vbatts/llama.cpp
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Merge remote-tracking branch 'origin/master' into tool-call

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ochafik 2024-10-31 01:45:07 +00:00
commit 61655b9cdd
59 changed files with 4992 additions and 3208 deletions
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2
ci/run.sh
View file

@ -53,7 +53,7 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then
exit 1
fi
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
fi
if [ ! -z ${GG_BUILD_VULKAN} ]; then

80
common/arg.cpp
View file

@ -128,13 +128,13 @@ static void common_params_handle_model_default(common_params & params) {
}
params.hf_file = params.model;
} else if (params.model.empty()) {
params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
params.model = fs_get_cache_file(string_split<std::string>(params.hf_file, '/').back());
}
} else if (!params.model_url.empty()) {
if (params.model.empty()) {
auto f = string_split(params.model_url, '#').front();
f = string_split(f, '?').front();
params.model = fs_get_cache_file(string_split(f, '/').back());
auto f = string_split<std::string>(params.model_url, '#').front();
f = string_split<std::string>(f, '?').front();
params.model = fs_get_cache_file(string_split<std::string>(f, '/').back());
}
} else if (params.model.empty()) {
params.model = DEFAULT_MODEL_PATH;
@ -251,6 +251,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
for (auto & antiprompt : params.antiprompt) {
string_process_escapes(antiprompt);
}
for (auto & seq_breaker : params.sparams.dry_sequence_breakers) {
string_process_escapes(seq_breaker);
}
}
if (!params.kv_overrides.empty()) {
@ -879,7 +882,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"--samplers"}, "SAMPLERS",
string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
[](common_params & params, const std::string & value) {
const auto sampler_names = string_split(value, ';');
const auto sampler_names = string_split<std::string>(value, ';');
params.sparams.samplers = common_sampler_types_from_names(sampler_names, true);
}
).set_sparam());
@ -940,13 +943,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.sparams.min_p = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--tfs"}, "N",
string_format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
[](common_params & params, const std::string & value) {
params.sparams.tfs_z = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--xtc-probability"}, "N",
string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability),
@ -997,6 +993,64 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.sparams.penalty_freq = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dry-multiplier"}, "N",
string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sparams.dry_multiplier),
[](common_params & params, const std::string & value) {
params.sparams.dry_multiplier = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dry-base"}, "N",
string_format("set DRY sampling base value (default: %.2f)", (double)params.sparams.dry_base),
[](common_params & params, const std::string & value) {
float potential_base = std::stof(value);
if (potential_base >= 1.0f)
{
params.sparams.dry_base = potential_base;
}
}
).set_sparam());
add_opt(common_arg(
{"--dry-allowed-length"}, "N",
string_format("set allowed length for DRY sampling (default: %d)", params.sparams.dry_allowed_length),
[](common_params & params, int value) {
params.sparams.dry_allowed_length = value;
}
).set_sparam());
add_opt(common_arg(
{"--dry-penalty-last-n"}, "N",
string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sparams.dry_penalty_last_n),
[](common_params & params, int value) {
params.sparams.dry_penalty_last_n = value;
}
).set_sparam());
add_opt(common_arg(
{"--dry-sequence-breaker"}, "STRING",
string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n",
params.sparams.dry_sequence_breakers.empty() ? "none" :
std::accumulate(std::next(params.sparams.dry_sequence_breakers.begin()),
params.sparams.dry_sequence_breakers.end(),
std::string("'") + (params.sparams.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sparams.dry_sequence_breakers[0]) + "'",
[](const std::string& a, const std::string& b) {
std::string formatted_b = (b == "\n") ? "\\n" : b;
return a + ", '" + formatted_b + "'";
}).c_str()),
[](common_params & params, const std::string & value) {
static bool defaults_cleared = false;
if (!defaults_cleared) {
params.sparams.dry_sequence_breakers.clear();
defaults_cleared = true;
}
if (value == "none") {
params.sparams.dry_sequence_breakers.clear();
} else {
params.sparams.dry_sequence_breakers.emplace_back(value);
}
}
).set_sparam());
add_opt(common_arg(
{"--dynatemp-range"}, "N",
string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
@ -1013,7 +1067,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam());
add_opt(common_arg(
{"--mirostat"}, "N",
string_format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n"
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
[](common_params & params, int value) {
params.sparams.mirostat = value;

18
common/common.cpp
View file

@ -418,19 +418,6 @@ std::string string_format(const char * fmt, ...) {
return std::string(buf.data(), size);
}
std::vector<std::string> string_split(std::string input, char separator) {
std::vector<std::string> parts;
size_t separator_pos = input.find(separator);
while (separator_pos != std::string::npos) {
std::string part = input.substr(0, separator_pos);
parts.emplace_back(part);
input = input.substr(separator_pos + 1);
separator_pos = input.find(separator);
}
parts.emplace_back(input);
return parts;
}
std::string string_strip(const std::string & str) {
size_t start = 0;
size_t end = str.size();
@ -2116,6 +2103,10 @@ void yaml_dump_non_result_info(FILE * stream, const common_params & params, cons
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "dry_allowed_length: %d # default: 2\n", sparams.dry_allowed_length);
fprintf(stream, "dry_base: %.2f # default: 1.75\n", sparams.dry_base);
fprintf(stream, "dry_multiplier: %.1f # default: 0.0\n", sparams.dry_multiplier);
fprintf(stream, "dry_penalty_last_n: %d # default: -1 (0 = disable, -1 = context size)\n", sparams.dry_penalty_last_n);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
@ -2196,7 +2187,6 @@ void yaml_dump_non_result_info(FILE * stream, const common_params & params, cons
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);

89
common/common.h
View file

@ -90,14 +90,15 @@ enum llama_example {
enum common_sampler_type {
COMMON_SAMPLER_TYPE_NONE = 0,
COMMON_SAMPLER_TYPE_TOP_K = 1,
COMMON_SAMPLER_TYPE_TOP_P = 2,
COMMON_SAMPLER_TYPE_MIN_P = 3,
COMMON_SAMPLER_TYPE_TFS_Z = 4,
COMMON_SAMPLER_TYPE_TYPICAL_P = 5,
COMMON_SAMPLER_TYPE_TEMPERATURE = 6,
COMMON_SAMPLER_TYPE_XTC = 7,
COMMON_SAMPLER_TYPE_INFILL = 8,
COMMON_SAMPLER_TYPE_DRY = 1,
COMMON_SAMPLER_TYPE_TOP_K = 2,
COMMON_SAMPLER_TYPE_TOP_P = 3,
COMMON_SAMPLER_TYPE_MIN_P = 4,
//COMMON_SAMPLER_TYPE_TFS_Z = 5,
COMMON_SAMPLER_TYPE_TYPICAL_P = 6,
COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
COMMON_SAMPLER_TYPE_XTC = 8,
COMMON_SAMPLER_TYPE_INFILL = 9,
};
// dimensionality reduction methods, used by cvector-generator
@ -110,34 +111,39 @@ enum dimre_method {
struct common_sampler_params {
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float xtc_probability = 0.00f; // 0.0 = disabled
float xtc_threshold = 0.10f; // > 0.5 disables XTC
float tfs_z = 1.00f; // 1.0 = disabled
float typ_p = 1.00f; // typical_p, 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float xtc_probability = 0.00f; // 0.0 = disabled
float xtc_threshold = 0.10f; // > 0.5 disables XTC
float typ_p = 1.00f; // typical_p, 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition:
float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length)
int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty
int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY
std::vector<enum common_sampler_type> samplers = {
COMMON_SAMPLER_TYPE_DRY,
COMMON_SAMPLER_TYPE_TOP_K,
COMMON_SAMPLER_TYPE_TFS_Z,
COMMON_SAMPLER_TYPE_TYPICAL_P,
COMMON_SAMPLER_TYPE_TOP_P,
COMMON_SAMPLER_TYPE_MIN_P,
@ -389,8 +395,6 @@ bool set_process_priority(enum ggml_sched_priority prio);
LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
std::string string_format(const char * fmt, ...);
std::vector<std::string> string_split(std::string input, char separator);
std::string string_strip(const std::string & str);
std::string string_get_sortable_timestamp();
@ -398,6 +402,7 @@ void string_replace_all(std::string & s, const std::string & search, const std::
template<class T>
static std::vector<T> string_split(const std::string & str, char delim) {
static_assert(!std::is_same<T, std::string>::value, "Please use the specialized version for std::string");
std::vector<T> values;
std::istringstream str_stream(str);
std::string token;
@ -410,6 +415,22 @@ static std::vector<T> string_split(const std::string & str, char delim) {
return values;
}
template<>
std::vector<std::string> string_split<std::string>(const std::string & input, char separator)
{
std::vector<std::string> parts;
size_t begin_pos = 0;
size_t separator_pos = input.find(separator);
while (separator_pos != std::string::npos) {
std::string part = input.substr(begin_pos, separator_pos - begin_pos);
parts.emplace_back(part);
begin_pos = separator_pos + 1;
separator_pos = input.find(separator, begin_pos);
}
parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos));
return parts;
}
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
void string_process_escapes(std::string & input);

30
common/sampling.cpp
View file

@ -130,10 +130,12 @@ std::string common_sampler_params::print() const {
snprintf(result, sizeof(result),
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
"\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
"\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
mirostat, mirostat_eta, mirostat_tau);
return std::string(result);
@ -183,6 +185,17 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
if (params.mirostat == 0) {
for (const auto & cnstr : params.samplers) {
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY:
{
std::vector<const char*> c_breakers;
c_breakers.reserve(params.dry_sequence_breakers.size());
for (const auto& str : params.dry_sequence_breakers) {
c_breakers.push_back(str.c_str());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
}
break;
case COMMON_SAMPLER_TYPE_TOP_K:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k));
break;
@ -195,9 +208,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
case COMMON_SAMPLER_TYPE_XTC:
llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
break;
case COMMON_SAMPLER_TYPE_TFS_Z:
llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_TYPICAL_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
@ -371,8 +381,8 @@ std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_
char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY: return 'd';
case COMMON_SAMPLER_TYPE_TOP_K: return 'k';
case COMMON_SAMPLER_TYPE_TFS_Z: return 'f';
case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y';
case COMMON_SAMPLER_TYPE_TOP_P: return 'p';
case COMMON_SAMPLER_TYPE_MIN_P: return 'm';
@ -385,8 +395,8 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY: return "dry";
case COMMON_SAMPLER_TYPE_TOP_K: return "top_k";
case COMMON_SAMPLER_TYPE_TFS_Z: return "tfs_z";
case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
case COMMON_SAMPLER_TYPE_TOP_P: return "top_p";
case COMMON_SAMPLER_TYPE_MIN_P: return "min_p";
@ -399,11 +409,11 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
std::vector<common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
std::unordered_map<std::string, common_sampler_type> sampler_canonical_name_map {
{ "dry", COMMON_SAMPLER_TYPE_DRY },
{ "top_k", COMMON_SAMPLER_TYPE_TOP_K },
{ "top_p", COMMON_SAMPLER_TYPE_TOP_P },
{ "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min_p", COMMON_SAMPLER_TYPE_MIN_P },
{ "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z },
{ "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
{ "xtc", COMMON_SAMPLER_TYPE_XTC },
{ "infill", COMMON_SAMPLER_TYPE_INFILL },
@ -420,8 +430,6 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
{ "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typ", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min-p", COMMON_SAMPLER_TYPE_MIN_P },
{ "tfs-z", COMMON_SAMPLER_TYPE_TFS_Z },
{ "tfs", COMMON_SAMPLER_TYPE_TFS_Z },
{ "temp", COMMON_SAMPLER_TYPE_TEMPERATURE },
};
@ -447,8 +455,8 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
std::vector<common_sampler_type> common_sampler_types_from_chars(const std::string & chars) {
std::unordered_map<char, common_sampler_type> sampler_name_map = {
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TFS_Z), COMMON_SAMPLER_TYPE_TFS_Z },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P },

3
convert_hf_to_gguf.py
View file

@ -573,6 +573,9 @@ class Model:
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
# ref: https://huggingface.co/BAAI/bge-small-en-v1.5
res = "bert-bge"
if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7":
# ref: https://huggingface.co/BAAI/bge-large-zh-v1.5
res = "bert-bge-large"
if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
# ref: https://huggingface.co/mosaicml/mpt-7b
res = "mpt"

1
convert_hf_to_gguf_update.py
View file

@ -72,6 +72,7 @@ models = [
{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
{"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },

6
convert_lora_to_gguf.py
View file

@ -230,7 +230,7 @@ def get_base_tensor_name(lora_tensor_name: str) -> str:
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Convert a huggingface PEFT LoRA adapter to a GGML compatible file")
description="Convert a Hugging Face PEFT LoRA adapter to a GGUF file")
parser.add_argument(
"--outfile", type=Path,
help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
@ -257,11 +257,11 @@ def parse_args() -> argparse.Namespace:
)
parser.add_argument(
"--base", type=Path, required=True,
help="directory containing base model file",
help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required",
)
parser.add_argument(
"lora_path", type=Path,
help="directory containing LoRA adapter file",
help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)",
)
return parser.parse_args()

132
examples/llama-bench/llama-bench.cpp
View file

@ -21,12 +21,6 @@
#include "ggml.h"
#include "llama.h"
#include "common.h"
#include "ggml-cuda.h"
#include "ggml-sycl.h"
#ifdef GGML_USE_CANN
#include "ggml-cann.h"
#endif
#ifdef _WIN32
#define WIN32_LEAN_AND_MEAN
@ -82,95 +76,27 @@ static T stdev(const std::vector<T> & v) {
}
static std::string get_cpu_info() {
std::string id;
#ifdef __linux__
FILE * f = fopen("/proc/cpuinfo", "r");
if (f) {
char buf[1024];
while (fgets(buf, sizeof(buf), f)) {
if (strncmp(buf, "model name", 10) == 0) {
char * p = strchr(buf, ':');
if (p) {
p++;
while (std::isspace(*p)) {
p++;
}
while (std::isspace(p[strlen(p) - 1])) {
p[strlen(p) - 1] = '\0';
}
id = p;
break;
}
}
}
fclose(f);
}
#elif defined(_WIN32)
HKEY hKey;
if (RegOpenKeyEx(HKEY_LOCAL_MACHINE,
TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"),
0,
KEY_READ,
&hKey) != ERROR_SUCCESS) {
// fail to open registry key
return "";
}
char cpu_brand[256];
DWORD cpu_brand_size = sizeof(cpu_brand);
if (RegQueryValueExA(hKey,
TEXT("ProcessorNameString"),
NULL,
NULL,
(LPBYTE)cpu_brand,
&cpu_brand_size) == ERROR_SUCCESS) {
id.assign(cpu_brand, cpu_brand_size);
if (id.find('\0') != std::string::npos) {
id.resize(id.find('\0'));
std::vector<std::string> cpu_list;
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
auto * dev = ggml_backend_dev_get(i);
auto dev_type = ggml_backend_dev_type(dev);
if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU || dev_type == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
cpu_list.push_back(ggml_backend_dev_description(dev));
}
}
RegCloseKey(hKey);
#endif
// TODO: other platforms
return id;
return join(cpu_list, ", ");
}
static std::string get_gpu_info() {
std::string id;
#ifdef GGML_USE_CUDA
int count = ggml_backend_cuda_get_device_count();
for (int i = 0; i < count; i++) {
char buf[128];
ggml_backend_cuda_get_device_description(i, buf, sizeof(buf));
id += buf;
if (i < count - 1) {
id += "/";
std::vector<std::string> gpu_list;
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
auto * dev = ggml_backend_dev_get(i);
auto dev_type = ggml_backend_dev_type(dev);
if (dev_type == GGML_BACKEND_DEVICE_TYPE_GPU) {
gpu_list.push_back(ggml_backend_dev_description(dev));
}
}
#endif
#ifdef GGML_USE_SYCL
int count = ggml_backend_sycl_get_device_count();
for (int i = 0; i < count; i++) {
char buf[128];
ggml_backend_sycl_get_device_description(i, buf, sizeof(buf));
id += buf;
if (i < count - 1) {
id += "/";
}
}
#endif
#ifdef GGML_USE_CANN
uint32_t count = ggml_backend_cann_get_device_count();
for (uint32_t i = 0; i < count; i++) {
char buf[128];
ggml_backend_cann_get_device_description(i, buf, sizeof(buf));
id += buf;
if (i < count - 1) {
id += "/";
}
}
#endif
// TODO: other backends
return id;
return join(gpu_list, ", ");
}
// command line params
@ -938,29 +864,15 @@ struct test {
}
static std::string get_backend() {
if (cuda) {
return GGML_CUDA_NAME;
std::vector<std::string> backends;
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
auto * reg = ggml_backend_reg_get(i);
std::string name = ggml_backend_reg_name(reg);
if (name != "CPU") {
backends.push_back(ggml_backend_reg_name(reg));
}
}
if (vulkan) {
return "Vulkan";
}
if (kompute) {
return "Kompute";
}
if (metal) {
return "Metal";
}
if (sycl) {
return GGML_SYCL_NAME;
}
if (gpu_blas) {
return "GPU BLAS";
}
if (blas) {
return "BLAS";
}
return "CPU";
return backends.empty() ? "CPU" : join(backends, ",");
}
static const std::vector<std::string> & get_fields() {

170
examples/llama.vim
View file

@ -2,7 +2,7 @@
"
" requires:
"
" - neovim
" - neovim or vim
" - curl
" - llama.cpp server instance
" - FIM-compatible model
@ -10,7 +10,7 @@
" sample config:
"
" - Tab - accept the current suggestion
" - Shift+Tab - accept just the first line of the segguestion
" - Shift+Tab - accept just the first line of the suggestion
" - Ctrl+F - toggle FIM completion manually
"
" make symlink or copy this file to ~/.config/nvim/autoload/llama.vim
@ -43,8 +43,8 @@
"
" colors (adjust to your liking)
highlight llama_hl_hint guifg=#ff772f
highlight llama_hl_info guifg=#77ff2f
highlight llama_hl_hint guifg=#ff772f ctermfg=202
highlight llama_hl_info guifg=#77ff2f ctermfg=119
" general parameters:
"
@ -81,7 +81,7 @@ let s:default_config = {
\ 'n_suffix': 64,
\ 'n_predict': 128,
\ 't_max_prompt_ms': 500,
\ 't_max_predict_ms': 1000,
\ 't_max_predict_ms': 3000,
\ 'show_info': 2,
\ 'auto_fim': v:true,
\ 'max_line_suffix': 8,
@ -93,6 +93,18 @@ let s:default_config = {
let g:llama_config = get(g:, 'llama_config', s:default_config)
function! s:get_indent(str)
let l:count = 0
for i in range(len(a:str))
if a:str[i] == "\t"
let l:count += &tabstop - 1
else
break
endif
endfor
return l:count
endfunction
function! s:rand(i0, i1) abort
return a:i0 + rand() % (a:i1 - a:i0 + 1)
endfunction
@ -129,6 +141,21 @@ function! llama#init()
let s:current_job = v:null
let s:ghost_text_nvim = exists('*nvim_buf_get_mark')
let s:ghost_text_vim = has('textprop')
if s:ghost_text_vim
let s:hlgroup_hint = 'llama_hl_hint'
let s:hlgroup_info = 'llama_hl_info'
if empty(prop_type_get(s:hlgroup_hint))
call prop_type_add(s:hlgroup_hint, {'highlight': s:hlgroup_hint})
endif
if empty(prop_type_get(s:hlgroup_info))
call prop_type_add(s:hlgroup_info, {'highlight': s:hlgroup_info})
endif
endif
augroup llama
autocmd!
autocmd InsertEnter * inoremap <expr> <silent> <C-F> llama#fim_inline(v:false)
@ -317,13 +344,22 @@ function! s:ring_update()
\ 't_max_predict_ms': 1
\ })
let l:curl_command = printf(
\ "curl --silent --no-buffer --request POST --url %s --header \"Content-Type: application/json\" --data %s",
\ g:llama_config.endpoint, shellescape(l:request)
\ )
let l:curl_command = [
\ "curl",
\ "--silent",
\ "--no-buffer",
\ "--request", "POST",
\ "--url", g:llama_config.endpoint,
\ "--header", "Content-Type: application/json",
\ "--data", l:request
\ ]
" no callbacks because we don't need to process the response
call jobstart(l:curl_command, {})
if s:ghost_text_nvim
call jobstart(l:curl_command, {})
elseif s:ghost_text_vim
call job_start(l:curl_command, {})
endif
endfunction
" necessary for 'inoremap <expr>'
@ -418,24 +454,37 @@ function! llama#fim(is_auto) abort
\ 't_max_predict_ms': g:llama_config.t_max_predict_ms
\ })
let l:curl_command = printf(
\ "curl --silent --no-buffer --request POST --url %s --header \"Content-Type: application/json\" --data %s",
\ g:llama_config.endpoint, shellescape(l:request)
\ )
let l:curl_command = [
\ "curl",
\ "--silent",
\ "--no-buffer",
\ "--request", "POST",
\ "--url", g:llama_config.endpoint,
\ "--header", "Content-Type: application/json",
\ "--data", l:request
\ ]
if s:current_job != v:null
call jobstop(s:current_job)
if s:ghost_text_nvim
call jobstop(s:current_job)
elseif s:ghost_text_vim
call job_stop(s:current_job)
endif
endif
" send the request asynchronously
let s:current_job = jobstart(l:curl_command, {
\ 'on_stdout': function('s:fim_on_stdout'),
\ 'on_exit': function('s:fim_on_exit'),
\ 'stdout_buffered': v:true,
\ 'pos_x': s:pos_x,
\ 'pos_y': s:pos_y,
\ 'is_auto': a:is_auto
\ })
if s:ghost_text_nvim
let s:current_job = jobstart(l:curl_command, {
\ 'on_stdout': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]),
\ 'on_exit': function('s:fim_on_exit'),
\ 'stdout_buffered': v:true
\ })
elseif s:ghost_text_vim
let s:current_job = job_start(l:curl_command, {
\ 'out_cb': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]),
\ 'exit_cb': function('s:fim_on_exit')
\ })
endif
" TODO: per-file location
let l:delta_y = abs(s:pos_y - s:pos_y_pick)
@ -482,9 +531,13 @@ function! llama#fim_cancel()
" clear the virtual text
let l:bufnr = bufnr('%')
let l:id_vt_fim = nvim_create_namespace('vt_fim')
call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1)
if s:ghost_text_nvim
let l:id_vt_fim = nvim_create_namespace('vt_fim')
call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1)
elseif s:ghost_text_vim
call prop_remove({'type': s:hlgroup_hint, 'all': v:true})
call prop_remove({'type': s:hlgroup_info, 'all': v:true})
endif
" remove the mappings
silent! iunmap <buffer> <Tab>
@ -499,13 +552,18 @@ function! s:on_move()
endfunction
" callback that processes the FIM result from the server and displays the suggestion
function! s:fim_on_stdout(job_id, data, event) dict
let l:raw = join(a:data, "\n")
function! s:fim_on_stdout(pos_x, pos_y, is_auto, job_id, data, event = v:null)
if s:ghost_text_nvim
let l:raw = join(a:data, "\n")
elseif s:ghost_text_vim
let l:raw = a:data
endif
if len(l:raw) == 0
return
endif
if self.pos_x != col('.') - 1 || self.pos_y != line('.')
if a:pos_x != col('.') - 1 || a:pos_y != line('.')
return
endif
@ -514,14 +572,14 @@ function! s:fim_on_stdout(job_id, data, event) dict
return
endif
let s:pos_x = self.pos_x
let s:pos_y = self.pos_y
let s:pos_x = a:pos_x
let s:pos_y = a:pos_y
let s:can_accept = v:true
let l:has_info = v:false
if s:can_accept && v:shell_error
if !self.is_auto
if !a:is_auto
call add(s:content, "<| curl error: is the server on? |>")
endif
let s:can_accept = v:false
@ -642,7 +700,9 @@ function! s:fim_on_stdout(job_id, data, event) dict
" display virtual text with the suggestion
let l:bufnr = bufnr('%')
let l:id_vt_fim = nvim_create_namespace('vt_fim')
if s:ghost_text_nvim
let l:id_vt_fim = nvim_create_namespace('vt_fim')
endif
" construct the info message
if g:llama_config.show_info > 0 && l:has_info
@ -671,15 +731,41 @@ function! s:fim_on_stdout(job_id, data, event) dict
endif
" display the suggestion and append the info to the end of the first line
call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, {
\ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']],
\ 'virt_text_win_col': virtcol('.') - 1
\ })
if s:ghost_text_nvim
call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, {
\ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']],
\ 'virt_text_win_col': virtcol('.') - 1
\ })
call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, {
\ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}),
\ 'virt_text_win_col': virtcol('.')
\ })
call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, {
\ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}),
\ 'virt_text_win_col': virtcol('.')
\ })
elseif s:ghost_text_vim
let l:new_suffix = s:content[0]
if !empty(l:new_suffix)
call prop_add(s:pos_y, s:pos_x + 1, {
\ 'type': s:hlgroup_hint,
\ 'text': l:new_suffix
\ })
endif
for line in s:content[1:]
call prop_add(s:pos_y, 0, {
\ 'type': s:hlgroup_hint,
\ 'text': line,
\ 'text_padding_left': s:get_indent(line),
\ 'text_align': 'below'
\ })
endfor
if !empty(l:info)
call prop_add(s:pos_y, 0, {
\ 'type': s:hlgroup_info,
\ 'text': l:info,
\ 'text_padding_left': col('$'),
\ 'text_wrap': 'truncate'
\ })
endif
endif
" setup accept shortcuts
inoremap <buffer> <Tab> <C-O>:call llama#fim_accept(v:false)<CR>
@ -688,7 +774,7 @@ function! s:fim_on_stdout(job_id, data, event) dict
let s:hint_shown = v:true
endfunction
function! s:fim_on_exit(job_id, exit_code, event) dict
function! s:fim_on_exit(job_id, exit_code, event = v:null)
if a:exit_code != 0
echom "Job failed with exit code: " . a:exit_code
endif

43
examples/main/README.md
View file

@ -187,6 +187,30 @@ Use the `--no-penalize-nl` option to disable newline penalization when applying
Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
### DRY Repetition Penalty
DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).
- `--dry-multiplier N`: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled).
- `--dry-base N`: Set the DRY sampling base value (default: 1.75).
- `--dry-allowed-length N`: Set the allowed length for DRY sampling (default: 2).
- `--dry-penalty-last-n N`: Set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size).
- `--dry-sequence-breaker STRING`: Add a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: `['\n', ':', '"', '*']`. If the string `"none"` is supplied, no sequence breakers are used.
The `dry-multiplier` option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8.
The `dry-base` option sets the base value for the exponential penalty calculation in DRY sampling. Higher values lead to more aggressive penalization of repetitions.
The `dry-allowed-length` option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words.
The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of -1 considers the entire context. Use a positive value to limit the consideration to a specific number of recent tokens.
The `dry-sequence-breaker` option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied.
DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence.
Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n -1 --dry-sequence-breaker "—" --dry-sequence-breaker "##"`
### Top-K Sampling
- `--top-k N`: Limit the next token selection to the K most probable tokens (default: 40).
@ -211,14 +235,6 @@ The Min-P sampling method was designed as an alternative to Top-P, and aims to e
Example usage: `--min-p 0.05`
### Tail-Free Sampling (TFS)
- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
Tail-free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks at how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens and thus disables the effect of TFS.
Example usage: `--tfs 0.95`
### Locally Typical Sampling
- `--typical N`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
@ -317,6 +333,15 @@ These options help improve the performance and memory usage of the LLaMA models.
For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize).
## LoRA (Low-Rank Adaptation) adapters
- `--lora FNAME`: Optional path to a LoRA adapter to use with scaling of 1.0. Can be mixed with `--lora-scaled` and can be repeated to use multiple adapters.
- `--lora-scaled FNAME`: Optional path to a LoRA adapter with user-defined scaling. Can be mixed with `--lora` and can repeated to use multiple adapters.
You can add LoRA adapters using `--lora` or `--lora-scaled`. For example: `--lora my_adapter_1.gguf --lora my_adapter_2.gguf ...` or `--lora-scaled lora_task_A.gguf 0.5 --lora-scaled lora_task_B.gguf 0.5`.
LoRA adapters should be in GGUF format. To convert from Hugging Face format use the `convert-lora-to-gguf.py` script. LoRA adapters are loaded separately and applied during inference - they are not merged with the main model. This means that mmap model loading is fully supported when using LoRA adapters. The old `--lora-base` flag has been removed now that merging is no longer performed.
## Additional Options
These options provide extra functionality and customization when running the LLaMA models:
@ -325,6 +350,4 @@ These options provide extra functionality and customization when running the LLa
- `--verbose-prompt`: Print the prompt before generating text.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
- `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache.

