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Merge branch 'master' into build-metal-default

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This commit is contained in:
Georgi Gerganov 2023-09-03 10:03:59 +03:00
commit 4de22829d9
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13 changed files with 288 additions and 160 deletions
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149
Makefile
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@ -71,21 +71,21 @@ OPT = -Ofast
else else
OPT = -O3 OPT = -O3
endif endif
CFLAGS = -I. $(OPT) -std=c11 -fPIC MK_CPPFLAGS = -I. -Icommon
CXXFLAGS = -I. -I./common $(OPT) -std=c++11 -fPIC MK_CFLAGS = $(CPPFLAGS) $(OPT) -std=c11 -fPIC
LDFLAGS = MK_CXXFLAGS = $(CPPFLAGS) $(OPT) -std=c++11 -fPIC
MK_LDFLAGS =
ifdef LLAMA_DEBUG ifdef LLAMA_DEBUG
CFLAGS += -O0 -g MK_CFLAGS += -O0 -g
CXXFLAGS += -O0 -g MK_CXXFLAGS += -O0 -g
LDFLAGS += -g MK_LDFLAGS += -g
else else
CFLAGS += -DNDEBUG MK_CPPFLAGS += -DNDEBUG
CXXFLAGS += -DNDEBUG
endif endif
ifdef LLAMA_SERVER_VERBOSE ifdef LLAMA_SERVER_VERBOSE
CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
endif endif
ifdef LLAMA_DISABLE_LOGS ifdef LLAMA_DISABLE_LOGS
@ -94,9 +94,9 @@ ifdef LLAMA_DISABLE_LOGS
endif # LLAMA_DISABLE_LOGS endif # LLAMA_DISABLE_LOGS
# warnings # warnings
CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ MK_CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \
-Wmissing-prototypes -Werror=implicit-int -Wno-unused-function -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function
CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
ifeq '' '$(findstring clang++,$(CXX))' ifeq '' '$(findstring clang++,$(CXX))'
# g++ only # g++ only
@ -105,29 +105,9 @@ endif
# OS specific # OS specific
# TODO: support Windows # TODO: support Windows
ifeq ($(UNAME_S),Linux) ifneq '' '$(filter $(UNAME_S),Linux Darwin FreeBSD NetBSD OpenBSD Haiku)'
CFLAGS += -pthread MK_CFLAGS += -pthread
CXXFLAGS += -pthread MK_CXXFLAGS += -pthread
endif
ifeq ($(UNAME_S),Darwin)
CFLAGS += -pthread
CXXFLAGS += -pthread
endif
ifeq ($(UNAME_S),FreeBSD)
CFLAGS += -pthread
CXXFLAGS += -pthread
endif
ifeq ($(UNAME_S),NetBSD)
CFLAGS += -pthread
CXXFLAGS += -pthread
endif
ifeq ($(UNAME_S),OpenBSD)
CFLAGS += -pthread
CXXFLAGS += -pthread
endif
ifeq ($(UNAME_S),Haiku)
CFLAGS += -pthread
CXXFLAGS += -pthread
endif endif
# detect Windows # detect Windows
@ -153,12 +133,11 @@ ifeq ($(_WIN32),1)
endif endif
ifdef LLAMA_GPROF ifdef LLAMA_GPROF
CFLAGS += -pg MK_CFLAGS += -pg
CXXFLAGS += -pg MK_CXXFLAGS += -pg
endif endif
ifdef LLAMA_PERF ifdef LLAMA_PERF
CFLAGS += -DGGML_PERF MK_CPPFLAGS += -DGGML_PERF
CXXFLAGS += -DGGML_PERF
endif endif
# Architecture specific # Architecture specific
@ -169,16 +148,16 @@ ifndef RISCV
ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
# Use all CPU extensions that are available: # Use all CPU extensions that are available:
CFLAGS += -march=native -mtune=native MK_CFLAGS += -march=native -mtune=native
CXXFLAGS += -march=native -mtune=native MK_CXXFLAGS += -march=native -mtune=native
# Usage AVX-only # Usage AVX-only
#CFLAGS += -mfma -mf16c -mavx #MK_CFLAGS += -mfma -mf16c -mavx
