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Update vendor ggml code to a5bb8ba4 (#13832)

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Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
This commit is contained in:
Jeffrey Morgan
2026-02-02 17:31:59 -08:00
committed by GitHub
parent 8f4a008139
commit ef00199fb4
241 changed files with 21271 additions and 5074 deletions
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66
llama/llama.cpp/src/llama-hparams.h vendored
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@@ -3,6 +3,7 @@
#include "llama.h"
#include <array>
#include <cassert>
// bump if necessary
#define LLAMA_MAX_LAYERS 512
@@ -52,8 +53,8 @@ struct llama_hparams {
uint32_t n_rel_attn_bkts = 0;
// note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
uint32_t n_embd_head_k_mla = 0;
uint32_t n_embd_head_v_mla = 0;
uint32_t n_embd_head_k_mla_impl = 0;
uint32_t n_embd_head_v_mla_impl = 0;
// for WavTokenizer
struct llama_hparams_posnet posnet;
@@ -107,9 +108,9 @@ struct llama_hparams {
float rope_attn_factor = 1.0f;
float rope_freq_base_train;
float rope_freq_base_train_swa;
float rope_freq_base_train_swa = 10000.0f;
float rope_freq_scale_train;
float rope_freq_scale_train_swa;
float rope_freq_scale_train_swa = 1.0f;
uint32_t n_ctx_orig_yarn;
float rope_yarn_log_mul = 0.0f;
@@ -125,10 +126,11 @@ struct llama_hparams {
llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
// the size of the sliding window (0 - no SWA)
uint32_t n_swa = 0;
// if swa_layers[il] == true, then layer il is SWA
// if swa_layers[il] == false, then layer il is dense (i.e. non-SWA)
// if swa_layers[il] == 1, then layer il is SWA
// if swa_layers[il] == 0, then layer il is dense (i.e. non-SWA)
// by default, all layers are dense
std::array<bool, LLAMA_MAX_LAYERS> swa_layers;
// note: using uint32_t type for compatibility reason
std::array<uint32_t, LLAMA_MAX_LAYERS> swa_layers;
// for State Space Models
uint32_t ssm_d_conv = 0;
@@ -163,6 +165,9 @@ struct llama_hparams {
// for Classifiers
uint32_t n_cls_out = 1;
// output embedding dimension (0 = use n_embd)
uint32_t n_embd_out_impl = 0;
// llama4 smallthinker
uint32_t n_moe_layer_step = 0;
uint32_t n_no_rope_layer_step = 4;
@@ -235,6 +240,9 @@ struct llama_hparams {
// dimension of main + auxiliary input embeddings
uint32_t n_embd_inp() const;
// dimension of output embeddings
uint32_t n_embd_out() const;
// dimension of key embeddings across all k-v heads
uint32_t n_embd_k_gqa(uint32_t il = 0) const;
@@ -266,15 +274,57 @@ struct llama_hparams {
bool is_swa(uint32_t il) const;
// note: currently only support if either all or none of the layers are MLA
bool is_mla() const;
uint32_t n_embd_head_k_mla() const;
uint32_t n_embd_head_v_mla() const;
bool has_kv(uint32_t il) const;
// number of layers for which has_kv() returns true
uint32_t n_layer_kv() const;
// note that this function uses different SWA parameters from those in the hparams
// note: inlined on purpose for performance reasons
// 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);
static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {
assert(p0 >= 0 && p1 >= 0);
switch (swa_type) {
case LLAMA_SWA_TYPE_NONE:
{
} break;
case LLAMA_SWA_TYPE_STANDARD:
{
if (p1 - p0 >= (int32_t) n_swa) {
return true;
}
} break;
case LLAMA_SWA_TYPE_CHUNKED:
{
const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
if (p0 < pos_chunk_start) {
return true;
}
} break;
case LLAMA_SWA_TYPE_SYMMETRIC:
{
const int32_t half_n_swa = (int32_t) n_swa / 2;
const int32_t pos_diff = p1 - p0;
// Mask if outside the symmetric window
if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
return true;
}
} break;
}
return false;
}
bool use_mrope() const;
};