Merge branch 'ggerganov:master' into master
This commit is contained in:
Marcus Dunn
GitHub
commit
911a871968
|
|
@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
|
|||
./quantize "$@"
|
||||
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
|
||||
./main "$@"
|
||||
elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
|
||||
./finetune "$@"
|
||||
elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
|
||||
echo "Converting PTH to GGML..."
|
||||
for i in `ls $1/$2/ggml-model-f16.bin*`; do
|
||||
|
|
@ -34,6 +36,8 @@ else
|
|||
echo " ex: --outtype f16 \"/models/7B/\" "
|
||||
echo " --quantize (-q): Optimize with quantization process ggml"
|
||||
echo " ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
|
||||
echo " --finetune (-f): Run finetune command to create a lora finetune of the model"
|
||||
echo " See documentation for finetune for command-line parameters"
|
||||
echo " --all-in-one (-a): Execute --convert & --quantize"
|
||||
echo " ex: \"/models/\" 7B"
|
||||
echo " --server (-s): Run a model on the server"
|
||||
|
|
|
|||
11
.github/workflows/build.yml
vendored
11
.github/workflows/build.yml
vendored
|
|
@ -498,6 +498,17 @@ jobs:
|
|||
path: |
|
||||
cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
|
||||
|
||||
ios-xcode-build:
|
||||
runs-on: macos-latest
|
||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Build Xcode project
|
||||
run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
|
||||
|
||||
|
||||
# freeBSD-latest:
|
||||
# runs-on: macos-12
|
||||
# steps:
|
||||
|
|
|
|||
26
.gitignore
vendored
26
.gitignore
vendored
|
|
@ -47,6 +47,7 @@ models-mnt
|
|||
/libllama.so
|
||||
/llama-bench
|
||||
/llava-cli
|
||||
/lookahead
|
||||
/main
|
||||
/metal
|
||||
/perplexity
|
||||
|
|
@ -87,15 +88,16 @@ poetry.lock
|
|||
poetry.toml
|
||||
|
||||
# Test binaries
|
||||
tests/test-grammar-parser
|
||||
tests/test-llama-grammar
|
||||
tests/test-double-float
|
||||
tests/test-grad0
|
||||
tests/test-opt
|
||||
tests/test-quantize-fns
|
||||
tests/test-quantize-perf
|
||||
tests/test-sampling
|
||||
tests/test-tokenizer-0-llama
|
||||
tests/test-tokenizer-0-falcon
|
||||
tests/test-tokenizer-1-llama
|
||||
tests/test-tokenizer-1-bpe
|
||||
/tests/test-grammar-parser
|
||||
/tests/test-llama-grammar
|
||||
/tests/test-double-float
|
||||
/tests/test-grad0
|
||||
/tests/test-opt
|
||||
/tests/test-quantize-fns
|
||||
/tests/test-quantize-perf
|
||||
/tests/test-sampling
|
||||
/tests/test-tokenizer-0-llama
|
||||
/tests/test-tokenizer-0-falcon
|
||||
/tests/test-tokenizer-1-llama
|
||||
/tests/test-tokenizer-1-bpe
|
||||
/tests/test-rope
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ else()
|
|||
endif()
|
||||
|
||||
# general
|
||||
option(BUILD_SHARED_LIBS "build shared libraries" OFF)
|
||||
option(LLAMA_STATIC "llama: static link libraries" OFF)
|
||||
option(LLAMA_NATIVE "llama: enable -march=native flag" ON)
|
||||
option(LLAMA_LTO "llama: enable link time optimization" OFF)
|
||||
|
|
@ -100,6 +101,9 @@ option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALO
|
|||
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
|
||||
|
||||
# Required for relocatable CMake package
|
||||
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
|
||||
|
||||
#
|
||||
# Compile flags
|
||||
#
|
||||
|
|
@ -112,6 +116,11 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
|
|||
find_package(Threads REQUIRED)
|
||||
include(CheckCXXCompilerFlag)
|
||||
|
||||
# enable libstdc++ assertions for debug builds
|
||||
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
|
||||
add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
|
||||
endif()
|
||||
|
||||
if (NOT MSVC)
|
||||
if (LLAMA_SANITIZE_THREAD)
|
||||
add_compile_options(-fsanitize=thread)
|
||||
|
|
@ -161,7 +170,7 @@ if (LLAMA_METAL)
|
|||
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
|
||||
|
||||
# copy ggml-metal.metal to bin directory
|
||||
configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
|
||||
configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
|
||||
|
||||
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
|
||||
${FOUNDATION_LIBRARY}
|
||||
|
|
|
|||
34
Makefile
34
Makefile
|
|
@ -2,13 +2,13 @@
|
|||
BUILD_TARGETS = \
|
||||
main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
|
||||
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
|
||||
speculative infill tokenize benchmark-matmult parallel finetune export-lora tests/test-c.o
|
||||
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
|
||||
|
||||
# Binaries only useful for tests
|
||||
TEST_TARGETS = \
|
||||
tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
|
||||
tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \
|
||||
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe
|
||||
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope
|
||||
|
||||
# Code coverage output files
|
||||
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
|
||||
|
|
@ -30,7 +30,7 @@ ifeq '' '$(findstring clang,$(shell $(CC) --version))'
|
|||
CC_VER := $(shell $(CC) -dumpfullversion -dumpversion | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
|
||||
else
|
||||
CC_IS_CLANG=1
|
||||
ifeq '' '$(findstring Apple LLVM,$(shell $(CC) --version))'
|
||||
ifeq '' '$(findstring Apple,$(shell $(CC) --version))'
|
||||
CC_IS_LLVM_CLANG=1
|
||||
else
|
||||
CC_IS_APPLE_CLANG=1
|
||||
|
|
@ -174,6 +174,10 @@ ifdef LLAMA_DEBUG
|
|||
MK_CFLAGS += -O0 -g
|
||||
MK_CXXFLAGS += -O0 -g
|
||||
MK_LDFLAGS += -g
|
||||
|
||||
ifeq ($(UNAME_S),Linux)
|
||||
MK_CXXFLAGS += -Wp,-D_GLIBCXX_ASSERTIONS
|
||||
endif
|
||||
else
|
||||
MK_CPPFLAGS += -DNDEBUG
|
||||
endif
|
||||
|
|
@ -648,7 +652,7 @@ beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS)
|
|||
finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
export-lora: examples/export-lora/export-lora.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
|
||||
|
|
@ -657,6 +661,9 @@ speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS)
|
|||
parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
ifdef LLAMA_METAL
|
||||
metal: examples/metal/metal.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
|
||||
|
|
@ -698,28 +705,28 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
|
|||
q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
|
||||
tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
|
||||
tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-double-float: tests/test-double-float.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-grad0: tests/test-grad0.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-opt: tests/test-opt.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
|
|
@ -734,5 +741,8 @@ tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMM
|
|||
tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-c.o: tests/test-c.c llama.h
|
||||
$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
|
||||
|
|
|
|||
|
|
@ -10,6 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
|
|||
|
||||
### Hot topics
|
||||
|
||||
- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225
|
||||
- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
|
||||
- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167
|
||||
|
||||
----
|
||||
|
|
@ -114,6 +116,8 @@ as the main playground for developing new features for the [ggml](https://github
|
|||
- [nat/openplayground](https://github.com/nat/openplayground)
|
||||
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
|
||||
- [withcatai/catai](https://github.com/withcatai/catai)
|
||||
- [semperai/amica](https://github.com/semperai/amica)
|
||||
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -320,7 +324,7 @@ mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
|
|||
|
||||
### BLAS Build
|
||||
|
||||
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
|
||||
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
|
||||
|
||||
- #### Accelerate Framework:
|
||||
|
||||
|
|
@ -892,7 +896,7 @@ Additionally, there the following images, similar to the above:
|
|||
- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
|
||||
- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
|
||||
|
||||
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the Gitlab Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
|
||||
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
|
||||
|
||||
#### Usage
|
||||
|
||||
|
|
|
|||
|
|
@ -11,7 +11,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
|
|||
if(NOT IS_DIRECTORY "${GIT_DIR}")
|
||||
file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
|
||||
string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
|
||||
set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
|
||||
string(FIND "${REAL_GIT_DIR}" "/" SLASH_POS)
|
||||
if (SLASH_POS EQUAL 0)
|
||||
set(GIT_DIR "${REAL_GIT_DIR}")
|
||||
else()
|
||||
set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
set(GIT_INDEX "${GIT_DIR}/index")
|
||||
|
|
@ -26,7 +31,7 @@ add_custom_command(
|
|||
COMMENT "Generating build details from Git"
|
||||
COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
|
||||
-DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
|
||||
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake"
|
||||
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
|
||||
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
|
||||
DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
|
||||
VERBATIM
|
||||
|
|
|
|||
|
|
@ -190,7 +190,7 @@ namespace grammar_parser {
|
|||
pos = parse_space(pos + 1, is_nested);
|
||||
} else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
|
||||
if (last_sym_start == out_elements.size()) {
|
||||
throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos);
|
||||
throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
|
||||
}
|
||||
|
||||
// apply transformation to previous symbol (last_sym_start to end) according to
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ import re
|
|||
import sys
|
||||
from enum import IntEnum
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
|
||||
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
|
@ -59,7 +59,7 @@ class Model:
|
|||
from safetensors import safe_open
|
||||
ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
|
||||
else:
|
||||
ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu"))
|
||||
ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
|
||||
|
||||
with ctx as model_part:
|
||||
for name in model_part.keys():
|
||||
|
|
@ -168,6 +168,8 @@ class Model:
|
|||
return PersimmonModel
|
||||
if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
|
||||
return StableLMModel
|
||||
if model_architecture == "QWenLMHeadModel":
|
||||
return QwenModel
|
||||
return Model
|
||||
|
||||
def _is_model_safetensors(self) -> bool:
|
||||
|
|
@ -203,6 +205,8 @@ class Model:
|
|||
return gguf.MODEL_ARCH.PERSIMMON
|
||||
if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
|
||||
return gguf.MODEL_ARCH.STABLELM
|
||||
if arch == "QWenLMHeadModel":
|
||||
return gguf.MODEL_ARCH.QWEN
|
||||
|
||||
raise NotImplementedError(f'Architecture "{arch}" not supported!')
|
||||
|
||||
|
|
@ -832,6 +836,131 @@ class StableLMModel(Model):
|
|||
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
|
||||
self.gguf_writer.add_layer_norm_eps(1e-5)
|
||||
|
||||
|
||||
class QwenModel(Model):
|
||||
@staticmethod
|
||||
def token_bytes_to_string(b):
|
||||
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
|
||||
byte_encoder = bytes_to_unicode()
|
||||
return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
|
||||
|
||||
@staticmethod
|
||||
def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
|
||||
parts = [bytes([b]) for b in token]
|
||||
while True:
|
||||
min_idx = None
|
||||
min_rank = None
|
||||
for i, pair in enumerate(zip(parts[:-1], parts[1:])):
|
||||
rank = mergeable_ranks.get(pair[0] + pair[1])
|
||||
if rank is not None and (min_rank is None or rank < min_rank):
|
||||
min_idx = i
|
||||
min_rank = rank
|
||||
if min_rank is None or (max_rank is not None and min_rank >= max_rank):
|
||||
break
|
||||
assert min_idx is not None
|
||||
parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
|
||||
return parts
|
||||
|
||||
def set_vocab(self):
|
||||
dir_model = self.dir_model
|
||||
hparams = self.hparams
|
||||
tokens: list[bytearray] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
from transformers import AutoTokenizer # type: ignore[attr-defined]
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
|
||||
vocab_size = hparams["vocab_size"]
|
||||
assert max(tokenizer.get_vocab().values()) < vocab_size
|
||||
|
||||
merges = []
|
||||
vocab = {}
|
||||
mergeable_ranks = tokenizer.mergeable_ranks
|
||||
for token, rank in mergeable_ranks.items():
|
||||
vocab[self.token_bytes_to_string(token)] = rank
|
||||
if len(token) == 1:
|
||||
continue
|
||||
merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
|
||||
assert len(merged) == 2
|
||||
merges.append(' '.join(map(self.token_bytes_to_string, merged)))
|
||||
|
||||
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()}
|
||||
added_vocab = tokenizer.special_tokens
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
pad_token = f"[PAD{i}]".encode("utf-8")
|
||||
tokens.append(bytearray(pad_token))
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
self.gguf_writer.add_tokenizer_model("gpt2")
|
||||
self.gguf_writer.add_token_list(tokens)
|
||||
self.gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
|
||||
special_vocab.merges = merges
|
||||
special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
|
||||
special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
|
||||
special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
self.gguf_writer.add_name("Qwen")
|
||||
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
|
||||
self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
|
||||
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
||||
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
||||
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
|
||||
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
|
||||
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
|
||||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams["num_hidden_layers"]
|
||||
model_kv = dict(self.get_tensors())
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
for name, data_torch in model_kv.items():
|
||||
# we don't need these
|
||||
if name.endswith(".rotary_emb.inv_freq"):
|
||||
continue
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.ftype == 0 and data_dtype == np.float16:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
||||
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
||||
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
###### CONVERSION LOGIC ######
|
||||
|
||||
|
||||
|
|
|
|||
2
convert.py
Normal file → Executable file
2
convert.py
Normal file → Executable file
|
|
@ -267,7 +267,7 @@ class Params:
|
|||
n_ctx = 2048
|
||||
|
||||
return Params(
|
||||
n_vocab = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
|
||||
n_vocab = model["tok_embeddings.weight"].shape[0],
|
||||
n_embd = config["dim"],
|
||||
n_layer = config["n_layers"],
|
||||
n_ctx = n_ctx,
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ else()
|
|||
add_subdirectory(save-load-state)
|
||||
add_subdirectory(simple)
|
||||
add_subdirectory(speculative)
|
||||
add_subdirectory(lookahead)
|
||||
add_subdirectory(train-text-from-scratch)
|
||||
if (LLAMA_METAL)
|
||||
add_subdirectory(metal)
|
||||
|
|
|
|||
|
|
@ -155,7 +155,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq);
|
||||
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %d, n_threads_batch = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
|
||||
LOG_TEE("\n");
|
||||
|
||||
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
This is a swift clone of `examples/batched`.
|
||||
|
||||
$ `make`
|
||||
$ `./swift MODEL_PATH [PROMPT] [PARALLEL]`
|
||||
$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]`
|
||||
|
|
|
|||
|
|
@ -215,9 +215,10 @@ print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end
|
|||
llama_print_timings(context)
|
||||
|
||||
private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
|
||||
let n_tokens = text.count + (add_bos ? 1 : 0)
|
||||
let utf8Count = text.utf8.count
|
||||
let n_tokens = utf8Count + (add_bos ? 1 : 0)
|
||||
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
|
||||
let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
|
||||
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
|
||||
var swiftTokens: [llama_token] = []
|
||||
for i in 0 ..< tokenCount {
|
||||
swiftTokens.append(tokens[Int(i)])
|
||||
|
|
@ -230,18 +231,15 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
|
|||
var result = [CChar](repeating: 0, count: 8)
|
||||
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
|
||||
if nTokens < 0 {
|
||||
if result.count >= -Int(nTokens) {
|
||||
result.removeLast(-Int(nTokens))
|
||||
} else {
|
||||
result.removeAll()
|
||||
}
|
||||
let actualTokensCount = -Int(nTokens)
|
||||
result = .init(repeating: 0, count: actualTokensCount)
|
||||
let check = llama_token_to_piece(
|
||||
model,
|
||||
token,
|
||||
&result,
|
||||
Int32(result.count)
|
||||
)
|
||||
assert(check == nTokens)
|
||||
assert(check == actualTokensCount)
|
||||
} else {
|
||||
result.removeLast(result.count - Int(nTokens))
|
||||
}
|
||||
|
|
@ -259,5 +257,4 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
|
|||
buffer = []
|
||||
return bufferString
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
|
|
|||
1
examples/llama.swiftui/.gitignore
vendored
Normal file
1
examples/llama.swiftui/.gitignore
vendored
Normal file
|
|
@ -0,0 +1 @@
|
|||
xcuserdata
|
||||
7
examples/llama.swiftui/README.md
Normal file
7
examples/llama.swiftui/README.md
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
# llama.swiftui
|
||||
|
||||
Local inference of llama.cpp on an iPhone.
|
||||
So far I only tested with starcoder 1B model, but it can most likely handle 7B models as well.
