Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-25 22:08:46 +01:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'master' into xsn/fix_lora

Browse Source
This commit is contained in:
ngxson 2024-07-15 11:06:47 +02:00
commit 703573f608
51 changed files with 2212 additions and 146823 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.devops/nix/package.nix
View File

@ -18,6 +18,7 @@
vulkan-headers,
vulkan-loader,
curl,
shaderc,
useBlas ? builtins.all (x: !x) [
useCuda
useMetalKit
@ -146,6 +147,7 @@ let
vulkanBuildInputs = [
vulkan-headers
vulkan-loader
shaderc
];
in

2
.devops/tools.sh
View File

@ -8,7 +8,7 @@ arg1="$1"
shift
if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
python3 ./convert-hf-to-gguf.py "$@"
python3 ./convert_hf_to_gguf.py "$@"
elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
./llama-quantize "$@"
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then

6
.github/workflows/build.yml vendored
View File

@ -355,8 +355,10 @@ jobs:
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libvulkan-dev
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
sudo apt-get update -y
sudo apt-get install -y build-essential vulkan-sdk
- name: Build
id: cmake_build

5
.gitignore vendored
View File

@ -61,6 +61,11 @@ llama-batched-swift
out/
tmp/
# Deprecated
/main
/server
# CI
!.github/workflows/*.yml

11
CMakeLists.txt
View File

@ -132,7 +132,16 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o
set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files")
set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files")
get_directory_property(LLAMA_TRANSIENT_DEFINES COMPILE_DEFINITIONS)
# At the moment some compile definitions are placed within the ggml/src
# directory but not exported on the `ggml` target. This could be improved by
# determining _precisely_ which defines are necessary for the llama-config
# package.
#
get_directory_property(GGML_DIR_DEFINES DIRECTORY ggml/src COMPILE_DEFINITIONS)
get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
set(GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES} ${GGML_DIR_DEFINES})
get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h)
install(TARGETS llama LIBRARY PUBLIC_HEADER)

43
Makefile
View File

@ -197,6 +197,10 @@ ifdef GGML_RPC
BUILD_TARGETS += rpc-server
endif
ifdef GGML_VULKAN
BUILD_TARGETS += vulkan-shaders-gen
endif
default: $(BUILD_TARGETS) $(LEGACY_TARGETS_BUILD)
test: $(TEST_TARGETS)
@ -547,11 +551,17 @@ ifdef GGML_OPENBLAS64
endif # GGML_OPENBLAS64
ifdef GGML_BLIS
MK_CPPFLAGS += -DGGML_USE_BLAS -I/usr/local/include/blis -I/usr/include/blis
MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis
MK_LDFLAGS += -lblis -L/usr/local/lib
OBJ_GGML += ggml/src/ggml-blas.o
endif # GGML_BLIS
ifdef GGML_NVPL
MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas
MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp
OBJ_GGML += ggml/src/ggml-blas.o
endif # GGML_NVPL
ifndef GGML_NO_LLAMAFILE
MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
OBJ_GGML += ggml/src/llamafile/sgemm.o
@ -704,8 +714,8 @@ endif # GGML_CUDA
ifdef GGML_VULKAN
MK_CPPFLAGS += -DGGML_USE_VULKAN
MK_LDFLAGS += -lvulkan
OBJ_GGML += ggml/src/ggml-vulkan.o
MK_LDFLAGS += $(shell pkg-config --libs vulkan)
OBJ_GGML += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o
ifdef GGML_VULKAN_CHECK_RESULTS
MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS
@ -727,10 +737,28 @@ ifdef GGML_VULKAN_RUN_TESTS
MK_CPPFLAGS += -DGGML_VULKAN_RUN_TESTS
endif
ggml/src/ggml-vulkan.o: \
ggml/src/ggml-vulkan.cpp \
ggml/include/ggml-vulkan.h
$(CXX) $(CXXFLAGS) -c $< -o $@
GLSLC_CMD = glslc
_ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen
_ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp
_ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp
_ggml_vk_input_dir = ggml/src/vulkan-shaders
_ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp)
ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source)
$(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@
$(_ggml_vk_header): $(_ggml_vk_source)
$(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen
$(_ggml_vk_genshaders_cmd) \
--glslc $(GLSLC_CMD) \
--input-dir $(_ggml_vk_input_dir) \
--target-hpp $(_ggml_vk_header) \
--target-cpp $(_ggml_vk_source)
vulkan-shaders-gen: ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
$(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
endif # GGML_VULKAN
ifdef GGML_HIPBLAS
@ -1110,6 +1138,7 @@ clean:
rm -vrf ggml/src/ggml-cuda/template-instances/*.o
rm -rvf $(BUILD_TARGETS)
rm -rvf $(TEST_TARGETS)
rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp
rm -rvf $(LEGACY_TARGETS_CLEAN)
find examples pocs -type f -name "*.o" -delete

29
cmake/llama-config.cmake.in
View File

@ -8,6 +8,13 @@ set(GGML_CUDA @GGML_CUDA@)
set(GGML_METAL @GGML_METAL@)
set(GGML_HIPBLAS @GGML_HIPBLAS@)
set(GGML_ACCELERATE @GGML_ACCELERATE@)
set(GGML_VULKAN @GGML_VULKAN@)
set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@)
set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@)
set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@)
set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@)
set(GGML_SYCL @GGML_SYCL@)
set(GGML_OPENMP @GGML_OPENMP@)
@PACKAGE_INIT@
@ -37,18 +44,36 @@ if (GGML_METAL)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
endif()
if (GGML_VULKAN)
find_package(Vulkan REQUIRED)
endif()
if (GGML_HIPBLAS)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
endif()
if (GGML_SYCL)
find_package(IntelSYCL REQUIRED)
find_package(MKL REQUIRED)
endif()
if (GGML_OPENMP)
find_package(OpenMP REQUIRED)
endif()
find_library(ggml_LIBRARY ggml
REQUIRED
HINTS ${LLAMA_LIB_DIR})
find_library(llama_LIBRARY llama
REQUIRED
HINTS ${LLAMA_LIB_DIR})
set(_llama_link_deps "Threads::Threads" "@LLAMA_EXTRA_LIBS@")
set(_llama_transient_defines "@LLAMA_TRANSIENT_DEFINES@")
set(_llama_link_deps "${ggml_LIBRARY}" "@GGML_LINK_LIBRARIES@")
set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
add_library(llama UNKNOWN IMPORTED)

120
convert_hf_to_gguf.py
View File

@ -373,6 +373,29 @@ class Model:
except KeyError:
raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
def does_token_look_special(self, token: str | bytes) -> bool:
if isinstance(token, (bytes, bytearray)):
token_text = token.decode(encoding="utf-8")
elif isinstance(token, memoryview):
token_text = token.tobytes().decode(encoding="utf-8")
else:
token_text = token
# Some models mark some added tokens which ought to be control tokens as not special.
# (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
seems_special = token_text in (
"<pad>", # deepseek-coder
"<mask>", "<2mass>", "[@BOS@]", # gemma{,-2}
)
seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>")) # deepseek-coder
# TODO: should these be marked as UNUSED instead? (maybe not)
seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">")) # gemma{,-2}
return seems_special
# used for GPT-2 BPE and WordPiece vocabs
def get_vocab_base(self) -> tuple[list[str], list[int], str]:
tokens: list[str] = []
@ -391,16 +414,18 @@ class Model:
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
if tokenizer.added_tokens_decoder[i].special:
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.USER_DEFINED)
toktypes.append(gguf.TokenType.UNUSED)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
token: str = reverse_vocab[i]
if token in added_vocab:
if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
toktypes.append(gguf.TokenType.CONTROL)
else:
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
toktypes.append(gguf.TokenType.USER_DEFINED)
else:
toktypes.append(gguf.TokenType.NORMAL)
tokens.append(token)
return tokens, toktypes, tokpre
@ -559,7 +584,7 @@ class Model:
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
toktypes.append(gguf.TokenType.UNUSED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.CONTROL)
@ -609,7 +634,7 @@ class Model:
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(token_id)
@ -644,6 +669,25 @@ class Model:
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
for token_id, token_data in added_tokens_decoder.items():
token_id = int(token_id)
token: str = token_data["content"]
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert tokens[token_id] == token.encode("utf-8")
if token_data.get("special") or self.does_token_look_special(token):
toktypes[token_id] = SentencePieceTokenTypes.CONTROL
else:
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
scores[token_id] = -1000.0
tokens[token_id] = token.encode("utf-8")
if vocab_size > len(tokens):
pad_count = vocab_size - len(tokens)
logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
@ -1203,11 +1247,10 @@ class RefactModel(Model):
# TODO: how to determine special FIM tokens automatically?
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
special_vocab._set_special_token("prefix", 1)
special_vocab._set_special_token("suffix", 3)
special_vocab._set_special_token("middle", 2)
special_vocab._set_special_token("fsep", 4) # is this correct?
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
@ -1267,7 +1310,7 @@ class StableLMModel(Model):
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
else:
# StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
# StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
self._set_vocab_qwen()
def set_gguf_parameters(self):
@ -1579,7 +1622,6 @@ class DbrxModel(Model):
self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
@ -1873,7 +1915,7 @@ class Phi3MiniModel(Model):
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
for token_id in range(tokenizer.vocab_size()):
@ -1918,7 +1960,7 @@ class Phi3MiniModel(Model):
for token_id, foken_data in added_tokens_decoder.items():
token_id = int(token_id)
token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert tokens[token_id] == token
tokens[token_id] = token
scores[token_id] = -1000.0
@ -1934,7 +1976,7 @@ class Phi3MiniModel(Model):
for foken_data in added_tokens:
token_id = int(foken_data["id"])
token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert tokens[token_id] == token
tokens[token_id] = token
scores[token_id] = -1000.0
@ -2146,7 +2188,7 @@ class InternLM2Model(Model):
toktype = SentencePieceTokenTypes.BYTE
# take care of ununsed raw token
if piece.startswith('[UNUSED'):
toktype = SentencePieceTokenTypes.UNKNOWN
toktype = SentencePieceTokenTypes.UNUSED
tokens.append(text)
scores.append(score)
@ -2176,7 +2218,7 @@ class InternLM2Model(Model):
if token == chat_eos_token:
chat_eos_token_id = token_id
token = token.encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert(tokens[token_id] == token)
tokens[token_id] = token
scores[token_id] = -1000.0
@ -2195,7 +2237,7 @@ class InternLM2Model(Model):
if token == chat_eos_token:
chat_eos_token_id = token_id
token = token.encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
assert(tokens[token_id] == token)
tokens[token_id] = token
scores[token_id] = -1000.0
@ -2424,19 +2466,7 @@ class Gemma2Model(Model):
model_arch = gguf.MODEL_ARCH.GEMMA2
def set_vocab(self):
tokens, scores, toktypes = self._create_vocab_sentencepiece()
# hack: This is required so that we can properly use start/end-of-turn for chat template
for i in range(108):
# including <unusedX>, <start_of_turn>, <end_of_turn>
toktypes[i] = SentencePieceTokenTypes.CONTROL
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
self._set_vocab_sentencepiece()
self.gguf_writer.add_add_space_prefix(False)
@ -2463,11 +2493,6 @@ class Gemma2Model(Model):
)
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
# sanity check
attn_scalar = self.hparams["query_pre_attn_scalar"]
if attn_scalar != hparams["hidden_size"] / hparams["num_attention_heads"]:
raise ValueError("query_pre_attn_scalar must be equal to n_embd / n_head")
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
@ -2760,7 +2785,7 @@ class ArcticModel(Model):
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
for token_id in range(tokenizer.vocab_size()):
@ -3015,7 +3040,7 @@ class T5Model(Model):
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(token_id)
@ -3233,15 +3258,14 @@ class ChatGLMModel(Model):
if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():
score = tokenizer.tokenizer.sp_model.get_score(token_id)
if len(piece) == 0:
text = f"[PAD{token_id}]".encode("utf-8")
if token_id >= tokenizer.tokenizer.sp_model.vocab_size():
if piece in special_tokens:
# show special tokens in prompt
toktype = SentencePieceTokenTypes.USER_DEFINED
toktype = SentencePieceTokenTypes.CONTROL
elif len(piece) == 0:
text = f"[PAD{token_id}]".encode("utf-8")
toktype = SentencePieceTokenTypes.UNUSED
else:
toktype = SentencePieceTokenTypes.UNKNOWN
toktype = SentencePieceTokenTypes.USER_DEFINED
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
@ -3330,7 +3354,7 @@ class ChatGLMModel(Model):
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
toktypes.append(gguf.TokenType.UNUSED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
if tokenizer.added_tokens_decoder[i].special:

39
docs/build.md
View File

@ -242,6 +242,45 @@ The following compilation options are also available to tweak performance (yes,
### Vulkan
**Windows**
#### w64devkit
Download and extract [w64devkit](https://github.com/skeeto/w64devkit/releases).
Download and install the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home#windows). When selecting components, only the Vulkan SDK Core is required.
Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies:
```sh
SDK_VERSION=1.3.283.0
cp /VulkanSDK/$SDK_VERSION/Bin/glslc.exe $W64DEVKIT_HOME/bin/
cp /VulkanSDK/$SDK_VERSION/Lib/vulkan-1.lib $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/
cp -r /VulkanSDK/$SDK_VERSION/Include/* $W64DEVKIT_HOME/x86_64-w64-mingw32/include/
cat > $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/pkgconfig/vulkan.pc <<EOF
Name: Vulkan-Loader
Description: Vulkan Loader
Version: $SDK_VERSION
Libs: -lvulkan-1
EOF
```
Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`.
#### MSYS2
Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
```sh
pacman -S git \
mingw-w64-ucrt-x86_64-gcc \
mingw-w64-ucrt-x86_64-cmake \
mingw-w64-ucrt-x86_64-vulkan-devel \
mingw-w64-ucrt-x86_64-shaderc
```
Switch into `llama.cpp` directory and build using CMake.
```sh
cmake -B build -DGGML_VULKAN=ON
cmake --build build --config Release
```
**With docker**:
You don't need to install Vulkan SDK. It will be installed inside the container.

