readme : update model list (#8851 )

llama : better replace_all (#8852 )
vulkan : fix Qantized Mat-Vec Mul on AMD GPUs for ncols < 64 (#8855 )
2026-02-12 14:03:20 +02:00 · 2024-08-05 08:54:10 +03:00 · 2024-08-05 08:53:39 +03:00 · 2024-08-05 08:52:55 +03:00 · 2024-08-05 08:50:57 +03:00 · 2024-08-05 08:50:57 +03:00
74 changed files with 1854 additions and 519 deletions
--- a/.devops/llama-server.Dockerfile
+++ b/.devops/llama-server.Dockerfile
@@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=22.04
 FROM ubuntu:$UBUNTU_VERSION AS build

 RUN apt-get update && \
-    apt-get install -y build-essential git libcurl4-openssl-dev curl
+    apt-get install -y build-essential git libcurl4-openssl-dev

 WORKDIR /app

@@ -16,7 +16,7 @@ RUN make -j$(nproc) llama-server
 FROM ubuntu:$UBUNTU_VERSION AS runtime

 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl

 COPY --from=build /app/llama-server /llama-server

--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -126,16 +126,9 @@ let
    ++ optionals useMetalKit [ MetalKit ];

  cudaBuildInputs = with cudaPackages; [
-    cuda_cccl.dev # <nv/target>
-
-    # A temporary hack for reducing the closure size, remove once cudaPackages
-    # have stopped using lndir: https://github.com/NixOS/nixpkgs/issues/271792
-    cuda_cudart.dev
-    cuda_cudart.lib
-    cuda_cudart.static
-    libcublas.dev
-    libcublas.lib
-    libcublas.static
+    cuda_cudart
+    cuda_cccl # <nv/target>
+    libcublas
  ];

  rocmBuildInputs = with rocmPackages; [
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -139,7 +139,8 @@ set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location o
 # determining _precisely_ which defines are necessary for the llama-config
 # package.
 #
-get_directory_property(GGML_DIR_DEFINES DIRECTORY ggml/src COMPILE_DEFINITIONS)
+get_target_property(GGML_DIRECTORY ggml SOURCE_DIR)
+get_directory_property(GGML_DIR_DEFINES DIRECTORY ${GGML_DIRECTORY} COMPILE_DEFINITIONS)
 get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
 set(GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES} ${GGML_DIR_DEFINES})
 get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
--- a/29
+++ b/29
@@ -1605,42 +1605,41 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
 # Mark legacy binary targets as .PHONY so that they are always checked.
 .PHONY: main quantize perplexity embedding server

+# Define the object file target
+examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 # NOTE: We currently will always build the deprecation-warning `main` and `server` binaries to help users migrate.
 #  Eventually we will want to remove these target from building all the time.
-main: examples/deprecation-warning/deprecation-warning.cpp
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+main: examples/deprecation-warning/deprecation-warning.o
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
 	@echo "NOTICE: The 'main' binary is deprecated. Please use 'llama-cli' instead."

-server: examples/deprecation-warning/deprecation-warning.cpp
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+server: examples/deprecation-warning/deprecation-warning.o
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
 	@echo "NOTICE: The 'server' binary is deprecated. Please use 'llama-server' instead."

-quantize: examples/deprecation-warning/deprecation-warning.cpp
+quantize: examples/deprecation-warning/deprecation-warning.o
 ifneq (,$(wildcard quantize))
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
 	@echo "#########"
 	@echo "WARNING: The 'quantize' binary is deprecated. Please use 'llama-quantize' instead."
 	@echo "  Remove the 'quantize' binary to remove this warning."
 	@echo "#########"
 endif

-perplexity: examples/deprecation-warning/deprecation-warning.cpp
+perplexity: examples/deprecation-warning/deprecation-warning.o
 ifneq (,$(wildcard perplexity))
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
 	@echo "#########"
 	@echo "WARNING: The 'perplexity' binary is deprecated. Please use 'llama-perplexity' instead."
 	@echo "  Remove the 'perplexity' binary to remove this warning."
 	@echo "#########"
 endif

-embedding: examples/deprecation-warning/deprecation-warning.cpp
+embedding: examples/deprecation-warning/deprecation-warning.o
 ifneq (,$(wildcard embedding))
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS)
 	@echo "#########"
 	@echo "WARNING: The 'embedding' binary is deprecated. Please use 'llama-embedding' instead."
 	@echo "  Remove the 'embedding' binary to remove this warning."
--- a/README.md
+++ b/README.md
@@ -95,8 +95,16 @@ Typically finetunes of the base models below are supported as well.
 - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
 - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
 - [x] [OLMo](https://allenai.org/olmo)
+- [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
 - [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
+- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520)
+- [x] [Smaug](https://huggingface.co/models?search=Smaug)
+- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B)
+- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
+- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
+- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
 - [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
+- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)

 (instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))

--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1634,7 +1634,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
    options.push_back({ "server",      "       --host HOST",            "ip address to listen (default: %s)", params.hostname.c_str() });
    options.push_back({ "server",      "       --port PORT",            "port to listen (default: %d)", params.port });
    options.push_back({ "server",      "       --path PATH",            "path to serve static files from (default: %s)", params.public_path.c_str() });
-    options.push_back({ "server",      "       --embedding(s)",         "enable embedding endpoint (default: %s)", params.embedding ? "enabled" : "disabled" });
+    options.push_back({ "server",      "       --embedding(s)",         "restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled" });
    options.push_back({ "server",      "       --api-key KEY",          "API key to use for authentication (default: none)" });
    options.push_back({ "server",      "       --api-key-file FNAME",   "path to file containing API keys (default: none)" });
    options.push_back({ "server",      "       --ssl-key-file FNAME",   "path to file a PEM-encoded SSL private key" });
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -316,7 +316,7 @@ class Model:
                if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
                    if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
                        data = gguf.quantize_bf16(data)
-                        assert data.dtype == np.int16
+                        assert data.dtype == np.uint16
                        data_qtype = gguf.GGMLQuantizationType.BF16