#CXXFLAGS += -mfma -mf16c -mavx #MK_CXXFLAGS += -mfma -mf16c -mavx
# Usage SSSE3-only (Not is SSE3!) # Usage SSSE3-only (Not is SSE3!)
#CFLAGS += -mssse3 #MK_CFLAGS += -mssse3
#CXXFLAGS += -mssse3 #MK_CXXFLAGS += -mssse3
endif endif
# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. # The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves.
@ -192,34 +171,33 @@ endif
ifneq ($(filter aarch64%,$(UNAME_M)),) ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc. # Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit) # Raspberry Pi 3, 4, Zero 2 (64-bit)
CFLAGS += -mcpu=native MK_CFLAGS += -mcpu=native
CXXFLAGS += -mcpu=native MK_CXXFLAGS += -mcpu=native
endif endif
ifneq ($(filter armv6%,$(UNAME_M)),) ifneq ($(filter armv6%,$(UNAME_M)),)
# Raspberry Pi 1, Zero # Raspberry Pi 1, Zero
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
endif endif
ifneq ($(filter armv7%,$(UNAME_M)),) ifneq ($(filter armv7%,$(UNAME_M)),)
# Raspberry Pi 2 # Raspberry Pi 2
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
endif endif
ifneq ($(filter armv8%,$(UNAME_M)),) ifneq ($(filter armv8%,$(UNAME_M)),)
# Raspberry Pi 3, 4, Zero 2 (32-bit) # Raspberry Pi 3, 4, Zero 2 (32-bit)
CFLAGS += -mfp16-format=ieee -mno-unaligned-access MK_CFLAGS += -mfp16-format=ieee -mno-unaligned-access
MK_CXXFLAGS += -mfp16-format=ieee -mno-unaligned-access
endif endif
ifneq ($(filter ppc64%,$(UNAME_M)),) ifneq ($(filter ppc64%,$(UNAME_M)),)
POWER9_M := $(shell grep "POWER9" /proc/cpuinfo) POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
ifneq (,$(findstring POWER9,$(POWER9_M))) ifneq (,$(findstring POWER9,$(POWER9_M)))
CFLAGS += -mcpu=power9 MK_CFLAGS += -mcpu=power9
CXXFLAGS += -mcpu=power9 MK_CXXFLAGS += -mcpu=power9
endif
# Require c++23's std::byteswap for big-endian support.
ifeq ($(UNAME_M),ppc64)
CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
endif endif
endif endif
@ -229,12 +207,10 @@ else
endif endif
ifndef LLAMA_NO_K_QUANTS ifndef LLAMA_NO_K_QUANTS
CFLAGS += -DGGML_USE_K_QUANTS MK_CPPFLAGS += -DGGML_USE_K_QUANTS
CXXFLAGS += -DGGML_USE_K_QUANTS
OBJS += k_quants.o OBJS += k_quants.o
ifdef LLAMA_QKK_64 ifdef LLAMA_QKK_64
CFLAGS += -DGGML_QKK_64 MK_CPPFLAGS += -DGGML_QKK_64
CXXFLAGS += -DGGML_QKK_64
endif endif
endif endif
@ -242,8 +218,8 @@ ifndef LLAMA_NO_ACCELERATE
# Mac OS - include Accelerate framework. # Mac OS - include Accelerate framework.
# `-framework Accelerate` works both with Apple Silicon and Mac Intel # `-framework Accelerate` works both with Apple Silicon and Mac Intel
ifeq ($(UNAME_S),Darwin) ifeq ($(UNAME_S),Darwin)
CFLAGS += -DGGML_USE_ACCELERATE MK_CPPFLAGS += -DGGML_USE_ACCELERATE
LDFLAGS += -framework Accelerate MK_LDFLAGS += -framework Accelerate
endif endif
endif # LLAMA_NO_ACCELERATE endif # LLAMA_NO_ACCELERATE
@ -258,25 +234,26 @@ ifndef LLAMA_NO_METAL
endif # LLAMA_NO_METAL endif # LLAMA_NO_METAL
ifdef LLAMA_MPI ifdef LLAMA_MPI
CFLAGS += -DGGML_USE_MPI -Wno-cast-qual MK_CPPFLAGS += -DGGML_USE_MPI
CXXFLAGS += -DGGML_USE_MPI -Wno-cast-qual MK_CFLAGS += -Wno-cast-qual
MK_CXXFLAGS += -Wno-cast-qual
