server : update readme + return json for "meta" field

This shader uses coopmat1 to do the Q*K^T multiply. The P*V multiply is more
difficult for various reasons so I haven't done it. Performance for this
shader is around 2.5x better than for the scalar shader when doing prompt
processing. Some of the benefit may be from other optimizations like staging
through shared memory, or splitting by rows.

build-xcframework.sh

@@ -117,6 +117,7 @@ setup_framework_structure() {
     # Copy all required headers (common for all platforms)
     cp include/llama.h             ${header_path}
     cp ggml/include/ggml.h         ${header_path}
+    cp ggml/include/ggml-opt.h     ${header_path}
     cp ggml/include/ggml-alloc.h   ${header_path}
     cp ggml/include/ggml-backend.h ${header_path}
     cp ggml/include/ggml-metal.h   ${header_path}

common/common.cpp

@@ -1565,3 +1565,20 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
 
     return result;
 }
+
+ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) {
+    const int64_t ne_datapoint = llama_n_ctx(ctx);
+    const int64_t ndata        = (tokens.size() - ne_datapoint - 1) / stride;
+    ggml_opt_dataset_t result = ggml_opt_dataset_init(
+        GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1);
+
+    llama_token * data   = (llama_token *) ggml_opt_dataset_data(result)->data;
+    llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data;
+
+    for (int64_t idata = 0; idata < ndata; ++idata) {
+        memcpy(data   + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token));
+        memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token));
+    }
+
+    return result;
+}

common/common.h

@@ -666,3 +666,9 @@ const char * const LLM_KV_SPLIT_COUNT         = "split.count";
 const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 
 }
+
+//
+// training utils
+//
+
+ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);

convert_hf_to_gguf.py

@@ -5746,11 +5746,20 @@ class GraniteModel(LlamaModel):
             logger.info("gguf: (granite) logits_scale = %s", logits_scale)
 
 
-@ModelBase.register("GraniteMoeForCausalLM")
+@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM")
 class GraniteMoeModel(GraniteModel):
     """Conversion for IBM's GraniteMoeForCausalLM"""
     model_arch = gguf.MODEL_ARCH.GRANITE_MOE
 
+    def set_gguf_parameters(self):
+        """GraniteMoeShared uses GraniteMoe parameters plus the following:
+        - shared_intermediate_size
+        """
+        super().set_gguf_parameters()
+        if shared_feed_forward_length := self.hparams.get("shared_intermediate_size"):
+            self.gguf_writer.add_expert_shared_feed_forward_length(shared_feed_forward_length)
+            logger.info("gguf: (granitemoeshared) shared_feed_forward_length = %s", shared_feed_forward_length)
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         """In modeling_granitemoe, the JetMoe implementation of parallel experts
         is used. This essentially merges w1 and w3 into a single tensor with 2x
@@ -5761,12 +5770,21 @@ class GraniteMoeModel(GraniteModel):
         if name.endswith("block_sparse_moe.input_linear.weight"):
             ffn_dim = self.hparams["intermediate_size"]
             assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
-            gate, up = data_torch[..., :ffn_dim, :], data_torch[..., ffn_dim:, :]
+            gate, up = data_torch.split(ffn_dim, dim=-2)
             return [
                 (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate),
                 (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up),
             ]
 
+        if name.endswith("shared_mlp.input_linear.weight"):
+            ffn_dim = self.hparams["shared_intermediate_size"]
+            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size"
+            gate, up = data_torch.split(ffn_dim, dim=-2)
+            return [
+                (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), gate),
+                (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), up),
+            ]
+
         return super().modify_tensors(data_torch, name, bid)
 
 

docs/multimodal.md

@@ -31,7 +31,7 @@ llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload
 
 ## Pre-quantized models
 
-These are ready-to-use models, most of them come with `Q4_K_M` quantization by default.
+These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/ggml-org
 
 Replaces the `(tool_name)` with the name of binary you want to use. For example, `llama-mtmd-cli` or `llama-server`
 

examples/CMakeLists.txt

@@ -32,6 +32,7 @@ else()
     add_subdirectory(speculative)
     add_subdirectory(speculative-simple)
     add_subdirectory(gen-docs)
+    add_subdirectory(training)
     if (NOT GGML_BACKEND_DL)
         add_subdirectory(convert-llama2c-to-ggml)
         # these examples use the backends directly and cannot be built with dynamic loading

examples/training/CMakeLists.txt

@@ -0,0 +1,5 @@
+set(TARGET llama-finetune)
+add_executable(${TARGET} finetune.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/training/README.md

@@ -0,0 +1,17 @@
+# llama.cpp/examples/training
+
+This directory contains examples related to language model training using llama.cpp/GGML.
+So far finetuning is technically functional (for FP32 models and limited hardware setups) but the code is very much WIP.
+Finetuning of Stories 260K and LLaMA 3.2 1b seems to work with 24 GB of memory.
+**For CPU training, compile llama.cpp without any additional backends such as CUDA.**
+**For CUDA training, use the maximum number of GPU layers.**
+
+Proof of concept:
+
+``` sh
+export model_name=llama_3.2-1b && export quantization=f32
+./build/bin/finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512
+./build/bin/perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf
+```
+
+The perplexity value of the finetuned model should be lower after training on the test set for 2 epochs.

examples/training/finetune.cpp

@@ -0,0 +1,96 @@
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <vector>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.escape = false;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
+        return 1;
+    }
+
+    if (params.use_mmap) {
+        LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", __func__);
+        params.use_mmap = false;
+    }
+    if (params.cache_type_k != GGML_TYPE_F32) {
+        LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
+        params.cache_type_k = GGML_TYPE_F32;
+    }
+    if (params.cache_type_v != GGML_TYPE_F32) {
+        LOG_INF("%s: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
+        params.cache_type_v = GGML_TYPE_F32;
+    }
+
+    common_init();
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // load the model and apply lora adapter, if any
+    common_init_result llama_init = common_init_from_params(params);
+    llama_model_ptr   & model = llama_init.model;
+    llama_context_ptr & ctx   = llama_init.context;
+
+    if (model == NULL) {
+        LOG_ERR("%s: unable to load model\n", __func__);
+        return 1;
+    }
+
+    // print system information
+    {
+        LOG_INF("\n");
+        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+    }
+
+    constexpr float val_split = 0.05f;
+
+    std::vector<llama_token> tokens = common_tokenize(ctx.get(), params.prompt, true);
+    ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get())/2);
+
+    struct ggml_opt_optimizer_params optimizer_params = ggml_opt_get_default_optimizer_params(nullptr);
+    optimizer_params.adamw.alpha = 1e-7f; // learning rate
+
+    struct llama_opt_params lopt_params {
+        /*n_ctx_train     =*/ 0,
+        /*param_filter    =*/ llama_opt_param_filter_all,
+        /*param_filter_ud =*/ nullptr,
+        /*get_opt_pars    =*/ ggml_opt_get_constant_optimizer_params,
+        /*get_opt_pars_ud =*/ &optimizer_params,
+    };
+    llama_opt_init(ctx.get(), model.get(), lopt_params);
+
+    const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - val_split);
+
+    ggml_opt_result_t result_train = ggml_opt_result_init();
+    ggml_opt_result_t result_eval  = ggml_opt_result_init();
+
+    for (int epoch = 0; epoch < 2; ++epoch) {
+        llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
+            ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
+        fprintf(stderr, "\n");
+
+        ggml_opt_result_reset(result_train);
+        ggml_opt_result_reset(result_eval);
+    }
+    ggml_opt_result_free(result_train);
+    ggml_opt_result_free(result_eval);
+
+    llama_model_save_to_file(model.get(), "finetuned-model.gguf");
+
+    llama_backend_free();
+
+    return 0;
+}

ggml/include/ggml-opt.h

@@ -37,13 +37,16 @@ extern "C" {
     // ====== Dataset ======
 
     GGML_API ggml_opt_dataset_t ggml_opt_dataset_init(
-            int64_t ne_datapoint, // number of elements per datapoint
-            int64_t ne_label,     // number of elements per label
-            int64_t ndata,        // total number of datapoints/labels
-            int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
+            enum ggml_type type_data,    // the type for the internal data tensor
+            enum ggml_type type_label,   // the type for the internal labels tensor
+            int64_t        ne_datapoint, // number of elements per datapoint
+            int64_t        ne_label,     // number of elements per label
+            int64_t        ndata,        // total number of datapoints/labels
+            int64_t        ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
     GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset);
 
     // get underlying tensors that store the data
+    GGML_API int64_t              ggml_opt_dataset_ndata (ggml_opt_dataset_t dataset);
     GGML_API struct ggml_tensor * ggml_opt_dataset_data  (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata]
     GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label,     ndata]
 
@@ -56,13 +59,19 @@ extern "C" {
             struct ggml_tensor * data_batch,   // shape = [ne_datapoint, ndata_batch]
             struct ggml_tensor * labels_batch, // shape = [ne_label,     ndata_batch]
             int64_t              ibatch);
+    GGML_API void ggml_opt_dataset_get_batch_host(
+            ggml_opt_dataset_t   dataset,
+            void               * data_batch,
+            size_t               nb_data_batch,
+            void               * labels_batch,
+            int64_t              ibatch);
 
     // ====== Model / Context ======
 
     enum ggml_opt_build_type {
-        GGML_OPT_BUILD_TYPE_FORWARD,
-        GGML_OPT_BUILD_TYPE_GRAD,
-        GGML_OPT_BUILD_TYPE_OPT,
+        GGML_OPT_BUILD_TYPE_FORWARD = 10,
+        GGML_OPT_BUILD_TYPE_GRAD    = 20,
+        GGML_OPT_BUILD_TYPE_OPT     = 30,
     };
 
     // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
@@ -81,20 +90,22 @@ extern "C" {
     // userdata can be used to pass arbitrary data
     typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata);
 
-    // returns the default optimizer params (constant)
+    // returns the default optimizer params (constant, hard-coded values)
     // userdata is not used
     GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata);
 
+    // casts userdata to ggml_opt_optimizer_params and returns it
+    GGML_API struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata);
+
     // parameters for initializing a new optimization context
     struct ggml_opt_params {
         ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs
 
-        struct ggml_context * ctx_compute; // created in user code, holds non-static tensors
-
-        // the forward graph is defined by inputs and outputs
-        // those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts
-        struct ggml_tensor * inputs;
-        struct ggml_tensor * outputs;
+        // by default the forward graph needs to be reconstructed for each eval
+        // if ctx_compute, inputs, and outputs are set the graphs are instead allocated statically
+        struct ggml_context * ctx_compute;
+        struct ggml_tensor  * inputs;
+        struct ggml_tensor  * outputs;
 
         enum ggml_opt_loss_type  loss_type;
         enum ggml_opt_build_type build_type;
@@ -107,12 +118,9 @@ extern "C" {
 
     // get parameters for an optimization context with defaults set where possible
     // parameters for which no sensible defaults exist are supplied as arguments to this function
-    GGML_API ggml_opt_params ggml_opt_default_params(
-            ggml_backend_sched_t      backend_sched,
-            struct ggml_context     * ctx_compute,
-            struct ggml_tensor      * inputs,
-            struct ggml_tensor      * outputs,
-            enum ggml_opt_loss_type   loss_type);
+    GGML_API struct ggml_opt_params ggml_opt_default_params(
+            ggml_backend_sched_t    backend_sched,
+            enum ggml_opt_loss_type loss_type);
 
     GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params);
     GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx);
@@ -121,6 +129,7 @@ extern "C" {
     GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer);
 
     // get underlying tensors that store data
+    // if not using static graphs these pointers become invalid with the next call to ggml_opt_alloc
     GGML_API struct ggml_tensor * ggml_opt_inputs(  ggml_opt_context_t opt_ctx); // forward graph input tensor
     GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor
     GGML_API struct ggml_tensor * ggml_opt_labels(  ggml_opt_context_t opt_ctx); // labels to compare outputs against
@@ -128,11 +137,12 @@ extern "C" {
     GGML_API struct ggml_tensor * ggml_opt_pred(    ggml_opt_context_t opt_ctx); // predictions made by outputs
     GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels
 
+    // get the gradient accumulator for a node from the forward graph
     GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node);
 
     // ====== Optimization Result ======
 
-    GGML_API ggml_opt_result_t ggml_opt_result_init();
+    GGML_API ggml_opt_result_t ggml_opt_result_init(void);
     GGML_API void ggml_opt_result_free(ggml_opt_result_t result);
     GGML_API void ggml_opt_result_reset(ggml_opt_result_t result);
 
@@ -144,11 +154,20 @@ extern "C" {
 
     // ====== Computation ======
 
-    // do forward pass, increment result if not NULL
-    GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
+    // if not using static graphs, this function must be called prior to ggml_opt_alloc
+    GGML_API void ggml_opt_prepare_alloc(
+        ggml_opt_context_t    opt_ctx,
+        struct ggml_context * ctx_compute,
+        struct ggml_cgraph  * gf,
+        struct ggml_tensor  * inputs,
+        struct ggml_tensor  * outputs);
 
-    // do forward pass, increment result if not NULL, do backward pass
-    GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
+    // allocate the next graph for evaluation, either forward or forward + backward
+    // must be called exactly once prior to calling ggml_opt_eval
+    GGML_API void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward);
+
+    // do forward pass, increment result if not NULL, do backward pass if allocated
+    GGML_API void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
 
     // ############################################################################
     // ## The high-level functions start here. They do not depend on any private ##
@@ -200,9 +219,9 @@ extern "C" {
     // fit model defined by inputs and outputs to dataset
     GGML_API void ggml_opt_fit(
             ggml_backend_sched_t            backend_sched,  // backend scheduler for constructing the compute graphs
-            ggml_context                  * ctx_compute,    // context with temporarily allocated tensors to calculate the outputs
-            ggml_tensor                   * inputs,         // input tensor with shape [ne_datapoint, ndata_batch]
-            ggml_tensor                   * outputs,        // output tensor, must have shape [ne_label, ndata_batch] if labels are used
+            struct ggml_context           * ctx_compute,    // context with temporarily allocated tensors to calculate the outputs
+            struct ggml_tensor            * inputs,         // input tensor with shape [ne_datapoint, ndata_batch]
+            struct ggml_tensor            * outputs,        // output tensor, must have shape [ne_label, ndata_batch] if labels are used
             ggml_opt_dataset_t              dataset,        // dataset with data and optionally also labels
             enum ggml_opt_loss_type         loss_type,      // loss to minimize
             ggml_opt_get_optimizer_params   get_opt_pars,   // callback to get optimizer params, userdata is pointer to epoch (of type int64_t)

ggml/include/ggml.h

@@ -768,7 +768,7 @@ extern "C" {
     // Tensor flags
     GGML_API void ggml_set_input(struct ggml_tensor * tensor);
     GGML_API void ggml_set_output(struct ggml_tensor * tensor);
-    GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
+    GGML_API void ggml_set_param(struct ggml_tensor * tensor);
     GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
 
     //
@@ -938,7 +938,7 @@ extern "C" {
     GGML_API struct ggml_tensor * ggml_repeat_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b); // sum up values that are adjacent in dims > 0 instead of repeated with same stride
 
     // concat a and b along dim
     // used in stable-diffusion
@@ -2049,15 +2049,14 @@ extern "C" {
 
     GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
     GGML_API void ggml_build_backward_expand(
-        struct ggml_context * ctx_static,  // context for static gradients (loss + gradient accumulation)
-        struct ggml_context * ctx_compute, // context for gradient computation
-        struct ggml_cgraph  * cgraph,
-        bool                  accumulate); // whether or not gradients should be accumulated, requires static allocation of tensors in ctx_static
+        struct ggml_context *  ctx,        // context for gradient computation
+        struct ggml_cgraph  *  cgraph,
+        struct ggml_tensor  ** grad_accs);
 
     // graph allocation in a context
     GGML_API struct ggml_cgraph * ggml_new_graph       (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
     GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
-    GGML_API struct ggml_cgraph * ggml_graph_dup       (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
+    GGML_API struct ggml_cgraph * ggml_graph_dup       (struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads);
     GGML_API void                 ggml_graph_cpy       (struct ggml_cgraph * src, struct ggml_cgraph * dst);
     GGML_API void                 ggml_graph_reset     (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
     GGML_API void                 ggml_graph_clear     (struct ggml_cgraph * cgraph);

ggml/src/ggml-backend.cpp

@@ -1111,7 +1111,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
             const int node_backend_id = tensor_backend_id(node);
 
-            assert(node_backend_id != -1); // all nodes should be assigned by now
+            assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
 
             // check if we should start a new split based on the sources of the current node
             bool need_new_split = false;

ggml/src/ggml-cpu/CMakeLists.txt

@@ -385,9 +385,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.5.0")
+        set(KLEIDIAI_COMMIT_TAG "v1.6.0")
         set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "ea22e1aefb800e9bc8c74d91633cc58e")
+        set(KLEIDIAI_ARCHIVE_MD5  "75b4ad68f25ab673dcc01065e5a0b05f")
 
         if (POLICY CMP0135)
             cmake_policy(SET CMP0135 NEW)

ggml/src/ggml-cpu/kleidiai/kernels.h

@@ -5,6 +5,7 @@
 #pragma once
 
 #include <functional>
+#include <variant>
 #include "ggml.h"
 
 enum cpu_feature {

ggml/src/ggml-cpu/kleidiai/kleidiai.cpp

@@ -3,7 +3,9 @@
 //
 #include <arm_neon.h>
 #include <assert.h>
+#include <atomic>
 #include <cfloat>
+#include <stdexcept>
 #include <stdint.h>
 #include <string.h>
 #if defined(__linux__)

ggml/src/ggml-cuda/acc.cu

@@ -1,47 +1,61 @@
 #include "acc.cuh"
 
-static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, int offset) {
-    const int i = blockDim.x * blockIdx.x + threadIdx.x;
+static __global__ void acc_f32(const float * x, const float * y, float * dst, const int64_t ne,
+        const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
+        const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) {
+    const int64_t i = blockDim.x * blockIdx.x + threadIdx.x;
+
     if (i >= ne) {
         return;
     }
-    int src1_idx = i - offset;
-    int oz = src1_idx / nb2;
-    int oy = (src1_idx - (oz * nb2)) / nb1;
-    int ox = src1_idx % nb1;
-    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
-        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
-    } else {
-        dst[i] = x[i];
+
+    int64_t src1_idx = i - offset;
+
+    int64_t tmp = src1_idx;
+    const int64_t i13 = tmp / s13;
+    tmp -= i13 * s13;
+    const int64_t i12 = tmp / s12;
+    tmp -= i12 * s12;
+    const int64_t i11 = tmp / s11;
+    tmp -= i11 * s11;
+    const int64_t i10 = tmp;
+
+    float val = x[i];
+    if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) {
+        val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10];
     }
+    dst[i] = val;
 }
 
-static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, const int offset, cudaStream_t stream) {
-    int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
-    acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
+static void acc_f32_cuda(const float * x, const float * y, float * dst, const int64_t n_elements,
+        const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
+        const int64_t s1, const int64_t s2, const int64_t s3, const int64_t offset, cudaStream_t stream) {
+    const int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
+    acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset);
 }
 
 void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
-    const float * src0_d = (const float *)src0->data;
-    const float * src1_d = (const float *)src1->data;
-    float * dst_d = (float *)dst->data;
+
+    const float * src0_d = (const float *) src0->data;
+    const float * src1_d = (const float *) src1->data;
+    float       * dst_d  = (float       *)  dst->data;
+
     cudaStream_t stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
 
-    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
-    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
-    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
-    int offset = dst->op_params[3] / 4; // offset in bytes
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(dst->nb[0] == ggml_element_size(dst));
+    GGML_ASSERT(ggml_is_contiguously_allocated(dst));
 
-    acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, stream);
+    const int64_t s1     = dst->op_params[0] / sizeof(float);
+    const int64_t s2     = dst->op_params[1] / sizeof(float);
+    const int64_t s3     = dst->op_params[2] / sizeof(float);
+    const int64_t offset = dst->op_params[3] / sizeof(float);
+
+    acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], s1, s2, s3, offset, stream);
 }

ggml/src/ggml-cuda/sum.cu

@@ -31,7 +31,7 @@ void ggml_cuda_op_sum(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguously_allocated(src0));
 
     const float * src0_d = (const float *) src0->data;
     float * dst_d = (float *) dst->data;

ggml/src/ggml-metal/ggml-metal.m

@@ -4358,7 +4358,7 @@ static bool ggml_metal_encode_node(
                 // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0)
                 //       for now avoiding mainly to keep the number of templates/kernels a bit lower
                 //       these are now trivial to add after: https://github.com/ggml-org/llama.cpp/pull/12612
-                if (ne01 >= 4 || (ne00%128 != 0 && ne00 != 96 && ne00 != 192 && ne00 != 576)) {
+                if (ne01 >= 20 || (ne00%128 != 0 && ne00 != 96 && ne00 != 192 && ne00 != 576)) {
                     switch (src1->type) {
                         case GGML_TYPE_F16:
                             {

ggml/src/ggml-metal/ggml-metal.metal

@@ -3887,6 +3887,11 @@ kernel void kernel_flash_attn_ext_vec(
                 sm[tiisg] = pm[ic + tiisg];
             }
 
+            // skip -INF blocks
+            if (simd_max(sm[tiisg]) == -INFINITY) {
+                continue;
+            }
+
             // Q*K^T
             {
                 // each simdgroup processes 1 query and NE (NW/NL) head elements

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -4855,8 +4855,6 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
             if (!any_on_device) {
                 return false;
             }
-            GGML_ASSERT(ggml_is_contiguous(src0));
-            GGML_ASSERT(ggml_is_contiguous(src1));
             func = ggml_cl_add;
             break;
         case GGML_OP_MUL:

ggml/src/ggml-opt.cpp

@@ -28,16 +28,19 @@ struct ggml_opt_dataset {
 };
 
 struct ggml_opt_context {
-    ggml_backend_sched_t    backend_sched        = nullptr;
-    ggml_cgraph           * allocated_graph      = nullptr;
-    ggml_cgraph           * allocated_graph_copy = nullptr;
-    struct ggml_context   * ctx_static           = nullptr;
-    struct ggml_context   * ctx_static_cpu       = nullptr;
-    struct ggml_context   * ctx_compute          = nullptr;
-    struct ggml_context   * ctx_copy             = nullptr;
-    ggml_backend_buffer_t   buf_static           = nullptr;
-    ggml_backend_buffer_t   buf_static_cpu       = nullptr;
-    std::mt19937            rng;
+    ggml_backend_sched_t       backend_sched        = nullptr;
+    ggml_cgraph              * allocated_graph      = nullptr;
+    ggml_cgraph              * allocated_graph_copy = nullptr;
+    struct ggml_context      * ctx_static           = nullptr;
+    struct ggml_context      * ctx_cpu              = nullptr;
+    struct ggml_context      * ctx_compute          = nullptr;
+    struct ggml_context      * ctx_copy             = nullptr;
+    ggml_backend_buffer_t      buf_static           = nullptr;
+    ggml_backend_buffer_t      buf_cpu              = nullptr;
+    std::mt19937               rng;
+    enum ggml_opt_loss_type    loss_type;
+    enum ggml_opt_build_type   build_type;
+    enum ggml_opt_build_type   build_type_alloc;
 
     struct ggml_tensor * inputs  = nullptr;
     struct ggml_tensor * outputs = nullptr;
@@ -50,6 +53,11 @@ struct ggml_opt_context {
     struct ggml_cgraph * gf      = nullptr;
     struct ggml_cgraph * gb_grad = nullptr;
     struct ggml_cgraph * gb_opt  = nullptr;
+    bool static_graphs           = false;
+    bool eval_ready              = false;
+    std::vector<struct ggml_tensor *> grad_accs;
+    std::vector<struct ggml_tensor *> grad_m;
+    std::vector<struct ggml_tensor *> grad_v;
 
     int64_t iter               = 1;
     int32_t opt_period         = 1;
@@ -73,7 +81,13 @@ struct ggml_opt_result {
 
 // ====== Dataset ======
 
-ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label, int64_t ndata, int64_t ndata_shard) {
+ggml_opt_dataset_t ggml_opt_dataset_init(
+        enum ggml_type type_data,
+        enum ggml_type type_label,
+        int64_t        ne_datapoint,
+        int64_t        ne_label,
+        int64_t        ndata,
+        int64_t        ndata_shard) {
     GGML_ASSERT(ne_datapoint >  0);
     GGML_ASSERT(ne_label     >= 0);
     GGML_ASSERT(ndata        >  0);
@@ -92,11 +106,11 @@ ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label,
         result->ctx = ggml_init(params);
     }
 
-    result->data = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_datapoint, ndata);
+    result->data = ggml_new_tensor_2d(result->ctx, type_data, ne_datapoint, ndata);
     result->nbs_data = ggml_nbytes(result->data) * ndata_shard/ndata;
 
     if (ne_label > 0) {
-        result->labels = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_label, ndata);
+        result->labels = ggml_new_tensor_2d(result->ctx, type_label, ne_label, ndata);
         result->nbs_labels = ggml_nbytes(result->labels) * ndata_shard/ndata;
     } else {
         result->labels = nullptr;
@@ -119,6 +133,10 @@ void ggml_opt_dataset_free(ggml_opt_dataset_t dataset) {
     delete dataset;
 }
 
+int64_t ggml_opt_dataset_ndata(ggml_opt_dataset_t dataset) {
+    return dataset->ndata;
+}
+
 struct ggml_tensor * ggml_opt_dataset_data(ggml_opt_dataset_t dataset) {
     return dataset->data;
 }
@@ -144,6 +162,8 @@ void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor *
     GGML_ASSERT(   data_batch && ggml_is_contiguous(data_batch));
     GGML_ASSERT(!labels_batch || ggml_is_contiguous(labels_batch));
     GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr));
+    GGML_ASSERT(                   data_batch->type == dataset->data->type);
+    GGML_ASSERT(!labels_batch || labels_batch->type == dataset->labels->type);
 
     const size_t nb_data_batch = ggml_nbytes(data_batch);
     GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0);
@@ -171,6 +191,31 @@ void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor *
     }
 }
 
+void ggml_opt_dataset_get_batch_host(ggml_opt_dataset_t dataset, void * data_batch, size_t nb_data_batch, void * labels_batch, int64_t ibatch) {
+    GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr));
+    GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0);
+
+    const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data;
+
+    GGML_ASSERT((ibatch + 1)*shards_per_batch <= int64_t(dataset->permutation.size()));
+
+    for (int64_t ishard_batch = 0; ishard_batch < shards_per_batch; ++ishard_batch) {
+        const int64_t ishard = dataset->permutation[ibatch*shards_per_batch + ishard_batch];
+
+        const char * ptr_data       = (const char *) dataset->data->data + ishard      *dataset->nbs_data;
+        char       * ptr_data_batch = (char       *) data_batch          + ishard_batch*dataset->nbs_data;
+        memcpy(ptr_data_batch, ptr_data, dataset->nbs_data);
+
+        if (!labels_batch) {
+            continue;
+        }
+
+        const char * ptr_labels       = (const char *) dataset->labels->data + ishard      *dataset->nbs_labels;
+        char       * ptr_labels_batch = (char       *) labels_batch          + ishard_batch*dataset->nbs_labels;
+        memcpy(ptr_labels_batch, ptr_labels, dataset->nbs_labels);
+    }
+}
+
 // ====== Model / Context ======
 
 struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata) {
@@ -187,17 +232,18 @@ struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * us
     return result;
 }
 
+struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata) {
+    return *((struct ggml_opt_optimizer_params *) userdata);
+}
+
 struct ggml_opt_params ggml_opt_default_params(
         ggml_backend_sched_t      backend_sched,
-        struct ggml_context     * ctx_compute,
-        struct ggml_tensor      * inputs,
-        struct ggml_tensor      * outputs,
         enum ggml_opt_loss_type   loss_type) {
     return {
         /*backend_sched   =*/ backend_sched,
-        /*ctx_compute     =*/ ctx_compute,
-        /*inputs          =*/ inputs,
-        /*logits          =*/ outputs,
+        /*ctx_compute     =*/ nullptr,
+        /*inputs          =*/ nullptr,
+        /*logits          =*/ nullptr,
         /*loss_type       =*/ loss_type,
         /*build_type      =*/ GGML_OPT_BUILD_TYPE_OPT,
         /*opt_period      =*/ 1,
@@ -266,195 +312,246 @@ static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) {
     return dst;
 }
 
-static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) {
-    GGML_ASSERT(graph);
-    if (opt_ctx->allocated_graph == graph) {
-        return;
-    }
+static void ggml_opt_build(ggml_opt_context_t opt_ctx) {
+    GGML_ASSERT(opt_ctx->ctx_compute && "no compute context set, either use static graphs or set one with ggml_opt_prepare_alloc");
+    GGML_ASSERT((!opt_ctx->static_graphs || opt_ctx->inputs->data) && "when using static graphs the inputs must be allocated statically");
 
-    ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph
+    const bool accumulate = opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD &&
+        !(opt_ctx->static_graphs && opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period == 1);
 
-    {
-        ggml_init_params params = {
-            /*.mem_size   =*/ ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE,
-            /*.mem_buffer =*/ nullptr,
-            /*.no_alloc   =*/ true,
-        };
-        ggml_free(opt_ctx->ctx_copy);
-        opt_ctx->ctx_copy = ggml_init(params);
-    }
-
-    opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph);
-
-    ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
-    opt_ctx->allocated_graph = graph;
-}
-
-ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
-    ggml_opt_context_t result = new struct ggml_opt_context;
-    result->backend_sched   = params.backend_sched;
-    result->ctx_compute     = params.ctx_compute;
-    result->inputs          = params.inputs;
-    result->outputs         = params.outputs;
-    result->opt_period      = params.opt_period;
-    result->get_opt_pars    = params.get_opt_pars;
-    result->get_opt_pars_ud = params.get_opt_pars_ud;
-
-    GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically");
-    GGML_ASSERT(result->opt_period >= 1);
-
-    const bool accumulate = params.build_type == GGML_OPT_BUILD_TYPE_GRAD ||
-        (params.build_type == GGML_OPT_BUILD_TYPE_OPT && result->opt_period > 1);
-
-    ggml_set_input(result->inputs);
-    ggml_set_output(result->outputs);
-
-    result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass.
-    ggml_build_forward_expand(result->gf, result->outputs);
+    ggml_set_input(opt_ctx->inputs);
+    ggml_set_output(opt_ctx->outputs);
 
     int n_param = 0;
-    for (int i = 0; i < result->gf->n_nodes; ++i) {
-        if (result->gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) {
+    for (int i = 0; i < opt_ctx->gf->n_nodes; ++i) {
+        const struct ggml_tensor * node = opt_ctx->gf->nodes[i];
+        if (node->flags & GGML_TENSOR_FLAG_PARAM) {
             n_param++;
         }
+        GGML_ASSERT(!(node->flags & GGML_TENSOR_FLAG_LOSS) && "support for extra loss terms not implemented");
     }
 
-    {
+    if (!opt_ctx->ctx_static) {
         // The static context is used for:
-        //   - gradients (1 tensor per param if using gradient accumulation)
+        //   - gradients (1 per loss, 1 tensor per param if using gradient accumulation)
         //   - optimizer momenta (2 tensors per param)
-        //   - labels
-        //   - loss + its gradient (up to 5 tensors)
-        //   - pred
-        //   - ncorrect (2 tensors).
-        const size_t tensors_per_param = (accumulate ? 1 : 0) + (params.build_type == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0);
-        const size_t size_meta = (tensors_per_param*n_param + 9) * ggml_tensor_overhead();
+        //   - labels (if using static graphs)
+        //   - loss (if using static graphs, up to 5 tensors)
+        //   - pred (if using static graphs)
+        //   - ncorrect (if using static graphs, 2 tensors).
+        constexpr size_t n_loss = 1;
+        const size_t tensors_per_param = (accumulate ? 1 : 0) +
+            (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0);
+        const size_t tensors_const = opt_ctx->static_graphs ? 9 : 0;
+        const size_t size_meta = (n_loss + tensors_per_param*n_param + tensors_const) * ggml_tensor_overhead();
         struct ggml_init_params params = {
             /*.mem_size   =*/ size_meta,
             /*.mem_buffer =*/ nullptr,
             /*.no_alloc   =*/ true,
         };
-        result->ctx_static = ggml_init(params);
+        opt_ctx->ctx_static = ggml_init(params);
     }
+    GGML_ASSERT(opt_ctx->build_type <= opt_ctx->build_type_alloc);
+
     {
-        // The static cpu context is used for:
-        //   - optimizer parameters (1 for the entire context)
+        // The cpu context is allocated statically if using static graphs, dynamically otherwise.
+        // It is used for:
+        //   - optimizer parameters (1 shared for all optimizer invocations)
         const size_t size_meta = 1 * ggml_tensor_overhead();
         struct ggml_init_params params = {
             /*.mem_size   =*/ size_meta,
             /*.mem_buffer =*/ nullptr,
             /*.no_alloc   =*/ true,
         };
-        result->ctx_static_cpu = ggml_init(params);
+        ggml_free(opt_ctx->ctx_cpu);
+        opt_ctx->ctx_cpu = ggml_init(params);
+
+        ggml_backend_buffer_free(opt_ctx->buf_cpu);
+        opt_ctx->buf_cpu = nullptr;
     }
 
+    struct ggml_context * ctx_results = opt_ctx->static_graphs ? opt_ctx->ctx_static : opt_ctx->ctx_compute;
 
