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Merge remote-tracking branch 'origin' into add-support-for-phi3-vision

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This commit is contained in:
Andrei Betlen 2024-08-27 18:13:54 -04:00
commit 951f1d9053
57 changed files with 9189 additions and 7109 deletions
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2
.devops/llama-server-cuda.Dockerfile
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@ -24,6 +24,8 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ENV GGML_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
RUN make -j$(nproc) llama-server

2
.devops/llama-server-intel.Dockerfile
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@ -26,6 +26,8 @@ RUN apt-get update && \
COPY --from=build /app/build/bin/llama-server /llama-server
ENV LC_ALL=C.utf8
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]

2
.devops/llama-server-rocm.Dockerfile
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@ -39,6 +39,8 @@ ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
ENV GGML_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
# Enable cURL
ENV LLAMA_CURL=1

2
.devops/llama-server-vulkan.Dockerfile
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@ -23,6 +23,8 @@ RUN cp /app/build/bin/llama-server /llama-server && \
rm -rf /app
ENV LC_ALL=C.utf8
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]

2
.devops/llama-server.Dockerfile
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@ -21,6 +21,8 @@ RUN apt-get update && \
COPY --from=build /app/llama-server /llama-server
ENV LC_ALL=C.utf8
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]

2
.ecrc
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@ -1,5 +1,5 @@
{
"Exclude": ["^\\.gitmodules$"],
"Exclude": ["^\\.gitmodules$", "stb_image\\.h"],
"Disable": {
"IndentSize": true
}

5
CMakePresets.json
View File

@ -28,6 +28,7 @@
{ "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
{ "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
{ "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
{ "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } },
{
"name": "arm64-windows-msvc", "hidden": true,
@ -60,6 +61,8 @@
{ "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
{ "name": "x64-windows-sycl-debug" , "inherits": [ "sycl-base", "debug" ] },
{ "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }
{ "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] },
{ "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] },
{ "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] }
]
}

29
ci/run.sh
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@ -13,6 +13,9 @@
# # with SYCL support
# GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
#
# # with VULKAN support
# GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
#
if [ -z "$2" ]; then
echo "usage: $0 <output-dir> <mnt-dir>"
@ -40,7 +43,7 @@ if [ ! -z ${GG_BUILD_METAL} ]; then
fi
if [ ! -z ${GG_BUILD_CUDA} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=1"
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native"
fi
if [ ! -z ${GG_BUILD_SYCL} ]; then
@ -52,6 +55,10 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
fi
if [ ! -z ${GG_BUILD_VULKAN} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
fi
## helpers
# download a file if it does not exist or if it is outdated
@ -107,7 +114,7 @@ function gg_run_ctest_debug {
gg_check_build_requirements
(time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
(time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
@ -138,7 +145,7 @@ function gg_run_ctest_release {
gg_check_build_requirements
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
if [ -z ${GG_BUILD_LOW_PERF} ]; then
(time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log
@ -266,7 +273,6 @@ function gg_sum_ctest_with_model_release {
}
# open_llama_7b_v2
# requires: GG_BUILD_CUDA
function gg_run_open_llama_7b_v2 {
cd ${SRC}
@ -290,8 +296,8 @@ function gg_run_open_llama_7b_v2 {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
@ -425,7 +431,7 @@ function gg_run_pythia_1_4b {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
@ -535,7 +541,6 @@ function gg_sum_pythia_1_4b {
}
# pythia_2_8b
# requires: GG_BUILD_CUDA
function gg_run_pythia_2_8b {
cd ${SRC}
@ -556,8 +561,8 @@ function gg_run_pythia_2_8b {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
@ -692,7 +697,7 @@ function gg_run_embd_bge_small {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
@ -761,7 +766,7 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
fi
if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
if [ -z ${GG_BUILD_CUDA} ]; then
if [ -z ${GG_BUILD_CUDA} ] && [ -z ${GG_BUILD_VULKAN} ]; then
test $ret -eq 0 && gg_run pythia_1_4b
else
test $ret -eq 0 && gg_run pythia_2_8b

23
common/common.cpp
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@ -327,6 +327,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
void gpt_params_parse_from_env(gpt_params & params) {
// we only care about server-related params for now
get_env("LLAMA_ARG_MODEL", params.model);
get_env("LLAMA_ARG_MODEL_URL", params.model_url);
get_env("LLAMA_ARG_MODEL_ALIAS", params.model_alias);
get_env("LLAMA_ARG_HF_REPO", params.hf_repo);
get_env("LLAMA_ARG_HF_FILE", params.hf_file);
get_env("LLAMA_ARG_THREADS", params.n_threads);
get_env("LLAMA_ARG_CTX_SIZE", params.n_ctx);
get_env("LLAMA_ARG_N_PARALLEL", params.n_parallel);
@ -341,6 +345,9 @@ void gpt_params_parse_from_env(gpt_params & params) {
get_env("LLAMA_ARG_EMBEDDINGS", params.embedding);
get_env("LLAMA_ARG_FLASH_ATTN", params.flash_attn);
get_env("LLAMA_ARG_DEFRAG_THOLD", params.defrag_thold);
get_env("LLAMA_ARG_CONT_BATCHING", params.cont_batching);
get_env("LLAMA_ARG_HOST", params.hostname);
get_env("LLAMA_ARG_PORT", params.port);
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
@ -901,7 +908,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
}
return true;
}
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") {
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--n-gpu-layers-draft") {
CHECK_ARG
params.n_gpu_layers_draft = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
@ -1861,13 +1868,19 @@ std::string string_get_sortable_timestamp() {
void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return; // Avoid infinite loop if 'search' is an empty string
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
while ((pos = s.find(search, pos)) != std::string::npos) {
s.replace(pos, search.length(), replace);
pos += replace.length();
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
void string_process_escapes(std::string & input) {

5186
common/stb_image.h
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File diff suppressed because it is too large Load Diff

11
convert_hf_to_gguf.py
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@ -63,6 +63,7 @@ class Model:
model_name: str | None
metadata_override: Path | None
dir_model_card: Path
is_lora: bool
# subclasses should define this!
model_arch: gguf.MODEL_ARCH
@ -70,7 +71,7 @@ class Model:
def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False,
use_temp_file: bool = False, eager: bool = False,
metadata_override: Path | None = None, model_name: str | None = None,
split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False, is_lora: bool = False):
if type(self) is Model:
raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
@ -92,6 +93,7 @@ class Model:
self.metadata_override = metadata_override
self.model_name = model_name
self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py
self.is_lora = is_lora # true if model is used inside convert_lora_to_gguf.py
# Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
if self.ftype == gguf.LlamaFileType.GUESSED:
@ -1570,7 +1572,7 @@ class LlamaModel(Model):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -1593,6 +1595,7 @@ class LlamaModel(Model):
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor + smooth))
if not self.is_lora:
self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
super().prepare_tensors()
@ -2140,6 +2143,7 @@ class Phi3MiniModel(Model):
if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
if not self.is_lora:
self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG] + ".weight", np.array(long_factors, dtype=np.float32))
self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32))
@ -3816,7 +3820,7 @@ class ExaoneModel(Model):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -3839,6 +3843,7 @@ class ExaoneModel(Model):
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor + smooth))
if not self.is_lora:
self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
super().prepare_tensors()

1
convert_lora_to_gguf.py
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@ -386,6 +386,7 @@ if __name__ == '__main__':
dry_run=args.dry_run,
dir_lora_model=dir_lora,
lora_alpha=alpha,
is_lora=True,
)
logger.info("Exporting model...")

14
docs/backend/SYCL.md
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@ -20,7 +20,7 @@
**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL and oneDNN)*.
- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
@ -28,10 +28,6 @@
The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).
When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
## Recommended Release
The SYCL backend would be broken by some PRs due to no online CI.
@ -45,6 +41,10 @@ The following release is verified with good quality:
## News
- 2024.8
- Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs.
- 2024.5
- Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
- Arch Linux is verified successfully.
@ -196,7 +196,7 @@ Please follow the instructions for downloading and installing the Toolkit for Li
Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs.
- **Adding support to Nvidia GPUs**
@ -255,8 +255,6 @@ or
# Export relevant ENV variables
source /opt/intel/oneapi/setvars.sh
# Build LLAMA with MKL BLAS acceleration for intel GPU
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

