Fix opencl by wrap #if-else-endif with \n (#2086)

1. guess n_layers;
2. relax warnings on context size;
3. add a note that its derivations are also supported.

Co-authored-by: Judd <foldl@boxvest.com>

.github/workflows/build.yml

@@ -137,9 +137,10 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
+          sysctl -a
           mkdir build
           cd build
-          cmake -DLLAMA_AVX2=OFF ..
+          cmake -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF ..
           cmake --build . --config Release
 
       - name: Test

CMakeLists.txt

@@ -68,8 +68,9 @@ option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
 option(LLAMA_CUBLAS                          "llama: use cuBLAS"                                OFF)
+option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
-set(LLAMA_CUDA_DMMV_Y       "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
+set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
 option(LLAMA_CUDA_DMMV_F16                   "llama: use 16 bit floats for dmmv CUDA kernels"   OFF)
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
@@ -246,8 +247,14 @@ if (LLAMA_CUBLAS)
         set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
 
         add_compile_definitions(GGML_USE_CUBLAS)
+        if (LLAMA_CUDA_FORCE_DMMV)
+            add_compile_definitions(GGML_CUDA_FORCE_DMMV)
+        endif()
         add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
-        add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
+        add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
+        if (DEFINED LLAMA_CUDA_DMMV_Y)
+            add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
+        endif()
         if (LLAMA_CUDA_DMMV_F16)
             add_compile_definitions(GGML_CUDA_DMMV_F16)
         endif()
@@ -263,7 +270,7 @@ if (LLAMA_CUBLAS)
         if (LLAMA_CUDA_DMMV_F16)
             set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics
         else()
-            set(CMAKE_CUDA_ARCHITECTURES "52") # lowest CUDA 12 standard
+            set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
         endif()
     endif()
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")

Makefile

@@ -164,16 +164,21 @@ ifdef LLAMA_CUBLAS
 	OBJS      += ggml-cuda.o
 	NVCC      = nvcc
 	NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
+ifdef LLAMA_CUDA_FORCE_DMMV
+	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
+endif # LLAMA_CUDA_FORCE_DMMV
 ifdef LLAMA_CUDA_DMMV_X
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
 else
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
 endif # LLAMA_CUDA_DMMV_X
-ifdef LLAMA_CUDA_DMMV_Y
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
+ifdef LLAMA_CUDA_MMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
+else ifdef LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
 else
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
-endif # LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
+endif # LLAMA_CUDA_MMV_Y
 ifdef LLAMA_CUDA_DMMV_F16
 	NVCCFLAGS += -DGGML_CUDA_DMMV_F16
 endif # LLAMA_CUDA_DMMV_F16

README.md

@@ -86,7 +86,7 @@ as the main playground for developing new features for the [ggml](https://github
 - [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
 - [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
 - [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
-- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
+- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) and its derivations (such as [baichuan-7b-sft](https://huggingface.co/hiyouga/baichuan-7b-sft))
 
 **Bindings:**
 
@@ -345,8 +345,9 @@ Building the program with BLAS support may lead to some performance improvements
 
   | Option                  | Legal values           | Default | Description |
   |-------------------------|------------------------|---------|-------------|
+  | LLAMA_CUDA_FORCE_DMMV   | Boolean                |   false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 |      32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_DMMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_MMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_F16     | Boolean                |   false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
   | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
 

convert.py

@@ -154,9 +154,15 @@ class Params:
         # try transformer naming first
         if "model.layers.0.self_attn.q_proj.weight" in model:
             n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
+        elif "model.layers.0.self_attn.W_pack.weight" in model:   # next: try baichuan naming
+            n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
         else:
             n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
 
+        if n_layer < 1:
+            raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
+                            "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
+
         n_head=n_embd // 128 # guessed
 
         return Params(

examples/alpaca.sh

@@ -7,7 +7,7 @@
 cd `dirname $0`
 cd ..
 
-./main -m ./models/ggml-alpaca-7b-q4.bin \
+./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \
        --color \
        -f ./prompts/alpaca.txt \
        --ctx_size 2048 \

examples/embedding/embedding.cpp

@@ -18,7 +18,7 @@ int main(int argc, char ** argv) {
     params.embedding = true;
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     }
 

examples/main/main.cpp

@@ -85,7 +85,7 @@ int main(int argc, char ** argv) {
     }
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     } else if (params.n_ctx < 8) {
         fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);

examples/perplexity/perplexity.cpp

@@ -130,7 +130,7 @@ int main(int argc, char ** argv) {
     params.n_batch = std::min(params.n_batch, params.n_ctx);
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     }
 

examples/quantize-stats/quantize-stats.cpp

@@ -147,7 +147,7 @@ void test_roundtrip_on_chunk(
         const ggml_tensor * layer,
         int64_t offset,
         int64_t chunk_size,
-        const quantize_fns_t & qfns,
+        const ggml_type_traits_t & qfns,
         bool use_reference,
         float * input_scratch,
         char * quantized_scratch,
@@ -163,11 +163,11 @@ void test_roundtrip_on_chunk(
     }
 
     if (use_reference) {
-        qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
     } else {
-        qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float(input_scratch, quantized_scratch, chunk_size);
     }
-    qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
+    qfns.to_float(quantized_scratch, output_scratch, chunk_size);
 
     update_error_stats(chunk_size, input_scratch, output_scratch, stats);
 }
@@ -177,7 +177,7 @@ void test_roundtrip_on_chunk(
 void test_roundtrip_on_layer(
         std::string & name,
         bool print_layer_stats,
-        const quantize_fns_t & qfns,
+        const ggml_type_traits_t & qfns,
         bool use_reference,
         const ggml_tensor * layer,
         std::vector<float> & input_scratch,
@@ -388,8 +388,8 @@ int main(int argc, char ** argv) {
         if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
             continue;
         }
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
+        if (qfns.from_float && qfns.to_float) {
             if (params.verbose) {
                 printf("testing %s ...\n",  ggml_type_name(type));
             }

examples/server/README.md

@@ -1,13 +1,13 @@
 # llama.cpp/example/server
 
-This example demonstrates a simple HTTP API server to interact with llama.cpp.
+This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
 
 Command line options:
 
 -   `--threads N`, `-t N`: Set the number of threads to use during computation.
 -   `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
 -   `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
--   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
 -   `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
 -   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
@@ -21,24 +21,22 @@ Command line options:
 -   `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
 -   `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
 -   `--port`: Set the port to listen. Default: `8080`.
+-   `--path`: path from which to serve static files (default examples/server/public)
 -   `--embedding`: Enable embedding extraction, Default: disabled.
 
 ## Build
 
-Build llama.cpp with server from repository root with either make or CMake.
+server is build alongside everything else from the root of the project
 
 - Using `make`:
 
   ```bash
-  LLAMA_BUILD_SERVER=1 make
+  make
   ```
 
 - Using `CMake`:
 
   ```bash
-  mkdir build-server
-  cd build-server
-  cmake -DLLAMA_BUILD_SERVER=ON ..
   cmake --build . --config Release
   ```
 
@@ -59,7 +57,7 @@ server.exe -m models\7B\ggml-model.bin -c 2048
 ```
 
 The above command will start a server that by default listens on `127.0.0.1:8080`.
-You can consume the endpoints with Postman or NodeJS with axios library.
+You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.
 
 ## Testing with CURL
 
@@ -206,3 +204,33 @@ openai.api_base = "http://<Your api-server IP>:port"
 ```
 
 Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
+
+### Extending or building alternative Web Front End
+
+The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
+
+Read the documentation in `/completion.js` to see convenient ways to access llama.
+
+A simple example is below:
+
+```html
+<html>
+  <body>
+    <pre>
+      <script type="module">
+        import { llama } from '/completion.js'
+
+        const prompt = `### Instruction:
+Write dad jokes, each one paragraph.
+You can use html formatting if needed.
+
+### Response:`
+
+        for await (const chunk of llama(prompt)) {
+          document.write(chunk.data.content)
+        }
+      </script>
+    </pre>
+  </body>
+</html>
+```

examples/server/completion.js.hpp

@@ -7,187 +7,369 @@ unsigned char completion_js[] = {
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+  0x20, 0x28, 0x72, 0x65, 0x73, 0x6f, 0x6c, 0x76, 0x65, 0x2c, 0x20, 0x72,
+  0x65, 0x6a, 0x65, 0x63, 0x74, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74,
+  0x65, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x22, 0x22, 0x3b, 0x0a, 0x20, 0x20,
+  0x20, 0x20, 0x74, 0x72, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20,
+  0x28, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b,
+  0x20, 0x6f, 0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x72,
+  0x6f, 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73,
+  0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x29, 0x29, 0x20, 0x7b,
+  0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e,
+  0x74, 0x65, 0x6e, 0x74, 0x20, 0x2b, 0x3d, 0x20, 0x63, 0x68, 0x75, 0x6e,
+  0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65,
+  0x6e, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x6f, 0x6c, 0x76,
+  0x65, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x63, 0x61, 0x74, 0x63, 0x68, 0x20,
+  0x28, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x65,
+  0x72, 0x72, 0x6f, 0x72, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d,
+  0x0a, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x7d, 0x3b, 0x0a, 0x0a, 0x2f,
+  0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x28, 0x64, 0x65, 0x70, 0x72, 0x65,
+  0x63, 0x61, 0x74, 0x65, 0x64, 0x29, 0x0a, 0x20, 0x2a, 0x2f, 0x0a, 0x65,
+  0x78, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20,
+  0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x43, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74,
+  0x65, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x70,
+  0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72,
+  0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2c, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62,
+  0x61, 0x63, 0x6b, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20,
+  0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x28, 0x63,
+  0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x20, 0x6f,
+  0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x61, 0x72, 0x61,
+  0x6d, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x70,
+  0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x7b, 0x20, 0x63, 0x6f, 0x6e,
+  0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x7d, 0x29, 0x29, 0x20,
+  0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61,
+  0x63, 0x6b, 0x28, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x29, 0x3b, 0x0a, 0x20,
+  0x20, 0x7d, 0x0a, 0x7d, 0x0a, 0x0a, 0x2f, 0x2f, 0x20, 0x47, 0x65, 0x74,
+  0x20, 0x74, 0x68, 0x65, 0x20, 0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x20, 0x69,
+  0x6e, 0x66, 0x6f, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x74, 0x68, 0x65,
+  0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x2e, 0x20, 0x54, 0x68, 0x69,
+  0x73, 0x20, 0x69, 0x73, 0x20, 0x75, 0x73, 0x65, 0x66, 0x75, 0x6c, 0x20,
+  0x66, 0x6f, 0x72, 0x20, 0x67, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x20,
+  0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x20,
+  0x77, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73,
+  0x6f, 0x20, 0x6f, 0x6e, 0x2e, 0x0a, 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74,
+  0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61,
+  0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d, 0x20,
+  0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20,
+  0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x67, 0x65, 0x6e,
+  0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74,
+  0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20,
+  0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73,
+  0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x61, 0x77,
+  0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22, 0x2f,
+  0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x22, 0x29,
+  0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20, 0x72,
+  0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20,
+  0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67,
+  0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65,
+  0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
 };
-unsigned int completion_js_len = 2275;
+unsigned int completion_js_len = 4462;

examples/server/deps.sh

@@ -4,10 +4,6 @@
 # get the directory of this script file
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 PUBLIC=$DIR/public
-OUTPUT=$DIR/templats.hpp
-
-echo "// Generated file, do not edit" > $OUTPUT
-echo "" > $OUTPUT
 
 echo "download js bundle files"
 curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js

examples/server/public/completion.js

@@ -5,20 +5,29 @@ const paramDefaults = {
   stop: ["</s>"]
 };
 
-/**
- * This function completes the input text using a llama dictionary.
- * @param {object} params - The parameters for the completion request.
- * @param {object} controller - an instance of AbortController if you need one, or null.
- * @param {function} callback - The callback function to call when the completion is done.
- * @returns {string} the completed text as a string. Ideally ignored, and you get at it via the callback.
- */
-export const llamaComplete = async (params, controller, callback) => {
+let generation_settings = null;
+
+
+// Completes the prompt as a generator. Recommended for most use cases.
+//
+// Example:
+//
+//    import { llama } from '/completion.js'
+//
+//    const request = llama("Tell me a joke", {n_predict: 800})
+//    for await (const chunk of request) {
+//      document.write(chunk.data.content)
+//    }
+//
+export async function* llama(prompt, params = {}, config = {}) {
+  let controller = config.controller;
+
   if (!controller) {
     controller = new AbortController();
   }
-  const completionParams = { ...paramDefaults, ...params };
 
-  // we use fetch directly here becasue the built in fetchEventSource does not support POST
+  const completionParams = { ...paramDefaults, ...params, prompt };
+
   const response = await fetch("/completion", {
     method: 'POST',
     body: JSON.stringify(completionParams),
@@ -36,7 +45,6 @@ export const llamaComplete = async (params, controller, callback) => {
   let content = "";
 
   try {
-
     let cont = true;
 
     while (cont) {
@@ -59,18 +67,21 @@ export const llamaComplete = async (params, controller, callback) => {
       result.data = JSON.parse(result.data);
       content += result.data.content;
 
-      // callack
-      if (callback) {
-        cont = callback(result) != false;
-      }
+      // yield
+      yield result;
 
       // if we got a stop token from server, we will break here
       if (result.data.stop) {
+        if (result.data.generation_settings) {
+          generation_settings = result.data.generation_settings;
+        }
         break;
       }
     }
   } catch (e) {
-    console.error("llama error: ", e);
+    if (e.name !== 'AbortError') {
+      console.error("llama error: ", e);
+    }
     throw e;
   }
   finally {
@@ -79,3 +90,79 @@ export const llamaComplete = async (params, controller, callback) => {
 
   return content;
 }
+
+// Call llama, return an event target that you can subcribe to
+//
+// Example:
+//
+//    import { llamaEventTarget } from '/completion.js'
+//
+//    const conn = llamaEventTarget(prompt)
+//    conn.addEventListener("message", (chunk) => {
+//      document.write(chunk.detail.content)
+//    })
+//
+export const llamaEventTarget = (prompt, params = {}, config = {}) => {
+  const eventTarget = new EventTarget();
+  (async () => {
+    let content = "";
+    for await (const chunk of llama(prompt, params, config)) {
+      if (chunk.data) {
+        content += chunk.data.content;
+        eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
+      }
+      if (chunk.data.generation_settings) {
+        eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
+      }
+      if (chunk.data.timings) {
+        eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
+      }
+    }
+    eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
+  })();
+  return eventTarget;
+}
+
+// Call llama, return a promise that resolves to the completed text. This does not support streaming
+//
+// Example:
+//
+//     llamaPromise(prompt).then((content) => {
+//       document.write(content)
+//     })
+//
+//     or
+//
+//     const content = await llamaPromise(prompt)
+//     document.write(content)
+//
+export const llamaPromise = (prompt, params = {}, config = {}) => {
+  return new Promise(async (resolve, reject) => {
+    let content = "";
+    try {
+      for await (const chunk of llama(prompt, params, config)) {
+        content += chunk.data.content;
+      }
+      resolve(content);
+    } catch (error) {
+      reject(error);
+    }
+  });
+};
+
+/**
+ * (deprecated)
+ */
+export const llamaComplete = async (params, controller, callback) => {
+  for await (const chunk of llama(params.prompt, params, { controller })) {
+    callback(chunk);
+  }
+}
+
+// Get the model info from the server. This is useful for getting the context window and so on.
+export const llamaModelInfo = async () => {
+  if (!generation_settings) {
+    generation_settings = await fetch("/model.json").then(r => r.json());
+  }
+  return generation_settings;
+}

examples/server/public/index.html

@@ -6,7 +6,6 @@
   <title>llama.cpp - chat</title>
 
   <style>
-
     body {
       background-color: #fff;
       color: #000;
@@ -22,10 +21,6 @@
       height: 100%;
     }
 
-    header, footer {
-      text-align: center;
-    }
-
     main {
       margin: 3px;
       display: flex;
@@ -99,6 +94,15 @@
       margin: 0.5em 0;
       display: block;
     }
+
+    header, footer {
+      text-align: center;
+    }
+
+    footer {
+      font-size: 80%;
+      color: #888;
+    }
   </style>
 
   <script type="module">
@@ -106,10 +110,10 @@
       html, h, signal, effect, computed, render, useSignal, useEffect, useRef
     } from '/index.js';
 
-    import { llamaComplete } from '/completion.js';
+    import { llama } from '/completion.js';
 
     const session = signal({
-      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in markdown.",
+      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
       template: "{{prompt}}\n\n{{history}}\n{{char}}:",
       historyTemplate: "{{name}}: {{message}}",
       transcript: [],
@@ -118,15 +122,6 @@
       user: "User",
     })
 