|
||||
|
||||
https://github.com/bachittle/llama.cpp/assets/39804642/e290827a-4edb-4093-9642-2a5e399ec545
|
||||
|
||||
208
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
Normal file
208
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
Normal file
|
|
@ -0,0 +1,208 @@
|
|||
import Foundation
|
||||
|
||||
// import llama
|
||||
|
||||
enum LlamaError: Error {
|
||||
case couldNotInitializeContext
|
||||
}
|
||||
|
||||
actor LlamaContext {
|
||||
private var model: OpaquePointer
|
||||
private var context: OpaquePointer
|
||||
private var batch: llama_batch
|
||||
private var tokens_list: [llama_token]
|
||||
/// This variable is used to store temporarily invalid cchars
|
||||
private var temporary_invalid_cchars: [CChar]
|
||||
|
||||
var n_len: Int32 = 512
|
||||
var n_cur: Int32 = 0
|
||||
var n_decode: Int32 = 0
|
||||
|
||||
init(model: OpaquePointer, context: OpaquePointer) {
|
||||
self.model = model
|
||||
self.context = context
|
||||
self.tokens_list = []
|
||||
self.batch = llama_batch_init(512, 0, 1)
|
||||
self.temporary_invalid_cchars = []
|
||||
}
|
||||
|
||||
deinit {
|
||||
llama_free(context)
|
||||
llama_free_model(model)
|
||||
llama_backend_free()
|
||||
}
|
||||
|
||||
static func createContext(path: String) throws -> LlamaContext {
|
||||
llama_backend_init(false)
|
||||
let model_params = llama_model_default_params()
|
||||
|
||||
let model = llama_load_model_from_file(path, model_params)
|
||||
guard let model else {
|
||||
print("Could not load model at \(path)")
|
||||
throw LlamaError.couldNotInitializeContext
|
||||
}
|
||||
var ctx_params = llama_context_default_params()
|
||||
ctx_params.seed = 1234
|
||||
ctx_params.n_ctx = 2048
|
||||
ctx_params.n_threads = 8
|
||||
ctx_params.n_threads_batch = 8
|
||||
|
||||
let context = llama_new_context_with_model(model, ctx_params)
|
||||
guard let context else {
|
||||
print("Could not load context!")
|
||||
throw LlamaError.couldNotInitializeContext
|
||||
}
|
||||
|
||||
return LlamaContext(model: model, context: context)
|
||||
}
|
||||
|
||||
func get_n_tokens() -> Int32 {
|
||||
return batch.n_tokens;
|
||||
}
|
||||
|
||||
func completion_init(text: String) {
|
||||
print("attempting to complete \"\(text)\"")
|
||||
|
||||
tokens_list = tokenize(text: text, add_bos: true)
|
||||
temporary_invalid_cchars = []
|
||||
|
||||
let n_ctx = llama_n_ctx(context)
|
||||
let n_kv_req = tokens_list.count + (Int(n_len) - tokens_list.count)
|
||||
|
||||
print("\n n_len = \(n_len), n_ctx = \(n_ctx), n_kv_req = \(n_kv_req)")
|
||||
|
||||
if n_kv_req > n_ctx {
|
||||
print("error: n_kv_req > n_ctx, the required KV cache size is not big enough")
|
||||
}
|
||||
|
||||
for id in tokens_list {
|
||||
print(String(cString: token_to_piece(token: id) + [0]))
|
||||
}
|
||||
|
||||
// batch = llama_batch_init(512, 0) // done in init()
|
||||
batch.n_tokens = Int32(tokens_list.count)
|
||||
|
||||
for i1 in 0..<batch.n_tokens {
|
||||
let i = Int(i1)
|
||||
batch.token[i] = tokens_list[i]
|
||||
batch.pos[i] = i1
|
||||
batch.n_seq_id[Int(i)] = 1
|
||||
batch.seq_id[Int(i)]![0] = 0
|
||||
batch.logits[i] = 0
|
||||
}
|
||||
batch.logits[Int(batch.n_tokens) - 1] = 1 // true
|
||||
|
||||
if llama_decode(context, batch) != 0 {
|
||||
print("llama_decode() failed")
|
||||
}
|
||||
|
||||
n_cur = batch.n_tokens
|
||||
}
|
||||
|
||||
func completion_loop() -> String {
|
||||
var new_token_id: llama_token = 0
|
||||
|
||||
let n_vocab = llama_n_vocab(model)
|
||||
let logits = llama_get_logits_ith(context, batch.n_tokens - 1)
|
||||
|
||||
var candidates = Array<llama_token_data>()
|
||||
candidates.reserveCapacity(Int(n_vocab))
|
||||
|
||||
for token_id in 0..<n_vocab {
|
||||
candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
|
||||
}
|
||||
candidates.withUnsafeMutableBufferPointer() { buffer in
|
||||
var candidates_p = llama_token_data_array(data: buffer.baseAddress, size: buffer.count, sorted: false)
|
||||
|
||||
new_token_id = llama_sample_token_greedy(context, &candidates_p)
|
||||
}
|
||||
|
||||
if new_token_id == llama_token_eos(context) || n_cur == n_len {
|
||||
print("\n")
|
||||
let new_token_str = String(cString: temporary_invalid_cchars + [0])
|
||||
temporary_invalid_cchars.removeAll()
|
||||
return new_token_str
|
||||
}
|
||||
|
||||
let new_token_cchars = token_to_piece(token: new_token_id)
|
||||
temporary_invalid_cchars.append(contentsOf: new_token_cchars)
|
||||
let new_token_str: String
|
||||
if let string = String(validatingUTF8: temporary_invalid_cchars + [0]) {
|
||||
temporary_invalid_cchars.removeAll()
|
||||
new_token_str = string
|
||||
} else if (0 ..< temporary_invalid_cchars.count).contains(where: {$0 != 0 && String(validatingUTF8: Array(temporary_invalid_cchars.suffix($0)) + [0]) != nil}) {
|
||||
// in this case, at least the suffix of the temporary_invalid_cchars can be interpreted as UTF8 string
|
||||
let string = String(cString: temporary_invalid_cchars + [0])
|
||||
temporary_invalid_cchars.removeAll()
|
||||
new_token_str = string
|
||||
} else {
|
||||
new_token_str = ""
|
||||
}
|
||||
print(new_token_str)
|
||||
// tokens_list.append(new_token_id)
|
||||
|
||||
batch.n_tokens = 0
|
||||
|
||||
batch.token[Int(batch.n_tokens)] = new_token_id
|
||||
batch.pos[Int(batch.n_tokens)] = n_cur
|
||||
batch.n_seq_id[Int(batch.n_tokens)] = 1
|
||||
batch.seq_id[Int(batch.n_tokens)]![0] = 0
|
||||
batch.logits[Int(batch.n_tokens)] = 1 // true
|
||||
batch.n_tokens += 1
|
||||
|
||||
n_decode += 1
|
||||
|
||||
n_cur += 1
|
||||
|
||||
if llama_decode(context, batch) != 0 {
|
||||
print("failed to evaluate llama!")
|
||||
}
|
||||
|
||||
return new_token_str
|
||||
}
|
||||
|
||||
func clear() {
|
||||
tokens_list.removeAll()
|
||||
temporary_invalid_cchars.removeAll()
|
||||
}
|
||||
|
||||
private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
|
||||
let utf8Count = text.utf8.count
|
||||
let n_tokens = utf8Count + (add_bos ? 1 : 0)
|
||||
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
|
||||
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
|
||||
|
||||
var swiftTokens: [llama_token] = []
|
||||
for i in 0..<tokenCount {
|
||||
swiftTokens.append(tokens[Int(i)])
|
||||
}
|
||||
|
||||
tokens.deallocate()
|
||||
|
||||
return swiftTokens
|
||||
}
|
||||
|
||||
/// - note: The result does not contain null-terminator
|
||||
private func token_to_piece(token: llama_token) -> [CChar] {
|
||||
let result = UnsafeMutablePointer<Int8>.allocate(capacity: 8)
|
||||
result.initialize(repeating: Int8(0), count: 8)
|
||||
defer {
|
||||
result.deallocate()
|
||||
}
|
||||
let nTokens = llama_token_to_piece(model, token, result, 8)
|
||||
|
||||
if nTokens < 0 {
|
||||
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
|
||||
newResult.initialize(repeating: Int8(0), count: Int(-nTokens))
|
||||
defer {
|
||||
newResult.deallocate()
|
||||
}
|
||||
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
|
||||
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
|
||||
return Array(bufferPointer)
|
||||
} else {
|
||||
let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nTokens))
|
||||
return Array(bufferPointer)
|
||||
}
|
||||
}
|
||||
}
|
||||
5
examples/llama.swiftui/llama.cpp.swift/bridging-header.h
Normal file
5
examples/llama.swiftui/llama.cpp.swift/bridging-header.h
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
//
|
||||
// Use this file to import your target's public headers that you would like to expose to Swift.
|
||||
//
|
||||
|
||||
#import "llama.h"
|
||||
481
examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
Normal file
481
examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
Normal file
|
|
@ -0,0 +1,481 @@
|
|||
// !$*UTF8*$!
|
||||
{
|
||||
archiveVersion = 1;
|
||||
classes = {
|
||||
};
|
||||
objectVersion = 56;
|
||||
objects = {
|
||||
|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
/* End PBXBuildFile section */
|
||||
|
||||
/* Begin PBXFileReference section */
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
GCC_WARN_UNDECLARED_SELECTOR = YES;
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GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
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||||
GCC_WARN_UNUSED_FUNCTION = YES;
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||||
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||||
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CLANG_WARN_ENUM_CONVERSION = YES;
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CLANG_WARN_INFINITE_RECURSION = YES;
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CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
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CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
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CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
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CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
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CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
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CLANG_WARN_STRICT_PROTOTYPES = YES;
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CLANG_WARN_SUSPICIOUS_MOVE = YES;
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CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
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CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
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COPY_PHASE_STRIP = NO;
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DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
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ENABLE_NS_ASSERTIONS = NO;
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ENABLE_STRICT_OBJC_MSGSEND = YES;
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ENABLE_USER_SCRIPT_SANDBOXING = YES;
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GCC_NO_COMMON_BLOCKS = YES;
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GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
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GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
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GCC_WARN_UNDECLARED_SELECTOR = YES;
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GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
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GCC_WARN_UNUSED_FUNCTION = YES;
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MTL_FAST_MATH = YES;
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VALIDATE_PRODUCT = YES;
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};
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name = Release;
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8A1C83822AC328BE0096AF73 /* Debug */ = {
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DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
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MARKETING_VERSION = 1.0;
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PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
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SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
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8A1C83832AC328BE0096AF73 /* Release */ = {
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isa = XCBuildConfiguration;
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buildSettings = {
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||||
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INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
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INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
|
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INFOPLIST_KEY_UILaunchScreen_Generation = YES;
|
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INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
|
||||
INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
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IPHONEOS_DEPLOYMENT_TARGET = 16.0;
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"$(inherited)",
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MARKETING_VERSION = 1.0;
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||||
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
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PRODUCT_NAME = "$(TARGET_NAME)";
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||||
SWIFT_EMIT_LOC_STRINGS = YES;
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SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
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||||
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||||
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name = Release;
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};
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/* End XCBuildConfiguration section */
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/* Begin XCConfigurationList section */
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8A1C836E2AC328BD0096AF73 /* Build configuration list for PBXProject "llama.swiftui" */ = {
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||||
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buildConfigurations = (
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||||
8A1C837F2AC328BE0096AF73 /* Debug */,
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8A1C83802AC328BE0096AF73 /* Release */,
|
||||
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|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Release;
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8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */ = {
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isa = XCConfigurationList;
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||||
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|
||||
8A1C83822AC328BE0096AF73 /* Debug */,
|
||||
8A1C83832AC328BE0096AF73 /* Release */,
|
||||
);
|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Release;
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/* End XCConfigurationList section */
|
||||
};
|
||||
rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
|
||||
}
|
||||
7
examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/contents.xcworkspacedata
generated
Normal file
7
examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/contents.xcworkspacedata
generated
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<Workspace
|
||||
version = "1.0">
|
||||
<FileRef
|
||||
location = "self:">
|
||||
</FileRef>
|
||||
</Workspace>
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>IDEDidComputeMac32BitWarning</key>
|
||||
<true/>
|
||||
</dict>
|
||||
</plist>
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
{
|
||||
"colors" : [
|
||||
{
|
||||
"idiom" : "universal"
|
||||
}
|
||||
],
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
{
|
||||
"images" : [
|
||||
{
|
||||
"idiom" : "universal",
|
||||
"platform" : "ios",
|
||||
"size" : "1024x1024"
|
||||
}
|
||||
],
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
45
examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
Normal file
45
examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
import Foundation
|
||||
|
||||
@MainActor
|
||||
class LlamaState: ObservableObject {
|
||||
@Published var messageLog = ""
|
||||
|
||||
private var llamaContext: LlamaContext?