38
examples/server/README.md
View file

@ -99,7 +99,7 @@ The project is under active development, and we are [looking for feedback and co
| Argument | Explanation |
| -------- | ----------- |
| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;typ_p;top_p;min_p;temperature) |
| `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
@ -108,15 +108,19 @@ The project is under active development, and we are [looking for feedback and co
| `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) |
| `--tfs N` | tail free sampling, parameter z (default: 1.0, 1.0 = disabled) |
| `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) |
| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) |
| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) |
| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) |
| `--dry-multiplier N` | DRY sampling multiplier (default: 0.0, 0.0 = disabled) |
| `--dry-base N` | DRY sampling base value (default: 1.75) |
| `--dry-allowed-length N` | allowed length for DRY sampling (default: 2) |
| `--dry-penalty-last-n N` | DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) |
| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers (`['\n', ':', '"', '*']`) in the process; use `"none"` to not use any sequence breakers
| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) |
| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) |
| `--mirostat N` | use Mirostat sampling.<br/>Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.<br/>(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) |
| `--mirostat N` | use Mirostat sampling.<br/>Top K, Nucleus and Locally Typical samplers are ignored if used.<br/>(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) |
| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) |
| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) |
| `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,<br/>i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',<br/>or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' |
@ -319,6 +323,18 @@ node index.js
- The prompt is a string or an array with the first element given as a string
- The model's `tokenizer.ggml.add_bos_token` metadata is `true`
These input shapes and data type are allowed for `prompt`:
- Single string: `"string"`
- Single sequence of tokens: `[12, 34, 56]`
- Mixed tokens and strings: `[12, 34, "string", 56, 78]`
Multiple prompts are also supported. In this case, the completion result will be an array.
- Only strings: `["string1", "string2"]`
- Strings and sequences of tokens: `["string1", [12, 34, 56]]`
- Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]`
`temperature`: Adjust the randomness of the generated text. Default: `0.8`
`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.
@ -343,8 +359,6 @@ node index.js
`stop`: Specify a JSON array of stopping strings.
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
`tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled.
`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
@ -357,6 +371,16 @@ node index.js
`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
`dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled.
`dry_base`: Set the DRY repetition penalty base value. Default: `1.75`
`dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2`
`dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size.
`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']`
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
@ -385,7 +409,7 @@ node index.js
`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false`
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
**Response format**
@ -790,7 +814,6 @@ Example:
"repeat_penalty": 1.100000023841858,
"samplers": [
"top_k",
"tfs_z",
"typical_p",
"top_p",
"min_p",
@ -804,7 +827,6 @@ Example:
"stream": false,
"task_id": 0,
"temperature": 0.0,
"tfs_z": 1.0,
"top_k": 40,
"top_p": 0.949999988079071,
"typical_p": 1.0

15
examples/server/public/index-new.html
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@ -40,12 +40,15 @@
repeat_last_n: 0, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.0, // 1.0 = disabled
penalize_nl: false, // true only useful for infinite completion
dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
dry_base: 1.75, // 0.0 = disabled
dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
dry_penalty_last_n: -1, // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
top_k: 0, // <= 0 to use vocab size
top_p: 1.0, // 1.0 = disabled
min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4
xtc_probability: 0.0, // 0 = disabled;
xtc_threshold: 0.1, // > 0.5 disables XTC;
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
frequency_penalty: 0.0, // 0.0 = disabled
@ -833,13 +836,16 @@ return html`
<fieldset class="params">
${IntField({ label: "Top-K", title: "Limits the selection of the next token to the K most probable tokens. 1 means no randomness = greedy sampling. If set to 0, it means the entire vocabulary size is considered.", max: 100, min: 0, step: 1, name: "top_k", value: params.value.top_k })}
${IntField({ label: "Penalize Last N", title: "The last n tokens that are taken into account to penalise repetitions. A value of 0 means that this function is deactivated and -1 means that the entire size of the context is taken into account.", max: 2048, min: 0, step: 16, name: "repeat_last_n", value: params.value.repeat_last_n })}
${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Presence Penalty", title: "A penalty that is applied if certain tokens appear repeatedly in the generated text. A higher value leads to fewer repetitions.", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
${FloatField({ label: "Top-P", title: "Limits the selection of the next token to a subset of tokens whose combined probability reaches a threshold value P = top-P. If set to 1, it means the entire vocabulary size is considered.", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
${FloatField({ label: "DRY Penalty Multiplier", title: "Set the DRY repetition penalty multiplier. Default is 0.0, which disables DRY.", max: 5.0, min: 0.0, name: "dry_multiplier", step: 0.01, value: params.value.dry_multiplier })}
${FloatField({ label: "DRY Base", title: "Set the DRY repetition penalty base value. Default is 1.75", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })}
${IntField({ label: "DRY Allowed Length", title: "Tokens that extend repetition beyond this receive exponentially increasing penalty. Default is 2", max: 10, min: 1, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })}
${IntField({ label: "DRY Penalty Last N", title: "How many tokens to scan for repetitions. Default is -1, where 0 is disabled and -1 is context size", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })}
${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
</fieldset>
@ -1139,11 +1145,12 @@ document.addEventListener('DOMContentLoaded', (event) => {
xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 },
xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 },
top_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 },
typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
repeat_penalty: { snapValue: 1.0, snapRangeMultiplier: 4 },
presence_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 },
frequency_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 },
dry_multiplier: { snapValue: 0.0, snapRangeMultiplier: 4 },
dry_base: { snapValue: 1.75, snapRangeMultiplier: 4 },
};
// add an event listener for each slider
Object.keys(snapSettings).forEach(sliderName => {

10
examples/server/public/index.html
View file

@ -304,12 +304,15 @@
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
penalize_nl: false,
dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
dry_base: 1.75, // 0.0 = disabled
dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
dry_penalty_last_n: -1, // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
xtc_probability: 0.0, // 0 = disabled;
xtc_threshold: 0.1, // > 0.5 disables XTC;
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
frequency_penalty: 0.0, // 0.0 = disabled
@ -1011,10 +1014,13 @@
<details>
<summary>More options</summary>
<fieldset class="two">
${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
${FloatField({ label: "DRY Penalty Multiplier", max: 5.0, min: 0.0, name: "dry_multiplier", step: 0.01, value: params.value.dry_multiplier })}
${FloatField({ label: "DRY Base", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })}
${IntField({ label: "DRY Allowed Length", max: 10, min: 2, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })}
${IntField({ label: "DRY Penalty Last N", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })}
${FloatField({ label: "XTC probability", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
${FloatField({ label: "XTC threshold", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
</fieldset>

0
examples/server/public/style.css Executable file → Normal file
View file

579
examples/server/server.cpp
View file

@ -43,21 +43,6 @@
#include <unordered_map>
#include <unordered_set>
#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
using json = nlohmann::ordered_json;
enum stop_type {
@ -68,6 +53,7 @@ enum stop_type {
// state diagram: https://github.com/ggerganov/llama.cpp/pull/9283
enum slot_state {
SLOT_STATE_IDLE,
SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future
SLOT_STATE_PROCESSING_PROMPT,
SLOT_STATE_DONE_PROMPT,
SLOT_STATE_GENERATING,
@ -79,7 +65,7 @@ enum server_state {
};
enum server_task_type {
SERVER_TASK_TYPE_COMPLETION,
SERVER_TASK_TYPE_INFERENCE,
SERVER_TASK_TYPE_CANCEL,
SERVER_TASK_TYPE_NEXT_RESPONSE,
SERVER_TASK_TYPE_METRICS,
@ -89,21 +75,22 @@ enum server_task_type {
SERVER_TASK_TYPE_SET_LORA,
};
enum server_task_cmpl_type {
SERVER_TASK_CMPL_TYPE_NORMAL,
SERVER_TASK_CMPL_TYPE_EMBEDDING,
SERVER_TASK_CMPL_TYPE_RERANK,
SERVER_TASK_CMPL_TYPE_INFILL,
enum server_task_inf_type {
SERVER_TASK_INF_TYPE_COMPLETION,
SERVER_TASK_INF_TYPE_EMBEDDING,
SERVER_TASK_INF_TYPE_RERANK,
SERVER_TASK_INF_TYPE_INFILL,
};
struct server_task {
int id = -1; // to be filled by server_queue
int id_target = -1; // used by SERVER_TASK_TYPE_CANCEL
llama_tokens prompt_tokens;
server_task_type type;
json data;
server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION;
// utility function
static std::unordered_set<int> get_list_id(const std::vector<server_task> & tasks) {
@ -163,26 +150,20 @@ struct server_slot {
int32_t i_batch = -1;
int32_t n_predict = -1; // TODO: disambiguate from params.n_predict
// n_prompt_tokens may not be equal to prompt_tokens.size(), because prompt maybe truncated
int32_t n_prompt_tokens = 0;
int32_t n_prompt_tokens_processed = 0;
json prompt; // can be either a string, array of strings or array of token ids
json input_prefix;
json input_suffix;
json input_extra;
// when a task is submitted, we first tokenize the prompt and store it here
std::vector<llama_token> prompt_tokens;
std::vector<llama_token> extra_tokens;
// input prompt tokens
llama_tokens prompt_tokens;
size_t last_nl_pos = 0;
std::string generated_text;
std::vector<llama_token> cache_tokens;
llama_tokens cache_tokens;
std::vector<completion_token_output> generated_token_probs;
server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION;
bool has_next_token = true;
bool has_new_line = false;
@ -233,7 +214,7 @@ struct server_slot {
n_past = 0;
n_sent_text = 0;
n_sent_token_probs = 0;
cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
inf_type = SERVER_TASK_INF_TYPE_COMPLETION;
generated_token_probs.clear();
}
@ -724,42 +705,6 @@ struct server_context {
metrics.init();
}
std::vector<llama_token> tokenize(const json & json_prompt, bool add_special, bool parse_special) const {
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
// or the first element of the json_prompt array is a string.
std::vector<llama_token> prompt_tokens;
if (json_prompt.is_array()) {
bool first = true;
for (const auto & p : json_prompt) {
if (p.is_string()) {
auto s = p.template get<std::string>();
std::vector<llama_token> p;
if (first) {
p = common_tokenize(ctx, s, add_special, parse_special);
first = false;
} else {
p = common_tokenize(ctx, s, false, parse_special);
}
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
} else {
if (first) {
first = false;
}
prompt_tokens.push_back(p.template get<llama_token>());
}
}
} else {
auto s = json_prompt.template get<std::string>();
prompt_tokens = common_tokenize(ctx, s, add_special, parse_special);
}
return prompt_tokens;
}
server_slot * get_slot_by_id(int id) {
for (server_slot & slot : slots) {
if (slot.id == id) {
@ -784,22 +729,16 @@ struct server_context {
continue;
}
// skip the slot if it does not contains prompt
if (!slot.prompt.is_string()) {
// skip the slot if it does not contains cached tokens
if (slot.prompt_tokens.empty()) {
continue;
}
// current slot's prompt
std::string slot_prompt = slot.prompt.get<std::string>();
// length of the current slot's prompt
int slot_prompt_len = slot_prompt.size();
// length of the Longest Common Prefix between the current slot's prompt and the input prompt
int lcp_len = longest_common_prefix(slot_prompt, prompt);
int lcp_len = longest_common_prefix(slot.cache_tokens, slot.prompt_tokens);
// fraction of the common substring length compared to the current slot's prompt length
similarity = static_cast<float>(lcp_len) / slot_prompt_len;
similarity = static_cast<float>(lcp_len) / static_cast<int>(slot.prompt_tokens.size());
// select the current slot if the criteria match
if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) {
@ -851,35 +790,57 @@ struct server_context {
slot.oaicompat_model = "";
}
slot.params.stream = json_value(data, "stream", false);
slot.params.cache_prompt = json_value(data, "cache_prompt", false);
slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict));
slot.params.n_indent = json_value(data, "n_indent", default_params.n_indent);
slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability);
slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold);
slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p);
slot.sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot.params.n_keep = json_value(data, "n_keep", default_params.n_keep);
slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
slot.sparams.seed = json_value(data, "seed", default_sparams.seed);
slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
//slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", default_params.t_max_prompt_ms); // TODO: implement
slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", default_params.t_max_predict_ms);
slot.params.stream = json_value(data, "stream", false);
slot.params.cache_prompt = json_value(data, "cache_prompt", false);
slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict));
slot.params.n_indent = json_value(data, "n_indent", default_params.n_indent);
slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability);
slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold);
slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p);
slot.sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
slot.sparams.dry_multiplier = json_value(data, "dry_multiplier", default_sparams.dry_multiplier);
slot.sparams.dry_base = json_value(data, "dry_base", default_sparams.dry_base);
slot.sparams.dry_allowed_length = json_value(data, "dry_allowed_length", default_sparams.dry_allowed_length);
slot.sparams.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", default_sparams.dry_penalty_last_n);
slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot.params.n_keep = json_value(data, "n_keep", default_params.n_keep);
slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
slot.sparams.seed = json_value(data, "seed", default_sparams.seed);
slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
//slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", default_params.t_max_prompt_ms); // TODO: implement
slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", default_params.t_max_predict_ms);
if (slot.sparams.dry_base < 1.0f)
{
slot.sparams.dry_base = default_sparams.dry_base;
}
// sequence breakers for DRY
{
// Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
// Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
if (data.contains("dry_sequence_breakers")) {
slot.sparams.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
if (slot.sparams.dry_sequence_breakers.empty()) {
send_error(task, "Error: dry_sequence_breakers must be a non-empty array of strings", ERROR_TYPE_INVALID_REQUEST);
return false;
}
}
}
// process "json_schema" and "grammar"
if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) {
@ -904,57 +865,6 @@ struct server_context {
SLT_WRN(slot, "n_predict = %d exceeds server configuration, setting to %d", slot.n_predict, slot.n_predict);
}
// infill
slot.input_prefix = json_value(data, "input_prefix", json());
slot.input_suffix = json_value(data, "input_suffix", json());
slot.input_extra = json_value(data, "input_extra", json());
SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.input_extra.size());
for (const auto & chunk : slot.input_extra) {
// { "text": string, "filename": string }
if (!chunk.contains("text") || !chunk["text"].is_string()) {
send_error(task, "extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST);
return false;
}
// filename is optional
if (chunk.contains("filename") && !chunk["filename"].is_string()) {
send_error(task, "extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST);
return false;
}
SLT_DBG(slot, "extra_context chunk in file '%s':\n%s\n", chunk.value("filename", "").c_str(), chunk.value("text", "").c_str());
}
// get prompt
{
const auto & prompt = data.find("prompt");
if (prompt == data.end()) {
send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
return false;
}
if ((prompt->is_string()) ||
(prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) ||
(prompt->is_array() && !prompt->empty() && prompt->at(0).is_number_integer())) {
slot.prompt = *prompt;
} else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
slot.prompt = prompt->at(0);
} else if (prompt->is_array() && prompt->size() > 1) {
// array of strings
for (const auto & el : *prompt) {
if (!el.is_string()) {
send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
return false;
}
}
slot.prompt = *prompt;
} else {
send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
return false;
}
}
{
slot.sparams.logit_bias.clear();
@ -1045,8 +955,7 @@ struct server_context {
}
}
slot.state = SLOT_STATE_PROCESSING_PROMPT;
slot.prompt_tokens.clear();
slot.state = SLOT_STATE_STARTED;
SLT_INF(slot, "%s", "processing task\n");
@ -1264,12 +1173,16 @@ struct server_context {
{"min_p", slot.sparams.min_p},
{"xtc_probability", slot.sparams.xtc_probability},
{"xtc_threshold", slot.sparams.xtc_threshold},
{"tfs_z", slot.sparams.tfs_z},
{"typical_p", slot.sparams.typ_p},
{"repeat_last_n", slot.sparams.penalty_last_n},
{"repeat_penalty", slot.sparams.penalty_repeat},
{"presence_penalty", slot.sparams.penalty_present},
{"frequency_penalty", slot.sparams.penalty_freq},
{"dry_multiplier", slot.sparams.dry_multiplier},
{"dry_base", slot.sparams.dry_base},
{"dry_allowed_length", slot.sparams.dry_allowed_length},
{"dry_penalty_last_n", slot.sparams.dry_penalty_last_n},
{"dry_sequence_breakers", slot.sparams.dry_sequence_breakers},
{"mirostat", slot.sparams.mirostat},
{"mirostat_tau", slot.sparams.mirostat_tau},
{"mirostat_eta", slot.sparams.mirostat_eta},
@ -1323,7 +1236,7 @@ struct server_context {
};
if (slot.sparams.n_probs > 0) {
const std::vector<llama_token> to_send_toks = common_tokenize(ctx, tkn.text_to_send, false);
const llama_tokens to_send_toks = common_tokenize(ctx, tkn.text_to_send, false);
const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size());
const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
@ -1359,7 +1272,7 @@ struct server_context {
{"tokens_predicted", slot.n_decoded},
{"tokens_evaluated", slot.n_prompt_tokens},
{"generation_settings", get_formated_generation(slot)},
{"prompt", slot.prompt},
{"prompt", common_detokenize(ctx, slot.prompt_tokens)},
{"has_new_line", slot.has_new_line},
{"truncated", slot.truncated},
{"stopped_eos", slot.stopped_eos},
@ -1374,7 +1287,7 @@ struct server_context {
if (slot.sparams.n_probs > 0) {
std::vector<completion_token_output> probs;
if (!slot.params.stream && slot.stopped_word) {
const std::vector<llama_token> stop_word_toks = common_tokenize(ctx, slot.stopping_word, false);
const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false);
size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
probs = std::vector<completion_token_output>(
@ -1483,19 +1396,17 @@ struct server_context {
// Functions to create new task(s) and receive result(s)
//
std::vector<server_task> create_tasks_cmpl(json data, server_task_cmpl_type cmpl_type) {
// break the input "prompt" into multiple tasks if needed, then format and tokenize the input prompt(s)
std::vector<server_task> create_tasks_inference(json data, server_task_inf_type inf_type) {
std::vector<server_task> tasks;
auto create_task = [&](json & task_data, bool replace_prompt, json prompt) {
auto create_task = [&](json & task_data, llama_tokens & prompt_tokens) {
SRV_DBG("create task, n_tokens = %d\n", (int) prompt_tokens.size());
server_task task;
task.id = queue_tasks.get_new_id();
task.cmpl_type = cmpl_type;
task.type = SERVER_TASK_TYPE_COMPLETION;
if (replace_prompt) {
task.data = task_data;
task.data["prompt"] = std::move(prompt);
} else {
task.data = std::move(task_data);
}
task.id = queue_tasks.get_new_id();
task.inf_type = inf_type;
task.type = SERVER_TASK_TYPE_INFERENCE;
task.data = task_data;
task.prompt_tokens = std::move(prompt_tokens);
tasks.push_back(std::move(task));
};
@ -1504,41 +1415,49 @@ struct server_context {
throw std::runtime_error(error_msg);
}
json prompt = data.at("prompt");
// if the prompt is a singleton (i.e. a string or a list of tokens), we only need to create single task
if (prompt.is_string() || json_is_array_of_numbers(prompt)) {
data["index"] = 0;
create_task(data, false, nullptr);
} else if (prompt.is_array()) {
// otherwise, it's a multiple-prompt task, we break it into smaller tasks
std::vector<json> prompts = prompt;
if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
// prompts[0] is the question
// the rest are the answers/documents
SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) prompts.size() - 1);
for (size_t i = 1; i < prompts.size(); i++) {
json qd;
qd.push_back(prompts[0]);
qd.push_back(prompts[i]);
data["index"] = i - 1;
create_task(data, true, qd);
}
} else {
SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) prompts.size());
for (size_t i = 0; i < prompts.size(); i++) {
const auto & e = prompts[i];
if (e.is_string() || json_is_array_of_numbers(e)) {
// because llama_tokenize api is thread-safe, we can tokenize the prompt from HTTP thread
bool add_special = inf_type != SERVER_TASK_INF_TYPE_RERANK && inf_type != SERVER_TASK_INF_TYPE_INFILL;
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx, data.at("prompt"), add_special, true);
switch (inf_type) {
case SERVER_TASK_INF_TYPE_RERANK:
{
// prompts[0] is the question
// the rest are the answers/documents
GGML_ASSERT(tokenized_prompts.size() > 1);
SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) tokenized_prompts.size() - 1);
for (size_t i = 1; i < tokenized_prompts.size(); i++) {
data["index"] = i - 1;
auto tokens = format_rerank(model, tokenized_prompts[0], tokenized_prompts[i]);
create_task(data, tokens);
}
} break;
case SERVER_TASK_INF_TYPE_INFILL:
{
SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
for (size_t i = 0; i < tokenized_prompts.size(); i++) {
data["index"] = i;
create_task(data, true, e);
} else {
throw std::runtime_error(error_msg);
auto tokens = format_infill(
ctx,
data.at("input_prefix"),
data.at("input_suffix"),
data.at("input_extra"),
params.n_batch,
params.n_predict,
slots[0].n_ctx, // TODO: there should be a better way
params.spm_infill,
tokenized_prompts[i]
);
create_task(data, tokens);
}
} break;
default:
{
SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
for (size_t i = 0; i < tokenized_prompts.size(); i++) {
data["index"] = i;
create_task(data, tokenized_prompts[i]);
}
}
}
} else {
// invalid case
throw std::runtime_error(error_msg);
}
return tasks;
@ -1560,7 +1479,7 @@ struct server_context {
queue_tasks.post(cancel_tasks, true);
}
// receive the results from task(s) created by create_tasks_cmpl
// receive the results from task(s) created by create_tasks_inference
void receive_cmpl_results(
const std::unordered_set<int> & id_tasks,
const std::function<void(std::vector<server_task_result>&)> & result_handler,
@ -1584,7 +1503,7 @@ struct server_context {
result_handler(results);
}
// receive the results from task(s) created by create_tasks_cmpl, in stream mode
// receive the results from task(s) created by create_tasks_inference, in stream mode
void receive_cmpl_results_stream(
const std::unordered_set<int> & id_tasks, const
std::function<bool(server_task_result&)> & result_handler, const
@ -1617,7 +1536,7 @@ struct server_context {
void process_single_task(const server_task & task) {
switch (task.type) {
case SERVER_TASK_TYPE_COMPLETION:
case SERVER_TASK_TYPE_INFERENCE:
{
const int id_slot = json_value(task.data, "id_slot", -1);
@ -1649,9 +1568,10 @@ struct server_context {
slot->reset();
slot->id_task = task.id;
slot->cmpl_type = task.cmpl_type;
slot->index = json_value(task.data, "index", 0);
slot->id_task = task.id;
slot->inf_type = task.inf_type;
slot->index = json_value(task.data, "index", 0);
slot->prompt_tokens = std::move(task.prompt_tokens);
if (!launch_slot_with_task(*slot, task)) {
SRV_ERR("failed to launch slot with task, id_task = %d\n", task.id);
@ -1684,7 +1604,7 @@ struct server_context {
slot_data["id"] = slot.id;
slot_data["id_task"] = slot.id_task;
slot_data["state"] = slot.state;
slot_data["prompt"] = slot.prompt;
slot_data["prompt"] = common_detokenize(ctx, slot.prompt_tokens);
slot_data["next_token"] = {
{"has_next_token", slot.has_next_token},
{"has_new_line", slot.has_new_line},
@ -1811,9 +1731,6 @@ struct server_context {
}
slot->cache_tokens.resize(token_count);
// TODO: maybe detokenize the slot->cache_tokens instead?
slot->prompt = string_format("[restored %d tokens from file]", (int) token_count);
const int64_t t_end = ggml_time_us();
const double t_restore_ms = (t_end - t_start) / 1000.0;
@ -1980,142 +1897,19 @@ struct server_context {
if (params.cont_batching || batch.n_tokens == 0) {
for (auto & slot : slots) {
// this slot still has a prompt to be processed
if (slot.state == SLOT_STATE_PROCESSING_PROMPT) {
if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) {
auto & prompt_tokens = slot.prompt_tokens;
// we haven't tokenized the prompt yet - do it now:
if (prompt_tokens.empty()) {
SLT_INF(slot, "tokenizing prompt, len = %d\n", (int) slot.prompt.size());
// TODO: maybe move branch to outside of this loop in the future
if (slot.state == SLOT_STATE_STARTED) {
slot.t_start_process_prompt = ggml_time_us();
slot.t_start_generation = 0;
switch (slot.cmpl_type) {
case SERVER_TASK_CMPL_TYPE_NORMAL:
case SERVER_TASK_CMPL_TYPE_EMBEDDING:
{
prompt_tokens = tokenize(slot.prompt, llama_add_bos_token(model), true);
} break;
case SERVER_TASK_CMPL_TYPE_RERANK:
{
// require slot.prompt to be array of 2 strings
if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
slot.release();
send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
continue;
}
// prompt: [BOS]query[EOS][SEP]doc[EOS]
prompt_tokens.clear();
prompt_tokens.push_back(llama_token_bos(model));
{
const auto part = tokenize(slot.prompt[0], false, false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
prompt_tokens.push_back(llama_token_sep(model));
{
const auto part = tokenize(slot.prompt[1], false, false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
} break;
case SERVER_TASK_CMPL_TYPE_INFILL:
{
// TODO: optimize this block by reducing memory allocations and movement
// use FIM repo-level pattern:
// ref: https://arxiv.org/pdf/2409.12186
//
// [FIM_REP]myproject
// [FIM_SEP]filename0
// extra chunk 0
// [FIM_SEP]filename1
// extra chunk 1
// ...
// [FIM_SEP]filename
// [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt
//
auto tokens_prefix = tokenize(slot.input_prefix, false, false);
auto tokens_suffix = tokenize(slot.input_suffix, false, false);
auto tokens_prompt = tokenize(slot.prompt, false, false);
slot.extra_tokens.clear();
if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) {
static const auto k_fim_repo = tokenize("myproject\n", false, false);
slot.extra_tokens.push_back(llama_token_fim_rep(model));
slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
}
for (const auto & chunk : slot.input_extra) {
// { "text": string, "filename": string }
const std::string text = chunk.value("text", "");
const std::string filename = chunk.value("filename", "tmp");
if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
const auto k_fim_file = tokenize(filename + "\n", false, false);
slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model));
slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
} else {
// chunk separator in binary form to avoid confusing the AI
static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00};
static const auto k_chunk_prefix_tokens = tokenize(k_chunk_prefix_str, false, false);
slot.extra_tokens.insert(slot.extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end());
}
const auto chunk_tokens = tokenize(text, false, false);
slot.extra_tokens.insert(slot.extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end());
}
if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
// TODO: current filename
static const auto k_fim_file = tokenize("filename\n", false, false);
slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model));
slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
}
// for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?)
const int n_suffix_take = std::min<int>(tokens_suffix.size(), (n_batch/4));
const int n_prefix_take = std::min<int>(tokens_prefix.size(), 3*(n_batch/4) - 3);
// fill the rest of the context with extra chunks
const int n_extra_take = std::min<int>(std::max<int>(0, slot.n_ctx - (n_batch) - 2*slot.n_predict), slot.extra_tokens.size());
tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
tokens_suffix.resize(n_suffix_take);
tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model));
tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end());
tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model));
auto embd_inp = params.spm_infill ? tokens_suffix : tokens_prefix;
auto embd_end = params.spm_infill ? tokens_prefix : tokens_suffix;
if (llama_add_bos_token(model)) {
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
}
SLT_DBG(slot, "extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", slot.n_ctx, n_extra_take, (int) slot.extra_tokens.size());
// put the extra context before the FIM prefix
embd_inp.insert(embd_inp.begin(), slot.extra_tokens.end() - n_extra_take, slot.extra_tokens.end());
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
embd_inp.push_back(llama_token_fim_mid(model));
prompt_tokens = std::move(embd_inp);
} break;
}
slot.n_past = 0;
slot.n_prompt_tokens = prompt_tokens.size();
slot.state = SLOT_STATE_PROCESSING_PROMPT;
SLT_INF(slot, "prompt tokenized, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens);
SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens);
// print prompt tokens (for debugging)
if (1) {
@ -2140,13 +1934,18 @@ struct server_context {
continue;
}
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
// this prompt is too large to process - discard it
if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) {
if (slot.n_prompt_tokens > n_ubatch) {
slot.release();
send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
continue;
}
if (slot.n_prompt_tokens > slot.n_ctx) {
slot.release();
send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_SERVER);
continue;
}
} else {
if (!params.ctx_shift) {
// if context shift is disabled, we make sure prompt size is smaller than KV size
@ -2170,7 +1969,7 @@ struct server_context {
const int n_block_size = n_left / 2;
const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
std::vector<llama_token> new_tokens(
llama_tokens new_tokens(
prompt_tokens.begin(),
prompt_tokens.begin() + slot.params.n_keep);
@ -2189,17 +1988,10 @@ struct server_context {
GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
}
common_sampler_reset(slot.smpl);
if (slot.params.cache_prompt) {
// reuse any previously computed tokens that are common with the new prompt
slot.n_past = longest_common_prefix(slot.cache_tokens, prompt_tokens);
// push the prompt into the sampling context (do not apply grammar)
for (int i = 0; i < slot.n_past; ++i) {
common_sampler_accept(slot.smpl, slot.cache_tokens[i], false);
}
// reuse chunks from the cached prompt by shifting their KV cache in the new position
if (params.n_cache_reuse > 0) {
size_t head_c = slot.n_past; // cache
@ -2231,9 +2023,6 @@ struct server_context {
for (size_t i = 0; i < n_match; i++) {
slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i];
common_sampler_accept(slot.smpl, slot.cache_tokens[head_p + i], false);
slot.n_past++;
}
@ -2260,7 +2049,7 @@ struct server_context {
}
// non-causal tasks require to fit the entire prompt in the physical batch
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) {
// cannot fit the prompt in the current batch - will try next iter
if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
continue;
@ -2269,8 +2058,8 @@ struct server_context {
// check that we are in the right batch_type, if not defer the slot
const bool slot_type =
slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ||
slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK ? 1 : 0;
slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING ||
slot.inf_type == SERVER_TASK_INF_TYPE_RERANK ? 1 : 0;
if (batch_type == -1) {
batch_type = slot_type;
@ -2285,8 +2074,6 @@ struct server_context {
// there is no common part left
slot.n_past = 0;
common_sampler_reset(slot.smpl);
}
SLT_INF(slot, "kv cache rm [%d, end)\n", slot.n_past);
@ -2314,6 +2101,13 @@ struct server_context {
GGML_ASSERT(batch.n_tokens > 0);
common_sampler_reset(slot.smpl);
// Process all prompt tokens through sampler system
for (int i = 0; i < slot.n_prompt_tokens; ++i) {
common_sampler_accept(slot.smpl, prompt_tokens[i], false);
}
// extract the logits only for the last token
batch.logits[batch.n_tokens - 1] = true;
@ -2383,7 +2177,7 @@ struct server_context {
}
if (slot.state == SLOT_STATE_DONE_PROMPT) {
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING) {
// prompt evaluated for embedding
send_embedding(slot, batch_view);
slot.release();
@ -2391,7 +2185,7 @@ struct server_context {
continue; // continue loop of slots
}
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
if (slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) {
send_rerank(slot, batch_view);
slot.release();
slot.i_batch = -1;
@ -2638,7 +2432,7 @@ int main(int argc, char ** argv) {
auto middleware_server_state = [&res_error, &state](const httplib::Request & req, httplib::Response & res) {
server_state current_state = state.load();
if (current_state == SERVER_STATE_LOADING_MODEL) {
auto tmp = string_split(req.path, '.');
auto tmp = string_split<std::string>(req.path, '.');
if (req.path == "/" || tmp.back() == "html") {
res.set_content(reinterpret_cast<const char*>(loading_html), loading_html_len, "text/html; charset=utf-8");
res.status = 503;
@ -2954,13 +2748,13 @@ int main(int argc, char ** argv) {
res_ok(res, {{ "success", true }});
};
const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_cmpl_type cmpl_type, json & data, httplib::Response & res) {
const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_inf_type inf_type, json & data, httplib::Response & res) {
if (ctx_server.params.embedding || ctx_server.params.reranking) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
std::vector<server_task> tasks = ctx_server.create_tasks_cmpl(data, cmpl_type);
std::vector<server_task> tasks = ctx_server.create_tasks_inference(data, inf_type);
ctx_server.queue_results.add_waiting_tasks(tasks);
ctx_server.queue_tasks.post(tasks);
@ -3006,10 +2800,11 @@ int main(int argc, char ** argv) {
const auto handle_completions = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
json data = json::parse(req.body);
return handle_completions_generic(SERVER_TASK_CMPL_TYPE_NORMAL, data, res);
return handle_completions_generic(SERVER_TASK_INF_TYPE_COMPLETION, data, res);
};
const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
// check model compatibility
std::string err;
if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) {
err += "prefix token is missing. ";
@ -3020,14 +2815,42 @@ int main(int argc, char ** argv) {
if (llama_token_fim_mid(ctx_server.model) == LLAMA_TOKEN_NULL) {
err += "middle token is missing. ";
}
if (!err.empty()) {
res_error(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED));
return;
}
json data = json::parse(req.body);
return handle_completions_generic(SERVER_TASK_CMPL_TYPE_INFILL, data, res);
// validate input
if (!data.contains("input_prefix")) {
res_error(res, format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST));
}
if (!data.contains("input_suffix")) {
res_error(res, format_error_response("\"input_suffix\" is required", ERROR_TYPE_INVALID_REQUEST));
}
if (data.contains("input_extra") && !data.at("input_extra").is_array()) {
res_error(res, format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST));
return;
}
json input_extra = json_value(data, "input_extra", json::array());
for (const auto & chunk : input_extra) {
// { "text": string, "filename": string }
if (!chunk.contains("text") || !chunk.at("text").is_string()) {
res_error(res, format_error_response("extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST));
return;
}
// filename is optional
if (chunk.contains("filename") && !chunk.at("filename").is_string()) {
res_error(res, format_error_response("extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST));
return;
}
}
data["input_extra"] = input_extra; // default to empty array if it's not exist
return handle_completions_generic(SERVER_TASK_INF_TYPE_INFILL, data, res);
};
// TODO: maybe merge this function with "handle_completions_generic"
@ -3050,7 +2873,7 @@ int main(int argc, char ** argv) {
return;
}
std::vector<server_task> tasks = ctx_server.create_tasks_cmpl(data, SERVER_TASK_CMPL_TYPE_NORMAL);
std::vector<server_task> tasks = ctx_server.create_tasks_inference(data, SERVER_TASK_INF_TYPE_COMPLETION);
ctx_server.queue_results.add_waiting_tasks(tasks);
ctx_server.queue_tasks.post(tasks);
@ -3127,7 +2950,7 @@ int main(int argc, char ** argv) {
const bool add_special = json_value(body, "add_special", false);
const bool with_pieces = json_value(body, "with_pieces", false);
std::vector<llama_token> tokens = ctx_server.tokenize(body.at("content"), add_special, true);
llama_tokens tokens = tokenize_mixed(ctx_server.ctx, body.at("content"), add_special, true);
if (with_pieces) {
for (const auto& token : tokens) {
@ -3164,7 +2987,7 @@ int main(int argc, char ** argv) {
std::string content;
if (body.count("tokens") != 0) {
const std::vector<llama_token> tokens = body.at("tokens");
const llama_tokens tokens = body.at("tokens");
content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend());
}
@ -3198,7 +3021,7 @@ int main(int argc, char ** argv) {
json responses = json::array();
bool error = false;
{
std::vector<server_task> tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_EMBEDDING);
std::vector<server_task> tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_EMBEDDING);
ctx_server.queue_results.add_waiting_tasks(tasks);
ctx_server.queue_tasks.post(tasks);
@ -3275,7 +3098,7 @@ int main(int argc, char ** argv) {
json responses = json::array();
bool error = false;
{
std::vector<server_task> tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_RERANK);
std::vector<server_task> tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_RERANK);
ctx_server.queue_results.add_waiting_tasks(tasks);
ctx_server.queue_tasks.post(tasks);

