extern/whisper.cpp
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talk-llama : sync llama.cpp

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-05-27 17:08:24 +03:00
parent 546928c33f
commit 26eb48cb08
18 changed files with 1968 additions and 1178 deletions
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39
examples/talk-llama/llama-hparams.h
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@@ -14,6 +14,12 @@ enum llama_expert_gating_func_type {
LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID = 2,
};
enum llama_swa_type {
LLAMA_SWA_TYPE_NONE = 0,
LLAMA_SWA_TYPE_STANDARD = 1,
LLAMA_SWA_TYPE_CHUNKED = 2,
};
struct llama_hparams_posnet {
uint32_t n_embd;
uint32_t n_layer;
@@ -35,8 +41,6 @@ struct llama_hparams {
uint32_t n_embd_features = 0;
uint32_t n_layer;
uint32_t n_rot;
uint32_t n_swa = 0; // sliding window attention (SWA)
uint32_t n_swa_pattern = 1; // by default, all layers use non-sliding-window attention
uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
uint32_t n_expert = 0;
@@ -96,6 +100,15 @@ struct llama_hparams {
std::array<int, 4> rope_sections;
// Sliding Window Attention (SWA)
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)
// by default, all layers are dense
std::array<bool, LLAMA_MAX_LAYERS> swa_layers;
// for State Space Models
uint32_t ssm_d_conv = 0;
uint32_t ssm_d_inner = 0;
@@ -116,11 +129,10 @@ struct llama_hparams {
bool causal_attn = true;
bool use_alibi = false;
bool attn_soft_cap = false;
bool use_kq_norm = true;
// llama4
uint32_t n_moe_layer_step = 0;
bool use_kq_norm = true;
uint32_t n_attn_chunk = 0;
// values below seems to be fixed on llama4
uint32_t n_no_rope_layer_step = 4;
uint32_t n_attn_temp_floor_scale = 8192;
float f_attn_temp_scale = 0.1;
@@ -133,6 +145,23 @@ struct llama_hparams {
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
// this value n_pattern means that every nth layer is dense (i.e. non-SWA)
// note that if n_pattern == 0, all layers are SWA
// if n_pattern == 1, all layers are dense
// example: n_pattern = 3
// il == 0: swa
// il == 1: swa
// il == 2: dense
// il == 3: swa
// il == 4: swa
// il == 5: dense
// il == 6: swa
// etc ...
void set_swa_pattern(uint32_t n_pattern);
// return true if one of the layers is SWA
bool is_swa_any() const;
uint32_t n_head(uint32_t il = 0) const;
uint32_t n_head_kv(uint32_t il = 0) const;