Merge branch 'ggerganov:master' into mannix-server-startup
This commit is contained in:
ManniX-ITA
GitHub
commit
ca0409fae4
46 changed files with 703 additions and 7365 deletions
2
.github/workflows/bench.yml
vendored
2
.github/workflows/bench.yml
vendored
|
|
@ -32,7 +32,7 @@ on:
|
|||
- cron: '04 2 * * *'
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}-${{ github.event.inputs.sha }}
|
||||
group: ${{ github.workflow }}-${{ github.ref || github.run_id }}-${{ github.event.inputs.sha }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
|
|
|
|||
33
.github/workflows/build.yml
vendored
33
.github/workflows/build.yml
vendored
|
|
@ -32,6 +32,8 @@ jobs:
|
|||
- name: Clone
|
||||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
|
|
@ -88,6 +90,8 @@ jobs:
|
|||
- name: Clone
|
||||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
|
|
@ -206,6 +210,8 @@ jobs:
|
|||
- name: Clone
|
||||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
|
|
@ -238,6 +244,33 @@ jobs:
|
|||
./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
|
||||
./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
|
||||
|
||||
- name: Determine tag name
|
||||
id: tag
|
||||
shell: bash
|
||||
run: |
|
||||
BUILD_NUMBER="$(git rev-list --count HEAD)"
|
||||
SHORT_HASH="$(git rev-parse --short=7 HEAD)"
|
||||
if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
|
||||
echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
|
||||
else
|
||||
SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
|
||||
echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
|
||||
fi
|
||||
|
||||
- name: Pack artifacts
|
||||
id: pack_artifacts
|
||||
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
|
||||
run: |
|
||||
cp LICENSE ./build/bin/
|
||||
zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/*
|
||||
|
||||
- name: Upload artifacts
|
||||
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip
|
||||
name: llama-bin-ubuntu-x64.zip
|
||||
|
||||
# ubuntu-latest-cmake-sanitizer:
|
||||
# runs-on: ubuntu-latest
|
||||
#
|
||||
|
|
|
|||
2
.github/workflows/server.yml
vendored
2
.github/workflows/server.yml
vendored
|
|
@ -23,7 +23,7 @@ on:
|
|||
- cron: '2 4 * * *'
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
|
||||
group: ${{ github.workflow }}-${{ github.ref || github.run_id }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
|
|
|
|||
4
.gitignore
vendored
4
.gitignore
vendored
|
|
@ -34,6 +34,7 @@ lcov-report/
|
|||
gcovr-report/
|
||||
|
||||
build*
|
||||
!build.zig
|
||||
cmake-build-*
|
||||
out/
|
||||
tmp/
|
||||
|
|
@ -100,6 +101,9 @@ qnt-*.txt
|
|||
perf-*.txt
|
||||
|
||||
examples/jeopardy/results.txt
|
||||
examples/server/*.html.hpp
|
||||
examples/server/*.js.hpp
|
||||
examples/server/*.mjs.hpp
|
||||
|
||||
poetry.lock
|
||||
poetry.toml
|
||||
|
|
|
|||
13
Makefile
13
Makefile
|
|
@ -699,7 +699,7 @@ OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
|
|||
llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
|
||||
$(CXX) $(CXXFLAGS) -c $< -o $@
|
||||
|
||||
COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
|
||||
COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h
|
||||
COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o
|
||||
|
||||
common.o: common/common.cpp $(COMMON_H_DEPS)
|
||||
|
|
@ -800,10 +800,19 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
|
|||
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
|
||||
|
||||
server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
|
||||
server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
|
||||
|
||||
# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
|
||||
examples/server/%.hpp: examples/server/public/% Makefile
|
||||
@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
|
||||
echo "unsigned char $${NAME}[] = {" && \
|
||||
cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
|
||||
echo "};" && \
|
||||
echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
|
||||
) > $@
|
||||
|
||||
gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
|
||||
|
|
|
|||
|
|
@ -229,12 +229,11 @@ source /opt/intel/oneapi/setvars.sh
|
|||
# Build LLAMA with MKL BLAS acceleration for intel GPU
|
||||
mkdir -p build && cd build
|
||||
|
||||
# Option 1: Use FP16 for better performance in long-prompt inference
|
||||
cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
|
||||
# Or without "--build", run "make" next
|
||||
# Option 1: Use FP16 for better performance in long-prompt inference
|
||||
#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
|
||||
|
||||
# Option 2: Use FP32 by default
|
||||
cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
|
||||
#build all binary
|
||||
cmake --build . --config Release -j -v
|
||||
|
|
@ -252,10 +251,10 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
|
|||
mkdir -p build && cd build
|
||||
|
||||
# Option 1: Use FP16 for better performance in long-prompt inference
|
||||
cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
|
||||
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
|
||||
|
||||
# Option 2: Use FP32 by default
|
||||
cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
|
||||
#build all binary
|
||||
cmake --build . --config Release -j -v
|
||||
|
|
|
|||
12
README.md
12
README.md
|
|
@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
|
|||
|
||||
### Recent API changes
|
||||
|
||||
- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
|
||||
- [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
|
||||
- [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
|
||||
- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
|
||||
|
|
@ -95,7 +96,7 @@ Typically finetunes of the base models below are supported as well.
|
|||
- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
|
||||
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
|
||||
- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
|
||||
- [X] Falcon
|
||||
- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
|
||||
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
|
||||
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
|
||||
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
|
||||
|
|
@ -122,6 +123,7 @@ Typically finetunes of the base models below are supported as well.
|
|||
- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
|
||||
- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
|
||||
- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
|
||||
- [x] [OLMo](https://allenai.org/olmo)
|
||||
|
||||
(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))
|
||||
|
||||
|
|
@ -548,7 +550,7 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
|
||||
|
||||
You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
|
||||
- For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed.
|
||||
- For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed.
|
||||
|
||||
- For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
|
||||
|
||||
|
|
@ -573,6 +575,12 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
|
||||
Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
|
||||
|
||||
Linux packaging:
|
||||
Fedora Linux:
|
||||
```bash
|
||||
sudo dnf install clblast
|
||||
```
|
||||
|
||||
Alternatively, they may be built from source.
|
||||
|
||||
- <details>
|
||||
|
|
|
|||
29
build.zig
29
build.zig
|
|
@ -140,4 +140,33 @@ pub fn build(b: *std.build.Builder) !void {
|
|||
if (server.target.isWindows()) {
|
||||
server.linkSystemLibrary("ws2_32");
|
||||
}
|
||||
|
||||
const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" };
|
||||
for (server_assets) |asset| {
|
||||
const input_path = b.fmt("examples/server/public/{s}", .{asset});
|
||||
const output_path = b.fmt("examples/server/{s}.hpp", .{asset});
|
||||
|
||||
// Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`:
|
||||
|
||||
const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize));
|
||||
defer b.allocator.free(input);
|
||||
|
||||
var buf = std.ArrayList(u8).init(b.allocator);
|
||||
defer buf.deinit();
|
||||
|
||||
for (input) |byte| {
|
||||
try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte});
|
||||
}
|
||||
|
||||
var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_");
|
||||
defer b.allocator.free(name);
|
||||
std.mem.replaceScalar(u8, name, '.', '_');
|
||||
|
||||
try std.fs.cwd().writeFile(output_path, b.fmt(
|
||||
"unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n",
|
||||
.{ name, buf.items, name, input.len },
|
||||
));
|
||||
|
||||
std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path });
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -108,7 +108,7 @@ int32_t get_num_physical_cores() {
|
|||
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
|
||||
}
|
||||
|
||||
#if defined(__x86_64__) && defined(__linux__)
|
||||
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
|
||||
#include <pthread.h>
|
||||
|
||||
static void cpuid(unsigned leaf, unsigned subleaf,
|
||||
|
|
@ -162,7 +162,7 @@ static int count_math_cpus(int cpu_count) {
|
|||
* Returns number of CPUs on system that are useful for math.