36
examples/server/tests/features/infill.feature Normal file
View file

@ -0,0 +1,36 @@
@llama.cpp
@infill
Feature: llama.cpp server
# The current model is made by adding FIM tokens to the existing stories260K
# We may want to use a better model in the future, maybe something like SmolLM 360M
Background: Server startup
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K-infill.gguf from HF repo ggml-org/models
And a model file test-model-infill.gguf
And a model alias tinyllama-infill
And 42 as server seed
And 1024 as batch size
And 1024 as ubatch size
And 2048 KV cache size
And 64 max tokens to predict
And 0.0 temperature
Then the server is starting
Then the server is healthy
Scenario: Infill without input_extra
Given a prompt "Complete this"
And an infill input extra none none
And an infill input prefix "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_"
And an infill input suffix "}\n"
And an infill request with no api error
Then 64 tokens are predicted matching One|day|she|saw|big|scary|bird
Scenario: Infill with input_extra
Given a prompt "Complete this"
And an infill input extra "llama.h" "LLAMA_API int32_t llama_n_threads();\n"
And an infill input prefix "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_"
And an infill input suffix "}\n"
And an infill request with no api error
Then 64 tokens are predicted matching cuts|Jimmy|mom|came|into|the|room"

46
examples/server/tests/features/steps/steps.py
View file

@ -94,6 +94,11 @@ def step_server_config(context, server_fqdn: str, server_port: str):
context.use_jinja = False
context.chat_template_file = None
# infill
context.infill_input_extra = None
context.infill_input_suffix = ''
context.infill_input_prefix = ''
context.tasks_result = []
context.concurrent_tasks = []
context.prompts = []
@ -326,6 +331,28 @@ async def step_request_completion(context, api_error: Literal['raised'] | str):
assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}"
@step('an infill request with {api_error} api error')
@async_run_until_complete
async def step_request_completion(context, api_error: Literal['raised'] | str):
if api_error != 'no':
raise ValueError(f'api_error={api_error} is not yet implemented')
payload = {
"prompt": context.prompts[0],
"input_suffix": context.infill_input_suffix,
"input_prefix": context.infill_input_prefix,
"n_predict": context.n_predict,
"seed": context.seed,
"temperature": context.temperature,
}
if context.infill_input_extra is not None:
payload['input_extra'] = context.infill_input_extra
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/infill',
json=payload) as response:
assert response.status == 200
context.tasks_result = [await response.json()]
@step('{predicted_n:d} tokens are predicted matching {re_content}')
def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
context.completion = context.tasks_result.pop()
@ -624,6 +651,25 @@ def step_a_prompt_prompt(context, prompt):
context.n_prompts = len(context.prompts)
# TODO: allow this to be repeated
@step('an infill input extra {filename} {text}')
def step_infill_input_extra(context, filename, text):
if filename == 'none':
context.infill_input_extra = None
else:
context.infill_input_extra = [{'filename': filename, 'text': text}]
@step('an infill input suffix {text}')
def step_infill_input_suffix(context, text):
context.infill_input_suffix = text
@step('an infill input prefix {text}')
def step_infill_input_prefix(context, text):
context.infill_input_prefix = text
@step('{num_prompts:d} prompts {prompt} with seed {seed:d}')
def step_many_prompts(context, num_prompts, prompt, seed):
if context.seed is None:

2
examples/server/themes/buttons-top/index.html
View file

@ -226,7 +226,6 @@
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
frequency_penalty: 0.0, // 0.0 = disabled
@ -788,7 +787,6 @@
<details>
<summary>More options</summary>
<fieldset class="two">
${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}

2
examples/server/themes/wild/index.html
View file

@ -229,7 +229,6 @@
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
frequency_penalty: 0.0, // 0.0 = disabled
@ -791,7 +790,6 @@
<details>
<summary>More options</summary>
<fieldset class="two">
${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}

260
examples/server/utils.hpp
View file

@ -26,6 +26,22 @@
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
using json = nlohmann::ordered_json;
using llama_tokens = std::vector<llama_token>;
#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__)
#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__)
// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
enum error_type {
@ -54,9 +70,237 @@ static T json_value(const json & body, const std::string & key, const T & defaul
}
//
// chat template utils
// tokenizer and input processing utils
//
static bool json_is_array_of_numbers(const json & data) {
if (data.is_array()) {
for (const auto & e : data) {
if (!e.is_number_integer()) {
return false;
}
}
return true;
}
return false;
}
// is array having BOTH numbers & strings?
static bool json_is_array_of_mixed_numbers_strings(const json & data) {
bool seen_string = false;
bool seen_number = false;
if (data.is_array()) {
for (const auto & e : data) {
seen_string |= e.is_string();
seen_number |= e.is_number_integer();
if (seen_number && seen_string) {
return true;
}
}
}
return false;
}
/**
* this handles 2 cases:
* - only string, example: "string"
* - mixed string and tokens, example: [12, 34, "string", 56, 78]
*/
static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
// or the first element of the json_prompt array is a string.
llama_tokens prompt_tokens;
if (json_prompt.is_array()) {
bool first = true;
for (const auto & p : json_prompt) {
if (p.is_string()) {
auto s = p.template get<std::string>();
llama_tokens p;
if (first) {
p = common_tokenize(ctx, s, add_special, parse_special);
first = false;
} else {
p = common_tokenize(ctx, s, false, parse_special);
}
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
} else {
if (first) {
first = false;
}
prompt_tokens.push_back(p.template get<llama_token>());
}
}
} else {
auto s = json_prompt.template get<std::string>();
prompt_tokens = common_tokenize(ctx, s, add_special, parse_special);
}
return prompt_tokens;
}
/**
* break the input "prompt" object into multiple prompt if needed, then tokenize them
* this supports these cases:
* - "prompt": "string"
* - "prompt": [12, 34, 56]
* - "prompt": [12, 34, "string", 56, 78]
* and multiple prompts (multi-tasks):
* - "prompt": ["string1", "string2"]
* - "prompt": ["string1", [12, 34, 56]]
* - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]]
*/
static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) {
std::vector<llama_tokens> result;
if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) {
// string or mixed
result.push_back(tokenize_mixed(ctx, json_prompt, add_special, parse_special));
} else if (json_is_array_of_numbers(json_prompt)) {
// array of tokens
result.push_back(json_prompt.get<llama_tokens>());
} else if (json_prompt.is_array()) {
// array of prompts
result.reserve(json_prompt.size());
for (const auto & p : json_prompt) {
if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) {
result.push_back(tokenize_mixed(ctx, p, add_special, parse_special));
} else if (json_is_array_of_numbers(p)) {
// array of tokens
result.push_back(p.get<llama_tokens>());
} else {
throw std::runtime_error("element of \"prompt\" must be a string, an list of tokens, or a list of mixed strings & tokens");
}
}
} else {
throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts");
}
return result;
}
//
// template utils
//
// format rerank task: [BOS]query[EOS][SEP]doc[EOS]
static llama_tokens format_rerank(const struct llama_model * model, const llama_tokens & query, const llama_tokens & doc) {
llama_tokens result;
result.reserve(doc.size() + query.size() + 4);
result.push_back(llama_token_bos(model));
result.insert(result.end(), query.begin(), query.end());
result.push_back(llama_token_eos(model));
result.push_back(llama_token_sep(model));
result.insert(result.end(), doc.begin(), doc.end());
result.push_back(llama_token_eos(model));
return result;
}
// format infill task
static llama_tokens format_infill(
const llama_context * ctx,
const json & input_prefix,
const json & input_suffix,
const json & input_extra,
const int n_batch,
const int n_predict,
const int n_ctx,
const bool spm_infill,
const llama_tokens & tokens_prompt
) {
// TODO: optimize this block by reducing memory allocations and movement
// use FIM repo-level pattern:
// ref: https://arxiv.org/pdf/2409.12186
//
// [FIM_REP]myproject
// [FIM_SEP]filename0
// extra chunk 0
// [FIM_SEP]filename1
// extra chunk 1
// ...
// [FIM_SEP]filename
// [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt
//
llama_tokens extra_tokens;
extra_tokens.reserve(n_ctx);
auto model = llama_get_model(ctx);
auto tokens_prefix = tokenize_mixed(ctx, input_prefix, false, false);
auto tokens_suffix = tokenize_mixed(ctx, input_suffix, false, false);
if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) {
// TODO: make project name an input
static const auto k_fim_repo = common_tokenize(ctx, "myproject\n", false, false);
extra_tokens.push_back(llama_token_fim_rep(model));
extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
}
for (const auto & chunk : input_extra) {
// { "text": string, "filename": string }
const std::string text = json_value(chunk, "text", std::string());
const std::string filename = json_value(chunk, "filename", std::string("tmp"));
if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
const auto k_fim_file = common_tokenize(ctx, filename + "\n", false, false);
extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model));
extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
} else {
// chunk separator in binary form to avoid confusing the AI
static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00};
static const auto k_chunk_prefix_tokens = common_tokenize(ctx, k_chunk_prefix_str, false, false);
extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end());
}
const auto chunk_tokens = common_tokenize(ctx, text, false, false);
extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end());
}
if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
// TODO: current filename
static const auto k_fim_file = common_tokenize(ctx, "filename\n", false, false);
extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model));
extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
}
// for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?)
const int n_prefix_take = std::min<int>(tokens_prefix.size(), 3*(n_batch/4));
const int n_suffix_take = std::min<int>(tokens_suffix.size(), std::max<int>(0, (n_batch/4) - (2 + tokens_prompt.size())));
SRV_DBG("n_prefix_take = %d, n_suffix_take = %d, total = %d\n", n_prefix_take, n_suffix_take, (n_prefix_take + n_suffix_take));
// fill the rest of the context with extra chunks
const int n_extra_take = std::min<int>(std::max<int>(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size());
tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
tokens_suffix.resize(n_suffix_take);
tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model));
tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end());
tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model));
auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix;
auto embd_end = spm_infill ? tokens_prefix : tokens_suffix;
if (llama_add_bos_token(model)) {
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
}
SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size());
// put the extra context before the FIM prefix
embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end());
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
embd_inp.push_back(llama_token_fim_mid(model));
return embd_inp;
}
// Format given chat. If tmpl is empty, we take the template from model metadata
inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
std::vector<common_chat_msg> chat;
@ -214,18 +458,6 @@ static size_t find_partial_stop_string(const std::string & stop, const std::stri
return std::string::npos;
}
static bool json_is_array_of_numbers(const json & data) {
if (data.is_array()) {
for (const auto & e : data) {
if (!e.is_number()) {
return false;
}
}
return true;
}
return false;
}
// TODO: reuse llama_detokenize
template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
@ -423,7 +655,7 @@ static json oaicompat_completion_params_parse(
}
// Copy remaining properties to llama_params
// This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint.
// This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint.
// See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
for (const auto & item : body.items()) {
// Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"

6
flake.lock generated
View file

@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1729256560,
"narHash": "sha256-/uilDXvCIEs3C9l73JTACm4quuHUsIHcns1c+cHUJwA=",
"lastModified": 1729665710,
"narHash": "sha256-AlcmCXJZPIlO5dmFzV3V2XF6x/OpNWUV8Y/FMPGd8Z4=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "4c2fcb090b1f3e5b47eaa7bd33913b574a11e0a0",
"rev": "2768c7d042a37de65bb1b5b3268fc987e534c49d",
"type": "github"
},
"original": {

19
ggml/include/ggml-backend.h
View file

@ -114,11 +114,12 @@ extern "C" {
//
enum ggml_backend_dev_type {
// CPU device using system memory
GGML_BACKEND_DEVICE_TYPE_CPU,
// GPU device using dedicated memory
GGML_BACKEND_DEVICE_TYPE_GPU,
// devices with full capabilities (excludes backends such as BLAS that only support matrix multiplication)
GGML_BACKEND_DEVICE_TYPE_CPU_FULL,
GGML_BACKEND_DEVICE_TYPE_GPU_FULL
// accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
GGML_BACKEND_DEVICE_TYPE_ACCEL
};
// functionality supported by the device
@ -167,10 +168,14 @@ extern "C" {
GGML_API ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index);
GGML_API void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name);
// Common functions that may be obtained using ggml_backend_reg_get_proc_address
// Functions that may be obtained using ggml_backend_reg_get_proc_address
typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(const float *);
typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t, int);
// Split buffer type for tensor parallelism
typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split);
// Set the number of threads for the backend
typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads);
// Get additional buffer types provided by the device (returns a NULL-terminated array)
typedef ggml_backend_buffer_type_t * (*ggml_backend_dev_get_extra_bufts_t)(ggml_backend_dev_t device);
//
// Backend registry
@ -192,7 +197,7 @@ extern "C" {
GGML_API ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params);
// = ggml_backend_dev_init(ggml_backend_dev_by_type(type), params)
GGML_API ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params);
// = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU_FULL) OR ggml_backend_dev_by_type(CPU_FULL), NULL)
// = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU) OR ggml_backend_dev_by_type(CPU), NULL)
GGML_API ggml_backend_t ggml_backend_init_best(void);
//

2
ggml/include/ggml-cuda.h
View file

@ -28,7 +28,7 @@ GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
// split tensor buffer that splits matrices by rows across multiple devices
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);

4
ggml/include/ggml-kompute.h
View file

@ -11,6 +11,8 @@
extern "C" {
#endif
#define GGML_KOMPUTE_MAX_DEVICES 16
struct ggml_vk_device {
int index;
int type; // same as VkPhysicalDeviceType
@ -41,6 +43,8 @@ GGML_API bool ggml_backend_is_kompute(ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device);
GGML_API ggml_backend_reg_t ggml_backend_kompute_reg(void);
#ifdef __cplusplus
}
#endif

268
ggml/src/ggml-aarch64.c
View file

@ -991,6 +991,73 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
}
}
return;
#elif defined(__riscv_v_intrinsic)
if (__riscv_vlenb() >= QK4_0) {
const size_t vl = QK4_0;
const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
for (int x = 0; x < nc / ncols_interleaved; x++) {
const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
vfloat32m1_t sumf = __riscv_vfmv_v_f_f32m1(0.0, vl / 4);
for (int l = 0; l < nb; l++) {
const int64_t a0 = *(const int64_t *)&a_ptr[l].qs[0];
const int64_t a1 = *(const int64_t *)&a_ptr[l].qs[8];
const int64_t a2 = *(const int64_t *)&a_ptr[l].qs[16];
const int64_t a3 = *(const int64_t *)&a_ptr[l].qs[24];
__asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment
const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a0, vl / 4));
const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a1, vl / 4));
const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a2, vl / 4));
const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a3, vl / 4));
const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4);
const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4);
const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4);
const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0);
const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1);
const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0);
const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1);
const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2);
const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2);
const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2);
const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2);
const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_hi_m));
const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl);
const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl);
const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl);
const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2);
const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2);
const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2);
const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2);
const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4);
const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4));
const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4));
const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4);
const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4);
// vector version needs Zvfhmin extension
const float a_scale = GGML_FP16_TO_FP32(a_ptr[l].d);
const float b_scales[8] = {
GGML_FP16_TO_FP32(b_ptr[l].d[0]),
GGML_FP16_TO_FP32(b_ptr[l].d[1]),
GGML_FP16_TO_FP32(b_ptr[l].d[2]),
GGML_FP16_TO_FP32(b_ptr[l].d[3]),
GGML_FP16_TO_FP32(b_ptr[l].d[4]),
GGML_FP16_TO_FP32(b_ptr[l].d[5]),
GGML_FP16_TO_FP32(b_ptr[l].d[6]),
GGML_FP16_TO_FP32(b_ptr[l].d[7])
};
const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4);
const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scale, vl / 4);
sumf = __riscv_vfmacc_vv_f32m1(sumf, tmp1, b_scales_vec, vl / 4);
}
__riscv_vse32_v_f32m1(s + x * ncols_interleaved, sumf, vl / 4);
}
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
{
float sumf[8];
@ -3171,6 +3238,207 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
}
}
}
return;
}
#elif defined(__riscv_v_intrinsic)
if (__riscv_vlenb() >= QK4_0) {
const size_t vl = QK4_0;
for (int y = 0; y < nr / 4; y++) {
const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
for (int x = 0; x < nc / ncols_interleaved; x++) {
const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
vfloat32m1_t sumf0 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4);
vfloat32m1_t sumf1 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4);
vfloat32m1_t sumf2 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4);
vfloat32m1_t sumf3 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4);
for (int l = 0; l < nb; l++) {
const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4);
const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4);
const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4);
const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0);
const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1);
const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0);
const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1);
// vector version needs Zvfhmin extension
const float a_scales[4] = {
GGML_FP16_TO_FP32(a_ptr[l].d[0]),
GGML_FP16_TO_FP32(a_ptr[l].d[1]),
GGML_FP16_TO_FP32(a_ptr[l].d[2]),
GGML_FP16_TO_FP32(a_ptr[l].d[3])
};
const float b_scales[8] = {
GGML_FP16_TO_FP32(b_ptr[l].d[0]),
GGML_FP16_TO_FP32(b_ptr[l].d[1]),
GGML_FP16_TO_FP32(b_ptr[l].d[2]),
GGML_FP16_TO_FP32(b_ptr[l].d[3]),
GGML_FP16_TO_FP32(b_ptr[l].d[4]),
GGML_FP16_TO_FP32(b_ptr[l].d[5]),
GGML_FP16_TO_FP32(b_ptr[l].d[6]),
GGML_FP16_TO_FP32(b_ptr[l].d[7])
};
const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4);
const int64_t A0 = *(const int64_t *)&a_ptr[l].qs[0];
const int64_t A4 = *(const int64_t *)&a_ptr[l].qs[32];
const int64_t A8 = *(const int64_t *)&a_ptr[l].qs[64];
const int64_t Ac = *(const int64_t *)&a_ptr[l].qs[96];
__asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment
vint16m4_t sumi_l0;
{
const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A0, vl / 4));
const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A4, vl / 4));
const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A8, vl / 4));
const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ac, vl / 4));
const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2);
const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2);
const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2);
const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2);
sumi_l0 = sumi_hi_m;
}
{
const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l0));
const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl);
const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl);
const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl);
const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2);
const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2);
const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2);
const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2);
const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4);
const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4));
const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4));
const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4);
const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4);
const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[0], vl / 4);
sumf0 = __riscv_vfmacc_vv_f32m1(sumf0, tmp1, b_scales_vec, vl / 4);
}
const int64_t A1 = *(const int64_t *)&a_ptr[l].qs[8];
const int64_t A5 = *(const int64_t *)&a_ptr[l].qs[40];
const int64_t A9 = *(const int64_t *)&a_ptr[l].qs[72];
const int64_t Ad = *(const int64_t *)&a_ptr[l].qs[104];
__asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment
vint16m4_t sumi_l1;
{
const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A1, vl / 4));
const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A5, vl / 4));
const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A9, vl / 4));
const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ad, vl / 4));
const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2);
const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2);
const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2);
const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2);
sumi_l1 = sumi_hi_m;
}
{
const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l1));
const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl);
const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl);
const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl);
const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2);
const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2);
const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2);
const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2);
const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4);
const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4));
const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4));
const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4);
const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4);
const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[1], vl / 4);
sumf1 = __riscv_vfmacc_vv_f32m1(sumf1, tmp1, b_scales_vec, vl / 4);
}
const int64_t A2 = *(const int64_t *)&a_ptr[l].qs[16];
const int64_t A6 = *(const int64_t *)&a_ptr[l].qs[48];
const int64_t Aa = *(const int64_t *)&a_ptr[l].qs[80];
const int64_t Ae = *(const int64_t *)&a_ptr[l].qs[112];
__asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment
vint16m4_t sumi_l2;
{
const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A2, vl / 4));
const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A6, vl / 4));
const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Aa, vl / 4));
const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ae, vl / 4));
const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2);
const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2);
const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2);
const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2);
sumi_l2 = sumi_hi_m;
}
{
const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l2));
const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl);
const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl);
const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl);
const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2);
const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2);
const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2);
const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2);
const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4);
const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4));
const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4));
const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4);
const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4);
const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[2], vl / 4);
sumf2 = __riscv_vfmacc_vv_f32m1(sumf2, tmp1, b_scales_vec, vl / 4);
}
const int64_t A3 = *(const int64_t *)&a_ptr[l].qs[24];
const int64_t A7 = *(const int64_t *)&a_ptr[l].qs[56];
const int64_t Ab = *(const int64_t *)&a_ptr[l].qs[88];
const int64_t Af = *(const int64_t *)&a_ptr[l].qs[120];
__asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment
vint16m4_t sumi_l3;
{
const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A3, vl / 4));
const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A7, vl / 4));
const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ab, vl / 4));
const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Af, vl / 4));
const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2);
const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2);
const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2);
const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2);
sumi_l3 = sumi_hi_m;
}
{
const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l3));
const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl);
const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl);
const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl);
const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2);
const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2);
const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2);
const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2);
const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4);
const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4));
const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4));
const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4);
const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4);
const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[3], vl / 4);
sumf3 = __riscv_vfmacc_vv_f32m1(sumf3, tmp1, b_scales_vec, vl / 4);
}
}
__riscv_vse32_v_f32m1(&s[(y * 4 + 0) * bs + x * ncols_interleaved], sumf0, vl / 4);
__riscv_vse32_v_f32m1(&s[(y * 4 + 1) * bs + x * ncols_interleaved], sumf1, vl / 4);
__riscv_vse32_v_f32m1(&s[(y * 4 + 2) * bs + x * ncols_interleaved], sumf2, vl / 4);
__riscv_vse32_v_f32m1(&s[(y * 4 + 3) * bs + x * ncols_interleaved], sumf3, vl / 4);
}
}
return;
}
#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)

33
ggml/src/ggml-amx.cpp
View file

@ -16,12 +16,6 @@
#if defined(__AMX_INT8__)
// AMX buffer interface
static const char * ggml_backend_amx_buffer_get_name(ggml_backend_buffer_t buffer) {
return "AMX";
GGML_UNUSED(buffer);
}
static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
}
@ -72,7 +66,6 @@ static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t
}
static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
/* .get_name = */ ggml_backend_amx_buffer_get_name,
/* .free_buffer = */ ggml_backend_amx_buffer_free_buffer,
/* .get_base = */ ggml_backend_amx_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
@ -121,14 +114,14 @@ static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft
ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() {
static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = {
/* .iface = */ {
/* .get_name = */ ggml_backend_amx_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size,
/* .is_host = */ ggml_backend_amx_buffer_type_is_host,
/* .get_name = */ ggml_backend_amx_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size,
/* .is_host = */ ggml_backend_amx_buffer_type_is_host,
},
/* .device = */ NULL,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0),
/* .context = */ NULL,
};
@ -149,12 +142,6 @@ static void ggml_backend_amx_free(ggml_backend_t backend) {
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_amx_get_default_buffer_type(ggml_backend_t backend) {
return ggml_backend_amx_buffer_type();
GGML_UNUSED(backend);
}
static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context;
@ -187,7 +174,6 @@ static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, s
static struct ggml_backend_i ggml_backend_amx_i = {
/* .get_name = */ ggml_backend_amx_name,
/* .free = */ ggml_backend_amx_free,
/* .get_default_buffer_type = */ ggml_backend_amx_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
@ -197,9 +183,6 @@ static struct ggml_backend_i ggml_backend_amx_i = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_amx_graph_compute,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
@ -279,7 +262,7 @@ static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t *
}
static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_CPU;
return GGML_BACKEND_DEVICE_TYPE_ACCEL;
GGML_UNUSED(dev);
}

19
ggml/src/ggml-backend-impl.h
View file

@ -22,7 +22,7 @@ extern "C" {
size_t (*get_max_size) (ggml_backend_buffer_type_t buft);
// (optional) data size needed to allocate the tensor, including padding (defaults to ggml_nbytes)
size_t (*get_alloc_size)(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor);
// (optional) check if tensor data is in host memory (defaults to false)
// (optional) check if tensor data is in host memory and uses standard ggml tensor layout (defaults to false)
bool (*is_host) (ggml_backend_buffer_type_t buft);
};
@ -37,7 +37,6 @@ extern "C" {
//
struct ggml_backend_buffer_i {
const char * (*get_name) (ggml_backend_buffer_t buffer);
// (optional) free the buffer
void (*free_buffer) (ggml_backend_buffer_t buffer);
// base address of the buffer
@ -88,19 +87,16 @@ extern "C" {
void (*free)(ggml_backend_t backend);
// Will be moved to the device interface
// buffer allocation
ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
// (optional) asynchronous tensor data access
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
// (optional) complete all pending operations
// (optional) complete all pending operations (required if the backend supports async operations)
void (*synchronize)(ggml_backend_t backend);
// (optional) compute graph with a plan (not used currently)
// (optional) graph plans (not used currently)
// compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// update the plan with a new graph - this should be faster than creating a new plan when the graph has the same topology
@ -111,13 +107,6 @@ extern "C" {
// compute graph (always async if supported by the backend)
enum ggml_status (*graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
// IMPORTANT: these functions have been moved to the device interface and will be removed from the backend interface
// new backends should implement the device interface instead
// These functions are being moved to the device interface
bool (*supports_op) (ggml_backend_t backend, const struct ggml_tensor * op);
bool (*supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
bool (*offload_op) (ggml_backend_t backend, const struct ggml_tensor * op);
// (optional) event synchronization
// record an event on this stream
void (*event_record)(ggml_backend_t backend, ggml_backend_event_t event);

242
ggml/src/ggml-backend.cpp
View file

@ -34,6 +34,11 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
}
ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
if (size == 0) {
// return a dummy buffer for zero-sized allocations
return ggml_backend_buffer_init(buft, {}, NULL, 0);
}
return buft->iface.alloc_buffer(buft, size);
}
@ -89,7 +94,7 @@ ggml_backend_buffer_t ggml_backend_buffer_init(
}
const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name(buffer);
return ggml_backend_buft_name(ggml_backend_buffer_get_type(buffer));
}
void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
@ -108,6 +113,11 @@ size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
}
void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
// get_base is optional if the buffer is zero-sized
if (buffer->size == 0) {
return NULL;
}
void * base = buffer->iface.get_base(buffer);
GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");
@ -122,6 +132,15 @@ void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_t
}
}
void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
// clear is optional if the buffer is zero-sized
if (buffer->size == 0) {
return;
}
buffer->iface.clear(buffer, value);
}
size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) {
return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer));
}
@ -134,10 +153,6 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g
return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor);
}
void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
buffer->iface.clear(buffer, value);
}
bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer));
}
@ -198,7 +213,7 @@ void ggml_backend_free(ggml_backend_t backend) {
}
ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
return backend->iface.get_default_buffer_type(backend);
return ggml_backend_dev_buffer_type(backend->device);
}
ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
@ -238,43 +253,42 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
if (size == 0) {
return;
}
GGML_ASSERT(buf != NULL && "tensor buffer not set");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
if (!size) {
return;
}
buf->iface.set_tensor(buf, tensor, data, offset, size);
}
void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
if (size == 0) {
return;
}
GGML_ASSERT(buf != NULL && "tensor buffer not set");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
if (!size) {
return;
}
buf->iface.get_tensor(buf, tensor, data, offset, size);
}
GGML_API void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
GGML_ASSERT(buf != NULL && "tensor buffer not set");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
if (!size) {
if (size == 0) {
return;
}
GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not supported by backend buffer");
GGML_ASSERT(buf != NULL && "tensor buffer not set");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not implemented by backend buffer");
buf->iface.memset_tensor(buf, tensor, value, offset, size);
}
@ -316,32 +330,15 @@ enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct
}
bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
// helper to ease transition to device interface
if (backend->device) {
return ggml_backend_dev_supports_op(backend->device, op);
}
return backend->iface.supports_op(backend, op);
return ggml_backend_dev_supports_op(backend->device, op);
}
bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
// helper to ease transition to device interface
if (backend->device) {
return ggml_backend_dev_supports_buft(backend->device, buft);
}
return backend->iface.supports_buft(backend, buft);
return ggml_backend_dev_supports_buft(backend->device, buft);
}
bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
// helper to ease transition to device interface
if (backend->device) {
return ggml_backend_dev_offload_op(backend->device, op);
}
if (backend->iface.offload_op != NULL) {
return backend->iface.offload_op(backend, op);
}
return false;
return ggml_backend_dev_offload_op(backend->device, op);
}
ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
@ -565,6 +562,10 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na
#include "ggml-cann.h"
#endif
#ifdef GGML_USE_KOMPUTE
#include "ggml-kompute.h"
#endif
struct ggml_backend_registry {
std::vector<ggml_backend_reg_t> backends;
std::vector<ggml_backend_dev_t> devices;
@ -582,6 +583,9 @@ struct ggml_backend_registry {
#ifdef GGML_USE_VULKAN
register_backend(ggml_backend_vk_reg());
#endif
#ifdef GGML_USE_CANN
register_backend(ggml_backend_cann_reg());
#endif
#ifdef GGML_USE_BLAS
register_backend(ggml_backend_blas_reg());
#endif
@ -591,12 +595,10 @@ struct ggml_backend_registry {
#ifdef GGML_USE_AMX
register_backend(ggml_backend_amx_reg());
#endif
#ifdef GGML_USE_CANN
register_backend(ggml_backend_cann_reg());
#ifdef GGML_USE_KOMPUTE
register_backend(ggml_backend_kompute_reg());
#endif
// TODO: kompute
register_backend(ggml_backend_cpu_reg());
}
@ -701,9 +703,9 @@ ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const
}
ggml_backend_t ggml_backend_init_best(void) {
ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU_FULL);
ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
if (!dev) {
dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU_FULL);
dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
}
if (!dev) {
return NULL;
@ -711,13 +713,7 @@ ggml_backend_t ggml_backend_init_best(void) {
return ggml_backend_dev_init(dev, NULL);
}
// backend CPU
static const char * ggml_backend_cpu_buffer_get_name(ggml_backend_buffer_t buffer) {
return "CPU";
GGML_UNUSED(buffer);
}
// CPU backend - buffer
static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
uintptr_t data = (uintptr_t)buffer->context;
@ -767,7 +763,6 @@ static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t
}
static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = {
/* .get_name = */ ggml_backend_cpu_buffer_get_name,
/* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer,
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
@ -780,7 +775,6 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = {
};
static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = {
/* .get_name = */ ggml_backend_cpu_buffer_get_name,
/* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
@ -792,6 +786,8 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = {
/* .reset = */ NULL,
};
// CPU backend - buffer type
static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "CPU";
@ -799,19 +795,14 @@ static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_ty
}
static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
auto alloc_size = size;
if (alloc_size == 0) {
alloc_size = 1;
}
void * data = ggml_aligned_malloc(alloc_size);
void * data = ggml_aligned_malloc(size);
if (data == NULL) {
GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, alloc_size);
GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size);
return NULL;
}
return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, alloc_size);
return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size);
}
static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
@ -843,6 +834,29 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
return &ggml_backend_cpu_buffer_type;
}
static const char * ggml_backend_cpu_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
return "CPU_Mapped";
GGML_UNUSED(buft);
}
static ggml_backend_buffer_type_t ggml_backend_cpu_buffer_from_ptr_type(void) {
static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
/* .iface = */ {
/* .get_name = */ ggml_backend_cpu_buffer_from_ptr_type_get_name,
/* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment,
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_cpu_buffer_type_is_host,
},
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
/* .context = */ NULL,
};
return &ggml_backend_cpu_buffer_type;
}
#ifdef GGML_USE_CPU_HBM
// buffer type HBM
@ -855,18 +869,11 @@ static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffe
GGML_UNUSED(buft);
}
static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
return "CPU_HBM";
GGML_UNUSED(buf);
}
static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
hbw_free(buffer->context);
}
static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
//void * ptr = hbw_malloc(size);
void * ptr;
int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
if (result != 0) {
@ -876,7 +883,6 @@ static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
buffer->buft = buft;
buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name;
buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer;
return buffer;
@ -899,6 +905,21 @@ ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
}
#endif
static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backend_dev_t device) {
static ggml_backend_buffer_type_t bufts[] = {
#ifdef GGML_USE_CPU_HBM
ggml_backend_cpu_hbm_buffer_type(),
#endif
NULL
};
return bufts;
GGML_UNUSED(device);
}
// CPU backend - backend (stream)
struct ggml_backend_cpu_context {
int n_threads;
ggml_threadpool_t threadpool;
@ -923,12 +944,6 @@ static void ggml_backend_cpu_free(ggml_backend_t backend) {
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
return ggml_backend_cpu_buffer_type();
GGML_UNUSED(backend);
}
struct ggml_backend_plan_cpu {
struct ggml_cplan cplan;
struct ggml_cgraph cgraph;
@ -998,7 +1013,6 @@ static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, s
static const struct ggml_backend_i ggml_backend_cpu_i = {
/* .get_name = */ ggml_backend_cpu_get_name,
/* .free = */ ggml_backend_cpu_free,
/* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
@ -1008,9 +1022,6 @@ static const struct ggml_backend_i ggml_backend_cpu_i = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute,
/* .graph_compute = */ ggml_backend_cpu_graph_compute,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
@ -1081,10 +1092,10 @@ void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_
ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned");
return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size);
return ggml_backend_buffer_init(ggml_backend_cpu_buffer_from_ptr_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size);
}
////////////////////////
// CPU backend - device
struct ggml_backend_cpu_device_context {
std::string description = "CPU";
@ -1171,7 +1182,7 @@ static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t *
}
static enum ggml_backend_dev_type ggml_backend_cpu_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_CPU_FULL;
return GGML_BACKEND_DEVICE_TYPE_CPU;
GGML_UNUSED(dev);
}
@ -1189,7 +1200,7 @@ static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggm
};
}
static ggml_backend_t ggml_backend_cpu_device_init(ggml_backend_dev_t dev, const char * params) {
static ggml_backend_t ggml_backend_cpu_device_init_backend(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_cpu_init();
GGML_UNUSED(dev);
@ -1202,7 +1213,7 @@ static ggml_backend_buffer_type_t ggml_backend_cpu_device_get_buffer_type(ggml_b
GGML_UNUSED(dev);
}
static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
GGML_UNUSED(dev);
@ -1244,10 +1255,10 @@ static const struct ggml_backend_device_i ggml_backend_cpu_device_i = {
/* .get_memory = */ ggml_backend_cpu_device_get_memory,
/* .get_type = */ ggml_backend_cpu_device_get_type,
/* .get_props = */ ggml_backend_cpu_device_get_props,
/* .init_backend = */ ggml_backend_cpu_device_init,
/* .init_backend = */ ggml_backend_cpu_device_init_backend,
/* .get_buffer_type = */ ggml_backend_cpu_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_ptr,
/* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_host_ptr,
/* .supports_op = */ ggml_backend_cpu_device_supports_op,
/* .supports_buft = */ ggml_backend_cpu_device_supports_buft,
/* .offload_op = */ NULL,
@ -1256,7 +1267,7 @@ static const struct ggml_backend_device_i ggml_backend_cpu_device_i = {
/* .event_synchronize = */ NULL,
};
////////////////////////
// CPU backend - backend (reg)
static const char * ggml_backend_cpu_reg_get_name(ggml_backend_reg_t reg) {
return "CPU";
@ -1287,6 +1298,10 @@ static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const ch
if (strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_cpu_set_n_threads;
}
if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) {
return (void *)ggml_backend_cpu_get_extra_bufts;
}
return NULL;
GGML_UNUSED(reg);
@ -1315,12 +1330,6 @@ struct ggml_backend_multi_buffer_context {
size_t n_buffers;
};
static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) {
ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
return ctx->buffers[0]->iface.get_name(ctx->buffers[0]);
}
static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
for (size_t i = 0; i < ctx->n_buffers; i++) {
@ -1339,7 +1348,6 @@ static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_
}
static const struct ggml_backend_buffer_i ggml_backend_multi_buffer_i = {
/* .get_name = */ ggml_backend_multi_buffer_get_name,
/* .free_buffer = */ ggml_backend_multi_buffer_free_buffer,
/* .get_base = */ NULL,
/* .init_tensor = */ NULL,
@ -1368,7 +1376,7 @@ ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer
}
bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name == ggml_backend_multi_buffer_get_name;
return buffer->iface.free_buffer == ggml_backend_multi_buffer_free_buffer;
}
void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
@ -1460,7 +1468,7 @@ struct ggml_backend_sched {
char * context_buffer;
size_t context_buffer_size;
bool debug;
int debug;
};
#define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor)
@ -1500,7 +1508,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co
return -1;
}
#if 0
#if 1
#define GGML_SCHED_MAX_SPLITS_DEBUG 4096
static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
#define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
@ -1548,7 +1556,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
if (src == NULL) {
continue;
}
if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
// skip ROPE since the rope freqs tensor is too small to choose a backend based on it
// not an ideal solution
if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
// check if a backend with higher prio wants to offload the op
if (src_backend_id == sched->n_backends - 1) {
@ -1595,19 +1605,21 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
continue;
if (sched->debug > 1) {
ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
continue;
}
ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
}
ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
GGML_LOG_DEBUG("\n");
}
GGML_LOG_DEBUG("\n");
}
}
@ -1899,11 +1911,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
if (src == NULL) {
continue;
}
// check if a weight is on a different backend
// check if a weight is on a different and incompatible backend
// by starting a new split, the memory of the previously offloaded weights can be reused
if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
int src_backend_id = tensor_backend_id(src);
if (src_backend_id != cur_backend_id) {
if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) {
need_new_split = true;
break;
}
@ -1915,7 +1927,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
int src_backend_id = sched->hv_tensor_backend_ids[id];
bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id);
if (src_backend_id != cur_backend_id && tensor_id_copy(id, cur_backend_id, 0) == NULL && !supported) {
//printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name);
need_new_split = true;
break;
}
@ -2240,7 +2251,8 @@ ggml_backend_sched_t ggml_backend_sched_new(
struct ggml_backend_sched * sched = (ggml_backend_sched *) calloc(1, sizeof(struct ggml_backend_sched));
sched->debug = getenv("GGML_SCHED_DEBUG") != NULL;
const char * GGML_SCHED_DEBUG = getenv("GGML_SCHED_DEBUG");
sched->debug = GGML_SCHED_DEBUG ? atoi(GGML_SCHED_DEBUG) : 0;
sched->n_backends = n_backends;
sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;

20
ggml/src/ggml-blas.cpp
View file

@ -224,12 +224,6 @@ static void ggml_backend_blas_free(ggml_backend_t backend) {
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_blas_get_default_buffer_type(ggml_backend_t backend) {
return ggml_backend_cpu_buffer_type();
GGML_UNUSED(backend);
}
static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
@ -265,7 +259,6 @@ static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend,
static struct ggml_backend_i blas_backend_i = {
/* .get_name = */ ggml_backend_blas_get_name,
/* .free = */ ggml_backend_blas_free,
/* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
@ -275,9 +268,6 @@ static struct ggml_backend_i blas_backend_i = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_blas_graph_compute,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
@ -356,7 +346,7 @@ static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t *
}
static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_CPU;
return GGML_BACKEND_DEVICE_TYPE_ACCEL;
GGML_UNUSED(dev);
}
@ -374,7 +364,7 @@ static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct gg
};
}
static ggml_backend_t ggml_backend_blas_device_init(ggml_backend_dev_t dev, const char * params) {
static ggml_backend_t ggml_backend_blas_device_init_backend(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_blas_init();
GGML_UNUSED(dev);
@ -387,7 +377,7 @@ static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_
GGML_UNUSED(dev);
}
static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
GGML_UNUSED(dev);
@ -456,10 +446,10 @@ static const struct ggml_backend_device_i ggml_backend_blas_device_i = {
/* .get_memory = */ ggml_backend_blas_device_get_memory,
/* .get_type = */ ggml_backend_blas_device_get_type,
/* .get_props = */ ggml_backend_blas_device_get_props,
/* .init_backend = */ ggml_backend_blas_device_init,
/* .init_backend = */ ggml_backend_blas_device_init_backend,
/* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_ptr,
/* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_host_ptr,
/* .supports_op = */ ggml_backend_blas_device_supports_op,
/* .supports_buft = */ ggml_backend_blas_device_supports_buft,
/* .offload_op = */ NULL,

50
ggml/src/ggml-cann.cpp
View file

@ -489,23 +489,6 @@ struct ggml_backend_cann_buffer_context {
~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
};
/**
* @brief Retrieve the name associated with a CANN buffer.
*
* This function returns the name of a CANN buffer, which is stored in the
* context of the buffer.
*
* @param buffer The CANN buffer whose name is to be retrieved.
* @return A pointer to a C-string containing the name of the buffer.
*/
static const char* ggml_backend_cann_buffer_get_name(
ggml_backend_buffer_t buffer) {
return "CANN";
GGML_UNUSED(buffer);
}
/**
* @brief Check if a buffer is a CANN buffer.
*
@ -515,9 +498,10 @@ static const char* ggml_backend_cann_buffer_get_name(
* @param buffer The buffer to check.
* @return true if the buffer is a CANN buffer, false otherwise.
*/
static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft);
static bool ggml_backend_buffer_is_cann(
ggml_backend_buffer_t buffer) {
return buffer->iface.get_name == ggml_backend_cann_buffer_get_name;
return ggml_backend_buft_is_cann(buffer->buft);
}
/**
@ -965,7 +949,6 @@ static void ggml_backend_cann_buffer_clear(
* on a CANN buffer within the backend.
*/
static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
/* .get_name = */ ggml_backend_cann_buffer_get_name,
/* .free_buffer = */ ggml_backend_cann_buffer_free_buffer,
/* .get_base = */ ggml_backend_cann_buffer_get_base,
/* .init_tensor = */ ggml_backend_cann_buffer_init_tensor,
@ -999,9 +982,10 @@ struct ggml_backend_cann_buffer_type_context {
*/
static const char* ggml_backend_cann_buffer_type_name(
ggml_backend_buffer_type_t buft) {
return "CANN";
ggml_backend_cann_buffer_type_context* buft_ctx =
(ggml_backend_cann_buffer_type_context*)buft->context;
GGML_UNUSED(buft);
return buft_ctx->name.c_str();
}
/**
@ -1465,24 +1449,6 @@ static void ggml_backend_cann_free(ggml_backend_t backend) {
delete backend;
}
/**
* @brief Retrieves the default buffer type associated with the CANN backend.
*
* This function returns the buffer type specific to the device associated
* with the CANN backend. It is used to allocate buffers for computations
* performed by the backend.
*
* @param backend Pointer to the CANN backend structure.
* @return Pointer to the buffer type structure for the CANN backend.
*/
static ggml_backend_buffer_type_t
ggml_backend_cann_get_default_buffer_type(ggml_backend_t backend) {
ggml_backend_cann_context* cann_ctx =
(ggml_backend_cann_context*)backend->context;
return ggml_backend_cann_buffer_type(cann_ctx->device);
}
/**
* @brief Sets tensor data asynchronously in the CANN backend.
*
@ -1863,7 +1829,6 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend,
static const ggml_backend_i ggml_backend_cann_interface = {
/* .get_name = */ ggml_backend_cann_name,
/* .free = */ ggml_backend_cann_free,
/* .get_default_buffer_type = */ ggml_backend_cann_get_default_buffer_type,
/* .set_tensor_async = */ ggml_backend_cann_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_cann_get_tensor_async,
/* .cpy_tensor_async = */ ggml_backend_cann_cpy_tensor_async,
@ -1873,9 +1838,6 @@ static const ggml_backend_i ggml_backend_cann_interface = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_cann_graph_compute,
/* .supports_op = */ NULL, // moved to device
/* .supports_buft = */ NULL, // moved to device
/* .offload_op = */ NULL, // moved to device
/* .event_record = */ ggml_backend_cann_event_record,
/* .event_wait = */ ggml_backend_cann_event_wait,
};
@ -1918,7 +1880,7 @@ static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t *
static enum ggml_backend_dev_type ggml_backend_cann_device_get_type(ggml_backend_dev_t dev) {
GGML_UNUSED(dev);
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
return GGML_BACKEND_DEVICE_TYPE_GPU;
}
static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {

169
ggml/src/ggml-cuda.cu
View file

@ -421,20 +421,15 @@ struct ggml_backend_cuda_buffer_context {
}
};
static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
return ctx->name.c_str();
}
static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name;
}
static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
delete ctx;
}
static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
return buffer->iface.free_buffer == ggml_backend_cuda_buffer_free_buffer;
}
static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
return ctx->dev_ptr;
@ -515,7 +510,6 @@ static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t
}
static const ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
/* .get_name = */ ggml_backend_cuda_buffer_get_name,
/* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer,
/* .get_base = */ ggml_backend_cuda_buffer_get_base,
/* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor,
@ -548,8 +542,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
ggml_cuda_set_device(buft_ctx->device);
size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
void * dev_ptr;
cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device);
if (err != cudaSuccess) {
@ -657,7 +649,9 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl
}
struct ggml_backend_cuda_split_buffer_type_context {
int main_device;
std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
std::string name;
};
struct ggml_backend_cuda_split_buffer_context {
@ -680,16 +674,6 @@ struct ggml_backend_cuda_split_buffer_context {
std::vector<ggml_tensor_extra_gpu *> tensor_extras;
};
static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
return GGML_CUDA_NAME "_Split";
GGML_UNUSED(buffer);
}
static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name;
GGML_UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds
}
static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
@ -833,7 +817,6 @@ static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, u
}
static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
/* .get_name = */ ggml_backend_cuda_split_buffer_get_name,
/* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer,
/* .get_base = */ ggml_backend_cuda_split_buffer_get_base,
/* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor,
@ -848,9 +831,9 @@ static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
// cuda split buffer type
static const char * ggml_backend_cuda_split_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return GGML_CUDA_NAME "_Split";
ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context;
GGML_UNUSED(buft);
return ctx->name.c_str();
}
static bool ggml_backend_buft_is_cuda_split(ggml_backend_buffer_type_t buft) {
@ -915,11 +898,11 @@ static const ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_inte
/* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host,
};
ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split) {
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
static std::map<std::array<float, GGML_CUDA_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
static std::map<std::pair<int, std::array<float, GGML_CUDA_MAX_DEVICES>>, struct ggml_backend_buffer_type> buft_map;
std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split_arr = {};
@ -937,18 +920,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten
}
}
auto it = buft_map.find(tensor_split_arr);
auto it = buft_map.find({main_device, tensor_split_arr});
if (it != buft_map.end()) {
return &it->second;
}
auto * ctx = new ggml_backend_cuda_split_buffer_type_context{
main_device,
tensor_split_arr,
GGML_CUDA_NAME + std::to_string(main_device) + "_Split",
};
struct ggml_backend_buffer_type buft {
/* .iface = */ ggml_backend_cuda_split_buffer_type_interface,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), 0),
/* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr},
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), main_device),
/* .context = */ ctx,
};
auto result = buft_map.emplace(tensor_split_arr, buft);
auto result = buft_map.emplace(std::make_pair(main_device, tensor_split_arr), buft);
return &result.first->second;
}
@ -960,12 +948,6 @@ static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_
GGML_UNUSED(buft);
}
static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
return GGML_CUDA_NAME "_Host";
GGML_UNUSED(buffer);
}
static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
CUDA_CHECK(cudaFreeHost(buffer->context));
}
@ -998,7 +980,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
buffer->buft = buft;
buffer->iface.get_name = ggml_backend_cuda_host_buffer_name;
buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer;
return buffer;
@ -1151,8 +1132,8 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {
GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer));
char * src_ptr = (char *) src->data;
char * dst_ptr = (char *) dst;
const char * src_ptr = (const char *) src->data;
char * dst_ptr = (char *) dst;
const int64_t ne0 = src->ne[0];
const int64_t nb0 = src->nb[0];
@ -1162,7 +1143,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
const enum ggml_type type = src->type;
const int64_t ts = ggml_type_size(type);
const int64_t bs = ggml_blck_size(type);
int64_t i1_diff = i1_high - i1_low;
const int64_t i1_diff = i1_high - i1_low;
const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
if (nb0 == ts && nb1 == ts*ne0/bs) {
@ -1400,7 +1381,7 @@ static void ggml_cuda_op_mul_mat(
const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft);
GGML_ASSERT(!(split && ne02 > 1));
GGML_ASSERT(!(split && ne03 > 1));
GGML_ASSERT(!(split && ne02 < ne12));
@ -1479,14 +1460,24 @@ static void ggml_cuda_op_mul_mat(
if (src0_is_contiguous) {
dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data;
} else {
dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0));
// If src0 is not contiguous it will be copied to a temporary buffer.
// This buffer needs to be cleared entirely because multiple regions will function as padding.
const size_t nbytes_data = ggml_nbytes(src0);
const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding);
// TODO: remove this for MUSA once the Guilty Lockup issue is resolved
#ifndef GGML_USE_MUSA
CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd, 0, nbytes_data + nbytes_padding, stream));
#else // GGML_USE_MUSA
CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream));
#endif // !GGML_USE_MUSA
}
// If src0 is on a temporary compute buffers (partial offloading) there may be some padding that needs to be cleared:
// If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared:
if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) {
const int64_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00);
const int64_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream));
const size_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00);
const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream));
}
if (src1_on_device && src1_is_contiguous) {
@ -1880,7 +1871,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
}
static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft);
bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
@ -2007,7 +1998,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
GGML_TENSOR_BINARY_OP_LOCALS
GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers");
GGML_ASSERT(!ggml_backend_buft_is_cuda_split(src0->buffer->buft) && "mul_mat_id does not support split buffers");
cudaStream_t stream = ctx.stream();
@ -2140,7 +2131,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) {
// why is this here instead of mul_mat?
if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) {
if (dst->src[0] != nullptr && ggml_backend_buft_is_cuda_split(dst->src[0]->buffer->buft)) {
ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device);
}
@ -2361,12 +2352,6 @@ static void ggml_backend_cuda_free(ggml_backend_t backend) {
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
return ggml_backend_cuda_buffer_type(cuda_ctx->device);
}
static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
@ -2572,7 +2557,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
continue;
}
if (node->src[0] && node->src[0]->buffer && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
if (node->src[0] && node->src[0]->buffer && ggml_backend_buft_is_cuda_split(node->src[0]->buffer->buft)) {
use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__);
@ -2659,7 +2644,8 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->buffer);
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) ||
ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft));
}
}
#endif
@ -2752,7 +2738,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
if (stat == cudaErrorGraphExecUpdateFailure) {
#ifndef NDEBUG
GGML_LOG_ERROR("%s: CUDA graph update failed\n", __func__);
GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__);
#endif
// The pre-existing graph exec cannot be updated due to violated constraints
// so instead clear error and re-instantiate
@ -2801,7 +2787,6 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev
static const ggml_backend_i ggml_backend_cuda_interface = {
/* .get_name = */ ggml_backend_cuda_get_name,
/* .free = */ ggml_backend_cuda_free,
/* .get_default_buffer_type = */ ggml_backend_cuda_get_default_buffer_type,
/* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async,
/* .cpy_tensor_async = */ ggml_backend_cuda_cpy_tensor_async,
@ -2811,9 +2796,6 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_cuda_graph_compute,
/* .supports_op = */ NULL, // moved to device
/* .supports_buft = */ NULL, // moved to device
/* .offload_op = */ NULL, // moved to device
/* .event_record = */ ggml_backend_cuda_event_record,
/* .event_wait = */ ggml_backend_cuda_event_wait,
};
@ -2903,7 +2885,7 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t *
static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) {
GGML_UNUSED(dev);
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
return GGML_BACKEND_DEVICE_TYPE_GPU;
}
static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
@ -2927,7 +2909,7 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
};
}
static ggml_backend_t ggml_backend_cuda_device_init(ggml_backend_dev_t dev, const char * params) {
static ggml_backend_t ggml_backend_cuda_device_init_backend(ggml_backend_dev_t dev, const char * params) {
GGML_UNUSED(params);
ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
return ggml_backend_cuda_init(ctx->device);
@ -2943,18 +2925,29 @@ static ggml_backend_buffer_type_t ggml_backend_cuda_device_get_host_buffer_type(
return ggml_backend_cuda_host_buffer_type();
}
static ggml_backend_buffer_t ggml_backend_cuda_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
GGML_UNUSED(dev);
GGML_UNUSED(ptr);
GGML_UNUSED(size);
GGML_UNUSED(max_tensor_size);
return nullptr;
}
// TODO: move these functions here
static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) dev->context;
// split buffers can only be used with GGML_OP_MUL_MAT
if (op->op != GGML_OP_MUL_MAT) {
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda_split(op->src[i]->buffer->buft)) {
return false;
}
}
}
// check if all the sources are allocated on this device
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda(op->src[i]->buffer->buft)) {
ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)op->src[i]->buffer->buft->context;
if (buft_ctx->device != dev_ctx->device) {
return false;
}
}
}
switch (op->op) {
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
@ -3141,7 +3134,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_ROPE:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_IM2COL:
return op->src[0]->type == GGML_TYPE_F16;
case GGML_OP_POOL_2D:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
@ -3181,24 +3173,27 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
}
static bool ggml_backend_cuda_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
if (ggml_backend_buft_is_cuda_split(buft)) {
return true;
}
return (ggml_backend_buft_is_cuda(buft) || ggml_backend_buft_is_cuda_split(buft)) && buft->device == dev;
}
if (ggml_backend_buft_is_cuda(buft)) {
ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *)dev->context;
ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
return buft_ctx->device == dev_ctx->device;
static int64_t get_op_batch_size(const ggml_tensor * op) {
switch (op->op) {
case GGML_OP_GET_ROWS:
return 0;
case GGML_OP_MUL_MAT:
return op->ne[1];
case GGML_OP_MUL_MAT_ID:
case GGML_OP_ROPE:
return op->ne[2];
default:
return ggml_nrows(op);
}
return false;
}
static bool ggml_backend_cuda_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
const int min_batch_size = 32;
return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) ||
(op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID);
return get_op_batch_size(op) >= min_batch_size;
GGML_UNUSED(dev);
}
@ -3239,10 +3234,10 @@ static const ggml_backend_device_i ggml_backend_cuda_device_interface = {
/* .get_memory = */ ggml_backend_cuda_device_get_memory,
/* .get_type = */ ggml_backend_cuda_device_get_type,
/* .get_props = */ ggml_backend_cuda_device_get_props,
/* .init_backend = */ ggml_backend_cuda_device_init,
/* .init_backend = */ ggml_backend_cuda_device_init_backend,
/* .get_buffer_type = */ ggml_backend_cuda_device_get_buffer_type,
/* .get_host_buffer_type = */ ggml_backend_cuda_device_get_host_buffer_type,
/* .buffer_from_host_ptr = */ ggml_backend_cuda_device_buffer_from_host_ptr,
/* .buffer_from_host_ptr = */ NULL,
/* .supports_op = */ ggml_backend_cuda_device_supports_op,
/* .supports_buft = */ ggml_backend_cuda_device_supports_buft,
/* .offload_op = */ ggml_backend_cuda_device_offload_op,

2
ggml/src/ggml-cuda/cpy.cuh
View file

@ -1,6 +1,6 @@
#include "common.cuh"
#define CUDA_CPY_BLOCK_SIZE 32
#define CUDA_CPY_BLOCK_SIZE 64
void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1);

6
ggml/src/ggml-cuda/im2col.cu
View file

@ -91,9 +91,9 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int64_t OH = is_2D ? dst->ne[2] : 1;
const int64_t OW = dst->ne[1];
const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
const int64_t batch = src1->ne[3];
const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32
const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
const int64_t batch = src1->ne[is_2D ? 3 : 2];
const size_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
if(dst->type == GGML_TYPE_F16) {
im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);

4
ggml/src/ggml-cuda/mmq.cu
View file

@ -8,8 +8,6 @@ void ggml_cuda_op_mul_mat_q(
const int64_t ne00 = src0->ne[0];
const int64_t nb01 = src0->nb[1];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
GGML_ASSERT(ne10 % QK8_1 == 0);
@ -17,7 +15,7 @@ void ggml_cuda_op_mul_mat_q(
const int64_t ne0 = dst->ne[0];
const int64_t row_diff = row_high - row_low;
const int64_t stride00 = nb01 / ggml_type_size(src0->type);
const int64_t stride00 = ne00 / ggml_blck_size(src0->type);
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;

266
ggml/src/ggml-kompute.cpp
View file

@ -42,6 +42,7 @@
#include <cstring>
#include <iostream>
#include <memory>
#include <mutex>
#include <stdexcept>
#include <string>
#include <unordered_map>
@ -273,18 +274,9 @@ static std::vector<ggml_vk_device> ggml_vk_available_devices_internal(size_t mem
return results;
}
// public API returns a C-style array
ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count) {
auto devices = ggml_vk_available_devices_internal(memoryRequired);
*count = devices.size();
if (devices.empty()) {
return nullptr;
}
size_t nbytes = sizeof (ggml_vk_device) * (devices.size());
auto * arr = static_cast<ggml_vk_device *>(malloc(nbytes));
memcpy(arr, devices.data(), nbytes);
return arr;
static std::vector<ggml_vk_device>& ggml_vk_available_devices() {
static std::vector<ggml_vk_device> devices = ggml_vk_available_devices_internal(0);
return devices;
}
static void ggml_vk_filterByVendor(std::vector<ggml_vk_device>& devices, const std::string& targetVendor) {
@ -341,7 +333,7 @@ ggml_vk_device ggml_vk_current_device() {
if (!komputeManager()->hasDevice())
return ggml_vk_device();
auto devices = ggml_vk_available_devices_internal(0);
auto devices = ggml_vk_available_devices();
ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName.data());
GGML_ASSERT(!devices.empty());
return devices.front();
@ -1323,17 +1315,7 @@ static void ggml_vk_cpy_f16_f32(Args&&... args) {
ggml_vk_cpy(spirv, 2, 4, std::forward<Args>(args)...);
}
static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
switch (op->type) {
case GGML_TYPE_F16:
case GGML_TYPE_F32:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
break;
default:
return false;
}
static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
switch (op->op) {
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
@ -1410,6 +1392,8 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
;
}
return false;
GGML_UNUSED(dev);
}
static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml_cgraph * gf) {
@ -1458,11 +1442,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
any_commands_recorded = true;
if (!ggml_vk_supports_op(dst)) {
fprintf(stderr, "%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
GGML_ABORT("unsupported op");
}
const int32_t ne00 = src0 ? src0->ne[0] : 0;
const int32_t ne01 = src0 ? src0->ne[1] : 0;
const int32_t ne02 = src0 ? src0->ne[2] : 0;
@ -1820,11 +1799,6 @@ static void ggml_backend_kompute_device_unref(ggml_backend_buffer_type_t buft) {
}
}
static const char * ggml_backend_kompute_buffer_get_name(ggml_backend_buffer_t buffer) {
auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buffer->buft->context);
return ctx->name.c_str();
}
static void ggml_backend_kompute_buffer_free_buffer(ggml_backend_buffer_t buffer) {
auto * memory = (ggml_vk_memory *)buffer->context;
if (ggml_vk_has_device()) {
@ -1868,7 +1842,6 @@ static void ggml_backend_kompute_buffer_clear(ggml_backend_buffer_t buffer, uint
}
static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = {
/* .get_name = */ ggml_backend_kompute_buffer_get_name,
/* .free_buffer = */ ggml_backend_kompute_buffer_free_buffer,
/* .get_base = */ ggml_backend_kompute_buffer_get_base,
/* .init_tensor = */ NULL,
@ -1913,25 +1886,31 @@ static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = {
};
ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device) {
static std::vector<ggml_backend_buffer_type> bufts = []() {
std::vector<ggml_backend_buffer_type> vec;
auto devices = ggml_vk_available_devices_internal(0);
vec.reserve(devices.size());
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
for (const auto & dev : devices) {
vec.push_back({
/* .iface = */ ggml_backend_kompute_buffer_type_interface,
/* .device = */ nullptr,
/* .context = */ new ggml_backend_kompute_buffer_type_context(dev.index, dev.bufferAlignment, dev.maxAlloc)
});
auto devices = ggml_vk_available_devices();
int32_t device_count = (int32_t) devices.size();
GGML_ASSERT(device < device_count);
GGML_ASSERT(devices.size() <= GGML_KOMPUTE_MAX_DEVICES);
static ggml_backend_buffer_type
ggml_backend_kompute_buffer_types[GGML_KOMPUTE_MAX_DEVICES];
static bool ggml_backend_kompute_buffer_type_initialized = false;
if (!ggml_backend_kompute_buffer_type_initialized) {
for (int32_t i = 0; i < device_count; i++) {
ggml_backend_kompute_buffer_types[i] = {
/* .iface = */ ggml_backend_kompute_buffer_type_interface,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), i),
/* .context = */ new ggml_backend_kompute_buffer_type_context{ i, devices[i].bufferAlignment, devices[i].maxAlloc },
};
}
return vec;
}();
ggml_backend_kompute_buffer_type_initialized = true;
}
auto it = std::find_if(bufts.begin(), bufts.end(), [device](const ggml_backend_buffer_type & t) {
return device == static_cast<ggml_backend_kompute_buffer_type_context *>(t.context)->device;
});
return it < bufts.end() ? &*it : nullptr;
return &ggml_backend_kompute_buffer_types[device];
}
// backend
@ -1953,31 +1932,15 @@ static void ggml_backend_kompute_free(ggml_backend_t backend) {
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(ggml_backend_t backend) {
auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
return ggml_backend_kompute_buffer_type(ctx->device);
}
static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
ggml_vk_graph_compute(ctx, cgraph);
return GGML_STATUS_SUCCESS;
}
static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
GGML_UNUSED(backend);
return ggml_vk_supports_op(op);
}
static bool ggml_backend_kompute_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
GGML_UNUSED(backend);
return buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name;
}
static struct ggml_backend_i kompute_backend_i = {
/* .get_name = */ ggml_backend_kompute_name,
/* .free = */ ggml_backend_kompute_free,
/* .get_default_buffer_type = */ ggml_backend_kompute_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
@ -1987,9 +1950,6 @@ static struct ggml_backend_i kompute_backend_i = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_kompute_graph_compute,
/* .supports_op = */ ggml_backend_kompute_supports_op,
/* .supports_buft = */ ggml_backend_kompute_supports_buft,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
@ -2006,7 +1966,7 @@ ggml_backend_t ggml_backend_kompute_init(int device) {
ggml_backend_t kompute_backend = new ggml_backend {
/* .guid = */ ggml_backend_kompute_guid(),
/* .interface = */ kompute_backend_i,
/* .device = */ nullptr,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), device),
/* .context = */ s_kompute_context,
};
@ -2016,3 +1976,167 @@ ggml_backend_t ggml_backend_kompute_init(int device) {
bool ggml_backend_is_kompute(ggml_backend_t backend) {
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid());
}
static size_t ggml_backend_kompute_get_device_count() {
auto devices = ggml_vk_available_devices();
return devices.size();
}
static void ggml_backend_kompute_get_device_description(int device, char * description, size_t description_size) {
auto devices = ggml_vk_available_devices();
GGML_ASSERT((size_t) device < devices.size());
snprintf(description, description_size, "%s", devices[device].name);
}
static void ggml_backend_kompute_get_device_memory(int device, size_t * free, size_t * total) {
auto devices = ggml_vk_available_devices();
GGML_ASSERT((size_t) device < devices.size());
*total = devices[device].heapSize;
*free = devices[device].heapSize;
}
//////////////////////////
struct ggml_backend_kompute_device_context {
int device;
std::string name;
std::string description;
};
static const char * ggml_backend_kompute_device_get_name(ggml_backend_dev_t dev) {
ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context;
return ctx->name.c_str();
}
static const char * ggml_backend_kompute_device_get_description(ggml_backend_dev_t dev) {
ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context;
return ctx->description.c_str();
}
static void ggml_backend_kompute_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context;
ggml_backend_kompute_get_device_memory(ctx->device, free, total);
}
static ggml_backend_buffer_type_t ggml_backend_kompute_device_get_buffer_type(ggml_backend_dev_t dev) {
ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context;
return ggml_backend_kompute_buffer_type(ctx->device);
}
static bool ggml_backend_kompute_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
if (buft->iface.get_name != ggml_backend_kompute_buffer_type_get_name) {
return false;
}
ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context;
ggml_backend_kompute_buffer_type_context * buft_ctx = (ggml_backend_kompute_buffer_type_context *)buft->context;
return buft_ctx->device == ctx->device;
}
static enum ggml_backend_dev_type ggml_backend_kompute_device_get_type(ggml_backend_dev_t dev) {
GGML_UNUSED(dev);
return GGML_BACKEND_DEVICE_TYPE_GPU;
}
static void ggml_backend_kompute_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_kompute_device_get_name(dev);
props->description = ggml_backend_kompute_device_get_description(dev);
props->type = ggml_backend_kompute_device_get_type(dev);
ggml_backend_kompute_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* async = */ false,
/* host_buffer = */ false,
/* .buffer_from_host_ptr = */ false,
/* events = */ false,
};
}
static ggml_backend_t ggml_backend_kompute_device_init(ggml_backend_dev_t dev, const char * params) {
GGML_UNUSED(params);
ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context;
return ggml_backend_kompute_init(ctx->device);
}
static bool ggml_backend_kompute_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
const int min_batch_size = 32;
return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) ||
(op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID);
GGML_UNUSED(dev);
}
static const struct ggml_backend_device_i ggml_backend_kompute_device_i = {
/* .get_name = */ ggml_backend_kompute_device_get_name,
/* .get_description = */ ggml_backend_kompute_device_get_description,
/* .get_memory = */ ggml_backend_kompute_device_get_memory,
/* .get_type = */ ggml_backend_kompute_device_get_type,
/* .get_props = */ ggml_backend_kompute_device_get_props,
/* .init_backend = */ ggml_backend_kompute_device_init,
/* .get_buffer_type = */ ggml_backend_kompute_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ NULL,
/* .supports_op = */ ggml_backend_kompute_device_supports_op,
/* .supports_buft = */ ggml_backend_kompute_device_supports_buft,
/* .offload_op = */ ggml_backend_kompute_device_offload_op,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
static const char * ggml_backend_kompute_reg_get_name(ggml_backend_reg_t reg) {
GGML_UNUSED(reg);
return "Kompute";
}
static size_t ggml_backend_kompute_reg_get_device_count(ggml_backend_reg_t reg) {
GGML_UNUSED(reg);
return ggml_backend_kompute_get_device_count();
}
static ggml_backend_dev_t ggml_backend_kompute_reg_get_device(ggml_backend_reg_t reg, size_t device) {
static std::vector<ggml_backend_dev_t> devices;
static bool initialized = false;
{
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
if (!initialized) {
for (size_t i = 0; i < ggml_backend_kompute_get_device_count(); i++) {
ggml_backend_kompute_device_context * ctx = new ggml_backend_kompute_device_context;
char desc[256];
ggml_backend_kompute_get_device_description(i, desc, sizeof(desc));
ctx->device = i;
ctx->name = "Kompute" + std::to_string(i);
ctx->description = desc;
devices.push_back(new ggml_backend_device {
/* .iface = */ ggml_backend_kompute_device_i,
/* .reg = */ reg,
/* .context = */ ctx,
});
}
initialized = true;
}
}
GGML_ASSERT(device < devices.size());
return devices[device];
}
static const struct ggml_backend_reg_i ggml_backend_kompute_reg_i = {
/* .get_name = */ ggml_backend_kompute_reg_get_name,
/* .get_device_count = */ ggml_backend_kompute_reg_get_device_count,
/* .get_device = */ ggml_backend_kompute_reg_get_device,
/* .get_proc_address = */ NULL,
};
ggml_backend_reg_t ggml_backend_kompute_reg() {
static ggml_backend_reg reg = {
/* .iface = */ ggml_backend_kompute_reg_i,
/* .context = */ nullptr,
};
return &reg;
}

247
ggml/src/ggml-metal.m
View file

@ -241,6 +241,8 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16,
GGML_METAL_KERNEL_TYPE_IM2COL_F16,
GGML_METAL_KERNEL_TYPE_IM2COL_F32,
GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16,
GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32,
GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
GGML_METAL_KERNEL_TYPE_PAD_F32,
GGML_METAL_KERNEL_TYPE_ARANGE_F32,
@ -272,6 +274,8 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_SIN,
GGML_METAL_KERNEL_TYPE_COS,
GGML_METAL_KERNEL_TYPE_SUM_ROWS,
GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,
GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,
GGML_METAL_KERNEL_TYPE_COUNT
};
@ -685,6 +689,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, rope_neox_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, im2col_ext_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, im2col_ext_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
@ -716,6 +722,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN, sin, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, pool_2d_avg_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, pool_2d_max_f32, true);
}
[metal_library release];
@ -844,8 +852,8 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
case GGML_OP_IM2COL:
return op->src[0]->type == GGML_TYPE_F16;
case GGML_OP_POOL_1D:
case GGML_OP_POOL_2D:
return false;
case GGML_OP_POOL_2D:
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_ARANGE:
@ -1007,19 +1015,21 @@ static void ggml_metal_encode_node(
id<MTLBuffer> id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil;
id<MTLBuffer> id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil;
//GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
//if (src0) {
// GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
// ggml_is_contiguous(src0), src0->name);
//}
//if (src1) {
// GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
// ggml_is_contiguous(src1), src1->name);
//}
//if (dst) {
// GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2,
// dst->name);
//}
#if 0
GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
if (src0) {
GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03,
ggml_is_contiguous(src0), src0->name);
}
if (src1) {
GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13,
ggml_is_contiguous(src1), src1->name);
}
if (dst) {
GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, ne3, nb0, nb1, nb2, nb3,
dst->name);
}
#endif
id<MTLDevice> device = ctx_dev->mtl_device;
@ -1802,14 +1812,16 @@ static void ggml_metal_encode_node(
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:13];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:14];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:7];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:9];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:10];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:11];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:12];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:15];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:16];
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
} else {
@ -1978,20 +1990,22 @@ static void ggml_metal_encode_node(
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:17];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:18];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:13];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:14];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:15];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:16];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:19];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:20];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 ||
src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
@ -2040,6 +2054,9 @@ static void ggml_metal_encode_node(
GGML_ASSERT(src1t == GGML_TYPE_F32);
GGML_ASSERT(ne03 == 1);
GGML_ASSERT(ne13 == 1);
// find the break-even point where the matrix-matrix kernel becomes more efficient compared
// to the matrix-vector kernel
// ne20 = n_used_experts
@ -2545,6 +2562,8 @@ static void ggml_metal_encode_node(
} break;
case GGML_OP_IM2COL:
{
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
@ -2574,30 +2593,54 @@ static void ggml_metal_encode_node(
const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4;
const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4;
id<MTLComputePipelineState> pipeline = nil;
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline;
const bool is_gt_mttpt = ((size_t)(N * KH * KW)) > pipeline.maxTotalThreadsPerThreadgroup;
switch (dst->type) {
case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break;
case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break;
case GGML_TYPE_F32: {
pipeline = (is_gt_mttpt ?
ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32].pipeline
:
ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline);
} break;
case GGML_TYPE_F16: {
pipeline = (is_gt_mttpt ?
ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16].pipeline
:
ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline);
} break;
default: GGML_ABORT("fatal error");
};
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2];
[encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3];
[encoder setBytes:&IW length:sizeof( int32_t) atIndex:4];
[encoder setBytes:&IH length:sizeof( int32_t) atIndex:5];
[encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6];
[encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7];
[encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8];
[encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9];
[encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10];
[encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11];
[encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ofs0 length:sizeof(int32_t) atIndex:2];
[encoder setBytes:&ofs1 length:sizeof(int32_t) atIndex:3];
[encoder setBytes:&IW length:sizeof(int32_t) atIndex:4];
[encoder setBytes:&IH length:sizeof(int32_t) atIndex:5];
[encoder setBytes:&CHW length:sizeof(int32_t) atIndex:6];
[encoder setBytes:&s0 length:sizeof(int32_t) atIndex:7];
[encoder setBytes:&s1 length:sizeof(int32_t) atIndex:8];
[encoder setBytes:&p0 length:sizeof(int32_t) atIndex:9];
[encoder setBytes:&p1 length:sizeof(int32_t) atIndex:10];
[encoder setBytes:&d0 length:sizeof(int32_t) atIndex:11];
[encoder setBytes:&d1 length:sizeof(int32_t) atIndex:12];
[encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)];
if (is_gt_mttpt) {
[encoder setBytes:&N length:sizeof(int32_t) atIndex:13];
[encoder setBytes:&KH length:sizeof(int32_t) atIndex:14];
[encoder setBytes:&KW length:sizeof(int32_t) atIndex:15];
const uint64_t n_threads = MIN(pipeline.maxTotalThreadsPerThreadgroup, (uint64_t)N);
const int64_t quotient = N / n_threads + (N % n_threads > 0 ? 1 : 0);
[encoder dispatchThreadgroups:MTLSizeMake(quotient * CHW, OH, OW) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)];
} else {
[encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)];
}
} break;
case GGML_OP_UPSCALE:
{
@ -3001,6 +3044,64 @@ static void ggml_metal_encode_node(
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_POOL_2D:
{
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0t == GGML_TYPE_F32 && src0t == dstt);
const int32_t * opts = dst->op_params;
enum ggml_op_pool op = opts[0];
id<MTLComputePipelineState> pipeline = nil;
switch (src0t) {
case GGML_TYPE_F32: {
switch(op) {
case GGML_OP_POOL_AVG:
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32].pipeline; break;
case GGML_OP_POOL_MAX:
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32].pipeline; break;
default: GGML_ASSERT(false && "not implemented");
}
} break;
default: GGML_ASSERT(false && "not implemented");
}
const int32_t k0 = opts[1];
const int32_t k1 = opts[2];
const int32_t s0 = opts[3];
const int32_t s1 = opts[4];
const int32_t p0 = opts[5];
const int32_t p1 = opts[6];
const int64_t IH = src0->ne[1];
const int64_t IW = src0->ne[0];
const int64_t N = dst->ne[3];
const int64_t OC = dst->ne[2];
const int64_t OH = dst->ne[1];
const int64_t OW = dst->ne[0];
const int64_t parallel_elements = N * OC * OH * OW;
const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements);
const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&k0 length:sizeof(int32_t) atIndex:2];
[encoder setBytes:&k1 length:sizeof(int32_t) atIndex:3];
[encoder setBytes:&s0 length:sizeof(int32_t) atIndex:4];
[encoder setBytes:&s1 length:sizeof(int32_t) atIndex:5];
[encoder setBytes:&p0 length:sizeof(int32_t) atIndex:6];
[encoder setBytes:&p1 length:sizeof(int32_t) atIndex:7];
[encoder setBytes:&IH length:sizeof(int64_t) atIndex:8];
[encoder setBytes:&IW length:sizeof(int64_t) atIndex:9];
[encoder setBytes:&OH length:sizeof(int64_t) atIndex:10];
[encoder setBytes:&OW length:sizeof(int64_t) atIndex:11];
[encoder setBytes:&parallel_elements length:sizeof(int64_t) atIndex:12];
[encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)];
} break;
default:
{
GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
@ -3146,12 +3247,6 @@ static enum ggml_status ggml_metal_graph_compute(
// backend interface
static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) {
return "Metal";
UNUSED(buffer);
}
static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
@ -3206,7 +3301,6 @@ static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_
}
static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
/* .get_name = */ ggml_backend_metal_buffer_get_name,
/* .free_buffer = */ ggml_backend_metal_buffer_free_buffer,
/* .get_base = */ ggml_backend_metal_buffer_get_base,
/* .init_tensor = */ NULL,
@ -3331,6 +3425,29 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
return &ggml_backend_buffer_type_metal;
}
static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
return "Metal_Mapped";
UNUSED(buft);
}
static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) {
static struct ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_metal = {
/* .iface = */ {
/* .get_name = */ ggml_backend_metal_buffer_from_ptr_type_get_name,
/* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment,
/* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_metal_buffer_type_is_host,
},
/* .device = */ &g_ggml_backend_metal_device,
/* .context = */ NULL,
};
return &ggml_backend_buffer_from_ptr_type_metal;
}
// TODO: obsoleted by ggml_backend_metal_device_buffer_from_ptr
ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context));
@ -3407,7 +3524,7 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
}
}
return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size);
return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size);
}
// backend
@ -3428,12 +3545,6 @@ static void ggml_backend_metal_free(ggml_backend_t backend) {
free(backend);
}
static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) {
return ggml_backend_metal_buffer_type();
UNUSED(backend);
}
static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
return ggml_metal_graph_compute(backend, cgraph);
}
@ -3500,7 +3611,6 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
static struct ggml_backend_i ggml_backend_metal_i = {
/* .get_name = */ ggml_backend_metal_name,
/* .free = */ ggml_backend_metal_free,
/* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
@ -3510,9 +3620,6 @@ static struct ggml_backend_i ggml_backend_metal_i = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_metal_graph_compute,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
@ -3607,7 +3714,7 @@ static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t
}
static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
return GGML_BACKEND_DEVICE_TYPE_GPU;
GGML_UNUSED(dev);
}

756
ggml/src/ggml-metal.metal
View file

File diff suppressed because it is too large Load diff

20
ggml/src/ggml-rpc.cpp
View file

@ -178,7 +178,6 @@ struct ggml_backend_rpc_buffer_context {
std::shared_ptr<socket_t> sock;
std::unordered_map<ggml_backend_buffer_t, void *> base_cache;
uint64_t remote_ptr;
std::string name;
};
// RPC helper functions
@ -409,11 +408,6 @@ static std::shared_ptr<socket_t> get_socket(const std::string & endpoint) {
return sock;
}
static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) {
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
return ctx->name.c_str();
}
static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
rpc_msg_free_buffer_req request = {ctx->remote_ptr};
@ -524,7 +518,6 @@ static void ggml_backend_rpc_buffer_clear(ggml_backend_buffer_t buffer, uint8_t
}
static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = {
/* .get_name = */ ggml_backend_rpc_buffer_get_name,
/* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer,
/* .get_base = */ ggml_backend_rpc_buffer_get_base,
/* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor,
@ -551,7 +544,7 @@ static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_back
if (response.remote_ptr != 0) {
ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft,
ggml_backend_rpc_buffer_interface,
new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"},
new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr},
response.remote_size);
return buffer;
} else {
@ -609,11 +602,6 @@ static void ggml_backend_rpc_free(ggml_backend_t backend) {
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) {
ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context;
return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str());
}
static void ggml_backend_rpc_synchronize(ggml_backend_t backend) {
UNUSED(backend);
// this is no-op because we don't have any async operations
@ -670,7 +658,6 @@ static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, g
static ggml_backend_i ggml_backend_rpc_interface = {
/* .get_name = */ ggml_backend_rpc_name,
/* .free = */ ggml_backend_rpc_free,
/* .get_default_buffer_type = */ ggml_backend_rpc_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
@ -680,9 +667,6 @@ static ggml_backend_i ggml_backend_rpc_interface = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_rpc_graph_compute,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
@ -1278,7 +1262,7 @@ static void ggml_backend_rpc_device_get_memory(ggml_backend_dev_t dev, size_t *
static enum ggml_backend_dev_type ggml_backend_rpc_device_get_type(ggml_backend_dev_t dev) {
// TODO: obtain value from the server
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
return GGML_BACKEND_DEVICE_TYPE_GPU;
UNUSED(dev);
}

54
ggml/src/ggml-sycl.cpp
View file

@ -249,13 +249,10 @@ struct ggml_backend_sycl_buffer_context {
}
};
static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) {
ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
return ctx->name.c_str();
}
static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_type_t buft);
static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name;
return buffer->buft->iface.get_name == ggml_backend_sycl_buffer_type_get_name;
}
static void
@ -440,7 +437,6 @@ catch (sycl::exception const &exc) {
}
static const ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = {
/* .get_name = */ ggml_backend_sycl_buffer_get_name,
/* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer,
/* .get_base = */ ggml_backend_sycl_buffer_get_base,
/* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor,
@ -698,16 +694,6 @@ struct ggml_backend_sycl_split_buffer_context {
std::vector<queue_ptr> streams;
};
static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) {
return GGML_SYCL_NAME "_Split";
GGML_UNUSED(buffer);
}
static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name;
}
static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
delete ctx;
@ -915,7 +901,6 @@ static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, u
}
static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = {
/* .get_name = */ ggml_backend_sycl_split_buffer_get_name,
/* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer,
/* .get_base = */ ggml_backend_sycl_split_buffer_get_base,
/* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor,
@ -935,6 +920,10 @@ static const char * ggml_backend_sycl_split_buffer_type_get_name(ggml_backend_bu
GGML_UNUSED(buft);
}
static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) {
return buffer->buft->iface.get_name == ggml_backend_sycl_split_buffer_type_get_name;
}
static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
// since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
// instead, we allocate them for each tensor separately in init_tensor
@ -1040,12 +1029,6 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_
GGML_UNUSED(buft);
}
static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) {
return GGML_SYCL_NAME "_Host";
GGML_UNUSED(buffer);
}
static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_sycl_host_free(buffer->context);
}
@ -1061,7 +1044,6 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm
// FIXME: this is a hack to avoid having to implement a new buffer type
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
buffer->buft = buft;
buffer->iface.get_name = ggml_backend_sycl_host_buffer_name;
buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer;
return buffer;
@ -4889,12 +4871,6 @@ static void ggml_backend_sycl_free(ggml_backend_t backend) {
delete backend;
}
static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) {
ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
return ggml_backend_sycl_buffer_type(sycl_ctx->device);
}
static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
ggml_tensor *tensor,
const void *data, size_t offset,
@ -5031,7 +5007,6 @@ static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_ev
static ggml_backend_i ggml_backend_sycl_interface = {
/* .get_name = */ ggml_backend_sycl_get_name,
/* .free = */ ggml_backend_sycl_free,
/* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type,
/* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async,
/* .cpy_tensor_async = */ NULL, // ggml_backend_sycl_cpy_tensor_async,
@ -5043,9 +5018,6 @@ static ggml_backend_i ggml_backend_sycl_interface = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_sycl_graph_compute,
/* .supports_op = */ NULL, // moved to device
/* .supports_buft = */ NULL, // moved to device
/* .offload_op = */ NULL, // moved to device
/* .event_record = */ ggml_backend_sycl_event_record,
/* .event_wait = */ ggml_backend_sycl_event_wait,
};
@ -5092,7 +5064,7 @@ static void ggml_backend_sycl_device_get_memory(ggml_backend_dev_t dev, size_t *
static enum ggml_backend_dev_type ggml_backend_sycl_device_get_type(ggml_backend_dev_t dev) {
GGML_UNUSED(dev);
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
return GGML_BACKEND_DEVICE_TYPE_GPU;
}
static void ggml_backend_sycl_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
@ -5388,12 +5360,14 @@ static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t re
return ctx->devices[index];
}
static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name)
{
static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) {
GGML_UNUSED(reg);
if (strcmp(name, "ggml_backend_split_buffer_type") == 0) {
return (void *)ggml_backend_sycl_split_buffer_type;
}
// TODO: update to the current function signature
//if (strcmp(name, "ggml_backend_split_buffer_type") == 0) {
// return (void *)ggml_backend_sycl_split_buffer_type;
//}
// SYCL doesn't support registering host memory, left here for reference
// "ggml_backend_register_host_buffer"
// "ggml_backend_unregister_host_buffer"

98
ggml/src/ggml-vulkan.cpp
View file

@ -213,6 +213,7 @@ struct vk_device_struct {
vk_pipeline pipeline_sum_rows_f32;
vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
vk_pipeline pipeline_timestep_embedding_f32;
vk_pipeline pipeline_pool2d_f32;
std::unordered_map<std::string, vk_pipeline_ref> pipelines;
std::unordered_map<std::string, uint64_t> pipeline_descriptor_set_requirements;
@ -403,6 +404,17 @@ struct vk_op_timestep_embedding_push_constants {
uint32_t max_period;
};
struct vk_op_pool2d_push_constants {
uint32_t IW; uint32_t IH;
uint32_t OW; uint32_t OH;
uint32_t OC;
uint32_t pelements;
uint32_t op;
int32_t k0; int32_t k1;
int32_t s0; int32_t s1;
int32_t p0; int32_t p1;
};
// Allow pre-recording command buffers
struct vk_staging_memcpy {
vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
@ -1803,6 +1815,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1);
for (auto &c : compiles) {
c.wait();
}
@ -4234,6 +4248,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_timestep_embedding_f32;
}
return nullptr;
case GGML_OP_POOL_2D:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_pool2d_f32;
}
return nullptr;
case GGML_OP_LEAKY_RELU:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_leaky_relu_f32;
@ -4464,6 +4483,14 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
uint32_t half_ceil = (dim + 1) / 2;
elements = { half_ceil, (uint32_t)src0->ne[0], 1 };
} break;
case GGML_OP_POOL_2D:
{
const uint32_t N = dst->ne[3];
const uint32_t OC = dst->ne[2];
const uint32_t OH = dst->ne[1];
const uint32_t OW = dst->ne[0];
elements = { N * OC * OH * OW, 1, 1};
} break;
case GGML_OP_ADD:
case GGML_OP_DIV:
case GGML_OP_MUL:
@ -4914,6 +4941,34 @@ static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context
}, dryrun);
}
static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
uint32_t op = static_cast<uint32_t>(dst->op_params[0]);
const int32_t k1 = dst->op_params[1];
const int32_t k0 = dst->op_params[2];
const int32_t s1 = dst->op_params[3];
const int32_t s0 = dst->op_params[4];
const int32_t p1 = dst->op_params[5];
const int32_t p0 = dst->op_params[6];
const uint32_t IH = src0->ne[1];
const uint32_t IW = src0->ne[0];
const uint32_t N = dst->ne[3];
const uint32_t OC = dst->ne[2];
const uint32_t OH = dst->ne[1];
const uint32_t OW = dst->ne[0];
const uint32_t parallel_elements = N * OC * OH * OW;
ggml_vk_op_f32<vk_op_pool2d_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_POOL_2D, {
IW, IH, OW, OH, OC,
parallel_elements,
op,
k0, k1, s0, s1, p0, p1,
}, dryrun);
}
static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
const float * op_params = (const float *)dst->op_params;
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun);
@ -5792,6 +5847,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_SUM_ROWS:
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_LEAKY_RELU:
break;
default:
@ -5927,6 +5983,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_TIMESTEP_EMBEDDING:
ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node, dryrun);
break;
case GGML_OP_POOL_2D:
ggml_vk_pool_2d(ctx, compute_ctx, src0, node, dryrun);
break;
case GGML_OP_LEAKY_RELU:
ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun);
@ -6018,6 +6078,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
case GGML_OP_SUM_ROWS:
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_LEAKY_RELU:
case GGML_OP_REPEAT:
buf = tensor->buffer;
@ -6186,13 +6247,8 @@ static void ggml_vk_get_device_description(int device, char * description, size_
// device backend
static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) {
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
return ctx->name.c_str();
}
static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name == ggml_backend_vk_buffer_get_name;
return buffer->buft->iface.get_name == ggml_backend_vk_buffer_type_name;
}
static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
@ -6256,7 +6312,6 @@ static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t v
}
static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
/* .get_name = */ ggml_backend_vk_buffer_get_name,
/* .free_buffer = */ ggml_backend_vk_buffer_free_buffer,
/* .get_base = */ ggml_backend_vk_buffer_get_base,
/* .init_tensor = */ ggml_backend_vk_buffer_init_tensor,
@ -6352,7 +6407,6 @@ static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
buffer->buft = buft;
buffer->iface.get_name = ggml_backend_vk_host_buffer_name;
buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer;
return buffer;
@ -6585,7 +6639,6 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
static ggml_backend_i ggml_backend_vk_interface = {
/* .get_name = */ ggml_backend_vk_name,
/* .free = */ ggml_backend_vk_free,
/* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type,
/* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async,
/* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async,
/* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async,
@ -6595,9 +6648,6 @@ static ggml_backend_i ggml_backend_vk_interface = {
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_vk_graph_compute,
/* .supports_op = */ NULL,
/* .supports_buft = */ NULL,
/* .offload_op = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
};
@ -6656,7 +6706,7 @@ void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total
//////////////////////////
struct ggml_backend_vk_device_context {
int device;
size_t device;
std::string name;
std::string description;
};
@ -6688,7 +6738,7 @@ static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(gg
static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) {
UNUSED(dev);
return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
return GGML_BACKEND_DEVICE_TYPE_GPU;
}
static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
@ -6697,9 +6747,10 @@ static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml
props->type = ggml_backend_vk_device_get_type(dev);
ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* async */ false,
/* host_buffer */ true,
/* events */ false,
/* .async = */ false,
/* .host_buffer = */ true,
/* .buffer_from_host_ptr = */ false,
/* .events = */ false,
};
}
@ -6821,6 +6872,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_SUM_ROWS:
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_LEAKY_RELU:
return true;
default:
@ -6887,7 +6939,7 @@ static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg,
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
if (!initialized) {
for (size_t i = 0; i < ggml_backend_vk_get_device_count(); i++) {
for (int i = 0; i < ggml_backend_vk_get_device_count(); i++) {
ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context;
char desc[256];
ggml_backend_vk_get_device_description(i, desc, sizeof(desc));
@ -7334,6 +7386,16 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
const int32_t dim = tensor->op_params[0];
const int32_t max_period = tensor->op_params[1];
tensor_clone = ggml_timestep_embedding(ggml_ctx, src0_clone, dim, max_period);
} else if (tensor->op == GGML_OP_POOL_2D) {
enum ggml_op_pool op = static_cast<ggml_op_pool>(dst->op_params[0]);
const int32_t k0 = tensor->op_params[1];
const int32_t k1 = tensor->op_params[2];
const int32_t s0 = tensor->op_params[3];
const int32_t s1 = tensor->op_params[4];
const int32_t p0 = tensor->op_params[5];
const int32_t p1 = tensor->op_params[6];
tensor_clone = ggml_pool_2d(ggml_ctx, src0_clone, op, k0, k1, s0, s1, p0, p1);
} else if (tensor->op == GGML_OP_LEAKY_RELU) {
const float * op_params = (const float *)tensor->op_params;
tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false);

77
ggml/src/ggml.c
View file

@ -3464,7 +3464,7 @@ int64_t ggml_nrows(const struct ggml_tensor * tensor) {
size_t ggml_nbytes(const struct ggml_tensor * tensor) {
size_t nbytes;
size_t blck_size = ggml_blck_size(tensor->type);
const size_t blck_size = ggml_blck_size(tensor->type);
if (blck_size == 1) {
nbytes = ggml_type_size(tensor->type);
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
@ -3852,10 +3852,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
},
};
for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) {
g_state.contexts[i].used = false;
}
const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
@ -4032,7 +4028,9 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml
if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
GGML_LOG_WARN("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
__func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size);
assert(false);
#ifndef NDEBUG
GGML_ABORT("not enough space in the context's memory pool");
#endif
return NULL;
}
@ -22138,7 +22136,11 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) {
return false;
}
p->data = GGML_CALLOC(p->n + 1, 1);
p->data = calloc(p->n + 1, 1);
if (!p->data) {
fprintf(stderr, "%s: failed to allocate memory for string of length %" PRIu64 "\n", __func__, p->n);
return false;
}
ok = ok && gguf_fread_el(file, p->data, p->n, offset);
@ -22172,7 +22174,11 @@ static void gguf_free_kv(struct gguf_kv * kv) {
}
struct gguf_context * gguf_init_empty(void) {
struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));
struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context));
if (!ctx) {
fprintf(stderr, "%s: failed to allocate memory for context\n", __func__);
return NULL;
}
memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
ctx->header.version = GGUF_VERSION;
@ -22218,7 +22224,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
bool ok = true;
struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));
struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context));
if (!ctx) {
fprintf(stderr, "%s: failed to allocate memory for context\n", __func__);
fclose(file);
return NULL;
}
// read the header
{
@ -22257,9 +22268,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
{
const uint64_t n_kv = ctx->header.n_kv;
// header.n_kv will hold the actual value of pairs that were successfully read in the loop below
ctx->header.n_kv = 0;
ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv));
ctx->kv = calloc(n_kv, sizeof(struct gguf_kv));
if (!ctx->kv) {
fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
for (uint64_t i = 0; i < n_kv; ++i) {
struct gguf_kv * kv = &ctx->kv[i];
@ -22310,7 +22325,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
return NULL;
}
kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type));
if (!kv->value.arr.data) {
fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset);
} break;
@ -22324,24 +22345,36 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
return NULL;
}
kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str));
kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str));
if (!kv->value.arr.data) {
fprintf(stderr, "%s: failed to allocate memory for array\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset);
}
} break;
case GGUF_TYPE_ARRAY:
default: GGML_ABORT("invalid type");
default:
{
fprintf(stderr, "%s: invalid array type %d\n", __func__, kv->value.arr.type);
ok = false;
} break;
}
} break;
default: GGML_ABORT("invalid type");
default:
{
fprintf(stderr, "%s: invalid type %d\n", __func__, kv->type);
ok = false;
} break;
}
if (!ok) {
break;
}
ctx->header.n_kv++;
}
if (!ok) {
@ -22354,7 +22387,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
// read the tensor infos
if (ctx->header.n_tensors > 0) {
ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));
if (!ctx->infos) {
fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__);
fclose(file);
gguf_free(ctx);
return NULL;
}
for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
struct gguf_tensor_info * info = &ctx->infos[i];

57
ggml/src/llamafile/sgemm.cpp
View file

@ -942,6 +942,36 @@ class tinyBLAS_Q0_AVX {
return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)), _mm_set1_epi8(8));
}
inline __m256i load(const block_q5_0 *b) {
return _mm256_or_si256(denibble(b->qs), bittobyte(b->qh));
}
inline __m128i load0(const block_q5_0* b) {
const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs));
uint32_t x32;
memcpy(&x32, b->qh, sizeof(uint32_t));
__m128i qxl = _mm_and_si128(_mm_set1_epi8(15), x);
__m128i bytesl = _mm_cmpeq_epi8(_mm_set1_epi64x(-1),
_mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe),
_mm_shuffle_epi8(_mm_set1_epi32(x32),
_mm_set_epi64x(0x0101010101010101, 0x0000000000000000))));
bytesl = _mm_andnot_si128(bytesl, _mm_set1_epi8((char)0xF0));
return _mm_or_si128(qxl, bytesl);
}
inline __m128i load1(const block_q5_0* b) {
const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs));
uint32_t x32;
memcpy(&x32, b->qh, sizeof(uint32_t));
__m128i qxh = _mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4));
__m128i bytesh = _mm_cmpeq_epi8(_mm_set1_epi64x(-1),
_mm_or_si128(_mm_set1_epi64x(0x7fbfdfeff7fbfdfe),
_mm_shuffle_epi8(_mm_set1_epi32(x32),
_mm_set_epi64x(0x0303030303030303, 0x0202020202020202))));
bytesh = _mm_andnot_si128(bytesh, _mm_set1_epi8((char)0xF0));
return _mm_or_si128(qxh, bytesh);
}
inline __m256i load(const block_iq4_nl *b) {
return MM256_SET_M128I(load1(b), load0(b));
}
@ -973,6 +1003,17 @@ class tinyBLAS_Q0_AVX {
_mm_srli_epi16(x, 4), 1));
}
static inline __m256i bittobyte(const uint8_t *p) {
uint32_t x32;
memcpy(&x32, p, sizeof(uint32_t));
__m256i bytes = _mm256_cmpeq_epi8(_mm256_set1_epi64x(-1),
_mm256_or_si256(_mm256_set1_epi64x(0x7fbfdfeff7fbfdfe),
_mm256_shuffle_epi8(_mm256_set1_epi32(x32),
_mm256_set_epi64x(0x0303030303030303, 0x0202020202020202,
0x0101010101010101, 0x0000000000000000))));
return _mm256_andnot_si256(bytes, _mm256_set1_epi8((char)0xF0));
}
const TA *const A;
const TB *const B;
TC *const C;
@ -1182,6 +1223,22 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda
#endif
}
case GGML_TYPE_Q5_0: {
if (Btype != GGML_TYPE_Q8_0)
return false;
#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__)
tinyBLAS_Q0_AVX<block_q5_0, block_q8_0, float> tb{
k, (const block_q5_0 *)A, lda,
(const block_q8_0 *)B, ldb,
(float *)C, ldc,
ith, nth};
tb.matmul(m, n);
return true;
#else
return false;
#endif
}
case GGML_TYPE_IQ4_NL: {
if (Btype != GGML_TYPE_Q8_0)
return false;

74
ggml/src/vulkan-shaders/pool2d.comp Normal file
View file

@ -0,0 +1,74 @@
#version 450
#include "types.comp"
#extension GL_EXT_shader_16bit_storage : require
layout(push_constant) uniform parameter {
uint IW; uint IH;
uint OW; uint OH;
uint OC;
uint pelements;
uint op;
int k0; int k1;
int s0; int s1;
int p0; int p1;
} p;
#define BLOCK_SIZE 512
#define FLT_MAX 3.402823466e+38F
#define OP_POOL_MAX 0u
#define OP_POOL_AVG 1u
layout (local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
layout(binding = 0) readonly buffer X {A_TYPE data_a[];};
layout(binding = 1) writeonly buffer D {D_TYPE data_d[];};
void main() {
const uint idx = gl_GlobalInvocationID.x;
if (idx >= p.pelements) {
return;
}
const uint O_HW = p.OW * p.OH;
const uint nc = idx / O_HW;
const uint cur_oh = (idx % O_HW) / p.OW;
const uint cur_ow = (idx % O_HW) % p.OW;
const int start_h = int(cur_oh) * p.s0 - p.p0;
const uint bh = max(start_h, 0);
const uint eh = min(start_h + p.k0, p.IH);
const int start_w = int(cur_ow) * p.s1 - p.p1;
const uint bw = max(start_w, 0);
const uint ew = min(start_w + p.k1, p.IW);
const float scale = 1.0 / float(p.k0 * p.k1);
float res;
if (p.op == OP_POOL_AVG) {
res = 0.0;
} else if (p.op == OP_POOL_MAX) {
res = -FLT_MAX;
} else {
return;
}
#pragma unroll
for (uint i = bh; i < eh; i++) {
#pragma unroll
for (uint j = bw; j < ew; j++) {
const float cur = D_TYPE(data_a[nc * p.IH * p.IW + i * p.IW + j]);
if (p.op == OP_POOL_AVG) {
res += cur * scale;
} else if (p.op == OP_POOL_MAX) {
res = max(res, cur);
}
}
}
data_d[nc * O_HW + cur_oh * p.OW + cur_ow] = res;
}

4
ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
View file

@ -493,6 +493,10 @@ void process_shaders(std::vector<std::future<void>>& tasks) {
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
tasks.push_back(std::async(std::launch::async, [=] {
string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
}));
}
void write_output_files() {

20
include/llama.h
View file

@ -205,7 +205,7 @@ extern "C" {
enum llama_split_mode {
LLAMA_SPLIT_MODE_NONE = 0, // single GPU
LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported
};
// TODO: simplify (https://github.com/ggerganov/llama.cpp/pull/9294#pullrequestreview-2286561979)
@ -274,10 +274,7 @@ extern "C" {
int32_t n_gpu_layers; // number of layers to store in VRAM
enum llama_split_mode split_mode; // how to split the model across multiple GPUs
// main_gpu interpretation depends on split_mode:
// LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model
// LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results
// LLAMA_SPLIT_MODE_LAYER: ignored
// the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
int32_t main_gpu;
// proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
@ -1089,9 +1086,6 @@ extern "C" {
/// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep);
/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
LLAMA_API struct llama_sampler * llama_sampler_init_tail_free (float z, size_t min_keep);
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
LLAMA_API struct llama_sampler * llama_sampler_init_typical (float p, size_t min_keep);
@ -1145,6 +1139,16 @@ extern "C" {
bool penalize_nl, // consider newlines as a repeatable token
bool ignore_eos); // ignore the end-of-sequence token
/// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
LLAMA_API struct llama_sampler * llama_sampler_init_dry(
const struct llama_model * model,
float dry_multiplier,
float dry_base,
int32_t dry_allowed_length,
int32_t dry_penalty_last_n,
const char ** seq_breakers,
size_t num_breakers);
LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias(
int32_t n_vocab,
int32_t n_logit_bias,

2
scripts/compare-llama-bench.py
View file

@ -20,7 +20,7 @@ logger = logging.getLogger("compare-llama-bench")
# Properties by which to differentiate results per commit:
KEY_PROPERTIES = [
"cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas",
"blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads",
"blas", "model_filename", "model_type", "n_batch", "n_ubatch", "embeddings", "n_threads",
"type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen"
]

2
scripts/run-with-preset.py
View file

@ -20,7 +20,7 @@ CLI_ARGS_LLAMA_CLI_PERPLEXITY = [
"np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
"prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n",
"repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
"simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
"simple-io", "tensor-split", "threads", "temp", "top-k", "top-p", "typical",
"verbose-prompt"
]

7
scripts/sync-ggml-am.sh
View file

@ -76,6 +76,7 @@ while read c; do
src/ggml*.m \
src/ggml*.metal \
src/ggml*.cu \
src/ggml-amx/* \
src/ggml-cann/* \
src/ggml-cuda/* \
src/ggml-sycl/* \
@ -121,6 +122,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
# src/ggml-aarch64.c -> ggml/src/ggml-aarch64.c
# src/ggml-aarch64.h -> ggml/src/ggml-aarch64.h
# src/ggml-alloc.c -> ggml/src/ggml-alloc.c
# src/ggml-amx/* -> ggml/src/ggml-amx/
# src/ggml-amx.cpp -> ggml/src/ggml-amx.cpp
# src/ggml-backend-impl.h -> ggml/src/ggml-backend-impl.h
# src/ggml-backend.cpp -> ggml/src/ggml-backend.cpp
# src/ggml-cann/* -> ggml/src/ggml-cann/
@ -141,6 +144,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
#
# include/ggml.h -> ggml/include/ggml.h
# include/ggml-alloc.h -> ggml/include/ggml-alloc.h
# include/ggml-amx.h -> ggml/include/ggml-amx.h
# include/ggml-backend.h -> ggml/include/ggml-backend.h
# include/ggml-blas.h -> ggml/include/ggml-blas.h
# include/ggml-cann.h -> ggml/include/ggml-cann.h
@ -168,6 +172,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
-e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.c/\1ggml\/src\/ggml-aarch64.c/g' \
-e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.h/\1ggml\/src\/ggml-aarch64.h/g' \
-e 's/([[:space:]]|[ab]\/)src\/ggml-alloc\.c/\1ggml\/src\/ggml-alloc.c/g' \
-e 's/([[:space:]]|[ab]\/)src\/ggml-amx\//\1ggml\/src\/ggml-amx\//g' \
-e 's/([[:space:]]|[ab]\/)src\/ggml-amx\.cpp/\1ggml\/src\/ggml-amx.cpp/g' \
-e 's/([[:space:]]|[ab]\/)src\/ggml-backend-impl\.h/\1ggml\/src\/ggml-backend-impl.h/g' \
-e 's/([[:space:]]|[ab]\/)src\/ggml-backend\.cpp/\1ggml\/src\/ggml-backend.cpp/g' \
-e 's/([[:space:]]|[ab]\/)src\/ggml-cann\//\1ggml\/src\/ggml-cann\//g' \
@ -187,6 +193,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
-e 's/([[:space:]]|[ab]\/)src\/vulkan-shaders\//\1ggml\/src\/vulkan-shaders\//g' \
-e 's/([[:space:]]|[ab]\/)include\/ggml\.h/\1ggml\/include\/ggml.h/g' \
-e 's/([[:space:]]|[ab]\/)include\/ggml-alloc\.h/\1ggml\/include\/ggml-alloc.h/g' \
-e 's/([[:space:]]|[ab]\/)include\/ggml-amx\.h/\1ggml\/include\/ggml-amx.h/g' \
-e 's/([[:space:]]|[ab]\/)include\/ggml-backend\.h/\1ggml\/include\/ggml-backend.h/g' \
-e 's/([[:space:]]|[ab]\/)include\/ggml-blas\.h/\1ggml\/include\/ggml-blas.h/g' \
-e 's/([[:space:]]|[ab]\/)include\/ggml-cann\.h/\1ggml\/include\/ggml-cann.h/g' \

2
scripts/sync-ggml.last
View file

@ -1 +1 @@
2327bda7a55ac6b72614ac5ebd5c5a5e02553b9b
162e232411ee98ceb0cccfa84886118d917d2123

3
scripts/sync-ggml.sh
View file

@ -8,6 +8,8 @@ cp -rpv ../ggml/src/ggml.c ./ggml/src/ggml.c
cp -rpv ../ggml/src/ggml-aarch64.c ./ggml/src/ggml-aarch64.c
cp -rpv ../ggml/src/ggml-aarch64.h ./ggml/src/ggml-aarch64.h
cp -rpv ../ggml/src/ggml-alloc.c ./ggml/src/ggml-alloc.c
cp -rpv ../ggml/src/ggml-amx/* ./ggml/src/ggml-amx/
cp -rpv ../ggml/src/ggml-amx.cpp ./ggml/src/ggml-amx.cpp
cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml/src/ggml-backend-impl.h
cp -rpv ../ggml/src/ggml-backend.cpp ./ggml/src/ggml-backend.cpp
cp -rpv ../ggml/src/ggml-cann/* ./ggml/src/ggml-cann/
@ -29,6 +31,7 @@ cp -rpv ../ggml/src/vulkan-shaders/* ./ggml/src/vulkan-shaders/
cp -rpv ../ggml/include/ggml.h ./ggml/include/ggml.h
cp -rpv ../ggml/include/ggml-alloc.h ./ggml/include/ggml-alloc.h
cp -rpv ../ggml/include/ggml-amx.h ./ggml/include/ggml-amx.h
cp -rpv ../ggml/include/ggml-backend.h ./ggml/include/ggml-backend.h
cp -rpv ../ggml/include/ggml-blas.h ./ggml/include/ggml-blas.h
cp -rpv ../ggml/include/ggml-cann.h ./ggml/include/ggml-cann.h

489
src/llama-sampling.cpp
View file

@ -113,7 +113,7 @@ static void llama_sampler_softmax_impl(llama_token_data_array * cur_p) {
}
static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k) {
// TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
// TODO: move bucket sort to separate function so that top_p/typical/softmax first is equally fast
// if (k >= (int32_t)cur_p->size) {
// return;
// }
@ -746,102 +746,6 @@ struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
};
}
// tail-free
struct llama_sampler_tail_free {
const float z;
const size_t min_keep;
};
static const char * llama_sampler_tail_free_name(const struct llama_sampler * /*smpl*/) {
return "tail-free";
}
static void llama_sampler_tail_free_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
const auto * ctx = (llama_sampler_tail_free *) smpl->ctx;
if (ctx->z >= 1.0f || cur_p->size <= 2) {
return;
}
llama_sampler_softmax_impl(cur_p);
// Compute the first and second derivatives
std::vector<float> first_derivatives(cur_p->size - 1);
std::vector<float> second_derivatives(cur_p->size - 2);
for (size_t i = 0; i < first_derivatives.size(); ++i) {
first_derivatives[i] = cur_p->data[i].p - cur_p->data[i + 1].p;
}
for (size_t i = 0; i < second_derivatives.size(); ++i) {
second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
}
// Calculate absolute value of second derivatives
for (size_t i = 0; i < second_derivatives.size(); ++i) {
second_derivatives[i] = std::abs(second_derivatives[i]);
}
// Normalize the second derivatives
{
const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
if (second_derivatives_sum > 1e-6f) {
for (float & value : second_derivatives) {
value /= second_derivatives_sum;
}
} else {
for (float & value : second_derivatives) {
value = 1.0f / second_derivatives.size();
}
}
}
float cum_sum = 0.0f;
size_t last_idx = cur_p->size;
for (size_t i = 0; i < second_derivatives.size(); ++i) {
cum_sum += second_derivatives[i];
// Check if the running sum is greater than z or if we have kept at least min_keep tokens
if (cum_sum > ctx->z && i >= ctx->min_keep) {
last_idx = i;
break;
}
}
// Resize the output vector to keep only the tokens above the tail location
cur_p->size = last_idx;
}
static struct llama_sampler * llama_sampler_tail_free_clone(const struct llama_sampler * smpl) {
const auto * ctx = (const llama_sampler_tail_free *) smpl->ctx;
return llama_sampler_init_tail_free(ctx->z, ctx->min_keep);
}
static void llama_sampler_tail_free_free(struct llama_sampler * smpl) {
delete (llama_sampler_tail_free *) smpl->ctx;
}
static struct llama_sampler_i llama_sampler_tail_free_i = {
/* .name = */ llama_sampler_tail_free_name,
/* .accept = */ nullptr,
/* .accept_str = */ nullptr,
/* .apply = */ llama_sampler_tail_free_apply,
/* .reset = */ nullptr,
/* .clone = */ llama_sampler_tail_free_clone,
/* .free = */ llama_sampler_tail_free_free,
};
struct llama_sampler * llama_sampler_init_tail_free(float z, size_t min_keep) {
return new llama_sampler {
/* .iface = */ &llama_sampler_tail_free_i,
/* .ctx = */ new llama_sampler_tail_free {
/* .z = */ z,
/*. min_keep = */ min_keep,
},
};
}
// typical
struct llama_sampler_typical {
@ -1717,6 +1621,397 @@ struct llama_sampler * llama_sampler_init_penalties(
};
}
// DRY
struct llama_sampler_dry {
int32_t total_context_size;
const float dry_multiplier;
const float dry_base;
const int32_t dry_allowed_length;
const int32_t dry_penalty_last_n;
std::unordered_multimap<llama_token, std::vector<llama_token>> dry_processed_breakers;
std::vector<int> dry_repeat_count;
std::unordered_map<llama_token, int> dry_max_token_repeat;
ring_buffer<llama_token> last_tokens;
};
// Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am)
static void get_overlapping_token_sequences(const llama_vocab & vocab, const std::string& str, std::unordered_multimap<llama_token, std::vector<llama_token>>& token_sequences, int max_tail_len = -1) {
for (llama_token token_id = 0; token_id < (llama_token)vocab.n_vocab; token_id++) {
std::string word = llama_detokenize(vocab, {token_id}, true);
if (word.find(str) != std::string::npos) {
token_sequences.emplace(token_id, std::vector<llama_token>());
} else {
size_t word_len = word.size(), str_len = str.size();
size_t pos = -1;
while ((pos = word.find(str[0], pos + 1)) != std::string::npos) {
bool match = true;
size_t i;
for (i = 1; i < str_len && i + pos < word_len; ++i) {
if (word[pos + i] != str[i]) {
match = false;
break;
}
}
if (match) {
std::vector<llama_token> tokenization = llama_tokenize_internal(vocab, str.substr(i), false, false);
if (max_tail_len >= 0 && tokenization.size() > (size_t)max_tail_len) {
tokenization.resize(max_tail_len);
}
// Ensure we don't already have a duplicate matching tokenization
auto its = token_sequences.equal_range(token_id);
bool found = false;
for (auto it = its.first; it != its.second; ++it) {
if (tokenization == it->second) {
found = true;
break;
}
}
if (!found) {
token_sequences.emplace(token_id, tokenization);
}
}
}
}
}
}
static const char * llama_sampler_dry_name(const struct llama_sampler * /*smpl*/) {
return "dry";
}
static void llama_sampler_dry_accept(struct llama_sampler * smpl, llama_token token) {
auto * ctx = (llama_sampler_dry *) smpl->ctx;
if (ctx->dry_multiplier == 0.0f || ctx->dry_base < 1.0f || ctx->dry_penalty_last_n == 0) {
return;
}
ctx->last_tokens.push_back(token);
}
// Ported from Koboldcpp, original PR: https://github.com/LostRuins/koboldcpp/pull/982 (Original author: pi6am)
static void llama_sampler_dry_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (llama_sampler_dry *) smpl->ctx;
if (ctx->dry_multiplier == 0.0f || ctx->dry_base < 1.0f || ctx->dry_penalty_last_n == 0) {
return;
}
int32_t effective_dry_penalty_last_n = (ctx->dry_penalty_last_n == -1) ? ctx->total_context_size : std::max(ctx->dry_penalty_last_n, 0);
int last_n_repeat = std::min(std::min((int)ctx->last_tokens.size(), effective_dry_penalty_last_n), ctx->total_context_size);
if (last_n_repeat <= ctx->dry_allowed_length) {
return;
}
ctx->dry_repeat_count.assign(last_n_repeat, 0);
ctx->dry_max_token_repeat.clear();
// Step 1: Look for restart sequences to limit the maximum repetition length.
// Work backwards through the context looking for any token that begins a restart sequence.
//
// The collection `restart_sequences` is a mapping from a "head" token to all "tail"
// sequences that together comprise a restart sequence. This allows us to quickly check
// whether each token is the head of a complete sequence. Most restart sequences are actually
// a single token, and for these the "tail" is an empty vector.
//
// If the token is a "head", test all restart sequences that begin with this token
// (there will often only be one sequence for each token, but if sequences like 'aaaq1' and
// 'aaa1' are used as restart strings, both could start with 'aaa' when tokenized). The
// longest matching sequence (if any) is used to limit the maximum repetition length.
//
// Note that in the case case of a short sequence contained in a longer one, this might fail to
// find the smallest value for `rep_limit`. For example, if 'amniotic' and 'ni' are both used as
// restart sequences, 'ni' will be found first, and since it's shorter it will fail to suppress
// 'otic'. This is a minor issue since fully contained restart sequences are likely to be rare.
//
// This is theoretically worst-case O(N^2) for arbitrary restart sequences, which is why we
// have already clamped the maximum tail sequence length when generating `restart_sequences`.
// With clamping, this scan is O(N) in the context length.
int rep_limit = last_n_repeat;
for (int i = 0; i < last_n_repeat; ++i) {
llama_token token = ctx->last_tokens.rat(i);
auto its = ctx->dry_processed_breakers.equal_range(token);
if (its.first == ctx->dry_processed_breakers.end()) {
continue;
}
int longest_match = -1;
for (auto it = its.first; it != its.second; ++it) {
// Note that (*it) does not contain the head character, so seq_len will be
// the restart sequence length minus 1.
// In the common case of a single-token restart sequence, (*it) will be empty
// and we will trivially match.
int seq_len = (int)it->second.size();
if (seq_len > longest_match && seq_len <= (int)i) {
bool match = true;
for (int offset = 0; offset < seq_len; ++offset) {
// The -1 when indexing `last_tokens` is because we already matched the head.
if (it->second[offset] != ctx->last_tokens.rat(i - offset - 1)) {
match = false;
break;
}
}
if (match) {
longest_match = seq_len;
}
}
}
if (longest_match >= 0) {
// We found a restart sequence starting `i` tokens from the end and continuing for
// `longest_match` tokens.
rep_limit = i - longest_match;
break;
}
}
if (rep_limit < ctx->dry_allowed_length) {
return;
}
// Step 2: Iterate in reverse over the last N tokens of the context, using the "Z-algorithm" (in
// the reverse direction) to efficiently compute the positions and lengths of suffixes appearing
// elsewhere in the context. We limit the suffix length to `rep_limit` to respect restart sequences.
//
// This algorithm is not currently documented on Wikipedia, but there is a clear description here:
// https://ivanyu.me/blog/2014/10/15/z-algorithm/
//
// The code below is adapted from the public domain implementation by the same author here:
// https://github.com/ivanyu/string-algorithms/blob/master/z_algorithm.py
//
// Example:
// Last N tokens: a b c c b c y a b c
// Repeat counts: 0 0 3 1 0 2 0 0 0 0
// ^
// This `3` means that the last three tokens of the context (a b c) also appear here.
//
// This step is worst case O(N) since the Z-algorithm is linear, despite the appearance of nested
// for/while loops. This can be seen by observing that the `lt` and `rt` bounds are set after each
// repeated suffix is detected (i.e. after each while loop when n > 0). These bound variables
// ensure that the inner while loops only examine each token in the context once as the outer
// for loop iterates over the context.
{
const int last = last_n_repeat - 1;
int rt = 0, lt = 0;
for (int k = 1; k < last_n_repeat; ++k) {
if (k > rt) {
// If k is outside the current Z-box, do naive computation.
int n = 0;
while (n + k < last_n_repeat && ctx->last_tokens.rat(n) == ctx->last_tokens.rat(n+k)) {
++n;
}
ctx->dry_repeat_count[last - k] = std::min(n, rep_limit);
if (n > 0) {
lt = k;
rt = k+n-1;
}
} else {
// If k is inside the current Z-box, consider two cases.
int p = k - lt; // Pair index.
int right_part_len = rt - k + 1;
if (ctx->dry_repeat_count[last - p] < right_part_len) {
int n = std::min(ctx->dry_repeat_count[last - p], rep_limit);
ctx->dry_repeat_count[last - k] = n;
} else {
int i = rt + 1;
while (i < last_n_repeat && ctx->last_tokens.rat(i) == ctx->last_tokens.rat(i - k)) {
i += 1;
}
int n = std::min(i - k, rep_limit);
ctx->dry_repeat_count[last - k] = n;
lt = k;
rt = i - 1;
}
}
}
}
// Step 3: Iterate over dry_repeat_count and last_tokens, examining the maximum repeat length
// that would be generated by emitting each new token that would extend a sequence.
//
// Following the same example as above:
// Last N tokens: a b c c b c y a b c
// Repeat counts: 0 0 3 1 0 2 0 0 0 0
//
// For each non-zero, look ahead one token. This token, if emitted, would extend the repetition.
// c: 3 -> 4 (from `a b c` to `a b c c`)
// b: 1 -> 2 (from `c` to `c b`)
// y: 2 -> 3 (from `b c` to `b c y`)
for (int i = 0; i < last_n_repeat - 1; ++i) {
int repeat_len = ctx->dry_repeat_count[i];
if (repeat_len >= ctx->dry_allowed_length) {
// This token ends a repeat, so the next token would continue one.
// By convention, the value of `repeat_len` only includes the tokens currently
// in the context, not the new token that would be added.
llama_token token = ctx->last_tokens.rat(last_n_repeat - 2 - i);
// Track the maximum sequence ending in this token.
const auto& it = ctx->dry_max_token_repeat.find(token);
if (it == ctx->dry_max_token_repeat.end() || it->second < repeat_len) {
ctx->dry_max_token_repeat[token] = repeat_len;
}
}
}
// Step 4: Apply logit penalties based on the maximum repeat length for relevant tokens.
// Prevent floating point overflow in `pow(penalty_base, exponent)` by clamping to `max_exponent`.
// Compute it from `penalty_base` and the approximate log of `std::numeric_limits<float>::max()`
const float FLOAT_MAX_LOG = 88.7228391f;
int max_exponent = 0;
if (ctx->dry_base > 1.000001f) {
max_exponent = FLOAT_MAX_LOG / std::log(ctx->dry_base);
}
for (size_t i = 0; i < cur_p->size; ++i) {
const auto& af_kvp = ctx->dry_max_token_repeat.find(cur_p->data[i].id);
if (af_kvp != ctx->dry_max_token_repeat.end()) {
// Check all sequence breakers starting with this token
auto range = ctx->dry_processed_breakers.equal_range(cur_p->data[i].id);
bool is_single_token_breaker = false;
for (auto it = range.first; it != range.second; ++it) {
if (it->second.empty()) {
is_single_token_breaker = true;
break;
}
}
// Apply penalty only if it's not a single-token sequence breaker
if (!is_single_token_breaker) {
int repeat_exp = af_kvp->second - ctx->dry_allowed_length;
if (max_exponent > 0 && repeat_exp > max_exponent) {
repeat_exp = max_exponent;
}
float penalty = ctx->dry_multiplier * std::pow(ctx->dry_base, repeat_exp);
cur_p->data[i].logit -= penalty;
}
}
}
cur_p->sorted = false;
}
static void llama_sampler_dry_reset(struct llama_sampler * smpl) {
auto * ctx = (llama_sampler_dry *) smpl->ctx;
ctx->last_tokens.clear();
ctx->dry_repeat_count.clear();
ctx->dry_max_token_repeat.clear();
}
static struct llama_sampler * llama_sampler_dry_clone(const struct llama_sampler * smpl) {
const auto * ctx = (llama_sampler_dry *) smpl->ctx;
// nullptr is passed as vocab because it is only needed for raw sequence breaker processing, which we have already done and will be copying
auto * result = llama_sampler_init_dry(nullptr, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0);
// Copy the state, including the processed breakers
{
auto * result_ctx = (llama_sampler_dry *) result->ctx;
result_ctx->dry_processed_breakers = ctx->dry_processed_breakers;
result_ctx->dry_repeat_count = ctx->dry_repeat_count;
result_ctx->dry_max_token_repeat = ctx->dry_max_token_repeat;
result_ctx->last_tokens = ctx->last_tokens;
}
return result;
}
static void llama_sampler_dry_free(struct llama_sampler * smpl) {
delete (llama_sampler_dry *) smpl->ctx;
}
static struct llama_sampler_i llama_sampler_dry_i = {
/* .name = */ llama_sampler_dry_name,
/* .accept = */ llama_sampler_dry_accept,
/* .apply = */ llama_sampler_dry_apply,
/* .reset = */ llama_sampler_dry_reset,
/* .clone = */ llama_sampler_dry_clone,
/* .free = */ llama_sampler_dry_free,
};
struct llama_sampler * llama_sampler_init_dry_impl(const struct llama_vocab & vocab, int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
int32_t effective_dry_penalty_last_n = (dry_penalty_last_n == -1) ? context_size : std::max(dry_penalty_last_n, 0);
std::unordered_multimap<llama_token, std::vector<llama_token>> processed_breakers;
const int MAX_CHAR_LEN = 40;
const int MAX_SEQ_LEN = 20;
const bool dry_enabled = (dry_multiplier != 0.0f && dry_base >= 1.0f && dry_penalty_last_n != 0);
if (dry_enabled && seq_breakers != nullptr && num_breakers > 0) {
// Process sequence breakers
for (size_t i = 0; i < num_breakers; ++i) {
if (seq_breakers[i] == nullptr || std::strlen(seq_breakers[i]) == 0) {
LLAMA_LOG_WARN("skipping null or empty DRY sequence breaker at index %zu\n", i);
continue;
}
std::string sequence_break(seq_breakers[i]);
if (sequence_break.empty()) {
LLAMA_LOG_WARN("skipping empty DRY sequence breaker\n");
continue;
}
if (sequence_break.size() > MAX_CHAR_LEN) {
LLAMA_LOG_WARN("truncating DRY sequence breaker to %d characters\n", MAX_CHAR_LEN);
sequence_break.resize(MAX_CHAR_LEN);
}
get_overlapping_token_sequences(vocab, sequence_break, processed_breakers, MAX_SEQ_LEN);
}
}
return new llama_sampler {
/* .iface = */ &llama_sampler_dry_i,
/* .ctx = */ new llama_sampler_dry {
/* .total_context_size = */ context_size,
/* .dry_multiplier = */ dry_multiplier,
/* .dry_base = */ dry_base,
/* .dry_allowed_length = */ dry_allowed_length,
/* .dry_penalty_last_n = */ dry_penalty_last_n,
/* .dry_processed_breakers = */ std::move(processed_breakers),
/* .dry_repeat_count = */ dry_enabled ? std::vector<int>(effective_dry_penalty_last_n, 0) : std::vector<int>{},
/* .dry_max_token_repeat = */ {},
/* .last_tokens = */ dry_enabled ? ring_buffer<llama_token>(effective_dry_penalty_last_n) : ring_buffer<llama_token>(0),
},
};
}
// wrapper for test-sampling.cpp
struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers) {
llama_vocab dummy_vocab;
auto * result = llama_sampler_init_dry_impl(dummy_vocab, context_size, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, NULL, 0);
auto * ctx = (llama_sampler_dry *) result->ctx;
// Process the token-based sequence breakers
ctx->dry_processed_breakers.clear();
if (seq_breakers.empty()) {
LLAMA_LOG_WARN("empty DRY sequence breakers list in llama_sampler_init_dry_testing\n");
} else {
for (const auto& breaker : seq_breakers) {
if (breaker.empty()) {
LLAMA_LOG_WARN("skipping DRY empty sequence breaker\n");
continue;
}
llama_token head_token = breaker[0];
std::vector<llama_token> tail_tokens(breaker.begin() + 1, breaker.end());
ctx->dry_processed_breakers.emplace(head_token, std::move(tail_tokens));
}
if (ctx->dry_processed_breakers.empty()) {
LLAMA_LOG_WARN("no valid DRY sequence breakers processed in llama_sampler_init_dry_testing\n");
}
}
return result;
}
// logit-bias
struct llama_sampler_logit_bias {

18
src/llama-sampling.h
View file

@ -30,3 +30,21 @@ bool llama_sampler_is_grammar_empty_impl(struct llama_sampler * gsmpl);
struct llama_sampler * llama_sampler_init_infill_impl(
const struct llama_vocab & vocab);
struct llama_sampler * llama_sampler_init_dry_impl(
const struct llama_vocab & vocab,
int32_t context_size,
float dry_multiplier,
float dry_base,
int32_t dry_allowed_length,
int32_t dry_penalty_last_n,
const char ** seq_breakers,
size_t num_breakers);
struct llama_sampler * llama_sampler_init_dry_testing(
int32_t context_size,
float dry_multiplier,
float dry_base,
int32_t dry_allowed_length,
int32_t dry_penalty_last_n,
const std::vector<std::vector<llama_token>>& seq_breakers);

16
src/llama-vocab.cpp
View file

@ -1966,3 +1966,19 @@ int32_t llama_detokenize_impl(
return total <= text_len_max ? total : -total;
}
std::string llama_detokenize(const struct llama_vocab & vocab, const std::vector<llama_token> & tokens, bool special) {
std::string text;
text.resize(std::max(text.capacity(), tokens.size()));
int32_t n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
if (n_chars < 0) {
text.resize(-n_chars);
n_chars = llama_detokenize_impl(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization
}
text.resize(n_chars);
// NOTE: the original tokenizer decodes bytes after collecting the pieces.
return text;
}

5
src/llama-vocab.h
View file

@ -163,3 +163,8 @@ int32_t llama_detokenize_impl(
int32_t text_len_max,
bool remove_special,
bool unparse_special);
std::string llama_detokenize(
const struct llama_vocab & vocab,
const std::vector<llama_token> & tokens,
bool special);

3127
src/llama.cpp
View file

File diff suppressed because it is too large Load diff

122
tests/test-backend-ops.cpp
View file

@ -1650,11 +1650,12 @@ struct test_mul_mat : public test_case {
const int64_t m;
const int64_t n;
const int64_t k;
const std::array<int64_t, 2> bs; // dims 3 and 4
const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
const std::array<int64_t, 2> bs; // dims 3 and 4
const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
const std::array<int64_t, 4> per; // permutation of dimensions
std::string vars() override {
return VARS_TO_STR7(type_a, type_b, m, n, k, bs, nr);
return VARS_TO_STR8(type_a, type_b, m, n, k, bs, nr, per);
}
double max_nmse_err() override {
@ -1669,17 +1670,44 @@ struct test_mul_mat : public test_case {
test_mul_mat(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
int64_t m = 32, int64_t n = 32, int64_t k = 32,
std::array<int64_t, 2> bs = {10, 10},
std::array<int64_t, 2> nr = {2, 2})
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr) {}
std::array<int64_t, 2> nr = {2, 2},
std::array<int64_t, 4> per = {0, 1, 2, 3})
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), nr(nr), per(per) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0] , bs[1]);
ggml_tensor * b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
ggml_set_param(ctx, a);
ggml_set_param(ctx, b);
ggml_set_name(a, "a");
ggml_set_name(b, "b");
ggml_tensor * a;
ggml_tensor * b;
const int npermuted = (per[0] != 0) + (per[1] != 1) + (per[2] != 2) + (per[3] != 3);
if (npermuted > 0) {
GGML_ASSERT(npermuted == 2);
GGML_ASSERT(!ggml_is_quantized(type_a) || per[0] == 0);
GGML_ASSERT(!ggml_is_quantized(type_b) || per[0] == 0);
// Create tensors with the permuted dimensions, then permute them back to the dimensions given by m,n,k.
const int64_t ne_a[4] = {k, m, bs[0], bs[1]};
const int64_t ne_b[4] = {k, n, bs[0]*nr[0], bs[1]*nr[1]};
a = ggml_new_tensor_4d(ctx, type_a, ne_a[per[0]], ne_a[per[1]], ne_a[per[2]], ne_a[per[3]]);
b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]);
ggml_set_param(ctx, a);
ggml_set_param(ctx, b);
ggml_set_name(a, "a");
ggml_set_name(b, "b");
a = ggml_permute(ctx, a, per[0], per[1], per[2], per[3]);
b = ggml_permute(ctx, b, per[0], per[1], per[2], per[3]);
ggml_set_name(a, "a_permuted");
ggml_set_name(b, "b_permuted");
} else {
a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]);
b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
ggml_set_param(ctx, a);
ggml_set_param(ctx, b);
ggml_set_name(a, "a");
ggml_set_name(b, "b");
}
ggml_tensor * out = ggml_mul_mat(ctx, a, b);
ggml_set_name(out, "out");
@ -3308,13 +3336,49 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
}
}
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
// test cases for 1D im2col
// im2col 1D
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {3000, 128, 1, 1}, {3, 128, 1280, 1}, 1, 0, 1, 0, 1, 0, false));
for (int s0 : {1, 3}) {
for (int p0 : {0, 3}) {
for (int d0 : {1, 3}) {
test_cases.emplace_back(new test_im2col(
GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {20, 2, 2, 1}, {3, 2, 2, 1},
s0, 0, p0, 0, d0, 0, false));
}
}
}
// im2col 2D
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
for (int s0 : {1, 3}) {
for (int s1 : {1, 3}) {
for (int p0 : {0, 3}) {
for (int p1 : {0, 3}) {
for (int d0 : {1, 3}) {
for (int d1 : {1, 3}) {
test_cases.emplace_back(new test_im2col(
GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32, {20, 20, 2, 2}, {3, 3, 2, 2},
s0, s1, p0, p1, d0, d1, true));
}
}
}
}
}
}
// extra tests for im2col 2D
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 32}, {3, 3, 1, 32}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 32}, {3, 3, 2, 32}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 1024}, {3, 3, 1, 1024}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 1024}, {3, 3, 2, 1024}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 2048}, {3, 3, 1, 2048}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 2048}, {3, 3, 2, 2048}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 1, 2560}, {3, 3, 1, 2560}, 1, 1, 1, 1, 1, 1, true));
test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {12, 12, 2, 2560}, {3, 3, 2, 2560}, 1, 1, 1, 1, 1, 1, true));
// sycl backend will limit task global_range < MAX_INT
// test cases for 2D im2col with large input W and H (occurs in stable-diffusion)
@ -3442,13 +3506,14 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
#if 1
for (ggml_type type_a : base_types) {
for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2}));
// test cases without permutation
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {1, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 10}, {2, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, { 1, 1}, {1, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 1}, {1, 1}));
@ -3457,6 +3522,19 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {1, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
// test cases with permutation
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 1, 3, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {2, 3}, {1, 1}, {0, 3, 2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 1, 3, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 8, 256, {2, 3}, {1, 1}, {0, 3, 2, 1}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 2, 1, 3}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 1, 3, 2}));
test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {2, 3}, {1, 1}, {0, 3, 2, 1}));
}
}
for (ggml_type type_a : other_types) {

270
tests/test-chat-template.cpp
View file

@ -128,13 +128,7 @@ static void test_jinja_templates() {
}
static void test_legacy_templates() {
struct test_template {
std::string name;
std::string tmpl;
std::string expected_output;
};
std::vector<llama_chat_message> conversation {
llama_chat_message conversation[] = {
{"system", "You are a helpful assistant"},
{"user", "Hello"},
{"assistant", "Hi there"},
@ -142,161 +136,131 @@ static void test_legacy_templates() {
{"assistant", " I am an assistant "},
{"user", "Another question"},
};
std::vector<test_template> templates {
{
"teknium/OpenHermes-2.5-Mistral-7B",
"{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
"<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
},
{
"mistralai/Mistral-7B-Instruct-v0.2",
"{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
"[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
},
{
"TheBloke/FusionNet_34Bx2_MoE-AWQ",
"{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}",
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s><s>[INST] Who are you [/INST] I am an assistant </s><s>[INST] Another question [/INST]",
},
{
"bofenghuang/vigogne-2-70b-chat",
"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
},
{
"mlabonne/AlphaMonarch-7B",
"{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
// TODO: should start w/ <s>
"system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n I am an assistant </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
},
{
"google/gemma-7b-it",
"{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
"<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
},
{
"OrionStarAI/Orion-14B-Chat",
"{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
// TODO: should start w/ <s>
"Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s> I am an assistant </s>Human: Another question\n\nAssistant: </s>",
},
{
// The included chat_template differs from the author's suggestions here: https://huggingface.co/openchat/openchat_3.5/discussions/5#65448109b4a3f3a2f486fd9d,
// So we match against the included template but implement the suggested version.
"openchat/openchat-3.5-0106",
"{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
"You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant: I am an assistant <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
},
{
"deepseek-ai/deepseek-coder-33b-instruct",
"{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
"You are a helpful assistant### Instruction:\nHello\n### Response:\nHi there\n<|EOT|>\n### Instruction:\nWho are you\n### Response:\n I am an assistant \n<|EOT|>\n### Instruction:\nAnother question\n### Response:\n",
},
{
// No template included in tokenizer_config.json, so this template likely needs to be manually set.,
"eachadea/vicuna-13b-1.1",
"{%- for message in messages %}{%- if message['role'] == 'system' -%}{{- '' + message['content'] + '\n\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
"You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT: I am an assistant </s>\nUSER: Another question\nASSISTANT:",
},
{
// No template included in tokenizer_config.json, so this template likely needs to be manually set.
"Orca-Vicuna",
"{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
"SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT: I am an assistant </s>\nUSER: Another question\nASSISTANT:",
},
{
"CohereForAI/c4ai-command-r-plus",
"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
"<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
},
{
"Llama-3",
"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
"<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
},
{
"Phi-3-mini",
"{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
},
{
"Phi-3-small",
"{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
},
{
"Phi-3-medium",
"{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
},
{
"Phi-3-vision",
"{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
},
{
"ChatGLM3",
"{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
"[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n I am an assistant <|user|>\n Another question<|assistant|>",
},
{
"ChatGLM4",
u8"[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
"[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>",
},
{
"MiniCPM-3B-OpenHermes-2.5-v2-GGUF",
u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
},
{
"DeepSeek-V2",
"{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:",
},
size_t message_count = 6;
std::vector<std::string> templates = {
// teknium/OpenHermes-2.5-Mistral-7B
"{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
// mistralai/Mistral-7B-Instruct-v0.2
"{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
// TheBloke/FusionNet_34Bx2_MoE-AWQ
"{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <<SYS>>\\\\n' + messages[idx]['content'] + '\\\\n<</SYS>>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}",
// bofenghuang/vigogne-2-70b-chat
"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
// mlabonne/AlphaMonarch-7B
"{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
// google/gemma-7b-it
"{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
// OrionStarAI/Orion-14B-Chat
"{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
// openchat/openchat-3.5-0106
// The included chat_template differs from the author's suggestions here: https://huggingface.co/openchat/openchat_3.5/discussions/5#65448109b4a3f3a2f486fd9d
// So we match against the included template but implement the suggested version.
"{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
// deepseek-ai/deepseek-coder-33b-instruct
"{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
// eachadea/vicuna-13b-1.1
// No template included in tokenizer_config.json, so this template likely needs to be manually set.
"{%- for message in messages %}{%- if message['role'] == 'system' -%}{{- '' + message['content'] + '\n\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
// Orca-Vicuna
// No template included in tokenizer_config.json, so this template likely needs to be manually set.
"{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
// CohereForAI/c4ai-command-r-plus
"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}",
// Llama-3
"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
//Phi-3-mini
"{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
//Phi-3-small
"{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
//Phi-3-medium
"{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
//Phi-3-vision
"{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
// ChatGLM3
"{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
// ChatGLM4
u8"[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n......{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
u8"{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
// DeepSeek-V2
"{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
// ibm-granite/granite-3.0-8b-instruct
"{%- if tools %}\n {{- '<|start_of_role|>available_tools<|end_of_role|>\n' }}\n {%- for tool in tools %}\n {{- tool | tojson(indent=4) }}\n {%- if not loop.last %}\n {{- '\n\n' }}\n {%- endif %}\n {%- endfor %}\n {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n {{- '<|start_of_role|>system<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'user' %}\n {{- '<|start_of_role|>user<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant' %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant_tool_call' %}\n {{- '<|start_of_role|>assistant<|end_of_role|><|tool_call|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'tool_response' %}\n {{- '<|start_of_role|>tool_response<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- endif %}\n {%- if loop.last and add_generation_prompt %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' }}\n {%- endif %}\n{%- endfor %}",
};
std::vector<std::string> expected_output = {
// teknium/OpenHermes-2.5-Mistral-7B
"<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
// mistralai/Mistral-7B-Instruct-v0.2
"[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
// TheBloke/FusionNet_34Bx2_MoE-AWQ
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s><s>[INST] Who are you [/INST] I am an assistant </s><s>[INST] Another question [/INST]",
// bofenghuang/vigogne-2-70b-chat
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
// mlabonne/AlphaMonarch-7B
"system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n I am an assistant </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
// google/gemma-7b-it
"<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
// OrionStarAI/Orion-14B-Chat
"Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s> I am an assistant </s>Human: Another question\n\nAssistant: </s>",
// openchat/openchat-3.5-0106
"You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant: I am an assistant <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
// deepseek-ai/deepseek-coder-33b-instruct
"You are a helpful assistant### Instruction:\nHello\n### Response:\nHi there\n<|EOT|>\n### Instruction:\nWho are you\n### Response:\n I am an assistant \n<|EOT|>\n### Instruction:\nAnother question\n### Response:\n",
// eachadea/vicuna-13b-1.1
"You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT: I am an assistant </s>\nUSER: Another question\nASSISTANT:",
// Orca-Vicuna
"SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT: I am an assistant </s>\nUSER: Another question\nASSISTANT:",
// CohereForAI/c4ai-command-r-plus
"<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
// Llama 3
"<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
//Phi-3-mini
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
//Phi-3-small
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
//Phi-3-medium
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
//Phi-3-vision
"<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
// ChatGLM3
"[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n I am an assistant <|user|>\n Another question<|assistant|>",
// ChatGLM4
"[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n I am an assistant <|user|>\nAnother question<|assistant|>",
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
u8"You are a helpful assistant<用户>Hello<AI>Hi there<用户>Who are you<AI>I am an assistant<用户>Another question<AI>",
// DeepSeek-V2
u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:",
// ibm-granite/granite-3.0-8b-instruct
"<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|> I am an assistant <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n",
};
std::vector<char> formatted_chat(1024);
int32_t res;
// test invalid chat template
res = llama_chat_apply_template(nullptr, "INVALID TEMPLATE", conversation.data(), conversation.size(), true, formatted_chat.data(), formatted_chat.size());
res = llama_chat_apply_template(nullptr, "INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size());
assert(res < 0);
for (auto use_jinja : std::vector<bool> { false, true }) {
printf("\n\n=== Using Jinja: %s ===\n\n", use_jinja ? "true" : "false");
for (const auto & tmpl : templates) {
printf("=== %s ===\n", tmpl.name.c_str());
formatted_chat.resize(1024);
res = llama_chat_apply_template(
nullptr,
tmpl.tmpl.c_str(),
conversation.data(),
conversation.size(),
true,
formatted_chat.data(),
formatted_chat.size()
);
if (res < 0) {
printf("Error: %d\n", res);
continue;
}
formatted_chat.resize(res);
std::string output(formatted_chat.data(), formatted_chat.size());
if (output != tmpl.expected_output) {
printf("# Failure!\n");
printf("Template: %s\n", tmpl.tmpl.c_str());
printf("Expected:\n");
printf("%s\n", tmpl.expected_output.c_str());
printf("-------------------------\n");
printf("Actual:\n");
printf("%s\n", output.c_str());
// assert(output == expected);
}
}
for (size_t i = 0; i < templates.size(); i++) {
std::string custom_template = templates[i];
std::string expected = expected_output[i];
formatted_chat.resize(1024);
res = llama_chat_apply_template(
nullptr,
custom_template.c_str(),
conversation,
message_count,
true,
formatted_chat.data(),
formatted_chat.size()
);
formatted_chat.resize(res);
std::string output(formatted_chat.data(), formatted_chat.size());
printf("%s\n", output.c_str());
printf("-------------------------\n");
assert(output == expected);
}
// test llama_chat_format_single for system message
printf("\n\n=== llama_chat_format_single (system message) ===\n\n");
std::vector<common_chat_msg> chat2;

58
tests/test-sampling.cpp
View file

@ -10,6 +10,8 @@
#include <string>
#include <vector>
extern struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers);
static void dump(const llama_token_data_array * cur_p) {
for (size_t i = 0; i < cur_p->size; i++) {
printf("%d: %f (%f)\n", cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
@ -103,16 +105,6 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
tester.check();
}
static void test_tfs(const std::vector<float> & probs, const std::vector<float> & probs_expected, float z) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_tail_free(z, 1));
DUMP(&tester.cur_p);
tester.check();
}
static void test_min_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
@ -167,6 +159,29 @@ static void test_penalties(
tester.check();
}
static void test_dry(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & expected_probs, float dry_multiplier, float dry_base,
int dry_allowed_length, int dry_penalty_last_n,
const std::vector<std::vector<llama_token>> & seq_breakers
) {
GGML_ASSERT(probs.size() == expected_probs.size());
sampler_tester tester(probs, expected_probs);
auto * sampler = llama_sampler_init_dry_testing(1024, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers);
for (size_t i = 0; i < last_tokens.size(); i++) {
llama_sampler_accept(sampler, last_tokens[i]);
}
DUMP(&tester.cur_p);
tester.apply(sampler);
tester.apply(llama_sampler_init_dist(0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p
) {
sampler_tester tester(n_vocab);
@ -177,7 +192,6 @@ static void test_sampler_queue(const size_t n_vocab, const std::string & sampler
for (auto s : samplers_sequence) {
switch (s){
case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break;
case 'f': GGML_ABORT("tail_free test not implemented");
case 'y': GGML_ABORT("typical test not implemented");
case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break;
case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); break;
@ -274,12 +288,11 @@ static void test_perf() {
data.emplace_back(llama_token_data{i, logit, 0.0f});
}
BENCH(llama_sampler_init_top_k (40), data, 32);
BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32);
BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32);
BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32);
BENCH(llama_sampler_init_typical (0.5f, 1), data, 32);
BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32);
BENCH(llama_sampler_init_top_k (40), data, 32);
BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32);
BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32);
BENCH(llama_sampler_init_typical(0.5f, 1), data, 32);
BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32);
}
int main(void) {
@ -318,10 +331,6 @@ int main(void) {
printf("XTC should not:\n");
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.99f, 0.39f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
@ -333,6 +342,13 @@ int main(void) {
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f);
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1}, {0.25f, 0.25f, 0.25f, 0.25f}, 1.0f, 1.1f, 2, 4, {});
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.296923f, 0.109232f}, 1.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 2, 6, {{3}});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.241818f, 0.241818f, 0.032727f}, 2.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {});
test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
test_sampler_queue(10000, "k", 1, 1.0f, 1.0f);
test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f);