2
examples/eval-callback/eval-callback.cpp
View File

@ -99,7 +99,7 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
char src1_str[128] = {0};
if (src1) {
sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
}
printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,

14
examples/gguf-hash/gguf-hash.cpp
View File

@ -347,7 +347,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
char hex_result[17];
for (int offset = 0; offset < 8; offset++) {
unsigned int shift_bits_by = (8 * (8 - offset - 1));
sprintf( ( hex_result + (2*offset)), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
}
if (hash_params.manifest_is_usable) {
@ -384,7 +384,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
char hex_result[41] = {0};
for (int offset = 0; offset < 20; offset++) {
sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
}
if (hash_params.manifest_is_usable) {
@ -421,7 +421,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0};
for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) {
sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
}
if (hash_params.manifest_is_usable) {
@ -460,7 +460,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
char hex_result[17];
for (int offset = 0; offset < 8; offset++) {
unsigned int shift_bits_by = (8 * (8 - offset - 1));
sprintf( ( hex_result + (2*offset)), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
}
if (hash_params.manifest_is_usable) {
@ -490,7 +490,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
char hex_result[41];
for (int offset = 0; offset < 20; offset++) {
sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
}
if (hash_params.manifest_is_usable) {
@ -520,7 +520,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0};
for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) {
sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
}
if (hash_params.manifest_is_usable) {
@ -552,7 +552,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
generate_uuidv5(result, uuid);
char string_buffer[37] = {0};
sprintf(string_buffer, "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
snprintf(string_buffer, sizeof(string_buffer), "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
uuid[0], uuid[1], uuid[2], uuid[3],
uuid[4], uuid[5], uuid[6], uuid[7],
uuid[8], uuid[9], uuid[10], uuid[11],

9
examples/main/main.cpp
View File

@ -289,8 +289,13 @@ int main(int argc, char ** argv) {
// Should not run without any tokens
if (embd_inp.empty()) {
embd_inp.push_back(llama_token_bos(model));
LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
if (add_bos) {
embd_inp.push_back(llama_token_bos(model));
LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
} else {
LOG_TEE("error: input is empty\n");
return -1;
}
}
// Tokenize negative prompt

77
examples/pydantic_models_to_grammar.py
View File

@ -6,7 +6,7 @@ import re
from copy import copy
from enum import Enum
from inspect import getdoc, isclass
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin, get_type_hints
from docstring_parser import parse
from pydantic import BaseModel, create_model
@ -53,35 +53,38 @@ class PydanticDataType(Enum):
def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str:
if isclass(pydantic_type) and issubclass(pydantic_type, str):
origin_type = get_origin(pydantic_type)
origin_type = pydantic_type if origin_type is None else origin_type
if isclass(origin_type) and issubclass(origin_type, str):
return PydanticDataType.STRING.value
elif isclass(pydantic_type) and issubclass(pydantic_type, bool):
elif isclass(origin_type) and issubclass(origin_type, bool):
return PydanticDataType.BOOLEAN.value
elif isclass(pydantic_type) and issubclass(pydantic_type, int):
elif isclass(origin_type) and issubclass(origin_type, int):
return PydanticDataType.INTEGER.value
elif isclass(pydantic_type) and issubclass(pydantic_type, float):
elif isclass(origin_type) and issubclass(origin_type, float):
return PydanticDataType.FLOAT.value
elif isclass(pydantic_type) and issubclass(pydantic_type, Enum):
elif isclass(origin_type) and issubclass(origin_type, Enum):
return PydanticDataType.ENUM.value
elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel):
return format_model_and_field_name(pydantic_type.__name__)
elif get_origin(pydantic_type) is list:
elif isclass(origin_type) and issubclass(origin_type, BaseModel):
return format_model_and_field_name(origin_type.__name__)
elif origin_type is list:
element_type = get_args(pydantic_type)[0]
return f"{map_pydantic_type_to_gbnf(element_type)}-list"
elif get_origin(pydantic_type) is set:
elif origin_type is set:
element_type = get_args(pydantic_type)[0]
return f"{map_pydantic_type_to_gbnf(element_type)}-set"
elif get_origin(pydantic_type) is Union:
elif origin_type is Union:
union_types = get_args(pydantic_type)
union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types]
return f"union-{'-or-'.join(union_rules)}"
elif get_origin(pydantic_type) is Optional:
elif origin_type is Optional:
element_type = get_args(pydantic_type)[0]
return f"optional-{map_pydantic_type_to_gbnf(element_type)}"
elif isclass(pydantic_type):
return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}"
elif get_origin(pydantic_type) is dict:
elif isclass(origin_type):
return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(origin_type.__name__)}"
elif origin_type is dict:
key_type, value_type = get_args(pydantic_type)
return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}"
else:
@ -118,7 +121,7 @@ def get_members_structure(cls, rule_name):
# Modify this comprehension
members = [
f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}'
for name, param_type in cls.__annotations__.items()
for name, param_type in get_type_hints(cls).items()
if name != "self"
]
@ -297,17 +300,20 @@ def generate_gbnf_rule_for_type(
field_name = format_model_and_field_name(field_name)
gbnf_type = map_pydantic_type_to_gbnf(field_type)
if isclass(field_type) and issubclass(field_type, BaseModel):
origin_type = get_origin(field_type)
origin_type = field_type if origin_type is None else origin_type
if isclass(origin_type) and issubclass(origin_type, BaseModel):
nested_model_name = format_model_and_field_name(field_type.__name__)
nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules)
rules.extend(nested_model_rules)
gbnf_type, rules = nested_model_name, rules
elif isclass(field_type) and issubclass(field_type, Enum):
elif isclass(origin_type) and issubclass(origin_type, Enum):
enum_values = [f'"\\"{e.value}\\""' for e in field_type] # Adding escaped quotes
enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}"
rules.append(enum_rule)
gbnf_type, rules = model_name + "-" + field_name, rules
elif get_origin(field_type) == list: # Array
elif origin_type is list: # Array
element_type = get_args(field_type)[0]
element_rule_name, additional_rules = generate_gbnf_rule_for_type(
model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
@ -317,7 +323,7 @@ def generate_gbnf_rule_for_type(
rules.append(array_rule)
gbnf_type, rules = model_name + "-" + field_name, rules
elif get_origin(field_type) == set or field_type == set: # Array
elif origin_type is set: # Array
element_type = get_args(field_type)[0]
element_rule_name, additional_rules = generate_gbnf_rule_for_type(
model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
@ -371,7 +377,7 @@ def generate_gbnf_rule_for_type(
gbnf_type = f"{model_name}-{field_name}-optional"
else:
gbnf_type = f"{model_name}-{field_name}-union"
elif isclass(field_type) and issubclass(field_type, str):
elif isclass(origin_type) and issubclass(origin_type, str):
if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None:
triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False)
markdown_string = field_info.json_schema_extra.get("markdown_code_block", False)
@ -387,8 +393,8 @@ def generate_gbnf_rule_for_type(
gbnf_type = PydanticDataType.STRING.value
elif (
isclass(field_type)
and issubclass(field_type, float)
isclass(origin_type)
and issubclass(origin_type, float)
and field_info
and hasattr(field_info, "json_schema_extra")
and field_info.json_schema_extra is not None
@ -413,8 +419,8 @@ def generate_gbnf_rule_for_type(
)
elif (
isclass(field_type)
and issubclass(field_type, int)
isclass(origin_type)
and issubclass(origin_type, int)
and field_info
and hasattr(field_info, "json_schema_extra")
and field_info.json_schema_extra is not None
@ -462,7 +468,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas
if not issubclass(model, BaseModel):
# For non-Pydantic classes, generate model_fields from __annotations__ or __init__
if hasattr(model, "__annotations__") and model.__annotations__:
model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} # pyright: ignore[reportGeneralTypeIssues]
model_fields = {name: (typ, ...) for name, typ in get_type_hints(model).items()}
else:
init_signature = inspect.signature(model.__init__)
parameters = init_signature.parameters
@ -470,7 +476,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas
name != "self"}
else:
# For Pydantic models, use model_fields and check for ellipsis (required fields)
model_fields = model.__annotations__
model_fields = get_type_hints(model)
model_rule_parts = []
nested_rules = []
@ -706,7 +712,7 @@ def generate_markdown_documentation(
else:
documentation += f" Fields:\n" # noqa: F541
if isclass(model) and issubclass(model, BaseModel):
for name, field_type in model.__annotations__.items():
for name, field_type in get_type_hints(model).items():
# if name == "markdown_code_block":
# continue
if get_origin(field_type) == list:
@ -754,14 +760,17 @@ def generate_field_markdown(
field_info = model.model_fields.get(field_name)
field_description = field_info.description if field_info and field_info.description else ""
if get_origin(field_type) == list:
origin_type = get_origin(field_type)
origin_type = field_type if origin_type is None else origin_type
if origin_type == list:
element_type = get_args(field_type)[0]
field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
if field_description != "":
field_text += ":\n"
else:
field_text += "\n"
elif get_origin(field_type) == Union:
elif origin_type == Union:
element_types = get_args(field_type)
types = []
for element_type in element_types:
@ -792,9 +801,9 @@ def generate_field_markdown(
example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
field_text += f"{indent} Example: {example_text}\n"
if isclass(field_type) and issubclass(field_type, BaseModel):
if isclass(origin_type) and issubclass(origin_type, BaseModel):
field_text += f"{indent} Details:\n"
for name, type_ in field_type.__annotations__.items():
for name, type_ in get_type_hints(field_type).items():
field_text += generate_field_markdown(name, type_, field_type, depth + 2)
return field_text
@ -855,7 +864,7 @@ def generate_text_documentation(
if isclass(model) and issubclass(model, BaseModel):
documentation_fields = ""
for name, field_type in model.__annotations__.items():
for name, field_type in get_type_hints(model).items():
# if name == "markdown_code_block":
# continue
if get_origin(field_type) == list:
@ -948,7 +957,7 @@ def generate_field_text(
if isclass(field_type) and issubclass(field_type, BaseModel):
field_text += f"{indent} Details:\n"
for name, type_ in field_type.__annotations__.items():
for name, type_ in get_type_hints(field_type).items():
field_text += generate_field_text(name, type_, field_type, depth + 2)
return field_text

2
examples/pydantic_models_to_grammar_examples.py
View File

@ -20,6 +20,8 @@ def create_completion(prompt, grammar):
response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data)
data = response.json()
assert data.get("error") is None, data
print(data["content"])
return data["content"]

2
examples/quantize-stats/quantize-stats.cpp
View File

@ -154,7 +154,7 @@ static void test_roundtrip_on_chunk(
}
if (use_reference) {
qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
qfns.from_float_ref(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.from_float(input_scratch, quantized_scratch, chunk_size);
}

40
examples/server/server.cpp
View File

@ -737,6 +737,8 @@ struct server_context {
slot.ga_n = ga_n;
slot.ga_w = ga_w;
slot.sparams = params.sparams;
slot.reset();
slots.push_back(slot);
@ -2003,6 +2005,11 @@ struct server_context {
int32_t n_batch = llama_n_batch(ctx);
int32_t n_ubatch = llama_n_ubatch(ctx);
// track if this is an embedding or non-embedding batch
// if we've added sampled tokens above, we are in non-embedding mode
// -1: none, 0: non-embedding, 1: embedding
int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
// next, batch any pending prompts without exceeding n_batch
if (params.cont_batching || batch.n_tokens == 0) {
for (auto & slot : slots) {
@ -2173,6 +2180,14 @@ struct server_context {
}
}
// check that we are in the right batch_type, if not defer the slot
bool slot_type = slot.embedding ? 1 : 0;
if (batch_type == -1) {
batch_type = slot_type;
} else if (batch_type != slot_type) {
continue;
}
// keep only the common part
int p0 = (int) system_tokens.size() + slot.n_past;
if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
@ -2274,6 +2289,9 @@ struct server_context {
{"n_tokens", batch.n_tokens},
});
// make sure we're in the right embedding mode
llama_set_embeddings(ctx, batch_type == 1);
// process the created batch of tokens
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
@ -2988,6 +3006,11 @@ int main(int argc, char ** argv) {
};
const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
if (ctx_server.params.embedding) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = json::parse(req.body);
@ -3083,6 +3106,11 @@ int main(int argc, char ** argv) {
};
const auto handle_chat_completions = [&ctx_server, &params, &res_error](const httplib::Request & req, httplib::Response & res) {
if (ctx_server.params.embedding) {
res_error(res, format_error_response("This server does not support chat completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template);
@ -3155,6 +3183,11 @@ int main(int argc, char ** argv) {
};
const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
if (ctx_server.params.embedding) {
res_error(res, format_error_response("This server does not support infill. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = json::parse(req.body);
@ -3241,13 +3274,8 @@ int main(int argc, char ** argv) {
return res.set_content(data.dump(), "application/json; charset=utf-8");
};
const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
const auto handle_embeddings = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
if (!params.embedding) {
res.status = 501;
res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
return;
}
const json body = json::parse(req.body);
bool is_openai = false;

22
examples/server/utils.hpp
View File

@ -122,8 +122,26 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
for (size_t i = 0; i < messages.size(); ++i) {
const auto & curr_msg = messages[i];
std::string role = json_value(curr_msg, "role", std::string(""));
std::string content = json_value(curr_msg, "content", std::string(""));
std::string role = json_value(curr_msg, "role", std::string(""));
std::string content;
if (curr_msg.contains("content")) {
if (curr_msg["content"].is_string()) {
content = curr_msg["content"].get<std::string>();
} else if (curr_msg["content"].is_array()) {
for (const auto & part : curr_msg["content"]) {
if (part.contains("text")) {
content += "\n" + part["text"].get<std::string>();
}
}
} else {
throw std::runtime_error("Invalid 'content' type (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
}
} else {
throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
}
chat.push_back({role, content});
}

8
examples/tokenize/tokenize.cpp
View File

@ -29,6 +29,7 @@ static void print_usage_information(const char * argv0, FILE * stream) {
fprintf(stream, " -p PROMPT, --prompt PROMPT read prompt from the argument.\n");
fprintf(stream, " --stdin read prompt from standard input.\n");
fprintf(stream, " --no-bos do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
fprintf(stream, " --no-parse-special do not parse control tokens.\n");
fprintf(stream, " --log-disable disable logs. Makes stderr quiet when loading the model.\n");
fprintf(stream, " --show-count print the total number of tokens.\n");
}
@ -195,6 +196,7 @@ int main(int raw_argc, char ** raw_argv) {
// variables where to put any arguments we see.
bool printing_ids = false;
bool no_bos = false;
bool no_parse_special = false;
bool disable_logging = false;
bool show_token_count = false;
const char * model_path = NULL;
@ -229,6 +231,9 @@ int main(int raw_argc, char ** raw_argv) {
else if (arg == "--no-bos") {
no_bos = true;
}
else if (arg == "--no-parse-special") {
no_parse_special = true;
}
else if (arg == "-p" || arg == "--prompt") {
if (prompt_set) {
fprintf(stderr, "Error: -p or --prompt specified multiple times.\n");
@ -359,9 +364,10 @@ int main(int raw_argc, char ** raw_argv) {
const bool model_wants_add_bos = llama_should_add_bos_token(model);
const bool add_bos = model_wants_add_bos && !no_bos;
const bool parse_special = !no_parse_special;
std::vector<llama_token> tokens;
tokens = ::llama_tokenize(model, prompt, add_bos, true);
tokens = ::llama_tokenize(model, prompt, add_bos, parse_special);
if (printing_ids) {
printf("[");

6
flake.lock generated
View File

@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1720031269,
"narHash": "sha256-rwz8NJZV+387rnWpTYcXaRNvzUSnnF9aHONoJIYmiUQ=",
"lastModified": 1720768451,
"narHash": "sha256-EYekUHJE2gxeo2pM/zM9Wlqw1Uw2XTJXOSAO79ksc4Y=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "9f4128e00b0ae8ec65918efeba59db998750ead6",
"rev": "7e7c39ea35c5cdd002cd4588b03a3fb9ece6fad9",
"type": "github"
},
"original": {

