Fix llama minitron

CMakePresets.json

@@ -28,6 +28,7 @@
     { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
     { "name": "reldbg",  "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
     { "name": "static",  "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
+    { "name": "sycl_f16",  "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } },
 
     {
         "name": "arm64-windows-msvc", "hidden": true,
@@ -60,6 +61,8 @@
     { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
 
     { "name": "x64-windows-sycl-debug"  , "inherits": [ "sycl-base", "debug"   ] },
-    { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }
+    { "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] },
+    { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] },
+    { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] }
   ]
 }

common/common.cpp

@@ -77,6 +77,41 @@
 
 using json = nlohmann::ordered_json;
 
+//
+// Environment variable utils
+//
+
+template<typename T>
+static typename std::enable_if<std::is_same<T, std::string>::value, void>::type
+get_env(std::string name, T & target) {
+    char * value = std::getenv(name.c_str());
+    target = value ? std::string(value) : target;
+}
+
+template<typename T>
+static typename std::enable_if<!std::is_same<T, bool>::value && std::is_integral<T>::value, void>::type
+get_env(std::string name, T & target) {
+    char * value = std::getenv(name.c_str());
+    target = value ? std::stoi(value) : target;
+}
+
+template<typename T>
+static typename std::enable_if<std::is_floating_point<T>::value, void>::type
+get_env(std::string name, T & target) {
+    char * value = std::getenv(name.c_str());
+    target = value ? std::stof(value) : target;
+}
+
+template<typename T>
+static typename std::enable_if<std::is_same<T, bool>::value, void>::type
+get_env(std::string name, T & target) {
+    char * value = std::getenv(name.c_str());
+    if (value) {
+        std::string val(value);
+        target = val == "1" || val == "true";
+    }
+}
+
 //
 // CPU utils
 //
@@ -220,12 +255,6 @@ int32_t cpu_get_num_math() {
 // CLI argument parsing
 //
 
-void gpt_params_handle_hf_token(gpt_params & params) {
-    if (params.hf_token.empty() && std::getenv("HF_TOKEN")) {
-        params.hf_token = std::getenv("HF_TOKEN");
-    }
-}
-
 void gpt_params_handle_model_default(gpt_params & params) {
     if (!params.hf_repo.empty()) {
         // short-hand to avoid specifying --hf-file -> default it to --model
@@ -273,7 +302,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
 
     gpt_params_handle_model_default(params);
 
-    gpt_params_handle_hf_token(params);
+    if (params.hf_token.empty()) {
+        get_env("HF_TOKEN", params.hf_token);
+    }
 
     if (params.escape) {
         string_process_escapes(params.prompt);
@@ -293,6 +324,25 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
     return true;
 }
 
+void gpt_params_parse_from_env(gpt_params & params) {
+    // we only care about server-related params for now
+    get_env("LLAMA_ARG_MODEL",            params.model);
+    get_env("LLAMA_ARG_THREADS",          params.n_threads);
+    get_env("LLAMA_ARG_CTX_SIZE",         params.n_ctx);
+    get_env("LLAMA_ARG_N_PARALLEL",       params.n_parallel);
+    get_env("LLAMA_ARG_BATCH",            params.n_batch);
+    get_env("LLAMA_ARG_UBATCH",           params.n_ubatch);
+    get_env("LLAMA_ARG_N_GPU_LAYERS",     params.n_gpu_layers);
+    get_env("LLAMA_ARG_THREADS_HTTP",     params.n_threads_http);
+    get_env("LLAMA_ARG_CHAT_TEMPLATE",    params.chat_template);
+    get_env("LLAMA_ARG_N_PREDICT",        params.n_predict);
+    get_env("LLAMA_ARG_ENDPOINT_METRICS", params.endpoint_metrics);
+    get_env("LLAMA_ARG_ENDPOINT_SLOTS",   params.endpoint_slots);
+    get_env("LLAMA_ARG_EMBEDDINGS",       params.embedding);
+    get_env("LLAMA_ARG_FLASH_ATTN",       params.flash_attn);
+    get_env("LLAMA_ARG_DEFRAG_THOLD",     params.defrag_thold);
+}
+
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     const auto params_org = params; // the example can modify the default params
 

common/common.h

@@ -267,7 +267,7 @@ struct gpt_params {
     std::string lora_outfile = "ggml-lora-merged-f16.gguf";
 };
 
