Update ggml-sycl.cpp

This reverts commit 8c570c9496.

.devops/main-intel.Dockerfile

@@ -1,15 +1,7 @@
-ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
-    rm /etc/apt/sources.list.d/intel-graphics.list && \
-    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
-    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
-    chmod 644 /usr/share/keyrings/intel-graphics.gpg
-
 ARG LLAMA_SYCL_F16=OFF
 RUN apt-get update && \
     apt-get install -y git

.devops/server-intel.Dockerfile

@@ -1,15 +1,7 @@
-ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
-    rm /etc/apt/sources.list.d/intel-graphics.list && \
-    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
-    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
-    chmod 644 /usr/share/keyrings/intel-graphics.gpg
-
 ARG LLAMA_SYCL_F16=OFF
 RUN apt-get update && \
     apt-get install -y git libcurl4-openssl-dev
@@ -27,14 +19,6 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
-    rm /etc/apt/sources.list.d/intel-graphics.list && \
-    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
-    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
-    chmod 644 /usr/share/keyrings/intel-graphics.gpg
-
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev
 

.github/pull_request_template.md

@@ -2,4 +2,4 @@
     - [ ] Review Complexity : Low
     - [ ] Review Complexity : Medium
     - [ ] Review Complexity : High
-- [ ] I have read the [contributing guidelines](CONTRIBUTING.md)
+- [ ] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md)

.github/workflows/build.yml

@@ -13,7 +13,7 @@ on:
     paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
+    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@@ -684,7 +684,7 @@ jobs:
             cmake --build build --config ${{ matrix.build }} -j $(nproc)
 
   windows-latest-cmake:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     env:
       OPENBLAS_VERSION: 0.3.23
@@ -829,7 +829,7 @@ jobs:
           name: llama-bin-win-${{ matrix.build }}.zip
 
   windows-latest-cmake-cuda:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     strategy:
       matrix:
@@ -843,8 +843,9 @@ jobs:
         with:
           fetch-depth: 0
 
-      - uses: Jimver/cuda-toolkit@v0.2.11
+      - name: Install CUDA toolkit
         id: cuda-toolkit
+        uses: Jimver/cuda-toolkit@v0.2.15
         with:
           cuda: ${{ matrix.cuda }}
           method: 'network'

.github/workflows/server.yml

@@ -16,11 +16,9 @@ on:
     branches:
       - master
     paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-  pull_request_target:
+  pull_request:
     types: [opened, synchronize, reopened]
     paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-  schedule:
-    -  cron: '2 4 * * *'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
@@ -113,60 +111,59 @@ jobs:
           cd examples/server/tests
           PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow
 
-# note : stopped working on windows for some reason
-#        https://github.com/ggerganov/llama.cpp/actions/runs/9375600457/job/25813996327#step:8:96
-#  server-windows:
-#    runs-on: windows-latest
-#
-#    steps:
-#      - name: Clone
-#        id: checkout
-#        uses: actions/checkout@v4
-#        with:
-#          fetch-depth: 0
-#          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-#
-#      - name: libCURL
-#        id: get_libcurl
-#        env:
-#          CURL_VERSION: 8.6.0_6
-#        run: |
-#          curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
-#          mkdir $env:RUNNER_TEMP/libcurl
-#          tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
-#
-#      - name: Build
-#        id: cmake_build
-#        run: |
-#          cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
-#          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
-#
-#      - name: Python setup
-#        id: setup_python
-#        uses: actions/setup-python@v5
-#        with:
-#          python-version: '3.11'
-#
-#      - name: Tests dependencies
-#        id: test_dependencies
-#        run: |
-#          pip install -r examples/server/tests/requirements.txt
-#
-#      - name: Copy Libcurl
-#        id: prepare_libcurl
-#        run: |
-#          cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
-#
-#      - name: Tests
-#        id: server_integration_tests
-#        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
-#        run: |
-#          cd examples/server/tests
-#          behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
-#
-#      - name: Slow tests
-#        id: server_integration_tests_slow
-#        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-#        run: |
-#          cd examples/server/tests
-#          behave.exe --stop --no-skipped --no-capture --tags slow
+
+  server-windows:
+    runs-on: windows-2019
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: libCURL
+        id: get_libcurl
+        env:
+          CURL_VERSION: 8.6.0_6
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
+          mkdir $env:RUNNER_TEMP/libcurl
+          tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
+          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
+
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r examples/server/tests/requirements.txt
+
+      - name: Copy Libcurl
+        id: prepare_libcurl
+        run: |
+          cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
+        run: |
+          cd examples/server/tests
+          behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd examples/server/tests
+          behave.exe --stop --no-skipped --no-capture --tags slow

CMakeLists.txt

@@ -402,12 +402,26 @@ if (LLAMA_CUBLAS)
 endif()
 
 if (LLAMA_CUDA)
-    cmake_minimum_required(VERSION 3.17)
+    cmake_minimum_required(VERSION 3.18)  # for CMAKE_CUDA_ARCHITECTURES
 
     find_package(CUDAToolkit)
     if (CUDAToolkit_FOUND)
         message(STATUS "CUDA found")
 
+        if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
+            # 52 == lowest CUDA 12 standard
+            # 60 == f16 CUDA intrinsics
+            # 61 == integer CUDA intrinsics
+            # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster
+            if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
+                set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
+            else()
+                set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
+                #set(CMAKE_CUDA_ARCHITECTURES "OFF") # use this to compile much faster, but only F16 models work
+            endif()
+        endif()
+        message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
+
         enable_language(CUDA)
 
         set(GGML_HEADERS_CUDA ggml-cuda.h)
@@ -472,21 +486,6 @@ if (LLAMA_CUDA)
         else()
             set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
         endif()
-
-    if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
-        # 52 == lowest CUDA 12 standard
-        # 60 == f16 CUDA intrinsics
-        # 61 == integer CUDA intrinsics
-        # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster
-        if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
-            set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
-        else()
-            set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
-            #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work
-        endif()
-    endif()
-    message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
-
     else()
         message(WARNING "CUDA not found")
     endif()