|
||||
private var modelUrl: URL? {
|
||||
Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models")
|
||||
// Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
|
||||
}
|
||||
init() {
|
||||
do {
|
||||
try loadModel()
|
||||
} catch {
|
||||
messageLog += "Error!\n"
|
||||
}
|
||||
}
|
||||
|
||||
private func loadModel() throws {
|
||||
messageLog += "Loading model...\n"
|
||||
if let modelUrl {
|
||||
llamaContext = try LlamaContext.createContext(path: modelUrl.path())
|
||||
messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
|
||||
} else {
|
||||
messageLog += "Could not locate model\n"
|
||||
}
|
||||
}
|
||||
|
||||
func complete(text: String) async {
|
||||
guard let llamaContext else {
|
||||
return
|
||||
}
|
||||
messageLog += "Attempting to complete text...\n"
|
||||
await llamaContext.completion_init(text: text)
|
||||
messageLog += "\(text)"
|
||||
|
||||
while await llamaContext.n_cur <= llamaContext.n_len {
|
||||
let result = await llamaContext.completion_loop()
|
||||
messageLog += "\(result)"
|
||||
}
|
||||
await llamaContext.clear()
|
||||
messageLog += "\n\ndone\n"
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
0
examples/llama.swiftui/llama.swiftui/Resources/models/.gitignore
vendored
Normal file
0
examples/llama.swiftui/llama.swiftui/Resources/models/.gitignore
vendored
Normal file
42
examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
Normal file
42
examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
Normal file
|
|
@ -0,0 +1,42 @@
|
|||
import SwiftUI
|
||||
|
||||
struct ContentView: View {
|
||||
@StateObject var llamaState = LlamaState()
|
||||
|
||||
@State private var multiLineText = ""
|
||||
|
||||
var body: some View {
|
||||
VStack {
|
||||
ScrollView(.vertical) {
|
||||
Text(llamaState.messageLog)
|
||||
}
|
||||
|
||||
TextEditor(text: $multiLineText)
|
||||
.frame(height: 200)
|
||||
.padding()
|
||||
.border(Color.gray, width: 0.5)
|
||||
Button(action: {
|
||||
sendText()
|
||||
}) {
|
||||
Text("Send")
|
||||
.padding()
|
||||
.background(Color.blue)
|
||||
.foregroundColor(.white)
|
||||
.cornerRadius(8)
|
||||
}
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
|
||||
func sendText() {
|
||||
Task {
|
||||
await llamaState.complete(text: multiLineText)
|
||||
multiLineText = ""
|
||||
}
|
||||
}
|
||||
}
|
||||
/*
|
||||
#Preview {
|
||||
ContentView()
|
||||
}
|
||||
*/
|
||||
10
examples/llama.swiftui/llama.swiftui/llama_swiftuiApp.swift
Normal file
10
examples/llama.swiftui/llama.swiftui/llama_swiftuiApp.swift
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
import SwiftUI
|
||||
|
||||
@main
|
||||
struct llama_swiftuiApp: App {
|
||||
var body: some Scene {
|
||||
WindowGroup {
|
||||
ContentView()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -5,7 +5,7 @@ import json
|
|||
import torch
|
||||
import numpy as np
|
||||
from gguf import *
|
||||
from transformers import CLIPModel, CLIPProcessor
|
||||
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel
|
||||
|
||||
TEXT = "clip.text"
|
||||
VISION = "clip.vision"
|
||||
|
|
@ -78,11 +78,19 @@ ap.add_argument("--text-only", action="store_true", required=False,
|
|||
help="Save a text-only model. It can't be used to encode images")
|
||||
ap.add_argument("--vision-only", action="store_true", required=False,
|
||||
help="Save a vision-only model. It can't be used to encode texts")
|
||||
ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
|
||||
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
|
||||
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
|
||||
ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
|
||||
ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
|
||||
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
|
||||
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
|
||||
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
|
||||
default_image_std = [0.26862954, 0.26130258, 0.27577711]
|
||||
ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
|
||||
ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
|
||||
|
||||
# with proper
|
||||
args = ap.parse_args()
|
||||
|
||||
|
||||
|
|
@ -96,15 +104,22 @@ if args.use_f32:
|
|||
# output in the same directory as the model if output_dir is None
|
||||
dir_model = args.model_dir
|
||||
|
||||
|
||||
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
|
||||
vocab = json.load(f)
|
||||
tokens = [key for key in vocab]
|
||||
if args.clip_model_is_vision:
|
||||
vocab = None
|
||||
tokens = None
|
||||
else:
|
||||
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
|
||||
vocab = json.load(f)
|
||||
tokens = [key for key in vocab]
|
||||
|
||||
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
|
||||
config = json.load(f)
|
||||
v_hparams = config["vision_config"]
|
||||
t_hparams = config["text_config"]
|
||||
if args.clip_model_is_vision:
|
||||
v_hparams = config
|
||||
t_hparams = None
|
||||
else:
|
||||
v_hparams = config["vision_config"]
|
||||
t_hparams = config["text_config"]
|
||||
|
||||
# possible data types
|
||||
# ftype == 0 -> float32
|
||||
|
|
@ -117,9 +132,12 @@ ftype = 1
|
|||
if args.use_f32:
|
||||
ftype = 0
|
||||
|
||||
|
||||
model = CLIPModel.from_pretrained(dir_model)
|
||||
processor = CLIPProcessor.from_pretrained(dir_model)
|
||||
if args.clip_model_is_vision:
|
||||
model = CLIPVisionModel.from_pretrained(dir_model)
|
||||
processor = None
|
||||
else:
|
||||
model = CLIPModel.from_pretrained(dir_model)
|
||||
processor = CLIPProcessor.from_pretrained(dir_model)
|
||||
|
||||
fname_middle = None
|
||||
has_text_encoder = True
|
||||
|
|
@ -128,13 +146,13 @@ has_llava_projector = False
|
|||
if args.text_only:
|
||||
fname_middle = "text-"
|
||||
has_vision_encoder = False
|
||||
elif args.vision_only:
|
||||
fname_middle = "vision-"
|
||||
has_text_encoder = False
|
||||
elif args.llava_projector is not None:
|
||||
fname_middle = "mmproj-"
|
||||
has_text_encoder = False
|
||||
has_llava_projector = True
|
||||
elif args.vision_only:
|
||||
fname_middle = "vision-"
|
||||
has_text_encoder = False
|
||||
else:
|
||||
fname_middle = ""
|
||||
|
||||
|
|
@ -182,8 +200,12 @@ if has_vision_encoder:
|
|||
block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
|
||||
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
|
||||
|
||||
image_mean = processor.image_processor.image_mean if args.image_mean is None else args.image_mean
|
||||
image_std = processor.image_processor.image_std if args.image_std is None else args.image_std
|
||||
if processor is not None:
|
||||
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
|
||||
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
|
||||
else:
|
||||
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
|
||||
image_std = args.image_std if args.image_std is not None else default_image_std
|
||||
fout.add_array("clip.vision.image_mean", image_mean)
|
||||
fout.add_array("clip.vision.image_std", image_std)
|
||||
|
||||
|
|
|
|||
5
examples/lookahead/CMakeLists.txt
Normal file
5
examples/lookahead/CMakeLists.txt
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
set(TARGET lookahead)
|
||||
add_executable(${TARGET} lookahead.cpp)
|
||||
install(TARGETS ${TARGET} RUNTIME)
|
||||
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
|
||||
target_compile_features(${TARGET} PRIVATE cxx_std_11)
|
||||
7
examples/lookahead/README.md
Normal file
7
examples/lookahead/README.md
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
# llama.cpp/examples/lookahead
|
||||
|
||||
Demonstartion of lookahead decoding technique:
|
||||
|
||||
https://lmsys.org/blog/2023-11-21-lookahead-decoding/
|
||||
|
||||
More info: https://github.com/ggerganov/llama.cpp/pull/4207
|
||||
487
examples/lookahead/lookahead.cpp
Normal file
487
examples/lookahead/lookahead.cpp
Normal file
|
|
@ -0,0 +1,487 @@
|
|||
#include "common.h"
|
||||
#include "llama.h"
|
||||
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
struct ngram_data {
|
||||
bool active = false;
|
||||
|
||||
llama_seq_id seq_id = -1;
|
||||
|
||||
std::vector<int> i_batch;
|
||||
|
||||
std::vector<llama_token> tokens;
|
||||
};
|
||||
|
||||
// n-gram container
|
||||
struct ngram_container {
|
||||
ngram_container(int n_vocab, int N, int G) {
|
||||
cnt.resize(n_vocab);
|
||||
head.resize(n_vocab);
|
||||
tokens.resize(n_vocab * G * (N - 1));
|
||||
}
|
||||
|
||||
int n_total = 0;
|
||||
|
||||
std::vector<int> cnt;
|
||||
std::vector<int> head;
|
||||
|
||||
// [n_vocab][G][N - 1]
|
||||
// for each token of the vocab, keep a ring-buffer of capacity G of n-grams of size N - 1
|
||||
std::vector<llama_token> tokens;
|
||||
};
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
|
||||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
const int W = 15; // lookahead window
|
||||
const int N = 5; // n-gram size
|
||||
const int G = 15; // max verification n-grams
|
||||
|
||||
const bool dump_kv_cache = params.dump_kv_cache;
|
||||
|
||||
#ifndef LOG_DISABLE_LOGS
|
||||
log_set_target(log_filename_generator("lookahead", "log"));
|
||||
LOG_TEE("Log start\n");
|
||||
log_dump_cmdline(argc, argv);
|
||||
#endif // LOG_DISABLE_LOGS
|
||||
|
||||
// init llama.cpp
|
||||
llama_backend_init(params.numa);
|
||||
|
||||
llama_model * model = NULL;
|
||||
llama_context * ctx = NULL;
|
||||
|
||||
// load the target model
|
||||
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
||||
|
||||
// Tokenize the prompt
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
LOG("add_bos tgt: %d\n", add_bos);
|
||||
|
||||
std::vector<llama_token> inp;
|
||||
std::vector<llama_token> all;
|
||||
|
||||
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
all = inp;
|
||||
|
||||
const int max_context_size = llama_n_ctx(ctx);
|
||||
const int max_tokens_list_size = max_context_size - 4;
|
||||
|
||||
if ((int) inp.size() > max_tokens_list_size) {
|
||||
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
|
||||
return 1;
|
||||
}
|
||||
|
||||
fprintf(stderr, "\n\n");
|
||||
|
||||
for (auto id : inp) {
|
||||
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
|
||||
}
|
||||
|
||||
fflush(stderr);
|
||||
|
||||
const int n_input = inp.size();
|
||||
|
||||
const auto t_enc_start = ggml_time_us();
|
||||
|
||||
// eval the prompt
|
||||
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
|
||||
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
|
||||
|
||||
for (int s = 1; s < W + G + 1; ++s) {
|
||||
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
|
||||
}
|
||||
|
||||
const auto t_enc_end = ggml_time_us();
|
||||
|
||||
int n_predict = 0;
|
||||
int n_accept = 0;
|
||||
|
||||
int n_past = inp.size();
|
||||
|
||||
llama_token id = 0;
|
||||
|
||||
// used to determine end of generation
|
||||
bool has_eos = false;
|
||||
|
||||
// for each decoded batch, we have at most W + G + 1 distinct sequences:
|
||||
// seq_id == 0 : the current input token
|
||||
// seq_id [1, W] : tokens from the past N - 1 Jacobi iterations
|
||||
// seq_id [W + 1, W + G] : verification n-grams
|
||||
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
|
||||
|
||||
// target model sampling context
|
||||
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
|
||||
|
||||
// verification n-grams
|
||||
std::vector<ngram_data> ngrams_cur(G);
|
||||
|
||||
// tokens for the past N - 1 Jacobi iterations
|
||||
std::vector<llama_token> tokens_j_prev(W);
|
||||
std::vector<std::vector<llama_token>> tokens_j(N - 1);
|
||||
for (int j = 0; j < N - 1; j++) {
|
||||
tokens_j[j].resize(W);
|
||||
|
||||
for (int i = 0; i < W; i++) {
|
||||
// there are different ways to init these tokens
|
||||
if (0) {
|
||||
// initialize randomly from the prompt tokens
|
||||
tokens_j[j][i] = all[1 + rand() % (all.size() - 1)];
|
||||
} else {
|
||||
// initialize with a sequence of increasing numbers
|
||||
tokens_j[j][i] = 100 + i;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<llama_seq_id> seq_id_look;
|
||||
|
||||
// the input token belongs both to all sequences
|
||||
std::vector<llama_seq_id> seq_id_all(W + G + 1);
|
||||
for (int i = 0; i < W + G + 1; i++) {
|
||||
seq_id_all[i] = i;
|
||||
}
|
||||
|
||||
// here we keep adding new n-grams as we go
|
||||
ngram_container ngrams_observed(llama_n_vocab(model), N, G);
|
||||
|
||||
// debug
|
||||
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
|
||||
|
||||
const auto t_dec_start = ggml_time_us();
|
||||
|
||||
// sample first token
|
||||
{
|
||||
id = llama_sampling_sample(ctx_sampling, ctx, NULL, 0);
|
||||
|
||||
llama_sampling_accept(ctx_sampling, ctx, id, true);
|
||||
|
||||
{
|
||||
const std::string token_str = llama_token_to_piece(ctx, id);
|
||||
|
||||
printf("%s", token_str.c_str());
|
||||
fflush(stdout);
|
||||
}
|
||||
}
|
||||
|
||||
while (true) {
|
||||
// debug
|
||||
if (dump_kv_cache) {
|
||||
llama_kv_cache_view_update(ctx, &kvc_view);
|
||||
dump_kv_cache_view_seqs(kvc_view, 40);
|
||||
}
|
||||
|
||||
// build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
|
||||
//
|
||||
// Example for W = 5, N = 4, G = 2:
|
||||
// (I = input, L = lookahead, V = verification)
|
||||
//
|
||||
// Batch: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
|
||||
// T: -2 -2 -2 -2 -1 -1 -1 -1 -1 0 0 0 0 0 0
|
||||
// Info: I L L L L L L L L L L L L L L V V V V V V
|
||||
// Pos: 0 1 2 3 4 1 2 3 4 5 2 3 4 5 6 1 2 3 1 2 3 (+ n_past)
|
||||
// Logits: 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
|
||||
// ---------------------------------------------------------------------
|
||||
// Seq: 0
|
||||
// 1 1 1
|
||||
// 2 2 2 2
|
||||
// 3 3 3 3 3
|
||||
// 4 4 4 4 4 4
|
||||
// 5 5 5 5 5 5 5
|
||||
// 6 6 6 6
|
||||
// 7 7 7 7
|
||||
// ---------------------------------------------------------------------
|
||||
// | | | | | | | | | | |
|
||||
// V V V V V | | | | | |
|
||||
// j_tokens | | | | | |
|
||||
// V V V V V V
|
||||
// id
|
||||
{
|
||||
llama_batch_clear(batch);
|
||||
|
||||
// current token - first token of the first level
|
||||
llama_batch_add(batch, id, n_past, seq_id_all, true);
|
||||
|
||||
// verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
|
||||
{
|
||||
const int g_cur = ngrams_observed.cnt[id];
|
||||
|
||||
ngrams_cur.resize(g_cur);
|
||||
for (int g = 0; g < g_cur; g++) {
|
||||
ngrams_cur[g].active = true;
|
||||
ngrams_cur[g].tokens.resize(N);
|
||||
ngrams_cur[g].i_batch.resize(N);
|
||||
ngrams_cur[g].seq_id = W + 1 + g;
|
||||
ngrams_cur[g].i_batch[0] = 0;
|
||||
ngrams_cur[g].tokens [0] = id;
|
||||
}
|
||||
|
||||
for (int j = 0; j < N - 1; j++) {
|
||||
for (int g = 0; g < g_cur; g++) {
|
||||
const int idx = id*(N - 1)*G + g*(N - 1);
|
||||
|
||||
const llama_token t = ngrams_observed.tokens[idx + j];
|
||||
|
||||
ngrams_cur[g].tokens [j + 1] = t;
|
||||
ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
|
||||
|
||||
llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// fill the remaining W - 1 tokens for the first level
|
||||
for (int i = 1; i < W; i++) {
|
||||
seq_id_look.resize(W - i);
|
||||
for (int j = 0; j < W - i; j++) {
|
||||
seq_id_look[j] = i + j + 1;
|
||||
}
|
||||
|
||||
llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
|
||||
}
|
||||
|
||||
// fill the rest of the levels
|
||||
for (int j = 1; j < N - 1; j++) {
|
||||
for (int i = 0; i < W; i++) {
|
||||
llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (llama_decode(ctx, batch) != 0) {
|
||||
fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
int seq_id_best = 0;
|
||||
|
||||
for (int v = 0; v < N; ++v) {
|
||||
int i_batch = 0;
|
||||
|
||||
// if no active ngrams are left, it means the sampled token does not pass the verification
|
||||
if (v > 0) {
|
||||
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
|
||||
if (ngrams_cur[g].active) {
|
||||
i_batch = ngrams_cur[g].i_batch[v];
|
||||
seq_id_best = ngrams_cur[g].seq_id;
|
||||
|
||||
++n_accept;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// no more matches -> create a new batch
|
||||
if (i_batch == 0) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// sample the next token
|
||||
id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_batch);
|
||||
|
||||
llama_sampling_accept(ctx_sampling, ctx, id, true);
|
||||
|
||||
// print
|
||||
{
|
||||
const std::string token_str = llama_token_to_piece(ctx, id);
|
||||
|
||||
if (v == 0) {
|
||||
printf("%s", token_str.c_str());
|
||||
} else {
|
||||
// print light cyan
|
||||
printf("\033[0;96m%s\033[0m", token_str.c_str());
|
||||
}
|
||||
fflush(stdout);
|
||||
|
||||
if (id == llama_token_eos(model)) {
|
||||
has_eos = true;
|
||||
}
|
||||
|
||||
all.push_back(id);
|
||||
}
|
||||
|
||||
++n_predict;
|
||||
++n_past;
|
||||
|
||||
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
|
||||
break;
|
||||
}
|
||||
|
||||
// verify across active n-grams
|
||||
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
|
||||
if (ngrams_cur[g].active) {
|
||||
if (v == N - 1) {
|
||||
ngrams_cur[g].active = false;
|
||||
} else {
|
||||
if (id != ngrams_cur[g].tokens[v + 1]) {
|
||||
ngrams_cur[g].active = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// print known n-grams starting with token id (debug)
|
||||
if (0 && v == 0) {
|
||||
if (ngrams_observed.cnt[id] > 0) {
|
||||
printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
|
||||
}
|
||||
|
||||
for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
|
||||
printf(" - ngram %2d: ", i);
|
||||
|
||||
const int idx = id*(N - 1)*G + i*(N - 1);
|
||||
|
||||
for (int j = 0; j < N - 1; j++) {
|
||||
const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
|
||||
|
||||
printf("%s", token_str.c_str());
|
||||
}
|
||||
|
||||
printf("\n");
|
||||
}
|
||||
}
|
||||
|
||||
// update lookahead tokens
|
||||
{
|
||||
for (int i = 0; i < W; i++) {
|
||||
tokens_j_prev[i] = tokens_j[0][i];
|
||||
}
|
||||
|
||||
for (int j = 0; j < N - 2; j++) {
|
||||
tokens_j[j] = tokens_j[j + 1];
|
||||
}
|
||||
|
||||
if (v == 0) {
|
||||
// sample from the last level
|
||||
for (int i = 0; i < W; i++) {
|
||||
tokens_j[N - 2][i] = llama_sampling_sample(ctx_sampling, ctx, NULL, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
|
||||
}
|
||||
} else {
|
||||
for (int i = 0; i < W; i++) {
|
||||
// there are different ways to init these tokens
|
||||
if (0) {
|
||||
// random init
|
||||
tokens_j[N - 2][i] = all[1 + rand() % (all.size() - 1)];
|
||||
} else {
|
||||
// init from the previous level
|
||||
tokens_j[N - 2][i] = tokens_j[0][i];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// update observed ngrams
|
||||
if (v == 0) {
|
||||
// the first token of the n-gram is determined by the index in the container so it is not stored
|
||||
std::vector<llama_token> ngram(N - 1);
|
||||
|
||||
// n-gram generation
|
||||
// ref: https://github.com/hao-ai-lab/LookaheadDecoding/issues/14#issuecomment-1826198518
|
||||
for (int f = 0; f < W; ++f) {
|
||||
const int ft = tokens_j_prev[f]; // first token of the n-gram
|
||||
|
||||
for (int j = 0; j < N - 1; ++j) {
|
||||
ngram[j] = tokens_j[j][f];
|
||||
}
|
||||
|
||||
// filter-out repeating n-grams
|
||||
{
|
||||
bool is_unique = true;
|
||||
|
||||
for (int k = 0; k < ngrams_observed.cnt[ft]; ++k) {
|
||||
const int idx = ft*(N - 1)*G + k*(N - 1);
|
||||
|
||||
bool is_match = true;
|
||||
for (int j = 0; j < N - 1; ++j) {
|
||||
if (ngrams_observed.tokens[idx + j] != ngram[j]) {
|
||||
is_match = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (is_match) {
|
||||
is_unique = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (!is_unique) {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
const int head = ngrams_observed.head[ft];
|
||||
const int idx = ft*(N - 1)*G + head*(N - 1);
|
||||
|
||||
for (int i = 0; i < N - 1; i++) {
|
||||
ngrams_observed.tokens[idx + i] = ngram[i];
|
||||
}
|
||||
|
||||
ngrams_observed.cnt[ft] = std::min(G, ngrams_observed.cnt[ft] + 1);
|
||||
ngrams_observed.head[ft] = (head + 1) % G;
|
||||
|
||||
ngrams_observed.n_total++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
|
||||
break;
|
||||
}
|
||||
|
||||
// KV cache management
|
||||
// if no verification token matched, we simply remove all cells from this batch -> no fragmentation
|
||||
llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
|
||||
|
||||
if (seq_id_best != 0) {
|
||||
// if a verification token matched, we keep the best sequence and remove the rest
|
||||
// this leads to some KV cache fragmentation
|
||||
llama_kv_cache_seq_keep(ctx, seq_id_best);
|
||||
llama_kv_cache_seq_cp (ctx, seq_id_best, 0, -1, -1);
|
||||
llama_kv_cache_seq_rm (ctx, seq_id_best, -1, -1);
|
||||
|
||||
for (int s = 1; s < W + G + 1; ++s) {
|
||||
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
auto t_dec_end = ggml_time_us();
|
||||
|
||||
LOG_TEE("\n\n");
|
||||
|
||||
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
|
||||
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
|
||||
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("W = %2d\n", W);
|
||||
LOG_TEE("N = %2d\n", N);
|
||||
LOG_TEE("G = %2d\n", G);
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("n_predict = %d\n", n_predict);
|
||||
LOG_TEE("n_accept = %d\n", n_accept);
|
||||
|
||||
llama_print_timings(ctx);
|
||||
|
||||
llama_kv_cache_view_free(&kvc_view);
|
||||
llama_sampling_free(ctx_sampling);
|
||||
|
||||
llama_batch_free(batch);
|
||||
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
|
||||
llama_backend_free();
|
||||
|
||||
fprintf(stderr, "\n\n");
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -100,6 +100,12 @@ static void sigint_handler(int signo) {
|
|||
}
|
||||
#endif
|
||||
|
||||
static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
|
||||
(void) level;
|
||||
(void) user_data;
|
||||
LOG_TEE("%s", text);
|
||||
}
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
g_params = ¶ms;
|
||||
|
|
@ -113,6 +119,7 @@ int main(int argc, char ** argv) {
|
|||
log_set_target(log_filename_generator("main", "log"));
|
||||
LOG_TEE("Log start\n");
|
||||
log_dump_cmdline(argc, argv);
|
||||
llama_log_set(llama_log_callback_logTee, nullptr);
|
||||
#endif // LOG_DISABLE_LOGS
|
||||
|
||||
// TODO: Dump params ?