-void gpt_params_handle_hf_token(gpt_params & params);
+void gpt_params_parse_from_env(gpt_params & params);
 void gpt_params_handle_model_default(gpt_params & params);
 
 bool gpt_params_parse_ex   (int argc, char ** argv, gpt_params & params);

convert_hf_to_gguf.py

@@ -1570,7 +1570,7 @@ def prepare_tensors(self):
         if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
             if rope_scaling.get("rope_type", '').lower() == "llama3":
                 base = self.hparams.get("rope_theta", 10000.0)
-                dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
                 factor = rope_scaling.get("factor", 8.0)
@@ -3816,7 +3816,7 @@ def prepare_tensors(self):
         if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
             if rope_scaling.get("rope_type", '').lower() == "llama3":
                 base = self.hparams.get("rope_theta", 10000.0)
-                dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+                dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
                 factor = rope_scaling.get("factor", 8.0)

docs/backend/SYCL.md

@@ -20,18 +20,14 @@
 **oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
 
 - **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
-- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
+- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL and oneDNN)*.
 - **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
 - **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
 
 ### Llama.cpp + SYCL
 
 The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).
 
-When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
-
-It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
-
 ## Recommended Release
 
 The SYCL backend would be broken by some PRs due to no online CI.
@@ -45,6 +41,10 @@ The following release is verified with good quality:
 
 ## News
 
+
+- 2024.8
+  - Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs.
+
 - 2024.5
   - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
   - Arch Linux is verified successfully.
@@ -196,7 +196,7 @@ Please follow the instructions for downloading and installing the Toolkit for Li
 
 Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
 
-Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
+Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs.
 
 - **Adding support to Nvidia GPUs**
 
@@ -255,8 +255,6 @@ or
 # Export relevant ENV variables
 source /opt/intel/oneapi/setvars.sh
 
-# Build LLAMA with MKL BLAS acceleration for intel GPU
-
 # Option 1: Use FP32 (recommended for better performance in most cases)
 cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 