|
||||
*/
|
||||
int get_math_cpu_count() {
|
||||
#if defined(__x86_64__) && defined(__linux__)
|
||||
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
|
||||
int cpu_count = sysconf(_SC_NPROCESSORS_ONLN);
|
||||
if (cpu_count < 1) {
|
||||
return get_num_physical_cores();
|
||||
|
|
@ -2328,10 +2328,10 @@ std::vector<llama_token> llama_tokenize(
|
|||
|
||||
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
|
||||
std::vector<char> result(8, 0);
|
||||
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
||||
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), true);
|
||||
if (n_tokens < 0) {
|
||||
result.resize(-n_tokens);
|
||||
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
||||
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), true);
|
||||
GGML_ASSERT(check == -n_tokens);
|
||||
} else {
|
||||
result.resize(n_tokens);
|
||||
|
|
|
|||
|
|
@ -1301,15 +1301,23 @@ class LlamaModel(Model):
|
|||
try:
|
||||
self. _set_vocab_sentencepiece()
|
||||
except FileNotFoundError:
|
||||
self._set_vocab_llama_hf()
|
||||
try:
|
||||
self._set_vocab_llama_hf()
|
||||
except (FileNotFoundError, TypeError):
|
||||
# Llama 3
|
||||
self._set_vocab_gpt2()
|
||||
|
||||
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
|
||||
special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
|
||||
special_vocab._set_special_token("prefix", 32007)
|
||||
special_vocab._set_special_token("suffix", 32008)
|
||||
special_vocab._set_special_token("middle", 32009)
|
||||
special_vocab._set_special_token("eot", 32010)
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
# Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
|
||||
if self.hparams.get("vocab_size", 32000) == 32016:
|
||||
special_vocab = gguf.SpecialVocab(
|
||||
self.dir_model, load_merges=False,
|
||||
special_token_types = ['prefix', 'suffix', 'middle', 'eot']
|
||||
)
|
||||
special_vocab._set_special_token("prefix", 32007)
|
||||
special_vocab._set_special_token("suffix", 32008)
|
||||
special_vocab._set_special_token("middle", 32009)
|
||||
special_vocab._set_special_token("eot", 32010)
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
super().set_gguf_parameters()
|
||||
|
|
@ -2194,6 +2202,8 @@ class InternLM2Model(Model):
|
|||
old_eos = special_vocab.special_token_ids["eos"]
|
||||
if "chat" in os.path.basename(self.dir_model.absolute()):
|
||||
# For the chat model, we replace the eos with '<|im_end|>'.
|
||||
# TODO: this is a hack, should be fixed
|
||||
# https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
|
||||
special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
|
||||
print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
|
||||
in chat mode so that the conversation can end normally.")
|
||||
|
|
@ -2429,12 +2439,15 @@ class GemmaModel(Model):
|
|||
|
||||
def set_vocab(self):
|
||||
self._set_vocab_sentencepiece()
|
||||
|
||||
# TODO: these special tokens should be exported only for the CodeGemma family
|
||||
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
|
||||
special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
|
||||
special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
|
||||
special_vocab._set_special_token("prefix", 67)
|
||||
special_vocab._set_special_token("suffix", 69)
|
||||
special_vocab._set_special_token("middle", 68)
|
||||
special_vocab._set_special_token("eot", 70)
|
||||
special_vocab._set_special_token("fsep", 70)
|
||||
special_vocab._set_special_token("eot", 107)
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
|
|
@ -2523,28 +2536,34 @@ class MambaModel(Model):
|
|||
|
||||
field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
|
||||
self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
|
||||
|
||||
field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
|
||||
self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
|
||||
|
||||
field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
|
||||
self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
|
||||
|
||||
field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
|
||||
self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
|
||||
|
||||
field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
|
||||
self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
|
||||
|
||||
field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
|
||||
self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
|
||||
|
||||
field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
|
||||
self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
d_model = self.find_hparam(["hidden_size", "d_model"])
|
||||
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
|
||||
d_model = self.find_hparam(["hidden_size", "d_model"])
|
||||
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
|
||||
d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
|
||||
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
|
||||
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
|
||||
# ceiling division
|
||||
# ref: https://stackoverflow.com/a/17511341/22827863
|
||||
# ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
|
||||
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
|
||||
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
|
||||
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
|
||||
|
||||
# Fail early for models which don't have a block expansion factor of 2
|
||||
|
|
@ -2636,6 +2655,66 @@ class CommandR2Model(Model):
|
|||
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
|
||||
|
||||
|
||||
@Model.register("OlmoForCausalLM")
|
||||
@Model.register("OLMoForCausalLM")
|
||||
class OlmoModel(Model):
|
||||
model_arch = gguf.MODEL_ARCH.OLMO
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
super().set_gguf_parameters()
|
||||
self.gguf_writer.add_layer_norm_eps(1e-5)
|
||||
if "clip_qkv" in self.hparams is not None:
|
||||
self.gguf_writer.add_clamp_kqv(self.hparams["clip_qkv"])
|
||||
|
||||
# Same as super class, but permuting q_proj, k_proj
|
||||
# Copied from: LlamaModel
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
n_head = self.hparams.get("num_attention_heads")
|
||||
n_kv_head = self.hparams.get("num_key_value_heads")
|
||||
for name, data_torch in self.get_tensors():
|
||||
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.numpy()
|
||||
|
||||
if name.endswith("q_proj.weight"):
|
||||
data = permute(data, n_head, n_head)
|
||||
if name.endswith("k_proj.weight"):
|
||||
data = permute(data, n_head, n_kv_head)
|
||||
|
||||
data = data.squeeze()
|
||||
|
||||
# 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)
|
||||
|
||||
# 1d tensors need to be converted to 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 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 ######
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -525,7 +525,14 @@ class LlamaHfVocab(Vocab):
|
|||
|
||||
# pre-check so we know if we need transformers
|
||||
tokenizer_model: dict[str, Any] = tokenizer_json['model']
|
||||
if (
|
||||
is_llama3 = (
|
||||
tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
|
||||
and not tokenizer_model.get('byte_fallback', True)
|
||||
)
|
||||
if is_llama3:
|
||||
raise TypeError('Llama 3 must be converted with BpeVocab')
|
||||
|
||||
if not is_llama3 and (
|
||||
tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
|
||||
or tokenizer_json['decoder']['type'] != 'Sequence'
|
||||
):
|
||||
|
|
|
|||
|
|
@ -153,7 +153,7 @@ while n_cur <= n_len {
|
|||
// const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
// is it an end of stream? -> mark the stream as finished
|
||||
if new_token_id == llama_token_eos(model) || n_cur == n_len {
|
||||
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
|
||||
i_batch[i] = -1
|
||||
// print("")
|
||||
if n_parallel > 1 {
|
||||
|
|
@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
|
|||
|
||||
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))
|
||||
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
|
||||
if nTokens < 0 {
|
||||
let actualTokensCount = -Int(nTokens)
|
||||
result = .init(repeating: 0, count: actualTokensCount)
|
||||
|
|
@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
|
|||
model,
|
||||
token,
|
||||
&result,
|
||||
Int32(result.count)
|
||||
Int32(result.count),
|
||||
false
|
||||
)
|
||||
assert(check == actualTokensCount)
|
||||
} else {
|
||||
|
|
|
|||
|
|
@ -191,8 +191,8 @@ int main(int argc, char ** argv) {
|
|||
|
||||
//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
// is it an end of stream? -> mark the stream as finished
|
||||
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
|
||||
// is it an end of generation? -> mark the stream as finished
|
||||
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
|
||||
i_batch[i] = -1;
|
||||
LOG_TEE("\n");
|
||||
if (n_parallel > 1) {
|
||||
|
|
|
|||
|
|
@ -47,7 +47,7 @@ struct beam_search_callback_data {
|
|||
// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
|
||||
// For example, eob can be flagged due to maximum token length, stop words, etc.
|
||||
static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
|
||||
return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
|
||||
return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
|
||||
}
|
||||
|
||||
// Function matching type llama_beam_search_callback_fn_t.