OBJS += ggml-mpi.o OBJS += ggml-mpi.o
endif # LLAMA_MPI endif # LLAMA_MPI
ifdef LLAMA_OPENBLAS ifdef LLAMA_OPENBLAS
CFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags openblas) MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas)
LDFLAGS += $(shell pkg-config --libs openblas) MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas)
MK_LDFLAGS += $(shell pkg-config --libs openblas)
endif # LLAMA_OPENBLAS endif # LLAMA_OPENBLAS
ifdef LLAMA_BLIS ifdef LLAMA_BLIS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
LDFLAGS += -lblis -L/usr/local/lib MK_LDFLAGS += -lblis -L/usr/local/lib
endif # LLAMA_BLIS endif # LLAMA_BLIS
ifdef LLAMA_CUBLAS ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
OBJS += ggml-cuda.o OBJS += ggml-cuda.o
NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
ifdef LLAMA_CUDA_NVCC ifdef LLAMA_CUDA_NVCC
@ -327,14 +304,15 @@ endif # LLAMA_CUBLAS
ifdef LLAMA_CLBLAST ifdef LLAMA_CLBLAST
CFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL)
CXXFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
# Mac provides OpenCL as a framework # Mac provides OpenCL as a framework
ifeq ($(UNAME_S),Darwin) ifeq ($(UNAME_S),Darwin)
LDFLAGS += -lclblast -framework OpenCL MK_LDFLAGS += -lclblast -framework OpenCL
else else
LDFLAGS += $(shell pkg-config --libs clblast OpenCL) MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL)
endif endif
OBJS += ggml-opencl.o OBJS += ggml-opencl.o
@ -349,10 +327,9 @@ ifdef LLAMA_HIPBLAS
LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_DMMV_X ?= 32
LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_MMV_Y ?= 1
LLAMA_CUDA_KQUANTS_ITER ?= 2 LLAMA_CUDA_KQUANTS_ITER ?= 2
CFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
CXXFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas
LDFLAGS += -lhipblas -lamdhip64 -lrocblas
HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS))
HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
@ -366,6 +343,12 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
endif # LLAMA_HIPBLAS endif # LLAMA_HIPBLAS
ifndef LLAMA_NO_METAL
MK_CPPFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG
MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
OBJS += ggml-metal.o
endif # LLAMA_METAL
ifndef LLAMA_NO_METAL ifndef LLAMA_NO_METAL
ggml-metal.o: ggml-metal.m ggml-metal.h ggml-metal.o: ggml-metal.m ggml-metal.h
$(CC) $(CFLAGS) -c $< -o $@ $(CC) $(CFLAGS) -c $< -o $@
@ -376,11 +359,17 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
$(CC) $(CFLAGS) -c $< -o $@ $(CC) $(CFLAGS) -c $< -o $@
endif # LLAMA_MPI endif # LLAMA_MPI
ifdef LLAMA_NO_K_QUANTS ifndef LLAMA_NO_K_QUANTS
k_quants.o: k_quants.c k_quants.h k_quants.o: k_quants.c k_quants.h
$(CC) $(CFLAGS) -c $< -o $@ $(CC) $(CFLAGS) -c $< -o $@
endif # LLAMA_NO_K_QUANTS endif # LLAMA_NO_K_QUANTS
# combine build flags with cmdline overrides
override CPPFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS)
override CFLAGS := $(MK_CFLAGS) $(CFLAGS)
override CXXFLAGS := $(MK_CXXFLAGS) $(CXXFLAGS)
override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
# #
# Print build information # Print build information
# #