|
||||
|
|
|
|||
|
|
@ -234,6 +234,55 @@ node index.js
|
|||
|
||||
- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
|
||||
|
||||
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
|
||||
|
||||
*Options:*
|
||||
|
||||
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
|
||||
|
||||
*Examples:*
|
||||
|
||||
You can use either Python `openai` library with appropriate checkpoints:
|
||||
|
||||
```python
|
||||
import openai
|
||||
|
||||
client = openai.OpenAI(
|
||||
base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
|
||||
api_key = "sk-no-key-required"
|
||||
)
|
||||
|
||||
completion = client.chat.completions.create(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[
|
||||
{"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
|
||||
{"role": "user", "content": "Write a limerick about python exceptions"}
|
||||
]
|
||||
)
|
||||
|
||||
print(completion.choices[0].message)
|
||||
```
|
||||
... or raw HTTP requests:
|
||||
|
||||
```shell
|
||||
curl http://localhost:8080/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "Authorization: Bearer no-key" \
|
||||
-d '{
|
||||
"model": "gpt-3.5-turbo",
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Write a limerick about python exceptions"
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
## More examples
|
||||
|
||||
### Change system prompt on runtime
|
||||
|
|
|
|||
|
|
@ -11,10 +11,10 @@ app = Flask(__name__)
|
|||
slot_id = -1
|
||||
|
||||
parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
|
||||
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
|
||||
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
|
||||
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
|
||||
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
|
||||
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
|
||||
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
|
||||
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
|
||||
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
|
||||
parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
|
||||
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
|
||||
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
|
||||
|
|
@ -34,19 +34,19 @@ def is_present(json, key):
|
|||
|
||||
#convert chat to prompt
|
||||
def convert_chat(messages):
|
||||
prompt = "" + args.chat_prompt.replace("\\n", "\n")
|
||||
|
||||
system_n = args.system_name.replace("\\n", "\n")
|
||||
user_n = args.user_name.replace("\\n", "\n")
|
||||
ai_n = args.ai_name.replace("\\n", "\n")
|
||||
stop = args.stop.replace("\\n", "\n")
|
||||
system_n = args.system_name
|
||||
user_n = args.user_name
|
||||
ai_n = args.ai_name
|
||||
stop = args.stop
|
||||
|
||||
prompt = "" + args.chat_prompt + stop
|
||||
|
||||
for line in messages:
|
||||
if (line["role"] == "system"):
|
||||
prompt += f"{system_n}{line['content']}"
|
||||
prompt += f"{system_n}{line['content']}{stop}"
|
||||
if (line["role"] == "user"):
|
||||
prompt += f"{user_n}{line['content']}"
|
||||
prompt += f"{user_n}{line['content']}{stop}"
|
||||
if (line["role"] == "assistant"):
|
||||
prompt += f"{ai_n}{line['content']}{stop}"
|
||||
prompt += ai_n.rstrip()
|
||||
|
|
@ -70,6 +70,7 @@ def make_postData(body, chat=False, stream=False):
|
|||
if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
|
||||
if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
|
||||
if(is_present(body, "seed")): postData["seed"] = body["seed"]
|
||||
if(is_present(body, "grammar")): postData["grammar"] = body["grammar"]
|
||||
if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
|
||||
if (args.stop != ""):
|
||||
postData["stop"] = [args.stop]
|
||||
|
|
@ -130,7 +131,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
|
|||
}
|
||||
]
|
||||
}
|
||||
slot_id = data["slot_id"]
|
||||
slot_id = data.get("slot_id")
|
||||
if (chat):
|
||||
if (start):
|
||||
resData["choices"][0]["delta"] = {
|
||||
|
|
@ -150,11 +151,13 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
|
|||
return resData
|
||||
|
||||
|
||||
@app.route('/chat/completions', methods=['POST'])
|
||||
@app.route('/v1/chat/completions', methods=['POST'])
|
||||
@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
|
||||
@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
|
||||
def chat_completions():
|
||||
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
|
||||
return Response(status=403)
|
||||
if request.method == 'OPTIONS':
|
||||
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
|
||||
body = request.get_json()
|
||||
stream = False
|
||||
tokenize = False
|
||||
|
|
@ -177,20 +180,22 @@ def chat_completions():
|
|||
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
|
||||
time_now = int(time.time())
|
||||
resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
|
||||
yield 'data: {}\n'.format(json.dumps(resData))
|
||||
yield 'data: {}\n\n'.format(json.dumps(resData))
|
||||
for line in data.iter_lines():
|
||||
if line:
|
||||
decoded_line = line.decode('utf-8')
|
||||
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
|
||||
yield 'data: {}\n'.format(json.dumps(resData))
|
||||
return Response(generate(), mimetype='text/event-stream')
|
||||
yield 'data: {}\n\n'.format(json.dumps(resData))
|
||||
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
|
||||
|
||||
|
||||
@app.route('/completions', methods=['POST'])
|
||||
@app.route('/v1/completions', methods=['POST'])
|
||||
@app.route('/completions', methods=['POST', 'OPTIONS'])
|
||||
@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
|
||||
def completion():
|
||||
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
|
||||
return Response(status=403)
|
||||
if request.method == 'OPTIONS':
|
||||
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
|
||||
body = request.get_json()
|
||||
stream = False
|
||||
tokenize = False
|
||||
|
|
@ -216,8 +221,8 @@ def completion():
|
|||
if line:
|
||||
decoded_line = line.decode('utf-8')
|
||||
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
|
||||
yield 'data: {}\n'.format(json.dumps(resData))
|
||||
return Response(generate(), mimetype='text/event-stream')
|
||||
yield 'data: {}\n\n'.format(json.dumps(resData))
|
||||
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(args.host, port=args.port)
|
||||
|
|
|
|||
|
|
@ -29,6 +29,8 @@
|
|||
#define SERVER_VERBOSE 1
|
||||
#endif
|
||||
|
||||
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
|
||||
|
||||
using json = nlohmann::json;
|
||||
|
||||
struct server_params
|
||||
|
|
@ -59,6 +61,10 @@ static bool server_verbose = false;
|
|||
#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
|
||||
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
|
||||
|
||||
json oaicompat_completion_params_parse(const json &body);
|
||||
std::string format_chatml(std::vector<json> messages);
|
||||
|
||||
|
||||
//
|
||||
// base64 utils (TODO: move to common in the future)
|
||||
//
|
||||
|
|
@ -149,15 +155,23 @@ struct task_server {
|
|||
json data;
|
||||
bool infill_mode = false;
|
||||
bool embedding_mode = false;
|
||||
int multitask_id = -1;
|
||||
};
|
||||
|
||||
struct task_result {
|
||||
int id;
|
||||
int multitask_id = -1;
|
||||
bool stop;
|
||||
bool error;
|
||||
json result_json;
|
||||
};
|
||||
|
||||
struct task_multi {
|
||||
int id;
|
||||
std::set<int> subtasks_remaining{};
|
||||
std::vector<task_result> results{};
|
||||
};
|
||||
|
||||
// TODO: can become bool if we can't find use of more states
|
||||
enum slot_state
|
||||
{
|
||||
|
|
@ -378,6 +392,9 @@ struct llama_client_slot
|
|||
bool stopped_word = false;
|
||||
bool stopped_limit = false;
|
||||
|
||||
bool oaicompat = false;
|
||||
std::string oaicompat_model;
|
||||
|
||||
std::string stopping_word;
|
||||
|
||||
// sampling
|
||||
|
|
@ -397,6 +414,9 @@ struct llama_client_slot
|
|||
double t_prompt_processing; // ms
|
||||
double t_token_generation; // ms
|
||||
|
||||
// multitasks
|
||||
int multitask_id = -1;
|
||||
|
||||
void reset() {
|
||||
num_prompt_tokens = 0;
|
||||
generated_text = "";
|
||||
|
|
@ -477,7 +497,7 @@ struct llama_client_slot
|
|||
};
|
||||
}
|
||||
|
||||
void print_timings() {
|
||||
void print_timings() const {
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
|
||||
__func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
|
||||
|
|
@ -520,7 +540,8 @@ struct llama_server_context
|
|||
|
||||
std::vector<task_server> queue_tasks;
|
||||
std::vector<task_result> queue_results;
|
||||
std::mutex mutex_tasks;
|
||||
std::vector<task_multi> queue_multitasks;
|
||||
std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks
|
||||
std::mutex mutex_results;
|
||||
|
||||
~llama_server_context()
|
||||
|
|
@ -609,6 +630,11 @@ struct llama_server_context
|
|||
|
||||
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
|
||||
{
|
||||
// TODO: currently, we tokenize using special tokens by default
|
||||
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
|
||||
// but it's better compared to completely ignoring ChatML and other chat templates
|
||||
const bool TMP_FORCE_SPECIAL = true;
|
||||
|
||||
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
|
||||
// or the first element of the json_prompt array is a string.
|
||||
std::vector<llama_token> prompt_tokens;
|
||||
|
|
@ -624,12 +650,12 @@ struct llama_server_context
|
|||
std::vector<llama_token> p;
|
||||
if (first)
|
||||
{
|
||||
p = ::llama_tokenize(ctx, s, add_bos);
|
||||
p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
|
||||
first = false;
|
||||
}
|
||||
else
|
||||
{
|
||||
p = ::llama_tokenize(ctx, s, false);
|
||||
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
|
||||
}
|
||||
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
|
||||
}
|
||||
|
|
@ -646,7 +672,7 @@ struct llama_server_context
|
|||
else
|
||||
{
|
||||
auto s = json_prompt.template get<std::string>();
|
||||
prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
|
||||
prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
|
||||
}
|
||||
|
||||
return prompt_tokens;
|
||||
|
|
@ -677,6 +703,14 @@ struct llama_server_context
|
|||
slot_params default_params;
|
||||
llama_sampling_params default_sparams;
|
||||
|
||||
if (data.count("__oaicompat") != 0) {
|
||||
slot->oaicompat = true;
|
||||
slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
|
||||
} else {
|
||||
slot->oaicompat = false;
|
||||
slot->oaicompat_model = "";
|
||||
}
|
||||
|
||||
slot->params.stream = json_value(data, "stream", false);
|
||||
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
|
||||
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
|
||||
|
|
@ -1090,17 +1124,40 @@ struct llama_server_context
|
|||
return slot.images.size() > 0;
|
||||
}
|
||||
|
||||
void send_error(int id, std::string error)
|
||||
void send_error(task_server& task, std::string error)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = id;
|
||||
res.id = task.id;
|
||||
res.multitask_id = task.multitask_id;
|
||||
res.stop = false;
|
||||
res.error = true;
|
||||
res.result_json = { { "content", error } };
|
||||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
void add_multi_task(int id, std::vector<int>& sub_ids)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_tasks);
|
||||
task_multi multi;
|
||||
multi.id = id;
|
||||
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
|
||||
queue_multitasks.push_back(multi);
|
||||
}
|
||||
|
||||
void update_multi_task(int multitask_id, int subtask_id, task_result& result)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_tasks);
|
||||
for (auto& multitask : queue_multitasks)
|
||||
{
|
||||
if (multitask.id == multitask_id)
|
||||
{
|
||||
multitask.subtasks_remaining.erase(subtask_id);
|
||||
multitask.results.push_back(result);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
json get_model_props()
|
||||
{
|
||||
return get_formated_generation(slots[0]);
|
||||
|
|
@ -1145,6 +1202,7 @@ struct llama_server_context
|
|||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = slot.task_id;
|
||||
res.multitask_id = slot.multitask_id;
|
||||
res.error = false;
|
||||
res.stop = false;
|
||||
|
||||
|
|
@ -1170,6 +1228,12 @@ struct llama_server_context
|
|||
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
|
||||
}
|
||||
|
||||
if (slot.oaicompat)
|
||||
{
|
||||
res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
|
||||
res.result_json["model"] = slot.oaicompat_model;
|
||||
}
|
||||
|
||||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
|
|
@ -1178,6 +1242,7 @@ struct llama_server_context
|
|||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = slot.task_id;
|
||||
res.multitask_id = slot.multitask_id;
|
||||
res.error = false;
|
||||
res.stop = true;
|
||||
|
||||
|
|
@ -1217,6 +1282,18 @@ struct llama_server_context
|
|||
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
|
||||
}
|
||||
|
||||
if (slot.oaicompat)
|
||||
{
|
||||
res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
|
||||
res.result_json["model"] = slot.oaicompat_model;
|
||||
}
|
||||
|
||||
// parent multitask, if any, needs to be updated
|
||||
if (slot.multitask_id != -1)
|
||||
{
|
||||
update_multi_task(slot.multitask_id, slot.task_id, res);
|
||||
}
|
||||
|
||||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
|
|
@ -1225,6 +1302,7 @@ struct llama_server_context
|
|||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
task_result res;
|
||||
res.id = slot.task_id;
|
||||
res.multitask_id = slot.multitask_id;
|
||||
res.error = false;
|
||||
res.stop = true;
|
||||
|
||||
|
|
@ -1251,16 +1329,26 @@ struct llama_server_context
|
|||
queue_results.push_back(res);
|
||||
}
|
||||
|
||||
int request_completion(json data, bool infill, bool embedding)
|
||||
int request_completion(json data, bool infill, bool embedding, int multitask_id)
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_tasks);
|
||||
std::unique_lock<std::mutex> lock(mutex_tasks);
|
||||
task_server task;
|
||||
task.id = id_gen++;
|
||||
task.target_id = 0;
|
||||
task.data = data;
|
||||
task.data = std::move(data);
|
||||
task.infill_mode = infill;
|
||||
task.embedding_mode = embedding;
|
||||
task.type = COMPLETION_TASK;
|
||||
task.multitask_id = multitask_id;
|
||||
|
||||
// when a completion task's prompt array is not a singleton, we split it into multiple requests
|
||||
if (task.data.at("prompt").size() > 1)
|
||||
{
|
||||
lock.unlock(); // entering new func scope
|
||||
return split_multiprompt_task(task);
|
||||
}
|
||||
|
||||
// otherwise, it's a single-prompt task, we actually queue it
|
||||
queue_tasks.push_back(task);
|
||||
return task.id;
|
||||
}
|
||||
|
|
@ -1279,8 +1367,17 @@ struct llama_server_context
|
|||
|
||||
for (int i = 0; i < (int) queue_results.size(); i++)
|
||||
{
|
||||
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
|
||||
if (queue_results[i].multitask_id == task_id)
|
||||
{
|
||||
update_multi_task(task_id, queue_results[i].id, queue_results[i]);
|
||||
queue_results.erase(queue_results.begin() + i);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (queue_results[i].id == task_id)
|
||||
{
|
||||
assert(queue_results[i].multitask_id == -1);
|
||||
task_result res = queue_results[i];
|
||||
queue_results.erase(queue_results.begin() + i);
|
||||
return res;
|
||||
|
|
@ -1370,6 +1467,27 @@ struct llama_server_context
|
|||
queue_tasks.push_back(task);
|
||||
}
|
||||
|
||||
int split_multiprompt_task(task_server& multiprompt_task)
|
||||
{
|
||||
int prompt_count = multiprompt_task.data.at("prompt").size();
|
||||
assert(prompt_count > 1);
|
||||
|
||||
int multitask_id = id_gen++;
|
||||
std::vector<int> subtask_ids(prompt_count);
|
||||
for (int i = 0; i < prompt_count; i++)
|
||||
{
|
||||
json subtask_data = multiprompt_task.data;
|
||||
subtask_data["prompt"] = subtask_data["prompt"][i];
|
||||
|
||||
// subtasks inherit everything else (infill mode, embedding mode, etc.)