4
examples/llava/README-minicpmv2.5.md
View File

@ -15,8 +15,8 @@ cd llama.cpp
Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)
```bash
python ./examples/minicpmv/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
python ./examples/minicpmv/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
# quantize int4 version

14
examples/llava/clip.cpp
View File

@ -218,13 +218,19 @@ static std::string gguf_data_to_str(enum gguf_type type, const void * data, int
static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return; // Avoid infinite loop if 'search' is an empty string
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
while ((pos = s.find(search, pos)) != std::string::npos) {
s.replace(pos, search.length(), replace);
pos += replace.length();
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {

2
examples/quantize/quantize.cpp
View File

@ -104,7 +104,7 @@ static void usage(const char * executable) {
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --keep-split: will generate quatized model in the same shards as input");
printf(" --keep-split: will generate quantized model in the same shards as input\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
printf("Note: --include-weights and --exclude-weights cannot be used together\n");

60
examples/server/README.md
View File

@ -249,23 +249,49 @@ logging:
Available environment variables (if specified, these variables will override parameters specified in arguments):
- `LLAMA_CACHE` (cache directory, used by `--hf-repo`)
- `HF_TOKEN` (Hugging Face access token, used when accessing a gated model with `--hf-repo`)
- `LLAMA_ARG_MODEL`
- `LLAMA_ARG_THREADS`
- `LLAMA_ARG_CTX_SIZE`
- `LLAMA_ARG_N_PARALLEL`
- `LLAMA_ARG_BATCH`
- `LLAMA_ARG_UBATCH`
- `LLAMA_ARG_N_GPU_LAYERS`
- `LLAMA_ARG_THREADS_HTTP`
- `LLAMA_ARG_CHAT_TEMPLATE`
- `LLAMA_ARG_N_PREDICT`
- `LLAMA_ARG_ENDPOINT_METRICS`
- `LLAMA_ARG_ENDPOINT_SLOTS`
- `LLAMA_ARG_EMBEDDINGS`
- `LLAMA_ARG_FLASH_ATTN`
- `LLAMA_ARG_DEFRAG_THOLD`
- `LLAMA_CACHE`: cache directory, used by `--hf-repo`
- `HF_TOKEN`: Hugging Face access token, used when accessing a gated model with `--hf-repo`
- `LLAMA_ARG_MODEL`: equivalent to `-m`
- `LLAMA_ARG_MODEL_URL`: equivalent to `-mu`
- `LLAMA_ARG_MODEL_ALIAS`: equivalent to `-a`
- `LLAMA_ARG_HF_REPO`: equivalent to `--hf-repo`
- `LLAMA_ARG_HF_FILE`: equivalent to `--hf-file`
- `LLAMA_ARG_THREADS`: equivalent to `-t`
- `LLAMA_ARG_CTX_SIZE`: equivalent to `-c`
- `LLAMA_ARG_N_PARALLEL`: equivalent to `-np`
- `LLAMA_ARG_BATCH`: equivalent to `-b`
- `LLAMA_ARG_UBATCH`: equivalent to `-ub`
- `LLAMA_ARG_N_GPU_LAYERS`: equivalent to `-ngl`
- `LLAMA_ARG_THREADS_HTTP`: equivalent to `--threads-http`
- `LLAMA_ARG_CHAT_TEMPLATE`: equivalent to `--chat-template`
- `LLAMA_ARG_N_PREDICT`: equivalent to `-n`
- `LLAMA_ARG_ENDPOINT_METRICS`: if set to `1`, it will enable metrics endpoint (equivalent to `--metrics`)
- `LLAMA_ARG_ENDPOINT_SLOTS`: if set to `0`, it will **disable** slots endpoint (equivalent to `--no-slots`). This feature is enabled by default.
- `LLAMA_ARG_EMBEDDINGS`: if set to `1`, it will enable embeddings endpoint (equivalent to `--embeddings`)
- `LLAMA_ARG_FLASH_ATTN`: if set to `1`, it will enable flash attention (equivalent to `-fa`)
- `LLAMA_ARG_CONT_BATCHING`: if set to `0`, it will **disable** continuous batching (equivalent to `--no-cont-batching`). This feature is enabled by default.
- `LLAMA_ARG_DEFRAG_THOLD`: equivalent to `-dt`
- `LLAMA_ARG_HOST`: equivalent to `--host`
- `LLAMA_ARG_PORT`: equivalent to `--port`
Example usage of docker compose with environment variables:
```yml
services:
llamacpp-server:
image: ghcr.io/ggerganov/llama.cpp:server
ports:
- 8080:8080
volumes:
- ./models:/models
environment:
# alternatively, you can use "LLAMA_ARG_MODEL_URL" to download the model
LLAMA_ARG_MODEL: /models/my_model.gguf
LLAMA_ARG_CTX_SIZE: 4096
LLAMA_ARG_N_PARALLEL: 2
LLAMA_ARG_ENDPOINT_METRICS: 1 # to disable, either remove or set to 0
LLAMA_ARG_PORT: 8080
```
## Build

2
examples/server/public/index.js
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File diff suppressed because one or more lines are too long

1
ggml/include/ggml-backend.h
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@ -63,6 +63,7 @@ extern "C" {
GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
// "offset" refers to the offset of the tensor data for setting/getting data
GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);

131
ggml/include/ggml.h
View File

@ -220,7 +220,7 @@
#include <stdio.h>
#define GGML_FILE_MAGIC 0x67676d6c // "ggml"
#define GGML_FILE_VERSION 1
#define GGML_FILE_VERSION 2
#define GGML_QNT_VERSION 2 // bump this on quantization format changes
#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
@ -453,6 +453,8 @@ extern "C" {
GGML_OP_SQR,
GGML_OP_SQRT,
GGML_OP_LOG,
GGML_OP_SIN,
GGML_OP_COS,
GGML_OP_SUM,
GGML_OP_SUM_ROWS,
GGML_OP_MEAN,
@ -490,9 +492,11 @@ extern "C" {
GGML_OP_CLAMP,
GGML_OP_CONV_TRANSPOSE_1D,
GGML_OP_IM2COL,
GGML_OP_IM2COL_BACK,
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
GGML_OP_POOL_2D_BACK,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_ARANGE,
@ -969,6 +973,22 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_sin(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_sin_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_cos(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_cos_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// return scalar
GGML_API struct ggml_tensor * ggml_sum(
struct ggml_context * ctx,
@ -1566,34 +1586,49 @@ extern "C" {
float min,
float max);
// im2col
// converts data into a format that effectively results in a convolution when combined with matrix multiplication
GGML_API struct ggml_tensor * ggml_im2col(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1, // dilation dimension 1
bool is_2D,
enum ggml_type dst_type);
GGML_API struct ggml_tensor * ggml_im2col_back(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // gradient of im2col output
int64_t * ne, // shape of im2col input
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1, // dilation dimension 1
bool is_2D);
GGML_API struct ggml_tensor * ggml_conv_depthwise_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1);
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1); // dilation dimension 1
GGML_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride
int p0, // padding
int d0); // dilation
@ -1602,29 +1637,29 @@ extern "C" {
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s,
int d);
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s, // stride
int d); // dilation
GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int p0,
int d0);
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride
int p0, // padding
int d0); // dilation
GGML_API struct ggml_tensor * ggml_conv_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1);
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int s0, // stride dimension 0
int s1, // stride dimension 1
int p0, // padding dimension 0
int p1, // padding dimension 1
int d0, // dilation dimension 0
int d1); // dilation dimension 1
// kernel size is a->ne[0] x a->ne[1]
@ -1686,6 +1721,18 @@ extern "C" {
float p0,
float p1);
GGML_API struct ggml_tensor * ggml_pool_2d_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * af, // "a"/input used in forward pass
enum ggml_op_pool op,
int k0,
int k1,
int s0,
int s1,
float p0,
float p1);
// nearest interpolate
// multiplies ne0 and ne1 by scale factor
// used in stable-diffusion
@ -1760,7 +1807,8 @@ extern "C" {
struct ggml_tensor * v,
struct ggml_tensor * mask,
float scale,
float max_bias);
float max_bias,
float logit_softcap);
GGML_API void ggml_flash_attn_ext_set_prec(
struct ggml_tensor * a,
@ -1777,10 +1825,8 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_ssm_conv(
struct ggml_context * ctx,
struct ggml_tensor * s,
struct ggml_tensor * x,
struct ggml_tensor * c,
struct ggml_tensor * sq);
struct ggml_tensor * sx,
struct ggml_tensor * c);
GGML_API struct ggml_tensor * ggml_ssm_scan(
struct ggml_context * ctx,
@ -1789,8 +1835,7 @@ extern "C" {
struct ggml_tensor * dt,
struct ggml_tensor * A,
struct ggml_tensor * B,
struct ggml_tensor * C,
struct ggml_tensor * sq);
struct ggml_tensor * C);
// partition into non-overlapping windows with padding if needed
// example:

10
ggml/src/CMakeLists.txt
View File

@ -549,6 +549,13 @@ if (GGML_SYCL)
file(GLOB GGML_SOURCES_SYCL "ggml-sycl/*.cpp")
list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp")
find_package(DNNL)
message("-- DNNL found:" ${DNNL_FOUND})
if (GGML_SYCL_TARGET STREQUAL "INTEL")
add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND})
else()
add_compile_definitions(GGML_SYCL_DNNL=0)
endif()
if (WIN32)
find_package(IntelSYCL REQUIRED)
find_package(MKL REQUIRED)
@ -561,6 +568,9 @@ if (GGML_SYCL)
set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl pthread m dl onemkl)
endif()
endif()
if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
list(APPEND GGML_EXTRA_LIBS DNNL::dnnl)
endif()
endif()
if (GGML_RPC)

21
ggml/src/ggml-aarch64.c
View File

@ -337,33 +337,18 @@ static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict ds
}
size_t quantize_q4_0_4x4(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
if (!quant_weights) {
UNUSED(quant_weights);
return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 4);
}
else {
assert(false);
return 0;
}
}
size_t quantize_q4_0_4x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
if (!quant_weights) {
UNUSED(quant_weights);
return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 8);
}
else {
assert(false);
return 0;
}
}
size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
if (!quant_weights) {
UNUSED(quant_weights);
return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8);
}
else {
assert(false);
return 0;
}
}
void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {

21
ggml/src/ggml-cuda.cu
View File

@ -9,8 +9,10 @@
#include "ggml-cuda/binbcast.cuh"
#include "ggml-cuda/clamp.cuh"
#include "ggml-cuda/concat.cuh"
#include "ggml-cuda/conv-transpose-1d.cuh"
#include "ggml-cuda/convert.cuh"
#include "ggml-cuda/cpy.cuh"
#include "ggml-cuda/cross-entropy-loss.cuh"
#include "ggml-cuda/diagmask.cuh"
#include "ggml-cuda/dmmv.cuh"
#include "ggml-cuda/fattn.cuh"
@ -29,7 +31,6 @@
#include "ggml-cuda/tsembd.cuh"
#include "ggml-cuda/unary.cuh"
#include "ggml-cuda/upscale.cuh"
#include "ggml-cuda/conv-transpose-1d.cuh"
#include <algorithm>
#include <array>
@ -2181,6 +2182,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_ADD:
ggml_cuda_op_add(ctx, dst);
break;
case GGML_OP_SUB:
ggml_cuda_op_sub(ctx, dst);
break;
case GGML_OP_ACC:
ggml_cuda_op_acc(ctx, dst);
break;
@ -2267,6 +2271,12 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_SQRT:
ggml_cuda_op_sqrt(ctx, dst);
break;
case GGML_OP_SIN:
ggml_cuda_op_sin(ctx, dst);
break;
case GGML_OP_COS:
ggml_cuda_op_cos(ctx, dst);
break;
case GGML_OP_CLAMP:
ggml_cuda_op_clamp(ctx, dst);
break;
@ -2303,6 +2313,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_FLASH_ATTN_EXT:
ggml_cuda_flash_attn_ext(ctx, dst);
break;
case GGML_OP_CROSS_ENTROPY_LOSS:
ggml_cuda_cross_entropy_loss(ctx, dst);
break;
default:
return false;
}
@ -2610,6 +2623,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->buffer);
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
}
}
@ -2853,12 +2867,15 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_TRANSPOSE:
case GGML_OP_NORM:
case GGML_OP_ADD:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_RMS_NORM:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_SIN:
case GGML_OP_COS:
case GGML_OP_CLAMP:
case GGML_OP_CONT:
case GGML_OP_DIAG_MASK_INF:
@ -2890,6 +2907,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
}
return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA &&
op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
case GGML_OP_CROSS_ENTROPY_LOSS:
return true;
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
default:
return false;

8
ggml/src/ggml-cuda/binbcast.cu
View File

@ -9,6 +9,10 @@ static __device__ __forceinline__ float op_add(const float a, const float b) {
return a + b;
}
static __device__ __forceinline__ float op_sub(const float a, const float b) {
return a - b;
}
static __device__ __forceinline__ float op_mul(const float a, const float b) {
return a * b;
}
@ -271,6 +275,10 @@ void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_add>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
}
void ggml_cuda_op_sub(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_sub>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
}
void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_mul>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
}

1
ggml/src/ggml-cuda/binbcast.cuh
View File

@ -2,5 +2,6 @@
void ggml_cuda_op_repeat(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_sub(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

106
ggml/src/ggml-cuda/cross-entropy-loss.cu Normal file
View File

@ -0,0 +1,106 @@
#include "common.cuh"
#include "cross-entropy-loss.cuh"
#include "sumrows.cuh"
#include <cmath>
#include <cstdint>
static __global__ void cross_entropy_loss_f32(const float * logits, const float * labels, float * dst, const int nclasses, const int k) {
const int warp_id = threadIdx.x / WARP_SIZE;
const int lane_id = threadIdx.x % WARP_SIZE;
const int i0 = blockDim.x*blockIdx.x + warp_id*WARP_SIZE;
const int ne_tmp = WARP_SIZE*nclasses;
extern __shared__ float tmp_all[];
float * tmp_logits = tmp_all + (2*warp_id + 0)*ne_tmp;
float * tmp_labels = tmp_all + (2*warp_id + 1)*ne_tmp;
// Each warp first loads ne_tmp logits/labels into shared memory:
for (int i = lane_id; i < ne_tmp; i += WARP_SIZE) {
const int ig = i0*nclasses + i; // ig == i global
tmp_logits[i] = ig < k*nclasses ? logits[ig] : 0.0f;
tmp_labels[i] = ig < k*nclasses ? labels[ig] : 0.0f;
}
// Each thread in the warp then calculates the cross entropy loss for a single row.
// TODO: pad in order to avoid shared memory bank conflicts.
// Find maximum for softmax:
float max = -INFINITY;
for (int i = 0; i < nclasses; ++i) {
max = fmaxf(max, tmp_logits[lane_id*nclasses + i]);
}
// Calculate log(softmax(logits)) which is just logits - max:
float sum = 0.0f;
for (int i = 0; i < nclasses; ++i) {
float val = tmp_logits[lane_id*nclasses + i] - max;
sum += expf(val);
tmp_logits[lane_id*nclasses + i] = val;
}
sum = logf(sum);
// log(exp(logits - max) / sum) = (logits - max) - log(sum)
float loss = 0.0f;
for (int i = 0; i < nclasses; ++i) {
loss += (tmp_logits[lane_id*nclasses + i] - sum) * tmp_labels[lane_id*nclasses + i];
}
loss = -warp_reduce_sum(loss) / (float)k;
__syncthreads();
if (lane_id == 0) {
tmp_all[warp_id] = loss;
}
__syncthreads();
if (warp_id != 0) {
return;
}
loss = lane_id < CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE/WARP_SIZE ? tmp_all[lane_id] : 0.0f;
loss = warp_reduce_sum(loss);
if (lane_id != 0) {
return;
}
dst[blockIdx.x] = loss;
}
void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_contiguous(dst));
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
const float * src0_d = (const float *) src0->data;
const float * src1_d = (const float *) src1->data;
float * dst_d = (float *) dst->data;
ggml_cuda_pool & pool = ctx.pool();
cudaStream_t stream = ctx.stream();
const dim3 blocks_dim(CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE, 1, 1);
const dim3 blocks_num((nrows + CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE - 1) / CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE, 1, 1);
const int shmem = 2*CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE*ne00*sizeof(float);
ggml_cuda_pool_alloc<float> dst_tmp(pool, blocks_num.x);
cross_entropy_loss_f32<<<blocks_num, blocks_dim, shmem, stream>>>(src0_d, src1_d, dst_tmp.ptr, ne00, nrows);
// Combine results from individual blocks:
sum_rows_f32_cuda(dst_tmp.ptr, dst_d, blocks_num.x, 1, stream);
}

5
ggml/src/ggml-cuda/cross-entropy-loss.cuh Normal file
View File

@ -0,0 +1,5 @@
#include "common.cuh"
#define CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE 256
void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

9
ggml/src/ggml-cuda/fattn-common.cuh
View File

@ -22,6 +22,7 @@ typedef void (* fattn_kernel_t)(
const float m0,
const float m1,
const uint32_t n_head_log2,
const float logit_softcap,
const int ne00,
const int ne01,
const int ne02,
@ -659,9 +660,15 @@ void launch_fattn(
float scale = 1.0f;
float max_bias = 0.0f;
float logit_softcap = 0.0f;
memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
memcpy(&logit_softcap, (float *) KQV->op_params + 2, sizeof(float));
if (logit_softcap != 0.0f) {
scale /= logit_softcap;
}
const uint32_t n_head = Q->ne[2];
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
@ -675,7 +682,7 @@ void launch_fattn(
V_data,
mask ? ((const char *) mask->data) : nullptr,
(parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
scale, max_bias, m0, m1, n_head_log2,
scale, max_bias, m0, m1, n_head_log2, logit_softcap,
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,

52
ggml/src/ggml-cuda/fattn-tile-f16.cu
View File

@ -4,7 +4,7 @@
#define FATTN_KQ_STRIDE_TILE_F16 64
template<int D, int ncols, int nwarps, int parallel_blocks> // D == head size
template<int D, int ncols, int nwarps, int parallel_blocks, bool use_logit_softcap> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(nwarps*WARP_SIZE, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@ -20,6 +20,7 @@ static __global__ void flash_attn_tile_ext_f16(
const float m0,
const float m1,
const uint32_t n_head_log2,
const float logit_softcap,
const int ne00,
const int ne01,
const int ne02,
@ -44,6 +45,12 @@ static __global__ void flash_attn_tile_ext_f16(
const int ne2,
const int ne3) {
#ifdef FP16_AVAILABLE
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
return;
}
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
@ -154,7 +161,13 @@ static __global__ void flash_attn_tile_ext_f16(
for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) {
const int j_KQ = j_KQ_0 + threadIdx.y;
half sum = __low2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]) + __high2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
half sum;
if (use_logit_softcap) {
const float2 tmp = __half22float2(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
sum = logit_softcap * tanhf(tmp.x + tmp.y);
} else {
sum = __low2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]) + __high2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
}
sum += mask ? slopeh*maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
kqmax_new[j_KQ_0/nwarps] = ggml_cuda_hmax(kqmax_new[j_KQ_0/nwarps], sum);
@ -270,20 +283,20 @@ static __global__ void flash_attn_tile_ext_f16(
#endif // FP16_AVAILABLE
}
template <int cols_per_block, int parallel_blocks>
template <int cols_per_block, int parallel_blocks, bool use_logit_softcap>
void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
case 64: {
constexpr int D = 64;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
case 128: {
constexpr int D = 128;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks>;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
default: {
@ -296,24 +309,45 @@ void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_ten
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
const int32_t precision = KQV->op_params[2];
const int32_t precision = KQV->op_params[3];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
float logit_softcap;
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
if (Q->ne[1] <= 16) {
constexpr int cols_per_block = 16;
constexpr int parallel_blocks = 4;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] <= 32) {
constexpr int cols_per_block = 32;
constexpr int parallel_blocks = 4;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
}
return;
}
constexpr int cols_per_block = 32;
constexpr int parallel_blocks = 1;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
launch_fattn_tile_f16_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
}
}

47
ggml/src/ggml-cuda/fattn-tile-f32.cu
View File

@ -4,7 +4,7 @@
#define FATTN_KQ_STRIDE_TILE_F32 32
template<int D, int ncols, int nwarps, int parallel_blocks> // D == head size
template<int D, int ncols, int nwarps, int parallel_blocks, bool use_logit_softcap> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(nwarps*WARP_SIZE, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@ -20,6 +20,7 @@ static __global__ void flash_attn_tile_ext_f32(
const float m0,
const float m1,
const uint32_t n_head_log2,
const float logit_softcap,
const int ne00,
const int ne01,
const int ne02,
@ -43,6 +44,12 @@ static __global__ void flash_attn_tile_ext_f32(
const int ne1,
const int ne2,
const int ne3) {
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
return;
}
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
@ -151,6 +158,10 @@ static __global__ void flash_attn_tile_ext_f32(
for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) {
const int j_KQ = j_KQ_0 + threadIdx.y;
if (use_logit_softcap) {
sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] = logit_softcap * tanhf(sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
}
sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
kqmax_new[j_KQ_0/nwarps] = fmaxf(kqmax_new[j_KQ_0/nwarps], sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]);
@ -267,20 +278,20 @@ static __global__ void flash_attn_tile_ext_f32(
}
}
template <int cols_per_block, int parallel_blocks>
template <int cols_per_block, int parallel_blocks, bool use_logit_softcap>
void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
case 64: {
constexpr int D = 64;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
case 128: {
constexpr int D = 128;
constexpr int nwarps = 8;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks>;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
} break;
default: {
@ -290,23 +301,45 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
}
void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
float logit_softcap;
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
if (Q->ne[1] <= 16) {
constexpr int cols_per_block = 16;
constexpr int parallel_blocks = 4;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] <= 32) {
constexpr int cols_per_block = 32;
constexpr int parallel_blocks = 4;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
}
return;
}
constexpr int cols_per_block = 32;
constexpr int parallel_blocks = 1;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
launch_fattn_tile_f32_64_128<cols_per_block, parallel_blocks, use_logit_softcap>(ctx, dst);
}
}

71
ggml/src/ggml-cuda/fattn-vec-f16.cuh
View File

@ -1,7 +1,7 @@
#include "common.cuh"
#include "fattn-common.cuh"
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@ -17,6 +17,7 @@ static __global__ void flash_attn_vec_ext_f16(
const float m0,
const float m1,
const uint32_t n_head_log2,
const float logit_softcap,
const int ne00,
const int ne01,
const int ne02,
@ -41,6 +42,12 @@ static __global__ void flash_attn_vec_ext_f16(
const int ne2,
const int ne3) {
#ifdef FP16_AVAILABLE
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
return;
}
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16<D>(type_K);
@ -190,6 +197,11 @@ static __global__ void flash_attn_vec_ext_f16(
for (int j = 0; j < ncols; ++j) {
half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]);
sum = warp_reduce_sum(sum);
if (use_logit_softcap) {
sum = logit_softcap*tanhf(sum);
}
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
if (ncols == 1) {
@ -286,10 +298,10 @@ static __global__ void flash_attn_vec_ext_f16(
#endif // FP16_AVAILABLE
}
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap>
void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V>;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
constexpr bool need_f16_K = D != 128;
constexpr bool need_f16_V = D != 128 && D != 64;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
@ -297,48 +309,81 @@ void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx,
template <int D, ggml_type type_K, ggml_type type_V>
void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_tensor * KQV = dst;
ggml_tensor * Q = dst->src[0];
ggml_tensor * K = dst->src[1];
ggml_tensor * V = dst->src[2];
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
const int32_t precision = KQV->op_params[2];
const int32_t precision = KQV->op_params[3];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
GGML_ASSERT(K->type == type_K);
GGML_ASSERT(V->type == type_V);
float logit_softcap;
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f16_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
}
#define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V) \

68
ggml/src/ggml-cuda/fattn-vec-f32.cuh
View File

@ -1,7 +1,7 @@
#include "common.cuh"
#include "fattn-common.cuh"
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // D == head size
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@ -17,6 +17,7 @@ static __global__ void flash_attn_vec_ext_f32(
const float m0,
const float m1,
const uint32_t n_head_log2,
const float logit_softcap,
const int ne00,
const int ne01,
const int ne02,
@ -40,6 +41,12 @@ static __global__ void flash_attn_vec_ext_f32(
const int ne1,
const int ne2,
const int ne3) {
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
return;
}
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32<D>(type_K);
@ -180,6 +187,11 @@ static __global__ void flash_attn_vec_ext_f32(
for (int j = 0; j < ncols; ++j) {
float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]);
sum = warp_reduce_sum(sum);
if (use_logit_softcap) {
sum = logit_softcap*tanhf(sum);
}
sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum);
@ -267,10 +279,10 @@ static __global__ void flash_attn_vec_ext_f32(
}
}
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V, bool use_logit_softcap>
void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
constexpr int nwarps = D/WARP_SIZE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V>;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
constexpr bool need_f16_K = D != 128;
constexpr bool need_f16_V = D != 128 && D != 64;
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
@ -278,44 +290,78 @@ void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx,
template <int D, ggml_type type_K, ggml_type type_V>
void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_tensor * Q = dst->src[0];
ggml_tensor * K = dst->src[1];
ggml_tensor * V = dst->src[2];
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
GGML_ASSERT(K->type == type_K);
GGML_ASSERT(V->type == type_V);
float logit_softcap;
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
} else {
constexpr bool use_logit_softcap = true;
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>(ctx, dst);
}
}
#define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V) \

61
ggml/src/ggml-cuda/fattn-wmma-f16.cuh
View File

@ -6,7 +6,7 @@
#endif // FP16_MMA_AVAILABLE
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t, bool use_logit_softcap>
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(nwarps*WARP_SIZE, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@ -22,6 +22,7 @@ static __global__ void flash_attn_ext_f16(
const float m0,
const float m1,
const uint32_t n_head_log2,
const float logit_softcap,
const int ne00,
const int ne01,
const int ne02,
@ -46,6 +47,12 @@ static __global__ void flash_attn_ext_f16(
const int ne2,
const int ne3) {
#ifdef FP16_MMA_AVAILABLE
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
return;
}
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
@ -85,6 +92,8 @@ static __global__ void flash_attn_ext_f16(
const half slopeh = __float2half(slopef);
const half2 slope2 = make_half2(slopef, slopef);
const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
frag_b Q_b[D/16][ncols/frag_n];
// A single buffer for temporarily holding tiles of KQ and VKQ parts:
@ -194,6 +203,10 @@ static __global__ void flash_attn_ext_f16(
const int k = k0 + threadIdx.x;
KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
if (use_logit_softcap) {
KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
}
}
float KQ_max_new = KQ_max_f[j0/nwarps];
@ -237,6 +250,15 @@ static __global__ void flash_attn_ext_f16(
const int k = k0 + threadIdx.x;
KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
if (use_logit_softcap) {
// There is no dedicated tangens hyperbolicus function for half2.
KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
/(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
}
}
half2 KQ_max_new = KQ_max_h2[j0/nwarps];
@ -427,6 +449,7 @@ static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed.");
template <int D, int cols_per_block, typename KQ_acc_t>
void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
constexpr int nwarps = 4;
@ -435,20 +458,50 @@ void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggm
const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
float logit_softcap;
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
if (4*blocks_num_pb1 < 2*nsm) {
constexpr int parallel_blocks = 4;
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
fattn_kernel_t fattn_kernel;
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
fattn_kernel = flash_attn_ext_f16<
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
} else {
constexpr bool use_logit_softcap = true;
fattn_kernel = flash_attn_ext_f16<
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
}
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
return;
}
if (2*blocks_num_pb1 < 2*nsm) {
constexpr int parallel_blocks = 2;
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
fattn_kernel_t fattn_kernel;
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
fattn_kernel = flash_attn_ext_f16<
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
} else {
constexpr bool use_logit_softcap = true;
fattn_kernel = flash_attn_ext_f16<
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
}
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
return;
}
constexpr int parallel_blocks = 1;
fattn_kernel_t fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>;
fattn_kernel_t fattn_kernel;
if (logit_softcap == 0.0f) {
constexpr bool use_logit_softcap = false;
fattn_kernel = flash_attn_ext_f16<
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
} else {
constexpr bool use_logit_softcap = true;
fattn_kernel = flash_attn_ext_f16<
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
}
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
}

4
ggml/src/ggml-cuda/fattn.cu
View File

@ -13,7 +13,7 @@ static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, g
const ggml_tensor * KQV = dst;
const ggml_tensor * Q = dst->src[0];
const int32_t precision = KQV->op_params[2];
const int32_t precision = KQV->op_params[3];
if (precision != GGML_PREC_DEFAULT) {
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
@ -301,7 +301,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
ggml_cuda_set_device(ctx.device);
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const int32_t precision = KQV->op_params[2];
const int32_t precision = KQV->op_params[3];
// On AMD the tile kernels perform poorly, use the vec kernel instead:
if (cc >= CC_OFFSET_AMD) {

3
ggml/src/ggml-cuda/sumrows.cu
View File

@ -16,7 +16,7 @@ static __global__ void k_sum_rows_f32(const float * x, float * dst, const int nc
}
}
static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
const dim3 block_dims(WARP_SIZE, 1, 1);
const dim3 block_nums(nrows, 1, 1);
k_sum_rows_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
@ -32,7 +32,6 @@ void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
const int64_t ncols = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);

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

@ -1,3 +1,5 @@
#include "common.cuh"
void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream);
void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

56
ggml/src/ggml-cuda/unary.cu
View File

@ -101,6 +101,24 @@ static __global__ void sqrt_f32(const float * x, float * dst, const int k) {
dst[i] = sqrtf(x[i]);
}
static __global__ void sin_f32(const float * x, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = sinf(x[i]);
}
static __global__ void cos_f32(const float * x, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = cosf(x[i]);
}
static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
@ -156,6 +174,16 @@ static void sqrt_f32_cuda(const float * x, float * dst, const int k, cudaStream_
sqrt_f32<<<num_blocks, CUDA_SQRT_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void sin_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_SIN_BLOCK_SIZE - 1) / CUDA_SIN_BLOCK_SIZE;
sin_f32<<<num_blocks, CUDA_SIN_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void cos_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_COS_BLOCK_SIZE - 1) / CUDA_COS_BLOCK_SIZE;
cos_f32<<<num_blocks, CUDA_COS_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
@ -312,3 +340,31 @@ void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
sqrt_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
}
void ggml_cuda_op_sin(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
sin_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
}
void ggml_cuda_op_cos(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
cos_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
}

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

@ -9,6 +9,8 @@
#define CUDA_HARDSWISH_BLOCK_SIZE 256
#define CUDA_SQR_BLOCK_SIZE 256
#define CUDA_SQRT_BLOCK_SIZE 256
#define CUDA_SIN_BLOCK_SIZE 256
#define CUDA_COS_BLOCK_SIZE 256
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
@ -31,3 +33,7 @@ void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_sin(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_cos(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

194
ggml/src/ggml-metal.m
View File

@ -31,6 +31,8 @@ struct ggml_metal_kernel {
enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_ADD,
GGML_METAL_KERNEL_TYPE_ADD_ROW,
GGML_METAL_KERNEL_TYPE_SUB,
GGML_METAL_KERNEL_TYPE_SUB_ROW,
GGML_METAL_KERNEL_TYPE_MUL,
GGML_METAL_KERNEL_TYPE_MUL_ROW,
GGML_METAL_KERNEL_TYPE_DIV,
@ -82,6 +84,8 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_RMS_NORM,
GGML_METAL_KERNEL_TYPE_GROUP_NORM,
GGML_METAL_KERNEL_TYPE_NORM,
GGML_METAL_KERNEL_TYPE_SSM_CONV_F32,
GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,
GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,
@ -205,6 +209,9 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,
GGML_METAL_KERNEL_TYPE_CONCAT,
GGML_METAL_KERNEL_TYPE_SQR,
GGML_METAL_KERNEL_TYPE_SQRT,
GGML_METAL_KERNEL_TYPE_SIN,
GGML_METAL_KERNEL_TYPE_COS,
GGML_METAL_KERNEL_TYPE_SUM_ROWS,
GGML_METAL_KERNEL_TYPE_COUNT
@ -491,6 +498,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB, sub, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB_ROW, sub_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true);
@ -542,6 +551,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, ssm_conv_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, ssm_scan_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction);
@ -665,6 +676,9 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQRT, sqrt, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN, sin, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
}
@ -765,15 +779,20 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_context * ctx
case GGML_OP_PERMUTE:
case GGML_OP_CONCAT:
case GGML_OP_ADD:
case GGML_OP_SUB:
case GGML_OP_ACC:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_REPEAT:
case GGML_OP_SCALE:
case GGML_OP_CLAMP:
case GGML_OP_SQR:
case GGML_OP_SUM_ROWS:
return true;
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_SIN:
case GGML_OP_COS:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_SUM_ROWS:
case GGML_OP_SOFT_MAX:
case GGML_OP_RMS_NORM:
case GGML_OP_GROUP_NORM:
@ -803,6 +822,9 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_context * ctx
return false;
}
return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels
case GGML_OP_SSM_CONV:
case GGML_OP_SSM_SCAN:
return true;
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
return ctx->support_simdgroup_reduction &&
@ -1050,6 +1072,7 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ADD:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
{
@ -1073,6 +1096,7 @@ static enum ggml_status ggml_metal_graph_compute(
nb = ne00 / 4;
switch (dst->op) {
case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break;
case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW].pipeline; break;
case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break;
case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break;
default: GGML_ABORT("fatal error");
@ -1082,6 +1106,7 @@ static enum ggml_status ggml_metal_graph_compute(
} else {
switch (dst->op) {
case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break;
case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB].pipeline; break;
case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break;
case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break;
default: GGML_ABORT("fatal error");
@ -1409,6 +1434,48 @@ static enum ggml_status ggml_metal_graph_compute(
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SQRT:
{
GGML_ASSERT(ggml_is_contiguous(src0));
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQRT].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SIN:
{
GGML_ASSERT(ggml_is_contiguous(src0));
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIN].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_COS:
{
GGML_ASSERT(ggml_is_contiguous(src0));
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_COS].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SUM_ROWS:
@ -1538,6 +1605,121 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
}
} break;
case GGML_OP_SSM_CONV:
{
GGML_ASSERT(src0t == GGML_TYPE_F32);
GGML_ASSERT(src1t == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:11];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:12];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:15];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:16];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:17];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:18];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne1, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SSM_SCAN:
{
struct ggml_tensor * src3 = gf->nodes[i]->src[3];
struct ggml_tensor * src4 = gf->nodes[i]->src[4];
struct ggml_tensor * src5 = gf->nodes[i]->src[5];
GGML_ASSERT(src3);
GGML_ASSERT(src4);
GGML_ASSERT(src5);
size_t offs_src3 = 0;
size_t offs_src4 = 0;
size_t offs_src5 = 0;
id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
id<MTLBuffer> id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil;
id<MTLBuffer> id_src5 = src5 ? ggml_metal_get_buffer(src5, &offs_src5) : nil;
const int64_t ne30 = src3->ne[0]; GGML_UNUSED(ne30);
const int64_t ne31 = src3->ne[1]; GGML_UNUSED(ne31);
const uint64_t nb30 = src3->nb[0];
const uint64_t nb31 = src3->nb[1];
const int64_t ne40 = src4->ne[0]; GGML_UNUSED(ne40);
const int64_t ne41 = src4->ne[1]; GGML_UNUSED(ne41);
const int64_t ne42 = src4->ne[2]; GGML_UNUSED(ne42);
const uint64_t nb40 = src4->nb[0];
const uint64_t nb41 = src4->nb[1];
const uint64_t nb42 = src4->nb[2];
const int64_t ne50 = src5->ne[0]; GGML_UNUSED(ne50);
const int64_t ne51 = src5->ne[1]; GGML_UNUSED(ne51);
const int64_t ne52 = src5->ne[2]; GGML_UNUSED(ne52);
const uint64_t nb50 = src5->nb[0];
const uint64_t nb51 = src5->nb[1];
const uint64_t nb52 = src5->nb[2];
const int64_t d_state = ne00;
const int64_t d_inner = ne01;
const int64_t n_seq_tokens = ne11;
const int64_t n_seqs = ne02;
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
[encoder setBuffer:id_src3 offset:offs_src3 atIndex:3];
[encoder setBuffer:id_src4 offset:offs_src4 atIndex:4];
[encoder setBuffer:id_src5 offset:offs_src5 atIndex:5];
[encoder setBuffer:id_dst offset:offs_dst atIndex:6];
[encoder setBytes:&d_state length:sizeof(d_state) atIndex:7];
[encoder setBytes:&d_inner length:sizeof(d_inner) atIndex:8];
[encoder setBytes:&n_seq_tokens length:sizeof(n_seq_tokens) atIndex:9];
[encoder setBytes:&n_seqs length:sizeof(n_seqs) atIndex:10];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:11];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:12];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:13];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17];
[encoder setBytes:&nb20 length:sizeof(nb20) atIndex:18];
[encoder setBytes:&nb21 length:sizeof(nb21) atIndex:19];
[encoder setBytes:&nb22 length:sizeof(nb22) atIndex:20];
[encoder setBytes:&nb30 length:sizeof(nb30) atIndex:21];
[encoder setBytes:&nb31 length:sizeof(nb31) atIndex:22];
[encoder setBytes:&nb40 length:sizeof(nb40) atIndex:23];
[encoder setBytes:&nb41 length:sizeof(nb41) atIndex:24];
[encoder setBytes:&nb42 length:sizeof(nb42) atIndex:25];
[encoder setBytes:&nb50 length:sizeof(nb50) atIndex:26];
[encoder setBytes:&nb51 length:sizeof(nb51) atIndex:27];
[encoder setBytes:&nb52 length:sizeof(nb52) atIndex:28];
[encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_MUL_MAT:
{
GGML_ASSERT(ne00 == ne10);
@ -2624,9 +2806,14 @@ static enum ggml_status ggml_metal_graph_compute(
float scale;
float max_bias;
float logit_softcap;
memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale));
memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
memcpy(&logit_softcap, ((int32_t *) dst->op_params) + 2, sizeof(logit_softcap));
if (logit_softcap != 0.0f) {
scale /= logit_softcap;
}
const uint32_t n_head = src0->ne[2];
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
@ -2701,6 +2888,7 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBytes:&m0 length:sizeof(m0) atIndex:25];
[encoder setBytes:&m1 length:sizeof(m1) atIndex:26];
[encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27];
[encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28];
if (!use_vec_kernel) {
// half8x8 kernel

244
ggml/src/ggml-metal.metal
View File

@ -17,7 +17,7 @@ enum ggml_sort_order {
GGML_SORT_ORDER_DESC,
};
// general-purpose kernel for addition, multiplication and division of two tensors
// general-purpose kernel for addition, subtraction, multiplication and division of two tensors
// pros: works for non-contiguous tensors, supports broadcast across all dims
// cons: not very efficient
kernel void kernel_add(
@ -70,6 +70,56 @@ kernel void kernel_add(
}
}
kernel void kernel_sub(
device const char * src0,
device const char * src1,
device char * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant uint64_t & nb13,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
constant int64_t & offs,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig.z;
const int64_t i02 = tgpig.y;
const int64_t i01 = tgpig.x;
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + offs;
device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + offs;
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
const int i10 = i0 % ne10;
*((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) - *((device float *)(src1_ptr + i10*nb10));
}
}
kernel void kernel_mul(
device const char * src0,
device const char * src1,
@ -226,6 +276,15 @@ kernel void kernel_add_row(
dst[tpig] = src0[tpig] + src1[tpig % nb];
}
kernel void kernel_sub_row(
device const float4 * src0,
device const float4 * src1,
device float4 * dst,
constant uint64_t & nb [[buffer(28)]],
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] - src1[tpig % nb];
}
kernel void kernel_mul_row(
device const float4 * src0,
device const float4 * src1,
@ -358,6 +417,27 @@ kernel void kernel_sqr(
dst[tpig] = src0[tpig] * src0[tpig];
}
kernel void kernel_sqrt(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = sqrt(src0[tpig]);
}
kernel void kernel_sin(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = sin(src0[tpig]);
}
kernel void kernel_cos(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = cos(src0[tpig]);
}
kernel void kernel_sum_rows(
device const float * src0,
device float * dst,
@ -667,6 +747,127 @@ kernel void kernel_diag_mask_inf_8(
}
}
// ref: ggml.c:ggml_compute_forward_ssm_conv_f32
// TODO: optimize
kernel void kernel_ssm_conv_f32(
device const void * src0,
device const void * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t ir = tgpig.x;
const int64_t i2 = tgpig.y;
const int64_t i3 = tgpig.z;
const int64_t nc = ne10;
const int64_t ncs = ne00;
const int64_t nr = ne01;
const int64_t n_t = ne1;
const int64_t n_s = ne2;
device const float * s = (device const float *) ((device const char *) src0 + ir*nb01 + i2*nb00 + i3*nb02);
device const float * c = (device const float *) ((device const char *) src1 + ir*nb11);
device float * x = (device float *) ((device char *) dst + ir*nb0 + i2*nb1 + i3*nb2);
float sumf = 0.0f;
for (int64_t i0 = 0; i0 < nc; ++i0) {
sumf += s[i0] * c[i0];
}
x[0] = sumf;
}
// ref: ggml.c:ggml_compute_forward_ssm_scan_f32
// TODO: optimize
kernel void kernel_ssm_scan_f32(
device const void * src0,
device const void * src1,
device const void * src2,
device const void * src3,
device const void * src4,
device const void * src5,
device float * dst,
constant int64_t & d_state,
constant int64_t & d_inner,
constant int64_t & n_seq_tokens,
constant int64_t & n_seqs,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant uint64_t & nb13,
constant uint64_t & nb20,
constant uint64_t & nb21,
constant uint64_t & nb22,
constant uint64_t & nb30,
constant uint64_t & nb31,
constant uint64_t & nb40,
constant uint64_t & nb41,
constant uint64_t & nb42,
constant uint64_t & nb50,
constant uint64_t & nb51,
constant uint64_t & nb52,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t ir = tgpig.x;
const int64_t i3 = tgpig.y;
const int64_t nc = d_state;
const int64_t nr = d_inner;
const int64_t n_t = n_seq_tokens;
const int64_t n_s = n_seqs;
for (int64_t i2 = 0; i2 < n_t; ++i2) {
device const float * s0 = (device const float *) ((device const char *) src0 + ir*nb01 + i3*nb02);
device const float * x = (device const float *) ((device const char *) src1 + ir*nb10 + i2*nb11 + i3*nb12);
device const float * dt = (device const float *) ((device const char *) src2 + ir*nb20 + i2*nb21 + i3*nb22);
device const float * A = (device const float *) ((device const char *) src3 + ir*nb31);
device const float * B = (device const float *) ((device const char *) src4 + i2*nb41 + i3*nb42);
device const float * C = (device const float *) ((device const char *) src5 + i2*nb51 + i3*nb52);
device float * y = (device float *) ((device char *) dst + ir*nb10 + i2*nb11 + i3*nb12); // TODO: do not use src1 strides
device float * s = (device float *) ((device char *) dst + ir*nb01 + i3*nb02 + nb13);
if (i2 > 0) {
s0 = s;
}
// i1 == 0
float dt_soft_plus = dt[0] <= 20.0f ? log(1.0f + exp(dt[0])) : dt[0];
float x_dt = x[0] * dt_soft_plus;
float sumf = 0.0f;
for (int64_t i0 = 0; i0 < nc; ++i0) {
int64_t i = i0;
float state = (s0[i] * exp(dt_soft_plus * A[i])) + (B[i0] * x_dt);
sumf += state * C[i0];
s[i] = state;
}
y[0] = sumf;
}
}
kernel void kernel_norm(
device const void * src0,
device float * dst,
@ -1976,6 +2177,7 @@ typedef void (flash_attn_ext_f16_t)(
constant float & m0,
constant float & m1,
constant uint32_t & n_head_log2,
constant float & logit_softcap,
threadgroup half * shared,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
@ -2014,6 +2216,7 @@ kernel void kernel_flash_attn_ext_f16(
constant float & m0,
constant float & m1,
constant uint32_t & n_head_log2,
constant float & logit_softcap,
threadgroup half * shared [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
@ -2138,19 +2341,6 @@ kernel void kernel_flash_attn_ext_f16(
}
simdgroup_store(mqk, ss + 8*cc, TF, 0, false);
const short tx = tiisg%4;
const short ty = tiisg/4;
if (mask != q) {
// mqk = mqk*scale + mask*slope
ss[8*cc + ty*TF + 2*tx + 0] = scale*ss[8*cc + ty*TF + 2*tx + 0] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 0];
ss[8*cc + ty*TF + 2*tx + 1] = scale*ss[8*cc + ty*TF + 2*tx + 1] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 1];
} else {
// mqk = mqk*scale
ss[8*cc + ty*TF + 2*tx + 0] *= scale;
ss[8*cc + ty*TF + 2*tx + 1] *= scale;
}
}
}
@ -2162,10 +2352,19 @@ kernel void kernel_flash_attn_ext_f16(
float ms[Q];
for (short j = 0; j < Q; ++j) {
const short p = tiisg;
const float m = M[j];
const float s = ss[j*TF + p];
// scale and apply the logitcap / mask
float s = ss[j*TF + tiisg]*scale;
if (logit_softcap != 0.0f) {
s = logit_softcap*precise::tanh(s);
}
if (mask != q) {
// mqk = mqk + mask*slope
s += slope*mp[ic + j*nb31/sizeof(half) + tiisg];
}
smax = simd_max(max(smax, s));
M[j] = simd_max(max(M[j], s));
@ -2176,7 +2375,7 @@ kernel void kernel_flash_attn_ext_f16(
S[j] = S[j]*ms[j] + simd_sum(vs);
// the P matrix from the paper (Q rows, C columns)
ss[j*TF + p] = vs;
ss[j*TF + tiisg] = vs;
}
// create a QxQ diagonal matrix for rescaling the output
@ -2345,6 +2544,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
constant float & m0,
constant float & m1,
constant uint32_t & n_head_log2,
constant float & logit_softcap,
threadgroup half * shared [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
@ -2479,7 +2679,13 @@ kernel void kernel_flash_attn_ext_vec_f16(
// mqk = mqk*scale + mask*slope
if (tiisg == 0) {
mqk = mqk*scale + ((mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f);
mqk *= scale;
if (logit_softcap != 0.0f) {
mqk = logit_softcap*precise::tanh(mqk);
}
mqk += (mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f;
ss4[cc] = mqk;
}

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

@ -3644,7 +3644,7 @@ void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) {
quantize_row_q8_K_ref(x, y, k);
}
//===================================== Dot ptoducts =================================
//===================================== Dot products =================================
//
// Helper functions

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

@ -38,6 +38,7 @@
#include "ggml-sycl/backend.hpp"
#include "ggml-sycl/presets.hpp"
#include "ggml-sycl/gemm.hpp"
bool ggml_sycl_loaded(void);
void ggml_sycl_free_data(struct ggml_tensor * tensor);
@ -2482,6 +2483,7 @@ inline void ggml_sycl_op_mul_mat_sycl(
const sycl::half alpha_f16 = 1.0f;
const sycl::half beta_f16 = 0.0f;
#if !GGML_SYCL_DNNL
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
*stream, oneapi::mkl::transpose::trans,
oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
@ -2491,6 +2493,13 @@ inline void ggml_sycl_op_mul_mat_sycl(
dpct::library_data_t::real_half)));
const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
#else
auto dnnl_stream = ctx.stream_dnnl(stream);
DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ptr, DnnlGemmWrapper::to_dt<sycl::half>(),
src0_ptr, DnnlGemmWrapper::to_dt<sycl::half>(), dst_f16.get(), DnnlGemmWrapper::to_dt<sycl::half>());
const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff* src1_ncols, stream);
#endif
}
else {
// GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
@ -2513,13 +2522,18 @@ inline void ggml_sycl_op_mul_mat_sycl(
const float alpha = 1.0f;
const float beta = 0.0f;
#if !GGML_SYCL_DNNL
SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
*stream, oneapi::mkl::transpose::trans,
oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
dpct::get_value(&alpha, *stream), src0_ddf_i, ne00,
src1_ddf1_i, ne10, dpct::get_value(&beta, *stream),
dst_dd_i, ldc)));
#else
auto dnnl_stream = ctx.stream_dnnl(stream);
DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ddf1_i, DnnlGemmWrapper::to_dt<float>(),
src0_ddf_i, DnnlGemmWrapper::to_dt<float>(), dst_dd_i, DnnlGemmWrapper::to_dt<float>());
#endif
}
(void) dst;
(void) src1_ddq_i;

50
ggml/src/ggml-sycl/common.hpp
View File

@ -19,6 +19,10 @@
#include "dpct/helper.hpp"
#include "ggml-sycl.h"
#include "presets.hpp"
#if GGML_SYCL_DNNL
#include "dnnl.hpp"
#include "dnnl_sycl.hpp"
#endif
#define GGML_COMMON_DECL_SYCL
#define GGML_COMMON_IMPL_SYCL
@ -277,6 +281,52 @@ struct ggml_backend_sycl_context {
return stream(device, 0);
}
#if GGML_SYCL_DNNL
dnnl::engine make_engine(sycl::queue* q) {
// Get the device associated with the queue
sycl::device dev = q->get_device();
// Get the context associated with the queue
sycl::context ctx = q->get_context();
const dnnl::engine eng = dnnl::sycl_interop::make_engine(dev, ctx);
return eng;
}
std::unordered_map<sycl::queue*, dnnl::stream> stream_map;
std::unordered_map<sycl::queue*, dnnl::engine> engine_map;
dnnl::stream stream_dnnl(int device, int _stream) {
auto q = stream(device, _stream);
return stream_dnnl(q);
}
dnnl::engine engine_dnnl(sycl::queue* qptr) {
auto it = engine_map.find(qptr);
if (it == engine_map.end()) {
auto eng = make_engine(qptr);
engine_map[qptr] = eng;
return eng;
}
else
{
return it->second;
}
}
dnnl::stream stream_dnnl(sycl::queue* qptr) {
auto it = stream_map.find(qptr);
if (it == stream_map.end()) {
auto eng = engine_dnnl(qptr);
auto stream = dnnl::sycl_interop::make_stream(eng, *qptr);
stream_map[qptr] = stream;
return stream;
}
else
{
return it->second;
}
}
dnnl::stream stream_dnnl() {
return stream_dnnl(device, 0);
}
#endif
// pool
std::unique_ptr<ggml_sycl_pool> pools[GGML_SYCL_MAX_DEVICES];

101
ggml/src/ggml-sycl/gemm.hpp Normal file
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@ -0,0 +1,101 @@
//
// MIT license
// Copyright (C) 2024 Intel Corporation
// SPDX-License-Identifier: MIT
//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
#ifndef GGML_SYCL_GEMM_HPP
#define GGML_SYCL_GEMM_HPP
#include <fstream>
#include <iostream>
#include "ggml-sycl.h"
#if GGML_SYCL_DNNL
#include "dnnl.hpp"
#include "dnnl_sycl.hpp"
class DnnlGemmWrapper {
public:
using dt = dnnl::memory::data_type;
using tag = dnnl::memory::format_tag;
template<typename T>
static constexpr dt to_dt() {
if constexpr (std::is_same_v<T, float>) return dt::f32;
else if constexpr (std::is_same_v<T, sycl::half>) return dt::f16;
else static_assert(0);
}
static inline void row_gemm(sycl::queue& q, bool a_trans,
bool b_trans, int m, int n, int k,
const void* a, dt at, const void* b, dt bt, void* c, dt ct)
{
// Get the device associated with the queue
sycl::device dev = q.get_device();
// Get the context associated with the queue
sycl::context ctx = q.get_context();
const dnnl::engine eng = dnnl::sycl_interop::make_engine(dev, ctx);
const dnnl::stream stream = dnnl::sycl_interop::make_stream(eng, q);
dnnl::memory::dims a_dims = { m, k };
dnnl::memory::dims b_dims = { k, n };
dnnl::memory::dims c_dims = { m, n };
const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
// Create the primitive.
auto matmul_prim = dnnl::matmul(matmul_pd);
// Primitive arguments.
std::unordered_map<int, dnnl::memory> matmul_args;
matmul_args.insert({ DNNL_ARG_SRC, a_mem });
matmul_args.insert({ DNNL_ARG_WEIGHTS, b_mem });
matmul_args.insert({ DNNL_ARG_DST, c_mem });
matmul_prim.execute(stream, matmul_args);
}
static inline void row_gemm(const dnnl::stream& stream, bool a_trans,
bool b_trans, int m, int n, int k,
const void* a, dt at, const void* b, dt bt, void* c, dt ct)
{
auto const eng = stream.get_engine();
dnnl::memory::dims a_dims = { m, k };
dnnl::memory::dims b_dims = { k, n };
dnnl::memory::dims c_dims = { m, n };
const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
// Create the primitive.
auto matmul_prim = dnnl::matmul(matmul_pd);
// Primitive arguments.
std::unordered_map<int, dnnl::memory> matmul_args;
matmul_args.insert({ DNNL_ARG_SRC, a_mem });
matmul_args.insert({ DNNL_ARG_WEIGHTS, b_mem });
matmul_args.insert({ DNNL_ARG_DST, c_mem });
matmul_prim.execute(stream, matmul_args);
}
};
#endif
#endif // GGML_SYCL_GEMM_HPP

62
ggml/src/ggml-vulkan.cpp
View File

@ -188,6 +188,8 @@ struct vk_device_struct {
vk_pipeline pipeline_upscale_f32;
vk_pipeline pipeline_scale_f32;
vk_pipeline pipeline_sqr_f32;
vk_pipeline pipeline_sin_f32;
vk_pipeline pipeline_cos_f32;
vk_pipeline pipeline_clamp_f32;
vk_pipeline pipeline_pad_f32;
vk_pipeline pipeline_repeat_f32;
@ -1702,6 +1704,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_sin_f32, "sin_f32", sin_f32_len, sin_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_cos_f32, "cos_f32", cos_f32_len, cos_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
@ -4023,6 +4027,16 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_sqr_f32;
}
return nullptr;
case GGML_OP_SIN:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_sin_f32;
}
return nullptr;
case GGML_OP_COS:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_cos_f32;
}
return nullptr;
case GGML_OP_CLAMP:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_clamp_f32;
@ -4171,6 +4185,8 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
case GGML_OP_UPSCALE:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SIN:
case GGML_OP_COS:
case GGML_OP_CLAMP:
case GGML_OP_PAD:
case GGML_OP_REPEAT:
@ -4381,6 +4397,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
case GGML_OP_MUL:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SIN:
case GGML_OP_COS:
case GGML_OP_CLAMP:
case GGML_OP_PAD:
case GGML_OP_REPEAT:
@ -4598,6 +4616,32 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const
}, dryrun);
}
static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
const uint32_t src0_type_size = ggml_type_size(src0->type);
const uint32_t dst_type_size = ggml_type_size(dst->type);
ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SIN, {
(uint32_t)ggml_nelements(src0),
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
0,
0.0f, 0.0f,
});
}
static void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
const uint32_t src0_type_size = ggml_type_size(src0->type);
const uint32_t dst_type_size = ggml_type_size(dst->type);
ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_COS, {
(uint32_t)ggml_nelements(src0),
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
0,
0.0f, 0.0f,
});
}
static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
float * op_params = (float *)dst->op_params;
const uint32_t src0_type_size = ggml_type_size(src0->type);
@ -5658,6 +5702,8 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_UPSCALE:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SIN:
case GGML_OP_COS:
case GGML_OP_CLAMP:
case GGML_OP_PAD:
case GGML_OP_CPY:
@ -5735,6 +5781,14 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_SQR:
ggml_vk_sqr(ctx, compute_ctx, src0, node, dryrun);
break;
case GGML_OP_SIN:
ggml_vk_sin(ctx, compute_ctx, src0, node);
break;
case GGML_OP_COS:
ggml_vk_cos(ctx, compute_ctx, src0, node);
break;
case GGML_OP_CLAMP:
ggml_vk_clamp(ctx, compute_ctx, src0, node, dryrun);
@ -5851,6 +5905,8 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
case GGML_OP_UPSCALE:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SIN:
case GGML_OP_COS:
case GGML_OP_CLAMP:
case GGML_OP_PAD:
case GGML_OP_CPY:
@ -6582,6 +6638,8 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
case GGML_OP_UPSCALE:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SIN:
case GGML_OP_COS:
case GGML_OP_CLAMP:
case GGML_OP_PAD:
case GGML_OP_CONT:
@ -7024,6 +7082,10 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]);
} else if (tensor->op == GGML_OP_SQR) {
tensor_clone = ggml_sqr(ggml_ctx, src0_clone);
} else if (tensor->op == GGML_OP_SIN) {
tensor_clone = ggml_sin(ggml_ctx, src0_clone);
} else if (tensor->op == GGML_OP_COS) {
tensor_clone = ggml_cos(ggml_ctx, src0_clone);
} else if (tensor->op == GGML_OP_CLAMP) {
tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
} else if (tensor->op == GGML_OP_PAD) {

936
ggml/src/ggml.c
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File diff suppressed because it is too large Load Diff

15
ggml/src/vulkan-shaders/cos.comp Normal file
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@ -0,0 +1,15 @@
#version 450
#include "types.comp"
#include "generic_unary_head.comp"
void main() {
const uint idx = get_idx();
if (idx >= p.ne) {
return;
}
const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
data_d[p.d_offset + dst_idx(idx)] = D_TYPE(cos(val));
}

15
ggml/src/vulkan-shaders/sin.comp Normal file
View File

@ -0,0 +1,15 @@
#version 450
#include "types.comp"
#include "generic_unary_head.comp"
void main() {
const uint idx = get_idx();
if (idx >= p.ne) {
return;
}
const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]);
data_d[p.d_offset + dst_idx(idx)] = D_TYPE(sin(val));
}

8
ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
View File

@ -396,6 +396,14 @@ void process_shaders(std::vector<std::future<void>>& tasks) {
string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));
tasks.push_back(std::async(std::launch::async, [] {
string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
}));

3
include/llama.h
View File

@ -511,6 +511,9 @@ extern "C" {
// to the decoder to start generating output sequence. For other models, it returns -1.
LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
// Returns true if the model is recurrent (like Mamba, RWKV, etc.)
LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model);
// Returns 0 on success
LLAMA_API uint32_t llama_model_quantize(
const char * fname_inp,

2
scripts/sync-ggml.last
View File

@ -1 +1 @@
797faa25af14126eb30134d4033139ae3c5428ed
28b7633d733bbeef0026570fbc61c79c5e9aa5ae

14
src/llama-impl.h
View File

@ -31,11 +31,17 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return; // Avoid infinite loop if 'search' is an empty string
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
while ((pos = s.find(search, pos)) != std::string::npos) {
s.replace(pos, search.length(), replace);
pos += replace.length();
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}

1465
src/llama.cpp
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File diff suppressed because it is too large Load Diff

150
tests/test-backend-ops.cpp
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@ -949,6 +949,58 @@ struct test_rms_norm : public test_case {
}
};
// GGML_OP_SSM_CONV
struct test_ssm_conv : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne_a;
const std::array<int64_t, 4> ne_b;
std::string vars() override {
return VARS_TO_STR3(type, ne_a, ne_b);
}
test_ssm_conv(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {10, 10, 10, 1},
std::array<int64_t, 4> ne_b = {3, 3, 1, 1})
: type(type), ne_a(ne_a), ne_b(ne_b) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
ggml_tensor * out = ggml_ssm_conv(ctx, a, b);
return out;
}
};
// GGML_OP_SSM_SCAN
struct test_ssm_scan : public test_case {
const ggml_type type;
const int64_t d_state;
const int64_t d_inner;
const int64_t n_seq_tokens;
const int64_t n_seqs;
std::string vars() override {
return VARS_TO_STR5(type, d_state, d_inner, n_seq_tokens, n_seqs);
}
test_ssm_scan(ggml_type type = GGML_TYPE_F32,
int64_t d_state = 32, int64_t d_inner = 32, int64_t n_seq_tokens = 32, int64_t n_seqs = 32)
: type(type), d_state(d_state), d_inner(d_inner), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * s = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ d_state, d_inner, n_seqs, 1 }.data());
ggml_tensor * x = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ d_inner, n_seq_tokens, n_seqs, 1 }.data());
ggml_tensor * dt = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ d_inner, n_seq_tokens, n_seqs, 1 }.data());
ggml_tensor * A = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ d_state, d_inner, 1 , 1 }.data());
ggml_tensor * B = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ d_state, n_seq_tokens, n_seqs, 1 }.data());
ggml_tensor * C = ggml_new_tensor(ctx, type, 4, std::vector<int64_t>{ d_state, n_seq_tokens, n_seqs, 1 }.data());
ggml_tensor * out = ggml_ssm_scan(ctx, s, x, dt, A, B, C);
return out;
}
};
// GGML_OP_MUL_MAT
struct test_mul_mat : public test_case {
const ggml_type type_a;
@ -1108,6 +1160,58 @@ struct test_sqrt : public test_case {
}
};
// GGML_OP_SIN
struct test_sin : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR2(type, ne);
}
test_sin(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10})
: type(type), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_sin(ctx, a);
return out;
}
void initialize_tensors(ggml_context * ctx) override {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
init_tensor_uniform(t, -100.0f, 100.0f);
}
}
};
// GGML_OP_COS
struct test_cos : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR2(type, ne);
}
test_cos(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10})
: type(type), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_cos(ctx, a);
return out;
}
void initialize_tensors(ggml_context * ctx) override {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
init_tensor_uniform(t, -100.0f, 100.0f);
}
}
};
// GGML_OP_CLAMP
struct test_clamp : public test_case {
const ggml_type type;
@ -1652,19 +1756,20 @@ struct test_flash_attn_ext : public test_case {
const bool mask; // use mask
const float max_bias; // ALiBi
const float logit_softcap; // Gemma 2
const ggml_type type_KV;
std::string vars() override {
return VARS_TO_STR7(hs, nh, kv, nb, mask, max_bias, type_KV);
return VARS_TO_STR8(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV);
}
double max_nmse_err() override {
return 5e-4;
}
test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
: hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {}
test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
: hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), type_KV(type_KV) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV));
@ -1673,7 +1778,28 @@ struct test_flash_attn_ext : public test_case {
ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1);
ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1);
ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr;
ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias);
ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias, logit_softcap);
return out;
}
};
// GGML_OP_CROSS_ENTROPY_LOSS
struct test_cross_entropy_loss : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
std::string vars() override {
return VARS_TO_STR2(type, ne);
}
test_cross_entropy_loss(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 10, 10, 10})
: type(type), ne(ne) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * logits = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * labels = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * out = ggml_cross_entropy_loss(ctx, logits, labels);
return out;
}
};
@ -2239,6 +2365,12 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
}
test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4, 1536, 1, 1}, {4, 1536, 1, 1}));
test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {8, 1536, 1, 1}, {4, 1536, 1, 1}));
test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4, 1536, 4, 1}, {4, 1536, 1, 1}));
test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 16, 1024, 32, 4));
#if 1
for (ggml_type type_a : base_types) {
for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
@ -2334,6 +2466,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_sqr());
test_cases.emplace_back(new test_sqrt());
test_cases.emplace_back(new test_sin());
test_cases.emplace_back(new test_cos());
test_cases.emplace_back(new test_clamp());
test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5));
@ -2437,11 +2571,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
for (bool mask : { true, false } ) {
for (float max_bias : { 0.0f, 8.0f }) {
if (!mask && max_bias > 0.0f) continue;
for (float logit_softcap : {0.0f, 10.0f}) {
if (hs != 128 && logit_softcap != 0.0f) continue;
for (int nh : { 32, }) {
for (int kv : { 512, 1024, }) {
for (int nb : { 1, 2, 4, 8, }) {
for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, type_KV));
test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV));
}
}
}
@ -2449,6 +2585,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
}
}
}
}
test_cases.emplace_back(new test_cross_entropy_loss());
// these tests are disabled to save execution time, but they can be handy for debugging
#if 0
@ -2483,7 +2622,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
}
GGML_ABORT("fatal error");
return false;
}
static void usage(char ** argv) {

245
tests/test-grad0.cpp
View File

@ -1,10 +1,14 @@
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows
#include "ggml.h"
#include <cfloat>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cassert>
#include <initializer_list>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
@ -217,7 +221,8 @@ static bool check_gradient(
int nargs,
float eps,
float max_error_abs,
float max_error_rel) {
float max_error_rel,
std::vector<double> expected_vals) {
static int n_threads = -1;
if (n_threads < 0) {
@ -248,9 +253,10 @@ static bool check_gradient(
// ggml_graph_dump_dot(gb, gf, "test-grad0-backward.dot");
for (int i = 0; i < nargs; ++i) {
bool all_g0_bad = true;
const int nelements = ggml_nelements(x[i]);
for (int k = 0; k < nelements; ++k) {
// compute gradient using finite differences
// Calculate gradient numerically:
const float x0 = ggml_get_f32_1d(x[i], k);
const float xm = x0 - eps;
const float xp = x0 + eps;
@ -267,6 +273,28 @@ static bool check_gradient(
const double f1 = ggml_get_f32_1d(f, 0);
const double g0 = (f0 - f1)/(2.0*(double) eps);
// The numerical calculation of the gradient fails around noncontinuities (e.g. 0 for ReLU).
// In such cases, provide a vector of expected values and skip the comparison for failed calculations.
if (!expected_vals.empty()) {
bool matches_any = false;
for (const double & ev : expected_vals) {
const double error_abs = std::fabs(g0 - ev);
if (error_abs > max_error_abs) {
continue;
}
const double error_rel = g0 != 0.0 ? fabs(g0 - ev)/fabs(g0) : 0.0;
if (error_rel > max_error_rel) {
continue;
}
matches_any = true;
break;
}
if (!matches_any) {
continue;
}
}
all_g0_bad = false;
ggml_set_f32_1d(x[i], k, x0);
// compute gradient using backward graph
@ -278,7 +306,7 @@ static bool check_gradient(
const double g1 = ggml_get_f32_1d(x[i]->grad, k);
const double error_abs = fabs(g0 - g1);
const double error_rel = g0 != 0 ? fabs(g0 - g1)/fabs(g0) : 0;
const double error_rel = g0 != 0.0 ? fabs(g0 - g1)/fabs(g0) : 0.0;
if (error_abs > max_error_abs || error_rel > max_error_rel) {
printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n",
@ -287,6 +315,10 @@ static bool check_gradient(
return false;
}
}
if (all_g0_bad) {
printf("%s: numerical calculation of the gradient failed for all values\n", op_name);
return false;
}
}
return true;
@ -404,7 +436,7 @@ int main(int argc, const char ** argv) {
seed_iter = rand();
unsigned seed = rand();
printf("test-grad0: iter:%d/%d\n", iter, niter);
printf("test-grad0: iter:%d/%d\n", (iter+1), niter);
struct ggml_context * ctx0 = ggml_init(params);
get_random_dims(ne, 4);
@ -424,7 +456,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
check_gradient("add f32", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f);
check_gradient("add f32", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f, {});
}
}
@ -441,7 +473,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
check_gradient("add f16", ctx0, x, f, ndims, nargs, 1e-1f, 2e-1f, 2e-1f);
check_gradient("add f16", ctx0, x, f, ndims, nargs, 1e-1f, 2e-1f, 2e-1f, {});
}
}
@ -458,7 +490,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sub(ctx0, x[0], x[1]));
check_gradient("sub", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("sub", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -475,7 +507,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_mul(ctx0, x[0], x[1]));
check_gradient("mul", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("mul", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -492,7 +524,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_div(ctx0, x[0], x[1]));
check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, 1e-1f, 1e-1f);
check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, 1e-1f, 1e-1f, {});
}
}
@ -509,7 +541,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, x[0]));
check_gradient("sqr", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("sqr", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -526,7 +558,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0]));
check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f);
check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f, {});
}
}
@ -543,7 +575,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_log(ctx0, x[0]));
check_gradient("log", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
check_gradient("log", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f, {});
}
}
@ -560,7 +592,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, x[0]);
check_gradient("sum", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("sum", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -578,7 +610,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sum_rows(ctx0, x[0])));
check_gradient("sum_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
check_gradient("sum_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {});
}
}
@ -596,7 +628,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_mean(ctx0, x[0]));
check_gradient("mean", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("mean", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -614,7 +646,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_argmax(ctx0, x[0]));
check_gradient("argmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("argmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -637,7 +669,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[1], ggml_repeat(ctx0, x[0], x[1]))));
check_gradient("repeat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
check_gradient("repeat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {});
}
}
@ -660,25 +692,25 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[0], ggml_repeat_back(ctx0, x[1], x[0]))));
check_gradient("repeat back", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
check_gradient("repeat back", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {});
}
}
// abs (finite differences do not work)
//{
// const int nargs = 1;
// abs
{
const int nargs = 1;
// for (int ndims = 1; ndims <= 2; ++ndims) {
// for (int i = 0; i < nargs; ++i) {
// x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
// ggml_set_param(ctx0, x[i]);
// }
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
// struct ggml_tensor * f = ggml_sum(ctx0, ggml_abs(ctx0, x[0]));
struct ggml_tensor * f = ggml_sum(ctx0, ggml_abs(ctx0, x[0]));
// check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f);
// }
//}
check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f, {-1.0, 1.0});
}
}
// sgn
{
@ -693,7 +725,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor* f = ggml_sum(ctx0, ggml_sgn(ctx0, x[0]));
check_gradient("sgn", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("sgn", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {0.0});
}
}
@ -710,7 +742,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor* f = ggml_sum(ctx0, ggml_neg(ctx0, x[0]));
check_gradient("neg", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("neg", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -727,7 +759,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor* f = ggml_sum(ctx0, ggml_step(ctx0, x[0]));
check_gradient("step", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("step", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {0.0});
}
}
@ -745,7 +777,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor* f = ggml_sum(ctx0, ggml_tanh(ctx0, x[0]));
check_gradient("tanh", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("tanh", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -776,7 +808,7 @@ int main(int argc, const char ** argv) {
GGML_PRINT_DEBUG("testing: mul_mat, [%lld, %lld] (%d) * [%lld, %lld] (%d)\n", x[1]->ne[0], x[1]->ne[1], x[1]->n_dims, x[0]->ne[0], x[0]->ne[1], x[0]->n_dims);
check_gradient("mul_mat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("mul_mat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
if (ndims == 2) {
// check_mat_mul does not support ndims > 2
check_mat_mul(m, x[1], x[0]);
@ -800,7 +832,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor* f = ggml_sum(ctx0, ggml_elu(ctx0, x[0]));
check_gradient("elu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("elu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -817,7 +849,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor* f = ggml_sum(ctx0, ggml_relu(ctx0, x[0]));
check_gradient("relu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("relu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {0.0, 1.0});
}
}
@ -835,7 +867,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor* f = ggml_sum(ctx0, ggml_gelu(ctx0, x[0]));
check_gradient("gelu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
check_gradient("gelu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {});
}
}
@ -854,9 +886,9 @@ int main(int argc, const char ** argv) {
#ifdef GGML_SILU_FP16
// due to GGML_SILU_FP16 the finite difference method will be slightly wrong -> increase error bounds.
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 0.5, INFINITY);
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 0.5, INFINITY, {});
#else
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
#endif
}
}
@ -874,7 +906,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rms_norm(ctx0, x[0], 1e-6f));
check_gradient("rms_norm", ctx0, x, f, ndims, nargs, 1e-4f, 1.0f, INFINITY);
check_gradient("rms_norm", ctx0, x, f, ndims, nargs, 1e-4f, 1.0f, INFINITY, {});
}
}
@ -892,7 +924,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], s));
check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -910,7 +942,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1]));
check_gradient("cpy f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("cpy f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -928,7 +960,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1]));
check_gradient("cpy f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
check_gradient("cpy f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY, {});
}
}
@ -952,7 +984,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1]));
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -976,7 +1008,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1]));
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1004,7 +1036,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
check_gradient("acc 1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("acc 1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1037,7 +1069,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
check_gradient("acc 2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("acc 2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1072,7 +1104,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
check_gradient("acc 3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("acc 3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1109,7 +1141,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
check_gradient("acc 4d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("acc 4d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1137,7 +1169,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_1d(ctx0, x[0], x[1], offset));
check_gradient("set_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("set_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1170,7 +1202,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_2d(ctx0, x[0], x[1], x[1]->nb[1], offset));
check_gradient("set_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("set_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1194,7 +1226,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_1d(ctx0, x[0], nelem, offset));
check_gradient("view_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("view_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1225,7 +1257,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_2d(ctx0, x[0], ne2[0], ne2[1], nb2[1], offset));
check_gradient("view_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("view_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1257,7 +1289,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_3d(ctx0, x[0], ne2[0], ne2[1], ne2[2], nb2[1], nb2[2], offset));
check_gradient("view_3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("view_3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1291,7 +1323,7 @@ int main(int argc, const char ** argv) {
// sum requires contiguous tensor rows
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cont(ctx0, ggml_permute(ctx0, x[0], ax0, ax1, ax2, ax3)));
check_gradient("permute", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("permute", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1319,7 +1351,7 @@ int main(int argc, const char ** argv) {
// sum requires contiguous tensor rows
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, x[0])));
check_gradient("transpose", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("transpose", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1337,7 +1369,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_get_rows(ctx0, x[0], x[1]));
check_gradient("get_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("get_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
// diag_mask_inf
@ -1353,7 +1385,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_diag_mask_inf(ctx0, x[0], n_past));
check_gradient("diag_mask_inf", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("diag_mask_inf", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
// diag_mask_zero
@ -1369,7 +1401,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_diag_mask_zero(ctx0, x[0], n_past));
check_gradient("diag_mask_zero", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("diag_mask_zero", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
// softmax
@ -1395,7 +1427,7 @@ int main(int argc, const char ** argv) {
1.0f - eps),
ggml_new_f32(ctx0, eps))));
check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY);
check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY, {});
// NOTE: softmax forward is computed using f16 table lookup instead of using actual expf, but backward assumes actual expf.
// this may result in different gradients too finite differences.
// when this test reports errors, first try to replace the table lookup with actual expf and test again to see if just that was the cause.
@ -1412,7 +1444,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 4);
for (int ndims = 1; ndims <= 4; ++ndims) {
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -0.1f, 0.1f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f);
// the second argument to cross_entropy_loss must sum up to 1 for each row
int nr = ggml_nrows(x[1]);
@ -1430,7 +1462,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]);
check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-4f, 1e-3f, INFINITY);
check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
}
}
@ -1468,7 +1500,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode));
GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY, {});
}
}
}
@ -1508,12 +1540,93 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode));
GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY, {});
}
}
}
}
// im2col f32
{
srand(seed);
const int nargs = 1;
const int ndims = 4;
for (const bool is_2D : {false, true}) {
int64_t ne0[ndims];
int64_t ne1[ndims];
get_random_dims(ne0, ndims);
get_random_dims(ne1, ndims);
// // Ensure that the output is not zero-sized:
ne1[0] += 8;
ne1[1] += 8;
if (is_2D) {
ne1[2] = ne0[2];
} else {
ne1[1] = ne0[1];
ne0[3] = 1;
ne1[3] = 1;
}
// The order of arguments is swapped because the first tensor is only used for its shape.
x[1] = get_random_tensor_f16(ctx0, ndims, ne0, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne1, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
const int s0 = 1 + irand(2);
const int s1 = is_2D ? 1 + irand(2) : 0;
const int p0 = 0 + irand(2);
const int p1 = is_2D ? 0 + irand(2) : 0;
const int d0 = 1 + irand(2);
const int d1 = is_2D ? 1 + irand(2) : 0;
struct ggml_tensor * f = ggml_sum(ctx0, ggml_im2col(ctx0, x[1], x[0], s0, s1, p0, p1, d0, d1, is_2D, GGML_TYPE_F32));
GGML_PRINT_DEBUG("im2col f32: is_2D=%s, s0=%d, s1=%d, p0=%d, p1=%d, d0=%d, d1=%d\n", is_2D ? "yes" : "no", s0, s1, p0, p1, d0, d1);
check_gradient("im2col f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY, {});
}
}
// pool_2d f32
{
srand(seed);
const int nargs = 1;
const int ndims = 4;
for (const enum ggml_op_pool op : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) {
int64_t ne0[ndims];
get_random_dims(ne0, ndims);
ne0[0] += 8;
ne0[1] += 8;
x[0] = get_random_tensor_f32(ctx0, ndims, ne0, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
const int k0 = 2 + irand(2);
const int k1 = 2 + irand(2);
const int s0 = 2 + irand(2);
const int s1 = 2 + irand(2);
const int p0 = 0 + irand(2);
const int p1 = 0 + irand(2);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_pool_2d(ctx0, x[0], op, k0, k1, s0, s1, p0, p1));
GGML_PRINT_DEBUG("ggml_pool_2d f32: op=%s k0=%d, k1=%d, s0=%d, s1=%d, p0=%d, p1=%d\n",
op == GGML_OP_POOL_MAX ? "max" : "avg", k0, k1, s0, s1, p0, p1);
std::vector<double> expected_vals;
if (op == GGML_OP_POOL_MAX) {
expected_vals.push_back(0.0);
expected_vals.push_back(1.0);
}
check_gradient("ggml_pool_2d f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, expected_vals);
}
}
// flash_attn f32
// TODO: adapt to ggml_flash_attn_ext() changes
//{
@ -1553,7 +1666,7 @@ int main(int argc, const char ** argv) {
// struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
// check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
// check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY, {});
// }
// }
// }

2
tests/test-lora-conversion-inference.sh
View File

@ -14,7 +14,7 @@ MODELS_REPO_URL=https://huggingface.co/ggml-org/$MODELS_REPO
# Clone the Hugging Face repository if the directory does not exist
if [ ! -d "$MODELS_REPO" ]; then
echo "Cloning the Hugging Face repository..."
git clone $MODELS_REPO_URL
git clone $MODELS_REPO_URL --depth 1
else
echo "Repository already exists. Skipping clone."
fi

8
tests/test-sampling.cpp
View File

@ -166,12 +166,12 @@ static void test_sampler_queue(
for (auto s : samplers_sequence) {
switch (s){
case 'k': llama_sample_top_k (nullptr, &candidates_p, top_k, 1); break;
case 'f': GGML_ABORT("tail_free test not implemented"); break;
case 'y': GGML_ABORT("typical test not implemented"); break;
case 'f': GGML_ABORT("tail_free test not implemented");
case 'y': GGML_ABORT("typical test not implemented");
case 'p': llama_sample_top_p (nullptr, &candidates_p, top_p, 1); break;
case 'm': llama_sample_min_p (nullptr, &candidates_p, min_p, 1); break;
case 't': GGML_ABORT("temperature test not implemented"); break;
default : GGML_ABORT("Unknown sampler"); break;
case 't': GGML_ABORT("temperature test not implemented");
default : GGML_ABORT("Unknown sampler");
}
llama_sample_softmax(nullptr, &candidates_p); // make sure tokens are sorted for tests