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llama.cpp
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Merge branch 'master' into compilade/mamba2

This commit is contained in:
Francis Couture-Harpin 2025-06-23 10:40:16 -04:00
parent 830e5542c2 bf2a99e3cb
commit afdb669206
99 changed files with 5168 additions and 4226 deletions
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4
.github/workflows/build.yml vendored
View File

@ -683,7 +683,7 @@ jobs:
env:
OPENBLAS_VERSION: 0.3.23
SDE_VERSION: 9.33.0-2024-01-07
VULKAN_VERSION: 1.4.309.0
VULKAN_VERSION: 1.4.313.2
strategy:
matrix:
@ -736,7 +736,7 @@ jobs:
id: get_vulkan
if: ${{ matrix.build == 'kompute-x64' || matrix.build == 'vulkan-x64' }}
run: |
curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe"
curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe"
& "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"

4
.github/workflows/release.yml vendored
View File

@ -302,7 +302,7 @@ jobs:
env:
OPENBLAS_VERSION: 0.3.23
VULKAN_VERSION: 1.4.309.0
VULKAN_VERSION: 1.4.313.2
strategy:
matrix:
@ -332,7 +332,7 @@ jobs:
id: get_vulkan
if: ${{ matrix.backend == 'vulkan' }}
run: |
curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe"
curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe"
& "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"

2
ci/run.sh
View File

@ -779,7 +779,7 @@ function gg_run_rerank_tiny {
model_f16="${path_models}/ggml-model-f16.gguf"
# for this model, the SEP token is "</s>"
(time ./bin/llama-embedding --model ${model_f16} -p "what is panda?</s></s>hi\nwhat is panda?</s></s>it's a bear\nwhat is panda?</s></s>The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
(time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
# sample output
# rerank score 0: 0.029

33
common/arg.cpp
View File

@ -2706,6 +2706,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.embd_sep = value;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(common_arg(
{"--cls-separator"}, "STRING",
"separator of classification sequences (default \\t) for example \"<#seq#>\"",
[](common_params & params, const std::string & value) {
params.cls_sep = value;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
add_opt(common_arg(
{"--host"}, "HOST",
string_format("ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: %s)", params.hostname.c_str()),
@ -3210,6 +3217,32 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.speculative.model.path = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
add_opt(common_arg(
{"-ctkd", "--cache-type-k-draft"}, "TYPE",
string_format(
"KV cache data type for K for the draft model\n"
"allowed values: %s\n"
"(default: %s)",
get_all_kv_cache_types().c_str(),
ggml_type_name(params.speculative.cache_type_k)
),
[](common_params & params, const std::string & value) {
params.speculative.cache_type_k = kv_cache_type_from_str(value);
}
).set_env("LLAMA_ARG_CACHE_TYPE_K_DRAFT"));
add_opt(common_arg(
{"-ctvd", "--cache-type-v-draft"}, "TYPE",
string_format(
"KV cache data type for V for the draft model\n"
"allowed values: %s\n"
"(default: %s)",
get_all_kv_cache_types().c_str(),
ggml_type_name(params.speculative.cache_type_v)
),
[](common_params & params, const std::string & value) {
params.speculative.cache_type_v = kv_cache_type_from_str(value);
}
).set_env("LLAMA_ARG_CACHE_TYPE_V_DRAFT"));
add_opt(common_arg(
{"-mv", "--model-vocoder"}, "FNAME",

9
common/common.cpp
View File

@ -706,11 +706,17 @@ bool fs_validate_filename(const std::string & filename) {
// disable C++17 deprecation warning for std::codecvt_utf8
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
#elif defined(__GNUC__)
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
#if defined(__clang__)
# pragma clang diagnostic pop
#elif defined(__GNUC__)
# pragma GCC diagnostic pop
#endif
filename_utf32 = converter.from_bytes(filename);
@ -1284,6 +1290,9 @@ std::vector<llama_token> common_tokenize(
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens == std::numeric_limits<int32_t>::min()) {
throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit");
}
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);

4
common/common.h
View File

@ -199,6 +199,9 @@ struct common_params_speculative {
float p_split = 0.1f; // speculative decoding split probability
float p_min = 0.75f; // minimum speculative decoding probability (greedy)
ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
struct cpu_params cpuparams;
struct cpu_params cpuparams_batch;
@ -355,6 +358,7 @@ struct common_params {
int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
std::string embd_sep = "\n"; // separator of embeddings
std::string cls_sep = "\t"; // separator of classification sequences
// server params
int32_t port = 8080; // server listens on this network port

49
common/json-schema-to-grammar.cpp
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@ -41,49 +41,6 @@ static std::string build_repetition(const std::string & item_rule, int min_items
return result;
}
/* Minimalistic replacement for std::string_view, which is only available from C++17 onwards */
class string_view {
const std::string & _str;
const size_t _start;
const size_t _end;
public:
string_view(const std::string & str, size_t start = 0, size_t end = std::string::npos) : _str(str), _start(start), _end(end == std::string::npos ? str.length() : end) {}
size_t size() const {
return _end - _start;
}
size_t length() const {
return size();
}
operator std::string() const {
return str();
}
std::string str() const {
return _str.substr(_start, _end - _start);
}
string_view substr(size_t pos, size_t len = std::string::npos) const {
return string_view(_str, _start + pos, len == std::string::npos ? _end : _start + pos + len);
}
char operator[](size_t pos) const {
auto index = _start + pos;
if (index >= _end) {
throw std::out_of_range("string_view index out of range");
}
return _str[_start + pos];
}
bool operator==(const string_view & other) const {
std::string this_str = *this;
std::string other_str = other;
return this_str == other_str;
}
};
static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) {
auto has_min = min_value != std::numeric_limits<int>::min();
auto has_max = max_value != std::numeric_limits<int>::max();
@ -112,14 +69,14 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream &
}
out << "}";
};
std::function<void(const string_view &, const string_view &)> uniform_range =
[&](const string_view & from, const string_view & to) {
std::function<void(const std::string_view &, const std::string_view &)> uniform_range =
[&](const std::string_view & from, const std::string_view & to) {
size_t i = 0;
while (i < from.length() && i < to.length() && from[i] == to[i]) {
i++;
}
if (i > 0) {
out << "\"" << from.substr(0, i).str() << "\"";
out << "\"" << from.substr(0, i) << "\"";
}
if (i < from.length() && i < to.length()) {
if (i > 0) {

36
convert_hf_to_gguf.py
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@ -2145,7 +2145,6 @@ class Llama4Model(LlamaModel):
def set_vocab(self):
self._set_vocab_gpt2()
self.gguf_writer.add_add_bos_token(True)
def set_gguf_parameters(self):
super().set_gguf_parameters()
@ -2194,7 +2193,7 @@ class Llama4VisionModel(MmprojModel):
name += ".weight"
if "multi_modal_projector.linear_1" in name:
# despite the name with number postfix, this is a single fully connected layer
return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC], data_torch)]
return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch)]
return [(self.map_tensor_name(name), data_torch)]
return []
@ -3918,9 +3917,6 @@ class BertModel(TextModel):
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
self.gguf_writer.add_add_bos_token(True)
self.gguf_writer.add_add_eos_token(True)
@ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification")
class DistilBertModel(BertModel):
@ -3962,8 +3958,6 @@ class RobertaModel(BertModel):
bpe_tok_path = self.dir_model / "tokenizer.json"
if bpe_tok_path.exists():
self._set_vocab_gpt2()
self.gguf_writer.add_add_bos_token(True)
self.gguf_writer.add_add_eos_token(True)
# we need this to validate the size of the token_type embeddings
# though currently we are passing all zeros to the token_type embeddings
@ -4950,8 +4944,6 @@ class JinaBertV2Model(BertModel):
self.gguf_writer.add_token_type_count(2)
else:
raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')
self.gguf_writer.add_add_bos_token(True)
self.gguf_writer.add_add_eos_token(True)
@ModelBase.register("OpenELMForCausalLM")
@ -5553,9 +5545,6 @@ class T5Model(TextModel):
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
self.gguf_writer.add_add_bos_token(False)
self.gguf_writer.add_add_eos_token(True)
def set_gguf_parameters(self):
if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
logger.warning("Couldn't find context length in config.json, assuming default value of 512")
@ -5693,9 +5682,6 @@ class T5EncoderModel(TextModel):
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
self.gguf_writer.add_add_bos_token(False)
self.gguf_writer.add_add_eos_token(True)
def set_gguf_parameters(self):
if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
logger.warning("Couldn't find context length in config.json, assuming default value of 512")
@ -6491,8 +6477,8 @@ def parse_args() -> argparse.Namespace:
help="model is executed on big endian machine",
)
parser.add_argument(
"model", type=Path,
help="directory containing model file",
"model", type=str,
help="directory containing model file or huggingface repository ID (if --remote)",
nargs="?",
)
parser.add_argument(
@ -6603,18 +6589,20 @@ def main() -> None:
else:
logging.basicConfig(level=logging.INFO)
dir_model = args.model
if args.remote:
hf_repo_id = args.model
from huggingface_hub import snapshot_download
local_dir = snapshot_download(
repo_id=str(dir_model),
repo_id=hf_repo_id,
allow_patterns=["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"])
dir_model = Path(local_dir)
logger.info(f"Downloaded config and tokenizer to {local_dir}")
else:
hf_repo_id = None
dir_model = Path(args.model)
if not dir_model.is_dir():
logger.error(f'Error: {args.model} is not a directory')
logger.error(f'Error: {dir_model} is not a directory')
sys.exit(1)
ftype_map: dict[str, gguf.LlamaFileType] = {
@ -6634,9 +6622,9 @@ def main() -> None:
if args.outfile is not None:
fname_out = args.outfile
elif args.remote:
elif hf_repo_id:
# if remote, use the model ID as the output file name
fname_out = Path("./" + str(args.model).replace("/", "-") + "-{ftype}.gguf")
fname_out = Path("./" + hf_repo_id.replace("/", "-") + "-{ftype}.gguf")
else:
fname_out = dir_model
@ -6665,7 +6653,7 @@ def main() -> None:
split_max_tensors=args.split_max_tensors,
split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
small_first_shard=args.no_tensor_first_split,
remote_hf_model_id=str(args.model) if args.remote else None)
remote_hf_model_id=hf_repo_id)
if args.vocab_only:
logger.info("Exporting model vocab...")

2
docs/build.md
View File

@ -1,6 +1,6 @@
# Build llama.cpp locally
The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h).
The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](../include/llama.h).
The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server.

34
examples/embedding/embedding.cpp
View File

@ -133,10 +133,36 @@ int main(int argc, char ** argv) {
// max batch size
const uint64_t n_batch = params.n_batch;
// get added sep and eos token, if any
const std::string added_sep_token = llama_vocab_get_add_sep(vocab) ? llama_vocab_get_text(vocab, llama_vocab_sep(vocab)) : "";
const std::string added_eos_token = llama_vocab_get_add_eos(vocab) ? llama_vocab_get_text(vocab, llama_vocab_eos(vocab)) : "";
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
for (const auto & prompt : prompts) {
auto inp = common_tokenize(ctx, prompt, true, true);
std::vector<llama_token> inp;
// split classification pairs and insert expected separator tokens
if (pooling_type == LLAMA_POOLING_TYPE_RANK && prompt.find(params.cls_sep) != std::string::npos) {
std::vector<std::string> pairs = split_lines(prompt, params.cls_sep);
std::string final_prompt;
for (size_t i = 0; i < pairs.size(); i++) {
final_prompt += pairs[i];
if (i != pairs.size() - 1) {
if (!added_eos_token.empty()) {
final_prompt += added_eos_token;
}
if (!added_sep_token.empty()) {
final_prompt += added_sep_token;
}
}
}
inp = common_tokenize(ctx, final_prompt, true, true);
} else {
inp = common_tokenize(ctx, prompt, true, true);
}
if (inp.size() > n_batch) {
LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
@ -145,11 +171,11 @@ int main(int argc, char ** argv) {
inputs.push_back(inp);
}
// check if the last token is SEP
// check if the last token is SEP/EOS
// it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
for (auto & inp : inputs) {
if (inp.empty() || inp.back() != llama_vocab_sep(vocab)) {
LOG_WRN("%s: last token in the prompt is not SEP\n", __func__);
if (inp.empty() || (inp.back() != llama_vocab_sep(vocab) && inp.back() != llama_vocab_eos(vocab))) {
LOG_WRN("%s: last token in the prompt is not SEP or EOS\n", __func__);
LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
}
}

2
examples/simple-chat/simple-chat.cpp
View File

@ -98,7 +98,7 @@ int main(int argc, char ** argv) {
auto generate = [&](const std::string & prompt) {
std::string response;
const bool is_first = llama_memory_seq_pos_max(llama_get_memory(ctx), 0) == 0;
const bool is_first = llama_memory_seq_pos_max(llama_get_memory(ctx), 0) == -1;
// tokenize the prompt
const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);

12
ggml/include/ggml.h
View File

@ -489,6 +489,7 @@ extern "C" {
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_PAD_REFLECT_1D,
GGML_OP_ROLL,
GGML_OP_ARANGE,
GGML_OP_TIMESTEP_EMBEDDING,
GGML_OP_ARGSORT,
@ -1801,6 +1802,17 @@ extern "C" {
int p0,
int p1);
// Move tensor elements by an offset given for each dimension. Elements that
// are shifted beyond the last position are wrapped around to the beginning.
GGML_API struct ggml_tensor * ggml_roll(
struct ggml_context * ctx,
struct ggml_tensor * a,
int shift0,
int shift1,
int shift2,
int shift3);
// Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
// timesteps: [N,]
// return: [N, dim]

17
ggml/src/CMakeLists.txt
View File

@ -286,6 +286,10 @@ function(ggml_add_cpu_backend_variant tag_name)
foreach (feat ${ARGN})
set(GGML_INTERNAL_${feat} ON)
endforeach()
elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC")
foreach (feat ${ARGN})
set(GGML_INTERNAL_${feat} ON)
endforeach()
endif()
ggml_add_cpu_backend_variant_impl(${tag_name})
@ -337,6 +341,19 @@ if (GGML_CPU_ALL_VARIANTS)
else()
message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}")
endif()
elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC")
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
ggml_add_cpu_backend_variant(power0)
ggml_add_cpu_backend_variant(power7_1 POWER7)
ggml_add_cpu_backend_variant(power7_2 POWER7 VSX)
ggml_add_cpu_backend_variant(power8_1 POWER8)
ggml_add_cpu_backend_variant(power8_2 POWER8 VSX)
ggml_add_cpu_backend_variant(power9 POWER9 VSX)
ggml_add_cpu_backend_variant(power10 POWER10 VSX)
ggml_add_cpu_backend_variant(power11 POWER11 VSX)
else()
message(FATAL_ERROR "Unsupported PowerPC target OS: ${CMAKE_SYSTEM_NAME}")
endif()
else()
message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS not yet supported with ${GGML_SYSTEM_ARCH} on ${CMAKE_SYSTEM_NAME}")
endif()

5
ggml/src/ggml-backend-reg.cpp
View File

@ -69,6 +69,9 @@
#if defined(__clang__)
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
#elif defined(__GNUC__)
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
namespace fs = std::filesystem;
@ -91,6 +94,8 @@ static std::string path_str(const fs::path & path) {
#if defined(__clang__)
# pragma clang diagnostic pop
#elif defined(__GNUC__)
# pragma GCC diagnostic pop
#endif
#ifdef _WIN32

25
ggml/src/ggml-cpu/CMakeLists.txt
View File

@ -388,6 +388,27 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
else()
list(APPEND ARCH_FLAGS -mcpu=native -mtune=native -mpowerpc64)
endif()
elseif(GGML_CPU_ALL_VARIANTS)
# Begin with the lowest baseline
set(ARCH_DEFINITIONS "")
# When a feature is selected, bump the MCPU to the first
# version that supported it
foreach(PVER RANGE 7 11)
if(DEFINED GGML_INTERNAL_POWER${PVER})
set(POWERPC_MCPU "power${PVER}")
list(APPEND ARCH_DEFINITIONS GGML_USE_POWER${PVER})
endif()
endforeach()
if (GGML_INTERNAL_VSX)
list(APPEND ARCH_DEFINITIONS GGML_USE_VSX)
list(APPEND ARCH_FLAGS -mvsx)
endif()
if (DEFINED POWERPC_MCPU)
list(APPEND ARCH_FLAGS -mcpu=${POWERPC_MCPU})
endif()
ggml_add_cpu_backend_features(${GGML_CPU_NAME} powerpc ${ARCH_DEFINITIONS})
else()
if (GGML_CPU_POWERPC_CPUTYPE)
list(APPEND ARCH_FLAGS -mcpu=${GGML_CPU_POWERPC_CPUTYPE})
@ -465,9 +486,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
# Fetch KleidiAI sources:
include(FetchContent)
set(KLEIDIAI_COMMIT_TAG "v1.6.0")
set(KLEIDIAI_COMMIT_TAG "v1.9.0")
set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
set(KLEIDIAI_ARCHIVE_MD5 "75b4ad68f25ab673dcc01065e5a0b05f")
set(KLEIDIAI_ARCHIVE_MD5 "2a8e1bb55d201557553545536489a017")
if (POLICY CMP0135)
cmake_policy(SET CMP0135 NEW)

18
ggml/src/ggml-cpu/arch/arm/repack.cpp
View File

@ -256,7 +256,6 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
UNUSED(blocklen);
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx;
for (int c = 0; c < nc; c += ncols_interleaved) {
@ -294,7 +293,6 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
s += ncols_interleaved;
}
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
float sumf[4];
int sumi;
@ -341,7 +339,6 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
UNUSED(blocklen);
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx;
for (int c = 0; c < nc; c += ncols_interleaved) {
@ -384,7 +381,6 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
s += ncols_interleaved;
}
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
float sumf[4];
int sumi;
@ -432,7 +428,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
#if defined(__ARM_FEATURE_SVE)
if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) {
if (ggml_cpu_get_sve_cnt() == QK8_0) {
const void * b_ptr = vx;
const void * a_ptr = vy;
float * res_ptr = s;
@ -547,7 +543,6 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
UNUSED(blocklen);
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl);
const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
float * res_ptr = s;
@ -594,7 +589,6 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
vst1q_f32(res_ptr + x * 4, sumf);
}
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
{
float sumf[4];
@ -643,8 +637,7 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
UNUSED(ncols_interleaved);
UNUSED(blocklen);
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
const void * b_ptr = vx;
const void * a_ptr = vy;
float * res_ptr = s;
@ -1101,7 +1094,6 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
: "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28"
);
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
{
float sumf[4][4];
@ -1160,7 +1152,6 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
UNUSED(blocklen);
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
const void * b_ptr = vx;
const void * a_ptr = vy;
float * res_ptr = s;
@ -1557,7 +1548,6 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
: "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28"
);
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
float sumf[4][4];
int sumi;
@ -1615,7 +1605,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) {
if (ggml_cpu_get_sve_cnt() == QK8_0) {
const void * b_ptr = vx;
const void * a_ptr = vy;
float * res_ptr = s;
@ -2083,7 +2073,6 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
UNUSED(blocklen);
#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl);
for (int y = 0; y < nr / 4; y++) {
@ -2135,7 +2124,6 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
}
}
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
{
float sumf[4][4];

82
ggml/src/ggml-cpu/arch/powerpc/cpu-feats.cpp Normal file
View File

@ -0,0 +1,82 @@
# include "ggml-backend-impl.h"
#if defined(__powerpc64__) || defined(__ppc64__) || defined(__PPC64__)
#if defined(__linux__)
#include <sys/auxv.h>
#endif
#include <string>
struct powerpc_features {
std::string platform = "";
int power_version = -1;
bool has_vsx = false;
powerpc_features() {
#if defined(__linux__)
unsigned long auxval = getauxval(AT_PLATFORM);
if (auxval) {
platform = std::string(reinterpret_cast<const char*>(auxval));
// TBD: Do systems exist that return this in uppercase?
if (platform.substr(0, 5) == "power") {
// Extractt a numeric suffix, if one exists
int vpos = -1;
for (int i = platform.length() - 1; i >= 0; i--) {
if (std::isdigit(platform[i])) {
vpos = i;
} else {
break;
}
}
if (vpos > -1) {
power_version = std::stoi(platform.substr(vpos));
}
}
}
#endif
if (power_version >= 9) {
has_vsx = true;
}
}
};
static int ggml_backend_cpu_powerpc_score() {
int score = 1;
powerpc_features pf;
// Platform scores
#if defined(GGML_USE_POWER7)
if (pf.power_version < 7) { return 0; }
score += 1<<1;
#endif
#if defined(GGML_USE_POWER8)
if (pf.power_version < 8) { return 0; }
score += 1<<2;
#endif
#if defined(GGML_USE_POWER9)
if (pf.power_version < 9) { return 0; }
score += 1<<3;
#endif
#if defined(GGML_USE_POWER10)
if (pf.power_version < 10) { return 0; }
score += 1<<4;
#endif
#if defined(GGML_USE_POWER11)
if (pf.power_version < 11) { return 0; }
score += 1<<5;
#endif
// Feature scores
#if defined(GGML_USE_VSX)
if (!pf.has_vsx) { return 0; }
score += 1<<6;
#endif
return score;
}
GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_powerpc_score)
#endif // defined(__powerpc64__) || defined(__ppc64__) || defined(__PPC64__)

100
ggml/src/ggml-cpu/ggml-cpu.c
View File

@ -74,13 +74,8 @@
#if defined(__ARM_ARCH)
struct ggml_arm_arch_features_type {
int has_neon;
int has_dotprod;
int has_i8mm;
int has_sve;
int sve_cnt;
int has_sme;
} ggml_arm_arch_features = {-1, -1, -1, -1, 0, -1};
} ggml_arm_arch_features = { 0 };
#endif
@ -678,87 +673,15 @@ bool ggml_is_numa(void) {
#if defined(__linux__) && defined(__aarch64__)
#include <sys/auxv.h>
#elif defined(__APPLE__)
#include <sys/sysctl.h>
#endif
#if !defined(HWCAP2_I8MM)
#define HWCAP2_I8MM (1 << 13)
#endif
#if !defined(HWCAP2_SME)
#define HWCAP2_SME (1 << 23)
#endif
static void ggml_init_arm_arch_features(void) {
#if defined(__linux__) && defined(__aarch64__)
uint32_t hwcap = getauxval(AT_HWCAP);
uint32_t hwcap2 = getauxval(AT_HWCAP2);
ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD);
ggml_arm_arch_features.has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM);
ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE);
ggml_arm_arch_features.has_sme = !!(hwcap2 & HWCAP2_SME);
#if defined(__ARM_FEATURE_SVE)
#if defined(__linux__) && defined(__aarch64__) && defined(__ARM_FEATURE_SVE)
ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL);
#endif
#elif defined(__APPLE__)
int oldp = 0;
size_t size = sizeof(oldp);
if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_neon = oldp;
if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_dotprod = oldp;
if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_i8mm = oldp;
if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_sme = oldp;
ggml_arm_arch_features.has_sve = 0;
ggml_arm_arch_features.sve_cnt = 0;
#else
// Run-time CPU feature detection not implemented for this platform, fallback to compile time
#if defined(__ARM_NEON)
ggml_arm_arch_features.has_neon = 1;
#else
ggml_arm_arch_features.has_neon = 0;
#endif
#if defined(__ARM_FEATURE_MATMUL_INT8)
ggml_arm_arch_features.has_i8mm = 1;
#else
ggml_arm_arch_features.has_i8mm = 0;
#endif
#if defined(__ARM_FEATURE_SVE)
ggml_arm_arch_features.has_sve = 1;
ggml_arm_arch_features.sve_cnt = 16;
#else
ggml_arm_arch_features.has_sve = 0;
ggml_arm_arch_features.sve_cnt = 0;
#endif
#if defined(__ARM_FEATURE_SME) || defined(__ARM_FEATURE_SME2)
ggml_arm_arch_features.has_sme = 1;
#else
ggml_arm_arch_features.has_sme = 0;
#endif
#endif
}
#endif
#endif // __ARM_ARCH
struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
GGML_ASSERT(!ggml_get_no_alloc(ctx));
@ -1967,6 +1890,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad_reflect_1d(params, tensor);
} break;
case GGML_OP_ROLL:
{
ggml_compute_forward_roll(params, tensor);
} break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
@ -2291,6 +2218,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
case GGML_OP_ROLL:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
@ -3443,7 +3371,7 @@ int ggml_cpu_has_vxe(void) {
int ggml_cpu_has_neon(void) {
#if defined(__ARM_ARCH) && defined(__ARM_NEON)
return ggml_arm_arch_features.has_neon;
return 1;
#else
return 0;
#endif
@ -3451,7 +3379,7 @@ int ggml_cpu_has_neon(void) {
int ggml_cpu_has_dotprod(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_DOTPROD)
return ggml_arm_arch_features.has_dotprod;
return 1;
#else
return 0;
#endif
@ -3459,7 +3387,7 @@ int ggml_cpu_has_dotprod(void) {
int ggml_cpu_has_sve(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE)
return ggml_arm_arch_features.has_sve;
return 1;
#else
return 0;
#endif
@ -3467,7 +3395,7 @@ int ggml_cpu_has_sve(void) {
int ggml_cpu_has_matmul_int8(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_MATMUL_INT8)
return ggml_arm_arch_features.has_i8mm;
return 1;
#else
return 0;
#endif
@ -3483,7 +3411,7 @@ int ggml_cpu_get_sve_cnt(void) {
int ggml_cpu_has_sme(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SME)
return ggml_arm_arch_features.has_sme;
return 1;
#else
return 0;
#endif

55
ggml/src/ggml-cpu/llamafile/sgemm.cpp
View File

@ -62,7 +62,7 @@
#define NOINLINE __attribute__((__noinline__))
#endif
#if defined(__ARM_NEON) || defined(__AVX512F__)
#if defined(__ARM_NEON) || defined(__AVX512F__) || defined(__VXE__) || defined(__VXE2__)
#define VECTOR_REGISTERS 32
#else
#define VECTOR_REGISTERS 16
@ -109,6 +109,12 @@ inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); }
inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); }
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#if defined(__VXE__) || defined(__VXE2__)
inline float32x4_t add(float32x4_t x, float32x4_t y) { return vec_add(x, y); }
inline float32x4_t sub(float32x4_t x, float32x4_t y) { return vec_sub(x, y); }
inline float32x4_t mul(float32x4_t x, float32x4_t y) { return vec_mul(x, y); }
#endif
#if defined(__MMA__)
typedef vector unsigned char vec_t;
typedef __vector_quad acc_t;
@ -162,6 +168,13 @@ inline float16x8_t madd(float16x8_t a, float16x8_t b, float16x8_t c) {
#endif
#endif
#if defined(__VXE__) || defined(__VXE2__)
template <>
inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) {
return vec_madd(a, b, c);
}
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
// VECTORIZED HORIZONTAL SUM
@ -178,6 +191,13 @@ inline float hsum(float16x8_t x) {
}
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#if defined(__VXE__) || defined(__VXE2__)
inline float hsum(float32x4_t x) {
float32x4_t tmp = x + vec_reve(x);
return tmp[0] + tmp[1];
}
#endif
#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
inline float hsum(__m128 x) {
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
@ -227,6 +247,21 @@ template <> inline float32x4_t load(const ggml_fp16_t *p) {
#endif // _MSC_VER
#endif // __ARM_NEON
#if defined(__VXE__) || defined(__VXE2__)
template <> inline float32x4_t load(const ggml_fp16_t * p) {
float tmp[4];
for (int i = 0; i < 4; i++) {
tmp[i] = GGML_FP16_TO_FP32(p[i]);
}
return vec_xl(0, (const float *)(tmp));
}
template <> inline float32x4_t load(const float * p) {
return vec_xl(0, p);
}
#endif
#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
template <> inline __m128 load(const float *p) {
return _mm_loadu_ps(p);
@ -3319,6 +3354,14 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64
(const float *)B, ldb,
(float *)C, ldc};
return tb.matmul(m, n);
#elif defined(__VXE__) || defined(__VXE2__)
if (n < 4)
return false;
tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ params,
k, (const float *)A, lda,
(const float *)B, ldb,
(float *)C, ldc};
return tb.matmul(m, n);
#elif defined(__MMA__)
if (k % 8)
return false;
@ -3410,6 +3453,16 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64
(float *)C, ldc};
return tb.matmul(m, n);
}
#elif defined(__VXE__) || defined(__VXE2__)
if (n < 4)
return false;
if (Btype == GGML_TYPE_F16) {
tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, ggml_fp16_t, float> tb{ params,
k, (const ggml_fp16_t *)A, lda,
(const ggml_fp16_t *)B, ldb,
(float *)C, ldc};
return tb.matmul(m, n);
}
#endif
return false;
}

5
ggml/src/ggml-cpu/llamafile/sgemm.h
View File

@ -1,6 +1,11 @@
#pragma once
#include <stdint.h>
#include <stdbool.h>
#if defined(__VXE__) || defined(__VXE2__)
#include <vecintrin.h>
#endif
#ifdef __cplusplus
extern "C" {
#endif

67
ggml/src/ggml-cpu/ops.cpp
View File

@ -6793,6 +6793,73 @@ void ggml_compute_forward_pad_reflect_1d(
}
}
// ggml_compute_forward_roll
static int64_t ggml_wrap_index(int64_t i, int64_t ne) {
if (i < 0) {
return i + ne;
} else if (i >= ne) {
return i - ne;
}
return i;
}
static void ggml_compute_forward_roll_f32(
const ggml_compute_params * params,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src_data = (const float *) src0->data;
float * dst_data = (float *) dst->data;
GGML_TENSOR_UNARY_OP_LOCALS
const int s0 = ggml_get_op_params_i32(dst, 0);
const int s1 = ggml_get_op_params_i32(dst, 1);
const int s2 = ggml_get_op_params_i32(dst, 2);
const int s3 = ggml_get_op_params_i32(dst, 3);
const int64_t total = ne1 * ne2 * ne3;
const int64_t per_thread = (total + params->nth) / params->nth;
const int64_t start = params->ith * per_thread;
const int64_t end = std::min(start + per_thread, total);
for (int64_t i = start; i < end; ++i) {
const int64_t i1 = i % ne1;
const int64_t i2 = (i / ne1) % ne2;
const int64_t i3 = i / (ne2 * ne1);
float * dst_row = dst_data + (i3*nb3 + i2*nb2 + i1*nb1) / sizeof(float);
const int64_t i01 = ggml_wrap_index(i1 - s1, ne01);
const int64_t i02 = ggml_wrap_index(i2 - s2, ne02);
const int64_t i03 = ggml_wrap_index(i3 - s3, ne03);
const float * src_row = src_data + (i03*nb03 + i02*nb02 + i01*nb01) / sizeof(float);
const int64_t s = ggml_wrap_index(-s0, ne00);
const int64_t n = ne00 - s;
ggml_vec_cpy_f32(n, dst_row, src_row + s);
ggml_vec_cpy_f32(s, dst_row + n, src_row);
}
}
void ggml_compute_forward_roll(
const ggml_compute_params * params,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_roll_f32(params, dst);
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
// ggml_compute_forward_arange
static void ggml_compute_forward_arange_f32(

1
ggml/src/ggml-cpu/ops.h
View File

@ -72,6 +72,7 @@ void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params
void ggml_compute_forward_upscale(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_pad(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_pad_reflect_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_roll(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_arange(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);

55
ggml/src/ggml-cpu/repack.cpp
View File

@ -1163,13 +1163,24 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
// not realy a GGML_TYPE_Q8_0 but same size.
switch (op->op) {
case GGML_OP_MUL_MAT:
{
size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
return true;
}
case GGML_OP_MUL_MAT_ID:
{
size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
size = GGML_PAD(size, sizeof(int64_t)); // + padding for next bloc.
size += sizeof(int64_t) * (1+op->src[0]->ne[2]) * op->src[1]->ne[2];
const int64_t ne02 = op->src[0]->ne[2]; // n_as, n_expert
const int64_t ne12 = op->src[1]->ne[2]; // n_tokens
const size_t sizeof_mmid_row_mapping = sizeof(int64_t);
size += sizeof_mmid_row_mapping*ne02*(ne12 + 1);
return true;
}
default:
// GGML_ABORT("fatal error");
break;
@ -1305,13 +1316,16 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
int32_t i2;
};
GGML_ASSERT(params->wsize >= (GGML_PAD(nbw3, sizeof(int64_t)) + n_as * sizeof(int64_t) +
n_as * ne12 * sizeof(mmid_row_mapping)));
GGML_ASSERT(params->wsize >=
(GGML_PAD(nbw3, sizeof(int64_t)) +
n_as*(ne12 + 1)*sizeof(mmid_row_mapping))
);
auto * wdata = (char *) params->wdata;
auto * wdata_src1_end = (char *) wdata + GGML_PAD(nbw3, sizeof(int64_t));
auto * wdata = (char *)params->wdata;
auto * wdata_src1_end = (char *)wdata + GGML_PAD(nbw3, sizeof(int64_t));
// total of [n_as][ne12 + 1] elemets of type mmid_row_mapping (2*int32_t = int64_t)
auto * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) (matrix_row_counts + n_as); // [n_as][ne12]
// src1: float32 => param type
@ -1397,44 +1411,45 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
}
};
// instance for Q4
static const tensor_traits<block_q4_0, 4, 4, GGML_TYPE_Q8_0> q4_0_4x4_q8_0;
static const tensor_traits<block_q4_0, 8, 4, GGML_TYPE_Q8_0> q4_0_4x8_q8_0;
static const tensor_traits<block_q4_0, 8, 8, GGML_TYPE_Q8_0> q4_0_8x8_q8_0;
static const tensor_traits<block_q4_K, 8, 8, GGML_TYPE_Q8_K> q4_K_8x8_q8_K;
// instance for IQ4
static const tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
} // namespace ggml::cpu::repack
static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(const struct ggml_tensor * cur) {
// instance for Q4
static const ggml::cpu::repack::tensor_traits<block_q4_0, 4, 4, GGML_TYPE_Q8_0> q4_0_4x4_q8_0;
static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 4, GGML_TYPE_Q8_0> q4_0_4x8_q8_0;
static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 8, GGML_TYPE_Q8_0> q4_0_8x8_q8_0;
static const ggml::cpu::repack::tensor_traits<block_q4_K, 8, 8, GGML_TYPE_Q8_K> q4_K_8x8_q8_K;
// instance for IQ4
static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
if (cur->type == GGML_TYPE_Q4_0) {
if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
if (cur->ne[1] % 8 == 0) {
return &ggml::cpu::repack::q4_0_8x8_q8_0;
return &q4_0_8x8_q8_0;
}
}
if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
if (cur->ne[1] % 4 == 0) {
return &ggml::cpu::repack::q4_0_4x8_q8_0;
return &q4_0_4x8_q8_0;
}
}
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
if (cur->ne[1] % 4 == 0) {
return &ggml::cpu::repack::q4_0_4x4_q8_0;
return &q4_0_4x4_q8_0;
}
}
} else if (cur->type == GGML_TYPE_Q4_K) {
if (ggml_cpu_has_avx2()) {
if (cur->ne[1] % 8 == 0) {
return &ggml::cpu::repack::q4_K_8x8_q8_K;
return &q4_K_8x8_q8_K;
}
}
} else if (cur->type == GGML_TYPE_IQ4_NL) {
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
if (cur->ne[1] % 4 == 0) {
return &ggml::cpu::repack::iq4_nl_4x4_q8_0;
return &iq4_nl_4x4_q8_0;
}
}
}

56
ggml/src/ggml-cuda/common.cuh
View File

@ -19,10 +19,10 @@
#endif
#include "ggml-common.h"
#include <cstdio>
#include <array>
#include <cassert>
#include <cfloat>
#include <cstdio>
#include <string>
#include <vector>
@ -241,8 +241,18 @@ static bool fp16_mma_available(const int cc) {
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
return false;
#else
return (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ||
GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc);
if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ||
GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc)) {
return true;
} else if (GGML_CUDA_CC_IS_RDNA4(cc)) {
#if defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12)
return true;
#else
return false;
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_HIP_ROCWMMA_FATTN_GFX12)
} else {
return false;
}
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
}
@ -252,6 +262,10 @@ static bool fp16_mma_hardware_available(const int cc) {
GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc);
}
static bool bf16_mma_hardware_available(const int cc) {
return GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_AMPERE;
}
// Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
static bool new_mma_available(const int cc) {
return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
@ -362,6 +376,26 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
#endif // FP16_AVAILABLE
}
// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
template<bool norm>
static __global__ void reduce_rows_f32(const float * x, float * dst, const int ncols) {
const int row = blockIdx.x;
const int col = threadIdx.x;
float sum = 0.0f;
for (int i = col; i < ncols; i += blockDim.x) {
sum += x[row * ncols + i];
}
sum = warp_reduce_sum(sum);
if (col != 0) {
return;
}
dst[row] = norm ? sum / ncols : sum;
}
template<int width = WARP_SIZE>
static __device__ __forceinline__ float warp_reduce_max(float x) {
#pragma unroll
@ -767,21 +801,7 @@ struct ggml_backend_cuda_context {
name(GGML_CUDA_NAME + std::to_string(device)) {
}
~ggml_backend_cuda_context() {
if (copy_event != nullptr) {
CUDA_CHECK(cudaEventDestroy(copy_event));
}
for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) {
for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) {
if (streams[i][j] != nullptr) {
CUDA_CHECK(cudaStreamDestroy(streams[i][j]));
}
}
if (cublas_handles[i] != nullptr) {
CUBLAS_CHECK(cublasDestroy(cublas_handles[i]));
}
}
}
~ggml_backend_cuda_context();
cudaStream_t stream(int device, int stream) {
if (streams[device][stream] == nullptr) {

161
ggml/src/ggml-cuda/conv2d-dw.cu Normal file
View File

@ -0,0 +1,161 @@
#include "conv2d-dw.cuh"
struct conv_params {
int in_w, in_h;
int out_w, out_h;
int kernel_w, kernel_h;
int stride_x, stride_y;
int padding_x, padding_y;
int dilation_x, dilation_y;
int channels, batches;
};
struct kernel_bounds {
int y_min, y_max;
int x_min, x_max;
};
__device__ __forceinline__ kernel_bounds calculate_kernel_bounds(int out_x, int out_y, const conv_params & params) {
kernel_bounds bounds;
bounds.y_min = max(0, (params.padding_y - out_y * params.stride_y + params.dilation_y - 1) / params.dilation_y);
bounds.y_max =
min(params.kernel_h,
(params.in_h + params.padding_y - out_y * params.stride_y + params.dilation_y - 1) / params.dilation_y);
bounds.x_min = max(0, (params.padding_x - out_x * params.stride_x + params.dilation_x - 1) / params.dilation_x);
bounds.x_max =
min(params.kernel_w,
(params.in_w + params.padding_x - out_x * params.stride_x + params.dilation_x - 1) / params.dilation_x);
return bounds;
}
__device__ __forceinline__ int calculate_input_coord(int out_coord, int kern_coord, int stride, int dilation, int padding) {
return out_coord * stride + kern_coord * dilation - padding;
}
struct whcn_layout {
__device__ static int input_index(int n, int c, int y, int x, const conv_params & params) {
return n * (params.channels * params.in_w * params.in_h) + c * params.in_w * params.in_h + y * params.in_w + x;
}
__device__ static int kernel_index(int c, int ky, int kx, const conv_params & params) {
return c * params.kernel_h * params.kernel_w + ky * params.kernel_w + kx;
}
__device__ static int output_index(int n, int c, int y, int x, const conv_params & params) {
return n * (params.channels * params.out_w * params.out_h) + c * params.out_w * params.out_h +
y * params.out_w + x;
}
__device__ static void unpack_indices(int global_idx, const conv_params & params, int & n, int & c, int & out_y,
int & out_x) {
out_x = global_idx % params.out_w;
out_y = (global_idx / params.out_w) % params.out_h;
c = (global_idx / (params.out_w * params.out_h)) % params.channels;
n = global_idx / (params.out_w * params.out_h * params.channels);
}
};
struct cwhn_layout {
__device__ static int input_index(int n, int c, int y, int x, const conv_params & params) {
return n * (params.channels * params.in_w * params.in_h) + (y * params.in_w + x) * params.channels + c;
}
__device__ static int kernel_index(int c, int ky, int kx, const conv_params & params) {
return (ky * params.kernel_w + kx) * params.channels + c;
}
__device__ static int output_index(int n, int c, int y, int x, const conv_params & params) {
return n * (params.channels * params.out_w * params.out_h) + y * (params.out_w * params.channels) +
x * params.channels + c;
}
__device__ static void unpack_indices(int global_idx, const conv_params & params, int & n, int & c, int & out_y,
int & out_x) {
c = global_idx % params.channels;
out_x = (global_idx / params.channels) % params.out_w;
out_y = (global_idx / (params.channels * params.out_w)) % params.out_h;
n = global_idx / (params.channels * params.out_w * params.out_h);
}
};
template <typename T, typename Layout>
__global__ void conv2d_dw_kernel(const T * __restrict__ input, const T * __restrict__ kernel, T * __restrict__ output,
const int in_w, const int in_h, const int out_w, const int out_h,
const int kernel_w, const int kernel_h, const int stride_x, const int stride_y,
const int padding_x, const int padding_y, const int dilation_x, const int dilation_y,
const int channels, const int batches) {
const int global_idx = blockIdx.x * blockDim.x + threadIdx.x;
const int total_elements = batches * channels * out_h * out_w;
if (global_idx >= total_elements) {
return;
}
conv_params params = { in_w, in_h, out_w, out_h, kernel_w, kernel_h, stride_x,
stride_y, padding_x, padding_y, dilation_x, dilation_y, channels, batches };
int batch_idx, channel_idx, out_y_idx, out_x_idx;
Layout::unpack_indices(global_idx, params, batch_idx, channel_idx, out_y_idx, out_x_idx);
T accumulator = 0;
kernel_bounds bounds = calculate_kernel_bounds(out_x_idx, out_y_idx, params);
for (int kern_y = bounds.y_min; kern_y < bounds.y_max; ++kern_y) {
int in_y_idx = calculate_input_coord(out_y_idx, kern_y, params.stride_y, params.dilation_y, params.padding_y);
for (int kern_x = bounds.x_min; kern_x < bounds.x_max; ++kern_x) {
int in_x_idx = calculate_input_coord(out_x_idx, kern_x, params.stride_x, params.dilation_x, params.padding_x);
const T input_val = input[Layout::input_index(batch_idx, channel_idx, in_y_idx, in_x_idx, params)];
const T kernel_val = kernel[Layout::kernel_index(channel_idx, kern_y, kern_x, params)];
accumulator += input_val * kernel_val;
}
}
output[Layout::output_index(batch_idx, channel_idx, out_y_idx, out_x_idx, params)] = accumulator;
}
void ggml_cuda_op_conv2d_dw(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * kernel = dst->src[0];
const ggml_tensor * input = dst->src[1];
GGML_ASSERT(kernel->type == GGML_TYPE_F32 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
const float * w_d = (const float *) kernel->data;
const float * x_d = (const float *) input->data;
float * y_d = (float *) dst->data;
const int32_t * p = (const int32_t *) dst->op_params;
const int stride_x = p[0];
const int stride_y = p[1];
const int padding_x = p[2];
const int padding_y = p[3];
const int dilation_x = p[4];
const int dilation_y = p[5];
const int in_w = input->ne[0];
const int in_h = input->ne[1];
const int kernel_w = kernel->ne[0];
const int kernel_h = kernel->ne[1];
const int out_w = dst->ne[0];
const int out_h = dst->ne[1];
const int channels = dst->ne[2];
const int batches = dst->ne[3];
cudaStream_t st = ctx.stream();
const int total = batches * channels * out_h * out_w;
const int blocks = (total + CUDA_CONV2D_DW_BLOCK_SIZE - 1) / CUDA_CONV2D_DW_BLOCK_SIZE;
if (ggml_is_contiguous(input)) {
conv2d_dw_kernel<float, whcn_layout><<<blocks, CUDA_CONV2D_DW_BLOCK_SIZE, 0, st>>>(
x_d, w_d, y_d, in_w, in_h, out_w, out_h, kernel_w, kernel_h, stride_x, stride_y, padding_x, padding_y,
dilation_x, dilation_y, channels, batches);
} else if (ggml_is_contiguous_channels(input)) {
conv2d_dw_kernel<float, cwhn_layout><<<blocks, CUDA_CONV2D_DW_BLOCK_SIZE, 0, st>>>(
x_d, w_d, y_d, in_w, in_h, out_w, out_h, kernel_w, kernel_h, stride_x, stride_y, padding_x, padding_y,
dilation_x, dilation_y, channels, batches);
} else {
GGML_ABORT("Unsupported memory layout for conv_2d_dw");
}
}

5
ggml/src/ggml-cuda/conv2d-dw.cuh Normal file
View File

@ -0,0 +1,5 @@
#pragma once
#include "common.cuh"
#define CUDA_CONV2D_DW_BLOCK_SIZE 256
void ggml_cuda_op_conv2d_dw(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

91
ggml/src/ggml-cuda/conv2d-transpose.cu Normal file
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@ -0,0 +1,91 @@
#include <algorithm>
#include "conv2d-transpose.cuh"
#include "ggml.h"
__global__ void conv2d_transpose_kernel(const float * __restrict__ input, const half * __restrict__ kernel,
float * __restrict__ output, const int in_w, const int in_h, const int out_w,
const int out_h, const int kernel_w, const int kernel_h, const int stride,
const int c_in, const int c_out, const int batches) {
const int global_idx = blockIdx.x * blockDim.x + threadIdx.x;
const int total_elements = out_w * out_h * c_out * batches;
if (global_idx >= total_elements) {
return;
}
const int out_x_idx = global_idx % out_w;
const int out_y_idx = (global_idx / out_w) % out_h;
const int c_idx = (global_idx / (out_w * out_h)) % c_out;
const int n_idx = global_idx / (out_w * out_h * c_out);
float accumulator = 0;
// For each output idx, find the inputs that contribute to it by checking stride alignment and bounds
for (int c_in_idx = 0; c_in_idx < c_in; c_in_idx++) {
for (int kh = 0; kh < kernel_h; ++kh) {
int in_y = out_y_idx - kh;
if (in_y < 0 || in_y % stride) continue;
in_y /= stride;
if (in_y >= in_h) continue;
for (int kw = 0; kw < kernel_w; ++kw) {
int in_x = out_x_idx - kw;
if (in_x < 0 || in_x % stride) continue;
in_x /= stride;
if (in_x >= in_w) continue;
const int input_idx = (in_w * in_h * c_in) * n_idx + (in_w * in_h) * c_in_idx + (in_w) *in_y + in_x;
const int kernel_idx =
(kernel_h * kernel_w * c_out) * c_in_idx + (kernel_h * kernel_w) * c_idx + (kernel_w) *kh + kw;
float input_val = input[input_idx];
half kern_val = kernel[kernel_idx];
accumulator += input_val * (float) kern_val;
}
}
}
output[(out_w * out_h * c_out) * n_idx + (out_w * out_h) * c_idx + (out_w) *out_y_idx + out_x_idx] = accumulator;
}
//input is (W, H, C_in, N), Kernel is (W, H, C_out, C_in)
void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * kernel = dst->src[0];
const ggml_tensor * input = dst->src[1];
GGML_ASSERT(kernel->type == GGML_TYPE_F16 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
const float * input_data = (const float *) input->data;
float * output_data = (float *) dst->data;
const half * kernel_data = (const half *) kernel->data;
const int input_w = input->ne[0];
const int input_h = input->ne[1];
const int output_w = dst->ne[0];
const int output_h = dst->ne[1];
const int channels_in = input->ne[2];
const int channels_out = kernel->ne[2];
const int kernel_w = kernel->ne[0];
const int kernel_h = kernel->ne[1];
const int stride = dst->op_params[0];
const int batches = input->ne[3];
GGML_ASSERT(channels_in == kernel->ne[3]);
GGML_ASSERT(stride > 0);
cudaStream_t st = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(input));
GGML_ASSERT(ggml_is_contiguous(kernel));
GGML_ASSERT(ggml_is_contiguous(dst));
const int total = (output_w * output_h * channels_out * batches);
const int blocks = (total + CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE - 1) / CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE;
conv2d_transpose_kernel<<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
input_data, kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w, kernel_h, stride,
channels_in, channels_out, batches);
}

4
ggml/src/ggml-cuda/conv2d-transpose.cuh Normal file
View File

@ -0,0 +1,4 @@
#include "common.cuh"
#define CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE 256
void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

76
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -11,6 +11,8 @@
#include "ggml-cuda/clamp.cuh"
#include "ggml-cuda/concat.cuh"
#include "ggml-cuda/conv-transpose-1d.cuh"
#include "ggml-cuda/conv2d-dw.cuh"
#include "ggml-cuda/conv2d-transpose.cuh"
#include "ggml-cuda/convert.cuh"
#include "ggml-cuda/count-equal.cuh"
#include "ggml-cuda/cpy.cuh"
@ -35,6 +37,7 @@
#include "ggml-cuda/ssm-scan.cuh"
#include "ggml-cuda/sum.cuh"
#include "ggml-cuda/sumrows.cuh"
#include "ggml-cuda/mean.cuh"
#include "ggml-cuda/tsembd.cuh"
#include "ggml-cuda/unary.cuh"
#include "ggml-cuda/upscale.cuh"
@ -47,6 +50,7 @@
#include <atomic>
#include <charconv>
#include <cinttypes>
#include <condition_variable>
#include <cstddef>
#include <cstdint>
#include <float.h>
@ -54,9 +58,8 @@
#include <map>
#include <memory>
#include <mutex>
#include <stdint.h>
#include <stdio.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string>
#include <vector>
@ -97,8 +100,7 @@ int ggml_cuda_get_device() {
static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) {
ggml_cuda_set_device(device);
cudaError_t err;
if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr)
{
if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr) {
err = cudaMallocManaged(ptr, size);
#if defined(GGML_USE_HIP)
if (err == hipSuccess) {
@ -116,9 +118,7 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device)
err = cudaMalloc(ptr, size);
}
#endif // defined(GGML_USE_HIP)
}
else
{
} else {
err = cudaMalloc(ptr, size);
}
return err;
@ -514,6 +514,33 @@ std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(i
return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
}
// destroying a cuBLAS handle while a graph is being captured in a different thread can result in a CUDA error
// this lock is used to ensure that no cuBLAS handle is destroyed while a graph is being captured
static std::mutex ggml_cuda_lock;
static std::condition_variable ggml_cuda_lock_cv;
static std::atomic<int> ggml_cuda_lock_counter;
ggml_backend_cuda_context::~ggml_backend_cuda_context() {
std::unique_lock<std::mutex> lock(ggml_cuda_lock);
ggml_cuda_lock_cv.wait(lock, []{ return ggml_cuda_lock_counter.load(std::memory_order_relaxed) == 0; });
if (copy_event != nullptr) {
CUDA_CHECK(cudaEventDestroy(copy_event));
}
for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) {
for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) {
if (streams[i][j] != nullptr) {
CUDA_CHECK(cudaStreamDestroy(streams[i][j]));
}
}
if (cublas_handles[i] != nullptr) {
CUBLAS_CHECK(cublasDestroy(cublas_handles[i]));
}
}
}
// cuda buffer
struct ggml_backend_cuda_buffer_context {
@ -1916,8 +1943,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
&& ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) && src0->view_src;
bool use_mul_mat_vec = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src0->ne[0] % 2 == 0 && src1->ne[1] == 1;
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
&& src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
@ -1925,7 +1951,6 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
bool any_gpus_with_slow_fp16 = false;
bool any_gpus_without_fp16_mma = false;
if (split) {
ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
@ -1938,14 +1963,14 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
const int cc = ggml_cuda_info().devices[id].cc;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_vec = use_mul_mat_vec && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_hardware_available(cc);
}
} else {
const int cc = ggml_cuda_info().devices[ctx.device].cc;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
use_mul_mat_vec = use_mul_mat_vec && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_hardware_available(cc);
any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_hardware_available(cc);
}
// debug helpers
@ -1956,7 +1981,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
//printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
//printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
if (!split && use_mul_mat_vec && (src0->ne[1] <= MMV_MAX_ROWS || any_gpus_without_fp16_mma)) {
if (!split && use_mul_mat_vec) {
// the custom F16 vector kernel can be used over batched cuBLAS GEMM
// but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention)
ggml_cuda_mul_mat_vec(ctx, src0, src1, nullptr, dst);
@ -2310,6 +2335,12 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_IM2COL:
ggml_cuda_op_im2col(ctx, dst);
break;
case GGML_OP_CONV_2D_DW:
ggml_cuda_op_conv2d_dw(ctx, dst);
break;
case GGML_OP_CONV_TRANSPOSE_2D:
ggml_cuda_conv_2d_transpose_p0(ctx, dst);
break;
case GGML_OP_CONV_TRANSPOSE_1D:
ggml_cuda_op_conv_transpose_1d(ctx,dst);
break;
@ -2322,6 +2353,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_SUM_ROWS:
ggml_cuda_op_sum_rows(ctx, dst);
break;
case GGML_OP_MEAN:
ggml_cuda_op_mean(ctx, dst);
break;
case GGML_OP_SSM_CONV:
ggml_cuda_op_ssm_conv(ctx, dst);
break;
@ -2685,6 +2719,11 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
graph_evaluated_or_captured = true; // CUDA graph has been captured
std::lock_guard<std::mutex> lock(ggml_cuda_lock);
if (ggml_cuda_lock_counter.fetch_sub(1, std::memory_order_relaxed) == 1) {
ggml_cuda_lock_cv.notify_all();
}
} else {
graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
}
@ -2760,7 +2799,13 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
}
}
if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
if (use_cuda_graph && cuda_graph_update_required) {
// Start CUDA graph capture
{
std::lock_guard<std::mutex> lock(ggml_cuda_lock);
ggml_cuda_lock_counter.fetch_add(1, std::memory_order_relaxed);
}
CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
}
@ -3220,9 +3265,12 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
return op->src[0]->nb[0] == ggml_type_size(op->src[0]->type) && ggml_is_contiguous_2(op->src[0]);
}
case GGML_OP_IM2COL:
case GGML_OP_CONV_2D_DW:
case GGML_OP_CONV_TRANSPOSE_2D:
case GGML_OP_POOL_2D:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_MEAN:
case GGML_OP_ARGSORT:
case GGML_OP_ACC:
return true;

19
ggml/src/ggml-cuda/mean.cu Normal file
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@ -0,0 +1,19 @@
#include "mean.cuh"
void ggml_cuda_op_mean(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(src0->type == GGML_TYPE_F32);
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);
const dim3 block_dims(WARP_SIZE, 1, 1);
const dim3 block_nums(nrows, 1, 1);
reduce_rows_f32</*norm*/ true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
}

3
ggml/src/ggml-cuda/mean.cuh Normal file
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@ -0,0 +1,3 @@
#include "common.cuh"
void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

314
ggml/src/ggml-cuda/mmv.cu
View File

@ -2,25 +2,26 @@
#include "common.cuh"
#include "mmv.cuh"
template <typename T, typename type_acc, int block_size>
template <typename T, typename type_acc, int ncols_dst, int block_size>
static __global__ void mul_mat_vec(
const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
const int64_t ncols2, const int64_t nchannels_y, const int64_t stride_row,
const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
const int64_t sample_ratio, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst) {
const int64_t row = blockIdx.x;
const int64_t channel_dst = blockIdx.y;
const int64_t channel_x = ids ? ids[channel_dst] : channel_dst / channel_ratio;
const int64_t channel_y = ids ? channel_dst % nchannels_y : channel_dst;
const int64_t sample_dst = blockIdx.z;
const int64_t sample_x = sample_dst / sample_ratio;
const int64_t sample_y = sample_dst;
const int ncols2, const int nchannels_y, const int stride_row, const int stride_col_y2, const int stride_col_dst,
const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
const int row = blockIdx.x;
const int channel_dst = blockIdx.y;
const int channel_x = ids ? ids[channel_dst] : channel_dst / channel_ratio;
const int channel_y = ids ? channel_dst % nchannels_y : channel_dst;
const int sample_dst = blockIdx.z;
const int sample_x = sample_dst / sample_ratio;
const int sample_y = sample_dst;
const int tid = threadIdx.x;
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
x += sample_x *stride_sample_x + channel_x *stride_channel_x + row*stride_row;
y += sample_y *stride_sample_y + channel_y *stride_channel_y;
dst += sample_dst*stride_sample_dst + channel_dst*stride_channel_dst;
x += int64_t(sample_x) *stride_sample_x + channel_x *stride_channel_x + row*stride_row;
y += int64_t(sample_y) *stride_sample_y + channel_y *stride_channel_y;
dst += int64_t(sample_dst)*stride_sample_dst + channel_dst*stride_channel_dst;
const float2 * y2 = (const float2 *) y;
@ -34,81 +35,108 @@ static __global__ void mul_mat_vec(
__syncthreads();
}
float sumf = 0.0f;
float sumf[ncols_dst] = {0.0f};
if constexpr (std::is_same<T, float>::value) {
const float2 * x2 = (const float2 *) x;
for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const float2 tmpx = x2[col2];
const float2 tmpy = y2[col2];
sumf += tmpx.x*tmpy.x;
sumf += tmpx.y*tmpy.y;
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
sumf[j] += tmpx.x*tmpy.x;
sumf[j] += tmpx.y*tmpy.y;
}
}
} else if constexpr (std::is_same<T, half>::value) {
const half2 * x2 = (const half2 *) x;
if (std::is_same<type_acc, float>::value) {
for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const float2 tmpx = __half22float2(x2[col2]);
const float2 tmpy = y2[col2];
sumf += tmpx.x * tmpy.x;
sumf += tmpx.y * tmpy.y;
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
sumf[j] += tmpx.x * tmpy.x;
sumf[j] += tmpx.y * tmpy.y;
}
}
} else {
#ifdef FP16_AVAILABLE
half2 sumh2 = make_half2(0.0f, 0.0f);
half2 sumh2[ncols_dst] = {{0.0f, 0.0f}};
for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
const float2 tmp = y2[col2];
sumh2 += x2[col2] * make_half2(tmp.x, tmp.y);
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const half2 tmpx = x2[col2];
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
sumh2[j] += tmpx * make_half2(tmpy.x, tmpy.y);
}
}
sumf = __low2float(sumh2) + __high2float(sumh2);
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
sumf[j] = __low2float(sumh2[j]) + __high2float(sumh2[j]);
}
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
}
} else if constexpr (std::is_same<T, nv_bfloat16>::value) {
const int * x2 = (const int *) x;
for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const int tmpx = x2[col2];
const float2 tmpy = y2[col2];
sumf += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
sumf += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
sumf[j] += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
sumf[j] += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
}
}
} else {
static_assert(std::is_same<T, void>::value, "unsupported type");
}
sumf = warp_reduce_sum<warp_size>(sumf);
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
sumf[j] = warp_reduce_sum<warp_size>(sumf[j]);
if (block_size > warp_size) {
buf_iw[tid/warp_size] = sumf;
buf_iw[tid/warp_size] = sumf[j];
__syncthreads();
if (tid >= warp_size) {
return;
if (tid < warp_size) {
sumf[j] = buf_iw[tid];
sumf[j] = warp_reduce_sum<warp_size>(sumf[j]);
}
if (j < ncols_dst) {
__syncthreads();
}
}
sumf = buf_iw[tid];
sumf = warp_reduce_sum<warp_size>(sumf);
}
if (tid != 0) {
if (tid >= ncols_dst) {
return;
}
dst[row] = sumf;
dst[tid*stride_col_dst + row] = sumf[tid];
}
template <typename T, typename type_acc>
template <typename T, typename type_acc, int ncols_dst>
static void launch_mul_mat_vec_cuda(
const T * x, const float * y, const int32_t * ids, float * dst,
const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
const int64_t ncols, const int64_t nrows,
const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
cudaStream_t stream) {
GGML_ASSERT(ncols % 2 == 0);
GGML_ASSERT(stride_row % 2 == 0);
GGML_ASSERT(stride_col_y % 2 == 0);
GGML_ASSERT(ids || nchannels_dst % nchannels_x == 0);
GGML_ASSERT( nsamples_dst % nsamples_x == 0);
const int64_t channel_ratio = nchannels_dst / nchannels_x;
@ -138,44 +166,52 @@ static void launch_mul_mat_vec_cuda(
const dim3 block_dims(block_size_best, 1, 1);
switch (block_size_best) {
case 32: {
mul_mat_vec<T, type_acc, 32><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 32><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 64: {
mul_mat_vec<T, type_acc, 64><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 64><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 96: {
mul_mat_vec<T, type_acc, 96><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 96><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 128: {
mul_mat_vec<T, type_acc, 128><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 160: {
mul_mat_vec<T, type_acc, 160><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 192: {
mul_mat_vec<T, type_acc, 192><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 224: {
mul_mat_vec<T, type_acc, 224><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
case 256: {
mul_mat_vec<T, type_acc, 256><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, channel_ratio, stride_channel_x, stride_channel_y,
stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
mul_mat_vec<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, smem, stream>>>
(x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
} break;
default: {
GGML_ABORT("fatal error");
@ -183,23 +219,91 @@ static void launch_mul_mat_vec_cuda(
}
}
template <typename T, typename type_acc>
static void mul_mat_vec_cuda_switch_ncols_dst(
const T * x, const float * y, const int32_t * ids, float * dst,
const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
cudaStream_t stream) {
switch (ncols_dst) {
case 1:
launch_mul_mat_vec_cuda<T, type_acc, 1>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 2:
launch_mul_mat_vec_cuda<T, type_acc, 2>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 3:
launch_mul_mat_vec_cuda<T, type_acc, 3>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 4:
launch_mul_mat_vec_cuda<T, type_acc, 4>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 5:
launch_mul_mat_vec_cuda<T, type_acc, 5>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 6:
launch_mul_mat_vec_cuda<T, type_acc, 6>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 7:
launch_mul_mat_vec_cuda<T, type_acc, 7>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 8:
launch_mul_mat_vec_cuda<T, type_acc, 8>
(x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
default:
GGML_ABORT("fatal error");
break;
}
}
template<typename T>
static void mul_mat_vec_cuda(
const T * x, const float * y, const int32_t * ids, float * dst,
const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
const int64_t stride_row, const int64_t stride_col_y, const int stride_col_dst,
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
enum ggml_prec prec, cudaStream_t stream) {
if constexpr(std::is_same<T, half>::value) {
if (prec == GGML_PREC_DEFAULT) {
launch_mul_mat_vec_cuda<T, half>
(x, y, ids, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
mul_mat_vec_cuda_switch_ncols_dst<T, half>
(x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
return;
}
}
launch_mul_mat_vec_cuda<T, float>
(x, y, ids, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
mul_mat_vec_cuda_switch_ncols_dst<T, float>
(x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
}
@ -246,24 +350,24 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
const int64_t stride_channel_dst = ids ? s1 : s2;
const int64_t stride_channel_y = ids ? s11 : s12;
GGML_ASSERT(ncols_dst == 1);
GGML_ASSERT(!ids || ncols_dst == 1);
switch (src0->type) {
case GGML_TYPE_F32: {
const float * src0_d = (const float *) src0->data;
mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, s01,
mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
ne03, ne3, s03, s13, s3, prec, ctx.stream());
} break;
case GGML_TYPE_F16: {
const half * src0_d = (const half *) src0->data;
mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, s01,
mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
ne03, ne3, s03, s13, s3, prec, ctx.stream());
} break;
case GGML_TYPE_BF16: {
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, s01,
mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
ne03, ne3, s03, s13, s3, prec, ctx.stream());
} break;
@ -282,16 +386,19 @@ void ggml_cuda_op_mul_mat_vec(
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = dst->ne[0];
const int64_t row_diff = row_high - row_low;
GGML_ASSERT(src1_ncols == 1);
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const int id = ggml_cuda_get_device();
const int cc = ggml_cuda_info().devices[id].cc;
const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
// ggml_cuda_op provides single, contiguous matrices
const int64_t stride_row = ne00;
const int64_t stride_col_y = ne10;
const int64_t stride_col_dst = id == ctx.device ? ne0 : row_diff; // main device has larger memory buffer
const int64_t nchannels_x = 1;
const int64_t nchannels_y = 1;
const int64_t nchannels_dst = 1;
@ -307,19 +414,19 @@ void ggml_cuda_op_mul_mat_vec(
switch (src0->type) {
case GGML_TYPE_F32: {
const float * src0_d = (const float *) src0_dd_i;
mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, stride_row,
mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
case GGML_TYPE_F16: {
const half * src0_d = (const half *) src0_dd_i;
mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, stride_row,
mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
case GGML_TYPE_BF16: {
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, stride_row,
mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
@ -334,3 +441,48 @@ void ggml_cuda_op_mul_mat_vec(
GGML_UNUSED(src1_ncols);
GGML_UNUSED(src1_padded_row_size);
}
bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
if (src0_ne[0] % 2 != 0) {
return false;
}
switch (type) {
case GGML_TYPE_F32:
if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
return ne11 <= 8;
}
if (cc >= GGML_CUDA_CC_TURING) {
return ne11 <= 4;
}
return ne11 <= 3;
}
return ne11 <= 8;
case GGML_TYPE_F16:
if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
return src0_small && ne11 <= 4;
}
if (fp16_mma_hardware_available(cc)) {
return src0_small && ne11 <= 3;
}
return ne11 <= 8;
}
return ne11 <= 8;
case GGML_TYPE_BF16:
if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
return src0_small && ne11 <= 4;
}
if (bf16_mma_hardware_available(cc)) {
return src0_small && ne11 <= 3;
}
return ne11 <= 8;
}
return ne11 <= 8;
default:
return false;
}
}

5
ggml/src/ggml-cuda/mmv.cuh
View File

@ -1,8 +1,5 @@
#include "common.cuh"
// maximum number of src0 rows with which to use mul_mat_vec over cuBLAS if FP16 tensor cores are available
#define MMV_MAX_ROWS 512
void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
void ggml_cuda_op_mul_mat_vec(
@ -10,3 +7,5 @@ void ggml_cuda_op_mul_mat_vec(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream);
bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);

23
ggml/src/ggml-cuda/sumrows.cu
View File

@ -1,25 +1,9 @@
#include "sumrows.cuh"
static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) {
const int row = blockIdx.x;
const int col = threadIdx.x;
float sum = 0.0f;
for (int i = col; i < ncols; i += blockDim.x) {
sum += x[row * ncols + i];
}
sum = warp_reduce_sum(sum);
if (col == 0) {
dst[row] = sum;
}
}
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);
reduce_rows_f32</*norm*/false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
}
void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@ -35,5 +19,8 @@ void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int64_t ncols = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
sum_rows_f32_cuda(src0_d, dst_d, ncols, nrows, stream);
const dim3 block_dims(WARP_SIZE, 1, 1);
const dim3 block_nums(nrows, 1, 1);
reduce_rows_f32</*norm=*/false><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
}

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

@ -1,5 +1,4 @@
#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);

80
ggml/src/ggml-metal/ggml-metal.m
View File

@ -48,22 +48,28 @@ static struct ggml_backend_metal_device_context {
int mtl_device_ref_count;
id<MTLLibrary> mtl_library;
NSLock * mtl_lock;
bool has_simdgroup_reduction;
bool has_simdgroup_mm;
bool has_residency_sets;
bool has_bfloat;
bool use_bfloat;
size_t max_size;
char name[128];
} g_ggml_ctx_dev_main = {
/*.mtl_device =*/ nil,
/*.mtl_device_ref_count =*/ 0,
/*.mtl_library =*/ nil,
/*.mtl_lock =*/ nil,
/*.has_simdgroup_reduction =*/ false,
/*.has_simdgroup_mm =*/ false,
/*.has_residency_sets =*/ false,
/*.has_bfloat =*/ false,
/*.use_bfloat =*/ false,
/*.max_size =*/ 0,
/*.name =*/ "",
};
@ -71,6 +77,10 @@ static struct ggml_backend_metal_device_context {
static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_device_context * ctx) {
assert(ctx != NULL);
if (ctx->mtl_lock == nil) {
ctx->mtl_lock = [[NSLock alloc] init];
}
if (ctx->mtl_device == nil) {
ctx->mtl_device = MTLCreateSystemDefaultDevice();
}
@ -94,6 +104,8 @@ static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_dev
ctx->use_bfloat = false;
#endif
ctx->max_size = ctx->mtl_device.maxBufferLength;
strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1);
}
@ -110,6 +122,11 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
ctx->mtl_device_ref_count--;
if (ctx->mtl_device_ref_count == 0) {
if (ctx->mtl_lock) {
[ctx->mtl_lock release];
ctx->mtl_lock = nil;
}
if (ctx->mtl_library) {
[ctx->mtl_library release];
ctx->mtl_library = nil;
@ -978,7 +995,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context));
struct ggml_backend_metal_device_context * ctx_dev = dev->context;
id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev);
id<MTLDevice> device = ctx_dev->mtl_device;
GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);
@ -992,9 +1009,16 @@ 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);
// load library
{
[ctx_dev->mtl_lock lock];
if (ctx_dev->mtl_library == nil) {
ctx_dev->mtl_library = ggml_metal_load_library(device, ctx_dev->use_bfloat);
}
[ctx_dev->mtl_lock unlock];
}
id<MTLLibrary> metal_library = ctx_dev->mtl_library;
if (metal_library == nil) {
GGML_LOG_ERROR("%s: error: metal library is nil\n", __func__);
@ -5313,7 +5337,6 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer)
}
ggml_backend_metal_buffer_rset_free(ctx);
ggml_backend_metal_device_rel(buffer->buft->device->context);
if (ctx->owned) {
#if TARGET_OS_OSX
@ -5422,7 +5445,10 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
}
struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)buft->device->context;
id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev);
GGML_ASSERT(ctx_dev->mtl_device != nil);
id<MTLDevice> device = ctx_dev->mtl_device;
ctx->all_data = ggml_metal_host_malloc(size_aligned);
ctx->all_size = size_aligned;
@ -5445,14 +5471,12 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) {
GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
free(ctx);
ggml_backend_metal_device_rel(ctx_dev);
return NULL;
}
if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
free(ctx);
ggml_backend_metal_device_rel(ctx_dev);
return NULL;
}
@ -5463,17 +5487,14 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
return 32;
GGML_UNUSED(buft);
}
static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
id<MTLDevice> device = ggml_backend_metal_device_acq(buft->device->context);
const size_t max_size = device.maxBufferLength;
ggml_backend_metal_device_rel(buft->device->context);
const size_t max_size = ((struct ggml_backend_metal_device_context *)buft->device->context)->max_size;
return max_size;
GGML_UNUSED(buft);
}
static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
@ -5546,7 +5567,10 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
}
struct ggml_backend_metal_device_context * ctx_dev = &g_ggml_ctx_dev_main;
id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev);
GGML_ASSERT(ctx_dev->mtl_device != nil);
id<MTLDevice> device = ctx_dev->mtl_device;
// the buffer fits into the max buffer size allowed by the device
if (size_aligned <= device.maxBufferLength) {
@ -5602,7 +5626,6 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
free(ctx);
ggml_backend_metal_device_rel(ctx_dev);
return NULL;
}
@ -5619,9 +5642,7 @@ static const char * ggml_backend_metal_name(ggml_backend_t backend) {
static void ggml_backend_metal_free(ggml_backend_t backend) {
struct ggml_backend_metal_context * ctx = backend->context;
struct ggml_backend_metal_device_context * ctx_dev = backend->device->context;
ggml_backend_metal_device_rel(ctx_dev);
ggml_metal_free(ctx);
free(backend);
@ -5761,6 +5782,8 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
struct ggml_backend_metal_device_context * ctx_dev = backend->device->context;
GGML_ASSERT(ctx_dev->mtl_device != nil);
return [ctx_dev->mtl_device supportsFamily:(MTLGPUFamilyApple1 + family - 1)];
}
@ -5780,10 +5803,7 @@ static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) {
}
static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) {
// acq/rel just to populate ctx->name in case it hasn't been done yet
struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context;
ggml_backend_metal_device_acq(ctx_dev);
ggml_backend_metal_device_rel(ctx_dev);
return ctx_dev->name;
}
@ -5791,12 +5811,10 @@ static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t
static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
if (@available(macOS 10.12, iOS 16.0, *)) {
struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context;
id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev);
id<MTLDevice> device = ctx_dev->mtl_device;
*total = device.recommendedMaxWorkingSetSize;
*free = *total - device.currentAllocatedSize;
ggml_backend_metal_device_rel(ctx_dev);
} else {
*free = 1;
*total = 1;
@ -5874,7 +5892,10 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back
}
struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context;
id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev);
GGML_ASSERT(ctx_dev->mtl_device != nil);
id<MTLDevice> device = ctx_dev->mtl_device;
// the buffer fits into the max buffer size allowed by the device
if (size_aligned <= device.maxBufferLength) {
@ -5930,7 +5951,6 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back
if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
free(ctx);
ggml_backend_metal_device_rel(ctx_dev);
return NULL;
}
@ -5944,7 +5964,8 @@ static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const
}
static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name ||
return
buft->iface.get_name == ggml_backend_metal_buffer_type_get_name ||
buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name;
GGML_UNUSED(dev);
@ -6030,8 +6051,19 @@ static struct ggml_backend_reg_i ggml_backend_metal_reg_i = {
/* .get_proc_address = */ ggml_backend_metal_get_proc_address,
};
// called upon program exit
static void ggml_metal_cleanup(void) {
ggml_backend_metal_device_rel(&g_ggml_ctx_dev_main);
}
// TODO: make thread-safe
ggml_backend_reg_t ggml_backend_metal_reg(void) {
// TODO: make this thread-safe somehow?
ggml_backend_metal_device_acq(&g_ggml_ctx_dev_main);
// register cleanup callback
// TODO: not ideal, but not sure if there is a better way to do this in Objective-C
atexit(ggml_metal_cleanup);
{
g_ggml_backend_metal_reg = (struct ggml_backend_reg) {
/* .api_version = */ GGML_BACKEND_API_VERSION,

11
ggml/src/ggml-sycl/binbcast.cpp
View File

@ -225,9 +225,9 @@ struct bin_bcast_sycl {
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) *
sycl::range<3>(1, 1, block_size),
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * sycl::range<3>(1, 1, block_size),
sycl::range<3>(1, 1, block_size)),
[=](sycl::nd_item<3> item_ct1) {
k_bin_bcast_unravel<bin_op>(
@ -246,9 +246,8 @@ struct bin_bcast_sycl {
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_bin_bcast<bin_op>(src0_dd, src1_dd, dst_dd, ne0, ne1,
ne2, ne3, ne10, ne11, ne12, ne13,
s1, s2, s3, s01, s02, s03, s11, s12, s13,

45
ggml/src/ggml-sycl/concat.cpp
View File

@ -89,32 +89,23 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst,
sycl::range<3> gridDim(ne2, ne1, num_blocks);
switch (dim) {
case 0:
stream->parallel_for(
sycl::nd_range<3>(gridDim *
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1); });
break;
case 1:
stream->parallel_for(
sycl::nd_range<3>(gridDim *
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1); });
break;
// dim >=2 will be dispatched to the default path
default:
stream->parallel_for(
sycl::nd_range<3>(gridDim *
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1); });
break;
}
}
@ -129,26 +120,22 @@ static void concat_f32_sycl_non_cont(
int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2,
uint64_t nb3, int32_t dim) {
sycl::range<3> gridDim(ne3, ne2, ne1);
stream->parallel_for(
sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(stream, sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
int64_t i3 = item_ct1.get_group(0);
int64_t i2 = item_ct1.get_group(1);
int64_t i1 = item_ct1.get_group(2);
int64_t o[4] = {0, 0, 0, 0};
int64_t o[4] = { 0, 0, 0, 0 };
o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
const float *x;
const float * x;
for (int i0 = item_ct1.get_local_id(2); i0 < ne0;
i0 += item_ct1.get_local_range(2)) {
for (int i0 = item_ct1.get_local_id(2); i0 < ne0; i0 += item_ct1.get_local_range(2)) {
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
x = (const float *)(src0 + (i3)*nb03 + (i2)*nb02 + (i1)*nb01 +
(i0)*nb00);
x = (const float *) (src0 + (i3) *nb03 + (i2) *nb02 + (i1) *nb01 + (i0) *nb00);
} else {
x = (const float *)(src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 +
(i1 - o[1]) * nb11 + (i0 - o[0]) * nb10);
x = (const float *) (src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 + (i1 - o[1]) * nb11 +
(i0 - o[0]) * nb10);
}
float *y = (float *)(dst + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);

12
ggml/src/ggml-sycl/conv.cpp
View File

@ -59,15 +59,9 @@ static void conv_transpose_1d_f32_f32_sycl(
const int num_blocks = (output_size + SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE - 1) / SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE;
const sycl::range<3> block_dims(1, 1, SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE);
const sycl::range<3> block_nums(1, 1, num_blocks);
stream->parallel_for(
sycl::nd_range<3>(
block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
conv_transpose_1d_kernel(
s0, output_size,
src0_ne0, src0_ne1, src0_ne2,
src1_ne0, dst_ne0,
src0, src1, dst, item_ct1);
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
conv_transpose_1d_kernel(s0, output_size, src0_ne0, src0_ne1, src0_ne2, src1_ne0, dst_ne0, src0, src1, dst,
item_ct1);
});
}

229
ggml/src/ggml-sycl/convert.cpp
View File

@ -33,14 +33,11 @@ static void dequantize_block_sycl(const void *__restrict__ vx,
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(
sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block<qk, qr, dequantize_kernel>(vx, y, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { dequantize_block<qk, qr, dequantize_kernel>(vx, y, k, item_ct1); });
}
}
@ -53,24 +50,18 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 64),
sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q2_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q2_K(vx, y, item_ct1); });
}
#else
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q2_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q2_K(vx, y, item_ct1); });
}
#endif
@ -85,24 +76,18 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 64),
sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q3_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q3_K(vx, y, item_ct1); });
}
#else
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q3_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q3_K(vx, y, item_ct1); });
}
#endif
}
@ -116,12 +101,9 @@ static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q4_0(vx, y, nb32, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q4_0(vx, y, nb32, item_ct1); });
}
}
@ -135,13 +117,12 @@ static void dequantize_row_q4_0_sycl_reorder(const void *vx, dst_t *y, const int
int constexpr WARP_K = WARP_SIZE * QK4_0;
const int n_warp = (k + WARP_K - 1) / WARP_K;
GGML_ASSERT(k % 2 == 0);
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, n_warp) *
sycl::range<3>(1, 1, WARP_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, n_warp) * sycl::range<3>(1, 1, WARP_SIZE),
sycl::range<3>(1, 1, WARP_SIZE)),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_block_q4_0_reorder(vx, y, k, item_ct1);
});
}
template <typename dst_t>
@ -153,12 +134,9 @@ static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q4_1(vx, y, nb32, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q4_1(vx, y, nb32, item_ct1); });
}
}
@ -171,11 +149,10 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q4_K(vx, y, get_pointer(scale_local_acc), item_ct1);
});
@ -191,10 +168,10 @@ static void dequantize_row_q4_K_sycl_reorder(const void * vx, dst_t * y, const i
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
stream->submit([&](sycl::handler & cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
cgh.parallel_for(sycl::nd_range<1>(sycl::range<1>(global_size), sycl::range<1>(local_size)),
sycl_parallel_for<1>(cgh, sycl::nd_range<1>(sycl::range<1>(global_size), sycl::range<1>(local_size)),
[=](sycl::nd_item<1> item_ct1) {
dequantize_block_q4_K_reorder(vx, y, get_pointer(scale_local_acc), item_ct1, nb);
});
@ -210,24 +187,18 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 64),
sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q5_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q5_K(vx, y, item_ct1); });
}
#else
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q5_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q5_K(vx, y, item_ct1); });
}
#endif
@ -242,24 +213,18 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 64),
sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q6_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K(vx, y, item_ct1); });
}
#else
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q6_K(vx, y, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K(vx, y, item_ct1); });
}
#endif
@ -271,7 +236,7 @@ static void dequantize_row_q6_K_sycl_reorder(const void * vx, dst_t * y, const i
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
stream->parallel_for(
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K_reorder(vx, y, item_ct1, nb); });
}
@ -284,15 +249,10 @@ static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq1_s(
vx, y, item_ct1, iq1s_grid_gpu
);
});
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_iq1_s(vx, y, item_ct1, iq1s_grid_gpu); });
});
}
}
@ -305,15 +265,10 @@ static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq1_m(
vx, y, item_ct1, iq1s_grid_gpu
);
});
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_iq1_m(vx, y, item_ct1, iq1s_grid_gpu); });
});
}
}
@ -326,14 +281,11 @@ static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int64_t
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq2_xxs(
vx, y, item_ct1, iq2xxs_grid,
ksigns_iq2xs, kmask_iq2xs);
dequantize_block_iq2_xxs(vx, y, item_ct1, iq2xxs_grid, ksigns_iq2xs, kmask_iq2xs);
});
});
}
@ -347,14 +299,11 @@ static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int64_t k
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq2_xs(
vx, y, item_ct1, iq2xs_grid,
ksigns_iq2xs, kmask_iq2xs);
dequantize_block_iq2_xs(vx, y, item_ct1, iq2xs_grid, ksigns_iq2xs, kmask_iq2xs);
});
});
}
@ -368,13 +317,10 @@ static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq2_s(vx, y, item_ct1);
});
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_iq2_s(vx, y, item_ct1); });
});
}
}
@ -388,14 +334,11 @@ static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int64_t
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq3_xxs(
vx, y, item_ct1, iq3xxs_grid,
ksigns_iq2xs, kmask_iq2xs);
dequantize_block_iq3_xxs(vx, y, item_ct1, iq3xxs_grid, ksigns_iq2xs, kmask_iq2xs);
});
});
}
@ -409,14 +352,10 @@ static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int64_t k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq3_s(
vx, y, item_ct1, kmask_iq2xs, iq3s_grid);
});
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_iq3_s(vx, y, item_ct1, kmask_iq2xs, iq3s_grid); });
});
}
}
@ -432,14 +371,11 @@ static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int64_t k
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq4_xs(vx, y, item_ct1);
});
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh,
sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_iq4_xs(vx, y, item_ct1); });
});
}
#endif
@ -453,14 +389,11 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_iq4_nl(vx, y, item_ct1);
});
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(
cgh,
sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) { dequantize_block_iq4_nl(vx, y, item_ct1); });
});
}
}

100
ggml/src/ggml-sycl/cpy.cpp
View File

@ -413,7 +413,8 @@ static void ggml_cpy_f16_f32_sycl(const char * cx, char * cdst, const int ne, co
{
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
stream->parallel_for(
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
@ -431,7 +432,8 @@ static void ggml_cpy_f32_f32_sycl(const char * cx, char * cdst, const int ne, co
{
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
stream->parallel_for(
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
@ -449,7 +451,8 @@ static void ggml_cpy_f32_f16_sycl(const char * cx, char * cdst, const int ne, co
{
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
stream->parallel_for(
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
@ -465,7 +468,7 @@ static void ggml_cpy_f32_q8_0_sycl(const char * cx, char * cdst, const int ne, c
const int nb12, const int nb13, queue_ptr stream) {
GGML_ASSERT(ne % QK8_0 == 0);
const int num_blocks = ne / QK8_0;
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@ -477,7 +480,7 @@ static void ggml_cpy_q8_0_f32_sycl(const char * cx, char * cdst, const int ne, c
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ne;
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_f32<cpy_blck_q8_0_f32, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@ -490,7 +493,7 @@ static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, c
const int nb12, const int nb13, queue_ptr stream) {
GGML_ASSERT(ne % QK4_0 == 0);
const int num_blocks = ne / QK4_0;
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@ -502,8 +505,9 @@ static void ggml_cpy_q4_0_f32_sycl(const char * cx, char * cdst, const int ne, c
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ne;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
@ -516,7 +520,7 @@ static void ggml_cpy_f32_q4_1_sycl(const char * cx, char * cdst, const int ne, c
const int nb12, const int nb13, queue_ptr stream) {
GGML_ASSERT(ne % QK4_1 == 0);
const int num_blocks = ne / QK4_1;
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@ -528,8 +532,9 @@ static void ggml_cpy_q4_1_f32_sycl(const char * cx, char * cdst, const int ne, c
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ne;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
@ -542,7 +547,7 @@ static void ggml_cpy_f32_q5_0_sycl(const char * cx, char * cdst, const int ne, c
const int nb12, const int nb13, queue_ptr stream) {
GGML_ASSERT(ne % QK5_0 == 0);
const int num_blocks = ne / QK5_0;
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@ -554,8 +559,9 @@ static void ggml_cpy_q5_0_f32_sycl(const char * cx, char * cdst, const int ne, c
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ne;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
@ -568,7 +574,7 @@ static void ggml_cpy_f32_q5_1_sycl(const char * cx, char * cdst, const int ne, c
const int nb12, const int nb13, queue_ptr stream) {
GGML_ASSERT(ne % QK5_1 == 0);
const int num_blocks = ne / QK5_1;
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@ -580,8 +586,9 @@ static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, c
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ne;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
item_ct1);
@ -594,10 +601,10 @@ static void ggml_cpy_f32_iq4_nl_sycl(const char * cx, char * cdst, const int ne,
const int nb12, const int nb13, queue_ptr stream) {
GGML_ASSERT(ne % QK4_NL == 0);
const int num_blocks = ne / QK4_NL;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
ne12, nb10, nb11, nb12, nb13, item_ct1);
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
[=](sycl::nd_item<3> item_ct1) {
cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
});
}
@ -609,7 +616,8 @@ static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, co
{
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
stream->parallel_for(
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
@ -628,7 +636,8 @@ static void ggml_cpy_i16_i16_sycl(const char * cx, char * cdst, const int ne, co
// dpct::has_capability_or_fail(stream->get_device(),
// {sycl::aspect::fp16});
stream->parallel_for(
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
@ -647,7 +656,8 @@ static void ggml_cpy_i32_i32_sycl(const char * cx, char * cdst, const int ne, co
// dpct::has_capability_or_fail(stream->get_device(),
// {sycl::aspect::fp16});
stream->parallel_for(
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
@ -662,10 +672,12 @@ static void ggml_cpy_q8_0_q8_0(const char * cx, char * cdst, const int ne, const
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
stream->parallel_for(
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
ne12, nb10, nb11, nb12, nb13, item_ct1);
});
}
@ -675,10 +687,12 @@ static void ggml_cpy_q5_0_q5_0(const char * cx, char * cdst, const int ne, const
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
stream->parallel_for(
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
ne12, nb10, nb11, nb12, nb13, item_ct1);
});
}
@ -689,10 +703,12 @@ static void ggml_cpy_q5_1_q5_1(const char * cx, char * cdst, const int ne, const
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
stream->parallel_for(
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
ne12, nb10, nb11, nb12, nb13, item_ct1);
});
}
@ -702,9 +718,12 @@ static void ggml_cpy_q4_0_q4_0(const char * cx, char * cdst, const int ne, const
const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
ne12, nb10, nb11, nb12, nb13, item_ct1);
});
}
@ -715,9 +734,12 @@ static void ggml_cpy_q4_1_q4_1(const char * cx, char * cdst, const int ne, const
const int nb12, const int nb13, queue_ptr stream) {
const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cpy_q_q<block_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
ne12, nb10, nb11, nb12, nb13, item_ct1);
});
}

58
ggml/src/ggml-sycl/dmmv.cpp
View File

@ -208,11 +208,9 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
nrows, item_ct1);
dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols, nrows, item_ct1);
});
}
}
@ -877,11 +875,10 @@ static void dequantize_mul_mat_vec_q4_0_sycl_reorder(const void *vx, const dfloa
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec_reorder<QK4_0, QR4_0, dequantize_q4_0_reorder>(
vx, y, dst, ncols, nrows, item_ct1);
dequantize_mul_mat_vec_reorder<QK4_0, QR4_0, dequantize_q4_0_reorder>(vx, y, dst, ncols,
nrows, item_ct1);
});
}
}
@ -900,11 +897,9 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
vx, y, dst, ncols, nrows, item_ct1);
dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(vx, y, dst, ncols, nrows, item_ct1);
});
}
}
@ -921,11 +916,9 @@ static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
vx, y, dst, ncols, nrows, item_ct1);
dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(vx, y, dst, ncols, nrows, item_ct1);
});
}
}
@ -942,11 +935,9 @@ static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
vx, y, dst, ncols, nrows, item_ct1);
dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(vx, y, dst, ncols, nrows, item_ct1);
});
}
}
@ -963,11 +954,9 @@ static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
vx, y, dst, ncols, nrows, item_ct1);
dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(vx, y, dst, ncols, nrows, item_ct1);
});
}
}
@ -984,11 +973,9 @@ static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
vx, y, dst, ncols, nrows, item_ct1);
dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(vx, y, dst, ncols, nrows, item_ct1);
});
}
}
@ -1002,8 +989,7 @@ static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
});
@ -1018,8 +1004,7 @@ static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
});
@ -1034,8 +1019,7 @@ static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
});
@ -1047,8 +1031,7 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const sycl::range<3> block_dims(1, 1, QK_WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
});
@ -1063,8 +1046,7 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
});

32
ggml/src/ggml-sycl/dpct/helper.hpp
View File

@ -13,10 +13,10 @@
#ifndef GGML_SYCL_DPCT_HELPER_HPP
#define GGML_SYCL_DPCT_HELPER_HPP
#include <map>
#include <sycl/sycl.hpp>
#include <sycl/half_type.hpp>
#include <syclcompat/math.hpp>
#include <map>
#ifdef GGML_SYCL_USE_INTEL_ONEMKL
#include <oneapi/mkl.hpp>
@ -118,6 +118,36 @@ inline auto get_onemath_backend(sycl::queue& queue)
#endif
}
#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
namespace syclex = sycl::ext::oneapi::experimental;
#endif
template <int NR, typename Func>
__dpct_inline__ void sycl_parallel_for(sycl::handler & cgh, sycl::nd_range<NR> nd_range, Func && func) {
#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
syclex::nd_launch(cgh, nd_range, func);
#else
cgh.parallel_for(nd_range, func);
#endif
}
template <int NR, typename Func>
__dpct_inline__ void sycl_parallel_for(sycl::queue * q, sycl::nd_range<NR> nd_range, Func && func) {
#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
syclex::nd_launch(*q, nd_range, func);
#else
q->parallel_for(nd_range, func);
#endif
}
template <typename Func> __dpct_inline__ void sycl_launch(sycl::queue * stream, Func && func) {
#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
syclex::submit(*stream, func);
#else
stream->submit(func);
#endif
}
namespace dpct
{
typedef sycl::queue *queue_ptr;

216
ggml/src/ggml-sycl/element_wise.cpp
View File

@ -329,13 +329,11 @@ static void acc_f32_sycl(const float *x, const float *y, float *dst,
const int ne12, const int nb1, const int nb2,
const int offset, queue_ptr stream) {
int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset,
item_ct1);
acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, item_ct1);
});
}
@ -343,46 +341,39 @@ template<typename T>
static void gelu_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
gelu(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { gelu(x, dst, k, item_ct1); });
}
template<typename T>
static void silu_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
silu(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { silu(x, dst, k, item_ct1); });
}
template<typename T>
static void sgn_sycl(const T * x, T * dst, const int k, queue_ptr stream) {
// hard code for now
const int num_blocks = ceil_div(k, 256);
stream->parallel_for(
sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range(1, 1, 256)), sycl::range(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) {
sgn(x, dst, k, item_ct1);
});
sycl_parallel_for(
stream, sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range(1, 1, 256)), sycl::range(1, 1, 256)),
[=](sycl::nd_item<3> item_ct1) { sgn(x, dst, k, item_ct1); });
}
template<typename T>
static void abs_sycl(const T * x, T * dst, const int k, queue_ptr stream) {
// hard code for now
const int num_blocks = ceil_div(k, 256);
stream->parallel_for(
sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) {
abs_op(x, dst, k, item_ct1);
});
sycl_parallel_for(
stream,
sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)),
[=](sycl::nd_item<3> item_ct1) { abs_op(x, dst, k, item_ct1); });
}
@ -390,23 +381,20 @@ template<typename T>
static void elu_sycl(const T * x, T * dst, const int k, queue_ptr stream) {
// hard code for now
const int num_blocks = ceil_div(k, 256);
stream->parallel_for(
sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)), [=](sycl::nd_item<3> item_ct1) {
elu_op(x, dst, k, item_ct1);
});
sycl_parallel_for(
stream,
sycl::nd_range<3>((sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, 256)), sycl::range<3>(1, 1, 256)),
[=](sycl::nd_item<3> item_ct1) { elu_op(x, dst, k, item_ct1); });
}
template<typename T>
static void gelu_quick_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
gelu_quick(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { gelu_quick(x, dst, k, item_ct1); });
}
@ -414,169 +402,133 @@ template<typename T>
static void gelu_erf_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
gelu_erf(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { gelu_erf(x, dst, k, item_ct1); });
}
template<typename T>
static void tanh_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
tanh(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { tanh(x, dst, k, item_ct1); });
}
template<typename T>
static void relu_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
relu(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { relu(x, dst, k, item_ct1); });
}
template<typename T>
static void hardsigmoid_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE),
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
hardsigmoid(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { hardsigmoid(x, dst, k, item_ct1); });
}
template<typename T>
static void hardswish_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE),
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
hardswish(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { hardswish(x, dst, k, item_ct1); });
}
template<typename T>
static void exp_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
exp(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { exp(x, dst, k, item_ct1); });
}
template<typename T>
static void log_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
log(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { log(x, dst, k, item_ct1); });
}
template<typename T>
static void neg_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
neg(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { neg(x, dst, k, item_ct1); });
}
template<typename T>
static void step_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
step(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { step(x, dst, k, item_ct1); });
}
template<typename T>
static void sigmoid_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_SIGMOID_BLOCK_SIZE - 1) / SYCL_SIGMOID_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE),
sycl_parallel_for(
stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
sigmoid(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { sigmoid(x, dst, k, item_ct1); });
}
template<typename T>
static void sqrt_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_SQRT_BLOCK_SIZE - 1) / SYCL_SQRT_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
sqrt(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { sqrt(x, dst, k, item_ct1); });
}
template<typename T>
static void sin_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
sin(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { sin(x, dst, k, item_ct1); });
}
template<typename T>
static void cos_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
cos(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { cos(x, dst, k, item_ct1); });
}
template<typename T>
@ -584,26 +536,20 @@ static void leaky_relu_sycl(const T *x, T *dst, const int k,
const float negative_slope,
queue_ptr stream) {
const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
leaky_relu(x, dst, k, negative_slope, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { leaky_relu(x, dst, k, negative_slope, item_ct1); });
}
template<typename T>
static void sqr_sycl(const T *x, T *dst, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
sqr(x, dst, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { sqr(x, dst, k, item_ct1); });
}
template<typename T>
@ -614,9 +560,8 @@ static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01,
int dst_size = ne10 * ne11 * ne12 * ne13;
int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE;
sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE);
stream->parallel_for(
sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)),
[=](sycl::nd_item<1> item_ct1) {
sycl_parallel_for<1>(
stream, sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
upscale(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1);
});
}
@ -627,12 +572,10 @@ static void pad_sycl(const T *x, T *dst, const int ne00,
const int ne1, const int ne2, queue_ptr stream) {
int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE;
sycl::range<3> gridDim(ne2, ne1, num_blocks);
stream->parallel_for(
sycl_parallel_for(stream,
sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
pad(x, dst, ne0, ne00, ne01, ne02, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { pad(x, dst, ne0, ne00, ne01, ne02, item_ct1); });
}
template<typename T>
@ -640,13 +583,10 @@ static void clamp_sycl(const T *x, T *dst, const float min,
const float max, const int k,
queue_ptr stream) {
const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE),
sycl_parallel_for(stream,
sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
clamp(x, dst, min, max, k, item_ct1);
});
[=](sycl::nd_item<3> item_ct1) { clamp(x, dst, min, max, k, item_ct1); });
}
inline void ggml_sycl_op_sgn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {

107
ggml/src/ggml-sycl/getrows.cpp
View File

@ -60,54 +60,6 @@ static void k_get_rows(
dst_row[iybs + iqs + y_offset] = v.y();
}
template<int qk, int qr, dequantize_kernel_t_reorder dequantize_kernel_recorder, typename dst_t>
static void k_get_rows_reorder(
const void * src0, const void *src0_dq, const int32_t * src1, dst_t * dst,
int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
/*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
/*size_t s0,*/ size_t s1, size_t s2, size_t s3,
/*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
size_t s10, size_t s11, size_t s12,
const sycl::nd_item<3> &item_ct1/*, size_t s13*/) {
const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) +
item_ct1.get_local_id(2)) *
2;
const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
item_ct1.get_local_id(1);
const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
item_ct1.get_local_id(0)) /
ne12;
const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
item_ct1.get_local_id(0)) %
ne12;
if (i00 >= ne00) {
return;
}
auto ncols = ne00;
const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
const int src0_off = i01 * ncols + i00;
const int ib = src0_off / QK4_0; // block index
const int iqs = (i00%qk)/qr; // x quant index
const int iybs = i00 - i00%qk; // dst block start index
const int y_offset = qr == 1 ? 1 : qk/2;
// dequantize
dfloat2 v;
dequantize_kernel_recorder((const void *)src0_dq, ib, (const void *)src0, src0_off/2, v);
dst_row[iybs + iqs + 0] = v.x();
dst_row[iybs + iqs + y_offset] = v.y();
GGML_UNUSED(nb01);
GGML_UNUSED(nb02);
GGML_UNUSED(nb03);
}
template<typename src0_t, typename dst_t>
static void k_get_rows_float(
const src0_t * src0, const int32_t * src1, dst_t * dst,
@ -166,58 +118,15 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
GGML_ASSERT(ne00 % 2 == 0);
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
k_get_rows<qk, qr, dq>(
src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_get_rows<qk, qr, dq>(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, s3, nb01, nb02, nb03, s10, s11, s12,
item_ct1);
});
GGML_UNUSED(dst);
GGML_UNUSED(ctx);
}
template <int qk, int qr, dequantize_kernel_t_reorder dq_reorder>
static void get_rows_sycl_reorder(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst, const void *src0_dd,
const int32_t *src1_dd, float *dst_dd,
queue_ptr stream) {
GGML_TENSOR_BINARY_OP_LOCALS
const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE);
const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE);
const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x);
// strides in elements
//const size_t s0 = nb0 / ggml_element_size(dst);
const size_t s1 = nb1 / ggml_element_size(dst);
const size_t s2 = nb2 / ggml_element_size(dst);
const size_t s3 = nb3 / ggml_element_size(dst);
const size_t s10 = nb10 / ggml_element_size(src1);
const size_t s11 = nb11 / ggml_element_size(src1);
const size_t s12 = nb12 / ggml_element_size(src1);
//const size_t s13 = nb13 / ggml_element_size(src1);
GGML_ASSERT(ne00 % 2 == 0);
const uint8_t* src0_q = (const uint8_t*)src0_dd;
const size_t ncols = ne00;
const size_t nrows = ne01;
const sycl::half* src0_dq = (const sycl::half*)(src0_q + nrows * ncols / 2);
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
k_get_rows_reorder<qk, qr, dq_reorder>(
src0_dd, src0_dq, src1_dd, dst_dd, ne00, ne12, s1, s2,
s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
});
GGML_UNUSED(dst);
GGML_UNUSED(ctx);
}
template <typename src0_t>
static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
@ -245,9 +154,8 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
});
@ -277,13 +185,8 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
src1_i32, (float *)dst->data, ctx.stream());
break;
case GGML_TYPE_Q4_0:
if (ctx.opt_feature.reorder && dst->op == GGML_OP_MUL_MAT) {
get_rows_sycl_reorder<QK4_0, QR4_0, dequantize_q4_0_reorder>(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data,
src1_i32, (float *)dst->data, ctx.stream());
} else {
get_rows_sycl<QK4_0, QR4_0, dequantize_q4_0>(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data,
src1_i32, (float *)dst->data, ctx.stream());
}
break;
case GGML_TYPE_Q4_1:
get_rows_sycl<QK4_1, QR4_1, dequantize_q4_1>(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data,

39
ggml/src/ggml-sycl/ggml-sycl.cpp
View File

@ -1887,13 +1887,12 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
const size_t shared_mem = ncols_pad * sizeof(int);
if (order == GGML_SORT_ORDER_ASC) {
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
sycl::range<1>(shared_mem), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(
x, dst, ncols, ncols_pad, item_ct1,
dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
@ -1901,13 +1900,12 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
});
});
} else if (order == GGML_SORT_ORDER_DESC) {
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
sycl::range<1>(shared_mem), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(
x, dst, ncols, ncols_pad, item_ct1,
dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
@ -1925,15 +1923,13 @@ static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols,
const sycl::range<3> block_nums(1, nrows, 1);
const size_t shared_mem = 256 * sizeof(float);
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> shared_data(
sycl::range<1>(shared_mem/sizeof(float)), cgh);
sycl::local_accessor<int, 1> shared_indices(
sycl::range<1>(shared_mem/sizeof(float)), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
const int tid = item_ct1.get_local_id(2);
const int row = item_ct1.get_global_id(1);
@ -1952,7 +1948,7 @@ static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols,
shared_indices[tid] = max_idx;
item_ct1.barrier(sycl::access::fence_space::local_space);
for (int stride = 256/2; stride > 0; stride >>= 1) {
for (int stride = 256 / 2; stride > 0; stride >>= 1) {
if (tid < stride) {
float val1 = shared_data[tid];
float val2 = shared_data[tid + stride];
@ -1964,7 +1960,6 @@ static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols,
item_ct1.barrier(sycl::access::fence_space::local_space);
}
if (tid == 0) {
dst[row] = shared_indices[0];
}
@ -2952,7 +2947,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
void ** ptrs_dst_get = ptrs_dst.get();
size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : s12 * sizeof(sycl::half);
size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : s13 * sizeof(sycl::half);
cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_compute_batched_ptrs(src0_f16, src1_f16, dst_ddf, ptrs_src_get, ptrs_dst_get, ne12, ne13, ne23, nb02,
nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1);
});
@ -3456,7 +3451,7 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
{
sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u));
sycl::range<3> grid_dims(1, n_ids, ids->ne[1]);
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 0> src1_row_acc(cgh);
char *__restrict src1_contiguous_get =
@ -3468,9 +3463,8 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
size_t ids_nb_ct6 = ids->nb[1];
size_t ids_nb_ct7 = ids->nb[0];
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_copy_src1_to_contiguous(
src1_original, src1_contiguous_get,
dev_cur_src1_row_get,
@ -3501,15 +3495,14 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
{
sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u));
sycl::range<3> grid_dims(1, 1, num_src1_rows);
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
const char *__restrict dst_contiguous_get =
dst_contiguous.get();
const mmid_row_mapping *__restrict dev_row_mapping_get =
dev_row_mapping.get();
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
k_copy_dst_from_contiguous(dst_original,
dst_contiguous_get,
dev_row_mapping_get,

4
ggml/src/ggml-sycl/gla.cpp
View File

@ -11,13 +11,13 @@ static void gated_linear_attn_f32_kernel(const dpct::queue_ptr stream, u_int B,
const u_int n_seq_tokens = T / B;
sycl::range<1> block_dims((C / H));
sycl::range<1> grid_dims((B * H));
stream->submit([&](sycl::handler & cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
/* local memory accessors*/
auto _k = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
auto _r = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
auto _td = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
cgh.parallel_for(sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) {
sycl_parallel_for<1>(cgh, sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) {
u_int tid = item.get_local_id(0);
u_int bid = item.get_group(0);

2
ggml/src/ggml-sycl/im2col.cpp
View File

@ -70,7 +70,7 @@ static void im2col_sycl_internal(const float * x, T * dst, int64_t IW, int64_t I
const int64_t CHW = IC * KH * KW;
stream->parallel_for(sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) {
im2col_kernel<T>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, CHW, s0, s1,
p0, p1, d0, d1, item_ct1);
});

140
ggml/src/ggml-sycl/mmq.cpp
View File

@ -1818,7 +1818,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
@ -1829,9 +1829,8 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q4_0<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -1853,7 +1852,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
@ -1864,9 +1863,8 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q4_0<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -1933,7 +1931,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
@ -1944,9 +1942,8 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q4_1<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -1968,7 +1965,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
@ -1979,9 +1976,8 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q4_1<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2048,7 +2044,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
@ -2059,9 +2055,8 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q5_0<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2083,7 +2078,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
@ -2094,9 +2089,8 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q5_0<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2163,7 +2157,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
@ -2174,9 +2168,8 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q5_1<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2198,7 +2191,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
@ -2209,9 +2202,8 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q5_1<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2278,7 +2270,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
@ -2289,9 +2281,8 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q8_0<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2313,7 +2304,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
@ -2324,9 +2315,8 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q8_0<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2393,7 +2383,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
@ -2406,9 +2396,8 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q2_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2431,7 +2420,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
@ -2444,9 +2433,8 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q2_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2516,7 +2504,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
@ -2531,9 +2519,8 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q3_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2557,7 +2544,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
@ -2572,9 +2559,8 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q3_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2644,7 +2630,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
@ -2657,9 +2643,8 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q4_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2682,7 +2667,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
@ -2695,9 +2680,8 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q4_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2765,7 +2749,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
@ -2778,9 +2762,8 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q5_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2803,7 +2786,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
@ -2816,9 +2799,8 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q5_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2886,7 +2868,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
@ -2899,9 +2881,8 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q6_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,
@ -2924,7 +2905,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
@ -2937,9 +2918,8 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
mul_mat_q6_K<need_check>(
vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
nrows_dst, item_ct1,

251
ggml/src/ggml-sycl/mmvq.cpp
View File

@ -544,8 +544,8 @@ static void reorder_mul_mat_vec_q4_0_q8_1_sycl(const void * vx, const void * vy,
const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, (block_num_y * WARP_SIZE));
const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size),
[=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_0>>(vx, vy, dst, ncols, nrows,
nd_item);
@ -561,8 +561,8 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void * vx, const void * vy, float *
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -580,15 +580,10 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -604,15 +599,10 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -628,15 +618,10 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -652,15 +637,10 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -676,15 +656,10 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -700,15 +675,10 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -724,15 +694,10 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -750,11 +715,11 @@ static void reorder_mul_mat_vec_q4_k_q8_1_sycl(const void * vx, const void * vy,
const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE);
const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size),
[=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_K>>(vx, vy, dst, ncols,
nrows, nd_item);
mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_K>>(vx, vy, dst, ncols, nrows,
nd_item);
});
});
}
@ -769,15 +734,10 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -794,8 +754,8 @@ static void reorder_mul_mat_vec_q6_k_q8_1_sycl(const void * vx, const void * vy,
const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE);
const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size),
[=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q6_K>>(vx, vy, dst, ncols, nrows,
nd_item);
@ -811,15 +771,10 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -836,13 +791,11 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS/2, block_iq2_xxs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS / 2, block_iq2_xxs, 1>(vx, vy, dst, ncols,
nrows, item_ct1);
});
});
}
@ -857,13 +810,11 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS/2, block_iq2_xs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS / 2, block_iq2_xs, 1>(vx, vy, dst, ncols,
nrows, item_ct1);
});
});
}
@ -878,14 +829,11 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S/2, block_iq2_s, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S / 2, block_iq2_s, 1>(vx, vy, dst, ncols, nrows,
item_ct1);
});
});
}
@ -900,14 +848,11 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS/2, block_iq3_xxs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS / 2, block_iq3_xxs, 1>(vx, vy, dst, ncols,
nrows, item_ct1);
});
});
}
@ -922,14 +867,11 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S/2, block_iq3_s, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S / 2, block_iq3_s, 1>(vx, vy, dst, ncols, nrows,
item_ct1);
});
});
}
@ -944,14 +886,11 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(vx, vy, dst, ncols, nrows,
item_ct1);
});
});
}
@ -966,13 +905,11 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(vx, vy, dst, ncols, nrows,
item_ct1);
});
});
}
@ -987,14 +924,11 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(vx, vy, dst, ncols, nrows,
item_ct1);
});
});
}
@ -1009,14 +943,11 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler &cgh) {
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS/4, block_iq4_xs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS / 4, block_iq4_xs, 1>(vx, vy, dst, ncols,
nrows, item_ct1);
});
});
}

143
ggml/src/ggml-sycl/norm.cpp
View File

@ -254,12 +254,11 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i
GGML_ASSERT(ncols % WARP_SIZE == 0);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
cgh.parallel_for(
sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
nullptr, WARP_SIZE);
});
});
}
@ -272,14 +271,13 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
sycl::range<1>(work_group_size / WARP_SIZE), cgh);
cgh.parallel_for(
sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
get_pointer(s_sum_acc_ct1), work_group_size);
});
});
}
@ -290,16 +288,12 @@ static void group_norm_f32_sycl(const float* x, float* dst,
const int ne_elements, queue_ptr stream, int device) {
if (group_size < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
const float eps_ct4 = eps;
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
group_norm_f32(
x, dst, group_size, ne_elements, eps_ct4, item_ct1,
nullptr, WARP_SIZE);
sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
group_norm_f32(x, dst, group_size, ne_elements, eps_ct4, item_ct1, nullptr,
WARP_SIZE);
});
});
}
@ -313,19 +307,15 @@ static void group_norm_f32_sycl(const float* x, float* dst,
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
cgh);
const float eps_ct4 = eps;
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
group_norm_f32(x, dst, group_size, ne_elements,
eps_ct4, item_ct1,
sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
group_norm_f32(x, dst, group_size, ne_elements, eps_ct4, item_ct1,
get_pointer(s_sum_acc_ct1), work_group_size);
});
});
@ -340,51 +330,10 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const
const sycl::range<3> global_dims(nsamples, nchannels, nrows);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
cgh.parallel_for(
sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
});
});
}
else {
const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
cgh);
cgh.parallel_for(
sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
});
});
}
}
static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream, int device) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
// printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
l2_norm_f32(x, dst, ncols, eps, item_ct1,
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
nullptr, WARP_SIZE);
});
});
@ -398,21 +347,53 @@ static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
l2_norm_f32(x, dst, ncols, eps, item_ct1,
sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
get_pointer(s_sum_acc_ct1), work_group_size);
});
});
}
}
static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream, int device) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
// printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
l2_norm_f32(x, dst, ncols, eps, item_ct1, nullptr, WARP_SIZE);
});
});
}
else {
const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
cgh);
sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
l2_norm_f32(x, dst, ncols, eps, item_ct1, get_pointer(s_sum_acc_ct1),
work_group_size);
});
});
}
}
void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
const ggml_tensor * src0 = dst->src[0];

36
ggml/src/ggml-sycl/rope.cpp
View File

@ -235,9 +235,10 @@ static void rope_norm_sycl(const T * x, T * dst, const int ne0, const int ne1, c
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rope_norm<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, item_ct1);
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_norm<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
});
} else {
/*
@ -245,9 +246,10 @@ static void rope_norm_sycl(const T * x, T * dst, const int ne0, const int ne1, c
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rope_norm<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, item_ct1);
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_norm<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
});
}
}
@ -267,14 +269,16 @@ static void rope_neox_sycl(const T * x, T * dst, const int ne0, const int ne1, c
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
if (freq_factors == nullptr) {
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, item_ct1);
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
});
} else {
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
theta_scale, freq_factors, item_ct1);
sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
});
}
}
@ -298,12 +302,12 @@ static void rope_multi_sycl(const T * x, T * dst, const int ne0, const int ne1,
}
// launch kernel
if (freq_factors == nullptr) {
stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
rope_multi<T, false>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
corr_dims, theta_scale, freq_factors, sections, item_ct1);
});
} else {
stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
rope_multi<T, true>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
corr_dims, theta_scale, freq_factors, sections, item_ct1);
});
@ -333,12 +337,12 @@ static void rope_vision_sycl(const T * x, T * dst, const int ne0, const int ne1,
}
// launch kernel
if (freq_factors == nullptr) {
stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
rope_vision<T, false>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
corr_dims, theta_scale, freq_factors, sections, item_ct1);
});
} else {
stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
rope_vision<T, true>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
corr_dims, theta_scale, freq_factors, sections, item_ct1);
});

6
ggml/src/ggml-sycl/softmax.cpp
View File

@ -127,11 +127,11 @@ static void soft_max_f32_submitter(const float * x, const T * mask, float * dst,
const int nrows_y, const float scale, const float max_bias, const float m0,
const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
const size_t n_local_scratch, queue_ptr stream) {
stream->submit([&](sycl::handler &cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
sycl_parallel_for(
cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
nrows_y, scale, max_bias, m0,

9
ggml/src/ggml-sycl/tsembd.cpp
View File

@ -45,13 +45,8 @@ static void timestep_embedding_f32_sycl(
int num_blocks = (half_ceil + SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE;
sycl::range<3> block_dims(1, 1, SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE);
sycl::range<3> gridDim(1, ne00, num_blocks);
stream->parallel_for(
sycl::nd_range<3>(
gridDim * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
timestep_embedding_f32(
x, dst, nb1, dim, max_period, item_ct1
);
sycl_parallel_for(stream, sycl::nd_range<3>(gridDim * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
timestep_embedding_f32(x, dst, nb1, dim, max_period, item_ct1);
});
}

28
ggml/src/ggml-sycl/wkv.cpp
View File

@ -207,12 +207,11 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
// Submit kernel
if (C / H == WKV_BLOCK_SIZE) {
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rwkv_wkv6_f32_kernel<WKV_BLOCK_SIZE>(
B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
@ -220,12 +219,11 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
});
});
} else {
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rwkv_wkv6_f32_kernel<WKV_BLOCK_SIZE * 2>(
B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
@ -264,12 +262,11 @@ void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
// Submit kernel
if (C / H == WKV_BLOCK_SIZE) {
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rwkv_wkv7_f32_kernel<WKV_BLOCK_SIZE>(
B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d,
item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
@ -277,12 +274,11 @@ void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
});
});
} else {
stream->submit([&](sycl::handler& cgh) {
sycl_launch(stream, [&](sycl::handler & cgh) {
sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
sycl_parallel_for(
cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
rwkv_wkv7_f32_kernel<WKV_BLOCK_SIZE * 2>(
B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d,
item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()

66
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

@ -1041,6 +1041,14 @@ void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) {
struct vk_instance_t {
vk::Instance instance;
bool debug_utils_support = false; // VK_EXT_debug_utils enabled
PFN_vkSetDebugUtilsObjectNameEXT pfn_vkSetDebugUtilsObjectNameEXT = {};
PFN_vkQueueBeginDebugUtilsLabelEXT pfn_vkQueueBeginDebugUtilsLabelEXT = {};
PFN_vkQueueEndDebugUtilsLabelEXT pfn_vkQueueEndDebugUtilsLabelEXT = {};
PFN_vkCmdBeginDebugUtilsLabelEXT pfn_vkCmdBeginDebugUtilsLabelEXT = {};
PFN_vkCmdEndDebugUtilsLabelEXT pfn_vkCmdEndDebugUtilsLabelEXT = {};
PFN_vkCmdInsertDebugUtilsLabelEXT pfn_vkCmdInsertDebugUtilsLabelEXT = {};
std::vector<size_t> device_indices;
vk_device devices[GGML_VK_MAX_DEVICES];
};
@ -1180,6 +1188,14 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
}
pipeline->compiled = true;
if (vk_instance.debug_utils_support) {
vk::DebugUtilsObjectNameInfoEXT duoni;
duoni.objectType = vk::ObjectType::ePipeline;
duoni.pObjectName = pipeline->name.c_str();
duoni.objectHandle = reinterpret_cast<uint64_t>(static_cast<VkPipeline_T*>(pipeline->pipeline));
vk_instance.pfn_vkSetDebugUtilsObjectNameEXT(device->device, &static_cast<VkDebugUtilsObjectNameInfoEXT &>(duoni));
}
{
std::lock_guard<std::mutex> guard(device->mutex);
device->pipelines.insert({ pipeline->name, pipeline });
@ -3561,6 +3577,8 @@ static void ggml_vk_print_gpu_info(size_t idx) {
static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
static bool ggml_vk_instance_debug_utils_ext_available(const std::vector<vk::ExtensionProperties> & instance_extensions);
static void ggml_vk_instance_init() {
if (vk_instance_initialized) {
return;
@ -3581,7 +3599,7 @@ static void ggml_vk_instance_init() {
#ifdef __APPLE__
const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
#endif
const bool debug_utils_ext = ggml_vk_instance_debug_utils_ext_available(instance_extensions) && getenv("GGML_VK_DEBUG_MARKERS") != nullptr;
std::vector<const char*> layers;
if (validation_ext) {
@ -3596,6 +3614,9 @@ static void ggml_vk_instance_init() {
extensions.push_back("VK_KHR_portability_enumeration");
}
#endif
if (debug_utils_ext) {
extensions.push_back("VK_EXT_debug_utils");
}
vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions);
#ifdef __APPLE__
if (portability_enumeration_ext) {
@ -3619,6 +3640,18 @@ static void ggml_vk_instance_init() {
vk_instance.instance = vk::createInstance(instance_create_info);
vk_instance_initialized = true;
if (debug_utils_ext) {
vk_instance.debug_utils_support = true;
vk_instance.pfn_vkSetDebugUtilsObjectNameEXT = (PFN_vkSetDebugUtilsObjectNameEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkSetDebugUtilsObjectNameEXT");
vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT = (PFN_vkQueueBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueBeginDebugUtilsLabelEXT");
vk_instance.pfn_vkQueueEndDebugUtilsLabelEXT = (PFN_vkQueueEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueEndDebugUtilsLabelEXT");
vk_instance.pfn_vkCmdBeginDebugUtilsLabelEXT = (PFN_vkCmdBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdBeginDebugUtilsLabelEXT");
vk_instance.pfn_vkCmdEndDebugUtilsLabelEXT = (PFN_vkCmdEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdEndDebugUtilsLabelEXT");
vk_instance.pfn_vkCmdInsertDebugUtilsLabelEXT = (PFN_vkCmdInsertDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdInsertDebugUtilsLabelEXT");
}
size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr;
// Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
@ -9495,6 +9528,12 @@ static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer
UNUSED(buft);
}
static size_t ggml_backend_vk_host_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
return vk_instance.devices[0]->suballocation_block_size;
UNUSED(buft);
}
// Should be changed to return device-specific host buffer type
// but that probably requires changes in llama.cpp
ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
@ -9503,7 +9542,7 @@ ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
/* .get_name = */ ggml_backend_vk_host_buffer_type_name,
/* .alloc_buffer = */ ggml_backend_vk_host_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_vk_host_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_max_size = */ ggml_backend_vk_host_buffer_type_get_max_size,
/* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
/* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host,
},
@ -9650,6 +9689,13 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)");
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
if (vk_instance.debug_utils_support) {
vk::DebugUtilsLabelEXT dul = {};
dul.pLabelName = "ggml_backend_vk_graph_compute";
dul.color = std::array<float,4>{1.0f, 1.0f, 1.0f, 1.0f};
vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue.queue, reinterpret_cast<VkDebugUtilsLabelEXT*>(&dul));
}
uint64_t total_mat_mul_bytes = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false, false, false);
@ -10339,6 +10385,22 @@ static bool ggml_vk_instance_portability_enumeration_ext_available(const std::ve
UNUSED(instance_extensions);
}
// Extension availability
static bool ggml_vk_instance_debug_utils_ext_available(
const std::vector<vk::ExtensionProperties> & instance_extensions) {
// Check for portability enumeration extension for MoltenVK support
for (const auto & properties : instance_extensions) {
if (strcmp("VK_EXT_debug_utils", properties.extensionName) == 0) {
return true;
}
}
std::cerr << "ggml_vulkan: WARNING: Instance extension VK_EXT_debug_utils not found." << std::endl;
return false;
UNUSED(instance_extensions);
}
static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch) {
switch (props.vendorID) {
case VK_VENDOR_ID_INTEL:

34
ggml/src/ggml.c
View File

@ -955,6 +955,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"UPSCALE",
"PAD",
"PAD_REFLECT_1D",
"ROLL",
"ARANGE",
"TIMESTEP_EMBEDDING",
"ARGSORT",
@ -985,7 +986,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"OPT_STEP_ADAMW",
};
static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -1050,6 +1051,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"upscale(x)",
"pad(x)",
"pad_reflect_1d(x)",
"roll(x)",
"arange(start, stop, step)",
"timestep_embedding(timesteps, dim, max_period)",
"argsort(x)",
@ -1080,7 +1082,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"adamw(x)",
};
static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -4341,6 +4343,34 @@ struct ggml_tensor * ggml_pad_reflect_1d(
return result;
}
// ggml_roll
struct ggml_tensor * ggml_roll(
struct ggml_context * ctx,
struct ggml_tensor * a,
int shift0,
int shift1,
int shift2,
int shift3) {
GGML_ASSERT(a->nb[0] == ggml_type_size(a->type));
GGML_ASSERT(abs(shift0) < a->ne[0]);
GGML_ASSERT(abs(shift1) < a->ne[1]);
GGML_ASSERT(abs(shift2) < a->ne[2]);
GGML_ASSERT(abs(shift3) < a->ne[3]);
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
ggml_set_op_params_i32(result, 0, shift0);
ggml_set_op_params_i32(result, 1, shift1);
ggml_set_op_params_i32(result, 2, shift2);
ggml_set_op_params_i32(result, 3, shift3);
result->op = GGML_OP_ROLL;
result->src[0] = a;
return result;
}
// ggml_arange
struct ggml_tensor * ggml_arange(

1
gguf-py/gguf/constants.py
View File

@ -199,6 +199,7 @@ class Keys:
MASK_ID = "tokenizer.ggml.mask_token_id"
ADD_BOS = "tokenizer.ggml.add_bos_token"
ADD_EOS = "tokenizer.ggml.add_eos_token"
ADD_SEP = "tokenizer.ggml.add_sep_token"
ADD_PREFIX = "tokenizer.ggml.add_space_prefix"
REMOVE_EXTRA_WS = "tokenizer.ggml.remove_extra_whitespaces"
PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap"

3
gguf-py/gguf/gguf_writer.py
View File

@ -894,6 +894,9 @@ class GGUFWriter:
def add_add_eos_token(self, value: bool) -> None:
self.add_bool(Keys.Tokenizer.ADD_EOS, value)
def add_add_sep_token(self, value: bool) -> None:
self.add_bool(Keys.Tokenizer.ADD_SEP, value)
def add_add_space_prefix(self, value: bool) -> None:
self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)

99
gguf-py/gguf/vocab.py
View File

@ -7,7 +7,10 @@ import os
from pathlib import Path
from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable
from sentencepiece import SentencePieceProcessor
try:
from sentencepiece import SentencePieceProcessor
except ImportError:
SentencePieceProcessor = None
import gguf
@ -116,6 +119,7 @@ class SpecialVocab:
logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping')
def _try_load_from_tokenizer_json(self, path: Path) -> bool:
tokenizer = None
tokenizer_file = path / 'tokenizer.json'
if tokenizer_file.is_file():
with open(tokenizer_file, encoding = 'utf-8') as f:
@ -149,11 +153,97 @@ class SpecialVocab:
added_tokens = tokenizer.get('added_tokens', {})
else:
added_tokens = {}
tokenizer_config = None
tokenizer_config_file = path / 'tokenizer_config.json'
if not tokenizer_config_file.is_file():
return True
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, encoding = 'utf-8') as f:
tokenizer_config = json.load(f)
if tokenizer:
special_bos = (tokenizer_config or {}).get('bos_token')
special_cls = (tokenizer_config or {}).get('cls_token')
special_eos = (tokenizer_config or {}).get('eos_token')
special_sep = (tokenizer_config or {}).get('sep_token')
if not special_bos and special_cls and tokenizer_config:
tokenizer_config['bos_token'] = special_bos = special_cls
if not special_eos and special_sep and tokenizer_config:
tokenizer_config['eos_token'] = special_eos = special_sep
if post_processor := tokenizer.get('post_processor'):
for processor in post_processor.get('processors', [post_processor]):
if processor.get('type') == 'RobertaProcessing':
self.add_special_token['bos'] = True
self.add_special_token['eos'] = True
self.add_special_token['sep'] = True
if not special_cls and tokenizer_config:
special_cls = processor.get('cls', [special_bos])[0]
tokenizer_config['cls_token'] = special_cls
if not special_sep and tokenizer_config:
special_sep = processor.get('sep', [special_eos])[0]
tokenizer_config['sep_token'] = special_sep
continue
# Crude parsing of TemplateProcessing to determine if BOS/SEP/EOS should be added
# Only works with simple templates, **will** get it wrong on unusual sequences
if processor.get('type') == 'TemplateProcessing':
tmpl_single = processor.get('single', [])
tmpl_pair = processor.get('pair', [])
special_first = None
special_last = None
if len(tmpl_single) > 1:
if special_first := tmpl_single[0].get('SpecialToken', {}).get('id'):
if not tokenizer_config:
special_bos = special_first
self.add_special_token['bos'] = True if special_first in (special_bos, special_cls) else False
if special_first not in (special_bos, special_cls):
logger.warning(f'Unknown leading special token {special_first!r} in TemplateProcessing<single>')
if special_last := tmpl_single[-1].get('SpecialToken', {}).get('id'):
if not tokenizer_config:
special_eos = special_last
elif special_last != special_eos:
if 'eot' not in self.special_token_types:
self.special_token_types = tuple(self.special_token_types) + ('eot', )
tokenizer_config['eot_token'] = special_eos
elif 'eom' not in self.special_token_types:
self.special_token_types = tuple(self.special_token_types) + ('eom', )
tokenizer_config['eom_token'] = special_eos
else:
logger.warning(f'Overriding EOS token {special_eos!r} with {special_last!r} without EOT/EOM fallback!')
tokenizer_config['eos_token'] = special_eos = special_last
self.add_special_token['eos'] = True if special_last == special_eos else False
if special_last != special_eos:
logger.warning(f'Unknown trailing special token {special_last!r} in TemplateProcessing<single>')
if tmpl_pair:
seq_start = 1 if special_first and tmpl_pair[0].get('SpecialToken', {}).get('id') == special_first else 0
seq_stop = -1 if special_last and tmpl_pair[-1].get('SpecialToken', {}).get('id') == special_last else None
if (special_first and seq_start == 0) or (special_last and seq_stop is None):
logger.warning('TemplateProcessing<single> leading/trailing special tokens do not match TemplateProcessing<pair>')
if tmpl_pair := tmpl_pair[slice(seq_start, seq_stop)]:
tmpl_a = tmpl_pair[0].get('Sequence', {}).get('id')
tmpl_b = tmpl_pair[-1].get('Sequence', {}).get('id')
if tmpl_a != 'A' or tmpl_b != 'B':
logger.warning(f'Unknown sequence {tmpl_a}...{tmpl_b} in TemplateProcessing<pair>')
# A [sep] [eos] B
if tmpl_a == 'A' and tmpl_b == 'B' and (tmpl_pair := tmpl_pair[1:-1]):
add_sep = False
if special_entry := tmpl_pair[0].get('SpecialToken', {}).get('id'):
if special_entry in (special_sep, special_eos) and not special_last:
add_sep = True
if special_entry not in (special_sep, special_eos):
logger.warning(f'Unknown separator token {special_entry!r} in TemplateProcessing<pair>')
else:
logger.warning(f'Unknown middle sequence {tmpl_pair[0]!r} in TemplateProcessing<pair>')
if len(tmpl_pair) == 2:
if special_entry := tmpl_pair[1].get('SpecialToken', {}).get('id'):
if special_entry in (special_sep, special_eos):
add_sep = True
if special_entry not in (special_sep, special_eos):
logger.warning(f'Unknown second separator token {special_entry!r} in TemplateProcessing<pair>')
else:
logger.warning(f'Unknown second middle sequence {tmpl_pair[1]!r} in TemplateProcessing<pair>')
self.add_special_token['sep'] = add_sep
if add_sep and not special_sep and tokenizer_config:
tokenizer_config['sep_token'] = special_eos
continue
if not tokenizer_config:
return True
chat_template_alt = None
chat_template_file = path / 'chat_template.json'
if chat_template_file.is_file():
@ -302,6 +392,9 @@ class SentencePieceVocab(Vocab):
name = "spm"
def __init__(self, base_path: Path):
if SentencePieceProcessor is None:
raise RuntimeError("sentencepiece is not installed")
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'tokenizer.model').exists():
# normal location

4
gguf-py/pyproject.toml
View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "gguf"
version = "0.17.0"
version = "0.17.1"
description = "Read and write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [
@ -22,7 +22,7 @@ python = ">=3.8"
numpy = ">=1.17"
tqdm = ">=4.27"
pyyaml = ">=5.1"
sentencepiece = ">=0.1.98,<=0.2.0"
sentencepiece = { version = ">=0.1.98,<=0.2.0", optional = true }
PySide6 = { version = "^6.9", python = ">=3.9,<3.14", optional = true }
[tool.poetry.dev-dependencies]

11
include/llama.h
View File

@ -390,6 +390,7 @@ extern "C" {
void * imatrix; // pointer to importance matrix data
void * kv_overrides; // pointer to vector containing overrides
void * tensor_types; // pointer to vector containing tensor types
void * prune_layers; // pointer to vector containing layer indices to prune
} llama_model_quantize_params;
typedef struct llama_logit_bias {
@ -943,12 +944,14 @@ extern "C" {
// Requires the context to have a memory.
// For encode-decoder contexts, processes the batch using the decoder.
// Positive return values does not mean a fatal error, but rather a warning.
// Upon non-zero return values, the memory state is restored to the state before this call
// Upon fatal-error or abort, the ubatches that managed to be been processed will remain in the memory state of the context
// To handle this correctly, query the memory state using llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
// Upon other return values, the memory state is restored to the state before this call
// 0 - success
// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
// 2 - aborted
// 2 - aborted (processed ubatches will remain in the context's memory)
// -1 - invalid input batch
// < -1 - error
// < -1 - fatal error (processed ubatches will remain in the context's memory)
LLAMA_API int32_t llama_decode(
struct llama_context * ctx,
struct llama_batch batch);
@ -1044,6 +1047,7 @@ extern "C" {
LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab);
LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab);
LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab);
@ -1087,6 +1091,7 @@ extern "C" {
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
/// @return Returns the number of tokens on success, no more than n_tokens_max
/// @return Returns a negative number on failure - the number of tokens that would have been returned
/// @return Returns INT32_MIN on overflow (e.g., tokenization result size exceeds int32_t limit)
/// @param add_special Allow to add BOS and EOS tokens if model is configured to do so.
/// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
/// as plaintext. Does not insert a leading space.

2
scripts/sync-ggml.last
View File

@ -1 +1 @@
8cda0a3c19f2c7dc493887353c42f6956bc268b1
9e4bee1c5afc2d677a5b32ecb90cbdb483e81fff

1
src/llama-arch.cpp
View File

@ -200,6 +200,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
{ LLM_KV_TOKENIZER_ADD_SEP, "tokenizer.ggml.add_sep_token" },
{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
{ LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" },
{ LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" },

1
src/llama-arch.h
View File

@ -196,6 +196,7 @@ enum llm_kv {
LLM_KV_TOKENIZER_MASK_ID,
LLM_KV_TOKENIZER_ADD_BOS,
LLM_KV_TOKENIZER_ADD_EOS,
LLM_KV_TOKENIZER_ADD_SEP,
LLM_KV_TOKENIZER_ADD_PREFIX,
LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,

875
src/llama-batch.cpp
View File

File diff suppressed because it is too large Load Diff

166
src/llama-batch.h
View File

@ -2,86 +2,44 @@
#include "llama.h"
#include "llama-cparams.h"
#include <array>
#include <vector>
#include <set>
#include <bitset>
#include <unordered_map>
// very similar to llama_batch,
// but has more metadata about sequences
// keep this struct lightweight
// it points to data in `llama_batch_allocr`
struct llama_ubatch {
bool equal_seqs;
// TODO: whole_seqs for embeddings?
uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs)
uint32_t n_seq_tokens; // tokens per sequence
uint32_t n_seqs;
uint32_t n_seq_tokens; // tokens per sequence set
uint32_t n_seqs; // sequence sets in the ubatch
uint32_t n_seqs_unq; // unique sequence ids in the ubatch
llama_token * token; // [n_tokens]
float * embd; // [n_embd, n_tokens]
llama_pos * pos; // [n_tokens]
int32_t * n_seq_id; // [n_seqs]
llama_seq_id ** seq_id; // [n_seqs]
int8_t * output; // [n_tokens]
// seq_id_unq: unique sequence ids in the ubatch
// seq_idx: indices of the unique sequence ids in the ubatch in [0, n_seqs_unq)
// used for extracting sequence pooled embeddings
// // size | idx | val
llama_token * token; // [n_tokens] | i | id, token
float * embd; // [n_embd, n_tokens] | i | embd
llama_pos * pos; // [n_tokens] | i | pos
int32_t * n_seq_id; // [n_tokens] | i | -
llama_seq_id ** seq_id; // [n_tokens] | s | s0, s1, seq_id
llama_seq_id * seq_id_unq; // [n_seqs_unq] | s | seq_id
int32_t * seq_idx; // [LLAMA_MAX_SEQ] | - | seq_idx
int8_t * output; // [n_tokens] | i | -
};
struct llama_sbatch_seq {
int32_t n_seq_id;
llama_seq_id * seq_id;
size_t offset;
size_t length;
};
// sequence-length-aware batch splitting
struct llama_sbatch {
// tokens left in this batch
size_t n_tokens;
size_t n_embd;
// sorted indices into the batch
std::vector<int64_t> ids;
// batch indices of the output
std::vector<int64_t> out_ids;
std::vector<llama_sbatch_seq> seq;
const llama_batch * batch = nullptr;
// buffers for the ubatches
// TODO: very hacky, this needs a complete rework
struct ubatch_data {
std::vector<llama_token> token;
std::vector<float> embd;
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<int8_t> output;
};
std::vector<ubatch_data> udatas;
llama_ubatch reserve_ubatch(size_t n_ubatch, bool has_embd = false);
void add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & seq, size_t length);
// simple split, unknown number of sequences of unequal lengths
llama_ubatch split_simple(size_t n_ubatch);
// make batches of equal-length sequences
llama_ubatch split_equal(size_t n_ubatch);
// sequence-wise split
llama_ubatch split_seq(size_t n_ubatch);
llama_sbatch() = default;
llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false);
};
// a helper for sanitizing and fulfilling a batch
// a helper for sanitizing, fulfilling and splitting a batch
class llama_batch_allocr {
public:
llama_batch_allocr();
llama_batch_allocr(uint32_t n_pos_per_embd);
// sanitize and auto-gen missing data in the input batch
// memory is optional. if provided will be used to check for sequence continuity and to determine the positions
@ -89,20 +47,57 @@ public:
const llama_batch & batch_inp,
const llama_vocab & vocab,
const llama_memory_i * memory,
bool embd_all);
uint32_t n_embd,
bool output_all);
const llama_batch & get_batch() const;
uint32_t get_n_tokens() const;
uint32_t get_n_outputs() const;
// the array of output indices in the order they were encountered during the ubatch splitting
std::vector<int32_t> & get_out_ids();
// min/max positions of each sequence in the current ubatch
llama_pos seq_pos_min(llama_seq_id seq_id) const;
llama_pos seq_pos_max(llama_seq_id seq_id) const;
// call once before splitting the batch to reset the internal state
void split_reset();
// simple split, unknown number of sequence sets of unequal lengths
llama_ubatch split_simple(uint32_t n_ubatch);
// make ubatches of equal-length sequences sets
llama_ubatch split_equal(uint32_t n_ubatch);
// sequence-set-wise split - each ubatch contains a single sequence-set
llama_ubatch split_seq(uint32_t n_ubatch);
// a helper method for creating a well-defined ubatch of tokens
// TODO: support embeddings if needed in the future
llama_ubatch ubatch_reserve(uint32_t n_seq_tokens, uint32_t n_seqs);
private:
void clear();
// create the next ubatch based on the provided batch indices (idxs) and the number of sequence sets (n_seqs)
// return llama_ubatch.n_tokens == 0 if the entire batch was consumed
llama_ubatch ubatch_add(const std::vector<int32_t> & idxs, uint32_t n_seqs, bool equal_seqs);
// for debugging, start with LLAMA_BATCH_DEBUG=2
void ubatch_print(const llama_ubatch & ubatch, int debug);
llama_batch batch;
// only for debugging purposes
const llama_vocab * vocab;
// TODO: this is more of a temporary solution until we have a better way to handle multiple positions per token/embd
// ref: https://github.com/ggml-org/llama.cpp/issues/13694#issuecomment-2983871762
const uint32_t n_pos_per_embd;
uint32_t n_embd;
uint32_t n_outputs;
std::array<llama_seq_id, 1> seq_id_0 = { 0 }; // default sequence id
@ -110,10 +105,43 @@ private:
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<llama_seq_id> seq_id_unq;
std::vector<int32_t> seq_idx;
std::vector<int8_t> output;
std::vector<std::set<llama_pos>> seq_pos; // seq_pos[s]: the set of positions in sequence s
std::vector<std::vector<bool>> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1
using pos_set_t = std::set<llama_pos>;
using seq_cpl_t = std::vector<bool>;
std::vector<pos_set_t> seq_pos; // seq_pos[s]: the set of positions in sequence s
std::vector<seq_cpl_t> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1
using idx_vec_t = std::vector<int32_t>;
using seq_set_t = std::bitset<LLAMA_MAX_SEQ>;
std::vector<seq_set_t> seq_set; // seq_set[i]: the sequence set of token i
std::unordered_map<seq_set_t, idx_vec_t> seq_set_map; // the indices at which the sequence set appears
// batch indices of the output
std::vector<int32_t> out_ids;
// used[i] indicates if token i has already been used in a previous ubatch
std::vector<bool> used;
// llama_ubatch points to this data:
struct ubatch {
std::vector<llama_token> token;
std::vector<float> embd;
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<llama_seq_id> seq_id_unq;
std::vector<int32_t> seq_idx;
std::vector<int8_t> output;
};
// current splitting state:
std::vector<ubatch> ubatches;
int debug;
};

17
src/llama-chat.cpp
View File

@ -528,12 +528,17 @@ int32_t llm_chat_apply_template(
}
} else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) {
// this template requires the model to have "\n\n" as EOT token
for (auto message : chat) {
std::string role(message->role);
if (role == "user") {
ss << "User: " << message->content << "\n\nAssistant:";
} else {
ss << message->content << "\n\n";
for (size_t i = 0; i < chat.size(); i++) {
std::string role(chat[i]->role);
if (role == "system") {
ss << "System: " << trim(chat[i]->content) << "\n\n";
} else if (role == "user") {
ss << "User: " << trim(chat[i]->content) << "\n\n";
if (i == chat.size() - 1) {
ss << "Assistant:";
}
} else if (role == "assistant") {
ss << "Assistant: " << trim(chat[i]->content) << "\n\n";
}
}
} else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) {

200
src/llama-context.cpp
View File

@ -20,7 +20,7 @@ llama_context::llama_context(
const llama_model & model,
llama_context_params params) :
model(model),
batch_allocr(std::make_unique<llama_batch_allocr>()) {
balloc(std::make_unique<llama_batch_allocr>(model.hparams.n_pos_per_embd())) {
LLAMA_LOG_INFO("%s: constructing llama_context\n", __func__);
t_start_us = model.t_start_us;
@ -280,8 +280,8 @@ llama_context::llama_context(
// simulate full KV cache
const auto mstate = memory->init_full();
if (!mstate) {
const auto mctx = memory->init_full();
if (!mctx) {
throw std::runtime_error("failed to initialize KV cache");
}
@ -289,7 +289,7 @@ llama_context::llama_context(
// reserve pp graph first so that buffers are only allocated once
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
@ -300,7 +300,7 @@ llama_context::llama_context(
// reserve with tg graph to get the number of splits and nodes
{
auto * gf = graph_reserve(1, 1, 1, mstate.get());
auto * gf = graph_reserve(1, 1, 1, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute tg buffers");
}
@ -311,7 +311,7 @@ llama_context::llama_context(
// reserve again with pp graph to avoid ggml-alloc reallocations during inference
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
@ -444,8 +444,8 @@ bool llama_context::kv_self_update(bool optimize) {
optimize |= memory_force_optimize;
memory_force_optimize = false;
const auto mstate = memory->init_update(this, optimize);
switch (mstate->get_status()) {
const auto mctx = memory->init_update(this, optimize);
switch (mctx->get_status()) {
case LLAMA_MEMORY_STATUS_SUCCESS:
{
// noop
@ -463,22 +463,22 @@ bool llama_context::kv_self_update(bool optimize) {
}
}
if (!mstate->apply()) {
if (!mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory update\n", __func__);
}
}
// if the memory module did any computation, we have to reserve a new worst-case graph
{
const auto mstate = memory->init_full();
if (!mstate) {
throw std::runtime_error("failed to initialize memory state");
const auto mctx = memory->init_full();
if (!mctx) {
throw std::runtime_error("failed to initialize memory context");
}
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
LLAMA_LOG_ERROR("%s: failed to reserve graph after the memory update\n", __func__);
}
@ -678,9 +678,9 @@ bool llama_context::apply_adapter_cvec(
return cvec.apply(model, data, len, n_embd, il_start, il_end);
}
llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_state_i * mstate, ggml_status & ret) {
if (mstate && !mstate->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory state\n", __func__);
llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_context_i * mctx, ggml_status & ret) {
if (mctx && !mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory context\n", __func__);
ret = GGML_STATUS_FAILED;
return nullptr;
}
@ -692,7 +692,7 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch,
return nullptr;
}
auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mstate);
auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mctx);
if (!res) {
LLAMA_LOG_ERROR("%s: failed to build graph\n", __func__);
ret = GGML_STATUS_FAILED;
@ -722,22 +722,26 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch,
}
int llama_context::encode(const llama_batch & batch_inp) {
GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT
if (batch_inp.n_tokens == 0) {
LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
return -1;
}
const auto & hparams = model.hparams;
const int64_t n_embd = hparams.n_embd;
// note: during encode, we always pass the full sequence starting from pos = 0
if (!batch_allocr->init(batch_inp, model.vocab, nullptr, true)) {
if (!balloc->init(batch_inp, model.vocab, nullptr, n_embd, true)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return -1;
}
const llama_batch & batch = batch_allocr->get_batch();
const uint32_t n_tokens = balloc->get_n_tokens();
const uint32_t n_tokens = batch.n_tokens;
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
const llama_ubatch ubatch = balloc->split_simple(n_tokens);
// micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
@ -751,14 +755,6 @@ int llama_context::encode(const llama_batch & batch_inp) {
n_queued_tokens += n_tokens;
const auto & hparams = model.hparams;
const int64_t n_embd = hparams.n_embd;
llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true);
const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
// reserve output buffer
if (output_reserve(n_tokens) < n_tokens) {
LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
@ -817,34 +813,28 @@ int llama_context::encode(const llama_batch & batch_inp) {
{
// extract sequence embeddings
auto & embd_seq_out = embd_seq;
embd_seq_out.clear();
GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits
for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
const llama_seq_id seq_id = ubatch.seq_id_unq[s];
const int32_t seq_idx = ubatch.seq_idx[seq_id];
// TODO: fix indexing [UBATCH_IDX]
for (uint32_t i = 0; i < n_tokens; i++) {
const llama_seq_id seq_id = ubatch.seq_id[i][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(n_embd);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_RANK:
{
// extract the rerank score - n_cls_out floats per sequence
auto & embd_seq_out = embd_seq;
const uint32_t n_cls_out = hparams.n_cls_out;
// TODO: fix indexing [UBATCH_IDX]
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
const llama_seq_id seq_id = ubatch.seq_id_unq[s];
const int32_t seq_idx = ubatch.seq_idx[seq_id];
embd_seq_out[seq_id].resize(n_cls_out);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_id)*sizeof(float), n_cls_out*sizeof(float));
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
@ -869,12 +859,16 @@ int llama_context::encode(const llama_batch & batch_inp) {
cross.v_embd.resize(cross.n_embd*cross.n_enc);
memcpy(cross.v_embd.data(), embd, ggml_nbytes(t_embd));
const auto & batch = balloc->get_batch();
// remember the sequence ids used during the encoding - needed for cross attention later
cross.seq_ids_enc.resize(n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
cross.seq_ids_enc[i].clear();
for (int s = 0; s < batch.n_seq_id[i]; s++) {
llama_seq_id seq_id = batch.seq_id[i][s];
const llama_seq_id seq_id = batch.seq_id[i][s];
cross.seq_ids_enc[i].insert(seq_id);
}
}
@ -884,6 +878,8 @@ int llama_context::encode(const llama_batch & batch_inp) {
}
int llama_context::decode(const llama_batch & batch_inp) {
GGML_ASSERT((!batch_inp.token && batch_inp.embd) || (batch_inp.token && !batch_inp.embd)); // NOLINT
if (!memory) {
LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__);
return encode(batch_inp);
@ -894,29 +890,24 @@ int llama_context::decode(const llama_batch & batch_inp) {
return -1;
}
// when computing embeddings, all tokens are output
const bool embd_all = cparams.embeddings;
if (!batch_allocr->init(batch_inp, model.vocab, memory.get(), embd_all)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return -1;
}
const llama_batch & batch = batch_allocr->get_batch();
const auto & vocab = model.vocab;
const auto & hparams = model.hparams;
const int32_t n_vocab = vocab.n_tokens();
const int64_t n_embd = hparams.n_embd;
const uint32_t n_tokens_all = batch.n_tokens;
// when computing embeddings, all tokens are output
const bool output_all = cparams.embeddings;
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
if (!balloc->init(batch_inp, vocab, memory.get(), n_embd, output_all)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return -1;
}
const uint32_t n_outputs_all = batch_allocr->get_n_outputs();
const uint32_t n_tokens_all = balloc->get_n_tokens();
const uint32_t n_outputs_all = balloc->get_n_outputs();
if (embd_all) {
if (output_all) {
// require that all tokens are output
if (n_outputs_all != n_tokens_all) {
LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %d, n_tokens_all = %d)\n",
@ -942,21 +933,21 @@ int llama_context::decode(const llama_batch & batch_inp) {
// handle any pending defrags/shifts
kv_self_update(false);
llama_memory_state_ptr mstate;
llama_memory_context_ptr mctx;
while (true) {
mstate = memory->init_batch(batch, cparams.n_ubatch, embd_all);
if (!mstate) {
mctx = memory->init_batch(*balloc, cparams.n_ubatch, output_all);
if (!mctx) {
return -2;
}
switch (mstate->get_status()) {
switch (mctx->get_status()) {
case LLAMA_MEMORY_STATUS_SUCCESS:
{
} break;
case LLAMA_MEMORY_STATUS_NO_UPDATE:
{
LLAMA_LOG_ERROR("%s: unexpected memory state status: %d\n", __func__, mstate->get_status());
LLAMA_LOG_ERROR("%s: unexpected memory context status: %d\n", __func__, mctx->get_status());
return -2;
}
@ -966,19 +957,19 @@ int llama_context::decode(const llama_batch & batch_inp) {
did_optimize = true;
if (kv_self_update(true)) {
LLAMA_LOG_DEBUG("%s: retrying batch size %d after cache optimization\n", __func__, batch.n_tokens);
LLAMA_LOG_DEBUG("%s: retrying batch size %d after cache optimization\n", __func__, balloc->get_n_tokens());
continue;
}
}
LLAMA_LOG_WARN("%s: failed to find a memory slot for batch of size %d\n", __func__, batch.n_tokens);
LLAMA_LOG_WARN("%s: failed to find a memory slot for batch of size %d\n", __func__, balloc->get_n_tokens());
return 1;
}
case LLAMA_MEMORY_STATUS_FAILED_COMPUTE:
{
LLAMA_LOG_ERROR("%s: compute failed while preparing batch of size %d\n", __func__, batch.n_tokens);
LLAMA_LOG_ERROR("%s: compute failed while preparing batch of size %d\n", __func__, balloc->get_n_tokens());
return -2;
}
@ -996,7 +987,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
int64_t n_outputs_prev = 0;
do {
const auto & ubatch = mstate->get_ubatch();
const auto & ubatch = mctx->get_ubatch();
// count the outputs in this ubatch
{
@ -1005,7 +996,6 @@ int llama_context::decode(const llama_batch & batch_inp) {
if (n_outputs_all == n_tokens_all) {
n_outputs_new = ubatch.n_tokens;
} else {
GGML_ASSERT(ubatch.output);
for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
n_outputs_new += (int32_t) (ubatch.output[i] != 0);
}
@ -1019,7 +1009,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
ggml_status status;
const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mstate.get(), status);
const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mctx.get(), status);
if (!res) {
// the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
@ -1028,7 +1018,6 @@ int llama_context::decode(const llama_batch & batch_inp) {
pos_min[s] = std::numeric_limits<llama_pos>::max();
}
// TODO: fix sequence indexing
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
const auto & seq_id = ubatch.seq_id[i][0];
@ -1105,27 +1094,27 @@ int llama_context::decode(const llama_batch & batch_inp) {
// extract sequence embeddings (cleared before processing each batch)
auto & embd_seq_out = embd_seq;
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
const llama_seq_id seq_id = ubatch.seq_id_unq[s];
const int32_t seq_idx = ubatch.seq_idx[seq_id];
embd_seq_out[seq_id].resize(n_embd);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_idx)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_RANK:
{
// extract the rerank score - a single float per sequence
// extract the rerank score - n_cls_out floats per sequence
auto & embd_seq_out = embd_seq;
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(1);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float));
const uint32_t n_cls_out = hparams.n_cls_out;
for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
const llama_seq_id seq_id = ubatch.seq_id_unq[s];
const int32_t seq_idx = ubatch.seq_idx[seq_id];
embd_seq_out[seq_id].resize(n_cls_out);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_cls_out*seq_idx)*sizeof(float), n_cls_out*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
@ -1136,7 +1125,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
}
n_outputs_prev += n_outputs;
} while (mstate->next());
} while (mctx->next());
// set to total number of outputs in the batch, for use in llama_get_logits_ith
n_outputs = n_outputs_all;
@ -1145,7 +1134,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
if (n_outputs > 0) {
bool sorted_output = true;
auto & out_ids = mstate->out_ids();
auto & out_ids = balloc->get_out_ids();
GGML_ASSERT(out_ids.size() == (size_t) n_outputs);
@ -1302,7 +1291,7 @@ ggml_cgraph * llama_context::graph_init() {
return ggml_new_graph_custom(ctx_compute.get(), graph_max_nodes(), false);
}
ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate) {
ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx) {
LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
if (n_tokens % n_seqs != 0) {
@ -1318,11 +1307,11 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
this->n_outputs = n_outputs;
llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
llama_batch_allocr balloc(model.hparams.n_pos_per_embd());
llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs);
auto * gf = graph_init();
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate);
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx);
this->n_outputs = save_n_outputs;
@ -1347,7 +1336,7 @@ llm_graph_result_ptr llama_context::graph_build(
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype,
const llama_memory_state_i * mstate) {
const llama_memory_context_i * mctx) {
return model.build_graph(
{
/*.ctx =*/ ctx,
@ -1359,7 +1348,7 @@ llm_graph_result_ptr llama_context::graph_build(
/*.backend_cpu =*/ backend_cpu,
/*.cvec =*/ &cvec,
/*.loras =*/ &loras,
/*.mstate =*/ mstate,
/*.mctx =*/ mctx,
/*.cross =*/ &cross,
/*.n_outputs =*/ n_outputs,
/*.cb =*/ graph_get_cb(),
@ -2039,7 +2028,12 @@ void llama_context::opt_epoch_iter(
batch.logits [pos_batch] = true;
}
const auto n_tokens_all = batch.n_tokens;
if (!balloc->init(batch, model.vocab, nullptr, model.hparams.n_embd, true)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return;
}
const uint32_t n_tokens_all = balloc->get_n_tokens();
n_queued_tokens += n_tokens_all;
@ -2047,8 +2041,8 @@ void llama_context::opt_epoch_iter(
uint32_t n_outputs_all = n_tokens_all;
auto mstate = memory->init_batch(batch, cparams.n_ubatch, true);
if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
auto mctx = memory->init_batch(*balloc, cparams.n_ubatch, true);
if (!mctx || mctx->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__);
break;
}
@ -2061,17 +2055,17 @@ void llama_context::opt_epoch_iter(
uint32_t pos_batch = 0;
do {
const auto & ubatch = mstate->get_ubatch();
const auto & ubatch = mctx->get_ubatch();
n_outputs = ubatch.n_tokens;
if (!mstate->apply()) {
LLAMA_LOG_ERROR("%s: failed to update the memory state\n", __func__);
if (!mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to update the memory context\n", __func__);
break;
}
auto * gf = graph_init();
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate.get());
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx.get());
struct ggml_context * ctx_compute_opt;
{
@ -2106,7 +2100,7 @@ void llama_context::opt_epoch_iter(
ggml_free(ctx_compute_opt);
pos_batch += ubatch.n_tokens;
} while (mstate->next());
} while (mctx->next());
}
}