|
||||
subtask_ids[i] = request_completion(subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
|
||||
}
|
||||
|
||||
// queue up the multitask so we can track its subtask progression
|
||||
add_multi_task(multitask_id, subtask_ids);
|
||||
return multitask_id;
|
||||
}
|
||||
|
||||
void process_tasks()
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mutex_tasks);
|
||||
|
|
@ -1385,7 +1503,7 @@ struct llama_server_context
|
|||
{
|
||||
LOG_TEE("slot unavailable\n");
|
||||
// send error result
|
||||
send_error(task.id, "slot unavailable");
|
||||
send_error(task, "slot unavailable");
|
||||
return;
|
||||
}
|
||||
|
||||
|
|
@ -1399,11 +1517,12 @@ struct llama_server_context
|
|||
slot->infill = task.infill_mode;
|
||||
slot->embedding = task.embedding_mode;
|
||||
slot->task_id = task.id;
|
||||
slot->multitask_id = task.multitask_id;
|
||||
|
||||
if (!launch_slot_with_data(slot, task.data))
|
||||
{
|
||||
// send error result
|
||||
send_error(task.id, "internal_error");
|
||||
send_error(task, "internal_error");
|
||||
break;
|
||||
}
|
||||
} break;
|
||||
|
|
@ -1419,6 +1538,38 @@ struct llama_server_context
|
|||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue
|
||||
auto queue_iterator = queue_multitasks.begin();
|
||||
while (queue_iterator != queue_multitasks.end())
|
||||
{
|
||||
if (queue_iterator->subtasks_remaining.empty())
|
||||
{
|
||||
// all subtasks done == multitask is done
|
||||
task_result aggregate_result;
|
||||
aggregate_result.id = queue_iterator->id;
|
||||
aggregate_result.stop = true;
|
||||
aggregate_result.error = false;
|
||||
|
||||
// collect json results into one json result
|
||||
std::vector<json> result_jsons;
|
||||
for (auto& subres : queue_iterator->results)
|
||||
{
|
||||
result_jsons.push_back(subres.result_json);
|
||||
aggregate_result.error = aggregate_result.error && subres.error;
|
||||
}
|
||||
aggregate_result.result_json = json{ "results", result_jsons };
|
||||
|
||||
std::lock_guard<std::mutex> lock(mutex_results);
|
||||
queue_results.push_back(aggregate_result);
|
||||
|
||||
queue_iterator = queue_multitasks.erase(queue_iterator);
|
||||
}
|
||||
else
|
||||
{
|
||||
++queue_iterator;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool update_slots() {
|
||||
|
|
@ -1810,6 +1961,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
|||
printf(" -spf FNAME, --system-prompt-file FNAME\n");
|
||||
printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
|
||||
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n");
|
||||
printf(" --log-disable disables logging to a file.\n");
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
|
|
@ -2164,6 +2316,11 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
|||
}
|
||||
params.mmproj = argv[i];
|
||||
}
|
||||
else if (arg == "--log-disable")
|
||||
{
|
||||
log_set_target(stdout);
|
||||
LOG_INFO("logging to file is disabled.", {});
|
||||
}
|
||||
else
|
||||
{
|
||||
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
||||
|
|
@ -2180,6 +2337,231 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
|||
}
|
||||
}
|
||||
|
||||
|
||||
static std::string random_string()
|
||||
{
|
||||
static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
|
||||
|
||||
std::random_device rd;
|
||||
std::mt19937 generator(rd());
|
||||
|
||||
std::string result(32, ' ');
|
||||
|
||||
for (int i = 0; i < 32; ++i) {
|
||||
result[i] = str[generator() % str.size()];
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
static std::string gen_chatcmplid()
|
||||
{
|
||||
std::stringstream chatcmplid;
|
||||
chatcmplid << "chatcmpl-" << random_string();
|
||||
return chatcmplid.str();
|
||||
}
|
||||
|
||||
std::string format_chatml(std::vector<json> messages)
|
||||
{
|
||||
std::ostringstream chatml_msgs;
|
||||
|
||||
for (auto it = messages.begin(); it != messages.end(); ++it) {
|
||||
chatml_msgs << "<|im_start|>"
|
||||
<< json_value(*it, "role", std::string("user")) << '\n';
|
||||
chatml_msgs << json_value(*it, "content", std::string(""))
|
||||
<< "<|im_end|>\n";
|
||||
}
|
||||
|
||||
chatml_msgs << "<|im_start|>assistant" << '\n';
|
||||
|
||||
return chatml_msgs.str();
|
||||
}
|
||||
|
||||
/* llama.cpp completion api semantics */
|
||||
json oaicompat_completion_params_parse(
|
||||
const json &body /* openai api json semantics */)
|
||||
{
|
||||
json llama_params;
|
||||
|
||||
llama_params["__oaicompat"] = true;
|
||||
|
||||
// Map OpenAI parameters to llama.cpp parameters
|
||||
llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
|
||||
llama_params["temperature"] = json_value(body, "temperature", 0.8);
|
||||
llama_params["top_k"] = json_value(body, "top_k", 40);
|
||||
llama_params["top_p"] = json_value(body, "top_p", 0.95);
|
||||
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
|
||||
llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
|
||||
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
|
||||
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
|
||||
llama_params["seed"] = json_value(body, "seed", 0);
|
||||
llama_params["stream"] = json_value(body, "stream", false);
|
||||
llama_params["mirostat"] = json_value(body, "mirostat", false);
|
||||
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0);
|
||||
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0);
|
||||
llama_params["penalize_nl"] = json_value(body, "penalize_nl", false);
|
||||
llama_params["typical_p"] = json_value(body, "typical_p", 0.0);
|
||||
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
|
||||
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
|
||||
llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
|
||||
|
||||
if (llama_params.count("grammar") != 0) {
|
||||
llama_params["grammar"] = json_value(body, "grammar", json::object());
|
||||
}
|
||||
|
||||
// Handle 'stop' field
|
||||
if (body.contains("stop") && body["stop"].is_string()) {
|
||||
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
|
||||
} else {
|
||||
llama_params["stop"] = json_value(body, "stop", json::array());
|
||||
}
|
||||
|
||||
// Ensure there is ChatML-specific end sequence among stop words
|
||||
llama_params["stop"].push_back("<|im_end|>");
|
||||
|
||||
return llama_params;
|
||||
}
|
||||
|
||||
static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
|
||||
{
|
||||
json result = response.result_json;
|
||||
|
||||
bool stopped_word = result.count("stopped_word") != 0;
|
||||
bool stopped_eos = json_value(result, "stopped_eos", false);
|
||||
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
|
||||
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
|
||||
std::string content = json_value(result, "content", std::string(""));
|
||||
|
||||
std::string finish_reason = "length";
|
||||
if (stopped_word || stopped_eos) {
|
||||
finish_reason = "stop";
|
||||
}
|
||||
|
||||
json choices =
|
||||
streaming ? json::array({json{{"finish_reason", finish_reason},
|
||||
{"index", 0},
|
||||
{"delta", json::object()}}})
|
||||
: json::array({json{{"finish_reason", finish_reason},
|
||||
{"index", 0},
|
||||
{"message", json{{"content", content},
|
||||
{"role", "assistant"}}}}});
|
||||
|
||||
std::time_t t = std::time(0);
|
||||
|
||||
json res =
|
||||
json{{"choices", choices},
|
||||
{"created", t},
|
||||
{"model",
|
||||
json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
|
||||
{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
|
||||
{"usage",
|
||||
json{{"completion_tokens", num_tokens_predicted},
|
||||
{"prompt_tokens", num_prompt_tokens},
|
||||
{"total_tokens", num_tokens_predicted + num_prompt_tokens}}},
|
||||
{"id", gen_chatcmplid()}};
|
||||
|
||||
if (server_verbose) {
|
||||
res["__verbose"] = result;
|
||||
}
|
||||
|
||||
if (result.contains("completion_probabilities")) {
|
||||
res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
// return value is vector as there is one case where we might need to generate two responses
|
||||
static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
|
||||
json result = response.result_json;
|
||||
|
||||
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
|
||||
return std::vector<json>({response.result_json});
|
||||
}
|
||||
|
||||
bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
|
||||
std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
|
||||
|
||||
bool stopped_word = json_value(result, "stopped_word", false);
|
||||
bool stopped_eos = json_value(result, "stopped_eos", false);
|
||||
bool stopped_limit = json_value(result, "stopped_limit", false);
|
||||
std::string content = json_value(result, "content", std::string(""));
|
||||
|
||||
std::string finish_reason;
|
||||
if (stopped_word || stopped_eos) {
|
||||
finish_reason = "stop";
|
||||
}
|
||||
if (stopped_limit) {
|
||||
finish_reason = "length";
|
||||
}
|
||||
|
||||
std::time_t t = std::time(0);
|
||||
|
||||
json choices;
|
||||
|
||||
if (!finish_reason.empty()) {
|
||||
choices = json::array({json{{"finish_reason", finish_reason},
|
||||
{"index", 0},
|
||||
{"delta", json::object()}}});
|
||||
} else {
|
||||
if (first) {
|
||||
if (content.empty()) {
|
||||
choices = json::array({json{{"finish_reason", nullptr},
|
||||
{"index", 0},
|
||||
{"delta", json{{"role", "assistant"}}}}});
|
||||
} else {
|
||||
// We have to send this as two updates to conform to openai behavior
|
||||
json initial_ret = json{{"choices", json::array({json{
|
||||
{"finish_reason", nullptr},
|
||||
{"index", 0},
|
||||
{"delta", json{
|
||||
{"role", "assistant"}
|
||||
}}}})},
|
||||
{"created", t},
|
||||
{"id", gen_chatcmplid()},
|
||||
{"model", modelname},
|
||||
{"object", "chat.completion.chunk"}};
|
||||
|
||||
json second_ret = json{
|
||||
{"choices", json::array({json{{"finish_reason", nullptr},
|
||||
{"index", 0},
|
||||
{"delta", json{
|
||||
{"content", content}}}
|
||||
}})},
|
||||
{"created", t},
|
||||
{"id", gen_chatcmplid()},
|
||||
{"model", modelname},
|
||||
{"object", "chat.completion.chunk"}};
|
||||
|
||||
return std::vector<json>({initial_ret, second_ret});
|
||||
}
|
||||
} else {
|
||||
// Some idiosyncrasy in task processing logic makes several trailing calls
|
||||
// with empty content, we ignore these at the calee site.
|
||||
if (content.empty()) {
|
||||
return std::vector<json>({json::object()});
|
||||
}
|
||||
|
||||
choices = json::array({json{
|
||||
{"finish_reason", nullptr},
|
||||
{"index", 0},
|
||||
{"delta",
|
||||
json{
|
||||
{"content", content},
|
||||
}},
|
||||
}});
|
||||
}
|
||||
}
|
||||
|
||||
json ret = json{{"choices", choices},
|
||||
{"created", t},
|
||||
{"id", gen_chatcmplid()},
|
||||
{"model", modelname},
|
||||
{"object", "chat.completion.chunk"}};
|
||||
|
||||
return std::vector<json>({ret});
|
||||
}
|
||||
|
||||
static json format_partial_response(
|
||||
llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
|
||||
) {
|
||||
|
|
@ -2335,7 +2717,7 @@ int main(int argc, char **argv)
|
|||
svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
json data = json::parse(req.body);
|
||||
const int task_id = llama.request_completion(data, false, false);
|
||||
const int task_id = llama.request_completion(data, false, false, -1);
|
||||
if (!json_value(data, "stream", false)) {
|
||||
std::string completion_text;
|
||||
task_result result = llama.next_result(task_id);
|
||||
|
|
@ -2356,9 +2738,9 @@ int main(int argc, char **argv)
|
|||
task_result result = llama.next_result(task_id);
|
||||
if (!result.error) {
|
||||
const std::string str =
|
||||
"data: " +
|
||||
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
"data: " +
|
||||
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
LOG_VERBOSE("data stream", {
|
||||
{ "to_send", str }
|
||||
});
|
||||
|
|
@ -2371,9 +2753,9 @@ int main(int argc, char **argv)
|
|||
}
|
||||
} else {
|
||||
const std::string str =
|
||||
"error: " +
|
||||
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
"error: " +
|
||||
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
LOG_VERBOSE("data stream", {
|
||||
{ "to_send", str }
|
||||
});
|
||||
|
|
@ -2398,10 +2780,102 @@ int main(int argc, char **argv)
|
|||
}
|
||||
});
|
||||
|
||||
|
||||
|
||||
svr.Get("/v1/models", [¶ms](const httplib::Request&, httplib::Response& res)
|
||||
{
|
||||
std::time_t t = std::time(0);
|
||||
|
||||
json models = {
|
||||
{"object", "list"},
|
||||
{"data", {
|
||||
{
|
||||
{"id", params.model_alias},
|
||||
{"object", "model"},
|
||||
{"created", t},
|
||||
{"owned_by", "llamacpp"}
|
||||
},
|
||||
}}
|
||||
};
|
||||
|
||||
res.set_content(models.dump(), "application/json");
|
||||
});
|
||||
|
||||
// TODO: add mount point without "/v1" prefix -- how?
|
||||
svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
json data = oaicompat_completion_params_parse(json::parse(req.body));
|
||||
|
||||
const int task_id = llama.request_completion(data, false, false, -1);
|
||||
|
||||
if (!json_value(data, "stream", false)) {
|
||||
std::string completion_text;
|
||||
task_result result = llama.next_result(task_id);
|
||||
|
||||
if (!result.error && result.stop) {
|
||||
json oaicompat_result = format_final_response_oaicompat(data, result);
|
||||
|
||||
res.set_content(oaicompat_result.dump(-1, ' ', false,
|
||||
json::error_handler_t::replace),
|
||||
"application/json");
|
||||
} else {
|
||||
res.status = 500;
|
||||
res.set_content(result.result_json["content"], "text/plain");
|
||||
return;
|
||||
}
|
||||
} else {
|
||||
const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
|
||||
while (true) {
|
||||
task_result llama_result = llama.next_result(task_id);
|
||||
if (!llama_result.error) {
|
||||
std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
|
||||
|
||||
for (auto it = result_array.begin(); it != result_array.end(); ++it)
|
||||
{
|
||||
if (!it->empty()) {
|
||||
const std::string str =
|
||||
"data: " +
|
||||
it->dump(-1, ' ', false, json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
LOG_VERBOSE("data stream", {{"to_send", str}});
|
||||
if (!sink.write(str.c_str(), str.size())) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (llama_result.stop) {
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
const std::string str =
|
||||
"error: " +
|
||||
llama_result.result_json.dump(-1, ' ', false,
|
||||
json::error_handler_t::replace) +
|
||||
"\n\n";
|
||||
LOG_VERBOSE("data stream", {{"to_send", str}});
|
||||
if (!sink.write(str.c_str(), str.size())) {
|
||||
return false;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
sink.done();
|
||||
return true;
|
||||
};
|
||||
|
||||
auto on_complete = [task_id, &llama](bool) {
|
||||
// cancel request
|
||||
llama.request_cancel(task_id);
|
||||
};
|
||||
|
||||
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
|
||||
}
|
||||
});
|
||||
|
||||
svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
|
||||
{
|
||||
json data = json::parse(req.body);
|
||||
const int task_id = llama.request_completion(data, true, false);
|
||||
const int task_id = llama.request_completion(data, true, false, -1);
|
||||
if (!json_value(data, "stream", false)) {
|
||||
std::string completion_text;
|
||||
task_result result = llama.next_result(task_id);
|
||||
|
|
@ -2505,7 +2979,7 @@ int main(int argc, char **argv)
|
|||
{
|
||||
prompt = "";
|
||||
}
|
||||
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true);
|
||||
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1);
|
||||
task_result result = llama.next_result(task_id);
|
||||
return res.set_content(result.result_json.dump(), "application/json");
|
||||
});
|
||||
|
|
|
|||
|
|
@ -75,7 +75,7 @@ int main(int argc, char ** argv) {
|
|||
// make sure the KV cache is big enough to hold all the prompt and generated tokens
|
||||
if (n_kv_req > n_ctx) {
|
||||
LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
|
||||
LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
|
||||
LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
8
examples/speculative/README.md
Normal file
8
examples/speculative/README.md
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
# llama.cpp/examples/speculative
|
||||
|
||||
Demonstartion of speculative decoding and tree-based speculative decoding techniques
|
||||
|
||||
More info:
|
||||
|
||||
- https://github.com/ggerganov/llama.cpp/pull/2926
|
||||
- https://github.com/ggerganov/llama.cpp/pull/3624
|
||||
|
|
@ -137,7 +137,7 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
|
|||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
add_allocated_tensor(alloc, tensor);
|
||||
size_t cur_max = (char*)addr - (char*)alloc->data + size;
|
||||
size_t cur_max = (char*)addr - (char*)alloc->base + size;
|
||||
if (cur_max > alloc->max_size) {
|
||||
printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
|
||||
for (int i = 0; i < 1024; i++) {
|
||||
|
|
|
|||
130
ggml-cuda.cu
130
ggml-cuda.cu
|
|
@ -443,6 +443,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
|
|||
#define CUDA_SCALE_BLOCK_SIZE 256
|
||||
#define CUDA_CLAMP_BLOCK_SIZE 256
|
||||
#define CUDA_ROPE_BLOCK_SIZE 256
|
||||
#define CUDA_SOFT_MAX_BLOCK_SIZE 1024
|
||||
#define CUDA_ALIBI_BLOCK_SIZE 32
|
||||
#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
|
||||
#define CUDA_QUANTIZE_BLOCK_SIZE 256
|
||||
|
|
@ -501,6 +502,31 @@ static size_t g_scratch_offset = 0;
|
|||
|
||||
static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
|
||||
|
||||
static __device__ __forceinline__ float warp_reduce_sum(float x) {
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
|
||||
}
|
||||
return x;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
|
||||
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
|
||||
}
|
||||
return a;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ float warp_reduce_max(float x) {
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
|
||||
}
|
||||
return x;
|
||||
}
|
||||
|
||||
static __global__ void add_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
|
||||
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
|
|
@ -577,15 +603,6 @@ static __global__ void sqr_f32(const float * x, float * dst, const int k) {
|
|||
dst[i] = x[i] * x[i];
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
|
||||
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
|
||||
}
|
||||
return a;
|
||||
}
|
||||
|
||||
template <int block_size>
|
||||
static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
|
||||
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
|
|
@ -624,14 +641,6 @@ static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
|
|||
}
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ float warp_reduce_sum(float x) {
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
|
||||
}
|
||||
return x;
|
||||
}
|
||||
|
||||
template <int block_size>
|
||||
static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
|
||||
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
|
|
@ -4717,45 +4726,74 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int
|
|||
dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
|
||||
}
|
||||
|
||||
// the CUDA soft max implementation differs from the CPU implementation
|
||||
// instead of doubles floats are used
|
||||
static __global__ void soft_max_f32(const float * x, float * dst, const int ncols) {
|
||||
const int row = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
const int block_size = blockDim.y;
|
||||
const int tid = threadIdx.y;
|
||||
static __global__ void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale) {
|
||||
const int tid = threadIdx.x;
|
||||
const int rowx = blockIdx.x;
|
||||
const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
|
||||
|
||||
const int block_size = blockDim.x;
|
||||
|
||||
const int warp_id = threadIdx.x / WARP_SIZE;
|
||||
const int lane_id = threadIdx.x % WARP_SIZE;
|
||||
|
||||
__shared__ float buf[CUDA_SOFT_MAX_BLOCK_SIZE/WARP_SIZE];
|
||||
|
||||
float max_val = -INFINITY;
|
||||
|
||||
for (int col = tid; col < ncols; col += block_size) {
|
||||
const int i = row*ncols + col;
|
||||
max_val = max(max_val, x[i]);
|
||||
const int ix = rowx*ncols + col;
|
||||
const int iy = rowy*ncols + col;
|
||||
max_val = max(max_val, x[ix]*scale + (y ? y[iy] : 0.0f));
|
||||
}
|
||||
|
||||
// find the max value in the block
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
max_val = max(max_val, __shfl_xor_sync(0xffffffff, max_val, mask, 32));
|
||||
max_val = warp_reduce_max(max_val);
|
||||
if (block_size > WARP_SIZE) {
|
||||
if (warp_id == 0) {
|
||||
buf[lane_id] = -INFINITY;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
if (lane_id == 0) {
|
||||
buf[warp_id] = max_val;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
max_val = buf[lane_id];
|
||||
max_val = warp_reduce_max(max_val);
|
||||
}
|
||||
|
||||
float tmp = 0.f;
|
||||
|
||||
for (int col = tid; col < ncols; col += block_size) {
|
||||
const int i = row*ncols + col;
|
||||
const float val = expf(x[i] - max_val);
|
||||
const int ix = rowx*ncols + col;
|
||||
const int iy = rowy*ncols + col;
|
||||
const float val = expf((x[ix]*scale + (y ? y[iy] : 0.0f)) - max_val);
|
||||
tmp += val;
|
||||
dst[i] = val;
|
||||
dst[ix] = val;
|
||||
}
|
||||
|
||||
// sum up partial sums
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
// find the sum of exps in the block
|
||||
tmp = warp_reduce_sum(tmp);
|
||||
if (block_size > WARP_SIZE) {
|
||||
if (warp_id == 0) {
|
||||
buf[lane_id] = 0.f;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
if (lane_id == 0) {
|
||||
buf[warp_id] = tmp;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
tmp = buf[lane_id];
|
||||
tmp = warp_reduce_sum(tmp);
|
||||
}
|
||||
|
||||
const float inv_tmp = 1.f / tmp;
|
||||
|
||||
for (int col = tid; col < ncols; col += block_size) {
|
||||
const int i = row*ncols + col;
|
||||
const int i = rowx*ncols + col;
|
||||
dst[i] *= inv_tmp;
|
||||
}
|
||||
}
|
||||
|
|
@ -5792,10 +5830,12 @@ static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols
|
|||
diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
|
||||
}
|
||||
|
||||
static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, cudaStream_t stream) {
|
||||
const dim3 block_dims(1, WARP_SIZE, 1);
|
||||
static void soft_max_f32_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) {
|
||||
int nth = WARP_SIZE;
|
||||
while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
|
||||
const dim3 block_dims(nth, 1, 1);
|
||||
const dim3 block_nums(nrows_x, 1, 1);
|
||||
soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
|
||||
soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
|
||||
}
|
||||
|
||||
static void im2col_f32_f16_cuda(const float * x, half * dst,
|
||||
|
|
@ -6846,14 +6886,18 @@ inline void ggml_cuda_op_soft_max(
|
|||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
||||
|
||||
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
|
||||
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t nrows = ggml_nrows(src0);
|
||||
const int64_t nrows_x = ggml_nrows(src0);
|
||||
const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1;
|
||||
|
||||
soft_max_f32_cuda(src0_dd, dst_dd, ne00, nrows, main_stream);
|
||||
float scale = 1.0f;
|
||||
memcpy(&scale, dst->op_params, sizeof(float));
|
||||
|
||||
soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
}
|
||||
|
||||
inline void ggml_cuda_op_scale(
|
||||
|
|
|
|||
48
ggml-metal.m
48
ggml-metal.m
|
|
@ -1028,20 +1028,27 @@ void ggml_metal_graph_compute(
|
|||
int nth = 32; // SIMD width
|
||||
|
||||
if (ne00%4 == 0) {
|
||||
while (nth < ne00/4 && nth < 256) {
|
||||
nth *= 2;
|
||||
}
|
||||
[encoder setComputePipelineState:ctx->pipeline_soft_max_4];
|
||||
} else {
|
||||
do {
|
||||
while (nth < ne00 && nth < 1024) {
|
||||
nth *= 2;
|
||||
} while (nth <= ne00 && nth <= 1024);
|
||||
nth /= 2;
|
||||
}
|
||||
[encoder setComputePipelineState:ctx->pipeline_soft_max];
|
||||
}
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
|
||||
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
|
||||
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
|
||||
[encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0];
|
||||
|
||||
const float scale = ((float *) dst->op_params)[0];
|
||||
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
|
||||
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
|
||||
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
|
||||
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
|
||||
[encoder setBytes:&scale length:sizeof(scale) atIndex:6];
|
||||
[encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
|
||||
} break;
|
||||
|
|
@ -1076,7 +1083,7 @@ void ggml_metal_graph_compute(
|
|||
|
||||
// find the break-even point where the matrix-matrix kernel becomes more efficient compared
|
||||
// to the matrix-vector kernel
|
||||
int ne11_mm_min = 1;
|
||||
int ne11_mm_min = src0t == GGML_TYPE_F16 ? 1 : 16;
|
||||
|
||||
#if 0
|
||||
// the numbers below are measured on M2 Ultra for 7B and 13B models
|
||||
|
|
@ -1351,15 +1358,19 @@ void ggml_metal_graph_compute(
|
|||
float eps;
|
||||
memcpy(&eps, dst->op_params, sizeof(float));
|
||||
|
||||
const int nth = MIN(512, ne00);
|
||||
int nth = 32; // SIMD width
|
||||
|
||||
while (nth < ne00/4 && nth < 1024) {
|
||||
nth *= 2;
|
||||
}
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_rms_norm];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
|
||||
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
|
||||
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
|
||||
[encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
|
||||
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
|
||||
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
|
||||
[encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
|
||||
|
||||
const int64_t nrows = ggml_nrows(src0);
|
||||
|
||||
|
|
@ -1433,7 +1444,8 @@ void ggml_metal_graph_compute(
|
|||
const int n_past = ((int32_t *) dst->op_params)[0];
|
||||
const int n_dims = ((int32_t *) dst->op_params)[1];
|
||||
const int mode = ((int32_t *) dst->op_params)[2];
|
||||
const int n_orig_ctx = ((int32_t *) dst->op_params)[3];
|
||||
// skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
|
||||
const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
|
||||
|
||||
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
|
||||
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
|
||||
|
|
|
|||
210
ggml-metal.metal
210
ggml-metal.metal
|
|
@ -39,6 +39,8 @@ typedef struct {
|
|||
int8_t qs[QK8_0]; // quants
|
||||
} block_q8_0;
|
||||
|
||||
#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
|
||||
|
||||
// general-purpose kernel for addition of two tensors
|
||||
// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3
|
||||
// cons: not very efficient
|
||||
|
|
@ -180,10 +182,12 @@ kernel void kernel_gelu(
|
|||
|
||||
kernel void kernel_soft_max(
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant float & scale,
|
||||
threadgroup float * buf [[threadgroup(0)]],
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
|
|
@ -194,73 +198,77 @@ kernel void kernel_soft_max(
|
|||
const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
|
||||
const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
|
||||
|
||||
device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
device const float * pmask = src1 ? src1 + i01*ne00 : nullptr;
|
||||
device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
|
||||
// parallel max
|
||||
float lmax = tpitg < ne00 ? psrc0[tpitg] : -INFINITY;
|
||||
float lmax = -INFINITY;
|
||||
|
||||
for (int i00 = tpitg + ntg; i00 < ne00; i00 += ntg) {
|
||||
lmax = MAX(lmax, psrc0[i00]);
|
||||
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f));
|
||||
}
|
||||
|
||||
float max = simd_max(lmax);
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = max;
|
||||
// find the max value in the block
|
||||
float max_val = simd_max(lmax);
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = -INFINITY;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = max_val;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
max_val = buf[tiisg];
|
||||
max_val = simd_max(max_val);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// broadcast, simd group number is ntg / 32
|
||||
for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
|
||||
if (tpitg < i) {
|
||||
buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]);
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
max = buf[0];
|
||||
|
||||
// parallel sum
|
||||
float lsum = 0.0f;
|
||||
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
const float exp_psrc0 = exp(psrc0[i00] - max);
|
||||
const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val);
|
||||
lsum += exp_psrc0;
|
||||
// Remember the result of exp here. exp is expensive, so we really do not
|
||||
// wish to compute it twice.
|
||||
pdst[i00] = exp_psrc0;
|
||||
}
|
||||
|
||||
float sum = simd_sum(lsum);
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = sum;
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sum = buf[tiisg];
|
||||
sum = simd_sum(sum);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// broadcast, simd group number is ntg / 32
|
||||
for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
|
||||
if (tpitg < i) {
|
||||
buf[tpitg] += buf[tpitg + i];
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sum = buf[0];
|
||||
const float inv_sum = 1.0f/sum;
|
||||
|
||||
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
pdst[i00] /= sum;
|
||||
pdst[i00] *= inv_sum;
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_soft_max_4(
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant float & scale,
|
||||
threadgroup float * buf [[threadgroup(0)]],
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
|
|
@ -271,64 +279,68 @@ kernel void kernel_soft_max_4(
|
|||
const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
|
||||
const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
|
||||
|
||||
device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
device const float4 * pmask = src1 ? (device const float4 *)(src1 + i01*ne00) : nullptr;
|
||||
device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
|
||||
// parallel max
|
||||
float4 lmax4 = tpitg < ne00/4 ? psrc4[tpitg] : -INFINITY;
|
||||
float4 lmax4 = -INFINITY;
|
||||
|
||||
for (int i00 = tpitg + ntg; i00 < ne00/4; i00 += ntg) {
|
||||
lmax4 = fmax(lmax4, psrc4[i00]);
|
||||
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
|
||||
lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f));
|
||||
}
|
||||
|
||||
const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
|
||||
float max = simd_max(lmax);
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = max;
|
||||
|
||||
float max_val = simd_max(lmax);
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = -INFINITY;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = max_val;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
max_val = buf[tiisg];
|
||||
max_val = simd_max(max_val);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// broadcast, simd group number is ntg / 32
|
||||
for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
|
||||
if (tpitg < i) {
|
||||
buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]);
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
max = buf[0];
|
||||
|
||||
// parallel sum
|
||||
float4 lsum4 = 0.0f;
|
||||
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
|
||||
const float4 exp_psrc4 = exp(psrc4[i00] - max);
|
||||
const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val);
|
||||
lsum4 += exp_psrc4;
|
||||
pdst4[i00] = exp_psrc4;
|
||||
}
|
||||
|
||||
const float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
|
||||
float sum = simd_sum(lsum);
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = sum;
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sum = buf[tiisg];
|
||||
sum = simd_sum(sum);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// broadcast, simd group number is ntg / 32
|
||||
for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
|
||||
if (tpitg < i) {
|
||||
buf[tpitg] += buf[tpitg + i];
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sum = buf[0];
|
||||
const float inv_sum = 1.0f/sum;
|
||||
|
||||
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
|
||||
pdst4[i00] /= sum;
|
||||
pdst4[i00] *= inv_sum;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -435,14 +447,13 @@ kernel void kernel_rms_norm(
|
|||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant float & eps,
|
||||
threadgroup float * sum [[threadgroup(0)]],
|
||||
threadgroup float * buf [[threadgroup(0)]],
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const float4 * x = (device const float4 *) ((device const char *) src0 + tgpig*nb01);
|
||||
device const float * x_scalar = (device const float *) x;
|
||||
device const float4 * x = (device const float4 *) ((device const char *) src0 + tgpig*nb01);
|
||||
|
||||
float4 sumf = 0;
|
||||
float all_sum = 0;
|
||||
|
|
@ -453,40 +464,30 @@ kernel void kernel_rms_norm(
|
|||
}
|
||||
all_sum = sumf[0] + sumf[1] + sumf[2] + sumf[3];
|
||||
all_sum = simd_sum(all_sum);
|
||||
if (tiisg == 0) {
|
||||
sum[sgitg] = all_sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// broadcast, simd group number is ntg / 32
|
||||
for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
|
||||
if (tpitg < i) {
|
||||
sum[tpitg] += sum[tpitg + i];
|
||||
}
|
||||
}
|
||||
if (tpitg == 0) {
|
||||
for (int i = 4 * (ne00 / 4); i < ne00; i++) {
|
||||
sum[0] += x_scalar[i];
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = 0.0f;
|
||||
}
|
||||
sum[0] /= ne00;
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = all_sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
all_sum = buf[tiisg];
|
||||
all_sum = simd_sum(all_sum);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const float mean = sum[0];
|
||||
const float mean = all_sum/ne00;
|
||||
const float scale = 1.0f/sqrt(mean + eps);
|
||||
|
||||
device float4 * y = (device float4 *) (dst + tgpig*ne00);
|
||||
device float * y_scalar = (device float *) y;
|
||||
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
|
||||
y[i00] = x[i00] * scale;
|
||||
}
|
||||
if (tpitg == 0) {
|
||||
for (int i00 = 4 * (ne00 / 4); i00 < ne00; i00++) {
|
||||
y_scalar[i00] = x_scalar[i00] * scale;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// function for calculate inner product between half a q4_0 block and 16 floats (yl), sumy is SUM(yl[i])
|
||||
|
|
@ -576,7 +577,6 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre
|
|||
// putting them in the kernel cause a significant performance penalty
|
||||
#define N_DST 4 // each SIMD group works on 4 rows
|
||||
#define N_SIMDGROUP 2 // number of SIMD groups in a thread group
|
||||
#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
|
||||
//Note: This is a template, but strictly speaking it only applies to
|
||||
// quantizations where the block size is 32. It also does not
|
||||
// giard against the number of rows not being divisible by
|
||||
|
|
|
|||
|
|
@ -1,20 +1,18 @@
|
|||
#include "ggml.h"
|
||||
#include "ggml-opencl.h"
|
||||
|
||||
#include <array>
|
||||
#include <atomic>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <cstring>
|
||||
#include <limits>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <limits>
|
||||
|
||||
#define CL_TARGET_OPENCL_VERSION 110
|
||||
#include <clblast.h>
|
||||
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <string.h>
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
|
|
|||
114
ggml.c
114
ggml.c
|
|
@ -4826,7 +4826,17 @@ struct ggml_tensor * ggml_diag_mask_zero_inplace(
|
|||
static struct ggml_tensor * ggml_soft_max_impl(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * mask,
|
||||
float scale,
|
||||
bool inplace) {
|
||||
GGML_ASSERT(ggml_is_contiguous(a));
|
||||
if (mask) {
|
||||
GGML_ASSERT(ggml_is_contiguous(mask));
|
||||
GGML_ASSERT(mask->ne[2] == 1);
|
||||
GGML_ASSERT(mask->ne[3] == 1);
|
||||
GGML_ASSERT(ggml_can_repeat_rows(mask, a));
|
||||
}
|
||||
|
||||
bool is_node = false;
|
||||
|
||||
if (a->grad) {
|
||||
|
|
@ -4835,9 +4845,13 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
|||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
float params[] = { scale };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_SOFT_MAX;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = a;
|
||||
result->src[1] = mask;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
|
@ -4845,13 +4859,21 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
|||
struct ggml_tensor * ggml_soft_max(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a) {
|
||||
return ggml_soft_max_impl(ctx, a, false);
|
||||
return ggml_soft_max_impl(ctx, a, NULL, 1.0f, false);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_soft_max_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a) {
|
||||
return ggml_soft_max_impl(ctx, a, true);
|
||||
return ggml_soft_max_impl(ctx, a, NULL, 1.0f, true);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_soft_max_ext(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * mask,
|
||||
float scale) {
|
||||
return ggml_soft_max_impl(ctx, a, mask, scale, false);
|
||||
}
|
||||
|
||||
// ggml_soft_max_back
|
||||
|
|
@ -9373,7 +9395,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
|
|||
// TODO: find the optimal values for these
|
||||
if (ggml_is_contiguous(src0) &&
|
||||
ggml_is_contiguous(src1) &&
|
||||
src0->type == GGML_TYPE_F32 &&
|
||||
//src0->type == GGML_TYPE_F32 &&
|
||||
src1->type == GGML_TYPE_F32 &&
|
||||
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
|
||||
|
||||
|
|
@ -10551,20 +10573,25 @@ static void ggml_compute_forward_diag_mask_zero(
|
|||
static void ggml_compute_forward_soft_max_f32(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
struct ggml_tensor * dst) {
|
||||
GGML_ASSERT(ggml_is_contiguous(src0));
|
||||
GGML_ASSERT(ggml_is_contiguous(dst));
|
||||
GGML_ASSERT(ggml_are_same_shape(src0, dst));
|
||||
const struct ggml_tensor * src1,
|
||||
struct ggml_tensor * dst) {
|
||||
assert(ggml_is_contiguous(dst));
|
||||
assert(ggml_are_same_shape(src0, dst));
|
||||
|
||||
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
float scale = 1.0f;
|
||||
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
|
||||
|
||||
// TODO: handle transposed/permuted matrices
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
const int64_t ne11 = src1 ? src1->ne[1] : 1;
|
||||
|
||||
const int nc = src0->ne[0];
|
||||
const int nr = ggml_nrows(src0);
|
||||
|
||||
|
|
@ -10575,29 +10602,40 @@ static void ggml_compute_forward_soft_max_f32(
|
|||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
|
||||
|
||||
for (int i1 = ir0; i1 < ir1; i1++) {
|
||||
float *sp = (float *)((char *) src0->data + i1*src0->nb[1]);
|
||||
float *dp = (float *)((char *) dst->data + i1*dst->nb[1]);
|
||||
float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
|
||||
float * dp = (float *)((char *) dst->data + i1*dst->nb[1]);
|
||||
|
||||
// broadcast the mask across rows
|
||||
float * mp = src1 ? (float *)((char *) src1->data + (i1%ne11)*src1->nb[1]) : NULL;
|
||||
|
||||
ggml_vec_cpy_f32 (nc, wp, sp);
|
||||
ggml_vec_scale_f32(nc, wp, scale);
|
||||
if (mp) {
|
||||
ggml_vec_acc_f32(nc, wp, mp);
|
||||
}
|
||||
|
||||
#ifndef NDEBUG
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
//printf("p[%d] = %f\n", i, p[i]);
|
||||
assert(!isnan(sp[i]));
|
||||
assert(!isnan(wp[i]));
|
||||
}
|
||||
#endif
|
||||
|
||||
float max = -INFINITY;
|
||||
ggml_vec_max_f32(nc, &max, sp);
|
||||
ggml_vec_max_f32(nc, &max, wp);
|
||||
|
||||
ggml_float sum = 0.0;
|
||||
|
||||
uint16_t scvt;
|
||||
for (int i = 0; i < nc; i++) {
|
||||
if (sp[i] == -INFINITY) {
|
||||
if (wp[i] == -INFINITY) {
|
||||
dp[i] = 0.0f;
|
||||
} else {
|
||||
// const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max);
|
||||
ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max);
|
||||
// const float val = (wp[i] == -INFINITY) ? 0.0 : exp(wp[i] - max);
|
||||
ggml_fp16_t s = GGML_FP32_TO_FP16(wp[i] - max);
|
||||
memcpy(&scvt, &s, sizeof(scvt));
|
||||
const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]);
|
||||
sum += (ggml_float)val;
|
||||
|
|
@ -10622,11 +10660,12 @@ static void ggml_compute_forward_soft_max_f32(
|
|||
static void ggml_compute_forward_soft_max(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
struct ggml_tensor * dst) {
|
||||
const struct ggml_tensor * src1,
|
||||
struct ggml_tensor * dst) {
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_soft_max_f32(params, src0, dst);
|
||||
ggml_compute_forward_soft_max_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
|
|
@ -13863,7 +13902,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
|||
} break;
|
||||
case GGML_OP_SOFT_MAX:
|
||||
{
|
||||
ggml_compute_forward_soft_max(params, tensor->src[0], tensor);
|
||||
ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor);
|
||||
} break;
|
||||
case GGML_OP_SOFT_MAX_BACK:
|
||||
{
|
||||
|
|
@ -15590,7 +15629,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
|||
} break;
|
||||
case GGML_OP_DIAG_MASK_ZERO:
|
||||
case GGML_OP_DIAG_MASK_INF:
|
||||
case GGML_OP_SOFT_MAX:
|
||||
case GGML_OP_SOFT_MAX_BACK:
|
||||
case GGML_OP_ROPE:
|
||||
case GGML_OP_ROPE_BACK:
|
||||
|
|
@ -15606,6 +15644,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
|||
{
|
||||
n_tasks = 1; //TODO
|
||||
} break;
|
||||
case GGML_OP_SOFT_MAX:
|
||||
{
|
||||
n_tasks = MIN(MIN(4, n_threads), ggml_nrows(node->src[0]));
|
||||
} break;
|
||||
case GGML_OP_CONV_TRANSPOSE_1D:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
|
@ -15689,13 +15731,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
|||
{
|
||||
n_tasks = 1;
|
||||
} break;
|
||||
case GGML_OP_COUNT:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
printf("%s: op %s not implemented\n", __func__, ggml_op_name(node->op));
|
||||
fprintf(stderr, "%s: op not implemented: ", __func__);
|
||||
if (node->op < GGML_OP_COUNT) {
|
||||
fprintf(stderr, "%s\n", ggml_op_name(node->op));
|
||||
} else {
|
||||
fprintf(stderr, "%d\n", node->op);
|
||||
}
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
|
|
@ -15836,18 +15879,16 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
|
||||
// thread scheduling for the different operations + work buffer size estimation
|
||||
for (int i = 0; i < cgraph->n_nodes; i++) {
|
||||
int n_tasks = 1;
|
||||
|
||||
struct ggml_tensor * node = cgraph->nodes[i];
|
||||
|
||||
const int n_tasks = ggml_get_n_tasks(node, n_threads);
|
||||
|
||||
size_t cur = 0;
|
||||
|
||||
switch (node->op) {
|
||||
case GGML_OP_CPY:
|
||||
case GGML_OP_DUP:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
if (ggml_is_quantized(node->type)) {
|
||||
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
|
||||
}
|
||||
|
|
@ -15855,16 +15896,12 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
case GGML_OP_ADD:
|
||||
case GGML_OP_ADD1:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
if (ggml_is_quantized(node->src[0]->type)) {
|
||||
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_ACC:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
if (ggml_is_quantized(node->src[0]->type)) {
|
||||
cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
|
||||
}
|
||||
|
|
@ -15892,12 +15929,14 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
} break;
|
||||
case GGML_OP_OUT_PROD:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
if (ggml_is_quantized(node->src[0]->type)) {
|
||||
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_SOFT_MAX:
|
||||
{
|
||||
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
|
||||
} break;
|
||||
case GGML_OP_CONV_TRANSPOSE_1D:
|
||||
{
|
||||
GGML_ASSERT(node->src[0]->ne[3] == 1);
|
||||
|
|
@ -15925,7 +15964,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
} break;
|
||||
case GGML_OP_IM2COL:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
} break;
|
||||
case GGML_OP_CONV_TRANSPOSE_2D:
|
||||
{
|
||||
|
|
@ -15943,8 +15981,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
} break;
|
||||
case GGML_OP_FLASH_ATTN:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
|
||||
|
||||
if (node->src[1]->type == GGML_TYPE_F32) {
|
||||
|
|
@ -15957,8 +15993,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
} break;
|
||||
case GGML_OP_FLASH_FF:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
if (node->src[1]->type == GGML_TYPE_F32) {
|
||||
cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
|
||||
cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
|
||||
|
|
@ -15969,8 +16003,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
} break;
|
||||
case GGML_OP_FLASH_ATTN_BACK:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
const int64_t D = node->src[0]->ne[0];
|
||||
const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
|
||||
const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back
|
||||
|
|
@ -15985,8 +16017,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
|||
|
||||
case GGML_OP_CROSS_ENTROPY_LOSS:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
|
||||
cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks);
|
||||
} break;
|
||||
case GGML_OP_COUNT:
|
||||
|
|
|
|||
13
ggml.h
13
ggml.h
|
|
@ -244,11 +244,10 @@
|
|||
#define GGML_ASSERT(x) \
|
||||
do { \
|
||||
if (!(x)) { \
|
||||
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
|
||||
fflush(stderr); \
|
||||
fflush(stdout); \
|
||||
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
|
||||
ggml_print_backtrace(); \
|
||||
exit(1); \
|
||||
abort(); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
|
|
@ -1283,6 +1282,14 @@ extern "C" {
|
|||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// fused soft_max(a*scale + mask)
|
||||
// mask is optional
|
||||
GGML_API struct ggml_tensor * ggml_soft_max_ext(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * mask,
|
||||
float scale);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_soft_max_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
|
|||
|
|
@ -92,6 +92,7 @@ class MODEL_ARCH(IntEnum):
|
|||
BERT = auto()
|
||||
BLOOM = auto()
|
||||
STABLELM = auto()
|
||||
QWEN = auto()
|
||||
|
||||
|
||||
class MODEL_TENSOR(IntEnum):
|
||||
|
|
@ -132,6 +133,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.BERT: "bert",
|
||||
MODEL_ARCH.BLOOM: "bloom",
|
||||
MODEL_ARCH.STABLELM: "stablelm",
|
||||
MODEL_ARCH.QWEN: "qwen",
|
||||
}
|
||||
|
||||
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
||||
|
|
@ -317,6 +319,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.QWEN: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.GPT2: [
|
||||
# TODO
|
||||
],
|
||||
|
|
@ -336,6 +352,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_ARCH.PERSIMMON: [
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
],
|
||||
MODEL_ARCH.QWEN: [
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD,
|
||||
],
|
||||
}
|
||||
|
||||
#
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ class TensorNameMap:
|
|||
# Token embeddings
|
||||
MODEL_TENSOR.TOKEN_EMBD: (
|
||||
"gpt_neox.embed_in", # gptneox
|
||||
"transformer.wte", # gpt2 gpt-j mpt refact
|
||||
"transformer.wte", # gpt2 gpt-j mpt refact qwen
|
||||
"transformer.word_embeddings", # falcon
|
||||
"word_embeddings", # bloom
|
||||
"model.embed_tokens", # llama-hf
|
||||
|
|
@ -38,7 +38,7 @@ class TensorNameMap:
|
|||
# Output
|
||||
MODEL_TENSOR.OUTPUT: (
|
||||
"embed_out", # gptneox
|
||||
"lm_head", # gpt2 mpt falcon llama-hf baichuan
|
||||
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
|
||||
"output", # llama-pth bloom
|
||||
"word_embeddings_for_head", # persimmon
|
||||
),
|
||||
|
|
@ -51,7 +51,7 @@ class TensorNameMap:
|
|||
"norm", # llama-pth
|
||||
"embeddings.LayerNorm", # bert
|
||||
"transformer.norm_f", # mpt
|
||||
"ln_f", # refact bloom
|
||||
"ln_f", # refact bloom qwen
|
||||
"language_model.encoder.final_layernorm", # persimmon
|
||||
),
|
||||
|
||||
|
|
@ -65,7 +65,7 @@ class TensorNameMap:
|
|||
# Attention norm
|
||||
MODEL_TENSOR.ATTN_NORM: (
|
||||
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
|
||||
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
|
||||
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
|
||||
"transformer.blocks.{bid}.norm_1", # mpt
|
||||
"transformer.h.{bid}.input_layernorm", # falcon7b
|
||||
"h.{bid}.input_layernorm", # bloom
|
||||
|
|
@ -85,7 +85,7 @@ class TensorNameMap:
|
|||
# Attention query-key-value
|
||||
MODEL_TENSOR.ATTN_QKV: (
|
||||
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
|
||||
"transformer.h.{bid}.attn.c_attn", # gpt2
|
||||
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