-    switch (params.loss_type) {
+    switch (opt_ctx->loss_type) {
         case GGML_OPT_LOSS_TYPE_MEAN: {
-            result->loss = ggml_sum(result->ctx_static, result->outputs);
-            ggml_set_name(result->loss, "loss_sum");
-            const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs));
-            result->loss = ggml_scale(result->ctx_static, result->loss, scale);
-            ggml_set_name(result->loss, "loss_mean");
-            result->loss_per_datapoint = true;
+            opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs);
+            ggml_set_name(opt_ctx->loss, "loss_sum");
+            const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs));
+            opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale);
+            ggml_set_name(opt_ctx->loss, "loss_mean");
+            opt_ctx->loss_per_datapoint = true;
             break;
         }
         case GGML_OPT_LOSS_TYPE_SUM: {
-            result->loss = ggml_sum(result->ctx_static, result->outputs);
-            ggml_set_name(result->loss, "loss_sum");
-            result->loss_per_datapoint = false;
+            opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs);
+            ggml_set_name(opt_ctx->loss, "loss_sum");
+            opt_ctx->loss_per_datapoint = false;
             break;
         }
         case GGML_OPT_LOSS_TYPE_CROSS_ENTROPY: {
-            result->labels = ggml_dup_tensor(result->ctx_static, result->outputs);
-            ggml_set_input(result->labels);
-            ggml_set_name(result->labels, "labels");
-            result->loss = ggml_cross_entropy_loss(result->ctx_static, result->outputs, result->labels);
-            ggml_set_name(result->loss, "loss_cross_entropy");
-            if (result->opt_period > 1) {
-                result->loss = ggml_scale(result->ctx_static, result->loss, 1.0f / result->opt_period);
-                ggml_set_name(result->loss, "loss_cross_entropy_scaled");
+            opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs);
+            ggml_set_input(opt_ctx->labels);
+            ggml_set_name(opt_ctx->labels, "labels");
+            opt_ctx->loss = ggml_cross_entropy_loss(ctx_results, opt_ctx->outputs, opt_ctx->labels);
+            ggml_set_name(opt_ctx->loss, "loss_cross_entropy");
+            if (opt_ctx->opt_period > 1) {
+                opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, 1.0f / opt_ctx->opt_period);
+                ggml_set_name(opt_ctx->loss, "loss_cross_entropy_scaled");
             }
-            result->loss_per_datapoint = true;
+            opt_ctx->loss_per_datapoint = true;
             break;
         }
         case GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR: {
-            result->labels = ggml_dup_tensor(result->ctx_static, result->outputs);
-            ggml_set_input(result->labels);
-            ggml_set_name(result->labels, "labels");
-            result->loss = ggml_sub(result->ctx_static, result->outputs, result->labels);
-            ggml_set_name(result->loss, "loss_error");
-            result->loss = ggml_sqr(result->ctx_static, result->loss);
-            ggml_set_name(result->loss, "loss_squared_error");
-            result->loss = ggml_sum(result->ctx_static, result->loss);
-            ggml_set_name(result->loss, "loss_sum_squared_error");
-            const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs));
-            result->loss = ggml_scale(result->ctx_static, result->loss, scale);
-            ggml_set_name(result->loss, "loss_mean_squared_error");
-            result->loss_per_datapoint = true;
+            opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs);
+            ggml_set_input(opt_ctx->labels);
+            ggml_set_name(opt_ctx->labels, "labels");
+            opt_ctx->loss = ggml_sub(ctx_results, opt_ctx->outputs, opt_ctx->labels);
+            ggml_set_name(opt_ctx->loss, "loss_error");
+            opt_ctx->loss = ggml_sqr(ctx_results, opt_ctx->loss);
+            ggml_set_name(opt_ctx->loss, "loss_squared_error");
+            opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->loss);
+            ggml_set_name(opt_ctx->loss, "loss_sum_squared_error");
+            const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs));
+            opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale);
+            ggml_set_name(opt_ctx->loss, "loss_mean_squared_error");
+            opt_ctx->loss_per_datapoint = true;
             break;
         }
     }
-    ggml_set_output(result->loss);
-    ggml_set_loss(result->loss);
-    ggml_build_forward_expand(result->gf, result->loss);
+    ggml_set_output(opt_ctx->loss);
+    ggml_set_loss(opt_ctx->loss);
+    ggml_build_forward_expand(opt_ctx->gf, opt_ctx->loss);
 
-    result->pred = ggml_argmax(result->ctx_static, result->outputs);
-    ggml_set_name(result->pred, "pred");
-    ggml_set_output(result->pred);
-    ggml_build_forward_expand(result->gf, result->pred);
+    if (opt_ctx->loss_type == GGML_OPT_LOSS_TYPE_CROSS_ENTROPY) {
+        opt_ctx->pred = ggml_argmax(ctx_results, opt_ctx->outputs);
+        ggml_set_name(opt_ctx->pred, "pred");
+        ggml_set_output(opt_ctx->pred);
+        ggml_build_forward_expand(opt_ctx->gf, opt_ctx->pred);
 
-    if (result->labels) {
-        result->ncorrect = ggml_count_equal(result->ctx_static, result->pred, ggml_argmax(result->ctx_static, result->labels));
-        ggml_set_name(result->ncorrect, "ncorrect");
-        ggml_set_output(result->ncorrect);
-        ggml_build_forward_expand(result->gf, result->ncorrect);
-    } else {
-        result->ncorrect = nullptr;
+        opt_ctx->ncorrect = ggml_count_equal(ctx_results, opt_ctx->pred, ggml_argmax(ctx_results, opt_ctx->labels));
+        ggml_set_name(opt_ctx->ncorrect, "ncorrect");
+        ggml_set_output(opt_ctx->ncorrect);
+        ggml_build_forward_expand(opt_ctx->gf, opt_ctx->ncorrect);
     }
 
-    if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
-        result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
-        return result;
+    if (opt_ctx->buf_static) {
+        if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
+            return;
+        }
+    } else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_FORWARD) {
+        opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(
+            opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
+        return;
     }
 
-    // gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients.
-    result->gb_grad = ggml_graph_dup(result->ctx_compute, result->gf);
-    ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate);
+    if (opt_ctx->grad_accs.empty()) {
+        GGML_ASSERT(opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD);
 
-    if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) {
-        result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
-        ggml_graph_reset(result->gb_grad);
-        return result;
-    }
+        const int n_nodes = opt_ctx->gf->n_nodes;
+        opt_ctx->grad_accs.resize(n_nodes);
+        for (int i = 0; i < n_nodes; ++i) {
+            ggml_tensor * node = opt_ctx->gf->nodes[i];
+            if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) {
+                opt_ctx->grad_accs[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
+            } else {
+                opt_ctx->grad_accs[i] = nullptr;
+            }
+        }
 
-    GGML_ASSERT(params.build_type == GGML_OPT_BUILD_TYPE_OPT);
-
-    // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
-    result->gb_opt = ggml_graph_dup(result->ctx_compute, result->gb_grad);
-
-    result->adamw_params = ggml_new_tensor_1d(result->ctx_static_cpu, GGML_TYPE_F32, 7);
-    ggml_set_input(result->adamw_params);
-    ggml_set_name(result->adamw_params, "adamw_params");
-
-    for (int i = result->gf->n_nodes-1; i >= 0; --i) {
-        struct ggml_tensor * node = result->gb_opt->nodes[i];
-        struct ggml_tensor * grad = ggml_graph_get_grad(result->gb_opt, node);
-
-        if (node->flags & GGML_TENSOR_FLAG_PARAM) {
-            struct ggml_tensor * m        = ggml_dup_tensor(result->ctx_static, node);
-            struct ggml_tensor * v        = ggml_dup_tensor(result->ctx_static, node);
-            struct ggml_tensor * opt_step = ggml_opt_step_adamw(result->ctx_compute, node, grad, m, v, result->adamw_params);
-            ggml_build_forward_expand(result->gb_opt, opt_step);
+        if (opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_OPT) {
+            opt_ctx->grad_m.resize(n_nodes);
+            opt_ctx->grad_v.resize(n_nodes);
+            for (int i = 0; i < n_nodes; ++i) {
+                ggml_tensor * node = opt_ctx->gf->nodes[i];
+                if (node->flags & GGML_TENSOR_FLAG_PARAM) {
+                    opt_ctx->grad_m[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
+                    opt_ctx->grad_v[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
+                } else {
+                    opt_ctx->grad_m[i] = nullptr;
+                    opt_ctx->grad_v[i] = nullptr;
+                }
+            }
         }
     }
 
-    result->buf_static = ggml_backend_alloc_ctx_tensors(
-        result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
+    // gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients.
+    opt_ctx->gb_grad = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gf, /*force_grads =*/ true);
+    ggml_build_backward_expand(opt_ctx->ctx_compute, opt_ctx->gb_grad, opt_ctx->grad_accs.data());
 
-    result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type());
+    if (opt_ctx->buf_static) {
+        if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_GRAD) {
+            return;
+        }
+    } else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_GRAD) {
+        opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
+        ggml_graph_reset(opt_ctx->gb_grad);
+    }
 
-    ggml_graph_reset(result->gb_opt);
+    GGML_ASSERT(opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT);
+
+    // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
+    opt_ctx->gb_opt = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gb_grad, /*force_grads =*/ true);
+
+    opt_ctx->adamw_params = ggml_new_tensor_1d(opt_ctx->ctx_cpu, GGML_TYPE_F32, 7);
+    ggml_set_input(opt_ctx->adamw_params);
+    ggml_set_name(opt_ctx->adamw_params, "adamw_params");
+
+    for (int i = opt_ctx->gf->n_nodes-1; i >= 0; --i) {
+        struct ggml_tensor * node = opt_ctx->gb_opt->nodes[i];
+        struct ggml_tensor * grad = ggml_graph_get_grad(opt_ctx->gb_opt, node);
+
+        if (grad && (node->flags & GGML_TENSOR_FLAG_PARAM)) {
+            struct ggml_tensor * m        = opt_ctx->grad_m[i];
+            struct ggml_tensor * v        = opt_ctx->grad_v[i];
+            struct ggml_tensor * opt_step = ggml_opt_step_adamw(opt_ctx->ctx_compute, node, grad, m, v, opt_ctx->adamw_params);
+
+            ggml_set_name(m,        (std::string("AdamW m for ")    + std::string(node->name)).c_str());
+            ggml_set_name(v,        (std::string("AdamW v for ")    + std::string(node->name)).c_str());
+            ggml_set_name(opt_step, (std::string("AdamW step for ") + std::string(node->name)).c_str());
+
+            ggml_build_forward_expand(opt_ctx->gb_opt, opt_step);
+        }
+    }
+
+    if (!opt_ctx->buf_static) {
+        opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(
+            opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
+        ggml_graph_reset(opt_ctx->gb_opt);
+    }
+
+    opt_ctx->buf_cpu = ggml_backend_alloc_ctx_tensors_from_buft(opt_ctx->ctx_cpu, ggml_backend_cpu_buffer_type());
+}
+
+ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
+    ggml_opt_context_t result = new struct ggml_opt_context;
+    result->backend_sched    = params.backend_sched;
+    result->ctx_compute      = params.ctx_compute;
+    result->loss_type        = params.loss_type;
+    result->build_type       = params.build_type;
+    result->build_type_alloc = params.build_type;
+    result->inputs           = params.inputs;
+    result->outputs          = params.outputs;
+    result->opt_period       = params.opt_period;
+    result->get_opt_pars     = params.get_opt_pars;
+    result->get_opt_pars_ud  = params.get_opt_pars_ud;
+
+    GGML_ASSERT(result->opt_period >= 1);
+
+    result->static_graphs = result->ctx_compute;
+
+    if (!result->static_graphs) {
+        GGML_ASSERT(!result->inputs);
+        GGML_ASSERT(!result->outputs);
+        return result;
+    }
+
+    GGML_ASSERT(result->inputs);
+    GGML_ASSERT(result->outputs);
+
+    result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass.
+    ggml_build_forward_expand(result->gf, result->outputs);
+
+    ggml_opt_build(result);
 
     return result;
 }
@@ -464,9 +561,9 @@ void ggml_opt_free(ggml_opt_context_t opt_ctx) {
         return;
     }
     ggml_backend_buffer_free(opt_ctx->buf_static);
-    ggml_backend_buffer_free(opt_ctx->buf_static_cpu);
+    ggml_backend_buffer_free(opt_ctx->buf_cpu);
     ggml_free(opt_ctx->ctx_static);
-    ggml_free(opt_ctx->ctx_static_cpu);
+    ggml_free(opt_ctx->ctx_cpu);
     delete opt_ctx;
 }
 
@@ -582,8 +679,79 @@ void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, doubl
 
 // ====== Computation ======
 
-static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, ggml_opt_result * result) {
-    if (graph != opt_ctx->gf) {
+void ggml_opt_prepare_alloc(
+        ggml_opt_context_t    opt_ctx,
+        struct ggml_context * ctx_compute,
+        struct ggml_cgraph  * gf,
+        struct ggml_tensor  * inputs,
+        struct ggml_tensor  * outputs) {
+    GGML_ASSERT(!opt_ctx->static_graphs);
+    opt_ctx->ctx_compute = ctx_compute;
+    opt_ctx->gf          = gf;
+    opt_ctx->inputs      = inputs;
+    opt_ctx->outputs     = outputs;
+}
+
+void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward) {
+    GGML_ASSERT(!opt_ctx->eval_ready);
+    if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period > 1 && opt_ctx->opt_i == 0) {
+        ggml_graph_reset(opt_ctx->gb_grad);
+    }
+    if (backward) {
+        const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
+        opt_ctx->build_type = opt_i_next == 0 ? GGML_OPT_BUILD_TYPE_OPT : GGML_OPT_BUILD_TYPE_GRAD;
+    } else {
+        opt_ctx->build_type = GGML_OPT_BUILD_TYPE_FORWARD;
+    }
+
+    if (!opt_ctx->static_graphs) {
+        ggml_opt_build(opt_ctx);
+    }
+
+    struct ggml_cgraph * graph = nullptr;
+    switch (opt_ctx->build_type) {
+        case GGML_OPT_BUILD_TYPE_FORWARD: {
+            graph = opt_ctx->gf;
+        } break;
+        case GGML_OPT_BUILD_TYPE_GRAD: {
+            graph = opt_ctx->gb_grad;
+        } break;
+        case GGML_OPT_BUILD_TYPE_OPT: {
+            graph = opt_ctx->gb_opt;
+        } break;
+    }
+    GGML_ASSERT(graph);
+
+    if (opt_ctx->allocated_graph == graph) {
+        opt_ctx->eval_ready = true;
+        return;
+    }
+
+    ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph
+
+    if (opt_ctx->static_graphs) {
+        ggml_init_params params = {
+            /*.mem_size   =*/ graph->size*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph->size, graph->grads),
+            /*.mem_buffer =*/ nullptr,
+            /*.no_alloc   =*/ true,
+        };
+        ggml_free(opt_ctx->ctx_copy);
+        opt_ctx->ctx_copy = ggml_init(params);
+
+        opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph);
+    } else {
+        opt_ctx->allocated_graph_copy = graph;
+    }
+
+    ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
+    opt_ctx->allocated_graph = graph;
+
+    opt_ctx->eval_ready = true;
+}
+
+void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result) {
+    GGML_ASSERT(opt_ctx->eval_ready);
+    if (opt_ctx->allocated_graph == opt_ctx->gb_opt) {
         struct ggml_opt_optimizer_params opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud);
 
         GGML_ASSERT(opt_pars.adamw.alpha >  0.0f);
@@ -609,9 +777,19 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph,
         adamw_par_data[6] = beta2h;
     }
 
-    ggml_opt_alloc_graph(opt_ctx, graph);
     ggml_backend_sched_graph_compute(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
     opt_ctx->iter += opt_ctx->allocated_graph == opt_ctx->gb_opt;
+    opt_ctx->opt_i = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
+
+    if (!opt_ctx->static_graphs) {
+        opt_ctx->gf                   = nullptr;
+        opt_ctx->gb_grad              = nullptr;
+        opt_ctx->gb_opt               = nullptr;
+        opt_ctx->allocated_graph      = nullptr;
+        opt_ctx->allocated_graph_copy = nullptr;
+    }
+
+    opt_ctx->eval_ready = false;
 
     if (!result) {
         return;
@@ -635,12 +813,14 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph,
     ggml_backend_tensor_get(opt_ctx->loss, &loss, 0, ggml_nbytes(opt_ctx->loss));
     result->loss.push_back(loss);
 
-    GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32);
-    std::vector<int32_t> pred(ndata);
-    ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred));
-    result->pred.insert(result->pred.end(), pred.begin(), pred.end());
+    if (opt_ctx->pred) {
+        GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32);
+        std::vector<int32_t> pred(ndata);
+        ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred));
+        result->pred.insert(result->pred.end(), pred.begin(), pred.end());
+    }
 
-    if (!opt_ctx->labels || result->ncorrect < 0) {
+    if (!opt_ctx->ncorrect || result->ncorrect < 0) {
         result->ncorrect = -1;
         return;
     }
@@ -652,26 +832,6 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph,
     result->ncorrect += ncorrect;
 }
 
-void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) {
-    ggml_opt_eval_graph(opt_ctx, opt_ctx->gf, result);
-}
-
-void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) {
-    if (opt_ctx->opt_period == 1) {
-        ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result);
-        return;
-    }
-
-    const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
-    if (opt_i_next == 0) {
-        ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result);
-        ggml_opt_reset(opt_ctx, /*optimizer =*/ false);
-    } else {
-        ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_grad, result);
-    }
-    opt_ctx->opt_i = opt_i_next;
-}
-
 // ====== High-Level Functions ======
 
 void ggml_opt_epoch(
@@ -700,16 +860,18 @@ void ggml_opt_epoch(
     int64_t ibatch = 0;
     int64_t t_loop_start = ggml_time_us();
     for (; ibatch < ibatch_split; ++ibatch) {
+        ggml_opt_alloc(opt_ctx, /*backward =*/ true);
         ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch);
-        ggml_opt_forward_backward(opt_ctx, result_train);
+        ggml_opt_eval(opt_ctx, result_train);
         if (callback_train) {
             callback_train(true, opt_ctx, dataset, result_train, ibatch+1, ibatch_split, t_loop_start);
         }
     }
     t_loop_start = ggml_time_us();
     for (; ibatch < nbatches; ++ibatch) {
+        ggml_opt_alloc(opt_ctx, /*backward =*/ false);
         ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch);
-        ggml_opt_forward(opt_ctx, result_eval);
+        ggml_opt_eval(opt_ctx, result_eval);
         if (callback_eval) {
             callback_eval(false, opt_ctx, dataset, result_eval, ibatch+1-ibatch_split, nbatches-ibatch_split, t_loop_start);
         }
@@ -726,13 +888,26 @@ void ggml_opt_epoch_callback_progress_bar(
         int64_t            t_start_us) {
     fprintf(stderr, "%s[", train ? "train: " : "val:   ");
 
-    constexpr int64_t bar_length = 25;
+    // The progress bar consists of partially filled blocks, unicode has 8 separate fill levels.
+    constexpr int64_t bar_length = 8;
+    const int64_t ibatch8 = 8 * ibatch;
     for (int64_t j = 0; j < bar_length; ++j) {
-        const int64_t ibatch_j = ibatch_max * j/bar_length;
-        if (ibatch_j < ibatch) {
-            fprintf(stderr, "=");
-        } else if (ibatch_max * (j - 1)/bar_length < ibatch) {
-            fprintf(stderr, ">");
+        if        (ibatch_max * (8*j + 8) / bar_length < ibatch8) {
+            fprintf(stderr, "\u2588"); // full block
+        } else if (ibatch_max * (8*j + 7) / bar_length < ibatch8) {
+            fprintf(stderr, "\u2589"); // 7/8 filled
+        } else if (ibatch_max * (8*j + 6) / bar_length < ibatch8) {
+            fprintf(stderr, "\u258A"); // 6/8 filled
+        } else if (ibatch_max * (8*j + 5) / bar_length < ibatch8) {
+            fprintf(stderr, "\u258B"); // 5/8 filled
+        } else if (ibatch_max * (8*j + 4) / bar_length < ibatch8) {
+            fprintf(stderr, "\u258C"); // 4/8 filled
+        } else if (ibatch_max * (8*j + 3) / bar_length < ibatch8) {
+            fprintf(stderr, "\u258D"); // 3/8 filled
+        } else if (ibatch_max * (8*j + 2) / bar_length < ibatch8) {
+            fprintf(stderr, "\u258E"); // 2/8 filled
+        } else if (ibatch_max * (8*j + 1) / bar_length < ibatch8) {
+            fprintf(stderr, "\u258F"); // 1/8 filled
         } else {
             fprintf(stderr, " ");
         }
@@ -764,8 +939,8 @@ void ggml_opt_epoch_callback_progress_bar(
     const int64_t t_eta_m = t_eta_s / 60;
     t_eta_s -= t_eta_m * 60;
 
-    fprintf(stderr, "| data=%06" PRId64 "/%06" PRId64 ", loss=%.6lf+-%.6lf, accuracy=%.2lf+-%.2lf%%, "
-            "t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 ", ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 "]\r",
+    fprintf(stderr, "] data=%07" PRId64 "/%07" PRId64 " loss=%.5lf±%.5lf acc=%.2lf±%.2lf%% "
+            "t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " \r",
             idata, idata_max, loss, loss_unc, 100.0*accuracy, 100.0*accuracy_unc,
             t_ibatch_h, t_ibatch_m, t_ibatch_s, t_eta_h, t_eta_m, t_eta_s);
     if (ibatch == ibatch_max) {
@@ -806,7 +981,10 @@ void ggml_opt_fit(
 
     int64_t epoch = 1;
 
-    ggml_opt_params params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type);
+    ggml_opt_params params = ggml_opt_default_params(backend_sched, loss_type);
+    params.ctx_compute     = ctx_compute;
+    params.inputs          = inputs;
+    params.outputs         = outputs;
     params.opt_period      = opt_period;
     params.get_opt_pars    = get_opt_pars;
     params.get_opt_pars_ud = &epoch;

ggml/src/ggml-vulkan/CMakeLists.txt

@@ -15,6 +15,32 @@ function(detect_host_compiler)
     set(HOST_CXX_COMPILER "${HOST_CXX_COMPILER}" PARENT_SCOPE)
 endfunction()
 
+# Function to test shader extension support
+# Parameters:
+#  EXTENSION_NAME - Name of the extension to test (e.g., "GL_EXT_integer_dot_product")
+#  TEST_SHADER_FILE - Path to the test shader file
+#  RESULT_VARIABLE - Name of the variable to set (ON/OFF) based on test result
+function(test_shader_extension_support EXTENSION_NAME TEST_SHADER_FILE RESULT_VARIABLE)
+    execute_process(
+        COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${TEST_SHADER_FILE}"
+        OUTPUT_VARIABLE glslc_output
+        ERROR_VARIABLE glslc_error
+    )
+
+    if (${glslc_error} MATCHES ".*extension not supported: ${EXTENSION_NAME}.*")
+        message(STATUS "${EXTENSION_NAME} not supported by glslc")
+        set(${RESULT_VARIABLE} OFF PARENT_SCOPE)
+    else()
+        message(STATUS "${EXTENSION_NAME} supported by glslc")
+        set(${RESULT_VARIABLE} ON PARENT_SCOPE)
+        add_compile_definitions(${RESULT_VARIABLE})
+
+        # Ensure the extension support is forwarded to vulkan-shaders-gen
+        list(APPEND VULKAN_SHADER_GEN_CMAKE_ARGS -D${RESULT_VARIABLE}=ON)
+        set(VULKAN_SHADER_GEN_CMAKE_ARGS "${VULKAN_SHADER_GEN_CMAKE_ARGS}" PARENT_SCOPE)
+    endif()
+endfunction()
+
 if (Vulkan_FOUND)
     message(STATUS "Vulkan found")
 
@@ -23,69 +49,35 @@ if (Vulkan_FOUND)
                              ../../include/ggml-vulkan.h
                             )
 
-    # Compile a test shader to determine whether GL_KHR_cooperative_matrix is supported.
-    # If it's not, there will be an error to stderr.
-    # If it's supported, set a define to indicate that we should compile those shaders
-    execute_process(COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_coopmat_support.comp"
-                    OUTPUT_VARIABLE glslc_output
-                    ERROR_VARIABLE glslc_error)
+    set(VULKAN_SHADER_GEN_CMAKE_ARGS
+        -DCMAKE_INSTALL_PREFIX=${CMAKE_BINARY_DIR}
+        -DCMAKE_RUNTIME_OUTPUT_DIRECTORY=${CMAKE_RUNTIME_OUTPUT_DIRECTORY}
+    )
 
-    if (${glslc_error} MATCHES ".*extension not supported: GL_KHR_cooperative_matrix.*")
-        message(STATUS "GL_KHR_cooperative_matrix not supported by glslc")
-        set(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT OFF)
-    else()
-        message(STATUS "GL_KHR_cooperative_matrix supported by glslc")
-        set(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT ON)
-        add_compile_definitions(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
-    endif()
+    # Test all shader extensions
+    test_shader_extension_support(
+        "GL_KHR_cooperative_matrix"
+        "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_coopmat_support.comp"
+        "GGML_VULKAN_COOPMAT_GLSLC_SUPPORT"
+    )
 
-    # Compile a test shader to determine whether GL_NV_cooperative_matrix2 is supported.
-    # If it's not, there will be an error to stderr.
-    # If it's supported, set a define to indicate that we should compile those shaders
-    execute_process(COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_coopmat2_support.comp"
-                    OUTPUT_VARIABLE glslc_output
-                    ERROR_VARIABLE glslc_error)
+    test_shader_extension_support(
+        "GL_NV_cooperative_matrix2"
+        "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_coopmat2_support.comp"
+        "GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT"
+    )
 
-    if (${glslc_error} MATCHES ".*extension not supported: GL_NV_cooperative_matrix2.*")
-        message(STATUS "GL_NV_cooperative_matrix2 not supported by glslc")
-        set(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT OFF)
-    else()
-        message(STATUS "GL_NV_cooperative_matrix2 supported by glslc")
-        set(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT ON)
-        add_compile_definitions(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
-    endif()
+    test_shader_extension_support(
+        "GL_EXT_integer_dot_product"
+        "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_integer_dot_support.comp"
+        "GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT"
+    )
 
-    # Compile a test shader to determine whether GL_EXT_integer_dot_product is supported.
-    # If it's not, there will be an error to stderr.
-    # If it's supported, set a define to indicate that we should compile those shaders
-    execute_process(COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_integer_dot_support.comp"
-                    OUTPUT_VARIABLE glslc_output
-                    ERROR_VARIABLE glslc_error)
-
-    if (${glslc_error} MATCHES ".*extension not supported: GL_EXT_integer_dot_product.*")
-        message(STATUS "GL_EXT_integer_dot_product not supported by glslc")
-        set(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT OFF)
-    else()
-        message(STATUS "GL_EXT_integer_dot_product supported by glslc")
-        set(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT ON)
-        add_compile_definitions(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
-    endif()
-
-    # Compile a test shader to determine whether GL_EXT_bfloat16 is supported.
-    # If it's not, there will be an error to stderr.
-    # If it's supported, set a define to indicate that we should compile those shaders
-    execute_process(COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_bfloat16_support.comp"
-                    OUTPUT_VARIABLE glslc_output
-                    ERROR_VARIABLE glslc_error)
-
-    if (${glslc_error} MATCHES ".*extension not supported: GL_EXT_bfloat16.*")
-        message(STATUS "GL_EXT_bfloat16 not supported by glslc")
-        set(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT OFF)
-    else()
-        message(STATUS "GL_EXT_bfloat16 supported by glslc")
-        set(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT ON)
-        add_compile_definitions(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
-    endif()
+    test_shader_extension_support(
+        "GL_EXT_bfloat16"
+        "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/test_bfloat16_support.comp"
+        "GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT"
+    )
 
     target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan)
     target_include_directories(ggml-vulkan PRIVATE ${CMAKE_CURRENT_BINARY_DIR})
@@ -124,16 +116,8 @@ if (Vulkan_FOUND)
         add_compile_definitions(GGML_VULKAN_RUN_TESTS)
     endif()
 
-    if (NOT CMAKE_CROSSCOMPILING)
-        add_subdirectory(vulkan-shaders)
-        if (MSVC)
-            foreach(CONFIG ${CMAKE_CONFIGURATION_TYPES})
-                string(TOUPPER ${CONFIG} CONFIG)
-                set_target_properties(vulkan-shaders-gen PROPERTIES
-                    RUNTIME_OUTPUT_DIRECTORY_${CONFIG} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
-            endforeach()
-        endif()
-    else()
+    # Set up toolchain for host compilation whether cross-compiling or not
+    if (CMAKE_CROSSCOMPILING)
         if (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN)
             set(HOST_CMAKE_TOOLCHAIN_FILE ${GGML_VULKAN_SHADERS_GEN_TOOLCHAIN})
         else()
@@ -146,25 +130,31 @@ if (Vulkan_FOUND)
             configure_file(${CMAKE_CURRENT_SOURCE_DIR}/cmake/host-toolchain.cmake.in ${CMAKE_BINARY_DIR}/host-toolchain.cmake @ONLY)
             set(HOST_CMAKE_TOOLCHAIN_FILE ${CMAKE_BINARY_DIR}/host-toolchain.cmake)
         endif()
-        message(STATUS "vulkan-shaders-gen toolchain file: ${HOST_CMAKE_TOOLCHAIN_FILE}")
-
-        include(ExternalProject)
-        # Native build through ExternalProject_Add
-        ExternalProject_Add(
-            vulkan-shaders-gen
-            SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders
-            CMAKE_ARGS -DCMAKE_TOOLCHAIN_FILE=${HOST_CMAKE_TOOLCHAIN_FILE}
-                    -DCMAKE_INSTALL_PREFIX=${CMAKE_BINARY_DIR}
-                    -DGGML_VULKAN_COOPMAT_GLSLC_SUPPORT=${GGML_VULKAN_COOPMAT_GLSLC_SUPPORT}
-                    -DGGML_VULKAN_COOPMAT2_GLSLC_SUPPORT=${GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT}
-                    -DGGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT=${GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT}
-                    -DGGML_VULKAN_BFLOAT16_GLSLC_SUPPORT=${GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT}
-            BUILD_COMMAND ${CMAKE_COMMAND} --build .
-            INSTALL_COMMAND ${CMAKE_COMMAND} --install .
-            INSTALL_DIR ${CMAKE_BINARY_DIR}
-        )
-        ExternalProject_Add_StepTargets(vulkan-shaders-gen build install)
+    else()
+        # For non-cross-compiling, use empty toolchain (use host compiler)
+        set(HOST_CMAKE_TOOLCHAIN_FILE "")
     endif()
+
+    # Always use ExternalProject_Add approach
+    include(ExternalProject)
+
+    # Add toolchain file if cross-compiling
+    if (CMAKE_CROSSCOMPILING)
+        list(APPEND VULKAN_SHADER_GEN_CMAKE_ARGS -DCMAKE_TOOLCHAIN_FILE=${HOST_CMAKE_TOOLCHAIN_FILE})
+        message(STATUS "vulkan-shaders-gen toolchain file: ${HOST_CMAKE_TOOLCHAIN_FILE}")
+    endif()
+
+    # Native build through ExternalProject_Add
+    ExternalProject_Add(
+        vulkan-shaders-gen
+        SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders
+        CMAKE_ARGS ${VULKAN_SHADER_GEN_CMAKE_ARGS}
+        BUILD_COMMAND ${CMAKE_COMMAND} --build .
+        INSTALL_COMMAND ${CMAKE_COMMAND} --install .
+        INSTALL_DIR ${CMAKE_BINARY_DIR}
+    )
+    ExternalProject_Add_StepTargets(vulkan-shaders-gen build install)
+
     set (_ggml_vk_host_suffix $<IF:$<STREQUAL:${CMAKE_HOST_SYSTEM_NAME},Windows>,.exe,>)
     set (_ggml_vk_genshaders_cmd ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/vulkan-shaders-gen${_ggml_vk_host_suffix})
     set (_ggml_vk_header     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp)
@@ -175,9 +165,8 @@ if (Vulkan_FOUND)
     file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp")
     set (_ggml_vk_shader_deps ${_ggml_vk_shader_deps} vulkan-shaders-gen)
 
-    if (CMAKE_CROSSCOMPILING)
-        set(_ggml_vk_shader_deps ${_ggml_vk_shader_deps} vulkan-shaders-gen-build vulkan-shaders-gen-install)
-    endif()
+    # Add build and install dependencies for all builds
+    set(_ggml_vk_shader_deps ${_ggml_vk_shader_deps} vulkan-shaders-gen-build vulkan-shaders-gen-install)
 
     add_custom_command(
         OUTPUT ${_ggml_vk_header}

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -288,6 +288,9 @@ struct vk_device_struct {
     bool coopmat_acc_f32_support {};
     bool coopmat_acc_f16_support {};
     bool coopmat_bf16_support {};
+    bool coopmat_support_16x16x16_f16acc {};
+    bool coopmat_support_16x16x16_f32acc {};
+    bool coopmat1_fa_support {};
     uint32_t coopmat_m;
     uint32_t coopmat_n;
     uint32_t coopmat_k;
@@ -410,6 +413,13 @@ struct vk_device_struct {
     vk_pipeline pipeline_flash_attn_f32_f16_D128_cm2[GGML_TYPE_COUNT][2][2][2];
     vk_pipeline pipeline_flash_attn_f32_f16_D256_cm2[GGML_TYPE_COUNT][2][2][2];
 
+    vk_pipeline pipeline_flash_attn_f32_f16_D64_cm1[GGML_TYPE_COUNT][2][2][2];
+    vk_pipeline pipeline_flash_attn_f32_f16_D80_cm1[GGML_TYPE_COUNT][2][2][2];
+    vk_pipeline pipeline_flash_attn_f32_f16_D96_cm1[GGML_TYPE_COUNT][2][2][2];
+    vk_pipeline pipeline_flash_attn_f32_f16_D112_cm1[GGML_TYPE_COUNT][2][2][2];
+    vk_pipeline pipeline_flash_attn_f32_f16_D128_cm1[GGML_TYPE_COUNT][2][2][2];
+    vk_pipeline pipeline_flash_attn_f32_f16_D256_cm1[GGML_TYPE_COUNT][2][2][2];
+
     vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2];
     vk_pipeline pipeline_flash_attn_f32_f16_D80[GGML_TYPE_COUNT][2][2][2];
     vk_pipeline pipeline_flash_attn_f32_f16_D96[GGML_TYPE_COUNT][2][2][2];
@@ -1588,19 +1598,36 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events
     );
 }
 
+enum FaCodePath {
+    FA_SCALAR,
+    FA_COOPMAT1,
+    FA_COOPMAT2,
+};
+
 // number of rows/cols for flash attention shader
 static constexpr uint32_t flash_attention_num_small_rows = 32;
 static constexpr uint32_t scalar_flash_attention_num_small_rows = 1;
 static constexpr uint32_t scalar_flash_attention_num_large_rows = 8;
 