-    const transcriptUpdate = (transcript) => {
-      session.value = {
-        ...session.value,
-        transcript
-      }
-    }
-
-    const chatStarted = computed(() => session.value.transcript.length > 0)
-
     const params = signal({
       n_predict: 400,
       temperature: 0.7,
@@ -136,8 +131,18 @@
       top_p: 0.5,
     })
 
+    const llamaStats = signal(null)
     const controller = signal(null)
+
     const generating = computed(() => controller.value == null )
+    const chatStarted = computed(() => session.value.transcript.length > 0)
+
+    const transcriptUpdate = (transcript) => {
+      session.value = {
+        ...session.value,
+        transcript
+      }
+    }
 
     // simple template replace
     const template = (str, extraSettings) => {
@@ -158,7 +163,7 @@
 
       transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
 
-      const payload = template(session.value.template, {
+      const prompt = template(session.value.template, {
         message: msg,
         history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
       });
@@ -168,22 +173,26 @@
 
       const llamaParams = {
         ...params.value,
-        prompt: payload,
         stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
       }
 
-      await llamaComplete(llamaParams, controller.value, (message) => {
-        const data = message.data;
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+        const data = chunk.data;
         currentMessage += data.content;
+
         // remove leading whitespace
         currentMessage = currentMessage.replace(/^\s+/, "")
 
         transcriptUpdate([...history, ["{{char}}", currentMessage]])
 
         if (data.stop) {
-          console.log("-->", data, ' response was:', currentMessage, 'transcript state:', session.value.transcript);
+          console.log("Completion finished: '", currentMessage, "', summary: ", data);
         }
-      })
+
+        if (data.timings) {
+          llamaStats.value = data.timings;
+        }
+      }
 
       controller.value = null;
     }
@@ -219,13 +228,12 @@
       return html`
         <form onsubmit=${submit}>
           <div>
-          <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
-
+            <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
           </div>
           <div class="right">
-          <button type="submit" disabled=${!generating.value} >Send</button>
-          <button onclick=${stop} disabled=${generating}>Stop</button>
-          <button onclick=${reset}>Reset</button>
+            <button type="submit" disabled=${!generating.value} >Send</button>
+            <button onclick=${stop} disabled=${generating}>Stop</button>
+            <button onclick=${reset}>Reset</button>
           </div>
         </form>
       `
@@ -243,7 +251,7 @@
       }, [messages])
 
       const chatLine = ([user, msg]) => {
-        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdown} text=${template(msg)} /></p>`
+        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
       };
 
       return html`
@@ -313,39 +321,52 @@
         </form>
       `
     }
-const Markdown = (params) => {
-  const md = params.text
-    .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
-    .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
-    .replace(/__(.*?)__/g, '<strong>$1</strong>')
-    .replace(/\*(.*?)\*/g, '<em>$1</em>')
-    .replace(/_(.*?)_/g, '<em>$1</em>')
-    .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
-    .replace(/`(.*?)`/g, '<code>$1</code>')
-    .replace(/\n/gim, '<br />');
-  return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
-};
+    // poor mans markdown replacement
+    const Markdownish = (params) => {
+      const md = params.text
+        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
+        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*/g, '<em>$1</em>')
+        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+        .replace(/`(.*?)`/g, '<code>$1</code>')
+        .replace(/\n/gim, '<br />');
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+    };
+
+    const ModelGenerationInfo = (params) => {
+      if (!llamaStats.value) {
+        return html`<span/>`
+      }
+      return html`
+        <span>
+          ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
+        </span>
+      `
+    }
 
     function App(props) {
 
       return html`
-      <div id="container">
-        <header>
-          <h1>llama.cpp</h1>
-        </header>
+        <div id="container">
+          <header>
+            <h1>llama.cpp</h1>
+          </header>
 
-        <main id="content">
-          <${chatStarted.value ? ChatLog : ConfigForm} />
-        </main>
+          <main id="content">
+            <${chatStarted.value ? ChatLog : ConfigForm} />
+          </main>
 
-        <footer id="write">
-          <${MessageInput} />
-        </footer>
+          <section id="write">
+            <${MessageInput} />
+          </section>
 
-        <footer>
-          <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a></p>
-        </footer>
-      </div>
+          <footer>
+            <p><${ModelGenerationInfo} /></p>
+            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+          </footer>
+        </div>
       `;
     }
 

ggml-cuda.cu

@@ -70,9 +70,11 @@ typedef void (*ggml_cuda_op_t)(
 
 // QK = number of values after dequantization
 // QR = QK / number of values before dequantization
+// QI = number of 32 bit integers before dequantization
 
 #define QK4_0 32
 #define QR4_0 2
+#define QI4_0 4
 typedef struct {
     half    d;              // delta
     uint8_t qs[QK4_0 / 2];  // nibbles / quants
@@ -81,6 +83,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0
 
 #define QK4_1 32
 #define QR4_1 2
+#define QI4_1 4
 typedef struct {
     half    d;              // delta
     half    m;              // min
@@ -90,6 +93,7 @@ static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong
 
 #define QK5_0 32
 #define QR5_0 2
+#define QI5_0 4
 typedef struct {
     half d;                 // delta
     uint8_t qh[4];          // 5-th bit of quants
@@ -99,6 +103,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5
 
 #define QK5_1 32
 #define QR5_1 2
+#define QI5_1 4
 typedef struct {
     half d;                 // delta
     half m;                 // min
@@ -109,12 +114,25 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) +
 
 #define QK8_0 32
 #define QR8_0 1
+#define QI8_0 8
 typedef struct {
     half    d;              // delta
     int8_t  qs[QK8_0];      // quants
 } block_q8_0;
 static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
 
+#define QK8_1 32
+#define QR8_1 1
+#define QI8_1 8
+typedef struct {
+    half    d;              // delta
+    half    s;              // unquantized sum
+    int8_t  qs[QK8_0];      // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
+
+typedef float (*vec_dot_q_cuda_t)(const void * vbq, const block_q8_1 * bq8_1, const int iqs);
+
 //================================= k-quants
 
 #ifdef GGML_QKK_64
@@ -198,14 +216,15 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define CUDA_SCALE_BLOCK_SIZE 256
 #define CUDA_ROPE_BLOCK_SIZE 256
 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
+#define CUDA_QUANTIZE_BLOCK_SIZE 256
 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256
 
 // dmmv = dequantize_mul_mat_vec
 #ifndef GGML_CUDA_DMMV_X
 #define GGML_CUDA_DMMV_X 32
 #endif
-#ifndef GGML_CUDA_DMMV_Y
-#define GGML_CUDA_DMMV_Y 1
+#ifndef GGML_CUDA_MMV_Y
+#define GGML_CUDA_MMV_Y 1
 #endif
 
 #ifndef K_QUANTS_PER_ITERATION
@@ -270,7 +289,6 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
     }
 
     // sum up partial sums
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -714,7 +732,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -819,7 +836,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -923,7 +939,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1028,7 +1043,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1139,7 +1153,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1158,6 +1171,41 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
     v.y = x[ib + iqs + 1];
 }
 
+static __global__ void quantize_q8_1(const float * x, void * vy, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int ib = i / QK8_0; // block index
+    const int iqs = i % QK8_0; // quant index
+
+    const float xi = x[i];
+    float amax = fabsf(xi);
+    float sum = xi;
+
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
+        sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
+    }
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    y[ib].d = d;
+    y[ib].s = sum;
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static __global__ void dequantize_block(const void * vx, float * y, const int k) {
     const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
@@ -1179,6 +1227,182 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k)
     y[iybs + iqs + y_offset] = v.y;
 }
 