                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
--- a/docs/build.md
+++ b/docs/build.md
@@ -178,7 +178,11 @@ For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](ht
  cmake --build build --config Release
  ```

-The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
+The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
+
+The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
+
+The following compilation options are also available to tweak performance:

 | Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
 |-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
--- a/examples/baby-llama/baby-llama.cpp
+++ b/examples/baby-llama/baby-llama.cpp
@@ -1,7 +1,6 @@
 #include "ggml.h"
 #include "train.h"

-#include <vector>
 #include <cassert>
 #include <cstdlib>
 #include <cstring>
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -69,7 +69,7 @@ int main(int argc, char ** argv) {
    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);

    // ensure enough sequences are available
-    ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
+    ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());

    llama_context * ctx = llama_new_context_with_model(model, ctx_params);

--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -247,7 +247,7 @@ server:
         --host HOST              ip address to listen (default: 127.0.0.1)
         --port PORT              port to listen (default: 8080)
         --path PATH              path to serve static files from (default: )
-         --embedding(s)           enable embedding endpoint (default: disabled)
+         --embedding(s)           restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
         --api-key KEY            API key to use for authentication (default: none)
         --api-key-file FNAME     path to file containing API keys (default: none)
         --ssl-key-file FNAME     path to file a PEM-encoded SSL private key
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -900,7 +900,7 @@ struct server_context {

        slot.params.stream             = json_value(data, "stream",            false);
        slot.params.cache_prompt       = json_value(data, "cache_prompt",      false);
-        slot.params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
+        slot.params.n_predict          = json_value(data, "n_predict",         json_value(data, "max_tokens", default_params.n_predict));
        slot.sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
        slot.sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
        slot.sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -355,24 +355,6 @@ static json oaicompat_completion_params_parse(

    llama_params["__oaicompat"] = true;

-    // Map OpenAI parameters to llama.cpp parameters
-    //
-    // For parameters that are defined by the OpenAI documentation (e.g.
-    // temperature), we explicitly specify OpenAI's intended default; we
-    // need to do that because sometimes OpenAI disagrees with llama.cpp
-    //
-    // https://platform.openai.com/docs/api-reference/chat/create
-    llama_sampling_params default_sparams;
-    llama_params["model"]             = json_value(body,   "model",             std::string("unknown"));
-    llama_params["frequency_penalty"] = json_value(body,   "frequency_penalty", 0.0);
-    llama_params["logit_bias"]        = json_value(body,   "logit_bias",        json::object());
-    llama_params["n_predict"]         = json_value(body,   "max_tokens",        -1);
-    llama_params["presence_penalty"]  = json_value(body,   "presence_penalty",  0.0);
-    llama_params["seed"]              = json_value(body,   "seed",              LLAMA_DEFAULT_SEED);
-    llama_params["stream"]            = json_value(body,   "stream",            false);
-    llama_params["temperature"]       = json_value(body,   "temperature",       1.0);
-    llama_params["top_p"]             = json_value(body,   "top_p",             1.0);
-
    // Apply chat template to the list of messages
    llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));

--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
        "nixpkgs-lib": "nixpkgs-lib"
      },
      "locked": {
-        "lastModified": 1719994518,
-        "narHash": "sha256-pQMhCCHyQGRzdfAkdJ4cIWiw+JNuWsTX7f0ZYSyz0VY=",
+        "lastModified": 1722555600,
+        "narHash": "sha256-XOQkdLafnb/p9ij77byFQjDf5m5QYl9b2REiVClC+x4=",
        "owner": "hercules-ci",
        "repo": "flake-parts",
-        "rev": "9227223f6d922fee3c7b190b2cc238a99527bbb7",
+        "rev": "8471fe90ad337a8074e957b69ca4d0089218391d",
        "type": "github"
      },
      "original": {
@@ -20,11 +20,11 @@
    },
    "nixpkgs": {
      "locked": {
-        "lastModified": 1721379653,
-        "narHash": "sha256-8MUgifkJ7lkZs3u99UDZMB4kbOxvMEXQZ31FO3SopZ0=",
+        "lastModified": 1722421184,
+        "narHash": "sha256-/DJBI6trCeVnasdjUo9pbnodCLZcFqnVZiLUfqLH4jA=",
        "owner": "NixOS",
        "repo": "nixpkgs",
-        "rev": "1d9c2c9b3e71b9ee663d11c5d298727dace8d374",
+        "rev": "9f918d616c5321ad374ae6cb5ea89c9e04bf3e58",
        "type": "github"
      },
      "original": {
@@ -36,14 +36,14 @@
    },
    "nixpkgs-lib": {
      "locked": {
-        "lastModified": 1719876945,
-        "narHash": "sha256-Fm2rDDs86sHy0/1jxTOKB1118Q0O3Uc7EC0iXvXKpbI=",
+        "lastModified": 1722555339,
+        "narHash": "sha256-uFf2QeW7eAHlYXuDktm9c25OxOyCoUOQmh5SZ9amE5Q=",
        "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/5daf0514482af3f97abaefc78a6606365c9108e2.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
      },
      "original": {
        "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/5daf0514482af3f97abaefc78a6606365c9108e2.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/a5d394176e64ab29c852d03346c1fc9b0b7d33eb.tar.gz"
      }
    },
    "root": {
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -207,6 +207,7 @@ set(GGML_PUBLIC_HEADERS
    include/ggml-alloc.h
    include/ggml-backend.h
    include/ggml-blas.h
+    include/ggml-cann.h
    include/ggml-cuda.h
    include/ggml.h
    include/ggml-kompute.h
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -349,6 +349,7 @@ extern "C" {
    GGML_API ggml_bf16_t ggml_fp32_to_bf16(float);
    GGML_API float       ggml_bf16_to_fp32(ggml_bf16_t);  // consider just doing << 16
    GGML_API void        ggml_bf16_to_fp32_row(const ggml_bf16_t *, float *, int64_t);
+    GGML_API void        ggml_fp32_to_bf16_row_ref(const float *, ggml_bf16_t *, int64_t);
    GGML_API void        ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t);

    struct ggml_object;
@@ -1455,7 +1456,6 @@ extern "C" {
    // if mode & 2 == 1, GPT-NeoX style
    //
    // b is an int32 vector with size a->ne[2], it contains the positions
-    // c is freq factors (e.g. phi3-128k), (optional)
    GGML_API struct ggml_tensor * ggml_rope(
            struct ggml_context * ctx,
            struct ggml_tensor  * a,
@@ -1472,6 +1472,7 @@ extern "C" {
            int                   mode);

    // custom RoPE
+    // c is freq factors (e.g. phi3-128k), (optional)
    GGML_API struct ggml_tensor * ggml_rope_ext(
            struct ggml_context * ctx,
            struct ggml_tensor  * a,
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -849,11 +849,6 @@ if (GGML_CANN)
                ${CANN_INSTALL_DIR}/acllib/include
            )

-            # TODO: find libs
-            link_directories(
-                ${CANN_INSTALL_DIR}/lib64
-            )
-
            add_subdirectory(ggml-cann/kernels)
            list(APPEND CANN_LIBRARIES
                ascendcl
@@ -872,6 +867,7 @@ if (GGML_CANN)

            set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     ${CANN_LIBRARIES} )
            set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CANN_INCLUDE_DIRS})
+            set(GGML_EXTRA_LIBDIRS  ${GGML_EXTRA_LIBDIRS}  ${CANN_INSTALL_DIR}/lib64)
            list(APPEND GGML_CDEF_PUBLIC GGML_USE_CANN)
        endif()
    else()
--- a/ggml/src/ggml-aarch64.c
+++ b/ggml/src/ggml-aarch64.c
@@ -384,8 +384,8 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void *
    UNUSED(blocklen);

 #if defined(__ARM_FEATURE_SVE)
-    if (svcntw() == 8) {
-        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
    }
 #endif
@@ -496,8 +496,8 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void *
    UNUSED(blocklen);

 #if defined(__ARM_FEATURE_SVE)
-    if (svcntw() == 8) {
-        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
    }
 #endif
@@ -614,7 +614,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
    UNUSED(blocklen);

 #if defined(__ARM_FEATURE_SVE) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
-    if (svcntw() == 8) {
+    if (ggml_sve_cnt_b == QK8_0) {
        const void * b_ptr = vx;
        const void * a_ptr = vy;
        float * res_ptr = s;
@@ -680,12 +680,12 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
        return;
    }
    else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
-        GGML_ASSERT((ggml_cpu_has_sve() && (svcntw() == 8)) &&
+        GGML_ASSERT((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
                    "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal "
                    "performance");
    }
    else if (ggml_cpu_has_neon()) {
-        GGML_ASSERT(((ggml_cpu_has_sve() && (svcntw() == 8)) || ggml_cpu_has_matmul_int8()) &&
+        GGML_ASSERT(((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) || ggml_cpu_has_matmul_int8()) &&
                    "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 "
                    "quantization format for optimal performance");
    }
@@ -745,8 +745,8 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void *
    UNUSED(blocklen);

 #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
-    if (svcntw() == 8) {
-        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
    }
 #endif
@@ -1266,8 +1266,8 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void *
    UNUSED(blocklen);

 #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
-    if (svcntw() == 8) {
-        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+    if (ggml_sve_cnt_b == QK8_0) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
    }
 #endif
@@ -1728,7 +1728,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
    UNUSED(blocklen);

 #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
-    if (svcntw() == 8) {
+    if (ggml_sve_cnt_b == QK8_0) {
        const void * b_ptr = vx;
        const void * a_ptr = vy;
        float * res_ptr = s;
@@ -2139,12 +2139,12 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
        return;
    }
    else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
-        GGML_ASSERT((ggml_cpu_has_sve() && (svcntw() == 8)) &&
+        GGML_ASSERT((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) &&
                    "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal "
                    "performance");
    }
    else if (ggml_cpu_has_neon()) {
-        GGML_ASSERT(((ggml_cpu_has_sve() && (svcntw() == 8)) || ggml_cpu_has_matmul_int8()) &&
+        GGML_ASSERT(((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) || ggml_cpu_has_matmul_int8()) &&
                    "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 "
                    "quantization format for optimal performance");
    }
--- a/ggml/src/ggml-cann.cpp
+++ b/ggml/src/ggml-cann.cpp
@@ -627,7 +627,6 @@ GGML_CALL static void* ggml_backend_cann_buffer_get_base(
 GGML_CALL static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor,
                                                       const void* src,
                                                       void* dst) {
-    GGML_ASSERT(tensor->op == GGML_OP_NONE);

    int64_t n_elems = ggml_nelements(tensor);
    int64_t groups = n_elems / QK4_0;
@@ -679,7 +678,6 @@ GGML_CALL static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor,
 */
 GGML_CALL static void ggml_backend_cann_transform_back_q4_0(
    const ggml_tensor* tensor, void* src, void* dst) {
-    GGML_ASSERT(tensor->op == GGML_OP_NONE);

    int64_t n_elems = ggml_nelements(tensor);
    int64_t groups = n_elems / QK4_0;
@@ -1666,10 +1664,17 @@ GGML_CALL static bool ggml_backend_cann_supports_op(ggml_backend_t backend,
            }
        case GGML_OP_MUL_MAT: {
            switch (op->src[0]->type) {
-                // case GGML_TYPE_Q4_0:
                case GGML_TYPE_F16:
                case GGML_TYPE_F32:
                case GGML_TYPE_Q8_0:
+                    // TODO: fix me
+                    // Current groupsize should not be greater than k-1 in
+                    // aclnnWeightQuantBatchMatmulV2GetWorkspaceSize().
+                    if (op->src[0]->ne[0]-1 > QK8_0) {
+                        return true;
+                    }
+                    return false;
+                case GGML_TYPE_Q4_0:
                    return true;
                default:
                    return false;
@@ -1694,6 +1699,7 @@ GGML_CALL static bool ggml_backend_cann_supports_op(ggml_backend_t backend,
                case GGML_TYPE_F32:
                case GGML_TYPE_F16:
                case GGML_TYPE_Q8_0:
+                case GGML_TYPE_Q4_0:
                    return true;
                default:
                    return false;
--- a/ggml/src/ggml-cann/acl_tensor.cpp
+++ b/ggml/src/ggml-cann/acl_tensor.cpp
@@ -37,6 +37,10 @@ aclDataType ggml_cann_type_mapping(ggml_type type) {
            return ACL_INT16;
        case GGML_TYPE_I32:
            return ACL_INT32;
+        case GGML_TYPE_Q4_0:
+            return ACL_INT4;
+        case GGML_TYPE_Q8_0:
+            return ACL_INT8;
        default:
            return ACL_DT_UNDEFINED;
    }
@@ -89,33 +93,6 @@ bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) {
    return false;
 }

-aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype,
-                                   size_t type_size, int64_t* ne, size_t* nb,
-                                   int64_t dims, aclFormat format,
-                                   size_t offset) {
-    int64_t tmp_ne[GGML_MAX_DIMS * 2];
-    int64_t tmp_stride[GGML_MAX_DIMS * 2];
-
-    memcpy(tmp_ne, ne, dims * sizeof(int64_t));
-    for (int i = 0; i < dims; i++) {
-        tmp_stride[i] = nb[i] / type_size;
-    }
-
-    std::reverse(tmp_ne, tmp_ne + dims);
-    std::reverse(tmp_stride, tmp_stride + dims);
-
-    int64_t acl_storage_len = 0;
-    for (int i = 0; i < dims; i++) {
-        acl_storage_len += (ne[i] - 1) * nb[i];
-    }
-
-    aclTensor* acl_tensor =
-        aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size,
-                        format, &acl_storage_len, 1, data_ptr);
-
-    return acl_tensor;
-}
-
 int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0,
                                  const ggml_tensor* src1,
                                  int64_t* bcast_src0_ne,
--- a/ggml/src/ggml-cann/acl_tensor.h
+++ b/ggml/src/ggml-cann/acl_tensor.h
@@ -23,6 +23,9 @@
 #ifndef CANN_ACL_TENSOR_H
 #define CANN_ACL_TENSOR_H

+#include <algorithm>
+#include <cstring>
+
 #include <aclnn/aclnn_base.h>
 #include "common.h"

@@ -65,7 +68,8 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = null
                             size_t offset = 0);

 /**
- * @brief   Creates an ACL tensor from provided parameters.
+ * @brief   Template for creating an ACL tensor from provided parameters. typename TYPE
+ *          should be size_t or float.
 *
 * @details This function creates an ACL tensor using the provided data pointer,
 *          data type, dimensions, strides, format, offset, and additional parameters.
@@ -83,10 +87,34 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = null
 * @param   offset      Offset in bytes for the ACL tensor data. Defaults to 0.
 * @return  Pointer to the created ACL tensor.
 */
+template<typename TYPE>
 aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype,
-                             size_t type_size, int64_t* ne, size_t* nb,
-                             int64_t dims, aclFormat format = ACL_FORMAT_ND,
-                             size_t offset = 0);
+                                   TYPE type_size, int64_t* ne, TYPE* nb,
+                                   int64_t dims,
+                                   aclFormat format = ACL_FORMAT_ND,
+                                   size_t offset = 0) {
+    int64_t tmp_ne[GGML_MAX_DIMS * 2];
+    int64_t tmp_stride[GGML_MAX_DIMS * 2];
+
+    memcpy(tmp_ne, ne, dims * sizeof(int64_t));
+    for (int i = 0; i < dims; i++) {
+        tmp_stride[i] = nb[i] / type_size;
+    }
+
+    std::reverse(tmp_ne, tmp_ne + dims);
+    std::reverse(tmp_stride, tmp_stride + dims);
+
+    int64_t acl_storage_len = 0;
+    for (int i = 0; i < dims; i++) {
+        acl_storage_len += (ne[i] - 1) * nb[i];
+    }
+
+    aclTensor* acl_tensor =
+        aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size,
+                        format, &acl_storage_len, 1, data_ptr);
+
+    return acl_tensor;
+}

 /**
 * @brief   Checks if tensors require broadcasting based on their shapes.
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -910,6 +910,13 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                ((ggml_tensor*)dst->extra)->ne);
            return;
        }
+        if (dst->type == GGML_TYPE_Q4_0) {
+            aclrtlaunch_ascendc_quantize_f16_to_q4_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
        if (dst->type == GGML_TYPE_F16) {
            if (ggml_are_same_shape(src, dst)) {
                cann_copy(ctx, acl_src, acl_dst);
@@ -971,6 +978,13 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                ((ggml_tensor*)dst->extra)->ne);
            return;
        }
+        if (dst->type == GGML_TYPE_Q4_0) {
+            aclrtlaunch_ascendc_quantize_f32_to_q4_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
        if (dst->type == GGML_TYPE_F32) {
            if (ggml_are_same_shape(src, dst)) {
                cann_copy(ctx, acl_src, acl_dst);
@@ -1312,6 +1326,111 @@ aclnnStatus aclnnIm2col(void* workspace, uint64_t workspaceSize,
 #ifdef __cplusplus
 }
 #endif
+
+static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx,
+                                             ggml_tensor* dst,
+                                             ggml_tensor* src1,
+                                             aclTensor* tmp_cast_tensor,
+                                             aclTensor* tmp_im2col_tensor) {
+    // Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW]
+    int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]};
+    size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]};
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1);
+
+    int64_t permute_dim[] = {0, 2, 1};
+    if (src1->type != dst->type) {
+        aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3);
+    } else {
+        aclnn_permute(ctx, tmp_im2col_tensor, acl_dst, permute_dim, 3);
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+static void ggml_cann_im2col_1d_post_process(
+    ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_tensor* src1,
+    aclTensor* tmp_cast_tensor, aclTensor* tmp_im2col_tensor,
+    const std::vector<int64_t>& im2col_op_params) {
+    // get params
+    const int64_t KH = im2col_op_params[0];
+    const int64_t KW = im2col_op_params[1];
+    const int64_t IW = im2col_op_params[2];
+    const int64_t IC = im2col_op_params[3];
+    const int64_t N = im2col_op_params[4];
+    const int64_t OH = im2col_op_params[5];
+    const int64_t OW = im2col_op_params[6];
+    const int64_t s0 = im2col_op_params[7];
+    const int64_t p0 = im2col_op_params[8];
+    const int64_t d0 = im2col_op_params[9];
+    const int64_t n_bytes_factor = im2col_op_params[10];
+
+    // Permute: [N, IC * KH * KW, OW * OH] ->
+    // [N, OW * OH * n_bytes_factor, IC * KH * KW]
+    aclTensor* tmp_permute_tensor = nullptr;
+    ggml_cann_pool_alloc tmp_permute_allocator(ctx.pool());
+    tmp_permute_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
+    void* tmp_permute_buffer = tmp_permute_allocator.get();
+
+    int64_t tmp_permute_ne[] = {IC * KH * KW, OW * OH * n_bytes_factor, N};
+    size_t tmp_permute_nb[GGML_MAX_DIMS - 1];
+    tmp_permute_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1];
+    }
+
+    tmp_permute_tensor = ggml_cann_create_tensor(
+        tmp_permute_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_permute_ne, tmp_permute_nb,
+        GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+
+    int64_t permute_dim[] = {0, 2, 1};
+    if (src1->type != dst->type) {
+        aclnn_permute(ctx, tmp_cast_tensor, tmp_permute_tensor, permute_dim, 3);
+    } else {
+        aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor, permute_dim,
+                      3);
+    }
+
+    // number of times the kernel moves in W dimension
+    const int n_step_w = (IW + 2 * p0 - d0 * (KW - 1) - 1) / s0 + 1;
+    size_t offset;
+    void *cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer;
+
+    // memory copy with offset to restore 1D im2col from 2d
+    if (IC > 1) {
+        offset = IC * KH * KW * n_step_w * ggml_type_size(dst->type);
+        size_t size_cpy = KH * KW * ggml_type_size(dst->type);
+
+        for (int c = 0; c < IC; c++) {
+            cur_permute_buffer = (char*)tmp_permute_buffer + offset +
+                                 KH * KW * c * ggml_type_size(dst->type);
+            cur_dst_buffer = (char*)dst->data +
+                             c * KH * KW * n_step_w * ggml_type_size(dst->type);
+
+            for (int i = 0; i < n_step_w; i++) {
+                ACL_CHECK(aclrtMemcpyAsync(
+                    cur_dst_buffer, size_cpy, cur_permute_buffer, size_cpy,
+                    ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+                cur_dst_buffer =
+                    (char*)cur_dst_buffer + KH * KW * ggml_type_size(dst->type);
+                cur_permute_buffer = (char*)cur_permute_buffer +
+                                     KH * KW * IC * ggml_type_size(dst->type);
+            }
+        }
+    } else {
+        offset = KH * KW * n_step_w *
+                 ggml_type_size(dst->type);  // equal to ggml_nbytes(dst)
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, offset,
+                                   (char*)tmp_permute_buffer + offset, offset,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(tmp_permute_tensor));
+}
+
 void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
    ggml_tensor* src0 = dst->src[0];  // kernel
    ggml_tensor* src1 = dst->src[1];  // input
@@ -1320,21 +1439,23 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);

-    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
-    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
-    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
-    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
-    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
-    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
-    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
-
    GGML_TENSOR_BINARY_OP_LOCALS;

-    const int64_t N = is_2D ? ne13 : ne12;
-    const int64_t IC = is_2D ? ne12 : ne11;
+    // aclnnIm2col only works on 2D. set s1, p1, d1 to 1 to perform 2D
+    // im2col and do post-processing to restore it to 1D.
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = is_2D ? ((const int32_t*)(dst->op_params))[1] : 1;
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = is_2D ? ((const int32_t*)(dst->op_params))[3] : 1;
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = is_2D ? ((const int32_t*)(dst->op_params))[5] : 1;

-    const int64_t KH = is_2D ? ne01 : 1;
+    const int64_t N = ne13;
+    const int64_t IC = ne12;
+    const int64_t KH = ne01;
    const int64_t KW = ne00;
+    const int64_t IW = ne10;

    const int64_t OH = is_2D ? ne2 : 1;
    const int64_t OW = ne1;
@@ -1342,9 +1463,12 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb10 == sizeof(float));

-    // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH]
+    // memory allocated increased to 3x when is_2D == false
+    const int64_t n_bytes_factor = is_2D ? 1 : 3;
+
+    // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH * n_bytes_factor]
    aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
-    int64_t tmp_im2col_ne[] = {OW * OH, IC * KH * KW, N};
+    int64_t tmp_im2col_ne[] = {OW * OH * n_bytes_factor, IC * KH * KW, N};
    size_t tmp_im2col_nb[GGML_MAX_DIMS - 1];

    tmp_im2col_nb[0] = ggml_type_size(src1->type);
@@ -1356,8 +1480,10 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
    // If dst is f16, tmp_buffer is f32, we need alloc src.typesize *
    // dst.elemcount.
    ggml_cann_pool_alloc im2col_allocator(
-        ctx.pool(), ggml_nelements(dst) * ggml_element_size(src1));
+        ctx.pool(),
+        ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor);
    void* tmp_im2col_buffer = im2col_allocator.get();
+
    aclTensor* tmp_im2col_tensor = ggml_cann_create_tensor(
        tmp_im2col_buffer, ggml_cann_type_mapping(src1->type),
        ggml_type_size(src1->type), tmp_im2col_ne, tmp_im2col_nb,
@@ -1380,8 +1506,9 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                                          paddings, strides, tmp_im2col_tensor,
                                          &workspaceSize, &executor));

+    ggml_cann_pool_alloc workspace_allocator(ctx.pool());
    if (workspaceSize > 0) {
-        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspace_allocator.alloc(workspaceSize);
        workspaceAddr = workspace_allocator.get();
    }

@@ -1391,9 +1518,10 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
    // Cast if dst is f16.
    aclTensor* tmp_cast_tensor = nullptr;
    ggml_cann_pool_alloc tmp_cast_allocator(ctx.pool());
+    void* tmp_cast_buffer = nullptr;
    if (src1->type != dst->type) {
-        tmp_cast_allocator.alloc(ggml_nbytes(dst));
-        void* tmp_cast_buffer = tmp_cast_allocator.get();
+        tmp_cast_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
+        tmp_cast_buffer = tmp_cast_allocator.get();
        size_t temp_cast_nb[GGML_MAX_DIMS - 1];
        temp_cast_nb[0] = ggml_type_size(dst->type);
        for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
@@ -1408,24 +1536,21 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                   ggml_cann_type_mapping(dst->type));
    }

-    // Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW]
-    int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]};
-    size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]};
-    aclTensor* acl_dst =
-        ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1);
-
-    int64_t permute_dim[] = {0, 2, 1};
-    if (src1->type != dst->type) {
-        aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3);
+    // post-processing
+    if (is_2D) {
+        ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor,
+                                         tmp_im2col_tensor);
    } else {
-        aclnn_permute(ctx, tmp_im2col_tensor, acl_dst, permute_dim, 3);
+        std::vector<int64_t> im2col_op_params = {
+            KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor};
+        ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor,
+                                         tmp_im2col_tensor, im2col_op_params);
    }

    // release
    ACL_CHECK(aclDestroyTensor(acl_src1));
    ACL_CHECK(aclDestroyTensor(tmp_im2col_tensor));
    ACL_CHECK(aclDestroyTensor(tmp_cast_tensor));
-    ACL_CHECK(aclDestroyTensor(acl_dst));
    ACL_CHECK(aclDestroyIntArray(kernel_size));
    ACL_CHECK(aclDestroyIntArray(dilations));
    ACL_CHECK(aclDestroyIntArray(paddings));
@@ -2352,21 +2477,33 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
 * @param dst The destination tensor where the result of the matrix
 * multiplication will be stored.
 */
-static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx,
-                                   ggml_tensor* dst) {
+static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
+                                   ggml_tensor* dst,
+                                   const enum ggml_type type) {
    ggml_tensor* src0 = dst->src[0];  // weight
    ggml_tensor* src1 = dst->src[1];  // input

    // The shape of the weight is NCHW. Matrix multiplication uses HW dims. HC
    // is regarded as batch. weight need transpose.
    int64_t weight_ne[] = {src0->ne[1], src0->ne[0]};
-    size_t weight_elem_size = sizeof(uint8_t);
-    size_t weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
+    float weight_elem_size;
+    if (type == GGML_TYPE_Q4_0) {
+        weight_elem_size = float(sizeof(uint8_t)) / 2;
+    }
+    else if (type == GGML_TYPE_Q8_0) {
+        weight_elem_size = float(sizeof(uint8_t));
+    }
+    else {
+        GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT");
+    }
+    float weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
+
    // size of one matrix is element_size * height * width.
    size_t weight_stride = weight_elem_size * src0->ne[0] * src0->ne[1];
    size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3];

    // scale stored at the end of weight. Also need transpose.
+    GGML_ASSERT(QK4_0 == QK8_0);
    int64_t scale_ne[] = {src0->ne[1], src0->ne[0] / QK8_0};
    size_t scale_elem_size = sizeof(uint16_t);
    size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size,
@@ -2381,10 +2518,10 @@ static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx,
    size_t input_nb[] = {input_elem_size, input_elem_size * src1->ne[0]};
    size_t input_stride = input_elem_size * src1->ne[0] * src1->ne[1];

+    ggml_cann_pool_alloc input_alloctor(ctx.pool());
    if (src1->type != GGML_TYPE_F16) {
        aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1);
-        ggml_cann_pool_alloc input_alloctor(
-            ctx.pool(), ggml_nelements(src1) * input_elem_size);
+        input_alloctor.alloc(ggml_nelements(src1) * input_elem_size);
        input_buffer = input_alloctor.get();

        int64_t* input_cast_ne = src1->ne;
@@ -2430,8 +2567,9 @@ static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx,
                (char*)input_buffer + batch1 * input_stride, ACL_FLOAT16,
                input_elem_size, input_ne, input_nb, 2);
            aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
-                (char*)src0->data + batch0 * weight_stride, ACL_INT8,
-                weight_elem_size, weight_ne, weight_nb, 2);
+                (char*)src0->data + batch0 * weight_stride,
+                ggml_cann_type_mapping(type), weight_elem_size, weight_ne,
+                weight_nb, 2);
            aclTensor* acl_scale_tensor = ggml_cann_create_tensor(
                scale_offset + batch0 * scale_stride, ACL_FLOAT16,
                scale_elem_size, scale_ne, scale_nb, 2);
@@ -2485,11 +2623,9 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
        case GGML_TYPE_F16:
            ggml_cann_mat_mul_fp(ctx, dst);
            break;
-        // case GGML_TYPE_Q4_0:
-        //     ggml_cann_mul_mat_q4_0(ctx, dst);
-        //     break;
+        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q8_0:
-            ggml_cann_mul_mat_q8_0(ctx, dst);
+            ggml_cann_mul_mat_quant(ctx, dst, type);
            break;
        default:
            GGML_ABORT("fatal error");
--- a/ggml/src/ggml-cann/kernels/CMakeLists.txt
+++ b/ggml/src/ggml-cann/kernels/CMakeLists.txt
@@ -9,6 +9,7 @@ file(GLOB SRC_FILES
    get_row_q8_0.cpp
    quantize_f32_q8_0.cpp
    quantize_f16_q8_0.cpp
+    quantize_float_to_q4_0.cpp
    dup.cpp
 )

@@ -29,4 +30,4 @@ ascendc_library(ascendc_kernels STATIC
    ${SRC_FILES}
 )

-#ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP)
+# ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP)
--- a/ggml/src/ggml-cann/kernels/ascendc_kernels.h
+++ b/ggml/src/ggml-cann/kernels/ascendc_kernels.h
@@ -8,6 +8,8 @@

 #include "aclrtlaunch_ascendc_quantize_f32_q8_0.h"
 #include "aclrtlaunch_ascendc_quantize_f16_q8_0.h"
+#include "aclrtlaunch_ascendc_quantize_f16_to_q4_0.h"
+#include "aclrtlaunch_ascendc_quantize_f32_to_q4_0.h"

 #include "aclrtlaunch_ascendc_dup_by_rows_fp16.h"
 #include "aclrtlaunch_ascendc_dup_by_rows_fp32.h"
--- a/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp
+++ b/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp
@@ -0,0 +1,273 @@
+#include "kernel_operator.h"
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+#define Group_Size 32
+
+template <typename SRC_T>
+class QUANTIZE_FLOAT_TO_Q4_0 {
+   public:
+    __aicore__ inline QUANTIZE_FLOAT_TO_Q4_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *output_ne_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        // input stride of data elements
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+            output_ne[i] = output_ne_ub[i];
+        }
+
+        // output stride of data elements
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
+        }
+
+        // scale saved one by one after data:. [group1_scale, group2_scale, ...]
+        scale_ne = input_ne;
+        scale_stride[0] = 1;
+        scale_stride[1] = input_ne[0] / Group_Size;
+        for (int i = 2; i < 4; i++) {
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        // split input tensor by rows.
+        uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
+        dr = nr / op_block_num;
+
+        uint64_t tails = nr % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        group_size_in_row = scale_stride[1];
+        int64_t scale_offset = output_ne[0] * output_ne[1] * output_ne[2] *
+                              output_ne[3] * sizeof(uint8_t) / 2;
+
+        input_gm.SetGlobalBuffer((__gm__ SRC_T *)input);
+        output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(output + scale_offset + ir *
+                                                 group_size_in_row *
+                                                 sizeof(half)));
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, Group_Size * sizeof(SRC_T));
+        pipe.InitBuffer(output_queue, BUFFER_NUM,
+                            Group_Size * sizeof(int8_t) / 2);
+        pipe.InitBuffer(cast_queue , BUFFER_NUM, Group_Size * sizeof(float));
+        pipe.InitBuffer(work_queue, BUFFER_NUM, Group_Size*sizeof(float));
+        pipe.InitBuffer(max_queue, BUFFER_NUM, Group_Size*sizeof(float));
+        pipe.InitBuffer(min_queue, BUFFER_NUM, Group_Size*sizeof(float));
+        pipe.InitBuffer(scale_queue, BUFFER_NUM, 16*sizeof(half));
+        pipe.InitBuffer(int8_queue, BUFFER_NUM, Group_Size * sizeof(int8_t));
+        pipe.InitBuffer(half_queue, BUFFER_NUM, Group_Size * sizeof(half));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<SRC_T> input_local = input_queue.AllocTensor<SRC_T>();
+        DataCopy(input_local, input_gm[offset], Group_Size);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        // reinterpretcast Group_Size(32) * int4b_t to Group_Size / 2 * int8_t,
+        // and using DataCopyPad to avoid 32 bits align.
+        LocalTensor<int4b_t> output_local = output_queue.DeQue<int4b_t>();
+        LocalTensor<int8_t> output_int8_local =
+                                    output_local.ReinterpretCast<int8_t>();
+
+        DataCopyExtParams dataCopyParams;
+        dataCopyParams.blockCount = 1;
+        dataCopyParams.blockLen = Group_Size / 2  * sizeof(int8_t);
+        DataCopyPad(output_gm[offset], output_int8_local, dataCopyParams);
+
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void input_to_cast(LocalTensor<float> cast_local,
+                                         LocalTensor<float> input_local) {
+        DataCopy(cast_local, input_local, Group_Size);
+    }
+
+    __aicore__ inline void input_to_cast(LocalTensor<float> cast_local,
+                                         LocalTensor<half> input_local) {
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, Group_Size);
+    }
+
+    __aicore__ inline half calculate_group(int64_t row, int64_t group) {
+        const int64_t i3 = row / (input_ne[1] * input_ne[2]);
+        const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
+        const int64_t i1 =
+            row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
+
+        const int64_t input_offset = i1 * input_stride[1] +
+                                     i2 * input_stride[2] +
+                                     i3 * input_stride[3] + Group_Size * group;
+
+        // output_offset is stride for output_gm which datatype is int8_t and
+        // divided by 2 is needed for int4b_t.
+        const int64_t output_offset = (i1 * output_stride[1] +
+                                       i2 * output_stride[2] +
+                                       i3 * output_stride[3] +
+                                       Group_Size * group) / 2;
+        copy_in(input_offset);
+
+        LocalTensor<SRC_T> input_local = input_queue.DeQue<SRC_T>();
+        LocalTensor<int4b_t> output_local = output_queue.AllocTensor<int4b_t>();
+        LocalTensor<float> cast_local = cast_queue.AllocTensor<float>();
+        LocalTensor<float> work_local = work_queue.AllocTensor<float>();
+        LocalTensor<float> max_local = max_queue.AllocTensor<float>();
+        LocalTensor<float> min_local = min_queue.AllocTensor<float>();
+        LocalTensor<int8_t> int8_local = int8_queue.AllocTensor<int8_t>();
+        LocalTensor<half> half_local = half_queue.AllocTensor<half>();
+
+        input_to_cast(cast_local, input_local);
+
+        ReduceMax(max_local, cast_local, work_local, Group_Size);
+        ReduceMin(min_local, cast_local, work_local, Group_Size);
+        const float max_value = max_local.GetValue(0);
+        const float min_value = min_local.GetValue(0);
+        float d = max_value;
+        if (min_value < 0 && (-1 * min_value) > max_value) {
+            d = min_value;
+        }
+
+        d = d / (-8);
+        if (d != 0) {
+            Muls(cast_local, cast_local, 1.0f / d, Group_Size);
+        }
+
+        // range: [-8,8] -> [0.5,16.5] -> [0,16] -> [0,15] -> [-8,7]
+        float scalar = 8.5f;
+        Adds(cast_local, cast_local, scalar, Group_Size);
+        Cast(cast_local, cast_local, RoundMode::CAST_FLOOR, Group_Size);
+        scalar = 15.0f;
+        Mins(cast_local, cast_local, scalar, Group_Size);
+        scalar = -8.0f;
+        Adds(cast_local, cast_local, scalar, Group_Size);
+
+        // float->half->int4b
+        Cast(half_local, cast_local, RoundMode::CAST_NONE, Group_Size);
+        Cast(output_local, half_local, RoundMode::CAST_NONE, Group_Size);
+
+        output_queue.EnQue(output_local);
+        copy_out(output_offset);
+
+        input_queue.FreeTensor(input_local);
+        work_queue.FreeTensor(work_local);
+        max_queue.FreeTensor(max_local);
+        min_queue.FreeTensor(min_local);
+        int8_queue.FreeTensor(int8_local);
+        half_queue.FreeTensor(half_local);
+        cast_queue.FreeTensor(cast_local);
+        return (half)d;
+    }
+
+    __aicore__ inline void calculate() {
+        LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
+        uint32_t scale_local_offset = 0;
+        uint32_t scale_global_offset = 0;
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                half scale = calculate_group(i, j);
+                scale_local.SetValue(scale_local_offset++, scale);
+                if (scale_local_offset == 16) {
+                    scale_local_offset = 0;
+                    // TODO: OPTIMIZE ME
+                    pipe_barrier(PIPE_ALL);
+                    DataCopy(scale_gm[scale_global_offset], scale_local, 16);
+                    pipe_barrier(PIPE_ALL);
+                    scale_global_offset += 16;
+                }
+            }
+        }
+
+        if (scale_local_offset != 0) {
+            pipe_barrier(PIPE_ALL);
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = scale_local_offset * sizeof(half);
+            DataCopyPad(scale_gm[scale_global_offset], scale_local,
+                        dataCopyParams);
+            pipe_barrier(PIPE_ALL);
+        }
+        scale_queue.FreeTensor(scale_local);
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t *scale_ne;
+    size_t scale_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t group_size_in_row;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<SRC_T> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int8_t> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, BUFFER_NUM> work_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> max_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> min_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> scale_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> cast_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> int8_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> half_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f16_to_q4_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_FLOAT_TO_Q4_0<half> op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_FLOAT_TO_Q4_0<float> op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -130,7 +130,22 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device)
    }
    return res;
 #else
+
+#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA)
+    cudaError_t err;
+    if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr)
+    {
+        err = cudaMallocManaged(ptr, size);
+    }
+    else
+    {
+        err = cudaMalloc(ptr, size);
+    }
+    return err;
+#else
    return cudaMalloc(ptr, size);
+#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA)
+
 #endif
 }

@@ -1885,10 +1900,9 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
 static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);

-    bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16)
+    bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type)
        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
-        && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src0->ne[0] >= GGML_CUDA_DMMV_X*2
-        && src1->ne[1] == 1;
+        && src0->ne[0] % (GGML_CUDA_DMMV_X*2) == 0 && src1->ne[1] == 1;
    bool          use_mul_mat_vec_q =  ggml_is_quantized(src0->type)
        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
        && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
--- a/ggml/src/ggml-cuda/dmmv.cu
+++ b/ggml/src/ggml-cuda/dmmv.cu
@@ -500,7 +500,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
 }

 static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
    const dim3 block_nums(block_num_y, 1, 1);
@@ -510,7 +510,7 @@ static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y,
 }

 static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@@ -519,7 +519,7 @@ static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y,
 }

 static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@@ -528,7 +528,7 @@ static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y,
 }

 static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@@ -537,7 +537,7 @@ static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y,
 }

 static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@@ -588,7 +588,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
 }

 static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0);
    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
    const dim3 block_nums(block_num_y, 1, 1);
    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
@@ -672,3 +672,12 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
    GGML_UNUSED(src1_ncols);
    GGML_UNUSED(src1_padded_row_size);
 }
+
+bool ggml_cuda_dmmv_type_supported(ggml_type src0_type) {
+    return src0_type == GGML_TYPE_Q4_0 || src0_type == GGML_TYPE_Q4_1 ||
+        src0_type == GGML_TYPE_Q5_0 || src0_type == GGML_TYPE_Q5_1 ||
+        src0_type == GGML_TYPE_Q8_0 || src0_type == GGML_TYPE_Q2_K ||
+        src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q4_K ||
+        src0_type == GGML_TYPE_Q5_K || src0_type == GGML_TYPE_Q6_K ||
+        src0_type == GGML_TYPE_F16;
+}
--- a/ggml/src/ggml-cuda/dmmv.cuh
+++ b/ggml/src/ggml-cuda/dmmv.cuh
@@ -16,3 +16,5 @@ void ggml_cuda_op_dequantize_mul_mat_vec(
    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
    const int64_t src1_padded_row_size, cudaStream_t stream);
+
+bool ggml_cuda_dmmv_type_supported(ggml_type src0_type);
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -80,8 +80,9 @@ static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
 /**
 * Converts float32 to brain16.
 *
- * This function is binary identical to AMD Zen4 VCVTNEPS2BF16.
- * Subnormals shall be flushed to zero, and NANs will be quiet.
+ * This is binary identical with Google Brain float conversion.
+ * Floats shall round to nearest even, and NANs shall be quiet.
+ * Subnormals aren't flushed to zero, except perhaps when used.
 * This code should vectorize nicely if using modern compilers.
 */
 static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
@@ -95,10 +96,6 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
        h.bits = (u.i >> 16) | 64; /* force to quiet */
        return h;
    }
-    if (!(u.i & 0x7f800000)) { /* subnormal */
-        h.bits = (u.i & 0x80000000) >> 16; /* flush to zero */
-        return h;
-    }
    h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16;
    return h;
 }
@@ -146,6 +143,7 @@ extern "C" {

 #if defined(__ARM_FEATURE_SVE)
 #include <arm_sve.h>
+#include <sys/prctl.h>
 #endif

 // 16-bit float
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -3818,7 +3818,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
    float sumf = 0;

 #if defined(__ARM_FEATURE_SVE)
-    if (svcntb() == QK8_0) {
+    if (ggml_sve_cnt_b == QK8_0) {
        const svbool_t ptrueh = svptrue_pat_b8(SV_VL16);
        const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh);

@@ -5303,7 +5303,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
    float sumf = 0;

 #if defined(__ARM_FEATURE_SVE)
-    if (svcntb() == QK8_0) {
+    if (ggml_sve_cnt_b == QK8_0) {
        svfloat32_t sumv0 = svdup_n_f32(0.0f);
        svfloat32_t sumv1 = svdup_n_f32(0.0f);

@@ -6449,22 +6449,22 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
            // compute mask for subtraction
            vuint8m1_t qh_m0 = __riscv_vand_vx_u8m1(vqh, m, vl);
            vbool8_t vmask_0 = __riscv_vmseq_vx_u8m1_b8(qh_m0, 0, vl);
-            vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_m(vmask_0, q3_0, 0x4, vl);
+            vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_mu(vmask_0, q3_0, q3_0, 0x4, vl);
            m <<= 1;

            vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl);
            vbool8_t vmask_1 = __riscv_vmseq_vx_u8m1_b8(qh_m1, 0, vl);
-            vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_m(vmask_1, q3_1, 0x4, vl);
+            vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_mu(vmask_1, q3_1, q3_1, 0x4, vl);
            m <<= 1;

            vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl);
            vbool8_t vmask_2 = __riscv_vmseq_vx_u8m1_b8(qh_m2, 0, vl);
-            vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_m(vmask_2, q3_2, 0x4, vl);
+            vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_mu(vmask_2, q3_2, q3_2, 0x4, vl);
            m <<= 1;

            vuint8m1_t qh_m3 = __riscv_vand_vx_u8m1(vqh, m, vl);
            vbool8_t vmask_3 = __riscv_vmseq_vx_u8m1_b8(qh_m3, 0, vl);
-            vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_m(vmask_3, q3_3, 0x4, vl);
+            vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_mu(vmask_3, q3_3, q3_3, 0x4, vl);
            m <<= 1;

            // load Q8 and take product with Q3
@@ -7720,13 +7720,13 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
            vint8m1_t q5_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q5_x, 0x0F, vl));
            vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl);
            vbool8_t vmask_1 = __riscv_vmsne_vx_u8m1_b8(qh_m1, 0, vl);
-            vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_m(vmask_1, q5_a, 16, vl);
+            vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_mu(vmask_1, q5_a, q5_a, 16, vl);
            m <<= 1;

            vint8m1_t q5_l = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q5_x, 0x04, vl));
            vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl);
            vbool8_t vmask_2 = __riscv_vmsne_vx_u8m1_b8(qh_m2, 0, vl);
-            vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_m(vmask_2, q5_l, 16, vl);
+            vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_mu(vmask_2, q5_l, q5_l, 16, vl);
            m <<= 1;

            vint16m2_t v0 = __riscv_vwmul_vv_i16m2(q5_m1, q8_y1, vl);
--- a/ggml/src/ggml-quants.h
+++ b/ggml/src/ggml-quants.h
@@ -127,6 +127,10 @@ void iq2xs_free_impl(enum ggml_type type);
 void iq3xs_init_impl(int grid_size);
 void iq3xs_free_impl(int grid_size);

+#if defined(__ARM_FEATURE_SVE)
+extern int ggml_sve_cnt_b;
+#endif
+
 #ifdef __cplusplus
 }
 #endif
--- a/ggml/src/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl.cpp
@@ -4108,6 +4108,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens
        case GGML_OP_ARGSORT:
            func = ggml_sycl_argsort;
            break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            func = ggml_sycl_op_timestep_embedding;
+            break;
        default:
            return false;
    }
@@ -5225,6 +5228,7 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
        case GGML_OP_UPSCALE:
        case GGML_OP_PAD:
        case GGML_OP_LEAKY_RELU:
+        case GGML_OP_TIMESTEP_EMBEDDING:
            return true;
        default:
            return false;
--- a/ggml/src/ggml-sycl/backend.hpp
+++ b/ggml/src/ggml-sycl/backend.hpp
@@ -24,5 +24,6 @@
 #include "rope.hpp"
 #include "norm.hpp"
 #include "softmax.hpp"
+#include "tsembd.hpp"

 #endif // GGML_SYCL_BACKEND_HPP
--- a/ggml/src/ggml-sycl/dpct/helper.hpp
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -1834,20 +1834,6 @@ namespace dpct
    template <typename T1, typename T2, typename T3>
    inline auto dp4a(T1 a, T2 b, T3 c)
    {
-#if defined(__SYCL_DEVICE_ONLY__) && defined(__NVPTX__) &&                     \
-    defined(__SYCL_CUDA_ARCH__) && __SYCL_CUDA_ARCH__ >= 610
-        dot_product_acc_t<T1, T2> res;
-        if constexpr (std::is_same_v<dot_product_acc_t<T1, T2>, uint32_t>) {
-            asm volatile("dp4a.u32.u32 %0, %1, %2, %3;"
-                         : "=r"(res)
-                         : "r"(a), "r"(b), "r"(c));
-        } else {
-            asm volatile("dp4a.s32.s32 %0, %1, %2, %3;"
-                         : "=r"(res)
-                         : "r"(a), "r"(b), "r"(c));
-        }
-        return res;
-#else
        dot_product_acc_t<T1, T2> res = c;
        auto va = extract_and_sign_or_zero_extend4(a);
        auto vb = extract_and_sign_or_zero_extend4(b);
@@ -1856,7 +1842,6 @@ namespace dpct
        res += va[2] * vb[2];
        res += va[3] * vb[3];
        return res;
-#endif
    }

    struct sub_sat
--- a/ggml/src/ggml-sycl/mmvq.cpp
+++ b/ggml/src/ggml-sycl/mmvq.cpp
@@ -902,7 +902,7 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
-                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 1>(
+                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
        });
--- a/ggml/src/ggml-sycl/presets.hpp
+++ b/ggml/src/ggml-sycl/presets.hpp
@@ -42,6 +42,7 @@
 #define SYCL_IM2COL_BLOCK_SIZE 256
 #define SYCL_POOL2D_BLOCK_SIZE 256
 #define SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE 256
+#define SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE 256

 // dmmv = dequantize_mul_mat_vec
 #ifndef GGML_SYCL_DMMV_X
--- a/ggml/src/ggml-sycl/tsembd.cpp
+++ b/ggml/src/ggml-sycl/tsembd.cpp
@@ -0,0 +1,71 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "tsembd.hpp"
+
+static void timestep_embedding_f32(
+        const float * timesteps, float * dst, const int nb1,
+        const int dim, const int max_period, const sycl::nd_item<3> &item_ct1) {
+    // item_ct1.get_group(1)(blockIDx.y): idx of timesteps->ne[0]
+    // item_ct1.get_group(2) (blockIDx.x): idx of ((dim + 1) / 2) / BLOCK_SIZE
+    int i = item_ct1.get_group(1);
+    int j = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    float * embed_data = (float *)((char *)dst +  i*nb1);
+
+    if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
+        embed_data[dim] = 0.f;
+    }
+
+    int half = dim / 2;
+    if (j >= half) {
+        return;
+    }
+
+    float timestep = timesteps[i];
+    float freq = (float)sycl::native::exp(-(sycl::log((float)max_period)) * j / half);
+    float arg = timestep * freq;
+    embed_data[j] = sycl::cos(arg);
+    embed_data[j + half] = sycl::sin(arg);
+}
+
+static void timestep_embedding_f32_sycl(
+        const float * x, float * dst, const int ne00, const int nb1,
+        const int dim, const int max_period, const queue_ptr& stream) {
+    // As the kernel returns when thread.idx is larger than dim/2, the half_ceil does not need to pad
+    int half_ceil = dim / 2;
+    int num_blocks = (half_ceil + SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE;
+    sycl::range<3> block_dims(1, 1, SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE);
+    sycl::range<3> gridDim(1, ne00, num_blocks);
+    stream->parallel_for(
+        sycl::nd_range<3>(
+            gridDim * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            timestep_embedding_f32(
+                x, dst, nb1, dim, max_period, item_ct1
+            );
+        });
+}
+
+void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+    const ggml_tensor *src1, ggml_tensor * dst) {
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    dpct::queue_ptr stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int dim = dst->op_params[0];
+    const int max_period = dst->op_params[1];
+
+    timestep_embedding_f32_sycl(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
+}
--- a/ggml/src/ggml-sycl/tsembd.hpp
+++ b/ggml/src/ggml-sycl/tsembd.hpp
@@ -0,0 +1,21 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_TSEMBD_HPP
+#define GGML_SYCL_TSEMBD_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+    const ggml_tensor *src1, ggml_tensor * dst);
+
+#endif // GGML_SYCL_TSEMBD_HPP
--- a/ggml/src/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan.cpp
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -37,6 +37,9 @@
 #include <unistd.h>
 #endif

+#if defined(__ARM_FEATURE_SVE)
+int ggml_sve_cnt_b = 0;
+#endif
 #if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
 #undef GGML_USE_LLAMAFILE
 #endif
@@ -141,7 +144,51 @@ typedef pthread_t ggml_thread_t;

 #include <sys/wait.h>

-#if defined(__linux__)
+#if defined(__ANDROID__)
+#include <unwind.h>
+#include <dlfcn.h>
+#include <stdio.h>
+
+struct backtrace_state {
+    void ** current;
+    void ** end;
+};
+
+static _Unwind_Reason_Code unwind_callback(struct _Unwind_Context* context, void* arg) {
+    struct backtrace_state * state = (struct backtrace_state *)arg;
+    uintptr_t pc = _Unwind_GetIP(context);
+    if (pc) {
+        if (state->current == state->end) {
+            return _URC_END_OF_STACK;
+        } else {
+            *state->current++ = (void*)pc;
+        }
+    }
+    return _URC_NO_REASON;
+}
+
+static void ggml_print_backtrace_symbols(void) {
+    const int max = 100;
+    void* buffer[max];
+
+    struct backtrace_state state = {buffer, buffer + max};
+    _Unwind_Backtrace(unwind_callback, &state);
+
+    int count = state.current - buffer;
+
+    for (int idx = 0; idx < count; ++idx) {
+        const void * addr = buffer[idx];
+        const char * symbol = "";
+
+        Dl_info info;
+        if (dladdr(addr, &info) && info.dli_sname) {
+            symbol = info.dli_sname;
+        }
+
+        fprintf(stderr, "%d: %p %s\n", idx, addr, symbol);
+    }
+}
+#elif defined(__linux__) && defined(__GLIBC__)
 #include <execinfo.h>
 static void ggml_print_backtrace_symbols(void) {
    void * trace[100];
@@ -436,9 +483,16 @@ void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) {
    }
 }

+void ggml_fp32_to_bf16_row_ref(const float * x, ggml_bf16_t * y, int64_t n) {
+    for (int i = 0; i < n; i++) {
+        y[i] = ggml_compute_fp32_to_bf16(x[i]);
+    }
+}
+
 void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) {
  int i = 0;
 #if defined(__AVX512BF16__)
+  // subnormals are flushed to zero on this platform
  for (; i + 32 <= n; i += 32) {
        _mm512_storeu_si512(
            (__m512i *)(y + i),
@@ -918,7 +972,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
        .is_quantized             = false,
        .to_float                 = (ggml_to_float_t) ggml_bf16_to_fp32_row,
        .from_float               = (ggml_from_float_t) ggml_fp32_to_bf16_row,
-        .from_float_ref           = (ggml_from_float_t) ggml_fp32_to_bf16_row,
+        .from_float_ref           = (ggml_from_float_t) ggml_fp32_to_bf16_row_ref,
        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_bf16,
        .vec_dot_type             = GGML_TYPE_BF16,
        .nrows                    = 1,
@@ -3507,6 +3561,12 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {

    GGML_ASSERT_ALIGNED(ctx->mem_buffer);

+#if defined(__ARM_FEATURE_SVE)
+    if (!ggml_sve_cnt_b) {
+        ggml_sve_cnt_b = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL);
+    }
+#endif
+
    GGML_PRINT_DEBUG("%s: context initialized\n", __func__);

    ggml_critical_section_end();
@@ -20606,7 +20666,7 @@ size_t ggml_quantize_chunk(
        case GGML_TYPE_BF16:
            {
                size_t elemsize = sizeof(ggml_bf16_t);
-                ggml_fp32_to_bf16_row(src + start, (ggml_bf16_t *)dst + start, n);
+                ggml_fp32_to_bf16_row_ref(src + start, (ggml_bf16_t *)dst + start, n);
                result = n * elemsize;
            } break;
        case GGML_TYPE_F32:
--- a/ggml/src/vulkan-shaders/add.comp
+++ b/ggml/src/vulkan-shaders/add.comp
@@ -4,9 +4,11 @@
 #include "generic_binary_head.comp"

 void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
        return;
    }

-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[src1_idx(idx)]));
 }
--- a/ggml/src/vulkan-shaders/clamp.comp
+++ b/ggml/src/vulkan-shaders/clamp.comp
@@ -4,10 +4,12 @@
 #include "generic_unary_head.comp"

 void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
        return;
    }

-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
 }
--- a/ggml/src/vulkan-shaders/concat.comp
+++ b/ggml/src/vulkan-shaders/concat.comp
@@ -0,0 +1,35 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+    const int dim = p.param3;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i3 = idx / (p.ne22*p.ne21*p.ne20);
+    const uint i3_offset = i3 * p.ne22*p.ne21*p.ne20;
+    const uint i2 = (idx - i3_offset) / (p.ne21*p.ne20);
+    const uint i2_offset = i2*p.ne21*p.ne20;
+    const uint i1 = (idx - i3_offset - i2_offset) / p.ne20;
+    const uint i0 = idx - i3_offset - i2_offset - i1*p.ne20;
+
+    uint o[4] = {0, 0, 0, 0};
+    o[dim] = dim == 0 ? p.ne00 : (dim == 1 ? p.ne01 : (dim == 2 ? p.ne02 : p.ne03));
+
+    const uint src0_idx = i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00;
+    const uint src1_idx = (i3 - o[3])*p.nb13 + (i2 - o[2])*p.nb12 + (i1 - o[1])*p.nb11 + (i0 - o[0])*p.nb10;
+    const uint dst_idx = i3*p.nb23 + i2*p.nb22 + i1*p.nb21 + i0*p.nb20;
+
+    const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03;
+
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : data_b[src1_idx]);
+#else
+    data_d[p.d_offset + dst_idx] = is_src0 ? data_a[src0_idx] : data_b[src1_idx];
+#endif
+}
--- a/ggml/src/vulkan-shaders/copy.comp
+++ b/ggml/src/vulkan-shaders/copy.comp
@@ -4,13 +4,15 @@
 #include "generic_unary_head.comp"

 void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
        return;
    }

 #ifndef OPTIMIZATION_ERROR_WORKAROUND
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx(idx)]);
 #else
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)];
+    data_d[p.d_offset + dst_idx(idx)] = data_a[src0_idx(idx)];
 #endif
 }
--- a/ggml/src/vulkan-shaders/div.comp
+++ b/ggml/src/vulkan-shaders/div.comp
@@ -4,9 +4,11 @@
 #include "generic_binary_head.comp"

 void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
        return;
    }

-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) / FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) / FLOAT_TYPE(data_b[src1_idx(idx)]));
 }
--- a/ggml/src/vulkan-shaders/gelu.comp
+++ b/ggml/src/vulkan-shaders/gelu.comp
@@ -13,7 +13,7 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 void main() {
    const float GELU_COEF_A    = 0.044715f;
    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-    const uint i = gl_GlobalInvocationID.x;
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;

    if (i >= p.KX) {
        return;
--- a/ggml/src/vulkan-shaders/gelu_quick.comp
+++ b/ggml/src/vulkan-shaders/gelu_quick.comp
@@ -0,0 +1,23 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const float GELU_QUICK_COEF = -1.702f;
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float x = float(data_a[i]);
+    data_d[i] = D_TYPE(x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x))));
+}
--- a/ggml/src/vulkan-shaders/generic_binary_head.comp
+++ b/ggml/src/vulkan-shaders/generic_binary_head.comp
@@ -7,7 +7,7 @@ layout (push_constant) uniform parameter
    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
    uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
    uint d_offset;
-    float param1; float param2;
+    float param1; float param2; int param3;
 } p;

 layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
@@ -16,6 +16,10 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};

+uint get_idx() {
+    return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+}
+
 uint src0_idx(uint idx) {
    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
--- a/ggml/src/vulkan-shaders/generic_unary_head.comp
+++ b/ggml/src/vulkan-shaders/generic_unary_head.comp
@@ -14,6 +14,10 @@ layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};

+uint get_idx() {
+    return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+}
+
 uint src0_idx(uint idx) {
    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
--- a/ggml/src/vulkan-shaders/group_norm.comp
+++ b/ggml/src/vulkan-shaders/group_norm.comp
@@ -0,0 +1,66 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared float tmp[BLOCK_SIZE];
+
+void main() {
+    const uint group_size = p.KX;
+    const float eps = p.param1;
+
+    const uint tid = gl_LocalInvocationID.x;
+    const uint start = gl_WorkGroupID.x * group_size + tid;
+    const uint end = start + group_size;
+
+    tmp[tid] = 0.0f;
+
+    // Calculate mean
+    [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
+        tmp[tid] += float(data_a[col]);
+    }
+
+    // tmp up partial tmps and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    const float mean = tmp[0] / group_size;
+    barrier();
+    tmp[tid] = 0.0f;
+
+    // Calculate variance
+    [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
+        const float xi = float(data_a[col]) - mean;
+        data_d[col] = D_TYPE(xi);
+        tmp[tid] += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    const float variance = tmp[0] / group_size;
+    const float scale = inversesqrt(variance + eps);
+
+    [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) {
+        data_d[col] *= D_TYPE(scale);
+    }
+}
--- a/ggml/src/vulkan-shaders/im2col.comp
+++ b/ggml/src/vulkan-shaders/im2col.comp
@@ -0,0 +1,57 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint batch_offset; uint offset_delta;
+    uint IC;
+    uint IW; uint IH;
+    uint OW; uint OH;
+    uint KW; uint KH;
+    uint pelements;
+    uint CHW;
+    int s0; int s1;
+    int p0; int p1;
+    int d0; int d1;
+} p;
+
+#include "types.comp"
+
+#define BLOCK_SIZE 256
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x;
+    if (i >= p.pelements) {
+        return;
+    }
+
+    const uint ksize = p.OW * (p.KH > 1 ? p.KW : 1);
+    const uint kx = i / ksize;
+    const uint kd = kx * ksize;
+    const uint ky = (i - kd) / p.OW;
+    const uint ix = i % p.OW;
+
+    const uint oh = gl_GlobalInvocationID.y;
+    const uint batch = gl_GlobalInvocationID.z / p.IC;
+    const uint ic = gl_GlobalInvocationID.z % p.IC;
+
+    const uint iiw = ix * p.s0 + kx * p.d0 - p.p0;
+    const uint iih = oh * p.s1 + ky * p.d1 - p.p1;
+
+    const uint offset_dst =
+        ((batch * p.OH + oh) * p.OW + ix) * p.CHW +
+        (ic * (p.KW * p.KH) + ky * p.KW + kx);
+
+    if (iih < 0 || iih >= p.IH || iiw < 0 || iiw >= p.IW) {
+        data_d[offset_dst] = D_TYPE(0.0f);
+    } else {
+        const uint offset_src = ic * p.offset_delta + batch * p.batch_offset;
+        data_d[offset_dst] = D_TYPE(data_a[offset_src + iih * p.IW + iiw]);
+    }
+}
--- a/ggml/src/vulkan-shaders/leaky_relu.comp
+++ b/ggml/src/vulkan-shaders/leaky_relu.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float val = float(data_a[i]);
+    data_d[i] = D_TYPE(max(val, 0.0f) + min(val, 0.0f) * p.param1);
+}
--- a/ggml/src/vulkan-shaders/mul.comp
+++ b/ggml/src/vulkan-shaders/mul.comp
@@ -4,9 +4,11 @@
 #include "generic_binary_head.comp"

 void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
        return;
    }

-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(data_b[src1_idx(idx)]));
 }
--- a/ggml/src/vulkan-shaders/mul_mat_vec.comp
+++ b/ggml/src/vulkan-shaders/mul_mat_vec.comp
@@ -16,6 +16,13 @@ void main() {
    const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
    const uint tid = gl_LocalInvocationID.x;

+    // There are not enough cols to use all threads
+    if (tid >= p.ncols) {
+        return;
+    }
+
+    const uint block_size = min(p.ncols, BLOCK_SIZE);
+
    uint a_offset, b_offset, d_offset;
    get_offsets(a_offset, b_offset, d_offset);

@@ -23,8 +30,8 @@ void main() {

    tmp[tid] = FLOAT_TYPE(0.0f);

-    [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) {
-        const uint col = i*BLOCK_SIZE + 2*tid;
+    [[unroll]] for (uint i = 0; i < p.ncols/block_size; i += 2) {
+        const uint col = i*block_size + 2*tid;
        const uint ib = (row*p.ncols + col)/QUANT_K; // block index
        const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
        const uint iybs = col - col%QUANT_K; // y block start index
@@ -38,7 +45,7 @@ void main() {

    // sum up partial sums and write back result
    barrier();
-    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
+    [[unroll]] for (uint s = block_size/2; s > 0; s >>= 1) {
        if (tid < s) {
            tmp[tid] += tmp[tid + s];
        }
--- a/ggml/src/vulkan-shaders/norm.comp
+++ b/ggml/src/vulkan-shaders/norm.comp
@@ -14,7 +14,7 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 shared vec2 sum[BLOCK_SIZE];

 void main() {
-    const uint row = gl_WorkGroupID.x;
+    const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
    const uint tid = gl_LocalInvocationID.x;

    sum[tid] = vec2(0.0f, 0.0f);
--- a/ggml/src/vulkan-shaders/pad.comp
+++ b/ggml/src/vulkan-shaders/pad.comp
@@ -0,0 +1,26 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i3 = idx / (p.ne12*p.ne11*p.ne10);
+    const uint i3_offset = i3 * p.ne12*p.ne11*p.ne10;
+    const uint i2 = (idx - i3_offset) / (p.ne11*p.ne10);
+    const uint i2_offset = i2*p.ne11*p.ne10;
+    const uint i1 = (idx - i3_offset - i2_offset) / p.ne10;
+    const uint i0 = idx - i3_offset - i2_offset - i1*p.ne10;
+
+    const uint src0_idx = i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00;
+    const uint dst_idx = i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0*p.nb10;
+
+    const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03;
+
+    data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : 0.0f);
+}
--- a/ggml/src/vulkan-shaders/relu.comp
+++ b/ggml/src/vulkan-shaders/relu.comp
@@ -11,7 +11,7 @@ layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};

 void main() {
-    const uint i = gl_GlobalInvocationID.x;
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;

    if (i >= p.KX) {
        return;
--- a/ggml/src/vulkan-shaders/rms_norm.comp
+++ b/ggml/src/vulkan-shaders/rms_norm.comp
@@ -14,7 +14,7 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 shared FLOAT_TYPE sum[BLOCK_SIZE];

 void main() {
-    const uint row = gl_WorkGroupID.x;
+    const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
    const uint tid = gl_LocalInvocationID.x;

    sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
--- a/ggml/src/vulkan-shaders/scale.comp
+++ b/ggml/src/vulkan-shaders/scale.comp
@@ -4,9 +4,11 @@
 #include "generic_unary_head.comp"

 void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
        return;
    }

-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(p.param1));
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(p.param1));
 }
--- a/ggml/src/vulkan-shaders/silu.comp
+++ b/ggml/src/vulkan-shaders/silu.comp
@@ -11,7 +11,7 @@ layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};

 void main() {
-    const uint i = gl_GlobalInvocationID.x;
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;

    if (i >= p.KX) {
        return;
--- a/ggml/src/vulkan-shaders/soft_max.comp
+++ b/ggml/src/vulkan-shaders/soft_max.comp
@@ -28,7 +28,7 @@ shared FLOAT_TYPE vals[BLOCK_SIZE];

 void main() {
    const uint tid = gl_LocalInvocationID.x;
-    const uint rowx = gl_WorkGroupID.x;
+    const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
    const uint rowy = rowx % p.KY;

    float slope = 1.0f;
--- a/ggml/src/vulkan-shaders/square.comp
+++ b/ggml/src/vulkan-shaders/square.comp
@@ -4,10 +4,12 @@
 #include "generic_unary_head.comp"

 void main() {
-    if (gl_GlobalInvocationID.x >= p.ne) {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
        return;
    }

-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
-    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val);
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
+    data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val * val);
 }
--- a/ggml/src/vulkan-shaders/sum_rows.comp
+++ b/ggml/src/vulkan-shaders/sum_rows.comp
@@ -14,7 +14,7 @@ layout (constant_id = 0) const uint BLOCK_SIZE = 32;
 shared FLOAT_TYPE tmp[BLOCK_SIZE];

 void main() {
-    const uint row = gl_WorkGroupID.x;
+    const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
    const uint col = gl_LocalInvocationID.x;

    tmp[col] = FLOAT_TYPE(0.0f);
--- a/ggml/src/vulkan-shaders/tanh.comp
+++ b/ggml/src/vulkan-shaders/tanh.comp
@@ -0,0 +1,21 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    data_d[i] = D_TYPE(tanh(data_a[i]));
+}
--- a/ggml/src/vulkan-shaders/timestep_embedding.comp
+++ b/ggml/src/vulkan-shaders/timestep_embedding.comp
@@ -0,0 +1,41 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint nb1;
+    uint dim;
+    uint max_period;
+} p;
+
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 256
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.y;
+    const uint j = gl_GlobalInvocationID.x;
+    const uint d_offset = i * p.nb1;
+
+    if (p.dim % 2 != 0 && j == ((p.dim + 1) / 2)) {
+        data_d[d_offset + p.dim] = 0.f;
+    }
+
+    const uint half_dim = p.dim / 2;
+    if (j >= half_dim) {
+        return;
+    }
+
+    const float timestep = float(data_a[i]);
+    const float freq = float(exp(-log(p.max_period) * j / half_dim));
+    const float arg = timestep * freq;
+    data_d[d_offset + j] = D_TYPE(cos(arg));
+    data_d[d_offset + j + half_dim] = D_TYPE(sin(arg));
+}
--- a/ggml/src/vulkan-shaders/types.comp
+++ b/ggml/src/vulkan-shaders/types.comp
@@ -6,7 +6,7 @@
 #define QUANT_K 1
 #define QUANT_R 1

-#ifndef LOAD_VEC_A
+#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1
 #define A_TYPE float
 #elif LOAD_VEC_A == 4
 #define A_TYPE vec4
@@ -19,7 +19,7 @@
 #define QUANT_K 1
 #define QUANT_R 1

-#ifndef LOAD_VEC_A
+#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1
 #define A_TYPE float16_t
 #elif LOAD_VEC_A == 4
 #define A_TYPE f16vec4
--- a/ggml/src/vulkan-shaders/upscale.comp
+++ b/ggml/src/vulkan-shaders/upscale.comp
@@ -0,0 +1,36 @@
+#version 450
+
+layout (push_constant) uniform parameter
+{
+    uint ne; uint d_offset;
+    uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13;
+    float sf0; float sf1; float sf2; float sf3;
+} p;
+
+#include "types.comp"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i10 = idx % p.ne10;
+    const uint i11 = (idx / p.ne10) % p.ne11;
+    const uint i12 = (idx / (p.ne10 * p.ne11)) % p.ne12;