-static uint32_t get_fa_num_small_rows(bool scalar) {
-    return scalar ? scalar_flash_attention_num_small_rows : flash_attention_num_small_rows;
+// The FA coopmat1 shader assumes 16x16x16 matrix multiply support.
+// 128 threads split into four subgroups, each subgroup does 1/4
+// of the Bc dimension.
+static constexpr uint32_t coopmat1_flash_attention_num_large_rows = 16;
+static constexpr uint32_t scalar_flash_attention_Bc = 64;
+static constexpr uint32_t scalar_flash_attention_workgroup_size = 128;
+
+static uint32_t get_fa_num_small_rows(FaCodePath path) {
+    if (path == FA_COOPMAT2) {
+        return flash_attention_num_small_rows;
+    } else {
+        return scalar_flash_attention_num_small_rows;
+    }
 }
 
-static std::array<uint32_t, 2> fa_rows_cols(bool scalar, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) {
+static std::array<uint32_t, 2> fa_rows_cols(FaCodePath path, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) {
     GGML_UNUSED(clamp);
 
-    if (scalar) {
+    if (path == FA_SCALAR) {
         if (small_rows) {
             return {scalar_flash_attention_num_small_rows, 64};
         } else {
@@ -1608,9 +1635,17 @@ static std::array<uint32_t, 2> fa_rows_cols(bool scalar, uint32_t D, uint32_t cl
         }
     }
 
+    if (path == FA_COOPMAT1) {
+        if (small_rows) {
+            return {scalar_flash_attention_num_small_rows, scalar_flash_attention_Bc};
+        } else {
+            return {coopmat1_flash_attention_num_large_rows, scalar_flash_attention_Bc};
+        }
+    }
+
     // small rows, large cols
     if (small_rows) {
-        return {get_fa_num_small_rows(scalar), 32};
+        return {get_fa_num_small_rows(FA_COOPMAT2), 32};
     }
 
     // small cols to reduce register count
@@ -1907,17 +1942,19 @@ static void ggml_vk_load_shaders(vk_device& device) {
                                       parameter_count, wg_denoms, specialization_constants, disable_robustness, require_full_subgroups, required_subgroup_size));
     };
 
-    auto const &fa_wg_denoms = [&](bool scalar, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::array<uint32_t, 3> {
-        return {fa_rows_cols(scalar, D, clamp, type, small_rows)[0], 1, 1};
+    auto const &fa_wg_denoms = [&](FaCodePath path, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::array<uint32_t, 3> {
+        return {fa_rows_cols(path, D, clamp, type, small_rows)[0], 1, 1};
     };
 
-    auto const &fa_spec_constants = [&](bool scalar, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::vector<uint32_t> {
+    auto const &fa_spec_constants = [&](FaCodePath path, uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::vector<uint32_t> {
         // For large number of rows, 128 invocations seems to work best.
         // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we
         // can't use 256 for D==80.
         // For scalar, use 128 (arbitrary)
-        uint32_t wg_size = scalar ? 128 : ((small_rows && (D % 32) == 0) ? 256 : 128);
-        auto rows_cols = fa_rows_cols(scalar, D, clamp, type, small_rows);
+        uint32_t wg_size = (path == FA_SCALAR || path == FA_COOPMAT1)
+                            ? scalar_flash_attention_workgroup_size
+                            : ((small_rows && (D % 32) == 0) ? 256 : 128);
+        auto rows_cols = fa_rows_cols(path, D, clamp, type, small_rows);
 
         // D_split can't be larger than a subgroup because we use subgroupShuffle to reduce it.
         // D_split can't be larger than the LSB of D divided by 4 due to vectorization in the shader.
@@ -1929,36 +1966,43 @@ static void ggml_vk_load_shaders(vk_device& device) {
         return {wg_size, rows_cols[0], rows_cols[1], (D), clamp, D_split};
     };
 
-#define CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, D) \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][0][0], "flash_attn_f32_f16_D" #D "_f16acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,false), fa_spec_constants(SCALAR, D,1,TYPE,false), 1, true);     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][0][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,false), fa_spec_constants(SCALAR, D,0,TYPE,false), fa_rows_cols(SCALAR,D,0,TYPE,false)[1], true);     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][0][0], "flash_attn_f32_f16_D" #D "_f32acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,false), fa_spec_constants(SCALAR, D,1,TYPE,false), 1, true);     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][0][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,false), fa_spec_constants(SCALAR, D,0,TYPE,false), fa_rows_cols(SCALAR,D,0,TYPE,false)[1], true);     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][1][0], "flash_attn_f32_f16_D" #D "_f16acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,true), fa_spec_constants(SCALAR, D,1,TYPE,true), 1, true);     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][1][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,true), fa_spec_constants(SCALAR, D,0,TYPE,true), fa_rows_cols(SCALAR,D,0,TYPE,true)[1], true);     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][1][0], "flash_attn_f32_f16_D" #D "_f32acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,1,TYPE,true), fa_spec_constants(SCALAR, D,1,TYPE,true), 1, true);     \
-        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][1][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(SCALAR, D,0,TYPE,true), fa_spec_constants(SCALAR, D,0,TYPE,true), fa_rows_cols(SCALAR,D,0,TYPE,true)[1], true);     \
+#define CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, D) \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][0][0], "flash_attn_f32_f16_D" #D "_f16acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,1,TYPE,false), fa_spec_constants(FAPATH, D,1,TYPE,false), 1,                                      true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][0][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,0,TYPE,false), fa_spec_constants(FAPATH, D,0,TYPE,false), fa_rows_cols(FAPATH,D,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][0][0], "flash_attn_f32_f16_D" #D "_f32acc"         #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,1,TYPE,false), fa_spec_constants(FAPATH, D,1,TYPE,false), 1,                                      true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][0][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc" #NAMELC #SUFFIX,           flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,0,TYPE,false), fa_spec_constants(FAPATH, D,0,TYPE,false), fa_rows_cols(FAPATH,D,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][1][0], "flash_attn_f32_f16_D" #D "_f16acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,1,TYPE,true), fa_spec_constants(FAPATH, D,1,TYPE,true),   1,                                      true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][0][1][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len,  flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data,  "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,0,TYPE,true), fa_spec_constants(FAPATH, D,0,TYPE,true),   fa_rows_cols(FAPATH,D,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][1][0], "flash_attn_f32_f16_D" #D "_f32acc_smallrows"         #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,1,TYPE,true), fa_spec_constants(FAPATH, D,1,TYPE,true),   1,                                      true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
+        ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D ## SUFFIX[TYPE][1][1][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _len,         flash_attn_f32_f16_ ## NAMELC ##     SUFFIX ## _data,         "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, D,0,TYPE,true), fa_spec_constants(FAPATH, D,0,TYPE,true),   fa_rows_cols(FAPATH,D,0,TYPE,true)[1],  true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0));     \
 
-#define CREATE_FA(TYPE, NAMELC, SCALAR, SUFFIX) \
-        CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 64) \
-        CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 80) \
-        CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 96) \
-        CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 112) \
-        CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 128) \
-        CREATE_FA2(TYPE, NAMELC, SCALAR, SUFFIX, 256)
+#define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \
+        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 64) \
+        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 80) \
+        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 96) \
+        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 112) \
+        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 128) \
+        CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 256)
 
-    CREATE_FA(GGML_TYPE_F16, f16, true, )
-    CREATE_FA(GGML_TYPE_Q4_0, q4_0, true, )
-    CREATE_FA(GGML_TYPE_Q8_0, q8_0, true, )
+    CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, )
+    CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_SCALAR, )
+    CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_SCALAR, )
+#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+    if (device->coopmat1_fa_support) {
+        CREATE_FA(GGML_TYPE_F16, f16, FA_COOPMAT1, _cm1)
+        CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_COOPMAT1, _cm1)
+        CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_COOPMAT1, _cm1)
+    }
+#endif
 #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
     if (device->coopmat2) {
-        CREATE_FA(GGML_TYPE_F16, f16, false, _cm2)
-        CREATE_FA(GGML_TYPE_Q4_0, q4_0, false, _cm2)
-        CREATE_FA(GGML_TYPE_Q4_1, q4_1, false, _cm2)
-        CREATE_FA(GGML_TYPE_Q5_0, q5_0, false, _cm2)
-        CREATE_FA(GGML_TYPE_Q5_1, q5_1, false, _cm2)
-        CREATE_FA(GGML_TYPE_Q8_0, q8_0, false, _cm2)
-        CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, false, _cm2)
+        CREATE_FA(GGML_TYPE_F16, f16, FA_COOPMAT2, _cm2)
+        CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_COOPMAT2, _cm2)
+        CREATE_FA(GGML_TYPE_Q4_1, q4_1, FA_COOPMAT2, _cm2)
+        CREATE_FA(GGML_TYPE_Q5_0, q5_0, FA_COOPMAT2, _cm2)
+        CREATE_FA(GGML_TYPE_Q5_1, q5_1, FA_COOPMAT2, _cm2)
+        CREATE_FA(GGML_TYPE_Q8_0, q8_0, FA_COOPMAT2, _cm2)
+        CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl, FA_COOPMAT2, _cm2)
     }
 #endif
 #undef CREATE_FA2
@@ -2041,17 +2085,17 @@ static void ggml_vk_load_shaders(vk_device& device) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, true);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, true);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _coopmat_len, NAMELC ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, true);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, true);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, true);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _coopmat_len, NAMELC ## _aligned ## F16ACC ## _coopmat_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, true);   \
 
         // Create 2 variants, {f16,f32} accumulator
 #define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
@@ -3009,6 +3053,11 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
 #if defined(VK_KHR_cooperative_matrix)
         device->coopmat_support = device->coopmat_support && coopmat_features.cooperativeMatrix;
+
+        // coopmat1 fa shader currently assumes 32 invocations per subgroup
+        device->coopmat1_fa_support = device->coopmat_support && device->subgroup_require_full_support &&
+                                      device->subgroup_size_control && device->subgroup_min_size <= 32 &&
+                                      device->subgroup_max_size >= 32;
 #endif
 
         if (coopmat2_support) {
@@ -3143,6 +3192,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
                             // Only enable if shape is identical
                             device->coopmat_acc_f32_support = true;
                         }
+                        if (prop.MSize == 16 && prop.NSize == 16 && prop.KSize == 16) {
+                            device->coopmat_support_16x16x16_f32acc = true;
+                        }
                     } else if ((vk::ComponentTypeKHR)prop.CType == vk::ComponentTypeKHR::eFloat16 &&
                                (vk::ComponentTypeKHR)prop.ResultType == vk::ComponentTypeKHR::eFloat16) {
                         // coopmat sizes not set yet
@@ -3155,6 +3207,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
                             // Only enable if shape is identical
                             device->coopmat_acc_f16_support = true;
                         }
+                        if (prop.MSize == 16 && prop.NSize == 16 && prop.KSize == 16) {
+                            device->coopmat_support_16x16x16_f16acc = true;
+                        }
                     }
                 } else if ((vk::ComponentTypeKHR)prop.AType      == vk::ComponentTypeKHR::eSint8 &&
                            (vk::ComponentTypeKHR)prop.BType      == vk::ComponentTypeKHR::eSint8 &&
@@ -5688,6 +5743,36 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx
     }
 }
 
+static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const uint32_t D, bool f32acc) {
+    // Needs to be kept up to date on shader changes
+    const uint32_t wg_size = scalar_flash_attention_workgroup_size;
+    const uint32_t Br = scalar_flash_attention_num_large_rows;
+    const uint32_t Bc = scalar_flash_attention_Bc;
+
+    const uint32_t acctype = f32acc ? 4 : 2;
+    const uint32_t f16vec4 = 8;
+
+    const uint32_t tmpsh = wg_size * sizeof(float);
+    const uint32_t tmpshv4 = wg_size * 4 * acctype;
+
+    const uint32_t Qf = Br * (D / 4 + 2) * f16vec4;
+
+    const uint32_t sfshstride = (D <= 128) ? (Br + 8) : Br;
+    const uint32_t sfsh = Bc * sfshstride * acctype;
+
+    const uint32_t kshstride = D / 4 + 2;
+    const uint32_t ksh = Bc * kshstride * f16vec4;
+
+    const uint32_t slope = Br * sizeof(float);
+
+    const uint32_t total_size = tmpsh + tmpshv4 + Qf + sfsh + ksh + slope;
+    const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
+
+    VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(D=" << D << ", f32acc=" << f32acc << ", total_size=" << total_size << ", supported=" << supported);
+
+    return supported;
+}
+
 static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, ggml_tensor * dst, bool dryrun = false) {
     VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3];
     std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3];
@@ -5738,7 +5823,19 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     assert(q->type == GGML_TYPE_F32);
     assert(k->type == v->type);
 
-    bool scalar = !ctx->device->coopmat2;
+    FaCodePath path = ctx->device->coopmat2 ? FA_COOPMAT2 :
+                      ctx->device->coopmat1_fa_support ? FA_COOPMAT1 : FA_SCALAR;
+
+    if (path == FA_COOPMAT1) {
+        const bool coopmat_shape_supported = (dst->op_params[3] == GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f32acc) ||
+                                             (dst->op_params[3] != GGML_PREC_F32 && ctx->device->coopmat_support_16x16x16_f16acc);
+
+        const bool coopmat_shmem_supported = ggml_vk_flash_attn_coopmat_shmem_support(ctx->device, D, dst->op_params[3] == GGML_PREC_F32);
+
+        if (!coopmat_shape_supported || !coopmat_shmem_supported) {
+            path = FA_SCALAR;
+        }
+    }
 
     uint32_t gqa_ratio = 1;
     uint32_t qk_ratio = neq2 / nek2;
@@ -5746,9 +5843,21 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     uint32_t workgroups_y = (uint32_t)neq2;
     uint32_t workgroups_z = (uint32_t)neq3;
 
-    // For scalar FA, we can use the "large" size to accommodate qga.
-    // For coopmat FA, we always use the small size (which is still pretty large for gqa).
-    const uint32_t max_gqa = scalar ? scalar_flash_attention_num_large_rows : get_fa_num_small_rows(false);
+    // For scalar/coopmat1 FA, we can use the "large" size to accommodate qga.
+    // For coopmat2 FA, we always use the small size (which is still pretty large for gqa).
+    uint32_t max_gqa;
+    switch (path) {
+    case FA_SCALAR:
+    case FA_COOPMAT1:
+        // We may switch from coopmat1 to scalar, so use the scalar limit for both
+        max_gqa = scalar_flash_attention_num_large_rows;
+        break;
+    case FA_COOPMAT2:
+        max_gqa = get_fa_num_small_rows(FA_COOPMAT2);
+        break;
+    default:
+        GGML_ASSERT(0);
+    }
 
     if (N == 1 && qk_ratio > 1 && qk_ratio <= max_gqa &&
         qk_ratio * nek2 == neq2 && nek2 == nev2 && neq3 == 1 && nek3 == 1 && nev3 == 1) {
@@ -5761,11 +5870,16 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
     }
 
     vk_pipeline *pipelines;
-    // XXX TODO other backends may be changing accumulator precision to default to f32 soon
-    bool f32acc = scalar || dst->op_params[3] == GGML_PREC_F32;
-    bool small_rows = N <= get_fa_num_small_rows(scalar);
+    bool small_rows = N <= get_fa_num_small_rows(path);
 
-    if (scalar) {
+    if (small_rows && path == FA_COOPMAT1) {
+        path = FA_SCALAR;
+    }
+
+    bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32;
+
+    switch (path) {
+    case FA_SCALAR:
         switch (D) {
         case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64[k->type][f32acc][small_rows][0]; break;
         case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80[k->type][f32acc][small_rows][0]; break;
@@ -5777,7 +5891,21 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
             GGML_ASSERT(!"unsupported D value");
             return;
         }
-    } else {
+        break;
+    case FA_COOPMAT1:
+        switch (D) {
+        case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64_cm1[k->type][f32acc][small_rows][0]; break;
+        case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80_cm1[k->type][f32acc][small_rows][0]; break;
+        case 96: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D96_cm1[k->type][f32acc][small_rows][0]; break;
+        case 112: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D112_cm1[k->type][f32acc][small_rows][0]; break;
+        case 128: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D128_cm1[k->type][f32acc][small_rows][0]; break;
+        case 256: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D256_cm1[k->type][f32acc][small_rows][0]; break;
+        default:
+            GGML_ASSERT(!"unsupported D value");
+            return;
+        }
+        break;
+    case FA_COOPMAT2:
         switch (D) {
         case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64_cm2[k->type][f32acc][small_rows][0]; break;
         case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80_cm2[k->type][f32acc][small_rows][0]; break;
@@ -5789,6 +5917,9 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
             GGML_ASSERT(!"unsupported D value");
             return;
         }
+        break;
+    default:
+        GGML_ASSERT(0);
     }
     assert(pipelines);
 

ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt

@@ -5,18 +5,35 @@ find_package (Threads REQUIRED)
 
 if (GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
     add_compile_definitions(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+    message(STATUS "Enabling coopmat glslc support")
 endif()
 if (GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
     add_compile_definitions(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
+    message(STATUS "Enabling coopmat2 glslc support")
 endif()
 if (GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
     add_compile_definitions(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
+    message(STATUS "Enabling dot glslc support")
 endif()
 if (GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
     add_compile_definitions(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
+    message(STATUS "Enabling bfloat16 glslc support")
 endif()
+
 set(TARGET vulkan-shaders-gen)
 add_executable(${TARGET} vulkan-shaders-gen.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads)
+
+# Configure output directories for MSVC builds
+if(MSVC)
+    # Get the main project's runtime output directory if possible
+    if(DEFINED CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+        foreach(CONFIG ${CMAKE_CONFIGURATION_TYPES})
+            string(TOUPPER ${CONFIG} CONFIG)
+            set_target_properties(${TARGET} PROPERTIES
+                RUNTIME_OUTPUT_DIRECTORY_${CONFIG} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
+        endforeach()
+    endif()
+endif()

ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp

@@ -12,6 +12,7 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
+layout (constant_id = 0) const uint32_t WorkGroupSize = 128;
 layout (constant_id = 1) const uint32_t Br = 1;
 layout (constant_id = 2) const uint32_t Bc = 32;
 layout (constant_id = 3) const uint32_t D = 32;
@@ -19,7 +20,7 @@ layout (constant_id = 3) const uint32_t D = 32;
 layout (constant_id = 5) const uint32_t D_split = 16;
 const uint32_t D_per_thread = D / D_split;
 
-const uint32_t cols_per_iter = gl_WorkGroupSize.x / D_split;
+const uint32_t cols_per_iter = WorkGroupSize / D_split;
 const uint32_t cols_per_thread = Bc / cols_per_iter;
 
 layout (push_constant) uniform parameter {
@@ -134,8 +135,8 @@ ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const i
     return ACC_TYPE(pow(base, ACC_TYPE(exph)));
 }
 
-shared FLOAT_TYPE tmpsh[gl_WorkGroupSize.x];
-shared vec4 tmpshv4[gl_WorkGroupSize.x];
+shared FLOAT_TYPE tmpsh[WorkGroupSize];
+shared vec4 tmpshv4[WorkGroupSize];
 
 shared float masksh[Bc][Br];
 shared vec4 Qf[Br][D / 4];

ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp

@@ -0,0 +1,506 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
+
+#extension GL_KHR_shader_subgroup_basic : enable
+#extension GL_KHR_memory_scope_semantics : enable
+#extension GL_KHR_cooperative_matrix : enable
+
+#include "types.comp"
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (constant_id = 1) const uint32_t Br = 1;
+layout (constant_id = 2) const uint32_t Bc = 32;
+layout (constant_id = 3) const uint32_t D = 32;
+
+layout (constant_id = 5) const uint32_t D_split = 16;
+
+const uint32_t D_per_thread = D / D_split;
+const uint32_t row_split = 4;
+const uint32_t rows_per_thread = Br / row_split;
+const uint32_t cols_per_iter = gl_WorkGroupSize.x / D_split / row_split;
+const uint32_t cols_per_thread = Bc / cols_per_iter;
+
+layout (push_constant) uniform parameter {
+    uint32_t N;
+    uint32_t KV;
+
+    uint32_t ne1;
+    uint32_t ne2;
+    uint32_t ne3;
+
+    uint32_t neq2;
+    uint32_t neq3;
+    uint32_t nek2;
+    uint32_t nek3;
+    uint32_t nev2;
+    uint32_t nev3;
+    uint32_t nem1;
+
+    uint32_t nb01;
+    uint32_t nb02;
+    uint32_t nb03;
+    uint32_t nb11;
+    uint32_t nb12;
+    uint32_t nb13;
+    uint32_t nb21;
+    uint32_t nb22;
+    uint32_t nb23;
+    uint32_t nb31;
+
+    float scale;
+    float max_bias;
+    float logit_softcap;
+
+    uint32_t mask;
+    uint32_t n_head_log2;
+    float m0;
+    float m1;
+
+    uint32_t gqa_ratio;
+    uint32_t split_kv;
+    uint32_t k_num;
+} p;
+
+layout (binding = 0) readonly buffer Q {float data_q[];};
+layout (binding = 0) readonly buffer QV4 {vec4 data_qv4[];};
+layout (binding = 1) readonly buffer K {float16_t data_k[];};
+layout (binding = 1) readonly buffer KV4 {f16vec4 data_kv4[];};
+layout (binding = 2) readonly buffer V {float16_t data_v[];};
+layout (binding = 2) readonly buffer VV4 {f16vec4 data_vv4[];};
+layout (binding = 3) readonly buffer M {float16_t data_m[];};
+layout (binding = 4) writeonly buffer O {D_TYPE data_o[];};
+
+#if defined(A_TYPE_PACKED16)
+#define BINDING_IDX_K 0
+#define BINDING_IDX_V 1
+layout (binding = 1) readonly buffer KV_PACKED16 {A_TYPE_PACKED16 data_packed16[];} kv_packed[2];
+#endif
+
+#if defined(DATA_A_Q4_0)
+#define BLOCK_BYTE_SIZE 18
+
+vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
+    uint vui_lo = uint(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]);
+    uint vui_hi = uint(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]);
+    uint shift = (iqs & 0x10) >> 2;
+    vui_lo >>= shift;
+    vui_hi >>= shift;
+
+    return float(kv_packed[binding_idx].data_packed16[a_offset + ib].d) * (vec4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, vui_hi & 0xF, (vui_hi >> 8) & 0xF) - 8.0f);
+}
+#endif
+
+#if defined(DATA_A_Q8_0)
+#define BLOCK_BYTE_SIZE 34
+vec4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
+    const i8vec2 v0 = unpack8(int32_t(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[iqs / 2])).xy; // vec4 used due to #12147
+    const i8vec2 v1 = unpack8(int32_t(kv_packed[binding_idx].data_packed16[a_offset + ib].qs[iqs / 2 + 1])).xy;
+
+    return float(kv_packed[binding_idx].data_packed16[a_offset + ib].d) * vec4(v0.x, v0.y, v1.x, v1.y);
+}
+#endif
+
+#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
+
+// Store the output when doing grouped query attention.
+// Rows index by Q's dimension 2, and the first N rows are valid.
+D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N)
+{
+    uint32_t offset = (iq2 + r) * D + c;
+    data_o[o_offset + offset] = D_TYPE(elem);
+    return elem;
+}
+
+// Store column zero. This is used to save per-row m and L values for split_k.
+ACC_TYPE perElemOpStoreCol0(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N)
+{
+    if (r < N && c == 0) {
+        uint32_t offset = iq2 + r;
+        data_o[o_offset + offset] = D_TYPE(elem);
+    }
+    return elem;
+}
+
+// Load the slope matrix, indexed by Q's dimension 2.
+ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2)
+{
+    const uint32_t h = iq2 + (r % p.gqa_ratio);
+
+    const ACC_TYPE base = ACC_TYPE(h < p.n_head_log2 ? p.m0 : p.m1);
+    const int      exph = int(h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1);
+
+    return ACC_TYPE(pow(base, ACC_TYPE(exph)));
+}
+
+// These need to be supported N,M values for a MatBc x MatBr x 16 coopmatmuladd
+const uint32_t MatBr = 16;
+const uint32_t MatBc = 16;
+
+shared FLOAT_TYPE tmpsh[gl_WorkGroupSize.x];
+shared ACC_TYPEV4 tmpshv4[gl_WorkGroupSize.x];
+
+const uint32_t qstride = D / 4 + 2; // in units of f16vec4
+shared f16vec4 Qf[Br * qstride];
+
+// Avoid padding for D==256 to make it fit in 48KB shmem.
+const uint32_t sfshstride = (D <= 128) ? (Br + 8) : Br;
+shared ACC_TYPE sfsh[Bc * sfshstride];
+
+const uint32_t kshstride = D / 4 + 2; // in units of f16vec4
+shared f16vec4 ksh[Bc * kshstride];
+
+shared float slope[Br];
+
+void main() {
+#ifdef NEEDS_INIT_IQ_SHMEM
+    init_iq_shmem(gl_WorkGroupSize);
+#endif
+
+    const uint32_t tid = gl_LocalInvocationIndex;
+    const uint32_t N = p.N;
+    const uint32_t KV = p.KV;
+
+    const uint32_t threads_per_rowgroup = gl_WorkGroupSize.x / row_split;
+    const uint32_t row_tid = gl_LocalInvocationIndex / threads_per_rowgroup;
+    const uint32_t d_tid = gl_LocalInvocationIndex % D_split;
+    const uint32_t col_tid = (gl_LocalInvocationIndex % threads_per_rowgroup) / D_split;
+
+#define tile_row(r) (row_tid * rows_per_thread + (r))
+
+    uint32_t i = gl_WorkGroupID.x;
+    uint32_t split_k_index = 0;
+
+    if (p.k_num > 1) {
+        i = 0;
+        split_k_index = gl_WorkGroupID.x;
+    }
+
+    const uint32_t Tr = CEIL_DIV(N, Br);
+
+    const uint32_t start_j = split_k_index * p.split_kv / Bc;
+    const uint32_t end_j = CEIL_DIV(min(KV, (split_k_index + 1) * p.split_kv), Bc);
+
+    // When not using grouped query attention, all rows share the same iq2, equal to gl_WorkGroupID.y.
+    // When using grouped query attention, each workgroup does gqa_ratio consecutive values of iq2.
+    const uint32_t iq2 = gl_WorkGroupID.y * p.gqa_ratio;
+    const uint32_t iq3 = gl_WorkGroupID.z;
+
+    // broadcast factors
+    const uint32_t rk2 = p.neq2/p.nek2;
+    const uint32_t rk3 = p.neq3/p.nek3;
+
+    const uint32_t rv2 = p.neq2/p.nev2;
+    const uint32_t rv3 = p.neq3/p.nev3;
+
+    // k indices
+    const uint32_t ik3 = iq3 / rk3;
+    const uint32_t ik2 = iq2 / rk2;
+
+    // v indices
+    const uint32_t iv3 = iq3 / rv3;
+    const uint32_t iv2 = iq2 / rv2;
+
+    // nb?1 are already divided by the type size and are in units of elements.
+    // When using grouped query attention, Q is indexed by iq2, so the stride
+    // should be nb02 (which is in bytes).
+    uint32_t q_stride = p.gqa_ratio > 1 ? (p.nb02 / 4) : p.nb01;
+    uint32_t k_stride = p.nb11;
+    uint32_t v_stride = p.nb21;
+    // When using grouped query attention, all rows use the same mask (stride 0).
+    // "p.gqa_ratio >> 16" is just a roundabout way of writing zero
+    // that prevents the compiler from folding the "&" through the select
+    // and breaking the alignment detection.
+    uint32_t m_stride = (p.gqa_ratio > 1) ? (p.gqa_ratio >> 16) : KV;
+
+    uint32_t q_offset = (iq2*p.nb02+iq3*p.nb03) / 4;
+
+    [[unroll]] for (uint32_t idx = 0; idx < Br * D / 4; idx += gl_WorkGroupSize.x) {
+        uint32_t d = (idx + tid) % (D / 4);
+        uint32_t r = (idx + tid) / (D / 4);
+        if (r < Br && d < D / 4 &&
+            i * Br + r < N) {
+            Qf[r * qstride + d] = f16vec4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d] * p.scale);
+        }
+    }
+    barrier();
+
+    ACC_TYPEV4 Of[rows_per_thread][D_per_thread / 4];
+    [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            Of[r][d] = ACC_TYPEV4(0.0);
+        }
+    }
+
+    float Lf[rows_per_thread], Mf[rows_per_thread];
+
+    // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M.
+    const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF);
+
+    [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+        Lf[r] = 0;
+        Mf[r] = NEG_FLT_MAX_OVER_2;
+    }
+
+    // ALiBi
+    if (p.max_bias > 0.0f) {
+        if (tid < Br) {
+            uint r = tid;
+            slope[r] = perElemOpComputeSlope(r, col_tid, ACC_TYPE(0), iq2);
+        }
+        barrier();
+    } else {
+        if (tid < Br) {
+            uint r = tid;
+            slope[r] = 1.0;
+        }
+        barrier();
+    }
+
+#if BLOCK_SIZE > 1
+    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE;
+    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE;
+#else
+    uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / 2;
+    uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / 2;
+#endif
+
+    [[dont_unroll]]
+    for (uint32_t j = start_j; j < end_j; ++j) {
+
+        [[unroll]] for (uint32_t idx = 0; idx < Bc * D / 4; idx += gl_WorkGroupSize.x) {
+            uint32_t d = (idx + tid) % (D / 4);
+            uint32_t c = (idx + tid) / (D / 4);
+            if (c < Bc && d < D / 4) {
+#if BLOCK_SIZE > 1
+                uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE + 4 * d;
+                uint ib = coord / BLOCK_SIZE;
+                uint iqs = (coord % BLOCK_SIZE);
+                f16vec4 K_Tf = f16vec4(dequantize4(ib, iqs, k_offset, BINDING_IDX_K));
+#else
+                f16vec4 K_Tf = f16vec4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]);
+#endif
+
+                ksh[c * kshstride + d] = K_Tf;
+            }
+        }
+        barrier();
+
+        // K * Q^T -> S^T: Bc x D * D x Br -> Bc x Br
+        // Bc split across workgroup (four subgroups), loop over D in chunks of 16: 16 x 16 * 16 x 16 -> 16 x 16
+        // This is written transposed in order to allow for N being 8 if implementations need it
+        coopmat<ACC_TYPE, gl_ScopeSubgroup, MatBc, MatBr, gl_MatrixUseAccumulator> SfMat = coopmat<ACC_TYPE, gl_ScopeSubgroup, MatBc, MatBr, gl_MatrixUseAccumulator>(0);
+        coopmat<float16_t, gl_ScopeSubgroup, MatBc, 16, gl_MatrixUseA> KMat;
+        coopmat<float16_t, gl_ScopeSubgroup, 16, MatBr, gl_MatrixUseB> QMat;
+
+        for (uint32_t d = 0; d < D / 16; ++d) {
+            coopMatLoad(QMat, Qf, d * 16 / 4, qstride, gl_CooperativeMatrixLayoutColumnMajor);
+
+            uint coord = (gl_SubgroupID * MatBc) * kshstride + d * 16 / 4;
+            coopMatLoad(KMat, ksh, coord, kshstride, gl_CooperativeMatrixLayoutRowMajor);
+
+            SfMat = coopMatMulAdd(KMat, QMat, SfMat);
+        }
+
+        uint coord = gl_SubgroupID * MatBc * sfshstride;
+        coopMatStore(SfMat, sfsh, coord, sfshstride, gl_CooperativeMatrixLayoutRowMajor);
+        barrier();
+
+        if (p.logit_softcap != 0.0f) {
+            [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) {
+                uint32_t c = (idx + tid) / Br;
+                uint32_t r = (idx + tid) % Br;
+                if (idx + tid < Bc * Br || idx + gl_WorkGroupSize.x <= Bc * Br) {
+                    sfsh[c * sfshstride + r] = ACC_TYPE(p.logit_softcap * tanh(sfsh[c * sfshstride + r]));
+                }
+            }
+            barrier();
+        }
+
+        if (p.mask != 0) {
+            [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) {
+                uint32_t c = (idx + tid) % Bc;
+                uint32_t r = (idx + tid) / Bc;
+                if (idx + tid < Bc * Br || idx + gl_WorkGroupSize.x <= Bc * Br) {
+                    sfsh[c * sfshstride + r] += ACC_TYPE(slope[r] * float(data_m[(i * Br + r) * m_stride + (j * Bc + c)]));
+                }
+            }
+            barrier();
+        }
+
+        float eMf[rows_per_thread];
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            float rowmaxf = sfsh[tile_row(r) + (0 * cols_per_iter + col_tid) * sfshstride];
+            [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) {
+                rowmaxf = max(rowmaxf, float(sfsh[tile_row(r) + (c * cols_per_iter + col_tid) * sfshstride]));
+            }
+            float Moldf = Mf[r];
+
+            // M = max(rowmax, Mold)
+            // P = e^(S - M)
+            // eM = e^(Mold - M)
+            Mf[r] = max(rowmaxf, Moldf);
+            eMf[r] = exp(Moldf - Mf[r]);
+        }
+
+        [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+            [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+                Of[r][d] = float16_t(eMf[r]) * Of[r][d];
+            }
+        }
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            Lf[r] = eMf[r]*Lf[r];
+        }
+
+        [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) {
+            float Pf[rows_per_thread];
+            [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+                Pf[r] = exp(sfsh[tile_row(r) + (c * cols_per_iter + col_tid) * sfshstride] - Mf[r]);
+                Lf[r] += Pf[r];
+            }
+            [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+#if BLOCK_SIZE > 1
+                uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid);
+                uint ib = coord / BLOCK_SIZE;
+                uint iqs = (coord % BLOCK_SIZE);
+                vec4 Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V);
+#else
+                vec4 Vf = vec4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]);
+#endif
+                [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+                    Of[r][d] += float16_t(Pf[r]) * ACC_TYPEV4(Vf);
+                }
+            }
+        }
+
+        barrier();
+    }
+
+    // reduce across threads
+
+    float rowmaxf[rows_per_thread], eMf[rows_per_thread], Moldf[rows_per_thread];
+    [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+        FLOAT_TYPE M = Mf[r];
+        tmpsh[tid] = M;
+        // Compute max across the row
+        barrier();
+        [[unroll]] for (int s = int(gl_WorkGroupSize.x / row_split) / 2; s >= D_split; s >>= 1) {
+            M = max(M, tmpsh[tid ^ s]);
+            barrier();
+            tmpsh[tid] = M;
+            barrier();
+        }
+        rowmaxf[r] = tmpsh[d_tid + row_tid * threads_per_rowgroup];
+        barrier();
+    }
+
+    [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+        Moldf[r] = Mf[r];
+
+        // M = max(rowmax, Mold)
+        // eM = e^(Mold - M)
+        Mf[r] = max(rowmaxf[r], Moldf[r]);
+        eMf[r] = exp(Moldf[r] - Mf[r]);
+
+        Lf[r] = eMf[r]*Lf[r];
+    }
+
+    [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+        FLOAT_TYPE L = Lf[r];
+        tmpsh[tid] = L;
+        // Compute sum across the row
+        barrier();
+        [[unroll]] for (int s = int(gl_WorkGroupSize.x / row_split) / 2; s >= D_split; s >>= 1) {
+            L += tmpsh[tid ^ s];
+            barrier();
+            tmpsh[tid] = L;
+            barrier();
+        }
+        Lf[r] = tmpsh[d_tid + row_tid * threads_per_rowgroup];
+        barrier();
+    }
+
+    [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+        [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+
+            Of[r][d] = float16_t(eMf[r]) * Of[r][d];
+            tmpshv4[tid] = Of[r][d];
+
+            barrier();
+            [[unroll]] for (int s = int(gl_WorkGroupSize.x / row_split) / 2; s >= D_split; s >>= 1) {
+                Of[r][d] += tmpshv4[tid ^ s];
+                barrier();
+                tmpshv4[tid] = Of[r][d];
+                barrier();
+            }
+            Of[r][d] = tmpshv4[d_tid + row_tid * threads_per_rowgroup];
+            barrier();
+        }
+    }
+
+    // If there is split_k, then the split_k resolve shader does the final
+    // division by L. Store the intermediate O value and per-row m and L values.
+    if (p.k_num > 1) {
+        uint32_t o_offset = D * p.ne1 * split_k_index;
+
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            if (tile_row(r) < N) {
+                [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+                    [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) {
+                        perElemOpGqaStore(tile_row(r), 4*(d * D_split + d_tid) + comp, float(Of[r][d][comp]), o_offset, iq2, N);
+                    }
+                }
+            }
+        }
+
+        o_offset = D * p.ne1 * p.k_num + p.ne1 * split_k_index * 2;
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            if (tile_row(r) < N) {
+                perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N);
+                perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N);
+            }
+        }
+
+        return;
+    }
+
+    float Lfrcp[rows_per_thread];
+    [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+        Lfrcp[r] = 1.0 / Lf[r];
+    }
+
+    [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            Of[r][d] *= float16_t(Lfrcp[r]);
+        }
+    }
+
+    uint32_t o_offset = iq3*p.ne2*p.ne1;
+
+    if (p.gqa_ratio > 1) {
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            if (tile_row(r) < N) {
+                [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+                    [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) {
+                        perElemOpGqaStore(tile_row(r), 4*(d * D_split + d_tid) + comp, float(Of[r][d][comp]), o_offset, iq2, N);
+                    }
+                }
+            }
+        }
+    } else {
+        [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
+            if (i * Br + tile_row(r) < N) {
+                [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) {
+                    [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) {
+                        data_o[o_offset + iq2 * D + (i * Br + tile_row(r)) * p.ne1 * D + 4*(d * D_split + d_tid) + comp] = D_TYPE(Of[r][d][comp]);
+                    }
+                }
+            }
+        }
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp

@@ -215,7 +215,7 @@ static std::mutex compile_count_mutex;
 static std::condition_variable compile_count_cond;
 
 void string_to_spv_func(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true, bool coopmat = false, bool coopmat2 = false, bool f16acc = false) {
-    std::string name = _name + (f16acc ? "_f16acc" : "") + (coopmat ? "_coopmat" : "") + (coopmat2 ? "_cm2" : (fp16 ? "" : "_fp32"));
+    std::string name = _name + (f16acc ? "_f16acc" : "") + (coopmat ? "_cm1" : "") + (coopmat2 ? "_cm2" : (fp16 ? "" : "_fp32"));
     std::string out_fname = join_paths(output_dir, name + ".spv");
     std::string in_path = join_paths(input_dir, in_fname);
 
@@ -424,6 +424,7 @@ void process_shaders() {
     // flash attention
     for (const auto& f16acc : {false, true}) {
         std::string acctype = f16acc ? "float16_t" : "float";
+        std::string acctypev4 = f16acc ? "f16vec4" : "vec4";
 
         for (const auto& tname : type_names) {
             if (tname == "f32") {
@@ -440,6 +441,16 @@ void process_shaders() {
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp",
                     merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, false, true, f16acc);
             }
+#endif
+#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+            if (tname == "f16") {
+                string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp",
+                    merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"ACC_TYPEV4", acctypev4}, {"COOPMAT", "1"}}), true, true, false, f16acc);
+            } else if (tname == "q4_0" || tname == "q8_0") {
+                std::string data_a_key = "DATA_A_" + to_uppercase(tname);
+                string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm1.comp",
+                    merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"ACC_TYPEV4", acctypev4}, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname)}, {"COOPMAT", "1"}}), true, true, false, f16acc);
+            }
 #endif
             if (tname == "f16") {
                 string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn.comp",

ggml/src/ggml.c

@@ -5499,7 +5499,7 @@ static void ggml_compute_backward(
             // tensor = src0 * 1 + src1 * 0
             if (src0_needs_grads) {
                 // dsrc0 = dtensor * 1
-                ggml_add_or_set(ctx, cgraph, isrc0, grad);
+                ggml_add_or_set(ctx, cgraph, isrc0, ggml_reshape(ctx, grad, src0));
             }
             if (src1_needs_grads) {
                 // dsrc1 = dtensor * 0 -> noop
@@ -5780,10 +5780,9 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor *
 }
 
 void ggml_build_backward_expand(
-        struct ggml_context * ctx_static,
-        struct ggml_context * ctx_compute,
-        struct ggml_cgraph  * cgraph,
-        bool                  accumulate) {
+        struct ggml_context *  ctx,
+        struct ggml_cgraph  *  cgraph,
+        struct ggml_tensor  ** grad_accs) {
     GGML_ASSERT(cgraph->n_nodes > 0);
     GGML_ASSERT(cgraph->grads);
     GGML_ASSERT(cgraph->grad_accs);
@@ -5856,21 +5855,24 @@ void ggml_build_backward_expand(
         GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW ||
             node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE);
 
-        const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
-        GGML_ASSERT(igrad != GGML_HASHSET_FULL);
-        GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, igrad));
-        if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) {
-            cgraph->grad_accs[igrad] = ggml_dup_tensor(ctx_static, node);
-            cgraph->grads[igrad]     = cgraph->grad_accs[igrad];
-            ggml_format_name(cgraph->grad_accs[igrad], "grad acc for %s", node->name);
+        const size_t ihash = ggml_hash_find(&cgraph->visited_hash_set, node);
+        GGML_ASSERT(ihash != GGML_HASHSET_FULL);
+        GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, ihash));
+        if (grad_accs && grad_accs[i]) {
+            cgraph->grad_accs[ihash] = grad_accs[i];
+            cgraph->grads[ihash]     = cgraph->grad_accs[ihash];
+        } else if (node->flags & GGML_TENSOR_FLAG_LOSS) {
+            // loss tensors always need a gradient accumulator
+            cgraph->grad_accs[ihash] = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
+            cgraph->grads[ihash]     = cgraph->grad_accs[ihash];
         }
-        grads_needed[igrad] = true;
+        grads_needed[ihash] = true;
     }
 
     for (int i = n_nodes_f - 1; i >= 0; --i) {
         // inplace operations to add gradients are not created by ggml_compute_backward except for gradient accumulation
         // use allocator to automatically make inplace operations
-        ggml_compute_backward(ctx_compute, cgraph, i, grads_needed);
+        ggml_compute_backward(ctx, cgraph, i, grads_needed);
     }
 
     free(grads_needed);
@@ -6016,8 +6018,8 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) {
     }
 }
 
-struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) {
-    struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads != NULL);
+struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads) {
+    struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads || force_grads);
     ggml_graph_cpy(cgraph, result);
     return result;
 }
@@ -6036,6 +6038,9 @@ struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
 }
 
 void ggml_graph_reset(struct ggml_cgraph * cgraph) {
+    if (!cgraph) {
+        return;
+    }
     GGML_ASSERT(cgraph->grads != NULL);
 
     for (int i = 0; i < cgraph->n_nodes; i++) {
@@ -6345,8 +6350,8 @@ void ggml_set_output(struct ggml_tensor * tensor) {
     tensor->flags |= GGML_TENSOR_FLAG_OUTPUT;
 }
 
-void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) {
-    GGML_UNUSED(ctx); // TODO: remove this parameter
+void ggml_set_param(struct ggml_tensor * tensor) {
+    GGML_ASSERT(tensor->op == GGML_OP_NONE);
     tensor->flags |= GGML_TENSOR_FLAG_PARAM;
 }
 

gguf-py/gguf/constants.py

@@ -1905,6 +1905,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_GATE_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
     ],
     MODEL_ARCH.CHAMELEON: [
         MODEL_TENSOR.TOKEN_EMBD,

gguf-py/gguf/tensor_mapping.py

@@ -428,6 +428,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_expert.down_proj",  # qwen2moe
             "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2
             "language_model.model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4
+            "model.layers.{bid}.shared_mlp.output_linear",     # granitemoe
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (

include/llama.h

@@ -4,6 +4,7 @@
 #include "ggml.h"
 #include "ggml-cpu.h"
 #include "ggml-backend.h"
+#include "ggml-opt.h"
 
 #include <stddef.h>
 #include <stdint.h>
@@ -344,7 +345,7 @@ extern "C" {
         float    yarn_beta_fast;   // YaRN low correction dim
         float    yarn_beta_slow;   // YaRN high correction dim
         uint32_t yarn_orig_ctx;    // YaRN original context size
-        float    defrag_thold;     // defragment the KV cache if holes/size > thold, < 0 disabled (default)
+        float    defrag_thold;     // defragment the KV cache if holes/size > thold, <= 0 disabled (default)
 
         ggml_backend_sched_eval_callback cb_eval;
         void * cb_eval_user_data;
@@ -445,6 +446,10 @@ extern "C" {
                                  size_t    n_paths,
               struct llama_model_params    params);
 
+    LLAMA_API void llama_model_save_to_file(
+            const struct llama_model * model,
+                        const char * path_model);
+
     DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
             "use llama_model_free instead");
 
@@ -1433,6 +1438,37 @@ extern "C" {
     LLAMA_API void                           llama_perf_sampler_print(const struct llama_sampler * chain);
     LLAMA_API void                           llama_perf_sampler_reset(      struct llama_sampler * chain);
 
+    //
+    // training
+    //
+
+    // function that returns whether or not a given tensor contains trainable parameters
+    typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata);
+
+    // always returns true
+    LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata);
+
+    struct llama_opt_params {
+        uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0
+
+        llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters
+        void * param_filter_ud;              // userdata for determining which tensors contain trainable parameters
+
+        ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
+        void * get_opt_pars_ud;                     // userdata for calculating optimizer parameters
+    };
+
+    LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params);
+
+    LLAMA_API void llama_opt_epoch(
+            struct llama_context    * lctx,
+            ggml_opt_dataset_t        dataset,
+            ggml_opt_result_t         result_train,
+            ggml_opt_result_t         result_eval,
+            int64_t                   idata_split,
+            ggml_opt_epoch_callback   callback_train,
+            ggml_opt_epoch_callback   callback_eval);
+
 #ifdef __cplusplus
 }
 #endif

scripts/compare-llama-bench.py

@@ -7,6 +7,10 @@ import sys
 import os
 from glob import glob
 import sqlite3
+import json
+import csv
+from typing import Optional, Union
+from collections.abc import Iterator, Sequence
 
 try:
     import git
@@ -17,6 +21,28 @@ except ImportError as e:
 
 logger = logging.getLogger("compare-llama-bench")
 
+# All llama-bench SQL fields
+DB_FIELDS = [
+    "build_commit", "build_number", "cpu_info",       "gpu_info",   "backends",     "model_filename",
+    "model_type",   "model_size",   "model_n_params", "n_batch",    "n_ubatch",     "n_threads",
+    "cpu_mask",     "cpu_strict",   "poll",           "type_k",     "type_v",       "n_gpu_layers",
+    "split_mode",   "main_gpu",     "no_kv_offload",  "flash_attn", "tensor_split", "tensor_buft_overrides",
+    "defrag_thold",
+    "use_mmap",     "embeddings",   "no_op_offload",  "n_prompt",   "n_gen",        "n_depth",
+    "test_time",    "avg_ns",       "stddev_ns",      "avg_ts",     "stddev_ts",
+]
+
+DB_TYPES = [
+    "TEXT",    "INTEGER", "TEXT",    "TEXT",    "TEXT",    "TEXT",
+    "TEXT",    "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
+    "TEXT",    "INTEGER", "INTEGER", "TEXT",    "TEXT",    "INTEGER",
+    "TEXT",    "INTEGER", "INTEGER", "INTEGER", "TEXT",    "TEXT",
+    "REAL",
+    "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
+    "TEXT",    "INTEGER", "INTEGER", "REAL",    "REAL",
+]
+assert len(DB_FIELDS) == len(DB_TYPES)
+
 # Properties by which to differentiate results per commit:
 KEY_PROPERTIES = [
     "cpu_info", "gpu_info", "backends", "n_gpu_layers", "tensor_buft_overrides", "model_filename", "model_type",
@@ -42,7 +68,7 @@ DEFAULT_HIDE = ["model_filename"]  # Always hide these properties by default.
 GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "]  # Strip prefixes for smaller tables.
 MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"}
 
-DESCRIPTION = """Creates tables from llama-bench data written to an SQLite database. Example usage (Linux):
+DESCRIPTION = """Creates tables from llama-bench data written to multiple JSON/CSV files, a single JSONL file or SQLite database. Example usage (Linux):
 
 $ git checkout master
 $ make clean && make llama-bench
@@ -70,12 +96,13 @@ help_c = (
 )
 parser.add_argument("-c", "--compare", help=help_c)
 help_i = (
-    "Input SQLite file for comparing commits. "
+    "JSON/JSONL/SQLite/CSV files for comparing commits. "
+    "Specify multiple times to use multiple input files (JSON/CSV only). "
     "Defaults to 'llama-bench.sqlite' in the current working directory. "
     "If no such file is found and there is exactly one .sqlite file in the current directory, "
     "that file is instead used as input."
 )
-parser.add_argument("-i", "--input", help=help_i)
+parser.add_argument("-i", "--input", action="append", help=help_i)
 help_o = (
     "Output format for the table. "
     "Defaults to 'pipe' (GitHub compatible). "
@@ -86,7 +113,7 @@ parser.add_argument("-o", "--output", help=help_o, default="pipe")
 help_s = (
     "Columns to add to the table. "
     "Accepts a comma-separated list of values. "
-    f"Legal values: {', '.join(KEY_PROPERTIES[:-2])}. "
+    f"Legal values: {', '.join(KEY_PROPERTIES[:-3])}. "
     "Defaults to model name (model_type) and CPU and/or GPU name (cpu_info, gpu_info) "
     "plus any column where not all data points are the same. "
     "If the columns are manually specified, then the results for each unique combination of the "
@@ -110,119 +137,321 @@ if unknown_args:
     sys.exit(1)
 
 input_file = known_args.input
-if input_file is None and os.path.exists("./llama-bench.sqlite"):
-    input_file = "llama-bench.sqlite"
-if input_file is None:
+if not input_file and os.path.exists("./llama-bench.sqlite"):
+    input_file = ["llama-bench.sqlite"]
+if not input_file:
     sqlite_files = glob("*.sqlite")
     if len(sqlite_files) == 1:
-        input_file = sqlite_files[0]
+        input_file = sqlite_files
 
-if input_file is None:
+if not input_file:
     logger.error("Cannot find a suitable input file, please provide one.\n")
     parser.print_help()
     sys.exit(1)
 
-connection = sqlite3.connect(input_file)
-cursor = connection.cursor()
 
-build_len_min: int = cursor.execute("SELECT MIN(LENGTH(build_commit)) from test;").fetchone()[0]
-build_len_max: int = cursor.execute("SELECT MAX(LENGTH(build_commit)) from test;").fetchone()[0]
+class LlamaBenchData:
+    repo: Optional[git.Repo]
+    build_len_min: int
+    build_len_max: int
+    build_len: int = 8
+    builds: list[str] = []
+    check_keys = set(KEY_PROPERTIES + ["build_commit", "test_time", "avg_ts"])
 
-if build_len_min != build_len_max:
-    logger.warning(f"{input_file} contains commit hashes of differing lengths. It's possible that the wrong commits will be compared. "
-                   "Try purging the the database of old commits.")
-    cursor.execute(f"UPDATE test SET build_commit = SUBSTRING(build_commit, 1, {build_len_min});")
+    def __init__(self):
+        try:
+            self.repo = git.Repo(".", search_parent_directories=True)
+        except git.InvalidGitRepositoryError:
+            self.repo = None
 
-build_len: int = build_len_min
+    def _builds_init(self):
+        self.build_len = self.build_len_min
 
-builds = cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall()
-builds = list(map(lambda b: b[0], builds))  # list[tuple[str]] -> list[str]
-
-if not builds:
-    raise RuntimeError(f"{input_file} does not contain any builds.")
-
-try:
-    repo = git.Repo(".", search_parent_directories=True)
-except git.InvalidGitRepositoryError:
-    repo = None
-
-
-def find_parent_in_data(commit: git.Commit):
-    """Helper function to find the most recent parent measured in number of commits for which there is data."""
-    heap: list[tuple[int, git.Commit]] = [(0, commit)]
-    seen_hexsha8 = set()
-    while heap:
-        depth, current_commit = heapq.heappop(heap)
-        current_hexsha8 = commit.hexsha[:build_len]
-        if current_hexsha8 in builds:
-            return current_hexsha8
-        for parent in commit.parents:
-            parent_hexsha8 = parent.hexsha[:build_len]
-            if parent_hexsha8 not in seen_hexsha8:
-                seen_hexsha8.add(parent_hexsha8)
-                heapq.heappush(heap, (depth + 1, parent))
-    return None
-
-
-def get_all_parent_hexsha8s(commit: git.Commit):
-    """Helper function to recursively get hexsha8 values for all parents of a commit."""
-    unvisited = [commit]
-    visited   = []
-
-    while unvisited:
-        current_commit = unvisited.pop(0)
-        visited.append(current_commit.hexsha[:build_len])
-        for parent in current_commit.parents:
-            if parent.hexsha[:build_len] not in visited:
-                unvisited.append(parent)
-
-    return visited
-
-
-def get_commit_name(hexsha8: str):
-    """Helper function to find a human-readable name for a commit if possible."""
-    if repo is None:
-        return hexsha8
-    for h in repo.heads:
-        if h.commit.hexsha[:build_len] == hexsha8:
-            return h.name
-    for t in repo.tags:
-        if t.commit.hexsha[:build_len] == hexsha8:
-            return t.name
-    return hexsha8
-
-
-def get_commit_hexsha8(name: str):
-    """Helper function to search for a commit given a human-readable name."""
-    if repo is None:
+    def _check_keys(self, keys: set) -> Optional[set]:
+        """Private helper method that checks against required data keys and returns missing ones."""
+        if not keys >= self.check_keys:
+            return self.check_keys - keys
         return None
-    for h in repo.heads:
-        if h.name == name:
-            return h.commit.hexsha[:build_len]
-    for t in repo.tags:
-        if t.name == name:
-            return t.commit.hexsha[:build_len]
-    for c in repo.iter_commits("--all"):
-        if c.hexsha[:build_len] == name[:build_len]:
-            return c.hexsha[:build_len]
-    return None
+
+    def find_parent_in_data(self, commit: git.Commit) -> Optional[str]:
+        """Helper method to find the most recent parent measured in number of commits for which there is data."""
+        heap: list[tuple[int, git.Commit]] = [(0, commit)]
+        seen_hexsha8 = set()
+        while heap:
+            depth, current_commit = heapq.heappop(heap)
+            current_hexsha8 = commit.hexsha[:self.build_len]
+            if current_hexsha8 in self.builds:
+                return current_hexsha8
+            for parent in commit.parents:
+                parent_hexsha8 = parent.hexsha[:self.build_len]
+                if parent_hexsha8 not in seen_hexsha8:
+                    seen_hexsha8.add(parent_hexsha8)
+                    heapq.heappush(heap, (depth + 1, parent))
+        return None
+
+    def get_all_parent_hexsha8s(self, commit: git.Commit) -> Sequence[str]:
+        """Helper method to recursively get hexsha8 values for all parents of a commit."""
+        unvisited = [commit]
+        visited   = []
+
+        while unvisited:
+            current_commit = unvisited.pop(0)
+            visited.append(current_commit.hexsha[:self.build_len])
+            for parent in current_commit.parents:
+                if parent.hexsha[:self.build_len] not in visited:
+                    unvisited.append(parent)
+
+        return visited
+
+    def get_commit_name(self, hexsha8: str) -> str:
+        """Helper method to find a human-readable name for a commit if possible."""
+        if self.repo is None:
+            return hexsha8
+        for h in self.repo.heads:
+            if h.commit.hexsha[:self.build_len] == hexsha8:
+                return h.name
+        for t in self.repo.tags:
+            if t.commit.hexsha[:self.build_len] == hexsha8:
+                return t.name
+        return hexsha8
+
+    def get_commit_hexsha8(self, name: str) -> Optional[str]:
+        """Helper method to search for a commit given a human-readable name."""
+        if self.repo is None:
+            return None
+        for h in self.repo.heads:
+            if h.name == name:
+                return h.commit.hexsha[:self.build_len]
+        for t in self.repo.tags:
+            if t.name == name:
+                return t.commit.hexsha[:self.build_len]
+        for c in self.repo.iter_commits("--all"):
+            if c.hexsha[:self.build_len] == name[:self.build_len]:
+                return c.hexsha[:self.build_len]
+        return None
+
+    def builds_timestamp(self, reverse: bool = False) -> Union[Iterator[tuple], Sequence[tuple]]:
+        """Helper method that gets rows of (build_commit, test_time) sorted by the latter."""
+        return []
+
+    def get_rows(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]:
+        """
+        Helper method that gets table rows for some list of properties.
+        Rows are created by combining those where all provided properties are equal.
+        The resulting rows are then grouped by the provided properties and the t/s values are averaged.
+        The returned rows are unique in terms of property combinations.
+        """
+        return []
+
+
+class LlamaBenchDataSQLite3(LlamaBenchData):
+    connection: sqlite3.Connection
+    cursor: sqlite3.Cursor
+
+    def __init__(self):
+        super().__init__()
+        self.connection = sqlite3.connect(":memory:")
+        self.cursor = self.connection.cursor()
+        self.cursor.execute(f"CREATE TABLE test({', '.join(' '.join(x) for x in zip(DB_FIELDS, DB_TYPES))});")
+
+    def _builds_init(self):
+        if self.connection:
+            self.build_len_min = self.cursor.execute("SELECT MIN(LENGTH(build_commit)) from test;").fetchone()[0]
+            self.build_len_max = self.cursor.execute("SELECT MAX(LENGTH(build_commit)) from test;").fetchone()[0]
+
+            if self.build_len_min != self.build_len_max:
+                logger.warning("Data contains commit hashes of differing lengths. It's possible that the wrong commits will be compared. "
+                               "Try purging the the database of old commits.")
+                self.cursor.execute(f"UPDATE test SET build_commit = SUBSTRING(build_commit, 1, {self.build_len_min});")
+
+            builds = self.cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall()
+            self.builds = list(map(lambda b: b[0], builds))  # list[tuple[str]] -> list[str]
+        super()._builds_init()
+
+    def builds_timestamp(self, reverse: bool = False) -> Union[Iterator[tuple], Sequence[tuple]]:
+        data = self.cursor.execute(
+            "SELECT build_commit, test_time FROM test ORDER BY test_time;").fetchall()
+        return reversed(data) if reverse else data
+
+    def get_rows(self, properties: list[str], hexsha8_baseline: str, hexsha8_compare: str) -> Sequence[tuple]:
+        select_string = ", ".join(
+            [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "tb.n_depth", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"])
+        equal_string = " AND ".join(
+            [f"tb.{p} = tc.{p}" for p in KEY_PROPERTIES] + [
+                f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"]
+        )
+        group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt", "tb.n_depth"])
+        query = (f"SELECT {select_string} FROM test tb JOIN test tc ON {equal_string} "
+                 f"GROUP BY {group_order_string} ORDER BY {group_order_string};")
+        return self.cursor.execute(query).fetchall()
+
+
+class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):
+    def __init__(self, data_file: str):
+        super().__init__()
+
+        self.connection.close()
+        self.connection = sqlite3.connect(data_file)
+        self.cursor = self.connection.cursor()
+        self._builds_init()
+
+    @staticmethod
+    def valid_format(data_file: str) -> bool:
+        connection = sqlite3.connect(data_file)
+        cursor = connection.cursor()
+
+        try:
+            if cursor.execute("PRAGMA schema_version;").fetchone()[0] == 0:
+                raise sqlite3.DatabaseError("The provided input file does not exist or is empty.")
+        except sqlite3.DatabaseError as e:
+            logger.debug(f'"{data_file}" is not a valid SQLite3 file.', exc_info=e)
+            cursor = None
+
+        connection.close()
+        return True if cursor else False
+
+
+class LlamaBenchDataJSONL(LlamaBenchDataSQLite3):
+    def __init__(self, data_file: str):
+        super().__init__()
+
+        with open(data_file, "r", encoding="utf-8") as fp:
+            for i, line in enumerate(fp):
+                parsed = json.loads(line)
+
+                for k in parsed.keys() - set(DB_FIELDS):
+                    del parsed[k]
+
+                if (missing_keys := self._check_keys(parsed.keys())):
+                    raise RuntimeError(f"Missing required data key(s) at line {i + 1}: {', '.join(missing_keys)}")
+
+                self.cursor.execute(f"INSERT INTO test({', '.join(parsed.keys())}) VALUES({', '.join('?' * len(parsed))});", tuple(parsed.values()))
+
+        self._builds_init()
+
+    @staticmethod
+    def valid_format(data_file: str) -> bool:
+        try:
+            with open(data_file, "r", encoding="utf-8") as fp:
+                for line in fp:
+                    json.loads(line)
+                    break
+        except Exception as e:
+            logger.debug(f'"{data_file}" is not a valid JSONL file.', exc_info=e)
+            return False
+
+        return True
+
+
+class LlamaBenchDataJSON(LlamaBenchDataSQLite3):
+    def __init__(self, data_files: list[str]):
+        super().__init__()
+
+        for data_file in data_files:
+            with open(data_file, "r", encoding="utf-8") as fp:
+                parsed = json.load(fp)
+
+                for i, entry in enumerate(parsed):
+                    for k in entry.keys() - set(DB_FIELDS):
+                        del entry[k]
+
+                    if (missing_keys := self._check_keys(entry.keys())):
+                        raise RuntimeError(f"Missing required data key(s) at entry {i + 1}: {', '.join(missing_keys)}")
+
+                    self.cursor.execute(f"INSERT INTO test({', '.join(entry.keys())}) VALUES({', '.join('?' * len(entry))});", tuple(entry.values()))
+
+        self._builds_init()
+
+    @staticmethod
+    def valid_format(data_files: list[str]) -> bool:
+        if not data_files:
+            return False
+
+        for data_file in data_files:
+            try:
+                with open(data_file, "r", encoding="utf-8") as fp:
+                    json.load(fp)
+            except Exception as e:
+                logger.debug(f'"{data_file}" is not a valid JSON file.', exc_info=e)
+                return False
+
+        return True
+
+
+class LlamaBenchDataCSV(LlamaBenchDataSQLite3):
+    def __init__(self, data_files: list[str]):
+        super().__init__()
+
+        for data_file in data_files:
+            with open(data_file, "r", encoding="utf-8") as fp:
+                for i, parsed in enumerate(csv.DictReader(fp)):
+                    keys = set(parsed.keys())
+
+                    for k in keys - set(DB_FIELDS):
+                        del parsed[k]
+
+                    if (missing_keys := self._check_keys(keys)):
+                        raise RuntimeError(f"Missing required data key(s) at line {i + 1}: {', '.join(missing_keys)}")
+
+                    self.cursor.execute(f"INSERT INTO test({', '.join(parsed.keys())}) VALUES({', '.join('?' * len(parsed))});", tuple(parsed.values()))
+
+        self._builds_init()
+
+    @staticmethod
+    def valid_format(data_files: list[str]) -> bool:
+        if not data_files:
+            return False
+
+        for data_file in data_files:
+            try:
+                with open(data_file, "r", encoding="utf-8") as fp:
+                    for parsed in csv.DictReader(fp):
+                        break
+            except Exception as e:
+                logger.debug(f'"{data_file}" is not a valid CSV file.', exc_info=e)
+                return False
+
+        return True
+
+
+bench_data = None
+if len(input_file) == 1:
+    if LlamaBenchDataSQLite3File.valid_format(input_file[0]):
+        bench_data = LlamaBenchDataSQLite3File(input_file[0])
+    elif LlamaBenchDataJSON.valid_format(input_file):
+        bench_data = LlamaBenchDataJSON(input_file)
+    elif LlamaBenchDataJSONL.valid_format(input_file[0]):
+        bench_data = LlamaBenchDataJSONL(input_file[0])
+    elif LlamaBenchDataCSV.valid_format(input_file):
+        bench_data = LlamaBenchDataCSV(input_file)
+else:
+    if LlamaBenchDataJSON.valid_format(input_file):
+        bench_data = LlamaBenchDataJSON(input_file)
+    elif LlamaBenchDataCSV.valid_format(input_file):
+        bench_data = LlamaBenchDataCSV(input_file)
+
+if not bench_data:
+    raise RuntimeError("No valid (or some invalid) input files found.")
+
+if not bench_data.builds:
+    raise RuntimeError(f"{input_file} does not contain any builds.")
 
 
 hexsha8_baseline = name_baseline = None
 
 # If the user specified a baseline, try to find a commit for it:
 if known_args.baseline is not None:
-    if known_args.baseline in builds:
+    if known_args.baseline in bench_data.builds:
         hexsha8_baseline = known_args.baseline
     if hexsha8_baseline is None:
-        hexsha8_baseline = get_commit_hexsha8(known_args.baseline)
+        hexsha8_baseline = bench_data.get_commit_hexsha8(known_args.baseline)
         name_baseline = known_args.baseline
     if hexsha8_baseline is None:
         logger.error(f"cannot find data for baseline={known_args.baseline}.")
         sys.exit(1)
 # Otherwise, search for the most recent parent of master for which there is data:
-elif repo is not None:
-    hexsha8_baseline = find_parent_in_data(repo.heads.master.commit)
+elif bench_data.repo is not None:
+    hexsha8_baseline = bench_data.find_parent_in_data(bench_data.repo.heads.master.commit)
 
     if hexsha8_baseline is None:
         logger.error("No baseline was provided and did not find data for any master branch commits.\n")
@@ -235,27 +464,25 @@ else:
     sys.exit(1)
 
 
-name_baseline = get_commit_name(hexsha8_baseline)
+name_baseline = bench_data.get_commit_name(hexsha8_baseline)
 
 hexsha8_compare = name_compare = None
 
 # If the user has specified a compare value, try to find a corresponding commit:
 if known_args.compare is not None:
-    if known_args.compare in builds:
+    if known_args.compare in bench_data.builds:
         hexsha8_compare = known_args.compare
     if hexsha8_compare is None:
-        hexsha8_compare = get_commit_hexsha8(known_args.compare)
+        hexsha8_compare = bench_data.get_commit_hexsha8(known_args.compare)
         name_compare = known_args.compare
     if hexsha8_compare is None:
         logger.error(f"cannot find data for compare={known_args.compare}.")
         sys.exit(1)
 # Otherwise, search for the commit for llama-bench was most recently run
 # and that is not a parent of master:
-elif repo is not None:
-    hexsha8s_master = get_all_parent_hexsha8s(repo.heads.master.commit)
-    builds_timestamp = cursor.execute(
-        "SELECT build_commit, test_time FROM test ORDER BY test_time;").fetchall()
-    for (hexsha8, _) in reversed(builds_timestamp):
+elif bench_data.repo is not None:
+    hexsha8s_master = bench_data.get_all_parent_hexsha8s(bench_data.repo.heads.master.commit)
+    for (hexsha8, _) in bench_data.builds_timestamp(reverse=True):
         if hexsha8 not in hexsha8s_master:
             hexsha8_compare = hexsha8
             break
@@ -270,26 +497,7 @@ else:
     parser.print_help()
     sys.exit(1)
 
-name_compare = get_commit_name(hexsha8_compare)
-
-
-def get_rows(properties):
-    """
-    Helper function that gets table rows for some list of properties.
-    Rows are created by combining those where all provided properties are equal.
-    The resulting rows are then grouped by the provided properties and the t/s values are averaged.
-    The returned rows are unique in terms of property combinations.
-    """
-    select_string = ", ".join(
-        [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "tb.n_depth", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"])
-    equal_string = " AND ".join(
-        [f"tb.{p} = tc.{p}" for p in KEY_PROPERTIES] + [
-            f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"]
-    )
-    group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt", "tb.n_depth"])
-    query = (f"SELECT {select_string} FROM test tb JOIN test tc ON {equal_string} "
-             f"GROUP BY {group_order_string} ORDER BY {group_order_string};")
-    return cursor.execute(query).fetchall()
+name_compare = bench_data.get_commit_name(hexsha8_compare)
 
 
 # If the user provided columns to group the results by, use them:
@@ -297,16 +505,16 @@ if known_args.show is not None:
     show = known_args.show.split(",")
     unknown_cols = []
     for prop in show:
-        if prop not in KEY_PROPERTIES[:-2]:  # Last two values are n_prompt, n_gen.
+        if prop not in KEY_PROPERTIES[:-3]:  # Last three values are n_prompt, n_gen, n_depth.
             unknown_cols.append(prop)
     if unknown_cols:
         logger.error(f"Unknown values for --show: {', '.join(unknown_cols)}")
         parser.print_usage()
         sys.exit(1)
-    rows_show = get_rows(show)
+    rows_show = bench_data.get_rows(show, hexsha8_baseline, hexsha8_compare)
 # Otherwise, select those columns where the values are not all the same:
 else:
-    rows_full = get_rows(KEY_PROPERTIES)
+    rows_full = bench_data.get_rows(KEY_PROPERTIES, hexsha8_baseline, hexsha8_compare)
     properties_different = []
     for i, kp_i in enumerate(KEY_PROPERTIES):
         if kp_i in DEFAULT_SHOW or kp_i in ["n_prompt", "n_gen", "n_depth"]:
@@ -336,7 +544,7 @@ else:
             show.remove(prop)
         except ValueError:
             pass
-    rows_show = get_rows(show)
+    rows_show = bench_data.get_rows(show, hexsha8_baseline, hexsha8_compare)
 
 if not rows_show:
     logger.error(f"No comparable data was found between {name_baseline} and {name_compare}.\n")

scripts/sync-ggml.last

@@ -1 +1 @@
-b59bddafe278877dfa22a80e53a637513862babb
+9b048bb72b811f50b0c30d9e5c84d6ff9f4bf005

src/CMakeLists.txt

@@ -23,6 +23,7 @@ add_library(llama
             llama-memory.cpp
             llama-mmap.cpp
             llama-model-loader.cpp
+            llama-model-saver.cpp
             llama-model.cpp
             llama-quant.cpp
             llama-sampling.cpp

src/llama-arch.cpp

@@ -1481,6 +1481,9 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
             { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
             { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
         },
     },
     {

src/llama-context.cpp

@@ -359,7 +359,9 @@ llama_context::llama_context(
     }
 }
 
-llama_context::~llama_context() = default;
+llama_context::~llama_context() {
+    ggml_opt_free(opt_ctx);
+}
 
 void llama_context::synchronize() {
     ggml_backend_sched_synchronize(sched.get());
@@ -1846,6 +1848,215 @@ void llama_context::perf_reset() {
     t_p_eval_us = n_p_eval = 0;
 }
 
+//
+// training
+//
+
+static void llama_set_param(struct ggml_tensor * tensor, llama_opt_param_filter param_filter, void * userdata) {
+    if (!tensor || tensor->type != GGML_TYPE_F32) {
+        return;
+    }
+    if (!param_filter(tensor, userdata)) {
+        return;
+    }
+    if (strcmp(tensor->name, "token_embd.weight") == 0) {
+        return; // FIXME
+    }
+    if (strcmp(tensor->name, "rope_freqs.weight") == 0) {
+        return; // FIXME
+    }
+    ggml_set_param(tensor);
+}
+
+void llama_context::opt_init(struct llama_model * model, struct llama_opt_params lopt_params) {
+    GGML_ASSERT(!opt_ctx);
+    model->hparams.n_ctx_train = lopt_params.n_ctx_train > 0 ? lopt_params.n_ctx_train : n_ctx();
+    const uint32_t n_batch     = std::min(this->n_batch(),  model->hparams.n_ctx_train);
+    const uint32_t n_ubatch    = std::min(this->n_ubatch(), n_batch);
+    GGML_ASSERT(model->hparams.n_ctx_train % n_batch  == 0);
+    GGML_ASSERT(n_batch                    % n_ubatch == 0);
+
+    ggml_opt_params opt_params = ggml_opt_default_params(sched.get(), GGML_OPT_LOSS_TYPE_CROSS_ENTROPY);
+    opt_params.opt_period      = n_batch / n_ubatch;
+    opt_params.get_opt_pars    = lopt_params.get_opt_pars;
+    opt_params.get_opt_pars_ud = lopt_params.get_opt_pars_ud;
+
+    opt_ctx = ggml_opt_init(opt_params);
+
+    llama_opt_param_filter param_filter = lopt_params.param_filter;
+    void * param_filter_ud              = lopt_params.param_filter_ud;
+
+  //llama_set_param(model->tok_embd,        param_filter, param_filter_ud); // FIXME
+    llama_set_param(model->type_embd,       param_filter, param_filter_ud);
+    llama_set_param(model->pos_embd,        param_filter, param_filter_ud);
+    llama_set_param(model->tok_norm,        param_filter, param_filter_ud);
+    llama_set_param(model->tok_norm_b,      param_filter, param_filter_ud);
+    llama_set_param(model->output_norm,     param_filter, param_filter_ud);
+    llama_set_param(model->output_norm_b,   param_filter, param_filter_ud);
+    llama_set_param(model->output,          param_filter, param_filter_ud);
+    llama_set_param(model->output_b,        param_filter, param_filter_ud);
+    llama_set_param(model->output_norm_enc, param_filter, param_filter_ud);
+    llama_set_param(model->cls,             param_filter, param_filter_ud);
+    llama_set_param(model->cls_b,           param_filter, param_filter_ud);
+    llama_set_param(model->cls_out,         param_filter, param_filter_ud);
+    llama_set_param(model->cls_out_b,       param_filter, param_filter_ud);
+
+    for (struct llama_layer & layer : model->layers) {
+        for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {
+            llama_set_param(reinterpret_cast<struct ggml_tensor **>(&layer)[i], param_filter, param_filter_ud);
+        }
+    }
+}
+
+void llama_context::opt_epoch_iter(
+        ggml_opt_dataset_t               dataset,
+        ggml_opt_result_t                result,
+        const std::vector<llama_token> & tokens,
+        const std::vector<llama_token> & labels_sparse,
+        llama_batch                    & batch,
+        ggml_opt_epoch_callback          callback,
+        bool                             train,
+        int64_t                          idata_in_loop,
+        int64_t                          ndata_in_loop,
+        int64_t                          t_loop_start) {
+    GGML_ASSERT(opt_ctx);
+    const uint32_t n_ctx    = llama_model_n_ctx_train(&model);
+    const uint32_t n_batch  = std::min(this->n_batch(),  n_ctx);
+    const uint32_t n_ubatch = std::min(this->n_ubatch(), n_batch);
+
+    llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+
+    kv_self->clear();
+    llama_kv_cache_guard kv_guard(kv_self);
+
+    for (uint32_t pos_ctx = 0; pos_ctx < n_ctx; pos_ctx += n_batch) {
+        batch.n_tokens = n_batch;
+        for (uint32_t pos_batch = 0; pos_batch < n_batch; ++pos_batch) {
+            batch.token   [pos_batch]    = tokens[pos_ctx + pos_batch];
+            batch.pos     [pos_batch]    = pos_ctx + pos_batch;
+            batch.n_seq_id[pos_batch]    = 1;
+            batch.seq_id  [pos_batch][0] = 0;
+            batch.logits  [pos_batch]    = true;
+        }
+
+        const auto n_tokens_all = batch.n_tokens;
+
+        n_queued_tokens += n_tokens_all;
+
+        // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
+        const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
+
+        embd_seq.clear();
+
+        int64_t n_outputs_all = n_tokens_all;
+
+        llama_sbatch sbatch = kv_self->sbatch_init(batch, /*logits_all =*/ true);
+
+        // reserve output buffer
+        if (output_reserve(n_outputs_all) < n_outputs_all) {
+            LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
+            GGML_ABORT("TODO: handle this error");
+        };
+
+        for (uint32_t pos_batch = 0; pos_batch < n_batch; pos_batch += n_ubatch) {
+            llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled);
+
+            n_outputs = ubatch.n_tokens;
+
+            // TODO: not sure if this is needed
+            if (!kv_self->find_slot(ubatch)) {
+                LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens);
+
+                GGML_ABORT("TODO: handle this error");
+            }
+
+            auto * gf = graph_init();
+            auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT);
+
+            struct ggml_context * ctx_compute_opt;
+            {
+                const size_t size_gf = ggml_graph_size(gf);
+                const size_t size_meta = 4*size_gf*ggml_tensor_overhead() + 2*ggml_graph_overhead_custom(size_gf, /*grads = */ true);
+                struct ggml_init_params params = {
+                    /*.mem_size   =*/ size_meta,
+                    /*.mem_buffer =*/ nullptr,
+                    /*.no_alloc   =*/ true,
+                };
+                ctx_compute_opt = ggml_init(params);
+            }
+            ggml_opt_prepare_alloc(opt_ctx, ctx_compute_opt, gf, res->get_tokens(), res->get_logits());
+            ggml_opt_alloc(opt_ctx, train);
+            res->set_inputs(&ubatch);
+            {
+                struct ggml_tensor * labels = ggml_opt_labels(opt_ctx);
+                GGML_ASSERT(labels->ne[1] == n_ubatch);
+                ggml_set_zero(labels);
+                const float onef = 1.0f;
+                for (uint32_t pos_ubatch = 0; pos_ubatch < n_ubatch; ++pos_ubatch) {
+                    const uint32_t ilabel = pos_ctx + pos_batch + pos_ubatch;
+                    GGML_ASSERT(labels_sparse[ilabel] < labels->ne[0]);
+                    ggml_backend_tensor_set(labels, &onef, (pos_ubatch*labels->ne[0] + labels_sparse[ilabel])*sizeof(float), sizeof(float));
+                }
+            }
+            ggml_opt_eval(opt_ctx, result);
+            if (callback) {
+                callback(train, opt_ctx, dataset, result, idata_in_loop + (pos_ctx + pos_batch)/n_ubatch + 1, ndata_in_loop, t_loop_start);
+            }
+            ggml_free(ctx_compute_opt);
+        }
+    }
+
+    kv_guard.commit();
+}
+
+void llama_context::opt_epoch(
+        ggml_opt_dataset_t        dataset,
+        ggml_opt_result_t         result_train,
+        ggml_opt_result_t         result_eval,
+        int64_t                   idata_split,
+        ggml_opt_epoch_callback   callback_train,
+        ggml_opt_epoch_callback   callback_eval) {
+    const uint32_t n_ctx    = this->n_ctx();
+    const uint32_t n_batch  = std::min(cparams.n_batch,  n_ctx);
+    const uint32_t n_ubatch = std::min(cparams.n_ubatch, n_batch);
+    const  int64_t ndata    = ggml_opt_dataset_ndata(dataset);
+
+    GGML_ASSERT(idata_split >= 0);
+    GGML_ASSERT(idata_split <= ndata);
+
+    const uint32_t ubatch_per_ctx = n_ctx / n_ubatch;
+
+    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+    std::vector<llama_token>        tokens(n_ctx);
+    std::vector<llama_token> labels_sparse(n_ctx);
+
+    int64_t idata = 0;
+
+    int64_t t_loop_start = ggml_time_us();
+    int64_t ndata_in_loop = idata_split*ubatch_per_ctx;
+    for (; idata < idata_split; ++idata) {
+        constexpr bool train = true;
+        const int64_t idata_in_loop = idata*ubatch_per_ctx;
+
+        ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata);
+        opt_epoch_iter(dataset, result_train, tokens, labels_sparse, batch,
+            callback_train, train, idata_in_loop, ndata_in_loop, t_loop_start);
+    }
+
+    t_loop_start = ggml_time_us();
+    ndata_in_loop = (ndata - idata_split)*ubatch_per_ctx;
+    for (; idata < ndata; ++idata) {
+        constexpr bool train = false;
+        const int64_t idata_in_loop = (idata - idata_split)*ubatch_per_ctx;
+
+        ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata);
+        opt_epoch_iter(dataset, result_eval, tokens, labels_sparse, batch,
+            callback_eval, train, idata_in_loop, ndata_in_loop, t_loop_start);
+    }
+
+    llama_batch_free(batch);
+}
+
 //
 // interface implementation
 //
@@ -2464,3 +2675,34 @@ void llama_perf_context_print(const llama_context * ctx) {
 void llama_perf_context_reset(llama_context * ctx) {
     ctx->perf_reset();
 }
+
+//
+// training
+//
+
+bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata) {
+    GGML_UNUSED(tensor);
+    GGML_UNUSED(userdata);
+    return true;
+}
+
+void llama_opt_init(struct llama_context * ctx, struct llama_model * model, struct llama_opt_params lopt_params) {
+    ctx->opt_init(model, lopt_params);
+}
+
+void llama_opt_epoch(
+        struct llama_context    * ctx,
+        ggml_opt_dataset_t        dataset,
+        ggml_opt_result_t         result_train,
+        ggml_opt_result_t         result_eval,
+        int64_t                   idata_split,
+        ggml_opt_epoch_callback   callback_train,
+        ggml_opt_epoch_callback   callback_eval) {
+    ctx->opt_epoch(
+        dataset,
+        result_train,
+        result_eval,
+        idata_split,
+        callback_train,
+        callback_eval);
+}

src/llama-context.h

@@ -7,6 +7,7 @@
 #include "llama-adapter.h"
 
 #include "ggml-cpp.h"
+#include "ggml-opt.h"
 
 #include <map>
 #include <vector>
@@ -133,6 +134,32 @@ struct llama_context {
     llama_perf_context_data perf_get_data() const;
     void perf_reset();
 
+    //
+    // training
+    //
+
+    void opt_init(struct llama_model * model, struct llama_opt_params lopt_params);
+
+    void opt_epoch(
+            ggml_opt_dataset_t      dataset,
+            ggml_opt_result_t       result_train,
+            ggml_opt_result_t       result_eval,
+            int64_t                 idata_split,
+            ggml_opt_epoch_callback callback_train,
+            ggml_opt_epoch_callback callback_eval);
+
+    void opt_epoch_iter(
+            ggml_opt_dataset_t               dataset,
+            ggml_opt_result_t                result,
+            const std::vector<llama_token> & tokens,
+            const std::vector<llama_token> & labels_sparse,
+            llama_batch                    & batch,
+            ggml_opt_epoch_callback          callback,
+            bool                             train,
+            int64_t                          idata_in_loop,
+            int64_t                          ndata_in_loop,
+            int64_t                          t_loop_start);
+
 private:
     //
     // output
@@ -212,6 +239,9 @@ private:
 
     ggml_context_ptr ctx_compute;
 
+    // training
+    ggml_opt_context_t opt_ctx = nullptr;
+
     ggml_threadpool_t threadpool       = nullptr;
     ggml_threadpool_t threadpool_batch = nullptr;
 

src/llama-graph.cpp

@@ -971,6 +971,7 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
         inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
         //cb(inp->tokens, "inp_tokens", -1);
         ggml_set_input(inp->tokens);
+        res->t_tokens = inp->tokens;
 
         cur = ggml_get_rows(ctx0, tok_embd, inp->tokens);
 

src/llama-graph.h

@@ -298,6 +298,7 @@ class llm_graph_result_i {
 public:
     virtual ~llm_graph_result_i() = default;
 
+    virtual ggml_tensor * get_tokens()      = 0;
     virtual ggml_tensor * get_logits()      = 0;
     virtual ggml_tensor * get_embd()        = 0;
     virtual ggml_tensor * get_embd_pooled() = 0;
@@ -312,6 +313,7 @@ class llm_graph_result : public llm_graph_result_i {
 public:
     virtual ~llm_graph_result() = default;
 
+    ggml_tensor * get_tokens()      override { return t_tokens; }
     ggml_tensor * get_logits()      override { return t_logits; }
     ggml_tensor * get_embd()        override { return t_embd; }
     ggml_tensor * get_embd_pooled() override { return t_embd_pooled; }
@@ -328,6 +330,7 @@ public:
     }
 
     // important graph nodes
+    ggml_tensor * t_tokens      = nullptr;
     ggml_tensor * t_logits      = nullptr;
     ggml_tensor * t_embd        = nullptr;
     ggml_tensor * t_embd_pooled = nullptr;

src/llama-model-loader.cpp

@@ -301,12 +301,12 @@ namespace GGUFMeta {
             GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
 
         switch (arr_info.gt) {
-            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
-            case GGUF_TYPE_INT32:   GGML_ASSERT(
-                                            (std::is_same<T,  int32_t>::value) ||
-                                            (std::is_same<T, uint32_t>::value));  break;
+            case GGUF_TYPE_UINT32:
+            case GGUF_TYPE_INT32:   GGML_ASSERT((std::is_same<T,  int32_t>::value) ||
+                                                (std::is_same<T, uint32_t>::value)); break;
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T,    float>::value)); break;
             default:
-                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+                throw std::runtime_error(format("%s is not a float32/uint32/int32 array", key.c_str()));
         }
 
         result.resize(arr_info.length);
@@ -330,12 +330,12 @@ namespace GGUFMeta {
             GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
 
         switch (arr_info.gt) {
-            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
-            case GGUF_TYPE_INT32:   GGML_ASSERT(
-                                            (std::is_same<T,  int32_t>::value) ||
-                                            (std::is_same<T, uint32_t>::value));  break;
+            case GGUF_TYPE_UINT32:
+            case GGUF_TYPE_INT32:   GGML_ASSERT((std::is_same<T,  int32_t>::value) ||
+                                                (std::is_same<T, uint32_t>::value)); break;
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T,    float>::value)); break;
             default:
-                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+                throw std::runtime_error(format("%s is not a float32/uint32/int32 array", key.c_str()));
         }
 
         if (arr_info.length > N_MAX) {

src/llama-model-saver.cpp

@@ -0,0 +1,281 @@
+#include "llama-model-saver.h"
+
+#include "gguf.h"
+
+#include "llama.h"
+#include "llama-hparams.h"
+#include "llama-model.h"
+#include "llama-vocab.h"
+
+#include <string>
+
+llama_model_saver::llama_model_saver(const struct llama_model & model) : model(model), llm_kv(model.arch) {
+    gguf_ctx = gguf_init_empty();
+}
+
+llama_model_saver::~llama_model_saver() {
+    gguf_free(gguf_ctx);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const uint32_t value) {
+    gguf_set_val_u32(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const int32_t value) {
+    gguf_set_val_i32(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const float value) {
+    gguf_set_val_f32(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const bool value) {
+    gguf_set_val_bool(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const char * value) {
+    gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+[[noreturn]]
+void llama_model_saver::add_kv(const enum llm_kv key, const char value) {
+    GGML_UNUSED(key);
+    GGML_UNUSED(value);
+    GGML_ABORT("fatal error"); // this should never be called, only needed to make the template below compile
+}
+
+template <typename Container>
+void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, const bool per_layer) {
+    const size_t n_values = per_layer ? size_t(model.hparams.n_layer) : value.size();
+    GGML_ASSERT(n_values <= value.size());
+
+    if (n_values == 0) {
+        return;
+    }
+
+    if (per_layer) {
+        bool all_values_the_same = true;
+        for (size_t i = 1; i < n_values; ++i) {
+            if (value[i] != value[0]) {
+                all_values_the_same = false;
+                break;
+            }
+        }
+        if (all_values_the_same) {
+            add_kv(key, value[0]);
+            return;
+        }
+    }
+
+    if (std::is_same<typename Container::value_type, uint8_t>::value) {
+        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT8, value.data(), n_values);
+    } else if (std::is_same<typename Container::value_type, int8_t>::value) {
+        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT8, value.data(), n_values);
+    } else if (std::is_same<typename Container::value_type, uint32_t>::value) {
+        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT32, value.data(), n_values);
+    } else if (std::is_same<typename Container::value_type, int32_t>::value) {
+        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT32, value.data(), n_values);
+    } else if (std::is_same<typename Container::value_type, float>::value) {
+        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_FLOAT32, value.data(), n_values);
+    } else if (std::is_same<Container, std::string>::value) {
+        gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), reinterpret_cast<const char *>(value.data()));
+    } else {
+        GGML_ABORT("fatal error");
+    }
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const std::vector<std::string> & value) {
+    std::vector<const char *> tmp(value.size());
+    for (size_t i = 0; i < value.size(); ++i) {
+        tmp[i] = value[i].c_str();
+    }
+    gguf_set_arr_str(gguf_ctx, llm_kv(key).c_str(), tmp.data(), tmp.size());
+}
+
+void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) {
+    if (!tensor) {
+        return;
+    }
+    if (gguf_find_tensor(gguf_ctx, tensor->name) >= 0) {
+        GGML_ASSERT(std::string(tensor->name) == "rope_freqs.weight"); // FIXME
+        return;
+    }
+    gguf_add_tensor(gguf_ctx, tensor);
+}
+
+void llama_model_saver::add_kv_from_model() {
+    const llama_hparams & hparams = model.hparams;
+    const llama_vocab   & vocab   = model.vocab;
+
+    const int32_t n_vocab = vocab.n_tokens();
+    std::vector<std::string> tokens(n_vocab);
+    std::vector<float>       scores(n_vocab);
+    std::vector<int32_t>     token_types(n_vocab);
+
+    for (int32_t id = 0; id < n_vocab; ++id) {
+        const llama_vocab::token_data & token_data = vocab.get_token_data(id);
+
+        tokens[id] = token_data.text;
+        scores[id] = token_data.score;
+
+        switch(token_data.attr) {
+            case LLAMA_TOKEN_ATTR_UNKNOWN:      token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN;      break;
+            case LLAMA_TOKEN_ATTR_UNUSED:       token_types[id] = LLAMA_TOKEN_TYPE_UNUSED;       break;
+            case LLAMA_TOKEN_ATTR_NORMAL:       token_types[id] = LLAMA_TOKEN_TYPE_NORMAL;       break;
+            case LLAMA_TOKEN_ATTR_CONTROL:      token_types[id] = LLAMA_TOKEN_TYPE_CONTROL;      break;
+            case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break;
+            case LLAMA_TOKEN_ATTR_BYTE:         token_types[id] = LLAMA_TOKEN_TYPE_BYTE;         break;
+            case LLAMA_TOKEN_ATTR_UNDEFINED:
+            default:                            token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED;    break;
+        }
+    }
+
+    // add_kv(LLM_KV_GENERAL_TYPE,                      ???);
+    add_kv(LLM_KV_GENERAL_ARCHITECTURE,              model.arch_name());
+    // add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION,      ???);
+    // add_kv(LLM_KV_GENERAL_ALIGNMENT,                 ???);
+    add_kv(LLM_KV_GENERAL_NAME,                      model.name);
+    // add_kv(LLM_KV_GENERAL_AUTHOR,                    ???);
+    // add_kv(LLM_KV_GENERAL_VERSION,                   ???);
+    // add_kv(LLM_KV_GENERAL_URL,                       ???);
+    // add_kv(LLM_KV_GENERAL_DESCRIPTION,               ???);
+    // add_kv(LLM_KV_GENERAL_LICENSE,                   ???);
+    // add_kv(LLM_KV_GENERAL_SOURCE_URL,                ???);
+    // add_kv(LLM_KV_GENERAL_SOURCE_HF_REPO,            ???);
+
+    add_kv(LLM_KV_VOCAB_SIZE,                        vocab.n_tokens());
+    add_kv(LLM_KV_CONTEXT_LENGTH,                    hparams.n_ctx_train);
+    add_kv(LLM_KV_EMBEDDING_LENGTH,                  hparams.n_embd);
+    add_kv(LLM_KV_BLOCK_COUNT,                       hparams.n_layer);
+    add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead);
+    add_kv(LLM_KV_FEED_FORWARD_LENGTH,               hparams.n_ff_arr, true);
+    add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
+    add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+    add_kv(LLM_KV_USE_PARALLEL_RESIDUAL,             hparams.use_par_res);
+    // add_kv(LLM_KV_TENSOR_DATA_LAYOUT,                ???);
+    add_kv(LLM_KV_EXPERT_COUNT,                      hparams.n_expert);
+    add_kv(LLM_KV_EXPERT_USED_COUNT,                 hparams.n_expert_used);
+    add_kv(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);
+    add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale);
+    add_kv(LLM_KV_POOLING_TYPE,                      uint32_t(hparams.pooling_type));
+    add_kv(LLM_KV_LOGIT_SCALE,                       hparams.f_logit_scale);
+    add_kv(LLM_KV_DECODER_START_TOKEN_ID,            hparams.dec_start_token_id);
+    add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING,            hparams.f_attn_logit_softcapping);
+    add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING,           hparams.f_final_logit_softcapping);
+    add_kv(LLM_KV_SWIN_NORM,                         hparams.swin_norm);
+    add_kv(LLM_KV_RESCALE_EVERY_N_LAYERS,            hparams.rescale_every_n_layers);
+    add_kv(LLM_KV_TIME_MIX_EXTRA_DIM,                hparams.time_mix_extra_dim);
+    add_kv(LLM_KV_TIME_DECAY_EXTRA_DIM,              hparams.time_decay_extra_dim);
+    add_kv(LLM_KV_RESIDUAL_SCALE,                    hparams.f_residual_scale);
+    add_kv(LLM_KV_EMBEDDING_SCALE,                   hparams.f_embedding_scale);
+
+    add_kv(LLM_KV_ATTENTION_HEAD_COUNT,              hparams.n_head_arr, true);
+    add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV,           hparams.n_head_kv_arr, true);
+    add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS,          hparams.f_max_alibi_bias);
+    add_kv(LLM_KV_ATTENTION_CLAMP_KQV,               hparams.f_clamp_kqv);
+    add_kv(LLM_KV_ATTENTION_KEY_LENGTH,              hparams.n_embd_head_k);
+    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,            hparams.n_embd_head_v);
+    add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS,           hparams.f_norm_eps);
+    add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+    add_kv(LLM_KV_ATTENTION_CAUSAL,                  hparams.causal_attn);
+    add_kv(LLM_KV_ATTENTION_Q_LORA_RANK,             hparams.n_lora_q);
+    add_kv(LLM_KV_ATTENTION_KV_LORA_RANK,            hparams.n_lora_kv);
+    add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,  hparams.n_rel_attn_bkts);
+    add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW,          hparams.n_swa);
+    add_kv(LLM_KV_ATTENTION_SCALE,                   hparams.f_attention_scale);
+
+    const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
+
+    add_kv(LLM_KV_ROPE_DIMENSION_COUNT,              hparams.n_rot);
+    add_kv(LLM_KV_ROPE_FREQ_BASE,                    hparams.rope_freq_base_train);
+    // add_kv(LLM_KV_ROPE_SCALE_LINEAR,                 rope_scaling_factor); // old name
+    add_kv(LLM_KV_ROPE_SCALING_TYPE,                 llama_rope_scaling_type_name(hparams.rope_scaling_type_train));
+    add_kv(LLM_KV_ROPE_SCALING_FACTOR,               rope_scaling_factor);
+    add_kv(LLM_KV_ROPE_SCALING_ATTN_FACTOR,          hparams.rope_attn_factor);
+    add_kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,         hparams.n_ctx_orig_yarn);
+    add_kv(LLM_KV_ROPE_SCALING_FINETUNED,            hparams.rope_finetuned);
+    add_kv(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,         hparams.rope_yarn_log_mul);
+
+    // TODO: implement split file support
+    // add_kv(LLM_KV_SPLIT_NO,                          ???);
+    // add_kv(LLM_KV_SPLIT_COUNT,                       ???);
+    // add_kv(LLM_KV_SPLIT_TENSORS_COUNT,               ???);
+
+    add_kv(LLM_KV_SSM_INNER_SIZE,                    hparams.ssm_d_inner);
+    add_kv(LLM_KV_SSM_CONV_KERNEL,                   hparams.ssm_d_conv);
+    add_kv(LLM_KV_SSM_STATE_SIZE,                    hparams.ssm_d_state);
+    add_kv(LLM_KV_SSM_TIME_STEP_RANK,                hparams.ssm_dt_rank);
+    add_kv(LLM_KV_SSM_DT_B_C_RMS,                    hparams.ssm_dt_b_c_rms);
+
+    add_kv(LLM_KV_WKV_HEAD_SIZE,                     hparams.wkv_head_size);
+
+    add_kv(LLM_KV_TOKENIZER_MODEL,                   vocab.get_tokenizer_model());
+    add_kv(LLM_KV_TOKENIZER_PRE,                     vocab.get_tokenizer_pre());
+    add_kv(LLM_KV_TOKENIZER_LIST,                    tokens);
+    add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE,              token_types);
+    add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,        vocab.n_token_types());
+    add_kv(LLM_KV_TOKENIZER_SCORES,                  scores);
+    add_kv(LLM_KV_TOKENIZER_MERGES,                  vocab.get_bpe_merges());
+    // FIXME llama_token is type i32 but when reading in a GGUF file u32 is expected, not an issue for writing though
+    add_kv(LLM_KV_TOKENIZER_BOS_ID,                  uint32_t(vocab.token_bos()));
+    add_kv(LLM_KV_TOKENIZER_EOS_ID,                  uint32_t(vocab.token_eos()));
+    add_kv(LLM_KV_TOKENIZER_EOT_ID,                  uint32_t(vocab.token_eot()));
+    add_kv(LLM_KV_TOKENIZER_EOM_ID,                  uint32_t(vocab.token_eom()));
+    add_kv(LLM_KV_TOKENIZER_UNK_ID,                  uint32_t(vocab.token_unk()));
+    add_kv(LLM_KV_TOKENIZER_SEP_ID,                  uint32_t(vocab.token_sep()));
+    add_kv(LLM_KV_TOKENIZER_PAD_ID,                  uint32_t(vocab.token_pad()));
+    // add_kv(LLM_KV_TOKENIZER_CLS_ID,                  uint32_t(vocab.token_bos())); // deprecated
+    // add_kv(LLM_KV_TOKENIZER_MASK_ID,                 ???);
+    add_kv(LLM_KV_TOKENIZER_ADD_BOS,                 vocab.get_add_bos());
+    add_kv(LLM_KV_TOKENIZER_ADD_EOS,                 vocab.get_add_eos());
+    add_kv(LLM_KV_TOKENIZER_ADD_PREFIX,              vocab.get_add_space_prefix());
+    add_kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,         vocab.get_remove_extra_whitespaces());
+    add_kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,    vocab.get_precompiled_charsmap());
+    // add_kv(LLM_KV_TOKENIZER_HF_JSON,                 ???);
+    // add_kv(LLM_KV_TOKENIZER_RWKV,                    ???);
+    add_kv(LLM_KV_TOKENIZER_FIM_PRE_ID,              uint32_t(vocab.token_fim_pre()));
+    add_kv(LLM_KV_TOKENIZER_FIM_SUF_ID,              uint32_t(vocab.token_fim_suf()));
+    add_kv(LLM_KV_TOKENIZER_FIM_MID_ID,              uint32_t(vocab.token_fim_mid()));
+    add_kv(LLM_KV_TOKENIZER_FIM_PAD_ID,              uint32_t(vocab.token_fim_pad()));
+    add_kv(LLM_KV_TOKENIZER_FIM_REP_ID,              uint32_t(vocab.token_fim_rep()));
+    add_kv(LLM_KV_TOKENIZER_FIM_SEP_ID,              uint32_t(vocab.token_fim_sep()));
+
+    // TODO: implement LoRA support
+    // add_kv(LLM_KV_ADAPTER_TYPE,                      ???);
+    // add_kv(LLM_KV_ADAPTER_LORA_ALPHA,                ???);
+
+    // deprecated
+    // add_kv(LLM_KV_TOKENIZER_PREFIX_ID,               ???);
+    // add_kv(LLM_KV_TOKENIZER_SUFFIX_ID,               ???);
+    // add_kv(LLM_KV_TOKENIZER_MIDDLE_ID,               ???);
+}
+
+void llama_model_saver::add_tensors_from_model() {
+    if (std::string(model.output->name) != std::string(model.tok_embd->name)) {
+        add_tensor(model.tok_embd); // some models use the same tensor for tok_embd and output
+    }
+    add_tensor(model.type_embd);
+    add_tensor(model.pos_embd);
+    add_tensor(model.tok_norm);
+    add_tensor(model.tok_norm_b);
+    add_tensor(model.output_norm);
+    add_tensor(model.output_norm_b);
+    add_tensor(model.output);
+    add_tensor(model.output_b);
+    add_tensor(model.output_norm_enc);
+    add_tensor(model.cls);
+    add_tensor(model.cls_b);
+    add_tensor(model.cls_out);
+    add_tensor(model.cls_out_b);
+
+    for (const struct llama_layer & layer : model.layers) {
+        for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {
+            add_tensor(reinterpret_cast<const struct ggml_tensor * const *>(&layer)[i]);
+        }
+    }
+}
+
+void llama_model_saver::save(const std::string & path_model) {
+    gguf_write_to_file(gguf_ctx, path_model.c_str(), false);
+}
+

src/llama-model-saver.h

@@ -0,0 +1,37 @@
+#pragma once
+
+#include "llama.h"
+#include "llama-arch.h"
+
+#include <vector>
+
+struct llama_model_saver {
+    struct gguf_context * gguf_ctx = nullptr;
+    const struct llama_model & model;
+    const struct LLM_KV llm_kv;
+
+    llama_model_saver(const struct llama_model & model);
+    ~llama_model_saver();
+
+    void add_kv(enum llm_kv key, uint32_t     value);
+    void add_kv(enum llm_kv key, int32_t      value);
+    void add_kv(enum llm_kv key, float        value);
+    void add_kv(enum llm_kv key, bool         value);
+    void add_kv(enum llm_kv key, const char * value);
+
+    [[noreturn]]
+    void add_kv(enum llm_kv key, char value); // needed to make the template below compile
+
+    template <typename Container>
+    void add_kv(enum llm_kv key, const Container & value, bool per_layer = false);
+
+    void add_kv(enum llm_kv key, const std::vector<std::string> & value);
+
+    void add_tensor(const struct ggml_tensor * tensor);
+
+    void add_kv_from_model();
+
+    void add_tensors_from_model();
+
+    void save(const std::string & path_model);
+};

src/llama-model.cpp

@@ -117,6 +117,10 @@ static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_
     { LLAMA_ROPE_SCALING_TYPE_LONGROPE,   "longrope"   },
 };
 