|
||||
|
|
|
|||
|
|
@ -586,7 +586,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
// deal with eot token in infill mode
|
||||
if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
|
||||
if(is_interacting && !params.interactive_first) {
|
||||
if (is_interacting && !params.interactive_first) {
|
||||
// print an eot token
|
||||
printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
|
||||
}
|
||||
|
|
@ -651,8 +651,8 @@ int main(int argc, char ** argv) {
|
|||
// LOG_TEE("took new input\n");
|
||||
is_interacting = false;
|
||||
}
|
||||
// deal with end of text token in interactive mode
|
||||
else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
|
||||
// deal with end of generation tokens in interactive mode
|
||||
else if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
|
||||
LOG("found EOS token\n");
|
||||
|
||||
if (params.interactive) {
|
||||
|
|
@ -731,8 +731,8 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
// end of text token
|
||||
if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
|
||||
// end of generation
|
||||
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
|
||||
break;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -408,7 +408,7 @@ Java_com_example_llama_Llm_completion_1loop(
|
|||
const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
|
||||
|
||||
const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
|
||||
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
|
||||
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
|
||||
return env->NewStringUTF("");
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -158,7 +158,7 @@ actor LlamaContext {
|
|||
new_token_id = llama_sample_token_greedy(context, &candidates_p)
|
||||
}
|
||||
|
||||
if new_token_id == llama_token_eos(model) || n_cur == n_len {
|
||||
if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
|
||||
print("\n")
|
||||
let new_token_str = String(cString: temporary_invalid_cchars + [0])
|
||||
temporary_invalid_cchars.removeAll()
|
||||
|
|
@ -322,7 +322,7 @@ actor LlamaContext {
|
|||
defer {
|
||||
result.deallocate()
|
||||
}
|
||||
let nTokens = llama_token_to_piece(model, token, result, 8)
|
||||
let nTokens = llama_token_to_piece(model, token, result, 8, false)
|
||||
|
||||
if nTokens < 0 {
|
||||
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
|
||||
|
|
@ -330,7 +330,7 @@ actor LlamaContext {
|
|||
defer {
|
||||
newResult.deallocate()
|
||||
}
|
||||
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
|
||||
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
|
||||
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
|
||||
return Array(bufferPointer)
|
||||
} else {
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@
|
|||
// I'll gradually clean and extend it
|
||||
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
|
||||
#include "clip.h"
|
||||
#include "log.h"
|
||||
#include "ggml.h"
|
||||
#include "ggml-alloc.h"
|
||||
#include "ggml-backend.h"
|
||||
|
|
@ -23,7 +24,6 @@
|
|||
#include <cstdlib>
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <map>
|
||||
#include <regex>
|
||||
#include <stdexcept>
|
||||
|
|
@ -145,7 +145,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
|
|||
static int get_key_idx(const gguf_context * ctx, const char * key) {
|
||||
int i = gguf_find_key(ctx, key);
|
||||
if (i == -1) {
|
||||
fprintf(stderr, "key %s not found in file\n", key);
|
||||
LOG_TEE("key %s not found in file\n", key);
|
||||
throw std::runtime_error(format("Missing required key: %s", key));
|
||||
}
|
||||
|
||||
|
|
@ -247,7 +247,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
|
|||
|
||||
static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
|
||||
size_t tensor_size = ggml_nbytes(tensor);
|
||||
printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
|
||||
LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
|
||||
prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
|
||||
tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
|
||||
}
|
||||
|
|
@ -265,7 +265,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
|
|||
static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
|
||||
std::ofstream file(filename, std::ios::binary);
|
||||
if (!file.is_open()) {
|
||||
std::cerr << "Failed to open file for writing: " << filename << std::endl;
|
||||
LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
|
||||
return;
|
||||
}
|
||||
|
||||
|
|
@ -284,7 +284,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s
|
|||
static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
|
||||
std::ofstream file(filename, std::ios::binary);
|
||||
if (!file.is_open()) {
|
||||
std::cerr << "Failed to open file for writing: " << filename << std::endl;
|
||||
LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
|
||||
return;
|
||||
}
|
||||
|
||||
|
|
@ -515,7 +515,7 @@ struct clip_ctx {
|
|||
|
||||
static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
LOG_TEE("This gguf file seems to have no vision encoder\n");
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
|
@ -879,21 +879,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
const int idx_name = gguf_find_key(ctx, KEY_NAME);
|
||||
if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
|
||||
const std::string name = gguf_get_val_str(ctx, idx_name);
|
||||
printf("%s: model name: %s\n", __func__, name.c_str());
|
||||
LOG_TEE("%s: model name: %s\n", __func__, name.c_str());
|
||||
}
|
||||
printf("%s: description: %s\n", __func__, description.c_str());
|
||||
printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
|
||||
printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
|
||||
printf("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
printf("%s: n_kv: %d\n", __func__, n_kv);
|
||||
printf("%s: ftype: %s\n", __func__, ftype_str.c_str());
|
||||
printf("\n");
|
||||
LOG_TEE("%s: description: %s\n", __func__, description.c_str());
|
||||
LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
|
||||
LOG_TEE("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
|
||||
LOG_TEE("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
LOG_TEE("%s: n_kv: %d\n", __func__, n_kv);
|
||||
LOG_TEE("%s: ftype: %s\n", __func__, ftype_str.c_str());
|
||||
LOG_TEE("\n");
|
||||
}
|
||||
const int n_tensors = gguf_get_n_tensors(ctx);
|
||||
|
||||
// kv
|
||||
const int n_kv = gguf_get_n_kv(ctx);
|
||||
printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
|
||||
LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
|
||||
__func__, n_kv, n_tensors, fname);
|
||||
{
|
||||
std::map<enum ggml_type, uint32_t> n_type;
|
||||
|
|
@ -904,7 +904,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
n_type[type]++;
|
||||
}
|
||||
|
||||
printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
|
||||
LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
|
||||
for (int i = 0; i < n_kv; i++) {
|
||||
const char * name = gguf_get_key(ctx, i);
|
||||
const enum gguf_type type = gguf_get_kv_type(ctx, i);
|
||||
|
|
@ -920,7 +920,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
}
|
||||
replace_all(value, "\n", "\\n");
|
||||
|
||||
printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
|
||||
LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
|
||||
}
|
||||
|
||||
// print type counts
|
||||
|
|
@ -929,7 +929,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
continue;
|
||||
}
|
||||
|
||||
printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
|
||||
LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -944,7 +944,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
size_t tensor_size = ggml_nbytes(cur);
|
||||
model_size += tensor_size;
|
||||
if (verbosity >= 3) {
|
||||
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
|
||||
LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
|
||||
__func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
|
||||
}
|
||||
}
|
||||
|
|
@ -971,18 +971,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
|
||||
#ifdef GGML_USE_CUDA
|
||||
new_clip->backend = ggml_backend_cuda_init(0);
|
||||
printf("%s: CLIP using CUDA backend\n", __func__);
|
||||
LOG_TEE("%s: CLIP using CUDA backend\n", __func__);
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
new_clip->backend = ggml_backend_metal_init();
|
||||
printf("%s: CLIP using Metal backend\n", __func__);
|
||||
LOG_TEE("%s: CLIP using Metal backend\n", __func__);
|
||||
#endif
|
||||
|
||||
|
||||
if (!new_clip->backend) {
|
||||
new_clip->backend = ggml_backend_cpu_init();
|
||||
printf("%s: CLIP using CPU backend\n", __func__);
|
||||
LOG_TEE("%s: CLIP using CPU backend\n", __func__);
|
||||
}
|
||||
|
||||
// model size and capabilities
|
||||
|
|
@ -1006,15 +1006,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
|
||||
|
||||
if (verbosity >= 1) {
|
||||
printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
|
||||
printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
|
||||
printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
|
||||
printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
|
||||
printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
|
||||
LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
|
||||
LOG_TEE("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
|
||||
LOG_TEE("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
|
||||
}
|
||||
}
|
||||
|
||||
printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
|
||||
LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
|
||||
|
||||
// load tensors
|
||||
{
|
||||
|
|
@ -1027,7 +1027,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
|
||||
new_clip->ctx_data = ggml_init(params);
|
||||
if (!new_clip->ctx_data) {
|
||||
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
||||
LOG_TEE("%s: ggml_init() failed\n", __func__);
|
||||
clip_free(new_clip);
|
||||
gguf_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -1035,7 +1035,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
|
||||
auto fin = std::ifstream(fname, std::ios::binary);
|
||||
if (!fin) {
|
||||
printf("cannot open model file for loading tensors\n");
|
||||
LOG_TEE("cannot open model file for loading tensors\n");
|
||||
clip_free(new_clip);
|
||||
gguf_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -1057,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
|
||||
fin.seekg(offset, std::ios::beg);
|
||||
if (!fin) {
|
||||
printf("%s: failed to seek for tensor %s\n", __func__, name);
|
||||
LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name);
|
||||
clip_free(new_clip);
|
||||
gguf_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -1128,23 +1128,23 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
}
|
||||
|
||||
if (verbosity >= 2) {
|
||||
printf("\n%s: vision model hparams\n", __func__);
|
||||
printf("image_size %d\n", hparams.image_size);
|
||||
printf("patch_size %d\n", hparams.patch_size);
|
||||
printf("v_hidden_size %d\n", hparams.hidden_size);
|
||||
printf("v_n_intermediate %d\n", hparams.n_intermediate);
|
||||
printf("v_projection_dim %d\n", hparams.projection_dim);
|
||||
printf("v_n_head %d\n", hparams.n_head);
|
||||
printf("v_n_layer %d\n", hparams.n_layer);
|
||||
printf("v_eps %f\n", hparams.eps);
|
||||
printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
|
||||
printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
|
||||
printf("v_image_grid_pinpoints: ");
|
||||
LOG_TEE("\n%s: vision model hparams\n", __func__);
|
||||
LOG_TEE("image_size %d\n", hparams.image_size);
|
||||
LOG_TEE("patch_size %d\n", hparams.patch_size);
|
||||
LOG_TEE("v_hidden_size %d\n", hparams.hidden_size);
|
||||
LOG_TEE("v_n_intermediate %d\n", hparams.n_intermediate);
|
||||
LOG_TEE("v_projection_dim %d\n", hparams.projection_dim);
|
||||
LOG_TEE("v_n_head %d\n", hparams.n_head);
|
||||
LOG_TEE("v_n_layer %d\n", hparams.n_layer);
|
||||
LOG_TEE("v_eps %f\n", hparams.eps);
|
||||
LOG_TEE("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
|
||||
LOG_TEE("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
|
||||
LOG_TEE("v_image_grid_pinpoints: ");
|
||||
for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
|
||||
printf("%d ", hparams.image_grid_pinpoints[i]);
|
||||
LOG_TEE("%d ", hparams.image_grid_pinpoints[i]);
|
||||
}
|
||||
printf("\n");
|
||||
printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
|
||||
|
||||
}
|
||||
|
||||
|
|
@ -1155,7 +1155,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
|
||||
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
|
||||
} catch(const std::exception& e) {
|
||||
fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
|
||||
LOG_TEE("%s: failed to load vision model tensors\n", __func__);
|
||||
}
|
||||
|
||||
// LLaVA projection
|
||||
|
|
@ -1184,7 +1184,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
} catch (std::runtime_error & e) { }
|
||||
try {
|
||||
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
|
||||
// fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
|
||||
// LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
|
||||
} catch (std::runtime_error & e) { }
|
||||
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
|
||||
// MobileVLM projection
|
||||
|
|
@ -1264,7 +1264,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
|
||||
ggml_gallocr_reserve(new_clip->compute_alloc, gf);
|
||||
size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
|
||||
printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
|
||||
LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
|
||||
}
|
||||
|
||||
return new_clip;
|
||||
|
|
@ -1304,7 +1304,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
|
|||
int nx, ny, nc;
|
||||
auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
|
||||
if (!data) {
|
||||
fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
|
||||
LOG_TEE("%s: failed to load image '%s'\n", __func__, fname);
|
||||
return false;
|
||||
}
|
||||
build_clip_img_from_data(data, nx, ny, img);
|
||||
|
|
@ -1316,7 +1316,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
|
|||
int nx, ny, nc;
|
||||
auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
|
||||
if (!data) {
|
||||
fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
|
||||
LOG_TEE("%s: failed to decode image bytes\n", __func__);
|
||||
return false;
|
||||
}
|
||||
build_clip_img_from_data(data, nx, ny, img);
|
||||
|
|
@ -1506,7 +1506,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
|
|||
int downscaled_height = static_cast<int>(original_height * scale);
|
||||
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
|
||||
int wasted_resolution = (width * height) - effective_resolution;
|
||||
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
// LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
|
||||
max_effective_resolution = effective_resolution;
|
||||
min_wasted_resolution = wasted_resolution;
|
||||
|
|
@ -1545,7 +1545,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
|
|||
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
|
||||
bool pad_to_square = true;
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
LOG_TEE("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
}
|
||||
auto & params = ctx->vision_model.hparams;
|
||||
|
|
@ -1622,7 +1622,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
|
|||
}
|
||||
|
||||
for (size_t i = 0; i < patches.size(); i++) {
|
||||
// printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
|
||||
// LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
|
||||
clip_image_u8_free(patches[i]);
|
||||
}
|
||||
|
||||
|
|
@ -1765,7 +1765,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
|
|||
|
||||
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
LOG_TEE("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -1777,7 +1777,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3
|
|||
|
||||
bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
LOG_TEE("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -1939,7 +1939,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
new_type = type;
|
||||
if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
|
||||
new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
|
||||
// fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
|
||||
// LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
|
||||
}
|
||||
const size_t n_elms = ggml_nelements(cur);
|
||||
float * f32_data;
|
||||
|
|
@ -1958,7 +1958,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
f32_data = (float *)conv_buf.data();
|
||||
break;
|
||||
default:
|
||||
printf("Please use an input file in f32 or f16\n");
|
||||
LOG_TEE("Please use an input file in f32 or f16\n");
|
||||
gguf_free(ctx_out);
|
||||
return false;
|
||||
}
|
||||
|
|
@ -1985,7 +1985,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
fout.put(0);
|
||||
}
|
||||
|
||||
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
|
||||
LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
|
||||
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
|
|
@ -2001,8 +2001,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
gguf_free(ctx_out);
|
||||
|
||||
{
|
||||
printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
|
||||
printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
return true;
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
#include "ggml.h"
|
||||
#include "log.h"
|
||||
#include "common.h"
|
||||
#include "clip.h"
|
||||
#include "llava.h"
|
||||
|
|
@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
|
|||
n_eval = n_batch;
|
||||
}
|
||||
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
|
||||
fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
|
||||
LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
|
||||
return false;
|
||||
}
|
||||
*n_past += n_eval;
|
||||
|
|
@ -45,7 +46,7 @@ static const char * sample(struct llama_sampling_context * ctx_sampling,
|
|||
const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
|
||||
llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
|
||||
static std::string ret;
|
||||
if (id == llama_token_eos(llama_get_model(ctx_llama))) {
|
||||
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
|
||||
ret = "</s>";
|
||||
} else {
|
||||
ret = llama_token_to_piece(ctx_llama, id);
|
||||
|
|
@ -73,7 +74,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
|
|||
size_t img_base64_str_start, img_base64_str_end;
|
||||
find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
|
||||
if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
|
||||
fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
|
||||
LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
|
|||
|
||||
auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
|
||||
if (!embed) {
|
||||
fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
|
||||
LOG_TEE("%s: could not load image from base64 string.\n", __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -112,8 +113,8 @@ struct llava_context {
|
|||
};
|
||||
|
||||
static void show_additional_info(int /*argc*/, char ** argv) {
|
||||
fprintf(stderr, "\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
|
||||
fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n");
|
||||
LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
|
||||
LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
|
||||
}
|
||||
|
||||
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
|
||||
|
|
@ -123,18 +124,18 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
|
|||
auto prompt = params->prompt;
|
||||
if (prompt_contains_image(prompt)) {
|
||||
if (!params->image.empty()) {
|
||||
fprintf(stderr, "using base64 encoded image instead of command line image path\n");
|
||||
LOG_TEE("using base64 encoded image instead of command line image path\n");
|
||||
}
|
||||
embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
|
||||
if (!embed) {
|
||||
fprintf(stderr, "%s: can't load image from prompt\n", __func__);
|
||||
LOG_TEE("%s: can't load image from prompt\n", __func__);
|
||||
return NULL;
|
||||
}
|
||||
params->prompt = remove_image_from_prompt(prompt);
|
||||
} else {
|
||||
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
|
||||
if (!embed) {
|
||||
fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
|
||||
LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str());
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
|
|
@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
|
||||
system_prompt = prompt.substr(0, image_pos);
|
||||
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
|
||||
printf("system_prompt: %s\n", system_prompt.c_str());
|
||||
LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
|
||||
if (params->verbose_prompt) {
|
||||
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
|
||||
for (int i = 0; i < (int) tmp.size(); i++) {
|
||||
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
}
|
||||
}
|
||||
printf("user_prompt: %s\n", user_prompt.c_str());
|
||||
LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
|
||||
if (params->verbose_prompt) {
|
||||
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
|
||||
for (int i = 0; i < (int) tmp.size(); i++) {
|
||||
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
}
|
||||
}
|
||||
} else {
|
||||
|
|
@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
if (params->verbose_prompt) {
|
||||
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
|
||||
for (int i = 0; i < (int) tmp.size(); i++) {
|
||||
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
|
||||
// generate the response
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
LOG_TEE("\n");
|
||||
|
||||
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
|
||||
std::string response = "";
|
||||
|
|
@ -224,7 +225,7 @@ static struct llava_context * llava_init(gpt_params * params) {
|
|||
|
||||
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
|
||||
if (model == NULL) {
|
||||
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
|
||||
LOG_TEE("%s: error: unable to load model\n" , __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -234,7 +235,7 @@ static struct llava_context * llava_init(gpt_params * params) {
|
|||
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
|
||||
|
||||
if (ctx_llama == NULL) {
|
||||
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
|
||||
LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -257,6 +258,12 @@ static void llava_free(struct llava_context * ctx_llava) {
|
|||
llama_backend_free();
|
||||
}
|
||||
|
||||
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) {
|
||||
ggml_time_init();
|
||||
|
||||
|
|