2
ggml/.gitignore vendored Normal file
View File

@ -0,0 +1,2 @@
src/ggml-vulkan-shaders.hpp
src/ggml-vulkan-shaders.cpp

220
ggml/ggml_vk_generate_shaders.py
View File

@ -1,220 +0,0 @@
#!/usr/bin/env python
import logging
import argparse
import asyncio
import os
from tempfile import gettempdir
logger = logging.getLogger("ggml-vk-generate-shaders")
GLSLC = "glslc"
type_names = [
"f32",
"f16",
"q4_0",
"q4_1",
"q5_0",
"q5_1",
"q8_0",
"q2_k",
"q3_k",
"q4_k",
"q5_k",
"q6_k",
]
ASYNCIO_CONCURRENCY = 64
input_dir = "vulkan-shaders"
output_dir = gettempdir()
lock = asyncio.Lock()
shader_fnames = []
async def string_to_spv(name, in_fname, defines, fp16=True):
name = f"{name}{'_fp32' if not fp16 else ''}"
out_fname = os.path.join(output_dir, f"{name}.spv")
in_path = os.path.join(input_dir, in_fname)
cmd = [GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname]
cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE)
stdout, stderr = await proc.communicate()
stdout = stdout.decode()
error = stderr.decode()
if proc.returncode:
cmd = " ".join(cmd)
logger.error(f"cannot compile {name}\n\n{cmd}\n\n{error}")
return
async with lock:
shader_fnames.append((name, out_fname))
def matmul_shaders(tasks, fp16, matmul_id):
if fp16:
load_vec = "8"
aligned_b_type_f32 = "mat2x4"
aligned_b_type_f16 = "f16mat2x4"
else:
load_vec = "4"
aligned_b_type_f32 = "vec4"
aligned_b_type_f16 = "f16vec4"
base_dict = {"FLOAT_TYPE": "float" if not fp16 else "float16_t"}
shader_name = "matmul"
if matmul_id:
base_dict["MUL_MAT_ID"] = "1"
shader_name = "matmul_id"
if fp16:
base_dict["FLOAT16"] = "1"
# Shaders with f16 B_TYPE
tasks.append(string_to_spv(f"{shader_name}_f32_f16", "mul_mm.comp", base_dict | {"DATA_A_F32": "1", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
tasks.append(string_to_spv(f"{shader_name}_f32_f16_aligned", "mul_mm.comp", base_dict | {"DATA_A_F32": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f16, "D_TYPE": "float"}, fp16))
tasks.append(string_to_spv(f"{shader_name}_f16", "mul_mm.comp", base_dict | {"DATA_A_F16": "1", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
tasks.append(string_to_spv(f"{shader_name}_f16_aligned", "mul_mm.comp", base_dict | {"DATA_A_F16": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f16, "D_TYPE": "float"}, fp16))
for tname in type_names:
data_a_key = f"DATA_A_{tname.upper()}"
load_vec_a = load_vec if tname in ("f32", "f16") else "2"
tasks.append(string_to_spv(f"{shader_name}_{tname}_f32", "mul_mm.comp", base_dict | {data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
tasks.append(string_to_spv(f"{shader_name}_{tname}_f32_aligned", "mul_mm.comp", base_dict | {data_a_key: "2", "LOAD_VEC_A": load_vec_a, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f32, "D_TYPE": "float"}, fp16))
async def main():
logger.info("ggml_vulkan: Generating and compiling shaders to SPIR-V")
tasks = []
base_dict = {"FLOAT_TYPE": "float"}
for fp16 in (False, True):
# MUL_MAT
matmul_shaders(tasks, fp16, False)
# MUL_MAT_ID
matmul_shaders(tasks, fp16, True)
for tname in type_names:
# mul mat vec
data_a_key = f"DATA_A_{tname.upper()}"
shader = f"mul_mat_vec_{tname}.comp" if tname.endswith("_k") else "mul_mat_vec.comp"
tasks.append(string_to_spv(f"mul_mat_vec_{tname}_f32_f32", shader, base_dict | {data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv(f"mul_mat_vec_{tname}_f16_f32", shader, base_dict | {data_a_key: "1", "B_TYPE": "float16_t", "D_TYPE": "float"}))
tasks.append(string_to_spv(f"mul_mat_vec_id_{tname}_f32", shader, base_dict | {"MUL_MAT_ID": "1", data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"}))
# Dequant shaders
if tname != "f16":
tasks.append(string_to_spv(f"dequant_{tname}", f"dequant_{tname}.comp", base_dict | {data_a_key: "1", "D_TYPE": "float16_t"}))
# get_rows
if not tname.endswith("_k"):
shader = "get_rows.comp" if tname in ("f32", "f16") else "get_rows_quant.comp"
if tname == "f16":
tasks.append(string_to_spv(f"get_rows_{tname}", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"}))
else:
tasks.append(string_to_spv(f"get_rows_{tname}", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float16_t"}))
tasks.append(string_to_spv(f"get_rows_{tname}_f32", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float"}))
tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
# Norms
tasks.append(string_to_spv("norm_f32", "norm.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("rms_norm_f32", "rms_norm.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("cpy_f32_f32", "copy.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("cpy_f32_f16", "copy.comp", {"A_TYPE": "float", "D_TYPE": "float16_t"}))
tasks.append(string_to_spv("cpy_f16_f16", "copy.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"}))
tasks.append(string_to_spv("add_f32", "add.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
tasks.append(string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}))
tasks.append(string_to_spv("mul_f32", "mul.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
tasks.append(string_to_spv("div_f32", "div.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
tasks.append(string_to_spv("scale_f32", "scale.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
tasks.append(string_to_spv("sqr_f32", "square.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
tasks.append(string_to_spv("clamp_f32", "clamp.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
tasks.append(string_to_spv("gelu_f32", "gelu.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("silu_f32", "silu.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("relu_f32", "relu.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("soft_max_f32", "soft_max.comp", base_dict | {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("soft_max_f32_f16", "soft_max.comp", base_dict | {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"}))
tasks.append(string_to_spv("rope_norm_f32", "rope_norm.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("rope_norm_f16", "rope_norm.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
tasks.append(string_to_spv("rope_neox_f32", "rope_neox.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
tasks.append(string_to_spv("rope_neox_f16", "rope_neox.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
tasks.append(string_to_spv("argsort_f32", "argsort.comp", {"A_TYPE": "float"}))
tasks.append(string_to_spv("sum_rows_f32", "sum_rows.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"}))
# Helper to decorate tasks with semaphore acquisition.
async def withSemaphore(sem, task):
async with sem:
return await task
# Run tasks concurrently guarded by a concurrency limit.
sem = asyncio.Semaphore(ASYNCIO_CONCURRENCY)
await asyncio.gather(*(withSemaphore(sem, task) for task in tasks))
with open("ggml-vulkan-shaders.hpp", "w") as f:
f.write("#include <cstdint>\n\n")
for name, path in sorted(shader_fnames):
with open(path, "rb") as spv:
counter = 0
newline_counter = 0
f.write(f"unsigned char {name}_data[] = {{\n")
for val in spv.read():
f.write(f"0x{val:02x},")
newline_counter += 1
counter += 1
if newline_counter >= 12:
newline_counter = 0
f.write("\n")
f.write("\n};\n")
f.write(f"const uint64_t {name}_len = {counter};\n\n")
os.remove(path)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="GGML Vulkan Shader Generator")
parser.add_argument("--glslc", help="Path to glslc")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
args = parser.parse_args()
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
if args.glslc:
GLSLC = args.glslc
asyncio.run(main())

50
ggml/include/ggml.h
View File

@ -714,9 +714,9 @@ extern "C" {
GGML_API GGML_CALL size_t ggml_nbytes (const struct ggml_tensor * tensor);
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
GGML_API GGML_CALL int ggml_blck_size(enum ggml_type type);
GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
GGML_API GGML_CALL int64_t ggml_blck_size(enum ggml_type type);
GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
GGML_DEPRECATED(
GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
@ -2410,31 +2410,31 @@ extern "C" {
#endif
typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
const void * GGML_RESTRICT y, size_t by, int nrc);
typedef void (*ggml_from_float_to_mat_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr,
int64_t k, int64_t bx);
typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef void (*ggml_from_float_to_mat_t)
(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
const void * GGML_RESTRICT y, size_t by, int nrc);
typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef struct {
const char * type_name;
int blck_size;
size_t type_size;
bool is_quantized;
ggml_to_float_t to_float;
ggml_from_float_t from_float;
ggml_from_float_t from_float_reference;
ggml_vec_dot_t vec_dot;
enum ggml_type vec_dot_type;
int64_t nrows; // number of rows to process simultaneously;
int64_t ncols; // number of columns to process simultaneously;
int64_t interleave_blcksize; // interleave elements in blocks of interleave_blcksize;
const char * type_name;
int64_t blck_size;
int64_t blck_size_interleave; // interleave elements in blocks
size_t type_size;
bool is_quantized;
ggml_to_float_t to_float;
ggml_from_float_t from_float;
ggml_from_float_t from_float_ref;
ggml_from_float_to_mat_t from_float_to_mat;
ggml_gemv_t gemv;
ggml_gemm_t gemm;
ggml_vec_dot_t vec_dot;
enum ggml_type vec_dot_type;
int64_t nrows; // number of rows to process simultaneously
int64_t ncols; // number of columns to process simultaneously
ggml_gemv_t gemv;
ggml_gemm_t gemm;
} ggml_type_traits_t;
GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);

37
ggml/src/CMakeLists.txt
View File

@ -527,14 +527,11 @@ if (GGML_RPC)
endif()
if (GGML_VULKAN)
find_package(Vulkan)
find_package(Vulkan COMPONENTS glslc REQUIRED)
if (Vulkan_FOUND)
message(STATUS "Vulkan found")
set(GGML_HEADERS_VULKAN ../include/ggml-vulkan.h)
set(GGML_SOURCES_VULKAN ggml-vulkan.cpp)
list(APPEND GGML_CDEF_PUBLIC GGML_USE_VULKAN)
# Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build
@ -563,7 +560,37 @@ if (GGML_VULKAN)
add_compile_definitions(GGML_VULKAN_RUN_TESTS)
endif()
set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} Vulkan::Vulkan)
add_subdirectory(vulkan-shaders)
set (_ggml_vk_genshaders_cmd vulkan-shaders-gen)
set (_ggml_vk_header ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp)
set (_ggml_vk_source ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp)
set (_ggml_vk_input_dir ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders)
set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv)
file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp")
add_custom_command(
OUTPUT ${_ggml_vk_header}
${_ggml_vk_source}
COMMAND ${_ggml_vk_genshaders_cmd}
--glslc ${Vulkan_GLSLC_EXECUTABLE}
--input-dir ${_ggml_vk_input_dir}
--output-dir ${_ggml_vk_output_dir}
--target-hpp ${_ggml_vk_header}
--target-cpp ${_ggml_vk_source}
--no-clean
DEPENDS ${_ggml_vk_shader_deps}
COMMENT "Generate vulkan shaders"
)
set(GGML_HEADERS_VULKAN ${CMAKE_CURRENT_SOURCE_DIR}/../include/ggml-vulkan.h ${_ggml_vk_header})
set(GGML_SOURCES_VULKAN ggml-vulkan.cpp ${_ggml_vk_source})
set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} Vulkan::Vulkan)
set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CMAKE_CURRENT_BINARY_DIR})
else()
message(WARNING "Vulkan not found")
endif()

68
ggml/src/ggml-aarch64.c
View File

@ -20,19 +20,19 @@
// Functions to create the interleaved data layout formats
// interleave 4 block_q4_0s in blocks of interleave_blcksize
// interleave 4 block_q4_0s in blocks of blck_size_interleave
// returns an interleaved block_q4_0x4
// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks
// first, then interleave quants from 4 block_q4_0s in blocks of interleave_blcksize
// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave
//
// - in : an array of block_q4_0 pointers
// - interleave_blcksize : the block_q4_0 quants bytes are interleaved in blocks of
// interleave_blcksize bytes
// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of
// blck_size_interleave bytes
// - xor_mask : the mask to convert the nibbles in block_q4_0 quants bytes
// from bias offset form to pure sign form (this saves subtract
// operations durin unpacking)
//
static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int interleave_blcksize, unsigned int xor_mask) {
static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) {
block_q4_0x4 out;
for (int i = 0; i < 4; i++) {
@ -40,9 +40,9 @@ static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int interleave_b
}
for (int i = 0; i < QK4_0 * 2; i++) {
int src_offset = (i / (4 * interleave_blcksize)) * interleave_blcksize;
int src_id = (i % (4 * interleave_blcksize)) / interleave_blcksize;
src_offset += (i % interleave_blcksize);
int src_offset = (i / (4 * blck_size_interleave)) * blck_size_interleave;
int src_id = (i % (4 * blck_size_interleave)) / blck_size_interleave;
src_offset += (i % blck_size_interleave);
out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
}
@ -50,11 +50,11 @@ static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int interleave_b
return out;
}
// interleave 8 block_q4_0s in blocks of interleave_blcksize
// interleave 8 block_q4_0s in blocks of blck_size_interleave
// returns an interleaved block_q4_0x8
// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks
// first, then interleave quants from 8 block_q4_0s in blocks of interleave_blcksize
static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int interleave_blcksize, unsigned int xor_mask) {
// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave
static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) {
block_q4_0x8 out;
for (int i = 0; i < 8; i++) {
@ -62,9 +62,9 @@ static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int interleave_b
}
for (int i = 0; i < QK4_0 * 4; i++) {
int src_offset = (i / (8 * interleave_blcksize)) * interleave_blcksize;
int src_id = (i % (8 * interleave_blcksize)) / interleave_blcksize;
src_offset += (i % interleave_blcksize);
int src_offset = (i / (8 * blck_size_interleave)) * blck_size_interleave;
int src_id = (i % (8 * blck_size_interleave)) / blck_size_interleave;
src_offset += (i % blck_size_interleave);
out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
}
@ -135,7 +135,7 @@ void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k)
}
#else
// scalar
const int interleave_blcksize = 4;
const int blck_size_interleave = 4;
float srcv[4][QK8_0];
float id[4];
@ -155,12 +155,12 @@ void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k)
}
for (int j = 0; j < QK8_0 * 4; j++) {
int src_offset = (j / (4 * interleave_blcksize)) * interleave_blcksize;
int src_id = (j % (4 * interleave_blcksize)) / interleave_blcksize;
src_offset += (j % interleave_blcksize);
int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
src_offset += (j % blck_size_interleave);
float x0 = srcv[src_id][src_offset] * id[src_id];
y[i].qs[j] = roundf(x0);;
y[i].qs[j] = roundf(x0);
}
}
#endif
@ -253,7 +253,7 @@ void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k)
}
#else
// scalar
const int interleave_blcksize = 8;
const int blck_size_interleave = 8;
float srcv[4][QK8_0];
float id[4];
@ -273,26 +273,30 @@ void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k)
}
for (int j = 0; j < QK8_0 * 4; j++) {
int src_offset = (j / (4 * interleave_blcksize)) * interleave_blcksize;
int src_id = (j % (4 * interleave_blcksize)) / interleave_blcksize;
src_offset += (j % interleave_blcksize);
int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
src_offset += (j % blck_size_interleave);
float x0 = srcv[src_id][src_offset] * id[src_id];
y[i].qs[j] = roundf(x0);;
y[i].qs[j] = roundf(x0);
}
}
#endif
}
void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t interleave_blcksize) {
void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) {
assert(nrow == 4);
UNUSED(nrow);
if (interleave_blcksize == 4) quantize_q8_0_4x4(x, vy, n_per_row);
else if (interleave_blcksize == 8) quantize_q8_0_4x8(x, vy, n_per_row);
else assert(false);
if (blck_size_interleave == 4) {
quantize_q8_0_4x4(x, vy, n_per_row);
} else if (blck_size_interleave == 8) {
quantize_q8_0_4x8(x, vy, n_per_row);
} else {
assert(false);
}
}
static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int interleave_blcksize) {
static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int blck_size_interleave) {
assert(n_per_row % QK4_0 == 0);
const int nb = n_per_row / QK4_0;
@ -311,15 +315,15 @@ static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict ds
for (int64_t x = 0; x < nb; x++) {
for (int i = 0; i < nrows_interleaved; i++ ) {
quantize_row_q4_0_reference(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
quantize_row_q4_0_ref(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
}
if (nrows_interleaved == 8) {
*(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, interleave_blcksize, 0x88);
*(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave, 0x88);
out_ptr = (block_q4_0x8 *) out_ptr + 1;
}
else if (nrows_interleaved == 4) {
*(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, interleave_blcksize, 0x88);
*(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave, 0x88);
out_ptr = (block_q4_0x4 *) out_ptr + 1;
}
}

14
ggml/src/ggml-aarch64.h
View File

@ -16,7 +16,7 @@ extern "C" {
void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t interleave_blcksize);
void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave);
// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
@ -24,14 +24,14 @@ size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT d
size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
// GEMV
void ggml_gemv_q4_0_4x4_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q4_0_4x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q4_0_8x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
// GEMM
void ggml_gemm_q4_0_4x4_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_4x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_8x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
#ifdef __cplusplus
}

2
ggml/src/ggml-backend.c
View File

@ -394,7 +394,7 @@ void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event)
// backend registry
#define GGML_REG_MAX_BACKENDS 16
#define GGML_REG_MAX_BACKENDS 64
struct ggml_backend_reg {
char name[128];