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int vi;
+    memcpy(&vi,  &bq4_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_0)]);
+
+    const float d = __half2float(bq4_0->d) * __half2float(bq8_1->d);
+
+    // subtract 8 from each quantized value
+    const int vi0 = __vsub4((vi >> 0) & 0x0F0F0F0F, 0x08080808);
+    const int vi1 = __vsub4((vi >> 4) & 0x0F0F0F0F, 0x08080808);
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi0, ui0, 0);
+    sumi     = __dp4a(vi1, ui1, sumi);
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    const int vi  = *((int *) &bq4_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_1)]);
+
+    const float d = __half2float(bq4_1->d) * __half2float(bq8_1->d);
+    const float m = bq4_1->m;
+    const float s = bq8_1->s;
+
+    const int vi0 = (vi >> 0) & 0x0F0F0F0F;
+    const int vi1 = (vi >> 4) & 0x0F0F0F0F;
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi0, ui0, 0);
+    sumi     = __dp4a(vi1, ui1, sumi);
+
+    return sumi*d + m*s / QI4_1; // scale sum by QI4_1 because there are QI4_1 threads working on this block
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int qs;
+    memcpy(&qs, &bq5_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int qh0 = bq5_0->qh[iqs/2 + 0] >> 4*(iqs%2);
+    const int qh1 = bq5_0->qh[iqs/2 + 2] >> 4*(iqs%2);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_0)]);
+
+    const float d = __half2float(bq5_0->d) * __half2float(bq8_1->d);
+
+    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
+    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
+    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
+    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
+    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
+    vi0     = __vsub4(vi0,  0x10101010); // subtract 16 from quantized values
+    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
+
+    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
+    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
+    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
+    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
+    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
+    vi1     = __vsub4(vi1,  0x10101010); // subtract 16 from quantized values
+    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    const int qs  = *((int *) &bq5_1->qs[sizeof(int) * (iqs + 0)]);
+    const int qh0 = bq5_1->qh[iqs/2 + 0] >> 4*(iqs%2);
+    const int qh1 = bq5_1->qh[iqs/2 + 2] >> 4*(iqs%2);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_1)]);
+
+    const float d = __half2float(bq5_1->d) * __half2float(bq8_1->d);
+    const float m = bq5_1->m;
+    const float s = bq8_1->s;
+
+    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
+    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
+    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
+    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
+    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
+    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
+
+    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
+    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
+    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
+    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
+    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
+    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
+
+    return sumi*d + m*s / QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int vi;
+    memcpy(&vi,  &bq8_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int ui = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+
+    const float d = __half2float(bq8_0->d) * __half2float(bq8_1->d);
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi, ui, 0);
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+template <int qk, int qi, typename block_q_t, vec_dot_q_cuda_t vec_dot_q_cuda>
+static __global__ void mul_mat_vec_q(const void * vx, const void * vy, float * dst, const int ncols, const int nrows) {
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i + threadIdx.x / qi; // x block index
+
+        const int iby = i + threadIdx.x / qi; // y block index
+
+        const int iqs  = threadIdx.x % qi; // x block quant index when casting the quants to int
+
+        tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows) {
     // qk = quantized weights per x block
@@ -1233,7 +1457,6 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y,
     }
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1284,7 +1507,6 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl
     const int idst = channel*nrows_dst + row_dst;
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1330,7 +1552,6 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
     }
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1440,7 +1661,6 @@ static __global__ void soft_max_f32(const float * x, float * dst, const int ncol
     }
 
     // sum up partial sums
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1494,6 +1714,11 @@ static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, con
     rms_norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
 }
 
+static void quantize_row_q8_1_cuda(const float * x, void * vy, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    quantize_q8_1<<<num_blocks, CUDA_QUANTIZE_BLOCK_SIZE, 0, stream>>>(x, vy, k);
+}
+
 static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
     dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@@ -1562,45 +1787,45 @@ static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cu
 
 static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -1647,6 +1872,51 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
     dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
+static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, vec_dot_q4_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, vec_dot_q4_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, vec_dot_q5_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, vec_dot_q5_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, vec_dot_q8_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
 static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
     dequantize_block<1, 1, convert_f16><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@@ -1654,9 +1924,9 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c
 
 static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<1, 1, convert_f16>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -1822,6 +2092,7 @@ static size_t g_scratch_offset = 0;
 
 static int g_device_count = -1;
 static int g_main_device = 0;
+static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
 static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
 
 static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
@@ -1839,9 +2110,12 @@ void ggml_init_cublas() {
         for (int id = 0; id < g_device_count; ++id) {
             cudaDeviceProp prop;
             CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
-            fprintf(stderr, "  Device %d: %s\n", id, prop.name);
+            fprintf(stderr, "  Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
+
             g_tensor_split[id] = total_vram;
             total_vram += prop.totalGlobalMem;
+
+            g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
         }
         for (int id = 0; id < g_device_count; ++id) {
             g_tensor_split[id] /= total_vram;
@@ -2057,7 +2331,7 @@ inline void ggml_cuda_op_rms_norm(
     (void) i1;
 }
 
-inline void ggml_cuda_op_dequantize_mul_mat_vec(
+inline void ggml_cuda_op_mul_mat_vec(
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
     float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
     cudaStream_t & cudaStream_main){
@@ -2069,69 +2343,116 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec(
     const int64_t ne00 = src0->ne[0];
     const int64_t nrows = i01_high - i01_low;
 
-// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
-#ifdef GGML_CUDA_DMMV_F16
-    size_t ash;
-    dfloat * src1_dfloat = nullptr; // dfloat == half
-
-    bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
-        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
-        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
-
-    if (src1_convert_f16) {
-        src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
-        ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00,
-                                ne00, 1, sizeof(float), 0, 0,
-                                ne00, 1, sizeof(half),  0, 0, cudaStream_main);
-    }
+#ifdef GGML_CUDA_FORCE_DMMV
+    const bool use_mul_mat_vec_q = false;
 #else
-    dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    const bool mul_mat_vec_q_implemented = src0->type == GGML_TYPE_Q4_0 ||
+        src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 ||
+        src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0;
+
+    // The integer intrinsics used in mul_mat_vec_q are available with compute capability 6.
+    // However, they have bad performance with Pascal cards.
+    // Therefore, in a multi GPU setting decide at runtime which GPUs should use mul_mat_vec_q.
+    const bool use_mul_mat_vec_q = g_compute_capabilities[id] >= 700 && mul_mat_vec_q_implemented;
+#endif
+
+    if (use_mul_mat_vec_q) {
+        size_t as;
+        void * src1_q8_1 = ggml_cuda_pool_malloc(ne00*sizeof(block_q8_1)/QK8_1, &as);
+        quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, cudaStream_main);
+
+        switch (src0->type) {
+            case GGML_TYPE_Q4_0:
+                mul_mat_vec_q4_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_1:
+                mul_mat_vec_q4_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_0:
+                mul_mat_vec_q5_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_1:
+                mul_mat_vec_q5_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q8_0:
+                mul_mat_vec_q8_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+
+        ggml_cuda_pool_free(src1_q8_1, as);
+    } else {
+        // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_CUDA_DMMV_F16
+        size_t ash;
+        dfloat * src1_dfloat = nullptr; // dfloat == half
+
+        bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+            src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+            src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+        if (src1_convert_f16) {
+            src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
+            ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00,
+                                    ne00, 1, sizeof(float), 0, 0,
+                                    ne00, 1, sizeof(half),  0, 0, cudaStream_main);
+        }
+#else
+        dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
 #endif // GGML_CUDA_DMMV_F16
 
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q4_1:
-            dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_0:
-            dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_1:
-            dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q8_0:
-            dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q2_K:
-            dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q3_K:
-            dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q4_K:
-            dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_K:
-            dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q6_K:
-            dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_F16:
-            convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
+        switch (src0->type) {
+            case GGML_TYPE_Q4_0:
+                dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_1:
+                dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_0:
+                dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_1:
+                dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q8_0:
+                dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q2_K:
+                dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q3_K:
+                dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_K:
+                dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_K:
+                dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q6_K:
+                dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_F16:
+                convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
 
 #ifdef GGML_CUDA_DMMV_F16
-    if (src1_convert_f16) {
-        ggml_cuda_pool_free(src1_dfloat, ash);
-    }
+        if (src1_convert_f16) {
+            ggml_cuda_pool_free(src1_dfloat, ash);
+        }
 #endif // GGML_CUDA_DMMV_F16
+    }
 
     (void) src1;
     (void) dst;
@@ -2701,8 +3022,8 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_
     }else if (src0->type == GGML_TYPE_F32) {
         ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
     } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
-        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src0->ne[1] % GGML_CUDA_DMMV_Y == 0) {
-            ggml_cuda_op(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false, false);
+        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
+            ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false);
         } else {
             ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
         }

ggml-opencl.cpp

@@ -653,13 +653,17 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
     const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
     const int in = tid - step*im;                        // 0...15 or 0...7
 
-#if K_QUANTS_PER_ITERATION == 1
+\n#if K_QUANTS_PER_ITERATION == 1\n
     const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
     const int is = 0;
-#else
+
+\n#else\n
+
     const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
     const int is = in / 4;
-#endif
+
+\n#endif\n
+
     const int ql_offset = 64*im + l0;
     const int qh_offset = 32*im + l0;
     const int s_offset  =  8*im + is;
@@ -676,7 +680,7 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
 
         const float d = vload_half(0, &x[i].d);
 