README.md

@@ -53,7 +53,6 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
         <li><a href="#quantization">Quantization</a></li>
         <li><a href="#interactive-mode">Interactive mode</a></li>
         <li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
-        <li><a href="#instruct-mode">Instruct mode</a></li>
         <li><a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a></li>
         <li><a href="#seminal-papers-and-background-on-the-models">Seminal papers and background on the models</a></li>
         <li><a href="#perplexity-measuring-model-quality">Perplexity (measuring model quality)</a></li>
@@ -577,7 +576,9 @@ Building the program with BLAS support may lead to some performance improvements
   vulkaninfo
   ```
 
-  Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+  Alternatively your package manager might be able to provide the appropriate libraries.
+  For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+  For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages.
 
   Then, build llama.cpp using the cmake command below:
 
@@ -769,34 +770,6 @@ The `grammars/` folder contains a handful of sample grammars. To write your own,
 
 For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one.
 
-### Instruct mode
-
-1. First, download and place the `ggml` model into the `./models` folder
-2. Run the `main` tool like this:
-
-```
-./examples/alpaca.sh
-```
-
-Sample run:
-
-```
-== Running in interactive mode. ==
- - Press Ctrl+C to interject at any time.
- - Press Return to return control to LLaMA.
- - If you want to submit another line, end your input in '\'.
-
- Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-> How many letters are there in the English alphabet?
-There 26 letters in the English Alphabet
-> What is the most common way of transportation in Amsterdam?
-The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
-> List 5 words that start with "ca".
-cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
->
-```
-
 ### Obtaining and using the Facebook LLaMA 2 model
 
 - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.

common/json-schema-to-grammar.cpp

@@ -40,7 +40,7 @@ static std::string build_repetition(const std::string & item_rule, int min_items
     return result;
 }
 
-const std::string SPACE_RULE = "\" \"?";
+const std::string SPACE_RULE = "| \" \" | \"\\n\" [ \\t]{0,20}";
 
 struct BuiltinRule {
     std::string content;
@@ -57,7 +57,7 @@ std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
     {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
     {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
     {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}},
-    {"char",   {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}},
+    {"char",   {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}},
     {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
     {"null", {"\"null\" space", {}}},
 };

examples/alpaca.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \
-       --color \
-       -f ./prompts/alpaca.txt \
-       --ctx_size 2048 \
-       -n -1 \
-       -ins -b 256 \
-       --top_k 10000 \
-       --temp 0.2 \
-       --repeat_penalty 1.1 \
-       -t 7

examples/gpt4all.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main --color --instruct --threads 4 \
-       --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \
-       --file ./prompts/alpaca.txt \
-       --batch_size 8 --ctx_size 2048 -n -1 \
-       --repeat_last_n 64 --repeat_penalty 1.3 \
-       --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95

examples/json_schema_to_grammar.py

@@ -29,9 +29,8 @@ class BuiltinRule:
         self.content = content
         self.deps = deps or []
 
-# whitespace is constrained to a single space char to prevent model "running away" in
-# whitespace. Also maybe improves generation quality?
-SPACE_RULE = '" "?'
+# Constraining spaces to prevent model "running away".
+SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}'
 
 PRIMITIVE_RULES = {
     'boolean'      : BuiltinRule('("true" | "false") space', []),
@@ -43,7 +42,7 @@ PRIMITIVE_RULES = {
     'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
     'array'        : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
     'uuid'         : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []),
-    'char'         : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})', []),
+    'char'         : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []),
     'string'       : BuiltinRule(r'"\"" char* "\"" space', ['char']),
     'null'         : BuiltinRule('"null" space', []),
 }

examples/llama-bench/llama-bench.cpp

@@ -1033,6 +1033,27 @@ struct markdown_printer : public printer {
         if (field == "n_gpu_layers") {
             return 3;
         }
+        if (field == "n_threads") {
+            return 7;
+        }
+        if (field == "n_batch") {
+            return 7;
+        }
+        if (field == "n_ubatch") {
+            return 8;
+        }
+        if (field == "type_k" || field == "type_v") {
+            return 6;
+        }
+        if (field == "split_mode") {
+            return 5;
+        }
+        if (field == "flash_attn") {
+            return 2;
+        }
+        if (field == "use_mmap") {
+            return 4;
+        }
         if (field == "test") {
             return 13;
         }

examples/llama2-13b.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \
-       --color \
-       --ctx_size 2048 \
-       -n -1 \
-       -ins -b 256 \
-       --top_k 10000 \
-       --temp 0.2 \
-       --repeat_penalty 1.1 \
-       -t 8

examples/llama2.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-#
-# Temporary script - will be removed in the future
-#
-
-cd `dirname $0`
-cd ..
-
-./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \
-       --color \
-       --ctx_size 2048 \
-       -n -1 \
-       -ins -b 256 \
-       --top_k 10000 \
-       --temp 0.2 \
-       --repeat_penalty 1.1 \
-       -t 8

examples/server/public/json-schema-to-grammar.mjs

@@ -1,5 +1,5 @@
 // WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first.
-const SPACE_RULE = '" "?';
+const SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}';
 
 function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
   if (minItems === 0 && maxItems === 1) {
@@ -41,7 +41,7 @@ const PRIMITIVE_RULES = {
   object         : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
   array          : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
   uuid           : new BuiltinRule('"\\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\\"" space', []),
-  char           : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F]{4})`, []),
+  char           : new BuiltinRule(`[^"\\\\\\x7F\\x00-\\x1F] | [\\\\] (["\\\\bfnrt] | "u" [0-9a-fA-F]{4})`, []),
   string         : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']),
   null           : new BuiltinRule('"null" space', []),
 };

examples/server/server.cpp

@@ -147,7 +147,7 @@ struct server_slot {
     int32_t n_prompt_tokens           = 0;
     int32_t n_prompt_tokens_processed = 0;
 
-    json prompt;
+    json prompt; // can be either a string, array of strings or array of token ids
 
     // when a task is submitted, we first tokenize the prompt and store it here
     std::vector<llama_token> prompt_tokens;
@@ -958,13 +958,16 @@ struct server_context {
         if (!task.infill) {
             const auto & prompt = data.find("prompt");
             if (prompt == data.end()) {
-                send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
+                send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
                 return false;
-            } else {
-                slot.prompt = *prompt;
             }
-            if (slot.prompt.is_array() && slot.prompt.size() == 0) {
-                send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST);
+
+            if ((prompt->is_string()) ||
+                (prompt->is_array() &&  prompt->size() == 1 && prompt->at(0).is_string()) ||
+                (prompt->is_array() && !prompt->empty()     && prompt->at(0).is_number_integer())) {
+                slot.prompt = *prompt;
+            } else {
+                send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
         }
@@ -1582,14 +1585,18 @@ struct server_context {
         switch (task.type) {
             case SERVER_TASK_TYPE_COMPLETION:
                 {
-                    int id_slot        = json_value(task.data, "id_slot", -1);
-                    std::string prompt = json_value(task.data, "prompt", std::string());
+                    const int id_slot = json_value(task.data, "id_slot", -1);
 
                     server_slot * slot;
 
                     if (id_slot != -1) {
                         slot = get_slot_by_id(id_slot);
                     } else {
+                        std::string prompt;
+                        if (task.data.contains("prompt") && task.data.at("prompt").is_string()) {
+                            json_value(task.data, "prompt", std::string());
+                        }
+
                         slot = get_available_slot(prompt);
                     }
 

ggml-alloc.c

@@ -886,7 +886,7 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
 #endif
         for (size_t i = 0; i < *n_buffers; i++) {
-            ggml_backend_buffer_free(*buffers[i]);
+            ggml_backend_buffer_free((*buffers)[i]);
         }
         free(*buffers);
         return false;

ggml-cuda.cu

@@ -2740,7 +2740,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                 case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
             }

ggml-cuda/common.cuh

@@ -139,6 +139,7 @@
 #define CC_PASCAL     600
 #define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
 #define CC_VOLTA      700
+#define CC_TURING     750
 #define CC_AMPERE     800
 #define CC_OFFSET_AMD 1000000
 #define CC_RDNA1      (CC_OFFSET_AMD + 1010)
@@ -326,9 +327,17 @@ static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int
 #endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000
 #endif // defined(GGML_USE_HIPBLAS)
 
-#define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
+#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
+#define FP16_AVAILABLE
+#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
 
-#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
+#define FP16_MMA_AVAILABLE
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
+
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING
+#define INT8_MMA_AVAILABLE
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING
 
 static bool fast_fp16_available(const int cc) {
     return cc >= CC_PASCAL && cc != 610;
@@ -338,6 +347,10 @@ static bool fp16_mma_available(const int cc) {
     return cc < CC_OFFSET_AMD && cc >= CC_VOLTA;
 }
 
+static bool int8_mma_available(const int cc) {
+    return cc < CC_OFFSET_AMD && cc >= CC_TURING;
+}
+
 [[noreturn]]
 static __device__ void no_device_code(
     const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
@@ -379,7 +392,7 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
 }
 
 static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
 
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 #pragma unroll
@@ -412,7 +425,7 @@ static __device__ __forceinline__ float warp_reduce_max(float x) {
 }
 
 static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
 
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
     return __float2half(fmaxf(__half2float(a), __half2float(b)));

ggml-cuda/fattn-common.cuh

@@ -74,7 +74,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
 
         const int sumi = __dp4a(v, u, 0);
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
             const half2  * Q_ds = (const half2  *) Q_ds_v;
 
@@ -122,7 +122,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
 
         const int sumi = __dp4a(v, u, 0);
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
             const half2  * Q_ds = (const half2  *) Q_ds_v;
 
@@ -181,7 +181,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
 
         const int sumi = __dp4a(v, u, 0);
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
             const half2  * Q_ds = (const half2  *) Q_ds_v;
 
@@ -236,7 +236,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
 
         const int sumi = __dp4a(v, u, 0);
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
             const half2  * Q_ds = (const half2  *) Q_ds_v;
 
@@ -314,7 +314,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16(
     GGML_UNUSED(Q_q8);
     GGML_UNUSED(Q_ds_v);
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     if (std::is_same<T, half>::value) {
         const half2 * Q_h2 = (const half2 *) Q_v;
 
@@ -407,7 +407,7 @@ static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__
     const int q0 = x[ib].qs[iqs];
     const int q  = ((q0 >> (4*shift)) & 0x0F) - 8;
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     if (std::is_same<T, half>::value) {
         return ((half) d)*((half) q);
     }
@@ -428,7 +428,7 @@ static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__
     const int   q0 = x[ib].qs[iqs];
     const int   q  = ((q0 >> (4*shift)) & 0x0F);
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     if (std::is_same<T, half>::value) {
         return __low2half(dm)*((half) q) + __high2half(dm);
     }
@@ -453,7 +453,7 @@ static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__
     const int qh  = ((qh0 >> idq) << 4) & 0x10;
     const int q   = (ql | qh) - 16;
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     if (std::is_same<T, half>::value) {
         return ((half) d)*((half) q);
     }
@@ -478,7 +478,7 @@ static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__
     const int   qh  = ((qh0 >> idq) << 4) & 0x10;
     const int   q   = (ql | qh);
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     if (std::is_same<T, half>::value) {
         return __low2half(dm)*((half) q) + __high2half(dm);
     }
@@ -497,7 +497,7 @@ static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__
     const T   d = x[ib].d;
     const int q = x[ib].qs[iqs];
 
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     if (std::is_same<T, half>::value) {
         return ((half) d)*((half) q);
     }

ggml-cuda/fattn-tile-f16.cu

@@ -43,7 +43,7 @@ static __global__ void flash_attn_tile_ext_f16(
         const int ne1,
         const int ne2,
         const int ne3) {
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.

ggml-cuda/fattn-vec-f16.cuh

@@ -40,7 +40,7 @@ static __global__ void flash_attn_vec_ext_f16(
         const int ne1,
         const int ne2,
         const int ne3) {
-#if FP16_AVAILABLE
+#ifdef FP16_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16<D>(type_K);

ggml-cuda/fattn-wmma-f16.cuh

@@ -1,9 +1,9 @@
 #include "common.cuh"
 #include "fattn-common.cuh"
 
-#if FP16_MMA_AVAILABLE
+#ifdef FP16_MMA_AVAILABLE
 #include <mma.h>
-#endif
+#endif // FP16_MMA_AVAILABLE
 
 // D == head size, VKQ_stride == num VKQ rows calculated in parallel:
 template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
@@ -45,7 +45,7 @@ static __global__ void flash_attn_ext_f16(
         const int ne1,
         const int ne2,
         const int ne3) {
-#if FP16_MMA_AVAILABLE
+#ifdef FP16_MMA_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.

ggml-cuda/mma.cuh

@@ -0,0 +1,161 @@
+#include "common.cuh"
+
+struct mma_int_A_I16K4 {
+    static constexpr int I  = 16;
+    static constexpr int K  = 4;
+    static constexpr int ne = 2;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l%2) * (I/2) + threadIdx.x / K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int /* l */) {
+        const int ret = threadIdx.x % K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+};
+
+struct mma_int_A_I16K8 {
+    static constexpr int I  = 16;
+    static constexpr int K  = 8;
+    static constexpr int ne = 4;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l%2) * (I/2) + threadIdx.x / (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int l) {
+        const int ret = (l/2) * (K/2) + threadIdx.x % (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+};
+
+struct mma_int_B_J8K4 {
+    static constexpr int J  = 8;
+    static constexpr int K  = 4;
+    static constexpr int ne = 1;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_j(const int /* l */) {
+        const int ret = threadIdx.x / K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int /* l */) {