+std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type) {
+    return LLAMA_ROPE_SCALING_TYPES.at(rope_scaling_type);
+}
+
 static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
     for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
         if (kv.second == name) {
@@ -1385,6 +1389,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     // Add additional layer/vocab/etc checks here for other model sizes
                     default: type = LLM_TYPE_UNKNOWN;
                 }
+
+                // For Granite MoE Shared
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
             } break;
         case LLM_ARCH_CHAMELEON:
             {
@@ -1768,6 +1775,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                             layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
                             layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
                             layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+
+                            // For Granite MoE Shared
+                            if (hparams.n_ff_shexp > 0) {
+                                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+                                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+                            }
                         }
                     }
                 } break;
@@ -4264,7 +4278,7 @@ uint64_t llama_model::n_elements() const {
 }
 
 void llama_model::print_info() const {
-    const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+    const std::string rope_scaling_type = llama_rope_scaling_type_name(hparams.rope_scaling_type_train);
 
     auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
         bool is_var = false;
@@ -4325,7 +4339,7 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
         LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
         LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
-        LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
+        LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type.c_str());
         LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
         LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
         LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
@@ -4381,10 +4395,13 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: n_ff_exp         = %d\n",     __func__, hparams.n_ff_exp);
     }
 
-    if (arch == LLM_ARCH_MINICPM || arch == LLM_ARCH_GRANITE || arch == LLM_ARCH_GRANITE_MOE) {
+    if (arch == LLM_ARCH_MINICPM ||
+        arch == LLM_ARCH_GRANITE ||
+        arch == LLM_ARCH_GRANITE_MOE) {
         LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
         LLAMA_LOG_INFO("%s: f_residual_scale  = %f\n", __func__, hparams.f_residual_scale);
         LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
+        LLAMA_LOG_INFO("%s: n_ff_shexp        = %d\n", __func__, hparams.n_ff_shexp);
     }
 
     if (arch == LLM_ARCH_BAILINGMOE) {
@@ -4594,11 +4611,6 @@ struct llm_build_llama : public llm_graph_context {
                 inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
-            // For Granite architecture
-            if (hparams.f_residual_scale) {
-                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-            }
-
             ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
             cb(ffn_inp, "ffn_inp", il);
 
@@ -4670,11 +4682,6 @@ struct llm_build_llama : public llm_graph_context {
                 cb(cur, "ffn_moe_out", il);
             }
 
-            // For Granite architecture
-            if (hparams.f_residual_scale) {
-                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-            }
-
             cur = ggml_add(ctx0, cur, ffn_inp);
             cb(cur, "ffn_out", il);
 
@@ -4697,11 +4704,6 @@ struct llm_build_llama : public llm_graph_context {
         // lm_head
         cur = build_lora_mm(model.output, cur);
 
-        // For Granite architecture
-        if (hparams.f_logit_scale) {
-            cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
-        }
-
         cb(cur, "result_output", -1);
         res->t_logits = cur;
 
@@ -4812,11 +4814,6 @@ struct llm_build_deci : public llm_graph_context {
                 continue;
             }
 
-            // For Granite architecture
-            if (hparams.f_residual_scale) {
-                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-            }
-
             // modified to support attention-free layer of Llama-3_1-Nemotron-51B
             ggml_tensor * ffn_inp = cur;
             if (n_head > 0) {
@@ -4840,11 +4837,6 @@ struct llm_build_deci : public llm_graph_context {
                 cb(cur, "ffn_out", il);
             }
 
-            // For Granite architecture
-            if (hparams.f_residual_scale) {
-                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-            }
-
             cur = ggml_add(ctx0, cur, ffn_inp);
             cb(cur, "ffn_out", il);
 
@@ -4867,11 +4859,6 @@ struct llm_build_deci : public llm_graph_context {
         // lm_head
         cur = build_lora_mm(model.output, cur);
 
-        // For Granite architecture
-        if (hparams.f_logit_scale) {
-            cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
-        }
-
         cb(cur, "result_output", -1);
         res->t_logits = cur;
 
@@ -12210,6 +12197,195 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
     }
 };
 
+
+struct llm_build_granite : public llm_graph_context {
+    llm_build_granite(
+        const llama_model & model,
+        const llm_graph_params & params,
+        ggml_cgraph * gf,
+        const bool use_rope = true)
+        : llm_graph_context(params) {
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - built only if rope enabled
+        ggml_tensor * inp_pos = nullptr;
+
+        auto * inp_attn = build_attn_inp_kv_unified();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and (optionally) RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                if (use_rope) {
+
+                    if (!inp_pos) {
+                        inp_pos = build_inp_pos();
+                    }
+                    ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il);
+                    Qcur = ggml_rope_ext(
+                            ctx0, Qcur, inp_pos, rope_factors,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                            );
+
+                    Kcur = ggml_rope_ext(
+                            ctx0, Kcur, inp_pos, rope_factors,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                            );
+                }
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            // For Granite architectures - scale residual
+            cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network (non-MoE)
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+
+            } else {
+                // MoE branch
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                ggml_tensor * moe_out = build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il);
+                cb(moe_out, "ffn_moe_out", il);
+
+                // For Granite MoE Shared
+                if (hparams.n_ff_shexp > 0) {
+                    ggml_tensor * ffn_shexp = build_ffn(cur,
+                        model.layers[il].ffn_up_shexp,   NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                    cb(ffn_shexp, "ffn_shexp", il);
+
+                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                    cb(cur, "ffn_out", il);
+                } else {
+                    cur = moe_out;
+                }
+            }
+
+            // For Granite architectures - scale residual
+            cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        // For Granite architectures - scale logits
+        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 // ref: https://github.com/facebookresearch/chameleon
 // based on the original build_llama() function, changes:
 //   * qk-norm
@@ -12917,8 +13093,6 @@ llm_graph_result_ptr llama_model::build_graph(
         case LLM_ARCH_LLAMA:
         case LLM_ARCH_LLAMA4:
         case LLM_ARCH_MINICPM:
-        case LLM_ARCH_GRANITE:
-        case LLM_ARCH_GRANITE_MOE:
             {
                 llm = std::make_unique<llm_build_llama>(*this, params, gf);
             } break;
@@ -13149,6 +13323,11 @@ llm_graph_result_ptr llama_model::build_graph(
             {
                 llm = std::make_unique<llm_build_arwkv7>(*this, params, gf);
             } break;
+        case LLM_ARCH_GRANITE:
+        case LLM_ARCH_GRANITE_MOE:
+            {
+                llm = std::make_unique<llm_build_granite>(*this, params, gf);
+            } break;
         case LLM_ARCH_CHAMELEON:
             {
                 llm = std::make_unique<llm_build_chameleon>(*this, params, gf);

src/llama-model.h

@@ -96,6 +96,8 @@ enum llm_type {
     LLM_TYPE_235B_A22B,
 };
 
+std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type);
+
 struct llama_layer_posnet {
     // resnet
     struct ggml_tensor * norm1   = nullptr;

src/llama-quant.cpp

@@ -14,6 +14,12 @@
 #include <thread>
 #include <unordered_map>
 
+// Quantization types. Changes to this struct must be replicated in quantize.cpp
+struct tensor_quantization {
+    std::string name;
+    ggml_type quant = GGML_TYPE_COUNT;
+};
+
 static void zeros(std::ofstream & file, size_t n) {
     char zero = 0;
     for (size_t i = 0; i < n; ++i) {
@@ -48,12 +54,6 @@ struct quantize_state_impl {
         {}
 };
 
-// changes to this struct must be replicated in quantize.cpp
-struct tensor_quantization {
-    std::string name;
-    ggml_type quant = GGML_TYPE_COUNT;
-};
-
 static void llama_tensor_dequantize_impl(
     ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
     const size_t nelements, const int nthread
@@ -519,7 +519,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         nthread = std::thread::hardware_concurrency();
     }
 
-    // mmap consistently increases speed Linux, and also increases speed on Windows with
+    // mmap consistently increases speed on Linux, and also increases speed on Windows with
     // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
 #if defined(__linux__) || defined(_WIN32)
     constexpr bool use_mmap = true;
@@ -529,7 +529,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
 
     llama_model_kv_override * kv_overrides = nullptr;
     if (params->kv_overrides) {
-        auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
+        auto * v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
         kv_overrides = v->data();
     }
 
@@ -796,17 +796,19 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
                 // unless the user specifies a type
                 if (params->tensor_types) {
                     const std::vector<tensor_quantization> & tensor_types = *static_cast<const std::vector<tensor_quantization> *>(params->tensor_types);
+                    const std::string tensor_name(tensor->name);
                     for (const auto & [tname, qtype] : tensor_types) {
-                        if (std::regex pattern(tname); std::regex_search(tensor->name, pattern)) {
-                            if (qtype != new_type) {
-                                LLAMA_LOG_DEBUG("(overriding %s -> %s), ", ggml_type_name(new_type), ggml_type_name(qtype));
+                        if (std::regex pattern(tname); std::regex_search(tensor_name, pattern)) {
+                            if  (qtype != new_type) {
+                                LLAMA_LOG_DEBUG("(overriding %s) ", ggml_type_name(new_type));
+                                new_type = qtype;
+                                break; // if two or more types are specified for the tensor, first match wins
                             }
-                            new_type = qtype;
-                            break;
                         }
                     }
                 }
             }
+
             if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
                 new_type = params->token_embedding_type;
             }

src/llama-vocab.cpp

@@ -1,5 +1,7 @@
 #include "llama-vocab.h"
 
+#include "ggml.h"
+#include "gguf.h"
 #include "llama-impl.h"
 #include "llama-model-loader.h"
 
@@ -1234,6 +1236,9 @@ struct fragment_buffer_variant {
 struct llama_vocab::impl {
     uint32_t n_token_types = 0; // for BERT-style token types
 
+    std::string tokenizer_model;
+    std::string tokenizer_pre;
+
     enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
     enum llama_vocab_pre_type pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
 
@@ -1369,9 +1374,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
     // determine vocab type
     {
-        std::string tokenizer_model;
-        std::string tokenizer_pre;
-
         ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
         ml.get_key(LLM_KV_TOKENIZER_PRE,   tokenizer_pre, false);
 
@@ -1466,7 +1468,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
             const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
             if (precompiled_charsmap_keyidx != -1) {
-                size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
+                const gguf_type pc_type = gguf_get_arr_type(ctx, precompiled_charsmap_keyidx);
+                GGML_ASSERT(pc_type == GGUF_TYPE_INT8 || pc_type == GGUF_TYPE_UINT8);
+
+                const size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
                 const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
                 precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
 #ifdef IS_BIG_ENDIAN
@@ -2789,6 +2794,14 @@ void llama_vocab::load(llama_model_loader & ml, const LLM_KV & kv) {
     pimpl->load(ml, kv);
 }
 
+std::string llama_vocab::get_tokenizer_model() const {
+    return pimpl->tokenizer_model;
+}
+
+std::string llama_vocab::get_tokenizer_pre() const {
+    return pimpl->tokenizer_pre;
+}
+
 enum llama_vocab_type llama_vocab::get_type() const {
     return pimpl->type;
 }
@@ -3011,6 +3024,20 @@ int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string
     return it->second;
 }
 
+std::vector<std::string> llama_vocab::get_bpe_merges() const {
+    std::vector<std::string> result(pimpl->bpe_ranks.size());
+
+    for (const auto & pair : pimpl->bpe_ranks) {
+        result[pair.second] = pair.first.first + " " + pair.first.second;
+    }
+
+    return result;
+}
+
+std::vector<char> llama_vocab::get_precompiled_charsmap() const {
+    return pimpl->precompiled_charsmap;
+}
+
 int32_t llama_vocab::tokenize(
                   const char * text,
                      int32_t   text_len,

src/llama-vocab.h

@@ -21,6 +21,9 @@ struct llama_vocab {
 
     void load(llama_model_loader & ml, const LLM_KV & kv);
 
+    std::string get_tokenizer_model() const;
+    std::string get_tokenizer_pre() const;
+
     enum llama_vocab_type     get_type()     const;
     enum llama_vocab_pre_type get_pre_type() const;
 
@@ -80,6 +83,9 @@ struct llama_vocab {
     int max_token_len() const;
 
     int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
+    std::vector<std::string> get_bpe_merges() const;
+
+    std::vector<char> get_precompiled_charsmap() const;
 
     int32_t tokenize(
                    const char * text,

src/llama.cpp

@@ -4,6 +4,7 @@
 #include "llama-mmap.h"
 #include "llama-vocab.h"
 #include "llama-model-loader.h"
+#include "llama-model-saver.h"
 #include "llama-model.h"
 
 #include "ggml.h"
@@ -253,6 +254,13 @@ struct llama_model * llama_model_load_from_splits(
     return llama_model_load_from_file_impl(splits.front(), splits, params);
 }
 
+void llama_model_save_to_file(const struct llama_model * model, const char * path_model) {
+    llama_model_saver ms(*model);
+    ms.add_kv_from_model();
+    ms.add_tensors_from_model();
+    ms.save(path_model);
+}
+
 //
 // chat templates
 //
@@ -338,3 +346,4 @@ const char * llama_print_system_info(void) {
 
     return s.c_str();
 }
+

tests/test-backend-ops.cpp

@@ -823,7 +823,7 @@ struct test_case {
 
         ggml_build_forward_expand(gf, out);
         ggml_graph_cpy(gf, gb);
-        ggml_build_backward_expand(ctx.get(), ctx.get(), gb, false);
+        ggml_build_backward_expand(ctx.get(), gb, nullptr);
         if (expect.size() != 1 || expect[0] != 0.0f) {
             GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf));
             for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
@@ -1026,7 +1026,7 @@ struct test_example : public test_case {
         // Step 3: return the output tensor.
         return out;
     }
-    // In order to also check the gradients for your op, add calls like ggml_set_param(ctx, a)
+    // In order to also check the gradients for your op, add calls like ggml_set_param(a)
     // immediately after you create the tensors.
     // This is optional and only makes sense if a backward pass has actually been implemented for the new op.
 };
@@ -1058,7 +1058,7 @@ struct test_unary : public test_case {
             auto ne = ne_a; ne[0] *= 3;
             a = ggml_new_tensor(ctx, type, 4, ne.data());
             if (grad_supported) {
-                ggml_set_param(ctx, a);
+                ggml_set_param(a);
             }
             ggml_set_name(a, "a");
 
@@ -1067,7 +1067,7 @@ struct test_unary : public test_case {
         } else {
             a = ggml_new_tensor(ctx, type, 4, ne_a.data());
             if (grad_supported) {
-                ggml_set_param(ctx, a);
+                ggml_set_param(a);
             }
             ggml_set_name(a, "a");
         }
@@ -1133,7 +1133,7 @@ struct test_get_rows : public test_case {
 
         const bool grad_supported = ggml_is_matrix(in) && ggml_is_vector(rows);
         if (grad_supported) {
-            ggml_set_param(ctx, in);
+            ggml_set_param(in);
             // rows is a constant input -> no gradients
         }
 
@@ -1322,7 +1322,7 @@ struct test_repeat : public test_case {
         ggml_set_name(target, "target");
 
         ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, src);
+        ggml_set_param(src);
         ggml_set_name(src, "src");
 
         ggml_tensor * out = ggml_repeat(ctx, src, target);
@@ -1406,7 +1406,7 @@ struct test_dup : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, src);
+        ggml_set_param(src);
         ggml_set_name(src, "src");
 
         if (_use_permute) {
@@ -1442,7 +1442,7 @@ struct test_set : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
-        ggml_set_param(ctx, src);
+        ggml_set_param(src);
         ggml_set_name(src, "src");
 
         auto ne_dst = ne;
@@ -1450,7 +1450,7 @@ struct test_set : public test_case {
             ne_dst[i] *= 2;
         }
         ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data());
-        ggml_set_param(ctx, dst);
+        ggml_set_param(dst);
         ggml_set_name(dst, "dst");
 
         size_t offset = 0;
@@ -1498,7 +1498,7 @@ struct test_cpy : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
-        ggml_set_param(ctx, src);
+        ggml_set_param(src);
         ggml_set_name(src, "src");
 
         if (_src_use_permute) {
@@ -1536,7 +1536,7 @@ struct test_cont : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, src);
+        ggml_set_param(src);
         ggml_set_name(src, "src");
 
         src = ggml_transpose(ctx, src);
@@ -1583,8 +1583,8 @@ struct test_bin_bcast : public test_case {
         // The backward pass supports broadcasting only for GGML_ADD:
         const bool grad_supported = op == ggml_add || ggml_are_same_shape(a, b);
         if (grad_supported) {
-            ggml_set_param(ctx, a);
-            ggml_set_param(ctx, b);
+            ggml_set_param(a);
+            ggml_set_param(b);
         }
 
         ggml_tensor * out = op(ctx, a, b);
@@ -1632,11 +1632,11 @@ struct test_add1 : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * b = ggml_new_tensor_1d(ctx, type, 1);
-        // ggml_set_param(ctx, b); // TODO: implement
+        // ggml_set_param(b); // TODO: implement
         ggml_set_name(b, "b");
 
         ggml_tensor * out = ggml_add1(ctx, a, b);
@@ -1667,7 +1667,7 @@ struct test_scale : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_scale(ctx, a, scale);
@@ -1762,7 +1762,7 @@ struct test_rms_norm : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         if (v) {
@@ -2028,9 +2028,9 @@ struct test_mul_mat : public test_case {
             b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]);
             if (!ggml_is_quantized(type_a)) {
                 if (bs[1] == 1 && nr[1] == 1) {
-                    ggml_set_param(ctx, a);
+                    ggml_set_param(a);
                 }
-                ggml_set_param(ctx, b);
+                ggml_set_param(b);
             }
             ggml_set_name(a, "a");
             ggml_set_name(b, "b");
@@ -2040,22 +2040,29 @@ struct test_mul_mat : public test_case {
             ggml_set_name(a, "a_permuted");
             ggml_set_name(b, "b_permuted");
         } else {
-
             if (v) {
                 a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0],       bs[1]);
                 b = ggml_new_tensor_4d(ctx, type_b, k*2, n, bs[0]*nr[0], bs[1]*nr[1]);
 
+                if (!ggml_is_quantized(type_a)) {
+                    if (bs[1] == 1 && nr[1] == 1) {
+                        ggml_set_param(a);
+                    }
+                    ggml_set_param(b);
+                }
+
                 a = ggml_view_4d(ctx, a, k, m, bs[0],       bs[1],       a->nb[1], a->nb[2], a->nb[3], 0);
                 b = ggml_view_4d(ctx, b, k, n, bs[0]*nr[0], bs[1]*nr[1], b->nb[1], b->nb[2], b->nb[3], 0);
             } else {
                 a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0],       bs[1]);
                 b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
-            }
-            if (!ggml_is_quantized(type_a)) {
-                if (bs[1] == 1 && nr[1] == 1) {
-                    ggml_set_param(ctx, a);
+
+                if (!ggml_is_quantized(type_a)) {
+                    if (bs[1] == 1 && nr[1] == 1) {
+                        ggml_set_param(a);
+                    }
+                    ggml_set_param(b);
                 }
-                ggml_set_param(ctx, b);
             }
             ggml_set_name(a, "a");
             ggml_set_name(b, "b");
@@ -2204,7 +2211,7 @@ struct test_sqr : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_sqr(ctx, a);
@@ -2233,7 +2240,7 @@ struct test_sqrt : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_sqrt(ctx, a);
@@ -2273,7 +2280,7 @@ struct test_log : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_log(ctx, a);
@@ -2309,7 +2316,7 @@ struct test_sin : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_sin(ctx, a);
@@ -2352,7 +2359,7 @@ struct test_cos : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_cos(ctx, a);
@@ -2432,7 +2439,7 @@ struct test_diag_mask_inf : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_diag_mask_inf(ctx, a, n_past);
@@ -2471,7 +2478,7 @@ struct test_soft_max : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * mask = nullptr;
@@ -2553,7 +2560,7 @@ struct test_rope : public test_case {
             auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
             a = ggml_new_tensor(ctx, type, 4, ne.data());
             if (forward) {
-                ggml_set_param(ctx, a);
+                ggml_set_param(a);
             }
             ggml_set_name(a, "a");
 
@@ -2562,7 +2569,7 @@ struct test_rope : public test_case {
         } else {
             a = ggml_new_tensor(ctx, type, 4, ne_a.data());
             if (forward) {
-                ggml_set_param(ctx, a);
+                ggml_set_param(a);
             }
             ggml_set_name(a, "a");
         }
@@ -2676,7 +2683,7 @@ struct test_pool2d : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
-        ggml_set_param(ctx, input);
+        ggml_set_param(input);
         ggml_set_name(input, "input");
 
         ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1);
@@ -2752,7 +2759,7 @@ struct test_im2col : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
-        ggml_set_param(ctx, input);
+        ggml_set_param(input);
         ggml_set_name(input, "input");
 
         ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
@@ -2929,7 +2936,7 @@ struct test_sum : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_sum(ctx, a);
@@ -2958,7 +2965,7 @@ struct test_sum_rows : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_sum_rows(ctx, a);
@@ -2983,7 +2990,7 @@ struct test_mean : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * out = ggml_mean(ctx, a);
@@ -3129,11 +3136,11 @@ struct test_acc : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
-        ggml_set_param(ctx, a);
+        ggml_set_param(a);
         ggml_set_name(a, "a");
 
         ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
-        ggml_set_param(ctx, b);
+        ggml_set_param(b);
         ggml_set_name(b, "b");
 
         ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]);
@@ -3370,7 +3377,7 @@ struct test_cross_entropy_loss : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * logits = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_set_param(ctx, logits);
+        ggml_set_param(logits);
         ggml_set_name(logits, "logits");
 
         ggml_tensor * labels = ggml_new_tensor(ctx, type, 4, ne.data());
@@ -3452,7 +3459,7 @@ struct test_opt_step_adamw : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
-        ggml_set_param(ctx, a); // Despite tensor a having gradients the output tensor will not.
+        ggml_set_param(a); // Despite tensor a having gradients the output tensor will not.
         ggml_set_name(a, "a");
 
         ggml_tensor * grad = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);

tests/test-opt.cpp

@@ -57,7 +57,8 @@ static helper_ctx_data helper_get_ctx_data(
         enum ggml_opt_loss_type loss_type          = GGML_OPT_LOSS_TYPE_SUM) {
     std::vector<ggml_opt_dataset_t> datasets(ndata);
     for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) {
-        ggml_opt_dataset_t dataset = ggml_opt_dataset_init(ne_datapoint, ne_label, ndata, ndata_shard);
+        ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
+            GGML_TYPE_F32, GGML_TYPE_F32, ne_datapoint, ne_label, ndata, ndata_shard);
 
         float * data   = ggml_get_data_f32(ggml_opt_dataset_data(  dataset));
         float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
@@ -74,7 +75,8 @@ static helper_ctx_data helper_get_ctx_data(
         datasets[ndata_shard-1] = dataset;
     }
 
-    ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(1, 0, ndata, /*ndata_shard =*/ 1);
+    ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(
+        GGML_TYPE_F32, GGML_TYPE_F32, 1, 0, ndata, /*ndata_shard =*/ 1);
 
     float * data = ggml_get_data_f32(ggml_opt_dataset_data(dataset_unsupervised));
 
@@ -113,7 +115,7 @@ static helper_ctx_data helper_get_ctx_data(
 
     struct ggml_tensor * weights = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
     ggml_set_name(weights, "weights");
-    ggml_set_param(ctx_static, weights);
+    ggml_set_param(weights);
 
     struct ggml_tensor * intermediary = ggml_add(ctx_compute, inputs, weights);
 
@@ -127,8 +129,11 @@ static helper_ctx_data helper_get_ctx_data(
     GGML_ASSERT(nbatch_logical % nbatch_physical == 0);
     const int32_t opt_period = nbatch_logical / nbatch_physical;
 
-    struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type);
-    opt_params.opt_period = opt_period;
+    struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, loss_type);
+    opt_params.ctx_compute = ctx_compute;
+    opt_params.inputs      = inputs;
+    opt_params.outputs     = outputs;
+    opt_params.opt_period  = opt_period;
     if (!optimizer_defaults) {
         opt_params.get_opt_pars = helper_get_test_opt_pars;
     }
@@ -264,8 +269,9 @@ static std::pair<int, int> test_grad(ggml_backend_sched_t backend_sched, ggml_ba
 
     for (int idata = 0; idata < ndata; ++idata) {
         const float idataf = idata;
+        ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
         ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
-        ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+        ggml_opt_eval(cd.opt_ctx, cd.result);
         ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float));
     }
 
@@ -334,8 +340,9 @@ static std::pair<int, int> test_forward_backward(
     } else {
         for (int idata = 0; idata < ndata; ++idata) {
             const float idataf = idata;
+            ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
             ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
-            ggml_opt_forward(cd.opt_ctx, cd.result);
+            ggml_opt_eval(cd.opt_ctx, cd.result);
             ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
         }
     }
@@ -367,7 +374,8 @@ static std::pair<int, int> test_forward_backward(
     float w0;
     ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float));
     for (int i = 0; i < 10; ++i) {
-        ggml_opt_forward_backward(cd.opt_ctx, nullptr);
+        ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
+        ggml_opt_eval(cd.opt_ctx, cd.result);
     }
     ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float));
 
@@ -387,8 +395,9 @@ static std::pair<int, int> test_forward_backward(
     } else {
         for (int idata = 0; idata < ndata; ++idata) {
             const float idataf = idata;
+            ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
             ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
-            ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+            ggml_opt_eval(cd.opt_ctx, cd.result);
             ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
         }
     }
@@ -492,14 +501,16 @@ static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched,
             int idata = 0;
             for (; idata < idata_split; ++idata) {
                 const float idataf = idata;
+                ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
                 ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
-                ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+                ggml_opt_eval(cd.opt_ctx, cd.result);
                 ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
             }
             for (; idata < ndata; ++idata) {
                 const float idataf = idata;
+                ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
                 ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
-                ggml_opt_forward(cd.opt_ctx, cd.result2);
+                ggml_opt_eval(cd.opt_ctx, cd.result2);
                 ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
             }
         }
@@ -573,7 +584,6 @@ static std::pair<int, int> test_gradient_accumulation(
 
     struct helper_ctx_data cd = helper_get_ctx_data(
         backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, /*nbatch_logical =*/ 6, nbatch_physical, loss_type);
-    struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx);
 
     std::vector<float> grad_history(ndata);
     for (int64_t idata = 0; idata < ndata; ++idata) {
@@ -584,15 +594,17 @@ static std::pair<int, int> test_gradient_accumulation(
         if (nbatch_physical == 1) {
             for (int idata = 0; idata < ndata; ++idata) {
                 const float idataf = idata;
+                ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
                 ggml_backend_tensor_set(cd.inputs, &idataf, 0, 1*sizeof(float));
-                ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+                ggml_opt_eval(cd.opt_ctx, cd.result);
                 ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, 1*sizeof(float));
             }
         } else if (nbatch_physical == 2) {
             for (int idata = 0; idata < ndata; idata += 2) {
                 const float idataf[2] = {float(idata + 0), float(idata + 1)};
+                ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
                 ggml_backend_tensor_set(cd.inputs, idataf, 0, 2*sizeof(float));
-                ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+                ggml_opt_eval(cd.opt_ctx, cd.result);
 
                 grad_history[idata + 0] = 0.0f;
                 ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata + 1, 0, 1*sizeof(float));
@@ -617,7 +629,7 @@ static std::pair<int, int> test_gradient_accumulation(
                 }
                 subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0, atol);
                 subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0, atol);
-                subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0, atol);
+                subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0, atol);
             } else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) {
                 if (nbatch_physical == 1) {
                     subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0/ndata, atol);
@@ -630,7 +642,7 @@ static std::pair<int, int> test_gradient_accumulation(
                 }
                 subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0/ndata, atol);
                 subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0/ndata, atol);
-                subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0/ndata, atol);
+                subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0/ndata, atol);
             } else {
                 GGML_ASSERT(false);
             }
@@ -692,7 +704,8 @@ static std::pair<int, int> test_regression(ggml_backend_sched_t backend_sched, g
     std::mt19937 gen(12345);
     std::normal_distribution<float> nd{0.0f, 0.1f};
 
-    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(1, 1, ndata_regression, ndata_regression);
+    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
+        GGML_TYPE_F32, GGML_TYPE_F32, 1, 1, ndata_regression, ndata_regression);
 
     float * data   = ggml_get_data_f32(ggml_opt_dataset_data(  dataset));
     float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
@@ -733,15 +746,14 @@ static std::pair<int, int> test_regression(ggml_backend_sched_t backend_sched, g
 
     struct ggml_tensor * a = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
     ggml_set_name(a, "a");
-    ggml_set_param(ctx_static, a);
+    ggml_set_param(a);
 
     struct ggml_tensor * b = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
     ggml_set_name(b, "b");
-    ggml_set_param(ctx_static, b);
+    ggml_set_param(b);
 
     struct ggml_tensor * f = ggml_add(ctx_compute, ggml_mul(ctx_compute, x, a), b);
     ggml_set_name(f, "f");
-    ggml_set_param(ctx_static, f);
 
     ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend);
     const float a0 = 1.0f;

tools/batched-bench/batched-bench.cpp

@@ -123,8 +123,8 @@ int main(int argc, char ** argv) {
 
                 common_batch_clear(batch);
 
-                for (int i = 0; i < pp; ++i) {
-                    for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
+                for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
+                    for (int i = 0; i < pp; ++i) {
                         common_batch_add(batch, 0, i, { j }, false);
                     }
                 }

tools/llama-bench/README.md

@@ -43,12 +43,13 @@ test parameters:
   -ub, --ubatch-size <n>                    (default: 512)
   -ctk, --cache-type-k <t>                  (default: f16)
   -ctv, --cache-type-v <t>                  (default: f16)
-  -t, --threads <n>                         (default: 16)
+  -dt, --defrag-thold <f>                   (default: -1)
+  -t, --threads <n>                         (default: system dependent)
   -C, --cpu-mask <hex,hex>                  (default: 0x0)
   --cpu-strict <0|1>                        (default: 0)
   --poll <0...100>                          (default: 50)
   -ngl, --n-gpu-layers <n>                  (default: 99)
-  -rpc, --rpc <rpc_servers>                 (default: )
+  -rpc, --rpc <rpc_servers>                 (default: none)
   -sm, --split-mode <none|layer|row>        (default: layer)
   -mg, --main-gpu <i>                       (default: 0)
   -nkvo, --no-kv-offload <0|1>              (default: 0)
@@ -62,7 +63,7 @@ test parameters:
 
 Multiple values can be given for each parameter by separating them with ','
 or by specifying the parameter multiple times. Ranges can be given as
-'start-end' or 'start-end+step' or 'start-end*mult'.
+'first-last' or 'first-last+step' or 'first-last*mult'.
 ```
 
 llama-bench can perform three types of tests:

tools/llama-bench/llama-bench.cpp

@@ -211,6 +211,8 @@ static std::vector<int> parse_int_range(const std::string & s) {
         for (int i = first; i <= last;) {
             result.push_back(i);
 
+            int prev_i = i;
+
             if (op == '+') {
                 i += step;
             } else if (op == '*') {
@@ -218,6 +220,10 @@ static std::vector<int> parse_int_range(const std::string & s) {
             } else {
                 throw std::invalid_argument("invalid range format");
             }
+
+            if (i <= prev_i) {
+                throw std::invalid_argument("invalid range");
+            }
         }
         search_start = match.suffix().first;
     }
@@ -239,6 +245,7 @@ struct cmd_params {
     std::vector<int>                 n_ubatch;
     std::vector<ggml_type>           type_k;
     std::vector<ggml_type>           type_v;
+    std::vector<float>               defrag_thold;
     std::vector<int>                 n_threads;
     std::vector<std::string>         cpu_mask;
     std::vector<bool>                cpu_strict;
@@ -274,6 +281,7 @@ static const cmd_params cmd_params_defaults = {
     /* n_ubatch             */ { 512 },
     /* type_k               */ { GGML_TYPE_F16 },
     /* type_v               */ { GGML_TYPE_F16 },
+    /* defrag_thold         */ { -1.0f },
     /* n_threads            */ { cpu_get_num_math() },
     /* cpu_mask             */ { "0x0" },
     /* cpu_strict           */ { false },
@@ -335,6 +343,8 @@ static void print_usage(int /* argc */, char ** argv) {
            join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
     printf("  -ctv, --cache-type-v <t>                  (default: %s)\n",
            join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
+    printf("  -dt, --defrag-thold <f>                   (default: %s)\n",
+           join(cmd_params_defaults.defrag_thold, ",").c_str());
     printf("  -t, --threads <n>                         (default: %s)\n",
            join(cmd_params_defaults.n_threads, ",").c_str());
     printf("  -C, --cpu-mask <hex,hex>                  (default: %s)\n",
@@ -368,7 +378,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf(
         "Multiple values can be given for each parameter by separating them with ','\n"
         "or by specifying the parameter multiple times. Ranges can be given as\n"
-        "'start-end' or 'start-end+step' or 'start-end*mult'.\n");
+        "'first-last' or 'first-last+step' or 'first-last*mult'.\n");
 }
 
 static ggml_type ggml_type_from_name(const std::string & s) {
@@ -519,6 +529,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                     break;
                 }
                 params.type_v.insert(params.type_v.end(), types.begin(), types.end());
+            } else if (arg == "-dt" || arg == "--defrag-thold") {
+                if (++i >= argc) {
+                    invalid_param = true;
+                    break;
+                }
+                auto p = string_split<float>(argv[i], split_delim);
+                params.defrag_thold.insert(params.defrag_thold.end(), p.begin(), p.end());
             } else if (arg == "-t" || arg == "--threads") {
                 if (++i >= argc) {
                     invalid_param = true;
@@ -825,6 +842,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.type_v.empty()) {
         params.type_v = cmd_params_defaults.type_v;
     }
+    if (params.defrag_thold.empty()) {
+        params.defrag_thold = cmd_params_defaults.defrag_thold;
+    }
     if (params.n_gpu_layers.empty()) {
         params.n_gpu_layers = cmd_params_defaults.n_gpu_layers;
     }
@@ -883,6 +903,7 @@ struct cmd_params_instance {
     int                n_ubatch;
     ggml_type          type_k;
     ggml_type          type_v;
+    float              defrag_thold;
     int                n_threads;
     std::string        cpu_mask;
     bool               cpu_strict;
@@ -959,15 +980,16 @@ struct cmd_params_instance {
     llama_context_params to_llama_cparams() const {
         llama_context_params cparams = llama_context_default_params();
 
-        cparams.n_ctx       = n_prompt + n_gen + n_depth;
-        cparams.n_batch     = n_batch;
-        cparams.n_ubatch    = n_ubatch;
-        cparams.type_k      = type_k;
-        cparams.type_v      = type_v;
-        cparams.offload_kqv = !no_kv_offload;
-        cparams.flash_attn  = flash_attn;
-        cparams.embeddings  = embeddings;
-        cparams.op_offload  = !no_op_offload;
+        cparams.n_ctx        = n_prompt + n_gen + n_depth;
+        cparams.n_batch      = n_batch;
+        cparams.n_ubatch     = n_ubatch;
+        cparams.type_k       = type_k;
+        cparams.type_v       = type_v;
+        cparams.defrag_thold = defrag_thold;
+        cparams.offload_kqv  = !no_kv_offload;
+        cparams.flash_attn   = flash_attn;
+        cparams.embeddings   = embeddings;
+        cparams.op_offload   = !no_op_offload;
 
         return cparams;
     }
@@ -992,6 +1014,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
     for (const auto & nub : params.n_ubatch)
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
+    for (const auto & defrag_thold : params.defrag_thold)
     for (const auto & nkvo : params.no_kv_offload)
     for (const auto & fa : params.flash_attn)
     for (const auto & nt : params.n_threads)
@@ -1012,6 +1035,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
+                /* .defrag_thold = */ defrag_thold,
                 /* .n_threads    = */ nt,
                 /* .cpu_mask     = */ cm,
                 /* .cpu_strict   = */ cs,
@@ -1044,6 +1068,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
+                /* .defrag_thold = */ defrag_thold,
                 /* .n_threads    = */ nt,
                 /* .cpu_mask     = */ cm,
                 /* .cpu_strict   = */ cs,
@@ -1076,6 +1101,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
+                /* .defrag_thold = */ defrag_thold,
                 /* .n_threads    = */ nt,
                 /* .cpu_mask     = */ cm,
                 /* .cpu_strict   = */ cs,
@@ -1117,6 +1143,7 @@ struct test {
     int                      poll;
     ggml_type                type_k;
     ggml_type                type_v;
+    float                    defrag_thold;
     int                      n_gpu_layers;
     llama_split_mode         split_mode;
     int                      main_gpu;
@@ -1151,6 +1178,7 @@ struct test {
         poll           = inst.poll;
         type_k         = inst.type_k;
         type_v         = inst.type_v;
+        defrag_thold   = inst.defrag_thold;
         n_gpu_layers   = inst.n_gpu_layers;
         split_mode     = inst.split_mode;
         main_gpu       = inst.main_gpu;
@@ -1206,6 +1234,7 @@ struct test {
             "model_type",   "model_size",   "model_n_params", "n_batch",    "n_ubatch",     "n_threads",
             "cpu_mask",     "cpu_strict",   "poll",           "type_k",     "type_v",       "n_gpu_layers",
             "split_mode",   "main_gpu",     "no_kv_offload",  "flash_attn", "tensor_split", "tensor_buft_overrides",
+            "defrag_thold",
             "use_mmap",     "embeddings",   "no_op_offload",   "n_prompt",       "n_gen",      "n_depth",      "test_time",
             "avg_ns",       "stddev_ns",    "avg_ts",         "stddev_ts",
         };
@@ -1225,7 +1254,7 @@ struct test {
             field == "use_mmap" || field == "embeddings") {
             return BOOL;
         }
-        if (field == "avg_ts" || field == "stddev_ts") {
+        if (field == "avg_ts" || field == "stddev_ts" || field == "defrag_thold") {
             return FLOAT;
         }
         return STRING;
@@ -1292,6 +1321,7 @@ struct test {
                                             std::to_string(flash_attn),
                                             tensor_split_str,
                                             tensor_buft_overrides_str,
+                                            std::to_string(defrag_thold),
                                             std::to_string(use_mmap),
                                             std::to_string(embeddings),
                                             std::to_string(no_op_offload),
@@ -1558,6 +1588,9 @@ struct markdown_printer : public printer {
         if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) {
             fields.emplace_back("type_v");
         }
+        if (params.defrag_thold.size() > 1 || params.defrag_thold != cmd_params_defaults.defrag_thold) {
+            fields.emplace_back("defrag_thold");
+        }
         if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
             fields.emplace_back("main_gpu");
         }

tools/mtmd/CMakeLists.txt

@@ -1,29 +1,3 @@
-# llava (legacy)
-
-add_library(llava OBJECT
-            llava.cpp
-            llava.h
-            clip.cpp
-            clip.h
-            )
-
-target_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-
-target_include_directories(llava PUBLIC .)
-target_include_directories(llava PUBLIC ../..)
-target_include_directories(llava PUBLIC ../../common)
-
-target_compile_features(llava PRIVATE cxx_std_17)
-
-add_library(llava_static STATIC $<TARGET_OBJECTS:llava>)
-if (BUILD_SHARED_LIBS)
-    set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON)
-    target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD)
-    add_library(llava_shared SHARED $<TARGET_OBJECTS:llava>)
-    target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-    install(TARGETS llava_shared LIBRARY)
-endif()
-
 # mtmd
 
 add_library(mtmd OBJECT
@@ -53,12 +27,10 @@ if (BUILD_SHARED_LIBS)
 endif()
 
 if (NOT MSVC)
-    target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h
     target_compile_options(mtmd PRIVATE -Wno-cast-qual) # stb_image.h
 endif()
 
 if(TARGET BUILD_INFO)
-    add_dependencies(llava BUILD_INFO)
     add_dependencies(mtmd BUILD_INFO)
 endif()
 
@@ -73,10 +45,3 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-cli)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-set(TARGET llama-llava-clip-quantize-cli)
-add_executable(${TARGET} clip-quantize-cli.cpp)
-set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)

tools/mtmd/README-quantize.md

@@ -1,44 +0,0 @@
-# Quantizing CLIP Visual Projector
-
-This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance.
-
-## Usage
-
-To quantize a CLIP visual projector model, use the following command:
-
-```sh
-./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf <type>
-```
-
-After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc).
-
-### Arguments
-
-- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format.
-- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved.
-- `<type>`: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`.
-
-### Quantization Types
-
-The following quantization types are supported, based on the `enum ggml_type` definition:
-
-- `2` - `q4_0`: 4-bit quantization with a single scale value.
-- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block.
-- `6` - `q5_0`: 5-bit quantization with a single scale value.
-- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block.
-- `8` - `q8_0`: 8-bit quantization with a single scale value.
-
-### Example
-
-To quantize a model using the `q4_0` quantization type, you would run:
-
-```sh
-./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2
-```
-
-This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method.
-
-## Notes
-
-- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements.
-- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments.

tools/mtmd/README.md

@@ -41,8 +41,8 @@ Built upon `clip.cpp` (similar to `llava.cpp`), `libmtmd` offers several advanta
 
 Multimodal projector (`mmproj`) files are specific to each model architecture.
 
-For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` flag to get the `mmproj` file:
-- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) - Note: 1B variant does not have vision support
+For the following models, you can use `convert_hf_to_gguf.py` with `--mmproj` flag to get the `mmproj` file:
+- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) ; See the guide [here](../../docs/multimodal/gemma3.md) - Note: 1B variant does not have vision support
 - SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
 - SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
 - [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint
@@ -52,6 +52,8 @@ For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` fla
 
 For older models, please refer to the relevant guide for instructions on how to obtain or create them:
 
+NOTE: conversion scripts are located under `tools/mtmd/legacy-models`
+
 - [LLaVA](../../docs/multimodal/llava.md)
 - [MobileVLM](../../docs/multimodal/MobileVLM.md)
 - [GLM-Edge](../../docs/multimodal/glmedge.md)
@@ -59,4 +61,3 @@ For older models, please refer to the relevant guide for instructions on how to
 - [MiniCPM-V 2.6](../../docs/multimodal/minicpmv2.6.md)
 - [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md)
 - [IBM Granite Vision](../../docs/multimodal/granitevision.md)
-- [Google Gemma 3](../../docs/multimodal/gemma3.md)

tools/mtmd/android/adb_run.sh

@@ -1,53 +0,0 @@
-#!/bin/bash
-
-model_dir="/Users/cxt/model/llm/mobileVLM/MobileVLM-1.7B_processed"
-projector_name="mmproj-model-f16.gguf"
-llama_name="ggml-model-q4_k.gguf"
-img_dir="/Users/cxt/model/llm"
-img_name="demo.jpg"
-prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
-# img_name="cat.jpeg"
-# prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
-
-program_dir="build_64/bin"
-binName="llama-mtmd-cli"
-n_threads=4
-
-
-deviceDir="/data/local/tmp"
-saveDir="output"
-if [ ! -d ${saveDir} ]; then
-    mkdir ${saveDir}
-fi
-
-
-function android_run() {
-    # # copy resource into device
-    # adb push ${model_dir}/${projector_name} ${deviceDir}/${projector_name}
-    # adb push ${model_dir}/${llama_name} ${deviceDir}/${llama_name}
-    adb push ${img_dir}/${img_name} ${deviceDir}/${img_name}
-    # copy program into device
-    adb push ${program_dir}/${binName} ${deviceDir}/${binName}
-    adb shell "chmod 0777 ${deviceDir}/${binName}"
-
-    # run
-    adb shell "echo cd ${deviceDir} ${deviceDir}/${binName} \
-                                                 -m ${deviceDir}/${llama_name} \
-                                                 --mmproj ${deviceDir}/${projector_name} \
-                                                 -t ${n_threads} \
-                                                 --image ${deviceDir}/${img_name} \
-                                                 -p \"${prompt}\" \
-                                                 > ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt"
-    adb shell "cd ${deviceDir}; pwd; ${deviceDir}/${binName} \
-                                                 -m ${deviceDir}/${llama_name} \
-                                                 --mmproj ${deviceDir}/${projector_name} \
-                                                 -t ${n_threads} \
-                                                 --image ${deviceDir}/${img_name} \
-                                                 -p \"${prompt}\" \
-                                                 >> ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt 2>&1"
-    adb pull ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt ${saveDir}
-}
-
-android_run
-
-echo "android_run is Done!"

tools/mtmd/android/build_64.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-cmake ../../../../ \
--DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
--DCMAKE_BUILD_TYPE=Release \
--DANDROID_ABI="arm64-v8a" \
--DANDROID_PLATFORM=android-23 $1
-
-make -j4

tools/mtmd/clip-quantize-cli.cpp

@@ -1,59 +0,0 @@
-#include "arg.h"
-#include "base64.hpp"
-#include "log.h"
-#include "common.h"
-#include "sampling.h"
-#include "clip.h"
-#include "llava.h"
-#include "llama.h"
-#include "ggml.h"
-
-static void print_usage(int argc, char ** argv) {
-    (void) argc;
-
-    fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]);
-    fprintf(stderr, "  type = 2 - q4_0\n");
-    fprintf(stderr, "  type = 3 - q4_1\n");
-    fprintf(stderr, "  type = 6 - q5_0\n");
-    fprintf(stderr, "  type = 7 - q5_1\n");
-    fprintf(stderr, "  type = 8 - q8_0\n");
-}
-
-int main(int argc, char ** argv) {
-    if (argc != 4) {
-        print_usage(argc, argv);
-        return 1;
-    }
-
-    const std::string fname_inp = argv[1];
-    const std::string fname_out = argv[2];
-
-    const int itype = atoi(argv[3]);
-
-    const int64_t t_main_start_us = ggml_time_us();
-
-    int64_t t_quantize_us = 0;
-
-    // load the model
-    {
-        const int64_t t_start_us = ggml_time_us();
-
-        if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
-            fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
-            return 1;
-        }
-
-        t_quantize_us = ggml_time_us() - t_start_us;
-    }
-
-    // report timing
-    {
-        const int64_t t_main_end_us = ggml_time_us();
-
-        printf("\n");
-        printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f);
-        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
-    }
-
-    return 0;
-}

tools/mtmd/clip.cpp

@@ -1909,16 +1909,20 @@ struct clip_model_loader {
                     } break;
                 case PROJECTOR_TYPE_QWEN2VL:
                     {
-                        // max image size = sqrt(max_pixels)
-                        // https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct/blob/main/preprocessor_config.json
-                        hparams.image_size = 3584;
+                        // max image size = sqrt(max_pixels) = 3584
+                        // ref: https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct/blob/main/preprocessor_config.json
+                        // however, the model use unreasonable memory past 1024 size, we force it to 1024 otherwise it's unusable
+                        // ref: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/discussions/10
+                        hparams.image_size = 1024;
                         hparams.warmup_image_size = hparams.patch_size * 8;
                     } break;
                 case PROJECTOR_TYPE_QWEN25VL:
                     {
                         // max image size = sqrt(max_pixels)
                         // https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/preprocessor_config.json
-                        hparams.image_size = 3584;
+                        // however, the model use unreasonable memory past 1024 size, we force it to 1024 otherwise it's unusable
+                        // ref: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/discussions/10
+                        hparams.image_size = 1024;
                         hparams.warmup_image_size = hparams.patch_size * 8;
                         get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern);
                     } break;
@@ -2305,14 +2309,6 @@ struct clip_model_loader {
     }
 };
 
-// read and create ggml_context containing the tensors and their data
-struct clip_ctx * clip_model_load(const char * fname, const int verbosity) {
-    return clip_init(fname, clip_context_params{
-        /* use_gpu */   true,
-        /* verbosity */ static_cast<ggml_log_level>(verbosity),
-    });
-}
-
 struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params) {
     g_logger_state.verbosity_thold = ctx_params.verbosity;
     clip_ctx * ctx_clip = nullptr;
@@ -3081,19 +3077,6 @@ size_t get_clip_image_grid_size(const struct clip_ctx * ctx) {
     return ctx->vision_model.hparams.image_grid_pinpoints.size();
 }
 
-// deprecated
-int clip_n_patches(const struct clip_ctx * ctx) {
-    clip_image_f32 img;
-    img.nx = ctx->vision_model.hparams.image_size;
-    img.ny = ctx->vision_model.hparams.image_size;
-    return clip_n_output_tokens(ctx, &img);
-}
-
-// deprecated
-int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
-    return clip_n_output_tokens(ctx, img);
-}
-
 int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
     const auto & params = ctx->vision_model.hparams;
     const int n_total = clip_n_output_tokens(ctx, img);
@@ -3582,141 +3565,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     return true;
 }
 
-bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
-    assert(itype < GGML_TYPE_COUNT);
-    ggml_type type = static_cast<ggml_type>(itype);
-
-    auto * ctx_clip = clip_init(fname_inp, clip_context_params{
-        /* use_gpu */   false,
-        /* verbosity */ GGML_LOG_LEVEL_ERROR,
-    });
-
-    const auto & ctx_src = ctx_clip->ctx_gguf.get();
-    const auto & ctx_data = ctx_clip->ctx_data.get();
-
-    auto * ctx_out = gguf_init_empty();
-    gguf_set_kv(ctx_out, ctx_src);
-    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
-    gguf_set_val_u32(ctx_out, "general.file_type", itype);
-
-    auto fout = std::ofstream(fname_out, std::ios::binary);
-
-    const int n_tensors = gguf_get_n_tensors(ctx_src);
-
-    for (int i = 0; i < n_tensors; ++i) {
-        const char * name = gguf_get_tensor_name(ctx_src, i);
-        ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
-        gguf_add_tensor(ctx_out, cur);
-    }
-
-    const size_t meta_size = gguf_get_meta_size(ctx_out);
-    for (size_t i = 0; i < meta_size; ++i) {
-        fout.put(0);
-    }
-
-    // regexes of tensor names to be quantized
-    const std::vector<std::string> k_names = {
-        ".*weight",
-    };
-
-    std::vector<uint8_t> work(512);
-    std::vector<float> conv_buf(512);
-    size_t total_size_org = 0;
-    size_t total_size_new = 0;
-
-    for (int i = 0; i < n_tensors; ++i) {
-        const std::string name = gguf_get_tensor_name(ctx_src, i);
-        ggml_tensor * cur = ggml_get_tensor(ctx_data, name.c_str());
-
-        enum ggml_type new_type;
-        void * new_data;
-        size_t new_size;
-
-        bool quantize = false;
-        for (const auto & s : k_names) {
-            if (std::regex_match(name, std::regex(s))) {
-                quantize = true;
-                break;
-            }
-        }
-
-        // quantize only 2D tensors and bigger than block size
-        quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type);
-
-        if (quantize) {
-            new_type = type;
-            if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
-                new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
-                // LOG_ERR("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
-            }
-            const size_t n_elms = ggml_nelements(cur);
-            float * f32_data;
-
-            switch (cur->type) {
-            case GGML_TYPE_F32:
-                f32_data = (float *)cur->data;
-                break;
-            case GGML_TYPE_F16:
-                if (conv_buf.size() < n_elms) {
-                    conv_buf.resize(n_elms);
-                }
-                for (size_t j = 0; j < n_elms; ++j) {
-                    conv_buf[j] = ggml_fp16_to_fp32(((ggml_fp16_t *)cur->data)[j]);
-                }
-                f32_data = (float *)conv_buf.data();
-                break;
-            default:
-                LOG_ERR("%s: Please use an input file in f32 or f16\n", __func__);
-                gguf_free(ctx_out);
-                return false;
-            }
-
-            if (work.size() < n_elms * 4) {
-                work.resize(n_elms * 4);
-            }
-            new_data = work.data();
-
-            new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
-        } else {
-            new_type = cur->type;
-            new_data = cur->data;
-            new_size = ggml_nbytes(cur);
-        }
-        const size_t orig_size = ggml_nbytes(cur);
-        total_size_org += orig_size;
-        total_size_new += new_size;
-        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
-        GGML_ASSERT(gguf_get_tensor_size(ctx_out, gguf_find_tensor(ctx_out, name.c_str())) == new_size);
-        gguf_set_tensor_data(ctx_out, name.c_str(), new_data);
-        fout.write((const char *)new_data, new_size);
-        size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size;
-        for (size_t j = 0; j < pad; ++j) {
-            fout.put(0);
-        }
-
-        LOG_INF("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
-               orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
-    }
-
-    // go back to beginning of file and write the updated metadata
-    fout.seekp(0, std::ios::beg);
-    std::vector<uint8_t> meta(meta_size);
-    gguf_get_meta_data(ctx_out, meta.data());
-    fout.write((const char *)meta.data(), meta_size);
-
-    fout.close();
-
-    clip_free(ctx_clip);
-    gguf_free(ctx_out);
-
-    {
-        LOG_INF("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
-        LOG_INF("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
-    }
-
-    return true;
-}
-
 int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
     switch (ctx->proj_type) {
         case PROJECTOR_TYPE_LDP:

tools/mtmd/clip.h

@@ -1,28 +1,9 @@
-#ifndef CLIP_H
-#define CLIP_H
+#pragma once
 
 #include "ggml.h"
 #include <stddef.h>
 #include <stdint.h>
 
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define CLIP_API __declspec(dllexport)
-#        else
-#            define CLIP_API __declspec(dllimport)
-#        endif
-#    else
-#        define CLIP_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define CLIP_API
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
 struct clip_ctx;
 
 struct clip_image_size {
@@ -39,97 +20,80 @@ struct clip_context_params {
     enum ggml_log_level verbosity;
 };
 
-// deprecated, use clip_init
-CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
+struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params);
 
-CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params);
+void clip_free(struct clip_ctx * ctx);
 
-CLIP_API void clip_free(struct clip_ctx * ctx);
+size_t clip_embd_nbytes(const struct clip_ctx * ctx);
+size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h);
 
-CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
-CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h);
-
-CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx);
-CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx);
-CLIP_API int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
+int32_t clip_get_image_size (const struct clip_ctx * ctx);
+int32_t clip_get_patch_size (const struct clip_ctx * ctx);
+int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
 
 // TODO: should be enum, not string
-CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
+const char * clip_patch_merge_type(const struct clip_ctx * ctx);
 
-CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
-CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
+const int32_t * clip_image_grid(const struct clip_ctx * ctx);
+size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
 
-GGML_DEPRECATED(CLIP_API int clip_n_patches(const struct clip_ctx * ctx),
-    "use clip_n_output_tokens instead");
-GGML_DEPRECATED(CLIP_API int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img),
-    "use clip_n_output_tokens instead");
-
-CLIP_API int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
+int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
 
 // for M-RoPE, this will be the number of token positions in X and Y directions
 // for other models, X will be the total number of tokens and Y will be 1
-CLIP_API int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img);
-CLIP_API int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img);
+int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img);
+int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img);
 
 // this should be equal to the embedding dimension of the text model
-CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+int clip_n_mmproj_embd(const struct clip_ctx * ctx);
 
-CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
-CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
-CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
+int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
+void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
+struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
 
-CLIP_API struct clip_image_size      * clip_image_size_init(void);
-CLIP_API struct clip_image_u8        * clip_image_u8_init (void);
-CLIP_API struct clip_image_f32       * clip_image_f32_init(void);
-CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava
+struct clip_image_size      * clip_image_size_init(void);
+struct clip_image_u8        * clip_image_u8_init (void);
+struct clip_image_f32       * clip_image_f32_init(void);
+struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava
 
 // nx, ny are the output image dimensions
-CLIP_API unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny);
+unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny);
 
-CLIP_API void clip_image_size_free (struct clip_image_size * img_size);
-CLIP_API void clip_image_u8_free (struct clip_image_u8  * img);
-CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
-CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
-CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
+void clip_image_size_free (struct clip_image_size * img_size);
+void clip_image_u8_free (struct clip_image_u8  * img);
+void clip_image_f32_free(struct clip_image_f32 * img);
+void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
+void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
 
 // use for accessing underlay data of clip_image_f32_batch
-CLIP_API size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size()
-CLIP_API size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx
-CLIP_API size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
-CLIP_API struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
+size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size()
+size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx
+size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
+struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
 
 /**
  * Build image from pixels decoded by other libraries instead of stb_image.h for better performance.
  * The memory layout is RGBRGBRGB..., input buffer length must be 3*nx*ny bytes
  */
-CLIP_API void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img);
+void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img);
 
-CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
+bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
 
 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */
-CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
+bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
 
 /** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
-CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
+bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
 
-CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
+struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
 
-CLIP_API bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
-CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
+bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
+bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
 
-CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
+int clip_is_minicpmv(const struct clip_ctx * ctx);
+bool clip_is_glm(const struct clip_ctx * ctx);
+bool clip_is_qwen2vl(const struct clip_ctx * ctx);
+bool clip_is_llava(const struct clip_ctx * ctx);
+bool clip_is_gemma3(const struct clip_ctx * ctx);
 
-CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_llava(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_gemma3(const struct clip_ctx * ctx);
-
-CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
-
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif // CLIP_H
+bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);

tools/mtmd/llava.cpp

@@ -1,591 +0,0 @@
-#include "clip.h"
-#include "llava.h"
-
-#include "llama.h"
-#include "ggml-cpp.h"
-
-#include <algorithm>
-#include <cerrno>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <limits>
-#include <vector>
-#include <memory>
-
-#if defined(LLAVA_LOG_OFF)
-#   define LOG_INF(...)
-#   define LOG_WRN(...)
-#   define LOG_ERR(...)
-#   define LOG_DBG(...)
-#else // defined(LLAVA_LOG_OFF)
-#   define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
-#   define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
-#   define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
-#   define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
-#endif // defined(LLAVA_LOG_OFF)
-
-// RGB uint8 image
-struct clip_image_u8 {
-    int nx;
-    int ny;
-
-    std::vector<uint8_t> buf;
-};
-
-// RGB float32 image (NHWC)
-// Memory layout: RGBRGBRGB...
-struct clip_image_f32 {
-    int nx;
-    int ny;
-
-    std::vector<float> buf;
-};
-
-struct clip_image_grid_shape {
-    int first;
-    int second;
-};
-
-// convenience cpp wrapper
-struct clip_image_f32_batch_deleter {
-    void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
-};
-typedef std::unique_ptr<clip_image_f32_batch, clip_image_f32_batch_deleter> clip_image_f32_batch_ptr;
-
-struct clip_image_size_deleter {
-    void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
-};
-typedef std::unique_ptr<clip_image_size, clip_image_size_deleter> clip_image_size_ptr;
-
-/**
- * Selects the best resolution from a list of possible resolutions based on the original size.
- *
- * @param original_size The original size of the image in the format (width, height).
- * @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
- * @return The best fit resolution in the format (width, height).
- */
-static std::pair<int, int> select_best_resolution(const std::pair<int, int>& original_size, const std::vector<std::pair<int, int>>& possible_resolutions) {
-    int original_width  = original_size.first;
-    int original_height = original_size.second;
-
-    std::pair<int, int> best_fit;
-    int max_effective_resolution = 0;
-    int min_wasted_resolution = std::numeric_limits<int>::max();
-
-    for (const auto& resolution : possible_resolutions) {
-        int width = resolution.first;
-        int height = resolution.second;
-        float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
-        int downscaled_width  = static_cast<int>(original_width * scale);
-        int downscaled_height = static_cast<int>(original_height * scale);
-        int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
-        int wasted_resolution = (width * height) - effective_resolution;
-        // LOG_DBG("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
-        if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
-            max_effective_resolution = effective_resolution;
-            min_wasted_resolution = wasted_resolution;
-            best_fit = resolution;
-        }
-    }
-
-    return best_fit;
-}
-
-/**
- * @brief Get the anyres image grid shape object
- *
- * @param image_size
- * @param grid_pinpoints
- * @param image_patch_size
- * @return <int, int>
- */
-static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair<int, int> & image_size, const std::vector<std::pair<int, int>> & grid_pinpoints, int image_patch_size) {
-    /**
-        Conversion from gguf flat array to vector:
-        std::vector<std::pair<int, int>> possible_resolutions;
-        for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
-            possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
-        }
-     */
-    auto best_resolution = select_best_resolution(image_size, grid_pinpoints);
-    return {best_resolution.first / image_patch_size, best_resolution.second / image_patch_size};
-}
-
-// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out)
-static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out, clip_image_f32 * img_input) {
-    struct {
-        struct ggml_context * ctx;
-    } model;
-
-    const int32_t image_size = clip_get_image_size(ctx_clip);
-    const int32_t patch_size = clip_get_patch_size(ctx_clip);
-
-    int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches)
-
-    int num_patches_width  = grid_shape.first;  // grid 1-4
-    int num_patches_height = grid_shape.second; // grid 1-4
-
-    const size_t num_images = num_patches_width * num_patches_height + 1;
-
-    // TODO: size calculation is not calculated - it's only tens of MB
-    size_t ctx_size = 0;
-
-    {
-        ctx_size += clip_embd_nbytes(ctx_clip) * num_images * 8; // image_features
-        ctx_size += 1024*1024 * ggml_type_size(GGML_TYPE_F32);
-    }
-
-    struct ggml_init_params params {
-        /*.mem_size   =*/ ctx_size,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ false, // NOTE: this should be false when using the legacy API
-    };
-
-    // Python reference code for full unpad:
-    /*
-        base_image_feature = image_feature[0]
-        image_feature = image_feature[1:]
-        image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
-        image_feature = image_feature.flatten(1, 2).flatten(2, 3)
-        image_feature = unpad_image(image_feature, image_sizes[image_idx])
-        image_feature = torch.cat((
-            image_feature,
-            self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1)
-        ), dim=-1)
-        image_feature = image_feature.flatten(1, 2).transpose(0, 1)
-        image_feature = torch.cat((base_image_feature, image_feature), dim=0)
-    */
-    // We now have two options: unpad or no unpad. Unpad removes tokens for faster llm eval.
-    // In terms of result quality it appears to make no difference, so we'll start with the easier approach given 5D tensors are not supported in ggml yet.
-    // Without unpad we have to split the sub-image embeddings into patches of 24 features each and permute them.
-    // Once all images are processed to prepended the base_image_features without any changes.
-
-    // Pytorch reference simplified, modified for ggml compatibility - confirmed identical output in python (for a 2x2 grid image (676x676 scaling))
-    /*
-        image_feature = image_feature.view(2, 2, 24, 24, 4096)
-        image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
-        image_feature = image_feature.view(2, 24, 2, 24, 4096)
-        image_feature = image_feature.flatten(0, 3)
-
-        // Reshape to 4D tensor by merging the last two dimensions
-        image_feature = image_feature.view(2, 2, 24, 24*4096)
-        image_feature = image_feature.permute(0, 2, 1, 3).contiguous()
-        image_feature = image_feature.view(-1, 4096)
-    */
-
-    model.ctx = ggml_init(params);
-
-    struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_output_tokens(ctx_clip, img_input), num_images - 1); // example: 4096 x 576 x 4
-    // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false);
-    // fill it with the image embeddings, ignoring the base
-    for (size_t i = 1; i < num_images; i++) {
-        size_t offset = (i-1) * clip_embd_nbytes(ctx_clip);
-        memcpy((uint8_t *)(image_features->data) + offset, image_embd_v[i], clip_embd_nbytes(ctx_clip));
-    }
-
-    struct ggml_cgraph  * gf = ggml_new_graph(model.ctx);
-    size_t size_ele = ggml_type_size(GGML_TYPE_F32);
-
-    struct ggml_tensor *image_features_patchview = ggml_view_4d(model.ctx, image_features,
-                                                                num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
-                                                                num_patches_per_side,
-                                                                num_patches_width,
-                                                                num_patches_height,
-                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
-                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side,
-                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side * num_patches_width, 0);
-    // ggml_tensor_printf(image_features_patchview,"image_features_patchview",__LINE__,false,false);
-    struct ggml_tensor *permuted_cont = ggml_cont(model.ctx, ggml_permute(model.ctx, image_features_patchview, 0, 2, 1, 3));
-    /**
-     At the end of each row we have to add the row_end embeddings, which are the same as the newline embeddings
-         image_feature = torch.cat((
-        image_feature,
-        self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
-    ), dim=-1)
-     *
-     */
-
-    // ggml_tensor_printf(permuted_cont,"permuted_cont",__LINE__,false,false);
-    struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side,  size_ele * clip_n_mmproj_embd(ctx_clip), 0);
-    // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
-    ggml_build_forward_expand(gf, flatten);
-
-    ggml_backend_ptr backend { ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr) };
-    GGML_ASSERT(backend != nullptr && "failed to initialize CPU backend");
-    ggml_backend_graph_compute(backend.get(), gf);
-
-    struct ggml_tensor* result = ggml_graph_node(gf, -1);
-
-    memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
-    // append without newline tokens (default behavior in llava_arch when not using unpad ):
-    memcpy(image_embd_out + clip_n_output_tokens(ctx_clip, img_input) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
-    *n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_output_tokens(ctx_clip, img_input));
-
-    // Debug: Test single segments
-    // Current findings: sending base image, sending a segment embedding all works similar to python
-    // However, permuted embeddings do not work yet (stride issue?)
-    // memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as context
-    // memcpy(image_embd_out, (float*)prepared_cont->data, clip_embd_nbytes(ctx_clip)); // main image as context
-    // *n_img_pos_out=576;
-
-    ggml_free(model.ctx);
-    return true;
-}
-
-static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) {
-    int width = image->nx;
-    int height = image->ny;
-    int num_patches = (height / patch_size) * (width / patch_size);
-    clip_image_f32 * patch = clip_image_f32_init();
-    patch->nx = patch_size * num_patches;
-    patch->ny = patch_size;
-    patch->buf.resize(3 * patch->nx * patch->ny);
-
-    int patch_index = 0;
-
-    for (int i = 0; i < height; i += patch_size) {
-        for (int j = 0; j < width; j += patch_size) {
-            for (int pi = 0; pi < patch_size; ++pi) {
-                for (int pj = 0; pj < patch_size; ++pj) {
-                    int input_index = ((i + pi) * width + (j + pj)) * 3;
-                    int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3;
-                    patch->buf[output_index] = image->buf[input_index];
-                    patch->buf[output_index+1] = image->buf[input_index+1];
-                    patch->buf[output_index+2] = image->buf[input_index+2];
-                }
-            }
-            patch_index++;
-        }
-    }
-    return patch;
-}
-
-static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
-    // std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
-    clip_image_f32_batch_ptr img_res_v(clip_image_f32_batch_init());
-    if (!clip_image_preprocess(ctx_clip, img, img_res_v.get())) {
-        LOG_ERR("%s: unable to preprocess image\n", __func__);
-        return false;
-    }
-
-    const int64_t t_img_enc_start_us = ggml_time_us();
-
-    const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
-
-    const size_t n_imgs = clip_image_f32_batch_n_images(img_res_v.get());
-
-    if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) {
-        std::vector<float *> image_embd_v;
-        image_embd_v.resize(n_imgs);
-        clip_image_size load_image_size;
-
-        for (size_t i = 0; i < n_imgs; i++) {
-            const int64_t t_img_enc_step_start_us = ggml_time_us();
-            int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
-            int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, nx, ny));
-            int patch_size = 14;
-            load_image_size.width = nx;
-            load_image_size.height = ny;
-            clip_add_load_image_size(ctx_clip, &load_image_size);
-
-            bool encoded = false;
-            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
-            if (clip_is_qwen2vl(ctx_clip)) {
-                encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]);
-            }
-            else {
-                encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(img_res, patch_size), image_embd_v[i]);
-            }
-
-            if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
-                return false;
-            }
-            const int64_t t_img_enc_steop_batch_us = ggml_time_us();
-            LOG_INF("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)n_imgs, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0);
-        }
-        const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
-
-        int n_img_pos_out = 0;
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
-            int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
-            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
-            std::memcpy(
-                image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
-                image_embd_v[i],
-                clip_embd_nbytes_by_img(ctx_clip, nx, ny));
-            n_img_pos_out += clip_n_output_tokens(ctx_clip, img_res);
-        }
-        *n_img_pos = n_img_pos_out;
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            free(image_embd_v[i]);
-        }
-        image_embd_v.clear();
-        load_image_size.width = img->nx;
-        load_image_size.height = img->ny;
-        clip_add_load_image_size(ctx_clip, &load_image_size);
-        LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size.width, load_image_size.height);
-    }
-    else if (clip_is_glm(ctx_clip)){
-        struct clip_image_size * load_image_size = clip_image_size_init();
-        load_image_size->width  = clip_image_f32_batch_nx(img_res_v.get(), 0);
-        load_image_size->height = clip_image_f32_batch_ny(img_res_v.get(), 0);
-        clip_add_load_image_size(ctx_clip, load_image_size);
-
-        clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
-        bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
-        int pos = int(load_image_size->width/clip_get_patch_size(ctx_clip)/2);
-        *n_img_pos = (pos * pos + 2);
-        if (!encoded){
-            LOG_ERR("Unable to encode image \n");
-            return false;
-        }
-    }
-    else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
-        // flat / default llava-1.5 type embedding
-        clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
-        *n_img_pos = clip_n_output_tokens(ctx_clip, img_res);
-        bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096
-        if (!encoded) {
-            LOG_ERR("Unable to encode image\n");
-
-            return false;
-        }
-    }
-    else {
-        // spatial_unpad llava-1.6 type embedding
-        // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
-        std::vector<float *> image_embd_v;
-        image_embd_v.resize(n_imgs);
-        for (size_t i = 0; i < n_imgs; i++) {
-            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
-            const bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
-            if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
-                return false;
-            }
-        }
-        const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
-
-        const int32_t * image_grid = clip_image_grid(ctx_clip);
-        const size_t num_gridpoints = get_clip_image_grid_size(ctx_clip);
-
-        std::vector<std::pair<int, int>> grid_pinpoints;
-        for (size_t i = 0; i < num_gridpoints; i += 2) {
-            grid_pinpoints.push_back({image_grid[i], image_grid[i+1]});
-        }
-
-        const int32_t image_size = clip_get_image_size(ctx_clip);
-
-        struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
-
-        int n_img_pos_out;
-        clip_image_f32 * img_input = clip_image_f32_get_img(img_res_v.get(), 0);
-        clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out, img_input);
-        *n_img_pos = n_img_pos_out;
-
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            free(image_embd_v[i]);
-        }
-        image_embd_v.clear();
-
-        // debug image/segment/normalization content:
-        // clip_image_u8 * tmp = clip_image_u8_init();
-        // clip_image_convert_f32_to_u8(*image_feature, *tmp);
-        // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
-    }
-
-    LOG_INF("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
-
-    const int64_t t_img_enc_end_us = ggml_time_us();
-    float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
-
-    LOG_INF("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
-
-    return true;
-}
-
-bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {
-        // make sure that the correct mmproj was used, i.e., compare apples to apples
-    int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama));
-    auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
-    if (n_image_embd != n_llama_embd) {
-        LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
-        return false;
-    }
-    return true;
-}
-
-bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
-    // Granite vision uses up to 10 patches + base patch
-    int num_max_patches = 11;
-    if (clip_is_minicpmv(ctx_clip)) {
-        num_max_patches = 10;
-    }
-    if (clip_is_glm(ctx_clip)) {
-        num_max_patches = 1;
-    }
-    float * image_embd;
-    if (clip_is_qwen2vl(ctx_clip)) {
-        // qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
-        image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny));
-    } else {
-        image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
-    }
-    if (!image_embd) {
-        LOG_ERR("Unable to allocate memory for image embeddings\n");
-        return false;
-    }
-
-    int n_img_pos;
-    if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
-        LOG_ERR("%s: cannot encode image, aborting\n", __func__);
-        free(image_embd);
-        return false;
-    }
-    *image_embd_out = image_embd;
-    *n_img_pos_out = n_img_pos;
-
-    return true;
-}
-
-struct llava_embd_batch {
-    std::vector<llama_pos>      pos;
-    std::vector<int32_t>        n_seq_id;
-    std::vector<llama_seq_id>   seq_id_0;
-    std::vector<llama_seq_id *> seq_ids;
-    std::vector<int8_t>         logits;
-    llama_batch batch;
-    llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
-        pos     .resize(n_tokens);
-        n_seq_id.resize(n_tokens);
-        seq_ids .resize(n_tokens + 1);
-        logits  .resize(n_tokens);
-        seq_id_0.resize(1);
-        seq_id_0[0] = seq_id;
-        seq_ids [n_tokens] = nullptr;
-        batch = {
-            /*n_tokens       =*/ n_tokens,
-            /*tokens         =*/ nullptr,
-            /*embd           =*/ embd,
-            /*pos            =*/ pos.data(),
-            /*n_seq_id       =*/ n_seq_id.data(),
-            /*seq_id         =*/ seq_ids.data(),
-            /*logits         =*/ logits.data(),
-        };
-        for (int i = 0; i < n_tokens; i++) {
-            batch.pos     [i] = pos_0 + i;
-            batch.n_seq_id[i] = 1;
-            batch.seq_id  [i] = seq_id_0.data();
-            batch.logits  [i] = false;
-        }
-    }
-};
-
-bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
-    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llama));
-
-    for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {
-        int n_eval = image_embed->n_image_pos - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        float * embd = image_embed->embed+i*n_embd;
-        llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
-        if (llama_decode(ctx_llama, llava_batch.batch)) {
-            LOG_ERR("%s : failed to eval\n", __func__);
-            return false;
-        }
-        *n_past += n_eval;
-    }
-    return true;
-}
-
-struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
-    clip_image_u8 * img = clip_image_u8_init();
-    if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
-        clip_image_u8_free(img);
-        LOG_ERR("%s: can't load image from bytes, is it a valid image?", __func__);
-        return NULL;
-    }
-
-    float* image_embed = NULL;
-    int n_image_pos = 0;
-    bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
-    if (!image_embed_result) {
-        clip_image_u8_free(img);
-        LOG_ERR("%s: couldn't embed the image\n", __func__);
-        return NULL;
-    }
-
-    clip_image_u8_free(img);
-    auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed));
-    result->embed = image_embed;
-    result->n_image_pos = n_image_pos;
-    return result;
-}
-
-static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
-    auto file = fopen(path, "rb");
-    if (file == NULL) {
-        LOG_ERR("%s: can't read file %s\n", __func__, path);
-        return false;
-    }
-
-    fseek(file, 0, SEEK_END);
-    auto fileSize = ftell(file);
-    fseek(file, 0, SEEK_SET);
-
-    auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
-    if (buffer == NULL) {
-        LOG_ERR("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
-        perror("Memory allocation error");
-        fclose(file);
-        return false;
-    }
-    errno = 0;
-    size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer
-    if (ferror(file)) {
-        LOG_ERR("read error: %s", strerror(errno));
-        free(buffer);
-        fclose(file);
-        return false;
-    }
-    if (ret != (size_t) fileSize) {
-        LOG_ERR("unexpectedly reached end of file");
-        free(buffer);
-        fclose(file);
-        return false;
-    }
-    fclose(file); // Close the file
-
-    *bytesOut = buffer;
-    *sizeOut = fileSize;
-    return true;
-}
-
-struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
-    unsigned char* image_bytes;
-    long image_bytes_length;
-    auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
-    if (!loaded) {
-        LOG_ERR("%s: failed to load %s\n", __func__, image_path);
-        return NULL;
-    }
-
-    llava_image_embed *embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
-    free(image_bytes);
-
-    return embed;
-}
-
-void llava_image_embed_free(struct llava_image_embed * embed) {
-    free(embed->embed);
-    free(embed);
-}

tools/mtmd/llava.h

@@ -1,49 +0,0 @@
-#ifndef LLAVA_H
-#define LLAVA_H
-
-#include "ggml.h"
-
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define LLAVA_API __declspec(dllexport)
-#        else
-#            define LLAVA_API __declspec(dllimport)
-#        endif
-#    else
-#        define LLAVA_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define LLAVA_API
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-struct clip_ctx;
-struct llava_image_embed {
-    float * embed;
-    int n_image_pos;
-};
-
-/** sanity check for clip <-> llava embed size match */
-LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
-
-LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
-
-/** build an image embed from image file bytes */
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
-/** build an image embed from a path to an image filename */
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
-/** free an embedding made with llava_image_embed_make_* */
-LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
-
-/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
-LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif

tools/mtmd/qwen2vl-test.cpp

@@ -1,636 +0,0 @@
-#include "arg.h"
-#include "base64.hpp"
-#include "log.h"
-#include "common.h"
-#include "sampling.h"
-#include "clip.h"
-#include "llava.h"
-#include "llama.h"
-#include "ggml.h"
-
-#ifdef GGML_USE_CUDA
-#include "ggml-cuda.h"
-#endif
-#ifdef NDEBUG
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-#endif
-
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <vector>
-#include <algorithm>
-#include <iostream>
-#include <fstream>
-#include <limits>
-#include <cassert>
-#include <cmath>
-
-// THIS FILE IS ONLY USED FOR TESTING THE QWEN2VL MODEL
-// IT IS NOT A PRODUCTION CODE
-
-static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
-                                     int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
-    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llama));
-    const int patch_size = 14 * 2;
-    const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
-    const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
-    auto img_tokens = image_embed->n_image_pos;
-    // llama_pos mrope_pos[img_tokens * 4];
-    std::vector<llama_pos> mrope_pos;
-    mrope_pos.resize(img_tokens * 4);
-
-    for (int y = 0; y < ph; y++)
-    {
-        for (int x = 0; x < pw; x++)
-        {
-            int i = y * pw + x;
-            mrope_pos[i] = *st_pos_id;
-            mrope_pos[i + img_tokens] = *st_pos_id + y;
-            mrope_pos[i + img_tokens * 2] = *st_pos_id + x;
-            mrope_pos[i + img_tokens * 3] = 0;
-        }
-    }
-    *st_pos_id += std::max(pw, ph);
-
-    int processed = 0;
-    std::vector<llama_pos> batch_mrope_pos;
-    batch_mrope_pos.resize(img_tokens * 4);
-
-    for (int i = 0; i < img_tokens; i += n_batch) {
-        int n_eval = img_tokens - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-
-        // llama_pos batch_mrope_pos[n_eval * 4];
-        std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0);
-        memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos));
-        memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos));
-        memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos));
-        memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos));
-
-        llama_batch batch = {
-            int32_t(n_eval),                // n_tokens
-            nullptr,                        // token
-            (image_embed->embed+i*n_embd),  // embed
-            batch_mrope_pos.data(),         // pos
-            nullptr,  // n_seq_id
-            nullptr,  // seq_id
-            nullptr,  // logits
-        };
-
-        if (llama_decode(ctx_llama, batch)) {
-            LOG_ERR("%s : failed to eval\n", __func__);
-            return false;
-        }
-        *n_past += n_eval;
-        processed += n_eval;
-    }
-    return true;
-}
-
-
-static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
-    int N = (int) tokens.size();
-    for (int i = 0; i < N; i += n_batch) {
-        int n_eval = (int) tokens.size() - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        auto batch = llama_batch_get_one(&tokens[i], n_eval);
-
-        if (llama_decode(ctx_llama, batch)) {
-            LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
-            return false;
-        }
-        *n_past += n_eval;
-        *st_pos_id += n_eval;
-    }
-    return true;
-}
-
-static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) {
-    std::vector<llama_token> tokens;
-    tokens.push_back(id);
-    return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id);
-}
-
-static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){
-    std::string              str2     = str;
-    std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
-    eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id);
-    return true;
-}
-
-static const char * sample(struct common_sampler * smpl,
-                           struct llama_context * ctx_llama,
-                           int * n_past, int * st_pos_id) {
-    const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
-    common_sampler_accept(smpl, id, true);
-
-    const llama_model * model = llama_get_model(ctx_llama);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    static std::string ret;
-    if (llama_vocab_is_eog(vocab, id)) {
-        ret = "</s>";
-    } else {
-        ret = common_token_to_piece(ctx_llama, id);
-    }
-    eval_id(ctx_llama, id, n_past, st_pos_id);
-    return ret.c_str();
-}
-
-static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
-static const char* IMG_BASE64_TAG_END = "\">";
-
-static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
-    begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
-    end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
-}
-
-static bool prompt_contains_image(const std::string& prompt) {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    return (begin != std::string::npos);
-}
-
-// replaces the base64 image tag in the prompt with `replacement`
-static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
-    size_t img_base64_str_start, img_base64_str_end;
-    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
-    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
-        return NULL;
-    }
-
-    auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
-    auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
-    auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
-
-    auto required_bytes = base64::required_encode_size(base64_str.size());
-    auto img_bytes = std::vector<unsigned char>(required_bytes);
-    base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
-
-    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
-    if (!embed) {
-        LOG_ERR("%s: could not load image from base64 string.\n", __func__);
-        return NULL;
-    }
-
-    return embed;
-}
-
-static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    if (begin == std::string::npos || end == std::string::npos) {
-        return prompt;
-    }
-    auto pre = prompt.substr(0, begin);
-    auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
-    return pre + replacement + post;
-}
-
-struct llava_context {
-    struct clip_ctx * ctx_clip = NULL;
-    struct llama_context * ctx_llama = NULL;
-    struct llama_model * model = NULL;
-};
-
-static void print_usage(int, char ** argv) {
-    LOG("\n example usage:\n");
-    LOG("\n     %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
-}
-
-static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
-
-    // load and preprocess the image
-    llava_image_embed * embed = NULL;
-    auto prompt = params->prompt;
-    if (prompt_contains_image(prompt)) {
-        if (!params->image.empty()) {
-            LOG_INF("using base64 encoded image instead of command line image path\n");
-        }
-        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
-        if (!embed) {
-            LOG_ERR("%s: can't load image from prompt\n", __func__);
-            return NULL;
-        }
-        params->prompt = remove_image_from_prompt(prompt);
-    } else {
-        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
-        if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
-            return NULL;
-        }
-    }
-
-    return embed;
-}
-
-static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
-    int n_past = 0;
-    int cur_pos_id = 0;
-
-    const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
-
-    std::string system_prompt, user_prompt;
-    size_t image_pos = prompt.find("<|vision_start|>");
-    if (image_pos != std::string::npos) {
-        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
-        system_prompt = prompt.substr(0, image_pos);
-        user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length());
-        LOG_INF("system_prompt: %s\n", system_prompt.c_str());
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-        LOG_INF("user_prompt: %s\n", user_prompt.c_str());
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-    } else {
-        // llava-1.5 native mode
-        system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>";
-        user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n";
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-    }
-
-    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true);
-    if (image_embed != nullptr) {
-        auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip);
-        qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size);
-    }
-    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false);
-
-    // generate the response
-
-    LOG("\n");
-
-    struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
-    if (!smpl) {
-        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
-        exit(1);
-    }
-
-    std::string response = "";
-    for (int i = 0; i < max_tgt_len; i++) {
-        const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id);
-        response += tmp;
-        if (strcmp(tmp, "</s>") == 0) break;
-        if (strstr(tmp, "###")) break; // Yi-VL behavior
-        LOG("%s", tmp);
-        if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
-        if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
-        if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
-
-        fflush(stdout);
-    }
-
-    common_sampler_free(smpl);
-    LOG("\n");
-}
-
-static struct llama_model * llava_init(common_params * params) {
-    llama_backend_init();
-    llama_numa_init(params->numa);
-
-    llama_model_params model_params = common_model_params_to_llama(*params);
-
-    llama_model * model = llama_model_load_from_file(params->model.path.c_str(), model_params);
-    if (model == NULL) {
-        LOG_ERR("%s: unable to load model\n" , __func__);
-        return NULL;
-    }
-    return model;
-}
-
-static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
-    const char * clip_path = params->mmproj.path.c_str();
-
-    auto prompt = params->prompt;
-    if (prompt.empty()) {
-        prompt = "describe the image in detail.";
-    }
-
-    auto ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO);
-
-    llama_context_params ctx_params = common_context_params_to_llama(*params);
-    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
-
-    llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
-
-    if (ctx_llama == NULL) {
-        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
-        return NULL;
-    }
-
-    auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
-
-    ctx_llava->ctx_llama = ctx_llama;
-    ctx_llava->ctx_clip = ctx_clip;
-    ctx_llava->model = model;
-    return ctx_llava;
-}
-
-static void llava_free(struct llava_context * ctx_llava) {
-    if (ctx_llava->ctx_clip) {
-        clip_free(ctx_llava->ctx_clip);
-        ctx_llava->ctx_clip = NULL;
-    }
-
-    llama_free(ctx_llava->ctx_llama);
-    llama_model_free(ctx_llava->model);
-    llama_backend_free();
-}
-
-#ifndef NDEBUG
-
-static void debug_test_mrope_2d() {
-    // 1. Initialize backend
-    ggml_backend_t backend = NULL;
-    std::string backend_name = "";
-// #ifdef GGML_USE_CUDA
-//     fprintf(stderr, "%s: using CUDA backend\n", __func__);
-//     backend = ggml_backend_cuda_init(0); // init device 0
-//     backend_name = "cuda";
-//     if (!backend) {
-//         fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
-//     }
-// #endif
-    // if there aren't GPU Backends fallback to CPU backend
-    if (!backend) {
-        backend = ggml_backend_cpu_init();
-        backend_name = "cpu";
-    }
-
-    // Calculate the size needed to allocate
-    size_t ctx_size = 0;
-    ctx_size += 2 * ggml_tensor_overhead(); // tensors
-    // no need to allocate anything else!
-
-    // 2. Allocate `ggml_context` to store tensor data
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ ctx_size,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
-    };
-    struct ggml_context * ctx = ggml_init(params);
-
-    struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30);
-    ggml_set_name(inp_raw, "inp_raw");
-    ggml_set_input(inp_raw);
-
-    struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4);
-    ggml_set_name(pos, "pos");
-    ggml_set_input(pos);
-
-    std::vector<float> dummy_q;
-    dummy_q.resize(128 * 12 * 30);
-    std::fill(dummy_q.begin(), dummy_q.end(), 0.1);
-    // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw));
-
-    std::vector<int> pos_id;
-    pos_id.resize(30 * 4);
-    for (int i = 0; i < 30; i ++) {
-        pos_id[i] = i;
-        pos_id[i + 30] = i + 10;
-        pos_id[i + 60] = i + 20;
-        pos_id[i + 90] = i + 30;
-    }
-    int sections[4] = {32, 32, 0, 0};
-
-    // 4. Allocate a `ggml_backend_buffer` to store all tensors
-    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
-
-    // 5. Copy tensor data from main memory (RAM) to backend buffer
-    ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw));
-    ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos));
-
-    // 6. Create a `ggml_cgraph` for mul_mat operation
-    struct ggml_cgraph * gf = NULL;
-    struct ggml_context * ctx_cgraph = NULL;
-
-    // create a temporally context to build the graph
-    struct ggml_init_params params0 = {
-        /*.mem_size   =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
-    };
-    ctx_cgraph = ggml_init(params0);
-    gf = ggml_new_graph(ctx_cgraph);
-
-    struct ggml_tensor * result0 = ggml_rope_multi(
-        ctx_cgraph, inp_raw, pos, nullptr,
-        128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1,
-        0, 1, 32, 1);
-
-    // Add "result" tensor and all of its dependencies to the cgraph
-    ggml_build_forward_expand(gf, result0);
-
-    // 7. Create a `ggml_gallocr` for cgraph computation
-    ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
-    ggml_gallocr_alloc_graph(allocr, gf);
-
-    // 9. Run the computation
-    int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading
-    if (ggml_backend_is_cpu(backend)) {
-        ggml_backend_cpu_set_n_threads(backend, n_threads);
-    }
-    ggml_backend_graph_compute(backend, gf);
-
-    // 10. Retrieve results (output tensors)
-    // in this example, output tensor is always the last tensor in the graph
-    struct ggml_tensor * result = result0;
-    // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1];
-    float * result_data = (float *)malloc(ggml_nbytes(result));
-    // because the tensor data is stored in device buffer, we need to copy it back to RAM
-    ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
-    const std::string bin_file = "mrope_2d_" + backend_name +".bin";
-    std::ofstream outFile(bin_file, std::ios::binary);
-
-    if (outFile.is_open()) {
-        outFile.write(reinterpret_cast<const char*>(result_data), ggml_nbytes(result));
-        outFile.close();
-        std::cout << "Data successfully written to " + bin_file << std::endl;
-    } else {
-        std::cerr << "Error opening file!" << std::endl;
-    }
-
-    free(result_data);
-    // 11. Free memory and exit
-    ggml_free(ctx_cgraph);
-    ggml_gallocr_free(allocr);
-    ggml_free(ctx);
-    ggml_backend_buffer_free(buffer);
-    ggml_backend_free(backend);
-}
-
-enum model_output_type {
-    conv3d,
-    patch_embed,
-    patch_win_attn_scatter,
-    first_attn_layer,
-    last_attn_layer,
-    attn_softmax,
-    final_layer,
-};
-
-static void debug_dump_img_embed(struct llava_context * ctx_llava, model_output_type output_type) {
-    constexpr int ih = 140;
-    constexpr int iw = 196;
-    // constexpr int ih = 56;
-    // constexpr int iw = 56;
-    // int n_embd  = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama));
-    int n_embd  = 1280;
-    int merge = 1;
-    if (output_type == model_output_type::final_layer) {
-        n_embd  = 2048;
-        merge = 2;
-    }
-    else if (output_type == model_output_type::attn_softmax) {
-        merge = 1;
-        n_embd = (ih/14/merge) * (iw/14/merge) * 16;
-    }
-
-    int ne = (ih/14/merge) * (iw/14/merge) * n_embd;
-    float vals[iw * ih * 3];
-    // float embd[ne];
-    std::vector<float> embd;
-    embd.resize(ne);
-
-    for (int i = 0; i < iw*ih; i++)
-    {
-        for (int c = 0; c < 3; c++)
-            vals[i * 3 + c] = (float)i / (iw*ih);
-    }
-
-    clip_encode_float_image(ctx_llava->ctx_clip, 8, vals, ih, iw, embd.data());
-
-    std::string file_postfix = "";
-    switch (output_type)
-    {
-    case model_output_type::conv3d:
-        file_postfix = "conv3d";
-        break;
-    case model_output_type::patch_embed:
-        file_postfix = "patch_embed";
-        break;
-    case model_output_type::patch_win_attn_scatter:
-        file_postfix = "scatter";
-        break;
-    case model_output_type::first_attn_layer:
-        file_postfix = "first_attn";
-        break;
-    case model_output_type::last_attn_layer:
-        file_postfix = "last_attn";
-        break;
-    case model_output_type::attn_softmax:
-        file_postfix = "attn_softmax";
-        break;
-    case model_output_type::final_layer:
-        file_postfix = "final";
-        break;
-    default:
-        break;
-    }
-    auto output_path = "img_embed_" + file_postfix + ".bin";
-
-    std::ofstream outFile(output_path, std::ios::binary);
-    if (outFile.is_open()) {
-        outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
-
-        outFile.close();
-        std::cout << "Data successfully written to ::[ " << output_path << std::endl;
-    } else {
-        std::cerr << "Error opening file!" << std::endl;
-    }
-}
-
-#endif
-
-
-int main(int argc, char ** argv) {
-    ggml_time_init();
-
-    common_params params;
-
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
-        return 1;
-    }
-
-    common_init();
-
-    if (params.mmproj.path.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
-        print_usage(argc, argv);
-        return 1;
-    }
-
-    auto * model = llava_init(&params);
-    if (model == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
-        return 1;
-    }
-
-    if (prompt_contains_image(params.prompt)) {
-        auto * ctx_llava = llava_init_context(&params, model);
-
-        auto * image_embed = load_image(ctx_llava, &params, "");
-
-        // process the prompt
-        process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-        llama_perf_context_print(ctx_llava->ctx_llama);
-        llava_image_embed_free(image_embed);
-        ctx_llava->model = NULL;
-        llava_free(ctx_llava);
-#ifndef NDEBUG
-    } else if (params.image[0].empty()) {
-        auto ctx_llava = llava_init_context(&params, model);
-
-        // debug_test_mrope_2d();
-        debug_dump_img_embed(ctx_llava, model_output_type::final_layer);
-        // debug_dump_img_embed(ctx_llava, model_output_type::last_attn_layer);
-
-        llama_perf_context_print(ctx_llava->ctx_llama);
-        ctx_llava->model = NULL;
-        llava_free(ctx_llava);
-#endif
-    } else {
-        for (auto & image : params.image) {
-            auto * ctx_llava = llava_init_context(&params, model);
-
-            auto * image_embed = load_image(ctx_llava, &params, image);
-            if (!image_embed) {
-                LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
-                return 1;
-            }
-
-            // process the prompt
-            process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-            llama_perf_context_print(ctx_llava->ctx_llama);
-            llava_image_embed_free(image_embed);
-            ctx_llava->model = NULL;
-            llava_free(ctx_llava);
-        }
-    }
-
-    llama_model_free(model);
-
-    return 0;
-}

tools/quantize/quantize.cpp

@@ -57,6 +57,12 @@ static const std::vector<quant_option> QUANT_OPTIONS = {
     { "COPY",     LLAMA_FTYPE_ALL_F32,         "only copy tensors, no quantizing",  },
 };
 
+// Quantization types. Changes to this struct must be replicated in llama-quantize.cpp
+struct tensor_quantization {
+    std::string name;
+    ggml_type quant = GGML_TYPE_COUNT;
+};
+
 static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE       = "quantize.imatrix.file";
 static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET    = "quantize.imatrix.dataset";
 static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES  = "quantize.imatrix.entries_count";
@@ -244,56 +250,10 @@ static ggml_type parse_ggml_type(const char * arg) {
             return type;
         }
     }
-    fprintf(stderr, "%s: invalid ggml_type '%s'\n", __func__, arg);
+    fprintf(stderr, "\n%s: invalid ggml_type '%s'\n\n", __func__, arg);
     return GGML_TYPE_COUNT;
 }
 
-// Allowed tensors for arbitrary quantization with --tensor-type option
-static const std::vector<std::string> ALLOWED_TENSOR_TYPE = {
-    "attn_k",
-    "attn_kv_a_mqa",
-    "attn_kv_b",
-    "attn_o",
-    "attn_output",
-    "attn_q",
-    "attn_q_a",
-    "attn_q_b",
-    "attn_qkv",
-    "attn_v",
-    "channel_mix_key",
-    "channel_mix_receptance",
-    "channel_mix_value",
-    "cls",
-    "cls.output",
-    "cross_attn_k",
-    "cross_attn_o",
-    "cross_attn_q",
-    "cross_attn_v",
-    "ffn_act",
-    "ffn_down",
-    "ffn_down_exps",
-    "ffn_down_shexp",
-    "ffn_gate",
-    "ffn_gate_exps",
-    "ffn_gate_shexp",
-    "ffn_up",
-    "ffn_up_exps",
-    "ffn_up_shexp",
-    "ssm_in",
-    "ssm_out",
-    "time_mix_gate",
-    "time_mix_key",
-    "time_mix_output",
-    "time_mix_receptance",
-    "time_mix_value",
-};
-
-// changes to this struct must be replicated in llama-quant.cpp
-struct tensor_quantization {
-    std::string name;
-    ggml_type quant = GGML_TYPE_COUNT;
-};
-
 static bool parse_tensor_type(const char * data, std::vector<tensor_quantization> & tensor_type) {
     const char * sep = strchr(data, '=');
     if (sep == nullptr) {
@@ -306,7 +266,6 @@ static bool parse_tensor_type(const char * data, std::vector<tensor_quantization
         printf("\n%s: missing tensor name\n\n", __func__);
         return false;
     }
-
     if (const size_t qt_len = strlen(sep); qt_len == 1) {
         printf("\n%s: missing quantization type\n\n", __func__);
         return false;
@@ -315,37 +274,15 @@ static bool parse_tensor_type(const char * data, std::vector<tensor_quantization
     std::string tn(data, tn_len);
     std::transform(tn.begin(), tn.end(), tn.begin(), tolower);
     sep++;
-    const std::string qt(sep);
-
-    bool found = false;
-    for (const auto & allowed : ALLOWED_TENSOR_TYPE) {
-        std::string tensor;
-        tensor = tn.rfind('.') != std::string::npos ? tn.substr(tn.rfind('.') + 1) : tn;
-        // handle special case of cls.output
-        std::string cls_output = "cls.output";
-        if (tn.find(cls_output) != std::string::npos) {
-            tensor = "cls.output";
-        }
-        // check if an allowed tensor exists and it's at the end of the kv string
-        if (tensor == allowed) {
-            found = true;
-            break;
-        }
-    }
-    if (!found) {
-        printf("\n%s: invalid tensor name '%s'\n\n", __func__, tn.c_str());
-        return false;
-    }
-
-    if (parse_ggml_type(qt.c_str()) == GGML_TYPE_COUNT) {
-        printf("\n%s: invalid quantization type '%s'\n\n", __func__, qt.c_str());
-        return false;
-    }
-
     tensor_quantization tqz;
     tqz.name = tn;
-    tqz.quant = parse_ggml_type(qt.c_str());
+    tqz.quant = parse_ggml_type(sep);
     tensor_type.emplace_back(std::move(tqz));
+    if (tqz.quant == GGML_TYPE_COUNT) {
+        printf("\n%s: invalid quantization type '%s'\n\n", __func__, sep);
+        return false;
+    }
+
     return true;
 }
 

tools/server/README.md

@@ -1040,7 +1040,7 @@ To know the `id` of the adapter, use GET `/lora-adapters`
 
 Returns information about the loaded model. See [OpenAI Models API documentation](https://platform.openai.com/docs/api-reference/models).
 
-The returned list always has one single element.
+The returned list always has one single element. The `meta` field can be `null` (for example, while the model is still loading).
 
 By default, model `id` field is the path to model file, specified via `-m`. You can set a custom value for model `id` field via `--alias` argument. For example, `--alias gpt-4o-mini`.
 

tools/server/server.cpp

@@ -3705,6 +3705,9 @@ int main(int argc, char ** argv) {
             if (req.path == "/" || tmp.back() == "html") {
                 res.set_content(reinterpret_cast<const char*>(loading_html), loading_html_len, "text/html; charset=utf-8");
                 res.status = 503;
+            } else if (req.path == "/models" || req.path == "/v1/models") {
+                // allow the models endpoint to be accessed during loading
+                return true;
             } else {
                 res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
             }
@@ -4363,7 +4366,13 @@ int main(int argc, char ** argv) {
         res_ok(res, {{ "prompt", std::move(data.at("prompt")) }});
     };
 
-    const auto handle_models = [&params, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
+    const auto handle_models = [&params, &ctx_server, &state, &res_ok](const httplib::Request &, httplib::Response & res) {
+        server_state current_state = state.load();
+        json model_meta = nullptr;
+        if (current_state == SERVER_STATE_READY) {
+            model_meta = ctx_server.model_meta();
+        }
+
         json models = {
             {"object", "list"},
             {"data", {
@@ -4372,7 +4381,7 @@ int main(int argc, char ** argv) {
                     {"object",   "model"},
                     {"created",  std::time(0)},
                     {"owned_by", "llamacpp"},
-                    {"meta",     ctx_server.model_meta()}
+                    {"meta",     model_meta},
                 },
              }}
         };

tools/server/webui/package-lock.json

@@ -44,6 +44,7 @@
         "eslint": "^9.17.0",
         "eslint-plugin-react-hooks": "^5.0.0",
         "eslint-plugin-react-refresh": "^0.4.16",
+        "fflate": "^0.8.2",
         "globals": "^15.14.0",
         "prettier": "^3.4.2",
         "sass-embedded": "^1.83.4",
@@ -2802,6 +2803,13 @@
         "reusify": "^1.0.4"
       }
     },
+    "node_modules/fflate": {
+      "version": "0.8.2",
+      "resolved": "https://registry.npmjs.org/fflate/-/fflate-0.8.2.tgz",
+      "integrity": "sha512-cPJU47OaAoCbg0pBvzsgpTPhmhqI5eJjh/JIu8tPj5q+T7iLvW/JAYUqmE7KOB4R1ZyEhzBaIQpQpardBF5z8A==",
+      "dev": true,
+      "license": "MIT"
+    },
     "node_modules/file-entry-cache": {
       "version": "8.0.0",
       "resolved": "https://registry.npmjs.org/file-entry-cache/-/file-entry-cache-8.0.0.tgz",

tools/server/webui/package.json

@@ -5,7 +5,7 @@
   "type": "module",
   "scripts": {
     "dev": "vite",
-    "build": "tsc -b && vite build",
+    "build": "npm run format && tsc -b && vite build",
     "format": "eslint . && prettier --write .",
     "lint": "eslint .",
     "preview": "vite preview"
@@ -47,6 +47,7 @@
     "eslint": "^9.17.0",
     "eslint-plugin-react-hooks": "^5.0.0",
     "eslint-plugin-react-refresh": "^0.4.16",
+    "fflate": "^0.8.2",
     "globals": "^15.14.0",
     "prettier": "^3.4.2",
     "sass-embedded": "^1.83.4",

tools/server/webui/src/components/ChatScreen.tsx

@@ -1,4 +1,4 @@
-import { useEffect, useMemo, useRef, useState } from 'react';
+import { ClipboardEvent, useEffect, useMemo, useRef, useState } from 'react';
 import { CallbackGeneratedChunk, useAppContext } from '../utils/app.context';
 import ChatMessage from './ChatMessage';
 import { CanvasType, Message, PendingMessage } from '../utils/types';
@@ -328,6 +328,17 @@ function ChatInput({
         {({ getRootProps, getInputProps }) => (
           <div
             className="flex flex-col rounded-xl border-1 border-base-content/30 p-3 w-full"
+            onPasteCapture={(e: ClipboardEvent<HTMLInputElement>) => {
+              const files = Array.from(e.clipboardData.items)
+                .filter((item) => item.kind === 'file')
+                .map((item) => item.getAsFile())
+                .filter((file) => file !== null);
+
+              if (files.length > 0) {
+                e.preventDefault();
+                extraContext.onFileAdded(files);
+              }
+            }}
             {...getRootProps()}
           >
             {!isGenerating && (

tools/server/webui/vite.config.ts

@@ -3,7 +3,7 @@ import react from '@vitejs/plugin-react';
 import { viteSingleFile } from 'vite-plugin-singlefile';
 import path from 'node:path';
 import fs from 'node:fs';
-import zlib from 'node:zlib';
+import * as fflate from 'fflate';
 
 /* eslint-disable */
 
@@ -33,9 +33,10 @@ const BUILD_PLUGINS = [
       },
       writeBundle() {
         const outputIndexHtml = path.join(config.build.outDir, 'index.html');
-        const content =
+        let content =
           GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
-        const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), {
+        content = content.replace(/\r/g, ''); // remove windows-style line endings
+        const compressed = fflate.gzipSync(Buffer.from(content, 'utf-8'), {
           level: 9,
         });