-#if K_QUANTS_PER_ITERATION == 1
+\n#if K_QUANTS_PER_ITERATION == 1\n
         float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
                   + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
                   + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
@@ -686,7 +690,7 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
                   + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
                   +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
         tmp[16 * ix + tid] += sum;
-#else
+\n#else\n
         float sum = 0;
         for (int l = 0; l < 4; ++l) {
             sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
@@ -695,7 +699,7 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
                  + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
         }
         tmp[16 * ix + tid] += sum;
-#endif
+\n#endif\n
 
     }
 

ggml.c

@@ -481,14 +481,14 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) {
     return GGML_FP32_TO_FP16(x);
 }
 
-void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n) {
-    for (size_t i = 0; i < n; i++) {
+void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) {
+    for (int i = 0; i < n; i++) {
         y[i] = GGML_FP16_TO_FP32(x[i]);
     }
 }
 
-void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) {
-    size_t i = 0;
+void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
+    int i = 0;
 #if defined(__F16C__)
     for (; i + 7 < n; i += 8) {
         __m256 x_vec = _mm256_loadu_ps(x + i);
@@ -1627,109 +1627,112 @@ static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, in
     }
 }
 
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
 static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 
-static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
+static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
+    [GGML_TYPE_F32] = {
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
+        .vec_dot_type             = GGML_TYPE_F32,
+    },
+    [GGML_TYPE_F16] = {
+        .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
+        .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
+        .vec_dot_type             = GGML_TYPE_F16,
+    },
     [GGML_TYPE_Q4_0] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q4_0,
-        .quantize_row_q           = quantize_row_q4_0,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference,
-        .quantize_row_q_dot       = quantize_row_q8_0,
-        .vec_dot_q                = ggml_vec_dot_q4_0_q8_0,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
+        .from_float               = quantize_row_q4_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
+        .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q4_1] = {
-        .dequantize_row_q         = (dequantize_row_q_t)dequantize_row_q4_1,
-        .quantize_row_q           = quantize_row_q4_1,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference,
-        .quantize_row_q_dot       = quantize_row_q8_1,
-        .vec_dot_q                = ggml_vec_dot_q4_1_q8_1,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
+        .from_float               = quantize_row_q4_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
+        .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q5_0] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q5_0,
-        .quantize_row_q           = quantize_row_q5_0,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_0_reference,
-        .quantize_row_q_dot       = quantize_row_q8_0,
-        .vec_dot_q                = ggml_vec_dot_q5_0_q8_0,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
+        .from_float               = quantize_row_q5_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
+        .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q5_1] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q5_1,
-        .quantize_row_q           = quantize_row_q5_1,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_1_reference,
-        .quantize_row_q_dot       = quantize_row_q8_1,
-        .vec_dot_q                = ggml_vec_dot_q5_1_q8_1,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
+        .from_float               = quantize_row_q5_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
+        .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q8_0] = {
-        .dequantize_row_q         = dequantize_row_q8_0,
-        .quantize_row_q           = quantize_row_q8_0,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_0_reference,
-        .quantize_row_q_dot       = quantize_row_q8_0,
-        .vec_dot_q                = ggml_vec_dot_q8_0_q8_0,
+        .to_float                 = dequantize_row_q8_0,
+        .from_float               = quantize_row_q8_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
+        .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q8_1] = {
-        .dequantize_row_q         = NULL,   // TODO
-        .quantize_row_q           = quantize_row_q8_1,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_1_reference,
-        .quantize_row_q_dot       = quantize_row_q8_1,
-        .vec_dot_q                = NULL,   // TODO
+        .from_float               = quantize_row_q8_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
 #ifdef GGML_USE_K_QUANTS
     [GGML_TYPE_Q2_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q2_K,
-        .quantize_row_q           = quantize_row_q2_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q2_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q2_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
+        .from_float               = quantize_row_q2_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
+        .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q3_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q3_K,
-        .quantize_row_q           = quantize_row_q3_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q3_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q3_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
+        .from_float               = quantize_row_q3_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
+        .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q4_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q4_K,
-        .quantize_row_q           = quantize_row_q4_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q4_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
+        .from_float               = quantize_row_q4_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
+        .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q5_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q5_K,
-        .quantize_row_q           = quantize_row_q5_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q5_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
+        .from_float               = quantize_row_q5_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
+        .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q6_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q6_K,
-        .quantize_row_q           = quantize_row_q6_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q6_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q6_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
+        .from_float               = quantize_row_q6_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
+        .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
+    [GGML_TYPE_Q8_K] = {
+        .from_float               = quantize_row_q8_K,
+    }
 #endif
 };
 
 // For internal test use
-quantize_fns_t ggml_internal_get_quantize_fn(size_t i) {
+ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) {
     GGML_ASSERT(i < GGML_TYPE_COUNT);
-    return quantize_fns[i];
+    return type_traits[i];
 }
 
 
@@ -2275,7 +2278,7 @@ inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)
 inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]*y[i];   }
 inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]/y[i];   }
 
-inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
 #ifdef GGML_SIMD
     float sumf = 0.0f;
     const int np = (n & ~(GGML_F32_STEP - 1));
@@ -2312,7 +2315,7 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
     *s = sumf;
 }
 
-inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
     ggml_float sumf = 0.0;
 
 #if defined(GGML_SIMD)
@@ -7825,8 +7828,8 @@ static void ggml_compute_forward_dup_f16(
                         id += ne00 * (ne01 - ir1);
                     }
                 }
-            } else if (ggml_is_quantized(dst->type)) {
-                quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
+            } else if (type_traits[dst->type].from_float) {
+                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
                 float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
 
                 size_t id = 0;
@@ -8078,26 +8081,8 @@ static void ggml_compute_forward_dup_f32(
                         id += rs * (ne01 - ir1);
                     }
                 }
-            } else if (dst->type == GGML_TYPE_F16) {
-                size_t id = 0;
-                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
-
-                for (int i03 = 0; i03 < ne03; i03++) {
-                    for (int i02 = 0; i02 < ne02; i02++) {
-                        id += ne00 * ir0;
-                        for (int i01 = ir0; i01 < ir1; i01++) {
-                            for (int i00 = 0; i00 < ne00; i00++) {
-                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-
-                                dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
-                                id++;
-                            }
-                        }
-                        id += ne00 * (ne01 - ir1);
-                    }
-                }
-            } else if (ggml_is_quantized(dst->type)) {
-                quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
+            } else if (type_traits[dst->type].from_float) {
+                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
 
                 size_t id = 0;
                 size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]);
@@ -8503,8 +8488,8 @@ static void ggml_compute_forward_add_q_f32(
     const int nth = params->nth;
 
     const enum ggml_type type = src0->type;
-    dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
-    quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+    ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
@@ -8777,8 +8762,8 @@ static void ggml_compute_forward_add1_q_f32(
     GGML_TENSOR_UNARY_OP_LOCALS;
 
     const enum ggml_type type = src0->type;
-    dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
-    quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+    ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0
     GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
@@ -10578,317 +10563,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
 }
 #endif
 