13
ggml/src/ggml-blas.cpp
View File

@ -8,11 +8,12 @@
# include <Accelerate/Accelerate.h>
#elif defined(GGML_BLAS_USE_MKL)
# include <mkl.h>
#elif defined(GGML_BLAS_USE_BLIS)
# include <blis.h>
#elif defined(GGML_BLAS_USE_NVPL)
# include <nvpl_blas.h>
#else
# include <cblas.h>
# ifdef BLIS_ENABLE_CBLAS
# include <blis.h>
# endif
#endif
struct ggml_backend_blas_context {
@ -140,10 +141,14 @@ static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct gg
openblas_set_num_threads(ctx->n_threads);
#endif
#if defined(BLIS_ENABLE_CBLAS)
#if defined(GGML_BLAS_USE_BLIS)
bli_thread_set_num_threads(ctx->n_threads);
#endif
#if defined(GGML_BLAS_USE_NVPL)
nvpl_blas_set_num_threads(ctx->n_threads);
#endif
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
const int64_t i03 = i13/r3;

2
ggml/src/ggml-cuda/common.cuh
View File

@ -104,7 +104,7 @@
#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
#define cudaStream_t hipStream_t
#define cudaSuccess hipSuccess
#define __trap abort
#define __trap() do { abort(); __builtin_unreachable(); } while(0)
#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED

4
ggml/src/ggml-cuda/mma.cuh
View File

@ -70,6 +70,10 @@ struct mma_int_A_I16K8 {
}
#endif // defined(INT8_MMA_AVAILABLE)
}
__device__ __forceinline__ void load_low(const int * __restrict__ xs0, const int & stride) {
((mma_int_A_I16K4 *) x)[0].load(xs0, stride);
}
};
struct mma_int_B_J8K4 {

1365
ggml/src/ggml-cuda/mmq.cuh
View File

File diff suppressed because it is too large Load Diff

107
ggml/src/ggml-cuda/quantize.cu
View File

@ -37,47 +37,92 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
reinterpret_cast<half&>(y[ib].ds.y) = sum;
}
template <bool need_sum>
template <mmq_q8_1_ds_layout ds_layout>
static __global__ void quantize_mmq_q8_1(
const float * __restrict__ x, void * __restrict__ vy, const int64_t kx0, const int64_t kx1, const int64_t kx0_padded) {
const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
constexpr int vals_per_scale = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 64 : 32;
constexpr int vals_per_sum = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 16 : 32;
const int64_t ix0 = ((int64_t)blockDim.x*blockIdx.x + threadIdx.x)*4;
if (ix0 >= kx0_padded) {
return;
}
const float4 * x4 = (const float4 *) x;
const int64_t ix1 = kx1*blockIdx.z + blockIdx.y;
block_q8_1_mmq * y = (block_q8_1_mmq *) vy;
const int64_t ib0 = blockIdx.z*(gridDim.y*gridDim.x*blockDim.x/(4*QK8_1)); // first block of channel
const int64_t ib = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y; // block index in channel
const int64_t iqs = ix0 % (4*QK8_1); // quant index in block
const int64_t ib0 = blockIdx.z*((int64_t)gridDim.y*gridDim.x*blockDim.x/QK8_1); // first block of channel
const int64_t ib = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y; // block index in channel
const int64_t iqs = ix0 % (4*QK8_1); // quant index in block
const float xi = ix0 < kx0 ? x[ix1*kx0 + ix0] : 0.0f;
float amax = fabsf(xi);
// Load 4 floats per thread and calculate max. abs. value between them:
const float4 xi = ix0 < kx0 ? x4[(ix1*kx0 + ix0)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f);
float amax = fabsf(xi.x);
amax = fmaxf(amax, fabsf(xi.y));
amax = fmaxf(amax, fabsf(xi.z));
amax = fmaxf(amax, fabsf(xi.w));
amax = warp_reduce_max(amax);
float sum;
if (need_sum) {
sum = warp_reduce_sum(xi);
// Exchange max. abs. value between vals_per_scale/4 threads.
#pragma unroll
for (int mask = vals_per_scale/8; mask > 0; mask >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE));
}
const float d = amax / 127;
const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
float sum;
if (ds_layout != MMQ_Q8_1_DS_LAYOUT_D4) {
sum = xi.x + xi.y + xi.z + xi.w;
y[ib].qs[iqs] = q;
// Exchange calculate sum across vals_per_sum/4 threads.
#pragma unroll
for (int mask = vals_per_sum/8; mask > 0; mask >>= 1) {
sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, WARP_SIZE);
}
}
const float d_inv = 127.0f / amax;
char4 q;
q.x = roundf(xi.x*d_inv);
q.y = roundf(xi.y*d_inv);
q.z = roundf(xi.z*d_inv);
q.w = roundf(xi.w*d_inv);
// Write back 4 int8 values as a single 32 bit value for better memroy bandwidth:
char4 * yqs4 = (char4 *) y[ib].qs;
yqs4[iqs/4] = q;
if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) {
if (iqs % 16 != 0 || iqs >= 96) {
return;
}
y[ib].d2s6[2 + iqs/16] = sum;
if (iqs % 64 != 0) {
return;
}
const float d = 1.0f / d_inv;
y[ib].d2s6[iqs/64] = d;
if (iqs % QK8_1 != 0) {
return;
}
if (need_sum) {
y[ib].ds[iqs/QK8_1] = make_half2(d, sum);
if (iqs % 32 != 0) {
return;
}
const float d = 1.0f / d_inv;
if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) {
y[ib].ds4[iqs/32] = make_half2(d, sum);
} else {
((float *) y[ib].ds)[iqs/QK8_1] = d;
y[ib].d4[iqs/32] = d;
}
}
@ -101,12 +146,24 @@ void quantize_mmq_q8_1_cuda(
GGML_ASSERT(kx0_padded % (4*QK8_1) == 0);
const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
const int64_t block_num_x = (kx0_padded + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ);
const dim3 num_blocks(block_num_x, kx1, channels);
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
if (mmq_need_sum(type_x)) {
quantize_mmq_q8_1<true><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
} else {
quantize_mmq_q8_1<false><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
switch (mmq_get_q8_1_ds_layout(type_x)) {
case MMQ_Q8_1_DS_LAYOUT_D4:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D4>
<<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
break;
case MMQ_Q8_1_DS_LAYOUT_DS4:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_DS4>
<<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
break;
case MMQ_Q8_1_DS_LAYOUT_D2S6:
quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D2S6>
<<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
break;
default:
GGML_ASSERT(false);
break;
}
}

6
ggml/src/ggml-cuda/quantize.cuh
View File

@ -5,7 +5,11 @@
#include <cstdint>
#define CUDA_QUANTIZE_BLOCK_SIZE 256
#define CUDA_QUANTIZE_BLOCK_SIZE 256
#define CUDA_QUANTIZE_BLOCK_SIZE_MMQ 128
static_assert(MATRIX_ROW_PADDING % CUDA_QUANTIZE_BLOCK_SIZE == 0, "Risk of out-of-bounds access.");
static_assert(MATRIX_ROW_PADDING % (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ) == 0, "Risk of out-of-bounds access.");
typedef void (*quantize_cuda_t)(
const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,

72
ggml/src/ggml-cuda/vecdotq.cuh
View File

@ -189,7 +189,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp
}
#define VDR_Q2_K_Q8_1_MMVQ 1
#define VDR_Q2_K_Q8_1_MMQ 2
#define VDR_Q2_K_Q8_1_MMQ 4
// contiguous v/x values
static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
@ -219,32 +219,56 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
return dm2f.x*sumf_d - dm2f.y*sumf_m;
}
// contiguous u/y values
// contiguous v/x + u/y values
template <int ns8>
static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8) {
const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8, const half2 * s8) {
float sumf_d = 0.0f;
float sumf_m = 0.0f;
float sumf = 0.0f;
float sumf_d8 = 0.0f;
#pragma unroll
for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
const float2 dm2f = __half22float2(dm2[i0/(QI8_1/2)]);
int sumi_d = 0;
int sumi_m = 0;
for (int i0 = 0; i0 < QR2_K*VDR_Q2_K_Q8_1_MMQ; i0 += QI8_1) {
const float2 dm2f0 = __half22float2(dm2[i0/(QI8_1/2) + 0]);
int sumi_d0 = 0;
const float2 dm2f1 = __half22float2(dm2[i0/(QI8_1/2) + 1]);
int sumi_d1 = 0;
const int vi0 = v[i0/(QI8_1/2)];
#pragma unroll
for (int i = i0; i < i0 + QI8_1/2; ++i) {
const int vi = (vi0 >> (2*(i % (QI8_1/2)))) & 0x03030303;
sumi_d = ggml_cuda_dp4a(vi, u[i], sumi_d); // SIMD dot product
sumi_m = ggml_cuda_dp4a(0x01010101, u[i], sumi_m);
sumi_d0 = ggml_cuda_dp4a(v[i], u[i], sumi_d0);
}
sumf_d8 += dm2f0.x * sumi_d0;
sumf_d += dm2f.x * sumi_d;
sumf_m += dm2f.y * sumi_m;
#pragma unroll
for (int i = i0 + QI8_1/2; i < i0 + QI8_1; ++i) {
sumi_d1 = ggml_cuda_dp4a(v[i], u[i], sumi_d1);
}
sumf_d8 += dm2f1.x * sumi_d1;
if (i0/QI8_1 < ns8) {
const float2 s8f = __half22float2(s8[i0/QI8_1]);
sumf -= dm2f0.y*s8f.x;
sumf -= dm2f1.y*s8f.y;
} else {
int sumi_m0 = 0;
#pragma unroll
for (int i = i0; i < i0 + QI8_1/2; ++i) {
sumi_m0 = ggml_cuda_dp4a(0x01010101, u[i], sumi_m0);
}
sumf_d8 -= dm2f0.y * sumi_m0;
int sumi_m1 = 0;
#pragma unroll
for (int i = i0 + QI8_1/2; i < i0 + QI8_1; ++i) {
sumi_m1 = ggml_cuda_dp4a(0x01010101, u[i], sumi_m1);
}
sumf_d8 -= dm2f1.y * sumi_m1;
}
}
return d8*(sumf_d - sumf_m);
return sumf + d8*sumf_d8;
}
#define VDR_Q3_K_Q8_1_MMVQ 1
@ -283,7 +307,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
return d3 * sumf;
}
// contiguous u/y values
// contiguous v/x + u/y values
static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
const float & d3, const float & d8) {
@ -296,8 +320,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
#pragma unroll
for (int i = i0; i < i0 + QI8_1/2; ++i) {
const int vi = __vsubss4((v[i/2] >> (4*(i%2))) & 0x0F0F0F0F, 0x04040404);
sumi_sc = ggml_cuda_dp4a(vi, u[i], sumi_sc); // SIMD dot product
sumi_sc = ggml_cuda_dp4a(v[i], u[i], sumi_sc); // SIMD dot product
}
sumi += sumi_sc * scales[i0 / (QI8_1/2)];
@ -334,7 +357,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
return dm4f.x*sumf_d - dm4f.y*sumf_m;
}
// contiguous u/y values
// contiguous v/x + u/y values
static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
@ -397,7 +420,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
return dm5f.x*sumf_d - dm5f.y*sumf_m;
}
// contiguous u/y values
// contiguous v/x + u/y values
static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
@ -451,13 +474,16 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
return d*sumf;
}
// contiguous u/y values
// contiguous v/x + u/y values
static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
const float & d6, const float * __restrict__ d8) {
float sumf_d = 0.0f;
const int sc_packed = get_int_b4(sc, 0);
const int8_t * sc_reg = (const int8_t *) &sc_packed;
#pragma unroll
for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
@ -471,7 +497,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
sumi_d.y = ggml_cuda_dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
}
sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
sumf_d += d8[i0/4] * (sc_reg[i0/2+0]*sumi_d.x + sc_reg[i0/2+1]*sumi_d.y);
}
return d6 * sumf_d;

28
ggml/src/ggml-metal.m
View File

@ -193,16 +193,16 @@ enum ggml_metal_kernel_type {
//GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
//GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261
GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,
GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,
GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
GGML_METAL_KERNEL_TYPE_CONCAT,
GGML_METAL_KERNEL_TYPE_SQR,
GGML_METAL_KERNEL_TYPE_SUM_ROWS,
@ -651,14 +651,14 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
@ -810,8 +810,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
switch (op->src[0]->type) {
case GGML_TYPE_F32:
switch (op->type) {
case GGML_TYPE_F16:
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
@ -824,8 +824,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
}
case GGML_TYPE_F16:
switch (op->type) {
case GGML_TYPE_F16:
case GGML_TYPE_F32:
case GGML_TYPE_F16:
return true;
default:
return false;
@ -837,7 +837,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_GET_ROWS:
{
return op->src[0]->type != GGML_TYPE_BF16 && op->ne[3] == 1;
return op->ne[3] == 1;
}
default:
return false;
@ -1580,8 +1580,8 @@ static enum ggml_status ggml_metal_graph_compute(
// some Metal matrix data types require aligned pointers
// ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
switch (src0->type) {
case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break;
case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break;
default: break;
}
@ -2775,8 +2775,8 @@ static enum ggml_status ggml_metal_graph_compute(
GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0);
switch (dstt) {
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break;
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break;
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break;
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break;
case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break;
case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break;
@ -2789,8 +2789,8 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_F16:
{
switch (dstt) {
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
default: GGML_ASSERT(false && "not implemented");
};
} break;

