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Unverified Commit 49a9c9ba authored by Daniel Hiltgen's avatar Daniel Hiltgen Committed by GitHub
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GGML update to ec98e2002 (#13451) 

* Revert "add support for NVIDIA Nemotron 3 Nano"

This reverts commit e7d2ae9d69421012e9a8765c06a3fdf0e45b12f3.

* GGML update to 380b4c984

Remove MaskBatchPadding as GGML_KQ_MASK_PAD is no longer present (no
padding required)

* update to c45f89d55

* ec98e2002

solar pro needed more adjusting - needs verification

* review comments
parent 1c094038
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Showing with 2561 additions and 2550 deletions
Makefile.sync
View file @ 49a9c9ba
UPSTREAM=https://github.com/ggml-org/llama.cpp.git
WORKDIR=llama/vendor
FETCH_HEAD=17f7f4baad8b3a716ee139da7bb56ae984e8c0fa
FETCH_HEAD=ec98e2002
.PHONY: help
help:
......
kvcache/causal.go
View file @ 49a9c9ba
......@@ -140,10 +140,6 @@ func (c *Causal) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity
c.config.CachePadding = 1
}
if c.config.MaskBatchPadding == 0 {
c.config.MaskBatchPadding = 1
}
if c.config.MaskDType == ml.DTypeOther {
c.config.MaskDType = ml.DTypeF32
}
......@@ -364,15 +360,12 @@ func roundUp(length, pad int) int {
// token in the history should apply. This is based on both the sequence and causality (the
// position of the history is not ahead of the token in the batch).
func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
// Align and pad the two dimensions as required by the backend
batchSize := roundUp(c.curBatchSize, c.config.MaskBatchPadding)
c.curCellRange.min = roundDown(c.curCellRange.min, c.config.CachePadding)
c.curCellRange.max = roundUp(c.curCellRange.max+1, c.config.CachePadding) - 1
length := c.curCellRange.max - c.curCellRange.min + 1
mask := make([]float32, batchSize*length)
mask := make([]float32, c.curBatchSize*length)
for i := range c.curBatchSize {
enabled := !slices.Contains(c.opts.Except, i)
......@@ -386,13 +379,7 @@ func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
}
}
// Mask out any padding tokens we added. For padding that we added to the cache history, this
// has already been masked out because the sequence doesn't match.
for i := c.curBatchSize * length; i < len(mask); i++ {
mask[i] = float32(math.Inf(-1))
}
maskTensor := ctx.Input().FromFloats(mask, length, batchSize)
maskTensor := ctx.Input().FromFloats(mask, length, c.curBatchSize)
if c.config.MaskDType != ml.DTypeF32 {
maskTensor = maskTensor.Cast(ctx, c.config.MaskDType)
......
llama/build-info.cpp
View file @ 49a9c9ba
int LLAMA_BUILD_NUMBER = 0;
char const *LLAMA_COMMIT = "17f7f4baad8b3a716ee139da7bb56ae984e8c0fa";
char const *LLAMA_COMMIT = "ec98e2002";
char const *LLAMA_COMPILER = "";
char const *LLAMA_BUILD_TARGET = "";
llama/llama.cpp/.rsync-filter
View file @ 49a9c9ba
......@@ -17,6 +17,9 @@ include /tools/mtmd/clip.cpp
include /tools/mtmd/mtmd.cpp
include /tools/mtmd/mtmd-audio.cpp
include /tools/mtmd/mtmd-helper.cpp
include /tools/mtmd/models/
include /tools/mtmd/models/*.h
include /tools/mtmd/models/*.cpp
include /src/
include /src/llama.*
include /src/llama-*.*
......
llama/llama.cpp/common/common.cpp
View file @ 49a9c9ba
......@@ -1013,31 +1013,40 @@ bool tty_can_use_colors() {
// Model utils
//
static inline void common_init_sampler_from_model(
// TODO: move to common/sampling
static void common_init_sampler_from_model(
const llama_model * model,
common_params_sampling & sparams) {
const uint64_t config = sparams.user_sampling_config;
auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
if (config & user_config) return;
if (config & user_config) {
return;
}
char buf[64] = {0};
if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
char * end = nullptr;
int32_t v = strtol(buf, &end, 10);
if (end && end != buf) dst = v;
if (end && end != buf) {
dst = v;
}
}
};
auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
if (config & user_config) return;
if (config & user_config) {
return;
}
char buf[128] = {0};
if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
char * end = nullptr;
float v = strtof(buf, &end);
if (end && end != buf) dst = v;
if (end && end != buf) {
dst = v;
}
}
};
......@@ -1065,31 +1074,125 @@ static inline void common_init_sampler_from_model(
get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA), sparams.mirostat_eta, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
}
struct common_init_result common_init_from_params(common_params & params) {
common_init_result iparams;
struct common_init_result::impl {
impl() = default;
~impl() = default;
llama_model_ptr model;
llama_context_ptr context;
std::vector<llama_adapter_lora_ptr> lora;
std::vector<common_sampler_ptr> samplers;
};
common_init_result::common_init_result(common_params & params) :
pimpl(new impl{}) {
auto mparams = common_model_params_to_llama(params);
auto cparams = common_context_params_to_llama(params);
if (params.fit_params) {
LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__);
llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
}
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
__func__, params.model.path.c_str());
return iparams;
return;
}
common_init_sampler_from_model(model, params.sampling);
pimpl->model.reset(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
auto cparams = common_context_params_to_llama(params);
// updates params.sampling
// TODO: fix naming
common_init_sampler_from_model(model, params.sampling);
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sampling.ignore_eos = false;
}
// initialize once
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
if (llama_vocab_is_eog(vocab, i)) {
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
params.sampling.logit_bias_eog.push_back({i, -INFINITY});
}
}
if (params.sampling.ignore_eos) {
// add EOG biases to the active set of logit biases
params.sampling.logit_bias.insert(
params.sampling.logit_bias.end(),
params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
}
//if (params.sampling.penalty_last_n == -1) {
// LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
// params.sampling.penalty_last_n = llama_n_ctx(lctx);
//}
//if (params.sampling.dry_penalty_last_n == -1) {
// LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
// params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
//}
pimpl->samplers.resize(cparams.n_seq_max);
for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
}
llama_context * lctx = llama_init_from_model(model, cparams);
if (lctx == NULL) {
LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
__func__, params.model.path.c_str());
llama_model_free(model);
return iparams;
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
return;
}
pimpl->context.reset(lctx);
}
llama_model * common_init_result::model() {
return pimpl->model.get();
}
llama_context * common_init_result::context() {
return pimpl->context.get();
}
common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
return pimpl->samplers[seq_id].get();
}
std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
return pimpl->lora;
}
void common_init_result::free_context() {
pimpl->context.reset();
}
common_init_result_ptr common_init_from_params(common_params & params) {
common_init_result_ptr res(new common_init_result(params));
llama_model * model = res->model();
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
return res;
}
llama_context * lctx = res->context();
if (lctx == NULL) {
LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
return res;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
params.ctx_shift = false;
......@@ -1101,10 +1204,7 @@ struct common_init_result common_init_from_params(common_params & params) {
const auto cvec = common_control_vector_load(params.control_vectors);
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
int err = llama_apply_adapter_cvec(
......@@ -1115,10 +1215,7 @@ struct common_init_result common_init_from_params(common_params & params) {
params.control_vector_layer_start,
params.control_vector_layer_end);
if (err) {
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
}
......@@ -1142,10 +1239,7 @@ struct common_init_result common_init_from_params(common_params & params) {
}
if (!ok) {
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
}
......@@ -1155,9 +1249,7 @@ struct common_init_result common_init_from_params(common_params & params) {
lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
if (lora == nullptr) {
LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
llama_model_free(model);
return iparams;
return res;
}
char buf[1024];
......@@ -1166,43 +1258,13 @@ struct common_init_result common_init_from_params(common_params & params) {
la.task_name = buf;
llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
la.prompt_prefix = buf;
iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
common_set_adapter_lora(lctx, params.lora_adapters);
}
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sampling.ignore_eos = false;
}
// initialize once
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
if (llama_vocab_is_eog(vocab, i)) {
LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
params.sampling.logit_bias_eog.push_back({i, -INFINITY});
}
}
if (params.sampling.ignore_eos) {
// add EOG biases to the active set of logit biases
params.sampling.logit_bias.insert(
params.sampling.logit_bias.end(),
params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
}
if (params.sampling.penalty_last_n == -1) {
LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
params.sampling.penalty_last_n = llama_n_ctx(lctx);
}
if (params.sampling.dry_penalty_last_n == -1) {
LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
}
if (params.warmup) {
LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
......@@ -1241,12 +1303,11 @@ struct common_init_result common_init_from_params(common_params & params) {
llama_set_warmup(lctx, false);
}
iparams.model.reset(model);
iparams.context.reset(lctx);
return iparams;
return res;
}
common_init_result::~common_init_result() = default;
std::string get_model_endpoint() {
const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
// We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
......@@ -1255,7 +1316,9 @@ std::string get_model_endpoint() {
std::string model_endpoint = "https://huggingface.co/";
if (endpoint_env) {
model_endpoint = endpoint_env;
if (model_endpoint.back() != '/') model_endpoint += '/';
if (model_endpoint.back() != '/') {
model_endpoint += '/';
}
}
return model_endpoint;
}
......
llama/llama.cpp/common/common.h
View file @ 49a9c9ba
......@@ -82,7 +82,8 @@ int32_t cpu_get_num_math();
enum llama_example {
LLAMA_EXAMPLE_COMMON,
LLAMA_EXAMPLE_SPECULATIVE,
LLAMA_EXAMPLE_MAIN,
LLAMA_EXAMPLE_COMPLETION,
LLAMA_EXAMPLE_CLI,
LLAMA_EXAMPLE_EMBEDDING,
LLAMA_EXAMPLE_PERPLEXITY,
LLAMA_EXAMPLE_RETRIEVAL,
......@@ -98,6 +99,7 @@ enum llama_example {
LLAMA_EXAMPLE_TTS,
LLAMA_EXAMPLE_DIFFUSION,
LLAMA_EXAMPLE_FINETUNE,
LLAMA_EXAMPLE_FIT_PARAMS,
LLAMA_EXAMPLE_COUNT,
};
......@@ -194,7 +196,6 @@ struct common_params_sampling {
std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY
std::vector<enum common_sampler_type> samplers = {
COMMON_SAMPLER_TYPE_PENALTIES,
COMMON_SAMPLER_TYPE_DRY,
......@@ -215,6 +216,10 @@ struct common_params_sampling {
std::vector<llama_logit_bias> logit_bias; // logit biases to apply
std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
bool has_logit_bias() const {
return !logit_bias.empty();
}
// print the parameters into a string
std::string print() const;
};
......@@ -302,8 +307,8 @@ struct lr_opt {
struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata);
struct common_params {
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 4096; // context size
int32_t n_predict = -1; // max. number of new tokens to predict, -1 == no limit
int32_t n_ctx = 0; // context size, 0 == context the model was trained with
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
......@@ -324,9 +329,12 @@ struct common_params {
// offload params
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
bool fit_params = true; // whether to fit unset model/context parameters to free device memory
size_t fit_params_target = 1024 * 1024*1024; // margin per device in bytes for fitting parameters to free memory
int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
......@@ -406,6 +414,7 @@ struct common_params {
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
bool cont_batching = true; // insert new sequences for decoding on-the-fly
bool no_perf = false; // disable performance metrics
bool show_timings = true; // show timing information on CLI
bool ctx_shift = false; // context shift on infinite text generation
bool swa_full = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
bool kv_unified = false; // enable unified KV cache
......@@ -462,7 +471,7 @@ struct common_params {
std::string public_path = ""; // NOLINT
std::string api_prefix = ""; // NOLINT
std::string chat_template = ""; // NOLINT
bool use_jinja = false; // NOLINT
bool use_jinja = true; // NOLINT
bool enable_chat_template = true;
common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
int reasoning_budget = -1;
......@@ -482,9 +491,10 @@ struct common_params {
bool endpoint_metrics = false;
// router server configs
std::string models_dir = ""; // directory containing models for the router server
int models_max = 4; // maximum number of models to load simultaneously
bool models_autoload = true; // automatically load models when requested via the router server
std::string models_dir = ""; // directory containing models for the router server
std::string models_preset = ""; // directory containing model presets for the router server
int models_max = 4; // maximum number of models to load simultaneously
bool models_autoload = true; // automatically load models when requested via the router server
bool log_json = false;
......@@ -666,15 +676,29 @@ bool tty_can_use_colors();
// Model utils
//
// note: defines object's lifetime
struct common_sampler;
// note: defines the model, context, samplers, ets. lifetimes
struct common_init_result {
llama_model_ptr model;
llama_context_ptr context;
common_init_result(common_params & params);
~common_init_result();
std::vector<llama_adapter_lora_ptr> lora;
llama_model * model();
llama_context * context();
common_sampler * sampler(llama_seq_id seq_id);
std::vector<llama_adapter_lora_ptr> & lora();
void free_context();
private:
struct impl;
std::unique_ptr<impl> pimpl;
};
struct common_init_result common_init_from_params(common_params & params);
using common_init_result_ptr = std::unique_ptr<common_init_result>;
common_init_result_ptr common_init_from_params(common_params & params);
struct llama_model_params common_model_params_to_llama ( common_params & params);
struct llama_context_params common_context_params_to_llama(const common_params & params);
......
llama/llama.cpp/common/json-schema-to-grammar.cpp
View file @ 49a9c9ba
......@@ -305,8 +305,9 @@ static std::string format_literal(const std::string & literal) {
std::string gbnf_format_literal(const std::string & literal) { return format_literal(literal); }
class SchemaConverter {
class common_schema_converter {
private:
friend class common_schema_info;
friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
std::function<json(const std::string &)> _fetch_json;
bool _dotall;
......@@ -729,7 +730,7 @@ private:
}
public:
SchemaConverter(
common_schema_converter(
const std::function<json(const std::string &)> & fetch_json,
bool dotall)
: _fetch_json(fetch_json), _dotall(dotall)
......@@ -990,6 +991,134 @@ public:
}
};
// common_schema_info implementation (pimpl)
common_schema_info::common_schema_info()
: impl_(std::make_unique<common_schema_converter>(
[](const std::string &) { return json(); },
false)) {}
common_schema_info::~common_schema_info() = default;
common_schema_info::common_schema_info(common_schema_info &&) noexcept = default;
common_schema_info & common_schema_info::operator=(common_schema_info &&) noexcept = default;
void common_schema_info::resolve_refs(nlohmann::ordered_json & schema) {
impl_->resolve_refs(schema, "");
}
// Determines if a JSON schema can resolve to a string type through any path.
// Some models emit raw string values rather than JSON-encoded strings for string parameters.
// If any branch of the schema (via oneOf, anyOf, $ref, etc.) permits a string, this returns
// true, allowing callers to handle the value as a raw string for simplicity.
bool common_schema_info::resolves_to_string(const nlohmann::ordered_json & schema) {
std::unordered_set<std::string> visited_refs;
std::function<bool(const json &)> check = [&](const json & s) -> bool {
if (!s.is_object()) {
return false;
}
// Handle $ref
if (s.contains("$ref")) {
const std::string & ref = s["$ref"];
if (visited_refs.find(ref) != visited_refs.end()) {
// Circular reference, assume not a string to be safe
return false;
}
visited_refs.insert(ref);
auto it = impl_->_refs.find(ref);
if (it != impl_->_refs.end()) {
return check(it->second);
}
return false;
}
// Check type field
if (s.contains("type")) {
const json & schema_type = s["type"];
if (schema_type.is_string()) {
if (schema_type == "string") {
return true;
}
} else if (schema_type.is_array()) {
// Type can be an array like ["string", "null"]
for (const auto & t : schema_type) {
if (t == "string") {
return true;
}
}
}
}
// Check oneOf/anyOf - if any alternative can be a string
if (s.contains("oneOf")) {
for (const auto & alt : s["oneOf"]) {
if (check(alt)) {
return true;
}
}
}
if (s.contains("anyOf")) {
for (const auto & alt : s["anyOf"]) {
if (check(alt)) {
return true;
}
}
}
// Check allOf - all components must be compatible with string type
if (s.contains("allOf")) {
bool all_string = true;
for (const auto & component : s["allOf"]) {
if (!check(component)) {
all_string = false;
break;
}
}
if (all_string) {
return true;
}
}
// Check const - if the constant value is a string
if (s.contains("const")) {
if (s["const"].is_string()) {
return true;
}
}
// Check enum - if any enum value is a string
if (s.contains("enum")) {
for (const auto & val : s["enum"]) {
if (val.is_string()) {
return true;
}
}
}
// String-specific keywords imply string type
if (s.contains("pattern") || s.contains("minLength") || s.contains("maxLength")) {
return true;
}
// Check format - many formats imply string
if (s.contains("format")) {
const std::string & fmt = s["format"];
if (fmt == "date" || fmt == "time" || fmt == "date-time" ||
fmt == "uri" || fmt == "email" || fmt == "hostname" ||
fmt == "ipv4" || fmt == "ipv6" || fmt == "uuid" ||
fmt.find("uuid") == 0) {
return true;
}
}
return false;
};
return check(schema);
}
std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
#ifdef LLAMA_USE_LLGUIDANCE
if (!force_gbnf) {
......@@ -1006,7 +1135,7 @@ std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
}
std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options) {
SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall);
common_schema_converter converter([&](const std::string &) { return json(); }, options.dotall);
common_grammar_builder builder {
/* .add_rule = */ [&](const std::string & name, const std::string & rule) {
return converter._add_rule(name, rule);
......
llama/llama.cpp/common/json-schema-to-grammar.h
View file @ 49a9c9ba
......@@ -3,11 +3,31 @@
#include <nlohmann/json_fwd.hpp>
#include <functional>
#include <memory>
#include <string>
std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
bool force_gbnf = false);
class common_schema_converter;
// Probes a JSON schema to extract information about its structure and type constraints.
class common_schema_info {
std::unique_ptr<common_schema_converter> impl_;
public:
common_schema_info();
~common_schema_info();
common_schema_info(const common_schema_info &) = delete;
common_schema_info & operator=(const common_schema_info &) = delete;
common_schema_info(common_schema_info &&) noexcept;
common_schema_info & operator=(common_schema_info &&) noexcept;
void resolve_refs(nlohmann::ordered_json & schema);
bool resolves_to_string(const nlohmann::ordered_json & schema);
};
struct common_grammar_builder {
std::function<std::string(const std::string &, const std::string &)> add_rule;
std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
......
llama/llama.cpp/common/log.cpp
View file @ 49a9c9ba
......@@ -420,6 +420,11 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps) {
log->set_timestamps(timestamps);
}
void common_log_flush(struct common_log * log) {
log->pause();
log->resume();
}
static int common_get_verbosity(enum ggml_log_level level) {
switch (level) {
case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
......
llama/llama.cpp/common/log.h
View file @ 49a9c9ba
......@@ -84,6 +84,7 @@ void common_log_set_file (struct common_log * log, const char * file); // n
void common_log_set_colors (struct common_log * log, log_colors colors); // not thread-safe
void common_log_set_prefix (struct common_log * log, bool prefix); // whether to output prefix to each log
void common_log_set_timestamps(struct common_log * log, bool timestamps); // whether to output timestamps in the prefix
void common_log_flush (struct common_log * log); // flush all pending log messages
// helper macros for logging
// use these to avoid computing log arguments if the verbosity of the log is higher than the threshold
......
llama/llama.cpp/common/sampling.cpp
View file @ 49a9c9ba
......@@ -104,9 +104,10 @@ struct ring_buffer {
struct common_sampler {
common_params_sampling params;
struct llama_sampler * grmr;
struct llama_sampler * chain;
bool grammar;
ring_buffer<llama_token> prev;
std::vector<llama_token_data> cur;
......@@ -116,7 +117,6 @@ struct common_sampler {
void reset() {
prev.clear();
llama_sampler_reset(grmr);
llama_sampler_reset(chain);
}
......@@ -167,10 +167,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
lparams.no_perf = params.no_perf;
struct llama_sampler * grmr;
llama_sampler * chain = llama_sampler_chain_init(lparams);
bool grammar = false;
std::vector<llama_sampler *> samplers;
if (params.grammar.compare(0, 11, "%llguidance") == 0) {
#ifdef LLAMA_USE_LLGUIDANCE
grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
samplers.push_back(llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()));
grammar = true;
#else
GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
#endif // LLAMA_USE_LLGUIDANCE
......@@ -217,30 +222,23 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
trigger_patterns_c.push_back(regex.c_str());
}
grmr = params.grammar_lazy
? llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
trigger_patterns_c.data(), trigger_patterns_c.size(),
trigger_tokens.data(), trigger_tokens.size())
: llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
if (!grmr) {
return nullptr;
if (!params.grammar.empty()) {
if (params.grammar_lazy) {
samplers.push_back(
llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
trigger_patterns_c.data(), trigger_patterns_c.size(),
trigger_tokens.data(), trigger_tokens.size()));
} else {
samplers.push_back(llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"));
}
grammar = true;
}
}
auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ grmr,
/* .chain = */ llama_sampler_chain_init(lparams),
/* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
/* .cur = */ {},
/* .cur_p = */ {},
};
llama_sampler_chain_add(result->chain,
llama_sampler_init_logit_bias(
llama_vocab_n_tokens(vocab),
params.logit_bias.size(),
params.logit_bias.data()));
if (params.has_logit_bias()) {
samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data()));
}
if (params.mirostat == 0) {
for (const auto & cnstr : params.samplers) {
......@@ -253,58 +251,70 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
c_breakers.push_back(str.c_str());
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
samplers.push_back(llama_sampler_init_dry (vocab, llama_model_n_ctx_train(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));
samplers.push_back(llama_sampler_init_top_k (params.top_k));
break;
case COMMON_SAMPLER_TYPE_TOP_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep));
samplers.push_back(llama_sampler_init_top_p (params.top_p, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma));
samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
break;
case COMMON_SAMPLER_TYPE_MIN_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep));
samplers.push_back(llama_sampler_init_min_p (params.min_p, params.min_keep));
break;
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));
samplers.push_back(llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
break;
case COMMON_SAMPLER_TYPE_TYPICAL_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
samplers.push_back(llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_TEMPERATURE:
llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
samplers.push_back(llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
break;
case COMMON_SAMPLER_TYPE_INFILL:
llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab));
samplers.push_back(llama_sampler_init_infill (vocab));
break;
case COMMON_SAMPLER_TYPE_PENALTIES:
llama_sampler_chain_add(result->chain, llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
samplers.push_back(llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
break;
default:
GGML_ASSERT(false && "unknown sampler type");
}
}
llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
samplers.push_back(llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
samplers.push_back(llama_sampler_init_temp(params.temp));
samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
} else if (params.mirostat == 2) {
llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
samplers.push_back(llama_sampler_init_temp(params.temp));
samplers.push_back(llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
} else {
GGML_ASSERT(false && "unknown mirostat version");
}
for (auto * smpl : samplers) {
llama_sampler_chain_add(chain, smpl);
}
auto * result = new common_sampler {
/* .params = */ params,
/* .chain = */ chain,
/* .grammar = */ grammar,
/* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
/* .cur = */ {},
/* .cur_p = */ {},
};
return result;
}
void common_sampler_free(struct common_sampler * gsmpl) {
if (gsmpl) {
llama_sampler_free(gsmpl->grmr);
llama_sampler_free(gsmpl->chain);
delete gsmpl;
......@@ -314,11 +324,24 @@ void common_sampler_free(struct common_sampler * gsmpl) {
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
const auto tm = gsmpl->tm();
if (accept_grammar) {
llama_sampler_accept(gsmpl->grmr, token);
}
if (gsmpl->grammar) {
const int n_smpl = llama_sampler_chain_n(gsmpl->chain);
llama_sampler_accept(gsmpl->chain, token);
for (int i = 0; i < n_smpl; i++) {
auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
// the grammar sampler is always the first one
if (i == 0) {
if (accept_grammar) {
llama_sampler_accept(smpl, token);
}
} else {
llama_sampler_accept(smpl, token);
}
}
} else {
llama_sampler_accept(gsmpl->chain, token);
}
gsmpl->prev.push_back(token);
}
......@@ -329,12 +352,12 @@ void common_sampler_reset(struct common_sampler * gsmpl) {
struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
return new common_sampler {
/* .params = */ gsmpl->params,
/* .grmr = */ llama_sampler_clone(gsmpl->grmr),
/* .chain = */ llama_sampler_clone(gsmpl->chain),
/* .prev = */ gsmpl->prev,
/* .cur = */ gsmpl->cur,
/* .cur_p = */ gsmpl->cur_p,
/* .params = */ gsmpl->params,
/* .chain = */ llama_sampler_clone(gsmpl->chain),
/* .grammar = */ gsmpl->grammar,
/* .prev = */ gsmpl->prev,
/* .cur = */ gsmpl->cur,
/* .cur_p = */ gsmpl->cur_p,
};
}
......@@ -383,58 +406,33 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
}
}
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
return gsmpl->chain;
}
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx) {
llama_synchronize(ctx);
// start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
const auto tm = gsmpl->tm();
gsmpl->set_logits(ctx, idx);
llama_token id = LLAMA_TOKEN_NULL;
auto & grmr = gsmpl->grmr;
auto & chain = gsmpl->chain;
auto & cur_p = gsmpl->cur_p; // initialized by set_logits
if (grammar_first) {
llama_sampler_apply(grmr, &cur_p);
}
gsmpl->set_logits(ctx, idx);
llama_sampler_apply(chain, &cur_p);
GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
const llama_token id = cur_p.data[cur_p.selected].id;
if (grammar_first) {
return id;
}
// check if it the sampled token fits the grammar
{
llama_token_data single_token_data = { id, 1.0f, 0.0f };
llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
llama_sampler_apply(grmr, &single_token_data_array);
const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
if (is_valid) {
return id;
}
}
// resampling:
// if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
gsmpl->set_logits(ctx, idx);
llama_sampler_apply(grmr, &cur_p);
llama_sampler_apply(chain, &cur_p);
GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration");
id = cur_p.data[cur_p.selected].id;
return cur_p.data[cur_p.selected].id;
return id;
}
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft) {
GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
std::vector<llama_token> result;
......@@ -442,7 +440,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
size_t i = 0;
for (; i < draft.size(); i++) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
common_sampler_accept(gsmpl, id, true);
......@@ -454,7 +452,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
}
if (i == draft.size()) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
common_sampler_accept(gsmpl, id, true);
......@@ -464,13 +462,13 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
return result;
}
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft) {
std::vector<int> idxs(draft.size() + 1);
for (size_t i = 0; i < idxs.size(); ++i) {
idxs[i] = i;
}
return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft);
}
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
......@@ -515,7 +513,8 @@ std::string common_sampler_print(const struct common_sampler * gsmpl) {
for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
result += std::string("-> ") + llama_sampler_name(smpl) + " ";
result += std::string("-> ");
result += std::string(llama_sampler_name(smpl)) + " ";
}
return result;
......
llama/llama.cpp/common/sampling.h
View file @ 49a9c9ba
......@@ -48,6 +48,8 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
// arguments can be nullptr to skip printing
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
// extended sampling implementation:
//
// - set logits
......@@ -55,10 +57,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
// - check if the token fits the grammar (if any)
// - if not: resample by first applying the grammar constraints and then sampling again (slower path)
//
// if grammar_first is true, the grammar is applied before the samplers (slower)
// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
//
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx);
// generalized version of common_sampler_sample
//
......@@ -76,10 +75,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
//
// returns at least 1 token, up to idxs.size()
//
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft);
// assume idxs == [ 0, 1, 2, ..., draft.size() ]
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft);
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
......@@ -107,3 +106,9 @@ std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std:
llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
const char * grammar_kind, const char * grammar_data);
struct common_sampler_deleter {
void operator()(common_sampler * s) { common_sampler_free(s); }
};
typedef std::unique_ptr<common_sampler, common_sampler_deleter> common_sampler_ptr;
llama/llama.cpp/include/llama.h
View file @ 49a9c9ba
......@@ -313,6 +313,7 @@ extern "C" {
bool check_tensors; // validate model tensor data
bool use_extra_bufts; // use extra buffer types (used for weight repacking)
bool no_host; // bypass host buffer allowing extra buffers to be used
bool no_alloc; // only load metadata and simulate memory allocations
};
// NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
......@@ -466,10 +467,24 @@ extern "C" {
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
// fits mparams and cparams to free device memory (assumes system memory is unlimited)
// returns true if the parameters could be successfully modified to fit device memory
// this function is NOT thread safe because it modifies the global llama logger state
LLAMA_API bool llama_params_fit(
const char * path_model,
struct llama_model_params * mparams,
struct llama_context_params * cparams,
float * tensor_split, // writable buffer for tensor split, needs at least llama_max_devices elements
struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
size_t margin, // margin of memory to leave per device in bytes
uint32_t n_ctx_min, // minimum context size to set when trying to reduce memory use
enum ggml_log_level log_level); // minimum log level to print during fitting, lower levels go to debug log
LLAMA_API int64_t llama_time_us(void);
LLAMA_API size_t llama_max_devices(void);
LLAMA_API size_t llama_max_parallel_sequences(void);
LLAMA_API size_t llama_max_tensor_buft_overrides(void);
LLAMA_API bool llama_supports_mmap (void);
LLAMA_API bool llama_supports_mlock (void);
......@@ -1354,7 +1369,9 @@ extern "C" {
// Set callback for all future logging events.
// If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
// The logger state is global so these functions are NOT thread safe.
LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data);
LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
//
// Performance utils
......
llama/llama.cpp/src/llama-arch.cpp
View file @ 49a9c9ba
This diff is collapsed.
llama/llama.cpp/src/llama-arch.h
View file @ 49a9c9ba
......@@ -3,6 +3,7 @@
#include "ggml.h" // ggml_op
#include <string>
#include <set>
//
// gguf constants (sync with gguf.py)
......@@ -318,6 +319,7 @@ enum llm_tensor {
LLM_TENSOR_DENSE_3_OUT,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_OUTPUT_NORM_LFM2, // fix for wrong tensor name
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ROPE_FACTORS_LONG,
LLM_TENSOR_ROPE_FACTORS_SHORT,
......@@ -529,6 +531,10 @@ struct LLM_TN_IMPL {
const int bid;
const int xid;
const std::set<llm_tensor> model_tensors;
LLM_TN_IMPL(llm_arch arch, llm_tensor tensor, const char * suffix, int bid, int xid);
std::string str() const;
operator std::string() const {
......@@ -550,11 +556,11 @@ struct LLM_TN {
llm_arch arch;
LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
return { arch, tensor, suffix, bid, xid };
return LLM_TN_IMPL(arch, tensor, suffix, bid, xid);
}
LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
return { arch, tensor, nullptr, bid, xid };
return LLM_TN_IMPL(arch, tensor, nullptr, bid, xid);
}
};
......
llama/llama.cpp/src/llama-batch.cpp
View file @ 49a9c9ba
......@@ -695,6 +695,8 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
udata->seq_idx .resize(LLAMA_MAX_SEQ, -1);
udata->output .resize(n_tokens);
udata->seq_id_data.reserve(n_tokens);
seq_set_t seq_set_unq;
for (size_t i = 0; i < idxs.size(); ++i) {
......@@ -716,11 +718,13 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
}
udata->n_seq_id[i] = batch.n_seq_id[idxs[i]];
udata->seq_id[i] = batch.seq_id[idxs[i]];
udata->output[i] = batch.logits[idxs[i]];
for (int s = 0; s < udata->n_seq_id[i]; ++s) {
seq_set_unq.set(udata->seq_id[i][s]);
const llama_seq_id seq_id = batch.seq_id[idxs[i]][s];
udata->seq_id_data.push_back(seq_id);
seq_set_unq.set(seq_id);
}
if (udata->output[i]) {
......@@ -728,6 +732,12 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
}
}
llama_seq_id * seq_id_ptr = udata->seq_id_data.data();
for (size_t i = 0; i < idxs.size(); ++i) {
udata->seq_id[i] = seq_id_ptr;
seq_id_ptr += udata->n_seq_id[i];
}
for (uint32_t s = 0; s < n_seq_max; ++s) {
if (seq_set_unq.test(s)) {
udata->seq_idx[s] = udata->seq_id_unq.size();
......
llama/llama.cpp/src/llama-batch.h
View file @ 49a9c9ba
......@@ -56,13 +56,15 @@ struct llama_ubatch {
std::vector<float> embd;
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<llama_seq_id *> seq_id; // these point into the seq_id_data below
std::vector<llama_seq_id> seq_id_unq;
std::vector<int32_t> seq_idx;
std::vector<int8_t> output;
std::vector<llama_seq_id> seq_id_data;
};
// the llama_ubatch pointers above point to this data if set. otherwise - points to non-owning data
// the llama_ubatch pointers above point to this data if set. otherwise - point to external non-owning data
std::shared_ptr<data_t> data;
};
......
llama/llama.cpp/src/llama-context.cpp
View file @ 49a9c9ba
......@@ -9,6 +9,7 @@
#include "llama-model.h"
#include <cinttypes>
#include <cmath>
#include <cstring>
#include <limits>
#include <stdexcept>
......@@ -72,6 +73,43 @@ llama_context::llama_context(
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
}
if (cparams.yarn_ext_factor != 0) {
static auto get_mscale = [](float scale, float mscale) {
return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
};
const float factor = 1.0f / cparams.rope_freq_scale;
// ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348
if (hparams.rope_yarn_log_mul != 0.0f) {
// note: here we assume `mscale == 1.0f`
// TODO: start reading the actual value of mscale and handle the case where it is not 1.0f
float mscale = 1.0f;
const float mscale_all_dims = hparams.rope_yarn_log_mul;
// [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
// special-case DEEPSEEK v2:
// https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43
if (model.arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) {
mscale = mscale_all_dims;
}
cparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n",
__func__, cparams.yarn_attn_factor, mscale, mscale_all_dims);
} else {
cparams.yarn_attn_factor = get_mscale(factor, 1.0f);
}
// when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor:
// https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544
//
// ref: https://github.com/ggml-org/llama.cpp/discussions/7416
// https://github.com/ggml-org/llama.cpp/pull/17945
cparams.yarn_attn_factor *= 1.0f / (1.0f + 0.1f * logf(factor));
}
cparams.yarn_attn_factor *= hparams.rope_attn_factor;
if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
......@@ -93,14 +131,6 @@ llama_context::llama_context(
// with causal attention, the batch size is limited by the context size
cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
// the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask
// this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext)
// ref: https://github.com/ggerganov/llama.cpp/pull/5021
// TODO: this padding is not needed for the cache-less context so we should probably move it to llama_memory
if (cparams.n_batch < GGML_KQ_MASK_PAD) {
LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD);
cparams.n_batch = GGML_KQ_MASK_PAD;
}
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
cparams.op_offload = params.op_offload;
......@@ -228,6 +258,7 @@ llama_context::llama_context(
backend_buft.clear();
backend_ptrs.clear();
backend_buf_exp_size.clear();
for (auto & backend : backends) {
auto * buft = ggml_backend_get_default_buffer_type(backend.get());
......@@ -244,6 +275,7 @@ llama_context::llama_context(
backend_buft.push_back(buft);
backend_ptrs.push_back(backend.get());
backend_buf_exp_size.push_back(0);
}
LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
......@@ -359,7 +391,8 @@ llama_context::llama_context(
// reserve pp (prompt processing) graph first so that buffers are only allocated once
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(),
model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr);
if (!gf) {
if (pipeline_parallel) {
LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__);
......@@ -377,7 +410,7 @@ llama_context::llama_context(
// reserve with tg (token generation) graph to get the number of splits and nodes
{
auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get());
auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get(), model.hparams.no_alloc);
if (!gf) {
throw std::runtime_error("failed to allocate compute tg buffers");
}
......@@ -392,7 +425,7 @@ llama_context::llama_context(
//
// auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get());
//
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc);
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
......@@ -401,11 +434,13 @@ llama_context::llama_context(
for (size_t i = 0; i < backend_ptrs.size(); ++i) {
ggml_backend_t backend = backend_ptrs[i];
ggml_backend_buffer_type_t buft = backend_buft[i];
size_t size = ggml_backend_sched_get_buffer_size(sched.get(), backend);
if (size > 1) {
if (!model.hparams.no_alloc) {
backend_buf_exp_size[i] = ggml_backend_sched_get_buffer_size(sched.get(), backend);
}
if (backend_buf_exp_size[i] > 1) {
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
backend_buf_exp_size[i] / 1024.0 / 1024.0);
}
}
......@@ -424,6 +459,23 @@ llama_context::llama_context(
}
llama_context::~llama_context() {
// FIXME this currently results in a use-after-free bug if the model is freed before the context
// if (!model.hparams.no_alloc) {
// for (size_t i = 0; i < backend_ptrs.size(); ++i) {
// ggml_backend_t backend = backend_ptrs[i];
// ggml_backend_buffer_type_t buft = backend_buft[i];
// const size_t size_exp = backend_buf_exp_size[i];
// const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend);
// if (size_exp == size_act) {
// LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n",
// __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
// } else {
// LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n",
// __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
// }
// }
// }
ggml_opt_free(opt_ctx);
}
......@@ -1325,6 +1377,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
// This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
synchronize();
buf_output = nullptr;
logits = nullptr;
embd = nullptr;
......@@ -1396,7 +1449,8 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
return static_cast<llm_graph_result *>(gf_res_reserve.get());
}
ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) {
ggml_cgraph * llama_context::graph_reserve(
uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) {
LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
GGML_ASSERT(n_outputs >= 1);
......@@ -1433,8 +1487,13 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
// initialize scheduler with the specified graph
if (split_only) {
ggml_backend_sched_split_graph(sched.get(), gf);
if (sizes) {
ggml_backend_sched_reserve_size(sched.get(), gf, sizes);
} else {
ggml_backend_sched_split_graph(sched.get(), gf);
}
} else if (!ggml_backend_sched_reserve(sched.get(), gf)) {
GGML_ASSERT(!sizes);
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
return nullptr;
}
......@@ -2056,15 +2115,26 @@ void llama_context::perf_reset() {
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> llama_context::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> ret;
for (const auto & buft_size : model.memory_breakdown()) {
ret[buft_size.first].model += buft_size.second;
for (const auto & [buft, size] : model.memory_breakdown()) {
ret[buft].model += size;
}
for (const auto & buft_size : memory->memory_breakdown()) {
ret[buft_size.first].context += buft_size.second;
if (memory) {
for (const auto & [buft, size] : memory->memory_breakdown()) {
ret[buft].context += size;
}
}
for (const auto & backend_ptr : backends) {
ggml_backend_t backend = backend_ptr.get();
ret[ggml_backend_sched_get_buffer_type(sched.get(), backend)].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
if (model.hparams.no_alloc) {
for (size_t i = 0; i < backends.size(); ++i) {
ggml_backend_t backend = backends[i].get();
ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend);
ret[buft].compute += backend_buf_exp_size[i];
}
} else {
for (const auto & backend_ptr : backends) {
ggml_backend_t backend = backend_ptr.get();
ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend);
ret[buft].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
}
}
return ret;
}
......
llama/llama.cpp/src/llama-context.h
View file @ 49a9c9ba
......@@ -26,6 +26,10 @@ struct llama_memory_breakdown_data {
size_t model = 0; // memory allocated for the model
size_t context = 0; // memory allocated for the context
size_t compute = 0; // memory allocated for temporary compute buffers
size_t total() const {
return model + context + compute;
}
};
struct llama_context {
......@@ -206,7 +210,8 @@ public:
ggml_status graph_compute(ggml_cgraph * gf, bool batched);
// reserve a graph with a dummy ubatch of the specified size
ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false);
ggml_cgraph * graph_reserve(
uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false, size_t * sizes = nullptr);
private:
llm_graph_params graph_params(
......@@ -281,9 +286,10 @@ private:
std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
// buffer types used for the compute buffer of each backend
// pointers and buffer types used for the compute buffer of each backend
std::vector<ggml_backend_t> backend_ptrs;
std::vector<ggml_backend_buffer_type_t> backend_buft;
std::vector<size_t> backend_buf_exp_size; // expected buffer sizes
llm_graph_result_ptr gf_res_prev;
llm_graph_result_ptr gf_res_reserve;
......
llama/llama.cpp/src/llama-graph.cpp
View file @ 49a9c9ba
......@@ -78,7 +78,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
for (int i = 0; i < n_tokens; ++i) {
const float pos = ubatch->pos[i];
attn_scale_data[i] = std::log(
std::floor((pos + 1.0f) / n_attn_temp_floor_scale) + 1.0
std::floor((pos + f_attn_temp_offset) / n_attn_temp_floor_scale) + 1.0
) * f_attn_temp_scale + 1.0;
}
......@@ -254,6 +254,24 @@ void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
}
}
bool llm_graph_input_rs::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_memory_recurrent_context *>(params.mctx);
this->mctx = mctx;
bool res = true;
res &= s_copy->ne[0] == mctx->get_n_rs();
res &= s_copy_main->ne[0] == params.ubatch.n_seqs;
res &= s_copy_extra->ne[0] == mctx->get_n_rs() - params.ubatch.n_seqs;
res &= head == mctx->get_head();
res &= rs_z == mctx->get_rs_z();
return res;
}
void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
......@@ -385,7 +403,7 @@ bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
//res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
res &= self_kq_mask->ne[0] == mctx->get_n_kv();
res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
res &= self_kq_mask->ne[1] == params.ubatch.n_tokens;
return res;
}
......@@ -416,10 +434,10 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
//res &= self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
res &= self_kq_mask->ne[0] == mctx->get_base()->get_n_kv();
res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
res &= self_kq_mask->ne[1] == params.ubatch.n_tokens;
res &= self_kq_mask_swa->ne[0] == mctx->get_swa()->get_n_kv();
res &= self_kq_mask_swa->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
res &= self_kq_mask_swa->ne[1] == params.ubatch.n_tokens;
return res;
}
......@@ -452,7 +470,7 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
}
}
for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
for (int i = n_tokens; i < n_tokens; ++i) {
for (int j = 0; j < n_enc; ++j) {
data[h*(n_enc*n_tokens) + i*n_enc + j] = -INFINITY;
}
......@@ -461,8 +479,46 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
}
void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
inp_attn->set_input(ubatch);
inp_rs->set_input(ubatch);
mctx->get_attn()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
mctx->get_attn()->set_input_v_idxs(inp_attn->self_v_idxs, ubatch);
mctx->get_attn()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
const int64_t n_rs = mctx->get_recr()->get_n_rs();
if (inp_rs->s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
int32_t * data = (int32_t *) inp_rs->s_copy->data;
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
data[i] = mctx->get_recr()->s_copy(i);
}
}
}
bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_memory_hybrid_context *>(params.mctx);
this->mctx = mctx;
bool res = true;
res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
//res &= inp_attn->self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
res &= inp_attn->self_kq_mask->ne[0] == mctx->get_attn()->get_n_kv();
res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();
res &= inp_rs->s_copy_main->ne[0] == params.ubatch.n_seqs;
res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;
res &= inp_rs->head == mctx->get_recr()->get_head();
res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();
return res;
}
//
......@@ -1097,8 +1153,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cur = ggml_relu(ctx0, cur);
cur = ggml_sqr(ctx0, cur);
cb(cur, "ffn_moe_relu_sqr", il);
}
break;
} break;
default:
GGML_ABORT("fatal error");
}
......@@ -1213,7 +1268,7 @@ ggml_tensor * llm_graph_context::build_inp_pos() const {
}
ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale, hparams.f_attn_temp_offset);
auto & cur = inp->attn_scale;
......@@ -1480,13 +1535,13 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams);
// note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens, 1, 1);
ggml_set_input(inp->self_kq_mask);
inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens, 1, 1);
ggml_set_input(inp->self_kq_mask_swa);
inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
......@@ -1568,7 +1623,7 @@ static std::unique_ptr<llm_graph_input_attn_kv> build_attn_inp_kv_impl(
inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch);
inp->self_v_idxs = mctx_cur->build_input_v_idxs(ctx0, ubatch);
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
ggml_set_input(inp->self_kq_mask);
inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
......@@ -1711,7 +1766,7 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
const int32_t n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, n_tokens, 1, 1);
ggml_set_input(inp->cross_kq_mask);
inp->cross_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->cross_kq_mask, GGML_TYPE_F16) : inp->cross_kq_mask;
......@@ -1777,7 +1832,7 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch);
inp->self_v_idxs = mctx_cur->get_base()->build_input_v_idxs(ctx0, ubatch);
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
ggml_set_input(inp->self_kq_mask);
inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
......@@ -1791,7 +1846,7 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
inp->self_v_idxs_swa = mctx_cur->get_swa()->build_input_v_idxs(ctx0, ubatch);
inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
ggml_set_input(inp->self_kq_mask_swa);
inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
......@@ -1851,6 +1906,9 @@ static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
inp->s_copy_main = ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0);
inp->s_copy_extra = ggml_view_1d(ctx0, inp->s_copy, n_rs - n_seqs, n_seqs * inp->s_copy->nb[0]);
inp->head = mctx_cur->get_head();
inp->rs_z = mctx_cur->get_rs_z();
return inp;
}
......@@ -1919,10 +1977,10 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
auto inp_rs = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
auto inp_rs = build_rs_inp_impl (ctx0, ubatch, mctx_cur->get_recr());
auto inp_attn = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
auto inp = std::make_unique<llm_graph_input_mem_hybrid>(cparams, std::move(inp_attn), std::move(inp_rs), mctx_cur);
return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
}
......
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