-static void ggml_compute_forward_mul_mat_f32(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    GGML_TENSOR_BINARY_OP_LOCALS;
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    assert(ne02 == ne12);
-    assert(ne03 == ne13);
-    assert(ne2  == ne12);
-    assert(ne3  == ne13);
-
-    // we don't support permuted src0 or src1
-    assert(nb00 == sizeof(float));
-    assert(nb10 == sizeof(float));
-
-    // dst cannot be transposed or permuted
-    assert(nb0 == sizeof(float));
-    assert(nb0 <= nb1);
-    assert(nb1 <= nb2);
-    assert(nb2 <= nb3);
-
-    assert(ne0 == ne01);
-    assert(ne1 == ne11);
-    assert(ne2 == ne02);
-    assert(ne3 == ne03);
-
-    // nb01 >= nb00 - src0 is not transposed
-    //   compute by src0 rows
-
-#if defined(GGML_USE_CLBLAST)
-    if (ggml_cl_can_mul_mat(src0, src1, dst)) {
-        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
-            ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
-        }
-        return;
-    }
-#endif
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
-        if (params->ith != 0) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_INIT) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_FINALIZE) {
-            return;
-        }
-
-        for (int64_t i03 = 0; i03 < ne03; i03++) {
-            for (int64_t i02 = 0; i02 < ne02; i02++) {
-                const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
-                const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
-                float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
-
-                cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
-                        ne11, ne01, ne10,
-                        1.0f,    y, ne10,
-                                 x, ne00,
-                        0.0f,    d, ne01);
-            }
-        }
-        //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
-
-        return;
-    }
-#endif
-
-    if (params->type == GGML_TASK_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_FINALIZE) {
-        return;
-    }
-
-    // parallelize by src0 rows using ggml_vec_dot_f32
-
-    // total rows in src0
-    const int nr = ne01*ne02*ne03;
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // src0 indices
-        const int i03 = ir/(ne02*ne01);
-        const int i02 = (ir - i03*ne02*ne01)/ne01;
-        const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
-
-        for (int64_t ic = 0; ic < ne11; ++ic) {
-            // src1 indices
-            const int i13 = i03;
-            const int i12 = i02;
-            const int i11 = ic;
-
-            // dst indices
-            const int i0 = i01;
-            const int i1 = i11;
-            const int i2 = i02;
-            const int i3 = i03;
-
-            ggml_vec_dot_f32(ne00,
-                    (float *) ((char *)  dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
-                    (float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)),
-                    (float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)));
-        }
-    }
-
-    //int64_t t1 = ggml_perf_time_us();
-    //static int64_t acc = 0;
-    //acc += t1 - t0;
-    //if (t1 - t0 > 10) {
-    //    printf("\n");
-    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
-    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
-    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
-    //    printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
-
-    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
-    //}
-}
-
-static void ggml_compute_forward_mul_mat_f16_f32(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    GGML_TENSOR_BINARY_OP_LOCALS;
-
-    //const int64_t ne   = ne0*ne1*ne2*ne3;
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    GGML_ASSERT(ne02 == ne12);
-    GGML_ASSERT(ne03 == ne13);
-    GGML_ASSERT(ne2  == ne12);
-    GGML_ASSERT(ne3  == ne13);
-
-    // TODO: we don't support permuted src0
-    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
-
-    // dst cannot be transposed or permuted
-    GGML_ASSERT(nb0 == sizeof(float));
-    GGML_ASSERT(nb0 <= nb1);
-    GGML_ASSERT(nb1 <= nb2);
-    GGML_ASSERT(nb2 <= nb3);
-
-    GGML_ASSERT(ne0 == ne01);
-    GGML_ASSERT(ne1 == ne11);
-    GGML_ASSERT(ne2 == ne02);
-    GGML_ASSERT(ne3 == ne03);
-
-    // nb01 >= nb00 - src0 is not transposed
-    //   compute by src0 rows
-
-#if defined(GGML_USE_CLBLAST)
-    if (ggml_cl_can_mul_mat(src0, src1, dst)) {
-        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
-            ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
-        }
-        return;
-    }
-#endif
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
-        GGML_ASSERT(nb10 == sizeof(float));
-
-        if (params->ith != 0) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_INIT) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_FINALIZE) {
-            return;
-        }
-
-        for (int64_t i03 = 0; i03 < ne03; i03++) {
-            for (int64_t i02 = 0; i02 < ne02; i02++) {
-                float * const wdata = params->wdata;
-                {
-                    size_t id = 0;
-                    for (int64_t i01 = 0; i01 < ne01; ++i01) {
-                        for (int64_t i00 = 0; i00 < ne00; ++i00) {
-                            wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00));
-                        }
-                    }
-
-                    assert(id*sizeof(float) <= params->wsize);
-                }
-
-                const float * x = wdata;
-                const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
-
-                float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
-
-                // zT = y * xT
-                cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
-                        ne11, ne01, ne10,
-                        1.0f,    y, ne10,
-                                 x, ne00,
-                        0.0f,    d, ne01);
-            }
-        }
-
-        /*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
-
-        return;
-    }
-#endif
-
-    if (params->type == GGML_TASK_INIT) {
-        ggml_fp16_t * const wdata = params->wdata;
-
-        size_t id = 0;
-        for (int64_t i13 = 0; i13 < ne13; ++i13) {
-            for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                    for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                        wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
-                    }
-                }
-            }
-        }
-
-        GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize);
-
-        return;
-    }
-
-    if (params->type == GGML_TASK_FINALIZE) {
-        return;
-    }
-
-    // fp16 -> half the size, so divide by 2
-    // TODO: do not support transposed src1
-    assert(nb10/2 == sizeof(ggml_fp16_t));
-
-    // parallelize by src0 rows using ggml_vec_dot_f16
-
-    // total rows in src0
-    const int nr = ne01*ne02*ne03;
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    ggml_fp16_t * wdata = params->wdata;
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // src0 indices
-        const int i03 = ir/(ne02*ne01);
-        const int i02 = (ir - i03*ne02*ne01)/ne01;
-        const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
-
-        const int i13 = i03;
-        const int i12 = i02;
-
-        const int i0 = i01;
-        const int i2 = i02;
-        const int i3 = i03;
-
-        ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
-        ggml_fp16_t * src1_col =                                wdata + (       0 + i12*ne11 + i13*ne12*ne11)*ne00;
-
-        float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
-
-        for (int64_t ic = 0; ic < ne11; ++ic) {
-            ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00);
-        }
-    }
-
-    //int64_t t1 = ggml_time_us();
-    //static int64_t acc = 0;
-    //acc += t1 - t0;
-    //if (t1 - t0 > 10) {
-    //    printf("\n");
-    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
-    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
-    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
-
-    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
-    //}
-}
-
-static void ggml_compute_forward_mul_mat_q_f32(
+static void ggml_compute_forward_mul_mat(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
@@ -10907,9 +10582,10 @@ static void ggml_compute_forward_mul_mat_q_f32(
     GGML_ASSERT(ne3  == ne13);
 
     const enum ggml_type type = src0->type;
-    quantize_row_q_t const quantize_row_q_dot = quantize_fns[type].quantize_row_q_dot;
-    vec_dot_q_t      const vec_dot_q          = quantize_fns[type].vec_dot_q;
-    enum ggml_type   const vec_dot_type       = quantize_fns[type].vec_dot_type;
+
+    ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
+    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
+    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
 
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
@@ -10952,27 +10628,27 @@ static void ggml_compute_forward_mul_mat_q_f32(
             return;
         }
 
-        float * const wdata = params->wdata;
-        dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
-
         for (int64_t i03 = 0; i03 < ne03; i03++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
+                const void * x = (char *) src0->data + i03*nb03 + i02*nb02;
                 const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
 
                 float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
 
-                {
+                if (type != GGML_TYPE_F32) {
+                    float * const wdata = params->wdata;
+                    ggml_to_float_t const to_float = type_traits[type].to_float;
+
                     size_t id = 0;
                     for (int64_t i01 = 0; i01 < ne01; ++i01) {
-                        dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
+                        to_float((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
                         id += ne00;
                     }
 
                     assert(id*sizeof(float) <= params->wsize);
+                    x = wdata;
                 }
 
-                const float * x = wdata;
-
                 cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
                         ne11, ne01, ne10,
                         1.0f,    y, ne10,
@@ -10988,14 +10664,16 @@ static void ggml_compute_forward_mul_mat_q_f32(
 #endif
 
     if (params->type == GGML_TASK_INIT) {
-        char * wdata = params->wdata;
-        const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
+        if (src1->type != vec_dot_type) {
+            char * wdata = params->wdata;
+            const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
 
-        for (int64_t i13 = 0; i13 < ne13; ++i13) {
-            for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                    quantize_row_q_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
-                    wdata += row_size;
+            for (int64_t i13 = 0; i13 < ne13; ++i13) {
+                for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
+                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
+                        wdata += row_size;
+                    }
                 }
             }
         }
@@ -11019,7 +10697,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    void * wdata = params->wdata;
+    void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
     const size_t row_size = ne00*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
 
     for (int ir = ir0; ir < ir1; ++ir) {
@@ -11043,7 +10721,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
         assert(ne00 % 32 == 0);
 
         for (int64_t ic = 0; ic < ne11; ++ic) {
-            vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
+            vec_dot(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
         }
     }
 
@@ -11060,40 +10738,6 @@ static void ggml_compute_forward_mul_mat_q_f32(
     //}
 }
 
-static void ggml_compute_forward_mul_mat(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        struct ggml_tensor * dst) {
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q8_1:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            {
-                ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
-            } break;
-        case GGML_TYPE_F16:
-            {
-                ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst);
-            } break;
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_mul_mat_f32(params, src0, src1, dst);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
 
