummy layer
+                    _ffn_multipliers.append(0.0)
+                else:
+                    _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
             assert self.block_count == len(self._num_kv_heads)
             assert self.block_count == len(self._num_heads)
             assert self.block_count == len(_ffn_multipliers)
@@ -5674,7 +5686,12 @@ def set_gguf_parameters(self):
         rope_dim = hparams.get("head_dim") or hparams["hidden_size"] // hparams["num_attention_heads"]
 
         self.gguf_writer.add_rope_dimension_count(rope_dim)
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        if (self.hparams.get("rope_scaling") or {}).get("type") == "yarn" and "factor" in self.hparams["rope_scaling"]:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
+        else:
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
         self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
         self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])

convert_hf_to_gguf.py

@@ -33,21 +33,21 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
-python ./tools/llava/llava_surgery.py -m path/to/MobileVLM-1.7B
+python ./tools/mtmd/llava_surgery.py -m path/to/MobileVLM-1.7B
 ```
 
 3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./tools/llava/convert_image_encoder_to_gguf.py \
+python ./tools/mtmd/convert_image_encoder_to_gguf.py \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B/llava.projector \
     --output-dir path/to/MobileVLM-1.7B \
     --projector-type ldp
 ```
 
 ```sh
-python ./tools/llava/convert_image_encoder_to_gguf.py \
+python ./tools/mtmd/convert_image_encoder_to_gguf.py \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
     --output-dir path/to/MobileVLM-1.7B_V2 \
@@ -69,10 +69,10 @@ Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directo
 
 ## Android compile and run
 ### compile
-refer to `tools/llava/android/build_64.sh`
+refer to `tools/mtmd/android/build_64.sh`
 ```sh
-mkdir tools/llava/android/build_64
-cd tools/llava/android/build_64
+mkdir tools/mtmd/android/build_64
+cd tools/mtmd/android/build_64
 ../build_64.sh
 ```
 ### run on Android

docs/multimodal/MobileVLM.md

@@ -25,13 +25,13 @@ git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/T
 2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
 
 ```sh
-python ./tools/llava/glmedge-surgery.py -m ../model_path
+python ./tools/mtmd/glmedge-surgery.py -m ../model_path
 ```
 
 4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
 
 ```sh
-python ./tools/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
+python ./tools/mtmd/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
 ```
 
 5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:

docs/multimodal/glmedge.md

@@ -37,19 +37,19 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 2. Install the required Python packages:
 
 ```sh
-pip install -r tools/llava/requirements.txt
+pip install -r tools/mtmd/requirements.txt
 ```
 
 3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
-python ./tools/llava/llava_surgery.py -m ../llava-v1.5-7b
+python ./tools/mtmd/llava_surgery.py -m ../llava-v1.5-7b
 ```
 
 4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./tools/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
+python ./tools/mtmd/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
 ```
 
 5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
@@ -69,12 +69,12 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
 2) Install the required Python packages:
 
 ```sh
-pip install -r tools/llava/requirements.txt
+pip install -r tools/mtmd/requirements.txt
 ```
 
 3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
 ```console
-python tools/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
+python tools/mtmd/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
 ```
 - you will find a llava.projector and a llava.clip file in your model directory
 
@@ -88,7 +88,7 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso
 
 5) Create the visual gguf model:
 ```console
-python ./tools/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
+python ./tools/mtmd/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
 ```
 - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP