Compare commits

...

25 Commits

Author SHA1 Message Date
Daniel Bevenius 7c7f3b7f43 ggml : skip intermediate .air file when compiling .metallib (#12247)
This commit updates the compilation of default.metallib to skip the
intermediate .air (Apple Intermediate Representation) file.

The motivation for this change is to simplify the custom command a
little and avoid generating and then removing the .air file.
2025-03-07 14:15:27 +01:00
Georgi Gerganov 102ac1891d sync : ggml
ggml-ci
2025-03-07 14:49:44 +02:00
vmobilis d6ae2fa061 ggml : ggml_compute_forward_concat() for arbitrary tensor type (ggml/1118)
* ggml_compute_forward_concat() for arbitrary tensor type

* Check that tensors' type match

* ggml-cpu.c: check type of source tensors

* ggml-cpu.c: move tensor type check to ggml_compute_forward_concat()

* ggml.c: check concatenated tensor type

* Remove tensor type check from ggml_compute_forward_concat() in ggml-cpu.c

..., as it was moved to ggml.c.
2025-03-07 14:49:44 +02:00
Rémy O 68d0027f3d ggml-cpu: faster AVX2 variant for IQ1_M (#12216) 2025-03-07 13:54:22 +02:00
Georgi Gerganov ea002810a2 ci : fix save-load test invocations (#12245) 2025-03-07 12:19:31 +02:00
Sigbjørn Skjæret 8fad3c7a7c server : Log original chat template parsing error (#12233) 2025-03-07 11:15:33 +01:00
Olivier Chafik 7cf64f6bee sync: minja - support QwQ-32B (#12235)
https://github.com/google/minja/commit/8a76f7815e8a3ae00bd233c2b5a8b7d4e86564ec
2025-03-07 09:33:37 +00:00
BB-fat 5e2d57b2b2 metal : simplify kernel arguments using a struct (#3229) (#12194)
* metal : refactor im2col parameters into a struct

* metal: Change im2col offset types from int32_t to uint64_t to support larger memory offsets

* metal : refactor sum_rows parameters into a struct

* metal : refactor soft_max parameters into a struct

* metal : refactor diag_mask_inf parameters into a struct

* metal : refactor ssm_conv parameters into a struct

* metal : refactor ssm_scan parameters into a struct

* metal : refactor get_rows parameters into a struct

* metal : refactor group_norm parameters into a struct

* metal : refactor conv_transpose_1d parameters into a struct

* metal : refactor upscale parameters into a struct

* metal : refactor pad parameters into a struct

* metal : refactor pad_reflect_1d parameters into a struct

* metal : refactor arange parameters into a struct

* metal : refactor timestep_embedding parameters into a struct

* metal : refactor argsort parameters into a struct

* metal : refactor leaky_relu parameters into a struct

* metal : refactor pool_2d parameters into a struct

* metal : fix trailing whitespace

---------

Co-authored-by: alexju <alexju@tencent.com>
2025-03-07 08:35:57 +01:00
David Huang f1648e91cf HIP: fix rocWMMA build flags under Windows (#12230) 2025-03-07 08:06:08 +01:00
Daniel Bevenius d6c95b0740 metal : fix default.metallib build (#12224)
This commit updates the custom command to build the default.metallib
file to use the correct path to ../ggml-common.h by using the variable
METALLIB_COMMON.

The motivation for this change is that currently when building and
specifying GGML_METAL_EMBED_LIBRARY=OFF the following error is
generated:
```console
[ 11%] Linking CXX shared library ../../bin/libggml.dylib
[ 11%] Built target ggml
make[2]: *** No rule to make target `ggml/src/ggml-metal/ggml-common.h', needed by `bin/default.metallib'.  Stop.
make[1]: *** [ggml/src/ggml-metal/CMakeFiles/ggml-metal-lib.dir/all] Error 2
```

With the above change the build could progress but there was a follow
on error about not being able to find the ggml-common.h file in
ggml-metal.metal where is was included as a relative path:
```console
[ 11%] Compiling Metal kernels
/Users/danbev/work/llama.cpp/build/bin/ggml-metal.metal:6:10: error: '../ggml-common.h' file not found, did you mean 'ggml-common.h'?
         ^~~~~~~~~~~~~~~~~~
         "ggml-common.h"
1 error generated.
```
Removing the relative path then allowed the build to complete
successfully.
2025-03-07 06:23:16 +01:00
lhez d76a86d967 opencl: Noncontiguous norm, rms_norm, disable fp16 for some ops (#12217)
* opencl: support noncontiguous `norm`

* opencl: support noncontiguous `rms_norm`

* opencl: disable fp16 for `ADD`, `MUL`, `SCALE`, `RELU`, `GELU`, `SILU`, `CLAMP`
2025-03-07 00:20:35 +00:00
xiaofei 776f9e59cc cmake : fix undefined reference errors for std::filesystem in ggml (#12092) (#12094)
Signed-off-by: Ray Lee <hburaylee@gmail.com>
Co-authored-by: Ray Lee <hburaylee@gmail.com>
2025-03-06 22:58:25 +00:00
Lucas Moura Belo 3d652bfddf readme : update bindings (#12229) 2025-03-06 21:15:13 +02:00
Johannes Gäßler 5220a16d18 CUDA: fix FA logic for PTX 7.0 and CC >= 7.5 (#12222) 2025-03-06 18:45:09 +01:00
David Huang 3ffbbd5ce1 HIP: rocWMMA documentation and enabling in workflow builds (#12179)
* Enable rocWMMA for Windows CI build

* Enable for Ubuntu

* GGML_HIP_ROCWMMA_FATTN documentation work
2025-03-06 14:14:11 +01:00
Olivier Chafik 42994048a3 update function-calling.md w/ template override for functionary-small-v3.2 (#12214) 2025-03-06 09:03:31 +00:00
Aaron Teo e9b2f84f14 llava: add big-endian conversion for image encoder (#12218)
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
2025-03-06 09:33:21 +01:00
uvos e721c05c93 HIP/CUDA: set the paramerter value in maintain_cuda_graph instead of replaceing it. (#12209)
This avoids conflict with internal cuda/hip runtimes memory managment behavior.
2025-03-06 08:20:52 +01:00
Han Yin 57b6abf85a android : fix KV cache log message condition (#12212) 2025-03-06 08:22:49 +02:00
Henry Linjamäki 94bb63e4f0 opencl : fix buffer alignment (#12197)
Fix the following error:

```
ggml-alloc.c:99: not enough space in the buffer
ggml_tallocr_alloc: not enough space in the buffer to allocate blk.17.ffn_down.weight (needed 27525120, available 27521024)
```

which occurs when `ggml_backend_opencl_context::alignment` is larger
than `cl_ptr_base` (hard-coded to `0x1000`).

Also, fix `ggml_backend_opencl_context::alignment` was set to
`CL_DEVICE_MEM_BASE_ADDR_ALIGN` which was treated as bytes but the
value is reported in bits.
2025-03-06 02:33:40 +01:00
Henry Linjamäki f79243992c opencl : fix ulong kernel args were set from int variables (#12174)
... which left garbage bits in the upper half of the kernel args. This
caused segmentation faults when running PoCL.
2025-03-06 02:31:14 +01:00
simon886212 ed4ce0dda2 opencl : fix profile-related errors (#12095)
Co-authored-by: ubuntu <ubuntu@localhost.localdomain>
2025-03-06 02:30:05 +01:00
Rémy O 07d1572347 ggml-cpu: Faster IQ1 mul_mat_vec on AVX2 using BMI2 instructions (#12154)
* ggml-cpu: Faster IQ1 mul_mat_vec on AVX2 using BMI2 instructions

* cmake: Add GGML_BMI2 build option

* ggml: enable BMI2 on relevant CPU variants

* ggml-cpu: include BMI2 in backend score

* ggml-cpu: register BMI2 in ggml_backend_cpu_get_features

* ggml-cpu: add __BMI2__ define when using MSVC
2025-03-06 02:26:10 +01:00
Akarshan Biswas 5e43f104cc SYCL: Disable f16 Unary OPs as not supported by the kernels (#12201) 2025-03-05 16:58:23 +01:00
Plamen Minev 16e4b22c5e ggml : fix GGMLMetalClass ODR (#12200)
-- it might happen if ggml is loaded from 2 separate libraries since each one of them will expose the class. This is more of a guard since we want to use only Metal as embedded library and don't care about the other case.
2025-03-05 17:16:01 +02:00
28 changed files with 1112 additions and 771 deletions
+16
View File
@@ -467,6 +467,7 @@ jobs:
run: |
cmake -B build -S . \
-DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-DGGML_HIP_ROCWMMA_FATTN=ON \
-DGGML_HIP=ON
cmake --build build --config Release -j $(nproc)
@@ -476,6 +477,7 @@ jobs:
cmake -B build2 -S . \
-DCMAKE_C_COMPILER=hipcc \
-DCMAKE_CXX_COMPILER=hipcc \
-DGGML_HIP_ROCWMMA_FATTN=ON \
-DGGML_HIP=ON
cmake --build build2 --config Release -j $(nproc)
@@ -1202,6 +1204,11 @@ jobs:
id: checkout
uses: actions/checkout@v4
- name: Clone rocWMMA repository
id: clone_rocwmma
run: |
git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
- name: Install
id: depends
run: |
@@ -1231,8 +1238,10 @@ jobs:
cmake -G "Unix Makefiles" -B build -S . `
-DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
-DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/" `
-DCMAKE_BUILD_TYPE=Release `
-DGGML_HIP=ON `
-DGGML_HIP_ROCWMMA_FATTN=ON `
-DGGML_RPC=ON
cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
@@ -1251,6 +1260,11 @@ jobs:
with:
fetch-depth: 0
- name: Clone rocWMMA repository
id: clone_rocwmma
run: |
git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
@@ -1280,8 +1294,10 @@ jobs:
cmake -G "Unix Makefiles" -B build -S . `
-DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
-DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/" `
-DCMAKE_BUILD_TYPE=Release `
-DAMDGPU_TARGETS=${{ matrix.gpu_target }} `
-DGGML_HIP_ROCWMMA_FATTN=ON `
-DGGML_HIP=ON `
-DGGML_RPC=ON
cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
+1
View File
@@ -157,6 +157,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
- Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi)
</details>
+4 -4
View File
@@ -352,10 +352,10 @@ function gg_run_open_llama_7b_v2 {
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
function check_ppl {
qnt="$1"
+37 -5
View File
@@ -1378,13 +1378,27 @@ struct ArgumentsExpression {
}
};
static std::string strip(const std::string & s) {
auto start = s.find_first_not_of(" \t\n\r");
static std::string strip(const std::string & s, const std::string & chars = "", bool left = true, bool right = true) {
auto charset = chars.empty() ? " \t\n\r" : chars;
auto start = left ? s.find_first_not_of(charset) : 0;
if (start == std::string::npos) return "";
auto end = s.find_last_not_of(" \t\n\r");
auto end = right ? s.find_last_not_of(charset) : s.size() - 1;
return s.substr(start, end - start + 1);
}
static std::vector<std::string> split(const std::string & s, const std::string & sep) {
std::vector<std::string> result;
size_t start = 0;
size_t end = s.find(sep);
while (end != std::string::npos) {
result.push_back(s.substr(start, end - start));
start = end + sep.length();
end = s.find(sep, start);
}
result.push_back(s.substr(start));
return result;
}
static std::string capitalize(const std::string & s) {
if (s.empty()) return s;
auto result = s;
@@ -1467,8 +1481,26 @@ public:
} else if (obj.is_string()) {
auto str = obj.get<std::string>();
if (method->get_name() == "strip") {
vargs.expectArgs("strip method", {0, 0}, {0, 0});
return Value(strip(str));
vargs.expectArgs("strip method", {0, 1}, {0, 0});
auto chars = vargs.args.empty() ? "" : vargs.args[0].get<std::string>();
return Value(strip(str, chars));
} else if (method->get_name() == "lstrip") {
vargs.expectArgs("lstrip method", {0, 1}, {0, 0});
auto chars = vargs.args.empty() ? "" : vargs.args[0].get<std::string>();
return Value(strip(str, chars, /* left= */ true, /* right= */ false));
} else if (method->get_name() == "rstrip") {
vargs.expectArgs("rstrip method", {0, 1}, {0, 0});
auto chars = vargs.args.empty() ? "" : vargs.args[0].get<std::string>();
return Value(strip(str, chars, /* left= */ false, /* right= */ true));
} else if (method->get_name() == "split") {
vargs.expectArgs("split method", {1, 1}, {0, 0});
auto sep = vargs.args[0].get<std::string>();
auto parts = split(str, sep);
Value result = Value::array();
for (const auto& part : parts) {
result.push_back(Value(part));
}
return result;
} else if (method->get_name() == "capitalize") {
vargs.expectArgs("capitalize method", {0, 0}, {0, 0});
return Value(capitalize(str));
+6
View File
@@ -235,6 +235,12 @@ You can download it from your Linux distro's package manager or from here: [ROCm
On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system.
The rocWMMA library is included by default when installing the ROCm SDK using the `rocm` meta package provided by AMD. Alternatively, if you are not using the meta package, you can install the library using the `rocwmma-dev` or `rocwmma-devel` package, depending on your system's package manager.
As an alternative, you can manually install the library by cloning it from the official [GitHub repository](https://github.com/ROCm/rocWMMA), checkout the corresponding version tag (e.g. `rocm-6.2.4`) and set `-DCMAKE_CXX_FLAGS="-I<path/to/rocwmma>/library/include/"` in CMake. This also works under Windows despite not officially supported by AMD.
Note that if you get the following error:
```
clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library
+12 -8
View File
@@ -287,30 +287,32 @@ Here are some models known to work (w/ chat template override when needed):
llama-server --jinja -fa -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q6_K_L
llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M
llama-server --jinja -fa -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Native support for DeepSeek R1 works best w/ our own template (official template buggy)
# Native support for DeepSeek R1 works best w/ our template override (official template is buggy, although we do work around it)
llama-server --jinja -fa -hf bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q6_K_L \
--chat-template-file models/templates/llama-cpp-deepseek-r1.jinja
--chat-template-file models/templates/llama-cpp-deepseek-r1.jinja
llama-server --jinja -fa -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M \
--chat-template-file models/templates/llama-cpp-deepseek-r1.jinja
--chat-template-file models/templates/llama-cpp-deepseek-r1.jinja
# Native support requires the right template for these GGUFs:
llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M
--chat-template-file models/templates/meetkai-functionary-medium-v3.2.jinja
llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \
--chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B tool_use )
--chat-template-file models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja
llama-server --jinja -fa -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M \
--chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use )
--chat-template-file models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja
llama-server --jinja -fa -hf bartowski/firefunction-v2-GGUF -hff firefunction-v2-IQ1_M.gguf \
--chat-template-file <( python scripts/get_chat_template.py fireworks-ai/llama-3-firefunction-v2 tool_use )
--chat-template-file models/templates/fireworks-ai-llama-3-firefunction-v2.jinja
llama-server --jinja -fa -hf bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L \
--chat-template-file <( python scripts/get_chat_template.py CohereForAI/c4ai-command-r7b-12-2024 tool_use )
--chat-template-file models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja
# Generic format support
llama-server --jinja -fa -hf bartowski/phi-4-GGUF:Q4_0
@@ -318,6 +320,8 @@ llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q8_0
llama-server --jinja -fa -hf bartowski/c4ai-command-r-v01-GGUF:Q2_K
```
To get the official template from original HuggingFace repos, you can use [scripts/get_chat_template.py](../scripts/get_chat_template.py) (see examples invocations in [models/templates/README.md](../models/templates/README.md))
> [!TIP]
> If there is no official `tool_use` Jinja template, you may want to set `--chat-template chatml` to use a default that works with many models (YMMV!), or write your own (e.g. we provide a custom [llama-cpp-deepseek-r1.jinja](../models/templates/llama-cpp-deepseek-r1.jinja) for DeepSeek R1 distills)
@@ -361,7 +361,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init(
const auto tokens_list = common_tokenize(context, text, true, parse_special);
auto n_ctx = llama_n_ctx(context);
auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
auto n_kv_req = tokens_list.size() + n_len;
LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req);
@@ -89,6 +89,7 @@ def bytes_to_unicode():
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument('--bigendian', action="store_true", default=False, help="Model is executed on big-endian machine")
ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
@@ -191,7 +192,7 @@ output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip")
fout = GGUFWriter(path=fname_out, arch="clip", endianess=GGUFEndian.LITTLE if not args.bigendian else GGUFEndian.BIG)
fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
+1
View File
@@ -1900,6 +1900,7 @@ struct server_context {
try {
common_chat_format_example(chat_templates.get(), params.use_jinja);
} catch (const std::exception & e) {
SRV_WRN("%s: Chat template parsing error: %s\n", __func__, e.what());
SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
chat_templates = common_chat_templates_init(model, "chatml");
}
+1
View File
@@ -106,6 +106,7 @@ option(GGML_CPU_KLEIDIAI "ggml: use KleidiAI optimized kernels if applicable
option(GGML_AVX "ggml: enable AVX" ${INS_ENB})
option(GGML_AVX_VNNI "ggml: enable AVX-VNNI" OFF)
option(GGML_AVX2 "ggml: enable AVX2" ${INS_ENB})
option(GGML_BMI2 "ggml: enable BMI2" ${INS_ENB})
option(GGML_AVX512 "ggml: enable AVX512F" OFF)
option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF)
option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF)
+1
View File
@@ -80,6 +80,7 @@ extern "C" {
GGML_BACKEND_API int ggml_cpu_has_avx (void);
GGML_BACKEND_API int ggml_cpu_has_avx_vnni (void);
GGML_BACKEND_API int ggml_cpu_has_avx2 (void);
GGML_BACKEND_API int ggml_cpu_has_bmi2 (void);
GGML_BACKEND_API int ggml_cpu_has_f16c (void);
GGML_BACKEND_API int ggml_cpu_has_fma (void);
GGML_BACKEND_API int ggml_cpu_has_avx512 (void);
+7 -7
View File
@@ -236,7 +236,7 @@ add_library(ggml
target_link_libraries(ggml PUBLIC ggml-base)
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
target_link_libraries(ggml PRIVATE dl)
target_link_libraries(ggml PRIVATE dl stdc++fs)
endif()
function(ggml_add_backend_library backend)
@@ -289,7 +289,7 @@ function(ggml_add_cpu_backend_variant tag_name)
set(GGML_CPU_TAG_NAME ${tag_name})
# other: OPENMP LLAMAFILE CPU_HBM
foreach (feat NATIVE
AVX AVX2 AVX_VNNI FMA F16C
AVX AVX2 BMI2 AVX_VNNI FMA F16C
AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16
AMX_TILE AMX_INT8 AMX_BF16)
set(GGML_${feat} OFF)
@@ -309,13 +309,13 @@ if (GGML_CPU_ALL_VARIANTS)
message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL")
endif()
ggml_add_cpu_backend_variant(sandybridge AVX)
ggml_add_cpu_backend_variant(haswell AVX F16C AVX2 FMA)
ggml_add_cpu_backend_variant(skylakex AVX F16C AVX2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake AVX F16C AVX2 FMA AVX_VNNI)
ggml_add_cpu_backend_variant(haswell AVX F16C AVX2 BMI2 FMA)
ggml_add_cpu_backend_variant(skylakex AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake AVX F16C AVX2 BMI2 FMA AVX_VNNI)
if (NOT MSVC)
# MSVC doesn't support AMX
ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
endif()
elseif (GGML_CPU)
ggml_add_cpu_backend_variant_impl("")
+8
View File
@@ -219,6 +219,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
if (GGML_AVX_VNNI)
list(APPEND ARCH_DEFINITIONS __AVXVNNI__ GGML_AVX_VNNI)
endif()
if (GGML_BMI2)
# MSVC does not define macro __BMI2__
list(APPEND ARCH_DEFINITIONS __BMI2__ GGML_BMI2)
endif()
else ()
if (GGML_NATIVE)
list(APPEND ARCH_FLAGS -march=native)
@@ -233,6 +237,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
list(APPEND ARCH_FLAGS -mfma)
list(APPEND ARCH_DEFINITIONS GGML_FMA)
endif()
if (GGML_BMI2)
list(APPEND ARCH_FLAGS -mbmi2)
list(APPEND ARCH_DEFINITIONS GGML_BMI2)
endif()
if (GGML_AVX)
list(APPEND ARCH_FLAGS -mavx)
list(APPEND ARCH_DEFINITIONS GGML_AVX)
+4
View File
@@ -278,6 +278,10 @@ static int ggml_backend_cpu_x86_score() {
if (!is.SSE42()) { return 0; }
score += 1<<2;
#endif
#ifdef GGML_BMI2
if (!is.BMI2()) { return 0; }
score += 1<<3;
#endif
#ifdef GGML_AVX
if (!is.AVX()) { return 0; }
score += 1<<4;
+48 -11
View File
@@ -11362,10 +11362,19 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
__m256i sumi = _mm256_setzero_si256();
int sumi1 = 0;
for (int ib = 0; ib < QK_K/32; ib += 2) {
#ifdef __BMI2__
const uint64_t packed_idx1 = _pdep_u64(*(const uint32_t *)qs, 0x00ff00ff00ff00ffULL) | _pdep_u64(qh[ib], 0x700070007000700ULL);
const uint64_t packed_idx2 = _pdep_u64(*(const uint32_t *)(qs + 4), 0x00ff00ff00ff00ffULL) | _pdep_u64(qh[ib + 1], 0x700070007000700ULL);
const uint16_t *idx1 = (const uint16_t *)(&packed_idx1);
const uint16_t *idx2 = (const uint16_t *)(&packed_idx2);
const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[idx1[3]], iq1s_grid[idx1[2]], iq1s_grid[idx1[1]], iq1s_grid[idx1[0]]);
const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[idx2[3]], iq1s_grid[idx2[2]], iq1s_grid[idx2[1]], iq1s_grid[idx2[0]]);
#else
const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)],
iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]);
const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)],
iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]);
#endif
qs += 8;
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
@@ -11711,6 +11720,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
const __m256i mask = _mm256_set1_epi16(0x7);
const __m256i mone = _mm256_set1_epi16(1);
const __m256i mone8 = _mm256_set1_epi8(1);
const __m256i mtwo8 = _mm256_set1_epi8(2);
// VPSHUFB cannot cross 128-bit lanes so odd shifts go to upper half.
const __m256i scales_shift = _mm256_set_epi64x(9, 3, 6, 0);
__m256 accum1 = _mm256_setzero_ps();
__m256 accum2 = _mm256_setzero_ps();
@@ -11722,10 +11735,33 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
const uint16_t * sc = (const uint16_t *)x[i].scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
// Extract 3-bit scales (16 values)
__m256i scales = _mm256_set1_epi64x(*(const uint64_t*)sc);
scales = _mm256_srlv_epi64(scales, scales_shift);
scales = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scales, mask), 1), mone);
// Indices to repeat each scale 8 times.
__m256i scales_idx1 = _mm256_set1_epi16(0x0100);
__m256i scales_idx2 = _mm256_add_epi8(scales_idx1, _mm256_set1_epi8(8));
__m256i sumi1 = _mm256_setzero_si256();
__m256i sumi2 = _mm256_setzero_si256();
for (int ib = 0; ib < QK_K/32; ib += 2) {
#ifdef __BMI2__
const uint64_t packed_idx1 = _pdep_u64(*(const uint32_t *)qs, 0x00ff00ff00ff00ffULL)
| _pdep_u64(*(const uint16_t*)(qh) & 0x7777, 0xf000f000f000f00ULL);
const uint64_t packed_idx2 = _pdep_u64(*(const uint32_t *)(qs + 4), 0x00ff00ff00ff00ffULL)
| _pdep_u64(*(const uint16_t*)(qh + 2) & 0x7777, 0xf000f000f000f00ULL);
const uint16_t *idx1 = (const uint16_t *)(&packed_idx1);
const uint16_t *idx2 = (const uint16_t *)(&packed_idx2);
const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[idx1[3]], iq1s_grid[idx1[2]], iq1s_grid[idx1[1]], iq1s_grid[idx1[0]]);
const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[idx2[3]], iq1s_grid[idx2[2]], iq1s_grid[idx2[1]], iq1s_grid[idx2[0]]);
// Convert signs to bytes 0x81 (negative) or 0x01 (positive)
const uint64_t delta_sign = _pdep_u64(*(const uint32_t*)(qh) & 0x88888888, 0xf0f0f0f0f0f0f0f0ULL);
const __m256i delta1 = _mm256_or_si256(mone8, _mm256_cvtepi8_epi64(_mm_set1_epi32(delta_sign)));
const __m256i delta2 = _mm256_or_si256(mone8, _mm256_cvtepi8_epi64(_mm_set1_epi32(delta_sign >> 32)));
#else
const __m256i q1b_1 = _mm256_set_epi64x(
iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)],
iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]
@@ -11734,11 +11770,6 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)],
iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]
);
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
@@ -11748,15 +11779,21 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
#endif
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
const __m256i dot3 = _mm256_maddubs_epi16(mone8, _mm256_sign_epi8(q8b_1, delta1));
const __m256i dot4 = _mm256_maddubs_epi16(mone8, _mm256_sign_epi8(q8b_2, delta2));
__m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
__m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
__m256i scale1 = _mm256_shuffle_epi8(scales, scales_idx1);
__m256i scale2 = _mm256_shuffle_epi8(scales, scales_idx2);
scales_idx1 = _mm256_add_epi8(scales_idx1, mtwo8);
scales_idx2 = _mm256_add_epi8(scales_idx2, mtwo8);
scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
const __m256i p2 = _mm256_madd_epi16(dot2, scale2);
const __m256i p3 = _mm256_madd_epi16(dot3, scale1);
+149 -2
View File
@@ -6648,6 +6648,135 @@ static void ggml_compute_forward_repeat_back(
// ggml_compute_forward_concat
static void ggml_compute_forward_concat_any(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const size_t len = ggml_type_size(src0->type);
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
const int32_t dim = ggml_get_op_params_i32(dst, 0);
GGML_ASSERT(dim >= 0 && dim < 4);
int64_t o[4] = {0, 0, 0, 0};
o[dim] = src0->ne[dim];
const char * x;
// TODO: smarter multi-theading
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03;
} else {
x = (const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13;
}
char * y = (char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3;
memcpy(y, x, len);
}
}
}
}
}
static void ggml_compute_forward_concat_i8(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(ggml_type_size(src0->type) == sizeof(int8_t));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
const int32_t dim = ggml_get_op_params_i32(dst, 0);
GGML_ASSERT(dim >= 0 && dim < 4);
int64_t o[4] = {0, 0, 0, 0};
o[dim] = src0->ne[dim];
const int8_t * x;
// TODO: smarter multi-theading
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const int8_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03);
} else {
x = (const int8_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
}
int8_t * y = (int8_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
*y = *x;
}
}
}
}
}
static void ggml_compute_forward_concat_f16(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(ggml_type_size(src0->type) == sizeof(ggml_fp16_t));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
const int32_t dim = ggml_get_op_params_i32(dst, 0);
GGML_ASSERT(dim >= 0 && dim < 4);
int64_t o[4] = {0, 0, 0, 0};
o[dim] = src0->ne[dim];
const ggml_fp16_t * x;
// TODO: smarter multi-theading
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const ggml_fp16_t *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03);
} else {
x = (const ggml_fp16_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
}
ggml_fp16_t * y = (ggml_fp16_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
*y = *x;
}
}
}
}
}
static void ggml_compute_forward_concat_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
@@ -6655,7 +6784,7 @@ static void ggml_compute_forward_concat_f32(
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->nb[0] == sizeof(float));
GGML_ASSERT(ggml_type_size(src0->type) == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
@@ -6698,6 +6827,16 @@ static void ggml_compute_forward_concat(
const struct ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F16:
case GGML_TYPE_BF16:
case GGML_TYPE_I16:
{
ggml_compute_forward_concat_f16(params, dst);
} break;
case GGML_TYPE_I8:
{
ggml_compute_forward_concat_i8(params, dst);
} break;
case GGML_TYPE_F32:
case GGML_TYPE_I32:
{
@@ -6705,7 +6844,7 @@ static void ggml_compute_forward_concat(
} break;
default:
{
GGML_ABORT("fatal error");
ggml_compute_forward_concat_any(params, dst);
}
}
}
@@ -15440,6 +15579,14 @@ int ggml_cpu_has_amx_int8(void) {
#endif
}
int ggml_cpu_has_bmi2(void) {
#if defined(__BMI2__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_fma(void) {
#if defined(__FMA__)
return 1;
+3
View File
@@ -511,6 +511,9 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r
if (ggml_cpu_has_fma()) {
features.push_back({ "FMA", "1" });
}
if (ggml_cpu_has_bmi2()) {
features.push_back({ "BMI2", "1" });
}
if (ggml_cpu_has_avx512()) {
features.push_back({ "AVX512", "1" });
}
+1 -1
View File
@@ -310,7 +310,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
}
// The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
if (cc == GGML_CUDA_CC_VOLTA) {
if (fp16_mma_available(cc) && !new_mma_available(cc)) {
ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
return;
}
+1 -1
View File
@@ -2571,7 +2571,7 @@ static void maintain_cuda_graph(ggml_backend_cuda_context * cuda_ctx, std::vecto
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
*(void**)cuda_ctx->cuda_graph->params[i].kernelParams[1] = *(void**)updated_kernel_arg_ptr;
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
}
}
+4 -5
View File
@@ -27,12 +27,12 @@ configure_file(../ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
configure_file(ggml-metal-impl.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal-impl.h COPYONLY)
set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/../ggml-common.h")
if (GGML_METAL_EMBED_LIBRARY)
enable_language(ASM)
add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/../ggml-common.h")
set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
set(METALLIB_IMPL "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal-impl.h")
@@ -88,12 +88,11 @@ else()
add_custom_command(
OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o - |
xcrun -sdk macosx metallib - -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
DEPENDS ggml-metal.metal ggml-common.h
DEPENDS ggml-metal.metal ${METALLIB_COMMON}
COMMENT "Compiling Metal kernels"
)
+235
View File
@@ -285,4 +285,239 @@ typedef struct {
float eps;
} ggml_metal_kargs_rms_norm;
typedef struct {
int64_t ne00;
int64_t ne01;
int64_t ne02;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
int32_t n_groups;
float eps;
} ggml_metal_kargs_group_norm;
typedef struct {
int32_t IC;
int32_t IL;
int32_t K;
int32_t s0;
uint64_t nb0;
uint64_t nb1;
} ggml_metal_kargs_conv_transpose_1d;
typedef struct {
uint64_t ofs0;
uint64_t ofs1;
int32_t IW;
int32_t IH;
int32_t CHW;
int32_t s0;
int32_t s1;
int32_t p0;
int32_t p1;
int32_t d0;
int32_t d1;
int32_t N;
int32_t KH;
int32_t KW;
int32_t KHW; // KH * KW, pre-computed on CPU to save GPU resources
} ggml_metal_kargs_im2col;
typedef struct {
int64_t ne00;
int64_t ne01;
int64_t ne02;
int64_t ne03;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
uint64_t nb03;
int64_t ne10;
int64_t ne11;
int64_t ne12;
int64_t ne13;
uint64_t nb10;
uint64_t nb11;
uint64_t nb12;
uint64_t nb13;
int64_t ne0;
int64_t ne1;
int64_t ne2;
int64_t ne3;
uint64_t nb0;
uint64_t nb1;
uint64_t nb2;
uint64_t nb3;
} ggml_metal_kargs_sum_rows;
typedef struct {
int64_t ne00;
int64_t ne01;
int64_t ne02;
float scale;
float max_bias;
float m0;
float m1;
uint32_t n_head_log2;
} ggml_metal_kargs_soft_max;
typedef struct {
int64_t ne00;
int64_t ne01;
int n_past;
} ggml_metal_kargs_diag_mask_inf;
typedef struct {
int64_t ne00;
int64_t ne01;
int64_t ne02;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
int64_t ne10;
int64_t ne11;
uint64_t nb10;
uint64_t nb11;
int64_t ne0;
int64_t ne1;
int64_t ne2;
uint64_t nb0;
uint64_t nb1;
uint64_t nb2;
} ggml_metal_kargs_ssm_conv;
typedef struct {
int64_t d_state;
int64_t d_inner;
int64_t n_seq_tokens;
int64_t n_seqs;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
uint64_t nb10;
uint64_t nb11;
uint64_t nb12;
uint64_t nb13;
uint64_t nb20;
uint64_t nb21;
uint64_t nb22;
uint64_t nb30;
uint64_t nb31;
uint64_t nb40;
uint64_t nb41;
uint64_t nb42;
uint64_t nb50;
uint64_t nb51;
uint64_t nb52;
} ggml_metal_kargs_ssm_scan;
typedef struct {
int64_t ne00;
uint64_t nb01;
uint64_t nb02;
int64_t ne10;
uint64_t nb10;
uint64_t nb11;
uint64_t nb1;
uint64_t nb2;
} ggml_metal_kargs_get_rows;
typedef struct {
int64_t ne00;
int64_t ne01;
int64_t ne02;
int64_t ne03;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
uint64_t nb03;
int64_t ne0;
int64_t ne1;
int64_t ne2;
int64_t ne3;
uint64_t nb0;
uint64_t nb1;
uint64_t nb2;
uint64_t nb3;
float sf0;
float sf1;
float sf2;
float sf3;
} ggml_metal_kargs_upscale;
typedef struct {
int64_t ne00;
int64_t ne01;
int64_t ne02;
int64_t ne03;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
uint64_t nb03;
int64_t ne0;
int64_t ne1;
int64_t ne2;
int64_t ne3;
uint64_t nb0;
uint64_t nb1;
uint64_t nb2;
uint64_t nb3;
} ggml_metal_kargs_pad;
typedef struct {
int64_t ne00;
int64_t ne01;
int64_t ne02;
int64_t ne03;
uint64_t nb00;
uint64_t nb01;
uint64_t nb02;
uint64_t nb03;
int64_t ne0;
int64_t ne1;
int64_t ne2;
int64_t ne3;
uint64_t nb0;
uint64_t nb1;
uint64_t nb2;
uint64_t nb3;
int32_t p0;
int32_t p1;
} ggml_metal_kargs_pad_reflect_1d;
typedef struct {
uint64_t nb1;
int dim;
int max_period;
} ggml_metal_kargs_timestep_embedding;
typedef struct {
float slope;
} ggml_metal_kargs_leaky_relu;
typedef struct {
int64_t ncols;
int64_t ncols_pad;
} ggml_metal_kargs_argsort;
typedef struct {
int64_t ne0;
float start;
float step;
} ggml_metal_kargs_arange;
typedef struct {
int32_t k0;
int32_t k1;
int32_t s0;
int32_t s1;
int32_t p0;
int32_t p1;
int64_t IH;
int64_t IW;
int64_t OH;
int64_t OW;
int64_t parallel_elements;
} ggml_metal_kargs_pool_2d;
#endif // GGML_METAL_IMPL
+285 -230
View File
@@ -467,11 +467,13 @@ struct ggml_backend_metal_context {
// for now it is easier to work in a separate file
// static NSString * const msl_library_source = @"see metal.metal";
#if !GGML_METAL_EMBED_LIBRARY
// Here to assist with NSBundle Path Hack
@interface GGMLMetalClass : NSObject
@end
@implementation GGMLMetalClass
@end
#endif
static void * ggml_metal_host_malloc(size_t n) {
void * data = NULL;
@@ -520,7 +522,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
id<MTLLibrary> metal_library;
id<MTLLibrary> metal_library = nil;
// load library
//
@@ -529,19 +531,23 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
// - if not found, load the source and compile it
// - if that fails, return NULL
{
NSBundle * bundle = nil;
#ifdef SWIFT_PACKAGE
bundle = SWIFTPM_MODULE_BUNDLE;
#else
bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
#endif
NSError * error = nil;
NSString * src = nil;
#if GGML_METAL_EMBED_LIBRARY
const bool try_metallib = false;
GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
extern const char ggml_metallib_start[];
extern const char ggml_metallib_end[];
src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
#else
const bool try_metallib = true;
#ifdef SWIFT_PACKAGE
NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
#else
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
#endif
NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
@@ -574,7 +580,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
path_lib = default_metallib_path;
}
if (try_metallib && path_lib != nil) {
if (path_lib != nil) {
// pre-compiled library found
NSURL * libURL = [NSURL fileURLWithPath:path_lib];
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
@@ -585,14 +591,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
return NULL;
}
} else {
#if GGML_METAL_EMBED_LIBRARY
GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
extern const char ggml_metallib_start[];
extern const char ggml_metallib_end[];
NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
#else
GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
NSString * path_source;
@@ -613,13 +611,15 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
if (error) {
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
#endif // GGML_METAL_EMBED_LIBRARY
}
#endif
if (!metal_library) {
@autoreleasepool {
// dictionary of preprocessor macros
NSMutableDictionary * prep = [NSMutableDictionary dictionary];
@@ -647,10 +647,11 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
[options release];
#endif
}
#if GGML_METAL_EMBED_LIBRARY
[src release];
#endif // GGML_METAL_EMBED_LIBRARY
}
#if GGML_METAL_EMBED_LIBRARY
[src release];
#endif // GGML_METAL_EMBED_LIBRARY
}
// print MTL GPU family:
@@ -1944,34 +1945,38 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_sum_rows args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.ne03 =*/ ne03,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne10 =*/ ne10,
/*.ne11 =*/ ne11,
/*.ne12 =*/ ne12,
/*.ne13 =*/ ne13,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.nb12 =*/ nb12,
/*.nb13 =*/ nb13,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.ne3 =*/ ne3,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&ne03 length:sizeof(ne03) 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:&nb03 length:sizeof(nb03) atIndex:9];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
[encoder setBytes:&ne13 length:sizeof(ne13) 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:&ne0 length:sizeof(ne0) atIndex:18];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
@@ -2020,8 +2025,17 @@ static void ggml_metal_encode_node(
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
// TODO: add ggml_metal_kargs struct
// TODO: optimize (see https://github.com/ggml-org/llama.cpp/pull/10238/commits/7941b6b9ec29a2866fec6fa6c51612515ca509f6)
ggml_metal_kargs_soft_max args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.scale =*/ scale,
/*.max_bias =*/ max_bias,
/*.m0 =*/ m0,
/*.m1 =*/ m1,
/*.n_head_log2 =*/ n_head_log2,
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
if (id_src1) {
@@ -2030,14 +2044,7 @@ static void ggml_metal_encode_node(
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
}
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
[encoder setBytes:&scale length:sizeof(scale) atIndex:6];
[encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7];
[encoder setBytes:&m0 length:sizeof(m0) atIndex:8];
[encoder setBytes:&m1 length:sizeof(m1) atIndex:9];
[encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10];
[encoder setBytes:&args length:sizeof(args) atIndex:3];
[encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
@@ -2055,13 +2062,16 @@ static void ggml_metal_encode_node(
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline;
}
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_diag_mask_inf args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.n_past =*/ n_past,
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&n_past length:sizeof(int) atIndex:4];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
if (ne00%8 == 0) {
[encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
@@ -2080,27 +2090,30 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_ssm_conv args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.ne10 =*/ ne10,
/*.ne11 =*/ ne11,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
};
[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 setBytes:&args length:sizeof(args) atIndex:3];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne1, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
@@ -2151,7 +2164,31 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_ssm_scan args = {
/*.d_state =*/ d_state,
/*.d_inner =*/ d_inner,
/*.n_seq_tokens =*/ n_seq_tokens,
/*.n_seqs =*/ n_seqs,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.nb12 =*/ nb12,
/*.nb13 =*/ nb13,
/*.nb20 =*/ nb20,
/*.nb21 =*/ nb21,
/*.nb22 =*/ nb22,
/*.nb30 =*/ nb30,
/*.nb31 =*/ nb31,
/*.nb40 =*/ nb40,
/*.nb41 =*/ nb41,
/*.nb42 =*/ nb42,
/*.nb50 =*/ nb50,
/*.nb51 =*/ nb51,
/*.nb52 =*/ nb52,
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
@@ -2160,30 +2197,7 @@ static void ggml_metal_encode_node(
[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 setBytes:&args length:sizeof(args) atIndex:7];
[encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
@@ -3040,19 +3054,22 @@ static void ggml_metal_encode_node(
default: GGML_ABORT("not implemented");
}
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_get_rows args = {
/*.ne00 =*/ ne00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.ne10 =*/ ne10,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
};
[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( int64_t) atIndex:3];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5];
[encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6];
[encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7];
[encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10];
[encoder setBytes:&args length:sizeof(args) atIndex:3];
[encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
} break;
@@ -3109,18 +3126,21 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_group_norm args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.n_groups =*/ n_groups,
/*.eps =*/ eps,
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8];
[encoder setBytes:&eps length:sizeof( float) atIndex:9];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
[encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
@@ -3278,8 +3298,8 @@ static void ggml_metal_encode_node(
const int32_t CHW = IC * KH * KW;
const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4;
const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4;
const uint64_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4;
const uint64_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4;
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline;
@@ -3301,27 +3321,30 @@ static void ggml_metal_encode_node(
default: GGML_ABORT("fatal error");
};
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_im2col args = {
/*.ofs0 =*/ ofs0,
/*.ofs1 =*/ ofs1,
/*.IW =*/ IW,
/*.IH =*/ IH,
/*.CHW =*/ CHW,
/*.s0 =*/ s0,
/*.s1 =*/ s1,
/*.p0 =*/ p0,
/*.p1 =*/ p1,
/*.d0 =*/ d0,
/*.d1 =*/ d1,
/*.N =*/ N,
/*.KH =*/ KH,
/*.KW =*/ KW,
/*.KHW =*/ KH * KW,
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ofs0 length:sizeof(int32_t) atIndex:2];
[encoder setBytes:&ofs1 length:sizeof(int32_t) atIndex:3];
[encoder setBytes:&IW length:sizeof(int32_t) atIndex:4];
[encoder setBytes:&IH length:sizeof(int32_t) atIndex:5];
[encoder setBytes:&CHW length:sizeof(int32_t) atIndex:6];
[encoder setBytes:&s0 length:sizeof(int32_t) atIndex:7];
[encoder setBytes:&s1 length:sizeof(int32_t) atIndex:8];
[encoder setBytes:&p0 length:sizeof(int32_t) atIndex:9];
[encoder setBytes:&p1 length:sizeof(int32_t) atIndex:10];
[encoder setBytes:&d0 length:sizeof(int32_t) atIndex:11];
[encoder setBytes:&d1 length:sizeof(int32_t) atIndex:12];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
if (is_gt_mttpt) {
[encoder setBytes:&N length:sizeof(int32_t) atIndex:13];
[encoder setBytes:&KH length:sizeof(int32_t) atIndex:14];
[encoder setBytes:&KW length:sizeof(int32_t) atIndex:15];
const uint64_t n_threads = MIN(pipeline.maxTotalThreadsPerThreadgroup, (uint64_t)N);
const int64_t quotient = N / n_threads + (N % n_threads > 0 ? 1 : 0);
@@ -3361,16 +3384,20 @@ static void ggml_metal_encode_node(
default: GGML_ABORT("fatal error");
};
ggml_metal_kargs_conv_transpose_1d args = {
/*.IC =*/ IC,
/*.IL =*/ IL,
/*.K =*/ K,
/*.s0 =*/ s0,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
};
[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:&IC length:sizeof( int32_t) atIndex:3];
[encoder setBytes:&IL length:sizeof( int32_t) atIndex:4];
[encoder setBytes:&K length:sizeof( int32_t) atIndex:5];
[encoder setBytes:&s0 length:sizeof( int32_t) atIndex:6];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&args length:sizeof(args) atIndex:3];
[encoder dispatchThreadgroups:MTLSizeMake(OL, OC, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
@@ -3385,30 +3412,33 @@ static void ggml_metal_encode_node(
const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_upscale args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.ne03 =*/ ne03,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.ne3 =*/ ne3,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
/*.sf0 =*/ sf0,
/*.sf1 =*/ sf1,
/*.sf2 =*/ sf2,
/*.sf3 =*/ sf3
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&ne03 length:sizeof(ne03) 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:&nb03 length:sizeof(nb03) atIndex:9];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17];
[encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18];
[encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19];
[encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20];
[encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);
@@ -3420,26 +3450,29 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_pad args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.ne03 =*/ ne03,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.ne3 =*/ ne3,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&ne03 length:sizeof(ne03) 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:&nb03 length:sizeof(nb03) atIndex:9];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
const int nth = MIN(1024, ne0);
@@ -3454,24 +3487,31 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32].pipeline;
ggml_metal_kargs_pad_reflect_1d args = {
/*.ne00 =*/ ne00,
/*.ne01 =*/ ne01,
/*.ne02 =*/ ne02,
/*.ne03 =*/ ne03,
/*.nb00 =*/ nb00,
/*.nb01 =*/ nb01,
/*.nb02 =*/ nb02,
/*.nb03 =*/ nb03,
/*.ne0 =*/ ne0,
/*.ne1 =*/ ne1,
/*.ne2 =*/ ne2,
/*.ne3 =*/ ne3,
/*.nb0 =*/ nb0,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
/*.p0 =*/ p0,
/*.p1 =*/ p1
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:6];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:11];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:12];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:13];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:14];
[encoder setBytes:&p0 length:sizeof(p0) atIndex:15];
[encoder setBytes:&p1 length:sizeof(p1) atIndex:16];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
const int nth = MIN(1024, ne0);
@@ -3489,12 +3529,15 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_arange args = {
/*.ne0 =*/ ne0,
/*.start =*/ start,
/*.step =*/ step
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_dst offset:offs_dst atIndex:0];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1];
[encoder setBytes:&start length:sizeof(start) atIndex:2];
[encoder setBytes:&step length:sizeof(step) atIndex:3];
[encoder setBuffer:id_dst offset:offs_dst atIndex:0];
[encoder setBytes:&args length:sizeof(args) atIndex:1];
const int nth = MIN(1024, ne0);
@@ -3511,13 +3554,16 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_timestep_embedding args = {
/*.nb1 =*/ nb1,
/*.dim =*/ dim,
/*.max_period =*/ max_period
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2];
[encoder setBytes:&dim length:sizeof(dim) atIndex:3];
[encoder setBytes:&max_period length:sizeof(max_period) atIndex:4];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
const int nth = MIN(1024, half);
@@ -3550,12 +3596,15 @@ static void ggml_metal_encode_node(
default: GGML_ABORT("fatal error");
};
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_argsort args = {
/*.ncols =*/ ne00,
/*.ncols_pad =*/ ne00_padded
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne00_padded length:sizeof( int64_t) atIndex:3];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)];
@@ -3569,11 +3618,14 @@ static void ggml_metal_encode_node(
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_leaky_relu args = {
/*.slope =*/ slope
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&slope length:sizeof(slope) atIndex:2];
[encoder setBytes:&args length:sizeof(args) atIndex:2];
const int64_t n = ggml_nelements(dst);
@@ -4149,21 +4201,24 @@ static void ggml_metal_encode_node(
const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements);
const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads;
// TODO: add ggml_metal_kargs struct
ggml_metal_kargs_pool_2d args_pool_2d = {
/* .k0 = */ k0,
/* .k1 = */ k1,
/* .s0 = */ s0,
/* .s1 = */ s1,
/* .p0 = */ p0,
/* .p1 = */ p1,
/* .IH = */ IH,
/* .IW = */ IW,
/* .OH = */ OH,
/* .OW = */ OW,
/* .parallel_elements = */ parallel_elements
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&k0 length:sizeof(int32_t) atIndex:2];
[encoder setBytes:&k1 length:sizeof(int32_t) atIndex:3];
[encoder setBytes:&s0 length:sizeof(int32_t) atIndex:4];
[encoder setBytes:&s1 length:sizeof(int32_t) atIndex:5];
[encoder setBytes:&p0 length:sizeof(int32_t) atIndex:6];
[encoder setBytes:&p1 length:sizeof(int32_t) atIndex:7];
[encoder setBytes:&IH length:sizeof(int64_t) atIndex:8];
[encoder setBytes:&IW length:sizeof(int64_t) atIndex:9];
[encoder setBytes:&OH length:sizeof(int64_t) atIndex:10];
[encoder setBytes:&OW length:sizeof(int64_t) atIndex:11];
[encoder setBytes:&parallel_elements length:sizeof(int64_t) atIndex:12];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&args_pool_2d length:sizeof(args_pool_2d) atIndex:2];
[encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)];
} break;
File diff suppressed because it is too large Load Diff
+60 -43
View File
@@ -278,7 +278,7 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
cl_int err;
#ifdef GGML_PROFILE_OPENCL
#ifdef GGML_OPENCL_PROFILING
GGML_LOG_INFO("ggml_opencl: OpenCL profiling enabled\n");
#endif
@@ -524,7 +524,10 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
return backend_ctx;
}
CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &backend_ctx->alignment, NULL));
cl_uint base_align_in_bits;
CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &base_align_in_bits, NULL));
GGML_ASSERT(base_align_in_bits % 8u == 0);
backend_ctx->alignment = base_align_in_bits / 8u;
GGML_LOG_INFO("ggml_opencl: mem base addr align: %u\n", backend_ctx->alignment);
clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &backend_ctx->max_alloc_size, NULL);
@@ -1004,17 +1007,18 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
case GGML_OP_ADD:
case GGML_OP_SCALE:
case GGML_OP_MUL:
return true;
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_RELU:
return ggml_is_contiguous(op->src[0]);
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
default:
return false;
}
case GGML_OP_CLAMP:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_SOFT_MAX:
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
@@ -1198,17 +1202,14 @@ struct ggml_backend_opencl_buffer_context {
std::string name;
};
static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000;
static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
delete ctx;
}
static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
return cl_ptr_base;
GGML_UNUSED(buffer);
ggml_backend_opencl_context * backend_ctx = ggml_cl2_init(buffer->buft->device);
return (void *) (uintptr_t) backend_ctx->alignment;
}
static enum ggml_status ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
@@ -1241,7 +1242,7 @@ static enum ggml_status ggml_backend_opencl_buffer_init_tensor(ggml_backend_buff
tensor->extra = view_extra;
} else {
{
size_t offset = (char *)tensor->data - (char *)cl_ptr_base;
size_t offset = (char *) tensor->data - (char *) ggml_backend_opencl_buffer_get_base(buffer);
ggml_tensor_extra_cl * extra = ctx->ggml_opencl_alloc_temp_tensor_extra();
extra->offset = offset;
@@ -2573,26 +2574,33 @@ static void ggml_cl_norm(ggml_backend_t backend, const ggml_tensor * src0, const
memcpy(&eps, dst->op_params, sizeof(float));
const int ne00 = src0 ? src0->ne[0] : 0;
const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
const int ne01 = src0 ? src0->ne[1] : 0;
const int ne02 = src0 ? src0->ne[2] : 0;
const int ne03 = src0 ? src0->ne[3] : 0;
GGML_ASSERT(ggml_is_contiguous_1(src0));
const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
const int nth = MIN(64, ne00);
cl_kernel kernel = backend_ctx->kernel_norm;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(float), &eps));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(float)*nth, NULL));
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne03));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(float), &eps));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float)*nth, NULL));
const int64_t nrows = ggml_nrows(src0);
size_t global_work_size[] = {(size_t)nrows*nth, 1, 1};
size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
size_t local_work_size[] = {(size_t)nth, 1, 1};
#ifdef GGML_OPENCL_PROFILING
@@ -2630,16 +2638,19 @@ static void ggml_cl_rms_norm(ggml_backend_t backend, const ggml_tensor * src0, c
memcpy(&eps, dst->op_params, sizeof(float));
const int ne00 = src0 ? src0->ne[0] : 0;
const int ne01 = src0 ? src0->ne[1] : 0;
const int ne02 = src0 ? src0->ne[2] : 0;
const int ne03 = src0 ? src0->ne[3] : 0;
const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
GGML_ASSERT(ne00 % 4 == 0);
GGML_ASSERT(ggml_is_contiguous_1(src0));
const int nth = MIN(64, ne00);
const int64_t nrows = ggml_nrows(src0);
size_t global_work_size[] = {(size_t)nrows*nth, 1, 1};
size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
size_t local_work_size[] = {(size_t)nth, 1, 1};
cl_kernel kernel = backend_ctx->kernel_rms_norm;
@@ -2654,15 +2665,20 @@ static void ggml_cl_rms_norm(ggml_backend_t backend, const ggml_tensor * src0, c
sizeof(local_work_size), local_work_size,
sizeof(size_t), &sgs, NULL));
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(float), &eps));
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne02));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne03));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(float), &eps));
// This is local memory - the size depends on subgroup size.
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(float)*nth/sgs, NULL));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float)*nth/sgs, NULL));
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
@@ -3023,6 +3039,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
// enqueue kernel with profiling
// <--------------------------------------------> //
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
g_profiling_info.emplace_back();
@@ -3764,10 +3781,10 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
const int ne02 = src0 ? src0->ne[2] : 0;
const int ne03 = src0 ? src0->ne[3] : 0;
const int nb00 = src0 ? src0->nb[0] : 0;
const int nb01 = src0 ? src0->nb[1] : 0;
const int nb02 = src0 ? src0->nb[2] : 0;
const int nb03 = src0 ? src0->nb[3] : 0;
const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
const int ne10 = src1 ? src1->ne[0] : 0;
const int ne11 = src1 ? src1->ne[1] : 0; UNUSED(ne11);
@@ -3779,10 +3796,10 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
const int ne2 = dst ? dst->ne[2] : 0;
const int ne3 = dst ? dst->ne[3] : 0;
const int nb0 = dst ? dst->nb[0] : 0;
const int nb1 = dst ? dst->nb[1] : 0;
const int nb2 = dst ? dst->nb[2] : 0;
const int nb3 = dst ? dst->nb[3] : 0;
const cl_ulong nb0 = dst ? dst->nb[0] : 0;
const cl_ulong nb1 = dst ? dst->nb[1] : 0;
const cl_ulong nb2 = dst ? dst->nb[2] : 0;
const cl_ulong nb3 = dst ? dst->nb[3] : 0;
GGML_ASSERT(ne10 % ne02 == 0);
GGML_ASSERT(ne10 >= ne02);
+22 -4
View File
@@ -506,14 +506,23 @@ kernel void kernel_norm(
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne03,
ulong nb01,
ulong nb02,
ulong nb03,
float eps,
local float * sum
) {
src0 = (global void*)((global char*)src0 + offset0);
dst = (global void*)((global char*)dst + offsetd);
global float * x = (global float *) ((global char *) src0 + get_group_id(0)*nb01);
int i03 = get_group_id(2);
int i02 = get_group_id(1);
int i01 = get_group_id(0);
global float * x = (global float *) ((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01);
// MEAN
// parallel sum
@@ -533,7 +542,7 @@ kernel void kernel_norm(
// recenter and VARIANCE
barrier(CLK_LOCAL_MEM_FENCE);
global float * y = dst + get_group_id(0)*ne00;
global float * y = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
sum[get_local_id(0)] = 0.0f;
for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
y[i00] = x[i00] - mean;
@@ -566,14 +575,23 @@ kernel void kernel_rms_norm(
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne03,
ulong nb01,
ulong nb02,
ulong nb03,
float eps,
local float * sum // Note, the size depends on number of subgroups
) {
src0 = (global void*)((global char*)src0 + offset0);
dst = (global float*)((global char*)dst + offsetd);
global float4 * x = (global float4 *) ((global char *) src0 + get_group_id(0)*nb01);
int i03 = get_group_id(2);
int i02 = get_group_id(1);
int i01 = get_group_id(0);
global float4 * x = (global float4 *) ((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01);
global float * x_scalar = (global float *) x;
float4 sumf = 0;
float all_sum = 0;
@@ -607,7 +625,7 @@ kernel void kernel_rms_norm(
const float mean = sum[0];
const float scale = 1.0f/sqrt(mean + eps);
global float4 * y = (global float4 *) (dst + get_group_id(0)*ne00);
global float4 * y = (global float4 *) (dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
global float * y_scalar = (global float *) y;
for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
y[i00] = x[i00] * scale;
+10 -8
View File
@@ -41,6 +41,7 @@
#include "ggml-sycl/gemm.hpp"
#include "ggml-sycl/sycl_hw.hpp"
#include "ggml-sycl/getrows.hpp"
#include "ggml.h"
static bool g_sycl_loaded = false;
int g_ggml_sycl_debug = 0;
@@ -3864,7 +3865,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_TANH:
case GGML_UNARY_OP_EXP:
return ggml_is_contiguous(op->src[0]);
return ggml_is_contiguous(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32);
default:
return false;
}
@@ -3981,23 +3982,24 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_LOG:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
return true;
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_GROUP_NORM:
return ggml_is_contiguous(op->src[0]);
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_LOG:
return (op->src[0]->type == GGML_TYPE_F32);
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_GROUP_NORM:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_SCALE:
return true;
case GGML_OP_CONT:
return op->src[0]->type != GGML_TYPE_BF16;
+1
View File
@@ -2332,6 +2332,7 @@ struct ggml_tensor * ggml_concat(
struct ggml_tensor * b,
int dim) {
GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS);
GGML_ASSERT(a->type == b->type);
int64_t ne[GGML_MAX_DIMS];
for (int d = 0; d < GGML_MAX_DIMS; ++d) {
+1 -1
View File
@@ -1 +1 @@
58ecf6b96d887e408b6869915863fa1126483d51
c7dfe3d174f98b14801f9ed12f129179d3e7b638