12
src/llama-context.h
View File

@ -18,7 +18,7 @@ class llama_io_read_i;
class llama_io_write_i;
struct llama_memory_i;
struct llama_memory_state_i;
struct llama_memory_context_i;
struct llama_context {
// init scheduler and compute buffers, reserve worst-case graphs
@ -93,13 +93,13 @@ struct llama_context {
int32_t il_end);
// process a single ubatch with a specific graph type
// if memory_state is provided, it will be applied first to the context's memory
// if memory_context is provided, it will be applied first to the context's memory
// ret contains the status of the graph computation
// returns nullptr only if ret != GGML_STATUS_SUCCESS
llm_graph_result_ptr process_ubatch(
const llama_ubatch & ubatch,
llm_graph_type gtype,
llama_memory_state_i * mstate,
llama_memory_context_i * mctx,
ggml_status & ret);
int encode(const llama_batch & batch_inp);
@ -197,7 +197,7 @@ public:
ggml_status graph_compute(ggml_cgraph * gf, bool batched);
// reserve a graph with a dummy ubatch of the specified size
ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate);
ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx);
private:
llm_graph_result_ptr graph_build(
@ -205,7 +205,7 @@ private:
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype,
const llama_memory_state_i * mstate);
const llama_memory_context_i * mctx);
llm_graph_cb graph_get_cb() const;
@ -247,7 +247,7 @@ private:
std::map<llama_seq_id, std::vector<float>> embd_seq;
// reuse the batch_allocr to avoid unnecessary memory allocations
std::unique_ptr<llama_batch_allocr> batch_allocr;
std::unique_ptr<llama_batch_allocr> balloc;
uint32_t n_outputs = 0; // number of actually-used outputs in the current ubatch or last logical batch

362
src/llama-graph.cpp
View File

@ -87,17 +87,13 @@ void llm_graph_input_pos_bucket::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_pos_bucket_kv::set_input(const llama_ubatch * ubatch) {
if (pos_bucket) {
kv_state->set_input_pos_bucket(pos_bucket, ubatch);
mctx->set_input_pos_bucket(pos_bucket, ubatch);
}
}
void llm_graph_input_out_ids::set_input(const llama_ubatch * ubatch) {
if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
//GGML_ASSERT(out_ids && "every model that can must skip unused outputs");
GGML_ASSERT(out_ids);
if (!out_ids) {
LLAMA_LOG_WARN("%s: 'out_ids' is not created\n", __func__);
} else {
const int64_t n_tokens = ubatch->n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(out_ids->buffer));
@ -107,133 +103,116 @@ void llm_graph_input_out_ids::set_input(const llama_ubatch * ubatch) {
for (int i = 0; i < n_tokens; ++i) {
data[i] = i;
}
} else if (ubatch->output) {
int32_t n_outputs = 0;
return;
}
GGML_ASSERT(ubatch->output);
int n_outputs = 0;
for (int i = 0; i < n_tokens; ++i) {
if (ubatch->output[i]) {
data[n_outputs++] = i;
}
}
// the graph needs to have been passed the correct number of outputs
GGML_ASSERT(n_outputs == n_outputs);
} else if (n_outputs == 1) {
// only keep last output
data[0] = n_tokens - 1;
} else {
GGML_ASSERT(n_outputs == 0);
}
}
}
}
void llm_graph_input_mean::set_input(const llama_ubatch * ubatch) {
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;
const int64_t n_seqs_unq = ubatch->n_seqs_unq;
GGML_ASSERT(mean);
GGML_ASSERT(ggml_backend_buffer_is_host(mean->buffer));
float * data = (float *) mean->data;
memset(mean->data, 0, n_tokens * n_tokens * ggml_element_size(mean));
memset(mean->data, 0, n_tokens*n_seqs_unq*ggml_element_size(mean));
std::vector<uint64_t> sum(n_tokens, 0);
std::vector<uint64_t> sums(n_seqs_unq, 0);
for (int i = 0; i < n_tokens; i += n_seq_tokens) {
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
// TODO: fix indexing [UBATCH_IDX]
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];
// TODO: adapt limits to n_seqs when ubatch->equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
sum[seq_id] += ubatch->n_seq_tokens;
}
std::vector<float> div(n_tokens, 0.0f);
for (int i = 0; i < n_tokens; ++i) {
const uint64_t s = sum[i];
if (s > 0) {
div[i] = 1.0f/float(s);
sums[seq_idx] += ubatch->n_seq_tokens;
}
}
// TODO: fix indexing [UBATCH_IDX]
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];
std::vector<float> div(n_seqs_unq, 0.0f);
for (int s = 0; s < n_seqs_unq; ++s) {
const uint64_t sum = sums[s];
if (sum > 0) {
div[s] = 1.0f/float(sum);
}
}
for (int i = 0; i < n_seq_tokens; ++i) {
data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id];
for (int i = 0; i < n_tokens; i += n_seq_tokens) {
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
for (int j = 0; j < n_seq_tokens; ++j) {
data[seq_idx*n_tokens + i + j] = div[seq_idx];
}
}
}
}
}
void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
if (cparams.embeddings && (
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;
const int64_t n_seqs_unq = ubatch->n_seqs_unq;
if (cparams.embeddings && (
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK
)) {
GGML_ASSERT(cls);
GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer));
uint32_t * data = (uint32_t *) cls->data;
memset(cls->data, 0, n_tokens * ggml_element_size(cls));
memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls));
// TODO: fix indexing [UBATCH_IDX]
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];
for (int i = 0; i < n_tokens; i += n_seq_tokens) {
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
// TODO: adapt limits to n_seqs when ubatch->equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
for (int i = 0; i < n_seq_tokens; ++i) {
const llama_pos pos = ubatch->pos[s*n_seq_tokens + i];
if (pos == 0) {
data[seq_id] = s*n_seq_tokens + i;
}
data[seq_idx] = i;
}
}
}
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;
GGML_ASSERT(cls);
GGML_ASSERT(ggml_backend_buffer_is_host(cls->buffer));
uint32_t * data = (uint32_t *) cls->data;
memset(cls->data, 0, n_tokens * ggml_element_size(cls));
memset(cls->data, 0, n_seqs_unq*ggml_element_size(cls));
std::vector<int> last_pos(n_tokens, -1);
std::vector<int> last_row(n_tokens, -1);
// TODO: fix indexing [UBATCH_IDX]
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];
// TODO: adapt limits to n_seqs when ubatch->equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");
for (int i = 0; i < n_seq_tokens; ++i) {
const llama_pos pos = ubatch->pos[s*n_seq_tokens + i];
if (pos >= last_pos[seq_id]) {
last_pos[seq_id] = pos;
last_row[seq_id] = s*n_seq_tokens + i;
}
}
}
std::vector<int> last_pos(n_seqs_unq, -1);
std::vector<int> last_row(n_seqs_unq, -1);
for (int i = 0; i < n_tokens; ++i) {
if (last_row[i] >= 0) {
data[i] = last_row[i];
const llama_pos pos = ubatch->pos[i];
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
const int32_t seq_idx = ubatch->seq_idx[seq_id];
if (pos >= last_pos[seq_idx]) {
last_pos[seq_idx] = pos;
last_row[seq_idx] = i;
}
}
}
for (int s = 0; s < n_seqs_unq; ++s) {
if (last_row[s] >= 0) {
data[s] = last_row[s];
}
}
}
@ -242,7 +221,7 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
const int64_t n_rs = mem_state->get_n_rs();
const int64_t n_rs = mctx->get_n_rs();
if (s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
@ -250,7 +229,7 @@ void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
data[i] = mem_state->s_copy(i);
data[i] = mctx->s_copy(i);
}
}
}
@ -266,33 +245,28 @@ void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
}
void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
if (kq_mask) {
if (cparams.causal_attn) {
const int64_t n_kv = ubatch->n_tokens;
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;
GGML_ASSERT(kq_mask);
GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
float * data = (float *) kq_mask->data;
for (int h = 0; h < 1; ++h) {
for (int s1 = 0; s1 < n_seqs; ++s1) {
const llama_seq_id seq_id = ubatch->seq_id[s1][0];
for (int i1 = 0; i1 < n_tokens; ++i1) {
const llama_seq_id s1 = ubatch->seq_id[i1][0];
for (int j = 0; j < n_seq_tokens; ++j) {
const int32_t tj = s1*n_seq_tokens + j;
for (int s0 = 0; s0 < n_seqs; ++s0) {
for (int i = 0; i < n_seq_tokens; ++i) {
const int32_t ti = s0*n_seq_tokens + i;
for (int i0 = 0; i0 < n_tokens; ++i0) {
float f = -INFINITY;
// TODO: fix indexing [UBATCH_IDX]
for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
if (ubatch->seq_id[s0][s] == seq_id && ubatch->pos[ti] <= ubatch->pos[tj]) {
for (int s = 0; s < ubatch->n_seq_id[i0]; ++s) {
const llama_seq_id s0 = ubatch->seq_id[i0][0];
// TODO: reimplement this like in llama_kv_cache_unified
if (s0 == s1 && (!cparams.causal_attn || ubatch->pos[i0] <= ubatch->pos[i1])) {
if (hparams.use_alibi) {
f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]);
} else {
f = 0.0f;
}
@ -300,55 +274,7 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
}
}
data[h*(n_kv*n_tokens) + tj*n_kv + ti] = f;
}
}
}
}
}
} else {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_seq_tokens = ubatch->n_seq_tokens;
const int64_t n_seqs = ubatch->n_seqs;
const int64_t n_stride = ubatch->n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(kq_mask->buffer));
float * data = (float *) kq_mask->data;
for (int h = 0; h < 1; ++h) {
for (int s1 = 0; s1 < n_seqs; ++s1) {
const llama_seq_id seq_id = ubatch->seq_id[s1][0];
for (int j = 0; j < n_seq_tokens; ++j) {
const int32_t tj = s1*n_seq_tokens + j;
for (int s0 = 0; s0 < n_seqs; ++s0) {
for (int i = 0; i < n_seq_tokens; ++i) {
const int32_t ti = s0*n_seq_tokens + i;
float f = -INFINITY;
// TODO: fix indexing [UBATCH_IDX]
for (int s = 0; s < ubatch->n_seq_id[s0]; ++s) {
if (ubatch->seq_id[s0][s] == seq_id) {
if (hparams.use_alibi) {
f = -std::abs(ubatch->pos[ti] - ubatch->pos[tj]);
} else {
f = 0.0f;
}
break;
}
}
data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f;
}
}
for (int i = n_tokens; i < n_stride; ++i) {
data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY;
}
}
}
data[h*(n_kv*n_tokens) + i1*n_kv + i0] = f;
}
}
}
@ -356,22 +282,23 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
kv_state->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
}
void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
kv_state->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
if (self_kq_mask_swa) {
kv_state->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
}
}
void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
if (cross_kq_mask) {
GGML_ASSERT(cross_kq_mask);
const int64_t n_enc = cross_kq_mask->ne[0];
const int64_t n_tokens = ubatch->n_tokens;
@ -381,17 +308,19 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
float * data = (float *) cross_kq_mask->data;
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_enc; ++i) {
for (int i = 0; i < n_tokens; ++i) {
for (int j = 0; j < n_enc; ++j) {
float f = -INFINITY;
// TODO: fix indexing [UBATCH_IDX]
for (int s = 0; s < ubatch->n_seq_id[j]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[j][s];
if (cross->seq_ids_enc[i].find(seq_id) != cross->seq_ids_enc[i].end()) {
for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[i][s];
if (cross->seq_ids_enc[j].find(seq_id) != cross->seq_ids_enc[j].end()) {
f = 0.0f;
}
}
data[h*(n_enc*n_tokens) + j*n_enc + i] = f;
data[h*(n_enc*n_tokens) + i*n_enc + j] = f;
}
}
@ -401,15 +330,14 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
}
}
}
}
}
void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
mem_state->get_state_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
mctx->get_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
const int64_t n_rs = mem_state->get_state_recr()->get_n_rs();
const int64_t n_rs = mctx->get_recr()->get_n_rs();
if (s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
@ -417,7 +345,7 @@ void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
data[i] = mem_state->get_state_recr()->s_copy(i);
data[i] = mctx->get_recr()->s_copy(i);
}
}
}
@ -461,16 +389,12 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
backend_cpu (params.backend_cpu),
cvec (params.cvec),
loras (params.loras),
mstate (params.mstate),
mctx (params.mctx),
cross (params.cross),
cb_func (params.cb),
res (std::make_unique<llm_graph_result>()) {
}
int64_t llm_graph_context::n_pos_per_embd() const {
return hparams.rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
}
void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
if (cb_func) {
cb_func(ubatch, cur, name, il);
@ -915,11 +839,11 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
}
ggml_tensor * llm_graph_context::build_inp_pos() const {
auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_embd());
auto inp = std::make_unique<llm_graph_input_pos>(hparams.n_pos_per_embd());
auto & cur = inp->pos;
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_embd());
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, (int64_t)n_tokens*hparams.n_pos_per_embd());
ggml_set_input(cur);
res->add_input(std::move(inp));
@ -942,6 +866,14 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
}
ggml_tensor * llm_graph_context::build_inp_out_ids() const {
// note: when all tokens are output, we could skip this optimization to spare the ggml_get_rows() calls,
// but this would make the graph topology depend on the number of output tokens, which can interere with
// features that require constant topology such as pipline parallelism
// ref: https://github.com/ggml-org/llama.cpp/pull/14275#issuecomment-2987424471
//if (n_outputs < n_tokens) {
// return nullptr;
//}
auto inp = std::make_unique<llm_graph_input_out_ids>(hparams, cparams, n_outputs);
auto & cur = inp->out_ids;
@ -959,7 +891,7 @@ ggml_tensor * llm_graph_context::build_inp_mean() const {
auto & cur = inp->mean;
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, ubatch.n_seqs_unq);
ggml_set_input(cur);
res->add_input(std::move(inp));
@ -972,7 +904,7 @@ ggml_tensor * llm_graph_context::build_inp_cls() const {
auto & cur = inp->cls;
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_seqs_unq);
ggml_set_input(cur);
res->add_input(std::move(inp));
@ -1018,11 +950,11 @@ ggml_tensor * llm_graph_context::build_inp_pos_bucket_enc() const {
}
ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const {
const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, kv_state);
auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, mctx_cur);
const auto n_kv = kv_state->get_n_kv();
const auto n_kv = mctx_cur->get_n_kv();
auto & cur = inp->pos_bucket;
@ -1050,14 +982,14 @@ ggml_tensor * llm_graph_context::build_pos_bias(ggml_tensor * pos_bucket, ggml_t
}
llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
const auto * mem_state = static_cast<const llama_memory_hybrid_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_mem_hybrid>(hparams, cparams, mem_state);
auto inp = std::make_unique<llm_graph_input_mem_hybrid>(hparams, cparams, mctx_cur);
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Hybrid recurrent is not supported with SWA attention layers");
const auto n_kv = inp->mem_state->get_state_attn()->get_n_kv();
const auto n_kv = inp->mctx->get_attn()->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
@ -1067,7 +999,7 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
}
{
const auto n_rs = mem_state->get_state_recr()->get_n_rs();
const auto n_rs = mctx_cur->get_recr()->get_n_rs();
inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
ggml_set_input(inp->s_copy);
@ -1251,14 +1183,14 @@ ggml_tensor * llm_graph_context::build_attn(
}
llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, kv_state);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, mctx_cur);
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA");
const auto n_kv = kv_state->get_n_kv();
const auto n_kv = mctx_cur->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
@ -1288,19 +1220,19 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
// store to KV cache
{
ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
ggml_tensor * k = kv_state->get_k(ctx0, il);
ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
@ -1338,23 +1270,23 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
const auto * kv_state_iswa = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
const auto * mctx_iswa = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
const bool is_swa = hparams.is_swa(il);
const auto * kv_state = is_swa ? kv_state_iswa->get_swa() : kv_state_iswa->get_base();
const auto * mctx_cur = is_swa ? mctx_iswa->get_swa() : mctx_iswa->get_base();
// store to KV cache
{
ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
ggml_tensor * q = q_cur;
ggml_tensor * k = kv_state->get_k(ctx0, il);
ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
@ -1447,19 +1379,19 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
const auto * kv_state = static_cast<const llama_memory_hybrid_state *>(mstate)->get_state_attn();
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_attn();
// store to KV cache
{
ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
ggml_tensor * k = kv_state->get_k(ctx0, il);
ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
@ -1480,12 +1412,12 @@ ggml_tensor * llm_graph_context::build_attn(
}
llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
const auto * kv_state = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, kv_state);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, mctx_cur);
{
const auto n_kv = kv_state->get_base()->get_n_kv();
const auto n_kv = mctx_cur->get_base()->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
@ -1497,7 +1429,7 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
{
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified for non-SWA");
const auto n_kv = kv_state->get_swa()->get_n_kv();
const auto n_kv = mctx_cur->get_swa()->get_n_kv();
inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
@ -1545,11 +1477,11 @@ ggml_tensor * llm_graph_context::build_rs(
}
llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_rs>(kv_state);
auto inp = std::make_unique<llm_graph_input_rs>(mctx_cur);
const auto n_rs = kv_state->get_n_rs();
const auto n_rs = mctx_cur->get_n_rs();
inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
ggml_set_input(inp->s_copy);
@ -1564,7 +1496,7 @@ ggml_tensor * llm_graph_context::build_rs(
int32_t state_size,
int32_t n_seqs,
const llm_graph_get_rows_fn & get_state_rows) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * kv_state = static_cast<const llama_memory_recurrent_context *>(mctx);
return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows);
}
@ -1576,7 +1508,7 @@ ggml_tensor * llm_graph_context::build_rs(
int32_t state_size,
int32_t n_seqs,
const llm_graph_get_rows_fn & get_state_rows) const {
const auto * kv_state = static_cast<const llama_memory_hybrid_state *>(mstate)->get_state_recr();
const auto * kv_state = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows);
}
@ -1586,13 +1518,13 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
ggml_cgraph * gf,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto token_shift_count = hparams.token_shift_count;
const int64_t n_seqs = ubatch.n_seqs;
ggml_tensor * token_shift_all = kv_state->get_r_l(il);
ggml_tensor * token_shift_all = mctx_cur->get_r_l(il);
ggml_tensor * token_shift = build_rs(
inp, gf, token_shift_all,
@ -1607,19 +1539,19 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
ggml_tensor * token_shift,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto token_shift_count = hparams.token_shift_count;
const auto n_embd = hparams.n_embd;
const int64_t n_seqs = ubatch.n_seqs;
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
return ggml_cpy(
ctx0,
ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * token_shift_count, 0),
ggml_view_1d(ctx0, kv_state->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(kv_state->get_r_l(il)))
ggml_view_1d(ctx0, mctx_cur->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(mctx_cur->get_r_l(il)))
);
}

47
src/llama-graph.h
View File

@ -17,12 +17,12 @@ struct ggml_tensor;
struct llama_ubatch;
struct llama_cparams;
struct llama_memory_state_i;
struct llama_memory_context_i;
class llama_kv_cache_unified_state;
class llama_kv_cache_unified_iswa_state;
class llama_memory_recurrent_state;
class llama_memory_hybrid_state;
class llama_kv_cache_unified_context;
class llama_kv_cache_unified_iswa_context;
class llama_memory_recurrent_context;
class llama_memory_hybrid_context;
// certain models (typically multi-modal) can produce different types of graphs
enum llm_graph_type {
@ -95,14 +95,14 @@ public:
class llm_graph_input_pos : public llm_graph_input_i {
public:
llm_graph_input_pos(int64_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {}
llm_graph_input_pos(uint32_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {}
virtual ~llm_graph_input_pos() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * pos = nullptr; // I32 [n_batch]
const int64_t n_pos_per_embd = 1;
const uint32_t n_pos_per_embd = 1;
};
// temperature tuning, used by llama4
@ -136,7 +136,7 @@ class llm_graph_input_pos_bucket_kv : public llm_graph_input_i {
public:
llm_graph_input_pos_bucket_kv(
const llama_hparams & hparams,
const llama_kv_cache_unified_state * kv_state) : hparams(hparams), kv_state(kv_state) {}
const llama_kv_cache_unified_context * mctx) : hparams(hparams), mctx(mctx) {}
virtual ~llm_graph_input_pos_bucket_kv() = default;
void set_input(const llama_ubatch * ubatch) override;
@ -144,7 +144,8 @@ public:
ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch]
const llama_hparams & hparams;
const llama_kv_cache_unified_state * kv_state;
const llama_kv_cache_unified_context * mctx;
};
class llm_graph_input_out_ids : public llm_graph_input_i {
@ -191,14 +192,14 @@ public:
class llm_graph_input_rs : public llm_graph_input_i {
public:
llm_graph_input_rs(const llama_memory_recurrent_state * mem_state) : mem_state(mem_state) {}
llm_graph_input_rs(const llama_memory_recurrent_context * mctx) : mctx(mctx) {}
virtual ~llm_graph_input_rs() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_copy; // I32 [kv_size]
const llama_memory_recurrent_state * mem_state;
const llama_memory_recurrent_context * mctx;
};
class llm_graph_input_cross_embd : public llm_graph_input_i {
@ -238,10 +239,10 @@ public:
llm_graph_input_attn_kv_unified(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_unified_state * kv_state) :
const llama_kv_cache_unified_context * mctx) :
hparams(hparams),
cparams(cparams),
kv_state(kv_state) {
mctx(mctx) {
}
~llm_graph_input_attn_kv_unified() = default;
@ -255,7 +256,7 @@ public:
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_kv_cache_unified_state * kv_state;
const llama_kv_cache_unified_context * mctx;
};
class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i {
@ -263,10 +264,10 @@ public:
llm_graph_input_attn_kv_unified_iswa(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_unified_iswa_state * kv_state) :
const llama_kv_cache_unified_iswa_context * mctx) :
hparams(hparams),
cparams(cparams),
kv_state(kv_state) {
mctx(mctx) {
}
~llm_graph_input_attn_kv_unified_iswa() = default;
@ -283,7 +284,7 @@ public:
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_kv_cache_unified_iswa_state * kv_state;
const llama_kv_cache_unified_iswa_context * mctx;
};
class llm_graph_input_attn_cross : public llm_graph_input_i {
@ -306,10 +307,10 @@ public:
llm_graph_input_mem_hybrid(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_memory_hybrid_state * mem_state) :
const llama_memory_hybrid_context * mctx) :
hparams(hparams),
cparams(cparams),
mem_state(mem_state) {
mctx(mctx) {
}
virtual ~llm_graph_input_mem_hybrid() = default;
@ -325,7 +326,7 @@ public:
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_memory_hybrid_state * mem_state;
const llama_memory_hybrid_context * mctx;
};
//
@ -403,7 +404,7 @@ struct llm_graph_params {
const llama_adapter_cvec * cvec;
const llama_adapter_loras * loras;
const llama_memory_state_i * mstate;
const llama_memory_context_i * mctx;
const llama_cross * cross;
uint32_t n_outputs;
@ -458,7 +459,7 @@ struct llm_graph_context {
const llama_adapter_cvec * cvec;
const llama_adapter_loras * loras;
const llama_memory_state_i * mstate;
const llama_memory_context_i * mctx;
const llama_cross * cross;
const llm_graph_cb & cb_func;
@ -467,8 +468,6 @@ struct llm_graph_context {
llm_graph_context(const llm_graph_params & params);
int64_t n_pos_per_embd() const;
void cb(ggml_tensor * cur, const char * name, int il) const;
//

4
src/llama-hparams.cpp
View File

@ -91,6 +91,10 @@ bool llama_hparams::is_recurrent(uint32_t il) const {
return recurrent_layer_arr[il];
}
uint32_t llama_hparams::n_pos_per_embd() const {
return rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
}
bool llama_hparams::is_swa(uint32_t il) const {
if (il < n_layer) {
return swa_layers[il];

2
src/llama-hparams.h
View File

@ -193,6 +193,8 @@ struct llama_hparams {
// whether or not the given layer is recurrent (for hybrid models)
bool is_recurrent(uint32_t il) const;
uint32_t n_pos_per_embd() const;
bool is_swa(uint32_t il) const;
};

104
src/llama-kv-cache-unified-iswa.cpp
View File

@ -95,19 +95,22 @@ llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
return kv_swa->seq_pos_max(seq_id);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) {
llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
GGML_UNUSED(embd_all);
// first try simple split
do {
auto sbatch = llama_sbatch(batch, hparams.n_embd, true);
balloc.split_reset();
std::vector<llama_ubatch> ubatches;
while (true) {
auto ubatch = balloc.split_simple(n_ubatch);
while (sbatch.n_tokens > 0) {
auto ubatch = sbatch.split_simple(n_ubatch);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(ubatch);
ubatches.push_back(std::move(ubatch)); // NOLINT
}
auto heads_base = kv_base->prepare(ubatches);
@ -122,20 +125,23 @@ llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch
assert(heads_base.size() == heads_swa.size());
return std::make_unique<llama_kv_cache_unified_iswa_state>(
this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
} while (false);
// if it fails, try equal split
do {
auto sbatch = llama_sbatch(batch, hparams.n_embd, false);
balloc.split_reset();
std::vector<llama_ubatch> ubatches;
while (true) {
auto ubatch = balloc.split_equal(n_ubatch);
while (sbatch.n_tokens > 0) {
auto ubatch = sbatch.split_equal(n_ubatch);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(ubatch);
ubatches.push_back(std::move(ubatch)); // NOLINT
}
auto heads_base = kv_base->prepare(ubatches);
@ -150,22 +156,22 @@ llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch
assert(heads_base.size() == heads_swa.size());
return std::make_unique<llama_kv_cache_unified_iswa_state>(
this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
} while (false);
// TODO: if we fail again, we should attempt different splitting strategies
// but to do that properly, we first have to refactor the batches to be more flexible
return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_kv_cache_unified_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() {
return std::make_unique<llama_kv_cache_unified_iswa_state>(this);
llama_memory_context_ptr llama_kv_cache_unified_iswa::init_full() {
return std::make_unique<llama_kv_cache_unified_iswa_context>(this);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_kv_cache_unified_iswa_state>(this, lctx, optimize);
llama_memory_context_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_kv_cache_unified_iswa_context>(this, lctx, optimize);
}
bool llama_kv_cache_unified_iswa::get_can_shift() const {
@ -191,48 +197,46 @@ llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const {
}
//
// llama_kv_cache_unified_iswa_state
// llama_kv_cache_unified_iswa_context
//
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv) :
state_base(kv->get_base()->init_full()),
state_swa (kv->get_swa ()->init_full()),
status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
ctx_base(kv->get_base()->init_full()),
ctx_swa (kv->get_swa ()->init_full()),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_context * lctx,
bool optimize) :
state_base(kv->get_base()->init_update(lctx, optimize)),
state_swa (kv->get_swa ()->init_update(lctx, optimize)),
status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
ctx_base(kv->get_base()->init_update(lctx, optimize)),
ctx_swa (kv->get_swa ()->init_update(lctx, optimize)),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_sbatch sbatch,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
std::vector<llama_ubatch> ubatches) :
sbatch(std::move(sbatch)),
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
state_base(new llama_kv_cache_unified_state(kv->get_base(), {}, std::move(heads_base), this->ubatches)),
state_swa (new llama_kv_cache_unified_state(kv->get_swa (), {}, std::move(heads_swa), this->ubatches)),
status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(heads_base), this->ubatches)),
ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(heads_swa), this->ubatches)),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default;
llama_kv_cache_unified_iswa_context:: ~llama_kv_cache_unified_iswa_context() = default;
bool llama_kv_cache_unified_iswa_state::next() {
bool llama_kv_cache_unified_iswa_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
state_base->next();
state_swa ->next();
ctx_base->next();
ctx_swa ->next();
if (++i_next >= ubatches.size()) {
return false;
@ -241,41 +245,35 @@ bool llama_kv_cache_unified_iswa_state::next() {
return true;
}
bool llama_kv_cache_unified_iswa_state::apply() {
bool llama_kv_cache_unified_iswa_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
bool res = true;
res = res & state_base->apply();
res = res & state_swa ->apply();
res = res & ctx_base->apply();
res = res & ctx_swa ->apply();
return res;
}
std::vector<int64_t> & llama_kv_cache_unified_iswa_state::out_ids() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return sbatch.out_ids;
}
llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const {
llama_memory_status llama_kv_cache_unified_iswa_context::get_status() const {
return status;
}
const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const {
const llama_ubatch & llama_kv_cache_unified_iswa_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const {
const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_base() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_unified_state *>(state_base.get());
return static_cast<const llama_kv_cache_unified_context *>(ctx_base.get());
}
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const {
const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_swa() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_unified_state *>(state_swa.get());
return static_cast<const llama_kv_cache_unified_context *>(ctx_swa.get());
}

43
src/llama-kv-cache-unified-iswa.h
View File

@ -31,14 +31,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
const llama_batch & batch,
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
@ -72,62 +72,57 @@ private:
std::unique_ptr<llama_kv_cache_unified> kv_swa;
};
class llama_kv_cache_unified_iswa_state : public llama_memory_state_i {
class llama_kv_cache_unified_iswa_context : public llama_memory_context_i {
public:
// used for errors
llama_kv_cache_unified_iswa_state(llama_memory_status status);
llama_kv_cache_unified_iswa_context(llama_memory_status status);
// used to create a full-cache state
llama_kv_cache_unified_iswa_state(
// used to create a full-cache context
llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv);
// used to create an update state
llama_kv_cache_unified_iswa_state(
// used to create an update context
llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_context * lctx,
bool optimize);
// used to create a state from a batch
llama_kv_cache_unified_iswa_state(
// used to create a batch processing context from a batch
llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_sbatch sbatch,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_unified_iswa_state();
virtual ~llama_kv_cache_unified_iswa_context();
//
// llama_memory_state_i
// llama_memory_context_i
//
bool next() override;
bool apply() override;
std::vector<int64_t> & out_ids() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
//
// llama_kv_cache_unified_iswa_state specific API
// llama_kv_cache_unified_iswa_context specific API
//
const llama_kv_cache_unified_state * get_base() const;
const llama_kv_cache_unified_state * get_swa() const;
const llama_kv_cache_unified_context * get_base() const;
const llama_kv_cache_unified_context * get_swa() const;
private:
//llama_kv_cache_unified_iswa * kv;
llama_sbatch sbatch;
// the index of the next ubatch to process
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;
const llama_memory_state_ptr state_base;
const llama_memory_state_ptr state_swa;
const llama_memory_context_ptr ctx_base;
const llama_memory_context_ptr ctx_swa;
const llama_memory_status status;
};

148
src/llama-kv-cache-unified.cpp
View File

@ -307,18 +307,24 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
return cells.seq_pos_max(seq_id);
}
llama_memory_state_ptr llama_kv_cache_unified::init_batch(
const llama_batch & batch,
llama_memory_context_ptr llama_kv_cache_unified::init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) {
GGML_UNUSED(embd_all);
do {
auto sbatch = llama_sbatch(batch, hparams.n_embd, true);
balloc.split_reset();
std::vector<llama_ubatch> ubatches;
while (sbatch.n_tokens > 0) {
ubatches.push_back(sbatch.split_simple(n_ubatch));
while (true) {
auto ubatch = balloc.split_simple(n_ubatch);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(std::move(ubatch)); // NOLINT
}
auto heads = prepare(ubatches);
@ -326,18 +332,18 @@ llama_memory_state_ptr llama_kv_cache_unified::init_batch(
break;
}
return std::make_unique<llama_kv_cache_unified_state>(
this, std::move(sbatch), std::move(heads), std::move(ubatches));
return std::make_unique<llama_kv_cache_unified_context>(
this, std::move(heads), std::move(ubatches));
} while (false);
return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_kv_cache_unified_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_state_ptr llama_kv_cache_unified::init_full() {
return std::make_unique<llama_kv_cache_unified_state>(this);
llama_memory_context_ptr llama_kv_cache_unified::init_full() {
return std::make_unique<llama_kv_cache_unified_context>(this);
}
llama_memory_state_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
bool do_shift = get_has_shift();
defrag_info dinfo;
@ -367,7 +373,7 @@ llama_memory_state_ptr llama_kv_cache_unified::init_update(llama_context * lctx,
}
}
return std::make_unique<llama_kv_cache_unified_state>(this, lctx, do_shift, std::move(dinfo));
return std::make_unique<llama_kv_cache_unified_context>(this, lctx, do_shift, std::move(dinfo));
}
llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
@ -644,12 +650,6 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
}
void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch) {
if (debug > 0) {
LLAMA_LOG_DEBUG("%s: ubatch info:\n", __func__);
LLAMA_LOG_DEBUG("%s: n_tokens = %d, equal_seqs = %d\n", __func__, ubatch.n_tokens, ubatch.equal_seqs);
LLAMA_LOG_DEBUG("%s: n_seq_tokens = %d, n_seqs = %d\n", __func__, ubatch.n_seq_tokens, ubatch.n_seqs);
}
// keep track of the max sequence position that we would overwrite with this ubatch
// for non-SWA cache, this would be always empty
llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
@ -657,27 +657,22 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
seq_pos_max_rm[s] = -1;
}
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
for (uint32_t j = 0; j < ubatch.n_seq_tokens; ++j) {
const uint32_t idx = s*ubatch.n_seq_tokens + j;
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
if (!cells.is_empty(head_cur + i)) {
assert(cells.seq_count(head_cur + i) == 1);
if (!cells.is_empty(head_cur + idx)) {
assert(cells.seq_count(head_cur + idx) == 1);
const llama_seq_id seq_id = cells.seq_get(head_cur + idx);
const llama_pos pos = cells.pos_get(head_cur + idx);
const llama_seq_id seq_id = cells.seq_get(head_cur + i);
const llama_pos pos = cells.pos_get(head_cur + i);
seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos);
cells.rm(head_cur + idx);
cells.rm(head_cur + i);
}
cells.pos_set(head_cur + idx, ubatch.pos[idx]);
cells.pos_set(head_cur + i, ubatch.pos[i]);
// TODO: fix indexing [UBATCH_IDX]
for (int32_t i = 0; i < ubatch.n_seq_id[s]; i++) {
cells.seq_add(head_cur + idx, ubatch.seq_id[s][i]);
}
for (int32_t s = 0; s < ubatch.n_seq_id[i]; s++) {
cells.seq_add(head_cur + i, ubatch.seq_id[i][s]);
}
}
@ -696,6 +691,7 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
seq_rm(s, cells.seq_pos_min(s), seq_pos_max_rm[s] + 1);
}
}
// move the head at the end of the slot
head = head_cur + ubatch.n_tokens;
}
@ -793,8 +789,6 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
const uint32_t n_tokens = ubatch->n_tokens;
const uint32_t n_seq_tokens = ubatch->n_seq_tokens;
const uint32_t n_seqs = ubatch->n_seqs;
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
float * data = (float *) dst->data;
@ -814,26 +808,23 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub
// xxxxx-----
// To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615
for (uint32_t h = 0; h < 1; ++h) {
for (uint32_t s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s][0];
for (uint32_t i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = ubatch->seq_id[i][0];
for (uint32_t j = 0; j < n_seq_tokens; ++j) {
const uint32_t idx = s*n_seq_tokens + j;
const llama_pos p1 = ubatch->pos[i];
const llama_pos p1 = ubatch->pos[idx];
for (uint32_t i = 0; i < n_kv; ++i) {
for (uint32_t j = 0; j < n_kv; ++j) {
float f = 0.0f;
bool masked = false;
if (cells.is_empty(i)) {
if (cells.is_empty(j)) {
masked = true;
} else {
const llama_pos p0 = cells.pos_get(i);
const llama_pos p0 = cells.pos_get(j);
// mask the token if not the same sequence
masked = masked || (!cells.seq_has(i, seq_id));
masked = masked || (!cells.seq_has(j, seq_id));
// mask future tokens
masked = masked || (causal_attn && p0 > p1);
@ -850,16 +841,15 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub
f = -INFINITY;
}
data[h*(n_kv*n_tokens) + idx*n_kv + i] = f;
}
data[h*(n_kv*n_tokens) + i*n_kv + j] = f;
}
}
// mask padded tokens
if (data) {
for (uint32_t j = n_tokens; j < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++j) {
for (uint32_t i = 0; i < n_kv; ++i) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
for (uint32_t i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (uint32_t j = 0; j < n_kv; ++j) {
data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
}
}
}
@ -887,12 +877,12 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama
const int32_t n_kv = dst->ne[0];
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_kv; ++i) {
for (int i = 0; i < n_tokens; ++i) {
for (int j = 0; j < n_kv; ++j) {
// the position when the cells is empty is irrelevant - it will be masked out later in the attention
const llama_pos p0 = cells.is_empty(i) ? -1 : cells.pos_get(i);
const llama_pos p0 = cells.is_empty(j) ? -1 : cells.pos_get(j);
data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(p0, ubatch->pos[j], hparams.n_rel_attn_bkts, false);
data[h*(n_kv*n_tokens) + i*n_kv + j] = llama_relative_position_bucket(p0, ubatch->pos[i], hparams.n_rel_attn_bkts, false);
}
}
}
@ -1509,12 +1499,9 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
seq_rm(dest_seq_id, -1, -1);
llama_sbatch sbatch;
llama_ubatch ubatch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
llama_batch_allocr balloc(hparams.n_pos_per_embd());
ubatch.n_tokens = cell_count;
ubatch.n_seq_tokens = cell_count;
ubatch.n_seqs = 1;
llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1);
for (uint32_t i = 0; i < cell_count; ++i) {
llama_pos pos;
@ -1723,18 +1710,18 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell
}
//
// llama_kv_cache_unified_state
// llama_kv_cache_unified_context
//
llama_kv_cache_unified_state::llama_kv_cache_unified_state(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
n_kv = kv->get_size();
head = 0;
}
llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_context * lctx,
bool do_shift,
@ -1744,16 +1731,15 @@ llama_kv_cache_unified_state::llama_kv_cache_unified_state(
}
}
llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_sbatch sbatch,
llama_kv_cache_unified::ubatch_heads heads,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), sbatch(std::move(sbatch)), heads(std::move(heads)), ubatches(std::move(ubatches)) {
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), heads(std::move(heads)), ubatches(std::move(ubatches)) {
}
llama_kv_cache_unified_state::~llama_kv_cache_unified_state() = default;
llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
bool llama_kv_cache_unified_state::next() {
bool llama_kv_cache_unified_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
@ -1763,7 +1749,7 @@ bool llama_kv_cache_unified_state::next() {
return true;
}
bool llama_kv_cache_unified_state::apply() {
bool llama_kv_cache_unified_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
// no ubatches -> this is a KV cache update
@ -1781,51 +1767,45 @@ bool llama_kv_cache_unified_state::apply() {
return true;
}
std::vector<int64_t> & llama_kv_cache_unified_state::out_ids() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return sbatch.out_ids;
}
llama_memory_status llama_kv_cache_unified_state::get_status() const {
llama_memory_status llama_kv_cache_unified_context::get_status() const {
return status;
}
const llama_ubatch & llama_kv_cache_unified_state::get_ubatch() const {
const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
uint32_t llama_kv_cache_unified_state::get_n_kv() const {
uint32_t llama_kv_cache_unified_context::get_n_kv() const {
return n_kv;
}
ggml_tensor * llama_kv_cache_unified_state::get_k(ggml_context * ctx, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::get_k(ggml_context * ctx, int32_t il) const {
return kv->get_k(ctx, il, n_kv);
}
ggml_tensor * llama_kv_cache_unified_state::get_v(ggml_context * ctx, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t il) const {
return kv->get_v(ctx, il, n_kv);
}
ggml_tensor * llama_kv_cache_unified_state::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
return kv->cpy_k(ctx, k_cur, il, head);
}
ggml_tensor * llama_kv_cache_unified_state::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
return kv->cpy_v(ctx, v_cur, il, head);
}
void llama_kv_cache_unified_state::set_input_k_shift(ggml_tensor * dst) const {
void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
kv->set_input_k_shift(dst);
}
void llama_kv_cache_unified_state::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
kv->set_input_kq_mask(dst, ubatch, causal_attn);
}
void llama_kv_cache_unified_state::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
void llama_kv_cache_unified_context::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
kv->set_input_pos_bucket(dst, ubatch);
}

39
src/llama-kv-cache-unified.h
View File

@ -56,14 +56,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
const llama_batch & batch,
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
@ -208,49 +208,46 @@ private:
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
};
class llama_kv_cache_unified_state : public llama_memory_state_i {
class llama_kv_cache_unified_context : public llama_memory_context_i {
public:
// some shorthands
using ubatch_heads = llama_kv_cache_unified::ubatch_heads;
using defrag_info = llama_kv_cache_unified::defrag_info;
// used for errors
llama_kv_cache_unified_state(llama_memory_status status);
llama_kv_cache_unified_context(llama_memory_status status);
// used to create a full-cache state
llama_kv_cache_unified_state(
// used to create a full-cache context
llama_kv_cache_unified_context(
llama_kv_cache_unified * kv);
// used to create an update state
llama_kv_cache_unified_state(
// used to create an update context
llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_context * lctx,
bool do_shift,
defrag_info dinfo);
// used to create a decode state from a batch
llama_kv_cache_unified_state(
// used to create a batch procesing context from a batch
llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_sbatch sbatch,
ubatch_heads heads,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_unified_state();
virtual ~llama_kv_cache_unified_context();
//
// llama_memory_state_i
// llama_memory_context_i
//
bool next() override;
bool apply() override;
std::vector<int64_t> & out_ids() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
//
// llama_kv_cache_unified_state specific API
// llama_kv_cache_unified_context specific API
//
uint32_t get_n_kv() const;
@ -275,7 +272,7 @@ private:
llama_context * lctx;
//
// update state
// update context
//
bool do_shift = false;
@ -283,11 +280,9 @@ private:
defrag_info dinfo;
//
// batch processing state
// batch processing context
//
llama_sbatch sbatch;
// the index of the next ubatch to process
size_t i_next = 0;

46
src/llama-kv-cells.h
View File

@ -7,6 +7,7 @@
#include <cassert>
#include <vector>
#include <set>
#include <map>
// meta information about KV cells that can be part of multiple sequences at the same time
// TODO: add unit tests
@ -164,7 +165,7 @@ public:
assert(seq_id >= 0);
seq[i].reset(seq_id);
seq_pos[seq_id].erase(pos[i]);
seq_pos_dec(seq_id, pos[i]);
if (seq[i].none()) {
pos[i] = -1;
@ -187,7 +188,7 @@ public:
seq[i].reset();
seq[i].set(seq_id);
seq_pos[seq_id].insert(pos[i]);
seq_pos_inc(seq_id, pos[i]);
return false;
}
@ -232,7 +233,7 @@ public:
assert(!seq[i].test(seq_id));
seq[i].set(seq_id);
seq_pos[seq_id].insert(pos[i]);
seq_pos_inc(seq_id, pos[i]);
}
// return the sequence id of this cell
@ -259,7 +260,9 @@ public:
return -1;
}
return *seq_pos[seq_id].begin();
assert(seq_pos[seq_id].begin()->second > 0);
return seq_pos[seq_id].begin()->first;
}
// the maximum position of sequence seq_id currently present in any of the cells
@ -272,7 +275,9 @@ public:
return -1;
}
return *seq_pos[seq_id].rbegin();
assert(seq_pos[seq_id].rbegin()->second > 0);
return seq_pos[seq_id].rbegin()->first;
}
// note: call only if the cell is not empty
@ -384,22 +389,41 @@ private:
//
std::vector<llama_pos> shift;
using bits_t = std::bitset<LLAMA_MAX_SEQ>;
using seq_set_t = std::bitset<LLAMA_MAX_SEQ>;
// the bitset seq[i] tells us which sequences are currently occupying the i-th cell
std::vector<bits_t> seq;
std::vector<seq_set_t> seq;
// the set seq_pos[s] tells us which positions are currently present for sequence s
// the set seq_pos[s][p] tells us how many times the position p is currently present for sequence s
// if the position p is not present, seq_pos[s][p] is not set
// this way seq_pos[s].begin() and seq_pos[s].rbegin() give us the min/max positions currently in the cache
std::set<llama_pos> seq_pos[LLAMA_MAX_SEQ];
//
// note that we cannot a use an std::set because in some cases a position can occur more than once for the same seq:
// - during performing a cache reuse via (rm + add)
// - some vision models have input embeddings with repeating positions
//
std::map<llama_pos, int> seq_pos[LLAMA_MAX_SEQ];
// helper functions for updating `seq_pos`, once cell at a time:
void seq_pos_dec(llama_seq_id s, llama_pos p) {
auto it = seq_pos[s].find(p);
assert(it != seq_pos[s].end());
if (--it->second == 0) {
seq_pos[s].erase(it);
}
}
void seq_pos_inc(llama_seq_id s, llama_pos p) {
seq_pos[s][p]++;
}
// remove cell i
void seq_pos_rm(uint32_t i) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq[i].test(s)) {
seq_pos[s].erase(pos[i]);
seq_pos_dec(s, pos[i]);
}
}
}
@ -408,7 +432,7 @@ private:
void seq_pos_add(uint32_t i) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (seq[i].test(s)) {
seq_pos[s].insert(pos[i]);
seq_pos_inc(s, pos[i]);
}
}
}

107
src/llama-memory-hybrid.cpp
View File

@ -32,7 +32,7 @@ llama_memory_hybrid::llama_memory_hybrid(
mem_attn(new llama_kv_cache_unified(
model,
filter_attn == nullptr ?
[&](int32_t il) { return !model.hparams.is_recurrent(il); }
[&](int32_t il) { return !hparams.is_recurrent(il); }
: filter_attn,
type_k,
type_v,
@ -47,7 +47,7 @@ llama_memory_hybrid::llama_memory_hybrid(
mem_recr(new llama_memory_recurrent(
model,
filter_recr == nullptr ?
[&](int32_t il) { return model.hparams.is_recurrent(il); }
[&](int32_t il) { return hparams.is_recurrent(il); }
: filter_recr,
type_r,
type_s,
@ -56,50 +56,57 @@ llama_memory_hybrid::llama_memory_hybrid(
n_seq_max
)) {}
llama_memory_state_ptr llama_memory_hybrid::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled) {
// since this includes a recurrent cache, we cannot use split_simple
auto sbatch = llama_sbatch(batch, hparams.n_embd, false);
llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
do {
balloc.split_reset();
// follow the recurrent pattern for creating the ubatch splits
std::vector<llama_ubatch> ubatches;
while (sbatch.n_tokens > 0) {
while (true) {
llama_ubatch ubatch;
if (embd_pooled) {
// Pooled embeddings cannot be split across ubatches (yet)
ubatch = sbatch.split_seq(n_ubatch);
if (embd_all) {
// if all tokens are output, split by sequence
ubatch = balloc.split_seq(n_ubatch);
} else {
ubatch = sbatch.split_equal(n_ubatch);
ubatch = balloc.split_equal(n_ubatch);
}
ubatches.push_back(ubatch);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(std::move(ubatch)); // NOLINT
}
// prepare the recurrent batches first
if (!mem_recr->prepare(ubatches)) {
// TODO: will the recurrent cache be in an undefined state at this point?
// TODO: will the recurrent cache be in an undefined context at this point?
LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__);
return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
// prepare the attention cache
auto heads_attn = mem_attn->prepare(ubatches);
if (heads_attn.empty()) {
LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__);
return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
return std::make_unique<llama_memory_hybrid_state>(
this, std::move(sbatch), std::move(heads_attn), std::move(ubatches));
return std::make_unique<llama_memory_hybrid_context>(
this, std::move(heads_attn), std::move(ubatches));
} while(false);
return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_state_ptr llama_memory_hybrid::init_full() {
return std::make_unique<llama_memory_hybrid_state>(this);
llama_memory_context_ptr llama_memory_hybrid::init_full() {
return std::make_unique<llama_memory_hybrid_context>(this);
}
llama_memory_state_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_memory_hybrid_state>(this, lctx, optimize);
llama_memory_context_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_memory_hybrid_context>(this, lctx, optimize);
}
bool llama_memory_hybrid::get_can_shift() const {
@ -169,41 +176,39 @@ llama_memory_recurrent * llama_memory_hybrid::get_mem_recr() const {
return mem_recr.get();
}
llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_status status) : status(status) {}
llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_status status) : status(status) {}
llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_hybrid * mem) :
state_attn(mem->get_mem_attn()->init_full()),
state_recr(mem->get_mem_recr()->init_full()),
status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_hybrid * mem) :
ctx_attn(mem->get_mem_attn()->init_full()),
ctx_recr(mem->get_mem_recr()->init_full()),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
llama_memory_hybrid_state::llama_memory_hybrid_state(
llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_context * lctx,
bool optimize) :
state_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
state_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
ctx_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
ctx_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
llama_memory_hybrid_state::llama_memory_hybrid_state(
llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_sbatch sbatch,
std::vector<uint32_t> heads_attn,
std::vector<llama_ubatch> ubatches) :
sbatch(std::move(sbatch)),
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
state_attn(new llama_kv_cache_unified_state(mem->get_mem_attn(), {}, std::move(heads_attn), this->ubatches)),
state_recr(new llama_memory_recurrent_state(mem->get_mem_recr(), {}, this->ubatches)),
status(LLAMA_MEMORY_STATUS_SUCCESS) {
ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
bool llama_memory_hybrid_state::next() {
bool llama_memory_hybrid_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
state_attn->next();
state_recr->next();
ctx_attn->next();
ctx_recr->next();
if (++i_next >= ubatches.size()) {
return false;
@ -212,36 +217,30 @@ bool llama_memory_hybrid_state::next() {
return true;
}
bool llama_memory_hybrid_state::apply() {
bool llama_memory_hybrid_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
bool res = true;
res = res & state_attn->apply();
res = res & state_recr->apply();
res = res & ctx_attn->apply();
res = res & ctx_recr->apply();
return res;
}
std::vector<int64_t> & llama_memory_hybrid_state::out_ids() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return sbatch.out_ids;
}
llama_memory_status llama_memory_hybrid_state::get_status() const {
llama_memory_status llama_memory_hybrid_context::get_status() const {
return status;
}
const llama_ubatch & llama_memory_hybrid_state::get_ubatch() const {
const llama_ubatch & llama_memory_hybrid_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
const llama_kv_cache_unified_state * llama_memory_hybrid_state::get_state_attn() const {
return static_cast<const llama_kv_cache_unified_state *>(state_attn.get());
const llama_kv_cache_unified_context * llama_memory_hybrid_context::get_attn() const {
return static_cast<const llama_kv_cache_unified_context *>(ctx_attn.get());
}
const llama_memory_recurrent_state * llama_memory_hybrid_state::get_state_recr() const {
return static_cast<const llama_memory_recurrent_state *>(state_recr.get());
const llama_memory_recurrent_context * llama_memory_hybrid_context::get_recr() const {
return static_cast<const llama_memory_recurrent_context *>(ctx_recr.get());
}

37
src/llama-memory-hybrid.h
View File

@ -49,14 +49,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
const llama_batch & batch,
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_pooled) override;
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
@ -90,54 +90,49 @@ private:
const std::unique_ptr<llama_memory_recurrent> mem_recr;
};
class llama_memory_hybrid_state : public llama_memory_state_i {
class llama_memory_hybrid_context : public llama_memory_context_i {
public:
// init failure
explicit llama_memory_hybrid_state(llama_memory_status status);
explicit llama_memory_hybrid_context(llama_memory_status status);
// init full
explicit llama_memory_hybrid_state(llama_memory_hybrid * mem);
explicit llama_memory_hybrid_context(llama_memory_hybrid * mem);
// init update
explicit llama_memory_hybrid_state(
explicit llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_context * lctx,
bool optimize);
// init success
llama_memory_hybrid_state(
llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_sbatch sbatch,
std::vector<uint32_t> heads_attn,
std::vector<llama_ubatch> ubatches);
~llama_memory_hybrid_state() = default;
~llama_memory_hybrid_context() = default;
bool next() override;
bool apply() override;
std::vector<int64_t> & out_ids() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
//
// llama_memory_hybrid_state
// llama_memory_hybrid_context
//
const llama_kv_cache_unified_state * get_state_attn() const;
const llama_memory_recurrent_state * get_state_recr() const;
const llama_kv_cache_unified_context * get_attn() const;
const llama_memory_recurrent_context * get_recr() const;
private:
llama_sbatch sbatch;
// the index of the next ubatch to process
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;
const llama_memory_state_ptr state_attn;
const llama_memory_state_ptr state_recr;
const llama_memory_context_ptr ctx_attn;
const llama_memory_context_ptr ctx_recr;
const llama_memory_status status;
};

118
src/llama-memory-recurrent.cpp
View File

@ -362,40 +362,42 @@ llama_pos llama_memory_recurrent::seq_pos_max(llama_seq_id seq_id) const {
return result;
}
llama_memory_state_ptr llama_memory_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) {
auto sbatch = llama_sbatch(batch, hparams.n_embd, false);
llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
std::vector<llama_ubatch> ubatches;
while (sbatch.n_tokens > 0) {
while (true) {
llama_ubatch ubatch;
if (embd_all) {
// if all tokens are output, split by sequence
ubatch = sbatch.split_seq(n_ubatch);
ubatch = balloc.split_seq(n_ubatch);
} else {
ubatch = sbatch.split_equal(n_ubatch);
ubatch = balloc.split_equal(n_ubatch);
}
ubatches.push_back(ubatch);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(std::move(ubatch)); // NOLINT
}
if (!prepare(ubatches)) {
return std::make_unique<llama_memory_recurrent_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
return std::make_unique<llama_memory_recurrent_state>(this, std::move(sbatch), std::move(ubatches));
return std::make_unique<llama_memory_recurrent_context>(this, std::move(ubatches));
}
llama_memory_state_ptr llama_memory_recurrent::init_full() {
return std::make_unique<llama_memory_recurrent_state>(this);
llama_memory_context_ptr llama_memory_recurrent::init_full() {
return std::make_unique<llama_memory_recurrent_context>(this);
}
llama_memory_state_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) {
llama_memory_context_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) {
GGML_UNUSED(lctx);
GGML_UNUSED(optimize);
return std::make_unique<llama_memory_recurrent_state>(LLAMA_MEMORY_STATUS_NO_UPDATE);
return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_NO_UPDATE);
}
bool llama_memory_recurrent::prepare(const std::vector<llama_ubatch> & ubatches) {
@ -423,9 +425,8 @@ bool llama_memory_recurrent::prepare(const std::vector<llama_ubatch> & ubatches)
}
bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
const uint32_t n_seqs = ubatch.n_seqs;
const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
const uint32_t n_seqs = ubatch.n_seqs;
// if we have enough unused cells before the current head ->
// better to start searching from the beginning of the cache, hoping to fill it
@ -445,9 +446,11 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
// everything should fit if all seq_ids are smaller than the max
for (uint32_t s = 0; s < n_seqs; ++s) {
const uint32_t n_seq_id = ubatch.n_seq_id[s];
const uint32_t i = s*n_seq_tokens; // first token of sequence set s
const uint32_t n_seq_id = ubatch.n_seq_id[i];
for (uint32_t j = 0; j < n_seq_id; ++j) {
const llama_seq_id seq_id = ubatch.seq_id[s][j];
const llama_seq_id seq_id = ubatch.seq_id[i][j];
if (seq_id < 0 || (uint32_t) seq_id >= size) {
// too big seq_id
@ -506,7 +509,8 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
// find usable cell range
for (uint32_t s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
const uint32_t i = s*n_seq_tokens;
const llama_seq_id seq_id = ubatch.seq_id[i][0];
auto & seq_meta = cells[seq_id];
bool has_cell = false;
if (seq_meta.tail >= 0) {
@ -530,7 +534,7 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
seq_meta.tail = next_empty_cell;
// find next empty cell
if (s + 1 < n_seqs) {
for (uint32_t i = 0; i < size; ++i) {
for (uint32_t j = 0; j < size; ++j) {
next_empty_cell += 1;
if (next_empty_cell >= size) { next_empty_cell -= size; }
auto & cell = cells[next_empty_cell];
@ -544,8 +548,9 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
// gather and re-order
for (uint32_t s = 0; s < n_seqs; ++s) {
const uint32_t i = s*n_seq_tokens;
const int32_t dst_id = s + min;
const int32_t src_id = cells[ubatch.seq_id[s][0]].tail;
const int32_t src_id = cells[ubatch.seq_id[i][0]].tail;
if (dst_id != src_id) {
auto & dst_cell = cells[dst_id];
auto & src_cell = cells[src_id];
@ -555,8 +560,8 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
std::swap(dst_cell.seq_id, src_cell.seq_id);
// swap tails
for (uint32_t i = 0; i < size; ++i) {
int32_t & tail = cells[i].tail;
for (uint32_t j = 0; j < size; ++j) {
int32_t & tail = cells[j].tail;
if (tail == src_id) {
tail = dst_id;
} else if (tail == dst_id) {
@ -568,7 +573,8 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
// update the pos of the used seqs
for (uint32_t s = 0; s < n_seqs; ++s) {
const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
const uint32_t i = s*n_seq_tokens;
const llama_pos last_pos = ubatch.pos[i + n_seq_tokens - 1];
const int32_t cell_id = s + min;
auto & cell = cells[cell_id];
@ -576,12 +582,12 @@ bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
// What should happen when the pos backtracks or skips a value?
// Clearing the state mid-batch would require special-casing which isn't done.
LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
__func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens);
__func__, last_pos, cell.pos, ubatch.seq_id[i][0], n_seq_tokens);
}
cell.pos = last_pos;
cell.seq_id.clear();
for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) {
const llama_seq_id seq_id = ubatch.seq_id[s][j];
for (int32_t j = 0; j < ubatch.n_seq_id[i]; ++j) {
const llama_seq_id seq_id = ubatch.seq_id[i][j];
cell.seq_id.insert(seq_id);
cells[seq_id].tail = cell_id;
}
@ -827,12 +833,9 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell
seq_rm(dest_seq_id, -1, -1);
llama_sbatch sbatch;
llama_ubatch batch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
llama_batch_allocr balloc(hparams.n_pos_per_embd());
batch.n_tokens = cell_count;
batch.n_seq_tokens = cell_count;
batch.n_seqs = 1;
llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1);
for (uint32_t i = 0; i < cell_count; ++i) {
llama_pos pos;
@ -846,12 +849,12 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell
return false;
}
batch.pos[i] = pos;
ubatch.pos[i] = pos;
}
batch.n_seq_id[0] = 1;
batch.seq_id[0] = &dest_seq_id;
ubatch.n_seq_id[0] = 1;
ubatch.seq_id[0] = &dest_seq_id;
if (!find_slot(batch)) {
if (!find_slot(ubatch)) {
LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
return false;
}
@ -859,8 +862,8 @@ bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell
// DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
// Assume that this is one contiguous block of cells
GGML_ASSERT(head + cell_count <= size);
GGML_ASSERT(cells[head].pos == batch.pos[0]);
GGML_ASSERT(cells[head + cell_count - 1].pos == batch.pos[cell_count - 1]);
GGML_ASSERT(cells[head].pos == ubatch.pos[0]);
GGML_ASSERT(cells[head + cell_count - 1].pos == ubatch.pos[cell_count - 1]);
GGML_ASSERT(cells[head].has_seq_id(dest_seq_id));
GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id));
} else {
@ -1037,23 +1040,22 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
}
//
// llama_memory_recurrent_state
// llama_memory_recurrent_context
//
llama_memory_recurrent_state::llama_memory_recurrent_state(llama_memory_status status) : status(status) {}
llama_memory_recurrent_context::llama_memory_recurrent_context(llama_memory_status status) : status(status) {}
llama_memory_recurrent_state::llama_memory_recurrent_state(
llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), is_full(true) {
}
llama_memory_recurrent_state::llama_memory_recurrent_state(
llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem,
llama_sbatch sbatch,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), sbatch(std::move(sbatch)), ubatches(std::move(ubatches)) {}
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), ubatches(std::move(ubatches)) {}
llama_memory_recurrent_state::~llama_memory_recurrent_state() = default;
llama_memory_recurrent_context::~llama_memory_recurrent_context() = default;
bool llama_memory_recurrent_state::next() {
bool llama_memory_recurrent_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
@ -1063,7 +1065,7 @@ bool llama_memory_recurrent_state::next() {
return true;
}
bool llama_memory_recurrent_state::apply() {
bool llama_memory_recurrent_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
mem->find_slot(ubatches[i_next]);
@ -1071,46 +1073,40 @@ bool llama_memory_recurrent_state::apply() {
return true;
}
std::vector<int64_t> & llama_memory_recurrent_state::out_ids() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return sbatch.out_ids;
}
llama_memory_status llama_memory_recurrent_state::get_status() const {
llama_memory_status llama_memory_recurrent_context::get_status() const {
return status;
}
const llama_ubatch & llama_memory_recurrent_state::get_ubatch() const {
const llama_ubatch & llama_memory_recurrent_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
uint32_t llama_memory_recurrent_state::get_n_rs() const {
uint32_t llama_memory_recurrent_context::get_n_rs() const {
return is_full ? mem->size : mem->n;
}
uint32_t llama_memory_recurrent_state::get_head() const {
uint32_t llama_memory_recurrent_context::get_head() const {
return is_full ? 0 : mem->head;
}
int32_t llama_memory_recurrent_state::get_rs_z() const {
int32_t llama_memory_recurrent_context::get_rs_z() const {
return is_full ? 0 : mem->rs_z;
}
uint32_t llama_memory_recurrent_state::get_size() const {
uint32_t llama_memory_recurrent_context::get_size() const {
return mem->size;
}
ggml_tensor * llama_memory_recurrent_state::get_r_l(int32_t il) const {
ggml_tensor * llama_memory_recurrent_context::get_r_l(int32_t il) const {
return mem->r_l[il];
}
ggml_tensor * llama_memory_recurrent_state::get_s_l(int32_t il) const {
ggml_tensor * llama_memory_recurrent_context::get_s_l(int32_t il) const {
return mem->s_l[il];
}
int32_t llama_memory_recurrent_state::s_copy(int i) const {
int32_t llama_memory_recurrent_context::s_copy(int i) const {
return mem->cells[i + mem->head].src0;
}

35
src/llama-memory-recurrent.h
View File

@ -11,8 +11,8 @@
// llama_memory_recurrent
//
// TODO: extract the cache state used for graph computation into llama_memory_recurrent_state_i
// see the implementation of llama_kv_cache_unified_state_i for an example how to do it
// TODO: extract the cache state used for graph computation into llama_memory_recurrent_context_i
// see the implementation of llama_kv_cache_unified_context_i for an example how to do it
class llama_memory_recurrent : public llama_memory_i {
public:
@ -34,14 +34,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
const llama_batch & batch,
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
void clear(bool data) override;
@ -125,37 +125,34 @@ private:
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
};
class llama_memory_recurrent_state : public llama_memory_state_i {
class llama_memory_recurrent_context : public llama_memory_context_i {
public:
// used for errors
llama_memory_recurrent_state(llama_memory_status status);
llama_memory_recurrent_context(llama_memory_status status);
// used to create a full-cache state
llama_memory_recurrent_state(
// used to create a full-cache or update context
llama_memory_recurrent_context(
llama_memory_recurrent * mem);
// used to create a state from a batch
llama_memory_recurrent_state(
// used to create a batch processing context from a batch
llama_memory_recurrent_context(
llama_memory_recurrent * mem,
llama_sbatch sbatch,
std::vector<llama_ubatch> ubatches);
virtual ~llama_memory_recurrent_state();
virtual ~llama_memory_recurrent_context();
//
// llama_memory_state_i
// llama_memory_context_i
//
bool next() override;
bool apply() override;
std::vector<int64_t> & out_ids() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
//
// llama_memory_recurrent_state specific API
// llama_memory_recurrent_context specific API
//
uint32_t get_n_rs() const;
@ -173,8 +170,6 @@ private:
llama_memory_recurrent * mem;
llama_sbatch sbatch;
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;

40
src/llama-memory.h
View File

@ -3,10 +3,11 @@
#include "llama.h"
#include <memory>
#include <vector>
struct llama_ubatch;
class llama_batch_allocr;
class llama_io_write_i;
class llama_io_read_i;
@ -26,23 +27,21 @@ enum llama_memory_status {
LLAMA_MEMORY_STATUS_FAILED_COMPUTE,
};
// helper function for combining the status of two memory states
// helper function for combining the status of two memory contexts
// useful for implementing hybrid memory types (e.g. iSWA)
llama_memory_status llama_memory_status_combine(llama_memory_status s0, llama_memory_status s1);
// the interface for managing the memory state during batch processing
// the interface for managing the memory context during batch processing
// this interface is implemented per memory type. see:
// - llama_kv_cache_unified_state
// - llama_kv_cache_unified_iswa_state
// - llama_kv_cache_unified_context
// - llama_kv_cache_unified_iswa_context
// ...
//
// the only method that can mutate the memory and the memory state is llama_memory_i::apply()
//
// TODO: rename to llama_memory_context_i ?
struct llama_memory_state_i {
virtual ~llama_memory_state_i() = default;
// the only method that should mutate the memory and the memory context is llama_memory_i::apply()
struct llama_memory_context_i {
virtual ~llama_memory_context_i() = default;
// consume the current ubatch from the state and proceed to the next one
// consume the current ubatch from the context and proceed to the next one
// return false if we are done
virtual bool next() = 0;
@ -50,17 +49,14 @@ struct llama_memory_state_i {
// return false on failure
virtual bool apply() = 0;
// TODO: this might get reworked in the future when refactoring llama_batch
virtual std::vector<int64_t> & out_ids() = 0;
// get the current ubatch
virtual const llama_ubatch & get_ubatch() const = 0;
// get the status of the memory state - used for error handling and checking if any updates would be applied
// get the status of the memory context - used for error handling and checking if any updates would be applied
virtual llama_memory_status get_status() const = 0;
};
using llama_memory_state_ptr = std::unique_ptr<llama_memory_state_i>;
using llama_memory_context_ptr = std::unique_ptr<llama_memory_context_i>;
// general concept of LLM memory
// the KV cache is a type of LLM memory, but there can be other types
@ -68,19 +64,19 @@ struct llama_memory_i {
virtual ~llama_memory_i() = default;
// split the input batch into a set of ubatches and verify that they can fit into the cache
// return a state object containing the ubatches and KV cache state required to process them
// check the llama_memory_state_i::get_status() for the result
virtual llama_memory_state_ptr init_batch(
const llama_batch & batch,
// return a context object containing the ubatches and memory state required to process them
// check the llama_memory_context_i::get_status() for the result
virtual llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) = 0;
// simulate full cache, used for allocating worst-case compute buffers
virtual llama_memory_state_ptr init_full() = 0;
virtual llama_memory_context_ptr init_full() = 0;
// prepare for any pending memory updates, such as shifts, defrags, etc.
// status == LLAMA_MEMORY_STATUS_NO_UPDATE if there is nothing to update
virtual llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) = 0;
virtual llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) = 0;
// getters
virtual bool get_can_shift() const = 0;

1
src/llama-model-saver.cpp
View File

@ -228,6 +228,7 @@ void llama_model_saver::add_kv_from_model() {
// add_kv(LLM_KV_TOKENIZER_MASK_ID, ???);
add_kv(LLM_KV_TOKENIZER_ADD_BOS, vocab.get_add_bos());
add_kv(LLM_KV_TOKENIZER_ADD_EOS, vocab.get_add_eos());
add_kv(LLM_KV_TOKENIZER_ADD_SEP, vocab.get_add_sep());
add_kv(LLM_KV_TOKENIZER_ADD_PREFIX, vocab.get_add_space_prefix());
add_kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.get_remove_extra_whitespaces());
add_kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, vocab.get_precompiled_charsmap());

517
src/llama-model.cpp
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File diff suppressed because it is too large Load Diff

83
src/llama-quant.cpp
View File

@ -1,5 +1,4 @@
#include "llama-quant.h"
#include "llama-impl.h"
#include "llama-model.h"
#include "llama-model-loader.h"
@ -27,6 +26,56 @@ static void zeros(std::ofstream & file, size_t n) {
}
}
static std::string remap_layer(const std::string & orig_name, const std::vector<int> & prune, std::map<int, std::string> & mapped, int & next_id) {
if (prune.empty()) {
return orig_name;
}
static const std::regex pattern(R"(blk\.(\d+)\.)");
if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
const int blk = std::stoi(match[1]);
std::string new_name = orig_name;
if (mapped.count(blk)) {
// Already mapped, do nothing
} else if (std::find(prune.begin(), prune.end(), blk) != prune.end()) {
mapped[blk] = "";
} else if (blk < prune.front()) {
mapped[blk] = std::to_string(blk);
next_id = blk + 1;
} else {
mapped[blk] = std::to_string(next_id);
++next_id;
}
return mapped[blk].empty() ? mapped[blk] : new_name.replace(match.position(1), match.length(1), mapped[blk]);
}
return orig_name;
}
static std::string remap_imatrix (const std::string & orig_name, const std::map<int, std::string> & mapped) {
if (mapped.empty()) {
return orig_name;
}
static const std::regex pattern(R"(blk\.(\d+)\.)");
if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
const std::string blk(match[1]);
std::string new_name = orig_name;
for (const auto & p : mapped) {
if (p.second == blk) {
LLAMA_LOG_DEBUG("(blk.%d imatrix) ", p.first);
return new_name.replace(match.position(1), match.length(1), std::to_string(p.first));
}
}
GGML_ABORT("\n%s: imatrix mapping error for %s\n", __func__, orig_name.c_str());
}
return orig_name;
}
struct quantize_state_impl {
const llama_model & model;
const llama_model_quantize_params * params;
@ -568,6 +617,11 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
const size_t align = GGUF_DEFAULT_ALIGNMENT;
gguf_context_ptr ctx_out { gguf_init_empty() };
std::vector<int> prune_list = {};
if (params->prune_layers) {
prune_list = *static_cast<const std::vector<int> *>(params->prune_layers);
}
// copy the KV pairs from the input file
gguf_set_kv (ctx_out.get(), ml.meta.get());
gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
@ -597,12 +651,32 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
}
}
std::map<int, std::string> mapped;
int blk_id = 0;
int pruned_attention_w = 0;
// make a list of weights
std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
tensors.reserve(ml.weights_map.size());
for (const auto & it : ml.weights_map) {
const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id));
if (remapped_name.empty()) {
if (it.first.find("attn_v.weight") != std::string::npos ||
it.first.find("attn_qkv.weight") != std::string::npos ||
it.first.find("attn_kv_b.weight") != std::string::npos) {
pruned_attention_w++;
}
LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str());
continue;
} else if (remapped_name != it.first) {
ggml_set_name(it.second.tensor, remapped_name.c_str());
LLAMA_LOG_DEBUG("%s: tensor %s remapped to %s\n", __func__, it.first.c_str(), ggml_get_name(it.second.tensor));
}
tensors.push_back(&it.second);
}
if (!prune_list.empty()) {
gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_BLOCK_COUNT).c_str(), blk_id);
}
// keep_split requires that the weights are sorted by split index
if (params->keep_split) {
@ -640,7 +714,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
GGML_ASSERT((qs.n_attention_wv == n_attn_layer - pruned_attention_w) && "n_attention_wv is unexpected");
}
size_t total_size_org = 0;
@ -681,7 +755,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
for (size_t i = 0; i < ctx_outs.size(); ++i) {
gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), (int32_t)tensors.size());
}
}
@ -832,7 +906,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
const float * imatrix = nullptr;
if (imatrix_data) {
auto it = imatrix_data->find(tensor->name);
auto it = imatrix_data->find(remap_imatrix(tensor->name, mapped));
if (it == imatrix_data->end()) {
LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
} else {
@ -947,6 +1021,7 @@ llama_model_quantize_params llama_model_quantize_default_params() {
/*.imatrix =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.tensor_type =*/ nullptr,
/*.prune_layers =*/ nullptr
};
return result;

33
src/llama-vocab.cpp
View File

@ -1269,6 +1269,7 @@ struct llama_vocab::impl {
bool add_space_prefix = false;
bool add_bos = false;
bool add_eos = false;
bool add_sep = false;
bool ignore_merges = false;
bool clean_spaces = false; // clean_up_tokenization_spaces
bool remove_extra_whitespaces = false;
@ -1421,6 +1422,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
special_sep_id = 102;
special_pad_id = 0;
special_mask_id = 103;
add_sep = true;
} else if (tokenizer_model == "gpt2") {
type = LLAMA_VOCAB_TYPE_BPE;
@ -1550,12 +1553,15 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "jina-es" ||
tokenizer_pre == "jina-de" ||
tokenizer_pre == "gigachat" ||
tokenizer_pre == "jina-v1-en" ||
tokenizer_pre == "jina-v2-es" ||
tokenizer_pre == "jina-v2-de" ||
tokenizer_pre == "jina-v2-de") {
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
} else if (
tokenizer_pre == "jina-v1-en" ||
tokenizer_pre == "jina-v2-code" ||
tokenizer_pre == "roberta-bpe") {
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
add_sep = true;
} else if (
tokenizer_pre == "refact") {
pre_type = LLAMA_VOCAB_PRE_TYPE_REFACT;
@ -1665,6 +1671,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
clean_spaces = true;
add_bos = true;
add_eos = false;
add_sep = true;
} else if (type == LLAMA_VOCAB_TYPE_UGM) {
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
add_bos = false;
@ -1801,7 +1808,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
}
}
// Handle add_bos and add_eos
// Handle add_bos, add_eos and add_sep
{
bool temp = true;
@ -1811,6 +1818,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
add_eos = temp;
}
if (ml.get_key(LLM_KV_TOKENIZER_ADD_SEP, temp, false)) {
add_sep = temp;
}
}
// auto-detect special tokens by text
@ -2060,9 +2070,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
//NOTE: Per token attributes are missing from the GGUF file.
//TODO: Extract attributes from GGUF file.
{
auto _contains_any = [] (const std::string & str, const std::vector<std::string> & substrs) -> bool {
auto _contains_any = [] (const std::string & str, const std::vector<std::string_view> & substrs) -> bool {
for (const auto & substr : substrs) {
if (str.find(substr) < std::string::npos) {
if (str.find(substr) != std::string::npos) {
return true;
}
}
@ -3000,6 +3010,10 @@ bool llama_vocab::get_add_eos() const {
return pimpl->add_eos;
}
bool llama_vocab::get_add_sep() const {
return pimpl->add_sep;
}
bool llama_vocab::get_ignore_merges() const {
return pimpl->ignore_merges;
}
@ -3060,6 +3074,11 @@ int32_t llama_vocab::tokenize(
bool add_special,
bool parse_special) const {
auto res = tokenize(std::string(text, text_len), add_special, parse_special);
if (res.size() >= static_cast<size_t>(std::numeric_limits<int32_t>::max())) {
LLAMA_LOG_ERROR("%s: tokenization result size %zu exceeds int32_t limit\n", __func__, res.size());
return std::numeric_limits<int32_t>::min();
}
if (n_tokens_max < (int) res.size()) {
// LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
return -((int) res.size());
@ -3191,6 +3210,10 @@ bool llama_vocab_get_add_eos(const struct llama_vocab * vocab) {
return vocab->get_add_eos();
}
bool llama_vocab_get_add_sep(const struct llama_vocab * vocab) {
return vocab->get_add_sep();
}
llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab) {
return vocab->token_fim_pre();
}

1
src/llama-vocab.h
View File

@ -74,6 +74,7 @@ struct llama_vocab {
bool get_add_space_prefix () const;
bool get_add_bos () const;
bool get_add_eos () const;
bool get_add_sep () const;
bool get_ignore_merges () const;
bool get_clean_spaces () const;
bool get_remove_extra_whitespaces () const;

5
src/unicode.cpp
View File

@ -204,12 +204,17 @@ static inline std::wstring unicode_wstring_from_utf8(const std::string & s) {
// disable C++17 deprecation warning for std::codecvt_utf8
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
#elif defined(__GNUC__)
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
std::wstring_convert<std::codecvt_utf8<wchar_t>> conv;
#if defined(__clang__)
# pragma clang diagnostic pop
#elif defined(__GNUC__)
# pragma GCC diagnostic pop
#endif
return conv.from_bytes(s);

36
tests/test-backend-ops.cpp
View File

@ -2755,6 +2755,35 @@ struct test_conv_transpose_1d : public test_case {
}
};
// GGML_OP_CONV_TRANSPOSE_2D
struct test_conv_transpose_2d : public test_case {
const std::array<int64_t, 4> ne_input;
const std::array<int64_t, 4> ne_kernel;
const int stride;
std::string vars() override {
return VARS_TO_STR3(ne_input, ne_kernel, stride);
}
test_conv_transpose_2d(std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
int stride = 1)
: ne_input(ne_input), ne_kernel(ne_kernel), stride(stride){}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
ggml_set_name(input, "input");
ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne_kernel.data());
ggml_set_name(kernel, "kernel");
ggml_tensor * out = ggml_conv_transpose_2d_p0(ctx, kernel, input, stride);
ggml_set_name(out, "out");
return out;
}
};
// GGML_OP_IM2COL
struct test_im2col : public test_case {
const ggml_type type_input;
@ -4080,6 +4109,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1));
test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 500, 1, 1}));
test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1}));
@ -4649,6 +4681,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false));
test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true));
test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1));
test_cases.emplace_back(new test_mean(GGML_TYPE_F32, {256, 256, 3, 1}));
return test_cases;
}

8
tools/llama-bench/llama-bench.cpp
View File

@ -267,6 +267,7 @@ struct cmd_params {
int delay;
bool verbose;
bool progress;
bool no_warmup;
output_formats output_format;
output_formats output_format_stderr;
};
@ -303,6 +304,7 @@ static const cmd_params cmd_params_defaults = {
/* delay */ 0,
/* verbose */ false,
/* progress */ false,
/* no_warmup */ false,
/* output_format */ MARKDOWN,
/* output_format_stderr */ NONE,
};
@ -325,6 +327,7 @@ static void print_usage(int /* argc */, char ** argv) {
output_format_str(cmd_params_defaults.output_format_stderr));
printf(" -v, --verbose verbose output\n");
printf(" --progress print test progress indicators\n");
printf(" --no-warmup skip warmup runs before benchmarking\n");
printf("\n");
printf("test parameters:\n");
printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
@ -425,6 +428,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
params.prio = cmd_params_defaults.prio;
params.delay = cmd_params_defaults.delay;
params.progress = cmd_params_defaults.progress;
params.no_warmup = cmd_params_defaults.no_warmup;
for (int i = 1; i < argc; i++) {
arg = argv[i];
@ -798,6 +802,8 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
params.verbose = true;
} else if (arg == "--progress") {
params.progress = true;
} else if (arg == "--no-warmup") {
params.no_warmup = true;
} else {
invalid_param = true;
break;
@ -1925,6 +1931,7 @@ int main(int argc, char ** argv) {
llama_attach_threadpool(ctx, threadpool, NULL);
// warmup run
if (!params.no_warmup) {
if (t.n_prompt > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count);
@ -1946,6 +1953,7 @@ int main(int argc, char ** argv) {
exit(1);
}
}
}
for (int i = 0; i < params.reps; i++) {
llama_memory_clear(llama_get_memory(ctx), false);

1
tools/main/main.cpp
View File

@ -292,6 +292,7 @@ int main(int argc, char ** argv) {
if (!params.system_prompt.empty() || !params.prompt.empty()) {
common_chat_templates_inputs inputs;
inputs.use_jinja = g_params->use_jinja;
inputs.messages = chat_msgs;
inputs.add_generation_prompt = !params.prompt.empty();

3
tools/mtmd/clip.cpp
View File

@ -2211,6 +2211,9 @@ struct clip_model_loader {
{
hparams.rope_theta = 10000.0f;
hparams.warmup_image_size = hparams.patch_size * 8;
// Mistral Small 2506 needs 1024x1024 image size cap to prevent OOM
// ref: https://github.com/ggml-org/llama.cpp/issues/14310
hparams.image_size = 1024;
get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false);
} break;
case PROJECTOR_TYPE_GEMMA3:

44
tools/quantize/quantize.cpp
View File

@ -107,13 +107,11 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
return false;
}
// usage:
// ./llama-quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type]\n", executable);
printf(" [--token-embedding-type] [--tensor-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights]\n", executable);
printf(" [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--tensor-type] [--prune-layers] [--keep-split] [--override-kv]\n");
printf(" model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
@ -124,6 +122,8 @@ static void usage(const char * executable) {
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n");
printf(" Advanced option to selectively quantize tensors. May be specified multiple times.\n");
printf(" --prune-layers L0,L1,L2...comma-separated list of layer numbers to prune from the model\n");
printf(" Advanced option to remove all tensors from the given layers\n");
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");
@ -286,6 +286,32 @@ static bool parse_tensor_type(const char * data, std::vector<tensor_quantization
return true;
}
static bool parse_layer_prune(const char * data, std::vector<int> & prune_layers) {
if (!data) {
printf("\n%s: no layer pruning ids provided\n\n", __func__);
return false;
}
const auto block_ids = string_split<std::string>(data, ',');
for (const auto & block_id : block_ids) {
int id;
try {
id = std::stoi(block_id);
} catch (...) {
id = -1;
}
if (id < 0) {
printf("\n%s: invalid layer id '%s'\n\n", __func__, block_id.c_str());
return false;
}
prune_layers.emplace_back(id);
}
sort(prune_layers.begin(), prune_layers.end());
prune_layers.erase(std::unique(prune_layers.begin(), prune_layers.end()), prune_layers.end());
return true;
}
int main(int argc, char ** argv) {
if (argc < 3) {
usage(argv[0]);
@ -298,6 +324,7 @@ int main(int argc, char ** argv) {
std::vector<std::string> included_weights, excluded_weights;
std::vector<llama_model_kv_override> kv_overrides;
std::vector<tensor_quantization> tensor_types;
std::vector<int> prune_layers;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
@ -324,6 +351,10 @@ int main(int argc, char ** argv) {
if (arg_idx == argc-1 || !parse_tensor_type(argv[++arg_idx], tensor_types)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--prune-layers") == 0) {
if (arg_idx == argc-1 || !parse_layer_prune(argv[++arg_idx], prune_layers)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) {
usage(argv[0]);
@ -411,6 +442,9 @@ int main(int argc, char ** argv) {
if (!tensor_types.empty()) {
params.tensor_types = &tensor_types;
}
if (!prune_layers.empty()) {
params.prune_layers = &prune_layers;
}
llama_backend_init();

34
tools/run/run.cpp
View File

@ -9,6 +9,9 @@
#include <nlohmann/json.hpp>
#if defined(_WIN32)
# ifndef NOMINMAX
# define NOMINMAX
# endif
# include <windows.h>
# include <io.h>
#else
@ -939,17 +942,30 @@ static int apply_chat_template(const struct common_chat_templates * tmpls, Llama
// Function to tokenize the prompt
static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt,
std::vector<llama_token> & prompt_tokens, const LlamaData & llama_data) {
const bool is_first = llama_memory_seq_pos_max(llama_get_memory(llama_data.context.get()), 0) == 0;
const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
prompt_tokens.resize(n_prompt_tokens);
if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), is_first,
true) < 0) {
printe("failed to tokenize the prompt\n");
const bool is_first = llama_memory_seq_pos_max(llama_get_memory(llama_data.context.get()), 0) == -1;
int n_tokens = prompt.size() + 2 * is_first;
prompt_tokens.resize(n_tokens);
n_tokens = llama_tokenize(vocab, prompt.c_str(), prompt.size(),
prompt_tokens.data(), prompt_tokens.size(),
is_first, /*parse_special =*/true);
if (n_tokens == std::numeric_limits<int32_t>::min()) {
printe("tokenization failed: input too large\n");
return -1;
}
return n_prompt_tokens;
if (n_tokens < 0) {
prompt_tokens.resize(-n_tokens);
int check = llama_tokenize(vocab, prompt.c_str(), prompt.size(),
prompt_tokens.data(), prompt_tokens.size(),
is_first, /*parse_special =*/true);
if (check != -n_tokens) {
printe("failed to tokenize the prompt (size mismatch)\n");
return -1;
}
n_tokens = check;
} else {
prompt_tokens.resize(n_tokens);
}
return n_tokens;
}
// Check if we have enough space in the context to evaluate this batch

2
tools/server/README.md
View File

@ -187,6 +187,8 @@ The project is under active development, and we are [looking for feedback and co
| `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for speculative decoding model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) |
| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for speculative decoding model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) |
| `-mv, --model-vocoder FNAME` | vocoder model for audio generation (default: unused) |
| `--tts-use-guide-tokens` | Use guide tokens to improve TTS word recall |
| `--embd-bge-small-en-default` | use default bge-small-en-v1.5 model (note: can download weights from the internet) |

41
tools/server/server.cpp
View File

@ -1969,10 +1969,8 @@ struct server_context {
params_dft.n_ctx = params_base.speculative.n_ctx == 0 ? params_base.n_ctx / params_base.n_parallel : params_base.speculative.n_ctx;
params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers;
params_dft.n_parallel = 1;
// force F16 KV cache for the draft model for extra performance
params_dft.cache_type_k = GGML_TYPE_F16;
params_dft.cache_type_v = GGML_TYPE_F16;
params_dft.cache_type_k = params_base.speculative.cache_type_k;
params_dft.cache_type_v = params_base.speculative.cache_type_v;
llama_init_dft = common_init_from_params(params_dft);
@ -3387,38 +3385,6 @@ struct server_context {
llama_set_embeddings(ctx, slot_batched->need_embd());
}
// pad the batch so that batch.n_tokens >= n_slots
// TODO: temporary workaround for https://github.com/ggml-org/llama.cpp/issues/13689
if (slot_batched->need_embd()) {
const int n_slots = slots.size();
if (batch.n_tokens < n_slots) {
std::set<llama_seq_id> seq_ids;
for (int j = 0; j < batch.n_tokens; ++j) {
seq_ids.insert(batch.seq_id[j][0]);
}
// find unused sequence id
llama_seq_id seq_id = -1;
for (int i = 0; i < n_slots; ++i) {
if (seq_ids.find(i) == seq_ids.end()) {
seq_id = i;
}
}
const int n_add = n_slots - batch.n_tokens;
SRV_WRN("adding %d dummy tokens to the batch, seq_id = %d\n", n_add, seq_id);
for (int j = 0; j < n_add; ++j) {
common_batch_add(batch, 0, j, { seq_id }, true);
}
slots[seq_id].cache_tokens.clear();
llama_memory_seq_rm(llama_get_memory(ctx), seq_id, -1, -1);
}
}
int32_t i_next = 0;
// process the created batch of tokens
@ -3452,9 +3418,12 @@ struct server_context {
}
if (ret < -1) {
// TODO: update slot state based on llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
err = "Compute error.";
}
// TODO: handle ret == 2 (abort) when we start aborting
if (!err.empty()) {
SRV_ERR("%s, i = %d, n_batch = %d, ret = %d\n", err.c_str(), i, n_batch, ret);
for (auto & slot : slots) {

8
tools/server/utils.hpp
View File

@ -271,12 +271,20 @@ static llama_tokens format_rerank(const struct llama_vocab * vocab, const llama_
}
result.reserve(doc.size() + query.size() + 4);
if (llama_vocab_get_add_bos(vocab)) {
result.push_back(llama_vocab_bos(vocab));
}
result.insert(result.end(), query.begin(), query.end());
if (llama_vocab_get_add_eos(vocab)) {
result.push_back(eos_token);
}
if (llama_vocab_get_add_sep(vocab)) {
result.push_back(llama_vocab_sep(vocab));
}
result.insert(result.end(), doc.begin(), doc.end());
if (llama_vocab_get_add_eos(vocab)) {
result.push_back(eos_token);
}
return result;
}