723
ggml/src/ggml-metal.metal
View File

@ -1219,9 +1219,10 @@ kernel void kernel_mul_mv_q8_0_f32(
kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
}
#define N_F32_F32 4
#define N_MV_T_T 4
void kernel_mul_mv_f32_f32_impl(
template<typename T0, typename T04, typename T1, typename T14>
void kernel_mul_mv_impl(
device const char * src0,
device const char * src1,
device float * dst,
@ -1239,13 +1240,12 @@ void kernel_mul_mv_f32_f32_impl(
uint64_t nb12,
int64_t ne0,
int64_t ne1,
uint r2,
uint r3,
uint3 tgpig,
uint tiisg) {
uint r2,
uint r3,
uint3 tgpig,
uint tiisg) {
const int64_t r0 = tgpig.x;
const int64_t rb = tgpig.y*N_F32_F32;
const int64_t rb = tgpig.y*N_MV_T_T;
const int64_t im = tgpig.z;
const uint i12 = im%ne12;
@ -1253,20 +1253,20 @@ void kernel_mul_mv_f32_f32_impl(
const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
device const float * x = (device const float *) (src0 + offset0);
device const T0 * x = (device const T0 *) (src0 + offset0);
if (ne00 < 128) {
for (int row = 0; row < N_F32_F32; ++row) {
for (int row = 0; row < N_MV_T_T; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12);
float sumf = 0;
for (int i = tiisg; i < ne00; i += 32) {
sumf += (float) x[i] * (float) y[i];
sumf += (T0) x[i] * (T1) y[i];
}
float all_sum = simd_sum(sumf);
@ -1275,32 +1275,32 @@ void kernel_mul_mv_f32_f32_impl(
}
}
} else {
device const float4 * x4 = (device const float4 *)x;
for (int row = 0; row < N_F32_F32; ++row) {
device const T04 * x4 = (device const T04 *) x;
for (int row = 0; row < N_MV_T_T; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
device const float4 * y4 = (device const float4 *) y;
device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12);
device const T14 * y4 = (device const T14 *) y;
float sumf = 0;
for (int i = tiisg; i < ne00/4; i += 32) {
for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]);
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
}
}
[[host_name("kernel_mul_mv_f32_f32")]]
kernel void kernel_mul_mv_f32_f32(
template<typename T0, typename T04, typename T1, typename T14>
kernel void kernel_mul_mv(
device const char * src0,
device const char * src1,
device float * dst,
@ -1322,90 +1322,38 @@ kernel void kernel_mul_mv_f32_f32(
constant uint & r3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]]) {
kernel_mul_mv_f32_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
kernel_mul_mv_impl<T0, T04, T1, T14>(
src0,
src1,
dst,
ne00,
ne01,
ne02,
nb00,
nb01,
nb02,
ne10,
ne11,
ne12,
nb10,
nb11,
nb12,
ne0,
ne1,
r2,
r3,
tgpig,
tiisg);
}
#define N_F16_F16 4
typedef decltype(kernel_mul_mv<half, half4, half, half4>) mul_mv_t;
kernel void kernel_mul_mv_f16_f16(
device const char * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]]) {
template [[host_name("kernel_mul_mv_f32_f32")]] kernel mul_mv_t kernel_mul_mv<float, float4, float, float4>;
template [[host_name("kernel_mul_mv_f16_f32")]] kernel mul_mv_t kernel_mul_mv<half, half4, float, float4>;
template [[host_name("kernel_mul_mv_f16_f16")]] kernel mul_mv_t kernel_mul_mv<half, half4, half, half4>;
const int64_t r0 = tgpig.x;
const int64_t rb = tgpig.y*N_F16_F16;
const int64_t im = tgpig.z;
const uint i12 = im%ne12;
const uint i13 = im/ne12;
const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
device const half * x = (device const half *) (src0 + offset0);
if (ne00 < 128) {
for (int row = 0; row < N_F16_F16; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12);
float sumf = 0;
for (int i = tiisg; i < ne00; i += 32) {
sumf += (half) x[i] * (half) y[i];
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
} else {
device const half4 * x4 = (device const half4 *)x;
for (int row = 0; row < N_F16_F16; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12);
device const half4 * y4 = (device const half4 *) y;
float sumf = 0;
for (int i = tiisg; i < ne00/4; i += 32) {
for (int k = 0; k < 4; ++k) sumf += (half) x4[i][k] * y4[i][k];
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (half) x[i] * y[i];
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
}
}
void kernel_mul_mv_f16_f32_1row_impl(
template<typename T, typename T4>
kernel void kernel_mul_mv_1row(
device const char * src0,
device const char * src1,
device float * dst,
@ -1437,7 +1385,7 @@ void kernel_mul_mv_f16_f32_1row_impl(
const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
device const half * x = (device const half *) (src0 + offset0);
device const T * x = (device const T *) (src0 + offset0);
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
float sumf = 0;
@ -1450,153 +1398,29 @@ void kernel_mul_mv_f16_f32_1row_impl(
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
} else {
device const half4 * x4 = (device const half4 *) x;
device const T4 * x4 = (device const T4 *) x;
device const float4 * y4 = (device const float4 *) y;
for (int i = tiisg; i < ne00/4; i += 32) {
for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k];
for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]);
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
}
[[host_name("kernel_mul_mv_f16_f32_1row")]]
kernel void kernel_mul_mv_f16_f32_1row(
device const char * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]]) {
kernel_mul_mv_f16_f32_1row_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
}
typedef decltype(kernel_mul_mv_1row<half, half4>) mul_mv_1row_t;
#define N_F16_F32 4
void kernel_mul_mv_f16_f32_impl(
device const char * src0,
device const char * src1,
device float * dst,
int64_t ne00,
int64_t ne01,
int64_t ne02,
uint64_t nb00,
uint64_t nb01,
uint64_t nb02,
int64_t ne10,
int64_t ne11,
int64_t ne12,
uint64_t nb10,
uint64_t nb11,
uint64_t nb12,
int64_t ne0,
int64_t ne1,
uint r2,
uint r3,
uint3 tgpig,
uint tiisg) {
const int64_t r0 = tgpig.x;
const int64_t rb = tgpig.y*N_F16_F32;
const int64_t im = tgpig.z;
const uint i12 = im%ne12;
const uint i13 = im/ne12;
const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
device const half * x = (device const half *) (src0 + offset0);
if (ne00 < 128) {
for (int row = 0; row < N_F16_F32; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
float sumf = 0;
for (int i = tiisg; i < ne00; i += 32) {
sumf += (float) x[i] * (float) y[i];
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
} else {
device const half4 * x4 = (device const half4 *)x;
for (int row = 0; row < N_F16_F32; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
device const float4 * y4 = (device const float4 *) y;
float sumf = 0;
for (int i = tiisg; i < ne00/4; i += 32) {
for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
}
}
[[host_name("kernel_mul_mv_f16_f32")]]
kernel void kernel_mul_mv_f16_f32(
device const char * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]]) {
kernel_mul_mv_f16_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
}
template [[host_name("kernel_mul_mv_f16_f32_1row")]] kernel mul_mv_1row_t kernel_mul_mv_1row<half, half4>;
// Assumes row size (ne00) is a multiple of 4
kernel void kernel_mul_mv_f16_f32_l4(
template<typename T, typename T4>
kernel void kernel_mul_mv_l4(
device const char * src0,
device const char * src1,
device float * dst,
@ -1628,14 +1452,14 @@ kernel void kernel_mul_mv_f16_f32_l4(
const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
device const half4 * x4 = (device const half4 *) (src0 + offset0);
device const T4 * x4 = (device const T4 *) (src0 + offset0);
for (int r1 = 0; r1 < nrows; ++r1) {
device const float4 * y4 = (device const float4 *) (src1 + r1*nb11 + im*nb12);
float sumf = 0;
for (int i = tiisg; i < ne00/4; i += 32) {
for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
}
float all_sum = simd_sum(sumf);
@ -1645,6 +1469,10 @@ kernel void kernel_mul_mv_f16_f32_l4(
}
}
typedef decltype(kernel_mul_mv_l4<half, half4>) mul_mv_l4_t;
template [[host_name("kernel_mul_mv_f16_f32_l4")]] kernel mul_mv_l4_t kernel_mul_mv_l4<half, half4>;
static float rope_yarn_ramp(const float low, const float high, const int i0) {
const float y = (i0 / 2 - low) / max(0.001f, high - low);
return 1.0f - min(1.0f, max(0.0f, y));
@ -2765,9 +2593,10 @@ kernel void kernel_flash_attn_ext_vec_f16(
template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>;
//template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
kernel void kernel_cpy_f16_f16(
device const half * src0,
device half * dst,
template<typename T0, typename T1>
kernel void kernel_cpy(
device const void * src0,
device void * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
@ -2798,138 +2627,20 @@ kernel void kernel_cpy_f16_f16(
const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
device T1 * dst_data = (device T1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
device const half * src = (device half *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = src[0];
device const T0 * src = (device T0 *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = (T1) src[0];
}
}
kernel void kernel_cpy_f16_f32(
device const half * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig[2];
const int64_t i02 = tgpig[1];
const int64_t i01 = tgpig[0];
typedef decltype(kernel_cpy<float, float>) kernel_cpy_t;
const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
const int64_t i3 = n / (ne2*ne1*ne0);
const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
device const half * src = (device half *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = src[0];
}
}
kernel void kernel_cpy_f32_f16(
device const float * src0,
device half * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig[2];
const int64_t i02 = tgpig[1];
const int64_t i01 = tgpig[0];
const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
const int64_t i3 = n / (ne2*ne1*ne0);
const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = src[0];
}
}
kernel void kernel_cpy_f32_f32(
device const float * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig[2];
const int64_t i02 = tgpig[1];
const int64_t i01 = tgpig[0];
const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
const int64_t i3 = n / (ne2*ne1*ne0);
const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = src[0];
}
}
template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy<float, float>;
template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy<float, half>;
template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy<half, half>;
template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy<half, float>;
kernel void kernel_cpy_f32_q8_0(
device const float * src0,
@ -5730,9 +5441,9 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4
}
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
kernel void kernel_get_rows(
kernel void kernel_get_rows_q(
device const void * src0,
device const char * src1,
device const void * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
@ -5745,27 +5456,24 @@ kernel void kernel_get_rows(
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint3 tptg [[threads_per_threadgroup]]) {
//const int64_t i = tgpig;
//const int64_t r = ((device int32_t *) src1)[i];
const int64_t i10 = tgpig.x;
const int64_t i11 = tgpig.y;
const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
const int64_t i02 = i11;
for (int64_t ind = tiitg; ind < ne00/16; ind += tptg.x) {
float4x4 temp;
dequantize_func(
((device const block_q *) ((device char *) src0 + r*nb01 + i02*nb02)) + ind/nl, ind%nl, temp);
dequantize_func(((device const block_q *) ((const device char *) src0 + r*nb01 + i02*nb02)) + ind/nl, ind%nl, temp);
*(((device float4x4 *) ((device char *) dst + i11*nb2 + i10*nb1)) + ind) = temp;
}
}
kernel void kernel_get_rows_f32(
template<typename T>
kernel void kernel_get_rows_f(
device const void * src0,
device const char * src1,
device const void * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
@ -5781,47 +5489,19 @@ kernel void kernel_get_rows_f32(
const int64_t i10 = tgpig.x;
const int64_t i11 = tgpig.y;
const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
const int64_t i02 = i11;
for (int ind = tiitg; ind < ne00; ind += tptg.x) {
((device float *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
((device float *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
}
}
kernel void kernel_get_rows_f16(
device const void * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb1,
constant uint64_t & nb2,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint3 tptg [[threads_per_threadgroup]]) {
const int64_t i10 = tgpig.x;
const int64_t i11 = tgpig.y;
const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
const int64_t i02 = i11;
for (int ind = tiitg; ind < ne00; ind += tptg.x) {
((device float *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
((device half *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
(( device float *) (( device char *) dst + i11*nb2 + i10*nb1))[ind] =
((const device T *) ((const device char *) src0 + i02*nb02 + r*nb01))[ind];
}
}
kernel void kernel_get_rows_i32(
device const void * src0,
device const char * src1,
device const void * src1,
device int32_t * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
@ -5837,13 +5517,13 @@ kernel void kernel_get_rows_i32(
const int64_t i10 = tgpig.x;
const int64_t i11 = tgpig.y;
const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
const int64_t i02 = i11;
for (int ind = tiitg; ind < ne00; ind += tptg.x) {
((device int32_t *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
((device int32_t *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
(( device int32_t *) (( device char *) dst + i11*nb2 + i10*nb1))[ind] =
((const device int32_t *) ((const device char *) src0 + i02*nb02 + r*nb01))[ind];
}
}
@ -5860,28 +5540,28 @@ kernel void kernel_get_rows_i32(
#define SG_MAT_ROW 8
// each block_q contains 16*nl weights
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
void kernel_mul_mm_impl(device const uchar * src0,
device const uchar * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne02,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
threadgroup uchar * shared_memory [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
template<typename T, typename T4x4, typename simdgroup_T8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
kernel void kernel_mul_mm(device const uchar * src0,
device const uchar * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne02,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
threadgroup uchar * shared_memory [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
threadgroup half * sa = (threadgroup half *)(shared_memory);
threadgroup T * sa = (threadgroup T *)(shared_memory);
threadgroup float * sb = (threadgroup float *)(shared_memory + 4096);
const uint r0 = tgpig.y;
@ -5896,7 +5576,7 @@ void kernel_mul_mm_impl(device const uchar * src0,
short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
simdgroup_half8x8 ma[4];
simdgroup_T8x8 ma[4];
simdgroup_float8x8 mb[2];
simdgroup_float8x8 c_res[8];
for (int i = 0; i < 8; i++){
@ -5919,7 +5599,7 @@ void kernel_mul_mm_impl(device const uchar * src0,
for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
// load data and store to threadgroup memory
half4x4 temp_a;
T4x4 temp_a;
dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup);
@ -5939,7 +5619,7 @@ void kernel_mul_mm_impl(device const uchar * src0,
threadgroup_barrier(mem_flags::mem_threadgroup);
// load matrices from threadgroup memory and conduct outer products
threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
threadgroup T * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
#pragma unroll(4)
@ -6115,48 +5795,6 @@ void kernel_mul_mm_id_impl(
}
}
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
kernel void kernel_mul_mm(device const uchar * src0,
device const uchar * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne02,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
threadgroup uchar * shared_memory [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
kernel_mul_mm_impl<block_q, nl, dequantize_func>(
src0,
src1,
dst,
ne00,
ne02,
nb01,
nb02,
ne12,
nb10,
nb11,
nb12,
ne0,
ne1,
r2,
r3,
shared_memory,
tgpig,
tiitg,
sgitg);
}
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
kernel void kernel_mul_mm_id(
device const uchar * src0s,
@ -6237,69 +5875,60 @@ kernel void kernel_mul_mm_id(
// get rows
//
typedef void (get_rows_t)(
device const void * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb1,
constant uint64_t & nb2,
uint3, uint, uint3);
typedef decltype(kernel_get_rows_f<float>) get_rows_f_t;
//template [[host_name("kernel_get_rows_f32")]] kernel get_rows_t kernel_get_rows<float4x4, 1, dequantize_f32>;
//template [[host_name("kernel_get_rows_f16")]] kernel get_rows_t kernel_get_rows<half4x4, 1, dequantize_f16>;
template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows<block_q4_0, 2, dequantize_q4_0>;
template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows<block_q4_1, 2, dequantize_q4_1>;
template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_t kernel_get_rows<block_q5_0, 2, dequantize_q5_0>;
template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_t kernel_get_rows<block_q5_1, 2, dequantize_q5_1>;
template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_t kernel_get_rows<block_q8_0, 2, dequantize_q8_0>;
template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_t kernel_get_rows<block_q2_K, QK_NL, dequantize_q2_K>;
template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows<block_q3_K, QK_NL, dequantize_q3_K>;
template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows<block_q4_K, QK_NL, dequantize_q4_K>;
template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows<block_q5_K, QK_NL, dequantize_q5_K>;
template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows<block_q6_K, QK_NL, dequantize_q6_K>;
template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows<block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_rows<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_t kernel_get_rows<block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
template [[host_name("kernel_get_rows_f32")]] kernel get_rows_f_t kernel_get_rows_f<float>;
template [[host_name("kernel_get_rows_f16")]] kernel get_rows_f_t kernel_get_rows_f<half>;
typedef decltype(kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>) get_rows_q_t;
template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>;
template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_1, 2, dequantize_q4_1>;
template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_0, 2, dequantize_q5_0>;
template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_1, 2, dequantize_q5_1>;
template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q8_0, 2, dequantize_q8_0>;
template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q2_K, QK_NL, dequantize_q2_K>;
template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q3_K, QK_NL, dequantize_q3_K>;
template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_K, QK_NL, dequantize_q4_K>;
template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_K, QK_NL, dequantize_q5_K>;
template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q6_K, QK_NL, dequantize_q6_K>;
template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_q_t kernel_get_rows_q<block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// matrix-matrix multiplication
//
typedef decltype(kernel_mul_mm<float4x4, 1, dequantize_f32>) mat_mm_t;
typedef decltype(kernel_mul_mm<half, half4x4, simdgroup_half8x8, float4x4, 1, dequantize_f32>) mat_mm_t;
template [[host_name("kernel_mul_mm_f32_f32")]] kernel mat_mm_t kernel_mul_mm<float4x4, 1, dequantize_f32>;
template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm<half4x4, 1, dequantize_f16>;
template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_0, 2, dequantize_q4_0>;
template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_1, 2, dequantize_q4_1>;
template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_0, 2, dequantize_q5_0>;
template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_1, 2, dequantize_q5_1>;
template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q8_0, 2, dequantize_q8_0>;
template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q2_K, QK_NL, dequantize_q2_K>;
template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q3_K, QK_NL, dequantize_q3_K>;
template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_K, QK_NL, dequantize_q4_K>;
template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_K, QK_NL, dequantize_q5_K>;
template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q6_K, QK_NL, dequantize_q6_K>;
template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
template [[host_name("kernel_mul_mm_f32_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, float4x4, 1, dequantize_f32>;
template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half4x4, 1, dequantize_f16>;
template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0>;
template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1>;
template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0>;
template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1>;
template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0>;
template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K>;
template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K>;
template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K>;
template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K>;
template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K>;
template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// indirect matrix-matrix multiplication
@ -6436,7 +6065,7 @@ void mmv_fn(
impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg);
}
typedef decltype(mmv_fn<kernel_mul_mv_f32_f32_impl>) mul_mv_impl_fn_t;
typedef decltype(mmv_fn<kernel_mul_mv_impl<half, half4, half, half4>>) mul_mv_impl_fn_t;
template<mul_mv_impl_fn_t impl_fn>
kernel void kernel_mul_mv_id(
@ -6514,20 +6143,20 @@ kernel void kernel_mul_mv_id(
sgitg);
}
typedef decltype(kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>) kernel_mul_mv_id_t;
typedef decltype(kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<float, float4, float, float4>>>) kernel_mul_mv_id_t;
template [[host_name("kernel_mul_mv_id_f32_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>;
template [[host_name("kernel_mul_mv_id_f16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f16_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q8_0_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q5_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q6_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_f32_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<float, float4, float, float4>>>;
template [[host_name("kernel_mul_mv_id_f16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<half, half4, float, float4>>>;
template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q8_0_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q5_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q6_K_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq1_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_s_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq1_m_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_m_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_xxs_f32_impl>>;