13
Package.swift
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@ -12,9 +12,18 @@ let package = Package(
name: "llama", name: "llama",
path: ".", path: ".",
exclude: ["ggml-metal.metal"], exclude: ["ggml-metal.metal"],
sources: ["ggml.c", "llama.cpp"], sources: [
"ggml.c",
"llama.cpp",
"ggml-alloc.c",
"k_quants.c"
],
publicHeadersPath: "spm-headers", publicHeadersPath: "spm-headers",
cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")], cSettings: [
.unsafeFlags(["-Wno-shorten-64-to-32"]),
.define("GGML_USE_K_QUANTS"),
.define("GGML_USE_ACCELERATE")
],
linkerSettings: [ linkerSettings: [
.linkedFramework("Accelerate") .linkedFramework("Accelerate")
] ]

41
README.md
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@ -120,6 +120,7 @@ as the main playground for developing new features for the [ggml](https://github
- [nat/openplayground](https://github.com/nat/openplayground) - [nat/openplayground](https://github.com/nat/openplayground)
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [withcatai/catai](https://github.com/withcatai/catai)
--- ---
@ -464,6 +465,8 @@ Building the program with BLAS support may lead to some performance improvements
You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK). You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
- For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed. - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed.
- For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
- <details> - <details>
<summary>Installing the OpenCL SDK from source</summary> <summary>Installing the OpenCL SDK from source</summary>
@ -481,10 +484,27 @@ Building the program with BLAS support may lead to some performance improvements
``` ```
</details> </details>
Installing CLBlast: it may be found in your operating system's packages. ##### Installing CLBlast
Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
Alternatively, they may be built from source.
- <details> - <details>
<summary>If not, then installing from source:</summary> <summary>Windows:</summary>
```cmd
set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast\build
cd CLBlast\build
cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/CLBlast
```
- <details>
<summary>Unix:</summary>
```sh ```sh
git clone https://github.com/CNugteren/CLBlast.git git clone https://github.com/CNugteren/CLBlast.git
@ -498,21 +518,32 @@ Building the program with BLAS support may lead to some performance improvements
Where `/some/path` is where the built library will be installed (default is `/usr/local`). Where `/some/path` is where the built library will be installed (default is `/usr/local`).
</details> </details>
Building: ##### Building Llama with CLBlast
- Build with make: - Build with make:
```sh ```sh
make LLAMA_CLBLAST=1 make LLAMA_CLBLAST=1
``` ```
- CMake: - CMake (Unix):
```sh ```sh
mkdir build mkdir build
cd build cd build
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_dir=/some/path cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_dir=/some/path
cmake --build . --config Release cmake --build . --config Release
``` ```
- CMake (Windows):
```cmd
set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP
```
Running: ##### Running Llama with CLBlast
The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does. The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does.

12
common/log.h
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@ -341,14 +341,14 @@ inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTri
} }
} }
if (_disabled)
{
// Log is disabled
return nullptr;
}
if (_initialized) if (_initialized)
{ {
if (_disabled)
{
// Log is disabled
return nullptr;
}
// with fallback in case something went wrong // with fallback in case something went wrong
return logfile ? logfile : stderr; return logfile ? logfile : stderr;
} }