 // ggml_compute_forward_out_prod
 
@@ -11483,7 +11127,7 @@ static void ggml_compute_forward_get_rows_q(
     const int nc = src0->ne[0];
     const int nr = ggml_nelements(src1);
     const enum ggml_type type = src0->type;
-    dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
 
     assert( dst->ne[0] == nc);
     assert( dst->ne[1] == nr);
@@ -16529,6 +16173,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                         //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks);
 
                         size_t cur = 0;
+                        const enum ggml_type vec_dot_type = type_traits[node->src0->type].vec_dot_type;
 
 #if defined(GGML_USE_CUBLAS)
                         if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) {
@@ -16544,39 +16189,20 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                         }
                         else
 #endif
-                        if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                            if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
-                                node->n_tasks = 1; // TODO: this actually is doing nothing
-                                                   //       the threads are still spinning
+                        if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
+                            node->n_tasks = 1; // TODO: this actually is doing nothing
+                                               //       the threads are still spinning
+                            if (node->src0->type != GGML_TYPE_F32) {
                                 // here we need memory just for single 2D matrix from src0
                                 cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
-                            } else {
-                                cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
                             }
-#else
-                            cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
+                        } else
 #endif
-                        } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
-                            cur = 0;
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                            if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
-                                node->n_tasks = 1;
-                            }
-#endif
-                        } else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                            if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
-                                node->n_tasks = 1;
-                                cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
-                            } else
-#endif
-                            {
-                                const enum ggml_type type_q = quantize_fns[node->src0->type].vec_dot_type;
-                                cur = GGML_TYPE_SIZE[type_q]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[type_q];
-                            }
+                        if (node->src1->type != vec_dot_type) {
+                            cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[vec_dot_type];
                         } else {
-                            GGML_ASSERT(false);
+                            cur = 0;
                         }
 
                         work_size = MAX(work_size, cur);

ggml.h

@@ -250,8 +250,8 @@ extern "C" {
     GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t x);
     GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
 
-    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
-    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
+    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
+    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
 
     struct ggml_object;
     struct ggml_context;
@@ -1515,25 +1515,24 @@ extern "C" {
     //
 
 #ifdef  __cplusplus
-    // restrict not standard in C++
+// restrict not standard in C++
 #define GGML_RESTRICT
 #else
 #define GGML_RESTRICT restrict
 #endif
-    typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-    typedef void (*quantize_row_q_t)  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-    typedef void (*vec_dot_q_t)       (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
+    typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+    typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int k);
+    typedef void (*ggml_vec_dot_t)   (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
 
     typedef struct {
-        dequantize_row_q_t dequantize_row_q;
-        quantize_row_q_t   quantize_row_q;
-        quantize_row_q_t   quantize_row_q_reference;
-        quantize_row_q_t   quantize_row_q_dot;
-        vec_dot_q_t        vec_dot_q;
-        enum ggml_type     vec_dot_type;
-    } quantize_fns_t;
+        ggml_to_float_t   to_float;
+        ggml_from_float_t from_float;
+        ggml_from_float_t from_float_reference;
+        ggml_vec_dot_t    vec_dot;
+        enum ggml_type    vec_dot_type;
+    } ggml_type_traits_t;
 
-    quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
+    ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i);
 
 #ifdef  __cplusplus
 }

llama.cpp

@@ -1156,6 +1156,7 @@ static void llama_model_load_internal(
             }
         }
 #endif // GGML_USE_CUBLAS
+
 #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
         const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
 
@@ -1164,6 +1165,10 @@ static void llama_model_load_internal(
             fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__);
         }
         size_t vram_kv_cache = 0;
+
+#ifdef GGML_USE_CUBLAS
+        const int max_backend_supported_layers = hparams.n_layer + 3;
+        const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
         if (n_gpu_layers > (int) hparams.n_layer + 1) {
             if (low_vram) {
                 fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
@@ -1180,14 +1185,18 @@ static void llama_model_load_internal(
                 vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2;
             }
         }
-        const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
+#elif defined(GGML_USE_CLBLAST)
+        const int max_backend_supported_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers = hparams.n_layer + 1;
+#endif // GGML_USE_CUBLAS
+
         fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n",
-                __func__, std::min(n_gpu_layers, max_offloadable_layers), hparams.n_layer + 3);
+                __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
         fprintf(stderr, "%s: total VRAM used: %zu MB\n",
                 __func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
 #else
         (void) n_gpu_layers;
-#endif
+#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
     }
 
     // populate `tensors_by_name`
@@ -1896,10 +1905,10 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     llama_sample_softmax(ctx, candidates);
 
+    const int64_t t_start_sample_us = ggml_time_us();
+
     // Compute the cumulative probabilities
     float cum_sum = 0.0f;
     size_t last_idx = candidates->size;
@@ -1928,9 +1937,8 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     llama_sample_softmax(nullptr, candidates);
+    const int64_t t_start_sample_us = ggml_time_us();
 
     // Compute the first and second derivatives
     std::vector<float> first_derivatives(candidates->size - 1);
@@ -1982,11 +1990,11 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     // Compute the softmax of logits and calculate entropy
     llama_sample_softmax(nullptr, candidates);
 
+    const int64_t t_start_sample_us = ggml_time_us();
+
     float entropy = 0.0f;
     for (size_t i = 0; i < candidates->size; ++i) {
         entropy += -candidates->data[i].p * logf(candidates->data[i].p);
@@ -2155,13 +2163,11 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_
 
     if (ctx) {
         ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-        ctx->n_sample++;
     }
     return X;
 }
 
 llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
-    assert(ctx);
     int64_t t_start_sample_us;
     t_start_sample_us = ggml_time_us();
 
@@ -2176,13 +2182,14 @@ llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_tok
         candidates->size = 1;
     }
 
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+
     // Normalize the probabilities of the remaining words
     llama_sample_softmax(ctx, candidates);
 
     // Sample the next word X from the remaining words
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
     llama_token X = llama_sample_token(ctx, candidates);
     t_start_sample_us = ggml_time_us();
 
@@ -2250,10 +2257,10 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
     }
     float * f32_output = (float *) output.addr;
 
-    quantize_fns_t qtype;
+    ggml_type_traits_t qtype;
     if (ggml_is_quantized(tensor.type)) {
-        qtype = ggml_internal_get_quantize_fn(tensor.type);
-        if (qtype.dequantize_row_q == NULL) {
+        qtype = ggml_internal_get_type_traits(tensor.type);
+        if (qtype.to_float == NULL) {
             throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type)));
         }
     } else if (tensor.type != GGML_TYPE_F16) {
@@ -2264,7 +2271,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
         if (tensor.type == GGML_TYPE_F16) {
             ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor.data, f32_output, nelements);
         } else if (ggml_is_quantized(tensor.type)) {
-            qtype.dequantize_row_q(tensor.data, f32_output, nelements);
+            qtype.to_float(tensor.data, f32_output, nelements);
         } else {
             LLAMA_ASSERT(false); // unreachable
         }
@@ -2289,7 +2296,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
             if (typ == GGML_TYPE_F16) {
                 ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
             } else {
-                qtype.dequantize_row_q(inbuf, outbuf, nels);
+                qtype.to_float(inbuf, outbuf, nels);
             }
         };
         workers.push_back(std::thread(compute, tensor.type, tensor.data + in_buff_offs, f32_output + out_buff_offs, thr_elems));
@@ -3473,23 +3480,35 @@ llama_token llama_token_nl() {
     return 13;
 }
 
+struct llama_timings llama_get_timings(struct llama_context * ctx) {
+    struct llama_timings result = {
+        /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
+        /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
+        /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
+        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
+        /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
+        /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
+
+        /*.n_sample =*/ std::max(1, ctx->n_sample),
+        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
+        /*.n_eval   =*/ std::max(1, ctx->n_eval),
+    };
+
+    return result;
+}
 
 void llama_print_timings(struct llama_context * ctx) {
-    const int64_t t_end_us = ggml_time_us();
-
-    const int32_t n_sample = std::max(1, ctx->n_sample);
-    const int32_t n_eval   = std::max(1, ctx->n_eval);
-    const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
+    const llama_timings timings = llama_get_timings(ctx);
 
     fprintf(stderr, "\n");
-    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
+    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, timings.t_load_ms);
     fprintf(stderr, "%s:      sample time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample, 1e6 / ctx->t_sample_us * n_sample);
+            __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
     fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval, 1e6 / ctx->t_p_eval_us * n_p_eval);
+            __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
     fprintf(stderr, "%s:        eval time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_eval_us,   n_eval,   1e-3 * ctx->t_eval_us   / n_eval,   1e6 / ctx->t_eval_us   * n_eval);
-    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
+            __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
+    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
 }
 
 void llama_reset_timings(struct llama_context * ctx) {

llama.h

@@ -134,6 +134,20 @@ extern "C" {
         bool quantize_output_tensor; // quantize output.weight
     } llama_model_quantize_params;
 
+    // performance timing information
+    struct llama_timings {
+        double t_start_ms;
+        double t_end_ms;
+        double t_load_ms;
+        double t_sample_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_sample;
+        int32_t n_p_eval;
+        int32_t n_eval;
+    };
+
     LLAMA_API struct llama_context_params llama_context_default_params();
     LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
 
@@ -331,6 +345,7 @@ extern "C" {
     LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
 
     // Performance information
+    LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
     LLAMA_API void llama_print_timings(struct llama_context * ctx);
     LLAMA_API void llama_reset_timings(struct llama_context * ctx);
 

pocs/vdot/q8dot.cpp

@@ -136,7 +136,7 @@ int main(int argc, char** argv) {
 
     auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
 
-    auto funcs = ggml_internal_get_quantize_fn(ggml_type);
+    auto funcs = ggml_internal_get_type_traits(ggml_type);
 
     Stat simple, ggml;
 
@@ -156,8 +156,8 @@ int main(int argc, char** argv) {
 
         t1 = std::chrono::high_resolution_clock::now();
         float fs;
-        if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data());
-        else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data());
+        if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data());
+        else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data());
         t2 = std::chrono::high_resolution_clock::now();
         t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
         if (iloop > 3) ggml.addResult(fs, t);

pocs/vdot/vdot.cpp

@@ -235,7 +235,7 @@ int main(int argc, char** argv) {
     int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64);
     int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64);
 
-    auto funcs = useQ4_1 ? ggml_internal_get_quantize_fn(GGML_TYPE_Q4_1) : ggml_internal_get_quantize_fn(GGML_TYPE_Q4_0);
+    auto funcs = useQ4_1 ? ggml_internal_get_type_traits(GGML_TYPE_Q4_1) : ggml_internal_get_type_traits(GGML_TYPE_Q4_0);
 
     std::vector<block_q4_0> q40;
     std::vector<block_q4_1> q41;
@@ -261,9 +261,9 @@ int main(int argc, char** argv) {
         // Note, we do not include this in the timing as in practical application
         // we already have the quantized model weights.
         if (useQ4_1) {
-            funcs.quantize_row_q(x1.data(), q41.data(), kVecSize);
+            funcs.from_float(x1.data(), q41.data(), kVecSize);
         } else {
-            funcs.quantize_row_q(x1.data(), q40.data(), kVecSize);
+            funcs.from_float(x1.data(), q40.data(), kVecSize);
         }
 
         // Now measure time the dot product needs using the "scalar" version above
@@ -282,9 +282,10 @@ int main(int argc, char** argv) {
             dot_q4_q8(kVecSize, &result, q40.data(), q8.data());
         }
         else {
-            funcs.quantize_row_q_dot(y1.data(), q8.data(), kVecSize);
-            if (useQ4_1) funcs.vec_dot_q(kVecSize, &result, q41.data(), q8.data());
-            else funcs.vec_dot_q(kVecSize, &result, q40.data(), q8.data());
+            auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type);
+            vdot.from_float(y1.data(), q8.data(), kVecSize);
+            if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data());
+            else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data());
         }
         sumq += result;
         t2 = std::chrono::high_resolution_clock::now();

scripts/sync-ggml.sh

@@ -8,4 +8,7 @@ cp -rpv ../ggml/src/ggml-opencl.cpp  ./ggml-opencl.cpp
 cp -rpv ../ggml/src/ggml-metal.h     ./ggml-metal.h
 cp -rpv ../ggml/src/ggml-metal.m     ./ggml-metal.m
 cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
-cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
+cp -rpv ../ggml/include/ggml/ggml.h  ./ggml.h
+
+cp -rpv ../ggml/tests/test-opt.c    ./tests/test-opt.c
+cp -rpv ../ggml/tests/test-grad0.c  ./tests/test-grad0.c

tests/test-grad0.c

@@ -1154,7 +1154,7 @@ int main(int argc, const char ** argv) {
                             continue;
                         }
 
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode));
+                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
 
                         GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
                         check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);

tests/test-quantize-fns.cpp

@@ -40,26 +40,26 @@ float array_rmse(const float * a1, const float * a2, size_t n) {
 }
 
 // Total quantization error on test data
-float total_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
+float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
     std::vector<uint8_t> tmp_q(2*test_size);
     std::vector<float> tmp_out(test_size);
 
-    qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
+    qfns.from_float(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
     return array_rmse(test_data, tmp_out.data(), test_size);
 }
 
 // Total quantization error on test data
-float reference_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
+float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
     std::vector<uint8_t> tmp_q(2*test_size);
     std::vector<float> tmp_out(test_size);
     std::vector<float> tmp_out_ref(test_size);
 
-    qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
+    qfns.from_float(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
 
-    qfns.quantize_row_q_reference(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out_ref.data(), test_size);
+    qfns.from_float_reference(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
 
     return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
 }
@@ -73,15 +73,17 @@ float dot_product(const float * a1, const float * a2, size_t test_size) {
 }
 
 // Total dot product error
-float dot_product_error(quantize_fns_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
+float dot_product_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
     std::vector<uint8_t> tmp_q1(2*test_size);
     std::vector<uint8_t> tmp_q2(2*test_size);
 
-    qfns.quantize_row_q    (test_data1, tmp_q1.data(), test_size);
-    qfns.quantize_row_q_dot(test_data2, tmp_q2.data(), test_size);
+    auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
+
+    qfns.from_float(test_data1, tmp_q1.data(), test_size);
+    vdot.from_float(test_data2, tmp_q2.data(), test_size);
 
     float result = INFINITY;
-    qfns.vec_dot_q(test_size, &result, tmp_q1.data(), tmp_q2.data());
+    qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data());
 
     const float dot_ref = dot_product(test_data1, test_data2, test_size);
 
@@ -123,9 +125,9 @@ int main(int argc, char * argv[]) {
 
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
 
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        if (qfns.from_float && qfns.to_float) {
             const float total_error = total_quantization_error(qfns, test_size, test_data.data());
             const float max_quantization_error =
                 type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :

tests/test-quantize-perf.cpp

@@ -123,9 +123,9 @@ void usage(char * argv[]) {
     printf("  --type TYPE           set test type as");
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(type);
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
         if (ggml_type_name(type) != NULL) {
-            if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+            if (qfns.from_float && qfns.to_float) {
                 printf(" %s", ggml_type_name(type));
             }
         }
@@ -271,12 +271,12 @@ int main(int argc, char * argv[]) {
 
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
         if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
             continue;
         }
 
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        if (qfns.from_float && qfns.to_float) {
             printf("%s\n", ggml_type_name(type));
 
             if (params.op_quantize_row_q_reference) {
@@ -284,7 +284,7 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q_reference(test_data1, test_q1, size);
+                        qfns.from_float_reference(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -298,7 +298,7 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q(test_data1, test_q1, size);
+                        qfns.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -309,11 +309,11 @@ int main(int argc, char * argv[]) {
 
             if (params.op_dequantize_row_q) {
                 printf("  dequantize_row_q\n");
-                qfns.quantize_row_q(test_data1, test_q1, largest);
+                qfns.from_float(test_data1, test_q1, largest);
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.dequantize_row_q(test_q1, test_out, size);
+                        qfns.to_float(test_q1, test_out, size);
                         return test_out[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -327,7 +327,8 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q_dot(test_data1, test_q1, size);
+                        auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
+                        vdot.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -338,13 +339,13 @@ int main(int argc, char * argv[]) {
 
             if (params.op_vec_dot_q) {
                 printf("  vec_dot_q\n");
-                qfns.quantize_row_q(test_data1, test_q1, largest);
-                qfns.quantize_row_q(test_data2, test_q2, largest);
+                qfns.from_float(test_data1, test_q1, largest);
+                qfns.from_float(test_data2, test_q2, largest);
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
                         float result;
-                        qfns.vec_dot_q(size, &result, test_q1, test_q2);
+                        qfns.vec_dot(size, &result, test_q1, test_q2);
                         return result;
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);