|
||||
"transformer.blocks.{bid}.attn.Wqkv", # mpt
|
||||
"transformer.h.{bid}.self_attention.query_key_value", # falcon
|
||||
"h.{bid}.self_attention.query_key_value", # bloom
|
||||
|
|
@ -119,7 +119,7 @@ class TensorNameMap:
|
|||
# Attention output
|
||||
MODEL_TENSOR.ATTN_OUT: (
|
||||
"gpt_neox.layers.{bid}.attention.dense", # gptneox
|
||||
"transformer.h.{bid}.attn.c_proj", # gpt2 refact
|
||||
"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
|
||||
"transformer.blocks.{bid}.attn.out_proj", # mpt
|
||||
"transformer.h.{bid}.self_attention.dense", # falcon
|
||||
"h.{bid}.self_attention.dense", # bloom
|
||||
|
|
@ -139,7 +139,7 @@ class TensorNameMap:
|
|||
# Feed-forward norm
|
||||
MODEL_TENSOR.FFN_NORM: (
|
||||
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
|
||||
"transformer.h.{bid}.ln_2", # gpt2 refact
|
||||
"transformer.h.{bid}.ln_2", # gpt2 refact qwen
|
||||
"h.{bid}.post_attention_layernorm", # bloom
|
||||
"transformer.blocks.{bid}.norm_2", # mpt
|
||||
"model.layers.{bid}.post_attention_layernorm", # llama-hf
|
||||
|
|
@ -161,18 +161,20 @@ class TensorNameMap:
|
|||
"encoder.layer.{bid}.intermediate.dense", # bert
|
||||
"transformer.h.{bid}.mlp.fc_in", # gpt-j
|
||||
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
|
||||
"transformer.h.{bid}.mlp.w1", # qwen
|
||||
),
|
||||
|
||||
# Feed-forward gate
|
||||
MODEL_TENSOR.FFN_GATE: (
|
||||
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
|
||||
"layers.{bid}.feed_forward.w1", # llama-pth
|
||||
"transformer.h.{bid}.mlp.w2", # qwen
|
||||
),
|
||||
|
||||
# Feed-forward down
|
||||
MODEL_TENSOR.FFN_DOWN: (
|
||||
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
|
||||
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact
|
||||
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen
|
||||
"transformer.blocks.{bid}.ffn.down_proj", # mpt
|
||||
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
|
||||
"h.{bid}.mlp.dense_4h_to_h", # bloom
|
||||
|
|
|
|||
337
llama.cpp
337
llama.cpp
|
|
@ -46,7 +46,6 @@
|
|||
#endif
|
||||
#include <windows.h>
|
||||
#include <io.h>
|
||||
#include <stdio.h> // for _fseeki64
|
||||
#endif
|
||||
|
||||
#include <algorithm>
|
||||
|
|
@ -193,6 +192,7 @@ enum llm_arch {
|
|||
LLM_ARCH_REFACT,
|
||||
LLM_ARCH_BLOOM,
|
||||
LLM_ARCH_STABLELM,
|
||||
LLM_ARCH_QWEN,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -209,6 +209,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_REFACT, "refact" },
|
||||
{ LLM_ARCH_BLOOM, "bloom" },
|
||||
{ LLM_ARCH_STABLELM, "stablelm" },
|
||||
{ LLM_ARCH_QWEN, "qwen" },
|
||||
};
|
||||
|
||||
enum llm_kv {
|
||||
|
|
@ -519,6 +520,22 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
|
|||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_QWEN,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
||||
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
||||
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
||||
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
||||
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
},
|
||||
},
|
||||
|
||||
{
|
||||
LLM_ARCH_UNKNOWN,
|
||||
|
|
@ -1249,6 +1266,9 @@ struct llama_layer {
|
|||
struct ggml_tensor * wqkv;
|
||||
|
||||
// attention bias
|
||||
struct ggml_tensor * bq;
|
||||
struct ggml_tensor * bk;
|
||||
struct ggml_tensor * bv;
|
||||
struct ggml_tensor * bo;
|
||||
struct ggml_tensor * bqkv;
|
||||
|
||||
|
|
@ -1971,10 +1991,13 @@ struct llama_model_loader {
|
|||
return tensor;
|
||||
}
|
||||
|
||||
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend) {
|
||||
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend, bool required = true) {
|
||||
struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
|
||||
|
||||
if (cur == NULL) {
|
||||
if (!required) {
|
||||
return NULL;
|
||||
}
|
||||
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
|
||||
}
|
||||
|
||||
|
|
@ -2348,6 +2371,15 @@ static void llm_load_hparams(
|
|||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_QWEN:
|
||||
{
|
||||
GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
|
||||
switch (hparams.n_layer) {
|
||||
case 32: model.type = e_model::MODEL_7B; break;
|
||||
case 40: model.type = e_model::MODEL_13B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
|
||||
default: (void)0;
|
||||
}
|
||||
|
|
@ -2645,15 +2677,15 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|||
}
|
||||
|
||||
// general kv
|
||||
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
|
||||
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
|
||||
|
||||
// special tokens
|
||||
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
|
||||
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
|
||||
if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
|
||||
if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
|
||||
if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
|
||||
if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
|
||||
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
|
||||
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
|
||||
if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
|
||||
if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
|
||||
if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
|
||||
if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
|
||||
}
|
||||
|
||||
static void llm_load_tensors(
|
||||
|
|
@ -2783,6 +2815,12 @@ static void llm_load_tensors(
|
|||
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
|
||||
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
||||
|
||||
// optional bias tensors
|
||||
layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend, false);
|
||||
layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend, false);
|
||||
layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend, false);
|
||||
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend, false);
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
|
|
@ -2791,9 +2829,14 @@ static void llm_load_tensors(
|
|||
|
||||
if (backend == GGML_BACKEND_GPU) {
|
||||
vram_weights +=
|
||||
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
|
||||
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
|
||||
ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
|
||||
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
|
||||
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) +
|
||||
(layer.bq ? ggml_nbytes(layer.bq) : 0) +
|
||||
(layer.bk ? ggml_nbytes(layer.bk) : 0) +
|
||||
(layer.bv ? ggml_nbytes(layer.bv) : 0) +
|
||||
(layer.bo ? ggml_nbytes(layer.bo) : 0) +
|
||||
ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
|
||||
ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
|
|
@ -3311,6 +3354,71 @@ static void llm_load_tensors(
|
|||
}
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_QWEN:
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
||||
{
|
||||
ggml_backend_type backend_norm;
|
||||
ggml_backend_type backend_output;
|
||||
|
||||
if (n_gpu_layers > int(n_layer)) {
|
||||
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
||||
// on Windows however this is detrimental unless everything is on the GPU
|
||||
#ifndef _WIN32
|
||||
backend_norm = llama_backend_offload;
|
||||
#else
|
||||
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
|
||||
#endif // _WIN32
|
||||
|
||||
backend_output = llama_backend_offload_split;
|
||||
} else {
|
||||
backend_norm = GGML_BACKEND_CPU;
|
||||
backend_output = GGML_BACKEND_CPU;
|
||||
}
|
||||
|
||||
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
||||
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
||||
|
||||
if (backend_norm == GGML_BACKEND_GPU) {
|
||||
vram_weights += ggml_nbytes(model.output_norm);
|
||||
}
|
||||
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
||||
vram_weights += ggml_nbytes(model.output);
|
||||
}
|
||||
}
|
||||
|
||||
const uint32_t n_ff = hparams.n_ff / 2;
|
||||
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
|
||||
model.layers.resize(n_layer);
|
||||
|
||||
for (uint32_t i = 0; i < n_layer; ++i) {
|
||||
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
|
||||
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
||||
|
||||
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split);
|
||||
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd * 3}, backend);
|
||||
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
|
||||
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
|
||||
if (backend == GGML_BACKEND_GPU) {
|
||||
vram_weights +=
|
||||
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) +
|
||||
ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
|
||||
ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
|
||||
default:
|
||||
throw std::runtime_error("unknown architecture");
|
||||
|
|
@ -3705,23 +3813,29 @@ static struct ggml_tensor * llm_build_kqv(
|
|||
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
|
||||
cb(kq, "kq", il);
|
||||
|
||||
kq = ggml_scale(ctx, kq, kq_scale);
|
||||
cb(kq, "kq_scaled", il);
|
||||
|
||||
if (max_alibi_bias > 0.0f) {
|
||||
// TODO: n_head or n_head_kv
|
||||
// TODO: K-shift is likely not working
|
||||
// TODO: change to ggml_add
|
||||
kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
|
||||
cb(kq, "kq_scaled_alibi", il);
|
||||
// temporary branch until we figure out how to handle ggml_alibi through ggml_add
|
||||
kq = ggml_scale(ctx, kq, kq_scale);
|
||||
cb(kq, "kq_scaled", il);
|
||||
|
||||
if (max_alibi_bias > 0.0f) {
|
||||
// TODO: n_head or n_head_kv
|
||||
// TODO: K-shift is likely not working
|
||||
// TODO: change to ggml_add
|
||||
kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
|
||||
cb(kq, "kq_scaled_alibi", il);
|
||||
}
|
||||
|
||||
kq = ggml_add(ctx, kq, kq_mask);
|
||||
cb(kq, "kq_masked", il);
|
||||
|
||||
kq = ggml_soft_max(ctx, kq);
|
||||
cb(kq, "kq_soft_max", il);
|
||||
} else {
|
||||
kq = ggml_soft_max_ext(ctx, kq, kq_mask, 1.0f/sqrtf(float(n_embd_head)));
|
||||
cb(kq, "kq_soft_max_ext", il);
|
||||
}
|
||||
|
||||
kq = ggml_add(ctx, kq, kq_mask);
|
||||
cb(kq, "kq_masked", il);
|
||||
|
||||
kq = ggml_soft_max(ctx, kq);
|
||||
cb(kq, "kq_soft_max", il);
|
||||
|
||||
// split cached v into n_head heads
|
||||
struct ggml_tensor * v =
|
||||
ggml_view_3d(ctx, kv.v,
|
||||
|
|
@ -3886,12 +4000,24 @@ struct llm_build_context {
|
|||
// compute Q and K and RoPE them
|
||||
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
if (model.layers[il].bq) {
|
||||
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
||||
cb(Qcur, "Qcur", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
if (model.layers[il].bk) {
|
||||
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
||||
cb(Kcur, "Kcur", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
if (model.layers[il].bv) {
|
||||
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
||||
cb(Vcur, "Vcur", il);
|
||||
}
|
||||
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
||||
|
|
@ -3910,7 +4036,7 @@ struct llm_build_context {
|
|||
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
|
||||
|
||||
cur = llm_build_kqv(ctx0, hparams, kv_self,
|
||||
model.layers[il].wo, NULL,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
|
||||
cb(cur, "kqv_out", il);
|
||||
}
|
||||
|
|
@ -4903,6 +5029,121 @@ struct llm_build_context {
|
|||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_qwen() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
|
||||
cb(inpL, "inp_embd", -1);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
cb(inp_pos, "inp_pos", -1);
|
||||
|
||||
// KQ_scale
|
||||
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
||||
cb(KQ_scale, "KQ_scale", -1);
|
||||
|
||||
// KQ_mask (mask for 1 head, it wil be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
||||
cb(KQ_mask, "KQ_mask", -1);
|
||||
|
||||
// shift the entire K-cache if needed
|
||||
if (do_rope_shift) {
|
||||
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
|
||||
}
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||
cb(cur, "bqkv", il);
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
// using mode = 2 for neox mode
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
|
||||
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_rope_custom(
|
||||
ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
|
||||
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
|
||||
|
||||
cur = llm_build_kqv(ctx0, hparams, kv_self,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
|
||||
cb(cur, "kqv_out", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward forward
|
||||
{
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, NULL,
|
||||
model.layers[il].ffn_gate, NULL,
|
||||
model.layers[il].ffn_down, NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
|
|
@ -5042,6 +5283,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
|
|||
{ "kq_scaled_alibi", OFFLOAD_FUNC_KQ },
|
||||
{ "kq_masked", OFFLOAD_FUNC_KQ },
|
||||
{ "kq_soft_max", OFFLOAD_FUNC_V },
|
||||
{ "kq_soft_max_ext", OFFLOAD_FUNC_V },
|
||||
{ "v", OFFLOAD_FUNC_V },
|
||||
{ "kqv", OFFLOAD_FUNC_V },
|
||||
{ "kqv_merged", OFFLOAD_FUNC_V },
|
||||
|
|
@ -5376,6 +5618,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_stablelm();
|
||||
} break;
|
||||
case LLM_ARCH_QWEN:
|
||||
{
|
||||
result = llm.build_qwen();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -5498,8 +5744,7 @@ static int llama_decode_internal(
|
|||
// a heuristic, to avoid attending the full cache if it is not yet utilized
|
||||
// after enough generations, the benefit from this heuristic disappears
|
||||
// if we start defragmenting the cache, the benefit from this will be more important
|
||||
//kv_self.n = std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)); // TODO: this might be better for CUDA?
|
||||
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, llama_kv_cache_cell_max(kv_self)));
|
||||
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
|
||||
|
||||
//printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
|
||||
|
||||
|
|
@ -5550,18 +5795,8 @@ static int llama_decode_internal(
|
|||
n_threads = std::min(4, n_threads);
|
||||
}
|
||||
|
||||
// If all tensors can be run on the GPU then using more than 1 thread is detrimental.
|
||||
const bool full_offload_supported =
|
||||
model.arch == LLM_ARCH_LLAMA ||
|
||||
model.arch == LLM_ARCH_BAICHUAN ||
|
||||
model.arch == LLM_ARCH_FALCON ||
|
||||
model.arch == LLM_ARCH_REFACT ||
|
||||
model.arch == LLM_ARCH_MPT ||
|
||||
model.arch == LLM_ARCH_STARCODER ||
|
||||
model.arch == LLM_ARCH_STABLELM;
|
||||
|
||||
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
|
||||
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
|
||||
if (ggml_cpu_has_cublas() && fully_offloaded) {
|
||||
n_threads = 1;
|
||||
}
|
||||
|
||||
|
|
@ -7037,6 +7272,7 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
|
|||
// Replace the data in candidates with the new_candidates data
|
||||
std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
|
||||
candidates->size = new_candidates.size();
|
||||
candidates->sorted = false;
|
||||
|
||||
if (ctx) {
|
||||
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
|
|
@ -7658,18 +7894,21 @@ static void llama_convert_tensor_internal(
|
|||
return;
|
||||
}
|
||||
|
||||
auto block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
|
||||
auto block_size_bytes = ggml_type_size(tensor->type);
|
||||
size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
|
||||
size_t block_size_bytes = ggml_type_size(tensor->type);
|
||||
|
||||
GGML_ASSERT(nelements % block_size == 0);
|
||||
auto nblocks = nelements / block_size;
|
||||
auto blocks_per_thread = nblocks / nthread;
|
||||
auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
|
||||
size_t nblocks = nelements / block_size;
|
||||
size_t blocks_per_thread = nblocks / nthread;
|
||||
size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
|
||||
|
||||
for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
|
||||
auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
|
||||
auto thr_elems = thr_blocks * block_size; // number of elements for this thread
|
||||
auto thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
|
||||
size_t in_buff_offs = 0;
|
||||
size_t out_buff_offs = 0;
|
||||
|
||||
for (int tnum = 0; tnum < nthread; tnum++) {
|
||||
size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
|
||||
size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
|
||||
size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
|
||||
|
||||
auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
|
||||
if (typ == GGML_TYPE_F16) {
|
||||
|
|
|
|||
2
llama.h
2
llama.h
|
|
@ -185,7 +185,7 @@ extern "C" {
|
|||
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
|
||||
float rope_freq_base; // RoPE base frequency, 0 = from model
|
||||
float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
|
||||
float yarn_ext_factor; // YaRN extrapolation mix factor, NaN = from model
|
||||
float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
|
||||
float yarn_attn_factor; // YaRN magnitude scaling factor
|
||||
float yarn_beta_fast; // YaRN low correction dim
|
||||
float yarn_beta_slow; // YaRN high correction dim
|
||||
|
|
|
|||
1
prompts/chat-with-qwen.txt
Normal file
1
prompts/chat-with-qwen.txt
Normal file
|
|
@ -0,0 +1 @@
|
|||
You are a helpful assistant.
|
||||
3
requirements-hf-to-gguf.txt
Normal file
3
requirements-hf-to-gguf.txt
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
-r requirements.txt
|
||||
torch==2.1.1
|
||||
transformers==4.35.2
|
||||
|
|
@ -1,5 +1,3 @@
|
|||
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
|
||||
set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
|
||||
set(BUILD_NUMBER 0)
|
||||
set(BUILD_COMMIT "unknown")
|
||||
set(BUILD_COMPILER "unknown")
|
||||
|
|
@ -58,23 +56,3 @@ else()
|
|||
)
|
||||
set(BUILD_TARGET ${OUT})
|
||||
endif()
|
||||
|
||||
# Only write the build info if it changed
|
||||
if(EXISTS ${OUTPUT_FILE})
|
||||
file(READ ${OUTPUT_FILE} CONTENTS)
|
||||
string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
|
||||
set(OLD_COMMIT ${CMAKE_MATCH_1})
|
||||
string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
|
||||
set(OLD_COMPILER ${CMAKE_MATCH_1})
|
||||
string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
|
||||
set(OLD_TARGET ${CMAKE_MATCH_1})
|
||||
if (
|
||||
NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR
|
||||
NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
|
||||
NOT OLD_TARGET STREQUAL BUILD_TARGET
|
||||
)
|
||||
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
|
||||
endif()
|
||||
else()
|
||||
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
|
||||
endif()
|
||||
|
|
|
|||
24
scripts/gen-build-info-cpp.cmake
Normal file
24
scripts/gen-build-info-cpp.cmake
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
|
||||
|
||||
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
|
||||
set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
|
||||
|
||||
# Only write the build info if it changed
|
||||
if(EXISTS ${OUTPUT_FILE})
|
||||
file(READ ${OUTPUT_FILE} CONTENTS)
|
||||
string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
|
||||
set(OLD_COMMIT ${CMAKE_MATCH_1})
|
||||
string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
|
||||
set(OLD_COMPILER ${CMAKE_MATCH_1})
|
||||
string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
|
||||
set(OLD_TARGET ${CMAKE_MATCH_1})
|
||||
if (
|
||||
NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR
|
||||
NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
|
||||
NOT OLD_TARGET STREQUAL BUILD_TARGET
|
||||
)
|
||||
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
|
||||
endif()
|
||||
else()
|
||||
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
|
||||
endif()
|
||||
Loading…
Reference in a new issue