@ -266,6 +273,14 @@ int main(int argc, char ** argv) {
|
|||
show_additional_info(argc, argv);
|
||||
return 1;
|
||||
}
|
||||
|
||||
#ifndef LOG_DISABLE_LOGS
|
||||
log_set_target(log_filename_generator("llava", "log"));
|
||||
LOG_TEE("Log start\n");
|
||||
log_dump_cmdline(argc, argv);
|
||||
llama_log_set(llama_log_callback_logTee, nullptr);
|
||||
#endif // LOG_DISABLE_LOGS
|
||||
|
||||
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
|
||||
gpt_print_usage(argc, argv, params);
|
||||
show_additional_info(argc, argv);
|
||||
|
|
@ -274,7 +289,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
auto ctx_llava = llava_init(¶ms);
|
||||
if (ctx_llava == NULL) {
|
||||
fprintf(stderr, "%s: error: failed to init llava\n", __func__);
|
||||
LOG_TEE("%s: error: failed to init llava\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -54,7 +54,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int>& ori
|
|||
int downscaled_height = static_cast<int>(original_height * scale);
|
||||
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
|
||||
int wasted_resolution = (width * height) - effective_resolution;
|
||||
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
// LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
|
||||
max_effective_resolution = effective_resolution;
|
||||
min_wasted_resolution = wasted_resolution;
|
||||
|
|
@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
|
|||
model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
|
||||
if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
|
||||
if (newline_tmp->buffer == NULL) {
|
||||
printf("newline_tmp tensor buffer is NULL\n");
|
||||
LOG_TEE("newline_tmp tensor buffer is NULL\n");
|
||||
}
|
||||
ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
|
||||
} else {
|
||||
model.newline->data = newline_tmp->data;
|
||||
if (model.newline->data == NULL) {
|
||||
printf("newline_tmp tensor data is NULL\n");
|
||||
LOG_TEE("newline_tmp tensor data is NULL\n");
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
img_res_v.size = 0;
|
||||
img_res_v.data = nullptr;
|
||||
if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
|
||||
fprintf(stderr, "%s: unable to preprocess image\n", __func__);
|
||||
LOG_TEE("%s: unable to preprocess image\n", __func__);
|
||||
delete[] img_res_v.data;
|
||||
return false;
|
||||
}
|
||||
|
|
@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
|
||||
delete[] img_res_v.data;
|
||||
if (!encoded) {
|
||||
fprintf(stderr, "Unable to encode image\n");
|
||||
LOG_TEE("Unable to encode image\n");
|
||||
|
||||
return false;
|
||||
}
|
||||
|
|
@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
|
||||
const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
|
||||
if (!encoded) {
|
||||
fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
|
||||
LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
const int64_t t_img_enc_batch_us = ggml_time_us();
|
||||
printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
|
||||
LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
|
||||
|
||||
const int32_t * image_grid = clip_image_grid(ctx_clip);
|
||||
|
||||
|
|
@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
// clip_image_save_to_bmp(*tmp, "image_feature.bmp");
|
||||
}
|
||||
|
||||
printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
|
||||
LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
|
||||
|
||||
const int64_t t_img_enc_end_us = ggml_time_us();
|
||||
float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
|
||||
|
||||
printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
|
||||
LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
|
@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
|
|||
int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
|
||||
auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
|
||||
if (n_image_embd != n_llama_embd) {
|
||||
printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
|
||||
LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
|
|
@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
|
|||
bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
|
||||
float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
|
||||
if (!image_embd) {
|
||||
fprintf(stderr, "Unable to allocate memory for image embeddings\n");
|
||||
LOG_TEE("Unable to allocate memory for image embeddings\n");
|
||||
return false;
|
||||
}
|
||||
|
||||
int n_img_pos;
|
||||
if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
|
||||
fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
|
||||
LOG_TEE("%s: cannot encode image, aborting\n", __func__);
|
||||
free(image_embd);
|
||||
return false;
|
||||
}
|
||||
|
|
@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
|
|||
}
|
||||
llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
|
||||
if (llama_decode(ctx_llama, batch)) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
LOG_TEE("%s : failed to eval\n", __func__);
|
||||
return false;
|
||||
}
|
||||
*n_past += n_eval;
|
||||
|
|
@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
|
|||
clip_image_u8 * img = clip_image_u8_init();
|
||||
if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
|
||||
clip_image_u8_free(img);
|
||||
fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
|
||||
LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
|
|||
bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
|
||||
if (!image_embed_result) {
|
||||
clip_image_u8_free(img);
|
||||
fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
|
||||
LOG_TEE("%s: coulnd't embed the image\n", __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
|
|||
static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
|
||||
auto file = fopen(path, "rb");
|
||||
if (file == NULL) {
|
||||
fprintf(stderr, "%s: can't read file %s\n", __func__, path);
|
||||
LOG_TEE("%s: can't read file %s\n", __func__, path);
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
|
|||
|
||||
auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
|
||||
if (buffer == NULL) {
|
||||
fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
|
||||
LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
|
||||
perror("Memory allocation error");
|
||||
fclose(file);
|
||||
return false;
|
||||
|
|
@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx
|
|||
long image_bytes_length;
|
||||
auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
|
||||
if (!loaded) {
|
||||
fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
|
||||
LOG_TEE("%s: failed to load %s\n", __func__, image_path);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -299,7 +299,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
fflush(stdout);
|
||||
|
||||
if (id == llama_token_eos(model)) {
|
||||
if (llama_token_is_eog(model, id)) {
|
||||
has_eos = true;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -141,7 +141,7 @@ int main(int argc, char ** argv){
|
|||
printf("%s", token_str.c_str());
|
||||
}
|
||||
|
||||
if (id == llama_token_eos(model)) {
|
||||
if (llama_token_is_eog(model, id)) {
|
||||
has_eos = true;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -795,8 +795,8 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
// deal with end of text token in interactive mode
|
||||
if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
|
||||
// deal with end of generation tokens in interactive mode
|
||||
if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
|
||||
LOG("found EOS token\n");
|
||||
|
||||
if (params.interactive) {
|
||||
|
|
@ -920,8 +920,8 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
// end of text token
|
||||
if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
|
||||
// end of generation
|
||||
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
|
||||
LOG_TEE(" [end of text]\n");
|
||||
break;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -359,7 +359,7 @@ int main(int argc, char ** argv) {
|
|||
// client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
|
||||
|
||||
if (client.n_decoded > 2 &&
|
||||
(id == llama_token_eos(model) ||
|
||||
(llama_token_is_eog(model, id) ||
|
||||
(params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
|
||||
client.response.find("User:") != std::string::npos ||
|
||||
client.response.find('\n') != std::string::npos)) {
|
||||
|
|
|
|||
|
|
@ -252,8 +252,8 @@ int main(int argc, char ** argv) {
|
|||
// sample the most likely token
|
||||
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
// is it an end of stream?
|
||||
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
|
||||
// is it an end of generation?
|
||||
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
|
||||
LOG_TEE("\n");
|
||||
|
||||
break;
|
||||
|
|
|
|||
|
|
@ -1,12 +1,29 @@
|
|||
set(TARGET server)
|
||||
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
|
||||
option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
|
||||
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
|
||||
add_executable(${TARGET}
|
||||
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
|
||||
set(TARGET_SRCS
|
||||
server.cpp
|
||||
utils.hpp
|
||||
httplib.h
|
||||
)
|
||||
set(PUBLIC_ASSETS
|
||||
index.html
|
||||
index.js
|
||||
completion.js
|
||||
json-schema-to-grammar.mjs
|
||||
)
|
||||
foreach(asset ${PUBLIC_ASSETS})
|
||||
set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
|
||||
set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
|
||||
list(APPEND TARGET_SRCS ${output})
|
||||
add_custom_command(
|
||||
DEPENDS "${input}"
|
||||
OUTPUT "${output}"
|
||||
COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
|
||||
)
|
||||
endforeach()
|
||||
add_executable(${TARGET} ${TARGET_SRCS})
|
||||
install(TARGETS ${TARGET} RUNTIME)
|
||||
target_compile_definitions(${TARGET} PRIVATE
|
||||
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
|
||||
|
|
|
|||
|
|
@ -1,496 +0,0 @@
|
|||
unsigned char completion_js[] = {
|
||||
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||||
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|
||||
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|
||||
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|
||||
0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72,
|
||||
0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61,
|
||||
0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67,
|
||||
0x73, 0x3b, 0x0a, 0x7d, 0x0a
|
||||
};
|
||||
unsigned int completion_js_len = 5909;
|
||||
|
|
@ -8,13 +8,3 @@ PUBLIC=$DIR/public
|
|||
echo "download js bundle files"
|
||||
curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
|
||||
echo >> $PUBLIC/index.js # add newline
|
||||
|
||||
FILES=$(ls $PUBLIC)
|
||||
|
||||
cd $PUBLIC
|
||||
for FILE in $FILES; do
|
||||
echo "generate $FILE.hpp"
|
||||
|
||||
# use simple flag for old version of xxd
|
||||
xxd -i $FILE > $DIR/$FILE.hpp
|
||||
done
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
File diff suppressed because one or more lines are too long
|
|
@ -1201,7 +1201,7 @@ struct server_context {
|
|||
});
|
||||
}
|
||||
|
||||
if (result.tok == llama_token_eos(model)) {
|
||||
if (llama_token_is_eog(model, result.tok)) {
|
||||
slot.stopped_eos = true;
|
||||
slot.has_next_token = false;
|
||||
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`.