32
convert.py
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@ -323,15 +323,27 @@ class BpeVocab:
self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
added_tokens: dict[str, int] added_tokens: dict[str, int]
if fname_added_tokens is not None: if fname_added_tokens is not None:
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
else: else:
added_tokens = {} # Fall back to trying to find the added tokens in tokenizer.json
tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json'
if not tokenizer_json_file.is_file():
added_tokens = {}
else:
tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
added_tokens = dict(
(item['content'], item['id'])
for item in tokenizer_json.get('added_tokens', [])
# Added tokens here can be duplicates of the main vocabulary.
if item['content'] not in self.bpe_tokenizer )
vocab_size: int = len(self.bpe_tokenizer) vocab_size: int = len(self.bpe_tokenizer)
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
actual_ids = sorted(added_tokens.values()) actual_ids = sorted(added_tokens.values())
if expected_ids != actual_ids: if expected_ids != actual_ids:
raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") expected_end_id = vocab_size + len(actual_ids) - 1
raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}")
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
self.added_tokens_list = [text for (text, idx) in items] self.added_tokens_list = [text for (text, idx) in items]
@ -345,10 +357,22 @@ class BpeVocab:
from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import]
byte_encoder = tokenization_gpt2.bytes_to_unicode() byte_encoder = tokenization_gpt2.bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()} byte_decoder = {v: k for k, v in byte_encoder.items()}
score = 0.0
for i, item in enumerate(tokenizer): for i, item in enumerate(tokenizer):
text: bytes = item.encode("utf-8") text: bytes = item.encode("utf-8")
score: float = -i # FIXME: These shouldn't be hardcoded, but it's probably better than the current behavior?
yield text, score, gguf.TokenType.USER_DEFINED if i <= 258 and text.startswith(b'<') and text.endswith(b'>'):
if i == 0 and text == b'<unk>':
toktype = gguf.TokenType.UNKNOWN
elif i == 1 or i == 2:
toktype = gguf.TokenType.CONTROL
elif i >= 3 and text.startswith(b'<0x'):
toktype = gguf.TokenType.BYTE
else:
toktype = gguf.TokenType.NORMAL
else:
toktype = gguf.TokenType.NORMAL
yield text, score, toktype
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list: for text in self.added_tokens_list:

13
examples/gptneox-wip/gptneox-main.cpp
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@ -660,9 +660,10 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2
ggml_tensor * gpt_neox_ff( ggml_tensor * gpt_neox_ff(
const gpt_neox_block &block, const gpt_neox_block &block,
ggml_context * ctx0, ggml_context * ctx0,
ggml_tensor * inp) { ggml_tensor * inp,
const gpt_neox_hparams &hparams) {
ggml_tensor * cur = ggml_norm(ctx0, inp); ggml_tensor * cur = ggml_norm(ctx0, inp, hparams.norm_eps);
cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur)); cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur));
cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur); cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur);
@ -753,7 +754,7 @@ bool gpt_neox_eval(
// self-attention // self-attention
{ {
{ {
cur = ggml_norm(ctx0, inpL); cur = ggml_norm(ctx0, inpL, hparams.norm_eps);
cur = ggml_add(ctx0, cur = ggml_add(ctx0,
ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur), ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur),
@ -844,7 +845,7 @@ bool gpt_neox_eval(
if (hparams.par_res == 0) { if (hparams.par_res == 0) {
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL); struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL);
cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF); cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF, hparams);
// input for next layer // input for next layer
inpL = ggml_add(ctx0, cur, inpFF); inpL = ggml_add(ctx0, cur, inpFF);
@ -853,7 +854,7 @@ bool gpt_neox_eval(
// this is independent of the self-attention result, so it could be done in parallel to the self-attention // this is independent of the self-attention result, so it could be done in parallel to the self-attention
// note here we pass inpL instead of cur // note here we pass inpL instead of cur
cur = gpt_neox_ff(model.blocks[il], ctx0, inpL); cur = gpt_neox_ff(model.blocks[il], ctx0, inpL, hparams);
// layer input + FF // layer input + FF
cur = ggml_add(ctx0, cur, inpFF); cur = ggml_add(ctx0, cur, inpFF);
@ -867,7 +868,7 @@ bool gpt_neox_eval(
// norm // norm
{ {
inpL = ggml_norm(ctx0, inpL); inpL = ggml_norm(ctx0, inpL, hparams.norm_eps);
// inpL = ln_f_g*inpL + ln_f_b // inpL = ln_f_g*inpL + ln_f_b
inpL = ggml_add(ctx0, inpL = ggml_add(ctx0,