+        const int ret = threadIdx.x % K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+};
+
+struct mma_int_B_J8K8 {
+    static constexpr int J  = 8;
+    static constexpr int K  = 8;
+    static constexpr int ne = 2;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_j(const int /* l */) {
+        const int ret = threadIdx.x / (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int l) {
+        const int ret = l * (K/2) + threadIdx.x % (K/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+};
+
+struct mma_int_C_I16J8 {
+    static constexpr int I  = 16;
+    static constexpr int J  = 8;
+    static constexpr int ne = 4;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l/2) * (I/2) + threadIdx.x / (J/2);
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_j(const int l) {
+        const int ret = 2 * (threadIdx.x % (J/2)) + l%2;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    __device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) {
+#ifdef INT8_MMA_AVAILABLE
+#if __CUDA_ARCH__ >= CC_AMPERE
+        asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
+            : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+#else
+        // On Turing m16n8k16 mma is not available, use 2x m8n8k16 mma instead:
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[0]), "+r"(x[1])
+            : "r"(mma_A.x[0]), "r"(mma_B.x[0]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+#endif // __CUDA_ARCH__ >= CC_AMPERE
+#else
+        GGML_UNUSED(mma_A);
+        GGML_UNUSED(mma_B);
+        NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    __device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) {
+#ifdef INT8_MMA_AVAILABLE
+#if __CUDA_ARCH__ >= CC_AMPERE
+        asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
+            : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_A.x[2]), "r"(mma_A.x[3]), "r"(mma_B.x[0]), "r"(mma_B.x[1]));
+#else
+        // On Turing m16n8k32 mma is not available, use 4x m8n8k16 mma instead:
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[0]), "+r"(x[1])
+            : "r"(mma_A.x[0]), "r"(mma_B.x[0]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[0]), "+r"(x[1])
+            : "r"(mma_A.x[2]), "r"(mma_B.x[1]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[3]), "r"(mma_B.x[1]));
+#endif // __CUDA_ARCH__ >= CC_AMPERE
+#else
+        GGML_UNUSED(mma_A);
+        GGML_UNUSED(mma_B);
+        NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+    }
+};

ggml-cuda/mmq.cuh

@@ -2,6 +2,7 @@
 
 #include "common.cuh"
 #include "vecdotq.cuh"
+#include "mma.cuh"
 
 #include <climits>
 #include <cstdint>
@@ -14,6 +15,7 @@ typedef void (*load_tiles_mmq_t)(
 typedef void (*vec_dot_mmq_t)(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0);
+typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1);
 
 struct block_q8_1_mmq {
     half2  ds[4];
@@ -141,15 +143,15 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
     GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
 
-    const float * x_dmf = (const float *) x_dm;
-    const int   * y_qs  = (const int   *) y + 4;
-    const half2 * y_ds  = (const half2 *) y;
+    const float * x_df = (const float *) x_dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
@@ -170,12 +172,76 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mul_mat(
             }
 
             sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
-                (&x_ql[i*(WARP_SIZE + 1) + k0], u, x_dmf[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0],
+                (&x_ql[i*(WARP_SIZE + 1) + k0], u, x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0],
                 y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
         }
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const float * x_df = (const float *) x_dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    mma_A A;
+    float dA[mma_C::ne/2];
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+#pragma unroll
+    for (int l = 0; l < mma_A::ne; ++l) {
+        const int i     = i0 + mma_A::get_i(l);
+        const int k     = k0 + mma_A::get_k(l) % QI4_0;
+        const int shift =   4*(mma_A::get_k(l) / QI4_0);
+
+        A.x[l] = __vsubss4((x_ql[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F, 0x08080808);
+    }
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dA[l] = x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0];
+    }
+
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        mma_C C;
+        mma_B B;
+        half2 dsB[mma_C::ne/2];
+
+#pragma unroll
+        for (int l = 0; l < mma_B::ne; ++l) {
+            const int j =    j0 + mma_B::get_j(l);
+            const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
+
+            B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+        }
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int j = j0 + mma_C::get_j(l);
+
+            dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
+        }
+
+        C.mma_K8(A, B);
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/B.J)*C.ne + l] += dA[l/2]*__low2float(dsB[l%2])*C.x[l];
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -215,7 +281,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -249,6 +315,70 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    mma_A A;
+    half2 dmA[mma_C::ne/2];
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+#pragma unroll
+    for (int l = 0; l < mma_A::ne; ++l) {
+        const int i     = i0 + mma_A::get_i(l);
+        const int k     = k0 + mma_A::get_k(l) % QI4_0;
+        const int shift =   4*(mma_A::get_k(l) / QI4_0);
+
+        A.x[l] = (x_ql[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F;
+    }
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dmA[l] = x_dm[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0];
+    }
+
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        mma_C C;
+        mma_B B;
+        half2 dsB[mma_C::ne/2];
+
+#pragma unroll
+        for (int l = 0; l < mma_B::ne; ++l) {
+            const int j =    j0 + mma_B::get_j(l);
+            const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
+
+            B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+        }
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int j = j0 + mma_C::get_j(l);
+
+            dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
+        }
+
+        C.mma_K8(A, B);
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            const half2 dmA_dsB = dmA[l/2]*dsB[l%2];
+            sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -308,7 +438,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q5_0_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -343,6 +473,68 @@ static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const float * x_df = (const float *) x_dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+    mma_A A;
+    float dA[mma_C::ne/2];
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+#pragma unroll
+    for (int l = 0; l < mma_A::ne; ++l) {
+        const int i     =    i0 + mma_A::get_i(l);
+        const int k     = 2*(k0 + mma_A::get_k(l) % QI5_0) + mma_A::get_k(l) / QI5_0;
+
+        A.x[l] = x_ql[i*(2*WARP_SIZE + 1) + k];
+    }
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dA[l] = x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0];
+    }
+
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        mma_C C;
+        mma_B B;
+        float dB[mma_C::ne/2];
+
+#pragma unroll
+        for (int l = 0; l < mma_B::ne; ++l) {
+            const int j =    j0 + mma_B::get_j(l);
+            const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
+
+            B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+        }
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int j = j0 + mma_C::get_j(l);
+
+            dB[l] = y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
+        }
+
+        C.mma_K8(A, B);
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/B.J)*C.ne + l] += dA[l/2]*dB[l%2]*C.x[l];
+        }
+    }
+}
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
@@ -400,7 +592,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q5_1_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -434,6 +626,69 @@ static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    mma_A A;
+    half2 dmA[mma_C::ne/2];
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+#pragma unroll
+    for (int l = 0; l < mma_A::ne; ++l) {
+        const int i     =    i0 + mma_A::get_i(l);
+        const int k     = 2*(k0 + mma_A::get_k(l) % QI5_1) + mma_A::get_k(l) / QI5_1;
+
+        A.x[l] = x_ql[i*(2*WARP_SIZE + 1) + k];
+    }
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dmA[l] = x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1];
+    }
+
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        mma_C C;
+        mma_B B;
+        half2 dsB[mma_C::ne/2];
+
+#pragma unroll
+        for (int l = 0; l < mma_B::ne; ++l) {
+            const int j =    j0 + mma_B::get_j(l);
+            const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
+
+            B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+        }
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int j = j0 + mma_C::get_j(l);
+
+            dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
+        }
+
+        C.mma_K8(A, B);
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            const half2 dmA_dsB = dmA[l/2]*dsB[l%2];
+            sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -475,7 +730,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -500,6 +755,69 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const float * x_df = (const float *) x_dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+    mma_A A;
+    float dA[mma_C::ne/2];
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+#pragma unroll
+    for (int l = 0; l < mma_A::ne; ++l) {
+        const int i = i0 + mma_A::get_i(l);
+        const int k = k0 + mma_A::get_k(l);
+
+        A.x[l] = x_ql[i*(WARP_SIZE + 1) + k];
+    }
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dA[l] = x_df[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0];
+    }
+
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        mma_C C;
+        mma_B B;
+        float dB[mma_C::ne/2];
+
+#pragma unroll
+        for (int l = 0; l < mma_B::ne; ++l) {
+            const int j = j0 + mma_B::get_j(l);
+            const int k = k0 + mma_B::get_k(l);
+
+            B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+        }
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int j = j0 + mma_C::get_j(l);
+
+            dB[l] = y_df[j*MMQ_TILE_Y_K + k0/QI8_1];
+        }
+
+        C.mma_K8(A, B);
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/B.J)*C.ne + l] += C.x[l]*dA[l/2]*dB[l%2];
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -771,7 +1089,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -797,6 +1115,97 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+    mma_A   A[2];
+    int   scA[mma_C::ne/2][2];
+    int    mA[mma_C::ne/2][2];
+    half2 dmA[mma_C::ne/2];
+#pragma unroll
+    for (int kvdr = 0; kvdr < VDR_Q4_K_Q8_1_MMQ; kvdr += 4) {
+#pragma unroll
+        for (int l = 0; l < mma_A::ne; ++l) {
+            const int i = i0 + mma_A::get_i(l);
+            const int k = k0 + mma_A::get_k(l);
+
+            A[kvdr/4].x[l] = (x_ql[i*(WARP_SIZE + 1) + k] >> kvdr) & 0x0F0F0F0F;
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + mma_C::get_i(2*l);
+
+            const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
+            const uint8_t *  m = sc + 8;
+
+            scA[l][kvdr/4] = sc[kvdr/4];
+            mA[l][kvdr/4]  =  m[kvdr/4];
+        }
+    }
+
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K];
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        float tmpd[mma_C::ne] = {0.0f};
+        float tmpm[mma_C::ne] = {0.0f};
+
+#pragma unroll
+        for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
+            mma_C   C;
+            mma_B   B;
+            half2 dsB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_B::ne; ++l) {
+                const int j = j0 + mma_B::get_j(l);
+                const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
+
+                B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+            }
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
+
+            C.mma_K8(A[kvdr/4], B);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) *  __low2float(dsB[l%2]);
+                tmpm[l] += mA[l/2][kvdr/4]           * __high2float(dsB[l%2]);
+            }
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l];
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -870,7 +1279,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -896,6 +1305,97 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+    mma_A   A[2];
+    int   scA[mma_C::ne/2][2];
+    int    mA[mma_C::ne/2][2];
+    half2 dmA[mma_C::ne/2];
+#pragma unroll
+    for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
+#pragma unroll
+        for (int l = 0; l < mma_A::ne; ++l) {
+            const int i = i0 + mma_A::get_i(l);
+            const int k = QR5_K*k0 + QR5_K*kvdr + mma_A::get_k(l);
+
+            A[kvdr/4].x[l] = x_ql[i*(QR5_K*WARP_SIZE + 1) + k];
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + mma_C::get_i(2*l);
+
+            const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
+            const uint8_t *  m = sc + 8;
+
+            scA[l][kvdr/4] = sc[kvdr/4];
+            mA[l][kvdr/4]  =  m[kvdr/4];
+        }
+    }
+
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K];
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        float tmpd[mma_C::ne] = {0.0f};
+        float tmpm[mma_C::ne] = {0.0f};
+
+#pragma unroll
+        for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
+            mma_C   C;
+            mma_B   B;
+            half2 dsB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_B::ne; ++l) {
+                const int j = j0 + mma_B::get_j(l);
+                const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
+
+                B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+            }
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
+
+            C.mma_K8(A[kvdr/4], B);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) *  __low2float(dsB[l%2]);
+                tmpm[l] += mA[l/2][kvdr/4]           * __high2float(dsB[l%2]);
+            }
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l];
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -962,7 +1462,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -989,6 +1489,148 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_B_J8K4  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const float * x_df = (const float *) x_dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+    mma_A   A[4];
+    int   scA[mma_C::ne/2][4];
+    float  dA[mma_C::ne/2];
+#pragma unroll
+    for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
+#pragma unroll
+        for (int l = 0; l < mma_A::ne; ++l) {
+            const int i = i0 + mma_A::get_i(l);
+            const int k = QR6_K*k0 + QR6_K*kvdr + mma_A::get_k(l);
+
+            A[kvdr/2 + 0].x[l] = x_ql[i*(QR6_K*WARP_SIZE + 1) + k + 0];
+            A[kvdr/2 + 1].x[l] = x_ql[i*(QR6_K*WARP_SIZE + 1) + k + mma_A::K];
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + mma_C::get_i(2*l);
+
+            const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]);
+
+            scA[l][kvdr/2 + 0] = sc[kvdr/2 + 0];
+            scA[l][kvdr/2 + 1] = sc[kvdr/2 + 1];
+        }
+    }
+
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dA[l] = x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K + k0/QI6_K];
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        float tmp[mma_C::ne] = {0.0f};
+
+#pragma unroll
+        for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
+            mma_C C[2];
+            mma_B B[2];
+            float dB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_B::ne; ++l) {
+                const int j = j0 + mma_B::get_j(l);
+                const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
+
+                B[0].x[l] = y_qs[j*MMQ_TILE_Y_K + k + 0];
+                B[1].x[l] = y_qs[j*MMQ_TILE_Y_K + k + mma_B::K];
+            }
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dB[l] = y_df[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
+