94
ggml/src/ggml-quants.c
View File

@ -658,7 +658,7 @@ static inline __m128i packNibbles( __m256i bytes ) {
#endif //__loongarch_asx
// reference implementation for deterministic creation of model files
void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
void quantize_row_q4_0_ref(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
static const int qk = QK4_0;
assert(k % qk == 0);
@ -696,11 +696,11 @@ void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict
}
void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) {
quantize_row_q4_0_reference(x, y, k);
quantize_row_q4_0_ref(x, y, k);
}
void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int64_t k) {
void quantize_row_q4_1_ref(const float * restrict x, block_q4_1 * restrict y, int64_t k) {
const int qk = QK4_1;
assert(k % qk == 0);
@ -738,10 +738,10 @@ void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict
}
void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) {
quantize_row_q4_1_reference(x, y, k);
quantize_row_q4_1_ref(x, y, k);
}
void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int64_t k) {
void quantize_row_q5_0_ref(const float * restrict x, block_q5_0 * restrict y, int64_t k) {
static const int qk = QK5_0;
assert(k % qk == 0);
@ -786,10 +786,10 @@ void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict
}
void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) {
quantize_row_q5_0_reference(x, y, k);
quantize_row_q5_0_ref(x, y, k);
}
void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int64_t k) {
void quantize_row_q5_1_ref(const float * restrict x, block_q5_1 * restrict y, int64_t k) {
const int qk = QK5_1;
assert(k % qk == 0);
@ -834,11 +834,11 @@ void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict
}
void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) {
quantize_row_q5_1_reference(x, y, k);
quantize_row_q5_1_ref(x, y, k);
}
// reference implementation for deterministic creation of model files
void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int64_t k) {
void quantize_row_q8_0_ref(const float * restrict x, block_q8_0 * restrict y, int64_t k) {
assert(k % QK8_0 == 0);
const int nb = k / QK8_0;
@ -1144,12 +1144,12 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
#else
GGML_UNUSED(nb);
// scalar
quantize_row_q8_0_reference(x, y, k);
quantize_row_q8_0_ref(x, y, k);
#endif
}
// reference implementation for deterministic creation of model files
void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int64_t k) {
void quantize_row_q8_1_ref(const float * restrict x, block_q8_1 * restrict y, int64_t k) {
assert(QK8_1 == 32);
assert(k % QK8_1 == 0);
const int nb = k / QK8_1;
@ -1508,7 +1508,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
#else
GGML_UNUSED(nb);
// scalar
quantize_row_q8_1_reference(x, y, k);
quantize_row_q8_1_ref(x, y, k);
#endif
}
@ -1899,7 +1899,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t *
//========================- 2-bit (de)-quantization
void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int64_t k) {
void quantize_row_q2_K_ref(const float * restrict x, block_q2_K * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
@ -2002,7 +2002,7 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6
}
void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) {
quantize_row_q2_K_reference(x, vy, k);
quantize_row_q2_K_ref(x, vy, k);
}
static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
@ -2226,7 +2226,7 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri
size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row);
if (!quant_weights) {
quantize_row_q2_K_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q2_K_ref(src, dst, (int64_t)nrow*n_per_row);
}
else {
char * qrow = (char *)dst;
@ -2241,7 +2241,7 @@ size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nr
//========================= 3-bit (de)-quantization
void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int64_t k) {
void quantize_row_q3_K_ref(const float * restrict x, block_q3_K * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
@ -2368,7 +2368,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6
}
void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) {
quantize_row_q3_K_reference(x, vy, k);
quantize_row_q3_K_ref(x, vy, k);
}
static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) {
@ -2458,7 +2458,7 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri
size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row);
if (!quant_weights) {
quantize_row_q3_K_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q3_K_ref(src, dst, (int64_t)nrow*n_per_row);
}
else {
char * qrow = (char *)dst;
@ -2473,7 +2473,7 @@ size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nr
// ====================== 4-bit (de)-quantization
void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int64_t k) {
void quantize_row_q4_K_ref(const float * restrict x, block_q4_K * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
@ -2572,7 +2572,7 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6
void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_q4_K * restrict y = vy;
quantize_row_q4_K_reference(x, y, k);
quantize_row_q4_K_ref(x, y, k);
}
static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) {
@ -2651,7 +2651,7 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row);
if (!quant_weights) {
quantize_row_q4_K_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q4_K_ref(src, dst, (int64_t)nrow*n_per_row);
}
else {
char * qrow = (char *)dst;
@ -2666,7 +2666,7 @@ size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nr
// ====================== 5-bit (de)-quantization
void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int64_t k) {
void quantize_row_q5_K_ref(const float * restrict x, block_q5_K * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int64_t nb = k / QK_K;
@ -2783,7 +2783,7 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6
void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_q5_K * restrict y = vy;
quantize_row_q5_K_reference(x, y, k);
quantize_row_q5_K_ref(x, y, k);
}
static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) {
@ -2882,7 +2882,7 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row);
if (!quant_weights) {
quantize_row_q5_K_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q5_K_ref(src, dst, (int64_t)nrow*n_per_row);
}
else {
char * qrow = (char *)dst;
@ -2897,7 +2897,7 @@ size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nr
// ====================== 6-bit (de)-quantization
void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int64_t k) {
void quantize_row_q6_K_ref(const float * restrict x, block_q6_K * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int64_t nb = k / QK_K;
@ -3001,7 +3001,7 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6
void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_q6_K * restrict y = vy;
quantize_row_q6_K_reference(x, y, k);
quantize_row_q6_K_ref(x, y, k);
}
static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) {
@ -3091,7 +3091,7 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri
size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row);
if (!quant_weights) {
quantize_row_q6_K_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q6_K_ref(src, dst, (int64_t)nrow*n_per_row);
}
else {
char * qrow = (char *)dst;
@ -3108,7 +3108,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
static_assert(QK4_0 == 32, "QK4_0 must be 32");
if (!quant_weights) {
quantize_row_q4_0_reference(x, y, n_per_row);
quantize_row_q4_0_ref(x, y, n_per_row);
return;
}
@ -3134,7 +3134,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
size_t quantize_q4_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
if (!quant_weights) {
quantize_row_q4_0_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q4_0_ref(src, dst, (int64_t)nrow*n_per_row);
return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
}
size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
@ -3151,7 +3151,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
static_assert(QK4_1 == 32, "QK4_1 must be 32");
if (!quant_weights) {
quantize_row_q4_1_reference(x, y, n_per_row);
quantize_row_q4_1_ref(x, y, n_per_row);
return;
}
@ -3179,7 +3179,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
size_t quantize_q4_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
if (!quant_weights) {
quantize_row_q4_1_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q4_1_ref(src, dst, (int64_t)nrow*n_per_row);
return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
}
size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
@ -3196,7 +3196,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
static_assert(QK5_0 == 32, "QK5_0 must be 32");
if (!quant_weights) {
quantize_row_q5_0_reference(x, y, n_per_row);
quantize_row_q5_0_ref(x, y, n_per_row);
return;
}
@ -3233,7 +3233,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
size_t quantize_q5_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
if (!quant_weights) {
quantize_row_q5_0_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q5_0_ref(src, dst, (int64_t)nrow*n_per_row);
return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
}
size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
@ -3250,7 +3250,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
static_assert(QK5_1 == 32, "QK5_1 must be 32");
if (!quant_weights) {
quantize_row_q5_1_reference(x, y, n_per_row);
quantize_row_q5_1_ref(x, y, n_per_row);
return;
}
@ -3286,7 +3286,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
if (!quant_weights) {
quantize_row_q5_1_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q5_1_ref(src, dst, (int64_t)nrow*n_per_row);
return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
}
size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
@ -3302,7 +3302,7 @@ size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nr
size_t quantize_q8_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
(void)quant_weights; // not used
const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row);
quantize_row_q8_0_reference(src, dst, (int64_t)nrow*n_per_row);
quantize_row_q8_0_ref(src, dst, (int64_t)nrow*n_per_row);
return nrow * row_size;
}
@ -3590,7 +3590,7 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y,
//===================================== Q8_K ==============================================
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int64_t k) {
void quantize_row_q8_K_ref(const float * restrict x, block_q8_K * restrict y, int64_t k) {
assert(k % QK_K == 0);
const int64_t nb = k / QK_K;
@ -3641,7 +3641,7 @@ void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int6
}
void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) {
quantize_row_q8_K_reference(x, y, k);
quantize_row_q8_K_ref(x, y, k);
}
//===================================== Dot ptoducts =================================
@ -13530,10 +13530,10 @@ size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int64_t
void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_iq3_xxs * restrict y = vy;
quantize_row_iq3_xxs_reference(x, y, k);
quantize_row_iq3_xxs_ref(x, y, k);
}
void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) {
void quantize_row_iq3_xxs_ref(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) {
assert(k % QK_K == 0);
quantize_row_iq3_xxs_impl(256, x, y, k, NULL);
}
@ -13746,10 +13746,10 @@ size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int64_t n
void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_iq3_s * restrict y = vy;
quantize_row_iq3_s_reference(x, y, k);
quantize_row_iq3_s_ref(x, y, k);
}
void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int64_t k) {
void quantize_row_iq3_s_ref(const float * restrict x, block_iq3_s * restrict y, int64_t k) {
assert(k % QK_K == 0);
quantize_iq3_s(x, y, 1, k, NULL);
}
@ -14487,7 +14487,7 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k
}
}
void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int64_t k) {
void quantize_row_iq4_nl_ref(const float * restrict x, block_iq4_nl * restrict y, int64_t k) {
assert(k % QK4_NL == 0);
quantize_row_iq4_nl(x, y, k);
}
@ -14515,10 +14515,10 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t
void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_iq4_xs * restrict y = vy;
quantize_row_iq4_xs_reference(x, y, k);
quantize_row_iq4_xs_ref(x, y, k);
}
void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int64_t k) {
void quantize_row_iq4_xs_ref(const float * restrict x, block_iq4_xs * restrict y, int64_t k) {
assert(k % QK_K == 0);
quantize_iq4_xs(x, y, 1, k, NULL);
}
@ -14705,7 +14705,7 @@ size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int64_t n
return nrow * nblock * sizeof(block_iq2_s);
}
void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int64_t k) {
void quantize_row_iq2_s_ref(const float * restrict x, block_iq2_s * restrict y, int64_t k) {
assert(k % QK_K == 0);
quantize_iq2_s(x, y, 1, k, NULL);
}
@ -14713,7 +14713,7 @@ void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restri
void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) {
assert(k % QK_K == 0);
block_iq2_s * restrict y = vy;
quantize_row_iq2_s_reference(x, y, k);
quantize_row_iq2_s_ref(x, y, k);
}
static bool validate_float(float f, size_t i) {

34
ggml/src/ggml-quants.h
View File

@ -12,25 +12,25 @@ extern "C" {
#endif
// Quantization
void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k);
void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_s_ref (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k);
void quantize_row_iq2_s_ref (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);

49
ggml/src/ggml-sycl.cpp
View File

@ -3768,37 +3768,13 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
const ggml_tensor_extra_gpu *src0_extra =
(const ggml_tensor_extra_gpu *)src0->extra;
const ggml_tensor_extra_gpu *src1_extra =
(const ggml_tensor_extra_gpu *)src1->extra;
const ggml_tensor_extra_gpu *dst_extra =
(const ggml_tensor_extra_gpu *)dst->extra;
ggml_tensor_extra_gpu src0_row_extra;
ggml_tensor_extra_gpu src1_row_extra;
ggml_tensor_extra_gpu dst_row_extra;
ggml_tensor src0_row = *src0;
ggml_tensor src1_row = *src1;
ggml_tensor dst_row = *dst;
src1_row.backend = GGML_BACKEND_TYPE_GPU;
dst_row.backend = GGML_BACKEND_TYPE_GPU;
src0_row.extra = &src0_row_extra;
src1_row.extra = &src1_row_extra;
dst_row.extra = &dst_row_extra;
char *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU
? (char *)src0->data
: (char *)src0_extra->data_device[ctx.device];
char *src1_original = src1->backend == GGML_BACKEND_TYPE_CPU
? (char *)src1->data
: (char *)src1_extra->data_device[ctx.device];
char *dst_original = dst->backend == GGML_BACKEND_TYPE_CPU
? (char *)dst->data
: (char *)dst_extra->data_device[ctx.device];
char *src0_original = (char *)src0->data;
char *src1_original = (char *)src1->data;
char *dst_original = (char *)dst->data;
src0_row.ne[2] = 1;
src0_row.ne[3] = 1;
@ -3827,12 +3803,9 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
const int64_t i1 = id;
const int64_t i2 = i12;
src0_row_extra.data_device[ctx.device] =
src0_original + i02*nb02;
src1_row_extra.data_device[ctx.device] =
src1_original + + i11*nb11 + i12*nb12;
dst_row_extra.data_device[ctx.device] =
dst_original + i1*nb1 + i2*nb2;
src0_row.data = src0_original + i02*nb02;
src1_row.data = src1_original + + i11*nb11 + i12*nb12;
dst_row.data = dst_original + i1*nb1 + i2*nb2;
ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
}
@ -3841,8 +3814,8 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
ggml_sycl_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
ggml_sycl_pool_alloc<char> dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
src1_row_extra.data_device[ctx.device] = src1_contiguous.get();
dst_row_extra.data_device[ctx.device] = dst_contiguous.get();
src1_row.data = src1_contiguous.get();
dst_row.data = dst_contiguous.get();
for (int64_t i02 = 0; i02 < n_as; i02++) {
int64_t num_src1_rows = 0;
@ -3898,7 +3871,7 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
});
}
src0_row_extra.data_device[ctx.device] = src0_original + i02*nb02;
src0_row.data = src0_original + i02*nb02;
GGML_ASSERT(nb11 == sizeof(float)*ne10);
GGML_ASSERT(nb1 == sizeof(float)*ne0);
@ -5221,6 +5194,10 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
return false;
}
}
ggml_type src0_type = op->src[0]->type;
if (src0_type == GGML_TYPE_BF16) {
return false;
}
return true;
} break;
case GGML_OP_GET_ROWS:

144956
ggml/src/ggml-vulkan-shaders.hpp
View File

File diff suppressed because it is too large Load Diff

16
ggml/src/ggml-vulkan.cpp
View File

@ -6561,7 +6561,7 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
ggml_vk_buffer_read(buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
}
std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
@ -6645,7 +6645,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
for (int i3 = 0; i3 < src0->ne[3]; i3++) {
for (int i2 = 0; i2 < src0->ne[2]; i2++) {
const int idx = i3*src0->ne[2] + i2;
ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
ggml_vk_buffer_read(buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
}
}
@ -6658,7 +6658,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
if (offset + src0_size >= buffer_gpu->size) {
src0_size = buffer_gpu->size - offset;
}
ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size);
ggml_vk_buffer_read(buffer_gpu, offset, src0_clone->data, src0_size);
memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
}
} else {
@ -6687,7 +6687,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
for (int i3 = 0; i3 < src1->ne[3]; i3++) {
for (int i2 = 0; i2 < src1->ne[2]; i2++) {
const int idx = i3*src1->ne[2] + i2;
ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
ggml_vk_buffer_read(buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
}
}
@ -6700,7 +6700,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
if (offset + src1_size >= buffer_gpu->size) {
src1_size = buffer_gpu->size - offset;
}
ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size);
ggml_vk_buffer_read(buffer_gpu, offset, src1_clone->data, src1_size);
memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
}
} else {
@ -6745,7 +6745,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
for (int i3 = 0; i3 < src2->ne[3]; i3++) {
for (int i2 = 0; i2 < src2->ne[2]; i2++) {
const int idx = i3*src2->ne[2] + i2;
ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
ggml_vk_buffer_read(buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
}
}
@ -6758,7 +6758,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
if (offset + src2_size >= buffer_gpu->size) {
src2_size = buffer_gpu->size - offset;
}
ggml_vk_buffer_read(ctx, buffer_gpu, offset, src2_clone->data, src2_size);
ggml_vk_buffer_read(buffer_gpu, offset, src2_clone->data, src2_size);
memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
}
} else {
@ -6922,7 +6922,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_tensor *
tensor_size = buffer_gpu->size - (extra->offset + tensor->view_offs);
}
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
ggml_vk_buffer_read(buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
}
float first_error_result = -1.0f;

111
ggml/src/ggml.c
View File

@ -592,7 +592,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = false,
.to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row,
.from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row,
.from_float_reference = (ggml_from_float_t) ggml_fp32_to_fp16_row,
.from_float_ref = (ggml_from_float_t) ggml_fp32_to_fp16_row,
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16,
.vec_dot_type = GGML_TYPE_F16,
.nrows = 1,
@ -604,7 +604,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q4_0,
.from_float = quantize_row_q4_0,
.from_float_reference = (ggml_from_float_t) quantize_row_q4_0_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q4_0_ref,
.vec_dot = ggml_vec_dot_q4_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
#if defined (__ARM_FEATURE_MATMUL_INT8)
@ -620,7 +620,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q4_1,
.from_float = quantize_row_q4_1,
.from_float_reference = (ggml_from_float_t) quantize_row_q4_1_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q4_1_ref,
.vec_dot = ggml_vec_dot_q4_1_q8_1,
.vec_dot_type = GGML_TYPE_Q8_1,
#if defined (__ARM_FEATURE_MATMUL_INT8)
@ -636,7 +636,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = false,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_COUNT,
.nrows = 1,
@ -648,7 +648,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = false,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_COUNT,
.nrows = 1,
@ -660,7 +660,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q5_0,
.from_float = quantize_row_q5_0,
.from_float_reference = (ggml_from_float_t) quantize_row_q5_0_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q5_0_ref,
.vec_dot = ggml_vec_dot_q5_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
@ -672,7 +672,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q5_1,
.from_float = quantize_row_q5_1,
.from_float_reference = (ggml_from_float_t) quantize_row_q5_1_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q5_1_ref,
.vec_dot = ggml_vec_dot_q5_1_q8_1,
.vec_dot_type = GGML_TYPE_Q8_1,
.nrows = 1,
@ -684,7 +684,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q8_0,
.from_float = quantize_row_q8_0,
.from_float_reference = (ggml_from_float_t) quantize_row_q8_0_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q8_0_ref,
.from_float_to_mat = quantize_mat_q8_0,
.vec_dot = ggml_vec_dot_q8_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
#if defined (__ARM_FEATURE_MATMUL_INT8)
@ -692,7 +693,6 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
#else
.nrows = 1,
#endif
.from_float_to_mat = quantize_mat_q8_0,
},
[GGML_TYPE_Q8_1] = {
.type_name = "q8_1",
@ -700,7 +700,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.type_size = sizeof(block_q8_1),
.is_quantized = true,
.from_float = quantize_row_q8_1,
.from_float_reference = (ggml_from_float_t) quantize_row_q8_1_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q8_1_ref,
.vec_dot_type = GGML_TYPE_Q8_1,
.nrows = 1,
},
@ -711,7 +711,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q2_K,
.from_float = quantize_row_q2_K,
.from_float_reference = (ggml_from_float_t) quantize_row_q2_K_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q2_K_ref,
.vec_dot = ggml_vec_dot_q2_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -723,7 +723,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q3_K,
.from_float = quantize_row_q3_K,
.from_float_reference = (ggml_from_float_t) quantize_row_q3_K_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q3_K_ref,
.vec_dot = ggml_vec_dot_q3_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -735,7 +735,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q4_K,
.from_float = quantize_row_q4_K,
.from_float_reference = (ggml_from_float_t) quantize_row_q4_K_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q4_K_ref,
.vec_dot = ggml_vec_dot_q4_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -747,7 +747,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q5_K,
.from_float = quantize_row_q5_K,
.from_float_reference = (ggml_from_float_t) quantize_row_q5_K_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q5_K_ref,
.vec_dot = ggml_vec_dot_q5_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -759,7 +759,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_q6_K,
.from_float = quantize_row_q6_K,
.from_float_reference = (ggml_from_float_t) quantize_row_q6_K_reference,
.from_float_ref = (ggml_from_float_t) quantize_row_q6_K_ref,
.vec_dot = ggml_vec_dot_q6_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -771,7 +771,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq2_xxs,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = ggml_vec_dot_iq2_xxs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -783,7 +783,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq2_xs,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = ggml_vec_dot_iq2_xs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -795,7 +795,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq3_xxs,
.from_float = quantize_row_iq3_xxs,
.from_float_reference = (ggml_from_float_t)quantize_row_iq3_xxs_reference,
.from_float_ref = (ggml_from_float_t)quantize_row_iq3_xxs_ref,
.vec_dot = ggml_vec_dot_iq3_xxs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -807,7 +807,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq3_s,
.from_float = quantize_row_iq3_s,
.from_float_reference = (ggml_from_float_t)quantize_row_iq3_s_reference,
.from_float_ref = (ggml_from_float_t)quantize_row_iq3_s_ref,
.vec_dot = ggml_vec_dot_iq3_s_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -819,7 +819,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq2_s,
.from_float = quantize_row_iq2_s,
.from_float_reference = (ggml_from_float_t)quantize_row_iq2_s_reference,
.from_float_ref = (ggml_from_float_t)quantize_row_iq2_s_ref,
.vec_dot = ggml_vec_dot_iq2_s_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -831,7 +831,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq1_s,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = ggml_vec_dot_iq1_s_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -843,7 +843,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq1_m,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = ggml_vec_dot_iq1_m_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -855,7 +855,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq4_nl,
.from_float = quantize_row_iq4_nl,
.from_float_reference = (ggml_from_float_t)quantize_row_iq4_nl_reference,
.from_float_ref = (ggml_from_float_t)quantize_row_iq4_nl_ref,
.vec_dot = ggml_vec_dot_iq4_nl_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
@ -867,7 +867,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq4_xs,
.from_float = quantize_row_iq4_xs,
.from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference,
.from_float_ref = (ggml_from_float_t)quantize_row_iq4_xs_ref,
.vec_dot = ggml_vec_dot_iq4_xs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
@ -886,7 +886,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.is_quantized = false,
.to_float = (ggml_to_float_t) ggml_bf16_to_fp32_row,
.from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row,
.from_float_reference = (ggml_from_float_t) ggml_fp32_to_bf16_row,
.from_float_ref = (ggml_from_float_t) ggml_fp32_to_bf16_row,
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16,
.vec_dot_type = GGML_TYPE_BF16,
.nrows = 1,
@ -894,48 +894,48 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0_4_4] = {
.type_name = "q4_0_4x4",
.blck_size = QK4_0,
.blck_size_interleave = 4,
.type_size = sizeof(block_q4_0),
.is_quantized = true,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 4,
.interleave_blcksize = 4,
.gemv = ggml_gemv_q4_0_4x4_q8_0,
.gemm = ggml_gemm_q4_0_4x4_q8_0,
},
[GGML_TYPE_Q4_0_4_8] = {
.type_name = "q4_0_4x8",
.blck_size = QK4_0,
.blck_size_interleave = 8,
.type_size = sizeof(block_q4_0),
.is_quantized = true,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 4,
.interleave_blcksize = 8,
.gemv = ggml_gemv_q4_0_4x8_q8_0,
.gemm = ggml_gemm_q4_0_4x8_q8_0,
},
[GGML_TYPE_Q4_0_8_8] = {
.type_name = "q4_0_8x8",
.blck_size = QK4_0,
.blck_size_interleave = 8,
.type_size = sizeof(block_q4_0),
.is_quantized = true,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.from_float_ref = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 8,
.interleave_blcksize = 8,
.gemv = ggml_gemv_q4_0_8x8_q8_0,
.gemm = ggml_gemm_q4_0_8x8_q8_0,
}
@ -3115,7 +3115,7 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
}
GGML_CALL int ggml_blck_size(enum ggml_type type) {
GGML_CALL int64_t ggml_blck_size(enum ggml_type type) {
return type_traits[type].blck_size;
}
@ -12192,15 +12192,14 @@ static void ggml_compute_forward_mul_mat(
const enum ggml_type type = src0->type;
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
int64_t const vec_dot_num_rows = type_traits[type].nrows;
int64_t const matmul_num_cols = type_traits[type].ncols;
int64_t const interleave_blcksize = type_traits[type].interleave_blcksize;
ggml_from_float_to_mat_t const from_float_to_mat
= type_traits[vec_dot_type].from_float_to_mat;
ggml_gemv_t const gemv = type_traits[type].gemv;
ggml_gemm_t const gemm = type_traits[type].gemm;
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
ggml_from_float_t const from_float = type_traits[vec_dot_type].from_float;
ggml_from_float_to_mat_t const from_float_to_mat = type_traits[vec_dot_type].from_float_to_mat;
int64_t const vec_dot_num_rows = type_traits[type].nrows;
int64_t const matmul_num_cols = type_traits[type].ncols;
int64_t const blck_size_interleave = type_traits[type].blck_size_interleave;
ggml_gemv_t const gemv = type_traits[type].gemv;
ggml_gemm_t const gemm = type_traits[type].gemm;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
@ -12264,14 +12263,14 @@ UseGgmlGemm1:;
for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
4, ne10, interleave_blcksize);
4, ne10, blck_size_interleave);
}
i11_processed = ne11 - ne11 % 4;
}
for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
}
}
}
@ -12355,7 +12354,7 @@ UseGgmlGemm2:;
int64_t src0_start = (ith * ne01) / nth;
int64_t src0_end = ((ith + 1) * ne01) / nth;
src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start;
src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end;
src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end;
if (src0_start >= src0_end) return;
// If there are more than three rows in src1, use gemm; otherwise, use gemv.
@ -12413,11 +12412,11 @@ static void ggml_compute_forward_mul_mat_id(
const bool src1_cont = ggml_is_contiguous(src1);
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
int64_t const matmul_num_cols = type_traits[type].ncols;
ggml_gemv_t const gemv = type_traits[type].gemv;
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
ggml_from_float_t const from_float = type_traits[vec_dot_type].from_float;
int64_t const matmul_num_cols = type_traits[type].ncols;
ggml_gemv_t const gemv = type_traits[type].gemv;
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
@ -12458,9 +12457,9 @@ static void ggml_compute_forward_mul_mat_id(
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
}
}
}
@ -21063,8 +21062,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
(int64_t) info->ne[3];
if (ne % ggml_blck_size(info->type) != 0) {
fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%d)\n",
__func__, info->name.data, (int)info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n",
__func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
fclose(file);
gguf_free(ctx);
return NULL;

5
ggml/src/vulkan-shaders/CMakeLists.txt Normal file
View File

@ -0,0 +1,5 @@
set(TARGET vulkan-shaders-gen)
add_executable(${TARGET} vulkan-shaders-gen.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_compile_features(${TARGET} PRIVATE cxx_std_11)

524
ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp Normal file
View File

@ -0,0 +1,524 @@
#include <iostream>
#include <fstream>
#include <sstream>
#include <string>
#include <stdexcept>
#include <array>
#include <vector>
#include <map>
#include <thread>
#include <mutex>
#include <future>
#include <queue>
#include <condition_variable>
#include <cstdio>
#include <cstring>
#include <cstdlib>
#include <sys/stat.h>
#include <sys/types.h>
#ifdef _WIN32
#include <windows.h>
#include <direct.h> // For _mkdir on Windows
#else
#include <unistd.h>
#include <sys/wait.h>
#include <fcntl.h>
#endif
#define ASYNCIO_CONCURRENCY 64
std::mutex lock;
std::vector<std::pair<std::string, std::string>> shader_fnames;
std::string GLSLC = "glslc";
std::string input_dir = "vulkan-shaders";
std::string output_dir = "/tmp";
std::string target_hpp = "ggml-vulkan-shaders.hpp";
std::string target_cpp = "ggml-vulkan-shaders.cpp";
bool no_clean = false;
const std::vector<std::string> type_names = {
"f32",
"f16",
"q4_0",
"q4_1",
"q5_0",
"q5_1",
"q8_0",
"q2_k",
"q3_k",
"q4_k",
"q5_k",
"q6_k"
};
void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) {
#ifdef _WIN32
HANDLE stdout_read, stdout_write;
HANDLE stderr_read, stderr_write;
SECURITY_ATTRIBUTES sa = { sizeof(SECURITY_ATTRIBUTES), NULL, TRUE };
if (!CreatePipe(&stdout_read, &stdout_write, &sa, 0) ||
!SetHandleInformation(stdout_read, HANDLE_FLAG_INHERIT, 0)) {
throw std::runtime_error("Failed to create stdout pipe");
}
if (!CreatePipe(&stderr_read, &stderr_write, &sa, 0) ||
!SetHandleInformation(stderr_read, HANDLE_FLAG_INHERIT, 0)) {
throw std::runtime_error("Failed to create stderr pipe");
}
PROCESS_INFORMATION pi;
STARTUPINFOA si = { sizeof(STARTUPINFOA) };
si.dwFlags = STARTF_USESTDHANDLES;
si.hStdOutput = stdout_write;
si.hStdError = stderr_write;
std::vector<char> cmd(command.begin(), command.end());
cmd.push_back('\0');
if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) {
throw std::runtime_error("Failed to create process");
}
CloseHandle(stdout_write);
CloseHandle(stderr_write);
std::array<char, 128> buffer;
DWORD bytes_read;
while (ReadFile(stdout_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) {
stdout_str.append(buffer.data(), bytes_read);
}
while (ReadFile(stderr_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) {
stderr_str.append(buffer.data(), bytes_read);
}
CloseHandle(stdout_read);
CloseHandle(stderr_read);
WaitForSingleObject(pi.hProcess, INFINITE);
CloseHandle(pi.hProcess);
CloseHandle(pi.hThread);
#else
int stdout_pipe[2];
int stderr_pipe[2];
if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) {
throw std::runtime_error("Failed to create pipes");
}
pid_t pid = fork();
if (pid < 0) {
throw std::runtime_error("Failed to fork process");
}
if (pid == 0) {
close(stdout_pipe[0]);
close(stderr_pipe[0]);
dup2(stdout_pipe[1], STDOUT_FILENO);
dup2(stderr_pipe[1], STDERR_FILENO);
close(stdout_pipe[1]);
close(stderr_pipe[1]);
execl("/bin/sh", "sh", "-c", command.c_str(), (char*) nullptr);
_exit(EXIT_FAILURE);
} else {
close(stdout_pipe[1]);
close(stderr_pipe[1]);
std::array<char, 128> buffer;
ssize_t bytes_read;
while ((bytes_read = read(stdout_pipe[0], buffer.data(), buffer.size())) > 0) {
stdout_str.append(buffer.data(), bytes_read);
}
while ((bytes_read = read(stderr_pipe[0], buffer.data(), buffer.size())) > 0) {
stderr_str.append(buffer.data(), bytes_read);
}
close(stdout_pipe[0]);
close(stderr_pipe[0]);
waitpid(pid, nullptr, 0);
}
#endif
}
bool directory_exists(const std::string& path) {
struct stat info;
if (stat(path.c_str(), &info) != 0) {
return false; // Path doesn't exist or can't be accessed
}
return (info.st_mode & S_IFDIR) != 0; // Check if it is a directory
}
bool create_directory(const std::string& path) {
#ifdef _WIN32
return _mkdir(path.c_str()) == 0 || errno == EEXIST; // EEXIST means the directory already exists
#else
return mkdir(path.c_str(), 0755) == 0 || errno == EEXIST; // 0755 is the directory permissions
#endif
}
std::string to_uppercase(const std::string& input) {
std::string result = input;
for (char& c : result) {
c = std::toupper(c);
}
return result;
}
bool string_ends_with(const std::string& str, const std::string& suffix) {
if (suffix.size() > str.size()) {
return false;
}
return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin());
}
#ifdef _WIN32
static const char path_separator = '\\';
#else
static const char path_separator = '/';
#endif
std::string join_paths(const std::string& path1, const std::string& path2) {
return path1 + path_separator + path2;
}
std::string basename(const std::string &path) {
return path.substr(path.find_last_of("/\\") + 1);
}
void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true) {
std::string name = _name + (fp16 ? "" : "_fp32");
std::string out_fname = join_paths(output_dir, name + ".spv");
std::string in_path = join_paths(input_dir, in_fname);
std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname};
for (const auto& define : defines) {
cmd.push_back("-D" + define.first + "=" + define.second);
}
std::string command;
for (const auto& part : cmd) {
command += part + " ";
}
std::string stdout_str, stderr_str;
try {
// std::cout << "Executing command: ";
// for (const auto& part : cmd) {
// std::cout << part << " ";
// }
// std::cout << std::endl;
execute_command(command, stdout_str, stderr_str);
if (!stderr_str.empty()) {
std::cerr << "cannot compile " << name << "\n\n" << command << "\n\n" << stderr_str << std::endl;
return;
}
std::lock_guard<std::mutex> guard(lock);
shader_fnames.push_back(std::make_pair(name, out_fname));
} catch (const std::exception& e) {
std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl;
}
}
std::map<std::string, std::string> merge_maps(const std::map<std::string, std::string>& a, const std::map<std::string, std::string>& b) {
std::map<std::string, std::string> result = a;
result.insert(b.begin(), b.end());
return result;
}
void matmul_shaders(std::vector<std::future<void>>& tasks, bool fp16, bool matmul_id) {
std::string load_vec = fp16 ? "8" : "4";
std::string aligned_b_type_f32 = fp16 ? "mat2x4" : "vec4";
std::string aligned_b_type_f16 = fp16 ? "f16mat2x4" : "f16vec4";
std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", fp16 ? "float16_t" : "float"}};
std::string shader_name = "matmul";
if (matmul_id) {
base_dict["MUL_MAT_ID"] = "1";
shader_name = "matmul_id";
}
if (fp16) {
base_dict["FLOAT16"] = "1";
}
// Shaders with f16 B_TYPE
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
}));
for (const auto& tname : type_names) {
std::string data_a_key = "DATA_A_" + to_uppercase(tname);
std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2";
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16);
}));
}
}
void process_shaders(std::vector<std::future<void>>& tasks) {
std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl;
std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", "float"}};
for (const auto& fp16 : {false, true}) {
matmul_shaders(tasks, fp16, false);
matmul_shaders(tasks, fp16, true);
}
for (const auto& tname : type_names) {
// mul mat vec
std::string data_a_key = "DATA_A_" + to_uppercase(tname);
std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
// Dequant shaders
if (tname != "f16") {
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}}));
}));
}
if (!string_ends_with(tname, "_k")) {
shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp";
if (tname == "f16") {
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
}));
} else {
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}});
}));
}
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("get_rows_" + tname + "_f32", shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}});
}));
}
}
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
}));
// Norms
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
}
void write_output_files() {
FILE* hdr = fopen(target_hpp.c_str(), "w");
FILE* src = fopen(target_cpp.c_str(), "w");
fprintf(hdr, "#include <cstdint>\n\n");
fprintf(src, "#include \"%s\"\n\n", basename(target_hpp).c_str());
for (const auto& pair : shader_fnames) {
const std::string& name = pair.first;
const std::string& path = pair.second;
FILE* spv = fopen(path.c_str(), "rb");
if (!spv) {
std::cerr << "Error opening SPIR-V file: " << path << "\n";
continue;
}
fseek(spv, 0, SEEK_END);
size_t size = ftell(spv);
fseek(spv, 0, SEEK_SET);
std::vector<unsigned char> data(size);
size_t read_size = fread(data.data(), 1, size, spv);
fclose(spv);
if (read_size != size) {
std::cerr << "Error reading SPIR-V file: " << path << "\n";
continue;
}
fprintf(hdr, "extern unsigned char %s_data[%zu];\n", name.c_str(), size);
fprintf(hdr, "const uint64_t %s_len = %zu;\n\n", name.c_str(), size);
fprintf(src, "unsigned char %s_data[%zu] = {\n", name.c_str(), size);
for (size_t i = 0; i < size; ++i) {
fprintf(src, "0x%02x,", data[i]);
if ((i + 1) % 12 == 0) fprintf(src, "\n");
}
fprintf(src, "\n};\n\n");
if (!no_clean) {
std::remove(path.c_str());
}
}
fclose(hdr);
fclose(src);
}
int main(int argc, char** argv) {
std::map<std::string, std::string> args;
for (int i = 1; i < argc; i += 2) {
if (i + 1 < argc) {
args[argv[i]] = argv[i + 1];
}
}
if (args.find("--glslc") != args.end()) {
GLSLC = args["--glslc"]; // Path to glslc
}
if (args.find("--input-dir") != args.end()) {
input_dir = args["--input-dir"]; // Directory containing shader sources
}
if (args.find("--output-dir") != args.end()) {
output_dir = args["--output-dir"]; // Directory for containing SPIR-V output
}
if (args.find("--target-hpp") != args.end()) {
target_hpp = args["--target-hpp"]; // Path to generated header file
}
if (args.find("--target-cpp") != args.end()) {
target_cpp = args["--target-cpp"]; // Path to generated cpp file
}
if (args.find("--no-clean") != args.end()) {
no_clean = true; // Keep temporary SPIR-V files in output-dir after build
}
if (!directory_exists(input_dir)) {
std::cerr << "\"" << input_dir << "\" must be a valid directory containing shader sources" << std::endl;
return EXIT_FAILURE;
}
if (!directory_exists(output_dir)) {
if (!create_directory(output_dir)) {
std::cerr << "Error creating output directory: " << output_dir << "\n";
return EXIT_FAILURE;
}
}
std::vector<std::future<void>> tasks;
process_shaders(tasks);
for (auto& task : tasks) {
task.get();
}
write_output_files();
return EXIT_SUCCESS;
}

27
gguf-py/scripts/gguf_hash.py
View File

@ -27,8 +27,9 @@ UUID_NAMESPACE_LLAMA_CPP = uuid.UUID('ef001206-dadc-5f6d-a15f-3359e577d4e5')
# For more information about what field.parts and field.data represent,
# please see the comments in the modify_gguf.py example.
def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar: bool, no_layer: bool) -> None:
sha1 = hashlib.sha1()
sha256 = hashlib.sha256()
uuidv5_sha1 = hashlib.sha1()
uuidv5_sha1.update(UUID_NAMESPACE_LLAMA_CPP.bytes)
@ -50,7 +51,7 @@ def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
bar = tqdm(desc="Hashing", total=total_weights, unit="weights", unit_scale=True, disable=disable_progress_bar)
# Hashing Process
for n, tensor in enumerate(reader.tensors, 1):
for tensor in reader.tensors:
# We don't need these
if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
@ -62,29 +63,39 @@ def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
sum_weights_in_tensor *= dim
bar.update(sum_weights_in_tensor)
sha1_layer = hashlib.sha1()
sha1_layer.update(tensor.data.data)
if not no_layer:
sha1_layer = hashlib.sha1()
sha1_layer.update(tensor.data.data)
print("sha1 {0} {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
sha256_layer = hashlib.sha256()
sha256_layer.update(tensor.data.data)
print("sha256 {0} {1}:{2}".format(sha256_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
sha1.update(tensor.data.data)
sha256.update(tensor.data.data)
uuidv5_sha1.update(tensor.data.data)
print("sha1 {0} {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
# Flush Hash Progress Bar
bar.close()
# Display Hash Output
print("sha1 {0} {1}".format(sha1.hexdigest(), filename)) # noqa: NP100
print("UUIDv5 {0} {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100
print("sha1 {0} {1}".format(sha1.hexdigest(), filename)) # noqa: NP100
print("sha256 {0} {1}".format(sha256.hexdigest(), filename)) # noqa: NP100
print("uuid {0} {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100
def main() -> None:
parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
parser.add_argument("model", type=str, help="GGUF format model filename")
parser.add_argument("--no-layer", action="store_true", help="exclude per layer hash")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
parser.add_argument("--progressbar", action="store_true", help="enable progressbar")
args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
reader = GGUFReader(args.model, 'r')
gguf_hash(reader, args.model, not args.progressbar)
gguf_hash(reader, args.model, not args.progressbar, args.no_layer)
if __name__ == '__main__':

1
requirements/requirements-pydantic.txt
View File

@ -1,2 +1,3 @@
docstring_parser~=0.15
pydantic~=2.6.3
requests

47
src/llama.cpp
View File

@ -5477,6 +5477,7 @@ static void llm_load_vocab(
} else if (
tokenizer_pre == "command-r") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
vocab.tokenizer_clean_spaces = false;
} else if (
tokenizer_pre == "qwen2") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
@ -5710,7 +5711,7 @@ static void llm_load_vocab(
// build special tokens cache
{
for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) {
if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
vocab.cache_special_tokens.push_back(id);
}
}
@ -5941,13 +5942,6 @@ static bool llm_load_tensors(
auto & hparams = model.hparams;
#ifdef GGML_USE_SYCL
// disable MoE with SYCL until mul_mat_id is updated
if (hparams.n_expert > 0) {
n_gpu_layers = 0;
}
#endif
model.split_mode = split_mode;
model.main_gpu = main_gpu;
model.n_gpu_layers = n_gpu_layers;
@ -8247,7 +8241,7 @@ static struct ggml_tensor * llm_build_kqv(
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
cb(kq, "kq", il);
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2) {
// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
@ -11800,7 +11794,12 @@ struct llm_build_context {
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur", il);
Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head)));
// ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
switch (model.type) {
case e_model::MODEL_9B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); break;
case e_model::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
default: GGML_ASSERT(false);
};
cb(Qcur, "Qcur_scaled", il);
Kcur = ggml_rope_ext(
@ -15532,17 +15531,6 @@ struct llm_tokenizer_bpe {
"[0-9][0-9][0-9]",
};
break;
case LLAMA_VOCAB_PRE_TYPE_MPT:
// TODO: MPT pre-tokenization regexes are unknown
// the following are close, but not exact. run the following:
// ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
regex_exprs = {
"\\s?\\p{L}+",
"\\s?\\p{P}+",
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
};
break;
case LLAMA_VOCAB_PRE_TYPE_STARCODER:
case LLAMA_VOCAB_PRE_TYPE_REFACT:
case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
@ -15552,6 +15540,7 @@ struct llm_tokenizer_bpe {
};
break;
case LLAMA_VOCAB_PRE_TYPE_GPT2:
case LLAMA_VOCAB_PRE_TYPE_MPT:
case LLAMA_VOCAB_PRE_TYPE_OLMO:
case LLAMA_VOCAB_PRE_TYPE_JAIS:
regex_exprs = {
@ -15578,8 +15567,8 @@ struct llm_tokenizer_bpe {
break;
case LLAMA_VOCAB_PRE_TYPE_VIKING:
regex_exprs = {
"\\p{N}",
" ?[^(\\s|.,!?…。，、।۔،)]+",
"\\p{N}",
};
break;
default:
@ -16299,12 +16288,20 @@ struct fragment_buffer_variant {
// #define PRETOKENIZERDEBUG
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
// for each special token
for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
const auto & data = vocab.id_to_token[special_id];
const auto & special_token = data.text;
if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
// Ignore control and unknown tokens when parse_special == false
continue;
// User-defined tokens are still pre-tokenized before everything else
// ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
// This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
}
// for each text fragment
std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
while (it != buffer.end()) {
@ -16417,7 +16414,7 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
if (!raw_text.empty()) {
fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
tokenizer_st_partition(vocab, fragment_buffer, parse_special);
}
switch (vocab.type) {
@ -21188,7 +21185,7 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
size--;
}
if (length < (int32_t)size) {
return (int32_t) -size;
return -(int32_t) size;
}
memcpy(buf, token, size);
return (int32_t) size;

4
tests/test-double-float.cpp
View File

@ -14,7 +14,7 @@
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wdouble-promotion"
// ggml.c::quantize_row_q4_0_reference
// ggml.c::quantize_row_q4_0_ref
inline static uint8_t round_orig(float v0) { return ((int8_t) (round(v0))) + 8; }
// ggml.c::ggml_silu_f32
@ -24,7 +24,7 @@ inline static float silu_orig(float x) {
#pragma GCC diagnostic pop
// ggml.c::quantize_row_q4_0_reference
// ggml.c::quantize_row_q4_0_ref
inline static uint8_t round_float(float v0) { return (int8_t)roundf(v0) + 8; }
// ggml.c::ggml_silu_f32

2
tests/test-quantize-fns.cpp
View File

@ -60,7 +60,7 @@ static float reference_quantization_error(ggml_type_traits_t & qfns, size_t test
qfns.from_float(test_data, tmp_q.data(), test_size);
qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
qfns.from_float_reference(test_data, tmp_q.data(), test_size);
qfns.from_float_ref(test_data, tmp_q.data(), test_size);
qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);

2
tests/test-quantize-perf.cpp
View File

@ -285,7 +285,7 @@ int main(int argc, char * argv[]) {
for (size_t size : params.test_sizes) {
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
auto quantize_fn = [&](void) -> float {
qfns.from_float_reference(test_data1, test_q1, size);
qfns.from_float_ref(test_data1, test_q1, size);
return test_q1[0];
};
size_t quantized_size = ggml_row_size(type, size);

4
tests/test-tokenizer-0.cpp
View File

@ -195,7 +195,7 @@ int main(int argc, char **argv) {
const bool add_special = false;
for (const auto & test_kv : k_tests) {
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, true);
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
printf("\n");
printf("src: '%s'\n", test_kv.first.c_str());
@ -253,7 +253,7 @@ int main(int argc, char **argv) {
{
const auto t_start = ggml_time_us();
res = llama_tokenize(ctx, text, add_special, true);
res = llama_tokenize(ctx, text, add_special, false);
const auto t_end = ggml_time_us();

7
tests/test-tokenizer-random.py
View File

@ -20,7 +20,7 @@ from typing import Any, Iterator, cast
from typing_extensions import Buffer
import cffi
from transformers import AutoTokenizer
from transformers import AutoTokenizer, PreTrainedTokenizer
logger = logging.getLogger("test-tokenizer-random")
@ -129,7 +129,7 @@ class Tokenizer:
class TokenizerGroundtruth (Tokenizer):
def __init__(self, dir_tokenizer: str):
self.model = AutoTokenizer.from_pretrained(dir_tokenizer)
self.model: PreTrainedTokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
# guess BOS and EOS
ids = self.encode("a")
assert 1 <= len(ids) <= 3
@ -143,7 +143,7 @@ class TokenizerGroundtruth (Tokenizer):
self.vocab = list(sorted(self.vocab))
# tokens and lists
self.special_tokens = list(self.model.all_special_tokens)
self.added_tokens = list(self.model.added_tokens_encoder)
self.added_tokens = self.model.batch_decode(self.model.added_tokens_encoder.values(), skip_special_tokens=False)
self.bos_token = self.model.bos_token
self.eos_token = self.model.eos_token
@ -232,6 +232,7 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
'a\na', # bert fail
'"`', # falcon
' \u2e4e', # falcon
'\n\x0b ', # falcon
'a\xa0\xa0\x00b', # jina-v2-es
'one <mask>', # jina-v2-es <mask> lstrip=true
'a </s> b', # rstrip phi-3