|
|||
cd ../../..
|
||||
mkdir build
|
||||
cd build
|
||||
cmake ../
|
||||
cmake -DLLAMA_CURL=ON ../
|
||||
cmake --build . --target server
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -381,10 +381,6 @@ static json oaicompat_completion_params_parse(
|
|||
} else {
|
||||
llama_params["stop"] = json_value(body, "stop", json::array());
|
||||
}
|
||||
// Some chat templates don't use EOS token to stop generation
|
||||
// We must add their end sequences to list of stop words
|
||||
llama_params["stop"].push_back("<|im_end|>"); // chatml
|
||||
llama_params["stop"].push_back("<end_of_turn>"); // gemma
|
||||
|
||||
// Handle "response_format" field
|
||||
if (body.contains("response_format")) {
|
||||
|
|
|
|||
|
|
@ -133,8 +133,8 @@ int main(int argc, char ** argv) {
|
|||
// sample the most likely token
|
||||
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
// is it an end of stream?
|
||||
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
|
||||
// is it an end of generation?
|
||||
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
|
||||
LOG_TEE("\n");
|
||||
|
||||
break;
|
||||
|
|
|
|||
|
|
@ -360,7 +360,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
if (token_id == llama_token_eos(model_tgt)) {
|
||||
if (llama_token_is_eog(model_tgt, token_id)) {
|
||||
has_eos = true;
|
||||
}
|
||||
++n_predict;
|
||||
|
|
|
|||
|
|
@ -73,6 +73,7 @@ struct my_llama_model {
|
|||
static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL = "train_model";
|
||||
static const char * LLM_KV_TRAINING_TYPE = "training.type";
|
||||
|
||||
static const char * LLM_KV_GENERAL_NAME = "general.name";
|
||||
static const char * LLM_KV_GENERAL_ARCHITECTURE = "general.architecture";
|
||||
static const char * LLM_KV_GENERAL_FILE_TYPE = "general.file_type";
|
||||
|
||||
|
|
@ -529,6 +530,7 @@ static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_contex
|
|||
|
||||
static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) {
|
||||
const char * arch = "llama";
|
||||
|
||||
enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
|
||||
|
||||
std::vector<char> keybuf;
|
||||
|
|
@ -540,6 +542,7 @@ static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vo
|
|||
|
||||
// set arch
|
||||
gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
|
||||
gguf_set_val_str(fctx, LLM_KV_GENERAL_NAME, arch);
|
||||
gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
|
||||
|
||||
// set hparams
|
||||
|
|
|
|||
6
flake.lock
generated
6
flake.lock
generated
|
|
@ -20,11 +20,11 @@
|
|||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1712791164,
|
||||
"narHash": "sha256-3sbWO1mbpWsLepZGbWaMovSO7ndZeFqDSdX0hZ9nVyw=",
|
||||
"lastModified": 1713537308,
|
||||
"narHash": "sha256-XtTSSIB2DA6tOv+l0FhvfDMiyCmhoRbNB+0SeInZkbk=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "1042fd8b148a9105f3c0aca3a6177fd1d9360ba5",
|
||||
"rev": "5c24cf2f0a12ad855f444c30b2421d044120c66f",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
|
|
|||
16
ggml-alloc.c
16
ggml-alloc.c
|
|
@ -371,16 +371,16 @@ struct ggml_gallocr {
|
|||
};
|
||||
|
||||
ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) {
|
||||
ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1);
|
||||
ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr));
|
||||
GGML_ASSERT(galloc != NULL);
|
||||
|
||||
galloc->bufts = calloc(sizeof(ggml_backend_buffer_type_t) * n_bufs, 1);
|
||||
galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
|
||||
GGML_ASSERT(galloc->bufts != NULL);
|
||||
|
||||
galloc->buffers = calloc(sizeof(ggml_backend_buffer_t) * n_bufs, 1);
|
||||
galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs);
|
||||
GGML_ASSERT(galloc->buffers != NULL);
|
||||
|
||||
galloc->buf_tallocs = calloc(sizeof(struct ggml_dyn_tallocr *) * n_bufs, 1);
|
||||
galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
|
||||
GGML_ASSERT(galloc->buf_tallocs != NULL);
|
||||
|
||||
for (int i = 0; i < n_bufs; i++) {
|
||||
|
|
@ -646,8 +646,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
|
|||
free(galloc->hash_set.keys);
|
||||
free(galloc->hash_values);
|
||||
galloc->hash_set.size = hash_size;
|
||||
galloc->hash_set.keys = calloc(sizeof(struct ggml_tensor *), hash_size);
|
||||
galloc->hash_values = calloc(sizeof(struct hash_node), hash_size);
|
||||
galloc->hash_set.keys = calloc(hash_size, sizeof(struct ggml_tensor *));
|
||||
galloc->hash_values = calloc(hash_size, sizeof(struct hash_node));
|
||||
GGML_ASSERT(galloc->hash_set.keys != NULL);
|
||||
GGML_ASSERT(galloc->hash_values != NULL);
|
||||
} else {
|
||||
|
|
@ -667,7 +667,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
|
|||
// set the node_allocs from the hash table
|
||||
if (galloc->n_nodes < graph->n_nodes) {
|
||||
free(galloc->node_allocs);
|
||||
galloc->node_allocs = calloc(sizeof(struct node_alloc), graph->n_nodes);
|
||||
galloc->node_allocs = calloc(graph->n_nodes, sizeof(struct node_alloc));
|
||||
GGML_ASSERT(galloc->node_allocs != NULL);
|
||||
}
|
||||
galloc->n_nodes = graph->n_nodes;
|
||||
|
|
@ -697,7 +697,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
|
|||
}
|
||||
if (galloc->n_leafs < graph->n_leafs) {
|
||||
free(galloc->leaf_allocs);
|
||||
galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
|
||||
galloc->leaf_allocs = calloc(graph->n_leafs, sizeof(galloc->leaf_allocs[0]));
|
||||
GGML_ASSERT(galloc->leaf_allocs != NULL);
|
||||
}
|
||||
galloc->n_leafs = graph->n_leafs;
|
||||
|
|
|
|||
|
|
@ -822,7 +822,11 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t
|
|||
GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
switch (op->op) {
|
||||
case GGML_OP_CPY:
|
||||
return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float
|
||||
return
|
||||
op->type != GGML_TYPE_IQ2_XXS &&
|
||||
op->type != GGML_TYPE_IQ2_XS &&
|
||||
op->type != GGML_TYPE_IQ1_S &&
|
||||
op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
|
||||
case GGML_OP_MUL_MAT:
|
||||
return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
|
||||
default:
|
||||
|
|
@ -1721,23 +1725,23 @@ ggml_backend_sched_t ggml_backend_sched_new(
|
|||
GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
|
||||
GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU
|
||||
|
||||
struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
|
||||
struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched));
|
||||
|
||||
// initialize hash table
|
||||
sched->hash_set = ggml_hash_set_new(graph_size);
|
||||
sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size);
|
||||
sched->tensor_copies = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size);
|
||||
sched->tensor_backend_id = calloc(sched->hash_set.size, sizeof(sched->tensor_backend_id[0]));
|
||||
sched->tensor_copies = calloc(sched->hash_set.size, sizeof(sched->tensor_copies[0]));
|
||||
|
||||
const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2;
|
||||
sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), nodes_size);
|
||||
sched->leaf_backend_ids = calloc(sizeof(sched->leaf_backend_ids[0]), nodes_size);
|
||||
sched->node_backend_ids = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
|
||||
sched->leaf_backend_ids = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
|
||||
|
||||
sched->n_backends = n_backends;
|
||||
|
||||
sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
|
||||
|
||||
const int initial_splits_capacity = 16;
|
||||
sched->splits = calloc(sizeof(sched->splits[0]), initial_splits_capacity);
|
||||
sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0]));
|
||||
sched->splits_capacity = initial_splits_capacity;
|
||||
|
||||
for (int b = 0; b < n_backends; b++) {
|
||||
|
|
@ -1968,10 +1972,10 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te
|
|||
struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
|
||||
struct ggml_hash_set hash_set = {
|
||||
/* .size = */ graph->visited_hash_table.size,
|
||||
/* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT
|
||||
/* .keys = */ calloc(graph->visited_hash_table.size, sizeof(hash_set.keys[0])) // NOLINT
|
||||
};
|
||||
struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT
|
||||
bool * node_init = calloc(sizeof(node_init[0]), hash_set.size);
|
||||
struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
|
||||
bool * node_init = calloc(hash_set.size, sizeof(node_init[0]));
|
||||
|
||||
struct ggml_init_params params = {
|
||||
/* .mem_size = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),
|
||||
|
|
|
|||
|
|
@ -135,6 +135,7 @@ class MODEL_ARCH(IntEnum):
|
|||
XVERSE = auto()
|
||||
COMMAND_R = auto()
|
||||
DBRX = auto()
|
||||
OLMO = auto()
|
||||
|
||||
|
||||
class MODEL_TENSOR(IntEnum):
|
||||
|
|
@ -210,6 +211,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.XVERSE: "xverse",
|
||||
MODEL_ARCH.COMMAND_R: "command-r",
|
||||
MODEL_ARCH.DBRX: "dbrx",
|
||||
MODEL_ARCH.OLMO: "olmo",
|
||||
}
|
||||
|
||||
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
||||
|
|
@ -695,6 +697,17 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_DOWN_EXP,
|
||||
MODEL_TENSOR.FFN_UP_EXP,
|
||||
],
|
||||
MODEL_ARCH.OLMO: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
# TODO
|
||||
}
|
||||
|
||||
|
|
@ -859,6 +872,7 @@ GGML_QUANT_SIZES = {
|
|||
GGMLQuantizationType.I32: (1, 4),
|
||||
GGMLQuantizationType.I64: (1, 8),
|
||||
GGMLQuantizationType.F64: (1, 8),
|
||||
GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32),
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
303
llama.cpp
303
llama.cpp
|
|
@ -222,6 +222,7 @@ enum llm_arch {
|
|||
LLM_ARCH_XVERSE,
|
||||
LLM_ARCH_COMMAND_R,
|
||||
LLM_ARCH_DBRX,
|
||||
LLM_ARCH_OLMO,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -256,6 +257,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_XVERSE, "xverse" },
|
||||
{ LLM_ARCH_COMMAND_R, "command-r" },
|
||||
{ LLM_ARCH_DBRX, "dbrx" },
|
||||
{ LLM_ARCH_OLMO, "olmo" },
|
||||
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
||||
};
|
||||
|
||||
|
|
@ -990,6 +992,20 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
|
|||
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_OLMO,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
||||
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
||||
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
||||
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||
{ 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,
|
||||
{
|
||||
|
|
@ -1584,12 +1600,12 @@ struct llama_mlock {
|
|||
};
|
||||
using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
|
||||
|
||||
static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
|
||||
static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
|
||||
std::vector<char> result(8, 0);
|
||||
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
||||
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
||||
if (n_tokens < 0) {
|
||||
result.resize(-n_tokens);
|
||||
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
||||
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
||||
GGML_ASSERT(check == -n_tokens);
|
||||
}
|
||||
else {
|
||||
|
|
@ -2104,7 +2120,7 @@ struct llama_vocab {
|
|||
id special_prefix_id = -1;
|
||||
id special_suffix_id = -1;
|
||||
id special_middle_id = -1;
|
||||
id special_eot_id = -1;
|
||||
id special_eot_id = -1; // TODO: move above after "eos_id", and here add "file separator" token
|
||||
|
||||
bool add_space_prefix = true;
|
||||
|
||||
|
|
@ -3754,7 +3770,7 @@ static void llm_load_hparams(
|
|||
switch (hparams.n_layer) {
|
||||
case 22: model.type = e_model::MODEL_1B; break;
|
||||
case 26: model.type = e_model::MODEL_3B; break;
|
||||
case 32: model.type = e_model::MODEL_7B; break;
|
||||
case 32: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_7B : e_model::MODEL_8B; break; // LLaMa 8B v3 uses GQA
|
||||
case 40: model.type = e_model::MODEL_13B; break;
|
||||
case 48: model.type = e_model::MODEL_34B; break;
|
||||
case 60: model.type = e_model::MODEL_30B; break;
|
||||
|
|
@ -4070,6 +4086,18 @@ static void llm_load_hparams(
|
|||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_OLMO:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
||||
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 22: model.type = e_model::MODEL_1B; break;
|
||||
case 32: model.type = e_model::MODEL_7B; break;
|
||||
case 80: model.type = e_model::MODEL_70B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
default: (void)0;
|
||||
}
|
||||
|
||||
|
|
@ -4151,7 +4179,10 @@ static void llm_load_vocab(
|
|||
vocab.special_prefix_id = 67;
|
||||
vocab.special_suffix_id = 69;
|
||||
vocab.special_middle_id = 68;
|
||||
vocab.special_eot_id = 70;
|
||||
// TODO: this is not EOT, it is "file separator" token, needs fix
|
||||
// https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
|
||||
//vocab.special_eot_id = 70;
|
||||
vocab.special_eot_id = 107;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -4280,6 +4311,7 @@ static void llm_load_vocab(
|
|||
{ LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
|
||||
{ LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id },
|
||||
};
|
||||
|
||||
for (const auto & it : special_token_types) {
|
||||
const std::string & key = kv(std::get<0>(it));
|
||||
int32_t & id = std::get<1>(it);
|
||||
|
|
@ -4294,7 +4326,6 @@ static void llm_load_vocab(
|
|||
} else {
|
||||
id = new_id;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
// Handle add_bos_token and add_eos_token
|
||||
|
|
@ -4308,6 +4339,27 @@ static void llm_load_vocab(
|
|||
vocab.special_add_eos = int(temp);
|
||||
}
|
||||
}
|
||||
|
||||
// find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
|
||||
//
|
||||
// TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
|
||||
// for now, we apply this workaround to find the EOT token based on its text
|
||||
if (vocab.special_eot_id == -1) {
|
||||
for (const auto & t : vocab.token_to_id) {
|
||||
if (
|
||||
// TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
|
||||
// need to fix convert script
|
||||
//vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
|
||||
(t.first == "<|eot_id|>" ||
|
||||
t.first == "<|im_end|>" ||
|
||||
t.first == "<end_of_turn>"
|
||||
)
|
||||
) {
|
||||
vocab.special_eot_id = t.second;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// build special tokens cache
|
||||
|
|
@ -4470,14 +4522,19 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|||
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.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
|
||||
if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_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.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
|
||||
if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_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_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
|
||||
if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
|
||||
if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
|
||||
if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
|
||||
}
|
||||
|
||||
// Returns false if cancelled by progress_callback
|
||||
|
|
@ -5666,6 +5723,37 @@ static bool llm_load_tensors(
|
|||
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
||||
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
||||
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
|
||||
// output
|
||||
{
|
||||
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
||||
// if output is NULL, init from the input tok embed
|
||||
if (model.output == NULL) {
|
||||
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
ml.n_created--; // artificial tensor
|
||||
ml.size_data += ggml_nbytes(model.output);
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
ggml_context * ctx_split = ctx_for_layer_split(i);
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
||||
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
||||
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
||||
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
||||
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
||||
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
|
|
@ -10096,6 +10184,139 @@ struct llm_build_context {
|
|||
return gf;
|
||||
|
||||
}
|
||||
|
||||
// ref: https://allenai.org/olmo
|
||||
// based on the original build_llama() function, changes:
|
||||
// * non-parametric layer norm
|
||||
// * clamp qkv
|
||||
// * removed bias
|
||||
// * removed MoE
|
||||
struct ggml_cgraph * build_olmo() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
||||
int32_t n_tokens = this->n_tokens;
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
// norm
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
NULL, NULL,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// 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 (hparams.f_clamp_kqv > 0.0f) {
|
||||
Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
||||
cb(Qcur, "Qcur", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
if (hparams.f_clamp_kqv > 0.0f) {
|
||||
Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
||||
cb(Kcur, "Kcur", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
if (hparams.f_clamp_kqv > 0.0f) {
|
||||
Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
||||
cb(Vcur, "Vcur", il);
|
||||
}
|
||||
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 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, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
||||
model.layers[il].wo, nullptr,
|
||||
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
n_tokens = n_outputs;
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward network
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
NULL, NULL,
|
||||
LLM_NORM, 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,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
||||
if (layer_dir != nullptr) {
|
||||
cur = ggml_add(ctx0, cur, layer_dir);
|
||||
}