+            C[0].mma_K4(A[kvdr/2 + 0], B[0]);
+            C[1].mma_K4(A[kvdr/2 + 1], B[1]);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                tmp[l] += (C[0].x[l]*scA[l/2][kvdr/2 + 0] + C[1].x[l]*scA[l/2][kvdr/2 + 1])*dB[l%2];
+            }
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/mma_B::J)*mma_C::ne + l] += tmp[l]*dA[l/2];
+        }
+    }
+}
+
+template<int mmq_x, int mmq_y, int nwarps, bool need_check>
+static __device__ __forceinline__ void mmq_write_back_dp4a(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) {
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+        const int j = blockIdx.y*mmq_x + j0 + threadIdx.y;
+
+        if (j >= ne1) {
+            return;
+        }
+
+#pragma unroll
+        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+            const int i = blockIdx.x*mmq_y + i0 + threadIdx.x;
+
+            if (need_check && i >= ne0) {
+                continue;
+            }
+
+            dst[j*ne0 + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
+        }
+    }
+}
+
+template<int mmq_x, int mmq_y, int nwarps, bool need_check>
+static __device__ __forceinline__ void mmq_write_back_mma(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) {
+    typedef mma_int_C_I16J8 mma_C;
+
+    const int i0 = threadIdx.y*mma_C::I;
+    static_assert(nwarps*mma_C::I == mmq_y, "nwarps*mma_C::I != mmq_y");
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_C::J) {
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            const int j = blockIdx.y*mmq_x + j0 + mma_C::get_j(l);
+
+            if (j >= ne1) {
+                continue;
+            }
+
+            const int i = blockIdx.x*mmq_y + i0 + mma_C::get_i(l);
+
+            if (need_check && i >= ne0) {
+                continue;
+            }
+
+            dst[j*ne0 + i] = sum[(j0/mma_C::J)*mma_C::ne + l];
+        }
+    }
+}
+
 // -------------------------------------------------------------------------------------------------------------------------------------
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check, ggml_type type>
@@ -998,35 +1640,65 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_0> {
     static constexpr int              vdr        = VDR_Q4_0_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_0<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_0_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_1> {
     static constexpr int              vdr        = VDR_Q4_1_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_1<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_1_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_0> {
     static constexpr int              vdr        = VDR_Q5_0_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_0<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_0_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_1> {
     static constexpr int              vdr        = VDR_Q5_1_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_1<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_1_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q8_0> {
     static constexpr int              vdr        = VDR_Q8_0_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q8_0<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q8_0_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
@@ -1034,6 +1706,7 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q2_K> {
     static constexpr int              vdr        = VDR_Q2_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q2_K<mmq_y, nwarps, need_check>;
     static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q2_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
@@ -1041,27 +1714,46 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q3_K> {
     static constexpr int              vdr        = VDR_Q3_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q3_K<mmq_y, nwarps, need_check>;
     static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q3_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_K> {
     static constexpr int              vdr        = VDR_Q4_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_K> {
     static constexpr int              vdr        = VDR_Q5_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q6_K> {
     static constexpr int              vdr        = VDR_Q6_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q6_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q6_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q6_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q6_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 static int mmq_need_sum(const ggml_type type_x) {
@@ -1118,6 +1810,7 @@ static __global__ void mul_mat_q(
     constexpr int              vdr        = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vdr;
     constexpr load_tiles_mmq_t load_tiles = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::load_tiles;
     constexpr vec_dot_mmq_t    vec_dot    = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot;
+    constexpr mmq_write_back_t write_back = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::write_back;
 
     constexpr tile_x_sizes txs = get_tile_x_sizes_device<mmq_y>(type);
 
@@ -1137,7 +1830,7 @@ static __global__ void mul_mat_q(
 
     const int * y = (const int *) yc + blockIdx.y*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int));
 
-    float sum[(mmq_x/nwarps) * (mmq_y/WARP_SIZE)] = {0.0f};
+    float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};
 
     for (int kb0 = 0; kb0 < blocks_per_row_x; kb0 += blocks_per_warp) {
 
@@ -1164,25 +1857,7 @@ static __global__ void mul_mat_q(
         }
     }
 
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = blockIdx.y*mmq_x + j0 + threadIdx.y;
-
-        if (j >= ne1) {
-            return;
-        }
-
-#pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = blockIdx.x*mmq_y + i0 + threadIdx.x;
-
-            if (need_check && i >= ne0) {
-                continue;
-            }
-
-            dst[j*ne0 + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
-        }
-    }
+    write_back(sum, dst, ne0, ne1);
 }
 
 struct mmq_args {
@@ -1256,10 +1931,10 @@ void mul_mat_q_case(const mmq_args & args, cudaStream_t stream) {
             launch_mul_mat_q<type,   8, 4>(args, stream);
             break;
         case  16:
-            launch_mul_mat_q<type,  16, 8>(args, stream);
+            launch_mul_mat_q<type,  16, 4>(args, stream);
             break;
         case  24:
-            launch_mul_mat_q<type,  24, 8>(args, stream);
+            launch_mul_mat_q<type,  24, 4>(args, stream);
             break;
         case  32:
             launch_mul_mat_q<type,  32, 8>(args, stream);

ggml-cuda/unary.cu

@@ -148,6 +148,8 @@ void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -160,6 +162,8 @@ void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -172,6 +176,8 @@ void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -184,6 +190,8 @@ void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -196,6 +204,8 @@ void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -208,6 +218,8 @@ void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -220,6 +232,8 @@ void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -232,6 +246,8 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -244,6 +260,8 @@ void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
@@ -259,6 +277,8 @@ void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     float * dst_d = (float *)dst->data;
     cudaStream_t stream = ctx.stream();
 
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 

ggml-kompute.cpp

@@ -1340,7 +1340,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
                 case GGML_UNARY_OP_RELU:
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_SILU:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     ;
             }

ggml-metal.m

@@ -744,7 +744,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_SILU:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
             }

ggml-sycl.cpp

@@ -7984,26 +7984,24 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_
     const int blocks_per_row = ncols / qk;
     const int blocks_per_warp = vdr * WARP_SIZE / qi;
 
-    const int qi_vdr = (qi / vdr); // N_threads processing 1 qk block
-
     // 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 = item_ct1.get_local_id(2) / qi_vdr; i < blocks_per_row;
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
          i += blocks_per_warp) {
-      const int ibx = row * blocks_per_row + i; // x block index
+        const int ibx = row*blocks_per_row + i; // x block index
 
-      const int iby = i * (qk / QK8_1); // y block index that aligns with ibx
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
 
-      const int iqs =
-          vdr *
-          (item_ct1.get_local_id(2) -
-           i * qi_vdr); // x block quant index when casting the quants to int
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
 
-      tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
+        tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
     }
 
     // sum up partial sums and write back result
@@ -8826,7 +8824,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
 }
 
 struct rope_corr_dims {
-    float v[4];
+    float v[2];
 };
 
 // YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
@@ -8850,29 +8848,38 @@ static void rope_yarn(
 }
 
 // rope == RoPE == rotary positional embedding
-template<typename T, bool has_pos>
-static void rope(
-    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims
-,
+template<typename T, bool has_ff>
+static void rope_norm(
+    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors,
     const sycl::nd_item<3> &item_ct1) {
-    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+    const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
                          item_ct1.get_local_id(1));
 
-    if (col >= ncols) {
+    if (i0 >= ne0) {
         return;
     }
 
     const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
                     item_ct1.get_local_id(2);
-    const int i = row*ncols + col;
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i = row*ne0 + i0;
     const int i2 = row/p_delta_rows;
 
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p * dpct::pow(freq_base, -float(col) / ncols);
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+    const float freq_factor = has_ff ? freq_factors[i0 / 2] : 1.0f;
 
     float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
     const float x0 = x[i + 0];
     const float x1 = x[i + 1];
@@ -8881,25 +8888,25 @@ static void rope(
     dst[i + 1] = x0*sin_theta + x1*cos_theta;
 }
 
-template<typename T, bool has_pos, bool has_freq_facs>
-static void rope_neox(
-    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims,
-    const float * freq_factors, const sycl::nd_item<3> &item_ct1) {
-    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+template <typename T, bool has_ff>
+static void rope_neox(const T *x, T *dst, int ne0, int n_dims,
+                      const int32_t *pos, float freq_scale, int p_delta_rows,
+                      float ext_factor, float attn_factor,
+                      rope_corr_dims corr_dims, float theta_scale,
+                      const float *freq_factors,
+                      const sycl::nd_item<3> &item_ct1) {
+    const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
                          item_ct1.get_local_id(1));
 
-    if (col >= ncols) {
+    if (i0 >= ne0) {
         return;
     }
 
     const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
                     item_ct1.get_local_id(2);
-    const int ib = col / n_dims;
-    const int ic = col % n_dims;
 
-    if (ib > 0) {
-        const int i = row*ncols + ib*n_dims + ic;
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
 
         dst[i + 0] = x[i + 0];
         dst[i + 1] = x[i + 1];
@@ -8907,19 +8914,14 @@ static void rope_neox(
         return;
     }
 
-    const int i  = row*ncols + ib*n_dims + ic/2;
+    const int i  = row*ne0 + i0/2;
     const int i2 = row/p_delta_rows;
 
-    float cur_rot = inv_ndims * ic - ib;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f;
-
-    const float theta_base =
-        p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor;
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
     float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
     const float x0 = x[i + 0];
     const float x1 = x[i + n_dims/2];
@@ -12375,15 +12377,18 @@ static void clamp_f32_sycl(const float *x, float *dst, const float min,
 }
 
 template <typename T>
-static void rope_sycl(const T *x, T *dst, int ncols, int nrows,
+static void rope_norm_sycl(const T *x, T *dst, int ne0, int n_dims, int nr,
                       const int32_t *pos, float freq_scale, int p_delta_rows,
                       float freq_base, float ext_factor, float attn_factor,
-                      rope_corr_dims corr_dims, dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % 2 == 0);
+                      rope_corr_dims corr_dims, const float * freq_factors, dpct::queue_ptr stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
     const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
-    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
-    if (pos == nullptr) {
+    const int n_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, n_blocks_x, nr);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    if (freq_factors == nullptr) {
         /*
         DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
         the limit. To get the device limit, query
@@ -12395,8 +12400,8 @@ static void rope_sycl(const T *x, T *dst, int ncols, int nrows,
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
             [=](sycl::nd_item<3> item_ct1) {
-                rope<T, false>(x, dst, ncols, pos, freq_scale, p_delta_rows,
-                               freq_base, ext_factor, attn_factor, corr_dims,
+                rope_norm<T, false>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
+                               ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
                                item_ct1);
             });
     } else {
@@ -12411,70 +12416,46 @@ static void rope_sycl(const T *x, T *dst, int ncols, int nrows,
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
             [=](sycl::nd_item<3> item_ct1) {
-                rope<T, true>(x, dst, ncols, pos, freq_scale, p_delta_rows,
-                              freq_base, ext_factor, attn_factor, corr_dims,
+                rope_norm<T, true>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
+                              ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
                               item_ct1);
             });
     }
 }
 
 template <typename T>
-static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
+static void rope_neox_sycl(const T *x, T *dst, int ne0, int n_dims, int nr,
                            const int32_t *pos, float freq_scale,
                            int p_delta_rows, float freq_base, float ext_factor,
                            float attn_factor, rope_corr_dims corr_dims,
                            const float * freq_factors, dpct::queue_ptr stream) {
-    GGML_ASSERT(ncols % 2 == 0);
+    GGML_ASSERT(ne0 % 2 == 0);
     const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
-    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
+    const int n_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, n_blocks_x, nr);
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.0f / n_dims;
 
-    if (pos == nullptr) {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-        if (freq_factors == nullptr) {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, false, false>(x, dst, ncols, n_dims, pos, freq_scale,
-                                        p_delta_rows, ext_factor, attn_factor,
-                                        corr_dims, theta_scale, inv_ndims, freq_factors,
-                                        item_ct1);
-                });
-        } else {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, false, true>(x, dst, ncols, n_dims, pos, freq_scale,
-                                        p_delta_rows, ext_factor, attn_factor,
-                                        corr_dims, theta_scale, inv_ndims, freq_factors,
-                                        item_ct1);
-                });
-        }
+    dpct::has_capability_or_fail(stream->get_device(),