+    const uint i13 = (idx / (p.ne10 * p.ne11 * p.ne12)) % p.ne13;
+
+    const uint i00 = uint(i10 / p.sf0);
+    const uint i01 = uint(i11 / p.sf1);
+    const uint i02 = uint(i12 / p.sf2);
+    const uint i03 = uint(i13 / p.sf3);
+
+    data_d[p.d_offset + idx] = D_TYPE(data_a[i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]);
+}
--- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -30,20 +30,6 @@

 #define ASYNCIO_CONCURRENCY 64

-// define prototypes
-void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str);
-bool directory_exists(const std::string& path);
-bool create_directory(const std::string& path);
-std::string to_uppercase(const std::string& input);
-bool string_ends_with(const std::string& str, const std::string& suffix);
-std::string join_paths(const std::string& path1, const std::string& path2);
-std::string basename(const std::string &path);
-void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16);
-std::map<std::string, std::string> merge_maps(const std::map<std::string, std::string>& a, const std::map<std::string, std::string>& b);
-void matmul_shaders(std::vector<std::future<void>>& tasks, bool fp16, bool matmul_id);
-void process_shaders(std::vector<std::future<void>>& tasks);
-void write_output_files();
-
 std::mutex lock;
 std::vector<std::pair<std::string, std::string>> shader_fnames;

@@ -52,7 +38,7 @@ std::string input_dir = "vulkan-shaders";
 std::string output_dir = "/tmp";
 std::string target_hpp = "ggml-vulkan-shaders.hpp";
 std::string target_cpp = "ggml-vulkan-shaders.cpp";
-bool clean = true;
+bool no_clean = false;

 const std::vector<std::string> type_names = {
    "f32",
@@ -283,9 +269,12 @@ void matmul_shaders(std::vector<std::future<void>>& tasks, bool fp16, bool matmu

    for (const auto& tname : type_names) {
        std::string data_a_key = "DATA_A_" + to_uppercase(tname);
+        // For unaligned, load one at a time for f32/f16, or two at a time for quants
+        std::string load_vec_a_unaligned = (tname == "f32" || tname == "f16") ? "1" : "2";
+        // For aligned matmul loads
        std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2";
        tasks.push_back(std::async(std::launch::async, [=] {
-            string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
+            string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
        }));
        tasks.push_back(std::async(std::launch::async, [=] {
            string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16);
@@ -354,6 +343,9 @@ void process_shaders(std::vector<std::future<void>>& tasks) {
    tasks.push_back(std::async(std::launch::async, [=] {
        string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
    tasks.push_back(std::async(std::launch::async, [=] {
        string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
    }));
@@ -371,6 +363,9 @@ void process_shaders(std::vector<std::future<void>>& tasks) {
    tasks.push_back(std::async(std::launch::async, [] {
        string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}});
+    }));

    tasks.push_back(std::async(std::launch::async, [] {
        string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {});
@@ -396,15 +391,42 @@ void process_shaders(std::vector<std::future<void>>& tasks) {
        string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
    }));

+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("concat_f32", "concat.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("concat_f16", "concat.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
    tasks.push_back(std::async(std::launch::async, [] {
        string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
    tasks.push_back(std::async(std::launch::async, [] {
        string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
    }));
    tasks.push_back(std::async(std::launch::async, [] {
        string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));

    tasks.push_back(std::async(std::launch::async, [] {
        string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
@@ -438,6 +460,17 @@ void process_shaders(std::vector<std::future<void>>& tasks) {
    tasks.push_back(std::async(std::launch::async, [=] {
        string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("im2col_f32_f16", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}));
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
 }

 void write_output_files() {
@@ -478,9 +511,8 @@ void write_output_files() {
        }
        fprintf(src, "\n};\n\n");

-        if (clean) {
+        if (!no_clean) {
            std::remove(path.c_str());
-            // fprintf(stderr, "Removed: %s\n", path.c_str());
        }
    }

@@ -496,18 +528,6 @@ int main(int argc, char** argv) {
        }
    }

-    if (argc <= 1 || args.find("--help") != args.end()) {
-        std::cout << "Usage:\n"
-                     "\tvulkan-shaders-gen [options]\n\n"
-                     "Options:\n"
-                     "\t--glslc <path>        Path to glslc executable (default: /usr/bin/glslc)\n"
-                     "\t--input-dir           Directory containing shader sources (required)\n"
-                     "\t--output-dir          Output directory for generated SPIR-V files and optional C++ headers\n"
-                     "\t--target-hpp <path>   Path to generate a header file with shader declarations in C++ format\n"
-                     "\t--target-cpp <path>   Path to generate a source code file implementing the declared shaders (optional)\n"
-                     "\t--no-clean            Keep temporary SPIR-V files after build (default: remove them)\n";
-        return EXIT_SUCCESS;
-    }
    if (args.find("--glslc") != args.end()) {
        GLSLC = args["--glslc"]; // Path to glslc
    }
@@ -524,7 +544,7 @@ int main(int argc, char** argv) {
        target_cpp = args["--target-cpp"]; // Path to generated cpp file
    }
    if (args.find("--no-clean") != args.end()) {
-        clean = false; // Keep temporary SPIR-V files in output-dir after build
+        no_clean = true; // Keep temporary SPIR-V files in output-dir after build
    }

    if (!directory_exists(input_dir)) {
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -312,6 +312,8 @@ class GGUFWriter:
        self.add_key_value(key, val, GGUFValueType.STRING)

    def add_array(self, key: str, val: Sequence[Any]) -> None:
+        if len(val) == 0:
+            return
        self.add_key_value(key, val, GGUFValueType.ARRAY)

    @staticmethod
@@ -845,7 +847,14 @@ class GGUFWriter:
            encoded_val = val.encode("utf-8") if isinstance(val, str) else val
            kv_data += self._pack("Q", len(encoded_val))
            kv_data += encoded_val
-        elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
+        elif vtype == GGUFValueType.ARRAY:
+
+            if not isinstance(val, Sequence):
+                raise ValueError("Invalid GGUF metadata array, expecting sequence")
+
+            if len(val) == 0:
+                raise ValueError("Invalid GGUF metadata array. Empty array")
+
            if isinstance(val, bytes):
                ltype = GGUFValueType.UINT8
            else:
--- a/gguf-py/gguf/quants.py
+++ b/gguf-py/gguf/quants.py
@@ -25,14 +25,12 @@ def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizati

 # same as ggml_compute_fp32_to_bf16 in ggml-impl.h
 def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray:
-    n = n.astype(np.float32, copy=False).view(np.int32)
+    n = n.astype(np.float32, copy=False).view(np.uint32)
    # force nan to quiet
-    n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n)
-    # flush subnormals to zero
-    n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n)
+    n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n)
    # round to nearest even
-    n = (n + (0x7fff + ((n >> 16) & 1))) >> 16
-    return n.astype(np.int16)
+    n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16
+    return n.astype(np.uint16)


 # This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
@@ -49,10 +47,10 @@ def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.


 def __quantize_bf16_array(n: np.ndarray) -> np.ndarray:
-    return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.int16, oshape=n.shape)
+    return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.uint16, oshape=n.shape)


-__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.int16)
+__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.uint16)


 def quantize_bf16(n: np.ndarray):
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -64,6 +64,7 @@ while read c; do
        src/ggml*.cu \
        src/ggml-cuda/* \
        src/ggml-sycl/* \
+        src/vulkan-shaders/* \
        include/ggml*.h \
        tests/test-opt.cpp \
        tests/test-grad0.cpp \
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-31d544f87835a55602883fe09156bb85a4c163d8
+18703ad600cc68dbdb04d57434c876989a841d12
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -122,17 +122,14 @@ static std::string trim(const std::string & str) {
 }

 static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
-    std::string result;
-    for (size_t pos = 0; ; pos += search.length()) {
-        auto new_pos = s.find(search, pos);
-        if (new_pos == std::string::npos) {
-            result += s.substr(pos, s.size() - pos);
-            break;
-        }
-        result += s.substr(pos, new_pos - pos) + replace;
-        pos = new_pos;
+    if (search.empty()) {
+        return; // Avoid infinite loop if 'search' is an empty string
+    }
+    size_t pos = 0;
+    while ((pos = s.find(search, pos)) != std::string::npos) {
+        s.replace(pos, search.length(), replace);
+        pos += replace.length();
    }
-    s = std::move(result);
 }

 static bool is_float_close(float a, float b, float abs_tol) {
@@ -4969,6 +4966,7 @@ static void llm_load_hparams(
                hparams.attn_soft_cap = true;

                switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_2B; break;
                    case 42: model.type = e_model::MODEL_9B; break;
                    case 46: model.type = e_model::MODEL_27B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
@@ -11736,6 +11734,7 @@ struct llm_build_context {

                // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
                switch (model.type) {
+                    case e_model::MODEL_2B:
                    case e_model::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
                    case e_model::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
                    default: GGML_ABORT("fatal error");
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -804,8 +804,7 @@ struct test_cpy : public test_case {

    test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
            std::array<int64_t, 4> ne = {10, 10, 10, 1},
-            std::array<int64_t, 4> permute = {0, 0, 0, 0},
-            bool _dst_use_permute = false)
+            std::array<int64_t, 4> permute = {0, 0, 0, 0})
        : type_src(type_src), type_dst(type_dst), ne(ne), permute(permute),
          _src_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}

@@ -2140,6 +2139,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op

    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
+    // test cases for 1D im2col
+    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
+    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));

    test_cases.emplace_back(new test_conv_transpose_1d());
    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 3, 0, 1));
@@ -2269,7 +2271,10 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op

    for (ggml_type type_a : other_types) {
        for (ggml_type type_b : {GGML_TYPE_F32}) {
-            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1,  1}, {1, 1}));
+            if (ggml_blck_size(type_a) != 256) {
+                test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, ggml_blck_size(type_a), {1,  1}, {1, 1}));
+            }
+            test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {1,  1}, {1, 1}));
        }
    }