14
examples/server/server.cpp
View File

@ -1379,7 +1379,13 @@ int main(int argc, char **argv)
} }
} }
const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs); auto probs = llama.generated_token_probs;
if (llama.params.n_probs > 0 && llama.stopped_word) {
const std::vector<llama_token> stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false);
probs = std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size());
}
const json data = format_final_response(llama, llama.generated_text, probs);
llama_print_timings(llama.ctx); llama_print_timings(llama.ctx);
@ -1456,7 +1462,11 @@ int main(int argc, char **argv)
if (!llama.has_next_token) { if (!llama.has_next_token) {
// Generation is done, send extra information. // Generation is done, send extra information.
const json data = format_final_response(llama, "", llama.generated_token_probs); const json data = format_final_response(
llama,
"",
std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index)
);
const std::string str = const std::string str =
"data: " + "data: " +

7
ggml-alloc.c
View File

@ -284,7 +284,14 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
// address and size of the buffer when measuring // address and size of the buffer when measuring
// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers // it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
static void * const MEASURE_BASE_ADDR = (void *) 0x1000; static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
#if defined(__ARM_NEON) && !defined(__aarch64__)
// 32-bit
// TODO: Use for 32-bit x86 as well
static const size_t MEASURE_MAX_SIZE = (1ULL<<32) - 1; // 4 GB
#else
// 64-bit
static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB
#endif
struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */); struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);

17
ggml-cuda.cu
View File

@ -81,12 +81,29 @@
#if defined(GGML_USE_HIPBLAS) #if defined(GGML_USE_HIPBLAS)
#define __CUDA_ARCH__ 1300 #define __CUDA_ARCH__ 1300
#ifndef __has_builtin
#define __has_builtin(x) 0
#endif
typedef int8_t int8x4_t __attribute__((ext_vector_type(4))); typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
static __device__ __forceinline__ int __vsubss4(const int a, const int b) { static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
const int8x4_t va = reinterpret_cast<const int8x4_t&>(a); const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b); const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
#if __has_builtin(__builtin_elementwise_sub_sat)
const int8x4_t c = __builtin_elementwise_sub_sat(va, vb); const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
return reinterpret_cast<const int&>(c); return reinterpret_cast<const int&>(c);
#else
int8x4_t c;
int16_t tmp;
#pragma unroll
for (int i = 0; i < 4; i++) {
tmp = va[i] - vb[i];
if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
c[i] = tmp;
}
return reinterpret_cast<int&>(c);
#endif // __has_builtin(__builtin_elementwise_sub_sat)
} }
static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {

18
ggml-metal.m
View File

@ -116,10 +116,24 @@ static NSString * const msl_library_source = @"see metal.metal";
struct ggml_metal_context * ggml_metal_init(int n_cb) { struct ggml_metal_context * ggml_metal_init(int n_cb) {
metal_printf("%s: allocating\n", __func__); metal_printf("%s: allocating\n", __func__);
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); // Show all the Metal device instances in the system
NSArray * devices = MTLCopyAllDevices();
id <MTLDevice> device;
NSString * s;
for (device in devices) {
s = [device name];
metal_printf("%s: found device: %s\n", __func__, [s UTF8String]);
}
// Pick and show default Metal device
device = MTLCreateSystemDefaultDevice();
s = [device name];
metal_printf("%s: picking default device: %s\n", __func__, [s UTF8String]);
// Configure context
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
ctx->device = device;
ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
ctx->device = MTLCreateSystemDefaultDevice();
ctx->queue = [ctx->device newCommandQueue]; ctx->queue = [ctx->device newCommandQueue];
ctx->n_buffers = 0; ctx->n_buffers = 0;
ctx->concur_list_len = 0; ctx->concur_list_len = 0;

46
ggml.c
View File

@ -817,46 +817,6 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
#if !defined(__aarch64__) #if !defined(__aarch64__)
inline static uint16_t vaddvq_u8(uint8x16_t v) {
return
(uint16_t)vgetq_lane_u8(v, 0) + (uint16_t)vgetq_lane_u8(v, 1) +
(uint16_t)vgetq_lane_u8(v, 2) + (uint16_t)vgetq_lane_u8(v, 3) +
(uint16_t)vgetq_lane_u8(v, 4) + (uint16_t)vgetq_lane_u8(v, 5) +
(uint16_t)vgetq_lane_u8(v, 6) + (uint16_t)vgetq_lane_u8(v, 7) +
(uint16_t)vgetq_lane_u8(v, 8) + (uint16_t)vgetq_lane_u8(v, 9) +
(uint16_t)vgetq_lane_u8(v, 10) + (uint16_t)vgetq_lane_u8(v, 11) +
(uint16_t)vgetq_lane_u8(v, 12) + (uint16_t)vgetq_lane_u8(v, 13) +
(uint16_t)vgetq_lane_u8(v, 14) + (uint16_t)vgetq_lane_u8(v, 15);
}
inline static int16_t vaddvq_s8(int8x16_t v) {
return
(int16_t)vgetq_lane_s8(v, 0) + (int16_t)vgetq_lane_s8(v, 1) +
(int16_t)vgetq_lane_s8(v, 2) + (int16_t)vgetq_lane_s8(v, 3) +
(int16_t)vgetq_lane_s8(v, 4) + (int16_t)vgetq_lane_s8(v, 5) +
(int16_t)vgetq_lane_s8(v, 6) + (int16_t)vgetq_lane_s8(v, 7) +
(int16_t)vgetq_lane_s8(v, 8) + (int16_t)vgetq_lane_s8(v, 9) +
(int16_t)vgetq_lane_s8(v, 10) + (int16_t)vgetq_lane_s8(v, 11) +
(int16_t)vgetq_lane_s8(v, 12) + (int16_t)vgetq_lane_s8(v, 13) +
(int16_t)vgetq_lane_s8(v, 14) + (int16_t)vgetq_lane_s8(v, 15);
}
inline static int32_t vaddvq_s16(int16x8_t v) {
return
(int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
(int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
(int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
(int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
}
inline static uint32_t vaddvq_u16(uint16x8_t v) {
return
(uint32_t)vgetq_lane_u16(v, 0) + (uint32_t)vgetq_lane_u16(v, 1) +
(uint32_t)vgetq_lane_u16(v, 2) + (uint32_t)vgetq_lane_u16(v, 3) +
(uint32_t)vgetq_lane_u16(v, 4) + (uint32_t)vgetq_lane_u16(v, 5) +
(uint32_t)vgetq_lane_u16(v, 6) + (uint32_t)vgetq_lane_u16(v, 7);
}
inline static int32_t vaddvq_s32(int32x4_t v) { inline static int32_t vaddvq_s32(int32x4_t v) {
return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
} }
@ -865,12 +825,6 @@ inline static float vaddvq_f32(float32x4_t v) {
return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
} }
inline static float vminvq_f32(float32x4_t v) {
return
MIN(MIN(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
MIN(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
}
inline static float vmaxvq_f32(float32x4_t v) { inline static float vmaxvq_f32(float32x4_t v) {
return return
MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),

40
k_quants.c
View File

@ -13,6 +13,26 @@
// //
#include <arm_neon.h> #include <arm_neon.h>
#if !defined(__aarch64__)
inline static int32_t vaddvq_s16(int16x8_t v) {
return
(int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
(int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
(int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
(int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
}
inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
return vcombine_s16(a0, b0);
}
inline static int32_t vaddvq_s32(int32x4_t v) {
return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
}
#endif
#else #else
#ifdef __wasm_simd128__ #ifdef __wasm_simd128__
@ -1302,7 +1322,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t m3 = vdupq_n_u8(0x3); const uint8x16_t m3 = vdupq_n_u8(0x3);
const uint8x16_t m4 = vdupq_n_u8(0xF); const uint8x16_t m4 = vdupq_n_u8(0xF);
#if defined(__ARM_FEATURE_DOTPROD)
const int32x4_t vzero = vdupq_n_s32(0); const int32x4_t vzero = vdupq_n_s32(0);
#endif
int8x16x2_t q2bytes; int8x16x2_t q2bytes;
uint8_t aux[16]; uint8_t aux[16];
@ -1608,7 +1630,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
#ifdef __ARM_NEON #ifdef __ARM_NEON
const uint8x16_t m3 = vdupq_n_u8(0x3); const uint8x16_t m3 = vdupq_n_u8(0x3);
#if defined(__ARM_FEATURE_DOTPROD)
const int32x4_t vzero = vdupq_n_s32(0); const int32x4_t vzero = vdupq_n_s32(0);
#endif
int8x16x4_t q2bytes; int8x16x4_t q2bytes;
@ -2592,8 +2616,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const uint8_t * restrict q4 = x[i].qs; const uint8_t * restrict q4 = x[i].qs;
const int8_t * restrict q8 = y[i].qs; const int8_t * restrict q8 = y[i].qs;
//int32x4_t isum = mzero;
int32_t sumi1 = 0; int32_t sumi1 = 0;
int32_t sumi2 = 0; int32_t sumi2 = 0;
@ -3092,9 +3114,11 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
#ifdef __ARM_NEON #ifdef __ARM_NEON
const uint8x16_t m4b = vdupq_n_u8(0xf); const uint8x16_t m4b = vdupq_n_u8(0xf);
const int32x4_t mzero = vdupq_n_s32(0);
const uint8x16_t mone = vdupq_n_u8(1); const uint8x16_t mone = vdupq_n_u8(1);
const uint8x16_t mtwo = vdupq_n_u8(2); const uint8x16_t mtwo = vdupq_n_u8(2);
#if defined(__ARM_FEATURE_DOTPROD)
const int32x4_t mzero = vdupq_n_s32(0);
#endif
int8x16x4_t q5bytes; int8x16x4_t q5bytes;
@ -3437,8 +3461,10 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
#ifdef __ARM_NEON #ifdef __ARM_NEON
const uint8x16_t m4b = vdupq_n_u8(0xf); const uint8x16_t m4b = vdupq_n_u8(0xf);
const int32x4_t mzero = vdupq_n_s32(0);
const uint8x16_t mh = vdupq_n_u8(16); const uint8x16_t mh = vdupq_n_u8(16);
#if defined(__ARM_FEATURE_DOTPROD)
const int32x4_t mzero = vdupq_n_s32(0);
#endif
int8x16x4_t q5bytes; int8x16x4_t q5bytes;
uint8x16x4_t q5h; uint8x16x4_t q5h;
@ -3656,7 +3682,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
float sum = 0; float sum = 0;
const uint8x16_t m4b = vdupq_n_u8(0xF); const uint8x16_t m4b = vdupq_n_u8(0xF);
#if defined(__ARM_FEATURE_DOTPROD)
const int32x4_t vzero = vdupq_n_s32(0); const int32x4_t vzero = vdupq_n_s32(0);
#endif
//const int8x16_t m32s = vdupq_n_s8(32); //const int8x16_t m32s = vdupq_n_s8(32);
const uint8x16_t mone = vdupq_n_u8(3); const uint8x16_t mone = vdupq_n_u8(3);
@ -4045,8 +4073,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
float sum = 0; float sum = 0;
const uint8x16_t m4b = vdupq_n_u8(0xF); const uint8x16_t m4b = vdupq_n_u8(0xF);
const int32x4_t vzero = vdupq_n_s32(0);
const int8x16_t m32s = vdupq_n_s8(32); const int8x16_t m32s = vdupq_n_s8(32);
#if defined(__ARM_FEATURE_DOTPROD)
const int32x4_t vzero = vdupq_n_s32(0);
#endif
const uint8x16_t mone = vdupq_n_u8(3); const uint8x16_t mone = vdupq_n_u8(3);

46
llama.cpp
View File

@ -325,6 +325,44 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
}, },
}, },
{
LLM_ARCH_GPT2,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_GPTJ,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_GPTNEOX,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_MPT,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
{
LLM_ARCH_UNKNOWN,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
},
},
}; };
static llm_arch llm_arch_from_string(const std::string & name) { static llm_arch llm_arch_from_string(const std::string & name) {
@ -1605,9 +1643,13 @@ static void llm_load_hparams(
GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT)); GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
if (hparams.n_rot != hparams.n_embd / hparams.n_head) { if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head)); if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
}
} }
// gpt-neox n_rot = rotary_pct * (n_embd / n_head)
// gpt-j n_rot = rotary_dim
} }
// arch-specific KVs // arch-specific KVs