+                                    {sycl::aspect::fp16});
+    if (freq_factors == nullptr) {
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, false>(x, dst, ne0, n_dims, pos, freq_scale,
+                                    p_delta_rows, ext_factor, attn_factor,
+                                    corr_dims, theta_scale, freq_factors,
+                                    item_ct1);
+            });
     } else {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        if (freq_factors == nullptr) {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, true, false>(x, dst, ncols, n_dims, pos, freq_scale,
-                                       p_delta_rows, ext_factor, attn_factor,
-                                       corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
-                });
-        } else {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, true, true>(x, dst, ncols, n_dims, pos, freq_scale,
-                                       p_delta_rows, ext_factor, attn_factor,
-                                       corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
-                });
-        }
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, true>(x, dst, ne0, n_dims, pos, freq_scale,
+                                    p_delta_rows, ext_factor, attn_factor,
+                                    corr_dims, theta_scale, freq_factors,
+                                    item_ct1);
+            });
     }
 }
 
@@ -13090,7 +13071,6 @@ void *ggml_sycl_host_malloc(size_t size) try {
     }
 
     void * ptr = nullptr;
-    //allow to use dpct::get_in_order_queue() for host malloc
     dpct::err0 err = CHECK_TRY_ERROR(
         ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue()));
 
@@ -13113,7 +13093,6 @@ catch (sycl::exception const &exc) {
 }
 
 void ggml_sycl_host_free(void *ptr) try {
-    //allow to use dpct::get_in_order_queue() for host malloc
     SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue())));
 }
 catch (sycl::exception const &exc) {
@@ -14002,8 +13981,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
 
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
+    const int64_t nr = ggml_nrows(src0);
 
     //const int n_past      = ((int32_t *) dst->op_params)[0];
     const int n_dims      = ((int32_t *) dst->op_params)[1];
@@ -14020,27 +13998,13 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
     memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
     memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
 
-    const float * freq_factors = nullptr;
-    const int32_t * pos = nullptr;
-    if ((mode & 1) == 0) {
-        GGML_ASSERT(src1->type == GGML_TYPE_I32);
-        GGML_ASSERT(src1->ne[0] == ne2);
-        pos = (const int32_t *) src1_dd;
-    }
-
     const bool is_neox = mode & 2;
 
-#pragma message("TODO: update rope NORM mode to match NEOX mode")
-#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7634")
+    const int32_t * pos = (const int32_t *) src1_dd;
 
-    if (is_neox) {
-        pos = (const int32_t *) src1_dd;
-
-        if (src2 != nullptr) {
-            freq_factors = (const float *) src2->data;
-        }
-    } else {
-        GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox");
+    const float * freq_factors = nullptr;
+    if (src2 != nullptr) {
+        freq_factors = (const float *) src2->data;
     }
 
     rope_corr_dims corr_dims;
@@ -14050,12 +14014,12 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
     if (is_neox) {
         if (src0->type == GGML_TYPE_F32) {
             rope_neox_sycl(
-                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
                 attn_factor, corr_dims, freq_factors, main_stream
             );
         } else if (src0->type == GGML_TYPE_F16) {
             rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd,
-                           ne00, n_dims, nrows, pos, freq_scale, ne01,
+                           ne00, n_dims, nr, pos, freq_scale, ne01,
                            freq_base, ext_factor, attn_factor, corr_dims,
                            freq_factors, main_stream);
         } else {
@@ -14063,14 +14027,14 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
         }
     } else {
         if (src0->type == GGML_TYPE_F32) {
-            rope_sycl(
-                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
+            rope_norm_sycl(
+                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
             );
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00,
-                      nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                      attn_factor, corr_dims, main_stream);
+            rope_norm_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00,
+                      n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                      attn_factor, corr_dims, freq_factors, main_stream);
         } else {
             GGML_ASSERT(false);
         }
@@ -17187,7 +17151,7 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
                 case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
             }
@@ -17264,7 +17228,12 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
         case GGML_OP_CONCAT:
             {
                 ggml_type src0_type = op->src[0]->type;
-                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
+                int dim = op->op_params[0];
+                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16 && dim == 2;
+            } break;
+        case GGML_OP_ROPE:
+            {
+                return ggml_is_contiguous(op->src[0]);
             } break;
         case GGML_OP_DUP:
         case GGML_OP_NONE:
@@ -17284,7 +17253,6 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
         case GGML_OP_CONT:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
-        case GGML_OP_ROPE:
         case GGML_OP_IM2COL:
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM_ROWS:

ggml-vulkan.cpp

@@ -1,5 +1,5 @@
 #include "ggml-vulkan.h"
-
+#include <vulkan/vulkan_core.h>
 #ifdef GGML_VULKAN_RUN_TESTS
 #include <chrono>
 #endif
@@ -9,12 +9,13 @@
 #include <algorithm>
 #include <cmath>
 #include <iostream>
-#include <limits>
 #include <tuple>
 #include <vector>
 #include <sstream>
 #include <utility>
 #include <memory>
+#include <limits>
+#include <map>
 
 #include "ggml.h"
 #include "ggml-backend-impl.h"
@@ -150,7 +151,7 @@ struct vk_device {
     vk_pipeline pipeline_relu_f32;
     vk_pipeline pipeline_diag_mask_inf_f32;
     vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
-    vk_pipeline pipeline_rope_f32, pipeline_rope_f16;
+    vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16;
     vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
     vk_pipeline pipeline_argsort_f32;
     vk_pipeline pipeline_sum_rows_f32;
@@ -283,26 +284,15 @@ struct vk_op_diag_mask_push_constants {
 
 struct vk_op_rope_push_constants {
     uint32_t ncols;
+    uint32_t n_dims;
     float freq_scale;
     uint32_t p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
-    float corr_dims[4];
-};
-
-struct vk_op_rope_neox_push_constants {
-    uint32_t ncols;
-    uint32_t ndims;
-    float freq_scale;
-    uint32_t p_delta_rows;
-    float freq_base;
-    float ext_factor;
-    float attn_factor;
-    float corr_dims[4];
+    float corr_dims[2];
     float theta_scale;
-    float inv_ndims;
-    uint32_t has_freq_facs;
+    uint32_t has_ff;
 };
 
 struct vk_op_soft_max_push_constants {
@@ -1534,11 +1524,11 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) {
     ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
 
     ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
 
@@ -1566,8 +1556,10 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     vk::PhysicalDeviceProperties2 props2;
     vk::PhysicalDeviceMaintenance3Properties props3;
     vk::PhysicalDeviceSubgroupProperties subgroup_props;
+    vk::PhysicalDeviceDriverProperties driver_props;
     props2.pNext = &props3;
     props3.pNext = &subgroup_props;
+    subgroup_props.pNext = &driver_props;
     physical_device.getProperties2(&props2);
 
     const size_t subgroup_size = subgroup_props.subgroupSize;
@@ -1611,7 +1603,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     fp16 = fp16 && vk12_features.shaderFloat16;
 
     std::string device_name = props2.properties.deviceName.data();
-    std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
+    std::cerr << GGML_VK_NAME << idx << ": " << device_name << " (" << driver_props.driverName << ") | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
 
     if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
         std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl;
@@ -1707,7 +1699,78 @@ void ggml_vk_instance_init() {
             vk::PhysicalDeviceProperties props = devices[i].getProperties();
 
             if (props.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) {
-                vk_instance.device_indices.push_back(i);
+                // Check if there are two physical devices corresponding to the same GPU
+                auto old_device = std::find_if(
+                    vk_instance.device_indices.begin(),
+                    vk_instance.device_indices.end(),
+                    [&devices, &props](const size_t k){ return devices[k].getProperties().deviceID == props.deviceID; }
+                );
+                if (old_device == vk_instance.device_indices.end()) {
+                    vk_instance.device_indices.push_back(i);
+                } else {
+                    // There can be two physical devices corresponding to the same GPU if there are 2 different drivers
+                    // This can cause error when splitting layers aross the devices, need to keep only 1
+#ifdef GGML_VULKAN_DEBUG
+                    std::cerr << "Device " << i << " and device " << *old_device << " have the same device id" << std::endl;
+#endif
+
+                    vk::PhysicalDeviceProperties2 old_prop;
+                    vk::PhysicalDeviceDriverProperties old_driver;
+                    old_prop.pNext = &old_driver;
+                    devices[*old_device].getProperties2(&old_prop);
+
+                    vk::PhysicalDeviceProperties2 new_prop;
+                    vk::PhysicalDeviceDriverProperties new_driver;
+                    new_prop.pNext = &new_driver;
+                    devices[i].getProperties2(&new_prop);
+
+                    std::map<vk::DriverId, int> driver_priorities {};
+                    int old_priority = std::numeric_limits<int>::max();
+                    int new_priority = std::numeric_limits<int>::max();
+
+                    // Check https://registry.khronos.org/vulkan/specs/1.3-extensions/man/html/VkDriverId.html for the list of driver id
+                    // Smaller number -> higher priority
+                    switch (old_prop.properties.vendorID) {
+                        case VK_VENDOR_ID_AMD:
+                            driver_priorities[vk::DriverId::eMesaRadv] = 1;
+                            driver_priorities[vk::DriverId::eAmdOpenSource] = 2;
+                            driver_priorities[vk::DriverId::eAmdProprietary] = 3;
+                            break;
+                        case VK_VENDOR_ID_INTEL:
+                            driver_priorities[vk::DriverId::eIntelOpenSourceMESA] = 1;
+                            driver_priorities[vk::DriverId::eIntelProprietaryWindows] = 2;
+                            break;
+                        case VK_VENDOR_ID_NVIDIA:
+                            driver_priorities[vk::DriverId::eNvidiaProprietary] = 1;
+#if defined(VK_API_VERSION_1_3) && VK_HEADER_VERSION >= 235
+                            driver_priorities[vk::DriverId::eMesaNvk] = 2;
+#endif
+                            break;
+                    }
+
+                    if (driver_priorities.count(old_driver.driverID)) {
+                        old_priority = driver_priorities[old_driver.driverID];
+                    }
+                    if (driver_priorities.count(new_driver.driverID)) {
+                        new_priority = driver_priorities[new_driver.driverID];
+                    }
+
+                    if (new_priority < old_priority) {
+                        auto r = std::remove(vk_instance.device_indices.begin(), vk_instance.device_indices.end(), *old_device);
+                        vk_instance.device_indices.erase(r, vk_instance.device_indices.end());
+                        vk_instance.device_indices.push_back(i);
+
+#ifdef GGML_VULKAN_DEBUG
+                        std::cerr << "Prioritize device " << i << " driver " << new_driver.driverName << " over device " << *old_device << " driver " << old_driver.driverName << std::endl;
+#endif
+                    }
+#ifdef GGML_VULKAN_DEBUG
+                    else {
+                        std::cerr << "Prioritize device " << *old_device << " driver " << old_driver.driverName << " over device " << i << " driver " << new_driver.driverName << std::endl;
+
+                    }
+#endif
+                }
             }
         }
 
@@ -3905,10 +3968,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
                 }
             } else {
                 if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return ctx->device->pipeline_rope_f32;
+                    return ctx->device->pipeline_rope_norm_f32;
                 }
                 if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-                    return ctx->device->pipeline_rope_f16;
+                    return ctx->device->pipeline_rope_norm_f16;
                 }
             }
             return nullptr;
@@ -4152,24 +4215,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
             ggml_vk_sync_buffers(subctx);
             ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         } else if (op == GGML_OP_ROPE) {
-            const int mode          = ((int32_t *) dst->op_params)[2];
-            const bool is_neox = mode & 2;
-
-            if (is_neox) {
-                // Empty src2 is possible in rope, but the shader needs a buffer
-                vk_subbuffer subbuf_z;
-                if (use_src2) {
-                    subbuf_z = { d_Z, z_buf_offset, z_sz };
-                } else {
-                    subbuf_z = { d_X, 0, d_X->size };
-                }
-
-                ggml_vk_sync_buffers(subctx);
-                ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+            // Empty src2 is possible in rope, but the shader needs a buffer
+            vk_subbuffer subbuf_z;
+            if (use_src2) {
+                subbuf_z = { d_Z, z_buf_offset, z_sz };
             } else {
-                ggml_vk_sync_buffers(subctx);
-                ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+                subbuf_z = { d_X, 0, d_X->size };
             }
+
+            ggml_vk_sync_buffers(subctx);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         } else if (use_src2) {
             ggml_vk_sync_buffers(subctx);
             ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_Z, z_buf_offset, z_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
@@ -4391,7 +4446,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx,
 
 static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
     const int n_dims        = ((int32_t *) dst->op_params)[1];
-    const int mode          = ((int32_t *) dst->op_params)[2];
+    // const int mode          = ((int32_t *) dst->op_params)[2];
     // const int n_ctx         = ((int32_t *) dst->op_params)[3];
     const int n_ctx_orig    = ((int32_t *) dst->op_params)[4];
     const float freq_base   = ((float *)   dst->op_params)[5];
@@ -4401,28 +4456,16 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con
     const float beta_fast   = ((float *)   dst->op_params)[9];
     const float beta_slow   = ((float *)   dst->op_params)[10];
 
-    const bool is_neox = mode & 2;
-
-#pragma message("TODO: update rope NORM mode to match NEOX mode")
-#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7634")
-
     float corr_dims[2];
     ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
 
-    if (is_neox) {
-        const float theta_scale = powf(freq_base, -2.0f/n_dims);
-        const float inv_ndims = -1.0f / n_dims;
-        ggml_vk_op_f32<vk_op_rope_neox_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, {
-            (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
-            freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims,
