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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 1144 additions and 380 deletions
llama/llama.cpp/src/llama-graph.h
View file @ 49a9c9ba
......@@ -132,8 +132,8 @@ public:
// temperature tuning, used by llama4
class llm_graph_input_attn_temp : public llm_graph_input_i {
public:
llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
: n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale, float f_attn_temp_offset)
: n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale), f_attn_temp_offset(f_attn_temp_offset) {}
virtual ~llm_graph_input_attn_temp() = default;
void set_input(const llama_ubatch * ubatch) override;
......@@ -142,6 +142,7 @@ public:
const uint32_t n_attn_temp_floor_scale;
const float f_attn_temp_scale;
const float f_attn_temp_offset;
};
class llm_graph_input_pos_bucket : public llm_graph_input_i {
......@@ -224,6 +225,8 @@ public:
void set_input(const llama_ubatch * ubatch) override;
bool can_reuse(const llm_graph_params & params) override;
ggml_tensor * s_copy; // I32 [n_rs]
// views of s_copy, computed once per graph
......@@ -232,6 +235,10 @@ public:
ggml_tensor * s_copy_extra; // I32 [n_rs - n_seqs]
const llama_memory_recurrent_context * mctx;
// used in view offsets, need to match for valid graph reuse
uint32_t head;
int32_t rs_z;
};
class llm_graph_input_cross_embd : public llm_graph_input_i {
......@@ -364,22 +371,28 @@ public:
class llm_graph_input_mem_hybrid : public llm_graph_input_i {
public:
llm_graph_input_mem_hybrid(
const llama_cparams & cparams,
std::unique_ptr<llm_graph_input_attn_kv> inp_attn,
std::unique_ptr<llm_graph_input_rs> inp_rs,
const llama_memory_hybrid_context * mctx) :
std::unique_ptr<llm_graph_input_rs> inp_rs,
const llama_memory_hybrid_context * mctx) :
inp_attn(std::move(inp_attn)),
inp_rs(std::move(inp_rs)),
cparams(cparams),
mctx(mctx) { }
virtual ~llm_graph_input_mem_hybrid() = default;
void set_input(const llama_ubatch * ubatch) override;
bool can_reuse(const llm_graph_params & params) override;
std::unique_ptr<llm_graph_input_attn_kv> inp_attn;
std::unique_ptr<llm_graph_input_rs> inp_rs;
llm_graph_input_attn_kv * get_attn() const { return inp_attn.get(); }
llm_graph_input_rs * get_recr() const { return inp_rs.get(); }
const llama_cparams cparams;
const llama_memory_hybrid_context * mctx;
};
......
llama/llama.cpp/src/llama-hparams.cpp
View file @ 49a9c9ba
#include "llama-hparams.h"
#include "ggml.h"
#include <algorithm>
#include <cassert>
void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
......@@ -237,3 +239,7 @@ bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama
return false;
}
bool llama_hparams::use_mrope() const {
return rope_sections[0] > 0 && rope_sections[1] > 0;
}
llama/llama.cpp/src/llama-hparams.h
View file @ 49a9c9ba
......@@ -34,6 +34,7 @@ struct llama_hparams_convnext {
struct llama_hparams {
bool vocab_only;
bool no_alloc;
bool rope_finetuned;
bool use_par_res;
bool swin_norm;
......@@ -109,6 +110,7 @@ struct llama_hparams {
float rope_freq_base_train_swa;
float rope_freq_scale_train;
float rope_freq_scale_train_swa;
uint32_t n_ctx_orig_yarn;
float rope_yarn_log_mul = 0.0f;
......@@ -166,6 +168,7 @@ struct llama_hparams {
uint32_t n_no_rope_layer_step = 4;
uint32_t n_attn_temp_floor_scale = 0;
float f_attn_temp_scale = 0.0f;
float f_attn_temp_offset = 0.0f; // offset position index
// gemma3n altup
uint32_t n_altup = 4; // altup_num_inputs
......@@ -272,7 +275,8 @@ struct llama_hparams {
// TODO: think of a better place for this function
// TODO: pack the SWA params in a struct?
static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1);
bool use_mrope() const;
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
llama/llama.cpp/src/llama-impl.cpp
View file @ 49a9c9ba
......@@ -25,6 +25,10 @@ time_meas::~time_meas() {
}
}
void llama_log_get(ggml_log_callback * log_callback, void ** user_data) {
ggml_log_get(log_callback, user_data);
}
void llama_log_set(ggml_log_callback log_callback, void * user_data) {
ggml_log_set(log_callback, user_data);
g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
......
llama/llama.cpp/src/llama-kv-cache.cpp
View file @ 49a9c9ba
......@@ -175,7 +175,15 @@ llama_kv_cache::llama_kv_cache(
// allocate tensors and initialize the buffers to avoid NaNs in the padding
for (auto & [buft, ctx] : ctx_map) {
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft);
ggml_backend_buffer_t buf;
if (model.hparams.no_alloc) {
buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) {
t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it
}
} else {
buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); // real buffer
}
if (!buf) {
throw std::runtime_error("failed to allocate buffer for kv cache");
}
......@@ -482,9 +490,18 @@ llama_pos llama_kv_cache::seq_pos_max(llama_seq_id seq_id) const {
std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, size_t> ret;
for (const auto & [_, buf] : ctxs_bufs) {
ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
for (const auto & [ctx, buf] : ctxs_bufs) {
ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf.get());
if (hparams.no_alloc) {
GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) == nullptr);
ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
} else {
// GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
ret[buft] += ggml_backend_buffer_get_size(buf.get());
}
}
return ret;
}
......@@ -1232,8 +1249,7 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u
GGML_ASSERT(n_tokens%n_stream == 0);
// n_tps == n_tokens_per_stream
const int64_t n_tps = n_tokens/n_stream;
const int64_t n_tps_pad = GGML_PAD(n_tps, GGML_KQ_MASK_PAD);
const int64_t n_tps = n_tokens/n_stream;
std::fill(data, data + ggml_nelements(dst), -INFINITY);
......@@ -1266,7 +1282,7 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u
const llama_pos p1_x = is_2d ? ubatch->pos[i + ubatch->n_tokens*2] : 0;
const llama_pos p1_y = is_2d ? ubatch->pos[i + ubatch->n_tokens] : 0;
const uint64_t idst = n_kv*(h*n_stream*n_tps_pad + s*n_tps_pad + ii);
const uint64_t idst = n_kv*(h*n_stream*n_tps + s*n_tps + ii);
for (uint32_t j = 0; j < n_kv; ++j) {
if (cells.is_empty(j)) {
......@@ -1370,9 +1386,10 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
float freq_scale) const {
const auto & n_ctx_orig = cparams.n_ctx_orig_yarn;
const auto & yarn_ext_factor = cparams.yarn_ext_factor;
const auto & yarn_beta_fast = cparams.yarn_beta_fast;
const auto & yarn_beta_slow = cparams.yarn_beta_slow;
const auto & yarn_ext_factor = cparams.yarn_ext_factor;
const auto & yarn_beta_fast = cparams.yarn_beta_fast;
const auto & yarn_beta_slow = cparams.yarn_beta_slow;
const auto & yarn_attn_factor = cparams.yarn_attn_factor;
const auto & n_rot = hparams.n_rot;
const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE
......@@ -1383,12 +1400,6 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
? LLAMA_ROPE_TYPE_NEOX
: hparams.rope_type;
// See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly.
// See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2
? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale))
: cparams.yarn_attn_factor;
ggml_tensor * tmp;
if (ggml_is_quantized(cur->type)) {
......@@ -1550,9 +1561,11 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama
const uint32_t strm = seq_id == -1 ? s : seq_to_stream[seq_id];
slot_info sinfo;
bool res = true;
res = res && state_read_meta(io, strm, cell_count, seq_id);
res = res && state_read_data(io, strm, cell_count);
res = res && state_read_meta(io, strm, cell_count, sinfo, seq_id);
res = res && state_read_data(io, strm, cell_count, sinfo);
if (!res) {
if (seq_id == -1) {
......@@ -1691,7 +1704,7 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
}
}
bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id) {
bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, slot_info & sinfo, llama_seq_id dest_seq_id) {
auto & cells = v_cells[strm];
auto & head = v_heads[strm];
......@@ -1728,7 +1741,7 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
ubatch.seq_id[i] = &dest_seq_id;
}
const auto sinfo = find_slot(ubatch, true);
sinfo = find_slot(ubatch, false);
if (sinfo.empty()) {
LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
return false;
......@@ -1738,20 +1751,16 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
// see: https://github.com/ggml-org/llama.cpp/pull/16825#issuecomment-3460868350
apply_ubatch(sinfo, ubatch);
const auto head_cur = sinfo.head();
// keep the head at the old position because we will read the KV data into it in state_read_data()
head = head_cur;
LLAMA_LOG_DEBUG("%s: cell_count = %d, dest_seq_id = %d\n", __func__, cell_count, dest_seq_id);
LLAMA_LOG_DEBUG("%s: head_cur = %d, head = %d, cell_count = %d, dest_seq_id = %d\n", __func__, head_cur, head, cell_count, dest_seq_id);
// DEBUG CHECK: head_cur should be our first cell, head_cur + cell_count - 1 should be our last cell (verify seq_id and pos values)
// Assume that this is one contiguous block of cells
GGML_ASSERT(head_cur + cell_count <= cells.size());
GGML_ASSERT(cells.pos_get(head_cur) == ubatch.pos[0]);
GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == ubatch.pos[cell_count - 1]);
GGML_ASSERT(cells.seq_has(head_cur, dest_seq_id));
GGML_ASSERT(cells.seq_has(head_cur + cell_count - 1, dest_seq_id));
// DEBUG CHECK: verify that all cells were allocated and have correct seq_id and pos values
GGML_ASSERT(sinfo.n_stream() == 1);
GGML_ASSERT(sinfo.idxs[0].size() == cell_count);
for (uint32_t i = 0; i < cell_count; ++i) {
const uint32_t idx = sinfo.idxs[0][i];
GGML_ASSERT(cells.pos_get(idx) == ubatch.pos[i]);
GGML_ASSERT(cells.seq_has(idx, dest_seq_id));
}
} else {
// whole KV cache restore
......@@ -1784,15 +1793,24 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
}
}
// Create contiguous slot_info for whole cache restore
sinfo.s0 = strm;
sinfo.s1 = strm;
sinfo.resize(1);
sinfo.strm[0] = strm;
sinfo.idxs[0].resize(cell_count);
for (uint32_t i = 0; i < cell_count; ++i) {
sinfo.idxs[0][i] = i;
}
head = 0;
}
return true;
}
bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count) {
bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo) {
auto & cells = v_cells[strm];
auto & head = v_heads[strm];
uint32_t v_trans;
uint32_t n_layer;
......@@ -1842,8 +1860,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
}
if (cell_count) {
// Read and set the keys for the whole cell range
ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row);
if (sinfo.is_contiguous()) {
// Fast path: contiguous cells, single memcpy
ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), sinfo.head() * k_size_row, cell_count * k_size_row);
} else {
// Slow path: scatter to non-contiguous positions
const void * src = io.read(cell_count * k_size_row);
for (uint32_t i = 0; i < cell_count; ++i) {
const size_t dst_offset = sinfo.idxs[0][i] * k_size_row;
ggml_backend_tensor_set(k, (const char*)src + i * k_size_row, dst_offset, k_size_row);
}
}
}
}
......@@ -1874,8 +1901,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
}
if (cell_count) {
// Read and set the values for the whole cell range
ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row);
if (sinfo.is_contiguous()) {
// Fast path: contiguous cells, single memcpy
ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), sinfo.head() * v_size_row, cell_count * v_size_row);
} else {
// Slow path: scatter to non-contiguous positions
const void * src = io.read(cell_count * v_size_row);
for (uint32_t i = 0; i < cell_count; ++i) {
const size_t dst_offset = sinfo.idxs[0][i] * v_size_row;
ggml_backend_tensor_set(v, (const char*)src + i * v_size_row, dst_offset, v_size_row);
}
}
}
}
} else {
......@@ -1914,10 +1950,22 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
}
if (cell_count) {
// For each row in the transposed matrix, read the values for the whole cell range
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
const size_t dst_offset = (head + j * cells.size()) * v_size_el;
ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
if (sinfo.is_contiguous()) {
// Fast path: contiguous cells
const uint32_t h = sinfo.head();
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
const size_t dst_offset = (h + j * cells.size()) * v_size_el;
ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
}
} else {
// Slow path: scatter to non-contiguous positions
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
const void * src = io.read(cell_count * v_size_el);
for (uint32_t i = 0; i < cell_count; ++i) {
const size_t dst_offset = (sinfo.idxs[0][i] + j * cells.size()) * v_size_el;
ggml_backend_tensor_set(v, (const char*)src + i * v_size_el, dst_offset, v_size_el);
}
}
}
}
}
......
llama/llama.cpp/src/llama-kv-cache.h
View file @ 49a9c9ba
......@@ -72,6 +72,23 @@ public:
void clear() {
idxs.clear();
}
// check if indices are contiguous starting from head()
bool is_contiguous() const {
if (idxs.empty() || idxs[0].empty()) {
return true;
}
if (idxs.size() > 1) {
return false;
}
const uint32_t h = idxs[0][0];
for (size_t i = 0; i < idxs[0].size(); ++i) {
if (idxs[0][i] != h + i) {
return false;
}
}
return true;
}
};
using slot_info_vec_t = std::vector<slot_info>;
......@@ -264,8 +281,8 @@ private:
void state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id = -1) const;
void state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const;
bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count);
bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, slot_info & sinfo, llama_seq_id dest_seq_id = -1);
bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo);
};
class llama_kv_cache_context : public llama_memory_context_i {
......
llama/llama.cpp/src/llama-memory-hybrid.cpp
View file @ 49a9c9ba
......@@ -222,7 +222,7 @@ llama_memory_hybrid_context::llama_memory_hybrid_context(
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
ctx_attn(new llama_kv_cache_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
......
llama/llama.cpp/src/llama-model-loader.cpp
View file @ 49a9c9ba
......@@ -473,6 +473,7 @@ llama_model_loader::llama_model_loader(
std::vector<std::string> & splits,
bool use_mmap,
bool check_tensors,
bool no_alloc,
const llama_model_kv_override * param_overrides_p,
const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) {
int trace = 0;
......@@ -716,6 +717,7 @@ llama_model_loader::llama_model_loader(
this->use_mmap = use_mmap;
this->check_tensors = check_tensors;
this->no_alloc = no_alloc;
}
std::string llama_model_loader::get_arch_name() const {
......
llama/llama.cpp/src/llama-model-loader.h
View file @ 49a9c9ba
......@@ -71,6 +71,7 @@ struct llama_model_loader {
bool use_mmap = false;
bool check_tensors;
bool no_alloc;
llama_files files;
llama_ftype ftype;
......@@ -97,6 +98,7 @@ struct llama_model_loader {
std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
bool use_mmap,
bool check_tensors,
bool no_alloc,
const llama_model_kv_override * param_overrides_p,
const llama_model_tensor_buft_override * param_tensor_buft_overrides_p);
......
llama/llama.cpp/src/llama-model.cpp
View file @ 49a9c9ba
......@@ -669,6 +669,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.n_swa = 8192;
hparams.n_attn_temp_floor_scale = 8192;
hparams.f_attn_temp_scale = 0.1f;
hparams.f_attn_temp_offset = 1.0f;
hparams.set_swa_pattern(4); // pattern: 3 chunked - 1 full
}
......@@ -1636,12 +1637,19 @@ void llama_model::load_hparams(llama_model_loader & ml) {
// that have no expert_gating_func model parameter set
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
}
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) {
// [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
// cancel the factor from the convert script
hparams.rope_yarn_log_mul /= 0.1f;
}
// (optional) temperature tuning - used by mistral-large
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false);
hparams.f_attn_temp_offset = 0.0f;
switch (hparams.n_layer) {
case 27: type = LLM_TYPE_16B; break;
case 60: type = LLM_TYPE_236B; break;
......@@ -1681,7 +1689,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_GLM4:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
switch (hparams.n_layer) {
case 40: type = LLM_TYPE_9B; break;
case 61: type = LLM_TYPE_32B; break;
......@@ -1690,8 +1699,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_GLM4_MOE:
{
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
// MoE parameters
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
......@@ -2282,7 +2292,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
switch (hparams.n_layer) {
case 80: type = LLM_TYPE_80B_A3B; break;
case 48: type = LLM_TYPE_80B_A3B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
......@@ -2291,9 +2301,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false);
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f);
hparams.f_attn_temp_offset = 0.0f;
// TODO: maybe add n_attn_temp_floor_scale as a separate KV?
if (hparams.f_attn_temp_scale != 0.0f) {
......@@ -2303,18 +2315,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
}
// TODO: this seems to be correct with the case of mscale == mscale_all_dims == 1.0f
// but may need further verification with other values
if (hparams.rope_yarn_log_mul != 0.0f) {
float factor = 1.0f / hparams.rope_freq_scale_train;
float mscale = 1.0f;
float mscale_all_dims = hparams.rope_yarn_log_mul;
static auto get_mscale = [](float scale, float mscale) {
return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
};
hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
}
switch (hparams.n_layer) {
case 26: type = LLM_TYPE_3B; break;
case 34: type = LLM_TYPE_8B; break;
......@@ -3414,9 +3414,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
// optional bias tensors
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
......@@ -6678,9 +6678,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
std::vector<ggml_backend_buffer_ptr> bufs;
if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
GGML_ASSERT(!ml.no_alloc);
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
// only the mmap region containing the tensors in the model is mapped to the backend buffer
// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer,
// then we could just use metal for all layers
// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
void * addr = nullptr;
size_t first, last; // NOLINT
......@@ -6696,9 +6698,16 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
bufs.emplace_back(buf);
buf_map.emplace(idx, buf);
}
}
else {
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
} else {
ggml_backend_buffer_t buf;
if (ml.no_alloc) {
buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them
}
} else {
buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
}
if (buf == nullptr) {
throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
}
......@@ -6753,6 +6762,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
}
if (ml.no_alloc) {
return true;
}
// load tensor data
for (auto & [ctx, buf_map] : ctx_buf_maps) {
if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
......@@ -6795,9 +6808,18 @@ size_t llama_model::n_devices() const {
std::map<ggml_backend_buffer_type_t, size_t> llama_model::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, size_t> ret;
for (const auto & [_, bufs] : pimpl->ctxs_bufs) {
for (const auto & buf : bufs) {
ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) {
if (hparams.no_alloc) {
GGML_ASSERT(bufs.size() == 1);
ggml_backend_buffer_t buf = bufs[0].get();
GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr);
ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf);
ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
} else {
for (const auto & buf : bufs) {
// GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
}
}
}
return ret;
......@@ -6842,6 +6864,7 @@ void llama_model::print_info() const {
// hparams
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, arch_name().c_str());
LLAMA_LOG_INFO("%s: vocab_only = %d\n", __func__, hparams.vocab_only);
LLAMA_LOG_INFO("%s: no_alloc = %d\n", __func__, hparams.no_alloc);
if (!hparams.vocab_only) {
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
......@@ -6876,6 +6899,7 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn);
LLAMA_LOG_INFO("%s: rope_yarn_log_mul= %.4f\n", __func__, hparams.rope_yarn_log_mul);
LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown");
// MRoPE (Multi-axis Rotary Position Embedding) sections
if (const auto & s = hparams.rope_sections; s[0] || s[1] || s[2] || s[3]) {
......@@ -6940,7 +6964,6 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul);
}
if (arch == LLM_ARCH_QWEN2MOE) {
......@@ -7697,6 +7720,7 @@ llama_model_params llama_model_default_params() {
/*.check_tensors =*/ false,
/*.use_extra_bufts =*/ true,
/*.no_host =*/ false,
/*.no_alloc =*/ false,
};
return result;
......@@ -7817,7 +7841,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_PLM:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GLM4:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_GRANITE_HYBRID:
......@@ -7880,7 +7903,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_SMALLTHINKER:
case LLM_ARCH_GLM4_MOE:
case LLM_ARCH_SEED_OSS:
case LLM_ARCH_GROVEMOE:
case LLM_ARCH_APERTUS:
......@@ -7897,6 +7919,11 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_QWEN3VLMOE:
return LLAMA_ROPE_TYPE_IMROPE;
case LLM_ARCH_GLM4:
return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NORM;
case LLM_ARCH_GLM4_MOE:
return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
// all model arches should be listed explicitly here
case LLM_ARCH_UNKNOWN:
GGML_ABORT("unknown architecture");
......
llama/llama.cpp/src/llama-quant.cpp
View file @ 49a9c9ba
......@@ -596,7 +596,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
}
std::vector<std::string> splits = {};
llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, kv_overrides, nullptr);
llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
ml.init_mappings(false); // no prefetching
llama_model model(llama_model_default_params());
......
llama/llama.cpp/src/llama-vocab.cpp
View file @ 49a9c9ba
......@@ -1884,7 +1884,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
clean_spaces = false;
} else if (
tokenizer_pre == "qwen2" ||
tokenizer_pre == "deepseek-r1-qwen") {
tokenizer_pre == "deepseek-r1-qwen" ||
tokenizer_pre == "kormo") {
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
clean_spaces = false;
} else if (
......
llama/llama.cpp/src/llama.cpp
View file @ 49a9c9ba
This diff is collapsed.
llama/llama.cpp/src/models/deepseek2.cpp
View file @ 49a9c9ba
#include "models.h"
llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
// lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
......@@ -20,9 +18,15 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
// See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
const float attn_factor = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
// And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
// first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor
GGML_ASSERT(ext_factor >= 0.0f);
const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));
// use the original attn_factor to pre-scale the kq_scale
const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
ggml_tensor * cur;
ggml_tensor * inpL;
......
llama/llama.cpp/src/models/glm4-moe.cpp
View file @ 49a9c9ba
......@@ -5,11 +5,20 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
int sections[4];
std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
bool use_mrope = hparams.use_mrope();
if (ubatch.embd && !use_mrope) {
// unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
}
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
......@@ -60,17 +69,25 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
}
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
if (use_mrope) {
Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
} else {
// Normal RoPE
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
}
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
......
llama/llama.cpp/src/models/glm4.cpp
View file @ 49a9c9ba
......@@ -8,11 +8,20 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
int sections[4];
std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
bool use_mrope = hparams.use_mrope();
if (ubatch.embd && !use_mrope) {
// unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
}
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
......@@ -63,11 +72,25 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
}
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
if (use_mrope) {
Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
} else {
// Normal RoPE
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
}
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
......
llama/llama.cpp/src/models/models.h
View file @ 49a9c9ba
......@@ -441,13 +441,14 @@ private:
ggml_tensor * cur,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_layer_ffn(
ggml_tensor * cur,
int il);
ggml_tensor * build_delta_net_recurrent(
ggml_tensor * build_delta_net_chunking(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
......@@ -456,18 +457,17 @@ private:
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_delta_net_chunking(
ggml_tensor * build_delta_net_autoregressive(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
int il);
int il);
ggml_tensor * build_norm_gated(
ggml_tensor * input,
......
llama/llama.cpp/src/models/qwen2.cpp
View file @ 49a9c9ba
......@@ -31,16 +31,25 @@ llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_para
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
......
llama/llama.cpp/src/models/qwen3next.cpp
View file @ 49a9c9ba
This diff is collapsed.
llama/llama.cpp/tools/mtmd/clip-graph.h 0 → 100644
View file @ 49a9c9ba
#pragma once
#include "ggml.h"
#include "ggml-cpp.h"
#include "clip.h"
#include "clip-impl.h"
#include "clip-model.h"
#include <vector>
#include <functional>
#define DEFAULT_INTERPOLATION_MODE (GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS)
struct clip_graph {
const clip_model & model;
const clip_hparams & hparams;
projector_type proj_type;
// we only support single image per batch
const clip_image_f32 & img;
const int patch_size;
const int n_patches_x;
const int n_patches_y;
const int n_patches;
const int n_embd;
const int n_head;
const int d_head;
const int n_layer;
const int n_mmproj_embd;
const float eps;
const float kq_scale;
const clip_flash_attn_type flash_attn_type;
// for debugging
const bool debug_graph;
std::vector<ggml_tensor *> & debug_print_tensors;
ggml_context_ptr ctx0_ptr;
ggml_context * ctx0;
ggml_cgraph * gf;
clip_graph(clip_ctx * ctx, const clip_image_f32 & img);
virtual ~clip_graph() = default;
virtual ggml_cgraph * build() = 0;
//
// utility functions
//
void cb(ggml_tensor * cur0, const char * name, int il) const;
// siglip2 naflex
ggml_tensor * resize_position_embeddings(uint32_t interpolation_mode = DEFAULT_INTERPOLATION_MODE);
// build vision transformer (ViT) cgraph
// this function should cover most of the models
// if your model has specific features, you should probably duplicate this function
ggml_tensor * build_vit(
ggml_tensor * inp,
int64_t n_pos,
norm_type norm_t,
ffn_op_type ffn_t,
ggml_tensor * learned_pos_embd,
std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos);
// build the input after conv2d (inp_raw --> patches)
// returns tensor with shape [n_embd, n_patches]
ggml_tensor * build_inp();
ggml_tensor * build_inp_raw(int channels = 3);
ggml_tensor * build_norm(
ggml_tensor * cur,
ggml_tensor * mw,
ggml_tensor * mb,
norm_type type,
float norm_eps,
int il) const;
ggml_tensor * build_ffn(
ggml_tensor * cur,
ggml_tensor * up,
ggml_tensor * up_b,
ggml_tensor * gate,
ggml_tensor * gate_b,
ggml_tensor * down,
ggml_tensor * down_b,
ffn_op_type type_op,
int il) const;
ggml_tensor * build_attn(
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
ggml_tensor * k_cur,
ggml_tensor * v_cur,
ggml_tensor * kq_mask,
float kq_scale,
int il) const;
// implementation of the 2D RoPE without adding a new op in ggml
// this is not efficient (use double the memory), but works on all backends
// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
ggml_tensor * build_rope_2d(
ggml_context * ctx0,
ggml_tensor * cur,
ggml_tensor * pos_a, // first half
ggml_tensor * pos_b, // second half
const float freq_base,
const bool interleave_freq
);
// aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
// support dynamic resolution
ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor);
// Generic function to stack frames for audio processing
// Abstracts out the StackAudioFrames logic used by ultravox
ggml_tensor * build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed);
};
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