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
NULL, NULL,
|
||||
LLM_NORM, 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;
|
||||
}
|
||||
};
|
||||
|
||||
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
||||
|
|
@ -10301,6 +10522,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_dbrx();
|
||||
} break;
|
||||
case LLM_ARCH_OLMO:
|
||||
{
|
||||
result = llm.build_olmo();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -13072,16 +13297,14 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
|
|||
GGML_ASSERT(ctx);
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
bool allow_eos = false;
|
||||
bool allow_eog = false;
|
||||
for (const auto & stack : grammar->stacks) {
|
||||
if (stack.empty()) {
|
||||
allow_eos = true;
|
||||
allow_eog = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
const llama_token eos = llama_token_eos(&ctx->model);
|
||||
|
||||
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
|
||||
candidates_decoded.reserve(candidates->size);
|
||||
std::vector<llama_grammar_candidate> candidates_grammar;
|
||||
|
|
@ -13089,9 +13312,10 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
|
|||
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
const llama_token id = candidates->data[i].id;
|
||||
const std::string piece = llama_token_to_piece(ctx, id);
|
||||
if (id == eos) {
|
||||
if (!allow_eos) {
|
||||
const std::string piece = llama_token_to_piece(ctx, id, false);
|
||||
|
||||
if (llama_token_is_eog(&ctx->model, id)) {
|
||||
if (!allow_eog) {
|
||||
candidates->data[i].logit = -INFINITY;
|
||||
}
|
||||
} else if (piece.empty() || piece[0] == 0) {
|
||||
|
|
@ -13280,7 +13504,7 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
|
|||
void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
if (token == llama_token_eos(&ctx->model)) {
|
||||
if (llama_token_is_eog(&ctx->model, token)) {
|
||||
for (const auto & stack : grammar->stacks) {
|
||||
if (stack.empty()) {
|
||||
return;
|
||||
|
|
@ -13289,7 +13513,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
|
|||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
const std::string piece = llama_token_to_piece(ctx, token);
|
||||
const std::string piece = llama_token_to_piece(ctx, token, false);
|
||||
|
||||
// Note terminating 0 in decoded string
|
||||
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
|
||||
|
|
@ -15154,6 +15378,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|||
case LLM_ARCH_MINICPM:
|
||||
case LLM_ARCH_XVERSE:
|
||||
case LLM_ARCH_COMMAND_R:
|
||||
case LLM_ARCH_OLMO:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
|
|
@ -16683,6 +16908,13 @@ llama_token_type llama_token_get_type(const struct llama_model * model, llama_to
|
|||
return model->vocab.id_to_token[token].type;
|
||||
}
|
||||
|
||||
bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
|
||||
return token != -1 && (
|
||||
token == llama_token_eos(model) ||
|
||||
token == llama_token_eot(model)
|
||||
);
|
||||
}
|
||||
|
||||
llama_token llama_token_bos(const struct llama_model * model) {
|
||||
return model->vocab.special_bos_id;
|
||||
}
|
||||
|
|
@ -16760,7 +16992,7 @@ static std::string llama_decode_text(const std::string & text) {
|
|||
}
|
||||
|
||||
// does not write null-terminator to buf
|
||||
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
|
||||
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
|
||||
if (0 <= token && token < llama_n_vocab(model)) {
|
||||
switch (llama_vocab_get_type(model->vocab)) {
|
||||
case LLAMA_VOCAB_TYPE_WPM:
|
||||
|
|
@ -16775,7 +17007,9 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
|
|||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
} else if (llama_is_user_defined_token(model->vocab, token)) {
|
||||
} else if (
|
||||
(llama_is_user_defined_token(model->vocab, token)) ||
|
||||
(llama_is_control_token (model->vocab, token) && special)) {
|
||||
std::string result = model->vocab.id_to_token[token].text;
|
||||
if (length < (int) result.length()) {
|
||||
return -(int) result.length();
|
||||
|
|
@ -16788,8 +17022,6 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
|
|||
}
|
||||
memcpy(buf, "\xe2\x96\x85", 3);
|
||||
return 3;
|
||||
} else if (llama_is_control_token(model->vocab, token)) {
|
||||
;
|
||||
} else if (llama_is_byte_token(model->vocab, token)) {
|
||||
if (length < 1) {
|
||||
return -1;
|
||||
|
|
@ -16810,15 +17042,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
|
|||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
} else if (llama_is_user_defined_token(model->vocab, token)) {
|
||||
} else if (
|
||||
(llama_is_user_defined_token(model->vocab, token)) ||
|
||||
(llama_is_control_token (model->vocab, token) && special)) {
|
||||
std::string result = model->vocab.id_to_token[token].text;
|
||||
if (length < (int) result.length()) {
|
||||
return -(int) result.length();
|
||||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
} else if (llama_is_control_token(model->vocab, token)) {
|
||||
;
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
|
@ -17016,6 +17248,15 @@ static int32_t llama_chat_apply_template_internal(
|
|||
if (add_ass) {
|
||||
ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
|
||||
}
|
||||
} else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
|
||||
// Llama 3
|
||||
for (auto message : chat) {
|
||||
std::string role(message->role);
|
||||
ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
|
||||
}
|
||||
if (add_ass) {
|
||||
ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
|
||||
}
|
||||
} else {
|
||||
// template not supported
|
||||
return -1;
|
||||
|
|
|
|||
9
llama.h
9
llama.h
|
|
@ -783,6 +783,9 @@ extern "C" {
|
|||
|
||||
LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
|
||||
|
||||
// Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
|
||||
LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
|
||||
|
||||
// Special tokens
|
||||
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
|
||||
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
|
||||
|
|
@ -796,7 +799,7 @@ extern "C" {
|
|||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||||
LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
|
||||
|
||||
// codellama infill tokens
|
||||
// Codellama infill tokens
|
||||
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
||||
LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
||||
LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
||||
|
|
@ -825,11 +828,13 @@ extern "C" {
|
|||
// Uses the vocabulary in the provided context.
|
||||
// Does not write null terminator to the buffer.
|
||||
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||||
// @param special If true, special tokens are rendered in the output.
|
||||
LLAMA_API int32_t llama_token_to_piece(
|
||||
const struct llama_model * model,
|
||||
llama_token token,
|
||||
char * buf,
|
||||
int32_t length);
|
||||
int32_t length,
|
||||
bool special);
|
||||
|
||||
/// Apply chat template. Inspired by hf apply_chat_template() on python.
|
||||
/// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
|
||||
|
|
|
|||
16
scripts/xxd.cmake
Normal file
16
scripts/xxd.cmake
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
# CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}`
|
||||
# Usage: cmake -DINPUT=examples/server/public/index.html -DOUTPUT=examples/server/index.html.hpp -P scripts/xxd.cmake
|
||||
|
||||
SET(INPUT "" CACHE STRING "Input File")
|
||||
SET(OUTPUT "" CACHE STRING "Output File")
|
||||
|
||||
get_filename_component(filename "${INPUT}" NAME)
|
||||
string(REGEX REPLACE "\\.|-" "_" name "${filename}")
|
||||
|
||||
file(READ "${INPUT}" hex_data HEX)
|
||||
string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}")
|
||||
|
||||
string(LENGTH ${hex_data} hex_len)
|
||||
math(EXPR len "${hex_len} / 2")
|
||||
|
||||
file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n")
|
||||
|
|
@ -46,7 +46,9 @@ int main(void) {
|
|||
// No template included in tokenizer_config.json, so this template likely needs to be manually set.
|
||||
"{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
|
||||
// CohereForAI/c4ai-command-r-plus
|
||||
"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}"
|
||||
"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
|
||||
// Llama-3
|
||||
"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
|
||||
};
|
||||
std::vector<std::string> expected_output = {
|
||||
// teknium/OpenHermes-2.5-Mistral-7B
|
||||
|
|
@ -73,6 +75,8 @@ int main(void) {
|
|||
"SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT: I am an assistant </s>\nUSER: Another question\nASSISTANT:",
|
||||
// CohereForAI/c4ai-command-r-plus
|
||||
"<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
|
||||
// Llama 3
|
||||
"<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
|
||||
};
|
||||
std::vector<char> formatted_chat(1024);
|
||||
int32_t res;
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue