From 2e5f70a25fc4576e9ed78603fe493eb7702c37a3 Mon Sep 17 00:00:00 2001
From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com>
Date: Thu, 24 Aug 2023 14:49:30 -0700
Subject: [PATCH 01/53] Added `enum` to `llama_token_get_type` return type
 (#2774)

---
 llama.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama.h b/llama.h
index 4e7638c04..2bcf94e0f 100644
--- a/llama.h
+++ b/llama.h
@@ -348,7 +348,7 @@ extern "C" {
 
     LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
 
-    LLAMA_API llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
+    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
 
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx);  // beginning-of-sentence

From b91ad7f46134d0d051dc516eb59a76f402de55c2 Mon Sep 17 00:00:00 2001
From: Shouzheng Liu <lshzh.hi@gmail.com>
Date: Fri, 25 Aug 2023 01:58:00 -0400
Subject: [PATCH 02/53] ggml-alloc : enlarge size of parse_seq (#2776)

Since we also store barriers in this array, we need to double its size.
---
 ggml-alloc.c | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml-alloc.c b/ggml-alloc.c
index af4affa4e..1ef011654 100644
--- a/ggml-alloc.c
+++ b/ggml-alloc.c
@@ -8,6 +8,7 @@
 
 #define UNUSED(x) (void)(x)
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
+#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
 
 //#define GGML_ALLOCATOR_DEBUG
 
@@ -67,7 +68,7 @@ struct ggml_allocr {
     struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
     size_t max_size;
     bool measure;
-    int parse_seq[GGML_MAX_NODES];
+    int parse_seq[GGML_MAX_CONCUR];
     int parse_seq_len;
 
 #ifdef GGML_ALLOCATOR_DEBUG

From 87e3733f24a85d894cc16e1cbdfa1ea1e81a76f3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= <yusufsarigoz@gmail.com>
Date: Fri, 25 Aug 2023 09:26:05 +0300
Subject: [PATCH 03/53] gguf : make gguf pip-installable

* gitignore : add dist and rm pyproject.toml

* gguf: prepare as Pip package

* gguf: prepare as Pip package

* gguf : fix line endings

* requirements : add gguf

* gguf : update readme with build notes

* gguf : update readme with build notes

* gguf : add notes for tests
---
 .gitignore                      |  2 +-
 gguf-py/LICENSE                 | 21 +++++++++++++
 gguf-py/README.md               | 55 +++++++++++++++++++++++++++++++++
 gguf-py/gguf/__init__.py        |  1 +
 gguf.py => gguf-py/gguf/gguf.py |  0
 gguf-py/pyproject.toml          | 28 +++++++++++++++++
 gguf-py/tests/test_gguf.py      |  7 +++++
 requirements.txt                |  1 +
 8 files changed, 114 insertions(+), 1 deletion(-)
 create mode 100644 gguf-py/LICENSE
 create mode 100644 gguf-py/README.md
 create mode 100644 gguf-py/gguf/__init__.py
 rename gguf.py => gguf-py/gguf/gguf.py (100%)
 mode change 100755 => 100644
 create mode 100644 gguf-py/pyproject.toml
 create mode 100644 gguf-py/tests/test_gguf.py

diff --git a/.gitignore b/.gitignore
index f3121794a..6cb7d9bc6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -60,6 +60,7 @@ compile_commands.json
 CMakeSettings.json
 
 __pycache__
+dist
 
 zig-out/
 zig-cache/
@@ -70,7 +71,6 @@ perf-*.txt
 
 examples/jeopardy/results.txt
 
-pyproject.toml
 poetry.lock
 poetry.toml
 
diff --git a/gguf-py/LICENSE b/gguf-py/LICENSE
new file mode 100644
index 000000000..76f67efdc
--- /dev/null
+++ b/gguf-py/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Georgi Gerganov
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/gguf-py/README.md b/gguf-py/README.md
new file mode 100644
index 000000000..03ad306ec
--- /dev/null
+++ b/gguf-py/README.md
@@ -0,0 +1,55 @@
+## gguf
+
+This is a Python package for writing binary files in the [GGUF](https://github.com/ggerganov/ggml/pull/302)
+(GGML Universal File) format.
+
+See [convert-llama-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-llama-hf-to-gguf.py)
+as an example for its usage.
+
+## Installation
+```sh
+pip install gguf
+```
+
+## Development
+Maintainers who participate in development of this package are advised to install it in editable mode:
+
+```sh
+cd /path/to/llama.cpp/gguf-py
+
+pip install --editable .
+```
+
+**Note**: This may require to upgrade your Pip installation, with a message saying that editable installation currently requires `setup.py`.
+In this case, upgrade Pip to the latest:
+
+```sh
+pip install --upgrade pip
+```
+
+## Publishing
+To publish the package, you need to have `twine` and `build` installed:
+
+```sh
+pip install build twine
+```
+
+Then, folow these steps to release a new version:
+
+1. Update the version in `pyproject.toml`.
+2. Build the package:
+
+```sh
+python -m build
+```
+
+3. Upload the generated distribution archives:
+
+```sh
+python -m twine upload dist/*
+```
+
+## TODO
+- [ ] Add tests
+- [ ] Include conversion scripts as command line entry points in this package.
+- Add CI workflow for releasing the package.
diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py
new file mode 100644
index 000000000..718ea71eb
--- /dev/null
+++ b/gguf-py/gguf/__init__.py
@@ -0,0 +1 @@
+from .gguf import GGUFWriter
diff --git a/gguf.py b/gguf-py/gguf/gguf.py
old mode 100755
new mode 100644
similarity index 100%
rename from gguf.py
rename to gguf-py/gguf/gguf.py
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
new file mode 100644
index 000000000..a6bce9460
--- /dev/null
+++ b/gguf-py/pyproject.toml
@@ -0,0 +1,28 @@
+[tool.poetry]
+name = "gguf"
+version = "0.2.0"
+description = "Write ML models in GGUF for GGML"
+authors = ["GGML <ggml@ggml.ai>"]
+packages = [
+    {include = "gguf"},
+]
+readme = "README.md"
+homepage = "https://ggml.ai"
+repository = "https://github.com/ggerganov/llama.cpp"
+keywords = ["ggml", "gguf", "llama.cpp"]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.8"
+numpy = ">=1.17"
+
+[tool.poetry.dev-dependencies]
+pytest = "^5.2"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
diff --git a/gguf-py/tests/test_gguf.py b/gguf-py/tests/test_gguf.py
new file mode 100644
index 000000000..512531dd2
--- /dev/null
+++ b/gguf-py/tests/test_gguf.py
@@ -0,0 +1,7 @@
+import gguf
+
+# TODO: add tests
+
+
+def test_write_gguf():
+    pass
diff --git a/requirements.txt b/requirements.txt
index 6c32cbd04..7dc51edb1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,3 @@
 numpy==1.24
 sentencepiece==0.1.98
+gguf>=0.1.0

From 3f460a2b723c8b936ac29ecfd02f244b3adeba55 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 25 Aug 2023 11:55:59 +0300
Subject: [PATCH 04/53] cuda : add RoPE kernel for mode == 2 (NeoX) (#2760)

* cuda : add RoPE kernel for mode == 2 (NeoX)

* falcon : do not offload the embeddings layer
---
 ggml-cuda.cu | 58 ++++++++++++++++++++++++++++++----------------------
 llama.cpp    | 22 +++++++++++++++++++-
 2 files changed, 54 insertions(+), 26 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 868b7a7b9..3bd1caf23 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -3907,28 +3907,27 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
     dst[i + 1] = x0*sin_theta + x1*cos_theta;
 }
 
-// TODO: this implementation is wrong!
-//static __global__ void rope_neox_f32(const float * x, float * dst, const int ncols, const float p0,
-//                                const float p_delta, const int p_delta_rows, const float theta_scale) {
-//    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
-//
-//    if (col >= ncols) {
-//        return;
-//    }
-//
-//    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-//    const int i = row*ncols + col/2;
-//
-//    const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
-//    const float sin_theta = sinf(theta);
-//    const float cos_theta = cosf(theta);
-//
-//    const float x0 = x[i + 0];
-//    const float x1 = x[i + ncols/2];
-//
-//    dst[i + 0]       = x0*cos_theta - x1*sin_theta;
-//    dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
-//}
+static __global__ void rope_neox_f32(const float * x, float * dst, const int ncols, const float p0,
+                                const float p_delta, const int p_delta_rows, const float theta_scale) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i = row*ncols + col/2;
+
+    const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
+    const float sin_theta = sinf(theta);
+    const float cos_theta = cosf(theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + ncols/2];
+
+    dst[i + 0]       = x0*cos_theta - x1*sin_theta;
+    dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
+}
 
 static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p, const float block_p, const float theta_scale) {
     const int col = blockDim.x*blockIdx.x + threadIdx.x;
@@ -4799,13 +4798,21 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
 
 static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
                           const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
-    GGML_ASSERT(nrows % 2 == 0);
+    GGML_ASSERT(nrows % 2 == 0); // GG: is this assert really needed? I don't see why
     const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
     rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
 }
 
+static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
+                          const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+    const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+    rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
+}
+
 static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p, const float block_p, const float theta_scale, cudaStream_t stream) {
     GGML_ASSERT(nrows % 4 == 0);
     const dim3 block_dims(4*CUDA_ROPE_BLOCK_SIZE, 1, 1);
@@ -5548,8 +5555,9 @@ inline void ggml_cuda_op_rope(
         const float block_p = max(p - (n_ctx - 2.f), 0.f);
         rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, id_p, block_p, theta_scale, cudaStream_main);
     } else if (is_neox) {
-        GGML_ASSERT(false && "RoPE NeoX not implemented yet");
-#pragma message("TODO: implement RoPE NeoX for CUDA")
+        GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
+        const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
+        rope_neox_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
     } else {
         const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
         rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
diff --git a/llama.cpp b/llama.cpp
index 67319396e..52ba31d79 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1958,6 +1958,14 @@ static void llm_load_tensors(
                         model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                         model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+
+                        if (backend_norm == GGML_BACKEND_GPU) {
+                            vram_weights += ggml_nbytes(model.output_norm);
+                            vram_weights += ggml_nbytes(model.output_norm_b);
+                        }
+                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
+                            vram_weights += ggml_nbytes(model.output);
+                        }
                     }
 
                     const uint32_t n_ff = hparams.n_ff;
@@ -1967,7 +1975,7 @@ static void llm_load_tensors(
                     model.layers.resize(n_layer);
 
                     for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
+                        const ggml_backend backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
                         const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
 
                         auto & layer = model.layers[i];
@@ -1978,6 +1986,11 @@ static void llm_load_tensors(
                         if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) {
                             layer.attn_norm_2   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend);
                             layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, backend);
+
+                            if (backend == GGML_BACKEND_GPU) {
+                                vram_weights += ggml_nbytes(layer.attn_norm_2);
+                                vram_weights += ggml_nbytes(layer.attn_norm_2_b);
+                            }
                         }
 
                         layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
@@ -1985,6 +1998,13 @@ static void llm_load_tensors(
 
                         layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
                         layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+
+                        if (backend == GGML_BACKEND_GPU) {
+                            vram_weights +=
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
+                                ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.wo)          +
+                                ggml_nbytes(layer.w2)        + ggml_nbytes(layer.w3);
+                        }
                     }
                 } break;
             default:

From 6bbc598a632560cb45dd2c51ad403bda8723b629 Mon Sep 17 00:00:00 2001
From: Henri Vasserman <henv@hot.ee>
Date: Fri, 25 Aug 2023 12:09:42 +0300
Subject: [PATCH 05/53] ROCm Port (#1087)

* use hipblas based on cublas
* Update Makefile for the Cuda kernels
* Expand arch list and make it overrideable
* Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5)
* add hipBLAS to README
* new build arg LLAMA_CUDA_MMQ_Y
* fix half2 decomposition
* Add intrinsics polyfills for AMD
* AMD assembly optimized __dp4a
* Allow overriding CC_TURING
* use "ROCm" instead of "CUDA"
* ignore all build dirs
* Add Dockerfiles
* fix llama-bench
* fix -nommq help for non CUDA/HIP

---------

Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com>
Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com>
Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com>
Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com>
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Co-authored-by: jammm <2500920+jammm@users.noreply.github.com>
Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
---
 .devops/full-rocm.Dockerfile         |  44 +++++++
 .devops/main-rocm.Dockerfile         |  44 +++++++
 .dockerignore                        |   9 +-
 .gitignore                           |  15 +--
 CMakeLists.txt                       |  38 ++++++
 Makefile                             |  24 ++++
 README.md                            |  29 +++++
 common/common.cpp                    |   4 +-
 examples/llama-bench/llama-bench.cpp |   4 +-
 ggml-cuda.cu                         | 173 ++++++++++++++++++++++-----
 ggml-cuda.h                          |   8 ++
 llama.cpp                            |   2 +-
 12 files changed, 335 insertions(+), 59 deletions(-)
 create mode 100644 .devops/full-rocm.Dockerfile
 create mode 100644 .devops/main-rocm.Dockerfile

diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile
new file mode 100644
index 000000000..6c521e9b4
--- /dev/null
+++ b/.devops/full-rocm.Dockerfile
@@ -0,0 +1,44 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} as build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH=\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102
+
+COPY requirements.txt requirements.txt
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV LLAMA_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+
+RUN make
+
+ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/main-rocm.Dockerfile b/.devops/main-rocm.Dockerfile
new file mode 100644
index 000000000..789deff6d
--- /dev/null
+++ b/.devops/main-rocm.Dockerfile
@@ -0,0 +1,44 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} as build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH=\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102
+
+COPY requirements.txt requirements.txt
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV LLAMA_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+
+RUN make
+
+ENTRYPOINT [ "/app/main" ]
diff --git a/.dockerignore b/.dockerignore
index 462fac23a..c6ef6c86c 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -5,14 +5,7 @@
 .vscode/
 .DS_Store
 
-build/
-build-em/
-build-debug/
-build-release/
-build-static/
-build-no-accel/
-build-sanitize-addr/
-build-sanitize-thread/
+build*/
 
 models/*
 
diff --git a/.gitignore b/.gitignore
index 6cb7d9bc6..e5faab774 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,20 +16,7 @@
 .vs/
 .vscode/
 
-build/
-build-em/
-build-debug/
-build-release/
-build-ci-debug/
-build-ci-release/
-build-static/
-build-cublas/
-build-opencl/
-build-metal/
-build-mpi/
-build-no-accel/
-build-sanitize-addr/
-build-sanitize-thread/
+build*/
 out/
 tmp/
 
diff --git a/CMakeLists.txt b/CMakeLists.txt
index bb63ef98e..ba008bcc6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -74,6 +74,7 @@ set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kern
 set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
 option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for some calculations"   OFF)
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
+option(LLAMA_HIPBLAS                         "llama: use hipBLAS"                               OFF)
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
 option(LLAMA_METAL                           "llama: use Metal"                                 OFF)
 option(LLAMA_MPI                             "llama: use MPI"                                   OFF)
@@ -352,6 +353,43 @@ if (LLAMA_CLBLAST)
     endif()
 endif()
 
+if (LLAMA_HIPBLAS)
+    list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
+
+    if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang")
+        message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang")
+    endif()
+    if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang")
+        message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++")
+    endif()
+
+    find_package(hip)
+    find_package(hipblas)
+    find_package(rocblas)
+
+    if (${hipblas_FOUND} AND ${hip_FOUND})
+        message(STATUS "HIP and hipBLAS found")
+        add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)
+        add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h)
+        if (LLAMA_CUDA_FORCE_DMMV)
+            target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV)
+        endif()
+        target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
+        target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
+        target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
+        target_compile_definitions(ggml-rocm PRIVATE CC_TURING=1000000000)
+        set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)
+        target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas)
+
+        if (LLAMA_STATIC)
+            message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
+        endif()
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-rocm)
+    else()
+        message(WARNING "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm")
+    endif()
+endif()
+
 if (LLAMA_ALL_WARNINGS)
     if (NOT MSVC)
         set(c_flags
diff --git a/Makefile b/Makefile
index d31acc450..a3400e491 100644
--- a/Makefile
+++ b/Makefile
@@ -280,6 +280,30 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 endif # LLAMA_CLBLAST
 
+ifdef LLAMA_HIPBLAS
+	ROCM_PATH	?= /opt/rocm
+	HIPCC	    ?= $(ROCM_PATH)/bin/hipcc
+	GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
+	LLAMA_CUDA_DMMV_X       ?= 32
+	LLAMA_CUDA_MMV_Y        ?= 1
+	LLAMA_CUDA_KQUANTS_ITER ?= 2
+	CFLAGS      += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
+	CXXFLAGS    += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
+	LDFLAGS     += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
+	LDFLAGS		+= -lhipblas -lamdhip64 -lrocblas
+	HIPFLAGS    += $(addprefix --offload-arch=,$(GPU_TARGETS))
+	HIPFLAGS    += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
+	HIPFLAGS    += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
+	HIPFLAGS    += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
+	HIPFLAGS    += -DCC_TURING=1000000000
+ifdef LLAMA_CUDA_FORCE_DMMV
+	HIPFLAGS 	+= -DGGML_CUDA_FORCE_DMMV
+endif # LLAMA_CUDA_FORCE_DMMV
+	OBJS        += ggml-cuda.o
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
+endif # LLAMA_HIPBLAS
+
 ifdef LLAMA_METAL
 	CFLAGS   += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
 	CXXFLAGS += -DGGML_USE_METAL
diff --git a/README.md b/README.md
index eebb11392..95471fdbb 100644
--- a/README.md
+++ b/README.md
@@ -422,6 +422,35 @@ Building the program with BLAS support may lead to some performance improvements
   | LLAMA_CUDA_F16          | Boolean                |   false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
   | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
 
+- #### hipBLAS
+
+  This provide BLAS acceleation on HIP supported GPU like AMD GPU.
+  Make sure to have ROCm installed.
+  You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html).
+  Windows support is coming soon...
+
+  - Using `make`:
+    ```bash
+    make LLAMA_HIPBLAS=1
+    ```
+  - Using `CMake`:
+    ```bash
+    mkdir build
+    cd build
+    CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON
+    cmake --build .
+    ```
+
+  The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
+  If your GPU is not officialy supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3.
+  The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
+
+  | Option                  | Legal values           | Default | Description |
+  |-------------------------|------------------------|---------|-------------|
+  | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 |      32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_MMV_Y        | Positive integer       |       1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
+
 - #### CLBlast
 
   OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
diff --git a/common/common.cpp b/common/common.cpp
index 53002ba30..ff19ec4e5 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -613,9 +613,11 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
     fprintf(stdout, "  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n");
     fprintf(stdout, "  -lv, --low-vram       don't allocate VRAM scratch buffer\n");
+#ifdef GGML_USE_CUBLAS
     fprintf(stdout, "  -nommq, --no-mul-mat-q\n");
-    fprintf(stdout, "                        use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
+    fprintf(stdout, "                        use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
     fprintf(stdout, "                        Not recommended since this is both slower and uses more VRAM.\n");
+#endif // GGML_USE_CUBLAS
 #endif
     fprintf(stdout, "  --mtest               compute maximum memory usage\n");
     fprintf(stdout, "  --export              export the computation graph to 'llama.ggml'\n");
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 36057bfca..7a2811584 100755
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -18,9 +18,7 @@
 #include "llama.h"
 #include "common.h"
 #include "build-info.h"
-#ifdef GGML_USE_CUBLAS
 #include "ggml-cuda.h"
-#endif
 
 // utils
 static uint64_t get_time_ns() {
@@ -504,7 +502,7 @@ struct test {
 
     static std::string get_backend() {
         if (cuda) {
-            return "CUDA";
+            return GGML_CUDA_NAME;
         }
         if (opencl) {
             return "OpenCL";
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 3bd1caf23..83d53c13c 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -6,15 +6,116 @@
 #include <atomic>
 #include <assert.h>
 
+#if defined(GGML_USE_HIPBLAS)
+#include <hip/hip_runtime.h>
+#include <hipblas/hipblas.h>
+#include <hip/hip_fp16.h>
+#ifdef __HIP_PLATFORM_AMD__
+// for rocblas_initialize()
+#include "rocblas/rocblas.h"
+#endif
+#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
+#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
+#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_OP_N HIPBLAS_OP_N
+#define CUBLAS_OP_T HIPBLAS_OP_T
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_TF32_TENSOR_OP_MATH 0
+#define CUDA_R_16F  HIPBLAS_R_16F
+#define CUDA_R_32F  HIPBLAS_R_32F
+#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
+#define cublasCreate hipblasCreate
+#define cublasGemmEx hipblasGemmEx
+#define cublasHandle_t hipblasHandle_t
+#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
+#define cublasSetStream hipblasSetStream
+#define cublasSgemm hipblasSgemm
+#define cublasStatus_t hipblasStatus_t
+#define cudaDeviceProp hipDeviceProp_t
+#define cudaDeviceSynchronize hipDeviceSynchronize
+#define cudaError_t hipError_t
+#define cudaEventCreateWithFlags hipEventCreateWithFlags
+#define cudaEventDisableTiming hipEventDisableTiming
+#define cudaEventRecord hipEventRecord
+#define cudaEvent_t hipEvent_t
+#define cudaEventDestroy hipEventDestroy
+#define cudaFree hipFree
+#define cudaFreeHost hipHostFree
+#define cudaGetDevice hipGetDevice
+#define cudaGetDeviceCount hipGetDeviceCount
+#define cudaGetDeviceProperties hipGetDeviceProperties
+#define cudaGetErrorString hipGetErrorString
+#define cudaGetLastError hipGetLastError
+#define cudaMalloc hipMalloc
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
+#define cudaMemcpy hipMemcpy
+#define cudaMemcpy2DAsync hipMemcpy2DAsync
+#define cudaMemcpyAsync hipMemcpyAsync
+#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
+#define cudaMemcpyKind hipMemcpyKind
+#define cudaMemset hipMemset
+#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
+#define cudaSetDevice hipSetDevice
+#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
+#define cudaStreamNonBlocking hipStreamNonBlocking
+#define cudaStreamSynchronize hipStreamSynchronize
+#define cudaStreamWaitEvent(stream, event) hipStreamWaitEvent(stream, event, 0)
+#define cudaStream_t hipStream_t
+#define cudaSuccess hipSuccess
+#else
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 #include <cuda_fp16.h>
+#endif
 
 #include "ggml-cuda.h"
 #include "ggml.h"
 
 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
+#ifndef CC_TURING
 #define CC_TURING   700
+#endif
+
+#if defined(GGML_USE_HIPBLAS)
+#define __CUDA_ARCH__ 1300
+
+typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
+static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
+    return reinterpret_cast<const int&>(c);
+}
+
+static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
+    c = __builtin_amdgcn_sdot4(a, b, c, false);
+#elif defined(__gfx1100__)
+    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
+#elif defined(__gfx1010__) || defined(__gfx900__)
+    int tmp1;
+    int tmp2;
+    asm("\n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        "
+        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
+        : "v"(a), "v"(b)
+    );
+#else
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
+#endif
+    return c;
+}
+#endif
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -424,8 +525,8 @@ static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const in
 static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
     const block_q4_1 * x = (const block_q4_1 *) vx;
 
-    const dfloat d = x[ib].dm.x;
-    const dfloat m = x[ib].dm.y;
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
 
     const int vui = x[ib].qs[iqs];
 
@@ -467,8 +568,8 @@ static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const in
 static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
     const block_q5_1 * x = (const block_q5_1 *) vx;
 
-    const dfloat d = x[ib].dm.x;
-    const dfloat m = x[ib].dm.y;
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
 
     uint32_t qh;
     memcpy(&qh, x[ib].qh, sizeof(qh));
@@ -520,8 +621,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
     const uint8_t q = x[i].qs[32*n + l];
     float * y = yy + i*QK_K + 128*n;
 
-    float dall = x[i].dm.x;
-    float dmin = x[i].dm.y;
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
     y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
     y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
     y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
@@ -531,8 +632,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
     const int il = tid%16;  // 0...15
     const uint8_t q = x[i].qs[il] >> (2*is);
     float * y = yy + i*QK_K + 16*is + il;
-    float dall = x[i].dm.x;
-    float dmin = x[i].dm.y;
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
     y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
     y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
 #endif
@@ -618,8 +719,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float
 
     float * y = yy + i*QK_K + 64*il + n*ir;
 
-    const float dall = x[i].dm.x;
-    const float dmin = x[i].dm.y;
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
 
     const uint8_t * q = x[i].qs + 32*il + n*ir;
 
@@ -657,8 +758,8 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, float
 
     float * y = yy + i*QK_K + 64*il + 2*ir;
 
-    const float dall = x[i].dm.x;
-    const float dmin = x[i].dm.y;
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
 
     const uint8_t * ql = x[i].qs + 32*il + 2*ir;
     const uint8_t * qh = x[i].qh + 2*ir;
@@ -770,8 +871,8 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
         const float   * y = yy + i * QK_K + y_offset;
         const uint8_t * q = x[i].qs + q_offset;
 
-        const float dall = x[i].dm.x;
-        const float dmin = x[i].dm.y;
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
 
         const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
         aux[0] = a[0] & 0x0f0f0f0f;
@@ -991,8 +1092,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
         const float   * y1 = yy + i*QK_K + y_offset;
         const float   * y2 = y1 + 128;
 
-        const float dall = x[i].dm.x;
-        const float dmin = x[i].dm.y;
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux[0] = a[im+0] & kmask1;
@@ -1124,8 +1225,8 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
         const float   * y1  = yy + i*QK_K + y_offset;
         const float   * y2  = y1 + 128;
 
-        const float dall = x[i].dm.x;
-        const float dmin = x[i].dm.y;
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux[0] = a[im+0] & kmask1;
@@ -1348,8 +1449,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
         return;
     }
 
-    y[ib].ds.x = d;
-    y[ib].ds.y = sum;
+    reinterpret_cast<half&>(y[ib].ds.x) = d;
+    reinterpret_cast<half&>(y[ib].ds.y) = sum;
 }
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
@@ -2346,7 +2447,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
         u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
     }
 
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, bq8_1->ds.x);
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
 }
 
 template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
@@ -2432,7 +2533,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR2_K; ++ i) {
         u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + i].ds.x;
+        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
     }
 
     return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
@@ -2551,7 +2652,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR3_K; ++i) {
         u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + i].ds.x;
+        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
     }
 
     return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
@@ -2720,7 +2821,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
 
     for (int i = 0; i < QR4_K; ++i) {
         const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = bq8i->ds.x;
+        d8[i] = __low2half(bq8i->ds);
 
         const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
         u[2*i+0] = q8[0];
@@ -2747,8 +2848,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
     const float dall = bq4_K->d[0];
     const float dmin = bq4_K->d[1];
 
-    const float d8_1 = bq8_1[0].ds.x;
-    const float d8_2 = bq8_1[1].ds.x;
+    const float d8_1 = __low2float(bq8_1[0].ds);
+    const float d8_2 = __low2float(bq8_1[1].ds);
 
     const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
     const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@@ -2901,7 +3002,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR5_K; ++i) {
         const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = bq8i->ds.x;
+        d8[i] = __low2float(bq8i->ds);
 
         const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
         u[2*i+0] = q8[0];
@@ -2919,8 +3020,8 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
 
     const float d = bq5_K->d;
 
-    const float d8_1 = bq8_1[0].ds.x;
-    const float d8_2 = bq8_1[1].ds.x;
+    const float d8_1 = __low2half(bq8_1[0].ds);
+    const float d8_2 = __low2half(bq8_1[1].ds);
 
     const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
     const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@@ -3075,7 +3176,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR6_K; ++i) {
         u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + 2*i].ds.x;
+        d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds);
     }
 
     return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
@@ -3243,7 +3344,7 @@ static __device__ __forceinline__ void mul_mat_q(
                     *dsi_dst = *dsi_src;
                 } else {
                     float * dfi_dst = (float *) dsi_dst;
-                    *dfi_dst = (*dsi_src).x;
+                    *dfi_dst = __low2half(*dsi_src);
                 }
             }
 
@@ -4944,10 +5045,18 @@ void ggml_init_cublas() {
     static bool initialized = false;
 
     if (!initialized) {
+
+#ifdef __HIP_PLATFORM_AMD__
+        // Workaround for a rocBLAS bug when using multiple graphics cards:
+        // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
+        rocblas_initialize();
+        CUDA_CHECK(cudaDeviceSynchronize());
+#endif
+
         CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
         GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
         int64_t total_vram = 0;
-        fprintf(stderr, "%s: found %d CUDA devices:\n", __func__, g_device_count);
+        fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
         for (int id = 0; id < g_device_count; ++id) {
             cudaDeviceProp prop;
             CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
diff --git a/ggml-cuda.h b/ggml-cuda.h
index f66bb1678..a72e82069 100644
--- a/ggml-cuda.h
+++ b/ggml-cuda.h
@@ -2,6 +2,14 @@
 
 #include "ggml.h"
 
+#ifdef GGML_USE_HIPBLAS
+#define GGML_CUDA_NAME "ROCm"
+#define GGML_CUBLAS_NAME "hipBLAS"
+#else
+#define GGML_CUDA_NAME "CUDA"
+#define GGML_CUBLAS_NAME "cuBLAS"
+#endif
+
 #ifdef  __cplusplus
 extern "C" {
 #endif
diff --git a/llama.cpp b/llama.cpp
index 52ba31d79..d12b6d1cb 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1836,7 +1836,7 @@ static void llm_load_tensors(
     (void) main_gpu;
     (void) mul_mat_q;
 #if defined(GGML_USE_CUBLAS)
-    LLAMA_LOG_INFO("%s: using CUDA for GPU acceleration\n", __func__);
+    LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
     ggml_cuda_set_main_device(main_gpu);
     ggml_cuda_set_mul_mat_q(mul_mat_q);
 #define LLAMA_BACKEND_OFFLOAD       GGML_BACKEND_GPU

From 8194cd8772c58b8a43aa07a2fc468f5366d7e320 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= <yusufsarigoz@gmail.com>
Date: Fri, 25 Aug 2023 12:43:41 +0300
Subject: [PATCH 06/53] gguf : export objects to user code (#2780)

* gguf export more objects to user code

* gguf export all objects to user code for now

* gguf : bump version
---
 gguf-py/gguf/__init__.py | 2 +-
 gguf-py/pyproject.toml   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py
index 718ea71eb..f9b70a85b 100644
--- a/gguf-py/gguf/__init__.py
+++ b/gguf-py/gguf/__init__.py
@@ -1 +1 @@
-from .gguf import GGUFWriter
+from .gguf import *
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index a6bce9460..cc70e28b7 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.2.0"
+version = "0.2.1"
 description = "Write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

From 5439a0ab57c16b556ffa91a0953df5e46b1e7fb4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 25 Aug 2023 13:03:25 +0300
Subject: [PATCH 07/53] ci : pip install gguf in editable mode (#2782)

ggml-ci
---
 ci/run.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ci/run.sh b/ci/run.sh
index 54ba6d710..e1486e7c1 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -391,6 +391,7 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
     ln -sfn ${mnt_models} ${SRC}/models-mnt
 
     python3 -m pip install -r ${SRC}/requirements.txt
+    python3 -m pip install --editable gguf-py
 fi
 
 ret=0

From 29674ab4e847fcaba60cc6558f0d46d5f74ae279 Mon Sep 17 00:00:00 2001
From: Jhen-Jie Hong <iainst0409@gmail.com>
Date: Fri, 25 Aug 2023 18:32:45 +0800
Subject: [PATCH 08/53] server : display token probabilities in the UI (#2489)

* server : add n_probs param in chat UI

* server : keep message data array & show in probabilites component

* server : add simple popover component

* server : fix completion_probabilities undefined if not set n_probs

* server : implement Probabilites

* server : handle bytes

* server : make n_probs max to 10 for easy scroll

* server : adjust for dark/light mode

* server : Fix regenerated prompt

* server : update index.html.hpp

* server : convert prob to percentage + show original value as div title

* server : fix Probabilites not used if included empty str

* server : skip byte pair in display probabilites

* server : remove array check of completion_probabilities in messages

* skip empty array or byte pair (> 1) in Probabilites

* generate index.html.hpp

* fix incorrect prob convert if the str is already a known token

* use final response to show probabilities on stop

* revert unnecessary change

* correct probabilites usage

* remove unused function

* always send partial response for get correct probs of last to_send

* fix typo

* fix content of format_final_response

* refactor probs render & make pColor transparent if not found

* send empty string when got stop_pos in partial

* avoid unnecessary empty data event & send rest of partial tokens on stop

* use <br /> for new line

* skip -1 tok in loop to avoid send '' on end

* trim last new lines on stop

* revert unnecessary change
---
 examples/server/index.html.hpp    | 2808 +++++++++++++++++------------
 examples/server/public/index.html |  245 ++-
 examples/server/server.cpp        |   86 +-
 3 files changed, 1979 insertions(+), 1160 deletions(-)

diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index 76851d9c6..84e6f97ba 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -152,149 +152,191 @@ unsigned char index_html[] = {
   0x6f, 0x70, 0x65, 0x6e, 0x5d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20,
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   0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a,
-  0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72,
-  0x65, 0x61, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70,
-  0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x35, 0x70, 0x78, 0x3b,
-  0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d,
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-  0x6c, 0x61, 0x79, 0x3a, 0x20, 0x62, 0x6c, 0x6f, 0x63, 0x6b, 0x3b, 0x0a,
+  0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x70, 0x72, 0x6f, 0x62, 0x2d, 0x73,
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+  0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72,
+  0x64, 0x65, 0x72, 0x2d, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x3a, 0x20,
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+  0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x61, 0x3e, 0x20, 0x61, 0x6e,
+  0x64, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68,
+  0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x67, 0x6d, 0x6c, 0x2e,
+  0x61, 0x69, 0x22, 0x3e, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x3c,
+  0x2f, 0x61, 0x3e, 0x2e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f,
+  0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20,
+  0x20, 0x20, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68,
+  0x28, 0x41, 0x70, 0x70, 0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d,
+  0x65, 0x6e, 0x74, 0x2e, 0x71, 0x75, 0x65, 0x72, 0x79, 0x53, 0x65, 0x6c,
+  0x65, 0x63, 0x74, 0x6f, 0x72, 0x28, 0x27, 0x23, 0x63, 0x6f, 0x6e, 0x74,
+  0x61, 0x69, 0x6e, 0x65, 0x72, 0x27, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20,
+  0x3c, 0x2f, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f,
+  0x68, 0x65, 0x61, 0x64, 0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79,
+  0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d,
+  0x22, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x22, 0x3e,
+  0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69,
+  0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c,
+  0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62,
+  0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c,
+  0x3e, 0x0a
 };
-unsigned int index_html_len = 20333;
+unsigned int index_html_len = 27218;
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 5eedb0b28..959a9b9a6 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -102,6 +102,17 @@
       padding: 0.5em;
     }
 
+    .prob-set {
+      padding: 0.3em;
+      border-bottom: 1px solid #ccc;
+    }
+
+    .popover-content {
+      position: absolute;
+      background-color: white;
+      padding: 0.2em;
+      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    }
 
     textarea {
       padding: 5px;
@@ -133,11 +144,17 @@
       font-size: 80%;
       color: #888;
     }
+
+    @media (prefers-color-scheme: dark) {
+      .popover-content {
+        background-color: black;
+      }
+    }
   </style>
 
   <script type="module">
     import {
-      html, h, signal, effect, computed, render, useSignal, useEffect, useRef
+      html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
     } from '/index.js';
 
     import { llama } from '/completion.js';
@@ -168,6 +185,7 @@
       mirostat_tau: 5, // target entropy
       mirostat_eta: 0.1, // learning rate
       grammar: '',
+      n_probs: 0, // no completion_probabilities
     })
 
     /* START: Support for storing prompt templates and parameters in borwser LocalStorage */
@@ -334,10 +352,21 @@
 
       const prompt = template(session.value.template, {
         message: msg,
-        history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
+        history: session.value.transcript.flatMap(
+          ([name, data]) =>
+            template(
+              session.value.historyTemplate,
+              {
+                name,
+                message: Array.isArray(data) ?
+                  data.map(msg => msg.content).join('').replace(/^\s/, '') :
+                  data,
+              }
+            )
+        ).join("\n"),
       });
 
-      let currentMessage = '';
+      const currentMessages = [];
       const history = session.value.transcript
 
       const llamaParams = {
@@ -347,15 +376,19 @@
 
       for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
         const data = chunk.data;
-        currentMessage += data.content;
-
-        // remove leading whitespace
-        currentMessage = currentMessage.replace(/^\s+/, "")
-
-        transcriptUpdate([...history, ["{{char}}", currentMessage]])
 
         if (data.stop) {
-          console.log("Completion finished: '", currentMessage, "', summary: ", data);
+          while (
+            currentMessages.length > 0 &&
+            currentMessages[currentMessages.length - 1].content.match(/\n$/) != null
+          ) {
+            currentMessages.pop();
+          }
+          transcriptUpdate([...history, ["{{char}}", currentMessages]])
+          console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data);
+        } else {
+          currentMessages.push(data);
+          transcriptUpdate([...history, ["{{char}}", currentMessages]])
         }
 
         if (data.timings) {
@@ -420,8 +453,18 @@
         }
       }, [messages])
 
-      const chatLine = ([user, msg]) => {
-        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
+      const chatLine = ([user, data], index) => {
+        let message
+        const isArrayMessage = Array.isArray(data)
+        if (params.value.n_probs > 0 && isArrayMessage) {
+          message = html`<${Probabilities} data=${data} />`
+        } else {
+          const text = isArrayMessage ?
+            data.map(msg => msg.content).join('').replace(/^\s+/, '') :
+            data;
+          message = html`<${Markdownish} text=${template(text)} />`
+        }
+        return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
       };
 
       return html`
@@ -568,10 +611,71 @@
               ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})}
               ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})}
             </fieldset>
+            <fieldset>
+              ${IntField({label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs})}
+            </fieldset>
           </details>
         </form>
       `
     }
+
+    const probColor = (p) => {
+      const r = Math.floor(192 * (1 - p));
+      const g = Math.floor(192 * p);
+      return `rgba(${r},${g},0,0.3)`;
+    }
+
+    const Probabilities = (params) => {
+      return params.data.map(msg => {
+        const { completion_probabilities } = msg;
+        if (
+          !completion_probabilities ||
+          completion_probabilities.length === 0
+        ) return msg.content
+
+        if (completion_probabilities.length > 1) {
+          // Not for byte pair
+          if (completion_probabilities[0].content.startsWith('byte: \\')) return msg.content
+
+          const splitData = completion_probabilities.map(prob => ({
+            content: prob.content,
+            completion_probabilities: [prob]
+          }))
+          return html`<${Probabilities} data=${splitData} />`
+        }
+
+        const { probs, content } = completion_probabilities[0]
+        const found = probs.find(p => p.tok_str === msg.content)
+        const pColor = found ? probColor(found.prob) : 'transparent'
+
+        const popoverChildren = html`
+          <div class="prob-set">
+            ${probs.map((p, index) => {
+              return html`
+                <div
+                  key=${index}
+                  title=${`prob: ${p.prob}`}
+                  style=${{
+                    padding: '0.3em',
+                    backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent'
+                  }}
+                >
+                  <span>${p.tok_str}: </span>
+                  <span>${Math.floor(p.prob * 100)}%</span>
+                </div>
+              `
+            })}
+          </div>
+        `
+
+        return html`
+          <${Popover} style=${{ backgroundColor: pColor }} popoverChildren=${popoverChildren}>
+            ${msg.content.match(/\n/gim) ? html`<br />` : msg.content}
+          </>
+        `
+      });
+    }
+
     // poor mans markdown replacement
     const Markdownish = (params) => {
       const md = params.text
@@ -600,10 +704,121 @@
       `
     }
 
+    // simple popover impl
+    const Popover = (props) => {
+      const isOpen = useSignal(false);
+      const position = useSignal({ top: '0px', left: '0px' });
+      const buttonRef = useRef(null);
+      const popoverRef = useRef(null);
+
+      const togglePopover = () => {
+        if (buttonRef.current) {
+          const rect = buttonRef.current.getBoundingClientRect();
+          position.value = {
+            top: `${rect.bottom + window.scrollY}px`,
+            left: `${rect.left + window.scrollX}px`,
+          };
+        }
+        isOpen.value = !isOpen.value;
+      };
+
+      const handleClickOutside = (event) => {
+        if (popoverRef.current && !popoverRef.current.contains(event.target) && !buttonRef.current.contains(event.target)) {
+          isOpen.value = false;
+        }
+      };
+
+      useEffect(() => {
+        document.addEventListener('mousedown', handleClickOutside);
+        return () => {
+          document.removeEventListener('mousedown', handleClickOutside);
+        };
+      }, []);
+
+      return html`
+        <span style=${props.style} ref=${buttonRef} onClick=${togglePopover}>${props.children}</span>
+        ${isOpen.value && html`
+          <${Portal} into="#portal">
+            <div
+              ref=${popoverRef}
+              class="popover-content"
+              style=${{
+                top: position.value.top,
+                left: position.value.left,
+              }}
+            >
+              ${props.popoverChildren}
+            </div>
+          </${Portal}>
+        `}
+      `;
+    };
+
+    // Source: preact-portal (https://github.com/developit/preact-portal/blob/master/src/preact-portal.js)
+    /** Redirect rendering of descendants into the given CSS selector */
+    class Portal extends Component {
+      componentDidUpdate(props) {
+        for (let i in props) {
+          if (props[i] !== this.props[i]) {
+            return setTimeout(this.renderLayer);
+          }
+        }
+      }
+
+      componentDidMount() {
+        this.isMounted = true;
+        this.renderLayer = this.renderLayer.bind(this);
+        this.renderLayer();
+      }
+
+      componentWillUnmount() {
+        this.renderLayer(false);
+        this.isMounted = false;
+        if (this.remote && this.remote.parentNode) this.remote.parentNode.removeChild(this.remote);
+      }
+
+      findNode(node) {
+        return typeof node === 'string' ? document.querySelector(node) : node;
+      }
+
+      renderLayer(show = true) {
+        if (!this.isMounted) return;
+
+        // clean up old node if moving bases:
+        if (this.props.into !== this.intoPointer) {
+          this.intoPointer = this.props.into;
+          if (this.into && this.remote) {
+            this.remote = render(html`<${PortalProxy} />`, this.into, this.remote);
+          }
+          this.into = this.findNode(this.props.into);
+        }
+
+        this.remote = render(html`
+          <${PortalProxy} context=${this.context}>
+            ${show && this.props.children || null}
+          </${PortalProxy}>
+        `, this.into, this.remote);
+      }
+
+      render() {
+        return null;
+      }
+    }
+    // high-order component that renders its first child if it exists.
+    // used as a conditional rendering proxy.
+    class PortalProxy extends Component {
+      getChildContext() {
+        return this.props.context;
+      }
+      render({ children }) {
+        return children || null;
+      }
+    }
+
     function App(props) {
 
       return html`
-        <div id="container">
+        <div>
           <header>
             <h1>llama.cpp</h1>
           </header>
@@ -624,11 +839,13 @@
       `;
     }
 
-    render(h(App), document.body);
+    render(h(App), document.querySelector('#container'));
   </script>
 </head>
 
 <body>
+  <div id="container"></div>
+  <div id="portal"></div>
 </body>
 
 </html>
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 1e6d10c1d..025b385cc 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -124,8 +124,9 @@ static void server_log(const char *level, const char *function, int line,
 static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
 {
     std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
-    // if first bit is 1, meaning it's a partial character
-    if (out.size() > 0 && (out[0] & 0x80) == 0x80)
+    // if the size is 1 and first bit is 1, meaning it's a partial character
+    //   (size > 1 meaning it's already a known token)
+    if (out.size() == 1 && (out[0] & 0x80) == 0x80)
     {
         std::stringstream ss;
         ss << std::hex << (out[0] & 0xff);
@@ -1321,59 +1322,86 @@ int main(int argc, char **argv)
 
                 while (llama.has_next_token) {
                     const completion_token_output token_with_probs = llama.doCompletion();
-                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
-                    if (llama.multibyte_pending > 0) {
+                    if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
                         continue;
                     }
+                    const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok);
 
                     size_t pos = std::min(sent_count, llama.generated_text.size());
 
                     const std::string str_test = llama.generated_text.substr(pos);
+                    bool is_stop_full = false;
                     size_t stop_pos =
                         llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
                     if (stop_pos != std::string::npos) {
+                        is_stop_full = true;
                         llama.generated_text.erase(
                             llama.generated_text.begin() + pos + stop_pos,
                             llama.generated_text.end());
                         pos = std::min(sent_count, llama.generated_text.size());
                     } else {
+                        is_stop_full = false;
                         stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
                             STOP_PARTIAL);
                     }
 
-                    const std::string to_send = llama.generated_text.substr(pos, stop_pos);
-                    sent_count += to_send.size();
+                    if (
+                        stop_pos == std::string::npos ||
+                        // Send rest of the text if we are at the end of the generation
+                        (!llama.has_next_token && !is_stop_full && stop_pos > 0)
+                    ) {
+                        const std::string to_send = llama.generated_text.substr(pos, std::string::npos);
 
-                    std::vector<completion_token_output> probs_output = {};
+                        sent_count += to_send.size();
 
-                    if (llama.params.n_probs > 0) {
-                        const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
-                        size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
-                        size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
-                        if (probs_pos < probs_stop_pos) {
-                            probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos);
+                        std::vector<completion_token_output> probs_output = {};
+
+                        if (llama.params.n_probs > 0) {
+                            const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
+                            size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
+                            size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
+                            if (probs_pos < probs_stop_pos) {
+                                probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos);
+                            }
+                            sent_token_probs_index = probs_stop_pos;
+                        }
+
+                        const json data = format_partial_response(llama, to_send, probs_output);
+
+                        const std::string str =
+                            "data: " +
+                            data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.data(), str.size())) {
+                            LOG_VERBOSE("stream closed", {});
+                            llama_print_timings(llama.ctx);
+                            return false;
                         }
-                        sent_token_probs_index = probs_stop_pos;
                     }
 
-                    const json data = llama.has_next_token
-                                          ? format_partial_response(llama, to_send, probs_output)
-                                          // Generation is done, send extra information.
-                                          : format_final_response(llama, to_send, llama.generated_token_probs);
+                    if (!llama.has_next_token) {
+                        // Generation is done, send extra information.
+                        const json data = format_final_response(llama, "", llama.generated_token_probs);
 
-                    const std::string str =
-                        "data: " +
-                        data.dump(-1, ' ', false, json::error_handler_t::replace) +
-                        "\n\n";
+                        const std::string str =
+                            "data: " +
+                            data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
 
-                    LOG_VERBOSE("data stream", {
-                        { "to_send", str }
-                    });
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
 
-                    if (!sink.write(str.data(), str.size())) {
-                        LOG_VERBOSE("stream closed", {});
-                        llama_print_timings(llama.ctx);
-                        return false;
+                        if (!sink.write(str.data(), str.size())) {
+                            LOG_VERBOSE("stream closed", {});
+                            llama_print_timings(llama.ctx);
+                            return false;
+                        }
                     }
                 }
 

From 12e2e33a977af73e75885eeee91c5575a77f4e5f Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Fri, 25 Aug 2023 14:08:53 +0200
Subject: [PATCH 09/53] convert.py : export rope freq_base when converting
 CodeLlama from an HF model (#2773)

---
 convert.py | 34 ++++++++++++++++++----------------
 1 file changed, 18 insertions(+), 16 deletions(-)

diff --git a/convert.py b/convert.py
index 10276bf63..e58ea46e0 100755
--- a/convert.py
+++ b/convert.py
@@ -160,13 +160,14 @@ class Params:
     def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
         config = json.load(open(config_path))
 
-        n_vocab    = config["vocab_size"]
-        n_embd     = config["hidden_size"]
-        n_layer    = config["num_hidden_layers"]
-        n_ff       = config["intermediate_size"]
-        n_head     = config["num_attention_heads"]
-        n_head_kv  = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head
-        f_norm_eps = config["rms_norm_eps"]
+        n_vocab          = config["vocab_size"]
+        n_embd           = config["hidden_size"]
+        n_layer          = config["num_hidden_layers"]
+        n_ff             = config["intermediate_size"]
+        n_head           = config["num_attention_heads"]
+        n_head_kv        = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head
+        f_norm_eps       = config["rms_norm_eps"]
+        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
 
         n_mult = Params.find_n_mult(n_ff, n_embd)
 
@@ -179,15 +180,16 @@ class Params:
                             "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
 
         return Params(
-            n_vocab    = n_vocab,
-            n_embd     = n_embd,
-            n_mult     = n_mult,
-            n_layer    = n_layer,
-            n_ctx      = n_ctx,
-            n_ff       = n_ff,
-            n_head     = n_head,
-            n_head_kv  = n_head_kv,
-            f_norm_eps = f_norm_eps,
+            n_vocab          = n_vocab,
+            n_embd           = n_embd,
+            n_mult           = n_mult,
+            n_layer          = n_layer,
+            n_ctx            = n_ctx,
+            n_ff             = n_ff,
+            n_head           = n_head,
+            n_head_kv        = n_head_kv,
+            f_norm_eps       = f_norm_eps,
+            f_rope_freq_base = f_rope_freq_base,
         )
 
     # LLaMA v2 70B params.json

From 154725c5436808e5c519685d0279e850596dbe62 Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Fri, 25 Aug 2023 15:16:19 +0200
Subject: [PATCH 10/53] llama-bench : add model sizes (#2771)

* llama-bench : add model sizes

* more compact markdown output

* back to GiB

* adjust column sizes
---
 examples/llama-bench/llama-bench.cpp | 60 ++++++++++++++++++++++++----
 llama.cpp                            | 18 ++++++++-
 llama.h                              |  6 ++-
 3 files changed, 74 insertions(+), 10 deletions(-)

diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 7a2811584..d0fe6d90d 100755
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -441,6 +441,8 @@ struct test {
     static const std::string gpu_info;
     std::string model_filename;
     std::string model_type;
+    uint64_t model_size;
+    uint64_t model_n_params;
     int n_batch;
     int n_threads;
     bool f32_kv;
@@ -457,8 +459,10 @@ struct test {
     test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) {
         model_filename = inst.model;
         char buf[128];
-        llama_model_type(lmodel, buf, sizeof(buf));
+        llama_model_desc(lmodel, buf, sizeof(buf));
         model_type = buf;
+        model_size = llama_model_size(lmodel);
+        model_n_params = llama_model_n_params(lmodel);
         n_batch = inst.n_batch;
         n_threads = inst.n_threads;
         f32_kv = inst.f32_kv;
@@ -524,7 +528,7 @@ struct test {
             "build_commit", "build_number",
             "cuda", "opencl", "metal", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
-            "model_filename", "model_type",
+            "model_filename", "model_type", "model_size", "model_n_params",
             "n_batch", "n_threads", "f16_kv",
             "n_gpu_layers", "main_gpu", "mul_mat_q", "low_vram", "tensor_split",
             "n_prompt", "n_gen", "test_time",
@@ -538,6 +542,7 @@ struct test {
 
     static field_type get_field_type(const std::string & field) {
         if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
+            field == "model_size" || field == "model_n_params" ||
             field == "n_gpu_layers" || field == "main_gpu" ||
             field == "n_prompt" || field == "n_gen" ||
             field == "avg_ns" || field == "stddev_ns") {
@@ -573,7 +578,7 @@ struct test {
             build_commit, std::to_string(build_number),
             std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
-            model_filename, model_type,
+            model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
             std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),
             std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), std::to_string(low_vram), tensor_split_str,
             std::to_string(n_prompt), std::to_string(n_gen), test_time,
@@ -709,8 +714,15 @@ struct markdown_printer : public printer {
             return -30;
         }
         if (field == "t/s") {
-            return 15;
+            return 16;
         }
+        if (field == "size" || field == "params") {
+            return 10;
+        }
+        if (field == "n_gpu_layers") {
+            return 3;
+        }
+
         int width = std::max((int)field.length(), 10);
 
         if (test::get_field_type(field) == test::STRING) {
@@ -719,9 +731,28 @@ struct markdown_printer : public printer {
         return width;
     }
 
+    static std::string get_field_display_name(const std::string & field) {
+        if (field == "n_gpu_layers") {
+            return "ngl";
+        }
+        if (field == "n_threads") {
+            return "threads";
+        }
+        if (field == "mul_mat_q") {
+            return "mmq";
+        }
+        if (field == "tensor_split") {
+            return "ts";
+        }
+        return field;
+    }
+
     void print_header(const cmd_params & params) override {
         // select fields to print
-        fields = { "model", "backend" };
+        fields.push_back("model");
+        fields.push_back("size");
+        fields.push_back("params");
+        fields.push_back("backend");
         bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
         if (!is_cpu_backend) {
             fields.push_back("n_gpu_layers");
@@ -752,7 +783,7 @@ struct markdown_printer : public printer {
 
         fprintf(fout, "|");
         for (const auto & field : fields) {
-            fprintf(fout, " %*s |", get_field_width(field), field.c_str());
+            fprintf(fout, " %*s |", get_field_width(field), get_field_display_name(field).c_str());
         }
         fprintf(fout, "\n");
         fprintf(fout, "|");
@@ -769,12 +800,26 @@ struct markdown_printer : public printer {
         fprintf(fout, "|");
         for (const auto & field : fields) {
             std::string value;
+            char buf[128];
             if (field == "model") {
                 value = t.model_type;
+            } else if (field == "size") {
+                if (t.model_size < 1024*1024*1024) {
+                    snprintf(buf, sizeof(buf), "%.2f MiB", t.model_size / 1024.0 / 1024.0);
+                } else {
+                    snprintf(buf, sizeof(buf), "%.2f GiB", t.model_size / 1024.0 / 1024.0 / 1024.0);
+                }
+                value = buf;
+            } else if (field == "params") {
+                if (t.model_n_params < 1000*1000*1000) {
+                    snprintf(buf, sizeof(buf), "%.2f M", t.model_n_params / 1e6);
+                } else {
+                    snprintf(buf, sizeof(buf), "%.2f B", t.model_n_params / 1e9);
+                }
+                value = buf;
             } else if (field == "backend") {
                 value = test::get_backend();
             } else if (field == "test") {
-                char buf[128];
                 if (t.n_prompt > 0 && t.n_gen == 0) {
                     snprintf(buf, sizeof(buf), "pp %d", t.n_prompt);
                 } else if (t.n_gen > 0 && t.n_prompt == 0) {
@@ -785,7 +830,6 @@ struct markdown_printer : public printer {
                 }
                 value = buf;
             } else if (field == "t/s") {
-                char buf[128];
                 snprintf(buf, sizeof(buf), "%.2f ± %.2f", t.avg_ts(), t.stdev_ts());
                 value = buf;
             } else if (vmap.find(field) != vmap.end()) {
diff --git a/llama.cpp b/llama.cpp
index d12b6d1cb..4529ac822 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -5297,13 +5297,29 @@ int llama_model_n_embd(const struct llama_model * model) {
     return model->hparams.n_embd;
 }
 
-int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size) {
+int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
     return snprintf(buf, buf_size, "%s %s %s",
             model->name.c_str(),
             llama_model_type_name(model->type),
             llama_model_ftype_name(model->ftype).c_str());
 }
 
+uint64_t llama_model_size(const struct llama_model * model) {
+    uint64_t size = 0;
+    for (const auto & it : model->tensors_by_name) {
+        size += ggml_nbytes(it.second);
+    }
+    return size;
+}
+
+uint64_t llama_model_n_params(const struct llama_model * model) {
+    uint64_t nparams = 0;
+    for (const auto & it : model->tensors_by_name) {
+        nparams += ggml_nelements(it.second);
+    }
+    return nparams;
+}
+
 int llama_model_quantize(
         const char * fname_inp,
         const char * fname_out,
diff --git a/llama.h b/llama.h
index 2bcf94e0f..d47468172 100644
--- a/llama.h
+++ b/llama.h
@@ -254,7 +254,11 @@ extern "C" {
     LLAMA_API int llama_model_n_embd (const struct llama_model * model);
 
     // Get a string describing the model type
-    LLAMA_API int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size);
+    LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
+    // Returns the total size of all the tensors in the model in bytes
+    LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
+    // Returns the total number of parameters in the model
+    LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
 
     // Returns 0 on success
     LLAMA_API int llama_model_quantize(

From 28b2c996ca0ab90a5669946084f13443ec98e241 Mon Sep 17 00:00:00 2001
From: Nigel Bosch <pnigelb@gmail.com>
Date: Fri, 25 Aug 2023 09:41:52 -0500
Subject: [PATCH 11/53] convert.py : Get rope scale from HuggingFace models
 (#2772)

* Get rope scale from HF models

* Save rope scale only for linear scaling

* Rewrite for clarity
---
 convert.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/convert.py b/convert.py
index e58ea46e0..4f3e92798 100755
--- a/convert.py
+++ b/convert.py
@@ -105,6 +105,7 @@ class Params:
     f_norm_eps: float
 
     f_rope_freq_base: Optional[float] = None
+    f_rope_scale: Optional[float] = None
 
     ftype: Optional[GGMLFileType] = None
 
@@ -169,6 +170,11 @@ class Params:
         f_norm_eps       = config["rms_norm_eps"]
         f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
 
+        if "rope_scaling" in config and config["rope_scaling"].get("type") == "linear":
+            f_rope_scale = config["rope_scaling"].get("factor")
+        else:
+            f_rope_scale = None
+
         n_mult = Params.find_n_mult(n_ff, n_embd)
 
         if "max_sequence_length" in config:
@@ -190,6 +196,7 @@ class Params:
             n_head_kv        = n_head_kv,
             f_norm_eps       = f_norm_eps,
             f_rope_freq_base = f_rope_freq_base,
+            f_rope_scale     = f_rope_scale,
         )
 
     # LLaMA v2 70B params.json
@@ -773,6 +780,9 @@ class OutputFile:
         if params.f_rope_freq_base:
             self.gguf.add_rope_freq_base(params.f_rope_freq_base)
 
+        if params.f_rope_scale:
+            self.gguf.add_rope_scale_linear(params.f_rope_scale)
+
         if params.ftype:
             self.gguf.add_file_type(params.ftype)
 

From c82742ac9cd96fd34aa961978805c1d8a361d589 Mon Sep 17 00:00:00 2001
From: Matt Pulver <matt.pulver@heavy.ai>
Date: Fri, 25 Aug 2023 11:18:48 -0400
Subject: [PATCH 12/53] llama : add llama_beam_search() (#2267)

* Add llama_beam_search().

* Add '// Beam search' heading to llama.{h,cpp} after llama_grammar_accept_token().

* Add space around * pointers and & references.

* Add spaces around comparison and assignment operators.

* Prefer west const.

* Use llama_ prefix for structs in global namespace.

* Delete obsolete comment from an earlier revision.

* Change eos to eob in llama_beam and llama_beam_view structs.
---
 common/common.h                      |   1 +
 examples/CMakeLists.txt              |   1 +
 examples/beam_search/CMakeLists.txt  |   8 +
 examples/beam_search/beam_search.cpp | 188 ++++++++++++++++++++
 examples/server/server.cpp           |  90 ++++++++--
 llama.cpp                            | 251 +++++++++++++++++++++++++++
 llama.h                              |  37 ++++
 7 files changed, 563 insertions(+), 13 deletions(-)
 create mode 100644 examples/beam_search/CMakeLists.txt
 create mode 100644 examples/beam_search/beam_search.cpp

diff --git a/common/common.h b/common/common.h
index 17d271e67..ce61265f8 100644
--- a/common/common.h
+++ b/common/common.h
@@ -28,6 +28,7 @@ struct gpt_params {
     int32_t main_gpu                        = 0;    // the GPU that is used for scratch and small tensors
     float   tensor_split[LLAMA_MAX_DEVICES] = {0};  // how split tensors should be distributed across GPUs
     int32_t n_probs                         = 0;    // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t n_beams                         = 0;    // if non-zero then use beam search of given width.
     float   rope_freq_base                  = 10000.0f; // RoPE base frequency
     float   rope_freq_scale                 = 1.0f;     // RoPE frequency scaling factor
 
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index d2176c910..94b785224 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -25,6 +25,7 @@ else()
     add_subdirectory(simple)
     add_subdirectory(embd-input)
     add_subdirectory(llama-bench)
+    add_subdirectory(beam_search)
     if (LLAMA_METAL)
         add_subdirectory(metal)
     endif()
diff --git a/examples/beam_search/CMakeLists.txt b/examples/beam_search/CMakeLists.txt
new file mode 100644
index 000000000..b29e01092
--- /dev/null
+++ b/examples/beam_search/CMakeLists.txt
@@ -0,0 +1,8 @@
+set(TARGET beam_search)
+add_executable(${TARGET} beam_search.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+  add_dependencies(${TARGET} BUILD_INFO)
+endif()
diff --git a/examples/beam_search/beam_search.cpp b/examples/beam_search/beam_search.cpp
new file mode 100644
index 000000000..1c04fabc2
--- /dev/null
+++ b/examples/beam_search/beam_search.cpp
@@ -0,0 +1,188 @@
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
+#include "common.h"
+#include "llama.h"
+#include "build-info.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <vector>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#define NOMINMAX
+#include <windows.h>
+#include <signal.h>
+#endif
+
+// Used for debugging to print out beam tokens.
+struct ostream_beam_view {
+    llama_context * ctx;
+    llama_beam_view beam_view;
+};
+std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) {
+    os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
+    for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
+        os << llama_token_to_str(obv.ctx, obv.beam_view.tokens[i]);
+    }
+    return os << ')';
+}
+
+// Put here anything you want back in beam_search_callback().
+struct beam_search_callback_data {
+    llama_context * ctx;
+    std::vector<llama_token> response;
+};
+
+// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
+// For example, eob can be flagged due to maximum token length, stop words, etc.
+bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, const size_t n_tokens) {
+    return n_tokens && tokens[n_tokens-1] == llama_token_eos(callback_data.ctx);
+}
+
+// Function matching type llama_beam_search_callback_fn_t.
+// Custom callback example is called each time the beams lengths increase:
+//  * Show progress by printing ',' following by number of convergent beam tokens if any.
+//  * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
+//    This is also called when the stop condition is met.
+//    Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
+void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) {
+    auto& callback_data = *static_cast<beam_search_callback_data*>(callback_data_ptr);
+    // Mark beams as EOS as needed.
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        llama_beam_view& beam_view = beams_state.beam_views[i];
+        if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) {
+            beam_view.eob = true;
+        }
+    }
+    printf(",");  // Show progress
+    if (const size_t n = beams_state.common_prefix_length) {
+        callback_data.response.resize(callback_data.response.size() + n);
+        assert(0u < beams_state.n_beams);
+        const llama_token * tokens = beams_state.beam_views[0].tokens;
+        std::copy(tokens, tokens + n, callback_data.response.end() - n);
+        printf("%lu", n);
+    }
+    fflush(stdout);
+#if 1 // DEBUG: print current beams for this iteration
+    std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n";
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        std::cout << "beams["<<i<<"]: " << ostream_beam_view{callback_data.ctx,beams_state.beam_views[i]} << std::endl;
+    }
+#endif
+}
+
+int main(int argc, char ** argv)
+{
+    gpt_params params;
+    //params.n_gpu_layers = 200;
+
+    //---------------------------------
+    // Print help :
+    //---------------------------------
+
+    if ( argc < 2 || argv[1][0] == '-' )
+    {
+        printf( "Usage: %s MODEL_PATH [BEAM_WIDTH=2] [PROMPT]\n" , argv[0] );
+        return 1 ;
+    }
+
+    //---------------------------------
+    // Load parameters :
+    //---------------------------------
+
+    params.model = argv[1];
+
+    params.n_beams = 2 < argc ? std::stoi(argv[2]) : 2;
+
+    if ( argc > 3 )
+    {
+        params.prompt = argv[3];
+    }
+
+    if ( params.prompt.empty() )
+    {
+        params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n";
+    }
+
+    //---------------------------------
+    // Init LLM :
+    //---------------------------------
+
+    llama_backend_init(params.numa);
+
+    llama_model * model;
+    llama_context * ctx;
+
+    std::tie(model, ctx) = llama_init_from_gpt_params( params );
+
+    if ( model == NULL )
+    {
+        fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
+        return 1;
+    }
+
+    //---------------------------------
+    // Tokenize the prompt :
+    //---------------------------------
+
+    std::vector<llama_token> tokens_list = llama_tokenize(ctx, params.prompt, true);
+
+    const size_t max_context_size     = llama_n_ctx( ctx );
+    const size_t max_tokens_list_size = max_context_size - 4 ;
+
+    if (tokens_list.size() > max_tokens_list_size)
+    {
+        fprintf( stderr , "%s: error: prompt too long (%lu tokens, max %lu)\n" ,
+             __func__ , tokens_list.size() , max_tokens_list_size );
+        return 1;
+    }
+
+    fprintf( stderr, "\n\n" );
+
+    // Print the tokens from the prompt :
+
+    for( auto id : tokens_list )
+    {
+        std::cout << llama_token_to_str(ctx, id);
+    }
+    std::cout << std::flush;
+
+    int n_past = llama_get_kv_cache_token_count(ctx);
+    if (llama_eval(ctx, tokens_list.data(), tokens_list.size(), n_past, params.n_threads))
+    {
+        fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ );
+        return 1;
+    }
+    n_past += tokens_list.size();
+
+    beam_search_callback_data callback_data{ctx, {}};
+    size_t const beam_width = static_cast<size_t>(params.n_beams);
+    int const n_predict = 256;
+    llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict, params.n_threads);
+
+    std::cout << "\n\n";
+    for (llama_token const token_id : callback_data.response) {
+        std::cout << llama_token_to_str(ctx,token_id);
+    }
+    std::cout << std::endl;
+
+    llama_free( ctx );
+    llama_free_model( model );
+
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 025b385cc..3300553f9 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1209,6 +1209,62 @@ static void log_server_request(const Request &req, const Response &res)
                            });
 }
 
+bool is_at_eob(llama_server_context & server_context, const llama_token * tokens, const size_t n_tokens) {
+    return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx);
+}
+
+// Function matching type llama_beam_search_callback_fn_t.
+// Custom callback example is called each time the beams lengths increase:
+//  * Show progress by printing ',' following by number of convergent beam tokens if any.
+//  * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
+//    This is also called when the stop condition is met.
+//    Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
+void beam_search_callback(void * callback_data, llama_beams_state beams_state) {
+    auto & llama = *static_cast<llama_server_context*>(callback_data);
+    // Mark beams as EOS as needed.
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        llama_beam_view& beam_view = beams_state.beam_views[i];
+        if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) {
+            beam_view.eob = true;
+        }
+    }
+    printf(",");  // Show progress
+    if (const size_t n = beams_state.common_prefix_length) {
+        llama.generated_token_probs.resize(llama.generated_token_probs.size() + n);
+        assert(0u < beams_state.n_beams);
+        const llama_token * tokens = beams_state.beam_views[0].tokens;
+        const auto map = [](llama_token tok) { return completion_token_output{{},tok}; };
+        std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map);
+        printf("%lu", n);
+    }
+    fflush(stdout);
+#if 0 // DEBUG: print current beams for this iteration
+    std::cout << "\n\nCurrent beams:\n";
+    for (size_t i=0 ; i < beams_state.n_beams ; ++i) {
+        std::cout << "beams["<<i<<"]: " << ostream_beam_view{state.ctx,beams_state.beam_views[i]} << std::endl;
+    }
+#endif
+}
+
+struct token_translator {
+    llama_context * ctx;
+    std::string operator()(llama_token tok) const { return llama_token_to_str(ctx, tok); }
+    std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
+};
+
+void append_to_generated_text_from_generated_token_probs(llama_server_context & llama) {
+    auto & gtps = llama.generated_token_probs;
+    auto translator = token_translator{llama.ctx};
+    auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
+    const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
+    if (llama.generated_text.capacity() < llama.generated_text.size() + len) {
+        llama.generated_text.reserve(llama.generated_text.size() + len);
+    }
+    for (const completion_token_output & cto : gtps) {
+        llama.generated_text += translator(cto);
+    }
+}
+
 int main(int argc, char **argv)
 {
     // own arguments required by this example
@@ -1291,22 +1347,30 @@ int main(int argc, char **argv)
         llama.beginCompletion();
 
         if (!llama.stream) {
-            size_t stop_pos = std::string::npos;
+            if (llama.params.n_beams) {
+                // Fill llama.generated_token_probs vector with final beam.
+                llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams,
+                                  llama.n_past, llama.n_remain, llama.params.n_threads);
+                // Translate llama.generated_token_probs to llama.generated_text.
+                append_to_generated_text_from_generated_token_probs(llama);
+            } else {
+                size_t stop_pos = std::string::npos;
 
-            while (llama.has_next_token) {
-                const completion_token_output token_with_probs = llama.doCompletion();
-                const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
+                while (llama.has_next_token) {
+                    const completion_token_output token_with_probs = llama.doCompletion();
+                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
 
-                stop_pos = llama.findStoppingStrings(llama.generated_text,
-                    token_text.size(), STOP_FULL);
-            }
+                    stop_pos = llama.findStoppingStrings(llama.generated_text,
+                        token_text.size(), STOP_FULL);
+                }
 
-            if (stop_pos == std::string::npos) {
-                stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL);
-            }
-            if (stop_pos != std::string::npos) {
-                llama.generated_text.erase(llama.generated_text.begin() + stop_pos,
-                    llama.generated_text.end());
+                if (stop_pos == std::string::npos) {
+                    stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL);
+                }
+                if (stop_pos != std::string::npos) {
+                    llama.generated_text.erase(llama.generated_text.begin() + stop_pos,
+                        llama.generated_text.end());
+                }
             }
 
             const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs);
diff --git a/llama.cpp b/llama.cpp
index 4529ac822..7d8b9a0ac 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4326,6 +4326,257 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
     ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
 }
 
+//
+// Beam search
+//
+
+struct llama_beam {
+    std::vector<llama_token> tokens;
+    float p;  // Cumulative beam probability (renormalized relative to all beams)
+    bool eob; // Initialize end-of-beam to false. Callback sets this to true.
+    // Sort beams by probability. In case of ties, prefer beams at eob.
+    bool operator<(const llama_beam & rhs) const {
+        return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
+    }
+    // Shift off first n tokens and discard them.
+    void shift_tokens(const size_t n) {
+        if (n) {
+            std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
+            tokens.resize(tokens.size() - n);
+        }
+    }
+    llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
+};
+
+// A struct for calculating logit-related info.
+struct llama_logit_info {
+    const float * const logits;
+    const int n_vocab;
+    const float max_l;
+    const float normalizer;
+    struct sum_exp {
+        float max_l;
+        float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
+    };
+    llama_logit_info(llama_context * ctx)
+      : logits(llama_get_logits(ctx))
+      , n_vocab(llama_n_vocab(ctx))
+      , max_l(*std::max_element(logits, logits + n_vocab))
+      , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
+      { }
+    llama_token_data get_token_data(const llama_token token_id) const {
+        constexpr auto p = std::numeric_limits<float>::quiet_NaN();  // never used
+        return {token_id, logits[token_id], p};
+    }
+    // Return top k token_data by logit.
+    std::vector<llama_token_data> top_k(size_t k) {
+        std::vector<llama_token_data> min_heap;  // min-heap by logit
+        const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
+        min_heap.reserve(k_min);
+        for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
+            min_heap.push_back(get_token_data(token_id));
+        }
+        auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
+        std::make_heap(min_heap.begin(), min_heap.end(), comp);
+        for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
+            if (min_heap.front().logit < logits[token_id]) {
+                std::pop_heap(min_heap.begin(), min_heap.end(), comp);
+                min_heap.back().id = token_id;
+                min_heap.back().logit = logits[token_id];
+                std::push_heap(min_heap.begin(), min_heap.end(), comp);
+            }
+        }
+        return min_heap;
+    }
+    float probability_from_logit(float logit) {
+        return normalizer * std::exp(logit - max_l);
+    }
+};
+
+struct llama_beam_search_data {
+    llama_context * ctx;
+    size_t n_beams;
+    int n_past;
+    int n_predict;
+    int n_threads;
+    std::vector<llama_beam> beams;
+    std::vector<llama_beam> next_beams;
+
+    // Re-calculated on each loop iteration
+    size_t common_prefix_length;
+
+    // Used to communicate to/from callback on beams state.
+    std::vector<llama_beam_view> beam_views;
+
+    llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict, int n_threads)
+      : ctx(ctx)
+      , n_beams(n_beams)
+      , n_past(n_past)
+      , n_predict(n_predict)
+      , n_threads(n_threads)
+      , beam_views(n_beams) {
+        beams.reserve(n_beams);
+        next_beams.reserve(n_beams);
+    }
+
+    // Collapse beams to a single beam given by index.
+    void collapse_beams(const size_t beam_idx) {
+        if (0u < beam_idx) {
+            std::swap(beams[0], beams[beam_idx]);
+        }
+        beams.resize(1);
+    }
+
+    // Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
+    // The repetative patterns below reflect the 2 stages of heaps:
+    //  * Gather elements until the vector is full, then call std::make_heap() on it.
+    //  * If the heap is full and a new element is found that should be included, pop the
+    //    least element to the back(), replace it with the new, then push it into the heap.
+    void fill_next_beams_by_top_probabilities(llama_beam & beam) {
+        // Min-heaps use a greater-than comparator.
+        const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
+        if (beam.eob) {
+            // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
+            if (next_beams.size() < n_beams) {
+                next_beams.push_back(std::move(beam));
+                if (next_beams.size() == n_beams) {
+                    std::make_heap(next_beams.begin(), next_beams.end(), comp);
+                }
+            } else if (next_beams.front().p < beam.p) {
+                std::pop_heap(next_beams.begin(), next_beams.end(), comp);
+                next_beams.back() = std::move(beam);
+                std::push_heap(next_beams.begin(), next_beams.end(), comp);
+            }
+        } else {
+            // beam is not at end-of-sentence, so branch with next top_k tokens.
+            if (!beam.tokens.empty()) {
+                llama_eval(ctx, beam.tokens.data(), beam.tokens.size(), n_past, n_threads);
+            }
+            llama_logit_info logit_info(ctx);
+            std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
+            size_t i=0;
+            if (next_beams.size() < n_beams) {
+                for (; next_beams.size() < n_beams ; ++i) {
+                    llama_beam next_beam = beam;
+                    next_beam.tokens.push_back(next_tokens[i].id);
+                    next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
+                    next_beams.push_back(std::move(next_beam));
+                }
+                std::make_heap(next_beams.begin(), next_beams.end(), comp);
+            } else {
+                for (; next_beams.front().p == 0.0f ; ++i) {
+                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
+                    next_beams.back() = beam;
+                    next_beams.back().tokens.push_back(next_tokens[i].id);
+                    next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
+                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
+                }
+            }
+            for (; i < n_beams ; ++i) {
+                const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
+                if (next_beams.front().p < next_p) {
+                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
+                    next_beams.back() = beam;
+                    next_beams.back().tokens.push_back(next_tokens[i].id);
+                    next_beams.back().p = next_p;
+                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
+                }
+            }
+        }
+    }
+
+    // Find common_prefix_length based on beams.
+    // Requires beams is not empty.
+    size_t find_common_prefix_length() {
+        size_t common_prefix_length = beams[0].tokens.size();
+        for (size_t i = 1 ; i < beams.size() ; ++i) {
+            common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
+            for (size_t j = 0 ; j < common_prefix_length ; ++j) {
+                if (beams[0].tokens[j] != beams[i].tokens[j]) {
+                    common_prefix_length = j;
+                    break;
+                }
+            }
+        }
+        return common_prefix_length;
+    }
+
+    // Construct beams_state to send back to caller via the callback function.
+    // Side effect: set common_prefix_length = find_common_prefix_length();
+    llama_beams_state get_beams_state(const bool last_call) {
+        for (size_t i = 0 ; i < beams.size() ; ++i) {
+            beam_views[i] = beams[i].view();
+        }
+        common_prefix_length = find_common_prefix_length();
+        return {beam_views.data(), beams.size(), common_prefix_length, last_call};
+    }
+
+    // Loop:
+    //  * while i < n_predict, AND
+    //  * any of the beams have not yet reached end-of-beam (eob), AND
+    //  * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
+    //    (since all other beam probabilities can only decrease)
+    void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
+        beams.push_back({{}, 1.0f, false});  // Start with one empty beam w/ probability = 1.0 and !eob.
+        const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
+        for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
+                       !beams[top_beam_index()].eob ; ++i) {
+            callback(callback_data, get_beams_state(false));  // Sets common_prefix_length
+            update_beams_from_beam_views();   // Update values (p,eob) that callback may have changed.
+            if (common_prefix_length) {
+                llama_eval(ctx, beams[0].tokens.data(), common_prefix_length, n_past, n_threads);
+                n_past += common_prefix_length;
+            }
+            // Zero-out next_beam probabilities to place them last in following min-heap.
+            std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
+            for (llama_beam & beam : beams) {
+                beam.shift_tokens(common_prefix_length);
+                fill_next_beams_by_top_probabilities(beam);
+            }
+            // next_beams become the beams of next/final iteration. Swap them to re-use memory.
+            beams.swap(next_beams);
+            renormalize_beam_probabilities(beams);
+        }
+        collapse_beams(top_beam_index());
+        callback(callback_data, get_beams_state(true));
+    }
+
+    // As beams grow, the cumulative probabilities decrease.
+    // Renormalize them to avoid floating point underflow.
+    static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
+        const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
+        const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
+        std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
+    }
+
+    // Assumes beams is non-empty.  Uses llama_beam::operator<() for ordering.
+    size_t top_beam_index() {
+        return std::max_element(beams.begin(), beams.end()) - beams.begin();
+    }
+
+    // Copy (p,eob) for each beam which may have been changed by the callback.
+    void update_beams_from_beam_views() {
+        for (size_t i = 0 ; i < beams.size() ; ++i) {
+            beams[i].p = beam_views[i].p;
+            beams[i].eob = beam_views[i].eob;
+        }
+    }
+};
+
+void llama_beam_search(llama_context * ctx,
+                       llama_beam_search_callback_fn_t callback, void * callback_data,
+                       size_t n_beams, int n_past, int n_predict, int n_threads) {
+    assert(ctx);
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict, n_threads);
+
+    beam_search_data.loop(callback, callback_data);
+
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    ctx->n_sample++;
+}
+
 //
 // quantization
 //
diff --git a/llama.h b/llama.h
index d47468172..86737200f 100644
--- a/llama.h
+++ b/llama.h
@@ -469,6 +469,43 @@ extern "C" {
     /// @details Accepts the sampled token into the grammar
     LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
 
+    //
+    // Beam search
+    //
+
+    struct llama_beam_view {
+        const llama_token * tokens;
+        size_t n_tokens;
+        float p;   // Cumulative beam probability (renormalized relative to all beams)
+        bool eob;  // Callback should set this to true when a beam is at end-of-beam.
+    };
+
+    // Passed to beam_search_callback function.
+    // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
+    // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
+    // These pointers are valid only during the synchronous callback, so should not be saved.
+    struct llama_beams_state {
+        llama_beam_view * beam_views;
+        size_t n_beams;               // Number of elements in beam_views[].
+        size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
+        bool last_call;               // True iff this is the last callback invocation.
+    };
+
+    // Type of pointer to the beam_search_callback function.
+    // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
+    // passed back to beam_search_callback. This avoids having to use global variables in the callback.
+    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, llama_beams_state);
+
+    /// @details Deterministically returns entire sentence constructed by a beam search.
+    /// @param ctx Pointer to the llama_context.
+    /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
+    /// @param callback_data A pointer that is simply passed back to callback.
+    /// @param n_beams Number of beams to use.
+    /// @param n_past Number of tokens already evaluated.
+    /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
+    /// @param n_threads Number of threads as passed to llama_eval().
+    LLAMA_API void llama_beam_search(struct llama_context * ctx, llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, int n_past, int n_predict, int n_threads);
+
     // Performance information
     LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
     LLAMA_API void llama_print_timings(struct llama_context * ctx);

From d046dcee081118c9071bbc63dacdb359a58c467a Mon Sep 17 00:00:00 2001
From: Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Date: Fri, 25 Aug 2023 19:05:02 +0300
Subject: [PATCH 13/53] Faster perplexity computation (#2786)

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
---
 examples/perplexity/perplexity.cpp | 67 +++++++++++++++++++++++++-----
 1 file changed, 56 insertions(+), 11 deletions(-)

diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index a7bd9db2a..18635932b 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -6,6 +6,8 @@
 #include <ctime>
 #include <sstream>
 #include <cstring>
+#include <thread>
+#include <mutex>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -27,6 +29,40 @@ std::vector<float> softmax(const std::vector<float>& logits) {
     return probs;
 }
 
+float log_softmax(int n_vocab, const float * logits, int tok) {
+    float max_logit = logits[0];
+    for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]);
+    double sum_exp = 0.0;
+    for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit);
+    return logits[tok] - max_logit - log(sum_exp);
+}
+
+void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread>& workers,
+        double& nll, double& nll2) {
+
+    std::mutex mutex;
+    int counter = 0;
+    auto compute = [&mutex, &counter, &nll, &nll2, n_vocab, logits, tokens, n_token] () {
+        double local_nll = 0, local_nll2 = 0;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int i = counter++;
+            if (i >= n_token) {
+                nll += local_nll; nll2 += local_nll2;
+                break;
+            }
+            lock.unlock();
+            double v = -log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
+            local_nll += v;
+            local_nll2 += v*v;
+        }
+    };
+    for (auto& w : workers) w = std::thread(compute);
+    compute();
+    for (auto& w : workers) w.join();
+
+}
+
 void perplexity_v2(llama_context * ctx, const gpt_params & params) {
     // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
     // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
@@ -166,9 +202,12 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
 
     int count = 0;
     double nll = 0.0;
+    double nll2 = 0.0;
 
     fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
 
+    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
     for (int i = 0; i < n_chunk; ++i) {
         const int start =     i * params.n_ctx;
         const int end   = start + params.n_ctx;
@@ -228,26 +267,32 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
         // Example, we have a context window of 512, we will compute perplexity for each of the
         // last 256 tokens.  Then, we split the input up into context window size chunks to
         // process the entire prompt.
-        for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) {
-            // Calculate probability of next token, given the previous ones.
-            const std::vector<float> tok_logits(
-                logits.begin() + (j + 0) * n_vocab,
-                logits.begin() + (j + 1) * n_vocab);
+        const int first = std::min(512, params.n_ctx/2);
+        process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2);
+        count += params.n_ctx - first - 1;
 
-            const float prob = softmax(tok_logits)[tokens[start + j + 1]];
-
-            nll += -std::log(prob);
-            ++count;
-        }
         // perplexity is e^(average negative log-likelihood)
         if (params.ppl_output_type == 0) {
             printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
         } else {
-            printf("%8d  %.4lf\n", i*params.n_ctx, std::exp(nll / count));
+            double av = nll/count;
+            double av2 = nll2/count - av*av;
+            if (av2 > 0) av2 = sqrt(av2/(count-1));
+            printf("%8d  %.4lf  %4lf  %4lf\n", i*params.n_ctx, std::exp(nll / count), av, av2);
         }
         fflush(stdout);
     }
     printf("\n");
+    nll2 /= count;
+    nll /= count;
+    nll2 -= nll * nll;
+    if (nll2 > 0) {
+        nll2 = sqrt(nll2/(count-1));
+        double ppl = exp(nll);
+        printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
+    } else {
+        printf("Unexpected negative standard deviation of log(prob)\n");
+    }
 }
 
 std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch,

From 232caf3c1581a6cb023571780ff41dc2d66d1ca0 Mon Sep 17 00:00:00 2001
From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com>
Date: Fri, 25 Aug 2023 09:17:15 -0700
Subject: [PATCH 14/53] llama : fix struct decl (#2790)

---
 llama.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama.h b/llama.h
index 86737200f..b77dd7735 100644
--- a/llama.h
+++ b/llama.h
@@ -485,7 +485,7 @@ extern "C" {
     // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
     // These pointers are valid only during the synchronous callback, so should not be saved.
     struct llama_beams_state {
-        llama_beam_view * beam_views;
+        struct llama_beam_view * beam_views;
         size_t n_beams;               // Number of elements in beam_views[].
         size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
         bool last_call;               // True iff this is the last callback invocation.

From bae5c5f679e043371bc2b4dffff8d4964d6cb953 Mon Sep 17 00:00:00 2001
From: lon <114724657+longregen@users.noreply.github.com>
Date: Sat, 26 Aug 2023 10:07:43 +0200
Subject: [PATCH 15/53] examples : skip unnecessary external lib in server
 README.md how-to (#2804)

---
 examples/server/README.md | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 77997f98d..7105e9020 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -77,34 +77,31 @@ You need to have [Node.js](https://nodejs.org/en) installed.
 ```bash
 mkdir llama-client
 cd llama-client
-npm init
-npm install axios
 ```
 
 Create a index.js file and put inside this:
 
 ```javascript
-const axios = require("axios");
-
 const prompt = `Building a website can be done in 10 simple steps:`;
 
 async function Test() {
-    let result = await axios.post("http://127.0.0.1:8080/completion", {
-        prompt,
-        n_predict: 512,
-    });
-
-    // the response is received until completion finish
-    console.log(result.data.content);
+    let response = await fetch("http://127.0.0.1:8080/completion", {
+        method: 'POST',
+        body: JSON.stringify({
+            prompt,
+            n_predict: 512,
+        })
+    })
+    console.log((await response.json()).content)
 }
 
-Test();
+Test()
 ```
 
 And run it:
 
 ```bash
-node .
+node index.js
 ```
 
 ## API Endpoints

From 2ba83c8685177faea3399db9564f9c52df75c366 Mon Sep 17 00:00:00 2001
From: klosax <131523366+klosax@users.noreply.github.com>
Date: Sat, 26 Aug 2023 13:45:53 +0200
Subject: [PATCH 16/53] Fix spm whitespaces (#2806)

* llama.cpp : fix spm whitespace escaping + clean up

* main.cpp : spm - add whitespace in front of prompt

* test-tokenizer-0.cpp : spm - add whitespace in front of prompt
---
 examples/main/main.cpp     | 17 ++++++++++----
 llama.cpp                  | 48 +++++++++++---------------------------
 tests/test-tokenizer-0.cpp |  3 ++-
 3 files changed, 27 insertions(+), 41 deletions(-)

diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index cb8747c2b..4665b82fe 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -189,12 +189,19 @@ int main(int argc, char ** argv) {
         }
     }
 
-    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    // Add BOS if SPM tokenizer
+    const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
 
     // tokenize the prompt
     std::vector<llama_token> embd_inp;
+
+    if (llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM) {
+        // Add a space in front of the first character to match OG llama tokenizer behavior
+        params.prompt.insert(0, 1, ' ');
+    }
+
     if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
-        embd_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
+        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
     } else {
         embd_inp = session_tokens;
     }
@@ -210,9 +217,9 @@ int main(int argc, char ** argv) {
     int original_prompt_len = 0;
     if (ctx_guidance) {
         params.cfg_negative_prompt.insert(0, 1, ' ');
-        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, is_spm);
+        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
 
-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
         original_prompt_len = original_inp.size();
         guidance_offset = (int)guidance_inp.size() - original_prompt_len;
     }
@@ -259,7 +266,7 @@ int main(int argc, char ** argv) {
     }
 
     // prefix & suffix for instruct mode
-    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", is_spm);
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos);
     const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false);
 
     // in instruct mode, we inject a prefix and a suffix to each input by the user
diff --git a/llama.cpp b/llama.cpp
index 7d8b9a0ac..b0a3b5768 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1635,7 +1635,7 @@ static void llm_load_hparams(
 }
 
 // TODO: This should probably be in llama.h
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape);
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos);
 
 static void llm_load_vocab(
         llama_model_loader & ml,
@@ -1737,7 +1737,7 @@ static void llm_load_vocab(
     }
 
     // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
-    vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false, false)[0];
+    vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0];
 
     // special tokens
     GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
@@ -3027,14 +3027,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
 }
 
 static std::string llama_escape_whitespace(const std::string& text) {
-    std::string result = "\xe2\x96\x81";
-    for (size_t offs = 0; offs < text.length(); ++offs) {
-        if (text[offs] == ' ') {
-            result += "\xe2\x96\x81";
-        } else {
-            result += text[offs];
-        }
-    }
+    std::string result = text;
+    replace_all(result, " ", "\xe2\x96\x81");
     return result;
 }
 
@@ -3219,7 +3213,7 @@ struct llm_bigram_bpe {
 };
 
 struct llm_tokenizer_bpe {
-    llm_tokenizer_bpe(const llama_vocab & vocab, bool g2ws): vocab(vocab) { flag_g2ws = g2ws; }
+    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}
 
     void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
         int final_prev_index = -1;
@@ -3371,8 +3365,6 @@ private:
         return words;
     }
 
-    bool flag_g2ws = false;
-
     const llama_vocab & vocab;
 
     std::vector<llm_symbol> symbols;
@@ -3381,39 +3373,26 @@ private:
     llm_bigram_bpe::queue work_queue;
 };
 
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape) {
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos) {
     std::vector<llama_vocab::id> output;
 
     if (raw_text.empty()) {
         return output;
     }
 
+    if (bos && vocab.special_bos_id != -1) {
+        output.push_back(vocab.special_bos_id);
+    }
+
     switch (vocab.type) {
         case LLAMA_VOCAB_TYPE_SPM:
             {
                 llm_tokenizer_spm tokenizer(vocab);
-
-                if (bos) {
-                    output.push_back(vocab.special_bos_id);
-                }
-
-                std::string text;
-                if (escape) {
-                    text = llama_escape_whitespace(raw_text);
-                } else {
-                    text = raw_text;
-                }
-
-                tokenizer.tokenize(text, output);
+                tokenizer.tokenize(llama_escape_whitespace(raw_text), output);
             } break;
         case LLAMA_VOCAB_TYPE_BPE:
             {
-                llm_tokenizer_bpe tokenizer(vocab, escape);
-
-                if (bos && vocab.special_bos_id != -1) {
-                    output.push_back(vocab.special_bos_id);
-                }
-
+                llm_tokenizer_bpe tokenizer(vocab);
                 tokenizer.tokenize(raw_text, output);
             } break;
     };
@@ -6095,8 +6074,7 @@ int llama_tokenize_with_model(
                  llama_token * tokens,
                          int   n_max_tokens,
                         bool   add_bos) {
-    auto escape = llama_vocab_get_type(model->vocab) == LLAMA_VOCAB_TYPE_SPM;
-    auto res = llama_tokenize_internal(model->vocab, text, add_bos, escape);
+    auto res = llama_tokenize_internal(model->vocab, text, add_bos);
 
     if (n_max_tokens < (int) res.size()) {
         LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
index f3ee851a3..7e9ac9188 100644
--- a/tests/test-tokenizer-0.cpp
+++ b/tests/test-tokenizer-0.cpp
@@ -100,7 +100,8 @@ int main(int argc, char **argv) {
     bool success = true;
 
     for (const auto & test_kv : k_tests()) {
-        std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, true);
+        // Add a space in front of the first character to match OG llama tokenizer behavior
+        std::vector<llama_token> res = llama_tokenize(ctx, " " + test_kv.first, true);
         fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
             __func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str());
 

From a2ca4e9de9da45ed0bb1c34935d5ec80cebc22d5 Mon Sep 17 00:00:00 2001
From: Nigel Bosch <pnigelb@gmail.com>
Date: Sat, 26 Aug 2023 07:11:17 -0500
Subject: [PATCH 17/53] Handle null rope scaling value (#2793)

---
 convert.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/convert.py b/convert.py
index 4f3e92798..d44e5a8c4 100755
--- a/convert.py
+++ b/convert.py
@@ -170,7 +170,8 @@ class Params:
         f_norm_eps       = config["rms_norm_eps"]
         f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
 
-        if "rope_scaling" in config and config["rope_scaling"].get("type") == "linear":
+        rope_scaling = config.get("rope_scaling")
+        if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear":
             f_rope_scale = config["rope_scaling"].get("factor")
         else:
             f_rope_scale = None

From f305bad11e10ad09e396faed2e37f4f845f5d566 Mon Sep 17 00:00:00 2001
From: Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com>
Date: Sat, 26 Aug 2023 14:25:39 +0100
Subject: [PATCH 18/53] flake : build llama.cpp on Intel with nix (#2795)

Problem
-------
`nix build` fails with missing `Accelerate.h`.

Changes
-------
- Fix build of the llama.cpp with nix for Intel: add the same SDK frameworks as
for ARM
- Add `quantize` app to the output of nix flake
- Extend nix devShell with llama-python so we can use convertScript

Testing
-------
Testing the steps with nix:
1. `nix build`
Get the model and then
2. `nix develop` and then `python convert.py models/llama-2-7b.ggmlv3.q4_0.bin`
3. `nix run llama.cpp#quantize -- open_llama_7b/ggml-model-f16.gguf ./models/ggml-model-q4_0.bin 2`
4. `nix run llama.cpp#llama -- -m models/ggml-model-q4_0.bin -p "What is nix?" -n 400 --temp 0.8 -e -t 8`

Co-authored-by: Volodymyr Vitvitskyi <volodymyrvitvitskyi@SamsungPro.local>
---
 flake.nix | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/flake.nix b/flake.nix
index 616b90252..d454cedc3 100644
--- a/flake.nix
+++ b/flake.nix
@@ -21,6 +21,12 @@
               CoreGraphics
               CoreVideo
             ]
+          else if isDarwin then
+            with pkgs.darwin.apple_sdk.frameworks; [
+              Accelerate
+              CoreGraphics
+              CoreVideo
+            ]
           else
             with pkgs; [ openblas ]
         );
@@ -80,8 +86,13 @@
           type = "app";
           program = "${self.packages.${system}.default}/bin/llama";
         };
+        apps.quantize = {
+          type = "app";
+          program = "${self.packages.${system}.default}/bin/quantize";
+        };
         apps.default = self.apps.${system}.llama;
         devShells.default = pkgs.mkShell {
+          buildInputs = [ llama-python ];
           packages = nativeBuildInputs ++ osSpecific;
         };
       });

From 771551a793c9976ed9cdfe7b8c69536af32af9f9 Mon Sep 17 00:00:00 2001
From: Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Date: Sat, 26 Aug 2023 16:48:53 +0300
Subject: [PATCH 19/53] Fix HellaSwag (#2805)

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
---
 examples/perplexity/perplexity.cpp | 20 +++++++++++++++++---
 1 file changed, 17 insertions(+), 3 deletions(-)

diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 18635932b..fd89852d6 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -351,6 +351,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
 
     const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    fprintf(stderr, "================================= is_spm = %d\n", is_spm);
 
     // This is needed as usual for LLaMA models
     const bool add_bos = is_spm;
@@ -406,6 +407,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     double acc = 0.0f;
     const int n_vocab = llama_n_vocab(ctx);
 
+    std::vector<std::vector<int>> ending_tokens(4);
+
     std::vector<float> tok_logits(n_vocab);
 
     for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
@@ -413,11 +416,21 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
         size_t context_size = context_embd.size();
 
+        for (int i = 0; i < 4; ++i) {
+            ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[i], add_bos);
+            for (int k = 0; k < int(context_size); ++k) {
+                if (ending_tokens[i][k] != context_embd[k]) {
+                    fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);
+                    break;
+                }
+            }
+        }
+
         // Do the 1st ending
         // In this case we include the context when evaluating
-        auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
+        //auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
+        auto query_embd = ending_tokens[0];
         auto query_size = query_embd.size();
-        //printf("First query: %d\n",(int)query_size);
 
         // Stop if query wont fit the ctx window
         if (query_size > (size_t)params.n_ctx) {
@@ -462,7 +475,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
 
             // Tokenize the query
-            query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
+            query_embd.resize(ending_tokens[ending_idx].size() - context_size);
+            std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int));
             query_size = query_embd.size();
 
             // Stop if query wont fit the ctx window

From 7592375403a0bd0456d5ec2cdf8350e591f04fb0 Mon Sep 17 00:00:00 2001
From: Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Date: Sat, 26 Aug 2023 17:27:49 +0300
Subject: [PATCH 20/53] Better perplexity for 2- and 3-bit quantization for
 LLaMA-v2-70B (#2807)

* Better perplexity for 2- and 3-bit quantization for the 70B model

* PR comment

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
---
 llama.cpp | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/llama.cpp b/llama.cpp
index b0a3b5768..52fcaceff 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4653,6 +4653,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
     std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname_inp, /*use_mmap*/ false));
 
+    llama_model model;
+    llm_load_arch(*ml, model);
+    llm_load_hparams(*ml, model, 0, 0, 0);
+
     const size_t align = GGUF_DEFAULT_ALIGNMENT;
     struct gguf_context * ctx_out = gguf_init_empty();
 
@@ -4678,6 +4682,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             ++n_feed_forward_w2;
         }
     }
+    if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) {
+        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
+                __func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer);
+    }
 
     int i_attention_wv = 0;
     int i_feed_forward_w2 = 0;
@@ -4769,6 +4777,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
                 else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
                         (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
+                if (model.type == MODEL_70B) {
+                    // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
+                    // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
+                    // nearly negligible increase in model size by quantizing this tensor with more bits:
+                    if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
+                }
                 ++i_attention_wv;
             } else if (name.find("ffn_down.weight") != std::string::npos) {
                 if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;

From 04f4b1eb10f3e25750ca3e530265ce2841730e6b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 26 Aug 2023 17:37:35 +0300
Subject: [PATCH 21/53] k-quants : remove unnecessary tensor shape restrictions
 (#2811)

---
 llama.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 52fcaceff..59105db1c 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4762,8 +4762,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
             if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
                 int nx = tensor->ne[0];
-                int ny = tensor->ne[1];
-                if (nx % QK_K == 0 && ny % QK_K == 0) {
+                if (nx % QK_K == 0) {
                     new_type = GGML_TYPE_Q6_K;
                 }
             } else if (name.find("attn_v.weight") != std::string::npos) {
@@ -4812,8 +4811,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
                 int nx = tensor->ne[0];
                 int ny = tensor->ne[1];
-                if (nx % QK_K != 0 || ny % QK_K != 0) {
-                    LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K);
+                if (nx % QK_K != 0) {
+                    LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for k-quants\n", __func__, nx, ny, QK_K);
                     convert_incompatible_tensor = true;
                 }
             }

From 50526f37eba0b28336700890242ff282b949cd83 Mon Sep 17 00:00:00 2001
From: Cebtenzzre <cebtenzzre@gmail.com>
Date: Sat, 26 Aug 2023 12:53:52 -0400
Subject: [PATCH 22/53] llama : use std::abs in llama_sample_tail_free (#2800)

Plain 'abs' casts the input to int.
---
 llama.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama.cpp b/llama.cpp
index 59105db1c..2b88485a8 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -3887,7 +3887,7 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
 
     // Calculate absolute value of second derivatives
     for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        second_derivatives[i] = abs(second_derivatives[i]);
+        second_derivatives[i] = std::abs(second_derivatives[i]);
     }
 
     // Normalize the second derivatives

From 72f895c923ba98b8f2af294440206f35915c0501 Mon Sep 17 00:00:00 2001
From: "Dr. Tom Murphy VII Ph.D" <499244+tom7@users.noreply.github.com>
Date: Sat, 26 Aug 2023 14:12:56 -0400
Subject: [PATCH 23/53] main : fix bug (penalize_nl=false doesn't work) +
 suppress warning on mingw (#1528)

* Fix bug in main.cpp where penalize_nl=false has no effect. It modifies the underlying logits array, but at this point we are already working on the candidates copy.

* Suppress redefinition warning for NOMINMAX on mingw. In my installation, this macro is already defined by /usr/lib/gcc/x86_64-w64-mingw32/11/include/c++/x86_64-w64-mingw32/bits/os_defines.h:45.

* main : fix indentation

* main : pass ctx to llama_token_nl()

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 examples/main/main.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 4665b82fe..11d7a7e4f 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -604,7 +604,12 @@ int main(int argc, char ** argv) {
                     last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
                     last_n_repeat, alpha_frequency, alpha_presence);
                 if (!penalize_nl) {
-                    logits[llama_token_nl(ctx)] = nl_logit;
+                    for (size_t idx = 0; idx < candidates_p.size; idx++) {
+                        if (candidates_p.data[idx].id == llama_token_nl(ctx)) {
+                            candidates_p.data[idx].logit = nl_logit;
+                            break;
+                        }
+                    }
                 }
 
                 if (grammar != NULL) {

From 741ca7dd1cec0a0349494742b9083d6ef4cd73c5 Mon Sep 17 00:00:00 2001
From: Cebtenzzre <cebtenzzre@gmail.com>
Date: Sat, 26 Aug 2023 14:17:51 -0400
Subject: [PATCH 24/53] llama : move #includes out of _GNU_SOURCE conditional
 (#2817)

---
 llama.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 2b88485a8..62889b3ed 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1,9 +1,6 @@
 // Defines fileno on msys:
 #ifndef _GNU_SOURCE
 #define _GNU_SOURCE
-#include <cstddef>
-#include <cstdint>
-#include <cstdio>
 #endif
 
 #include "llama.h"
@@ -62,6 +59,9 @@
 #include <cinttypes>
 #include <climits>
 #include <cstdarg>
+#include <cstddef>
+#include <cstdint>
+#include <cstdio>
 #include <cstring>
 #include <ctime>
 #include <fstream>

From 61d1a2895eeca55e0c8b7018492f6ab9c90cff78 Mon Sep 17 00:00:00 2001
From: Tungsten842 <quantmint@protonmail.com>
Date: Sat, 26 Aug 2023 20:19:44 +0200
Subject: [PATCH 25/53] flake.nix : add rocm support and cleanup (#2808)

---
 flake.lock | 12 ++++++------
 flake.nix  | 43 +++++++++++++++++++++++--------------------
 2 files changed, 29 insertions(+), 26 deletions(-)

diff --git a/flake.lock b/flake.lock
index 33164e096..a7777d05d 100644
--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
         "systems": "systems"
       },
       "locked": {
-        "lastModified": 1685518550,
-        "narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=",
+        "lastModified": 1692799911,
+        "narHash": "sha256-3eihraek4qL744EvQXsK1Ha6C3CR7nnT8X2qWap4RNk=",
         "owner": "numtide",
         "repo": "flake-utils",
-        "rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef",
+        "rev": "f9e7cf818399d17d347f847525c5a5a8032e4e44",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1685931219,
-        "narHash": "sha256-8EWeOZ6LKQfgAjB/USffUSELPRjw88A+xTcXnOUvO5M=",
+        "lastModified": 1692913444,
+        "narHash": "sha256-1SvMQm2DwofNxXVtNWWtIcTh7GctEVrS/Xel/mdc6iY=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "7409480d5c8584a1a83c422530419efe4afb0d19",
+        "rev": "18324978d632ffc55ef1d928e81630c620f4f447",
         "type": "github"
       },
       "original": {
diff --git a/flake.nix b/flake.nix
index d454cedc3..02095411e 100644
--- a/flake.nix
+++ b/flake.nix
@@ -6,6 +6,9 @@
   outputs = { self, nixpkgs, flake-utils }:
     flake-utils.lib.eachDefaultSystem (system:
       let
+        name = "llama.cpp";
+        src = ./.;
+        meta.mainProgram = "llama";
         inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin;
         buildInputs = with pkgs; [ openmpi ];
         osSpecific = with pkgs; buildInputs ++
@@ -31,7 +34,7 @@
             with pkgs; [ openblas ]
         );
         pkgs = import nixpkgs { inherit system; };
-        nativeBuildInputs = with pkgs; [ cmake pkgconfig ];
+        nativeBuildInputs = with pkgs; [ cmake ninja pkgconfig ];
         llama-python =
           pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]);
         postPatch = ''
@@ -44,35 +47,35 @@
           mv $out/bin/server $out/bin/llama-server
         '';
         cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" "-DLLAMA_MPI=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ];
-      in {
+      in
+      {
         packages.default = pkgs.stdenv.mkDerivation {
-          name = "llama.cpp";
-          src = ./.;
-          postPatch = postPatch;
-          nativeBuildInputs = nativeBuildInputs;
-          buildInputs = osSpecific;
+          inherit name src meta postPatch nativeBuildInputs buildInputs postInstall;
           cmakeFlags = cmakeFlags
             ++ (if isAarch64 && isDarwin then [
-              "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
-              "-DLLAMA_METAL=ON"
-            ] else [
-              "-DLLAMA_BLAS=ON"
-              "-DLLAMA_BLAS_VENDOR=OpenBLAS"
+            "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
+            "-DLLAMA_METAL=ON"
+          ] else [
+            "-DLLAMA_BLAS=ON"
+            "-DLLAMA_BLAS_VENDOR=OpenBLAS"
           ]);
-          postInstall = postInstall;
-          meta.mainProgram = "llama";
         };
         packages.opencl = pkgs.stdenv.mkDerivation {
-          name = "llama.cpp";
-          src = ./.;
-          postPatch = postPatch;
-          nativeBuildInputs = nativeBuildInputs;
+          inherit name src meta postPatch nativeBuildInputs postInstall;
           buildInputs = with pkgs; buildInputs ++ [ clblast ];
           cmakeFlags = cmakeFlags ++ [
             "-DLLAMA_CLBLAST=ON"
           ];
-          postInstall = postInstall;
-          meta.mainProgram = "llama";
+        };
+        packages.rocm = pkgs.stdenv.mkDerivation {
+          inherit name src meta postPatch nativeBuildInputs postInstall;
+          buildInputs = with pkgs; buildInputs ++ [ hip hipblas rocblas ];
+          cmakeFlags = cmakeFlags ++ [
+            "-DLLAMA_HIPBLAS=1"
+            "-DCMAKE_C_COMPILER=hipcc"
+            "-DCMAKE_CXX_COMPILER=hipcc"
+            "-DCMAKE_POSITION_INDEPENDENT_CODE=ON"
+          ];
         };
         apps.llama-server = {
           type = "app";

From c7d92e6dfec3f54849f3a0ba373054d29f321ea2 Mon Sep 17 00:00:00 2001
From: Tim Miller <drasticactions@users.noreply.github.com>
Date: Sun, 27 Aug 2023 03:27:07 +0900
Subject: [PATCH 26/53] llama : use Unicode Escape Sequence to replace encoded
 characters (#2814)

The use of special characters within source files can break compiling on some computers with different region and language settings. Using Unicode escape sequences should allow for the code to be compiled on all setups without needing to change your computers settings or switch regions.
---
 llama.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 62889b3ed..05c54c213 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -955,10 +955,10 @@ struct llama_vocab {
     id linefeed_id = 13;
 
     int find_bpe_rank(std::string token_left, std::string token_right) const {
-        replace_all(token_left,  " ",  "Ġ");
-        replace_all(token_left,  "\n", "Ċ");
-        replace_all(token_right, " ",  "Ġ");
-        replace_all(token_right, "\n", "Ċ");
+        replace_all(token_left,  " ",  "\u0120");
+        replace_all(token_left,  "\n", "\u010A");
+        replace_all(token_right, " ",  "\u0120");
+        replace_all(token_right, "\n", "\u010A");
 
         auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
         if (it == bpe_ranks.end()) {

From 730d9c681e339b76407659344e5a2cd50af7d7d5 Mon Sep 17 00:00:00 2001
From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date: Sat, 26 Aug 2023 14:13:36 -0600
Subject: [PATCH 27/53] convert.py : advanced option (#2753)

* Allow convert.py to convert to q8_0

Fix issue with bounded_parallel_map and greedy consuming iterator

Display elapsed time during conversion

* Add --concurrency option

Minor improvements to help text

Clean up bounded_parallel_map function a bit

* Massive speed improvement thanks to Cebtenzzre

* Refactor types
---
 convert.py | 206 ++++++++++++++++++++++++++++++++++-------------------
 1 file changed, 133 insertions(+), 73 deletions(-)

diff --git a/convert.py b/convert.py
index d44e5a8c4..a15e6ccd2 100755
--- a/convert.py
+++ b/convert.py
@@ -3,6 +3,7 @@
 import gguf
 import argparse
 import concurrent.futures
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
 import copy
 import enum
 import faulthandler
@@ -17,13 +18,14 @@ import re
 import signal
 import struct
 import sys
+import time
 import zipfile
 import numpy as np
 
 from abc import ABCMeta, abstractmethod
 from dataclasses import dataclass
 from pathlib import Path
-from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, Union)
+from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, TypeVar, Union)
 from sentencepiece import SentencePieceProcessor  # type: ignore
 
 if TYPE_CHECKING:
@@ -37,30 +39,70 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
 ARCH=gguf.MODEL_ARCH.LLAMA
 NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]
 
+DEFAULT_CONCURRENCY = 8
 #
 # data types
 #
 
 @dataclass(frozen=True)
-class UnquantizedDataType:
+class DataType:
     name: str
+    dtype: 'np.dtype[Any]'
+    valid_conversions: List[str]
 
-DT_F16  = UnquantizedDataType('F16')
-DT_F32  = UnquantizedDataType('F32')
-DT_I32  = UnquantizedDataType('I32')
-DT_BF16 = UnquantizedDataType('BF16')
+    def elements_to_bytes(self, n_elements: int) -> int:
+        return n_elements * self.dtype.itemsize
 
-DataType = Union[UnquantizedDataType]
+@dataclass(frozen=True)
+class UnquantizedDataType(DataType):
+    pass
 
-DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
-    DT_BF16: np.dtype(np.uint16),
-    DT_F16:  np.dtype(np.float16),
-    DT_F32:  np.dtype(np.float32),
-    DT_I32:  np.dtype(np.int32),
-}
+DT_F16  = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
+DT_F32  = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
+DT_I32  = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
+DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
 
-NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
-    {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
+@dataclass(frozen=True)
+class QuantizedDataType(DataType):
+    block_size: int
+    quantized_dtype: 'np.dtype[Any]'
+    ggml_type: gguf.GGMLQuantizationType
+
+    def quantize(self, arr: NDArray) -> NDArray:
+        raise NotImplementedError(f'Quantization for {self.name} not implemented')
+
+    def elements_to_bytes(self, n_elements: int) -> int:
+        assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
+        return self.quantized_dtype.itemsize * (n_elements // self.block_size)
+
+@dataclass(frozen=True)
+class Q8_0QuantizedDataType(QuantizedDataType):
+    # Mini Q8_0 quantization in Python!
+    def quantize(self, arr: NDArray) -> NDArray:
+        assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
+        assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
+        n_blocks = arr.size // self.block_size
+        blocks = arr.reshape((n_blocks, self.block_size))
+        # Much faster implementation of block quantization contributed by @Cebtenzzre
+        def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
+            d = abs(blocks).max(axis = 1) / np.float32(127)
+            with np.errstate(divide = 'ignore'):
+                qs = (blocks / d[:, None]).round()
+            qs[d == 0] = 0
+            yield from zip(d, qs)
+        return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
+
+DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
+    dtype = np.dtype(np.float32), valid_conversions = [],
+    ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
+    quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
+
+# Quantized types skipped here because they may also map to np.float32
+NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = {}
+for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
+    if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
+        raise ValueError(f'Invalid duplicate data type {dt}')
+    NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt
 
 SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
     'BF16': DT_BF16,
@@ -73,20 +115,22 @@ SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
 # TODO: rename to LLAMAFileType
 # TODO: move to `gguf.py`
 class GGMLFileType(enum.IntEnum):
-    AllF32    = 0
-    MostlyF16 = 1  # except 1d tensors
+    AllF32     = 0
+    MostlyF16  = 1  # except 1d tensors
+    MostlyQ8_0 = 7  # except 1d tensors
 
     def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
-        if len(tensor.shape) == 1:
-            # 1D tensors are always F32.
-            return DT_F32
-        elif self == GGMLFileType.AllF32:
-            return DT_F32
-        elif self == GGMLFileType.MostlyF16:
-            return DT_F16
-        else:
+        dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
+        if dt is None:
             raise ValueError(self)
+        # 1D tensors are always F32.
+        return dt if len(tensor.shape) > 1 else DT_F32
 
+GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
+    GGMLFileType.AllF32    : DT_F32,
+    GGMLFileType.MostlyF16 : DT_F16,
+    GGMLFileType.MostlyQ8_0: DT_Q8_0,
+}
 
 #
 # hparams loading
@@ -415,7 +459,7 @@ class UnquantizedTensor(Tensor):
         self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
 
     def astype(self, data_type: DataType) -> Tensor:
-        dtype = DATA_TYPE_TO_NUMPY[data_type]
+        dtype = data_type.dtype
         if self.data_type == DT_BF16:
             self.ndarray = bf16_to_fp32(self.ndarray)
         return UnquantizedTensor(self.ndarray.astype(dtype))
@@ -454,22 +498,6 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv
 GGMLCompatibleTensor = Union[UnquantizedTensor]
 
 
-class DeferredPermutedTensor(Tensor):
-    def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None:
-        self.base = base
-        self.n_head = n_head
-        self.data_type = self.base.data_type
-
-    def astype(self, data_type: DataType) -> Tensor:
-        return self.base.astype(data_type).permute(self.n_head, self.n_head_kv)
-
-    def to_ggml(self) -> GGMLCompatibleTensor:
-        return self.base.to_ggml().permute(self.n_head, self.n_head_kv)
-
-    def permute(self, n_head: int, n_head_kv: int) -> Tensor:
-        raise Exception("shouldn't permute twice")
-
-
 @dataclass
 class LazyTensor:
     _load: Callable[[], Tensor]
@@ -479,7 +507,9 @@ class LazyTensor:
 
     def load(self) -> Tensor:
         ret = self._load()
-        assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description)
+        # Should be okay if it maps to the same numpy type?
+        assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
+                (self.data_type, ret.data_type, self.description)
         return ret
 
     def astype(self, data_type: DataType) -> 'LazyTensor':
@@ -490,8 +520,8 @@ class LazyTensor:
         return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')
 
     def validate_conversion_to(self, data_type: DataType) -> None:
-        if data_type == self.data_type:
-            return
+        if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:
+            raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')
 
 
 LazyModel = Dict[str, LazyTensor]
@@ -617,9 +647,7 @@ class LazyUnpickler(pickle.Unpickler):
         info = self.zip_file.getinfo(filename)
 
         def load(offset: int, elm_count: int) -> NDArray:
-            dtype = DATA_TYPE_TO_NUMPY.get(data_type)
-            if dtype is None:
-                raise Exception("tensor stored in unsupported format")
+            dtype = data_type.dtype
             fp = self.zip_file.open(info)
             fp.seek(offset * dtype.itemsize)
             size = elm_count * dtype.itemsize
@@ -683,7 +711,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
 
     def convert(info: Dict[str, Any]) -> LazyTensor:
         data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
-        numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
+        numpy_dtype = data_type.dtype
         shape: List[int] = info['shape']
         begin, end = info['data_offsets']
         assert 0 <= begin <= end <= len(byte_buf)
@@ -723,23 +751,35 @@ def lazy_load_file(path: Path) -> ModelPlus:
 In = TypeVar('In')
 Out = TypeVar('Out')
 
-def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
+def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, factory: Callable = ThreadPoolExecutor) -> Iterable[Out]:
     '''Parallel map, but with backpressure.  If the caller doesn't call `next`
     fast enough, this will stop calling `func` at some point rather than
     letting results pile up in memory.  Specifically, there is a max of one
     output value buffered per thread.'''
-    with concurrent.futures.ThreadPoolExecutor() as executor:
+    if concurrency < 2:
+        yield from map(func, iterable)
+        # Not reached.
+    iterable = iter(iterable)
+    with factory(max_workers = max_workers) as executor:
         futures: List[concurrent.futures.Future[Out]] = []
-        items_rev = list(iterable)[::-1]
-        for i in range(min(concurrency, len(items_rev))):
-            futures.append(executor.submit(func, items_rev.pop()))
+        done = False
+        for _ in range(concurrency):
+            try:
+                futures.append(executor.submit(func, next(iterable)))
+            except StopIteration:
+                done = True
+                break
+
         while futures:
             result = futures.pop(0).result()
-            if items_rev:
-                futures.append(executor.submit(func, items_rev.pop()))
+            while not done and len(futures) < concurrency:
+                try:
+                    futures.append(executor.submit(func, next(iterable)))
+                except StopIteration:
+                    done = True
+                    break
             yield result
 
-
 def check_vocab_size(params: Params, vocab: Vocab) -> None:
     if params.n_vocab != vocab.vocab_size:
         assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
@@ -804,12 +844,11 @@ class OutputFile:
         self.gguf.add_token_types(toktypes)
 
     def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
-        n_elements = 1
-        for dim in tensor.shape:
-            n_elements *= dim
-        data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
-        data_nbytes = n_elements * data_type.itemsize
-        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
+        n_elements = int(np.prod(tensor.shape))
+        raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
+        data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
+        data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
+        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)
 
     def write_meta(self) -> None:
         self.gguf.write_header_to_file()
@@ -835,7 +874,20 @@ class OutputFile:
         of.close()
 
     @staticmethod
-    def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
+    def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]:
+        name, lazy_tensor = item
+        tensor = lazy_tensor.load().to_ggml()
+        return (lazy_tensor.data_type, tensor.ndarray)
+
+    @staticmethod
+    def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray:
+        dt, arr = item
+        if not isinstance(dt, QuantizedDataType):
+            return arr
+        return dt.quantize(arr)
+
+    @staticmethod
+    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
         check_vocab_size(params, vocab)
 
         of = OutputFile(fname_out)
@@ -851,16 +903,19 @@ class OutputFile:
         of.write_meta()
         of.write_tensor_info()
 
-        def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
-            name, lazy_tensor = item
-            return lazy_tensor.load().to_ggml().ndarray
-
         # tensor data
-        ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
+        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
+        if ftype == GGMLFileType.MostlyQ8_0:
+            ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, factory = ProcessPoolExecutor)
+        else:
+            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
+
+        start = time.time()
         for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
+            elapsed = time.time() - start
             size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
             padi = len(str(len(model)))
-            print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
+            print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}")
             of.gguf.write_tensor_data(ndarray)
 
         of.close()
@@ -872,6 +927,8 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
         return GGMLFileType.AllF32
     if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
         return GGMLFileType.MostlyF16
+    if output_type_str == "q8_0":
+        return GGMLFileType.MostlyQ8_0
 
     name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
 
@@ -918,7 +975,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
             print(f"skipping tensor {name_new}")
             continue
         else:
-            print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
+            print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
             out[name_new] = lazy_tensor
 
     return out
@@ -1023,6 +1080,7 @@ def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
     namestr = {
         GGMLFileType.AllF32:    "f32",
         GGMLFileType.MostlyF16: "f16",
+        GGMLFileType.MostlyQ8_0:"q8_0",
     }[file_type]
     ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
     if ret in model_paths:
@@ -1046,12 +1104,13 @@ def main(args_in: Optional[List[str]] = None) -> None:
     parser.add_argument("--dump",        action="store_true",    help="don't convert, just show what's in the model")
     parser.add_argument("--dump-single", action="store_true",    help="don't convert, just show what's in a single model file")
     parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
-    parser.add_argument("--outtype",     choices=["f32", "f16"], help="output format (default: based on input)")
+    parser.add_argument("--outtype",     choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
     parser.add_argument("--vocab-dir",   type=Path,              help="directory containing tokenizer.model, if separate from model file")
     parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
     parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
     parser.add_argument("--vocabtype",   choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
     parser.add_argument("--ctx",         type=int,               help="model training context (default: based on input)")
+    parser.add_argument("--concurrency", type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
     args = parser.parse_args(args_in)
 
     if args.dump_single:
@@ -1073,6 +1132,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
         params.ftype = {
             "f32": GGMLFileType.AllF32,
             "f16": GGMLFileType.MostlyF16,
+            "q8_0": GGMLFileType.MostlyQ8_0,
         }[args.outtype]
 
     print(f"params = {params}")
@@ -1104,7 +1164,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
         params.ftype = ftype
         print(f"Writing {outfile}, format {ftype}")
 
-        OutputFile.write_all(outfile, params, model, vocab)
+        OutputFile.write_all(outfile, ftype, params, model, vocab, concurrency = args.concurrency)
         print(f"Wrote {outfile}")
 
 

From c1ac54b77aaba10d029084d152be786102010eb2 Mon Sep 17 00:00:00 2001
From: Bruce MacDonald <brucewmacdonald@gmail.com>
Date: Sat, 26 Aug 2023 16:11:45 -0700
Subject: [PATCH 28/53] server : add `/detokenize` endpoint (#2802)

* Add a /detokenize endpoint to the example server

* remove trailing white-space
---
 examples/server/README.md  |  6 ++++++
 examples/server/server.cpp | 21 +++++++++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/examples/server/README.md b/examples/server/README.md
index 7105e9020..517608046 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -164,6 +164,12 @@ node index.js
 
     Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
 
+-   **POST** `/detokenize`: Convert tokens to text.
+
+    *Options:*
+
+    `tokens`: Set the tokens to detokenize.
+
 -   **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
 
     *Options:*
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 3300553f9..a4b4d6418 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1104,6 +1104,12 @@ static json format_tokenizer_response(const std::vector<llama_token> &tokens)
         {"tokens", tokens}};
 }
 
+static json format_detokenized_response(std::string content)
+{
+    return json{
+        {"content", content}};
+}
+
 template <typename T>
 static T json_value(const json &body, const std::string &key, const T &default_value)
 {
@@ -1501,6 +1507,21 @@ int main(int argc, char **argv)
         const json data = format_tokenizer_response(tokens);
         return res.set_content(data.dump(), "application/json"); });
 
+    svr.Post("/detokenize", [&llama](const Request &req, Response &res)
+             {
+        auto lock = llama.lock();
+
+        const json body = json::parse(req.body);
+        std::string content;
+        if (body.count("tokens") != 0)
+        {
+            const std::vector<llama_token> tokens = body["tokens"];
+            content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
+        }
+
+        const json data = format_detokenized_response(content);
+        return res.set_content(data.dump(), "application/json"); });
+
     svr.Post("/embedding", [&llama](const Request &req, Response &res)
              {
         auto lock = llama.lock();

From 789c8c945a2814e1487e18e68823d9926e3b1454 Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Sun, 27 Aug 2023 09:03:27 +0200
Subject: [PATCH 29/53] ci : add LoRA test to CI (#2650)

* ci : add lora test

ggml-ci

* move lora summary to the top, add lora logs

ggml-ci

* ci : decrease CPU ppl runs to 2 to avoide 20 min timeout

ggml-ci

* add 7b lora test

use 1 thread for CUDA generation tests

ggml-ci

* add test with q8_0 (cpu only)

ggml-ci

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ci/run.sh | 140 +++++++++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 118 insertions(+), 22 deletions(-)

diff --git a/ci/run.sh b/ci/run.sh
index e1486e7c1..942b2e00c 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -196,17 +196,17 @@ function gg_run_open_llama_3b_v2 {
     (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     function check_ppl {
         qnt="$1"
@@ -233,6 +233,48 @@ function gg_run_open_llama_3b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    # lora
+    function compare_ppl {
+        qnt="$1"
+        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
+            return 20
+        fi
+
+        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
+        return 0
+    }
+
+    path_lora="../models-mnt/open-llama/3B-v2/lora"
+    path_shakespeare="../models-mnt/shakespeare"
+
+    shakespeare="${path_shakespeare}/shakespeare.txt"
+    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
+
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin
+    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt
+
+    python3 ../convert-lora-to-ggml.py ${path_lora}
+
+    # f16
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
+    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
+    compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0 + f16 lora-base
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
+    compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+
     set +e
 }
 
@@ -242,6 +284,7 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf 'OpenLLaMA 3B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
     gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -253,6 +296,11 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
+    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
+    gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
+    gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
+    gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }
 
 # open_llama_7b_v2
@@ -310,17 +358,17 @@ function gg_run_open_llama_7b_v2 {
     ./bin/quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/main --model ${model_f16}  -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/main --model ${model_q8_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/main --model ${model_q4_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/main --model ${model_q4_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/main --model ${model_q5_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/main --model ${model_q5_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/main --model ${model_q2_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/main --model ${model_q3_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/main --model ${model_q4_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/main --model ${model_q5_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/main --model ${model_q6_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/main --model ${model_f16}  -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -359,6 +407,48 @@ function gg_run_open_llama_7b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    # lora
+    function compare_ppl {
+        qnt="$1"
+        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
+            return 20
+        fi
+
+        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
+        return 0
+    }
+
+    path_lora="../models-mnt/open-llama/7B-v2/lora"
+    path_shakespeare="../models-mnt/shakespeare"
+
+    shakespeare="${path_shakespeare}/shakespeare.txt"
+    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
+
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin
+    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt
+
+    python3 ../convert-lora-to-ggml.py ${path_lora}
+
+    # f16
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
+    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # currently not supported by the CUDA backend
+    # q8_0
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
+    #compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0 + f16 lora-base
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
+    #compare_ppl "q8_0 / f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
     set +e
 }
 
@@ -368,6 +458,7 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf 'OpenLLaMA 7B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
     gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -379,6 +470,11 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
+    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
+    #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
+    #gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
+    #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }
 
 ## main

From 1591e2e590762011b43b10a9b6e04f13f98f2aa5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Przemys=C5=82aw=20Pawe=C5=82czyk?= <przemoc@gmail.com>
Date: Sun, 27 Aug 2023 10:10:25 +0200
Subject: [PATCH 30/53] ggml : detect SSSE3 (#2825)

* ggml : add ggml_cpu_has_ssse3

* llama : show SSSE3 in system info
---
 ggml.c    | 8 ++++++++
 ggml.h    | 1 +
 llama.cpp | 1 +
 3 files changed, 10 insertions(+)

diff --git a/ggml.c b/ggml.c
index 8cb5c404f..394fb459f 100644
--- a/ggml.c
+++ b/ggml.c
@@ -20516,6 +20516,14 @@ int ggml_cpu_has_sse3(void) {
 #endif
 }
 
+int ggml_cpu_has_ssse3(void) {
+#if defined(__SSSE3__)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_vsx(void) {
 #if defined(__POWER9_VECTOR__)
     return 1;
diff --git a/ggml.h b/ggml.h
index 421c0df60..b418153ba 100644
--- a/ggml.h
+++ b/ggml.h
@@ -1944,6 +1944,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_clblast    (void);
     GGML_API int ggml_cpu_has_gpublas    (void);
     GGML_API int ggml_cpu_has_sse3       (void);
+    GGML_API int ggml_cpu_has_ssse3      (void);
     GGML_API int ggml_cpu_has_vsx        (void);
 
     //
diff --git a/llama.cpp b/llama.cpp
index 05c54c213..e956c0163 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -6194,6 +6194,7 @@ const char * llama_print_system_info(void) {
     s += "WASM_SIMD = "   + std::to_string(ggml_cpu_has_wasm_simd())   + " | ";
     s += "BLAS = "        + std::to_string(ggml_cpu_has_blas())        + " | ";
     s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
+    s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
     s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
 
     return s.c_str();

From edd4c1481708fcd788b0e423268304fd26e2b125 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 14:19:19 +0300
Subject: [PATCH 31/53] llama : more tokenizer fixes (#2810)

* tests : write a Python tokenizer test (wip)

* llama : prefix input text for tokenization with whitespace

* llama : distinguish pieces from decoded text + fix detokenization

* common : add comments

* examples : no longer manually add leading space when tokenizing

* tests : use Python to generate tokenizer tests for C++

* tests : add option to tokenize text files

ggml-ci

* tests : add test-tokenizer-1.py

* llama.cpp : fix LF token

* hellaswag : move the concat space for clarity

* tests : add falcon tests (py + cpp, currently do not pass Unicode)

ggml-ci

* common : temporary separate llama_detokenize calls for SPM and BPE

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
---
 common/common.cpp                             |  39 +++-
 common/common.h                               |  22 ++-
 examples/beam_search/beam_search.cpp          |   6 +-
 examples/embd-input/embd-input-lib.cpp        |   2 +-
 examples/embedding/embedding.cpp              |   5 +-
 examples/main/main.cpp                        |  20 +-
 examples/perplexity/perplexity.cpp            |   4 +-
 examples/save-load-state/save-load-state.cpp  |   4 +-
 examples/server/server.cpp                    |  16 +-
 examples/simple/simple.cpp                    |   4 +-
 .../train-text-from-scratch.cpp               |   4 +-
 llama.cpp                                     |  60 +++---
 llama.h                                       |  10 +-
 tests/CMakeLists.txt                          |   6 +-
 tests/test-tokenizer-0-falcon.cpp             | 178 +++++++++++++++++
 tests/test-tokenizer-0-falcon.py              |  83 ++++++++
 tests/test-tokenizer-0-llama.cpp              | 182 ++++++++++++++++++
 tests/test-tokenizer-0-llama.py               |  95 +++++++++
 tests/test-tokenizer-0.cpp                    | 141 --------------
 tests/test-tokenizer-1.cpp                    |  14 +-
 20 files changed, 671 insertions(+), 224 deletions(-)
 create mode 100644 tests/test-tokenizer-0-falcon.cpp
 create mode 100644 tests/test-tokenizer-0-falcon.py
 create mode 100644 tests/test-tokenizer-0-llama.cpp
 create mode 100644 tests/test-tokenizer-0-llama.py
 delete mode 100644 tests/test-tokenizer-0.cpp

diff --git a/common/common.cpp b/common/common.cpp
index ff19ec4e5..0d91a6a35 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -733,12 +733,12 @@ std::vector<llama_token> llama_tokenize(
     return result;
 }
 
-std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
+std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_str(ctx, token, result.data(), result.size());
+        int check = llama_token_to_piece(ctx, token, result.data(), result.size());
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -746,3 +746,36 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok
 
     return std::string(result.data(), result.size());
 }
+
+std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
+    const llama_token bos_id = llama_token_bos(ctx);
+
+    std::string piece;
+    std::string result;
+
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        piece = llama_token_to_piece(ctx, tokens[i]);
+
+        // remove the leading space of the first non-BOS token
+        if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
+            piece = piece.substr(1);
+        }
+
+        result += piece;
+    }
+
+    return result;
+}
+
+std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
+    std::string piece;
+    std::string result;
+
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        piece = llama_token_to_piece(ctx, tokens[i]);
+
+        result += piece;
+    }
+
+    return result;
+}
diff --git a/common/common.h b/common/common.h
index ce61265f8..97fda2be7 100644
--- a/common/common.h
+++ b/common/common.h
@@ -116,11 +116,31 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
 // Vocab utils
 //
 
+// tokenizes a string into a vector of tokens
+// should work similar to Python's `tokenizer.encode`
 std::vector<llama_token> llama_tokenize(
         struct llama_context * ctx,
            const std::string & text,
                         bool   add_bos);
 
-std::string llama_token_to_str(
+// tokenizes a token into a piece
+// should work similar to Python's `tokenizer.id_to_piece`
+std::string llama_token_to_piece(
         const struct llama_context * ctx,
                        llama_token   token);
+
+// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
+//       that takes into account the tokenizer type and decides how to handle the leading space
+//
+// detokenizes a vector of tokens into a string
+// should work similar to Python's `tokenizer.decode`
+// removes the leading space from the first non-BOS token
+std::string llama_detokenize_spm(
+                         llama_context * ctx,
+        const std::vector<llama_token> & tokens);
+
+// detokenizes a vector of tokens into a string
+// should work similar to Python's `tokenizer.decode`
+std::string llama_detokenize_bpe(
+                         llama_context * ctx,
+        const std::vector<llama_token> & tokens);
diff --git a/examples/beam_search/beam_search.cpp b/examples/beam_search/beam_search.cpp
index 1c04fabc2..42c7c7254 100644
--- a/examples/beam_search/beam_search.cpp
+++ b/examples/beam_search/beam_search.cpp
@@ -35,7 +35,7 @@ struct ostream_beam_view {
 std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) {
     os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
     for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
-        os << llama_token_to_str(obv.ctx, obv.beam_view.tokens[i]);
+        os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
     }
     return os << ')';
 }
@@ -156,7 +156,7 @@ int main(int argc, char ** argv)
 
     for( auto id : tokens_list )
     {
-        std::cout << llama_token_to_str(ctx, id);
+        std::cout << llama_token_to_piece(ctx, id);
     }
     std::cout << std::flush;
 
@@ -175,7 +175,7 @@ int main(int argc, char ** argv)
 
     std::cout << "\n\n";
     for (llama_token const token_id : callback_data.response) {
-        std::cout << llama_token_to_str(ctx,token_id);
+        std::cout << llama_token_to_piece(ctx,token_id);
     }
     std::cout << std::endl;
 
diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp
index 8a6ad882e..036bdb398 100644
--- a/examples/embd-input/embd-input-lib.cpp
+++ b/examples/embd-input/embd-input-lib.cpp
@@ -214,7 +214,7 @@ const char * sampling(struct MyModel * mymodel) {
     if (id == llama_token_eos(ctx)) {
         ret = "</s>";
     } else {
-        ret = llama_token_to_str(ctx, id);
+        ret = llama_token_to_piece(ctx, id);
     }
     eval_id(mymodel, id);
     return ret.c_str();
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 38395c75b..93d583b5c 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -56,9 +56,6 @@ int main(int argc, char ** argv) {
 
     int n_past = 0;
 
-    // Add a space in front of the first character to match OG llama tokenizer behavior
-    params.prompt.insert(0, 1, ' ');
-
     // tokenize the prompt
     auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
 
@@ -67,7 +64,7 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
         fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
         for (int i = 0; i < (int) embd_inp.size(); i++) {
-            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
+            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
         }
         fprintf(stderr, "\n");
     }
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 11d7a7e4f..3ce57f436 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -195,11 +195,6 @@ int main(int argc, char ** argv) {
     // tokenize the prompt
     std::vector<llama_token> embd_inp;
 
-    if (llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM) {
-        // Add a space in front of the first character to match OG llama tokenizer behavior
-        params.prompt.insert(0, 1, ' ');
-    }
-
     if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
         embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
     } else {
@@ -216,7 +211,6 @@ int main(int argc, char ** argv) {
     int guidance_offset = 0;
     int original_prompt_len = 0;
     if (ctx_guidance) {
-        params.cfg_negative_prompt.insert(0, 1, ' ');
         guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
 
         std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
@@ -285,7 +279,7 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
         fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
         for (int i = 0; i < (int) embd_inp.size(); i++) {
-            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
+            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
         }
 
         if (ctx_guidance) {
@@ -293,14 +287,14 @@ int main(int argc, char ** argv) {
             fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
             fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
             for (int i = 0; i < (int) guidance_inp.size(); i++) {
-                fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]).c_str());
+                fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
             }
         }
 
         if (params.n_keep > 0) {
         fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
-                fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]).c_str());
+                fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
             }
             fprintf(stderr, "'\n");
         }
@@ -456,7 +450,7 @@ int main(int argc, char ** argv) {
                 //printf("\n---\n");
                 //printf("resetting: '");
                 //for (int i = 0; i < (int) embd.size(); i++) {
-                //    printf("%s", llama_token_to_str(ctx, embd[i]));
+                //    printf("%s", llama_token_to_piece(ctx, embd[i]));
                 //}
                 //printf("'\n");
                 //printf("\n---\n");
@@ -509,7 +503,7 @@ int main(int argc, char ** argv) {
                     input_size = embd_guidance.size();
                     //fprintf(stderr, "\n---------------------\n");
                     //for (int i = 0; i < (int) embd_guidance.size(); i++) {
-                        //fprintf(stderr, "%s", llama_token_to_str(ctx, embd_guidance[i]));
+                        //fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_guidance[i]));
                     //}
                     //fprintf(stderr, "\n---------------------\n");
                 } else {
@@ -673,7 +667,7 @@ int main(int argc, char ** argv) {
         // display text
         if (input_echo) {
             for (auto id : embd) {
-                printf("%s", llama_token_to_str(ctx, id).c_str());
+                printf("%s", llama_token_to_piece(ctx, id).c_str());
             }
             fflush(stdout);
         }
@@ -689,7 +683,7 @@ int main(int argc, char ** argv) {
             if (params.antiprompt.size()) {
                 std::string last_output;
                 for (auto id : last_n_tokens) {
-                    last_output += llama_token_to_str(ctx, id);
+                    last_output += llama_token_to_piece(ctx, id);
                 }
 
                 is_antiprompt = false;
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index fd89852d6..b596d0626 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -392,7 +392,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         hs_data[i].context = prompt_lines[idx*6];
         hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
         for (size_t j=0; j < 4; j++) {
-            hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
+            hs_data[i].ending[j] = prompt_lines[idx*6+2+j];
         }
 
         // Delete the selected random example from the prompt
@@ -417,7 +417,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         size_t context_size = context_embd.size();
 
         for (int i = 0; i < 4; ++i) {
-            ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[i], add_bos);
+            ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + " " + hs_data[task_idx].ending[i], add_bos);
             for (int k = 0; k < int(context_size); ++k) {
                 if (ending_tokens[i][k] != context_embd[k]) {
                     fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index 3db61b754..573bc4ef9 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -87,7 +87,7 @@ int main(int argc, char ** argv) {
         }
         llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
         auto next_token = llama_sample_token(ctx, &candidates_p);
-        auto next_token_str = llama_token_to_str(ctx, next_token);
+        auto next_token_str = llama_token_to_piece(ctx, next_token);
         last_n_tokens_data.push_back(next_token);
 
         printf("%s", next_token_str.c_str());
@@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
         }
         llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
         auto next_token = llama_sample_token(ctx2, &candidates_p);
-        auto next_token_str = llama_token_to_str(ctx2, next_token);
+        auto next_token_str = llama_token_to_piece(ctx2, next_token);
         last_n_tokens_data.push_back(next_token);
 
         printf("%s", next_token_str.c_str());
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index a4b4d6418..89a3311f5 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -94,7 +94,7 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
     std::string ret;
     for (; begin != end; ++begin)
     {
-        ret += llama_token_to_str(ctx, *begin);
+        ret += llama_token_to_piece(ctx, *begin);
     }
     return ret;
 }
@@ -123,7 +123,7 @@ static void server_log(const char *level, const char *function, int line,
 // format incomplete utf-8 multibyte character for output
 static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
 {
-    std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
+    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
     // if the size is 1 and first bit is 1, meaning it's a partial character
     //   (size > 1 meaning it's already a known token)
     if (out.size() == 1 && (out[0] & 0x80) == 0x80)
@@ -286,7 +286,6 @@ struct llama_server_context
                     std::vector<llama_token> p;
                     if (first)
                     {
-                        s.insert(0, 1, ' '); // add a space if it's the first
                         p = ::llama_tokenize(ctx, s, add_bos);
                         first = false;
                     }
@@ -309,7 +308,6 @@ struct llama_server_context
         else
         {
             auto s = json_prompt.template get<std::string>();
-            s.insert(0, 1, ' '); // always add a first space
             prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
         }
 
@@ -566,7 +564,7 @@ struct llama_server_context
 
         if (!embd.empty() && embd.back() == llama_token_eos(ctx))
         {
-            // stopping_word = llama_token_to_str(ctx, embd.back());
+            // stopping_word = llama_token_to_piece(ctx, embd.back());
             has_next_token = false;
             stopped_eos = true;
             LOG_VERBOSE("eos token found", {});
@@ -613,7 +611,7 @@ struct llama_server_context
     {
         const completion_token_output token_with_probs = nextToken();
 
-        const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
+        const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
         generated_text += token_text;
 
         if (params.n_probs > 0)
@@ -1254,7 +1252,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) {
 
 struct token_translator {
     llama_context * ctx;
-    std::string operator()(llama_token tok) const { return llama_token_to_str(ctx, tok); }
+    std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
     std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
 };
 
@@ -1364,7 +1362,7 @@ int main(int argc, char **argv)
 
                 while (llama.has_next_token) {
                     const completion_token_output token_with_probs = llama.doCompletion();
-                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
+                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok);
 
                     stop_pos = llama.findStoppingStrings(llama.generated_text,
                         token_text.size(), STOP_FULL);
@@ -1395,7 +1393,7 @@ int main(int argc, char **argv)
                     if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
                         continue;
                     }
-                    const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok);
+                    const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok);
 
                     size_t pos = std::min(sent_count, llama.generated_text.size());
 
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index 132f7fbf9..4ee85faca 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -63,7 +63,7 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "\n\n");
 
     for (auto id : tokens_list) {
-        fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str());
+        fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
     }
 
     fflush(stderr);
@@ -112,7 +112,7 @@ int main(int argc, char ** argv) {
         }
 
         // print the new token :
-        printf("%s", llama_token_to_str(ctx, new_token_id).c_str());
+        printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
         fflush(stdout);
 
         // push this new token for next evaluation
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index 79b117df7..12d153417 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -1964,7 +1964,7 @@ void print_matrix(struct ggml_tensor * probs) {
 
 
 void print_token(struct llama_context * ctx, llama_token token) {
-    printf("%s", llama_token_to_str(ctx, token).c_str());
+    printf("%s", llama_token_to_piece(ctx, token).c_str());
 }
 
 void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) {
@@ -2202,7 +2202,7 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
         const char * in  = buf.data();
         const char * end = buf.data() + buf.size();
         for (int i = 0; i < (int) out.size(); ++i) {
-            std::string s = llama_token_to_str(lctx, out[i]);
+            std::string s = llama_token_to_piece(lctx, out[i]);
             int len = s.length();
             if (in >= end) {
                 printf("%s: unexpected end of original text.\n", __func__);
diff --git a/llama.cpp b/llama.cpp
index e956c0163..2a8af4ee9 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -796,12 +796,12 @@ static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
     (void) tensor;
 }
 
-static std::string llama_token_to_text(const struct llama_context * ctx, llama_token token) {
+static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_str(ctx, token, result.data(), result.size());
+        int check = llama_token_to_piece(ctx, token, result.data(), result.size());
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -1635,7 +1635,8 @@ static void llm_load_hparams(
 }
 
 // TODO: This should probably be in llama.h
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos);
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos);
+static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
 
 static void llm_load_vocab(
         llama_model_loader & ml,
@@ -1737,7 +1738,11 @@ static void llm_load_vocab(
     }
 
     // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
-    vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0];
+    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+        vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
+    } else {
+        vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0];
+    }
 
     // special tokens
     GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
@@ -3026,10 +3031,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
     return vocab.token_to_id.at(buf);
 }
 
-static std::string llama_escape_whitespace(const std::string& text) {
-    std::string result = text;
-    replace_all(result, " ", "\xe2\x96\x81");
-    return result;
+static void llama_escape_whitespace(std::string & text) {
+    replace_all(text, " ", "\xe2\x96\x81");
 }
 
 static void llama_unescape_whitespace(std::string & word) {
@@ -3373,22 +3376,31 @@ private:
     llm_bigram_bpe::queue work_queue;
 };
 
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos) {
+static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos) {
     std::vector<llama_vocab::id> output;
 
-    if (raw_text.empty()) {
-        return output;
-    }
+    // OG tokenizer behavior:
+    //
+    // tokenizer.encode('', add_bos=True)  returns [1]
+    // tokenizer.encode('', add_bos=False) returns []
 
     if (bos && vocab.special_bos_id != -1) {
         output.push_back(vocab.special_bos_id);
     }
 
+    if (raw_text.empty()) {
+        return output;
+    }
+
     switch (vocab.type) {
         case LLAMA_VOCAB_TYPE_SPM:
             {
+                // without adding this leading whitespace, we do not get the same results as the original tokenizer
+                raw_text = " " + raw_text;
+
                 llm_tokenizer_spm tokenizer(vocab);
-                tokenizer.tokenize(llama_escape_whitespace(raw_text), output);
+                llama_escape_whitespace(raw_text);
+                tokenizer.tokenize(raw_text, output);
             } break;
         case LLAMA_VOCAB_TYPE_BPE:
             {
@@ -4078,16 +4090,16 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
     std::vector<llama_grammar_candidate>                              candidates_grammar;
 
     for (size_t i = 0; i < candidates->size; ++i) {
-        const llama_token id   = candidates->data[i].id;
-        const std::string text = llama_token_to_text(ctx, id);
+        const llama_token id    = candidates->data[i].id;
+        const std::string piece = llama_token_to_str(ctx, id);
         if (id == eos) {
             if (!allow_eos) {
                 candidates->data[i].logit = -INFINITY;
             }
-        } else if (text.empty() || text[0] == 0) {
+        } else if (piece.empty() || piece[0] == 0) {
             candidates->data[i].logit = -INFINITY;
         } else {
-            candidates_decoded.push_back(decode_utf8(text.c_str(), grammar->partial_utf8));
+            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
             candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
         }
     }
@@ -4291,10 +4303,10 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
         GGML_ASSERT(false);
     }
 
-    const std::string text = llama_token_to_text(ctx, token);
+    const std::string piece = llama_token_to_str(ctx, token);
 
     // Note terminating 0 in decoded string
-    const auto   decoded     = decode_utf8(text.c_str(), grammar->partial_utf8);
+    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
     const auto & code_points = decoded.first;
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
         grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
@@ -6101,12 +6113,12 @@ int llama_tokenize_with_model(
     return res.size();
 }
 
-int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * buf, int length) {
-    return llama_token_to_str_with_model(&ctx->model, token, buf, length);
+int llama_token_to_piece(const struct llama_context * ctx, llama_token token, char * buf, int length) {
+    return llama_token_to_piece_with_model(&ctx->model, token, buf, length);
 }
 
-// does not write null-terminator to str
-int llama_token_to_str_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
+// does not write null-terminator to buf
+int llama_token_to_piece_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
     if (0 <= token && token < llama_model_n_vocab(model)) {
         if (llama_is_normal_token(model->vocab, token)) {
             std::string result = model->vocab.id_to_token[token].text;
diff --git a/llama.h b/llama.h
index b77dd7735..b084fe23c 100644
--- a/llama.h
+++ b/llama.h
@@ -381,15 +381,17 @@ extern "C" {
                              int   n_max_tokens,
                             bool   add_bos);
 
-    // Token Id -> String. Uses the vocabulary in the provided context
-    // Does not write null terminator to the buffer
-    LLAMA_API int llama_token_to_str(
+    // Token Id -> Piece.
+    // Uses the vocabulary in the provided context.
+    // Does not write null terminator to the buffer.
+    // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    LLAMA_API int llama_token_to_piece(
             const struct llama_context * ctx,
                            llama_token   token,
                                   char * buf,
                                   int    length);
 
-    LLAMA_API int llama_token_to_str_with_model(
+    LLAMA_API int llama_token_to_piece_with_model(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 2afaf86b1..ca1f39d31 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -25,8 +25,10 @@ endfunction()
 llama_build_and_test_executable(test-quantize-fns.cpp)
 llama_build_and_test_executable(test-quantize-perf.cpp)
 llama_build_and_test_executable(test-sampling.cpp)
-llama_build_executable(test-tokenizer-0.cpp)
-llama_test_executable (test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_build_executable(test-tokenizer-0-llama.cpp)
+llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_build_executable(test-tokenizer-0-falcon.cpp)
+#llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_build_executable(test-tokenizer-1.cpp)
 # test-tokenizer-1 requires a BPE vocab. re-enable when we have one.
 #llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
diff --git a/tests/test-tokenizer-0-falcon.cpp b/tests/test-tokenizer-0-falcon.cpp
new file mode 100644
index 000000000..836fb8ad2
--- /dev/null
+++ b/tests/test-tokenizer-0-falcon.cpp
@@ -0,0 +1,178 @@
+#include "llama.h"
+#include "common.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+#include <vector>
+#include <fstream>
+
+// generate using test-tokenizer-0-falcon.py
+static const std::map<std::string, std::vector<llama_token>> & k_tests() {
+    static std::map<std::string, std::vector<llama_token>> _k_tests = {
+        { ""                      , {  }, },
+        { " "                     , {     204, }, },
+        { "  "                    , {     258, }, },
+        { "   "                   , {     466, }, },
+        { "\t"                    , {     192, }, },
+        { "\n"                    , {     193, }, },
+        { "\t\n"                  , {   19125, }, },
+        { "Hello world"           , {    9856,   1079, }, },
+        { " Hello world"          , {   23090,   1079, }, },
+        { "Hello World"           , {    9856,   2889, }, },
+        { " Hello World"          , {   23090,   2889, }, },
+        { " Hello World!"         , {   23090,   2889,     12, }, },
+        { "Hello, world!"         , {    9856,     23,   1079,     12, }, },
+        { " Hello, world!"        , {   23090,     23,   1079,     12, }, },
+        { " this is 🦙.cpp"        , {     414,    304,   3346,    111,    231,     25,  29247, }, },
+        { "w048 7tuijk dsdfhu"    , {      98,  55866,    204,     34,  16682,   7149,  36190,   6869,  11481, }, },
+        { "нещо на Български"     , {     150,    133,   6207,    151,    215,    150,    134,   5052,    133,   6279,   5052,    223,    151,    216,  49679,    123,  53110,  47043,   7795, }, },
+        { "កាន់តែពិសេសអាចខលចេញ"   , {   38154,    206,  38154,    126,  38154,    225,    167,    237,    217,  38154,    221,    167,    237,    208,  38154,    228,  38154,    127,  38154,    237,    167,    237,    207,  38154,    237,  38154,    107,  38154,    126,  38154,    211,  38154,    207,  38154,    233,  38154,    211,    167,    237,    207,  38154,    215, }, },
+        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {    2571,    232,    206,    204,     19,  11003,     20,   8196,    126,    283,    219,  48778,    116,  13392,    204,     19,  51831,    732,  63209,   1741,   7955,    522,     20,  22438,    211,    204,     19,   7927,  53360,    325,    504,    701,    946,  10930,     20, }, },
+        { "Hello"                 , {    9856, }, },
+        { " Hello"                , {   23090, }, },
+        { "  Hello"               , {     204,  23090, }, },
+        { "   Hello"              , {     258,  23090, }, },
+        { "    Hello"             , {     466,  23090, }, },
+        { "    Hello\n    Hello"  , {     466,  23090,    742,  23090, }, },
+    };
+
+    return _k_tests;
+}
+
+int main(int argc, char **argv) {
+    if (argc < 2) {
+        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
+        return 1;
+    }
+
+    const std::string fname = argv[1];
+
+    std::string fname_text;
+    if (argc > 2) {
+        fname_text = argv[2];
+    }
+
+    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+    llama_model * model;
+    llama_context * ctx;
+
+    llama_backend_init(false);
+
+    // load the vocab
+    {
+        auto lparams = llama_context_default_params();
+
+        lparams.vocab_only = true;
+
+        model = llama_load_model_from_file(fname.c_str(), lparams);
+
+        if (model == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            return 1;
+        }
+
+        ctx = llama_new_context_with_model(model, lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            llama_free_model(model);
+            return 1;
+        }
+    }
+
+    if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_BPE) {
+        fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
+        llama_free_model(model);
+        llama_free(ctx);
+        return 2;
+    }
+
+    bool success = true;
+
+    for (const auto & test_kv : k_tests()) {
+        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, false);
+
+        printf("\n");
+        printf("src: '%s'\n", test_kv.first.c_str());
+        printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
+        printf("tok: ");
+        for (const auto & tok : res) {
+            printf("%d ", tok);
+        }
+        printf("\n");
+
+        bool correct = res.size() == test_kv.second.size();
+
+        for (int i = 0; i < (int) res.size() && correct; ++i) {
+            if (test_kv.second[i] != res[i]) {
+                correct = false;
+            }
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
+                llama_detokenize_bpe(ctx, res).c_str(),
+                llama_detokenize_bpe(ctx, test_kv.second).c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+
+            success = false;
+        }
+    }
+
+    if (!fname_text.empty()) {
+        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
+
+        std::string text;
+        {
+            std::ifstream ifs(fname_text);
+            if (!ifs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
+                return 1;
+            }
+            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
+        }
+
+        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
+
+        const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
+
+        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
+
+        {
+            const std::string fname_out = fname_text + ".tokcpp";
+
+            std::ofstream ofs(fname_out);
+            if (!ofs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
+                return 1;
+            }
+
+            for (const auto & tok : res) {
+                ofs << tok << " ";
+            }
+
+            ofs << "\n";
+        }
+
+        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
+    }
+
+    llama_free_model(model);
+    llama_free(ctx);
+
+    llama_backend_free();
+
+    return success ? 0 : 3;
+}
diff --git a/tests/test-tokenizer-0-falcon.py b/tests/test-tokenizer-0-falcon.py
new file mode 100644
index 000000000..9c8c1c7d1
--- /dev/null
+++ b/tests/test-tokenizer-0-falcon.py
@@ -0,0 +1,83 @@
+# tests with BPE tokenizer
+
+import os
+import sys
+import argparse
+
+from transformers import AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+parser.add_argument("--fname-tok",   help="path to a text file to tokenize")
+args = parser.parse_args()
+
+dir_tokenizer = args.dir_tokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
+
+tests = [
+        "",
+        " ",
+        "  ",
+        "   ",
+        "\t",
+        "\n",
+        "\t\n",
+        "Hello world",
+        " Hello world",
+        "Hello World",
+        " Hello World",
+        " Hello World!",
+        "Hello, world!",
+        " Hello, world!",
+        " this is 🦙.cpp",
+        "w048 7tuijk dsdfhu",
+        "нещо на Български",
+        "កាន់តែពិសេសអាចខលចេញ",
+        "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+        "Hello",
+        " Hello",
+        "  Hello",
+        "   Hello",
+        "    Hello",
+        "    Hello\n    Hello",
+    ]
+
+for text in tests:
+    print('text: ', text)
+    print(tokenizer.encode(text))
+    print(tokenizer.decode(tokenizer.encode(text)))
+
+print("\n\ntests for C++:\n")
+for text in tests:
+    res = tokenizer.encode(text)
+
+    k = text.replace('\n', '\\n')
+    k = k.replace('\t', '\\t')
+    k = '"' + k + '"'
+    print("{ %-24s, { " % k, end='')
+    for x in res:
+        print("%7d," % x, end='')
+    print(" }, },")
+
+print(tokenizer.encode('hello'))
+print(tokenizer.encode('world'))
+print(tokenizer.encode(' world'))
+print(tokenizer.encode('hello world'))
+
+fname_tok = args.fname_tok
+if fname_tok:
+    print('tokenizing file: ', fname_tok)
+    fname_out = fname_tok + '.tok'
+    with open(fname_tok, 'r') as f:
+        lines = f.readlines()
+        s = ''.join(lines)
+        res = tokenizer.encode(s)
+        # write to file
+        with open(fname_out, 'w') as f:
+            for x in res:
+                f.write(str(x) + ' ')
+            f.write('\n')
+        print('len(res): ', len(res))
+        print('len(lines): ', len(lines))
+    print('results written to: ', fname_out)
diff --git a/tests/test-tokenizer-0-llama.cpp b/tests/test-tokenizer-0-llama.cpp
new file mode 100644
index 000000000..8630742c6
--- /dev/null
+++ b/tests/test-tokenizer-0-llama.cpp
@@ -0,0 +1,182 @@
+#include "llama.h"
+#include "common.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+#include <vector>
+#include <fstream>
+
+// generate using test-tokenizer-0-llama.py
+static const std::map<std::string, std::vector<llama_token>> & k_tests() {
+    static std::map<std::string, std::vector<llama_token>> _k_tests = {
+        { ""                      , {  }, },
+        { " "                     , {     259, }, },
+        { "  "                    , {    1678, }, },
+        { "   "                   , {     268, }, },
+        { "\t"                    , {   29871,     12, }, },
+        { "\n"                    , {   29871,     13, }, },
+        { "\t\n"                  , {   29871,     12,     13, }, },
+        { "Hello world"           , {   15043,   3186, }, },
+        { " Hello world"          , {   29871,  15043,   3186, }, },
+        { "Hello World"           , {   15043,   2787, }, },
+        { " Hello World"          , {   29871,  15043,   2787, }, },
+        { " Hello World!"         , {   29871,  15043,   2787,  29991, }, },
+        { "Hello, world!"         , {   15043,  29892,   3186,  29991, }, },
+        { " Hello, world!"        , {   29871,  15043,  29892,   3186,  29991, }, },
+        { " this is 🦙.cpp"        , {   29871,    445,    338,  29871,    243,    162,    169,    156,  29889,   8223, }, },
+        { "w048 7tuijk dsdfhu"    , {     281,  29900,  29946,  29947,  29871,  29955,   9161,  13535,  18031,   2176,   6905, }, },
+        { "нещо на Български"     , {    1538,   4851,    665,   1386,  29713,   1305, }, },
+        { "កាន់តែពិសេសអាចខលចេញ"   , {   29871,  31849,  31324,  31934,    228,    162,    142,    228,    161,    146,    228,    162,    133,    228,    161,    153,    228,    161,    186,  31708,    228,    162,    132,  31708,    228,    161,    165,  31324,    228,    161,    136,    228,    161,    132,    228,    161,    158,    228,    161,    136,    228,    162,    132,    228,    161,    140, }, },
+        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {   29871,    243,    162,    157,    131,    313,   8945,  29897,  29871,    243,    162,    155,    185,  30722,    243,    162,    143,    174,  30598,    313,  20787,    953,   3848,    275,  16125,    630,  29897,  29871,  31681,    313,   6194,    953,  29877,   2397,    393,    756,    967,   1914,   5993,  29897, }, },
+        { "Hello"                 , {   15043, }, },
+        { " Hello"                , {   29871,  15043, }, },
+        { "  Hello"               , {     259,  15043, }, },
+        { "   Hello"              , {    1678,  15043, }, },
+        { "    Hello"             , {     268,  15043, }, },
+        { "    Hello\n    Hello"  , {     268,  15043,     13,   1678,  15043, }, },
+    };
+
+    return _k_tests;
+}
+
+int main(int argc, char **argv) {
+    if (argc < 2) {
+        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
+        return 1;
+    }
+
+    const std::string fname = argv[1];
+
+    std::string fname_text;
+    if (argc > 2) {
+        fname_text = argv[2];
+    }
+
+    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+    llama_model * model;
+    llama_context * ctx;
+
+    llama_backend_init(false);
+
+    // load the vocab
+    {
+        auto lparams = llama_context_default_params();
+
+        lparams.vocab_only = true;
+
+        model = llama_load_model_from_file(fname.c_str(), lparams);
+
+        if (model == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            return 1;
+        }
+
+        ctx = llama_new_context_with_model(model, lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            llama_free_model(model);
+            return 1;
+        }
+    }
+
+    if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_SPM) {
+        fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
+        llama_free_model(model);
+        llama_free(ctx);
+        return 2;
+    }
+
+    bool success = true;
+
+    for (const auto & test_kv : k_tests()) {
+        const std::vector<llama_token> res_bos   = llama_tokenize(ctx, test_kv.first, true);
+        const std::vector<llama_token> res_nobos = llama_tokenize(ctx, test_kv.first, false);
+
+        printf("\n");
+        printf("src: '%s'\n", test_kv.first.c_str());
+        printf("res: '%s'\n", llama_detokenize_spm(ctx, res_bos).c_str());
+        printf("tok: ");
+        for (const auto & tok : res_bos) {
+            printf("%d ", tok);
+        }
+        printf("\n");
+
+        bool correct = res_nobos.size() == test_kv.second.size() && res_bos.size() == res_nobos.size() + 1 && res_bos[0] == 1;
+
+        for (int i = 0; i < (int) res_nobos.size() && correct; ++i) {
+            if (test_kv.second[i] != res_bos[i + 1]) {
+                correct = false;
+            }
+            if (test_kv.second[i] != res_nobos[i]) {
+                correct = false;
+            }
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
+                llama_detokenize_spm(ctx, res_nobos).c_str(),
+                llama_detokenize_spm(ctx, test_kv.second).c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res_nobos) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+
+            success = false;
+        }
+    }
+
+    if (!fname_text.empty()) {
+        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
+
+        std::string text;
+        {
+            std::ifstream ifs(fname_text);
+            if (!ifs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
+                return 1;
+            }
+            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
+        }
+
+        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
+
+        const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
+
+        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
+
+        {
+            const std::string fname_out = fname_text + ".tokcpp";
+
+            std::ofstream ofs(fname_out);
+            if (!ofs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
+                return 1;
+            }
+
+            for (const auto & tok : res) {
+                ofs << tok << " ";
+            }
+
+            ofs << "\n";
+        }
+
+        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
+    }
+
+    llama_free_model(model);
+    llama_free(ctx);
+
+    llama_backend_free();
+
+    return success ? 0 : 3;
+}
diff --git a/tests/test-tokenizer-0-llama.py b/tests/test-tokenizer-0-llama.py
new file mode 100644
index 000000000..bc164ee29
--- /dev/null
+++ b/tests/test-tokenizer-0-llama.py
@@ -0,0 +1,95 @@
+# tests with SPM tokenizer
+
+import os
+import sys
+import argparse
+
+from sentencepiece import SentencePieceProcessor
+
+parser = argparse.ArgumentParser()
+parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+parser.add_argument("--fname-tok",   help="path to a text file to tokenize")
+args = parser.parse_args()
+
+dir_tokenizer = args.dir_tokenizer
+
+tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model')
+
+tests = [
+        "",
+        " ",
+        "  ",
+        "   ",
+        "\t",
+        "\n",
+        "\t\n",
+        "Hello world",
+        " Hello world",
+        "Hello World",
+        " Hello World",
+        " Hello World!",
+        "Hello, world!",
+        " Hello, world!",
+        " this is 🦙.cpp",
+        "w048 7tuijk dsdfhu",
+        "нещо на Български",
+        "កាន់តែពិសេសអាចខលចេញ",
+        "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+        "Hello",
+        " Hello",
+        "  Hello",
+        "   Hello",
+        "    Hello",
+        "    Hello\n    Hello",
+    ]
+
+
+for text in tests:
+    print('text: ', text)
+    print('\nwith bos:')
+    print(tokenizer.encode(text, add_bos=True))
+    print(tokenizer.decode(tokenizer.encode(text, add_bos=True)))
+    print('\nwithout bos:')
+    print(tokenizer.encode(text, add_bos=False))
+    print(tokenizer.decode(tokenizer.encode(text, add_bos=False)))
+
+print("'" + tokenizer.id_to_piece(15043) + "'") # '_Hello'
+print("'" + tokenizer.id_to_piece(29871) + "'") # '_'
+print("'" + tokenizer.decode([15043]) + "'")        # 'Hello'
+print("'" + tokenizer.decode([15043, 15043]) + "'") # 'Hello Hello'
+print("'" + tokenizer.decode([29871, 15043]) + "'")               # ' Hello'
+print("'" + tokenizer.decode([29871, 15043, 29871, 15043]) + "'") # ' Hello  Hello'
+
+print("\n\ntests for C++:\n")
+for text in tests:
+    res = tokenizer.encode(text, add_bos=False)
+
+    k = text.replace('\n', '\\n')
+    k = k.replace('\t', '\\t')
+    k = '"' + k + '"'
+    print("{ %-24s, { " % k, end='')
+    for x in res:
+        print("%7d," % x, end='')
+    print(" }, },")
+
+print(tokenizer.encode('hello'))
+print(tokenizer.encode('world'))
+print(tokenizer.encode(' world'))
+print(tokenizer.encode('hello world'))
+
+fname_tok = args.fname_tok
+if fname_tok:
+    print('tokenizing file: ', fname_tok)
+    fname_out = fname_tok + '.tok'
+    with open(fname_tok, 'r') as f:
+        lines = f.readlines()
+        s = ''.join(lines)
+        res = tokenizer.encode(s, add_bos=True)
+        # write to file
+        with open(fname_out, 'w') as f:
+            for x in res:
+                f.write(str(x) + ' ')
+            f.write('\n')
+        print('len(res): ', len(res))
+        print('len(lines): ', len(lines))
+    print('results written to: ', fname_out)
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
deleted file mode 100644
index 7e9ac9188..000000000
--- a/tests/test-tokenizer-0.cpp
+++ /dev/null
@@ -1,141 +0,0 @@
-#include "llama.h"
-#include "common.h"
-
-#include <cstdio>
-#include <string>
-#include <map>
-#include <vector>
-
-static std::string unescape_whitespace(llama_context* ctx, const std::vector<llama_token>& tokens) {
-    std::string result;
-    for (size_t i = 0; i < tokens.size(); ++i) {
-        result += llama_token_to_str(ctx, tokens[i]);
-    }
-    return result;
-}
-
-static const std::map<std::string, std::vector<llama_token>> & k_tests() {
-    static std::map<std::string, std::vector<llama_token>> _k_tests = {
-        { " ",                      {1,    259, }, },
-        { "  ",                     { 1,    1678, }, },
-        { "   ",                    { 1,     268, }, },
-        { "\t",                     { 1,    29871,   12, }, },
-        { "\n",                     { 1,    29871,   13, }, },
-        { "\t\n",                   { 1,    29871,   12,     13, }, },
-        { "Hello world",            { 1,  15043,   3186, }, },
-        { " Hello world",           { 1,  29871,  15043,   3186, }, },
-        { "Hello World",            { 1,  15043,   2787, }, },
-        { " Hello World",           { 1,  29871,  15043,   2787, }, },
-        { " Hello World!",          { 1,  29871,  15043,   2787,  29991, }, },
-        { " this is 🦙.cpp",        { 1,  29871,    445,    338,  29871,    243,    162,    169,    156,  29889,   8223, }, },
-        { "w048 7tuijk dsdfhu",     { 1,    281,  29900,  29946,  29947,  29871,  29955,   9161,  13535,  18031,   2176,   6905, }, },
-        { "нещо на Български",      { 1,   1538,   4851,    665,   1386,  29713,   1305, }, },
-        { "កាន់តែពិសេសអាចខលចេញ",   { 1,  29871,  31849,  31324,  31934,    228,    162,    142,    228,    161,
-                                     146,    228,    162,    133,    228,    161,    153,    228,    161,    186,
-                                     31708,    228,    162,    132,  31708,    228,    161,    165,  31324,    228,
-                                     161,    136,    228,    161,    132,    228,    161,    158,    228,    161,
-                                     136,    228,    162,    132,    228,    161,    140, }, },
-        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
-            { 1,  29871,    243,    162,    157,    131,    313,   8945,  29897,  29871,
-                243,    162,    155,    185,  30722,    243,    162,    143,    174,  30598,
-                313,  20787,    953,   3848,    275,  16125,    630,  29897,  29871,  31681,
-                313,   6194,    953,  29877,   2397,    393,    756,    967,   1914,   5993,  29897, }, },
-        { "Hello",                  { 1,    15043 }, },
-        { " Hello",                 { 1,    29871,  15043 }, },
-        { "  Hello",                { 1,    259,    15043 }, },
-        { "   Hello",               { 1,    1678,   15043 }, },
-        { "    Hello",              { 1,    268,    15043 }, },
-        { "    Hello\n    Hello",   { 1,    268,    15043,  13,     1678,   15043 }, },
-    };
-
-    return _k_tests;
-}
-
-int main(int argc, char **argv) {
-    if (argc < 2) {
-        fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
-        return 1;
-    }
-
-    const std::string fname = argv[1];
-
-    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
-
-    llama_model * model;
-    llama_context * ctx;
-
-    llama_backend_init(false);
-
-    // load the vocab
-    {
-        auto lparams = llama_context_default_params();
-
-        lparams.vocab_only = true;
-
-        model = llama_load_model_from_file(fname.c_str(), lparams);
-
-        if (model == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            return 1;
-        }
-
-        ctx = llama_new_context_with_model(model, lparams);
-
-        if (ctx == NULL) {
-            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
-            llama_free_model(model);
-            return 1;
-        }
-    }
-
-    const int n_vocab = llama_n_vocab(ctx);
-
-    if (n_vocab != 32000) {
-        fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
-        llama_free_model(model);
-        llama_free(ctx);
-        return 2;
-    }
-
-    bool success = true;
-
-    for (const auto & test_kv : k_tests()) {
-        // Add a space in front of the first character to match OG llama tokenizer behavior
-        std::vector<llama_token> res = llama_tokenize(ctx, " " + test_kv.first, true);
-        fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
-            __func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str());
-
-        bool correct = res.size() == test_kv.second.size();
-
-        for (int i = 0; i < (int) res.size() && correct; ++i) {
-            if (res[i] != test_kv.second[i]) {
-                correct = false;
-            }
-        }
-
-        if (!correct) {
-            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
-            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
-                unescape_whitespace(ctx, res).c_str(), unescape_whitespace(ctx, test_kv.second).c_str());
-            fprintf(stderr, "%s : expected tokens: ", __func__);
-            for (const auto & t : test_kv.second) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-            fprintf(stderr, "%s : got tokens:      ", __func__);
-            for (const auto & t : res) {
-                fprintf(stderr, "%6d, ", t);
-            }
-            fprintf(stderr, "\n");
-
-            success = false;
-        }
-    }
-
-    llama_free_model(model);
-    llama_free(ctx);
-
-    llama_backend_free();
-
-    return success ? 0 : 3;
-}
diff --git a/tests/test-tokenizer-1.cpp b/tests/test-tokenizer-1.cpp
index bd607d12b..ce4f2898c 100644
--- a/tests/test-tokenizer-1.cpp
+++ b/tests/test-tokenizer-1.cpp
@@ -22,14 +22,6 @@ static std::string escape_whitespace(const std::string& text) {
     return result;
 }
 
-static std::string unescape_whitespace(llama_context * ctx, const std::vector<llama_token> & tokens) {
-    std::string result;
-    for (size_t i = 0; i < tokens.size(); ++i) {
-        result += llama_token_to_str(ctx, tokens[i]);
-    }
-    return result;
-}
-
 int main(int argc, char **argv) {
     if (argc < 2) {
         fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
@@ -72,13 +64,13 @@ int main(int argc, char **argv) {
     const int n_vocab = llama_n_vocab(ctx);
 
     for (int i = 0; i < n_vocab; ++i) {
-        std::string forward = llama_token_to_str(ctx, i);
+        std::string forward = llama_token_to_piece(ctx, i);
         std::vector<llama_token> tokens = llama_tokenize(ctx, forward, false);
         if (tokens.size() == 1) {
             if (i != tokens[0]) {
-                std::string backward = llama_token_to_str(ctx, tokens[0]);
+                std::string backward = llama_token_to_piece(ctx, tokens[0]);
                 fprintf(stderr, "%s : error: token %d is string %s but bpe returns token %d %s\n",
-                    __func__, i, llama_token_to_str(ctx, i).c_str(), tokens[0], backward.c_str());
+                    __func__, i, llama_token_to_piece(ctx, i).c_str(), tokens[0], backward.c_str());
                 return 2;
             }
         }

From d0cee0d36d5be95a0d9088b674dbb27354107221 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 14:19:54 +0300
Subject: [PATCH 32/53] gguf : add 64-bit support (GGUF v2) (#2821)

* gguf : bump version to 2

* gguf : add support for 64-bit (no backwards comp yet)

* gguf : v1 backwards comp

* gguf.py : bump GGUF version

* gguf.py : uint64_t on all lengths, sizes and counts, enums still uint32_t

* gguf.py : string lengths uint32_t

* gguf : update all counts to 64-bit

* gguf.py : string len uint64_t and n_dims uint32_t

* gguf : fix typo

* llama.cpp : print gguf version

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
---
 examples/gguf/gguf.cpp |   3 +
 ggml.c                 | 137 +++++++++++++++++++++++++++++++++++------
 ggml.h                 |  11 +++-
 gguf-py/gguf/gguf.py   |  36 ++++++++---
 llama.cpp              |   4 +-
 5 files changed, 164 insertions(+), 27 deletions(-)

diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp
index dee00df87..cda517bde 100644
--- a/examples/gguf/gguf.cpp
+++ b/examples/gguf/gguf.cpp
@@ -30,6 +30,9 @@ bool gguf_ex_write(const std::string & fname) {
     gguf_set_val_u32 (ctx, "some.parameter.uint32",   0x12345678);
     gguf_set_val_i32 (ctx, "some.parameter.int32",   -0x12345679);
     gguf_set_val_f32 (ctx, "some.parameter.float32",  0.123456789f);
+    gguf_set_val_u64 (ctx, "some.parameter.uint64",   0x123456789abcdef0ull);
+    gguf_set_val_i64 (ctx, "some.parameter.int64",   -0x123456789abcdef1ll);
+    gguf_set_val_f64 (ctx, "some.parameter.float64",  0.1234567890123456789);
     gguf_set_val_bool(ctx, "some.parameter.bool",     true);
     gguf_set_val_str (ctx, "some.parameter.string",   "hello world");
 
diff --git a/ggml.c b/ggml.c
index 394fb459f..855d519bf 100644
--- a/ggml.c
+++ b/ggml.c
@@ -19394,7 +19394,7 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
 ////////////////////////////////////////////////////////////////////////////////
 
 struct gguf_str {
-    uint32_t n;
+    uint64_t n;  // GGUFv2
     char * data;
 };
 
@@ -19408,9 +19408,12 @@ static const size_t GGUF_TYPE_SIZE[GGUF_TYPE_COUNT] = {
     [GGUF_TYPE_FLOAT32] = sizeof(float),
     [GGUF_TYPE_BOOL]    = sizeof(bool),
     [GGUF_TYPE_STRING]  = sizeof(struct gguf_str),
+    [GGUF_TYPE_UINT64]  = sizeof(uint64_t),
+    [GGUF_TYPE_INT64]   = sizeof(int64_t),
+    [GGUF_TYPE_FLOAT64] = sizeof(double),
     [GGUF_TYPE_ARRAY]   = 0, // undefined
 };
-static_assert(GGUF_TYPE_COUNT == 10, "GGUF_TYPE_COUNT != 10");
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
 
 static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = {
     [GGUF_TYPE_UINT8]   = "u8",
@@ -19423,8 +19426,11 @@ static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = {
     [GGUF_TYPE_BOOL]    = "bool",
     [GGUF_TYPE_STRING]  = "str",
     [GGUF_TYPE_ARRAY]   = "arr",
+    [GGUF_TYPE_UINT64]  = "u64",
+    [GGUF_TYPE_INT64]   = "i64",
+    [GGUF_TYPE_FLOAT64] = "f64",
 };
-static_assert(GGUF_TYPE_COUNT == 10, "GGUF_TYPE_COUNT != 10");
+static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13");
 
 union gguf_value {
     uint8_t  uint8;
@@ -19434,6 +19440,9 @@ union gguf_value {
     uint32_t uint32;
     int32_t  int32;
     float    float32;
+    uint64_t uint64;
+    int64_t  int64;
+    double   float64;
     bool     bool_;
 
     struct gguf_str str;
@@ -19441,7 +19450,7 @@ union gguf_value {
     struct {
         enum gguf_type type;
 
-        uint32_t n;
+        uint64_t n;  // GGUFv2
         void * data;
     } arr;
 };
@@ -19449,8 +19458,6 @@ union gguf_value {
 struct gguf_kv {
     struct gguf_str key;
 
-    uint32_t n_bytes; // TODO: is this actually needed?
-
     enum  gguf_type  type;
     union gguf_value value;
 };
@@ -19458,15 +19465,15 @@ struct gguf_kv {
 struct gguf_header {
     uint32_t magic;
     uint32_t version;
-    uint32_t n_tensors;
-    uint32_t n_kv;
+    uint64_t n_tensors; // GGUFv2
+    uint64_t n_kv;      // GGUFv2
 };
 
 struct gguf_tensor_info {
     struct gguf_str name;
 
     uint32_t n_dims;
-    uint32_t ne[GGML_MAX_DIMS];
+    uint64_t ne[GGML_MAX_DIMS];
 
     enum ggml_type type;
 
@@ -19497,19 +19504,32 @@ static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset)
     return n == size;
 }
 
-static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) {
+// NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset) {
     p->n    = 0;
     p->data = NULL;
 
     bool ok = true;
 
-    // TODO: how to avoid mallocs for strings?
     ok = ok && gguf_fread_el(file, &p->n,    sizeof(p->n), offset); p->data = calloc(p->n + 1, 1);
     ok = ok && gguf_fread_el(file,  p->data, p->n,         offset);
 
     return ok;
 }
 
+static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) {
+    p->n    = 0;
+    p->data = NULL;
+
+    bool ok = true;
+
+    uint32_t n = 0;
+    ok = ok && gguf_fread_el(file, &n,       sizeof(n),   offset); p->data = calloc(n + 1, 1); p->n = n;
+    ok = ok && gguf_fread_el(file,  p->data, p->n,         offset);
+
+    return ok;
+}
+
 struct gguf_context * gguf_init_empty(void) {
     struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
 
@@ -19565,8 +19585,21 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         ctx->data  = NULL;
 
         ok = ok && gguf_fread_el(file, &ctx->header.version,   sizeof(ctx->header.version),   &offset);
-        ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
-        ok = ok && gguf_fread_el(file, &ctx->header.n_kv,      sizeof(ctx->header.n_kv),      &offset);
+
+        if (ctx->header.version == 1) {
+            // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+            uint32_t n_tensors = 0;
+            uint32_t n_kv      = 0;
+
+            ok = ok && gguf_fread_el(file, &n_tensors, sizeof(n_tensors), &offset);
+            ok = ok && gguf_fread_el(file, &n_kv,      sizeof(n_kv),      &offset);
+
+            ctx->header.n_tensors = n_tensors;
+            ctx->header.n_kv      = n_kv;
+        } else {
+            ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
+            ok = ok && gguf_fread_el(file, &ctx->header.n_kv,      sizeof(ctx->header.n_kv),      &offset);
+        }
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read header\n", __func__);
@@ -19576,6 +19609,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         }
     }
 
+    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+    bool (* gguf_fread_str)(FILE *, struct gguf_str *, size_t *) = gguf_fread_str_cur;
+    if (ctx->header.version == 1) {
+        gguf_fread_str = gguf_fread_str_v1;
+    }
+
     // read the kv pairs
     {
         ctx->kv = GGML_ALIGNED_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv));
@@ -19585,9 +19624,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
             //fprintf(stderr, "%s: reading kv %d\n", __func__, i);
 
-            ok = ok && gguf_fread_str(file, &kv->key,                          &offset);
-          //ok = ok && gguf_fread_el (file, &kv->n_bytes, sizeof(kv->n_bytes), &offset);
-            ok = ok && gguf_fread_el (file, &kv->type,    sizeof(kv->type),    &offset);
+            ok = ok && gguf_fread_str(file, &kv->key,                    &offset);
+            ok = ok && gguf_fread_el (file, &kv->type, sizeof(kv->type), &offset);
 
             //fprintf(stderr, "%s: reading kv with key %s\n", __func__, kv->key.data);
 
@@ -19599,12 +19637,23 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                 case GGUF_TYPE_UINT32:  ok = ok && gguf_fread_el (file, &kv->value.uint32,  sizeof(kv->value.uint32),  &offset); break;
                 case GGUF_TYPE_INT32:   ok = ok && gguf_fread_el (file, &kv->value.int32,   sizeof(kv->value.int32),   &offset); break;
                 case GGUF_TYPE_FLOAT32: ok = ok && gguf_fread_el (file, &kv->value.float32, sizeof(kv->value.float32), &offset); break;
+                case GGUF_TYPE_UINT64:  ok = ok && gguf_fread_el (file, &kv->value.uint64,  sizeof(kv->value.uint64),  &offset); break;
+                case GGUF_TYPE_INT64:   ok = ok && gguf_fread_el (file, &kv->value.int64,   sizeof(kv->value.int64),   &offset); break;
+                case GGUF_TYPE_FLOAT64: ok = ok && gguf_fread_el (file, &kv->value.float64, sizeof(kv->value.float64), &offset); break;
                 case GGUF_TYPE_BOOL:    ok = ok && gguf_fread_el (file, &kv->value.bool_,   sizeof(kv->value.bool_),   &offset); break;
                 case GGUF_TYPE_STRING:  ok = ok && gguf_fread_str(file, &kv->value.str,                                &offset); break;
                 case GGUF_TYPE_ARRAY:
                     {
                         ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset);
-                        ok = ok && gguf_fread_el(file, &kv->value.arr.n,    sizeof(kv->value.arr.n),    &offset);
+
+                        if (ctx->header.version == 1) {
+                            // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+                            uint32_t n = 0;
+                            ok = ok && gguf_fread_el(file, &n, sizeof(n), &offset);
+                            kv->value.arr.n = n;
+                        } else {
+                            ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset);
+                        }
 
                         switch (kv->value.arr.type) {
                             case GGUF_TYPE_UINT8:
@@ -19614,6 +19663,9 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                             case GGUF_TYPE_UINT32:
                             case GGUF_TYPE_INT32:
                             case GGUF_TYPE_FLOAT32:
+                            case GGUF_TYPE_UINT64:
+                            case GGUF_TYPE_INT64:
+                            case GGUF_TYPE_FLOAT64:
                             case GGUF_TYPE_BOOL:
                                 {
                                     kv->value.arr.data = malloc(kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
@@ -19660,7 +19712,14 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             ok = ok && gguf_fread_str(file, &info->name,                          &offset);
             ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims),  &offset);
             for (uint32_t j = 0; j < info->n_dims; ++j) {
-                ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
+                if (ctx->header.version == 1) {
+                    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
+                    uint32_t t = 0;
+                    ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset);
+                    info->ne[j] = t;
+                } else {
+                    ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
+                }
             }
             ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
             ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);
@@ -19954,6 +20013,18 @@ float gguf_get_val_f32(struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.float32;
 }
 
+uint64_t gguf_get_val_u64(struct gguf_context * ctx, int i) {
+    return ctx->kv[i].value.uint64;
+}
+
+int64_t gguf_get_val_i64(struct gguf_context * ctx, int i) {
+    return ctx->kv[i].value.int64;
+}
+
+double gguf_get_val_f64(struct gguf_context * ctx, int i) {
+    return ctx->kv[i].value.float64;
+}
+
 bool gguf_get_val_bool(struct gguf_context * ctx, int i) {
     return ctx->kv[i].value.bool_;
 }
@@ -20056,6 +20127,27 @@ void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) {
     ctx->kv[idx].value.float32 = val;
 }
 
+void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type         = GGUF_TYPE_UINT64;
+    ctx->kv[idx].value.uint64 = val;
+}
+
+void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type        = GGUF_TYPE_INT64;
+    ctx->kv[idx].value.int64 = val;
+}
+
+void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) {
+    const int idx = gguf_get_or_add_key(ctx, key);
+
+    ctx->kv[idx].type          = GGUF_TYPE_FLOAT64;
+    ctx->kv[idx].value.float64 = val;
+}
+
 void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) {
     const int idx = gguf_get_or_add_key(ctx, key);
 
@@ -20106,6 +20198,9 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
             case GGUF_TYPE_UINT32:  gguf_set_val_u32 (ctx, src->kv[i].key.data, src->kv[i].value.uint32);   break;
             case GGUF_TYPE_INT32:   gguf_set_val_i32 (ctx, src->kv[i].key.data, src->kv[i].value.int32);    break;
             case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, src->kv[i].key.data, src->kv[i].value.float32);  break;
+            case GGUF_TYPE_UINT64:  gguf_set_val_u64 (ctx, src->kv[i].key.data, src->kv[i].value.uint64);   break;
+            case GGUF_TYPE_INT64:   gguf_set_val_i64 (ctx, src->kv[i].key.data, src->kv[i].value.int64);    break;
+            case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, src->kv[i].key.data, src->kv[i].value.float64);  break;
             case GGUF_TYPE_BOOL:    gguf_set_val_bool(ctx, src->kv[i].key.data, src->kv[i].value.bool_);    break;
             case GGUF_TYPE_STRING:  gguf_set_val_str (ctx, src->kv[i].key.data, src->kv[i].value.str.data); break;
             case GGUF_TYPE_ARRAY:
@@ -20267,6 +20362,9 @@ static void gguf_write_to_buf(struct gguf_context * ctx, struct gguf_buf * buf,
             case GGUF_TYPE_UINT32:  gguf_bwrite_el (buf, &kv->value.uint32,  sizeof(kv->value.uint32) ); break;
             case GGUF_TYPE_INT32:   gguf_bwrite_el (buf, &kv->value.int32,   sizeof(kv->value.int32)  ); break;
             case GGUF_TYPE_FLOAT32: gguf_bwrite_el (buf, &kv->value.float32, sizeof(kv->value.float32)); break;
+            case GGUF_TYPE_UINT64:  gguf_bwrite_el (buf, &kv->value.uint64,  sizeof(kv->value.uint64) ); break;
+            case GGUF_TYPE_INT64:   gguf_bwrite_el (buf, &kv->value.int64,   sizeof(kv->value.int64)  ); break;
+            case GGUF_TYPE_FLOAT64: gguf_bwrite_el (buf, &kv->value.float64, sizeof(kv->value.float64)); break;
             case GGUF_TYPE_BOOL:    gguf_bwrite_el (buf, &kv->value.bool_,   sizeof(kv->value.bool_)  ); break;
             case GGUF_TYPE_STRING:  gguf_bwrite_str(buf, &kv->value.str                               ); break;
             case GGUF_TYPE_ARRAY:
@@ -20282,6 +20380,9 @@ static void gguf_write_to_buf(struct gguf_context * ctx, struct gguf_buf * buf,
                         case GGUF_TYPE_UINT32:
                         case GGUF_TYPE_INT32:
                         case GGUF_TYPE_FLOAT32:
+                        case GGUF_TYPE_UINT64:
+                        case GGUF_TYPE_INT64:
+                        case GGUF_TYPE_FLOAT64:
                         case GGUF_TYPE_BOOL:
                             {
                                 gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
diff --git a/ggml.h b/ggml.h
index b418153ba..792ca6e42 100644
--- a/ggml.h
+++ b/ggml.h
@@ -216,7 +216,7 @@
 #define GGML_EXIT_ABORTED 1
 
 #define GGUF_MAGIC   0x46554747 // "GGUF"
-#define GGUF_VERSION 1
+#define GGUF_VERSION 2
 
 #define GGUF_DEFAULT_ALIGNMENT 32
 
@@ -1827,6 +1827,9 @@ extern "C" {
         GGUF_TYPE_BOOL    = 7,
         GGUF_TYPE_STRING  = 8,
         GGUF_TYPE_ARRAY   = 9,
+        GGUF_TYPE_UINT64  = 10,
+        GGUF_TYPE_INT64   = 11,
+        GGUF_TYPE_FLOAT64 = 12,
         GGUF_TYPE_COUNT,       // marks the end of the enum
     };
 
@@ -1867,6 +1870,9 @@ extern "C" {
     GGML_API uint32_t     gguf_get_val_u32 (struct gguf_context * ctx, int i);
     GGML_API int32_t      gguf_get_val_i32 (struct gguf_context * ctx, int i);
     GGML_API float        gguf_get_val_f32 (struct gguf_context * ctx, int i);
+    GGML_API uint64_t     gguf_get_val_u64 (struct gguf_context * ctx, int i);
+    GGML_API int64_t      gguf_get_val_i64 (struct gguf_context * ctx, int i);
+    GGML_API double       gguf_get_val_f64 (struct gguf_context * ctx, int i);
     GGML_API bool         gguf_get_val_bool(struct gguf_context * ctx, int i);
     GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);
     GGML_API int          gguf_get_arr_n   (struct gguf_context * ctx, int i);
@@ -1886,6 +1892,9 @@ extern "C" {
     GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
     GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t  val);
     GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float    val);
+    GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
+    GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t  val);
+    GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double   val);
     GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool     val);
     GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
     GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py
index f4db7001b..838a2c0f8 100644
--- a/gguf-py/gguf/gguf.py
+++ b/gguf-py/gguf/gguf.py
@@ -13,7 +13,7 @@ from typing import Any, IO, List, Optional
 #
 
 GGUF_MAGIC             = 0x46554747
-GGUF_VERSION           = 1
+GGUF_VERSION           = 2
 GGUF_DEFAULT_ALIGNMENT = 32
 
 # general
@@ -365,6 +365,9 @@ class GGUFValueType(IntEnum):
     BOOL    = 7
     STRING  = 8
     ARRAY   = 9
+    UINT64  = 10
+    INT64   = 11
+    FLOAT64 = 12
 
     @staticmethod
     def get_type(val):
@@ -378,6 +381,7 @@ class GGUFValueType(IntEnum):
             return GGUFValueType.BOOL
         elif isinstance(val, int):
             return GGUFValueType.INT32
+        # TODO: need help with 64-bit types in Python
         else:
             print("Unknown type: "+str(type(val)))
             sys.exit()
@@ -400,8 +404,8 @@ class GGUFWriter:
     def write_header_to_file(self):
         self.fout.write(struct.pack("<I", GGUF_MAGIC))
         self.fout.write(struct.pack("<I", GGUF_VERSION))
-        self.fout.write(struct.pack("<I", self.ti_data_count))
-        self.fout.write(struct.pack("<I", self.kv_data_count))
+        self.fout.write(struct.pack("<Q", self.ti_data_count))
+        self.fout.write(struct.pack("<Q", self.kv_data_count))
         self.flush()
 #        print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
 
@@ -444,6 +448,18 @@ class GGUFWriter:
         self.add_key(key)
         self.add_val(val, GGUFValueType.FLOAT32)
 
+    def add_uint64(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.UINT64)
+
+    def add_int64(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.INT64)
+
+    def add_float64(self, key: str, val: float):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.FLOAT64)
+
     def add_bool(self, key: str, val: bool):
         self.add_key(key)
         self.add_val(val, GGUFValueType.BOOL)
@@ -483,17 +499,23 @@ class GGUFWriter:
             self.kv_data += struct.pack("<i", val)
         elif vtype == GGUFValueType.FLOAT32:
             self.kv_data += struct.pack("<f", val)
+        elif vtype == GGUFValueType.UINT64:
+            self.kv_data += struct.pack("<Q", val)
+        elif vtype == GGUFValueType.INT64:
+            self.kv_data += struct.pack("<q", val)
+        elif vtype == GGUFValueType.FLOAT64:
+            self.kv_data += struct.pack("<d", val)
         elif vtype == GGUFValueType.BOOL:
             self.kv_data += struct.pack("?", val)
         elif vtype == GGUFValueType.STRING:
             encoded_val = val.encode("utf8") if isinstance(val, str) else val
-            self.kv_data += struct.pack("<I", len(encoded_val))
+            self.kv_data += struct.pack("<Q", len(encoded_val))
             self.kv_data += encoded_val
         elif vtype == GGUFValueType.ARRAY:
             ltype = set([GGUFValueType.get_type(item) for item in val])
             assert len(ltype) == 1, "All items in a GGUF array should be of the same type"
             self.kv_data += struct.pack("<I", list(ltype)[0])
-            self.kv_data += struct.pack("<I", len(val))
+            self.kv_data += struct.pack("<Q", len(val))
             for item in val:
                 self.add_val(item, add_vtype=False)
         else:
@@ -507,12 +529,12 @@ class GGUFWriter:
         assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
 
         encoded_name = name.encode("utf8")
-        self.ti_data += struct.pack("<I", len(encoded_name))
+        self.ti_data += struct.pack("<Q", len(encoded_name))
         self.ti_data += encoded_name
         n_dims = len(tensor_shape)
         self.ti_data += struct.pack("<I", n_dims)
         for i in range(n_dims):
-            self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
+            self.ti_data += struct.pack("<Q", tensor_shape[n_dims - 1 - i])
         if raw_dtype is None:
             dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
         else:
diff --git a/llama.cpp b/llama.cpp
index 2a8af4ee9..df103a6e5 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1144,11 +1144,13 @@ static bool llama_kv_cache_init(
 
 enum llama_fver {
     GGUF_FILE_VERSION_V1 = 1,
+    GGUF_FILE_VERSION_V2 = 2,
 };
 
 static const char * llama_file_version_name(llama_fver version) {
     switch (version) {
-        case GGUF_FILE_VERSION_V1: return "GGUF V1 (latest)";
+        case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
+        case GGUF_FILE_VERSION_V2: return "GGUF V2 (latest)";
     }
 
     return "unknown";

From c48c5bb0b06385f6c708339188d2aaf2bc278477 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 14:44:35 +0300
Subject: [PATCH 33/53] readme : update hot topics

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 95471fdbb..f15a583b0 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,10 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ### Hot topics
 
+- ## IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810
+
+- ## GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821
+
 - Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717
 
 - A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398)

From a6d1189fdd4c1ab4ba23f9d777f8950901dcffb2 Mon Sep 17 00:00:00 2001
From: Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Date: Sun, 27 Aug 2023 15:19:59 +0300
Subject: [PATCH 34/53] k_quants tuning for Falcon-7b (#2816)

* Make ggml-cuda.cu build with QK_K = 64

Using LLAMA_CUDA_FORCE_DMMV = ON and -nommq it runs and produces
a meaningful result.

* k_quants tuning for Falcon-7b

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
---
 ggml-cuda.cu | 25 +++++++++++++++++--------
 llama.cpp    | 43 ++++++++++++++++++++++++++++++++++---------
 2 files changed, 51 insertions(+), 17 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 83d53c13c..d83aefc9a 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -306,11 +306,11 @@ typedef struct {
 #define QI4_K (QK_K / (4*QR4_K))
 #ifdef GGML_QKK_64
 typedef struct {
-    half    d[2];              // super-block scales/mins
+    half    dm[2];             // super-block scales/mins
     uint8_t scales[2];         // 4-bit block scales/mins
     uint8_t qs[QK_K/2];        // 4--bit quants
 } block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
+static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
 #else
 typedef struct {
     half2 dm;                  // super-block scale for quantized scales/mins
@@ -737,8 +737,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float
     const int tid = threadIdx.x;
     const uint8_t * q = x[i].qs;
     float * y = yy + i*QK_K;
-    const float d = (float)x[i].d[0];
-    const float m = (float)x[i].d[1];
+    const float d = (float)x[i].dm[0];
+    const float m = (float)x[i].dm[1];
     y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
     y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
 #endif
@@ -1155,8 +1155,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux16[0] = a[0] & 0x0f0f;
         aux16[1] = (a[0] >> 4) & 0x0f0f;
-        const float d = (float)x[i].d[0];
-        const float m = (float)x[i].d[1];
+        const float d = (float)x[i].dm[0];
+        const float m = (float)x[i].dm[1];
         float sum = 0.f;
         for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
             sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
@@ -2845,8 +2845,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
     aux16[0] = a[0] & 0x0f0f;
     aux16[1] = (a[0] >> 4) & 0x0f0f;
 
-    const float dall = bq4_K->d[0];
-    const float dmin = bq4_K->d[1];
+    const float dall = bq4_K->dm[0];
+    const float dmin = bq4_K->dm[1];
 
     const float d8_1 = __low2float(bq8_1[0].ds);
     const float d8_2 = __low2float(bq8_1[1].ds);
@@ -2929,7 +2929,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
 
+#if QK_K == 256
         x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+#else
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
+#endif
     }
 
 #pragma unroll
@@ -3119,7 +3123,9 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
 
+#if QK_K == 256
         x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+#endif
     }
 
 #pragma unroll
@@ -4709,6 +4715,8 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
     const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
 
+#if QK_K == 256
+
     int id;
     CUDA_CHECK(cudaGetDevice(&id));
     const int compute_capability = g_compute_capabilities[id];
@@ -4740,6 +4748,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
         mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
+#endif
 }
 
 static void ggml_mul_mat_q4_K_q8_1_cuda(
diff --git a/llama.cpp b/llama.cpp
index df103a6e5..e9868f5d0 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -4776,7 +4776,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
             if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
                 int nx = tensor->ne[0];
-                if (nx % QK_K == 0) {
+                if (model.arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+                    new_type = GGML_TYPE_Q8_0;
+                }
+                else if (new_type != GGML_TYPE_Q8_0) {
                     new_type = GGML_TYPE_Q6_K;
                 }
             } else if (name.find("attn_v.weight") != std::string::npos) {
@@ -4800,17 +4803,39 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             } else if (name.find("ffn_down.weight") != std::string::npos) {
                 if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
                 else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
-                    new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+                    new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
+                             : model.arch != LLM_ARCH_FALCON || use_more_bits(i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q4_K
+                             : GGML_TYPE_Q3_K;
+                }
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
+                    new_type = model.arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
+                }
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+                    if (model.arch == LLM_ARCH_FALCON) {
+                        new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
+                                   use_more_bits(i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+                    } else {
+                        if (use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+                    }
+                }
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && model.arch != LLM_ARCH_FALCON && i_feed_forward_w2 < 4) {
+                    new_type = GGML_TYPE_Q5_K;
                 }
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
-                else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
-                         use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < 4) new_type = GGML_TYPE_Q5_K;
                 ++i_feed_forward_w2;
             } else if (name.find("attn_output.weight") != std::string::npos) {
-                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K  ) new_type = GGML_TYPE_Q3_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
-                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+                if (model.arch != LLM_ARCH_FALCON) {
+                    if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K  ) new_type = GGML_TYPE_Q3_K;
+                    else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
+                    else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+                } else {
+                    if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+                }
+            }
+            else if (name.find("attn_qkv.weight") != std::string::npos) {
+                if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
             }
             else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) {
                 if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;

From 25423e9185b7c2a1881ed8f85cc752a12370be9d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 15:24:40 +0300
Subject: [PATCH 35/53] scripts : helper convert script

---
 scripts/convert-gg.sh   | 26 ++++++++++++++++++++++++++
 scripts/qnt-all.sh      |  2 ++
 scripts/run-all-perf.sh |  2 ++
 scripts/run-all-ppl.sh  |  2 ++
 4 files changed, 32 insertions(+)
 create mode 100755 scripts/convert-gg.sh

diff --git a/scripts/convert-gg.sh b/scripts/convert-gg.sh
new file mode 100755
index 000000000..01fda16fd
--- /dev/null
+++ b/scripts/convert-gg.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+set -e
+
+# LLaMA v1
+python3 convert.py ../llama1/7B  --outfile models/llama-7b/ggml-model-f16.gguf  --outtype f16
+python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
+
+# LLaMA v2
+python3 convert.py ../llama2/llama-2-7b  --outfile models/llama-7b-v2/ggml-model-f16.gguf  --outtype f16
+python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
+
+# Code Llama
+python3 convert.py ../codellama/CodeLlama-7b/  --outfile models/codellama-7b/ggml-model-f16.gguf  --outtype f16
+python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
+
+# Falcon
+python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b  1
+mv -v ../falcon/falcon-7b/ggml-model-f16.gguf models/falcon-7b/ggml-model-f16.gguf
+
+python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-40b 1
+mv -v ../falcon/falcon-40b/ggml-model-f16.gguf models/falcon-40b/ggml-model-f16.gguf
diff --git a/scripts/qnt-all.sh b/scripts/qnt-all.sh
index 1b3d07da5..1376e4194 100755
--- a/scripts/qnt-all.sh
+++ b/scripts/qnt-all.sh
@@ -20,6 +20,8 @@ fi
 model="$1"
 out="../tmp/results-${model}"
 
+set -e
+
 mkdir -p ${out}
 
 for q in ${qnt[@]}; do
diff --git a/scripts/run-all-perf.sh b/scripts/run-all-perf.sh
index 91a6d853f..7391e3dd5 100755
--- a/scripts/run-all-perf.sh
+++ b/scripts/run-all-perf.sh
@@ -20,6 +20,8 @@ fi
 model="$1"
 out="../tmp/results-${model}"
 
+set -e
+
 mkdir -p ${out}
 
 mstr=""
diff --git a/scripts/run-all-ppl.sh b/scripts/run-all-ppl.sh
index 366d0866c..f643ca3ae 100755
--- a/scripts/run-all-ppl.sh
+++ b/scripts/run-all-ppl.sh
@@ -17,6 +17,8 @@ if [ ! -z "$3" ]; then
     args="$3"
 fi
 
+set -e
+
 model="$1"
 out="../tmp/results-${model}"
 

From da7455d0467b5f5cc2e45d0dcffaf098df13db63 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 15:52:34 +0300
Subject: [PATCH 36/53] readme : fix headings

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index f15a583b0..bf3eb0b76 100644
--- a/README.md
+++ b/README.md
@@ -11,9 +11,9 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ### Hot topics
 
-- ## IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810
+- #### IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810
 
-- ## GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821
+- GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821
 
 - Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717
 

From eaa13a48ff4136f01c1cdb79cacd61b67ec53095 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 16:40:48 +0300
Subject: [PATCH 37/53] falcon : fix CUDA inference by making K and Q
 contiguous (#2830)

* falcon : fix CUDA inference by making K and Q contiguous

ggml-ci

* cuda : add assert to guard from non-cont ropes
---
 ggml-cuda.cu |  2 ++
 llama.cpp    | 10 ++++++----
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index d83aefc9a..d76a25dc2 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -6337,9 +6337,11 @@ void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml
 
 void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
 
     const int mode = ((int32_t *) dst->op_params)[2];
     const bool is_glm = mode & 4;
+
     ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm
 }
 
diff --git a/llama.cpp b/llama.cpp
index e9868f5d0..0d12d9cca 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -2642,18 +2642,20 @@ static struct ggml_cgraph * llm_build_falcon(
 
             const size_t wsize = ggml_type_size(cur->type);
 
-            struct ggml_tensor * tmpq = ggml_view_3d(
+            // TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for
+            //       non-contiguous views is added for the rope operator
+            struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
                 ctx0, cur, n_embd_head, n_head, N,
                 wsize * n_embd_head,
                 wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                0);
+                0));
             offload_func_kq(tmpq);
 
-            struct ggml_tensor * tmpk = ggml_view_3d(
+            struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d(
                 ctx0, cur, n_embd_head, n_head_kv, N,
                 wsize * n_embd_head,
                 wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                wsize * n_embd_head *  n_head);
+                wsize * n_embd_head *  n_head));
             offload_func_kq(tmpk);
 
             struct ggml_tensor * tmpv = ggml_view_3d(

From 463173a6c0ff353055eb90665794884c888c790f Mon Sep 17 00:00:00 2001
From: Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Date: Sun, 27 Aug 2023 16:50:33 +0300
Subject: [PATCH 38/53] llama : speedup tokenization (#2831)

* Speedup tokenization

On current master it takes ~3.2 seconds to tokenize
Wikitext. With this change it becomes ~525 ms.

* Fixit: it was missing the piece after the last found occurence

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
---
 examples/perplexity/perplexity.cpp |  4 ++++
 llama.cpp                          | 15 ++++++++++-----
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index b596d0626..ebafa0c29 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -190,10 +190,14 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
     const bool add_bos = is_spm;
 
+    auto tim1 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
     auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
 
+    auto tim2 = std::chrono::high_resolution_clock::now();
+    fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
+
     const int n_chunk_max = tokens.size() / params.n_ctx;
 
     const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
diff --git a/llama.cpp b/llama.cpp
index 0d12d9cca..0bb8fcd6e 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -114,12 +114,17 @@ static size_t utf8_len(char src) {
 }
 
 void replace_all(std::string & s, const std::string & search, const std::string & replace) {
-    for (size_t pos = 0; ; pos += replace.length()) {
-        pos = s.find(search, pos);
-        if (pos == std::string::npos) break;
-        s.erase(pos, search.length());
-        s.insert(pos, replace);
+    std::string result;
+    for (size_t pos = 0; ; pos += search.length()) {
+        auto new_pos = s.find(search, pos);
+        if (new_pos == std::string::npos) {
+            result += s.substr(pos, s.size() - pos);
+            break;
+        }
+        result += s.substr(pos, new_pos - pos) + replace;
+        pos = new_pos;
     }
+    s = std::move(result);
 }
 
 static void zeros(std::ofstream & file, size_t n) {

From 230d46c723edf5999752e4cb67fd94edb19ef9c7 Mon Sep 17 00:00:00 2001
From: Olivier Chafik <ochafik@users.noreply.github.com>
Date: Sun, 27 Aug 2023 15:13:31 +0100
Subject: [PATCH 39/53] examples : update llama2.c converter to read vocab and
 write models in GGUF format (#2751)

* llama2.c: direct gguf output (WIP)

* Simplify vector building logic

* llama2.c gguf conversion: fix token types in converter

* llama2.c: support copying vocab from a llama gguf model file

* llama2.c: update default path for vocab model + readme

* llama2.c: use defines for gguf keys

* llama2.c: escape whitespaces w/ U+2581 in vocab converter the llama.cpp way

* llama2.c converter: cleanups + take n_ff from config
---
 examples/convert-llama2c-to-ggml/README.md    |   8 +-
 .../convert-llama2c-to-ggml.cpp               | 340 +++++++++++-------
 2 files changed, 220 insertions(+), 128 deletions(-)

diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md
index fd561fcbc..0f37d295b 100644
--- a/examples/convert-llama2c-to-ggml/README.md
+++ b/examples/convert-llama2c-to-ggml/README.md
@@ -12,18 +12,14 @@ usage: ./convert-llama2c-to-ggml [options]
 
 options:
   -h, --help                       show this help message and exit
-  --copy-vocab-from-model FNAME    model path from which to copy vocab (default 'tokenizer.bin')
+  --copy-vocab-from-model FNAME    path of gguf llama model or llama2.c vocabulary from which to copy vocab (default 'models/7B/ggml-model-f16.gguf')
   --llama2c-model FNAME            [REQUIRED] model path from which to load Karpathy's llama2.c model
   --llama2c-output-model FNAME     model path to save the converted llama2.c model (default ak_llama_model.bin')
 ```
 
 An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows:
 
-`$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.ggmlv3.bin`
-
-For now the generated model is in the legacy GGJTv3 format, so you need to convert it to gguf manually:
-
-`$ python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input stories42M.ggmlv3.bin --output stories42M.gguf.bin`
+`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
 
 Now you can use the model with a command like:
 
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index f8a58dc7a..51d90ea6a 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -10,9 +10,48 @@
 #include <ctime>
 #include <random>
 #include <stdexcept>
+#include <sstream>
 #include <algorithm>
 #include <string>
 
+// GGUF keys & tensor names.
+
+#define KV_GENERAL_ARCHITECTURE          "general.architecture"
+#define KV_GENERAL_NAME                  "general.name"
+
+#define KV_TOKENIZER_MODEL               "tokenizer.ggml.model"
+#define KV_TOKENIZER_LIST                "tokenizer.ggml.tokens"
+#define KV_TOKENIZER_TOKEN_TYPE          "tokenizer.ggml.token_type"
+#define KV_TOKENIZER_SCORES              "tokenizer.ggml.scores"
+#define KV_TOKENIZER_BOS_ID              "tokenizer.ggml.bos_token_id"
+#define KV_TOKENIZER_EOS_ID              "tokenizer.ggml.eos_token_id"
+#define KV_TOKENIZER_UNK_ID              "tokenizer.ggml.unknown_token_id"
+#define KV_TOKENIZER_SEP_ID              "tokenizer.ggml.seperator_token_id"
+#define KV_TOKENIZER_PAD_ID              "tokenizer.ggml.padding_token_id"
+#define KV_TOKENIZER_HF_JSON             "tokenizer.huggingface.json"
+
+#define KV_CONTEXT_LENGTH                "llama.context_length"
+#define KV_EMBEDDING_LENGTH              "llama.embedding_length"
+#define KV_BLOCK_COUNT                   "llama.block_count"
+#define KV_FEED_FORWARD_LENGTH           "llama.feed_forward_length"
+#define KV_ATTENTION_HEAD_COUNT          "llama.attention.head_count"
+#define KV_ATTENTION_HEAD_COUNT_KV       "llama.attention.head_count_kv"
+#define KV_ATTENTION_LAYERNORM_RMS_EPS   "llama.attention.layer_norm_rms_epsilon"
+#define KV_ROPE_DIMENSION_COUNT          "llama.rope.dimension_count"
+
+#define TN_TOKEN_EMBD  "token_embd.weight"
+#define TN_OUTPUT_NORM "output_norm.weight"
+#define TN_OUTPUT      "output.weight"
+#define TN_ATTN_NORM   "blk.%d.attn_norm.weight"
+#define TN_ATTN_Q      "blk.%d.attn_q.weight"
+#define TN_ATTN_K      "blk.%d.attn_k.weight"
+#define TN_ATTN_V      "blk.%d.attn_v.weight"
+#define TN_ATTN_OUTPUT "blk.%d.attn_output.weight"
+#define TN_FFN_NORM    "blk.%d.ffn_norm.weight"
+#define TN_FFN_GATE    "blk.%d.ffn_gate.weight"
+#define TN_FFN_DOWN    "blk.%d.ffn_down.weight"
+#define TN_FFN_UP      "blk.%d.ffn_up.weight"
+
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
@@ -20,6 +59,11 @@
 #define LLAMA_FILE_MAGIC_GGJT        0x67676a74u // 'ggjt'
 #define LLAMA_FILE_VERSION_GGJT_V3   3
 
+#define TOKENIZER_NAME "llama"
+#define UNKNOWN_TOKEN_ID 0
+#define BOS_TOKEN_ID 1
+#define EOS_TOKEN_ID 2
+
 //////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
 typedef struct {
     int dim; // transformer dimension
@@ -183,6 +227,7 @@ struct my_llama_hparams {
     uint32_t n_vocab = 32000;
     uint32_t n_ctx   = 512;   // this is provided as user input?
     uint32_t n_embd  = 4096;
+    uint32_t n_ff    = 11008;
     uint32_t n_mult  = 4;
     uint32_t n_head  = 32;
     uint32_t n_layer = 32;
@@ -214,6 +259,8 @@ struct my_llama_layer {
 struct my_llama_model {
     struct ggml_context * ctx = NULL;
 
+    std::string name;
+
     my_llama_hparams hparams;
 
     struct ggml_tensor * tok_embeddings;
@@ -276,18 +323,13 @@ struct train_params {
     int mem_compute1_gb;
 };
 
-uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
-    const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
-    return n_ff;
-}
-
 void print_params(struct my_llama_hparams * params) {
     printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
     printf("%s: n_ctx:   %d\n", __func__, params->n_ctx);
     printf("%s: n_embd:  %d\n", __func__, params->n_embd);
     printf("%s: n_mult:  %d\n", __func__, params->n_mult);
     printf("%s: n_head:  %d\n", __func__, params->n_head);
-    printf("%s: n_ff:    %d\n", __func__, get_n_ff(params));
+    printf("%s: n_ff:    %d\n", __func__, params->n_ff);
     printf("%s: n_layer: %d\n", __func__, params->n_layer);
     printf("%s: n_rot:   %d\n", __func__, params->n_rot);
 }
@@ -299,7 +341,7 @@ void init_model(struct my_llama_model * model) {
     const uint32_t n_layer = hparams.n_layer;
     const uint32_t n_vocab = hparams.n_vocab;
 
-    const uint32_t n_ff = get_n_ff(&hparams);
+    const uint32_t n_ff = hparams.n_ff;
     struct ggml_context * ctx = model->ctx;
 
     model->train_its = 0;
@@ -481,21 +523,6 @@ struct llama_file {
         return std::string(chars.data(), len);
     }
 
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            throw std::runtime_error(format("write error: %s", strerror(errno)));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
     ~llama_file() {
         if (fp) {
             std::fclose(fp);
@@ -503,30 +530,6 @@ struct llama_file {
     }
 };
 
-void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
-    if (tensor == NULL) {
-        file->write_u32(0);
-        file->write_u32(0);
-        file->write_u32(GGML_TYPE_F32);
-        file->seek((0-file->tell()) & 31, SEEK_CUR);
-        return;
-    }
-    const char * name = ggml_get_name(tensor);
-    uint32_t name_len = strlen(name);
-    uint32_t nd = tensor->n_dims;
-    uint32_t ne[4] = { (uint32_t)tensor->ne[0],
-                       (uint32_t)tensor->ne[1],
-                       (uint32_t)tensor->ne[2],
-                       (uint32_t)tensor->ne[3] };
-    file->write_u32(nd);
-    file->write_u32(name_len);
-    file->write_u32(tensor->type);
-    file->write_raw(ne, sizeof(ne[0]) * nd);
-    file->write_raw(name, name_len);
-    file->seek((0-file->tell()) & 31, SEEK_CUR);
-    file->write_raw(tensor->data, ggml_nbytes(tensor));
-}
-
 bool is_ggml_file(const char *filename) {
     llama_file file(filename, "rb");
     if (file.size < 4) {
@@ -536,48 +539,96 @@ bool is_ggml_file(const char *filename) {
     return magic == GGUF_MAGIC;
 }
 
+static std::string llama_escape_whitespaces(const std::string& text) {
+    std::ostringstream out;
+    for (char c : text) {
+        if (c == ' ') out << "\xe2\x96\x81";
+        else out << c;
+    }
+    return out.str();
+}
+
 void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
-#pragma message("TODO: implement reading vocabulary using gguf")
-//    // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary
-//    if (is_ggml_file(filename)) {
-//
-//        struct llama_context_params llama_params = llama_context_default_params();
-//        llama_params.vocab_only = true;
-//
-//        struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
-//        struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
-//
-//        const int n_vocab = llama_n_vocab(lctx);
-//        vocab->id_to_token.resize(n_vocab);
-//        for (int i=0; i<n_vocab; ++i) {
-//            vocab->id_to_token[i].text  = llama_token_get_text(lctx, i);
-//            vocab->id_to_token[i].score = llama_token_get_score(lctx, i);
-//            vocab->id_to_token[i].type  = llama_token_get_type(lctx, i);
-//            vocab->token_to_id.emplace(vocab->id_to_token[i].text, i);
-//        }
-//        llama_free(lctx);
-//        llama_free_model(lmodel);
-//    } else
-    { // assume llama2.c vocabulary
-        printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename);
+    if (is_ggml_file(filename)) {
+        struct ggml_context * ctx_data = NULL;
+
+        struct gguf_init_params params = {
+            /*.no_alloc = */ false,
+            /*.ctx      = */ &ctx_data,
+        };
+
+        struct gguf_context * ctx = gguf_init_from_file(filename, params);
+        GGML_ASSERT(ctx != NULL);
+
+        const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL);
+        GGML_ASSERT(model_idx >= 0);
+        std::string tokenizer_name = gguf_get_val_str(ctx, model_idx);
+        GGML_ASSERT(tokenizer_name == TOKENIZER_NAME);
+
+        const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);
+        GGML_ASSERT(token_idx >= 0);
+
+        const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
+        GGML_ASSERT(score_idx >= 0);
+        const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+
+        const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);
+        GGML_ASSERT(toktype_idx >= 0);
+        const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+
+        const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
+
+        vocab->id_to_token.resize(n_vocab);
+
+        for (uint32_t i = 0; i < n_vocab; i++) {
+            std::string word = gguf_get_arr_str(ctx, token_idx, i);
+
+            vocab->token_to_id[word] = i;
+
+            auto & token_data = vocab->id_to_token[i];
+            token_data.text  = std::move(word);
+            token_data.score = scores[i];
+            token_data.type  = (llama_token_type) toktypes[i];
+        }
+        ggml_free(ctx_data);
+        gguf_free(ctx);
+    } else {
+        // assume llama2.c vocabulary
+        printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
         llama_file file(filename, "rb");
         const int  n_vocab = config->vocab_size;
         /* uint32_t max_token_length =  */ file.read_u32(); // unused
         vocab->id_to_token.resize(n_vocab);
-        for (int i=0; i<n_vocab; ++i) {
+        for (llama_vocab::id id=0; id<n_vocab; ++id) {
             float_t score = file.read_f32();
             uint32_t len = file.read_u32();
             std::string text = file.read_string(len);
-            // Special-case handling of <0xXX> single byte tokens.
-            char byte_val;
-            if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
-                char cstr[2] = { byte_val, 0 };
-                text = cstr;
+
+            unsigned char byte_val;
+            llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
+            if (id == UNKNOWN_TOKEN_ID) {
+                text = "<unk>";
+                type = LLAMA_TOKEN_TYPE_UNKNOWN;
+            } else if (id == BOS_TOKEN_ID) {
+                text = "<s>";
+                type = LLAMA_TOKEN_TYPE_CONTROL;
+            } else if (id == EOS_TOKEN_ID) {
+                text = "</s>";
+                type = LLAMA_TOKEN_TYPE_CONTROL;
+            } else if (text.empty()) {
+                type = LLAMA_TOKEN_TYPE_CONTROL;
+            } else if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
+                // Text of byte tokens is already in the expected format.
+                type = LLAMA_TOKEN_TYPE_BYTE;
+            } else {
+                type = LLAMA_TOKEN_TYPE_NORMAL;
             }
-            vocab->id_to_token[i].text = text;
-            vocab->id_to_token[i].score = score;
-            vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED;
-            vocab->token_to_id.emplace(text, i);
+            text = llama_escape_whitespaces(text);
+
+            vocab->id_to_token[id].text = text;
+            vocab->id_to_token[id].score = score;
+            vocab->id_to_token[id].type = type;
+            vocab->token_to_id.emplace(text, id);
         }
     }
 }
@@ -619,33 +670,6 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * kar
 }
 
 void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) {
-    struct llama_file file(filename, "wb");
-    if (file.fp == NULL) {
-        return;
-    }
-
-#pragma message("TODO: implement file saving using gguf")
-    // write_magic
-    file.write_u32(LLAMA_FILE_MAGIC_GGJT);   // magic
-    file.write_u32(LLAMA_FILE_VERSION_GGJT_V3); // version
-    // write_hparams
-    file.write_u32(model->hparams.n_vocab);
-    file.write_u32(model->hparams.n_embd);
-    file.write_u32(model->hparams.n_mult);
-    file.write_u32(model->hparams.n_head);
-    file.write_u32(model->hparams.n_layer);
-    file.write_u32(model->hparams.n_rot);
-    file.write_u32(LLAMA_FTYPE_ALL_F32);
-
-    // write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
-    uint32_t n_vocab = model->hparams.n_vocab;
-    for (uint32_t i = 0; i < n_vocab; i++) {
-        const auto & token_data = vocab->id_to_token.at(i);
-        file.write_u32((uint32_t) token_data.text.size());
-        file.write_raw(token_data.text.data(), token_data.text.size());
-        file.write_raw(&token_data.score, sizeof(token_data.score));
-    }
-
     // stuff AK weights into GG weights one by one.
     // w->token_embedding_table -> model->tok_embeddings
     // float*                   -> struct ggml_tensor
@@ -658,8 +682,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
     // for rms-att-weight
     int row_length = model->hparams.n_embd;
     const auto & hparams = model->hparams;
-    //int n_ff = model->hparams.n_embd;
-    int n_ff = get_n_ff(&hparams);
+    int n_ff = model->hparams.n_ff;
 
     for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
         auto & layer = model->layers[i];
@@ -677,28 +700,91 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
         stuff_karpathy_weights_into_gg(layer.w2            , &w->w2[i*n_ff*row_length]);
         stuff_karpathy_weights_into_gg(layer.w3            , &w->w3[i*row_length*n_ff]);
     }
+
+    struct gguf_context * ctx = gguf_init_empty();
+
+    std::vector<const char*> tokens;
+    std::vector<float> scores;
+    std::vector<llama_token_type> token_types;
+    for (const llama_vocab::token_data & token_data : vocab->id_to_token) {
+        tokens.push_back(token_data.text.c_str());
+        scores.push_back(token_data.score);
+        token_types.push_back(token_data.type);
+    }
+    gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size());
+    gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size());
+    gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size());
+
+    gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME);
+
+    gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, "llama");
+    gguf_set_val_str(ctx, KV_GENERAL_NAME, "llama");
+
+    // special tokens
+    gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
+
+    gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
+    gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
+    gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
+    gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
+    // n_head_kv is optional, default to n_head
+    // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);
+    gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
+    gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
+    gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
+
     // write tensors
-    write_tensor(&file, model->tok_embeddings);
-    write_tensor(&file, model->norm);
-    write_tensor(&file, model->output); // ?
+    ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD);
+    gguf_add_tensor(ctx, model->tok_embeddings);
+
+    ggml_set_name(model->norm, TN_OUTPUT_NORM);
+    gguf_add_tensor(ctx, model->norm);
+
+    ggml_set_name(model->output, TN_OUTPUT);
+    gguf_add_tensor(ctx, model->output);
+
     for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
         auto & layer = model->layers[i];
 
-        write_tensor(&file, layer.attention_norm);
-        write_tensor(&file, layer.wq);
-        write_tensor(&file, layer.wk);
-        write_tensor(&file, layer.wv);
-        write_tensor(&file, layer.wo);
-        write_tensor(&file, layer.ffn_norm);
-        write_tensor(&file, layer.w1);
-        write_tensor(&file, layer.w2);
-        write_tensor(&file, layer.w3);
+        ggml_format_name(layer.wq, TN_ATTN_Q, i);
+        gguf_add_tensor(ctx, layer.wq);
+
+        ggml_format_name(layer.wk, TN_ATTN_K, i);
+        gguf_add_tensor(ctx, layer.wk);
+
+        ggml_format_name(layer.wv, TN_ATTN_V, i);
+        gguf_add_tensor(ctx, layer.wv);
+
+        ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i);
+        gguf_add_tensor(ctx, layer.wo);
+
+        ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i);
+        gguf_add_tensor(ctx, layer.attention_norm);
+
+        ggml_format_name(layer.w1, TN_FFN_GATE, i);
+        gguf_add_tensor(ctx, layer.w1);
+
+        ggml_format_name(layer.w2, TN_FFN_DOWN, i);
+        gguf_add_tensor(ctx, layer.w2);
+
+        ggml_format_name(layer.w3, TN_FFN_UP, i);
+        gguf_add_tensor(ctx, layer.w3);
+
+        ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i);
+        gguf_add_tensor(ctx, layer.ffn_norm);
     }
+
+    gguf_write_to_file(ctx, filename, false);
+    gguf_free(ctx);
 }
 
 struct train_params get_default_train_params() {
     struct train_params params;
-    params.fn_vocab_model    = "tokenizer.bin";
+    params.fn_vocab_model    = "models/7B/ggml-model-f16.gguf";
     params.fn_llama2c_output_model = "ak_llama_model.bin";
     params.fn_train_data     = "shakespeare.txt";
     params.fn_checkpoint_in  = "checkpoint.bin";
@@ -751,7 +837,7 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params)
     fprintf(stderr, "\n");
     fprintf(stderr, "options:\n");
     fprintf(stderr, "  -h, --help                       show this help message and exit\n");
-    fprintf(stderr, "  --copy-vocab-from-model FNAME    llama2.c vocabulary or ggmlv3 model path from which to copy vocab (default '%s')\n", params->fn_vocab_model);
+    fprintf(stderr, "  --copy-vocab-from-model FNAME    path of gguf llama model or llama2.c vocabulary from which to copy vocab (default '%s')\n", params->fn_vocab_model);
     fprintf(stderr, "  --llama2c-model FNAME            [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
     fprintf(stderr, "  --llama2c-output-model FNAME     model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
     fprintf(stderr, "\n");
@@ -812,6 +898,14 @@ bool params_parse(int argc, char ** argv, struct train_params * params) {
     return true;
 }
 
+std::string basename(const std::string &path) {
+    size_t pos = path.find_last_of("/");
+    if (pos == std::string::npos) {
+        return path;
+    }
+    return path.substr(pos + 1);
+}
+
 int main(int argc, char ** argv) {
     struct train_params params = get_default_train_params();
     if (!params_parse(argc, argv, &params)) {
@@ -840,6 +934,7 @@ int main(int argc, char ** argv) {
     model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
     model.hparams.n_ctx   = params.n_ctx;
     model.hparams.n_embd  = config.dim; //params.n_embd;
+    model.hparams.n_ff    = config.hidden_dim;
     model.hparams.n_mult  = 32;//params.n_mult;
     model.hparams.n_head  = config.n_heads; //params.n_head;
     model.hparams.n_layer = config.n_layers; //params.n_layer;
@@ -853,6 +948,7 @@ int main(int argc, char ** argv) {
     model.ctx = ggml_init(lcparams);
 
     init_model(&model);
+    model.name = basename(params.fn_llama2c_model);
     save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
 
     printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);

From c10704d01e21e3dbe4d6ca1026ebff85349dd239 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 18:55:41 +0300
Subject: [PATCH 40/53] llama : fix MPI threads (close #2827)

---
 llama.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/llama.cpp b/llama.cpp
index 0bb8fcd6e..72d2d1de0 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -2845,7 +2845,6 @@ static bool llama_eval_internal(
 
     GGML_ASSERT(n_tokens > 0);
     GGML_ASSERT(n_past >= 0);
-    GGML_ASSERT(n_threads > 0);
     // TODO: keep the values of n_batch and n_ctx
     // GGML_ASSERT(n_tokens <= n_batch);
     // GGML_ASSERT(n_past + n_tokens <= n_ctx);
@@ -2856,6 +2855,8 @@ static bool llama_eval_internal(
     ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
 #endif
 
+    GGML_ASSERT(n_threads > 0);
+
     const int N = n_tokens;
 
     const auto & model   = lctx.model;

From 103cfafc774f6feb3172b5d4d39681c965b17eba Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 27 Aug 2023 21:50:22 +0300
Subject: [PATCH 41/53] gguf : fix strings to not be null-terminated (#2839)

* gguf : fix strings to not be null-terminated

ggml-ci

* gguf : fix gguf_add_tensor name
---
 ggml.c | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/ggml.c b/ggml.c
index 855d519bf..767c19ae2 100644
--- a/ggml.c
+++ b/ggml.c
@@ -19524,8 +19524,8 @@ static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset)
     bool ok = true;
 
     uint32_t n = 0;
-    ok = ok && gguf_fread_el(file, &n,       sizeof(n),   offset); p->data = calloc(n + 1, 1); p->n = n;
-    ok = ok && gguf_fread_el(file,  p->data, p->n,         offset);
+    ok = ok && gguf_fread_el(file, &n,       sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n;
+    ok = ok && gguf_fread_el(file,  p->data, p->n,      offset);
 
     return ok;
 }
@@ -20071,7 +20071,7 @@ static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) {
     const int n_kv = gguf_get_n_kv(ctx);
 
     ctx->kv = realloc(ctx->kv, (n_kv + 1) * sizeof(struct gguf_kv));
-    ctx->kv[n_kv].key.n    = strlen(key) + 1;
+    ctx->kv[n_kv].key.n    = strlen(key);
     ctx->kv[n_kv].key.data = strdup(key);
     ctx->header.n_kv++;
 
@@ -20159,7 +20159,7 @@ void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char *
     const int idx = gguf_get_or_add_key(ctx, key);
 
     ctx->kv[idx].type           = GGUF_TYPE_STRING;
-    ctx->kv[idx].value.str.n    = strlen(val) + 1;
+    ctx->kv[idx].value.str.n    = strlen(val);
     ctx->kv[idx].value.str.data = strdup(val);
 }
 
@@ -20182,7 +20182,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char **
     ctx->kv[idx].value.arr.data = malloc(n*sizeof(struct gguf_str));
     for (int i = 0; i < n; i++) {
         struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
-        str->n    = strlen(data[i]) + 1;
+        str->n    = strlen(data[i]);
         str->data = strdup(data[i]);
     }
 }
@@ -20229,7 +20229,7 @@ void gguf_add_tensor(
     const int idx = ctx->header.n_tensors;
     ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info));
 
-    ctx->infos[idx].name.n    = strlen(tensor->name) + 1;
+    ctx->infos[idx].name.n    = strlen(tensor->name);
     ctx->infos[idx].name.data = strdup(tensor->name);
 
     for (int i = 0; i < GGML_MAX_DIMS; ++i) {

From 3e8ff47af620a31e0810c58a41e4b089145982ef Mon Sep 17 00:00:00 2001
From: JohnnyB <jboero@users.noreply.github.com>
Date: Mon, 28 Aug 2023 07:31:24 +0100
Subject: [PATCH 42/53] devops : added systemd units and set versioning to use
 date. (#2835)

* Corrections and systemd units

* Missing dependency clblast
---
 ....srpm.spec => llama-cpp-clblast.srpm.spec} | 44 ++++++++++++++----
 ...s.srpm.spec => llama-cpp-cublas.srpm.spec} | 26 ++++++++++-
 .devops/llama-cpp.srpm.spec                   | 45 +++++++++++++++----
 3 files changed, 96 insertions(+), 19 deletions(-)
 rename .devops/{lamma-cpp-clblast.srpm.spec => llama-cpp-clblast.srpm.spec} (56%)
 rename .devops/{lamma-cpp-cublas.srpm.spec => llama-cpp-cublas.srpm.spec} (71%)

diff --git a/.devops/lamma-cpp-clblast.srpm.spec b/.devops/llama-cpp-clblast.srpm.spec
similarity index 56%
rename from .devops/lamma-cpp-clblast.srpm.spec
rename to .devops/llama-cpp-clblast.srpm.spec
index 739c68281..076f29695 100644
--- a/.devops/lamma-cpp-clblast.srpm.spec
+++ b/.devops/llama-cpp-clblast.srpm.spec
@@ -13,12 +13,13 @@
 #    It is up to the user to install the correct vendor-specific support.
 
 Name:           llama.cpp-clblast
-Version:        master
+Version:        %( date "+%%Y%%m%%d" )
 Release:        1%{?dist}
-Summary:        OpenCL Inference of LLaMA model in pure C/C++
+Summary:        OpenCL Inference of LLaMA model in C/C++
 License:        MIT
 Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
-BuildRequires:  coreutils make gcc-c++ git mesa-libOpenCL-devel
+BuildRequires:  coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel
+Requires:       clblast
 URL:            https://github.com/ggerganov/llama.cpp
 
 %define debug_package %{nil}
@@ -35,18 +36,43 @@ make -j LLAMA_CLBLAST=1
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p main %{buildroot}%{_bindir}/llamacppclblast
-cp -p server %{buildroot}%{_bindir}/llamacppclblastserver
-cp -p simple %{buildroot}%{_bindir}/llamacppclblastsimple
+cp -p main %{buildroot}%{_bindir}/llamaclblast
+cp -p server %{buildroot}%{_bindir}/llamaclblastserver
+cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple
+
+mkdir -p %{buildroot}/usr/lib/systemd/system
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamaclblast.service
+[Unit]
+Description=Llama.cpp server, CPU only (no GPU support in this build).
+After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
+
+[Service]
+Type=simple
+EnvironmentFile=/etc/sysconfig/llama
+ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS
+ExecReload=/bin/kill -s HUP $MAINPID
+Restart=never
+
+[Install]
+WantedBy=default.target
+EOF
+
+mkdir -p %{buildroot}/etc/sysconfig
+%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
+LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
+EOF
 
 %clean
 rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llamacppclblast
-%{_bindir}/llamacppclblastserver
-%{_bindir}/llamacppclblastsimple
+%{_bindir}/llamaclblast
+%{_bindir}/llamaclblastserver
+%{_bindir}/llamaclblastsimple
+/usr/lib/systemd/system/llamaclblast.service
+%config /etc/sysconfig/llama
+
 
 %pre
 
diff --git a/.devops/lamma-cpp-cublas.srpm.spec b/.devops/llama-cpp-cublas.srpm.spec
similarity index 71%
rename from .devops/lamma-cpp-cublas.srpm.spec
rename to .devops/llama-cpp-cublas.srpm.spec
index 75d32fbe7..f847ebb1e 100644
--- a/.devops/lamma-cpp-cublas.srpm.spec
+++ b/.devops/llama-cpp-cublas.srpm.spec
@@ -13,7 +13,7 @@
 #    It is up to the user to install the correct vendor-specific support.
 
 Name:           llama.cpp-cublas
-Version:        master
+Version:        %( date "+%%Y%%m%%d" )
 Release:        1%{?dist}
 Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
 License:        MIT
@@ -40,6 +40,28 @@ cp -p main %{buildroot}%{_bindir}/llamacppcublas
 cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
 cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple
 
+mkdir -p %{buildroot}/usr/lib/systemd/system
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacublas.service
+[Unit]
+Description=Llama.cpp server, CPU only (no GPU support in this build).
+After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
+
+[Service]
+Type=simple
+EnvironmentFile=/etc/sysconfig/llama
+ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS
+ExecReload=/bin/kill -s HUP $MAINPID
+Restart=never
+
+[Install]
+WantedBy=default.target
+EOF
+
+mkdir -p %{buildroot}/etc/sysconfig
+%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
+LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
+EOF
+
 %clean
 rm -rf %{buildroot}
 rm -rf %{_builddir}/*
@@ -48,6 +70,8 @@ rm -rf %{_builddir}/*
 %{_bindir}/llamacppcublas
 %{_bindir}/llamacppcublasserver
 %{_bindir}/llamacppcublassimple
+/usr/lib/systemd/system/llamacublas.service
+%config /etc/sysconfig/llama
 
 %pre
 
diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec
index c65251a5a..446213d69 100644
--- a/.devops/llama-cpp.srpm.spec
+++ b/.devops/llama-cpp.srpm.spec
@@ -6,6 +6,7 @@
 # Notes for llama.cpp:
 # 1. Tags are currently based on hash - which will not sort asciibetically.
 #    We need to declare standard versioning if people want to sort latest releases.
+#    In the meantime, YYYYMMDD format will be used.
 # 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
 # 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
 #    Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
@@ -13,12 +14,13 @@
 #    It is up to the user to install the correct vendor-specific support.
 
 Name:           llama.cpp
-Version:        master
+Version:        %( date "+%%Y%%m%%d" )
 Release:        1%{?dist}
 Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
 License:        MIT
 Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
-BuildRequires:  coreutils make gcc-c++ git
+BuildRequires:  coreutils make gcc-c++ git libstdc++-devel
+Requires:       libstdc++
 URL:            https://github.com/ggerganov/llama.cpp
 
 %define debug_package %{nil}
@@ -26,27 +28,52 @@ URL:            https://github.com/ggerganov/llama.cpp
 
 %description
 CPU inference for Meta's Lllama2 models using default options.
+Models are not included in this package and must be downloaded separately.
 
 %prep
-%autosetup
+%setup -n llama.cpp-master
 
 %build
 make -j
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p main %{buildroot}%{_bindir}/llamacpp
-cp -p server %{buildroot}%{_bindir}/llamacppserver
-cp -p simple %{buildroot}%{_bindir}/llamacppsimple
+cp -p main %{buildroot}%{_bindir}/llama
+cp -p server %{buildroot}%{_bindir}/llamaserver
+cp -p simple %{buildroot}%{_bindir}/llamasimple
+
+mkdir -p %{buildroot}/usr/lib/systemd/system
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llama.service
+[Unit]
+Description=Llama.cpp server, CPU only (no GPU support in this build).
+After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
+
+[Service]
+Type=simple
+EnvironmentFile=/etc/sysconfig/llama
+ExecStart=/usr/bin/llamaserver $LLAMA_ARGS
+ExecReload=/bin/kill -s HUP $MAINPID
+Restart=never
+
+[Install]
+WantedBy=default.target
+EOF
+
+mkdir -p %{buildroot}/etc/sysconfig
+%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
+LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
+EOF
 
 %clean
 rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llamacpp
-%{_bindir}/llamacppserver
-%{_bindir}/llamacppsimple
+%{_bindir}/llama
+%{_bindir}/llamaserver
+%{_bindir}/llamasimple
+/usr/lib/systemd/system/llama.service
+%config /etc/sysconfig/llama
 
 %pre
 

From ebcee207b6058b7f695bb5c203ad87b1066a9790 Mon Sep 17 00:00:00 2001
From: Cebtenzzre <cebtenzzre@gmail.com>
Date: Mon, 28 Aug 2023 02:32:25 -0400
Subject: [PATCH 43/53] quantize : make output filename optional again (#2823)

* quantize : make output filename optional again

* quantize : fix path parsing on Windows

suggested by @slaren
---
 examples/quantize/quantize.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index d172f645a..df9a214fc 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -100,7 +100,7 @@ int main(int argc, char ** argv) {
         }
     }
 
-    if (argc - arg_idx < 3) {
+    if (argc - arg_idx < 2) {
         usage(argv[0]);
     }
 
@@ -114,7 +114,7 @@ int main(int argc, char ** argv) {
     std::string ftype_str;
     if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
         std::string fpath;
-        const size_t pos = fname_inp.find_last_of('/');
+        const size_t pos = fname_inp.find_last_of("/\\");
         if (pos != std::string::npos) {
             fpath = fname_inp.substr(0, pos + 1);
         }

From f55538c3ccba9b926846ef862fa830cea08c433e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 28 Aug 2023 10:59:08 +0300
Subject: [PATCH 44/53] metal : fix memory leak (#2762)

* metal : fix memory leak

* metal : fix encoders memory leak

* metal : clean up more memory resources

* metal : fix more leaks

* metal : reuse dispatch queue + autoreleasepool

* metal : reuse array for command buffers and encoders

* ggml : assert for odd number of blocks on ARM

15M tinyllama is an example
---
 ggml-metal.h |   1 +
 ggml-metal.m | 100 +++++++++++++++++++++++++++++++++++++++++----------
 ggml.c       |  11 +++---
 3 files changed, 88 insertions(+), 24 deletions(-)

diff --git a/ggml-metal.h b/ggml-metal.h
index 00202b787..fca28d37e 100644
--- a/ggml-metal.h
+++ b/ggml-metal.h
@@ -24,6 +24,7 @@
 
 // max memory buffers that can be mapped to the device
 #define GGML_METAL_MAX_BUFFERS 16
+#define GGML_METAL_MAX_COMMAND_BUFFERS 32
 
 struct ggml_tensor;
 struct ggml_cgraph;
diff --git a/ggml-metal.m b/ggml-metal.m
index 06eb3872e..ad2ee8cf5 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -33,12 +33,15 @@ struct ggml_metal_buffer {
 struct ggml_metal_context {
     int n_cb;
 
-    float * logits;
-
     id<MTLDevice>       device;
     id<MTLCommandQueue> queue;
     id<MTLLibrary>      library;
 
+    id<MTLCommandBuffer>         command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS];
+    id<MTLComputeCommandEncoder> command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS];
+
+    dispatch_queue_t d_queue;
+
     int n_buffers;
     struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
 
@@ -114,12 +117,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 
     struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
 
-    ctx->n_cb   = n_cb;
+    ctx->n_cb   = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
     ctx->device = MTLCreateSystemDefaultDevice();
     ctx->queue  = [ctx->device newCommandQueue];
     ctx->n_buffers = 0;
     ctx->concur_list_len = 0;
 
+    ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
 
 #if 0
     // compile from source string and show compile log
@@ -239,9 +243,67 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 
 void ggml_metal_free(struct ggml_metal_context * ctx) {
     fprintf(stderr, "%s: deallocating\n", __func__);
+#define GGML_METAL_DEL_KERNEL(name) \
+    [ctx->function_##name release]; \
+    [ctx->pipeline_##name release];
+
+    GGML_METAL_DEL_KERNEL(add);
+    GGML_METAL_DEL_KERNEL(add_row);
+    GGML_METAL_DEL_KERNEL(mul);
+    GGML_METAL_DEL_KERNEL(mul_row);
+    GGML_METAL_DEL_KERNEL(scale);
+    GGML_METAL_DEL_KERNEL(silu);
+    GGML_METAL_DEL_KERNEL(relu);
+    GGML_METAL_DEL_KERNEL(gelu);
+    GGML_METAL_DEL_KERNEL(soft_max);
+    GGML_METAL_DEL_KERNEL(diag_mask_inf);
+    GGML_METAL_DEL_KERNEL(get_rows_f16);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DEL_KERNEL(get_rows_q8_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q2_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q3_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q5_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q6_K);
+    GGML_METAL_DEL_KERNEL(rms_norm);
+    GGML_METAL_DEL_KERNEL(norm);
+    GGML_METAL_DEL_KERNEL(mul_mat_f16_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q2_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q3_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q4_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q5_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q6_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
+    GGML_METAL_DEL_KERNEL(rope);
+    GGML_METAL_DEL_KERNEL(alibi_f32);
+    GGML_METAL_DEL_KERNEL(cpy_f32_f16);
+    GGML_METAL_DEL_KERNEL(cpy_f32_f32);
+    GGML_METAL_DEL_KERNEL(cpy_f16_f16);
+
+#undef GGML_METAL_DEL_KERNEL
+
     for (int i = 0; i < ctx->n_buffers; ++i) {
         [ctx->buffers[i].metal release];
     }
+
+    [ctx->library release];
+    [ctx->queue release];
+    [ctx->device release];
+
+    dispatch_release(ctx->d_queue);
+
     free(ctx);
 }
 
@@ -261,7 +323,7 @@ void ggml_metal_host_free(void * data) {
 }
 
 void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
-    ctx->n_cb = n_cb;
+    ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
 }
 
 int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
@@ -507,6 +569,8 @@ void ggml_metal_graph_compute(
                struct ggml_cgraph * gf) {
     metal_printf("%s: evaluating graph\n", __func__);
 
+    @autoreleasepool {
+
     // if there is ctx->concur_list, dispatch concurrently
     // else fallback to serial dispatch
     MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
@@ -521,29 +585,25 @@ void ggml_metal_graph_compute(
 
     const int n_cb = ctx->n_cb;
 
-    NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
-
     for (int i = 0; i < n_cb; ++i) {
-        command_buffers[i] = [ctx->queue commandBuffer];
+        ctx->command_buffers[i] = [ctx->queue commandBuffer];
 
         // enqueue the command buffers in order to specify their execution order
-        [command_buffers[i] enqueue];
-    }
+        [ctx->command_buffers[i] enqueue];
 
-    // TODO: is this the best way to start threads?
-    dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
+        ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc];
+    }
 
     for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
         const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
 
-        dispatch_async(queue, ^{
+        dispatch_async(ctx->d_queue, ^{
             size_t offs_src0 = 0;
             size_t offs_src1 = 0;
             size_t offs_dst  = 0;
 
-            id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
-
-            id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
+            id<MTLCommandBuffer> command_buffer  = ctx->command_buffers[cb_idx];
+            id<MTLComputeCommandEncoder> encoder = ctx->command_encoders[cb_idx];
 
             const int node_start =                                      (cb_idx + 0) * n_nodes_per_cb;
             const int node_end   = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
@@ -1117,17 +1177,19 @@ void ggml_metal_graph_compute(
     }
 
     // wait for all threads to finish
-    dispatch_barrier_sync(queue, ^{});
-
-    [command_buffers[n_cb - 1] waitUntilCompleted];
+    dispatch_barrier_sync(ctx->d_queue, ^{});
 
     // check status of command buffers
     // needed to detect if the device ran out-of-memory for example (#1881)
     for (int i = 0; i < n_cb; i++) {
-        MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status];
+        [ctx->command_buffers[i] waitUntilCompleted];
+
+        MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
         if (status != MTLCommandBufferStatusCompleted) {
             fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status);
             GGML_ASSERT(false);
         }
     }
+
+    }
 }
diff --git a/ggml.c b/ggml.c
index 767c19ae2..54f426bc0 100644
--- a/ggml.c
+++ b/ggml.c
@@ -2436,7 +2436,6 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
     const int nb = n / qk;
 
     assert(n % qk == 0);
-    assert(nb % 2 == 0);
 
     const block_q4_0 * restrict x = vx;
     const block_q8_0 * restrict y = vy;
@@ -2445,6 +2444,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
     for (int i = 0; i < nb; i += 2) {
         const block_q4_0 * restrict x0 = &x[i + 0];
         const block_q4_0 * restrict x1 = &x[i + 1];
@@ -2623,6 +2623,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
     }
 
     // Main loop
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
     for (int i = 2; i < nb; i+=2) {
         _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
         _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
@@ -2706,7 +2707,6 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
     const int nb = n / qk;
 
     assert(n % qk == 0);
-    assert(nb % 2 == 0);
 
     const block_q4_1 * restrict x = vx;
     const block_q8_1 * restrict y = vy;
@@ -2718,6 +2718,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
 
     float summs = 0;
 
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
     for (int i = 0; i < nb; i += 2) {
         const block_q4_1 * restrict x0 = &x[i + 0];
         const block_q4_1 * restrict x1 = &x[i + 1];
@@ -2832,7 +2833,6 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
     const int nb = n / qk;
 
     assert(n % qk == 0);
-    assert(nb % 2 == 0);
     assert(qk == QK5_0);
 
     const block_q5_0 * restrict x = vx;
@@ -2848,6 +2848,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void *
     uint64_t tmp0[4];
     uint64_t tmp1[4];
 
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
     for (int i = 0; i < nb; i += 2) {
         const block_q5_0 * restrict x0 = &x[i];
         const block_q5_0 * restrict x1 = &x[i + 1];
@@ -3072,7 +3073,6 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
     const int nb = n / qk;
 
     assert(n % qk == 0);
-    assert(nb % 2 == 0);
     assert(qk == QK5_1);
 
     const block_q5_1 * restrict x = vx;
@@ -3091,6 +3091,7 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
     uint64_t tmp0[4];
     uint64_t tmp1[4];
 
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
     for (int i = 0; i < nb; i += 2) {
         const block_q5_1 * restrict x0 = &x[i];
         const block_q5_1 * restrict x1 = &x[i + 1];
@@ -3328,7 +3329,6 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
     const int nb = n / qk;
 
     assert(n % qk == 0);
-    assert(nb % 2 == 0);
 
     const block_q8_0 * restrict x = vx;
     const block_q8_0 * restrict y = vy;
@@ -3337,6 +3337,7 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void *
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
+    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
     for (int i = 0; i < nb; i += 2) {
         const block_q8_0 * restrict x0 = &x[i + 0];
         const block_q8_0 * restrict x1 = &x[i + 1];

From dd0dc366dab10e8df28d3924e7f313b5c695e908 Mon Sep 17 00:00:00 2001
From: igarnier <igarnier@protonmail.com>
Date: Mon, 28 Aug 2023 10:19:59 +0200
Subject: [PATCH 45/53] llama.h : add missing struct keyword for C compat in
 callback type (#2847)

---
 llama.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama.h b/llama.h
index b084fe23c..7bb681d61 100644
--- a/llama.h
+++ b/llama.h
@@ -496,7 +496,7 @@ extern "C" {
     // Type of pointer to the beam_search_callback function.
     // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
     // passed back to beam_search_callback. This avoids having to use global variables in the callback.
-    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, llama_beams_state);
+    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
 
     /// @details Deterministically returns entire sentence constructed by a beam search.
     /// @param ctx Pointer to the llama_context.

From 92b1bbd2ec43c82ec0530ba3c8758846c5790c75 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Mon, 28 Aug 2023 13:23:55 +0200
Subject: [PATCH 46/53] CUDA: fix RoPE asserts, block sizes (#2833)

---
 ggml-cuda.cu | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index d76a25dc2..5fd625630 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -4908,8 +4908,8 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
 
 static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
                           const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
-    GGML_ASSERT(nrows % 2 == 0); // GG: is this assert really needed? I don't see why
-    const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1);
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
     rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
@@ -4917,7 +4917,8 @@ static void rope_f32_cuda(const float * x, float * dst, const int ncols, const i
 
 static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
                           const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
-    const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1);
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
     rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);

From 35feac6560387cf0484371af3d9b12bff678e0b9 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 28 Aug 2023 14:24:53 +0300
Subject: [PATCH 47/53] ggml : sync (mem align to header + conv_transpose_2d
 fixes + ggml_alloc) (#2852)

* ggml : sync (mem align to header + conv_transpose_2d fixes)

ggml-ci

* ggml-alloc : minor fix

* ggml-alloc : sync more fixes
---
 ggml-alloc.c |  6 +++---
 ggml.c       | 22 ++++++++--------------
 ggml.h       | 18 +++++++++++++-----
 3 files changed, 24 insertions(+), 22 deletions(-)

diff --git a/ggml-alloc.c b/ggml-alloc.c
index 1ef011654..140e9a2a7 100644
--- a/ggml-alloc.c
+++ b/ggml-alloc.c
@@ -268,7 +268,7 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
         /*.parse_seq     = */ {0},
         /*.parse_seq_len = */ 0,
 #ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ = {0},
+        /*.allocated_tensors = */ {0},
 #endif
     };
 
@@ -297,7 +297,7 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
         /*.parse_seq     = */ {0},
         /*.parse_seq_len = */ 0,
 #ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ = {0},
+        /*.allocated_tensors = */ {0},
 #endif
     };
 
@@ -556,7 +556,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
                                 struct ggml_tensor * view_src = get_view_source(parent);
                                 struct hash_node * view_src_hn = hash_get(ht, view_src);
                                 view_src_hn->n_views -= 1;
-                                AT_PRINTF("view_src %s\n", view_src->name);
+                                AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
                                 if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
                                     ggml_allocator_free_tensor(alloc, view_src);
                                 }
diff --git a/ggml.c b/ggml.c
index 54f426bc0..dadb30757 100644
--- a/ggml.c
+++ b/ggml.c
@@ -157,12 +157,6 @@ typedef void * thread_ret_t;
 //#define GGML_SOFT_MAX_ACCELERATE
 #endif
 
-#if UINTPTR_MAX == 0xFFFFFFFF
-    #define GGML_MEM_ALIGN 4
-#else
-    #define GGML_MEM_ALIGN 16
-#endif
-
 //
 // logging
 //
@@ -7098,11 +7092,13 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0(
     };
 
     struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    ggml_set_op_params_i32(result, 0, stride);
+
     result->op = GGML_OP_CONV_TRANSPOSE_2D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
     result->src[1] = b;
-    result->src[2] = ggml_new_i32(ctx, stride);
 
     return result;
 }
@@ -13498,7 +13494,6 @@ static void ggml_compute_forward_conv_transpose_2d(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
-        const struct ggml_tensor * opt0,
               struct ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
@@ -13558,7 +13553,7 @@ static void ggml_compute_forward_conv_transpose_2d(
         return;
     }
 
-    const int32_t stride = ((const int32_t*)(opt0->data))[0];
+    const int32_t stride = ggml_get_op_params_i32(dst, 0);
 
     // total patches in dst
     const int np = ne2;
@@ -13571,7 +13566,7 @@ static void ggml_compute_forward_conv_transpose_2d(
     const int ip1 = MIN(ip0 + dp, np);
 
     ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
-    ggml_fp16_t * const wdata_src = (ggml_fp16_t *) params->wdata + nk;
+    ggml_fp16_t * const wdata_src = wdata + nk;
 
     for (int i2 = ip0; i2 < ip1; i2++) { // Cout
         float * dst_data = (float *)((char *) dst->data + i2*nb2);
@@ -13583,9 +13578,8 @@ static void ggml_compute_forward_conv_transpose_2d(
                     for (int i00 = 0; i00 < ne00; i00++) {
                         float v = 0;
                         ggml_vec_dot_f16(ne03, &v,
-                                (ggml_fp16_t *) wdata_src + i1n,
-                                (ggml_fp16_t *) wdata_kernel + i01*ne00*ne03 + i00*ne03);
-
+                                wdata_src + i1n,
+                                wdata_kernel + i01*ne00*ne03 + i00*ne03);
                         dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
                     }
                 }
@@ -15732,7 +15726,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_CONV_TRANSPOSE_2D:
             {
-                ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor);
             } break;
         case GGML_OP_POOL_1D:
             {
diff --git a/ggml.h b/ggml.h
index 792ca6e42..4ef3d5253 100644
--- a/ggml.h
+++ b/ggml.h
@@ -130,13 +130,16 @@
 // The data of the tensor is accessed via the "data" pointer. For example:
 //
 //   {
-//       struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3);
+//       const int nx = 2;
+//       const int ny = 3;
 //
-//       // a[2, 1] = 1.0f;
-//       *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
+//       struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
 //
-//       // a[0, 2] = 2.0f;
-//       *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f;
+//       for (int y = 0; y < ny; y++) {
+//           for (int x = 0; x < nx; x++) {
+//               *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y;
+//           }
+//       }
 //
 //       ...
 //   }
@@ -211,6 +214,11 @@
 #define GGML_MAX_OP_PARAMS     32
 #define GGML_DEFAULT_N_THREADS 4
 
+#if UINTPTR_MAX == 0xFFFFFFFF
+    #define GGML_MEM_ALIGN 4
+#else
+    #define GGML_MEM_ALIGN 16
+#endif
 
 #define GGML_EXIT_SUCCESS 0
 #define GGML_EXIT_ABORTED 1

From 3af6b86301ddfb11bb68e91dfc030b611b0d8426 Mon Sep 17 00:00:00 2001
From: Ronny Brendel <ronnybrendel@gmail.com>
Date: Mon, 28 Aug 2023 14:51:08 +0200
Subject: [PATCH 48/53] ggml : tiny ggml_vec_dot_q4_K_q8_K AVX2 improvement
 (#2819)

---
 k_quants.c | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/k_quants.c b/k_quants.c
index 82bf81697..3a9b1dafd 100644
--- a/k_quants.c
+++ b/k_quants.c
@@ -2694,13 +2694,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
             const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
             __m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
             p16l = _mm256_madd_epi16(scale_l, p16l);
-            sumi = _mm256_add_epi32(sumi, p16l);
 
             const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
             __m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
             p16h = _mm256_madd_epi16(scale_h, p16h);
-            sumi = _mm256_add_epi32(sumi, p16h);
+            const __m256i sumj = _mm256_add_epi32(p16l, p16h);
 
+            sumi = _mm256_add_epi32(sumi, sumj);
         }
 
         __m256 vd = _mm256_set1_ps(d);

From be475f60af1a54e8de81466ccc907d080cf6df1a Mon Sep 17 00:00:00 2001
From: grahameth <96447521+grahameth@users.noreply.github.com>
Date: Mon, 28 Aug 2023 17:38:12 +0200
Subject: [PATCH 49/53] llama.cpp : fix wrong vsnprintf call in MS compiler
 (#2856)

Co-authored-by: grahameth <->
---
 llama.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 72d2d1de0..da8ff64d0 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -6257,10 +6257,6 @@ void llama_log_set(llama_log_callback log_callback, void * user_data) {
     g_state.log_callback_user_data = user_data;
 }
 
-#if defined(_MSC_VER) && !defined(vsnprintf)
-#define vsnprintf _vsnprintf
-#endif
-
 static void llama_log_internal_v(llama_log_level level, const char * format, va_list args) {
     va_list args_copy;
     va_copy(args_copy, args);

From 75fafcbcccc280a5b3883bc76d0a2dabf474d094 Mon Sep 17 00:00:00 2001
From: alonfaraj <alonfaraj@gmail.com>
Date: Mon, 28 Aug 2023 18:38:35 +0300
Subject: [PATCH 50/53] make : fix tests build (#2855)

* makefile:
- fix test name
- add missing tests build

* editorconfig : fixes

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 .gitignore |  5 ++++-
 Makefile   | 14 ++++++++++----
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/.gitignore b/.gitignore
index e5faab774..7a3f3fff4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -63,10 +63,13 @@ poetry.toml
 
 # Test binaries
 tests/test-grammar-parser
+tests/test-llama-grammar
 tests/test-double-float
 tests/test-grad0
 tests/test-opt
 tests/test-quantize-fns
 tests/test-quantize-perf
 tests/test-sampling
-tests/test-tokenizer-0
+tests/test-tokenizer-0-llama
+tests/test-tokenizer-0-falcon
+tests/test-tokenizer-1
diff --git a/Makefile b/Makefile
index a3400e491..e60821dd5 100644
--- a/Makefile
+++ b/Makefile
@@ -2,7 +2,7 @@
 BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf llama-bench
 
 # Binaries only useful for tests
-TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
+TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1
 
 default: $(BUILD_TARGETS)
 
@@ -442,10 +442,10 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
 vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
-tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o common.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
 
-tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
 
 tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS)
@@ -466,5 +466,11 @@ tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama
 tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0: tests/test-tokenizer-0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)

From 6b73ef120114beb5664ea94aab48d07ed248ee52 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Mon, 28 Aug 2023 17:59:39 +0200
Subject: [PATCH 51/53] YAML result logging + preset script (#2657)

---
 common/common.cpp                  | 331 +++++++++++++++++++++++++++--
 common/common.h                    |  18 ++
 examples/main/main.cpp             |  78 ++++++-
 examples/perplexity/perplexity.cpp | 141 +++++++++---
 examples/server/server.cpp         |   2 +-
 llama.cpp                          |  29 +++
 llama.h                            |   3 +
 run_with_preset.py                 | 140 ++++++++++++
 8 files changed, 700 insertions(+), 42 deletions(-)
 create mode 100755 run_with_preset.py

diff --git a/common/common.cpp b/common/common.cpp
index 0d91a6a35..4a0d43c13 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1,15 +1,20 @@
 #include "common.h"
+#include "build-info.h"
+#include "llama.h"
 
-#include <cassert>
-#include <iostream>
-#include <cstring>
-#include <fstream>
-#include <string>
-#include <iterator>
 #include <algorithm>
-#include <sstream>
-#include <unordered_set>
+#include <cassert>
+#include <cmath>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iterator>
+#include <iostream>
 #include <regex>
+#include <sstream>
+#include <string>
+#include <unordered_set>
+#include <vector>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -19,11 +24,14 @@
 #if defined(_WIN32)
 #define WIN32_LEAN_AND_MEAN
 #define NOMINMAX
+#include <codecvt>
+#include <locale>
 #include <windows.h>
 #include <fcntl.h>
 #include <io.h>
 #else
 #include <sys/ioctl.h>
+#include <sys/stat.h>
 #include <unistd.h>
 #endif
 
@@ -93,7 +101,6 @@ void process_escapes(std::string& input) {
 
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     bool invalid_param = false;
-    bool escape_prompt = false;
     std::string arg;
     gpt_params default_params;
     const std::string arg_prefix = "--";
@@ -125,8 +132,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.prompt = argv[i];
-        } else if (arg == "-e") {
-            escape_prompt = true;
+        } else if (arg == "-e" || arg == "--escape") {
+            params.escape = true;
         } else if (arg == "--prompt-cache") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -415,6 +422,16 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.antiprompt.push_back(argv[i]);
+        } else if (arg == "-ld" || arg == "--logdir") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.logdir = argv[i];
+
+            if (params.logdir.back() != DIRECTORY_SEPARATOR) {
+                params.logdir += DIRECTORY_SEPARATOR;
+            }
         } else if (arg == "--perplexity") {
             params.perplexity = true;
         } else if (arg == "--ppl-stride") {
@@ -520,7 +537,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         exit(1);
     }
 
-    if (escape_prompt) {
+    if (params.escape) {
         process_escapes(params.prompt);
         process_escapes(params.input_prefix);
         process_escapes(params.input_suffix);
@@ -546,7 +563,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
     fprintf(stdout, "  -p PROMPT, --prompt PROMPT\n");
     fprintf(stdout, "                        prompt to start generation with (default: empty)\n");
-    fprintf(stdout, "  -e                    process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
+    fprintf(stdout, "  -e, --escape          process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
     fprintf(stdout, "  --prompt-cache FNAME  file to cache prompt state for faster startup (default: none)\n");
     fprintf(stdout, "  --prompt-cache-all    if specified, saves user input and generations to cache as well.\n");
     fprintf(stdout, "                        not supported with --interactive or other interactive options\n");
@@ -627,6 +644,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
     fprintf(stdout, "  -m FNAME, --model FNAME\n");
     fprintf(stdout, "                        model path (default: %s)\n", params.model.c_str());
+    fprintf(stdout, "  -ld LOGDIR, --logdir LOGDIR\n");
+    fprintf(stdout, "                        path under which to save YAML logs (no logging if unset)\n");
     fprintf(stdout, "\n");
 }
 
@@ -779,3 +798,289 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
 
     return result;
 }
+
+// returns true if successful, false otherwise
+bool create_directory_with_parents(const std::string & path) {
+#ifdef _WIN32
+    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+    std::wstring wpath = converter.from_bytes(path);
+
+    // if the path already exists, check whether it's a directory
+    const DWORD attributes = GetFileAttributesW(wpath.c_str());
+    if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
+        return true;
+    }
+
+    size_t pos_slash = 0;
+
+    // process path from front to back, procedurally creating directories
+    while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
+        const std::wstring subpath = wpath.substr(0, pos_slash);
+        const wchar_t * test = subpath.c_str();
+
+        const bool success = CreateDirectoryW(test, NULL);
+        if (!success) {
+            const DWORD error = GetLastError();
+
+            // if the path already exists, ensure that it's a directory
+            if (error == ERROR_ALREADY_EXISTS) {
+                const DWORD attributes = GetFileAttributesW(subpath.c_str());
+                if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
+                    return false;
+                }
+            } else {
+                return false;
+            }
+        }
+
+        pos_slash += 1;
+    }
+
+    return true;
+#else
+    // if the path already exists, check whether it's a directory
+    struct stat info;
+    if (stat(path.c_str(), &info) == 0) {
+        return S_ISDIR(info.st_mode);
+    }
+
+    size_t pos_slash = 1; // skip leading slashes for directory creation
+
+    // process path from front to back, procedurally creating directories
+    while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
+        const std::string subpath = path.substr(0, pos_slash);
+        struct stat info;
+
+        // if the path already exists, ensure that it's a directory
+        if (stat(subpath.c_str(), &info) == 0) {
+            if (!S_ISDIR(info.st_mode)) {
+                return false;
+            }
+        } else {
+            // create parent directories
+            const int ret = mkdir(subpath.c_str(), 0755);
+            if (ret != 0) {
+                return false;
+            }
+        }
+
+        pos_slash += 1;
+    }
+
+    return true;
+#endif // _WIN32
+}
+
+void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data) {
+    if (data.empty()) {
+        fprintf(stream, "%s:\n", prop_name);
+        return;
+    }
+
+    fprintf(stream, "%s: [", prop_name);
+    for (size_t i = 0; i < data.size() - 1; ++i) {
+        fprintf(stream, "%e, ", data[i]);
+    }
+    fprintf(stream, "%e]\n", data.back());
+}
+
+void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data) {
+    if (data.empty()) {
+        fprintf(stream, "%s:\n", prop_name);
+        return;
+    }
+
+    fprintf(stream, "%s: [", prop_name);
+    for (size_t i = 0; i < data.size() - 1; ++i) {
+        fprintf(stream, "%d, ", data[i]);
+    }
+    fprintf(stream, "%d]\n", data.back());
+}
+
+void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) {
+    std::string data_str(data == NULL ? "" : data);
+
+    if (data_str.empty()) {
+        fprintf(stream, "%s:\n", prop_name);
+        return;
+    }
+
+    size_t pos_start = 0;
+    size_t pos_found = 0;
+
+    if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) {
+        data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
+        data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
+        data_str = "\"" + data_str + "\"";
+        fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
+        return;
+    }
+
+    if (data_str.find('\n') == std::string::npos) {
+        fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
+        return;
+    }
+
+    fprintf(stream, "%s: |\n", prop_name);
+    while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
+        fprintf(stream, "  %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
+        pos_start = pos_found + 1;
+    }
+}
+
+std::string get_sortable_timestamp() {
+    using clock = std::chrono::system_clock;
+
+    const clock::time_point current_time = clock::now();
+    const time_t as_time_t = clock::to_time_t(current_time);
+    char timestamp_no_ns[100];
+    std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
+
+    const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
+        current_time.time_since_epoch() % 1000000000).count();
+    char timestamp_ns[10];
+    snprintf(timestamp_ns, 11, "%09ld", ns);
+
+    return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
+}
+
+void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
+                               const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
+    fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
+    fprintf(stream, "build_number: %d\n", BUILD_NUMBER);
+    fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
+    fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
+    fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false");
+    fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
+    fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
+    fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
+    fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
+    fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
+    fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
+    fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
+    fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
+    fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
+    fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
+
+#ifdef NDEBUG
+    fprintf(stream, "debug: false\n");
+#else
+    fprintf(stream, "debug: true\n");
+#endif // NDEBUG
+
+    fprintf(stream, "model_desc: %s\n", model_desc);
+    fprintf(stream, "n_vocab: %d  # output size of the final layer, 32001 for some models\n", llama_n_vocab(lctx));
+
+#ifdef __OPTIMIZE__
+    fprintf(stream, "optimize: true\n");
+#else
+    fprintf(stream, "optimize: false\n");
+#endif // __OPTIMIZE__
+
+    fprintf(stream, "time: %s\n", timestamp.c_str());
+
+    fprintf(stream, "\n");
+    fprintf(stream, "###############\n");
+    fprintf(stream, "# User Inputs #\n");
+    fprintf(stream, "###############\n");
+    fprintf(stream, "\n");
+
+    fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
+    fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
+    dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str());
+    fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale);
+    fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
+    fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
+    fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
+    fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
+    fprintf(stream, "export: %s # default: false\n", params.export_cgraph ? "true" : "false");
+    fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
+    fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty);
+    dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
+    fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
+    fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
+    fprintf(stream, "hellaswag_tasks: %ld # default: 400\n", params.hellaswag_tasks);
+
+    const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx));
+    const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY;
+    fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
+
+    dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
+    fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
+    dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
+    fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
+    fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
+    fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
+    fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
+    fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
+
+    fprintf(stream, "logit_bias:\n");
+    for (std::pair<llama_token, float> lb : params.logit_bias) {
+        if (ignore_eos && lb.first == logit_bias_eos->first) {
+            continue;
+        }
+        fprintf(stream, "  %d: %f", lb.first, lb.second);
+    }
+
+    fprintf(stream, "lora: %s\n", params.lora_adapter.c_str());
+    fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
+    fprintf(stream, "low_vram: %s # default: false\n", params.low_vram ? "true" : "false");
+    fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
+    fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
+    fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat);
+    fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau);
+    fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
+    fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
+    fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
+    fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
+    fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
+    fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
+    fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
+    fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
+    fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
+    fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
+    fprintf(stream, "no_penalize_nl: %s # default: false\n", !params.penalize_nl ? "true" : "false");
+    fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false");
+    fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
+    fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
+    fprintf(stream, "presence_penalty: %f # default: 0.0\n", params.presence_penalty);
+    dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str());
+    fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
+    fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
+    fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
+    dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
+    fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
+    fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty);
+
+    fprintf(stream, "reverse_prompt:\n");
+    for (std::string ap : params.antiprompt) {
+        size_t pos = 0;
+        while ((pos = ap.find('\n', pos)) != std::string::npos) {
+            ap.replace(pos, 1, "\\n");
+            pos += 1;
+        }
+
+        fprintf(stream, "  - %s\n", ap.c_str());
+    }
+
+    fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
+    fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
+    fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
+    fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
+    fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
+
+    const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
+    dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
+
+    fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z);
+    fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
+    fprintf(stream, "top_k: %d # default: 40\n", params.top_k);
+    fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p);
+    fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
+    fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
+}
diff --git a/common/common.h b/common/common.h
index 97fda2be7..c15373144 100644
--- a/common/common.h
+++ b/common/common.h
@@ -11,6 +11,12 @@
 #include <unordered_map>
 #include <tuple>
 
+#ifdef _WIN32
+#define DIRECTORY_SEPARATOR '\\'
+#else
+#define DIRECTORY_SEPARATOR '/'
+#endif // _WIN32
+
 //
 // CLI argument parsing
 //
@@ -61,6 +67,7 @@ struct gpt_params {
     std::string input_suffix      = "";  // string to suffix user inputs with
     std::string grammar           = "";  // optional BNF-like grammar to constrain sampling
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
+    std::string logdir            = "";  // directory in which to save YAML log files
 
     std::string lora_adapter = "";  // lora adapter path
     std::string lora_base    = "";  // base model path for the lora adapter
@@ -82,6 +89,7 @@ struct gpt_params {
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
 
     bool embedding         = false; // get only sentence embedding
+    bool escape            = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
     bool interactive_first = false; // wait for user input immediately
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
@@ -144,3 +152,13 @@ std::string llama_detokenize_spm(
 std::string llama_detokenize_bpe(
                          llama_context * ctx,
         const std::vector<llama_token> & tokens);
+
+bool create_directory_with_parents(const std::string & path);
+void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
+void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
+void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
+std::string get_sortable_timestamp();
+
+void dump_non_result_info_yaml(
+    FILE * stream, const gpt_params & params, const llama_context * lctx,
+    const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 3ce57f436..89cc4f602 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -17,6 +17,7 @@
 #include <ctime>
 #include <fstream>
 #include <iostream>
+#include <sstream>
 #include <string>
 #include <vector>
 
@@ -36,9 +37,57 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static llama_context ** g_ctx;
+static llama_context           ** g_ctx;
+static llama_model             ** g_model;
+static gpt_params               * g_params;
+static std::vector<llama_token> * g_input_tokens;
+static std::ostringstream       * g_output_ss;
+static std::vector<llama_token> * g_output_tokens;
 static bool is_interacting = false;
 
+void write_logfile(
+    const llama_context * ctx, const gpt_params & params, const llama_model * model,
+    const std::vector<llama_token> input_tokens, const std::string output, const std::vector<llama_token> output_tokens) {
+
+    if (params.logdir.empty()) {
+        return;
+    }
+
+    const std::string timestamp = get_sortable_timestamp();
+
+    const bool success = create_directory_with_parents(params.logdir);
+    if (!success) {
+        fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
+                __func__, params.logdir.c_str());
+        return;
+    }
+
+    const std::string logfile_path = params.logdir + timestamp + ".yml";
+    FILE * logfile = fopen(logfile_path.c_str(), "w");
+
+    if (logfile == NULL) {
+        fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
+        return;
+    }
+
+    fprintf(logfile, "binary: main\n");
+    char model_desc[128];
+    llama_model_desc(model, model_desc, sizeof(model_desc));
+    dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
+
+    fprintf(logfile, "\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "# Generation Results #\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "\n");
+
+    dump_string_yaml_multiline(logfile, "output", output.c_str());
+    dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
+
+    llama_dump_timing_info_yaml(logfile, ctx);
+    fclose(logfile);
+}
+
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
 void sigint_handler(int signo) {
     if (signo == SIGINT) {
@@ -48,6 +97,7 @@ void sigint_handler(int signo) {
             console::cleanup();
             printf("\n");
             llama_print_timings(*g_ctx);
+            write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
             _exit(130);
         }
     }
@@ -56,6 +106,7 @@ void sigint_handler(int signo) {
 
 int main(int argc, char ** argv) {
     gpt_params params;
+    g_params = &params;
 
     if (gpt_params_parse(argc, argv, params) == false) {
         return 1;
@@ -116,6 +167,7 @@ int main(int argc, char ** argv) {
     llama_model * model;
     llama_context * ctx;
     llama_context * ctx_guidance = NULL;
+    g_model = &model;
     g_ctx = &ctx;
 
     // load the model and apply lora adapter, if any
@@ -397,6 +449,10 @@ int main(int argc, char ** argv) {
     int n_session_consumed = 0;
     int n_past_guidance    = 0;
 
+    std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
+    std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
+    std::ostringstream output_ss;     g_output_ss     = &output_ss;
+
     // the first thing we will do is to output the prompt, so set color accordingly
     console::set_display(console::prompt);
 
@@ -667,7 +723,15 @@ int main(int argc, char ** argv) {
         // display text
         if (input_echo) {
             for (auto id : embd) {
-                printf("%s", llama_token_to_piece(ctx, id).c_str());
+                const std::string token_str = llama_token_to_piece(ctx, id);
+                printf("%s", token_str.c_str());
+
+                if (embd.size() > 1) {
+                    input_tokens.push_back(id);
+                } else {
+                    output_tokens.push_back(id);
+                    output_ss << token_str;
+                }
             }
             fflush(stdout);
         }
@@ -761,6 +825,8 @@ int main(int argc, char ** argv) {
                         printf("%s", params.input_suffix.c_str());
                     }
 
+                    const size_t original_size = embd_inp.size();
+
                     // instruct mode: insert instruction prefix
                     if (params.instruct && !is_antiprompt) {
                         n_consumed = embd_inp.size();
@@ -775,6 +841,12 @@ int main(int argc, char ** argv) {
                         embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
                     }
 
+                    for (size_t i = original_size; i < embd_inp.size(); ++i) {
+                        const llama_token token = embd_inp[i];
+                        output_tokens.push_back(token);
+                        output_ss << llama_token_to_piece(ctx, token);
+                    }
+
                     n_remain -= line_inp.size();
                 }
 
@@ -817,6 +889,8 @@ int main(int argc, char ** argv) {
     }
 
     llama_print_timings(ctx);
+    write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
+
     if (ctx_guidance) { llama_free(ctx_guidance); }
     llama_free(ctx);
     llama_free_model(model);
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index ebafa0c29..aeb774c5f 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -3,16 +3,79 @@
 #include "build-info.h"
 
 #include <cmath>
+#include <cstdio>
+#include <cstring>
 #include <ctime>
 #include <sstream>
-#include <cstring>
 #include <thread>
 #include <mutex>
+#include <vector>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+struct results_perplexity {
+    std::vector<llama_token> tokens;
+    double                   ppl_value;
+    std::vector<float>       logits;
+    std::vector<float>       probs;
+};
+
+struct results_log_softmax {
+    double log_softmax;
+    float  logit;
+    float  prob;
+};
+
+void write_logfile(const llama_context * ctx, const gpt_params & params,
+                   const llama_model * model, const struct results_perplexity & results) {
+
+    if (params.logdir.empty()) {
+        return;
+    }
+
+    if (params.hellaswag) {
+        fprintf(stderr, "%s: warning: logging results is not implemented for HellaSwag. No files will be written.\n", __func__);
+        return;
+    }
+
+    const std::string timestamp = get_sortable_timestamp();
+
+    const bool success = create_directory_with_parents(params.logdir);
+    if (!success) {
+        fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
+                __func__, params.logdir.c_str());
+        return;
+    }
+
+    const std::string logfile_path = params.logdir + timestamp + ".yml";
+    FILE * logfile = fopen(logfile_path.c_str(), "w");
+
+    if (logfile == NULL) {
+        fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
+        return;
+    }
+
+    fprintf(logfile, "binary: main\n");
+    char model_desc[128];
+    llama_model_desc(model, model_desc, sizeof(model_desc));
+    dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc);
+
+    fprintf(logfile, "\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "# Perplexity Results #\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "\n");
+
+    dump_vector_float_yaml(logfile, "logits", results.logits);
+    fprintf(logfile, "ppl_value: %f\n", results.ppl_value);
+    dump_vector_float_yaml(logfile, "probs", results.probs);
+
+    llama_dump_timing_info_yaml(logfile, ctx);
+    fclose(logfile);
+}
+
 std::vector<float> softmax(const std::vector<float>& logits) {
     std::vector<float> probs(logits.size());
     float max_logit = logits[0];
@@ -29,20 +92,20 @@ std::vector<float> softmax(const std::vector<float>& logits) {
     return probs;
 }
 
-float log_softmax(int n_vocab, const float * logits, int tok) {
+results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
     float max_logit = logits[0];
     for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]);
     double sum_exp = 0.0;
     for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit);
-    return logits[tok] - max_logit - log(sum_exp);
+    return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
 }
 
-void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread>& workers,
-        double& nll, double& nll2) {
+void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
+        double & nll, double & nll2, float * logit_history, float * prob_history) {
 
     std::mutex mutex;
     int counter = 0;
-    auto compute = [&mutex, &counter, &nll, &nll2, n_vocab, logits, tokens, n_token] () {
+    auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
         double local_nll = 0, local_nll2 = 0;
         while (true) {
             std::unique_lock<std::mutex> lock(mutex);
@@ -52,34 +115,43 @@ void process_logits(int n_vocab, const float * logits, const int * tokens, int n
                 break;
             }
             lock.unlock();
-            double v = -log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
+            const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
+            const double v = -results.log_softmax;
             local_nll += v;
             local_nll2 += v*v;
+
+            logit_history[i] = results.logit;
+            prob_history[i]  = results.prob;
         }
     };
-    for (auto& w : workers) w = std::thread(compute);
+    for (auto & w : workers) w = std::thread(compute);
     compute();
-    for (auto& w : workers) w.join();
+    for (auto & w : workers) w.join();
 
 }
 
-void perplexity_v2(llama_context * ctx, const gpt_params & params) {
+results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
     // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
     // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
-    if (params.ppl_stride <= 0) {
-        fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
-        return;
-    }
-
     const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
     const bool add_bos = is_spm;
 
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
-    auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+    std::vector<float>       logit_history;
+    std::vector<float>       prob_history;
+
+    logit_history.resize(tokens.size());
+    prob_history.resize(tokens.size());
+
+    if (params.ppl_stride <= 0) {
+        fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
+        return {tokens, -1, logit_history, prob_history};
+    }
 
     const int calc_chunk = params.n_ctx;
 
@@ -88,7 +160,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
     if (int(tokens.size()) <= calc_chunk) {
         fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
                 tokens.size(), params.n_ctx, params.ppl_stride);
-        return;
+        return {tokens, -1, logit_history, prob_history};
     }
 
     const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1)  / params.ppl_stride;
@@ -120,7 +192,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
             //fprintf(stderr, "    Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
             if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
                 //fprintf(stderr, "%s : failed to eval\n", __func__);
-                return;
+                return {tokens, -1, logit_history, prob_history};
             }
 
             // save original token and restore it after eval
@@ -161,6 +233,8 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
                 logits.begin() + (j + 1) * n_vocab);
 
             const float prob = softmax(tok_logits)[tokens[start + j + 1]];
+            logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];
+            prob_history[start + j + 1]  = prob;
 
             nll += -std::log(prob);
             ++count;
@@ -174,12 +248,14 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
         fflush(stdout);
     }
     printf("\n");
+
+    return {tokens, std::exp(nll / count), logit_history, prob_history};
 }
 
-void perplexity(llama_context * ctx, const gpt_params & params) {
+results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
+
     if (params.ppl_stride > 0) {
-        perplexity_v2(ctx, params);
-        return;
+        return perplexity_v2(ctx, params);
     }
 
     // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
@@ -193,11 +269,17 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     auto tim1 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
-    auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
 
     auto tim2 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
 
+    std::vector<float> logit_history;
+    logit_history.resize(tokens.size());
+
+    std::vector<float> prob_history;
+    prob_history.resize(tokens.size());
+
     const int n_chunk_max = tokens.size() / params.n_ctx;
 
     const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
@@ -236,7 +318,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
 
             if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
-                return;
+                return {tokens, -1, logit_history, prob_history};
             }
 
             // restore the original token in case it was set to BOS
@@ -272,7 +354,8 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
         // last 256 tokens.  Then, we split the input up into context window size chunks to
         // process the entire prompt.
         const int first = std::min(512, params.n_ctx/2);
-        process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2);
+        process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first,
+                       workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
         count += params.n_ctx - first - 1;
 
         // perplexity is e^(average negative log-likelihood)
@@ -287,16 +370,19 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
         fflush(stdout);
     }
     printf("\n");
+
     nll2 /= count;
     nll /= count;
+    const double ppl = exp(nll);
     nll2 -= nll * nll;
     if (nll2 > 0) {
         nll2 = sqrt(nll2/(count-1));
-        double ppl = exp(nll);
         printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
     } else {
         printf("Unexpected negative standard deviation of log(prob)\n");
     }
+
+    return {tokens, ppl, logit_history, prob_history};
 }
 
 std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch,
@@ -604,13 +690,16 @@ int main(int argc, char ** argv) {
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
+    struct results_perplexity results;
     if (params.hellaswag) {
         hellaswag_score(ctx, params);
     } else {
-        perplexity(ctx, params);
+        results = perplexity(ctx, params);
     }
 
     llama_print_timings(ctx);
+    write_logfile(ctx, params, model, results);
+
     llama_free(ctx);
     llama_free_model(model);
 
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 89a3311f5..b485a5ead 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -719,7 +719,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stdout, "  -ts SPLIT --tensor-split SPLIT\n");
     fprintf(stdout, "                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
     fprintf(stdout, "  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n");
-    fprintf(stdout, "  -lv, --low-vram don't allocate VRAM scratch buffer\n");
+    fprintf(stdout, "  -lv, --low-vram       don't allocate VRAM scratch buffer\n");
     fprintf(stdout, "  -nommq, --no-mul-mat-q\n");
     fprintf(stdout, "                        use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
     fprintf(stdout, "                        Not recommended since this is both slower and uses more VRAM.\n");
diff --git a/llama.cpp b/llama.cpp
index da8ff64d0..11697ee65 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -6247,6 +6247,35 @@ const char * llama_print_system_info(void) {
     return s.c_str();
 }
 
+void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
+
+    fprintf(stream, "\n");
+    fprintf(stream, "###########\n");
+    fprintf(stream, "# Timings #\n");
+    fprintf(stream, "###########\n");
+    fprintf(stream, "\n");
+
+    fprintf(stream, "mst_eval: %.2f  # ms / token during generation\n",
+            1.0e-3 * ctx->t_eval_us / ctx->n_eval);
+    fprintf(stream, "mst_p_eval: %.2f  # ms / token during prompt processing\n",
+            1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
+    fprintf(stream, "mst_sample: %.2f  # ms / token during sampling\n",
+            1.0e-3 * ctx->t_sample_us / ctx->n_sample);
+    fprintf(stream, "n_eval: %d  # number of tokens generated (excluding the first one)\n", ctx->n_eval);
+    fprintf(stream, "n_p_eval: %d  # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
+    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->n_sample);
+    fprintf(stream, "t_eval_us: %ld  # total microseconds spent generating tokens\n", ctx->t_eval_us);
+    fprintf(stream, "t_load_us: %ld  # total microseconds spent loading the model\n", ctx->t_load_us);
+    fprintf(stream, "t_p_eval_us: %ld  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
+    fprintf(stream, "t_sample_us: %ld  # total microseconds spent sampling\n", ctx->t_sample_us);
+    fprintf(stream, "ts_eval: %.2f  # tokens / second during generation\n",
+            1.0e6 * ctx->n_eval / ctx->t_eval_us);
+    fprintf(stream, "ts_p_eval: %.2f  # tokens / second during prompt processing\n",
+            1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
+    fprintf(stream, "ts_sample: %.2f  # tokens / second during sampling\n",
+            1.0e6 * ctx->n_sample / ctx->t_sample_us);
+}
+
 // For internal test use
 const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
     return ctx->model.tensors_by_name;
diff --git a/llama.h b/llama.h
index 7bb681d61..b38d3be20 100644
--- a/llama.h
+++ b/llama.h
@@ -10,6 +10,7 @@
 #endif // GGML_USE_CUBLAS
 #include <stddef.h>
 #include <stdint.h>
+#include <stdio.h>
 #include <stdbool.h>
 
 #ifdef LLAMA_SHARED
@@ -520,6 +521,8 @@ extern "C" {
     // If this is not called, or NULL is supplied, everything is output on stderr.
     LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
 
+    LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/run_with_preset.py b/run_with_preset.py
new file mode 100755
index 000000000..8f90f52a9
--- /dev/null
+++ b/run_with_preset.py
@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import subprocess
+import sys
+
+import yaml
+
+CLI_ARGS_MAIN_PERPLEXITY = [
+    "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape",
+    "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag",
+    "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct",
+    "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base",
+    "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock",
+    "model", "mtest", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q",
+    "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
+    "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n",
+    "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
+    "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
+    "verbose-prompt"
+]
+
+CLI_ARGS_LLAMA_BENCH = [
+    "batch-size", "memory-f32", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers",
+    "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose"
+]
+
+CLI_ARGS_SERVER = [
+    "alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base",
+    "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q",
+    "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split",
+    "threads", "verbose"
+]
+
+description = """Run llama.cpp binaries with presets from YAML file(s).
+To specify which binary should be run, specify the "binary" property (main, perplexity, llama-bench, and server are supported).
+To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument.
+
+Formatting considerations:
+- The YAML property names are the same as the CLI argument names of the corresponding binary.
+- Properties must use the long name of their corresponding llama.cpp CLI arguments.
+- Like the llama.cpp binaries the property names do not differentiate between hyphens and underscores.
+- Flags must be defined as "<PROPERTY_NAME>: true" to be effective.
+- To define the logit_bias property, the expected format is "<TOKEN_ID>: <BIAS>" in the "logit_bias" namespace.
+- To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings.
+- To define a tensor split, pass a list of floats.
+"""
+usage = "run_with_preset.py [-h] [yaml_files ...] [--<ARG_NAME> <ARG_VALUE> ...]"
+epilog = ("  --<ARG_NAME> specify additional CLI ars to be passed to the binary (override all preset files). "
+          "Unknown args will be ignored.")
+
+parser = argparse.ArgumentParser(
+    description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter)
+parser.add_argument("-bin", "--binary", help="The binary to run.")
+parser.add_argument("yaml_files", nargs="*",
+                    help="Arbitrary number of YAML files from which to read preset values. "
+                    "If two files specify the same values the later one will be used.")
+
+known_args, unknown_args = parser.parse_known_args()
+
+if not known_args.yaml_files and not unknown_args:
+    parser.print_help()
+    sys.exit(0)
+
+props = dict()
+
+for yaml_file in known_args.yaml_files:
+    with open(yaml_file, "r") as f:
+        props.update(yaml.load(f, yaml.SafeLoader))
+
+props = {prop.replace("_", "-"): val for prop, val in props.items()}
+
+binary = props.pop("binary", "main")
+if known_args.binary:
+    binary = known_args.binary
+
+if os.path.exists(f"./{binary}"):
+    binary = f"./{binary}"
+
+if binary.lower().endswith("main") or binary.lower().endswith("perplexity"):
+    cli_args = CLI_ARGS_MAIN_PERPLEXITY
+elif binary.lower().endswith("llama-bench"):
+    cli_args = CLI_ARGS_LLAMA_BENCH
+elif binary.lower().endswith("server"):
+    cli_args = CLI_ARGS_SERVER
+else:
+    print(f"Unknown binary: {binary}")
+    sys.exit(1)
+
+command_list = [binary]
+
+for cli_arg in cli_args:
+    value = props.pop(cli_arg, None)
+
+    if not value or value == -1:
+        continue
+
+    if cli_arg == "logit-bias":
+        for token, bias in value.items():
+            command_list.append("--logit-bias")
+            command_list.append(f"{token}{bias:+}")
+        continue
+
+    if cli_arg == "reverse-prompt" and not isinstance(value, str):
+        for rp in value:
+            command_list.append("--reverse-prompt")
+            command_list.append(str(rp))
+        continue
+
+    command_list.append(f"--{cli_arg}")
+
+    if cli_arg == "tensor-split":
+        command_list.append(",".join([str(v) for v in value]))
+        continue
+
+    value = str(value)
+
+    if value != "True":
+        command_list.append(str(value))
+
+num_unused = len(props)
+if num_unused > 10:
+    print(f"The preset file contained a total of {num_unused} unused properties.")
+elif num_unused > 0:
+    print("The preset file contained the following unused properties:")
+    for prop, value in props.items():
+        print(f"  {prop}: {value}")
+
+command_list += unknown_args
+
+sp = subprocess.Popen(command_list)
+
+while sp.returncode is None:
+    try:
+        sp.wait()
+    except KeyboardInterrupt:
+        pass
+
+sys.exit(sp.returncode)

From 43033b7bb4858da4f591715b3babdf906c9b7cbc Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Mon, 28 Aug 2023 19:19:18 +0200
Subject: [PATCH 52/53] llama-bench : set locale to utf8 (#2832)

---
 examples/llama-bench/llama-bench.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index d0fe6d90d..bf3a487ab 100755
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -3,6 +3,9 @@
 #include <cassert>
 #include <chrono>
 #include <cinttypes>
+#include <clocale>
+#include <cmath>
+#include <cstdio>
 #include <cstring>
 #include <ctime>
 #include <iterator>
@@ -10,7 +13,6 @@
 #include <numeric>
 #include <regex>
 #include <sstream>
-#include <stdio.h>
 #include <string>
 #include <vector>
 
@@ -916,6 +918,9 @@ static void llama_null_log_callback(enum llama_log_level level, const char * tex
 }
 
 int main(int argc, char ** argv) {
+    // try to set locale for unicode characters in markdown
+    setlocale(LC_CTYPE, ".UTF-8");
+
 #if !defined(NDEBUG)
     fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
 #endif

From 44c117f41ee01c5ac8fb86bba041f08d8b87b46d Mon Sep 17 00:00:00 2001
From: xaedes <xaedes@gmail.com>
Date: Mon, 28 Aug 2023 21:51:47 +0200
Subject: [PATCH 53/53] train : mem usage and other improvements  (#2439)

* fix track_max_mem in forward_batch_wo_cache_flash_attn_train

* remove unnecessary Adam(W) optimizer tensors.

reduces optimizer memory overhead from 7*modelsize to 2*modelsize.

additionally allows to optimize models with more than 2^31 parameters by replacing int with int64_t.

bumps training checkpoint file version, but old checkpoints can still be read.
new version with less tensors is saved.

* add gradient clipping to AdamW

* Fix reset of unused g->nodes and g->grads to NULL

* implement gradient checkpointing for training

reduces memory overhead from O(n_layer) to O(sqrt(n_layer))

as explained in readme of https://github.com/cybertronai/gradient-checkpointing

* remove unused compute buffer 3

* add and use function ggml_build_backward_expand to avoid stack overflows with large maximum number of nodes

GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);

* change AdamW decay parameter to work like the torch AdamW decay parameter

It is now relative to Adam learning rate `alpha*sched`.
Before that it was relative to `sched` only.

`alpha` being the maximum learning rate and `sched` being a scaling parameter in [0..1]

* change default AdamW weight decay parameter used in training to 0.1 as used in nanoGPT

* change default AdamW weight decay parameter defined in ggml to 0.0, making Adam default instead of AdamW

btw: the default weight decay parameter for torch.optim.AdamW is 0.01

* bug fixes for cross entropy loss

ggml_cross_entropy_loss: sums where not correctly added in workload of each thread
ggml_cross_entropy_loss_back: simplify backward process, reducing numerical issues

guard usage of exp f16 lookup in cross entropy by #define GGML_CROSS_ENTROPY_EXP_FP16

cross entropy loss is only used once during training, but it is quite sensitive to numerical errors introduced by exp-f16-lookup.
so exp-f16-lookup for cross entropy loss is disabled by default, trading better gradients for very slightly worse runtime performance.

* fix test-grad0 for cross_entropy_loss

the second argument to cross_entropy_loss must sum up to 1 for each row

* fix test-grad0 for soft_max

dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)

* improve finite differences of test-grad0 by using double instead of float

* change cross_entropy_loss to output average over all rows

this helps keeping the loss and gradients in a sane range

* improve gradient checkpointing

sqrt(n_layers) is only the best checkpoint step when mem size of checkpoints and mem size of layers are equal.
since layers require more memory than the single-tensor-checkpoint we use, the optimal values are compute different:

```
  given: n, u, v
  objective: minimize(a*u+b*v) where a*b=n, a>0, b>0
  b=n/a
  minimize(a*u+v*n/a)
  diff(a*u+v*n/a, a) = u - (v*n/a)/a
  diff(a*u+v*n/a, a) == 0
  u - (v*n/a)/a == 0
  u == v*n/(a*a)
  u*a*a = v*n
  a*a = v*n/u
  a = sqrt(n*v/u)
```

this change results in more checkpoints, requiring less layers to store between checkpoints, overall improving memory usage.

* disable gradient checkpointing debug output

* llama : fix rope usage in train-text-from-scratch after ChatGLM change

* add more training parameters:

--enable-restart N         Only for Adam optimizer. Enable restarts of cos-decay
--disable-restart N        Only for Adam optimizer. Disable restarts of cos-decay
--opt-past N               Number of optimization iterations to track for delta convergence test. Disabled when zero.
--opt-delta N              Maximum delta for delta convergence test. Disabled when <= zero.
--opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero.
--adam-epsf N              AdamW epsilon for convergence test. Disabled when <= zero.
--adam-min-alpha N         Adam minimum learning rate alpha, usually 0.1 * alpha

* replace memcpy with reshape operation so that the graph is not cut at the input

this makes it possible to store other values into the input tensor and then simply recompute the graph without rebuilding it

* remove unused function argument from get_example_targets_batch

* measure and print total training time

* add optimization callback to ggml_opt_resume_g

this callback is called before each iteration with custom data and pointer to learning schedule parameter (only used in Adam(W)).

can be used for dynamic learning schedule and setting input data for batches before each iteration

* use optimization callback in training

allows dynamic learning schedule and different batch data for each iteration without relying on low n_iter and high n_examples parameters

reduces runtime by avoiding restart of optimization function and improves training convergence by providing a different batch for each iteration

* add minimum number of tensor dimensions to apply weight decay (default 2)

this allows to not apply weight decay to bias parameters

* rename training parameter cos-decay-alpha to cos-decay-min and clarify that adam-min-alpha also applies to warmup

* fix increase of model.train_samples and model.train_tokens

now that each optimizer iteration gets its own batch we need to multiply by number of opt iterations

* change sampling parameters for prediction after training to defaults of common.h

and clarify what is context for prediction and what are generated tokens

* tighten abs error bounds for cross_entropy_loss in test-grad0

* add conditional compilation of using F16 exp in flash attention

uncomment `// #define GGML_FLASH_ATTN_EXP_FP16` to enable usage of f16 exp in flash attention

* tighten abs error bounds for flash_attn in test-grad0

* tighten abs error bounds for sqrt in test-grad0

* remove out-commented vectorized code of opt_adam

the vectorized code might be bit faster for low number of parameters, but it had a big memory usage overhead

* ggml : update ggml_rms_norm_back with configurable eps

* llama training : fix ggml_rms_norm_back calls to pass configurable eps

* remove trailing whitespace

* add train function using automatic gradient checkpointing backward pass and allocator

* in train function replace add_inplace by regular add

because using add_inplace seems to result in different gradients

* don't use allocate hash_map on context

because the context has no_alloc=True when using memory allocator resulting in NULL data pointers

* correctly clone reshape and permute operations by also cloning tensor->nb values

* fix variable name and add missing type cast

* terminate recursive tensor cloning when reaching tensor without src tensors

* correctly clone view tensors by setting data pointers

without this the checkpointing would only work when being used together with memory allocator

* fix variable names

* swap arguments to commutative ops to be the same as in `forward_batch_wo_cache_flash_attn`

* add input tensors as checkpoints

so that recursive tensor cloning of gradient checkpointing terminates on input tensors

* fix variable name and add missing boolean negation

* make sure some tensors are not reallocated by inserting new temporary nodes depending on them:

output and parameter gradient tensors need to be available at the end of the graph execution

parameter gradient tensors also need to be available before the graph execution because they are set to zero before each optimizer iteration

checkpoint tensors are allocated all together to reduce memory allocator fragmentation

afterwards, in addition to the temporary nodes, we also need to reset the temporary leafs

* fix ASSERT to work with zero layers

* add training options whether to use allocator and/or unified training function

* integrate unified training function which may use memory allocator

the unified training function also supports arguments whether to use flash attention and/or gradient checkpointing

* format name of cloned tensors with " (clone)" suffix

* set names for tensors in unified train function for easier debugging

* allocate graph on context using ggml_new_graph

* remove handwritten training functions

* remove unused training parameters "use_scratch" and "use_unified"

* remove trailing whitespace

* remove unused train params: mem_compute1_gb & mem_compute2_gb

mem_compute_gb is used for compute when automatic memory allocator is not enabled, otherwise it can be very small to only hold the tensor definitions
mem_compute0_gb is used for automatic memory allocator (as long as measurement of max required size is not implemented)

* remove unused forward_batch function

* add debug asserts in ggml_allocr_alloc to some common pitfalls when using this function directly

* only use ggml_allocr_alloc when tensor has NULL data and is no view

* fix test when to create temporary backward graph

temporary backward graph is only necessary when using checkpointing

* fix memory "leak" in optimizers

each iteration a new cplan with new memory for work data was allocated.
now cplan creation only happens at the start of optimization, with each iteration reusing the cplan and its work data.

* reverse order of for loop in ggml_build_backward_expand to save memory when using gradient checkpointing and allocator

with this loop order gradient checkpointing with allocator on 16 layer model saves 13% memory; 2 layer memory it saves 2% memory.

the computation results are the same

* add missing lctx argument to get_example_targets_batch

* implement llama model file saving using gguf

checkpoint loading and saving disabled, to be replaced by loading and saving via gguf

* implement loading/saving of checkpointing files using GGUF

* bug fixes

* add checkpoint file version for future compatibility

* update readme with gguf filenames

* save & load opt->just_initialized value

* add first draft for checkpoint conversion script

* add gguf arch and ftype

* save opt parameter counter as uint64

* add gguf key and tensor names for optimizer and training

* add layer_norm_rms_eps to checkpoint convert script

* use same GGUF_GET_KEY macro as in llama.cpp

* use norm_rms_eps, and rope parameters and command line options to set them

* fix memory corruption bug in gguf

ctx->kv and ctx->infos was reallocated using not-aligned realloc, but freed with aligned free.
to fix this a GGML_ALIGNED_REALLOC was added, but there is no posix_memalign_realloc function.
so on non-windows and non-mingw32 platforms we fall back to aligned malloc, followed by copying
and freeing the old data.

* add gguf example cmake file

* bug fixes in tokenize_file

* bug fixes in load_llama_model_gguf

* bug fix: init model when no checkpoint was loaded

* bug fix in read_tensor_by_name

* bug fix in load_opt_context_gguf

* avoid printing lots of spaced on the unusual case that loss gets nan

* set name of tensors with empty name from what was read from gguf

* remove trailing whitespace

* print data checksums before saving and after loading to verify correctness

* bug fixes for convert-train-checkpoint-to-gguf

* temporarily add code to write old checkpoint files

used to verify that old checkpoint files are correctly converted to gguf

* bug fixes for convert-train-checkpoint-to-gguf.py loading checkpoints with opt_version=0

* remove code used to verify correctness of checkpoint file conversion

* remove trailing whitespace

* remove prediction related code

use main for prediction, it is better optimized

* update train-text-from-scratch README.md

* fix non-windows GGML_ALIGNED_REALLOC

* add missing blank line at end of file

* remove GGML_ALIGNED_REALLOC and use normal malloc/realloc/free for gguf ctx->kv & ctx->infos

* train : fix compile warnings

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 common/common.cpp                             |    5 +-
 .../convert-llama2c-to-ggml.cpp               |    1 -
 examples/gguf/CMakeLists.txt                  |    5 +
 examples/train-text-from-scratch/README.md    |   14 +-
 .../convert-train-checkpoint-to-gguf.py       |  492 +++
 .../train-text-from-scratch.cpp               | 3502 ++++++-----------
 ggml-alloc.c                                  |    4 +
 ggml.c                                        |  335 +-
 ggml.h                                        |   29 +-
 llama.cpp                                     |    9 +-
 tests/test-grad0.cpp                          |   52 +-
 11 files changed, 1940 insertions(+), 2508 deletions(-)
 create mode 100644 examples/gguf/CMakeLists.txt
 create mode 100644 examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py

diff --git a/common/common.cpp b/common/common.cpp
index 4a0d43c13..90fe2e84e 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -15,6 +15,7 @@
 #include <string>
 #include <unordered_set>
 #include <vector>
+#include <cinttypes>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -938,8 +939,8 @@ std::string get_sortable_timestamp() {
 
     const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
         current_time.time_since_epoch() % 1000000000).count();
-    char timestamp_ns[10];
-    snprintf(timestamp_ns, 11, "%09ld", ns);
+    char timestamp_ns[11];
+    snprintf(timestamp_ns, 11, "%09" PRId64, ns);
 
     return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
 }
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 51d90ea6a..e9e070b1f 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -681,7 +681,6 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
 
     // for rms-att-weight
     int row_length = model->hparams.n_embd;
-    const auto & hparams = model->hparams;
     int n_ff = model->hparams.n_ff;
 
     for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt
new file mode 100644
index 000000000..7d1806af3
--- /dev/null
+++ b/examples/gguf/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET gguf)
+add_executable(${TARGET} gguf.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/train-text-from-scratch/README.md b/examples/train-text-from-scratch/README.md
index 726ec47c0..f4ffcd987 100644
--- a/examples/train-text-from-scratch/README.md
+++ b/examples/train-text-from-scratch/README.md
@@ -8,15 +8,15 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s
 
 # train
 ./bin/train-text-from-scratch \
-        --vocab-model ../models/ggml-vocab.bin \
+        --vocab-model ../models/ggml-vocab-llama.gguf \
         --ctx 64 --embd 256 --head 8 --layer 16 \
-        --checkpoint-in  chk-shakespeare-256x16.bin \
-        --checkpoint-out chk-shakespeare-256x16.bin \
-        --model-out ggml-shakespeare-256x16-f32.bin \
+        --checkpoint-in  chk-shakespeare-256x16.gguf \
+        --checkpoint-out chk-shakespeare-256x16.gguf \
+        --model-out ggml-shakespeare-256x16-f32.gguf \
         --train-data "shakespeare.txt" \
-        -t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \
-        --print-details-interval 0 --predict 16 --use-flash
+        -t 6 -b 16 --seed 1 --adam-iter 256 \
+        --no-checkpointing
 
 # predict
-./bin/main -m ggml-shakespeare-256x16-f32.bin
+./bin/main -m ggml-shakespeare-256x16-f32.gguf
 ```
diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py
new file mode 100644
index 000000000..01b3ee92a
--- /dev/null
+++ b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py
@@ -0,0 +1,492 @@
+#!/usr/bin/env python3
+# train-text-from-scratch checkpoint --> gguf conversion
+
+import argparse
+import gguf
+import os
+import struct
+import sys
+import numpy as np
+from pathlib import Path
+
+# gguf constants
+LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
+LLM_KV_OPTIMIZER_TYPE_ADAM  = "adam"
+LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
+LLM_KV_OPTIMIZER_FILE_VERSION               = "optimizer.file_version"
+LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT     = "optimizer.convergence_past_count"
+LLM_KV_OPTIMIZER_PARAMETER_COUNT            = "optimizer.parameter_count"
+LLM_KV_OPTIMIZER_ITERATION_COUNT            = "optimizer.iteration_count"
+LLM_KV_OPTIMIZER_JUST_INITIALIZED           = "optimizer.just_initialized"
+LLM_KV_OPTIMIZER_ADAM_BEST_LOSS             = "optimizer.adam.best_loss"
+LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS         = "optimizer.adam.previous_loss"
+LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT  = "optimizer.adam.no_improvement_count"
+LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
+LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS            = "optimizer.lbfgs.best_loss"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP     = "optimizer.lbfgs.line_search_step"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J        = "optimizer.lbfgs.line_search_j"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K        = "optimizer.lbfgs.line_search_k"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END      = "optimizer.lbfgs.line_search_end"
+LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
+
+LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS    = "optimizer.adam.first_moments"
+LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS   = "optimizer.adam.second_moments"
+LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
+
+LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS  = "optimizer.lbfgs.current_parameters"
+LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
+LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS   = "optimizer.lbfgs.current_gradients"
+LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS  = "optimizer.lbfgs.previous_gradients"
+LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION    = "optimizer.lbfgs.search_direction"
+LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES    = "optimizer.lbfgs.past_loss_values"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA        = "optimizer.lbfgs.memory_alpha"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.lbfgs.memory_ys"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y"
+
+LLM_KV_TRAINING_FILE_VERSION    = "training.file_version"
+LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"
+LLM_KV_TRAINING_SAMPLE_COUNT    = "training.sample_count"
+LLM_KV_TRAINING_TOKEN_COUNT     = "training.token_count"
+
+class Tensor:
+    def __init__(self, dtype='f', ne=None):
+        if ne is None:
+            ne = []
+        self.dtype = dtype
+        self.ne = ne
+        self.nbytes = 0
+        if self.dtype == 'f':
+            if len(self.ne) == 0:
+                self.nbytes = 0
+            else:
+                self.nbytes = int(np.product(self.ne)) * 4
+        else:
+            raise ValueError(f"Unhandled data type '{self.dtype}'")
+
+    def load(self, data, offset):
+        nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+
+        assert(nd == len(self.ne))
+        ne = []
+        for d in range(nd):
+            n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+            ne.append(n)
+
+        assert(tuple(ne) == tuple(self.ne))
+
+        if self.dtype == 'f':
+            assert(dtype == 0)
+        else:
+            raise ValueError(f"Unhandled data type '{self.dtype}'")
+
+        self.name = bytes(data[offset:offset+namelen]); offset += namelen
+        # 32-byte alignment
+        offset += (0 - offset) & 31
+        self.data = data[offset:offset+self.nbytes]
+        offset += self.nbytes
+        return offset
+
+    def max_storage_size(self):
+        result = 0
+        result += 4 # nd
+        result += 4 # namelen
+        result += 4 # dtype
+        result += len(self.ne)*8 # ne
+        result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
+        result += 31 # 32-byte alignment
+        result += self.nbytes
+        return result
+
+    def save_gguf(self, gguf_writer, name):
+        gguf_writer.add_tensor(
+            name=name,
+            tensor=self.data,
+            raw_shape=np.array(list(reversed(self.ne))),
+            raw_dtype=gguf.GGMLQuantizationType.F32)
+
+class OptimizationParamsV0:
+    def __init__(self):
+        pass
+
+    def load(self, data, offset):
+        self.type                 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_threads            = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.past                 = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.delta                = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.print_forward_graph  = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
+        self.print_backward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
+        self.adam_n_iter          = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_sched           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_decay           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_alpha           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_beta1           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_beta2           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_eps             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_eps_f           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_eps_g           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_m              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_n_iter         = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_max_linesearch = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_eps            = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_ftol           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_wolfe          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_min_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_max_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_linesearch     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        return offset
+
+class OptimizationContext:
+    def __init__(self):
+        pass
+
+    def load(self, data, offset):
+        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
+        offset += 4
+
+        if self.version == 0:
+            params = OptimizationParamsV0()
+            offset = params.load(data, offset)
+            self.past = params.past
+            self.lbfgs_m = params.lbfgs_m
+            self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0];  offset += 8
+            self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
+            self.type = params.type
+
+            self.adam_m  = Tensor('f', [self.nx])
+            self.adam_v  = Tensor('f', [self.nx])
+            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
+
+            self.lbfgs_x    = Tensor('f', [self.nx])
+            self.lbfgs_xp   = Tensor('f', [self.nx])
+            self.lbfgs_g    = Tensor('f', [self.nx])
+            self.lbfgs_gp   = Tensor('f', [self.nx])
+            self.lbfgs_d    = Tensor('f', [self.nx])
+            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
+            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
+            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
+
+            if self.type == 0:
+                # these tensors are stored, but we don't need their data
+                x  = Tensor('f', [self.nx])
+                g  = Tensor('f', [self.nx])
+                g2 = Tensor('f', [self.nx])
+                mh = Tensor('f', [self.nx])
+                vh = Tensor('f', [self.nx])
+
+                offset = x.load(data, offset)
+                offset = g.load(data, offset)
+                offset = g2.load(data, offset)
+                offset = self.adam_m.load(data, offset)
+                offset = self.adam_v.load(data, offset)
+                offset = mh.load(data, offset)
+                offset = vh.load(data, offset)
+                offset = self.adam_pf.load(data, offset)
+
+                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+            elif self.type == 1:
+                offset = self.lbfgs_x.load(data, offset)
+                offset = self.lbfgs_xp.load(data, offset)
+                offset = self.lbfgs_g.load(data, offset)
+                offset = self.lbfgs_gp.load(data, offset)
+                offset = self.lbfgs_d.load(data, offset)
+                offset = self.lbfgs_pf.load(data, offset)
+                offset = self.lbfgs_lmal.load(data, offset)
+                offset = self.lbfgs_lmys.load(data, offset)
+                offset = self.lbfgs_lms.load(data, offset)
+                offset = self.lbfgs_lmy.load(data, offset)
+
+                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+            else:
+                raise ValueError('Unknown optimizer type')
+
+
+        elif self.version == 1:
+            self.past    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.nx      = struct.unpack('N',  bytes(data[offset:offset + 8]))[0];  offset += 8
+            self.iter    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
+
+            self.adam_m  = Tensor('f', [self.nx])
+            self.adam_v  = Tensor('f', [self.nx])
+            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
+
+            self.lbfgs_x    = Tensor('f', [self.nx])
+            self.lbfgs_xp   = Tensor('f', [self.nx])
+            self.lbfgs_g    = Tensor('f', [self.nx])
+            self.lbfgs_gp   = Tensor('f', [self.nx])
+            self.lbfgs_d    = Tensor('f', [self.nx])
+            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
+            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
+            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
+
+            # forgot to save type in version 1:
+            # guess self.type from number of remaining bytes
+            size_type_0 = 12 + sum([t.max_storage_size() for t in
+                                    [self.adam_m, self.adam_v]
+                                    +([self.adam_pf] if (self.past > 0) else [])])
+            size_type_1 = 24 + sum([t.max_storage_size() for t in
+                                    [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
+                                     self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
+                                     self.lbfgs_lmal, self.lbfgs_lmys,
+                                     self.lbfgs_lms, self.lbfgs_lmy]
+                                     +([self.lbfgs_pf] if (self.past > 0) else [])])
+            # due to alignment padding the size might not by exact
+            # but the difference in size for both types is significant,
+            # so we can just use whichever is closest
+            remaining = len(data) - offset
+            if abs(remaining - size_type_0) < abs(remaining - size_type_1):
+                self.type = 0
+            else:
+                self.type = 1
+
+            if self.type == 0:
+                offset = self.adam_m.load(data, offset)
+                offset = self.adam_v.load(data, offset)
+                offset = self.adam_pf.load(data,offset)
+
+                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+            elif self.type == 1:
+                offset = self.lbfgs_x.load(data, offset)
+                offset = self.lbfgs_xp.load(data, offset)
+                offset = self.lbfgs_g.load(data, offset)
+                offset = self.lbfgs_gp.load(data, offset)
+                offset = self.lbfgs_d.load(data, offset)
+                offset = self.lbfgs_pf.load(data, offset)
+                offset = self.lbfgs_lmal.load(data, offset)
+                offset = self.lbfgs_lmys.load(data, offset)
+                offset = self.lbfgs_lms.load(data, offset)
+                offset = self.lbfgs_lmy.load(data, offset)
+
+                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+        else:
+            raise ValueError('Invalid version of checkpoint file')
+
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
+        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
+        gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
+        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
+        gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
+
+        if self.type == 0:
+            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
+            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
+
+            self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
+            self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
+            if self.past > 0:
+                self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
+
+        elif self.type == 1:
+            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
+            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
+            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
+            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
+            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
+            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
+
+            self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
+            self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
+            self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
+            self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
+            self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
+            if self.past > 0:
+                self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
+            self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
+            self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
+            self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
+            self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
+        else:
+            raise ValueError('Unknown optimizer type')
+
+class ModelParams:
+    def __init__(self):
+        pass
+
+    def load(self, data, offset):
+        self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_embd  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_mult  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_head  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_rot   = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        return offset
+
+    def get_n_ff(self):
+        # struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
+        return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
+
+    def save_gguf(self, gguf_writer):
+        # self.n_vocab not saved
+        gguf_writer.add_embedding_length(self.n_embd)
+        gguf_writer.add_head_count(self.n_head)
+        gguf_writer.add_block_count(self.n_layer)
+        gguf_writer.add_rope_dimension_count(self.n_rot)
+        gguf_writer.add_feed_forward_length(self.get_n_ff())
+
+def tensor_name(key, bid=None):
+    return gguf.MODEL_TENSOR_NAMES[gguf.MODEL_ARCH.LLAMA][key].format(bid=bid) + ".weight"
+
+class Layer:
+    def __init__(self, params, bid):
+        self.bid = bid
+        self.att_norm = Tensor('f', [params.n_embd])
+        self.wq       = Tensor('f', [params.n_embd, params.n_embd])
+        self.wk       = Tensor('f', [params.n_embd, params.n_embd])
+        self.wv       = Tensor('f', [params.n_embd, params.n_embd])
+        self.wo       = Tensor('f', [params.n_embd, params.n_embd])
+        self.ffn_norm = Tensor('f', [params.n_embd])
+        self.w1       = Tensor('f', [params.n_embd, params.get_n_ff()])
+        self.w2       = Tensor('f', [params.get_n_ff(), params.n_embd])
+        self.w3       = Tensor('f', [params.n_embd, params.get_n_ff()])
+
+    def load(self, data, offset):
+        offset = self.att_norm.load(data, offset)
+        offset = self.wq.load(data, offset)
+        offset = self.wk.load(data, offset)
+        offset = self.wv.load(data, offset)
+        offset = self.wo.load(data, offset)
+        offset = self.ffn_norm.load(data, offset)
+        offset = self.w1.load(data, offset)
+        offset = self.w2.load(data, offset)
+        offset = self.w3.load(data, offset)
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        self.att_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid))
+        self.wq.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid))
+        self.wk.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid))
+        self.wv.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid))
+        self.wo.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid))
+        self.ffn_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid))
+        self.w1.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid))
+        self.w2.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid))
+        self.w3.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid))
+
+class Model:
+    def __init__(self):
+        self.params = ModelParams()
+        self.layers = []
+
+    def load(self, data, offset):
+        offset = self.params.load(data, offset)
+
+        self.tok_embd = Tensor('f', [self.params.n_embd, self.params.n_vocab])
+        self.norm     = Tensor('f', [self.params.n_embd])
+        self.output   = Tensor('f', [self.params.n_embd, self.params.n_vocab])
+
+        offset = self.tok_embd.load(data, offset)
+        offset = self.norm.load(data, offset)
+        offset = self.output.load(data, offset)
+
+        self.layers.clear()
+        for bid in range(self.params.n_layer):
+            layer = Layer(self.params, bid)
+            offset = layer.load(data, offset)
+            self.layers.append(layer)
+
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        self.params.save_gguf(gguf_writer)
+
+        self.tok_embd.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD))
+        self.norm.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM))
+        self.output.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT))
+
+        for layer in self.layers:
+            layer.save_gguf(gguf_writer)
+
+class Checkpoint:
+    def __init__(self):
+        self.model = Model()
+        self.opt_ctx = OptimizationContext()
+
+    def load(self, data, offset):
+        magic   = bytes(reversed(data[offset:offset + 4])); offset += 4
+        if magic != b'ggcp':
+            raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'")
+
+        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        if self.version != 0:
+            raise ValueError('Invalid version of checkpoint file')
+
+        self.train_its     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        self.train_tokens  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+
+        offset = self.model.load(data, offset)
+        offset = self.opt_ctx.load(data, offset)
+
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
+        gguf_writer.add_layer_norm_rms_eps(1e-5)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION,    0)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT,    self.train_samples)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT,     self.train_tokens)
+        self.model.save_gguf(gguf_writer)
+        self.opt_ctx.save_gguf(gguf_writer)
+
+def handle_args():
+    parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF')
+    parser.add_argument('--input',  '-i', type = Path, help = 'Input train checkpoint filename', required=True)
+    parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename', required=True)
+    return parser.parse_args()
+
+def main():
+    cfg = handle_args()
+    data = np.memmap(cfg.input, mode = 'r')
+    chk = Checkpoint()
+    offset = 0
+    offset = chk.load(data, offset)
+    # we should have read all available data
+    assert(offset == len(data))
+
+    gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
+    chk.save_gguf(gguf_writer)
+    print("    gguf: write header")
+    gguf_writer.write_header_to_file()
+    print("    gguf: write metadata")
+    gguf_writer.write_kv_data_to_file()
+    print("    gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+    gguf_writer.close()
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index 12d153417..6fe85d419 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -1,4 +1,5 @@
 #include "ggml.h"
+#include "ggml-alloc.h"
 #include "common.h"
 #include "llama.h"
 #include <unordered_map>
@@ -17,8 +18,6 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static const float rms_norm_eps = 1e-5f;
-
 struct random_normal_distribution {
     std::mt19937 gen;
     std::normal_distribution<float> rd;
@@ -63,17 +62,6 @@ float frand_uniform(struct random_uniform_distribution * rnd) {
     return rnd->rd(rnd->gen);
 }
 
-void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
-    struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
-
-    if (plan.work_size > 0) {
-        buf.resize(plan.work_size);
-        plan.work_data = buf.data();
-    }
-
-    ggml_graph_compute(graph, &plan);
-}
-
 struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {
     float scale = 1.0f; // xavier
     switch (tensor->n_dims) {
@@ -167,29 +155,20 @@ struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struc
     return tensor;
 }
 
-struct llama_vocab {
-    using id    = int32_t;
-    using token = std::string;
-    using ttype = llama_token_type;
-
-    struct token_data {
-        token text;
-        float score;
-        ttype type;
-    };
-
-    std::unordered_map<token, id> token_to_id;
-    std::vector<token_data> id_to_token;
-};
-
 struct my_llama_hparams {
     uint32_t n_vocab = 32000;
-    uint32_t n_ctx   = 512;   // this is provided as user input?
+    uint32_t n_ctx   = 512;
     uint32_t n_embd  = 4096;
-    uint32_t n_mult  = 4;
     uint32_t n_head  = 32;
     uint32_t n_layer = 32;
     uint32_t n_rot   = 64;
+    uint32_t n_ff    = 11008;
+
+    // float f_norm_eps     = 1e-5; // falcon
+    float f_norm_rms_eps = 1e-5; // llama
+
+    float rope_freq_base  = 10000.0f;
+    float rope_freq_scale = 1.0f;
 
     bool operator!=(const my_llama_hparams& other) const {
         return memcmp(this, &other, sizeof(my_llama_hparams));
@@ -215,17 +194,6 @@ struct my_llama_layer {
     struct ggml_tensor * w3;
 };
 
-struct my_llama_kv_cache {
-    struct ggml_context * ctx = NULL;
-
-    struct ggml_tensor * k;
-    struct ggml_tensor * v;
-
-    // llama_ctx_buffer buf;
-
-    int n; // number of tokens currently in the cache
-};
-
 struct my_llama_model {
     struct ggml_context * ctx = NULL;
 
@@ -243,18 +211,91 @@ struct my_llama_model {
     uint32_t train_tokens = 0;
 };
 
-uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
-    const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
-    return n_ff;
-}
+// gguf constants
+const char * LLM_KV_OPTIMIZER_TYPE = "optimizer.type";
+const char * LLM_KV_OPTIMIZER_TYPE_ADAM  = "adam";
+const char * LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs";
+const char * LLM_KV_OPTIMIZER_FILE_VERSION               = "optimizer.file_version";
+const char * LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT     = "optimizer.convergence_past_count";
+const char * LLM_KV_OPTIMIZER_PARAMETER_COUNT            = "optimizer.parameter_count";
+const char * LLM_KV_OPTIMIZER_ITERATION_COUNT            = "optimizer.iteration_count";
+const char * LLM_KV_OPTIMIZER_JUST_INITIALIZED           = "optimizer.just_initialized";
+const char * LLM_KV_OPTIMIZER_ADAM_BEST_LOSS             = "optimizer.adam.best_loss";
+const char * LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS         = "optimizer.adam.previous_loss";
+const char * LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT  = "optimizer.adam.no_improvement_count";
+const char * LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count";
+const char * LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS            = "optimizer.lbfgs.best_loss";
+const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP     = "optimizer.lbfgs.line_search_step";
+const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J        = "optimizer.lbfgs.line_search_j";
+const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K        = "optimizer.lbfgs.line_search_k";
+const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END      = "optimizer.lbfgs.line_search_end";
+const char * LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count";
+
+const char * LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS    = "optimizer.adam.first_moments";
+const char * LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS   = "optimizer.adam.second_moments";
+const char * LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values";
+
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS  = "optimizer.lbfgs.current_parameters";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS   = "optimizer.lbfgs.current_gradients";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS  = "optimizer.lbfgs.previous_gradients";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION    = "optimizer.lbfgs.search_direction";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES    = "optimizer.lbfgs.past_loss_values";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA        = "optimizer.lbfgs.memory_alpha";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.lbfgs.memory_ys";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s";
+const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y";
+
+const char * LLM_KV_TRAINING_FILE_VERSION    = "training.file_version";
+const char * LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count";
+const char * LLM_KV_TRAINING_SAMPLE_COUNT    = "training.sample_count";
+const char * LLM_KV_TRAINING_TOKEN_COUNT     = "training.token_count";
+
+// gguf constants (sync with gguf.py)
+
+const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
+const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";
+
+const char * LLM_KV_CONTEXT_LENGTH              = "%s.context_length";
+const char * LLM_KV_EMBEDDING_LENGTH            = "%s.embedding_length";
+const char * LLM_KV_BLOCK_COUNT                 = "%s.block_count";
+const char * LLM_KV_FEED_FORWARD_LENGTH         = "%s.feed_forward_length";
+const char * LLM_KV_ATTENTION_HEAD_COUNT        = "%s.attention.head_count";
+const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon";
+const char * LLM_KV_ROPE_DIMENSION_COUNT        = "%s.rope.dimension_count";
+const char * LLM_KV_ROPE_FREQ_BASE              = "%s.rope.freq_base"; // TODO load in llama.cpp
+const char * LLM_KV_ROPE_SCALE_LINEAR           = "%s.rope.scale_linear";
+
+const char * LLM_KV_TOKENIZER_MODEL             = "tokenizer.ggml.model";
+const char * LLM_KV_TOKENIZER_LIST              = "tokenizer.ggml.tokens";
+const char * LLM_KV_TOKENIZER_TOKEN_TYPE        = "tokenizer.ggml.token_type";
+const char * LLM_KV_TOKENIZER_SCORES            = "tokenizer.ggml.scores";
+const char * LLM_KV_TOKENIZER_MERGES            = "tokenizer.ggml.merges";
+const char * LLM_KV_TOKENIZER_BOS_ID            = "tokenizer.ggml.bos_token_id";
+const char * LLM_KV_TOKENIZER_EOS_ID            = "tokenizer.ggml.eos_token_id";
+const char * LLM_KV_TOKENIZER_UNK_ID            = "tokenizer.ggml.unknown_token_id";
+const char * LLM_KV_TOKENIZER_SEP_ID            = "tokenizer.ggml.seperator_token_id";
+const char * LLM_KV_TOKENIZER_PAD_ID            = "tokenizer.ggml.padding_token_id";
+
+const char * LLM_TENSOR_TOKEN_EMBD    = "token_embd";
+const char * LLM_TENSOR_OUTPUT_NORM   = "output_norm";
+const char * LLM_TENSOR_OUTPUT        = "output";
+const char * LLM_TENSOR_ATTN_NORM     = "blk.%d.attn_norm";
+const char * LLM_TENSOR_ATTN_Q        = "blk.%d.attn_q";
+const char * LLM_TENSOR_ATTN_K        = "blk.%d.attn_k";
+const char * LLM_TENSOR_ATTN_V        = "blk.%d.attn_v";
+const char * LLM_TENSOR_ATTN_OUT      = "blk.%d.attn_output";
+const char * LLM_TENSOR_FFN_NORM      = "blk.%d.ffn_norm";
+const char * LLM_TENSOR_FFN_GATE      = "blk.%d.ffn_gate";
+const char * LLM_TENSOR_FFN_DOWN      = "blk.%d.ffn_down";
+const char * LLM_TENSOR_FFN_UP        = "blk.%d.ffn_up";
 
 void print_params(struct my_llama_hparams * params) {
     printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
     printf("%s: n_ctx:   %d\n", __func__, params->n_ctx);
     printf("%s: n_embd:  %d\n", __func__, params->n_embd);
-    printf("%s: n_mult:  %d\n", __func__, params->n_mult);
     printf("%s: n_head:  %d\n", __func__, params->n_head);
-    printf("%s: n_ff:    %d\n", __func__, get_n_ff(params));
+    printf("%s: n_ff:    %d\n", __func__, params->n_ff);
     printf("%s: n_layer: %d\n", __func__, params->n_layer);
     printf("%s: n_rot:   %d\n", __func__, params->n_rot);
 }
@@ -265,8 +306,7 @@ void init_model(struct my_llama_model * model) {
     const uint32_t n_embd  = hparams.n_embd;
     const uint32_t n_layer = hparams.n_layer;
     const uint32_t n_vocab = hparams.n_vocab;
-
-    const uint32_t n_ff = get_n_ff(&hparams);
+    const uint32_t n_ff    = hparams.n_ff;
 
     struct ggml_context * ctx = model->ctx;
 
@@ -274,20 +314,31 @@ void init_model(struct my_llama_model * model) {
     model->train_samples = 0;
     model->train_tokens = 0;
 
+    std::vector<char> tn_buf;
+    tn_buf.resize(GGML_MAX_NAME);
+    auto tn = [&tn_buf](const char * key) -> const char * {
+        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
+        return tn_buf.data();
+    };
+    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
+        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
+        std::string s = tn_buf.data();
+        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
+        return tn_buf.data();
+    };
+
     model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
     model->norm           = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
     model->output         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
 
-    ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
-    ggml_set_name(model->norm,           "norm.weight");
-    ggml_set_name(model->output,         "output.weight");
+    ggml_set_name(model->tok_embeddings, tn(LLM_TENSOR_TOKEN_EMBD));
+    ggml_set_name(model->norm,           tn(LLM_TENSOR_OUTPUT_NORM));
+    ggml_set_name(model->output,         tn(LLM_TENSOR_OUTPUT));
 
     model->layers.resize(n_layer);
     for (uint32_t i = 0; i < n_layer; ++i) {
         auto & layer = model->layers[i];
 
-        std::string layers_i = "layers." + std::to_string(i);
-
         layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
 
         layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
@@ -301,18 +352,18 @@ void init_model(struct my_llama_model * model) {
         layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32,   n_ff, n_embd);
         layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
 
-        ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str());
+        ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i));
 
-        ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str());
-        ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str());
-        ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str());
-        ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str());
+        ggml_set_name(layer.wq,             tni(LLM_TENSOR_ATTN_Q, i));
+        ggml_set_name(layer.wk,             tni(LLM_TENSOR_ATTN_K, i));
+        ggml_set_name(layer.wv,             tni(LLM_TENSOR_ATTN_V, i));
+        ggml_set_name(layer.wo,             tni(LLM_TENSOR_ATTN_OUT, i));
 
-        ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str());
+        ggml_set_name(layer.ffn_norm,       tni(LLM_TENSOR_FFN_NORM, i));
 
-        ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str());
-        ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
-        ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
+        ggml_set_name(layer.w1,             tni(LLM_TENSOR_FFN_GATE, i));
+        ggml_set_name(layer.w2,             tni(LLM_TENSOR_FFN_DOWN, i));
+        ggml_set_name(layer.w3,             tni(LLM_TENSOR_FFN_UP, i));
     }
 }
 
@@ -371,267 +422,6 @@ void randomize_model(struct my_llama_model * model, int seed, float mean, float
     }
 }
 
-bool init_kv_cache(struct my_llama_kv_cache* cache, struct my_llama_model * model, int n_batch) {
-    const auto & hparams = model->hparams;
-
-    const uint32_t n_ctx   = hparams.n_ctx;
-    const uint32_t n_embd  = hparams.n_embd;
-    const uint32_t n_layer = hparams.n_layer;
-
-    const int64_t n_mem      = n_layer*n_ctx*n_batch;
-    const int64_t n_elements = n_embd*n_mem;
-
-    // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
-
-    // struct ggml_init_params params;
-    // params.mem_size   = cache.buf.size;
-    // params.mem_buffer = cache.buf.addr;
-    // params.no_alloc   = false;
-    if (!cache->ctx) {
-        struct ggml_init_params params;
-        params.mem_size   = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024;
-        params.mem_buffer = NULL;
-        params.no_alloc   = false;
-
-        cache->ctx = ggml_init(params);
-
-        if (!cache->ctx) {
-            fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
-            return false;
-        }
-    }
-
-    cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
-    cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
-
-    return true;
-}
-
-struct ggml_tensor * forward(
-        struct my_llama_model    * model,
-        struct my_llama_kv_cache * cache,
-        struct ggml_context   * ctx0,
-        struct ggml_cgraph    * gf,
-        struct ggml_tensor    * tokens_input,
-        const  int              n_tokens,
-        const  int              n_past) {
-
-    const int N = n_tokens;
-
-    struct my_llama_kv_cache& kv_self = *cache;
-    const auto & hparams = model->hparams;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_head  = hparams.n_head;
-    const int n_rot   = hparams.n_rot;
-
-    struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens));
-
-    struct ggml_tensor * kc = kv_self.k;
-    struct ggml_tensor * vc = kv_self.v;
-
-    // inpL shape [n_embd,N,1,1]
-    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * inpSA = inpL;
-
-        struct ggml_tensor * cur;
-
-        // lctx.use_buf(ctx0, 0);
-
-        // norm
-        {
-            // cur shape [n_embd,N,1,1]
-            cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-
-            // cur = attention_norm*cur
-            cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
-                        cur);
-        }
-
-        // self-attention
-        {
-            // compute Q and K and RoPE them
-            // wq   shape [n_embd, n_embd, 1, 1]
-            // wk   shape [n_embd, n_embd, 1, 1]
-            // Qcur shape [n_embd/n_head, n_head, N, 1]
-            // Kcur shape [n_embd/n_head, n_head, N, 1]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
-
-            // store key and value to memory
-            {
-                // compute the transposed [N, n_embd] V matrix
-                // wv   shape [n_embd, n_embd, 1, 1]
-                // Vcur shape [n_embd, N, 1, 1]
-                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wv, cur), n_embd, N)));
-
-                // kv_self.k shape [n_embd * n_ctx * n_layer, 1]
-                // kv_self.v shape [n_embd * n_ctx * n_layer, 1]
-                // k         shape [n_embd * N, 1]   == kv_self.k[:,n_past:n_past+N,il,0]
-                // v         shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0]
-
-                /* {
-                    struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
-                    struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
-                            (   n_ctx)*ggml_element_size(kv_self.v),
-                            (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
-
-                    // important: storing RoPE-ed version of K in the KV cache!
-                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-                } //*/
-
-                kc = ggml_set_1d_inplace(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
-                vc = ggml_set_2d_inplace(ctx0, vc, Vcur, (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
-            }
-
-            // Qcur shape [n_embd/n_head, n_head, N, 1]
-            // Q shape    [n_embd/n_head, N, n_head, 1]
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        Qcur,
-                        0, 2, 1, 3);
-
-            // kv_self.k shape [n_embd * n_ctx * n_layer, 1]
-            // K shape [n_embd/n_head, n_past + N, n_head, 1]
-            struct ggml_tensor * K =
-                ggml_permute(ctx0,
-                        ggml_reshape_3d(ctx0,
-                            ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        0, 2, 1, 3);
-
-            // K * Q
-            // KQ shape [n_past + N, N, n_head, 1]
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            // KQ_scaled shape [n_past + N, N, n_head, 1]
-            struct ggml_tensor * KQ_scaled =
-                ggml_scale(ctx0,
-                        KQ,
-                        ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
-
-            // KQ_masked = mask_past(KQ_scaled)
-            // KQ_masked shape [n_past + N, N, n_head, 1]
-            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
-
-            // KQ = soft_max(KQ_masked)
-            // KQ_soft_max shape [n_past + N, N, n_head, 1]
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-
-            // split cached V into n_head heads
-            //// V shape [n_past + N, n_embd/n_head, n_head, 1]
-            // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1]
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, vc,
-                        n_past + N, n_embd/n_head, n_head,
-                        n_ctx*ggml_element_size(vc),
-                        n_ctx*ggml_element_size(vc)*n_embd/n_head,
-                        il*n_ctx*ggml_element_size(vc)*n_embd);
-
-            // KQV shape [n_embd/n_head, N, n_head, 1]
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            // KQV_merged shape [n_embd/n_head, n_head, N, 1]
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            // KQV_merged shape
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            // cur shape [n_embd,N,1,1]
-            cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N);
-            // cur = ggml_cpy(ctx0,
-            //         KQV_merged,
-            //         ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
-
-            // projection (no bias)
-            // cur shape [n_embd,N,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].wo,
-                    cur);
-        }
-
-        // lctx.use_buf(ctx0, 1);
-
-        // inpFF shape [n_embd,N,1,1]
-        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
-
-        // feed-forward network
-        {
-            // norm
-            {
-                // cur shape [n_embd,N,1,1]
-                cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
-
-                // cur = ffn_norm*cur
-                // cur shape [n_embd,N,1,1]
-                cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
-                        cur);
-            }
-
-            // tmp shape [n_ff,N,1,1]
-            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
-                    model->layers[il].w3,
-                    cur);
-
-            // cur shape [n_ff,N,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w1,
-                    cur);
-
-            // SILU activation
-            // cur shape [n_ff,N,1,1]
-            cur = ggml_silu(ctx0, cur);
-
-            // cur shape [n_ff,N,1,1]
-            cur = ggml_mul(ctx0, cur, tmp);
-
-            // cur shape [n_embd,N,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w2,
-                    cur);
-        }
-
-        // cur shape [n_embd,N,1,1]
-        cur = ggml_add(ctx0, cur, inpFF);
-
-        // input for next layer
-        // inpL shape [n_embd,N,1,1]
-        inpL = cur;
-    }
-
-    // norm
-    {
-
-        // inpL shape [n_embd,N,1,1]
-        inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-
-        // inpL = norm*inpL
-        // inpL shape [n_embd,N,1,1]
-        inpL = ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model->norm, inpL),
-                    inpL);
-
-        //embeddings = inpL;
-    }
-
-    // lm_head
-    // inpL shape [n_vocab,N,1,1]
-    inpL = ggml_mul_mat(ctx0, model->output, inpL);
-
-    // run the computation
-    ggml_build_forward_expand(gf, inpL);
-
-    return inpL;
-}
-
 void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {
     GGML_ASSERT(tensor->n_dims == 1);
     GGML_ASSERT(tensor->ne[0] == ne0);
@@ -658,786 +448,222 @@ void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int6
     GGML_ASSERT(tensor->ne[3] == ne3);
 }
 
-struct ggml_tensor * forward_batch(
-        struct my_llama_model    * model,
-        struct my_llama_kv_cache * cache,
-        struct ggml_context   * ctx0,
-        struct ggml_cgraph    * gf,
-        struct ggml_tensor    * tokens_input,
-        const  int              n_tokens,
-        const  int              n_past,
-        const  int              n_batch) {
-
-    const int N = n_tokens;
-
-    struct my_llama_kv_cache& kv_self = *cache;
-    const auto & hparams = model->hparams;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_vocab = hparams.n_vocab;
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_head  = hparams.n_head;
-    const int n_rot   = hparams.n_rot;
-    const int n_ff    = get_n_ff(&hparams);
-
-    struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch);
-    memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch);
-
-    struct ggml_tensor * kc = kv_self.k;
-    struct ggml_tensor * vc = kv_self.v;
-
-    // inpL shape [n_embd,N*n_batch,1]
-    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
-    assert_shape_2d(inpL, n_embd, N*n_batch);
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * inpSA = inpL;
-
-        struct ggml_tensor * cur;
-
-        // lctx.use_buf(ctx0, 0);
-
-        // norm
-        {
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-
-            // cur = attention_norm*cur
-            cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
-                        cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        // self-attention
-        {
-            // compute Q and K and RoPE them
-            // wq   shape [n_embd, n_embd, 1, 1]
-            // wk   shape [n_embd, n_embd, 1, 1]
-            // Qcur shape [n_embd/n_head, n_head, N, n_batch]
-            // Kcur shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
-            assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
-            assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
-
-            // store key and value to memory
-            {
-                // compute the transposed [N, n_embd] V matrix
-                // wv   shape [n_embd, n_embd, 1, 1]
-                // Vcur shape [N, n_embd, n_batch, 1]
-                struct ggml_tensor * Vcur = ggml_cont(ctx0,
-                    ggml_permute(ctx0,
-                        ggml_reshape_3d(ctx0,
-                            ggml_mul_mat(ctx0,
-                                model->layers[il].wv,
-                                cur),
-                        n_embd, N, n_batch),
-                        1, 0, 2, 3));
-                assert_shape_3d(Vcur, N, n_embd, n_batch);
-
-                // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]
-                // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]
-                // k         shape [n_embd * N, n_batch]   == kv_self.k[:,n_past:n_past+N,:,il]
-                // v         shape [N, n_embd, n_batch, 1] == kv_self.v[:,n_past:n_past+N,:,il]
-
-                /* {
-                    struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
-                    struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
-                            (   n_ctx)*ggml_element_size(kv_self.v),
-                            (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
-
-                    // important: storing RoPE-ed version of K in the KV cache!
-                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-                } //*/
-
-                kc = ggml_set_2d_inplace(ctx0, kc,
-                        ggml_reshape_2d(ctx0, Kcur, n_embd*N, n_batch),
-                        ggml_element_size(kc)*n_embd*n_ctx,
-                        (ggml_element_size(kc)*n_embd)*(il*n_batch*n_ctx + n_past));
-                vc = ggml_set_2d_inplace(ctx0, vc,
-                        ggml_reshape_2d(ctx0, Vcur, N*n_embd, n_batch),
-                        ggml_element_size(vc)*n_ctx*n_embd,
-                        ggml_element_size(vc)*(n_past + il*n_embd*n_batch*n_ctx));
-
-                assert_shape_1d(kc, n_embd * n_ctx * n_batch * n_layer);
-                assert_shape_1d(vc, n_embd * n_ctx * n_batch * n_layer);
-            }
-
-            // Qcur shape [n_embd/n_head, n_head, N, n_batch]
-            // Q shape    [n_embd/n_head, N, n_head, n_batch]
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        Qcur,
-                        0, 2, 1, 3);
-            assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch);
-
-            // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]
-            // K shape [n_embd/n_head, n_past + N, n_head, n_batch]
-            struct ggml_tensor * K =
-                ggml_permute(ctx0,
-                        ggml_reshape_4d(ctx0,
-                            ggml_view_3d(ctx0,
-                                kc,
-                                n_embd,
-                                (n_past + N),
-                                n_batch,
-                                n_embd*ggml_element_size(kc),
-                                n_ctx*n_embd*ggml_element_size(kc),
-                                il*n_batch*n_ctx*n_embd*ggml_element_size(kc)),
-                            n_embd/n_head, n_head, n_past + N, n_batch),
-                        0, 2, 1, 3);
-            assert_shape_4d(K, n_embd/n_head, n_past + N, n_head, n_batch);
-
-            // K * Q
-            // KQ shape [n_past + N, N, n_head, n_batch]
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            assert_shape_4d(KQ, n_past + N, N, n_head, n_batch);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            // KQ_scaled shape [n_past + N, N, n_head, n_batch]
-            struct ggml_tensor * KQ_scaled =
-                ggml_scale_inplace(ctx0,
-                        KQ,
-                        ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
-            assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch);
-
-            // KQ_masked = mask_past(KQ_scaled)
-            // KQ_masked shape [n_past + N, N, n_head, n_batch]
-            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
-            assert_shape_4d(KQ_masked, n_past + N, N, n_head, n_batch);
-
-            // KQ = soft_max(KQ_masked)
-            // KQ_soft_max shape [n_past + N, N, n_head, n_batch]
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
-            assert_shape_4d(KQ_soft_max, n_past + N, N, n_head, n_batch);
-
-            // split cached V into n_head heads
-            // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]
-            // V shape [n_past + N, n_embd/n_head, n_head, n_batch] == kv_self.v[:(n_past+N),:,:,il]
-            struct ggml_tensor * V =
-                ggml_view_4d(ctx0, vc,
-                        n_past + N, n_embd/n_head, n_head, n_batch,
-                        ggml_element_size(vc)*n_ctx,
-                        ggml_element_size(vc)*n_ctx*n_embd/n_head,
-                        ggml_element_size(vc)*n_ctx*n_embd,
-                        il*n_batch*n_ctx*n_embd*ggml_element_size(vc));
-            assert_shape_4d(V, n_past + N, n_embd/n_head, n_head, n_batch);
-
-            // KQV shape [n_embd/n_head, N, n_head, n_batch]
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            // KQV_merged shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch);
-            // KQV_merged shape
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-            // cur = ggml_cpy(ctx0,
-            //         KQV_merged,
-            //         ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
-
-            // projection (no bias)
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].wo,
-                    cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        // lctx.use_buf(ctx0, 1);
-
-        // inpFF shape [n_embd,N*n_batch,1,1]
-        struct ggml_tensor * inpFF = ggml_add_inplace(ctx0, cur, inpSA);
-        assert_shape_2d(inpFF, n_embd, N*n_batch);
-
-        // feed-forward network
-        {
-            // norm
-            {
-                // cur shape [n_embd,N*n_batch,1,1]
-                cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
-                assert_shape_2d(cur, n_embd, N*n_batch);
-
-                // cur = ffn_norm*cur
-                // cur shape [n_embd,N*n_batch,1,1]
-                cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
-                        cur);
-                assert_shape_2d(cur, n_embd, N*n_batch);
-            }
-
-            // tmp shape [n_ff,N*n_batch,1,1]
-            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
-                    model->layers[il].w3,
-                    cur);
-            assert_shape_2d(tmp, n_ff, N*n_batch);
-
-            // cur shape [n_ff,N*n_batch,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w1,
-                    cur);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            // SILU activation
-            // cur shape [n_ff,N*n_batch,1,1]
-            cur = ggml_silu(ctx0, cur);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            // cur shape [n_ff,N*n_batch,1,1]
-            cur = ggml_mul(ctx0, cur, tmp);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w2,
-                    cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        // cur shape [n_embd,N*n_batch,1,1]
-        cur = ggml_add_inplace(ctx0, cur, inpFF);
-        assert_shape_2d(cur, n_embd, N*n_batch);
-
-        // input for next layer
-        // inpL shape [n_embd,N*n_batch,1,1]
-        inpL = cur;
-        assert_shape_2d(inpL, n_embd, N*n_batch);
-    }
-
-    // norm
-    {
-
-        // inpL shape [n_embd,N*n_batch,1,1]
-        inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-        assert_shape_2d(inpL, n_embd, N*n_batch);
-
-        // inpL = norm*inpL
-        // inpL shape [n_embd,N*n_batch,1,1]
-        inpL = ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model->norm, inpL),
-                    inpL);
-
-        assert_shape_2d(inpL, n_embd, N*n_batch);
-
-        //embeddings = inpL;
-    }
-
-    // lm_head
-    // inpL shape [n_vocab,N*n_batch,1,1]
-    inpL = ggml_mul_mat(ctx0, model->output, inpL);
-    assert_shape_2d(inpL, n_vocab, N*n_batch);
-
-    {
-        // inpL shape [n_vocab,N,n_batch,1]
-        inpL = ggml_reshape_3d(ctx0,
-                        inpL,
-                        n_vocab, N, n_batch);
-        assert_shape_3d(inpL, n_vocab, N, n_batch);
-    }
-
-    // run the computation
-    ggml_build_forward_expand(gf, inpL);
-
-    return inpL;
+static size_t hash(void * p) {
+    return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
 }
 
-struct ggml_tensor * forward_batch_wo_cache(
-        struct my_llama_model * model,
-        struct ggml_context   * ctx0,
-        struct ggml_cgraph    * gf,
-        struct ggml_tensor    * tokens_input,
-        const  int              n_tokens,
-        const  int              n_batch) {
+static size_t hash_find(void * hash_table[], void * p) {
+    size_t h = hash(p);
 
-    const int n_past = 0;
-    const int N = n_tokens;
-
-    const auto & hparams = model->hparams;
-    //const int n_ctx   = hparams.n_ctx;
-    const int n_vocab = hparams.n_vocab;
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_head  = hparams.n_head;
-    const int n_rot   = hparams.n_rot;
-    const int n_ff    = get_n_ff(&hparams);
-
-    struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch);
-    memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch);
-
-    // inpL shape [n_embd,N*n_batch,1]
-    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
-    assert_shape_2d(inpL, n_embd, N*n_batch);
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * inpSA = inpL;
-
-        struct ggml_tensor * cur;
-
-        // lctx.use_buf(ctx0, 0);
-
-        // norm
-        {
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-
-            // cur = attention_norm*cur
-            cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
-                        cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
+    // linear probing
+    size_t i = h;
+    while (hash_table[i] != NULL && hash_table[i] != p) {
+        i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
+        if (i == h) {
+            // visited all hash table entries -> not found
+            return GGML_GRAPH_HASHTABLE_SIZE;
         }
-
-        // self-attention
-        {
-            // compute Q and K and RoPE them
-            // wq   shape [n_embd, n_embd, 1, 1]
-            // wk   shape [n_embd, n_embd, 1, 1]
-            // Qcur shape [n_embd/n_head, n_head, N, n_batch]
-            // Kcur shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
-            assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
-            assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
-
-            // Vcur shape [N, n_batch, n_embd/n_head, n_head]
-            struct ggml_tensor * Vcur = ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, cur, model->layers[il].wv), N, n_batch, n_embd/n_head, n_head);
-            assert_shape_4d(Vcur, N, n_batch, n_embd/n_head, n_head);
-
-            // Qcur shape [n_embd/n_head, n_head, N, n_batch]
-            // Q shape    [n_embd/n_head, N, n_head, n_batch]
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        Qcur,
-                        0, 2, 1, 3);
-            assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch);
-
-            // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]
-            // K shape [n_embd/n_head, N, n_head, n_batch]
-            struct ggml_tensor * K =
-                ggml_permute(ctx0,
-                        Kcur,
-                        0, 2, 1, 3);
-            assert_shape_4d(K, n_embd/n_head, N, n_head, n_batch);
-
-            // K * Q
-            // KQ shape [N, N, n_head, n_batch]
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            assert_shape_4d(KQ, N, N, n_head, n_batch);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            // KQ_scaled shape [N, N, n_head, n_batch]
-            struct ggml_tensor * KQ_scaled =
-                ggml_scale_inplace(ctx0,
-                        KQ,
-                        ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
-            assert_shape_4d(KQ_scaled, N, N, n_head, n_batch);
-
-            // KQ_masked = mask_past(KQ_scaled)
-            // KQ_masked shape [N, N, n_head, n_batch]
-            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
-            assert_shape_4d(KQ_masked, N, N, n_head, n_batch);
-
-            // KQ = soft_max(KQ_masked)
-            // KQ_soft_max shape [N, N, n_head, n_batch]
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
-            assert_shape_4d(KQ_soft_max, N, N, n_head, n_batch);
-
-            // Vcur shape [N, n_batch, n_embd/n_head, n_head]
-            // V shape    [N, n_embd/n_head, n_head, n_batch]
-            struct ggml_tensor * V =
-                ggml_permute(ctx0,
-                    Vcur,
-                    0, 3, 1, 2);
-            assert_shape_4d(V, N, n_embd/n_head, n_head, n_batch);
-
-            // KQV shape [n_embd/n_head, N, n_head, n_batch]
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            // KQV_merged shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch);
-            // KQV_merged shape
-
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-
-            // projection (no bias)
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].wo,
-                    cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        // lctx.use_buf(ctx0, 1);
-
-        // inpFF shape [n_embd,N*n_batch,1,1]
-        struct ggml_tensor * inpFF = ggml_add_inplace(ctx0, cur, inpSA);
-        assert_shape_2d(inpFF, n_embd, N*n_batch);
-
-        // feed-forward network
-        {
-            // norm
-            {
-                // cur shape [n_embd,N*n_batch,1,1]
-                cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
-                assert_shape_2d(cur, n_embd, N*n_batch);
-
-                // cur = ffn_norm*cur
-                // cur shape [n_embd,N*n_batch,1,1]
-                cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
-                        cur);
-                assert_shape_2d(cur, n_embd, N*n_batch);
-            }
-
-            // tmp shape [n_ff,N*n_batch,1,1]
-            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
-                    model->layers[il].w3,
-                    cur);
-            assert_shape_2d(tmp, n_ff, N*n_batch);
-
-            // cur shape [n_ff,N*n_batch,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w1,
-                    cur);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            // SILU activation
-            // cur shape [n_ff,N*n_batch,1,1]
-            cur = ggml_silu(ctx0, cur);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            // cur shape [n_ff,N*n_batch,1,1]
-            cur = ggml_mul(ctx0, cur, tmp);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            // cur shape [n_embd,N*n_batch,1,1]
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w2,
-                    cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        // cur shape [n_embd,N*n_batch,1,1]
-        cur = ggml_add_inplace(ctx0, cur, inpFF);
-        assert_shape_2d(cur, n_embd, N*n_batch);
-
-        // input for next layer
-        // inpL shape [n_embd,N*n_batch,1,1]
-        inpL = cur;
-        assert_shape_2d(inpL, n_embd, N*n_batch);
     }
-
-    // norm
-    {
-
-        // inpL shape [n_embd,N*n_batch,1,1]
-        inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-        assert_shape_2d(inpL, n_embd, N*n_batch);
-
-        // inpL = norm*inpL
-        // inpL shape [n_embd,N*n_batch,1,1]
-        inpL = ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model->norm, inpL),
-                    inpL);
-
-        assert_shape_2d(inpL, n_embd, N*n_batch);
-
-        //embeddings = inpL;
-    }
-
-    // lm_head
-    // inpL shape [n_vocab,N*n_batch,1,1]
-    inpL = ggml_mul_mat(ctx0, model->output, inpL);
-    assert_shape_2d(inpL, n_vocab, N*n_batch);
-
-    {
-        // inpL shape [n_vocab,N,n_batch,1]
-        inpL = ggml_reshape_3d(ctx0,
-                        inpL,
-                        n_vocab, N, n_batch);
-        assert_shape_3d(inpL, n_vocab, N, n_batch);
-    }
-
-    // run the computation
-    ggml_build_forward_expand(gf, inpL);
-
-    return inpL;
+    return i;
 }
 
-struct ggml_tensor * forward_batch_wo_cache_flash_attn(
-        struct my_llama_model * model,
-        struct ggml_context   * ctx0,
-        struct ggml_cgraph    * gf,
-        struct ggml_tensor    * tokens_input,
-        const  int              n_tokens,
-        const  int              n_batch) {
+static bool hash_insert(void * hash_table[], void * p) {
+    //size_t h = hash(p);
+    size_t i = hash_find(hash_table, p);
 
-    const int n_past = 0;
-    const int N = n_tokens;
+    GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full
 
-    const auto & hparams = model->hparams;
-    //const int n_ctx   = hparams.n_ctx;
-    const int n_vocab = hparams.n_vocab;
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_head  = hparams.n_head;
-    const int n_rot   = hparams.n_rot;
-    const int n_ff    = get_n_ff(&hparams);
-
-    struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch);
-    memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch);
-
-    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
-    assert_shape_2d(inpL, n_embd, N*n_batch);
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * inpSA = inpL;
-
-        struct ggml_tensor * cur;
-
-        // norm
-        {
-            cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-
-            // cur = attention_norm*cur
-            cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
-                        cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        // self-attention
-        {
-            // compute Q and K and RoPE them
-            // wq   shape [n_embd, n_embd, 1, 1]
-            // wk   shape [n_embd, n_embd, 1, 1]
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
-            assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
-            assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
-
-            struct ggml_tensor * Vcur = ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, cur, model->layers[il].wv), N, n_batch, n_embd/n_head, n_head);
-            assert_shape_4d(Vcur, N, n_batch, n_embd/n_head, n_head);
-
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        Qcur,
-                        0, 2, 1, 3);
-            assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch);
-
-            struct ggml_tensor * K =
-                ggml_permute(ctx0,
-                        Kcur,
-                        0, 2, 1, 3);
-            assert_shape_4d(K, n_embd/n_head, N, n_head, n_batch);
-
-            struct ggml_tensor * V =
-                ggml_permute(ctx0,
-                    Vcur,
-                    0, 3, 1, 2);
-            assert_shape_4d(V, N, n_embd/n_head, n_head, n_batch);
-
-            bool masked = true;
-            struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, masked);
-            assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch);
-
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch);
-            cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-
-            // projection (no bias)
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].wo,
-                    cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        struct ggml_tensor * inpFF = ggml_add_inplace(ctx0, cur, inpSA);
-        assert_shape_2d(inpFF, n_embd, N*n_batch);
-
-        // feed-forward network
-        {
-            // norm
-            {
-                cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
-                assert_shape_2d(cur, n_embd, N*n_batch);
-
-                // cur = ffn_norm*cur
-                cur = ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
-                        cur);
-                assert_shape_2d(cur, n_embd, N*n_batch);
-            }
-
-            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
-                    model->layers[il].w3,
-                    cur);
-            assert_shape_2d(tmp, n_ff, N*n_batch);
-
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w1,
-                    cur);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            // SILU activation
-            cur = ggml_silu(ctx0, cur);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            cur = ggml_mul(ctx0, cur, tmp);
-            assert_shape_2d(cur, n_ff, N*n_batch);
-
-            cur = ggml_mul_mat(ctx0,
-                    model->layers[il].w2,
-                    cur);
-            assert_shape_2d(cur, n_embd, N*n_batch);
-        }
-
-        cur = ggml_add_inplace(ctx0, cur, inpFF);
-        assert_shape_2d(cur, n_embd, N*n_batch);
-
-        // input for next layer
-        inpL = cur;
-        assert_shape_2d(inpL, n_embd, N*n_batch);
+    if (hash_table[i] == p) {
+        return true;
     }
 
-    // norm
-    {
-
-        inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
-        assert_shape_2d(inpL, n_embd, N*n_batch);
-
-        // inpL = norm*inpL
-        inpL = ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model->norm, inpL),
-                    inpL);
-
-        assert_shape_2d(inpL, n_embd, N*n_batch);
-    }
-
-    // lm_head
-    inpL = ggml_mul_mat(ctx0, model->output, inpL);
-    assert_shape_2d(inpL, n_vocab, N*n_batch);
-
-    {
-        inpL = ggml_reshape_3d(ctx0,
-                        inpL,
-                        n_vocab, N, n_batch);
-        assert_shape_3d(inpL, n_vocab, N, n_batch);
-    }
-
-    // run the computation
-    ggml_build_forward_expand(gf, inpL);
-
-    return inpL;
+    // insert
+    GGML_ASSERT(hash_table[i] == NULL);
+    hash_table[i] = p;
+    return false;
 }
 
-// expand the graph nodes without creating leafs.
-struct ggml_tensor * expand(struct ggml_cgraph * g, struct ggml_tensor * t) {
-    // check if already visited
-    for (int i = 0; i < g->n_nodes; i++) {
-        if (g->nodes[i] == t) {
-            return t;
-        }
-    }
-
-    for (int i = 0; i < g->n_leafs; i++) {
-        if (g->leafs[i] == t) {
-            return t;
-        }
-    }
-
-    for (int i = 0; i < GGML_MAX_SRC; ++i) {
-        if (t->src[i]) {
-            expand(g, t->src[i]);
-        }
-    }
-
-    GGML_ASSERT(g->n_nodes < GGML_MAX_NODES);
-
-    if (strlen(t->name) == 0) {
-        snprintf(t->name, sizeof(t->name), "node_%d", g->n_nodes);
-    }
-
-    g->nodes[g->n_nodes] = t;
-    g->grads[g->n_nodes] = t->grad;
-    g->n_nodes++;
-    return t;
+static bool hash_contains(void * hash_table[], void * p) {
+    size_t i = hash_find(hash_table, p);
+    return (i < GGML_GRAPH_HASHTABLE_SIZE) && (hash_table[i] == p);
 }
 
-void graph_set_leafs_grads(struct ggml_cgraph * g) {
-    // moves leaf nodes to g->leafs.
-    // i.e. g->n_nodes might change.
-    int n_nodes = 0;
-    for (int i = 0; i < g->n_nodes; ++i) {
-        struct ggml_tensor * node = g->nodes[i];
-        const bool is_leaf = node->op == GGML_OP_NONE && node->grad == NULL;
-        if (is_leaf) {
-            GGML_ASSERT(g->n_leafs < GGML_MAX_NODES);
+struct hash_map {
+    void * keys[GGML_GRAPH_HASHTABLE_SIZE];
+    void * vals[GGML_GRAPH_HASHTABLE_SIZE];
+};
+//static const size_t HASH_MAP_SIZE = sizeof(struct hash_map);
 
-            if (strlen(node->name) == 0) {
-                snprintf(node->name, sizeof(node->name), "leaf_%d", g->n_leafs);
-            }
-
-            g->leafs[g->n_leafs] = node;
-            g->n_leafs++;
-        } else {
-            GGML_ASSERT(n_nodes < GGML_MAX_NODES);
-
-            if (strlen(node->name) == 0) {
-                snprintf(node->name, sizeof(node->name), "node_%d", n_nodes);
-            }
-
-            g->nodes[n_nodes] = node;
-            g->grads[n_nodes] = node->grad;
-            n_nodes++;
-        }
+struct hash_map * new_hash_map() {
+    struct hash_map * result = new struct hash_map;
+    for (int i=0; i<GGML_GRAPH_HASHTABLE_SIZE; ++i) {
+        result->keys[i] = NULL;
+        result->vals[i] = NULL;
     }
-    for (int i=n_nodes; i < g->n_nodes; ++i) {
-        g->nodes[n_nodes] = NULL;
-        g->grads[n_nodes] = NULL;
-    }
-    g->n_nodes = n_nodes;
+    return result;
+};
+
+void free_hash_map(struct hash_map * map) {
+    delete map;
 }
 
-struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
-        struct my_llama_model * model,
-        struct ggml_context   * ctx0,
+static bool ggml_is_view(struct ggml_tensor * t) {
+    return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
+           t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
+}
+
+static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
+    switch (t->op) {
+        case GGML_OP_PERMUTE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_VIEW:
+            return t->src[0];
+        case GGML_OP_CPY:
+            return t->src[1];
+        default:
+            return NULL;
+    }
+}
+
+static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
+    struct ggml_tensor * parent = t;
+    do {
+        parent = get_view_parent(parent);
+    } while (ggml_is_view(parent));
+    return parent;
+}
+
+struct ggml_tensor * ggml_recompute_graph_node(
+        struct ggml_context * ctx,
+        struct ggml_cgraph  * graph,
+        struct hash_map     * replacements,
+        struct ggml_tensor  * node) {
+
+    if (node == NULL) {
+        return NULL;
+    }
+
+    if (node->is_param) {
+        return node;
+    }
+
+    if (!hash_contains(graph->visited_hash_table, node)) {
+        return node;
+    }
+
+    int count_children = 0;
+    for (int k = 0; k < GGML_MAX_SRC; ++k) {
+        if (node->src[k]) {
+            ++count_children;
+        }
+    }
+
+    if (count_children == 0) {
+        return node;
+    }
+
+    size_t i = hash_find(replacements->keys, node);
+    GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full
+    if (replacements->keys[i] == node) {
+        return (struct ggml_tensor *) replacements->vals[i];
+    }
+
+    struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, node->n_dims, node->ne);
+
+    // insert clone into replacements
+    GGML_ASSERT(replacements->keys[i] == NULL); // assert that we don't overwrite
+    replacements->keys[i] = node;
+    replacements->vals[i] = clone;
+
+    clone->op       = node->op;
+    clone->grad     = node->grad;
+    clone->is_param = node->is_param;
+    clone->extra    = node->extra;
+    for (int k = 0; k < GGML_MAX_DIMS; ++k) {
+        clone->nb[k] = node->nb[k];
+    }
+    for (int k = 0; k < GGML_MAX_SRC; ++k) {
+        clone->src[k] = ggml_recompute_graph_node(ctx, graph, replacements, node->src[k]);
+    }
+    if (ggml_is_view(clone)) {
+        struct ggml_tensor * source = get_view_source(clone);
+        GGML_ASSERT(source != NULL);
+        clone->data = source->data;
+    }
+
+    GGML_ASSERT(sizeof(node->op_params) == sizeof(int32_t) * (GGML_MAX_OP_PARAMS / sizeof(int32_t)));
+    GGML_ASSERT(sizeof(node->name)      == GGML_MAX_NAME);
+    memcpy(clone->op_params, node->op_params, sizeof(node->op_params));
+    ggml_format_name(clone, "%s (clone)", ggml_get_name(node));
+
+    return clone;
+};
+
+void ggml_build_backward_gradient_checkpointing(
+        struct ggml_context   * ctx,
         struct ggml_cgraph    * gf,
         struct ggml_cgraph    * gb,
+        struct ggml_cgraph    * gb_tmp,
+        struct ggml_tensor  * * checkpoints,
+        int                     n_checkpoints) {
+    *gb_tmp = *gf;
+    ggml_build_backward_expand(ctx, gf, gb_tmp, true);
+
+    if (n_checkpoints <= 0) {
+        *gb = *gb_tmp;
+        return;
+    }
+
+    struct hash_map * replacements = new_hash_map();
+
+    // insert checkpoints in replacements
+    for (int i = 0; i < n_checkpoints; ++i) {
+        size_t k = hash_find(replacements->keys, checkpoints[i]);
+        GGML_ASSERT(k < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full
+        GGML_ASSERT(replacements->keys[k] == NULL); // assert that we don't overwrite
+        replacements->keys[k] = checkpoints[i];
+        replacements->vals[k] = checkpoints[i];
+    }
+
+    *gb = *gf;
+    // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes],
+    // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]),
+    // by recomputing them from checkpoints
+    for (int i = gf->n_nodes; i<gb_tmp->n_nodes; ++i) {
+        struct ggml_tensor * node = gb_tmp->nodes[i];
+        for (int k = 0; k < GGML_MAX_SRC; ++k) {
+            // insert new tensors recomputing src, reusing already made replacements,
+            // remember replacements: remember new tensors with mapping from corresponding gf nodes
+            // recurse for input tensors,
+            // unless (i.e. terminating when) input tensors are checkpoints
+            node->src[k] = ggml_recompute_graph_node(ctx, gf, replacements, node->src[k]);
+        }
+        // insert rewritten backward node with replacements made into resulting backward graph gb
+        ggml_build_forward_expand(gb, node);
+    }
+
+    free_hash_map(replacements);
+}
+
+struct ggml_tensor * llama_build_train_graphs(
+        struct my_llama_model * model,
+        struct ggml_allocr    * alloc,
+        struct ggml_context   * ctx,
+        struct ggml_cgraph    * gf,
+        struct ggml_cgraph    * gb,
+        struct ggml_cgraph    * gb_tmp,
         struct ggml_tensor  * * logits,
         struct ggml_tensor    * tokens_input,
         struct ggml_tensor    * targets,
-        void                  * compute_buf_0,
-        void                  * compute_buf_1,
-        size_t                  size_buf_0,
-        size_t                  size_buf_1,
         const  int              n_tokens,
-        const  int              n_batch) {
-
-    ggml_set_scratch(ctx0, { 0, 0, nullptr, });
+        const  int              n_batch,
+        const  bool             enable_flash_attn,
+        const  bool             enable_checkpointing) {
 
+    ggml_set_scratch(ctx, { 0, 0, nullptr, });
     const int n_past = 0;
     const int N = n_tokens;
-
-    gf->n_nodes = 0;
-    gf->n_leafs = 0;
-    gf->perf_runs = 0;
-    gf->perf_cycles = 0;
-    gf->perf_time_us = 0;
-
     const auto & hparams = model->hparams;
     const int n_ctx      = hparams.n_ctx;
     const int n_vocab    = hparams.n_vocab;
@@ -1445,476 +671,162 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
     const int n_layer    = hparams.n_layer;
     const int n_head     = hparams.n_head;
     const int n_rot      = hparams.n_rot;
-    const int n_ff       = get_n_ff(&hparams);
-    const int rope_mode  = 0;
+    const int n_ff       = hparams.n_ff;
+    const float f_norm_rms_eps  = hparams.f_norm_rms_eps;
+    const float rope_freq_base  = hparams.rope_freq_base;
+    const float rope_freq_scale = hparams.rope_freq_scale;
 
-    int last_buf = -1;
-    size_t buf_offs[2] = { 0, 0 };
-    size_t buf_size[2] = { size_buf_0,
-                           size_buf_1 };
-    void * buf_data[2] = { compute_buf_0,
-                           compute_buf_1 };
-    auto use_buf = [ctx0, &last_buf, &buf_offs, &buf_size, &buf_data] (int buf) {
-        size_t last_offs = 0;
-        last_offs = ggml_set_scratch(ctx0, { 0, 0, nullptr, });
-        if (last_buf >= 0) {
-            buf_offs[last_buf] = last_offs;
-        }
-        if (buf >= 0) {
-            size_t offs = buf_offs[buf];
-            size_t size = buf_size[buf];
-            void * data = buf_data[buf];
-            ggml_set_scratch(ctx0, { offs, size, data, });
-        }
-        last_buf = buf;
-    };
-
-    bool track_max_mem = false;
-    size_t buf_maxs[2] = { 0, 0 };
-
-    auto clr_buf = [ctx0, &last_buf, &buf_offs, &buf_size, &buf_data, &buf_maxs, track_max_mem] (int buf) {
-        if (buf < 0) return;
-        if (track_max_mem) {
-            size_t last_offs = 0;
-            last_offs = ggml_set_scratch(ctx0, { 0, 0, nullptr, });
-            if (last_buf >= 0) {
-                buf_offs[last_buf] = last_offs;
-                buf_maxs[last_buf] = std::max(buf_maxs[last_buf], buf_offs[last_buf]);
-            }
-        }
-        buf_offs[buf] = 0;
-        if (track_max_mem && last_buf >= 0) {
-            size_t offs = buf_offs[last_buf];
-            size_t size = buf_size[last_buf];
-            void * data = buf_data[last_buf];
-            ggml_set_scratch(ctx0, { offs, size, data, });
+    auto set_name = [](struct ggml_tensor * t, const char * n) {
+        ggml_set_name(t, n);
+        if (t->grad) {
+            ggml_format_name(t->grad, "%s->grad", n);
         }
     };
 
+    // rope has so much parameters that we make a custom function for it
+    auto rope = [ctx, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
+                (struct ggml_tensor * t) -> struct ggml_tensor * {
+        // not capturing these, to silcence warnings
+        const int n_past    = 0;
+        const int rope_mode = 0;
 
-    auto view__q = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * {
-        int64_t ne0 = n_embd/n_head;
-        int64_t ne1 = N;
-        int64_t ne2 = n_head;
-        int64_t ne3 = n_batch;
-        size_t  nb0 = ggml_element_size(t);
-        size_t  nb1 = nb0*ne0;
-        size_t  nb2 = nb1*ne1;
-        size_t  nb3 = nb2*ne2;
-        size_t offset = 0;
-        return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset);
+        return ggml_rope_custom(ctx,
+            t, n_past, n_rot, rope_mode, n_ctx,
+            rope_freq_base, rope_freq_scale);
     };
 
-    auto view__k = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * {
-        int64_t ne0 = n_embd/n_head;
-        int64_t ne1 = N;
-        int64_t ne2 = n_head;
-        int64_t ne3 = n_batch;
-        size_t  nb0 = ggml_element_size(t);
-        size_t  nb1 = nb0*ne0;
-        size_t  nb2 = nb1*ne1;
-        size_t  nb3 = nb2*ne2;
-        size_t offset = nb3*ne3;
-        return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset);
-    };
+    set_name(tokens_input, "tokens_input");
+    set_name(targets,      "targets");
 
-    auto view__v = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * {
-        int64_t ne0 = N;
-        int64_t ne1 = n_embd/n_head;
-        int64_t ne2 = n_head;
-        int64_t ne3 = n_batch;
-        size_t  nb0 = ggml_element_size(t);
-        size_t  nb1 = nb0*ne0;
-        size_t  nb2 = nb1*ne1;
-        size_t  nb3 = nb2*ne2;
-        size_t offset = 2*nb3*ne3;
-        return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset);
-    };
-
-    auto add_or_set = [ctx0] (struct ggml_tensor * a, struct ggml_tensor * b) -> struct ggml_tensor * {
-        if (a == NULL) {
-            return b;
-        } else {
-            return ggml_add_inplace(ctx0, a, b);
-        }
-    };
-
-    use_buf(-1);
-
-    model->tok_embeddings->grad    = NULL;
-    model->norm->grad              = NULL;
-    model->output->grad            = NULL;
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct my_llama_layer & layer = model->layers[il];
-        layer.attention_norm->grad = NULL;
-        layer.wq->grad             = NULL;
-        layer.wk->grad             = NULL;
-        layer.wv->grad             = NULL;
-        layer.wo->grad             = NULL;
-        layer.ffn_norm->grad       = NULL;
-        layer.w1->grad             = NULL;
-        layer.w2->grad             = NULL;
-        layer.w3->grad             = NULL;
-    }
-
-    clr_buf(0);
-    clr_buf(1);
-
-    use_buf(-1);
-
-    struct ggml_tensor * t00 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); assert_shape_1d(t00, N*n_batch);
-    memcpy(t00->data, tokens_input->data, ggml_element_size(t00)*N*n_batch);
-
-    use_buf(-1);
-
-    struct ggml_tensor * t01 = expand(gf, ggml_get_rows(ctx0, model->tok_embeddings, t00)); assert_shape_2d(t01, n_embd, N*n_batch);
-
-    // need to remember these for the backward pass
-    std::vector<struct ggml_tensor *> t02L; t02L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t03L; t03L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t04L; t04L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t05L; t05L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t06L; t06L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t07L; t07L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t08L; t08L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t09L; t09L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t10L; t10L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t11L; t11L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t12L; t12L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t13L; t13L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t14L; t14L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t15L; t15L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t16L; t16L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t17L; t17L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t18L; t18L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t19L; t19L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t20L; t20L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t21L; t21L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t22L; t22L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t23L; t23L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t24L; t24L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t25L; t25L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t26L; t26L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t27L; t27L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t28L; t28L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t29L; t29L.resize(n_layer, NULL);
-    std::vector<struct ggml_tensor *> t30L; t30L.resize(n_layer, NULL);
+    GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
+    struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch);  set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch);
+    struct ggml_tensor * t01 = ggml_get_rows(ctx, model->tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch);
 
     struct ggml_tensor * cur = t01;
 
+    std::vector<struct ggml_tensor *> checkpoints;
+    checkpoints.push_back(tokens_input);
+    checkpoints.push_back(targets);
+    checkpoints.push_back(t00);
+    checkpoints.push_back(t01);
+
+    struct ggml_tensor * kv_scale;
+    if (!enable_flash_attn) {
+        kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head));
+    }
+
     for (int il = 0; il < n_layer; ++il) {
-        clr_buf(0);
         struct my_llama_layer & layer = model->layers[il];
-        // tensors with values necessary for backward pass are in persistent buf(-1)
-        // other tensors with buf(0) and buf(1) are only temporary needed, and their memory reused after layer is completed.
-        use_buf(-1); struct ggml_tensor * t02 = expand(gf, ggml_rms_norm     (ctx0, cur, rms_norm_eps));                      assert_shape_2d(t02, n_embd, N*n_batch);
-        use_buf( 0); struct ggml_tensor * t03 = expand(gf, ggml_repeat       (ctx0, layer.attention_norm, t02));              assert_shape_2d(t03, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t04 = expand(gf, ggml_mul          (ctx0, t02, t03));                               assert_shape_2d(t04, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat      (ctx0, layer.wq, t04));                          assert_shape_2d(t05, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t06 = expand(gf, ggml_reshape_4d   (ctx0, t05, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode, 0));       assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t08 = expand(gf, ggml_mul_mat      (ctx0, layer.wk, t04));                          assert_shape_2d(t08, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t09 = expand(gf, ggml_reshape_4d   (ctx0, t08, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode, 0));       assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t11 = expand(gf, ggml_mul_mat      (ctx0, t04, layer.wv));                          assert_shape_2d(t11, N*n_batch, n_embd);
-        use_buf(-1); struct ggml_tensor * t12 = expand(gf, ggml_reshape_4d   (ctx0, t11, N, n_batch, n_embd/n_head, n_head)); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
-        use_buf(-1); struct ggml_tensor * t13 = expand(gf, ggml_permute      (ctx0, t07, 0, 2, 1, 3));                        assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
-        use_buf(-1); struct ggml_tensor * t14 = expand(gf, ggml_permute      (ctx0, t10, 0, 2, 1, 3));                        assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch);
-        use_buf(-1); struct ggml_tensor * t15 = expand(gf, ggml_permute      (ctx0, t12, 0, 3, 1, 2));                        assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
-        use_buf(-1); struct ggml_tensor * t16 = expand(gf, ggml_flash_attn   (ctx0, t13, t14, t15, true));                    assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
-        use_buf( 0); struct ggml_tensor * t17 = expand(gf, ggml_permute      (ctx0, t16, 0, 2, 1, 3));                        assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t18 = expand(gf, ggml_cont         (ctx0, t17));                                    assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
-        use_buf(-1); struct ggml_tensor * t19 = expand(gf, ggml_reshape_2d   (ctx0, t18, n_embd, N*n_batch));                 assert_shape_2d(t19, n_embd, N*n_batch);
-        use_buf( 0); struct ggml_tensor * t20 = expand(gf, ggml_mul_mat      (ctx0, layer.wo, t19));                          assert_shape_2d(t20, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t21 = expand(gf, ggml_add          (ctx0, t20, cur));                               assert_shape_2d(t21, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t22 = expand(gf, ggml_rms_norm     (ctx0, t21, rms_norm_eps));                      assert_shape_2d(t22, n_embd, N*n_batch);
-        use_buf( 0); struct ggml_tensor * t23 = expand(gf, ggml_repeat       (ctx0, layer.ffn_norm, t22));                    assert_shape_2d(t23, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t24 = expand(gf, ggml_mul          (ctx0, t23, t22));                               assert_shape_2d(t24, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t25 = expand(gf, ggml_mul_mat      (ctx0, layer.w3, t24));                          assert_shape_2d(t25, n_ff, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t26 = expand(gf, ggml_mul_mat      (ctx0, layer.w1, t24));                          assert_shape_2d(t26, n_ff, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t27 = expand(gf, ggml_silu         (ctx0, t26));                                    assert_shape_2d(t27, n_ff, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t28 = expand(gf, ggml_mul          (ctx0, t27, t25));                               assert_shape_2d(t28, n_ff, N*n_batch);
-        use_buf( 0); struct ggml_tensor * t29 = expand(gf, ggml_mul_mat      (ctx0, layer.w2, t28));                          assert_shape_2d(t29, n_embd, N*n_batch);
-        use_buf(-1); struct ggml_tensor * t30 = expand(gf, ggml_add          (ctx0, t21, t29));                               assert_shape_2d(t30, n_embd, N*n_batch);
-        t02L[il] = t02;
-        t03L[il] = t03;
-        t04L[il] = t04;
-        t05L[il] = t05;
-        t06L[il] = t06;
-        t07L[il] = t07;
-        t08L[il] = t08;
-        t09L[il] = t09;
-        t10L[il] = t10;
-        t11L[il] = t11;
-        t12L[il] = t12;
-        t13L[il] = t13;
-        t14L[il] = t14;
-        t15L[il] = t15;
-        t16L[il] = t16;
-        t17L[il] = t17;
-        t18L[il] = t18;
-        t19L[il] = t19;
-        t20L[il] = t20;
-        t21L[il] = t21;
-        t22L[il] = t22;
-        t23L[il] = t23;
-        t24L[il] = t24;
-        t25L[il] = t25;
-        t26L[il] = t26;
-        t27L[il] = t27;
-        t28L[il] = t28;
-        t29L[il] = t29;
-        t30L[il] = t30;
-
-        cur      = t30;
-    }
-    clr_buf(0);
-    use_buf(0);
-    struct ggml_tensor * t31   = expand(gf, ggml_rms_norm  (ctx0, cur, rms_norm_eps));         assert_shape_2d(t31, n_embd, N*n_batch);
-    struct ggml_tensor * t32   = expand(gf, ggml_repeat    (ctx0, model->norm, t31));          assert_shape_2d(t32, n_embd, N*n_batch);
-    struct ggml_tensor * t33   = expand(gf, ggml_mul       (ctx0, t32, t31));                  assert_shape_2d(t33, n_embd, N*n_batch);
-    use_buf(-1);
-    struct ggml_tensor * t34   = expand(gf, ggml_mul_mat   (ctx0, model->output, t33));        assert_shape_2d(t34, n_vocab, N*n_batch);
-    struct ggml_tensor * t35   = expand(gf, ggml_reshape_3d(ctx0, t34, n_vocab, N, n_batch));  assert_shape_3d(t35, n_vocab, N, n_batch);
-    struct ggml_tensor * t36   = expand(gf, ggml_cross_entropy_loss(ctx0, t35, targets));      assert_shape_1d(t36, 1);
-
-    {
-        /*
-        tok_embeddings                                                        | grad_tok_embeddings = ggml_get_rows_back(grad_t01, t00)
-        L0_att_norm                                                           | grad_L0_att_norm    = ggml_repeat_back(grad_t03L0, L0_att_norm.shape)
-        L0_wq                                                                 | grad_L0_wq          = ggml_out_prod(t04L0, grad_t05L0)
-        L0_wk                                                                 | grad_L0_wk          = ggml_out_prod(t04L0, grad_t08L0)
-        L0_wv                                                                 | grad_L0_wv          = ggml_out_prod(t04L0, ggml_transpose(grad_t11L0))
-        L0_wo                                                                 | grad_L0_wo          = ggml_out_prod(t19L0, grad_t20L0)
-        L0_ffn_norm                                                           | grad_L0_ffn_norm    = ggml_repeat_back(grad_t23L0, L0_ffn_norm.shape)
-        L0_w1                                                                 | grad_L0_w1          = ggml_out_prod(t24L0, grad_t26L0)
-        L0_w2                                                                 | grad_L0_w2          = ggml_out_prod(t28L0, grad_t29L0)
-        L0_w3                                                                 | grad_L0_w3          = ggml_out_prod(t24L0, grad_t25L0)
-        L1_att_norm                                                           | grad_L1_att_norm    = ggml_repeat_back(grad_t03L1, L1_att_norm.shape)
-        L1_wq                                                                 | grad_L1_wq          = ggml_out_prod(t04L1, grad_t05L1)
-        L1_wk                                                                 | grad_L1_wk          = ggml_out_prod(t04L1, grad_t08L1)
-        L1_wv                                                                 | grad_L1_wv          = ggml_out_prod(t04L1, ggml_transpose(grad_t11L1))
-        L1_wo                                                                 | grad_L1_wo          = ggml_out_prod(t19L1, grad_t20L1)
-        L1_ffn_norm                                                           | grad_L1_ffn_norm    = ggml_repeat_back(grad_t23L1, L1_ffn_norm.shape)
-        L1_w1                                                                 | grad_L1_w1          = ggml_out_prod(t24L1, grad_t26L1)
-        L1_w2                                                                 | grad_L1_w2          = ggml_out_prod(t28L1, grad_t29L1)
-        L1_w3                                                                 | grad_L1_w3          = ggml_out_prod(t24L1, grad_t25L1)
-        norm                                                                  | grad_norm           = ggml_repeat_back(grad_t32, norm.shape)
-        output                                                                | grad_output         = ggml_out_prod(t33, grad_t34)
-                                                                              |
-        t01 = ggml_get_rows(tok_embeddings, t00)                              | grad_t01   = grad_t21L0 + ggml_rms_norm_back(t01, grad_t02L0)
-        for layer:                                                            |
-        t02L0*= ggml_rms_norm     (t01)                                       | grad_t02L0 = ggml_mul(grad_t04L0, t03L0)
-        t03L0 = ggml_repeat       (L0_att_norm, t02L0_shape)                  | grad_t03L0 = ggml_mul(grad_t04L0, t02L0)
-        t04L0*= ggml_mul          (t02L0, t03L0)                              | grad_t04L0 = ggml_out_prod(L0_wv, grad_t11L0) + ggml_out_prod(L0_wk, ggml_transpose(grad_t08L0)) + ggml_out_prod(L0_wq, ggml_transpose(grad_t05L0))
-        t05L0 = ggml_mul_mat      (L0_wq, t04L0)                              | grad_t05L0 = ggml_reshape(grad_t06L0, t05L0_shape)
-        t06L0 = ggml_reshape_4d   (t05L0, n_embd/n_head, n_head, N, n_batch)  | grad_t06L0 = ggml_rope_back(grad_t07L0)
-        t07L0 = ggml_rope_inplace (t06L0)                                     | grad_t07L0 = ggml_permute_back(grad_t13L0, 0, 2, 1, 3) = ggml_permute(grad_t13L0, 0, 2, 1, 3)
-        t08L0 = ggml_mul_mat      (L0_wk, t04L0)                              | grad_t08L0 = ggml_reshape(grad_t09L0, t08L0_shape)
-        t09L0 = ggml_reshape_4d   (t08L0, n_embd/n_head, n_head, N, n_batch)  | grad_t09L0 = ggml_rope_back(grad_t10L0)
-        t10L0 = ggml_rope_inplace (t09L0)                                     | grad_t10L0 = ggml_permute_back(grad_t14L0, 0, 2, 1, 3) = ggml_permute(grad_t14L0, 0, 2, 1, 3)
-        t11L0 = ggml_mul_mat      (t04L0, L0_wv)                              | grad_t11L0 = ggml_reshape(grad_t12L0, t11L0_shape)
-        t12L0 = ggml_reshape_4d   (t11L0, N, n_batch, n_embd/n_head, n_head)  | grad_t12L0 = ggml_permute_back(grad_t15L0, 0, 3, 1, 2) = ggml_permute(grad_t15L0, 0, 2, 3, 1)
-        t13L0*= ggml_permute      (t07L0, 0, 2, 1, 3)                         | grad_t13L0 = view__q(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0))
-        t14L0*= ggml_permute      (t10L0, 0, 2, 1, 3)                         | grad_t14L0 = view__k(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0))
-        t15L0*= ggml_permute      (t12L0, 0, 3, 1, 2)                         | grad_t15L0 = view__v(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0))
-        t16L0 = ggml_flash_attn   (t13L0, t14L0, t15L0)                       | grad_t16L0 = ggml_permute_back(grad_t17L0, 0, 2, 1, 3) = ggml_permute(grad_t17L0, 0, 2, 1, 3)
-        t17L0 = ggml_permute      (t16L0, 0, 2, 1, 3)                         | grad_t17L0 = grad_t18L0
-        t18L0 = ggml_cont         (t17L0)                                     | grad_t18L0 = ggml_reshape(grad_t19L0, t18L0_shape)
-        t19L0*= ggml_reshape_2d   (t18L0, n_embd, N*n_batch)                  | grad_t19L0 = ggml_out_prod(L0_wo, ggml_transpose(grad_t20L0))
-        t20L0 = ggml_mul_mat      (L0_wo, t19L0)                              | grad_t20L0 = grad_t21L0
-        t21L0*= ggml_add          (t20L0, t01)                                | grad_t21L0 = grad_t30L0 + ggml_rms_norm_back(t21L0, grad_t22L0)
-        t22L0*= ggml_rms_norm     (t21L0)                                     | grad_t22L0 = ggml_mul(grad_t24L0, t23L0)
-        t23L0 = ggml_repeat       (L0_ffn_norm, t22L0_shape)                  | grad_t23L0 = ggml_mul(grad_t24L0, t22L0)
-        t24L0*= ggml_mul          (t23L0, t22L0)                              | grad_t24L0 = ggml_out_prod(L0_w1, ggml_transpose(grad_t26L0)) + ggml_out_prod(L0_w3, ggml_transpose(grad_t25L0))
-        t25L0*= ggml_mul_mat      (L0_w3, t24L0)                              | grad_t25L0 = ggml_mul(grad_t28L0, t27L0)
-        t26L0*= ggml_mul_mat      (L0_w1, t24L0)                              | grad_t26L0 = ggml_silu_back(t26L0, grad_t27L0)
-        t27L0*= ggml_silu         (t26L0)                                     | grad_t27L0 = ggml_mul(grad_t28L0, t25L0)
-        t28L0*= ggml_mul          (t27L0, t25L0)                              | grad_t28L0 = ggml_out_prod(L0_w2, ggml_transpose(grad_t29L0))
-        t29L0 = ggml_mul_mat      (L0_w2, t28L0)                              | grad_t29L0 = grad_t30L0
-        t30L0*= ggml_add          (t21L0, t29L0)                              | grad_t30L0 = ggml_rms_norm_back(t30L0, grad_t02L1) + grad_t21L1
-                                                                              ^
-        t02L1*= ggml_rms_norm     (t30L0)                                     | grad_t02L1 = ggml_mul(grad_t04L1, t03L1)
-        t03L1 = ggml_repeat       (L1_att_norm, t02L1_shape)                  | grad_t03L1 = ggml_mul(grad_t04L1, t02L1)
-        t04L1*= ggml_mul          (t02L1, t03L1)                              | grad_t04L1 = ggml_out_prod(L1_wv, grad_t11L1) + ggml_out_prod(L1_wk, ggml_transpose(grad_t08L1)) + ggml_out_prod(L1_wq, ggml_transpose(grad_t05L1))
-        t05L1 = ggml_mul_mat      (L1_wq, t04L1)                              | grad_t05L1 = ggml_reshape(grad_t06L1, t05L1_shape)
-        t06L1 = ggml_reshape_4d   (t05L1, n_embd/n_head, n_head, N, n_batch)  | grad_t06L1 = ggml_rope_back(grad_t07L1)
-        t07L1 = ggml_rope_inplace (t06L1)                                     | grad_t07L1 = ggml_permute_back(grad_t13L1, 0, 2, 1, 3) = ggml_permute(grad_t13L1, 0, 2, 1, 3)
-        t08L1 = ggml_mul_mat      (L1_wk, t04L1)                              | grad_t08L1 = ggml_reshape(grad_t09L1, t08L1_shape)
-        t09L1 = ggml_reshape_4d   (t08L1, n_embd/n_head, n_head, N, n_batch)  | grad_t09L1 = ggml_rope_back(grad_t10L1)
-        t10L1 = ggml_rope_inplace (t09L1)                                     | grad_t10L1 = ggml_permute_back(grad_t14L1, 0, 2, 1, 3) = ggml_permute(grad_t14L1, 0, 2, 1, 3)
-        t11L1 = ggml_mul_mat      (t04L1, L1_wv)                              | grad_t11L1 = ggml_reshape(grad_t12L1, t11L1_shape)
-        t12L1 = ggml_reshape_4d   (t11L1, N, n_batch, n_embd/n_head, n_head)  | grad_t12L1 = ggml_permute_back(grad_t15L1, 0, 3, 1, 2) = ggml_permute(grad_t15L1, 0, 2, 3, 1)
-        t13L1*= ggml_permute      (t07L1, 0, 2, 1, 3)                         | grad_t13L1 = view__q(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1))
-        t14L1*= ggml_permute      (t10L1, 0, 2, 1, 3)                         | grad_t14L1 = view__k(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1))
-        t15L1*= ggml_permute      (t12L1, 0, 3, 1, 2)                         | grad_t15L1 = view__v(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1))
-        t16L1 = ggml_flash_attn   (t13L1, t14L1, t15L1)                       | grad_t16L1 = ggml_permute_back(grad_t17L1, 0, 2, 1, 3) = ggml_permute(grad_t17L1, 0, 2, 1, 3)
-        t17L1 = ggml_permute      (t16L1, 0, 2, 1, 3)                         | grad_t17L1 = grad_t18L1
-        t18L1 = ggml_cont         (t17L1)                                     | grad_t18L1 = ggml_reshape(grad_t19L1, t18L1_shape)
-        t19L1*= ggml_reshape_2d   (t18L1, n_embd, N*n_batch)                  | grad_t19L1 = ggml_out_prod(L1_wo, ggml_transpose(grad_t20L1))
-        t20L1 = ggml_mul_mat      (L1_wo, t19L1)                              | grad_t20L1 = grad_t21L1
-        t21L1*= ggml_add          (t20L1, t30L0)                              | grad_t21L1 = grad_t30L1 + ggml_rms_norm_back(t21L1, grad_t22L1)
-        t22L1*= ggml_rms_norm     (t21L1)                                     | grad_t22L1 = ggml_mul(grad_t24L1, t23L1)
-        t23L1 = ggml_repeat       (L1_ffn_norm, t22L1_shape)                  | grad_t23L1 = ggml_mul(grad_t24L1, t22L1)
-        t24L1*= ggml_mul          (t23L1, t22L1)                              | grad_t24L1 = ggml_out_prod(L1_w1, ggml_transpose(grad_t26L1)) + ggml_out_prod(L1_w3, ggml_transpose(grad_t25L1))
-        t25L1*= ggml_mul_mat      (L1_w3, t24L1)                              | grad_t25L1 = ggml_mul(grad_t28L1, t27L1)
-        t26L1*= ggml_mul_mat      (L1_w1, t24L1)                              | grad_t26L1 = ggml_silu_back(t26L1, grad_t27L1)
-        t27L1*= ggml_silu         (t26L1)                                     | grad_t27L1 = ggml_mul(grad_t28L1, t25L1)
-        t28L1*= ggml_mul          (t27L1, t25L1)                              | grad_t28L1 = ggml_out_prod(L1_w2, ggml_transpose(grad_t29L1))
-        t29L1 = ggml_mul_mat      (L1_w2, t28L1)                              | grad_t29L1 = grad_t30L1
-        t30L1*= ggml_add          (t21L1, t29L1)                              | grad_t30L1 = ggml_rms_norm_back(t30L1, grad_t31)
-                                                                              ^
-        t31   = ggml_rms_norm     (t30L1)                                     | grad_t31   = ggml_mul(grad_t33, t32)
-        t32   = ggml_repeat       (norm, t31.shape)                           | grad_t32   = ggml_mul(grad_t33, t31)
-        t33   = ggml_mul          (t32, t31)                                  | grad_t33   = ggml_out_prod(output, ggml_transpose(grad_t34))
-        t34   = ggml_mul_mat      (output, t33)                               | grad_t34   = ggml_reshape(grad_t35, t34.shape)
-        t35   = ggml_reshape_3d   (t34, n_vocab, N, n_batch)                  | grad_t35   = ggml_cross_entropy_loss_back(t35, targets, grad_t36)
-        t36   = ggml_cross_entropy_loss(t35, targets)                         | grad_t36   = 1 (optimizer)
-        tensors marked with * need to be stored until grad computation
-        tensors during grad computation are all temporary
-        */
-    }
-
-    *gb = *gf;
-
-    // t36->grad gets set to one by optimizer, so we need the tensor.
-    // initialize it with 1.0f to make sure.
-    use_buf(-1);
-    t36->grad = expand(gb, ggml_new_f32(ctx0, 1.0f));
-
-    use_buf(0);
-    t35->grad = expand(gb, ggml_cross_entropy_loss_back(ctx0, t35, targets, t36->grad));              assert_shape_3d(t35->grad, n_vocab, N, n_batch);
-    t34->grad = expand(gb, ggml_reshape_2d (ctx0, t35->grad, n_vocab, N*n_batch));                    assert_shape_2d(t34->grad, n_vocab, N*n_batch);
-    t33->grad = expand(gb, ggml_out_prod   (ctx0, model->output, ggml_transpose(ctx0, t34->grad)));   assert_shape_2d(t33->grad, n_embd, N*n_batch);
-    t32->grad = expand(gb, ggml_mul        (ctx0, t33->grad, t31));                                   assert_shape_2d(t32->grad, n_embd, N*n_batch);
-
-    use_buf(-1);
-
-    model->norm->grad   = expand(gb, add_or_set(model->norm->grad,   ggml_repeat_back(ctx0, t32->grad, model->norm))); assert_shape_1d(model->norm->grad, n_embd);
-    model->output->grad = expand(gb, add_or_set(model->output->grad, ggml_out_prod(ctx0, t33, t34->grad)));            assert_shape_2d(model->output->grad, n_embd, n_vocab);
-
-    clr_buf(1);
-    use_buf(1);
-    t31->grad = expand(gb, ggml_mul(ctx0, t33->grad, t32));  assert_shape_2d(t31->grad, n_embd, N*n_batch);
-
-    struct ggml_tensor * back_layer_inp = t31;
-    struct ggml_tensor * grad_layer_inp = NULL;
-
-    for (int k = 0; k < n_layer; ++k) {
-        int il = n_layer-1-k;
-        struct my_llama_layer & layer = model->layers[il];
-
-        struct ggml_tensor * t02 = t02L[il];
-        struct ggml_tensor * t03 = t03L[il];
-        struct ggml_tensor * t04 = t04L[il];
-        struct ggml_tensor * t05 = t05L[il];
-        struct ggml_tensor * t06 = t06L[il];
-        struct ggml_tensor * t07 = t07L[il];
-        struct ggml_tensor * t08 = t08L[il];
-        struct ggml_tensor * t09 = t09L[il];
-        struct ggml_tensor * t10 = t10L[il];
-        struct ggml_tensor * t11 = t11L[il];
-        struct ggml_tensor * t12 = t12L[il];
-        struct ggml_tensor * t13 = t13L[il];
-        struct ggml_tensor * t14 = t14L[il];
-        struct ggml_tensor * t15 = t15L[il];
-        struct ggml_tensor * t16 = t16L[il];
-        struct ggml_tensor * t17 = t17L[il];
-        struct ggml_tensor * t18 = t18L[il];
-        struct ggml_tensor * t19 = t19L[il];
-        struct ggml_tensor * t20 = t20L[il];
-        struct ggml_tensor * t21 = t21L[il];
-        struct ggml_tensor * t22 = t22L[il];
-        struct ggml_tensor * t23 = t23L[il];
-        struct ggml_tensor * t24 = t24L[il];
-        struct ggml_tensor * t25 = t25L[il];
-        struct ggml_tensor * t26 = t26L[il];
-        struct ggml_tensor * t27 = t27L[il];
-        struct ggml_tensor * t28 = t28L[il];
-        struct ggml_tensor * t29 = t29L[il];
-        struct ggml_tensor * t30 = t30L[il];
-
-        clr_buf(0);
-        use_buf(0);
-        t30->grad = expand(gb, ggml_rms_norm_back(ctx0, t30, back_layer_inp->grad)); assert_shape_2d(t30->grad, n_embd, N*n_batch);
-        if (grad_layer_inp) {
-            t30->grad = expand(gb, ggml_add(ctx0, t30->grad, grad_layer_inp->grad)); assert_shape_2d(t30->grad, n_embd, N*n_batch);
+        struct ggml_tensor * t02 = ggml_rms_norm     (ctx, cur, f_norm_rms_eps);                    set_name(t02, "t02");     assert_shape_2d(t02, n_embd, N*n_batch);
+        struct ggml_tensor * t03 = ggml_repeat       (ctx, layer.attention_norm, t02);              set_name(t03, "t03");     assert_shape_2d(t03, n_embd, N*n_batch);
+        struct ggml_tensor * t04 = ggml_mul          (ctx, t03, t02);                               set_name(t04, "t04");     assert_shape_2d(t04, n_embd, N*n_batch);
+        struct ggml_tensor * t05 = ggml_mul_mat      (ctx, layer.wq, t04);                          set_name(t05, "t05");     assert_shape_2d(t05, n_embd, N*n_batch);
+        struct ggml_tensor * t06 = ggml_reshape_4d   (ctx, t05, n_embd/n_head, n_head, N, n_batch); set_name(t06, "t06");     assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
+        struct ggml_tensor * t07 = rope              (t06);                                         set_name(t07, "t07");     assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
+        struct ggml_tensor * t08 = ggml_mul_mat      (ctx, layer.wk, t04);                          set_name(t08, "t08");     assert_shape_2d(t08, n_embd, N*n_batch);
+        struct ggml_tensor * t09 = ggml_reshape_4d   (ctx, t08, n_embd/n_head, n_head, N, n_batch); set_name(t09, "t09");     assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
+        struct ggml_tensor * t10 = rope              (t09);                                         set_name(t10, "t10");     assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
+        struct ggml_tensor * t11 = ggml_mul_mat      (ctx, t04, layer.wv);                          set_name(t11, "t11");     assert_shape_2d(t11, N*n_batch, n_embd);
+        struct ggml_tensor * t12 = ggml_reshape_4d   (ctx, t11, N, n_batch, n_embd/n_head, n_head); set_name(t12, "t12");     assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
+        struct ggml_tensor * t13 = ggml_permute      (ctx, t07, 0, 2, 1, 3);                        set_name(t13, "t13");     assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
+        struct ggml_tensor * t14 = ggml_permute      (ctx, t10, 0, 2, 1, 3);                        set_name(t14, "t14");     assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch);
+        struct ggml_tensor * t15 = ggml_permute      (ctx, t12, 0, 3, 1, 2);                        set_name(t15, "t15");     assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
+        struct ggml_tensor * t16;
+        if (enable_flash_attn) {
+            t16 = ggml_flash_attn(ctx, t13, t14, t15, true);                                        set_name(t16, "t16");     assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
+        } else {
+            struct ggml_tensor * t16_0 = ggml_mul_mat              (ctx, t14, t13);                 set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
+            struct ggml_tensor * t16_1 = ggml_scale_inplace        (ctx, t16_0, kv_scale);          set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
+            struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past);            set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
+            struct ggml_tensor * t16_3 = ggml_soft_max_inplace     (ctx, t16_2);                    set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
+            t16 = ggml_mul_mat(ctx, t15, t16_3);                                                    set_name(t16, "t16");     assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
         }
-        clr_buf(1);
-        t29->grad = t30->grad;                                                                                        assert_shape_2d(t29->grad, n_embd, N*n_batch);
-        t28->grad = expand(gb, ggml_out_prod(ctx0, layer.w2, ggml_transpose(ctx0, t29->grad)));                       assert_shape_2d(t28->grad, n_ff, N*n_batch);
-        t27->grad = expand(gb, ggml_mul(ctx0, t28->grad, t25));                                                       assert_shape_2d(t27->grad, n_ff, N*n_batch);
-        t26->grad = expand(gb, ggml_silu_back(ctx0, t26, t27->grad));                                                 assert_shape_2d(t26->grad, n_ff, N*n_batch);
-        t25->grad = expand(gb, ggml_mul(ctx0, t28->grad, t27));                                                       assert_shape_2d(t25->grad, n_ff, N*n_batch);
-        t24->grad = expand(gb, ggml_add_inplace(ctx0,
-                        ggml_out_prod(ctx0, layer.w1, ggml_transpose(ctx0, t26->grad)),
-                        ggml_out_prod(ctx0, layer.w3, ggml_transpose(ctx0, t25->grad))));                             assert_shape_2d(t24->grad, n_embd, N*n_batch);
-        t23->grad = expand(gb, ggml_mul(ctx0, t24->grad, t22));                                                       assert_shape_2d(t23->grad, n_embd, N*n_batch);
-        t22->grad = expand(gb, ggml_mul(ctx0, t24->grad, ggml_repeat(ctx0, layer.ffn_norm, t24->grad)));              assert_shape_2d(t22->grad, n_embd, N*n_batch);
-        use_buf(1);
-        t21->grad = expand(gb, ggml_add(ctx0, t30->grad, ggml_rms_norm_back(ctx0, t21, t22->grad)));                  assert_shape_2d(t21->grad, n_embd, N*n_batch);
-        grad_layer_inp = t21;
-        use_buf(0);
-        t20->grad = t21->grad;                                                                                        assert_shape_2d(t20->grad, n_embd, N*n_batch);
-        t19->grad = expand(gb, ggml_out_prod(ctx0, layer.wo, ggml_transpose(ctx0, t20->grad)));                       assert_shape_2d(t19->grad, n_embd, N*n_batch);
-        t18->grad = expand(gb, ggml_reshape_4d(ctx0, t19->grad, n_embd/n_head, n_head, N, n_batch));                  assert_shape_4d(t18->grad, n_embd/n_head, n_head, N, n_batch);
-        t17->grad = t18->grad;                                                                                        assert_shape_4d(t17->grad, n_embd/n_head, n_head, N, n_batch);
-        t16->grad = expand(gb, ggml_permute(ctx0, t17->grad, 0, 2, 1, 3));                                            assert_shape_4d(t16->grad, n_embd/n_head, N, n_head, n_batch);
-        struct ggml_tensor * flash_attn = expand(gb, ggml_flash_attn_back(ctx0, t13, t14, t15, t16->grad, true));     assert_shape_4d(flash_attn, n_embd/n_head, N*3, n_head, n_batch);
-        t15->grad = expand(gb, view__v(flash_attn));                                                                  assert_shape_4d(t15->grad, N, n_embd/n_head, n_head, n_batch);
-        t14->grad = expand(gb, view__k(flash_attn));                                                                  assert_shape_4d(t14->grad, n_embd/n_head, N, n_head, n_batch);
-        t13->grad = expand(gb, view__q(flash_attn));                                                                  assert_shape_4d(t13->grad, n_embd/n_head, N, n_head, n_batch);
-        t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1));                                            assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head);
-        t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd));                 assert_shape_2d(t11->grad, N*n_batch, n_embd);
-        t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3));                                            assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch);
-        t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx, 10000.0f, 1.0f, 0.0f, false));        assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
-        t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch));                                  assert_shape_2d(t08->grad, n_embd, N*n_batch);
-        t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3));                                            assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch);
-        t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx, 10000.0f, 1.0f, 0.0f, false));        assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
-        t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch));                                  assert_shape_2d(t05->grad, n_embd, N*n_batch);
-        t04->grad = expand(gb, ggml_add_inplace(ctx0,
-                        ggml_add_inplace(ctx0,
-                            ggml_out_prod(ctx0, layer.wv, t11->grad),
-                            ggml_out_prod(ctx0, layer.wk, ggml_transpose(ctx0, t08->grad))),
-                        ggml_out_prod(ctx0, layer.wq, ggml_transpose(ctx0, t05->grad))));                             assert_shape_2d(t04->grad, n_embd, N*n_batch);
-        t03->grad = expand(gb, ggml_mul(ctx0, t04->grad, t02));                                                       assert_shape_2d(t04->grad, n_embd, N*n_batch);
-        use_buf(1);
-        t02->grad = expand(gb, ggml_mul(ctx0, t04->grad, ggml_repeat(ctx0, layer.attention_norm, t02)));              assert_shape_2d(t02->grad, n_embd, N*n_batch);
-        back_layer_inp = t02;
-        // use_buf(0);
-
-        use_buf(-1);
-        layer.attention_norm->grad = expand(gb, add_or_set(layer.attention_norm->grad, ggml_repeat_back(ctx0, t03->grad, layer.attention_norm)));   assert_shape_1d(layer.attention_norm->grad, n_embd);
-        layer.wq->grad             = expand(gb, add_or_set(layer.wq->grad,             ggml_out_prod(ctx0, t04, t05->grad)));                       assert_shape_2d(layer.wq->grad,             n_embd, n_embd);
-        layer.wk->grad             = expand(gb, add_or_set(layer.wk->grad,             ggml_out_prod(ctx0, t04, t08->grad)));                       assert_shape_2d(layer.wk->grad,             n_embd, n_embd);
-        layer.wv->grad             = expand(gb, add_or_set(layer.wv->grad,             ggml_out_prod(ctx0, t04, ggml_transpose(ctx0, t11->grad)))); assert_shape_2d(layer.wv->grad,             n_embd, n_embd);
-        layer.wo->grad             = expand(gb, add_or_set(layer.wo->grad,             ggml_out_prod(ctx0, t19, t20->grad)));                       assert_shape_2d(layer.wo->grad,             n_embd, n_embd);
-        layer.ffn_norm->grad       = expand(gb, add_or_set(layer.ffn_norm->grad,       ggml_repeat_back(ctx0, t23->grad, layer.ffn_norm)));         assert_shape_1d(layer.ffn_norm->grad,       n_embd);
-        layer.w1->grad             = expand(gb, add_or_set(layer.w1->grad,             ggml_out_prod(ctx0, t24, t26->grad)));                       assert_shape_2d(layer.w1->grad,             n_embd, n_ff);
-        layer.w2->grad             = expand(gb, add_or_set(layer.w2->grad,             ggml_out_prod(ctx0, t28, t29->grad)));                       assert_shape_2d(layer.w2->grad,             n_ff, n_embd);
-        layer.w3->grad             = expand(gb, add_or_set(layer.w3->grad,             ggml_out_prod(ctx0, t24, t25->grad)));                       assert_shape_2d(layer.w3->grad,             n_embd, n_ff);
-        // use_buf(0);
+        struct ggml_tensor * t17 = ggml_permute      (ctx, t16, 0, 2, 1, 3);                        set_name(t17, "t17");     assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch);
+        struct ggml_tensor * t18 = ggml_cont         (ctx, t17);                                    set_name(t18, "t18");     assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
+        struct ggml_tensor * t19 = ggml_reshape_2d   (ctx, t18, n_embd, N*n_batch);                 set_name(t19, "t19");     assert_shape_2d(t19, n_embd, N*n_batch);
+        struct ggml_tensor * t20 = ggml_mul_mat      (ctx, layer.wo, t19);                          set_name(t20, "t20");     assert_shape_2d(t20, n_embd, N*n_batch);
+        struct ggml_tensor * t21 = ggml_add          (ctx, t20, cur);                               set_name(t21, "t21");     assert_shape_2d(t21, n_embd, N*n_batch);
+        struct ggml_tensor * t22 = ggml_rms_norm     (ctx, t21, f_norm_rms_eps);                    set_name(t22, "t22");     assert_shape_2d(t22, n_embd, N*n_batch);
+        struct ggml_tensor * t23 = ggml_repeat       (ctx, layer.ffn_norm, t22);                    set_name(t23, "t23");     assert_shape_2d(t23, n_embd, N*n_batch);
+        struct ggml_tensor * t24 = ggml_mul          (ctx, t23, t22);                               set_name(t24, "t24");     assert_shape_2d(t24, n_embd, N*n_batch);
+        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, layer.w3, t24);                          set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
+        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, layer.w1, t24);                          set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
+        struct ggml_tensor * t27 = ggml_silu         (ctx, t26);                                    set_name(t27, "t27");     assert_shape_2d(t27, n_ff, N*n_batch);
+        struct ggml_tensor * t28 = ggml_mul          (ctx, t27, t25);                               set_name(t28, "t28");     assert_shape_2d(t28, n_ff, N*n_batch);
+        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, layer.w2, t28);                          set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
+        struct ggml_tensor * t30 = ggml_add          (ctx, t29, t21);                               set_name(t30, "t30");     assert_shape_2d(t30, n_embd, N*n_batch);
+        cur = t30;
+        checkpoints.push_back(cur);
+    }
+    struct ggml_tensor * t31   = ggml_rms_norm          (ctx, cur, f_norm_rms_eps);                 set_name(t31, "t31");     assert_shape_2d(t31, n_embd, N*n_batch);
+    struct ggml_tensor * t32   = ggml_repeat            (ctx, model->norm, t31);                    set_name(t32, "t32");     assert_shape_2d(t32, n_embd, N*n_batch);
+    struct ggml_tensor * t33   = ggml_mul               (ctx, t32, t31);                            set_name(t33, "t33");     assert_shape_2d(t33, n_embd, N*n_batch);
+    struct ggml_tensor * t34   = ggml_mul_mat           (ctx, model->output, t33);                  set_name(t34, "t34");     assert_shape_2d(t34, n_vocab, N*n_batch);
+    struct ggml_tensor * t35   = ggml_reshape_3d        (ctx, t34, n_vocab, N, n_batch);            set_name(t35, "t35");     assert_shape_3d(t35, n_vocab, N, n_batch);
+    struct ggml_tensor * t36   = ggml_cross_entropy_loss(ctx, t35, targets);                        set_name(t36, "t36");     assert_shape_1d(t36, 1);
+
+    checkpoints.push_back(t31);
+    checkpoints.push_back(t32);
+    checkpoints.push_back(t33);
+    checkpoints.push_back(t34);
+    checkpoints.push_back(t35);
+    checkpoints.push_back(t36);
+
+    ggml_build_forward_expand(gf, t36);
+
+    if (enable_checkpointing) {
+        ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
+    } else {
+        *gb = *gf;
+        ggml_build_backward_expand(ctx, gf, gb, true);
+    }
+
+    if (alloc) {
+        // make sure some tensors are not reallocated by inserting new temporary nodes depending on them
+        int n_leafs_before = gb->n_leafs;
+        int n_nodes_before = gb->n_nodes;
+        struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f);
+        // output tensors
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one));
+        // input gradient
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one));
+        GGML_ASSERT(t36->grad->data == NULL && !ggml_is_view(t36->grad));
+        ggml_allocr_alloc(alloc, t36->grad);
+        // gradient tensors (will be set to zero by ggml_graph_reset)
+        // pinning these produces large unnecessary memory overhead, which will be resolved by PR 2632
+        for (int i = 0; i < gf->n_nodes; ++i) {
+            if (!gf->grads[i]) continue;
+            if (gf->grads[i]->data == NULL && !ggml_is_view(gf->grads[i])) {
+                ggml_allocr_alloc(alloc, gf->grads[i]);
+            }
+            ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, gf->grads[i], one));
+        }
+        // allocating checkpoints in one block to reduce memory fragmentation
+        // note: they will be freed in reverse order
+        for (int i = 0; i < (int) checkpoints.size(); ++i) {
+            if (checkpoints[i]->data == NULL && !ggml_is_view(checkpoints[i])) {
+                ggml_allocr_alloc(alloc, checkpoints[i]);
+            }
+        }
+
+        //int n_leafs_after = gb->n_leafs;
+        //int n_nodes_after = gb->n_nodes;
+
+        ggml_allocr_alloc_graph(alloc, gb);
+
+        // remove the additional nodes and leafs
+        for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
+            gb->leafs[i] = NULL;
+        }
+        for (int i = n_nodes_before; i < gb->n_nodes; ++i) {
+            gb->nodes[i] = NULL;
+        }
+        gb->n_leafs = n_leafs_before;
+        gb->n_nodes = n_nodes_before;
     }
-    clr_buf(0);
-    use_buf(0);
-    t01->grad = expand(gb, ggml_add_inplace(ctx0, grad_layer_inp->grad, ggml_rms_norm_back(ctx0, t01, back_layer_inp->grad)));  assert_shape_2d(t01->grad, n_embd, N*n_batch);
-    use_buf(-1);
-    model->tok_embeddings->grad = expand(gb, ggml_get_rows_back(ctx0, t01->grad, t00, model->tok_embeddings));                  assert_shape_2d(model->tok_embeddings->grad, n_embd, n_vocab);
-    // clr_buf(1);
-    // clr_buf(0);
 
     *logits = t35;
-
-    if (track_max_mem) {
-        printf("%s: max size compute buf0: %zu\n", __func__, buf_maxs[0]);
-        printf("%s: max size compute buf1: %zu\n", __func__, buf_maxs[1]);
-    }
-
-    // now that all grads are created, set the graph leafs and grads
-    graph_set_leafs_grads(gf);
-    graph_set_leafs_grads(gb);
-
     return t36;
 }
 
@@ -1962,42 +874,6 @@ void print_matrix(struct ggml_tensor * probs) {
     }
 }
 
-
-void print_token(struct llama_context * ctx, llama_token token) {
-    printf("%s", llama_token_to_piece(ctx, token).c_str());
-}
-
-void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) {
-    for (int i=0; i<tokens->ne[0]; ++i) {
-        int token = ggml_get_i32_1d(tokens, i);
-        print_token(ctx, token);
-    }
-}
-
-void print_tokens_batch(struct llama_context* ctx, struct ggml_tensor * tokens) {
-    for (int i1=0; i1<tokens->ne[1]; ++i1) {
-        //int num_newline = 0;
-        for (int i0=0; i0<tokens->ne[0]; ++i0) {
-            int token = get_i32_2d(tokens, i0, i1);
-            print_token(ctx, token);
-            // bool isnl = (token == llama_token_nl());
-            // if (isnl) {
-            //     ++num_newline;
-            // }
-            // if (isnl) {
-            //     if (num_newline < 2) {
-            //         print_token(ctx, token);
-            //     } else {
-            //         printf("\\n");
-            //     }
-            // } else {
-            //     print_token(ctx, token);
-            // }
-        }
-        printf("\n--\n");
-    }
-}
-
 void get_example_targets(struct llama_context * lctx, const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) {
     int n_tokens = tokens_input->ne[0];
     int n_vocab  = target_logits->ne[0];
@@ -2033,51 +909,27 @@ void get_example_targets_batch(struct llama_context * lctx, const int * train_sa
 
     ggml_set_f32(target_logits, -1.0f/n_vocab);
     ggml_set_f32(target_probs, 0.0f);
+    // printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples);
     for (int k=0; k<n_batch; ++k) {
         // printf("%s: batch %d\n", __func__, k);
-        size_t sample = train_samples[(example_id*n_batch + k) % n_train_samples];
+        size_t sample_idx = (example_id*n_batch + k) % n_train_samples;
+        size_t sample = train_samples[sample_idx];
+        // printf("%s: sample_idx=%zu sample=%zu\n", __func__, sample_idx, sample);
         GGML_ASSERT(sample+n_tokens-1 < n_train_data);
 
         set_i32_2d(tokens_input, 0, k, llama_token_bos(lctx));
         for (int i=1; i<n_tokens+1; ++i) {
             int token = clamp(train_data[sample+i-1], 0, n_vocab-1);
-            // print_token(lctx, token);
             set_f32_3d(target_logits, token, i-1, k, +1.0f);
             set_f32_3d(target_probs,  token, i-1, k, +1.0f);
             if (i<n_tokens) {
                 set_i32_2d(tokens_input, i, k, token);
             }
         }
-        // printf("\n=\n");
-        // for (int i=0; i<n_tokens; ++i) {
-        //     int token = get_i32_2d(tokens_input, i, k);
-        //     print_token(lctx, token);
-        // }
-        // printf("\n-\n");
     }
 }
 
 
-void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs, int n_shift) {
-    int n_tokens = tokens_input->ne[0];
-    int n_vocab = target_logits->ne[0];
-    for (int i=0; i<n_tokens-n_shift; ++i) {
-        ggml_set_i32_1d(tokens_input, i, ggml_get_i32_1d(tokens_input, i + n_shift));
-        for (int k=0; k<n_vocab; ++k) {
-            ggml_set_f32_1d(target_logits, i*n_vocab + k, ggml_get_f32_1d(target_logits, (i + n_shift)*n_vocab + k));
-            ggml_set_f32_1d(target_probs, i*n_vocab + k,  ggml_get_f32_1d(target_probs,  (i + n_shift)*n_vocab + k));
-        }
-    }
-}
-
-struct ggml_tensor * square_error_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * target) {
-    return ggml_sum(ctx, ggml_sqr(ctx, ggml_sub(ctx, target, a)));
-}
-
-struct ggml_tensor * cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * probs) {
-    return ggml_cross_entropy_loss(ctx, a, probs);
-}
-
 #ifdef __GNUC__
 #ifdef __MINGW32__
 __attribute__((format(gnu_printf, 1, 2)))
@@ -2099,103 +951,53 @@ static std::string format(const char * fmt, ...) {
     return std::string(buf.data(), size);
 }
 
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
-
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            size = 0;
-        } else {
-            seek(0, SEEK_END);
-            size = tell();
-            seek(0, SEEK_SET);
-        }
-    }
-
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
-    }
-
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        GGML_ASSERT(ret == 0); // same
-    }
-
-    void read_raw(void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, size, 1, fp);
-        if (ferror(fp)) {
-            throw std::runtime_error(format("read error: %s", strerror(errno)));
-        }
-        if (ret != 1) {
-            throw std::runtime_error(std::string("unexpectedly reached end of file"));
-        }
-    }
-
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
-    }
-
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
-    }
-
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            throw std::runtime_error(format("write error: %s", strerror(errno)));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-};
-
 int tokenize_file(struct llama_context * lctx, const char * filename, std::vector<llama_token>& out) {
-    struct llama_file f(filename, "rb");
+    FILE * fp = std::fopen(filename, "rb");
+    if (fp == NULL) {
+        return 0;
+    }
+
+#ifdef _WIN32
+    GGML_ASSERT(_fseeki64(fp, (__int64) 0, SEEK_END) == 0);
+#else
+    GGML_ASSERT(std::fseek(fp, (long) 0, SEEK_END) == 0);
+#endif
+
+    size_t size = 0;
+#ifdef _WIN32
+    __int64 ret = _ftelli64(fp);
+    size = ret;
+#else
+    long ret = std::ftell(fp);
+    size = ret;
+#endif
+
+#ifdef _WIN32
+    GGML_ASSERT(_fseeki64(fp, (__int64) 0, SEEK_SET) == 0);
+#else
+    GGML_ASSERT(std::fseek(fp, (long) 0, SEEK_SET) == 0);
+#endif
 
     std::vector<char> buf;
-    buf.resize(f.size+1);
+    buf.resize(size+1);
+    out.resize(size+1);
 
-    f.read_raw(buf.data(), f.size);
-    buf[f.size] = '\0';
+    if (std::fread(buf.data(), size, 1, fp) != 1) {
+        throw std::runtime_error(std::string("unexpectedly reached end of file"));
+    }
+    if (ferror(fp)) {
+        throw std::runtime_error(format("read error: %s", strerror(errno)));
+    }
+
+    buf[size] = '\0';
 
     int n_tokens = llama_tokenize(lctx, buf.data(), out.data(), out.size(), false);
     if (n_tokens < 0) {
         out.resize(-n_tokens);
-        llama_tokenize(lctx, buf.data(), out.data(), out.size(), false);
+        n_tokens = llama_tokenize(lctx, buf.data(), out.data(), out.size(), false);
     }
+    GGML_ASSERT(n_tokens >= 0);
+    out.resize(n_tokens);
 
     bool verify = false;
     if (verify) {
@@ -2238,438 +1040,466 @@ void shuffle_ints(int * begin, int * end) {
     });
 }
 
-struct my_llama_sampler_params {
-    float temp            = 0.0f;  // <= 0.0 disabled
-    int   top_k           = 20;    // <= 0 to use vocab size
-    float top_p           = 0.95f; // 1.0 = disabled
-    float tfs_z           = 1.00f; // 1.0 = disabled
-    float typical_p       = 1.00f; // 1.0 = disabled
-    int   repeat_last_n   = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float repeat_penalty  = 1.0f;  // 1.0 = disabled
-    float alpha_presence  = 0.0f;  // 0.0 = disabled
-    float alpha_frequency = 0.0f;  // 0.0 = disabled
-    int   mirostat        = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float mirostat_tau    = 5.00f; // target entropy
-    float mirostat_eta    = 0.10f; // learning rate
-    bool  penalize_nl     = true;  // consider newlines as a repeatable token
-};
-
-struct my_llama_sampler {
-    struct llama_context * ctx = NULL;
-    my_llama_sampler_params params;
-
-    int n_vocab = 0;
-    int n_ctx = 0;
-
-    float mirostat_mu;
-
-    std::vector<llama_token_data> candidates;
-    llama_token_data_array candidates_p;
-
-};
-
-void init_sampler(struct my_llama_sampler * sampler, struct llama_context * ctx) {
-    sampler->ctx = ctx;
-    sampler->n_vocab = llama_n_vocab(sampler->ctx);
-    sampler->n_ctx   = llama_n_ctx(sampler->ctx);
-    sampler->mirostat_mu = 2.0f * sampler->params.mirostat_tau;
+#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
+{ \
+    const std::string skey(key); \
+    const int kid = gguf_find_key(ctx, skey.c_str()); \
+    if (kid >= 0) { \
+        enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
+        if (ktype != (type)) { \
+            throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \
+        } \
+        (dst) = func(ctx, kid); \
+    } else if (req) { \
+        throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \
+    } \
 }
 
-llama_token sample(struct my_llama_sampler * sampler, float * logits, const llama_token * last_tokens, int n_last_tokens) {
-    GGML_ASSERT(sampler->ctx != NULL);
 
-    struct llama_context * ctx = sampler->ctx;
+bool are_same_layout(struct ggml_tensor * a, struct ggml_tensor * b) {
+    GGML_ASSERT(a != NULL);
+    GGML_ASSERT(b != NULL);
+    GGML_ASSERT(a->type == b->type);
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+    GGML_ASSERT(ggml_is_contiguous(a) && ggml_is_contiguous(b));
 
-    sampler->candidates.resize(sampler->n_vocab);
-    for (llama_token token_id = 0; token_id < sampler->n_vocab; ++token_id) {
-        sampler->candidates[token_id].id = token_id;
-        sampler->candidates[token_id].logit = logits[token_id];
-        sampler->candidates[token_id].p = 0.0;
+    return true;
+}
+
+void read_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name) {
+    if (dst == NULL) {
+        return;
     }
+    struct ggml_tensor * t  = ggml_get_tensor(ctx, name);
+    GGML_ASSERT(are_same_layout(dst, t));
+    memcpy(dst->data, t->data, ggml_nbytes(t));
 
-    llama_token_data_array * candidates_p = & sampler->candidates_p;
-
-    candidates_p->data = sampler->candidates.data();
-    candidates_p->size = sampler->candidates.size();
-    candidates_p->sorted = false;
-
-    const auto params = sampler->params;
-
-    // Apply penalties
-    const float nl_logit = logits[llama_token_nl(ctx)];
-
-    const int n_last = std::min(std::min(n_last_tokens, params.repeat_last_n), sampler->n_ctx);
-
-    llama_sample_repetition_penalty(
-        ctx,
-        candidates_p,
-        last_tokens + n_last_tokens - n_last,
-        n_last,
-        params.repeat_penalty);
-    llama_sample_frequency_and_presence_penalties(
-        ctx,
-        candidates_p,
-        last_tokens + n_last_tokens - n_last,
-        n_last,
-        params.alpha_frequency,
-        params.alpha_presence);
-
-    if (!params.penalize_nl) {
-        logits[llama_token_nl(ctx)] = nl_logit;
+    if (strlen(ggml_get_name(dst)) == 0) {
+        ggml_set_name(dst, name);
     }
+}
 
-    llama_token token = 0;
-    if (params.temp <= 0) {
-        // Greedy sampling
-        token = llama_sample_token_greedy(ctx, candidates_p);
+void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt) {
+    // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
+
+    uint32_t file_version;
+    GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_FILE_VERSION);
+    GGML_ASSERT(file_version == 0);
+
+    GGUF_GET_KEY(fctx, opt->params.past, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT);
+    GGUF_GET_KEY(fctx, opt->iter, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ITERATION_COUNT);
+    GGUF_GET_KEY(fctx, opt->just_initialized, gguf_get_val_bool, GGUF_TYPE_BOOL, true, LLM_KV_OPTIMIZER_JUST_INITIALIZED);
+
+    uint64_t nx;
+    GGUF_GET_KEY(fctx, nx, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_OPTIMIZER_PARAMETER_COUNT);
+    opt->nx = (size_t) nx;
+
+    // don't call ggml_opt_init until optimizer type and optimizer specific parameters are know
+
+    std::string opt_type;
+    GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE);
+    if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) {
+        opt->params.type = GGML_OPT_ADAM;
+
+        GGUF_GET_KEY(fctx, opt->adam.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS);
+        GGUF_GET_KEY(fctx, opt->adam.fx_prev,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS);
+        GGUF_GET_KEY(fctx, opt->adam.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32,  true, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT);
+
+        GGML_ASSERT(opt->ctx != NULL);
+        ggml_opt_init(opt->ctx, opt, opt->params, opt->nx);
+
+        read_tensor_by_name(opt->adam.m,  f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS);
+        read_tensor_by_name(opt->adam.v,  f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
+        read_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
+    } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) {
+        opt->params.type = GGML_OPT_LBFGS;
+
+        GGUF_GET_KEY(fctx, opt->params.lbfgs.m,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT);
+        GGUF_GET_KEY(fctx, opt->lbfgs.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS);
+        GGUF_GET_KEY(fctx, opt->lbfgs.step,             gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP);
+        GGUF_GET_KEY(fctx, opt->lbfgs.j,                gguf_get_val_i32, GGUF_TYPE_INT32,   true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J);
+        GGUF_GET_KEY(fctx, opt->lbfgs.k,                gguf_get_val_i32, GGUF_TYPE_INT32,   true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K);
+        GGUF_GET_KEY(fctx, opt->lbfgs.end,              gguf_get_val_i32, GGUF_TYPE_INT32,   true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END);
+        GGUF_GET_KEY(fctx, opt->lbfgs.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32,  true, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT);
+
+        GGML_ASSERT(opt->ctx != NULL);
+        ggml_opt_init(opt->ctx, opt, opt->params, opt->nx);
+
+        read_tensor_by_name(opt->lbfgs.x,    f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS);
+        read_tensor_by_name(opt->lbfgs.xp,   f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS);
+        read_tensor_by_name(opt->lbfgs.g,    f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS);
+        read_tensor_by_name(opt->lbfgs.gp,   f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS);
+        read_tensor_by_name(opt->lbfgs.d,    f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION);
+        read_tensor_by_name(opt->lbfgs.pf,   f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES);
+        read_tensor_by_name(opt->lbfgs.lmal, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA);
+        read_tensor_by_name(opt->lbfgs.lmys, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS);
+        read_tensor_by_name(opt->lbfgs.lms,  f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);
+        read_tensor_by_name(opt->lbfgs.lmy,  f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);
     } else {
-        if (params.mirostat == 1) {
-            int mirostat_m = 100;
-            llama_sample_temperature(ctx, candidates_p, params.temp);
-            token = llama_sample_token_mirostat(ctx, candidates_p, params.mirostat_tau, params.mirostat_eta, mirostat_m, &sampler->mirostat_mu);
-        } else if (params.mirostat == 2) {
-            llama_sample_temperature(ctx, candidates_p, params.temp);
-            token = llama_sample_token_mirostat_v2(ctx, candidates_p, params.mirostat_tau, params.mirostat_eta, &sampler->mirostat_mu);
-        } else {
-            // Temperature sampling
-            llama_sample_top_k        (ctx, candidates_p, params.top_k, 1);
-            llama_sample_tail_free    (ctx, candidates_p, params.tfs_z, 1);
-            llama_sample_typical      (ctx, candidates_p, params.typical_p, 1);
-
-            llama_sample_top_p        (ctx, candidates_p, params.top_p, 1);
-            llama_sample_temperature  (ctx, candidates_p, params.temp);
-            token = llama_sample_token(ctx, candidates_p);
-        }
-    }
-    return token;
-}
-
-void set_logits_masked(struct ggml_tensor * logits, std::vector<bool>& mask, float value) {
-    GGML_ASSERT(logits->ne[0] == (int64_t) mask.size());
-    for (int i2 = 0; i2 < logits->ne[2]; ++i2) {
-        for (int i1 = 0; i1 < logits->ne[1]; ++i1) {
-            for (int i0 = 0; i0 < logits->ne[0]; ++i0) {
-                if (!mask[i0]) continue;
-                float * ptr = (float *) ((char *) logits->data + i2*logits->nb[2] + i1*logits->nb[1] + i0*logits->nb[0]);
-                *ptr = value;
-            }
-        }
+        throw std::runtime_error("unknown optimizer type\n");
     }
 }
 
-void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
-    if (tensor == NULL) {
-        file->write_u32(0);
-        file->write_u32(0);
-        file->write_u32(GGML_TYPE_F32);
-        file->seek((0-file->tell()) & 31, SEEK_CUR);
-        return;
-    }
-    const char * name = ggml_get_name(tensor);
-    uint32_t name_len = strlen(name);
-    uint32_t nd = tensor->n_dims;
-    uint32_t ne[4] = { (uint32_t)tensor->ne[0],
-                       (uint32_t)tensor->ne[1],
-                       (uint32_t)tensor->ne[2],
-                       (uint32_t)tensor->ne[3] };
-    file->write_u32(nd);
-    file->write_u32(name_len);
-    file->write_u32(tensor->type);
-    file->write_raw(ne, sizeof(ne[0]) * nd);
-    file->write_raw(name, name_len);
-    file->seek((0-file->tell()) & 31, SEEK_CUR);
-    file->write_raw(tensor->data, ggml_nbytes(tensor));
-}
-
-void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
-    int32_t nd = file->read_u32();
-    GGML_ASSERT(nd == tensor->n_dims);
-
-    uint32_t name_len       = file->read_u32();
-    enum     ggml_type type = (enum ggml_type) file->read_u32();
-    GGML_ASSERT(type == tensor->type);
-
-    uint32_t ne[4];
-    file->read_raw(ne, sizeof(ne[0]) * nd);
-    for (int i=0; i<nd; ++i) {
-        GGML_ASSERT(ne[i] == tensor->ne[i]);
-    }
-
-    std::string name = file->read_string(name_len);
-    GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0);
-
-    file->seek((0-file->tell()) & 31, SEEK_CUR);
-    file->read_raw(tensor->data, ggml_nbytes(tensor));
-}
-
-void write_opt_context(struct llama_file * file, struct ggml_opt_context * opt) {
-    const uint32_t version = 0;
-    GGML_ASSERT(opt->nx   >= 0);
-    GGML_ASSERT(opt->iter >= 0);
-    file->write_u32(version);
-    file->write_raw(&opt->params, sizeof(opt->params));
-    file->write_raw(&opt->nx,     sizeof(opt->nx));
-    file->write_raw(&opt->iter,   sizeof(opt->iter));
-    file->write_u32((uint32_t)  opt->just_initialized);
-    switch (opt->params.type) {
-        case GGML_OPT_ADAM:
-            {
-                GGML_ASSERT(opt->adam.x != NULL);
-                write_tensor(file, opt->adam.x);
-                write_tensor(file, opt->adam.g1);
-                write_tensor(file, opt->adam.g2);
-                write_tensor(file, opt->adam.m);
-                write_tensor(file, opt->adam.v);
-                write_tensor(file, opt->adam.mh);
-                write_tensor(file, opt->adam.vh);
-                write_tensor(file, opt->adam.pf);
-                file->write_raw(&opt->adam.fx_best,          sizeof(opt->adam.fx_best));
-                file->write_raw(&opt->adam.fx_prev,          sizeof(opt->adam.fx_prev));
-                file->write_raw(&opt->adam.n_no_improvement, sizeof(opt->adam.n_no_improvement));
-            } break;
-        case GGML_OPT_LBFGS:
-            {
-                GGML_ASSERT(opt->adam.x != NULL);
-                write_tensor(file, opt->lbfgs.x);
-                write_tensor(file, opt->lbfgs.xp);
-                write_tensor(file, opt->lbfgs.g);
-                write_tensor(file, opt->lbfgs.gp);
-                write_tensor(file, opt->lbfgs.d);
-                write_tensor(file, opt->lbfgs.pf);
-                write_tensor(file, opt->lbfgs.lmal);
-                write_tensor(file, opt->lbfgs.lmys);
-                write_tensor(file, opt->lbfgs.lms);
-                write_tensor(file, opt->lbfgs.lmy);
-                file->write_raw(&opt->lbfgs.fx_best,          sizeof(opt->lbfgs.fx_best));
-                file->write_raw(&opt->lbfgs.step,             sizeof(opt->lbfgs.step));
-                file->write_raw(&opt->lbfgs.j,                sizeof(opt->lbfgs.j));
-                file->write_raw(&opt->lbfgs.k,                sizeof(opt->lbfgs.k));
-                file->write_raw(&opt->lbfgs.end,              sizeof(opt->lbfgs.end));
-                file->write_raw(&opt->lbfgs.n_no_improvement, sizeof(opt->lbfgs.n_no_improvement));
-            } break;
-    }
-}
-
-void read_opt_context(struct llama_file * file, struct ggml_context * ctx, struct ggml_opt_context * opt) {
-    uint32_t version = file->read_u32();
-    GGML_ASSERT(version == 0);
-
-    file->read_raw(&opt->params, sizeof(opt->params));
-    file->read_raw(&opt->nx,     sizeof(opt->nx));
-    ggml_opt_init(ctx, opt, opt->params, opt->nx);
-
-    file->read_raw(&opt->iter,   sizeof(opt->iter));
-    opt->just_initialized = (bool) file->read_u32();
+void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt) {
+    gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_FILE_VERSION, 0);
+    gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, opt->params.past);
+    gguf_set_val_u64(fctx, LLM_KV_OPTIMIZER_PARAMETER_COUNT, (uint64_t) opt->nx);
+    gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ITERATION_COUNT, opt->iter);
+    gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized);
 
     switch (opt->params.type) {
         case GGML_OPT_ADAM:
             {
-                read_tensor(file, opt->adam.x);
-                read_tensor(file, opt->adam.g1);
-                read_tensor(file, opt->adam.g2);
-                read_tensor(file, opt->adam.m);
-                read_tensor(file, opt->adam.v);
-                read_tensor(file, opt->adam.mh);
-                read_tensor(file, opt->adam.vh);
-                if (opt->adam.pf) { read_tensor(file, opt->adam.pf); }
-                file->read_raw(&opt->adam.fx_best,          sizeof(opt->adam.fx_best));
-                file->read_raw(&opt->adam.fx_prev,          sizeof(opt->adam.fx_prev));
-                file->read_raw(&opt->adam.n_no_improvement, sizeof(opt->adam.n_no_improvement));
+                gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM);
+                gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS,            opt->adam.fx_best);
+                gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS,        opt->adam.fx_prev);
+                gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, opt->adam.n_no_improvement);
+
+                ggml_set_name(opt->adam.m, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS);
+                ggml_set_name(opt->adam.v, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
+                if (opt->adam.pf) {
+                    ggml_set_name(opt->adam.pf, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
+                }
+
+                gguf_add_tensor(fctx, opt->adam.m);
+                gguf_add_tensor(fctx, opt->adam.v);
+                if (opt->adam.pf) {
+                    gguf_add_tensor(fctx, opt->adam.pf);
+                }
             } break;
         case GGML_OPT_LBFGS:
             {
-                GGML_ASSERT(opt->adam.x != NULL);
-                read_tensor(file, opt->lbfgs.x);
-                read_tensor(file, opt->lbfgs.xp);
-                read_tensor(file, opt->lbfgs.g);
-                read_tensor(file, opt->lbfgs.gp);
-                read_tensor(file, opt->lbfgs.d);
-                if (opt->lbfgs.pf) { read_tensor(file, opt->lbfgs.pf); }
-                read_tensor(file, opt->lbfgs.lmal);
-                read_tensor(file, opt->lbfgs.lmys);
-                read_tensor(file, opt->lbfgs.lms);
-                read_tensor(file, opt->lbfgs.lmy);
-                file->read_raw(&opt->lbfgs.fx_best,          sizeof(opt->lbfgs.fx_best));
-                file->read_raw(&opt->lbfgs.step,             sizeof(opt->lbfgs.step));
-                file->read_raw(&opt->lbfgs.j,                sizeof(opt->lbfgs.j));
-                file->read_raw(&opt->lbfgs.k,                sizeof(opt->lbfgs.k));
-                file->read_raw(&opt->lbfgs.end,              sizeof(opt->lbfgs.end));
-                file->read_raw(&opt->lbfgs.n_no_improvement, sizeof(opt->lbfgs.n_no_improvement));
+                gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS);
+                gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m);
+                gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS,            opt->lbfgs.fx_best);
+                gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP,     opt->lbfgs.step);
+                gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J,        opt->lbfgs.j);
+                gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K,        opt->lbfgs.k);
+                gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END,      opt->lbfgs.end);
+                gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, opt->lbfgs.n_no_improvement);
+
+                ggml_set_name(opt->lbfgs.x,    LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS);
+                ggml_set_name(opt->lbfgs.xp,   LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS);
+                ggml_set_name(opt->lbfgs.g,    LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS);
+                ggml_set_name(opt->lbfgs.gp,   LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS);
+                ggml_set_name(opt->lbfgs.d,    LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION);
+                if (opt->lbfgs.pf) {
+                    ggml_set_name(opt->lbfgs.pf, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES);
+                }
+                ggml_set_name(opt->lbfgs.lmal, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA);
+                ggml_set_name(opt->lbfgs.lmys, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS);
+                ggml_set_name(opt->lbfgs.lms,  LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S);
+                ggml_set_name(opt->lbfgs.lmy,  LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y);
+
+                gguf_add_tensor(fctx, opt->lbfgs.x);
+                gguf_add_tensor(fctx, opt->lbfgs.xp);
+                gguf_add_tensor(fctx, opt->lbfgs.g);
+                gguf_add_tensor(fctx, opt->lbfgs.gp);
+                gguf_add_tensor(fctx, opt->lbfgs.d);
+                if (opt->lbfgs.pf) {
+                    gguf_add_tensor(fctx, opt->lbfgs.pf);
+                }
+                gguf_add_tensor(fctx, opt->lbfgs.lmal);
+                gguf_add_tensor(fctx, opt->lbfgs.lmys);
+                gguf_add_tensor(fctx, opt->lbfgs.lms);
+                gguf_add_tensor(fctx, opt->lbfgs.lmy);
             } break;
     }
 }
 
-void save_checkpoint(struct my_llama_model * model, struct ggml_opt_context * opt, const char * filename) {
-    struct llama_file file(filename, "wb");
-    if (file.fp == NULL) {
-        return;
+void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model) {
+    // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
+    std::string arch;
+
+    std::vector<char> keybuf;
+    keybuf.resize(512);
+    auto kv = [&arch, &keybuf](const char * key) -> const char * {
+        snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());
+        return keybuf.data();
+    };
+
+    std::vector<char> tn_buf;
+    tn_buf.resize(GGML_MAX_NAME);
+    auto tn = [&tn_buf](const char * key) -> const char * {
+        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
+        return tn_buf.data();
+    };
+    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
+        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
+        std::string s = tn_buf.data();
+        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
+        return tn_buf.data();
+    };
+
+    GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
+    GGML_ASSERT(arch == "llama");
+
+    uint32_t ftype_u;
+    GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);
+    GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);
+
+    // n_ctx was not saved in earlier checkpoint file versions, so we make it optional here
+    GGUF_GET_KEY(fctx, model->hparams.n_ctx,   gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
+
+    GGUF_GET_KEY(fctx, model->hparams.n_embd,  gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
+    GGUF_GET_KEY(fctx, model->hparams.n_ff,    gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
+    GGUF_GET_KEY(fctx, model->hparams.n_head,  gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
+    GGUF_GET_KEY(fctx, model->hparams.n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
+
+    model->hparams.n_rot = model->hparams.n_embd / model->hparams.n_head;
+    GGUF_GET_KEY(fctx, model->hparams.n_rot,   gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
+
+    float rope_freq_scale = 1.0f;
+    GGUF_GET_KEY(fctx, model->hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
+    GGUF_GET_KEY(fctx, model->hparams.rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
+    GGUF_GET_KEY(fctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
+    if (rope_freq_scale != 1.0f) {
+        model->hparams.rope_freq_scale = 1.0f / rope_freq_scale;
     }
 
-    const uint32_t magic   = 'ggcp';
-    const uint32_t version = 0;
+    init_model(model);
 
-    file.write_u32(magic);
-    file.write_u32(version);
-    file.write_u32(model->train_its);
-    file.write_u32(model->train_samples);
-    file.write_u32(model->train_tokens);
-    file.write_u32(model->hparams.n_vocab);
-    file.write_u32(model->hparams.n_embd);
-    file.write_u32(model->hparams.n_mult);
-    file.write_u32(model->hparams.n_head);
-    file.write_u32(model->hparams.n_layer);
-    file.write_u32(model->hparams.n_rot);
-
-    write_tensor(&file, model->tok_embeddings);
-    write_tensor(&file, model->norm);
-    write_tensor(&file, model->output);
+    read_tensor_by_name(model->tok_embeddings, f_ggml_ctx, tn(LLM_TENSOR_TOKEN_EMBD));
+    read_tensor_by_name(model->norm,           f_ggml_ctx, tn(LLM_TENSOR_OUTPUT_NORM));
+    read_tensor_by_name(model->output,         f_ggml_ctx, tn(LLM_TENSOR_OUTPUT));
 
     for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
         auto & layer = model->layers[i];
 
-        write_tensor(&file, layer.attention_norm);
-        write_tensor(&file, layer.wq);
-        write_tensor(&file, layer.wk);
-        write_tensor(&file, layer.wv);
-        write_tensor(&file, layer.wo);
-        write_tensor(&file, layer.ffn_norm);
-        write_tensor(&file, layer.w1);
-        write_tensor(&file, layer.w2);
-        write_tensor(&file, layer.w3);
+        read_tensor_by_name(layer.attention_norm, f_ggml_ctx, tni(LLM_TENSOR_ATTN_NORM, i));
+        read_tensor_by_name(layer.wq,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_Q, i));
+        read_tensor_by_name(layer.wk,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_K, i));
+        read_tensor_by_name(layer.wv,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i));
+        read_tensor_by_name(layer.wo,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i));
+        read_tensor_by_name(layer.ffn_norm,       f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i));
+        read_tensor_by_name(layer.w1,             f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
+        read_tensor_by_name(layer.w2,             f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
+        read_tensor_by_name(layer.w3,             f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
     }
-
-    write_opt_context(&file, opt);
 }
 
-bool load_checkpoint(struct my_llama_model * model, struct ggml_opt_context * opt, const char * filename, bool init) {
-    struct llama_file file(filename, "rb");
+void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) {
+    const char * arch = "llama";
+    enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
 
-    uint32_t magic;
-    uint32_t version;
+    std::vector<char> keybuf;
+    keybuf.resize(512);
+    auto kv = [arch, &keybuf](const char * key) -> const char * {
+        snprintf(keybuf.data(), keybuf.size(), key, arch);
+        return keybuf.data();
+    };
 
-    uint32_t train_its = 0;
-    uint32_t train_samples = 0;
-    uint32_t train_tokens = 0;
+    // set arch
+    gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
+    gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
 
-    if (file.fp) {
-        printf("%s: Loading model from '%s'.\n", __func__, filename);
-        magic                  = file.read_u32();
-        GGML_ASSERT(magic     == 'ggcp');
-        version                = file.read_u32();
-        GGML_ASSERT(version   == 0);
-        train_its              = file.read_u32();
-        train_samples          = file.read_u32();
-        train_tokens           = file.read_u32();
-        model->hparams.n_vocab = file.read_u32();
-        model->hparams.n_embd  = file.read_u32();
-        model->hparams.n_mult  = file.read_u32();
-        model->hparams.n_head  = file.read_u32();
-        model->hparams.n_layer = file.read_u32();
-        model->hparams.n_rot   = file.read_u32();
-        print_params(&model->hparams);
-    }
+    // set hparams
+    gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH),              model->hparams.n_ctx                  );
+    gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH),            model->hparams.n_embd                 );
+    gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH),         model->hparams.n_ff                   );
+    gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT),        model->hparams.n_head                 );
+    gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT),                 model->hparams.n_layer                );
+    gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT),        model->hparams.n_rot                  );
 
-    if (init) {
-        init_model(model);
-    }
+    gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps         );
+    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE),              model->hparams.rope_freq_base         ); // TODO load in llama.cpp
+    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR),           1.0f / model->hparams.rope_freq_scale );
 
-    if (file.fp) {
-        model->train_its = train_its;
-        model->train_samples = train_samples;
-        model->train_tokens = train_tokens;
-    }
+    // set vocab by copying from vocab_model gguf file
+    {
+        struct gguf_init_params params = {
+            /*.no_alloc = */ false,
+            /*.ctx      = */ NULL,
+        };
+        struct gguf_context * vctx = gguf_init_from_file(fn_vocab_model, params);
 
-    printf("%s: Training iterations: %u.\n", __func__, model->train_its);
-    printf("%s: Training samples:    %u.\n", __func__, model->train_samples);
-    printf("%s: Training tokens:     %u.\n", __func__, model->train_tokens);
+        const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST));
+        if (token_idx == -1) {
+            throw std::runtime_error("cannot find tokenizer vocab in model file\n");
+        }
+        const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx);
 
-    if (file.fp) {
-        read_tensor(&file, model->tok_embeddings);
-        read_tensor(&file, model->norm);
-        read_tensor(&file, model->output);
-
-        for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-            auto & layer = model->layers[i];
-
-            read_tensor(&file, layer.attention_norm);
-            read_tensor(&file, layer.wq);
-            read_tensor(&file, layer.wk);
-            read_tensor(&file, layer.wv);
-            read_tensor(&file, layer.wo);
-            read_tensor(&file, layer.ffn_norm);
-            read_tensor(&file, layer.w1);
-            read_tensor(&file, layer.w2);
-            read_tensor(&file, layer.w3);
+        const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES));
+        if (score_idx == -1) {
+            throw std::runtime_error("cannot find tokenizer scores in model file\n");
         }
 
-        read_opt_context(&file, model->ctx, opt);
+        const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx);
+
+        const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE));
+        if (toktype_idx == -1) {
+            throw std::runtime_error("cannot find token type list in GGUF file\n");
+        }
+
+        const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx);
+
+        std::string tokenizer_name;
+        GGUF_GET_KEY(vctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL));
+
+        gguf_set_val_str(fctx, kv(LLM_KV_TOKENIZER_MODEL), tokenizer_name.c_str());
+        gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_SCORES), GGUF_TYPE_FLOAT32, scores, n_vocab);
+        gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE), GGUF_TYPE_INT32, toktypes, n_vocab);
+
+        int32_t special_bos_id = 1;
+        int32_t special_eos_id = 2;
+        int32_t special_unk_id = 0;
+        int32_t special_sep_id = -1;
+        int32_t special_pad_id = -1;
+        if (tokenizer_name == "llama") {
+            // default special tokens
+            special_bos_id = 1;
+            special_eos_id = 2;
+            special_unk_id = 0;
+            special_sep_id = -1;
+            special_pad_id = -1;
+        } else if (tokenizer_name == "gpt2") {
+            // read and copy bpe merges
+            const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES));
+            if (merges_keyidx == -1) {
+                throw std::runtime_error("cannot find tokenizer merges in model file\n");
+            }
+
+            const int n_merges = gguf_get_arr_n(vctx, merges_keyidx);
+
+            std::vector<const char*> merges;
+            merges.resize(n_merges);
+            for (int i = 0; i < n_merges; i++) {
+                merges[i] = gguf_get_arr_str(vctx, merges_keyidx, i);
+            }
+            gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_MERGES), merges.data(), n_merges);
+
+            // default special tokens
+            special_bos_id = 11;
+            special_eos_id = 11;
+            special_unk_id = -1;
+            special_sep_id = -1;
+            special_pad_id = -1;
+        } else {
+            fprintf(stderr, "%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
+            fprintf(stderr, "%s: using default tokenizer: 'llama'", __func__);
+        }
+
+        std::vector<const char*> tokens;
+        tokens.resize(n_vocab);
+        for (uint32_t i = 0; i < n_vocab; i++) {
+            tokens[i] = gguf_get_arr_str(vctx, token_idx, i);
+        }
+        gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_LIST), tokens.data(), n_vocab);
+
+        GGUF_GET_KEY(vctx, special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
+        GGUF_GET_KEY(vctx, special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID));
+        GGUF_GET_KEY(vctx, special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID));
+        GGUF_GET_KEY(vctx, special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID));
+        GGUF_GET_KEY(vctx, special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID));
+
+        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_BOS_ID), special_bos_id);
+        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_EOS_ID), special_eos_id);
+        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_UNK_ID), special_unk_id);
+        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_SEP_ID), special_sep_id);
+        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_PAD_ID), special_pad_id);
+
+        gguf_free(vctx);
     }
 
-    return (file.fp != NULL);
+    // add tensors
+    gguf_add_tensor(fctx, model->tok_embeddings);
+    gguf_add_tensor(fctx, model->norm);
+    gguf_add_tensor(fctx, model->output);
+    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
+        auto & layer = model->layers[i];
+
+
+        gguf_add_tensor(fctx, layer.attention_norm);
+        gguf_add_tensor(fctx, layer.wq);
+        gguf_add_tensor(fctx, layer.wk);
+        gguf_add_tensor(fctx, layer.wv);
+        gguf_add_tensor(fctx, layer.wo);
+        gguf_add_tensor(fctx, layer.ffn_norm);
+        gguf_add_tensor(fctx, layer.w1);
+        gguf_add_tensor(fctx, layer.w2);
+        gguf_add_tensor(fctx, layer.w3);
+    }
 }
 
-void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, const char * filename) {
-    struct llama_file file(filename, "wb");
-    if (file.fp == NULL) {
-        return;
+void save_llama_model_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model) {
+    struct gguf_context * fctx = gguf_init_empty();
+
+    save_llama_model_gguf(fctx, fn_vocab_model, model);
+
+    // write file
+    const bool only_meta = false;
+    gguf_write_to_file(fctx, filename, only_meta);
+    gguf_free(fctx);
+}
+
+void load_checkpoint_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct ggml_opt_context * opt) {
+    load_llama_model_gguf(fctx, f_ggml_ctx, model);
+
+    uint32_t file_version;
+    GGUF_GET_KEY(fctx, file_version,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_FILE_VERSION);
+    GGML_ASSERT(file_version == 0);
+
+    GGUF_GET_KEY(fctx, model->train_its,     gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_ITERATION_COUNT);
+    GGUF_GET_KEY(fctx, model->train_samples, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_SAMPLE_COUNT);
+    GGUF_GET_KEY(fctx, model->train_tokens,  gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_TOKEN_COUNT);
+
+    load_opt_context_gguf(fctx, f_ggml_ctx, opt);
+}
+
+void save_checkpoint_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model, struct ggml_opt_context * opt) {
+    save_llama_model_gguf(fctx, fn_vocab_model, model);
+
+    gguf_set_val_u32(fctx, LLM_KV_TRAINING_FILE_VERSION,    0);
+    gguf_set_val_u32(fctx, LLM_KV_TRAINING_ITERATION_COUNT, model->train_its);
+    gguf_set_val_u32(fctx, LLM_KV_TRAINING_SAMPLE_COUNT,    model->train_samples);
+    gguf_set_val_u32(fctx, LLM_KV_TRAINING_TOKEN_COUNT,     model->train_tokens);
+
+    save_opt_context_gguf(fctx, opt);
+}
+
+bool load_checkpoint_file(const char * filename, struct my_llama_model * model, struct ggml_opt_context * opt) {
+    struct ggml_context * f_ggml_ctx;
+    struct gguf_init_params params;
+    params.no_alloc = false;
+    params.ctx = &f_ggml_ctx;
+    struct gguf_context * fctx = gguf_init_from_file(filename, params);
+    if (fctx == NULL) {
+        return false;
     }
 
-#pragma message("TODO: implement file saving using gguf")
-    (void) vocab;
-    (void) model;
-//    // write_magic
-//    file.write_u32(LLAMA_FILE_MAGIC);   // magic
-//    file.write_u32(LLAMA_FILE_VERSION); // version
-//    // write_hparams
-//    file.write_u32(model->hparams.n_vocab);
-//    file.write_u32(model->hparams.n_embd);
-//    file.write_u32(model->hparams.n_mult);
-//    file.write_u32(model->hparams.n_head);
-//    file.write_u32(model->hparams.n_layer);
-//    file.write_u32(model->hparams.n_rot);
-//    file.write_u32(LLAMA_FTYPE_ALL_F32);
-//    // write_vocab
-//    uint32_t n_vocab = model->hparams.n_vocab;
-//    for (uint32_t i = 0; i < n_vocab; i++) {
-//        const auto & token_data = vocab->id_to_token.at(i);
-//        file.write_u32((uint32_t) token_data.tok.size());
-//        file.write_raw(token_data.tok.data(), token_data.tok.size());
-//        file.write_raw(&token_data.score, sizeof(token_data.score));
-//    }
-//    // write tensors
-//    write_tensor(&file, model->tok_embeddings);
-//    write_tensor(&file, model->norm);
-//    write_tensor(&file, model->output);
-//    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-//        auto & layer = model->layers[i];
-//
-//        write_tensor(&file, layer.attention_norm);
-//        write_tensor(&file, layer.wq);
-//        write_tensor(&file, layer.wk);
-//        write_tensor(&file, layer.wv);
-//        write_tensor(&file, layer.wo);
-//        write_tensor(&file, layer.ffn_norm);
-//        write_tensor(&file, layer.w1);
-//        write_tensor(&file, layer.w2);
-//        write_tensor(&file, layer.w3);
-//    }
+    load_checkpoint_gguf(fctx, f_ggml_ctx, model, opt);
+
+    return true;
 }
 
-float cosine_decay(const int decay_steps, const float alpha, int step) {
+void save_checkpoint_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model, struct ggml_opt_context * opt) {
+    struct gguf_context * fctx = gguf_init_empty();
+
+    save_checkpoint_gguf(fctx, fn_vocab_model, model, opt);
+
+    // write file
+    const bool only_meta = false;
+    gguf_write_to_file(fctx, filename, only_meta);
+    gguf_free(fctx);
+}
+
+float cosine_decay(const int decay_steps, const float minimum, int step) {
     if (step > decay_steps) {
         step = decay_steps;
     }
     const float cosine_decay = 0.50f*(1.0f + cosf(3.14159265359f*step/decay_steps));
-    const float decay = (1 - alpha)*cosine_decay + alpha;
+    const float decay = (1 - minimum)*cosine_decay + minimum;
     return decay;
 }
 
-float cosine_decay_restart(int decay_steps, const float alpha, int step, float restart_step_mult) {
-    while (step > decay_steps) {
-        step -= decay_steps;
-        decay_steps = (int) restart_step_mult * decay_steps;
+float cosine_decay_restart(int decay_steps, const float minimum, int step, float restart_step_mult, bool enable_restart) {
+    if (enable_restart) {
+        while (step > decay_steps) {
+            step -= decay_steps;
+            decay_steps = (int) restart_step_mult * decay_steps;
+        }
     }
-    return cosine_decay(decay_steps, alpha, step);
+    return cosine_decay(decay_steps, minimum, step);
 }
 
 struct train_params {
@@ -2683,39 +1513,51 @@ struct train_params {
 
     int n_ctx;
     int n_embd;
-    int n_mult;
     int n_head;
     int n_layer;
-    int n_rotmax;
+    int n_ff;
 
     int n_threads;
     int n_batch;
     int n_examples;
-    int n_predict;
+
+    float f_norm_rms_eps;
+    float rope_freq_base;
+    float rope_freq_scale;
 
     int print_info_interval;
-    int print_details_interval;
 
     bool samples_start_after_nl;
     bool use_adam;
     bool use_flash;
-    bool use_scratch;
+    bool use_checkpointing;
+    bool use_alloc;
 
     // only adam
     int   warmup;
     int   cos_decay_steps;
     float cos_decay_restart;
-    float cos_decay_alpha;
+    float cos_decay_min;
+    bool  enable_restart;
+
+    int   opt_past;
+    float opt_delta;
+    int   opt_max_no_improvement;
 
     int   lbfgs_n_iter;
     int   adam_n_iter;
     float adam_alpha;
+    float adam_min_alpha;
     float adam_decay;
+    int   adam_decay_min_ndim;
+    float adam_beta1;
+    float adam_beta2;
+    float adam_gclip;
+    float adam_eps_f;
 
     int mem_model_gb;
     int mem_compute_gb;
     int mem_compute0_gb;
-    int mem_compute1_gb;
 };
 
 struct train_params get_default_train_params() {
@@ -2730,40 +1572,51 @@ struct train_params get_default_train_params() {
 
     params.n_ctx      =  128;
     params.n_embd     =  256;
-    params.n_mult     =  256;
     params.n_head     =    8;
     params.n_layer    =   16;
-    params.n_rotmax   =   64;
+    params.n_ff       =  768;
 
     params.n_threads  =    6;
     params.n_batch    =    8;
-    params.n_examples =    8;
-    params.n_predict  = 1024;
+    params.n_examples =    1;
+
+    params.f_norm_rms_eps  = 1e-5;
+    params.rope_freq_base  = 10000.0f;
+    params.rope_freq_scale = 1.0f;
 
     params.print_info_interval    = 1;
-    params.print_details_interval = 2;
 
     params.samples_start_after_nl = false;
     params.use_adam               = true;
     params.use_flash              = true;
-    params.use_scratch            = true;
+    params.use_checkpointing      = true;
+    params.use_alloc              = true;
+
+    params.opt_past               = 0;
+    params.opt_delta              = 1e-5f;
+    params.opt_max_no_improvement = 0;
 
     // only adam
     params.warmup            =  100;
     params.cos_decay_steps   = 1000;
     params.cos_decay_restart = 1.1f;
-    params.cos_decay_alpha   = 0.0f;
+    params.cos_decay_min     = 0.1f;
+    params.enable_restart    = false;
 
-    params.lbfgs_n_iter      = 16;
-    params.adam_n_iter       = 16;
-    params.adam_alpha        = 1e-3f;
-    params.adam_decay        = 1e-3f;
+    params.lbfgs_n_iter        = 256;
+    params.adam_n_iter         = 256;
+    params.adam_alpha          = 1e-3f;
+    params.adam_min_alpha      = 0;
+    params.adam_decay          = 1e-1f;
+    params.adam_decay_min_ndim = 2;
+    params.adam_beta1          = 0.9f;
+    params.adam_beta2          = 0.999f;
+    params.adam_gclip          = 1.0f;
+    params.adam_eps_f          = 0.0f;
 
-    params.mem_model_gb   = 2;
+    params.mem_model_gb   =  2;
     params.mem_compute_gb = 24;
     params.mem_compute0_gb = 8;
-    params.mem_compute1_gb = 2;
-
     return params;
 }
 
@@ -2780,35 +1633,47 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p
     fprintf(stderr, "  -s SEED, --seed SEED       RNG seed (default: -1, use random seed for -1)\n");
     fprintf(stderr, "  -c N, --ctx N              Context size used during training (default %d)\n", params->n_ctx);
     fprintf(stderr, "  --embd N                   Embedding size used for new models (default %d)\n", params->n_embd);
-    fprintf(stderr, "  --mult N                   Mult size used for new models, influences feedforward size. (default %d)\n", params->n_mult);
+    fprintf(stderr, "  --ff N                     Feedforward size used for new models. (default %d)\n", params->n_ff);
     fprintf(stderr, "  --head N                   Number of heads for new models (default %d)\n", params->n_head);
     fprintf(stderr, "  --layer N                  Number of layers for new models (default %d)\n", params->n_layer);
-    fprintf(stderr, "  --rotmax N                 Maximal number Rope dimensions for new models (default %d)\n", params->n_rotmax);
+    fprintf(stderr, "  --norm-rms-eps F           RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
+    fprintf(stderr, "  --rope-freq-base F         Frequency base for ROPE (default %f)\n", params->rope_freq_base);
+    fprintf(stderr, "  --rope-freq-scale F        Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
     fprintf(stderr, "  -t N, --threads N          Number of threads (default %d)\n", params->n_threads);
     fprintf(stderr, "  -b N, --batch N            Parallel batch size (default %d)\n", params->n_batch);
     fprintf(stderr, "  -n N, --examples N         Number of examples to train (default %d)\n", params->n_examples);
-    fprintf(stderr, "  --predict N                Number of tokens to generate after training (default %d)\n", params->n_predict);
     fprintf(stderr, "  --print-info-interval N    Print infos during training each N examples (default %d)\n", params->print_info_interval);
-    fprintf(stderr, "  --print-details-interval N Print details during training each N examples (default %d)\n", params->print_details_interval);
     fprintf(stderr, "  --samples-after-nl         Training samples start after newlines. (default %s)\n", params->samples_start_after_nl ? "on" : "off");
     fprintf(stderr, "  --use-lbfgs                Use LBFGS optimizer instead of default Adam\n");
     fprintf(stderr, "  --use-adam                 Use Adam optimizer (default)\n");
-    fprintf(stderr, "  --no-flash                 Don't use flash attention.\n");
+    fprintf(stderr, "  --no-flash                 Don't use flash attention \n");
     fprintf(stderr, "  --use-flash                Use flash attention (default)\n");
-    fprintf(stderr, "  --no-scratch               Don't use scratch buffers\n");
-    fprintf(stderr, "  --use-scratch              Use scratch buffers (default)\n");
-    fprintf(stderr, "  --warmup N                 Number of warmup steps (default %d)\n", params->warmup);
-    fprintf(stderr, "  --cos-decay-steps N        Number of cosine decay steps (default %d)\n", params->cos_decay_steps);
-    fprintf(stderr, "  --cos-decay-restart N      Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart);
-    fprintf(stderr, "  --cos-decay-alpha N        Cosine decay alpha (default %f)\n", params->cos_decay_alpha);
-    fprintf(stderr, "  --lbfgs-iter N             Maximum number of LBFGS optimization iterations for each batch (default %d)\n", params->lbfgs_n_iter);
+    fprintf(stderr, "  --no-checkpointing         Don't use gradient checkpointing\n");
+    fprintf(stderr, "  --use-checkpointing        Use gradient checkpointing (default)\n");
+    fprintf(stderr, "  --no-alloc                 Don't use allocator\n");
+    fprintf(stderr, "  --use-alloc                Use allocator (default)\n");
+    fprintf(stderr, "  --warmup N                 Only for Adam optimizer. Number of warmup steps (default %d)\n", params->warmup);
+    fprintf(stderr, "  --cos-decay-steps N        Only for Adam optimizer. Number of cosine decay steps (default %d)\n", params->cos_decay_steps);
+    fprintf(stderr, "  --cos-decay-restart N      Only for Adam optimizer. Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart);
+    fprintf(stderr, "  --cos-decay-min N          Only for Adam optimizer. Cosine decay minimum (default %f)\n", params->cos_decay_min);
+    fprintf(stderr, "  --enable-restart N         Only for Adam optimizer. Enable restarts of cos-decay %s\n", params->enable_restart ? "(default)" : "");
+    fprintf(stderr, "  --disable-restart N        Only for Adam optimizer. Disable restarts of cos-decay %s\n", !params->enable_restart ? "(default)" : "");
+    fprintf(stderr, "  --opt-past N               Number of optimization iterations to track for delta convergence test. Disabled when zero. (default %d)\n", params->opt_past);
+    fprintf(stderr, "  --opt-delta N              Maximum delta for delta convergence test. Disabled when <= zero. (default %f)\n", params->opt_delta);
+    fprintf(stderr, "  --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. (default %d)\n", params->opt_max_no_improvement);
+    fprintf(stderr, "  --adam-epsf N              AdamW epsilon for convergence test. Disabled when <= zero. (default %f)\n", params->adam_eps_f);
     fprintf(stderr, "  --adam-iter N              Maximum number of Adam optimization iterations for each batch (default %d)\n", params->adam_n_iter);
     fprintf(stderr, "  --adam-alpha N             Adam learning rate alpha (default %f)\n", params->adam_alpha);
+    fprintf(stderr, "  --adam-min-alpha N         Adam minimum learning rate alpha - including warmup phase (default %f)\n", params->adam_min_alpha);
     fprintf(stderr, "  --adam-decay N             AdamW weight decay. Values greater zero enable AdamW instead of regular Adam. (default %f)\n", params->adam_decay);
+    fprintf(stderr, "  --adam-decay-min-ndim N    Minimum number of tensor dimensions to apply AdamW weight decay. Weight decay is not applied to tensors with less n_dims. (default %d)\n", params->adam_decay_min_ndim);
+    fprintf(stderr, "  --adam-beta1 N             AdamW beta1 in interval [0,1). How much to smooth the first moment of gradients. (default %f)\n", params->adam_beta1);
+    fprintf(stderr, "  --adam-beta2 N             AdamW beta2 in interval [0,1). How much to smooth the second moment of gradients. (default %f)\n", params->adam_beta2);
+    fprintf(stderr, "  --adam-gclip N             AdamW gradient clipping. Disabled when zero. (default %f)\n", params->adam_gclip);
+    fprintf(stderr, "  --lbfgs-iter N             Maximum number of LBFGS optimization iterations for each batch (default %d)\n", params->lbfgs_n_iter);
     fprintf(stderr, "  --mem-model N              Memory to allocate for model and cache in gigabytes. (default %d)\n", params->mem_model_gb);
     fprintf(stderr, "  --mem-compute N            Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute_gb);
-    fprintf(stderr, "  --mem-compute0 N           Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute0_gb);
-    fprintf(stderr, "  --mem-compute1 N           Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute1_gb);
+    fprintf(stderr, "  --mem-compute0 N           Memory to allocate for automatic memory allocator in gigabytes. (default %d)\n", params->mem_compute0_gb);
     fprintf(stderr, "\n");
 }
 
@@ -2872,12 +1737,12 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
                 break;
             }
             params->n_embd = std::stoi(argv[i]);
-        } else if (arg == "--mult") {
+        } else if (arg == "--ff") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params->n_mult = std::stoi(argv[i]);
+            params->n_ff = std::stoi(argv[i]);
         } else if (arg == "--head") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -2890,12 +1755,24 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
                 break;
             }
             params->n_layer = std::stoi(argv[i]);
-        } else if (arg == "--rotmax") {
+        } else if (arg == "--norm-rms-eps") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params->n_rotmax = std::stoi(argv[i]);
+            params->f_norm_rms_eps = std::stof(argv[i]);
+        } else if (arg == "--rope-freq-base") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->rope_freq_base = std::stof(argv[i]);
+        } else if (arg == "--rope-freq-scale") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->rope_freq_scale = std::stof(argv[i]);
         } else if (arg == "-t" || arg == "--threads") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -2914,24 +1791,12 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
                 break;
             }
             params->n_examples = std::stoi(argv[i]);
-        } else if (arg == "--predict") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_predict = std::stoi(argv[i]);
         } else if (arg == "--print-info-interval") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params->print_info_interval = std::stoi(argv[i]);
-        } else if (arg == "--print-details-interval") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->print_details_interval = std::stoi(argv[i]);
         } else if (arg == "--samples-after-nl") {
             params->samples_start_after_nl = true;
         } else if (arg == "--use-lbfgs") {
@@ -2942,10 +1807,14 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
             params->use_flash = false;
         } else if (arg == "--use-flash") {
             params->use_flash = true;
-        } else if (arg == "--no-scratch") {
-            params->use_scratch = false;
-        } else if (arg == "--use-scratch") {
-            params->use_scratch = true;
+        } else if (arg == "--no-checkpointing") {
+            params->use_checkpointing = false;
+        } else if (arg == "--use-checkpointing") {
+            params->use_checkpointing = true;
+        } else if (arg == "--no-alloc") {
+            params->use_alloc = false;
+        } else if (arg == "--use-alloc") {
+            params->use_alloc = true;
         } else if (arg == "--warmup") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -2964,18 +1833,40 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
                 break;
             }
             params->cos_decay_restart = std::stof(argv[i]);
-        } else if (arg == "--cos-decay-alpha") {
+        } else if (arg == "--cos-decay-min") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params->cos_decay_alpha = std::stof(argv[i]);
-        } else if (arg == "--lbfgs-iter") {
+            params->cos_decay_min = std::stof(argv[i]);
+        } else if (arg == "--enable-restart") {
+            params->enable_restart = true;
+        } else if (arg == "--disable-restart") {
+            params->enable_restart = false;
+        } else if (arg == "--opt-past") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params->lbfgs_n_iter = std::stoi(argv[i]);
+            params->opt_past = std::stoi(argv[i]);
+        } else if (arg == "--opt-delta") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->opt_delta = std::stof(argv[i]);
+        } else if (arg == "--opt-max-no-improvement") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->opt_max_no_improvement = std::stoi(argv[i]);
+        } else if (arg == "--adam-epsf") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->adam_eps_f = std::stof(argv[i]);
         } else if (arg == "--adam-iter") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -2988,12 +1879,48 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
                 break;
             }
             params->adam_alpha = std::stof(argv[i]);
+        } else if (arg == "--adam-min-alpha") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->adam_min_alpha = std::stof(argv[i]);
         } else if (arg == "--adam-decay") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params->adam_decay = std::stof(argv[i]);
+        } else if (arg == "--adam-decay-min-ndim") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->adam_decay_min_ndim = std::stoi(argv[i]);
+        } else if (arg == "--adam-beta1") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->adam_beta1 = std::stof(argv[i]);
+        } else if (arg == "--adam-beta2") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->adam_beta2 = std::stof(argv[i]);
+        } else if (arg == "--adam-gclip") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->adam_gclip = std::stof(argv[i]);
+        } else if (arg == "--lbfgs-iter") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params->lbfgs_n_iter = std::stoi(argv[i]);
         } else if (arg == "--mem-model") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -3012,12 +1939,6 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
                 break;
             }
             params->mem_compute0_gb = std::stoi(argv[i]);
-        } else if (arg == "--mem-compute1") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->mem_compute1_gb = std::stoi(argv[i]);
         } else if (arg == "-h" || arg == "--help") {
             train_print_usage(argc, argv, &default_params);
             exit(0);
@@ -3036,6 +1957,63 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) {
     return true;
 }
 
+struct opt_callback_data {
+    struct train_params *     params;
+    struct ggml_opt_context * opt;
+    struct llama_context *    lctx;
+    llama_token *             tokens_data;
+    size_t                    tokens_size;
+    int *                     samples_data;
+    size_t                    samples_size;
+    int                       shuffle_countdown;
+    struct ggml_tensor *      tokens_input;
+    struct ggml_tensor *      target_logits;
+    struct ggml_tensor *      target_probs;
+};
+
+void opt_callback(void * vdata, float * sched) {
+    struct opt_callback_data * data = (struct opt_callback_data *) vdata;
+    struct train_params * params    = data->params;
+    struct ggml_opt_context * opt   = data->opt;
+    int n_batch = params->n_batch;
+
+    *sched = (opt->iter < params->warmup)
+                ? (float) opt->iter / (float) params->warmup
+                : cosine_decay_restart(
+                    params->cos_decay_steps,
+                    params->cos_decay_min,
+                    opt->iter - params->warmup,
+                    params->cos_decay_restart,
+                    params->enable_restart);
+    float min_sched = params->adam_min_alpha / params->adam_alpha;
+    *sched = min_sched + *sched * (1.0f - min_sched);
+
+    int impr_plot = std::isnan(opt->loss_after) ? 0 : -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
+    printf("%s: iter=%*d, sched=%f loss0=%f loss=%f | improvement: %*d>\n", __func__, 6, opt->iter, *sched, opt->loss_before, opt->loss_after, impr_plot, (int)0);
+
+    if (data->shuffle_countdown < n_batch) {
+        printf("%s: reshuffle samples\n", __func__);
+        shuffle_ints(data->samples_data, data->samples_data + data->samples_size);
+        for (int i = 0; i < (int) data->samples_size; ++i) {
+            GGML_ASSERT(data->samples_data[i]+params->n_ctx-1 < (int) data->tokens_size);
+        }
+        data->shuffle_countdown = data->samples_size;
+    }
+
+    get_example_targets_batch(
+        data->lctx,
+        data->samples_data,
+        data->samples_size,
+        data->tokens_data,
+        data->tokens_size,
+        opt->iter,
+        data->tokens_input,
+        data->target_logits,
+        data->target_probs);
+
+    data->shuffle_countdown -= n_batch;
+}
+
 int main(int argc, char ** argv) {
     struct train_params params = get_default_train_params();
 
@@ -3055,18 +2033,6 @@ int main(int argc, char ** argv) {
     struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, llama_params);
     struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
 
-    struct llama_vocab vocab;
-    {
-        const int n_vocab = llama_n_vocab(lctx);
-        vocab.id_to_token.resize(n_vocab);
-        for (int i=0; i<n_vocab; ++i) {
-            vocab.id_to_token[i].text  = llama_token_get_text(lctx, i);
-            vocab.id_to_token[i].score = llama_token_get_score(lctx, i);
-            vocab.id_to_token[i].type  = llama_token_get_type(lctx, i);
-            vocab.token_to_id.emplace(vocab.id_to_token[i].text, i);
-        }
-    }
-
     printf("%s: tokenize training data\n", __func__);
     std::vector<llama_token> train_tokens;
     if (tokenize_file(lctx, params.fn_train_data, train_tokens) < 0) {
@@ -3078,10 +2044,14 @@ int main(int argc, char ** argv) {
     model.hparams.n_vocab = llama_n_vocab(lctx);
     model.hparams.n_ctx   = params.n_ctx;
     model.hparams.n_embd  = params.n_embd;
-    model.hparams.n_mult  = params.n_mult;
     model.hparams.n_head  = params.n_head;
     model.hparams.n_layer = params.n_layer;
-    model.hparams.n_rot   = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
+    model.hparams.n_ff    = params.n_ff;
+    // llama.cpp requires n_rot to be exactly n_embd / n_head
+    model.hparams.n_rot   = model.hparams.n_embd / model.hparams.n_head;
+    model.hparams.f_norm_rms_eps  = params.f_norm_rms_eps;
+    model.hparams.rope_freq_base  = params.rope_freq_base;
+    model.hparams.rope_freq_scale = params.rope_freq_scale;
 
     print_params(&model.hparams);
 
@@ -3103,19 +2073,12 @@ int main(int argc, char ** argv) {
     }
     printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens);
 
-    struct my_llama_kv_cache kv_self;
-
-
     struct ggml_init_params lcparams;
     lcparams.mem_size   = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
     lcparams.mem_buffer = NULL;
     lcparams.no_alloc   = false;
 
     model.ctx = ggml_init(lcparams);
-    kv_self.ctx = model.ctx;
-
-    my_llama_sampler sampler;
-
 
     int n_tokens = model.hparams.n_ctx;
     int n_vocab  = model.hparams.n_vocab;
@@ -3126,24 +2089,38 @@ int main(int argc, char ** argv) {
 
     struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM);
     struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
-    opt_params_adam.print_forward_graph = false;
+    opt_params_adam.print_forward_graph  = false;
     opt_params_adam.print_backward_graph = false;
-    opt_params_adam.n_threads   = params.n_threads;
-    opt_params_adam.adam.n_iter = params.adam_n_iter;
-    opt_params_adam.adam.sched  = 1.0f;
-    opt_params_adam.adam.alpha  = params.adam_alpha;
-    opt_params_adam.adam.decay  = params.adam_decay;
+    opt_params_adam.n_threads            = params.n_threads;
+    opt_params_adam.past                 = params.opt_past;
+    opt_params_adam.delta                = params.opt_delta;
+    opt_params_adam.max_no_improvement   = params.opt_max_no_improvement;
+    opt_params_adam.adam.n_iter          = params.adam_n_iter;
+    opt_params_adam.adam.sched           = 1.0f;
+    opt_params_adam.adam.alpha           = params.adam_alpha;
+    opt_params_adam.adam.decay           = params.adam_decay;
+    opt_params_adam.adam.decay_min_ndim  = params.adam_decay_min_ndim;
+    opt_params_adam.adam.beta1           = params.adam_beta1;
+    opt_params_adam.adam.beta2           = params.adam_beta2;
+    opt_params_adam.adam.gclip           = params.adam_gclip;
+    opt_params_adam.adam.eps_f           = params.adam_eps_f;
 
-    opt_params_lbfgs.print_forward_graph = false;
+    opt_params_lbfgs.print_forward_graph  = false;
     opt_params_lbfgs.print_backward_graph = false;
-    opt_params_lbfgs.n_threads    = params.n_threads;
-    opt_params_lbfgs.lbfgs.n_iter = params.lbfgs_n_iter;
+    opt_params_lbfgs.n_threads            = params.n_threads;
+    opt_params_adam.past                  = params.opt_past;
+    opt_params_adam.delta                 = params.opt_delta;
+    opt_params_adam.max_no_improvement    = params.opt_max_no_improvement;
+    opt_params_lbfgs.lbfgs.n_iter         = params.lbfgs_n_iter;
 
     opt->ctx = model.ctx;
     opt->params = params.use_adam ? opt_params_adam : opt_params_lbfgs;
 
     printf("%s: init model\n", __func__);
-    bool existed = load_checkpoint(&model, opt, params.fn_checkpoint_in, true);
+    bool existed = load_checkpoint_file(params.fn_checkpoint_in, &model, opt);
+    if (!existed) {
+        init_model(&model);
+    }
     set_param_model(&model);
 
     opt->params = params.use_adam ? opt_params_adam : opt_params_lbfgs;
@@ -3156,11 +2133,7 @@ int main(int argc, char ** argv) {
         randomize_model(&model, params.seed, 0.0f, 1.0f, -1.0f, +1.0f);
     }
 
-    init_kv_cache(&kv_self, &model, 1);
-    // init_kv_cache(&kv_self, &model, n_batch);
-    init_sampler(&sampler, lctx);
-
-    printf("used_mem model+cache: %zu bytes\n", ggml_used_mem(model.ctx));
+    printf("used_mem model: %zu bytes\n", ggml_used_mem(model.ctx));
     // ggml_print_tensor_objects(model.ctx);
 
     // TODO: use std::vector<uint8_t> intead of "new"
@@ -3168,9 +2141,13 @@ int main(int argc, char ** argv) {
     uint8_t * compute_addr = new uint8_t[compute_size];
 
     size_t size_buf_0 = 1024ll*1024ll*1024ll*((size_t) params.mem_compute0_gb);
-    size_t size_buf_1 = 1024ll*1024ll*1024ll*((size_t) params.mem_compute1_gb);
     uint8_t * compute_buf_0 = new uint8_t[size_buf_0];
-    uint8_t * compute_buf_1 = new uint8_t[size_buf_1];
+
+    ggml_allocr * alloc = NULL;
+    if (params.use_alloc) {
+        static const size_t tensor_alignment = 32;
+        alloc = ggml_allocr_new(compute_buf_0, size_buf_0, tensor_alignment);
+    }
 
     GGML_ASSERT(n_tokens < (int) train_tokens.size());
     std::vector<int> train_samples;
@@ -3185,10 +2162,23 @@ int main(int argc, char ** argv) {
         GGML_ASSERT(train_samples[i]+n_tokens-1 < (int) train_tokens.size());
     }
 
-    std::vector<uint8_t> work_buffer;
-
     printf("%s: begin training\n", __func__);
 
+    struct opt_callback_data opt_cb_data;
+    opt_cb_data.params = &params;
+    opt_cb_data.opt = opt;
+    opt_cb_data.lctx = lctx;
+    opt_cb_data.tokens_data = train_tokens.data();
+    opt_cb_data.tokens_size = train_tokens.size();
+    opt_cb_data.samples_data = train_samples.data();
+    opt_cb_data.samples_size = train_samples.size();
+    opt_cb_data.shuffle_countdown = train_samples.size();
+    opt_cb_data.tokens_input  = NULL;
+    opt_cb_data.target_logits = NULL;
+    opt_cb_data.target_probs  = NULL;
+
+    int64_t t0 = ggml_time_ms();
+
     for (int ex = 0; ex < params.n_examples; ++ex) {
         if (ex*n_batch >= (int) train_samples.size()) {
             shuffle_ints(train_samples.data(), train_samples.data() + train_samples.size());
@@ -3198,198 +2188,110 @@ int main(int argc, char ** argv) {
         }
 
         struct ggml_init_params cparams = {
-            /*.mem_size   =*/ compute_size,
-            /*.mem_buffer =*/ compute_addr,
-            /*.no_alloc   =*/ false,
+            compute_size, // mem_size
+            compute_addr, // mem_buffer
+            false,        // no_alloc
         };
         struct ggml_context * ctx0 = ggml_init(cparams);
 
-        struct ggml_tensor * after_opt_best_samples = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch);
+        ggml_set_no_alloc(ctx0, false);
+
+        // don't use alloc for input tensors, so we can safely fill them with data
+        //struct ggml_tensor * after_opt_best_samples = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch);
         //struct ggml_tensor * after_opt_probs        = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
         struct ggml_tensor * tokens_input           = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch);
         struct ggml_tensor * target_logits          = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
         struct ggml_tensor * target_probs           = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
 
+        ggml_set_no_alloc(ctx0, (alloc != NULL));
+
+        if (alloc) {
+            ggml_allocr_reset(alloc);
+        }
+
+        opt_cb_data.tokens_input  = tokens_input;
+        opt_cb_data.target_logits = target_logits;
+        opt_cb_data.target_probs  = target_probs;
+
         int n_past = 0;
 
-        struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) + (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
-        struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) + (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
-
-        memset(gfbuf->data, 0, ggml_nbytes(gfbuf));
-        memset(gbbuf->data, 0, ggml_nbytes(gbbuf));
-
-        struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data;
-        struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data;
-
-
-        get_example_targets_batch(lctx, train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), ex,  tokens_input, target_logits, target_probs);
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+        struct ggml_cgraph * gb = ggml_new_graph(ctx0);
+        struct ggml_cgraph * gb_tmp = params.use_checkpointing
+            ? ggml_new_graph(ctx0)
+            : NULL;
 
         GGML_ASSERT(n_past == 0);
 
         struct ggml_tensor * loss   = NULL;
         struct ggml_tensor * logits = NULL;
 
-        if (params.use_scratch) {
-            loss = forward_batch_wo_cache_flash_attn_train(
-                    &model, ctx0,
-                    gf, gb,
-                    &logits, tokens_input, target_probs,
-                    compute_buf_0, compute_buf_1,
-                    size_buf_0, size_buf_1,
-                    n_tokens, n_batch);
-        } else if (params.use_flash) {
-            logits = forward_batch_wo_cache_flash_attn(&model, ctx0, gf, tokens_input, n_tokens, n_batch);
-            loss   = cross_entropy_loss(ctx0, logits, target_probs);
-            ggml_build_forward_expand(gf, loss);
-            *gb = ggml_build_backward(ctx0, gf, true);
-        } else {
-            logits = forward_batch_wo_cache(&model, ctx0, gf, tokens_input, n_tokens, n_batch);
-            loss   = cross_entropy_loss(ctx0, logits, target_probs);
-            ggml_build_forward_expand(gf, loss);
-            *gb = ggml_build_backward(ctx0, gf, true);
-        }
-
-        ggml_graph_compute_helper(work_buffer, gf, params.n_threads);
+        loss = llama_build_train_graphs(
+            &model, alloc, ctx0,
+            gf, gb, gb_tmp,
+            &logits, tokens_input, target_probs,
+            n_tokens, n_batch,
+            params.use_flash,
+            params.use_checkpointing
+        );
 
         size_t used_mem_before_opt = ggml_used_mem(ctx0);
 
-        float error_before_opt = ggml_get_f32_1d(loss, 0);
-
         opt->params.adam.sched = (opt->iter < params.warmup)
             ? (float) opt->iter / (float) params.warmup
             : cosine_decay_restart(
                 params.cos_decay_steps,
-                params.cos_decay_alpha,
+                params.cos_decay_min,
                 opt->iter - params.warmup,
-                params.cos_decay_restart);
+                params.cos_decay_restart,
+                params.enable_restart);
+
+        float min_sched = params.adam_min_alpha / params.adam_alpha;
+        opt->params.adam.sched = min_sched + opt->params.adam.sched * (1.0f - min_sched);
 
         printf("%s: opt->params.adam.sched %.5f\n", __func__, opt->params.adam.sched);
 
-        ggml_opt_resume_g(ctx0, opt, loss, gf, gb);
+        ggml_opt_resume_g(ctx0, opt, loss, gf, gb, &opt_callback, (void *) &opt_cb_data);
 
         size_t used_mem_after_opt = ggml_used_mem(ctx0);
 
+        int n_iter = params.use_adam ? params.adam_n_iter : params.lbfgs_n_iter;
         model.train_its = opt->iter;
-        model.train_samples += n_batch;
-        model.train_tokens  += n_batch * n_tokens;
-
-        ggml_graph_compute_helper(work_buffer, gf, params.n_threads);
-
-        float error_after_opt = ggml_get_f32_1d(loss, 0);
+        model.train_samples += n_batch * n_iter;
+        model.train_tokens  += n_batch * n_tokens * n_iter;
 
         if (params.print_info_interval > 0 && ex % params.print_info_interval == 0) {
             printf("Example %d, opt iter %d\n", ex, opt->iter);
-            printf("error_before_opt: %.6f\n", error_before_opt);
-            printf("error_after_opt:  %.6f\n", error_after_opt);
+            printf("error_before_opt: %.6f\n", opt->loss_before);
+            printf("error_after_opt:  %.6f\n", opt->loss_after);
             printf("used_mem_before_opt: %zu bytes\n", used_mem_before_opt);
             printf("used_mem_after_opt:  %zu bytes\n", used_mem_after_opt);
         }
 
-        if (params.print_details_interval > 0 && ex % params.print_details_interval == 0) {
-            // set_logits_masked(logits, token_notavail, -1e9);
-            for (int i=0; i<n_batch; ++i) {
-                init_sampler(&sampler, lctx);
-                for (int k=0; k<n_tokens; ++k) {
-                    int32_t token = sample(&sampler,
-                        (float *)       ((char *) logits->data + i*logits->nb[2] + k*logits->nb[1]),
-                        (llama_token *) ((char *) tokens_input->data + i*tokens_input->nb[1]),
-                        k);
-                    * ((int32_t *) ((char *) after_opt_best_samples->data + i*after_opt_best_samples->nb[1] + k*after_opt_best_samples->nb[0])) = token;
-                }
-            }
-
-            // printf("probabilities after optimization:\n");
-            // print_matrix(after_opt_probs);
-            printf("Example:\n---\n");
-            print_tokens_batch(lctx, tokens_input);
-            printf("\n---\n");
-
-            // printf("best samples after optimization:\n---\n");
-            printf("samples after optimization:\n---\n");
-            print_tokens_batch(lctx, after_opt_best_samples);
-            printf("\n---\n");
-        }
-
         ggml_free(ctx0);
     }
 
+    int64_t t1 = ggml_time_ms();
+    int64_t d  = t1-t0;
+    double  dd = (double) d * 1e-3;
+    printf("%s: total training time=%f seconds\n", __func__, dd);
+
     if (params.n_examples > 0) {
-        save_checkpoint(&model, opt, params.fn_checkpoint_out);
+        save_checkpoint_file(params.fn_checkpoint_out, params.fn_vocab_model, &model, opt);
     }
 
     if (strlen(params.fn_model_out) > 0) {
-        save_as_llama_model(&vocab, &model, params.fn_model_out);
+        save_llama_model_file(params.fn_model_out, params.fn_vocab_model, &model);
     }
 
-    {
-        int n_gen = params.n_predict;
-        int sample_ctx = n_tokens - n_tokens/8;
-
-        sampler.params.temp = 0.2f;
-        sampler.params.repeat_penalty = 1.1f;
-        sampler.params.mirostat = 2;
-        init_sampler(&sampler, lctx);
-
-        printf("Generating %d tokens.\n", n_gen);
-
-        struct ggml_tensor * tokens_input  = ggml_new_tensor_1d(model.ctx, GGML_TYPE_I32, n_tokens);
-        struct ggml_tensor * target_logits = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab,  n_tokens);
-        struct ggml_tensor * target_probs  = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab,  n_tokens);
-
-        get_example_targets(lctx, train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), rand()%train_samples.size(), tokens_input, target_logits, target_probs);
-        for (int i=sample_ctx; i<n_tokens; ++i) {
-            ggml_set_i32_1d(tokens_input, i, n_vocab/2);
-        }
-
-        for (int i=0; i<sample_ctx-1; ++i) {
-            print_token(lctx, ggml_get_i32_1d(tokens_input, i));
-        }
-
-        printf("---\n");
-        for (int i=0; i<n_gen; ++i) {
-            struct ggml_init_params cparams = {
-                /*.mem_size   =*/ compute_size,
-                /*.mem_buffer =*/ compute_addr,
-                /*.no_alloc   =*/ false,
-            };
-            struct ggml_context * ctx0 = ggml_init(cparams);
-
-            ggml_cgraph gf = {};
-
-            int n_past = 0;
-            struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, sample_ctx, n_past);
-
-            ggml_build_forward_expand(&gf, logits);
-            ggml_graph_compute_helper(work_buffer, &gf, params.n_threads);
-
-            //struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
-            //struct ggml_tensor * probs        = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
-
-            // set_logits_masked(logits, token_notavail, -1e9);
-            int token = sample(&sampler,
-                (float *) ((char *) logits->data + (sample_ctx-1)*logits->nb[1]),
-                (llama_token *) tokens_input->data,
-                sample_ctx-1);
-            //int token = ggml_get_i32_1d(best_samples, sample_ctx-1);
-
-            // print_row(probs, sample_at);
-            print_token(lctx, token);
-
-            lshift_examples(tokens_input, target_logits, target_probs, 1);
-            ggml_set_i32_1d(tokens_input, 0, 0);
-            ggml_set_i32_1d(tokens_input, sample_ctx-1, token);
-
-            ggml_free(ctx0);
-        }
+    if (alloc) {
+        ggml_allocr_free(alloc);
     }
 
     delete[] compute_addr;
     delete[] compute_buf_0;
-    delete[] compute_buf_1;
-
+    ggml_free(model.ctx);
     llama_free(lctx);
     llama_free_model(lmodel);
-    ggml_free(model.ctx);
-
     return 0;
 }
diff --git a/ggml-alloc.c b/ggml-alloc.c
index 140e9a2a7..63beb1d4e 100644
--- a/ggml-alloc.c
+++ b/ggml-alloc.c
@@ -107,6 +107,10 @@ static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct g
 }
 
 void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+#ifdef GGML_ALLOCATOR_DEBUG
+    GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources
+    GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
+#endif
     size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
     size = aligned_offset(NULL, size, alloc->alignment);
 
diff --git a/ggml.c b/ggml.c
index dadb30757..9a787863d 100644
--- a/ggml.c
+++ b/ggml.c
@@ -123,6 +123,8 @@ typedef void * thread_ret_t;
 #define GGML_GELU_FP16
 #define GGML_GELU_QUICK_FP16
 #define GGML_SILU_FP16
+// #define GGML_CROSS_ENTROPY_EXP_FP16
+// #define GGML_FLASH_ATTN_EXP_FP16
 
 #define GGML_SOFT_MAX_UNROLL 4
 #define GGML_VEC_DOT_UNROLL  2
@@ -186,8 +188,8 @@ typedef void * thread_ret_t;
 //
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
-#define GGML_ALIGNED_MALLOC(size)  _aligned_malloc(size, GGML_MEM_ALIGN)
-#define GGML_ALIGNED_FREE(ptr)     _aligned_free(ptr)
+#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
+#define GGML_ALIGNED_FREE(ptr)    _aligned_free(ptr)
 #else
 inline static void * ggml_aligned_malloc(size_t size) {
     void * aligned_memory = NULL;
@@ -212,8 +214,8 @@ inline static void * ggml_aligned_malloc(size_t size) {
     }
     return aligned_memory;
 }
-#define GGML_ALIGNED_MALLOC(size)  ggml_aligned_malloc(size)
-#define GGML_ALIGNED_FREE(ptr)     free(ptr)
+#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size)
+#define GGML_ALIGNED_FREE(ptr)    free(ptr)
 #endif
 
 #define UNUSED GGML_UNUSED
@@ -5857,7 +5859,8 @@ struct ggml_tensor * ggml_rms_norm_inplace(
 struct ggml_tensor * ggml_rms_norm_back(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        struct ggml_tensor  * b) {
+        struct ggml_tensor  * b,
+        float  eps) {
     bool is_node = false;
 
     if (a->grad) {
@@ -5867,6 +5870,8 @@ struct ggml_tensor * ggml_rms_norm_back(
 
     struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
 
+    ggml_set_op_params(result, &eps, sizeof(eps));
+
     result->op   = GGML_OP_RMS_NORM_BACK;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
@@ -9443,6 +9448,8 @@ static void ggml_compute_forward_div_f32(
 
 
 #ifdef GGML_USE_ACCELERATE
+            UNUSED(ggml_vec_div_f32);
+
             vDSP_vdiv(
                     (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
                     (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
@@ -10749,7 +10756,8 @@ static void ggml_compute_forward_rms_norm_back_f32(
 
     GGML_TENSOR_BINARY_OP_LOCALS;
 
-    const float eps = 1e-6f; // TODO: make this a parameter
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
 
     // TODO: optimize
     for (int64_t i03 = 0; i03 < ne03; i03++) {
@@ -12139,6 +12147,7 @@ static void ggml_compute_forward_soft_max_back_f32(
         // dx = J * dy
         // dxk = sum_i(Jki * dyi)
         // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk
+        // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk
         // dxk = sum_i(-yk*yi * dyi) + yk*dyk
         // dxk = -yk * sum_i(yi * dyi) + yk*dyk
         // dxk = -yk * dot(y, dy) + yk*dyk
@@ -13929,7 +13938,7 @@ static void ggml_compute_forward_flash_attn_f32(
                 vvexpf(S, S, &Mup);
                 ggml_vec_sum_f32(Mup, &sum, S);
 #else
-                uint16_t   scvt[GGML_SOFT_MAX_UNROLL];
+                uint16_t   scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt);
                 ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 };
 
                 for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
@@ -13939,9 +13948,13 @@ static void ggml_compute_forward_flash_attn_f32(
                         if (SS[j] == -INFINITY) {
                             SS[j] = 0.0f;
                         } else {
+#ifndef GGML_FLASH_ATTN_EXP_FP16
+                            const float val = expf(SS[j] - max);
+#else
                             ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
                             memcpy(&scvt[j], &s, sizeof(uint16_t));
                             const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+#endif
                             sump[j] += (ggml_float)val;
                             SS[j] = val;
                         }
@@ -14519,7 +14532,7 @@ static void ggml_compute_forward_flash_attn_back_f32(
                     vvexpf(SM, SM, &Mup);
                     ggml_vec_sum_f32(Mup, &sum, SM);
 #else
-                    uint16_t   scvt[GGML_SOFT_MAX_UNROLL];
+                    uint16_t   scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt);
                     ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 };
 
                     for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) {
@@ -14530,9 +14543,13 @@ static void ggml_compute_forward_flash_attn_back_f32(
                             if (SR[j] == -INFINITY) {
                                 SW[j] = 0.0f;
                             } else {
+#ifndef GGML_FLASH_ATTN_EXP_FP16
+                                const float val = expf(SR[j] - max);
+#else
                                 ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max);
                                 memcpy(&scvt[j], &s, sizeof(uint16_t));
                                 const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+#endif
                                 sump[j] += (ggml_float)val;
                                 SW[j] = val;
                             }
@@ -15270,6 +15287,8 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
     const int nc = src0->ne[0];
     const int nr = ggml_nrows(src0);
 
+    GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));
+
     if (params->type == GGML_TASK_INIT) {
         if (ith == 0) {
             memset(sums, 0, sizeof(float) * (nth + nth * nc));
@@ -15281,7 +15300,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
         if (ith == 0) {
             float * dp = (float *) dst->data;
             ggml_vec_sum_f32(nth, dp, sums);
-            dp[0] *= -1.0f;
+            dp[0] *= -1.0f / (float) nr;
         }
         return;
     }
@@ -15298,7 +15317,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
     for (int i1 = ir0; i1 < ir1; i1++) {
         float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]);
         float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]);
-        float * st = (float *) params->wdata + nth + ith*nc;
+        float * st = ((float *) params->wdata) + nth + ith*nc;
 
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
@@ -15313,15 +15332,19 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
             float max = -INFINITY;
             ggml_vec_max_f32(nc, &max, s0);
 
-            uint16_t scvt;
+            uint16_t scvt; UNUSED(scvt);
             for (int i = 0; i < nc; i++) {
                 if (s0[i] == -INFINITY) {
                     st[i] = 0.0f;
                 } else {
-                    // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max);
+#ifndef GGML_CROSS_ENTROPY_EXP_FP16
+                    const float s = s0[i] - max;
+                    const float val = expf(s);
+#else
                     ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
                     memcpy(&scvt, &s, sizeof(scvt));
                     const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+#endif
                     sum += (ggml_float)val;
                     st[i] = val;
                 }
@@ -15337,7 +15360,9 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
         ggml_vec_log_f32(nc, st, st);
         ggml_vec_mul_f32(nc, st, st, s1);
 
-        ggml_vec_sum_f32(nc, sums + ith, st);
+        float st_sum = 0;
+        ggml_vec_sum_f32(nc, &st_sum, st);
+        sums[ith] += st_sum;
 
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
@@ -15387,7 +15412,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
         return;
     }
 
-    const float eps = 1e-9f;
+    const double eps = 1e-9;
 
     // TODO: handle transposed/permuted matrices
     const int64_t nc = src0->ne[0];
@@ -15406,7 +15431,6 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
         float * ds0 = (float *)((char *) dst->data  + i1*dst->nb[1]);
         float * s0  = (float *)((char *) src0->data + i1*src0->nb[1]);
         float * s1  = (float *)((char *) src1->data + i1*src1->nb[1]);
-        float * sm  = (float *) params->wdata + ith*nc;
 
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
@@ -15415,54 +15439,6 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
             assert(!isnan(s1[i]));
         }
 #endif
-        // step by step explanation:
-        {
-            //float * sums = (float *) params->wdata;
-
-            // forward pass with annotated gradients from backward pass
-            // (built by going in reverse operation order, adding to gradients of current operation args)
-            // st0 = exp(s0-max(s0))                                                       grad[st0] = grad[st1]*(1.0 - eps)/sum
-                                                          // from softmax_back:            grad[s0]  = st1_k * (grad[st1]_k - dot(st1, grad[st1]))
-            // ggml_vec_scale_f32(nc, st, sum);           // st1 = st0*/sum = softmax(s0)  grad[st1] = grad[st2]*(1.0 - eps)
-            // ggml_vec_scale_f32(nc, st, (1.0f - eps));  // st2 = st1*(1.0 - eps)         grad[st2] = grad[st3]
-            // ggml_vec_add1_f32(nc, st, st, eps);        // st3 = st2 + eps               grad[st3] = grad[st4]/st3
-            // ggml_vec_log_f32(nc, st, st);              // st4 = log(st3)                grad[st4] = grad[st5] * s1
-            // ggml_vec_mul_f32(nc, st, st, s1);          // st5 = st4 * s1                grad[st5] = grad[sums[ith]]
-            // ggml_vec_sum_f32(nc, sums + ith, st);      // sums[ith] = st5               grad[sums[ith]] = grad[cross_entropy_loss] = -grad[cel]
-
-            // substitute into grad[st1], because we can reuse softmax_back from this point on
-            // grad[st1] = -grad[cel]*s1*(1.0 - eps)/(eps + softmax(s0)*(1.0 - eps))
-            // postorder:
-            // grad[st1] := softmax(s0)
-            // grad[st1] := grad[st1]*(1.0 - eps)
-            // grad[st1] := grad[st1] + eps
-            // grad[st1] := s1 / grad[st1]
-            // grad[st1] := grad[st1]*(1.0-eps)*-grad[cel]
-
-            // src0 gradients by going through softmax_back
-            // grad[s0] = st1_k * (grad[st1]_k - dot(st1, grad[st1]))
-            //   from softmax_back:
-            //   dxk = yk * (dyk - dot(y, dy))
-            //   dot_y_dy := dot(y, dy)
-            //   dx := dy
-            //   dx := dx - dot_y_dy
-            //   dx := dx * y
-            //   postorder:
-            //   dot_st1_dst1 := dot(st1, grad[st1])
-            //   grad[s0] := grad[st1]
-            //   grad[s0] := grad[s0] - dot_st1_dst1
-            //   grad[s0] := grad[s0] * st1
-
-            // prepend postorder from grad[st1] directly using grad[s0] as memory location, as we will grad[s0] := grad[st1]
-            // sm           := softmax(s0)
-            // grad[s0]     := sm*(1.0 - eps)
-            // grad[s0]     := grad[s0] + eps
-            // grad[s0]     := s1 / grad[s0]
-            // grad[s0]     := grad[s0]*(1.0-eps)*-grad[cel]
-            // dot_st1_dst1 := dot(sm, grad[s0])
-            // grad[s0]     := grad[s0] - dot_st1_dst1
-            // grad[s0]     := grad[s0] * sm
-        }
 
         // soft_max
         ggml_float sum = 0.0;
@@ -15470,39 +15446,37 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
             float max = -INFINITY;
             ggml_vec_max_f32(nc, &max, s0);
 
-            uint16_t scvt;
+            uint16_t scvt; UNUSED(scvt);
             for (int i = 0; i < nc; i++) {
                 if (s0[i] == -INFINITY) {
-                    sm[i] = 0.0f;
+                    ds0[i] = 0.0f;
                 } else {
-                    // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max);
+#ifndef GGML_CROSS_ENTROPY_EXP_FP16
+                    const float s = s0[i] - max;
+                    const float val = expf(s);
+#else
                     ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
                     memcpy(&scvt, &s, sizeof(scvt));
                     const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+#endif
                     sum += (ggml_float)val;
-                    sm[i] = val;
+                    ds0[i] = val;
                 }
             }
 
             assert(sum > 0.0);
-            sum = 1.0/sum;
+            sum = (1.0 - eps)/sum;
         }
 
-        float dot_st1_dst1 = 0;
-        ggml_vec_scale_f32(nc, sm, sum);
-        ggml_vec_cpy_f32  (nc, ds0, sm);
-        ggml_vec_scale_f32(nc, ds0, (1.0f - eps));
-        ggml_vec_add1_f32 (nc, ds0, ds0, eps);
-        ggml_vec_div_f32  (nc, ds0, s1, ds0);
-        ggml_vec_scale_f32(nc, ds0, -(1.0f - eps)*d[0]);
-        ggml_vec_dot_f32  (nc, &dot_st1_dst1, sm, ds0);
-        ggml_vec_acc1_f32 (nc, ds0, -dot_st1_dst1);
-        ggml_vec_mul_f32  (nc, ds0, ds0, sm);
+        // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr
+        ggml_vec_scale_f32(nc, ds0, sum);
+        ggml_vec_add1_f32(nc, ds0, ds0, eps);
+        ggml_vec_sub_f32(nc, ds0, ds0, s1);
+        ggml_vec_scale_f32(nc, ds0, d[0] / (float) nr);
+
 
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
-            assert(!isnan(sm[i]));
-            assert(!isinf(sm[i]));
             assert(!isnan(ds0[i]));
             assert(!isinf(ds0[i]));
         }
@@ -16057,9 +16031,12 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 // necessary for llama
                 if (src0->grad) {
+                    float eps;
+                    memcpy(&eps, tensor->op_params, sizeof(float));
+
                     src0->grad = ggml_add_impl(ctx,
                             src0->grad,
-                            ggml_rms_norm_back(ctx, src0, tensor->grad),
+                            ggml_rms_norm_back(ctx, src0, tensor->grad, eps),
                             inplace);
                 }
             } break;
@@ -16827,9 +16804,7 @@ struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) {
     return result;
 }
 
-struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) {
-    struct ggml_cgraph result = *gf;
-
+void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) {
     GGML_ASSERT(gf->n_nodes > 0);
 
     // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph
@@ -16853,15 +16828,19 @@ struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cg
         }
     }
 
-    for (int i = gf->n_nodes - 1; i >= 0; i--) {
+    for (int i = 0; i < gf->n_nodes; i++) {
         struct ggml_tensor * node = gf->nodes[i];
 
         if (node->is_param) {
             GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node);
-            ggml_build_forward_expand(&result, node->grad);
+            ggml_build_forward_expand(gb, node->grad);
         }
     }
+}
 
+struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) {
+    struct ggml_cgraph result = *gf;
+    ggml_build_backward_expand(ctx, gf, &result, keep);
     return result;
 }
 
@@ -17537,10 +17516,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
             case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
                 {
                     n_tasks = n_threads;
-
-                    size_t cur = ggml_type_size(node->type)*node->src[0]->ne[0]*n_tasks;
-
-                    work_size = MAX(work_size, cur);
                 } break;
             case GGML_OP_NONE:
                 {
@@ -18418,14 +18393,16 @@ static enum ggml_opt_result ggml_opt_adam(
         struct ggml_opt_params params,
         struct ggml_tensor * f,
         struct ggml_cgraph * gf,
-        struct ggml_cgraph * gb) {
+        struct ggml_cgraph * gb,
+        ggml_opt_callback callback,
+        void * callback_data) {
     GGML_ASSERT(ggml_is_scalar(f));
 
     // these will store the parameters we want to optimize
     struct ggml_tensor * ps[GGML_MAX_PARAMS];
 
     int np = 0;
-    int nx = 0;
+    int64_t nx = 0;
     for (int i = 0; i < gf->n_nodes; ++i) {
         if (gf->nodes[i]->is_param) {
             GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);
@@ -18444,31 +18421,32 @@ static enum ggml_opt_result ggml_opt_adam(
     }
 
     // constants
-    const float sched = params.adam.sched;
-    const float decay = params.adam.decay * sched;
-    const float alpha = params.adam.alpha * sched;
+    float sched = params.adam.sched;
+    const float alpha = params.adam.alpha;
+    const float decay = params.adam.decay * alpha;
     const float beta1 = params.adam.beta1;
     const float beta2 = params.adam.beta2;
     const float eps   = params.adam.eps;
+    const float gclip = params.adam.gclip;
+    const int decay_min_ndim = params.adam.decay_min_ndim;
 
-    float * x  = opt->adam.x->data;  // view of the parameters
-    float * g1 = opt->adam.g1->data; // gradient
-    float * g2 = opt->adam.g2->data; // gradient squared
     float * m  = opt->adam.m->data;  // first moment
     float * v  = opt->adam.v->data;  // second moment
-    float * mh = opt->adam.mh->data; // first moment hat
-    float * vh = opt->adam.vh->data; // second moment hat
 
     float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values
 
-    // update view
-    ggml_opt_get_params(np, ps, x);
+    if (callback) {
+        callback(callback_data, &sched);
+    }
 
     // compute the function value
     ggml_graph_reset  (gf);
     ggml_set_f32      (f->grad, 1.0f);
 
-    ggml_graph_compute_with_ctx(ctx, gb, params.n_threads);
+    struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
+    ggml_graph_compute(gb, &cplan);
 
     opt->adam.fx_prev = ggml_get_f32_1d(f, 0);
     opt->adam.fx_best = opt->adam.fx_prev;
@@ -18476,6 +18454,9 @@ static enum ggml_opt_result ggml_opt_adam(
         pf[opt->iter % params.past] = opt->adam.fx_prev;
     }
 
+    opt->loss_before = opt->adam.fx_prev;
+    opt->loss_after  = opt->adam.fx_prev;
+
     // initialize
     if (opt->just_initialized) {
         opt->adam.n_no_improvement = 0;
@@ -18508,50 +18489,55 @@ static enum ggml_opt_result ggml_opt_adam(
         UNUSED(t_start_cpu);
 
         {
-            // update the gradient
-            ggml_opt_get_grad(np, ps, g1);
+            float gnorm = 1.0f;
+            if (gclip > 0.0f) {
+                // gradient clipping
+                ggml_float sum = 0.0;
+                for (int p = 0; p < np; ++p) {
+                    const int64_t ne = ggml_nelements(ps[p]);
+                    for (int64_t j = 0; j < ne; ++j) {
+                        float g = ggml_get_f32_1d(ps[p]->grad, j);
+                        sum += (ggml_float)(g*g);
+                    }
+                }
+                ggml_float norm = sqrt(sum);
+                if (norm > (ggml_float) gclip) {
+                    gnorm = (float) ((ggml_float) gclip / norm);
+                }
+            }
+            const float beta1h = alpha*sched/(1.0f - powf(beta1, opt->iter));
+            const float beta2h =        1.0f/(1.0f - powf(beta2, opt->iter));
+            int64_t i = 0;
+            for (int p = 0; p < np; ++p) {
+                const int64_t ne = ggml_nelements(ps[p]);
+                const float p_decay = ((ps[p]->n_dims >= decay_min_ndim) ? decay : 0.0f) * sched;
+                for (int64_t j = 0; j < ne; ++j) {
+                    float x = ggml_get_f32_1d(ps[p], j);
+                    float g = ggml_get_f32_1d(ps[p]->grad, j)*gnorm;
+                    m[i] = m[i]*beta1 +   g*(1.0f - beta1);
+                    v[i] = v[i]*beta2 + g*g*(1.0f - beta2);
+                    float mh = m[i]*beta1h;
+                    float vh = v[i]*beta2h;
+                    vh = sqrtf(vh) + eps;
+                    x  = x*(1.0f - p_decay) - mh/vh;
+                    ggml_set_f32_1d(ps[p], j, x);
+                    ++i;
+                }
+            }
+        }
 
-            // m_t = beta1*m_t-1 + (1 - beta1)*g_t
-            ggml_vec_scale_f32(nx, m, beta1);
-            ggml_vec_mad_f32  (nx, m, g1, 1.0f - beta1);
-
-            // g2 = g1^2
-            ggml_vec_sqr_f32  (nx, g2, g1);
-
-            // v_t = beta2*v_t-1 + (1 - beta2)*g_t^2
-            ggml_vec_scale_f32(nx, v, beta2);
-            ggml_vec_mad_f32  (nx, v, g2, 1.0f - beta2);
-
-            // m^hat = m_t / (1 - beta1^t)
-            // v^hat = v_t / (1 - beta2^t)
-            // x_t = x_t-1 - sched*(alpha*m^hat/(sqrt(v^hat) + eps) + decay*x_t-1)
-            // x_t = x_t-1 - sched*alpha*m^hat/(sqrt(v^hat) + eps) - sched*decay*x_t-1
-            // x_t = x_t-1*(1-sched*decay) - sched*alpha*m^hat/(sqrt(v^hat) + eps)
-            // x_t = x_t-1*(1-sched*decay) + sched*decay*(-alpha/decay)*m^hat/(sqrt(v^hat) + eps)
-            // x_t = mix(x_t-1, (-alpha/decay)*m^hat/(sqrt(v^hat) + eps), sched*decay)
-            ggml_vec_cpy_f32  (nx, mh, m);
-            ggml_vec_cpy_f32  (nx, vh, v);
-
-            ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, opt->iter)));
-            ggml_vec_scale_f32(nx, vh,  1.0f/(1.0f - powf(beta2, opt->iter)));
-
-            ggml_vec_sqrt_f32 (nx, vh, vh);
-            ggml_vec_acc1_f32 (nx, vh, eps);
-
-            ggml_vec_div_f32  (nx, mh, mh, vh);
-            ggml_vec_scale_f32(nx, x,  1.0f - decay);
-            ggml_vec_sub_f32  (nx, x,  x,  mh);
-
-            // update the parameters
-            ggml_opt_set_params(np, ps, x);
+        if (callback) {
+            callback(callback_data, &sched);
         }
 
         ggml_graph_reset  (gf);
         ggml_set_f32      (f->grad, 1.0f);
 
-        ggml_graph_compute_with_ctx(ctx, gb, params.n_threads);
+        ggml_graph_compute(gb, &cplan);
 
         const float fx = ggml_get_f32_1d(f, 0);
+        opt->loss_after = fx;
+
 
         // check convergence
         if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) {
@@ -18620,7 +18606,6 @@ struct ggml_lbfgs_iteration_data {
 };
 
 static enum ggml_opt_result linesearch_backtracking(
-        struct ggml_context * ctx,
         const struct ggml_opt_params * params,
         int nx,
         float * x,
@@ -18632,8 +18617,11 @@ static enum ggml_opt_result linesearch_backtracking(
         struct ggml_tensor * f,
         struct ggml_cgraph * gf,
         struct ggml_cgraph * gb,
+        struct ggml_cplan  * cplan,
         const int np,
-        struct ggml_tensor * ps[]) {
+        struct ggml_tensor * ps[],
+        ggml_opt_callback callback,
+        void * callback_data) {
     int count = 0;
 
     float width  = 0.0f;
@@ -18662,6 +18650,12 @@ static enum ggml_opt_result linesearch_backtracking(
     dgtest = params->lbfgs.ftol*dginit;
 
     while (true) {
+        if (callback) {
+            // LBFG-S does not support learning rate -> ignore learning schedule
+            float sched = 0;
+            callback(callback_data, &sched);
+        }
+
         ggml_vec_cpy_f32(nx, x, xp);
         ggml_vec_mad_f32(nx, x, d, *step);
 
@@ -18672,7 +18666,7 @@ static enum ggml_opt_result linesearch_backtracking(
             ggml_graph_reset  (gf);
             ggml_set_f32      (f->grad, 1.0f);
 
-            ggml_graph_compute_with_ctx(ctx, gb, params->n_threads);
+            ggml_graph_compute(gb, cplan);
 
             ggml_opt_get_grad(np, ps, g);
 
@@ -18732,7 +18726,9 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         struct ggml_opt_params params,
         struct ggml_tensor * f,
         struct ggml_cgraph * gf,
-        struct ggml_cgraph * gb) {
+        struct ggml_cgraph * gb,
+        ggml_opt_callback callback,
+        void * callback_data) {
     if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE ||
         params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) {
         if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) {
@@ -18764,6 +18760,10 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         opt->iter = iter;
     }
 
+    struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
+
     float * x  = opt->lbfgs.x->data;  // current parameters
     float * xp = opt->lbfgs.xp->data; // previous parameters
     float * g  = opt->lbfgs.g->data;  // current gradient
@@ -18785,6 +18785,12 @@ static enum ggml_opt_result ggml_opt_lbfgs(
     float * lm_s     = opt->lbfgs.lms->data;
     float * lm_y     = opt->lbfgs.lmy->data;
 
+    if (callback) {
+        // LBFG-S does not support learning rate -> ignore learning schedule
+        float sched = 0;
+        callback(callback_data, &sched);
+    }
+
     // evaluate the function value and its gradient
     {
         ggml_opt_set_params(np, ps, x);
@@ -18792,11 +18798,14 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         ggml_graph_reset  (gf);
         ggml_set_f32      (f->grad, 1.0f);
 
-        ggml_graph_compute_with_ctx(ctx, gb, params.n_threads);
+        ggml_graph_compute(gb, &cplan);
 
         ggml_opt_get_grad(np, ps, g);
 
         fx = ggml_get_f32_1d(f, 0);
+
+        opt->loss_before = fx;
+        opt->loss_after  = fx;
     }
 
     // search direction = -gradient
@@ -18851,7 +18860,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         ggml_vec_cpy_f32(nx, xp, x);
         ggml_vec_cpy_f32(nx, gp, g);
 
-        ls = linesearch_backtracking(ctx, &params, nx, x, &fx, g, d, step, xp, f, gf, gb, np, ps);
+        ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gf, gb, &cplan, np, ps, callback, callback_data);
 
         if (ls < 0) {
             // linesearch failed - go back to the previous point and return
@@ -18861,6 +18870,8 @@ static enum ggml_opt_result ggml_opt_lbfgs(
             return ls;
         }
 
+        opt->loss_after = fx;
+
         ggml_vec_norm_f32(nx, &xnorm, x);
         ggml_vec_norm_f32(nx, &gnorm, g);
 
@@ -18918,7 +18929,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         //     ys = y^t \cdot s    -> 1 / \rho.
         //     yy = y^t \cdot y.
         //
-        ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0] *nx]);
+        ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0]*nx]);
         ggml_vec_dot_f32(nx, &yy, &lm_y[end[0]*nx], &lm_y[end[0]*nx]);
 
         lm_ys[end[0]] = ys;
@@ -18981,13 +18992,15 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
                     .adam = {
                         .n_iter = 10000,
                         .sched  = 1.000f,
-                        .decay  = 0.001f,
+                        .decay  = 0.0f,
+                        .decay_min_ndim = 2,
                         .alpha  = 0.001f,
                         .beta1  = 0.9f,
                         .beta2  = 0.999f,
                         .eps    = 1e-8f,
                         .eps_f  = 1e-5f,
                         .eps_g  = 1e-3f,
+                        .gclip  = 0.0f,
                     },
                 };
             } break;
@@ -19037,23 +19050,13 @@ GGML_API void ggml_opt_init(
     switch (opt->params.type) {
         case GGML_OPT_ADAM:
             {
-                opt->adam.x  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
-                opt->adam.g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
-                opt->adam.g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
                 opt->adam.m  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
                 opt->adam.v  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
-                opt->adam.mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
-                opt->adam.vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx);
                 opt->adam.pf = params.past > 0
                     ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past)
                     : NULL;
-                ggml_set_zero(opt->adam.x);
-                ggml_set_zero(opt->adam.g1);
-                ggml_set_zero(opt->adam.g2);
                 ggml_set_zero(opt->adam.m);
                 ggml_set_zero(opt->adam.v);
-                ggml_set_zero(opt->adam.mh);
-                ggml_set_zero(opt->adam.vh);
                 if (opt->adam.pf) {
                     ggml_set_zero(opt->adam.pf);
                 }
@@ -19137,7 +19140,7 @@ enum ggml_opt_result ggml_opt_resume(
     *gf = ggml_build_forward (f);
     *gb = ggml_build_backward(ctx, gf, true);
 
-    return ggml_opt_resume_g(ctx, opt, f, gf, gb);
+    return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL);
 }
 
 enum ggml_opt_result ggml_opt_resume_g(
@@ -19145,7 +19148,9 @@ enum ggml_opt_result ggml_opt_resume_g(
         struct ggml_opt_context * opt,
         struct ggml_tensor * f,
         struct ggml_cgraph * gf,
-        struct ggml_cgraph * gb) {
+        struct ggml_cgraph * gb,
+        ggml_opt_callback callback,
+        void * callback_data) {
 
     // build forward + backward compute graphs
     enum ggml_opt_result result = GGML_OPT_OK;
@@ -19153,11 +19158,11 @@ enum ggml_opt_result ggml_opt_resume_g(
     switch (opt->params.type) {
         case GGML_OPT_ADAM:
             {
-                result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb);
+                result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
             } break;
         case GGML_OPT_LBFGS:
             {
-                result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb);
+                result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
             } break;
     }
 
@@ -19612,7 +19617,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the kv pairs
     {
-        ctx->kv = GGML_ALIGNED_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv));
+        ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv));
 
         for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
             struct gguf_kv * kv = &ctx->kv[i];
@@ -19695,7 +19700,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the tensor infos
     {
-        ctx->infos = GGML_ALIGNED_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
+        ctx->infos = malloc(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
 
         for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
             struct gguf_tensor_info * info = &ctx->infos[i];
@@ -19896,7 +19901,7 @@ void gguf_free(struct gguf_context * ctx) {
             }
         }
 
-        GGML_ALIGNED_FREE(ctx->kv);
+        free(ctx->kv);
     }
 
     if (ctx->infos) {
@@ -19908,7 +19913,7 @@ void gguf_free(struct gguf_context * ctx) {
             }
         }
 
-        GGML_ALIGNED_FREE(ctx->infos);
+        free(ctx->infos);
     }
 
     GGML_ALIGNED_FREE(ctx);
diff --git a/ggml.h b/ggml.h
index 4ef3d5253..8b410cc85 100644
--- a/ggml.h
+++ b/ggml.h
@@ -952,11 +952,11 @@ extern "C" {
 
     // a - x
     // b - dy
-    // TODO: update with configurable eps
     GGML_API struct ggml_tensor * ggml_rms_norm_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            float                 eps);
 
     // A: n columns, m rows
     // B: n columns, p rows  (i.e. we transpose it internally)
@@ -1612,7 +1612,8 @@ extern "C" {
             struct ggml_tensor  * tensor);
 
 
-    GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
+    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, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
 
     GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
     GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
@@ -1677,6 +1678,8 @@ extern "C" {
         GGML_LINESEARCH_INVALID_PARAMETERS,
     };
 
+    typedef void (*ggml_opt_callback)(void * data, float * sched);
+
     // optimization parameters
     //
     //   see ggml.c (ggml_opt_default_params) for default values
@@ -1712,12 +1715,14 @@ extern "C" {
 
             float sched; // schedule multiplier (fixed, decay or warmup)
             float decay; // weight decay for AdamW, use 0.0f to disable
+            int   decay_min_ndim; // minimum number of tensor dimension to apply weight decay
             float alpha; // learning rate
             float beta1;
             float beta2;
             float eps;   // epsilon for numerical stability
             float eps_f; // epsilon for convergence test
             float eps_g; // epsilon for convergence test
+            float gclip; // gradient clipping
         } adam;
 
         // LBFGS parameters
@@ -1745,14 +1750,12 @@ extern "C" {
 
         bool just_initialized;
 
+        float loss_before;
+        float loss_after;
+
         struct {
-            struct ggml_tensor * x;  // view of the parameters
-            struct ggml_tensor * g1; // gradient
-            struct ggml_tensor * g2; // gradient squared
             struct ggml_tensor * m;  // first moment
             struct ggml_tensor * v;  // second moment
-            struct ggml_tensor * mh; // first moment hat
-            struct ggml_tensor * vh; // second moment hat
             struct ggml_tensor * pf; // past function values
             float fx_best;
             float fx_prev;
@@ -1789,10 +1792,10 @@ extern "C" {
 
     // initialize optimizer context
     GGML_API void ggml_opt_init(
-            struct ggml_context * ctx,
+            struct ggml_context     * ctx,
             struct ggml_opt_context * opt,
-            struct ggml_opt_params params,
-            int64_t nx);
+            struct ggml_opt_params    params,
+            int64_t                   nx);
 
     // continue optimizing the function defined by the tensor f
     GGML_API enum ggml_opt_result ggml_opt_resume(
@@ -1806,7 +1809,9 @@ extern "C" {
             struct ggml_opt_context * opt,
             struct ggml_tensor * f,
             struct ggml_cgraph * gf,
-            struct ggml_cgraph * gb);
+            struct ggml_cgraph * gb,
+            ggml_opt_callback callback,
+            void * callback_data);
 
     //
     // quantization
diff --git a/llama.cpp b/llama.cpp
index 11697ee65..7cb468538 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -6248,7 +6248,6 @@ const char * llama_print_system_info(void) {
 }
 
 void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
-
     fprintf(stream, "\n");
     fprintf(stream, "###########\n");
     fprintf(stream, "# Timings #\n");
@@ -6264,10 +6263,10 @@ void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
     fprintf(stream, "n_eval: %d  # number of tokens generated (excluding the first one)\n", ctx->n_eval);
     fprintf(stream, "n_p_eval: %d  # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
     fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->n_sample);
-    fprintf(stream, "t_eval_us: %ld  # total microseconds spent generating tokens\n", ctx->t_eval_us);
-    fprintf(stream, "t_load_us: %ld  # total microseconds spent loading the model\n", ctx->t_load_us);
-    fprintf(stream, "t_p_eval_us: %ld  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
-    fprintf(stream, "t_sample_us: %ld  # total microseconds spent sampling\n", ctx->t_sample_us);
+    fprintf(stream, "t_eval_us: %" PRId64 "  # total microseconds spent generating tokens\n", ctx->t_eval_us);
+    fprintf(stream, "t_load_us: %" PRId64 "  # total microseconds spent loading the model\n", ctx->t_load_us);
+    fprintf(stream, "t_p_eval_us: %" PRId64 "  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
+    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->t_sample_us);
     fprintf(stream, "ts_eval: %.2f  # tokens / second during generation\n",
             1.0e6 * ctx->n_eval / ctx->t_eval_us);
     fprintf(stream, "ts_p_eval: %.2f  # tokens / second during prompt processing\n",
diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp
index 75a698d73..468cde66a 100644
--- a/tests/test-grad0.cpp
+++ b/tests/test-grad0.cpp
@@ -275,14 +275,14 @@ static bool check_gradient(
 
             ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
 
-            const float f0 = ggml_get_f32_1d(f, 0);
+            const double f0 = ggml_get_f32_1d(f, 0);
 
             ggml_set_f32_1d(x[i], k, xm);
 
             ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
 
-            const float f1 = ggml_get_f32_1d(f, 0);
-            const float g0 = (f0 - f1)/(2.0f*eps);
+            const double f1 = ggml_get_f32_1d(f, 0);
+            const double g0 = (f0 - f1)/(2.0*(double) eps);
 
             ggml_set_f32_1d(x[i], k, x0);
 
@@ -292,10 +292,10 @@ static bool check_gradient(
 
             ggml_graph_compute_with_ctx(ctx0, &gb, n_threads);
 
-            const float g1 = ggml_get_f32_1d(x[i]->grad, k);
+            const double g1 = ggml_get_f32_1d(x[i]->grad, k);
 
-            const float error_abs = fabsf(g0 - g1);
-            const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabsf(g0) : 0;
+            const double error_abs = fabs(g0 - g1);
+            const double error_rel = g0 != 0 ? fabs(g0 - g1)/fabs(g0) : 0;
 
             if (error_abs > max_error_abs || error_rel > max_error_rel) {
                 printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n",
@@ -531,7 +531,7 @@ int main(int argc, const char ** argv) {
 
                 struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0]));
 
-                check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
+                check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f);
             }
         }
 
@@ -1345,9 +1345,18 @@ int main(int argc, const char ** argv) {
                 x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
                 ggml_set_param(ctx0, x[0]);
 
-                struct ggml_tensor * f = ggml_sum(ctx0, ggml_soft_max(ctx0, x[0]));
+                float eps = 1e-6f;
+                // dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
+                // instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)
+                struct ggml_tensor * f = ggml_sum(ctx0,
+                                            ggml_log(ctx0,
+                                                ggml_add1(ctx0,
+                                                    ggml_scale(ctx0,
+                                                        ggml_soft_max(ctx0, x[0]),
+                                                        ggml_new_f32(ctx0, 1.0f - eps)),
+                                                    ggml_new_f32(ctx0, eps))));
 
-                check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
+                check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY);
             }
         }
 
@@ -1358,15 +1367,26 @@ int main(int argc, const char ** argv) {
             int64_t ne2[4];
             get_random_dims(ne2, 4);
 
-            for (int ndims = 1; ndims <= 3; ++ndims) {
-                x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
+            for (int ndims = 1; ndims <= 4; ++ndims) {
+                x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -0.1f, 0.1f);
                 x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f);
+                // the second argument to cross_entropy_loss must sum up to 1 for each row
+                int nr = ggml_nrows(x[1]);
+                int nc = ggml_nelements(x[1]) / nr;
+                for (int ir = 0; ir < nr; ++ir) {
+                    float sum = 0;
+                    for (int ic = 0; ic < nc; ++ic) {
+                        sum += ((float *) x[1]->data)[ic + ir*nc];
+                    }
+                    for (int ic = 0; ic < nc; ++ic) {
+                        ((float *) x[1]->data)[ic + ir*nc] /= sum;
+                    }
+                }
                 ggml_set_param(ctx0, x[0]);
 
-                struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1]));
+                struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]);
 
-                check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-1f, 1e-2f, INFINITY);
-                // finite differences regularly fails!
+                check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-4f, 1e-3f, INFINITY);
             }
         }
 
@@ -1473,7 +1493,7 @@ int main(int argc, const char ** argv) {
 
                     struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
 
-                    check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
+                    check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
                 }
             }
         }
@@ -1514,7 +1534,7 @@ int main(int argc, const char ** argv) {
 
                     struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
 
-                    check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
+                    check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
                 }
             }
         }