-            src2 != nullptr,
-        });
-    } else {
-        ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, {
-            (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1],
-            freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}
-        });
-    }
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, {
+        (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
+        freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale,
+        src2 != nullptr,
+    });
 }
 
 static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
@@ -6070,7 +6113,13 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(
     std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl;
 #endif
     ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size);
+
+    vk_buffer dev_buffer = nullptr;
+    try {
+        dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size);
+    } catch (const vk::SystemError& e) {
+        return nullptr;
+    }
 
     ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name);
 
@@ -6390,7 +6439,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_SILU:
                 case GGML_UNARY_OP_RELU:
-                    return true;
+                    return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
             }
@@ -6466,7 +6515,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
         //         return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
         //     } break;
         case GGML_OP_ROPE:
-            return true;
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:

ggml.c

@@ -3212,35 +3212,42 @@ GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) {
     return tensor->nb[0] > tensor->nb[1];
 }
 
-GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+static bool ggml_is_contiguous_n(const struct ggml_tensor * tensor, int n) {
+    size_t next_nb = ggml_type_size(tensor->type);
+    if (tensor->ne[0] != ggml_blck_size(tensor->type) && tensor->nb[0] != next_nb) {
+        return false;
+    }
+    next_nb *= tensor->ne[0]/ggml_blck_size(tensor->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        if (tensor->ne[i] != 1) {
+            if (i > n) {
+                if (tensor->nb[i] != next_nb) {
+                    return false;
+                }
+                next_nb *= tensor->ne[i];
+            } else {
+                // this dimension does not need to be contiguous
+                next_nb = tensor->ne[i]*tensor->nb[i];
+            }
+        }
+    }
+    return true;
+}
 
-    return
-        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
-        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
-        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
+    return ggml_is_contiguous_0(tensor);
 }
 
 GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor) {
-    return ggml_is_contiguous(tensor);
+    return ggml_is_contiguous_n(tensor, 0);
 }
 
 GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
-
-    return
-        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
-        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+    return ggml_is_contiguous_n(tensor, 1);
 }
 
 GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
-
-    return
-        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+    return ggml_is_contiguous_n(tensor, 2);
 }
 
 GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) {
@@ -3272,20 +3279,20 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        (t0->ne[0] == t1->ne[0] ) &&
-        (t0->ne[1] == t1->ne[1] ) &&
-        (t0->ne[2] == t1->ne[2] ) &&
-        (t0->ne[3] == t1->ne[3] );
+        (t0->ne[0] == t1->ne[0]) &&
+        (t0->ne[1] == t1->ne[1]) &&
+        (t0->ne[2] == t1->ne[2]) &&
+        (t0->ne[3] == t1->ne[3]);
 }
 
 bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        (t0->nb[0] == t1->nb[0] ) &&
-        (t0->nb[1] == t1->nb[1] ) &&
-        (t0->nb[2] == t1->nb[2] ) &&
-        (t0->nb[3] == t1->nb[3] );
+        (t0->nb[0] == t1->nb[0]) &&
+        (t0->nb[1] == t1->nb[1]) &&
+        (t0->nb[2] == t1->nb[2]) &&
+        (t0->nb[3] == t1->nb[3]);
 }
 
 // check if t1 can be represented as a repeatition of t0
@@ -4078,32 +4085,26 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
     switch (tensor->type) {
         case GGML_TYPE_I8:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
                 return ((int8_t *)(tensor->data))[i];
             }
         case GGML_TYPE_I16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
                 return ((int16_t *)(tensor->data))[i];
             }
         case GGML_TYPE_I32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
                 return ((int32_t *)(tensor->data))[i];
             }
         case GGML_TYPE_F16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
             }
         case GGML_TYPE_BF16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
                 return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
             }
         case GGML_TYPE_F32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(float));
                 return ((float *)(tensor->data))[i];
             }
         default:
@@ -4125,32 +4126,26 @@ void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
     switch (tensor->type) {
         case GGML_TYPE_I8:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
                 ((int8_t *)(tensor->data))[i] = value;
             } break;
         case GGML_TYPE_I16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
                 ((int16_t *)(tensor->data))[i] = value;
             } break;
         case GGML_TYPE_I32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
                 ((int32_t *)(tensor->data))[i] = value;
             } break;
         case GGML_TYPE_F16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
                 ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
             } break;
         case GGML_TYPE_BF16:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
                 ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
             } break;
         case GGML_TYPE_F32:
             {
-                GGML_ASSERT(tensor->nb[0] == sizeof(float));
                 ((float *)(tensor->data))[i] = value;
             } break;
         default:
@@ -7343,13 +7338,15 @@ struct ggml_tensor * ggml_add_rel_pos_inplace(
     return ggml_add_rel_pos_impl(ctx, a, pw, ph, true);
 }
 
-// gmml_unary
+// ggml_unary
 
 static struct ggml_tensor * ggml_unary_impl(
         struct ggml_context * ctx,
         struct ggml_tensor * a,
         enum ggml_unary_op op,
         bool inplace) {
+    GGML_ASSERT(ggml_is_contiguous_1(a));
+
     bool is_node = false;
 
     if (!inplace && (a->grad)) {
@@ -11014,6 +11011,8 @@ static void ggml_compute_forward_abs_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11023,9 +11022,6 @@ static void ggml_compute_forward_abs_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_abs_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11060,6 +11056,8 @@ static void ggml_compute_forward_sgn_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11069,9 +11067,6 @@ static void ggml_compute_forward_sgn_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_sgn_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11106,6 +11101,8 @@ static void ggml_compute_forward_neg_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11115,9 +11112,6 @@ static void ggml_compute_forward_neg_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_neg_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11152,6 +11146,8 @@ static void ggml_compute_forward_step_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11161,9 +11157,6 @@ static void ggml_compute_forward_step_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_step_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11198,6 +11191,8 @@ static void ggml_compute_forward_tanh_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11207,9 +11202,6 @@ static void ggml_compute_forward_tanh_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_tanh_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11244,6 +11236,8 @@ static void ggml_compute_forward_elu_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11253,9 +11247,6 @@ static void ggml_compute_forward_elu_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_elu_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11290,6 +11281,8 @@ static void ggml_compute_forward_relu_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11299,9 +11292,6 @@ static void ggml_compute_forward_relu_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_relu_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11336,6 +11326,8 @@ static void ggml_compute_forward_sigmoid_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11345,9 +11337,6 @@ static void ggml_compute_forward_sigmoid_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_sigmoid_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11381,9 +11370,9 @@ static void ggml_compute_forward_gelu_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
@@ -11444,9 +11433,9 @@ static void ggml_compute_forward_gelu_quick_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
@@ -11507,9 +11496,9 @@ static void ggml_compute_forward_silu_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
@@ -11570,6 +11559,8 @@ static void ggml_compute_forward_leaky_relu_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11619,11 +11610,11 @@ static void ggml_compute_forward_silu_back_f32(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * grad = dst->src[1];
 
-    GGML_ASSERT(ggml_is_contiguous_1(grad));
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_1(dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
-    GGML_ASSERT(ggml_are_same_shape(src0, grad));
+    assert(ggml_is_contiguous_1(grad));
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+    assert(ggml_are_same_shape(src0, grad));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
@@ -11685,6 +11676,8 @@ static void ggml_compute_forward_hardswish_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11694,9 +11687,6 @@ static void ggml_compute_forward_hardswish_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_hardswish_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -11728,6 +11718,8 @@ static void ggml_compute_forward_hardsigmoid_f32(
     const struct ggml_tensor * src0 = dst->src[0];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11737,9 +11729,6 @@ static void ggml_compute_forward_hardsigmoid_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert(dst->nb[0]  == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         ggml_vec_hardsigmoid_f32(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -16686,7 +16675,10 @@ static void ggml_compute_forward_map_unary_f32(
 
     const struct ggml_tensor * src0 = dst->src[0];
 
-    GGML_ASSERT(ggml_are_same_shape(src0, dst));
+    assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
@@ -16695,9 +16687,6 @@ static void ggml_compute_forward_map_unary_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert( dst->nb[0] == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         fun(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),
@@ -16735,6 +16724,9 @@ static void ggml_compute_forward_map_binary_f32(
     const struct ggml_tensor * src1 = dst->src[1];
 
     assert(params->ith == 0);
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(src1));
+    assert(ggml_is_contiguous_1(dst));
     assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -16744,10 +16736,6 @@ static void ggml_compute_forward_map_binary_f32(
     const int n  = ggml_nrows(src0);
     const int nc = src0->ne[0];
 
-    assert( dst->nb[0] == sizeof(float));
-    assert(src0->nb[0] == sizeof(float));
-    assert(src1->nb[0] == sizeof(float));
-
     for (int i = 0; i < n; i++) {
         fun(nc,
                 (float *) ((char *) dst->data  + i*( dst->nb[1])),

ggml_vk_generate_shaders.py

@@ -2400,7 +2400,7 @@ void main() {
 """
 
 # ROPE
-rope_src = """
+rope_norm_src = """
 #version 450
 
 #extension GL_EXT_shader_16bit_storage : require
@@ -2408,17 +2408,21 @@ rope_src = """
 layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_b[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+layout (binding = 1) readonly buffer Y {int data_pos[];};
+layout (binding = 2) readonly buffer Z {float data_ff[];};
+layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
 
 layout (push_constant) uniform parameter {
     uint ncols;
+    uint n_dims;
     float freq_scale;
     uint p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
-    float corr_dims[4];
+    float corr_dims[2];
+    float theta_scale;
+    uint has_ff;
 } p;
 
 float rope_yarn_ramp(const float low, const float high, const uint i0) {
@@ -2450,14 +2454,24 @@ void main() {
         return;
     }
 
+    if (col >= p.n_dims) {
+        const uint i = row*p.ncols + col;
+
+        data_d[i + 0] = data_a[i + 0];
+        data_d[i + 1] = data_a[i + 1];
+
+        return;
+    }
+
     const uint i = row*p.ncols + col;
     const uint i2 = row/p.p_delta_rows;
 
-    const int pos = data_b[i2];
-    const float theta_base = pos * pow(p.freq_base, -float(col)/p.ncols);
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
 
     float cos_theta, sin_theta;
-    rope_yarn(theta_base, col, cos_theta, sin_theta);
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
 
     const float x0 = float(data_a[i + 0]);
     const float x1 = float(data_a[i + 1]);
@@ -2475,22 +2489,21 @@ rope_neox_src = """
 layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_b[];};
-layout (binding = 2) readonly buffer Z {float data_freq_factors[];};
+layout (binding = 1) readonly buffer Y {int data_pos[];};
+layout (binding = 2) readonly buffer Z {float data_ff[];};
 layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
 
 layout (push_constant) uniform parameter {
     uint ncols;
-    uint ndims;
+    uint n_dims;
     float freq_scale;
     uint p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
-    float corr_dims[4];
+    float corr_dims[2];
     float theta_scale;
-    float inv_ndims;
-    uint has_freq_facs;
+    uint has_ff;
 } p;
 
 float rope_yarn_ramp(const float low, const float high, const uint i0) {
@@ -2522,11 +2535,8 @@ void main() {
         return;
     }
 
-    const uint ib = col / p.ndims;
-    const uint ic = col % p.ndims;
-
-    if (ib > 0) {
-        const uint i = row*p.ncols + ib*p.ndims + ic;
+    if (col >= p.n_dims) {
+        const uint i = row*p.ncols + col;
 
         data_d[i + 0] = data_a[i + 0];
         data_d[i + 1] = data_a[i + 1];
@@ -2534,29 +2544,27 @@ void main() {
         return;
     }
 
-    const uint i  = row*p.ncols + ib*p.ndims + ic/2;
+    const uint i  = row*p.ncols + col/2;
     const uint i2 = row/p.p_delta_rows;
 
-    const int pos = data_b[i2];
-    const float freq_factor = p.has_freq_facs != 0 ? data_freq_factors[ic/2] : 1.0f;
-    const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f) / freq_factor;
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
 
     float cos_theta, sin_theta;
-    rope_yarn(theta_base, ic, cos_theta, sin_theta);
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
 
     const float x0 = float(data_a[i + 0]);
-    const float x1 = float(data_a[i + p.ndims/2]);
+    const float x1 = float(data_a[i + p.n_dims/2]);
 
     data_d[i + 0]        = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[i + p.ndims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
+    data_d[i + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
 }
 """
 
 argsort_src = """
 #version 450
 
-#extension GL_EXT_shader_16bit_storage : require
-
 #define BLOCK_SIZE 1024
 #define ASC 0
 
@@ -3039,8 +3047,8 @@ async def main():
     tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("soft_max_f32_f16", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float16_t", "C_TYPE": "float16_t", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
+    tasks.append(string_to_spv("rope_norm_f32", rope_norm_src, {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rope_norm_f16", rope_norm_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
 
     tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))

grammars/README.md

@@ -94,6 +94,8 @@ This guide provides a brief overview. Check out the GBNF files in this directory
 ./main -m <model> --grammar-file grammars/some-grammar.gbnf -p 'Some prompt'
 ```
 
+`llama.cpp` can also convert JSON schemas to grammars either ahead of time or at each request, see below.
+
 ## Troubleshooting
 
 Grammars currently have performance gotchas (see https://github.com/ggerganov/llama.cpp/issues/4218).
@@ -103,3 +105,40 @@ Grammars currently have performance gotchas (see https://github.com/ggerganov/ll
 A common pattern is to allow repetitions of a pattern `x` up to N times.
 
 While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) may result in extremely slow sampling. Instead, you can write `x{0,N}` (or `(x (x (x ... (x)?...)?)?)?` w/ N-deep nesting in earlier llama.cpp versions).
+
+## Using GBNF grammars
+
+You can use GBNF grammars:
+
+- In the [server](../examples/server)'s completion endpoints, passed as the `grammar` body field
+- In the [main](../examples/main) CLI, passed as the `--grammar` & `--grammar-file` flags
+- With the [gbnf-validator](../examples/gbnf-validator) tool, to test them against strings.
+
+## JSON Schemas → GBNF
+
+`llama.cpp` supports converting a subset of https://json-schema.org/ to GBNF grammars:
+
+- In the [server](../examples/server):
+    - For any completion endpoints, passed as the `json_schema` body field
+    - For the `/chat/completions` endpoint, passed inside the `result_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}`)
+- In the [main](../examples/main) CLI, passed as the `--json` / `-j` flag
+- To convert to a grammar ahead of time:
+    - in CLI, with [json_schema_to_grammar.py](../examples/json_schema_to_grammar.py)
+    - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI)
+
+Take a look at [tests](../../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggerganov/llama.cpp/pull/5978, https://github.com/ggerganov/llama.cpp/pull/6659 & https://github.com/ggerganov/llama.cpp/pull/6555).
+
+Here is also a non-exhaustive list of **unsupported** features:
+
+- `additionalProperties`: to be fixed in https://github.com/ggerganov/llama.cpp/pull/7840
+- `minimum`, `exclusiveMinimum`, `maximum`, `exclusiveMaximum`
+    - `integer` constraints to be implemented in https://github.com/ggerganov/llama.cpp/pull/7797
+- Remote `$ref`s in the C++ version (Python & JavaScript versions fetch https refs)
+- Mixing `properties` w/ `anyOf` / `oneOf` in the same type (https://github.com/ggerganov/llama.cpp/issues/7703)
+- `string` formats `uri`, `email`
+- [`contains`](https://json-schema.org/draft/2020-12/json-schema-core#name-contains) / `minContains`
+- `uniqueItems`
+- `$anchor` (cf. [dereferencing](https://json-schema.org/draft/2020-12/json-schema-core#name-dereferencing))
+- [`not`](https://json-schema.org/draft/2020-12/json-schema-core#name-not)
+- [Conditionals](https://json-schema.org/draft/2020-12/json-schema-core#name-keywords-for-applying-subsche) `if` / `then` / `else` / `dependentSchemas`
+- [`patternProperties`](https://json-schema.org/draft/2020-12/json-schema-core#name-patternproperties)