examples/server/README.md

@@ -247,6 +247,25 @@ logging:
          --log-append             Don't truncate the old log file.
 ```
 
+Available environment variables (if specified, these variables will override parameters specified in arguments):
+
+- `LLAMA_CACHE` (cache directory, used by `--hf-repo`)
+- `HF_TOKEN` (Hugging Face access token, used when accessing a gated model with `--hf-repo`)
+- `LLAMA_ARG_MODEL`
+- `LLAMA_ARG_THREADS`
+- `LLAMA_ARG_CTX_SIZE`
+- `LLAMA_ARG_N_PARALLEL`
+- `LLAMA_ARG_BATCH`
+- `LLAMA_ARG_UBATCH`
+- `LLAMA_ARG_N_GPU_LAYERS`
+- `LLAMA_ARG_THREADS_HTTP`
+- `LLAMA_ARG_CHAT_TEMPLATE`
+- `LLAMA_ARG_N_PREDICT`
+- `LLAMA_ARG_ENDPOINT_METRICS`
+- `LLAMA_ARG_ENDPOINT_SLOTS`
+- `LLAMA_ARG_EMBEDDINGS`
+- `LLAMA_ARG_FLASH_ATTN`
+- `LLAMA_ARG_DEFRAG_THOLD`
 
 ## Build
 

examples/server/server.cpp

@@ -2507,6 +2507,9 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    // parse arguments from environment variables
+    gpt_params_parse_from_env(params);
+
     // TODO: not great to use extern vars
     server_log_json = params.log_json;
     server_verbose = params.verbosity > 0;

ggml/include/ggml.h

@@ -1777,10 +1777,8 @@ extern "C" {
 
     GGML_API struct ggml_tensor * ggml_ssm_conv(
             struct ggml_context * ctx,
-            struct ggml_tensor  * s,
-            struct ggml_tensor  * x,
-            struct ggml_tensor  * c,
-            struct ggml_tensor  * sq);
+            struct ggml_tensor  * sx,
+            struct ggml_tensor  * c);
 
     GGML_API struct ggml_tensor * ggml_ssm_scan(
             struct ggml_context * ctx,
@@ -1789,8 +1787,7 @@ extern "C" {
             struct ggml_tensor  * dt,
             struct ggml_tensor  * A,
             struct ggml_tensor  * B,
-            struct ggml_tensor  * C,
-            struct ggml_tensor  * sq);
+            struct ggml_tensor  * C);
 
     // partition into non-overlapping windows with padding if needed
     // example:

ggml/src/CMakeLists.txt

@@ -549,6 +549,13 @@ if (GGML_SYCL)
     file(GLOB   GGML_SOURCES_SYCL "ggml-sycl/*.cpp")
     list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp")
 
+    find_package(DNNL)
+    message("-- DNNL found:" ${DNNL_FOUND})
+    if (GGML_SYCL_TARGET STREQUAL "INTEL")
+        add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND})
+    else()
+        add_compile_definitions(GGML_SYCL_DNNL=0)
+    endif()
     if (WIN32)
         find_package(IntelSYCL REQUIRED)
         find_package(MKL REQUIRED)
@@ -561,6 +568,9 @@ if (GGML_SYCL)
             set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl pthread m dl onemkl)
         endif()
     endif()
+    if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
+        list(APPEND GGML_EXTRA_LIBS DNNL::dnnl)
+    endif()
 endif()
 
 if (GGML_RPC)

ggml/src/ggml-sycl.cpp

@@ -38,6 +38,7 @@
 
 #include "ggml-sycl/backend.hpp"
 #include "ggml-sycl/presets.hpp"
+#include "ggml-sycl/gemm.hpp"
 
 bool   ggml_sycl_loaded(void);
 void   ggml_sycl_free_data(struct ggml_tensor * tensor);
@@ -2482,6 +2483,7 @@ inline void ggml_sycl_op_mul_mat_sycl(
 
         const sycl::half alpha_f16 = 1.0f;
         const sycl::half beta_f16 = 0.0f;
+#if !GGML_SYCL_DNNL
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
             *stream, oneapi::mkl::transpose::trans,
             oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
@@ -2491,6 +2493,13 @@ inline void ggml_sycl_op_mul_mat_sycl(
             dpct::library_data_t::real_half)));
         const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
         to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
+#else
+        auto dnnl_stream = ctx.stream_dnnl(stream);
+        DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ptr, DnnlGemmWrapper::to_dt<sycl::half>(),
+            src0_ptr, DnnlGemmWrapper::to_dt<sycl::half>(), dst_f16.get(), DnnlGemmWrapper::to_dt<sycl::half>());
+        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
+        to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff* src1_ncols, stream);
+#endif
     }
     else {
         // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
@@ -2513,13 +2522,18 @@ inline void ggml_sycl_op_mul_mat_sycl(
 
         const float alpha = 1.0f;
         const float beta = 0.0f;
-
+#if !GGML_SYCL_DNNL
         SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
             *stream, oneapi::mkl::transpose::trans,
             oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
             dpct::get_value(&alpha, *stream), src0_ddf_i, ne00,
             src1_ddf1_i, ne10, dpct::get_value(&beta, *stream),
             dst_dd_i, ldc)));
+#else
+        auto dnnl_stream = ctx.stream_dnnl(stream);
+         DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ddf1_i, DnnlGemmWrapper::to_dt<float>(),
+            src0_ddf_i, DnnlGemmWrapper::to_dt<float>(), dst_dd_i, DnnlGemmWrapper::to_dt<float>());
+#endif
     }
     (void) dst;
     (void) src1_ddq_i;

ggml/src/ggml-sycl/common.hpp

@@ -19,6 +19,10 @@
 #include "dpct/helper.hpp"
 #include "ggml-sycl.h"
 #include "presets.hpp"
+#if GGML_SYCL_DNNL
+#include "dnnl.hpp"
+#include "dnnl_sycl.hpp"
+#endif
 
 #define GGML_COMMON_DECL_SYCL
 #define GGML_COMMON_IMPL_SYCL
@@ -277,6 +281,52 @@ struct ggml_backend_sycl_context {
         return stream(device, 0);
     }
 
+#if GGML_SYCL_DNNL
+    dnnl::engine make_engine(sycl::queue* q) {
+        // Get the device associated with the queue
+        sycl::device dev = q->get_device();
+        // Get the context associated with the queue
+        sycl::context ctx = q->get_context();
+        const dnnl::engine eng = dnnl::sycl_interop::make_engine(dev, ctx);
+        return eng;
+    }
+
+    std::unordered_map<sycl::queue*, dnnl::stream> stream_map;
+    std::unordered_map<sycl::queue*, dnnl::engine> engine_map;
+    dnnl::stream stream_dnnl(int device, int _stream) {
+        auto q = stream(device, _stream);
+        return stream_dnnl(q);
+    }
+    dnnl::engine engine_dnnl(sycl::queue* qptr) {
+        auto it = engine_map.find(qptr);
+        if (it == engine_map.end()) {
+            auto eng = make_engine(qptr);
+            engine_map[qptr] = eng;
+            return eng;
+        }
+        else
+        {
+            return it->second;
+        }
+    }
+    dnnl::stream stream_dnnl(sycl::queue* qptr) {
+        auto it = stream_map.find(qptr);
+        if (it == stream_map.end()) {
+            auto eng = engine_dnnl(qptr);
+            auto stream = dnnl::sycl_interop::make_stream(eng, *qptr);
+            stream_map[qptr] = stream;
+            return stream;
+        }
+        else
+        {
+            return it->second;
+        }
+    }
+    dnnl::stream stream_dnnl() {
+        return stream_dnnl(device, 0);
+    }
+#endif
+
     // pool
     std::unique_ptr<ggml_sycl_pool> pools[GGML_SYCL_MAX_DEVICES];