grammars/json.gbnf

@@ -16,10 +16,10 @@ array  ::=
 string ::=
   "\"" (
     [^"\\\x7F\x00-\x1F] |
-    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
+    "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes
   )* "\"" ws
 
-number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws
 
 # Optional space: by convention, applied in this grammar after literal chars when allowed
-ws ::= ([ \t\n] ws)?
+ws ::= | " " | "\n" [ \t]{0,20}

grammars/json_arr.gbnf

@@ -25,10 +25,10 @@ array  ::=
 string ::=
   "\"" (
     [^"\\\x7F\x00-\x1F] |
-    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
+    "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes
   )* "\"" ws
 
-number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [1-9] [0-9]{0,15})? ws
 
 # Optional space: by convention, applied in this grammar after literal chars when allowed
-ws ::= ([ \t\n] ws)?
+ws ::= | " " | "\n" [ \t]{0,20}

tests/test-backend-ops.cpp

@@ -642,20 +642,29 @@ struct test_case {
 struct test_unary : public test_case {
     const ggml_unary_op op;
     const ggml_type type;
-    const std::array<int64_t, 4> ne;
+    const std::array<int64_t, 4> ne_a;
+    int v; // view (1 : non-contiguous a)
 
     std::string vars() override {
-        return VARS_TO_STR2(type, ne);
+        return VARS_TO_STR3(type, ne_a, v);
     }
 
     test_unary(ggml_unary_op op,
             ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {128, 10, 10, 10})
-        : op(op), type(type), ne(ne) {}
+            std::array<int64_t, 4> ne_a = {128, 10, 10, 10},
+            int v = 0)
+        : op(op), type(type), ne_a(ne_a), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * in = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * out = ggml_unary(ctx, in, op);
+        ggml_tensor * a;
+        if (v & 1) {
+            auto ne = ne_a; ne[0] *= 3;
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+        } else {
+            a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        }
+        ggml_tensor * out = ggml_unary(ctx, a, op);
         return out;
     }
 
@@ -2016,9 +2025,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     };
 
     // unary ops
-    for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
-        test_cases.emplace_back(new test_unary((ggml_unary_op) op));
-        test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }));
+    for (int v : {0, 1}) {
+        for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
+            test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 128, 10, 10, 10 }, v));
+            test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }, v));
+        }
     }
 
     test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));

tests/test-json-schema-to-grammar.cpp

@@ -105,14 +105,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             array ::= "[" space ( value ("," space value)* )? "]" space
             boolean ::= ("true" | "false") space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             null ::= "null" space
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
             root ::= object
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
             value ::= object | array | string | number | boolean | null
         )"""
@@ -135,7 +135,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             date-time ::= date "T" time
             date-time-string ::= "\"" date-time "\"" space
             root ::= "[" space tuple-0 "," space uuid "," space tuple-2 "," space tuple-3 "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             time ::= ([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )
             time-string ::= "\"" time "\"" space
             tuple-0 ::= date-string
@@ -152,9 +152,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "type": "string"
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "\"" char* "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -166,9 +166,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minLength": 1
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "\"" char+ "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -180,9 +180,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minLength": 3
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "\"" char{3,} "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -194,9 +194,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxLength": 3
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "\"" char{0,3} "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -209,9 +209,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxLength": 4
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "\"" char{1,4} "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -223,7 +223,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= ("true" | "false") space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -236,7 +236,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             root ::= ("-"? integral-part) space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -248,7 +248,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"foo\""
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -260,7 +260,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "123"
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -272,7 +272,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"red\"" | "\"amber\"" | "\"green\"" | "null" | "42" | "[\"foo\"]"
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -283,9 +283,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "prefixItems": [{ "type": "string" }]
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "[" space string "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -297,12 +297,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "prefixItems": [{ "type": "string" }, { "type": "number" }]
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "[" space string "," space number "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -317,7 +317,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             root ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -333,7 +333,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             boolean ::= ("true" | "false") space
             root ::= "[" space boolean ("," space boolean)+ "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -349,7 +349,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             boolean ::= ("true" | "false") space
             root ::= "[" space boolean? "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -365,7 +365,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             boolean ::= ("true" | "false") space
             root ::= "[" space (boolean ("," space boolean)?)? "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -386,7 +386,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             item ::= number | integer
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "[" space item ("," space item){2,4} "]" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -399,7 +399,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"" "ab" "c"? "d"* "ef" "g"+ ("hij")? "kl" "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -412,7 +412,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"" "[]{}()|+*?" "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -425,7 +425,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "\"" "\"" "\"" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -440,7 +440,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             dot ::= [^\x0A\x0D]
             root ::= "\"" ("(" root-1{1,3} ")")? root-1{3,3} "-" root-1{4,4} " " "a"{3,5} "nd" dot dot dot "\"" space
             root-1 ::= [0-9]
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -466,9 +466,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             a-kv ::= "\"a\"" space ":" space string
             b-kv ::= "\"b\"" space ":" space string
             c-kv ::= "\"c\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "{" space b-kv "," space c-kv "," space a-kv "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -486,9 +486,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             a-kv ::= "\"a\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "{" space  (a-kv )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -510,9 +510,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             b-kv ::= "\"b\"" space ":" space string
             b-rest ::= ( "," space c-kv )?
             c-kv ::= "\"c\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             root ::= "{" space  (a-kv a-rest | b-kv b-rest | c-kv )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -534,11 +534,11 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             a-kv ::= "\"a\"" space ":" space string
             b-kv ::= "\"b\"" space ":" space string
             c-kv ::= "\"c\"" space ":" space string
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             d-kv ::= "\"d\"" space ":" space string
             d-rest ::= ( "," space c-kv )?
             root ::= "{" space b-kv "," space a-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -554,12 +554,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             additional-kv ::= string ":" space additional-value
             additional-kvs ::= additional-kv ( "," space additional-kv )*
             additional-value ::= "[" space (number ("," space number)*)? "]" space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "{" space  (additional-kvs )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -574,14 +574,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             array ::= "[" space ( value ("," space value)* )? "]" space
             boolean ::= ("true" | "false") space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             null ::= "null" space
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
             root ::= object
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
             value ::= object | array | string | number | boolean | null
         )"""
@@ -596,14 +596,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             array ::= "[" space ( value ("," space value)* )? "]" space
             boolean ::= ("true" | "false") space
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             null ::= "null" space
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
             root ::= object
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
             value ::= object | array | string | number | boolean | null
         )"""
@@ -618,7 +618,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             root ::= "{" space  "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -637,12 +637,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             a-kv ::= "\"a\"" space ":" space number
             additional-kv ::= string ":" space string
             additional-kvs ::= additional-kv ( "," space additional-kv )*
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "{" space a-kv ( "," space ( additional-kvs ) )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -662,12 +662,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             a-rest ::= additional-kvs
             additional-kv ::= string ":" space number
             additional-kvs ::= additional-kv ( "," space additional-kv )*
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "{" space  (a-kv a-rest | additional-kvs )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -690,12 +690,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             additional-kvs ::= additional-kv ( "," space additional-kv )*
             b-kv ::= "\"b\"" space ":" space number
             b-rest ::= additional-kvs
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "{" space a-kv ( "," space ( b-kv b-rest | additional-kvs ) )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -721,11 +721,11 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             }
         })""",
         R"""(
-            char ::= [^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F]{4})
+            char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             foo ::= "{" space foo-a-kv "}" space
             foo-a-kv ::= "\"a\"" space ":" space string
             root ::= foo
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
             string ::= "\"" char* "\"" space
         )"""
     });
@@ -759,7 +759,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= alternative-0 | alternative-1
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -803,7 +803,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
             root ::= "{" space a-kv "," space b-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 
@@ -851,7 +851,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             number-number-kv ::= "\"number\"" space ":" space number-number
             number-number-root-kv ::= "\"root\"" space ":" space number
             root ::= "{" space number-kv "}" space
-            space ::= " "?
+            space ::= | " " | "\n" [ \t]{0,20}
         )"""
     });
 }
@@ -870,7 +870,7 @@ int main() {
         }
     });
 
-    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python --version") == 0)) {
+    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python -c \"import sys; exit(1) if sys.version_info < (3, 8) else print('Python version is sufficient')\"") == 0)) {
         test_all("Python", [](const TestCase & tc) {
             write("test-json-schema-input.tmp", tc.schema);
             tc.verify_status(std::system(
@@ -878,7 +878,7 @@ int main() {
             tc.verify(read("test-grammar-output.tmp"));
         });
     } else {
-        fprintf(stderr, "\033[33mWARNING: Python not found, skipping Python JSON schema -> grammar tests.\n\033[0m");
+        fprintf(stderr, "\033[33mWARNING: Python not found (min version required is 3.8), skipping Python JSON schema -> grammar tests.\n\033[0m");
     }
 
     if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {