mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2026-05-06 16:21:49 +00:00
model, mtmd: fix gguf conversion for audio/vision mmproj (#21309)
* fix gguf conversion for audio/vision mmproj * fix test
This commit is contained in:
Xuan-Son Nguyen
GitHub
parent
223373742b
commit
63f8fe0ef4
27 changed files with 1462 additions and 41 deletions
|
|
@ -73,6 +73,7 @@ add_library(llama
|
|||
models/gemma2-iswa.cpp
|
||||
models/gemma3.cpp
|
||||
models/gemma3n-iswa.cpp
|
||||
models/gemma4-iswa.cpp
|
||||
models/glm4-moe.cpp
|
||||
models/glm4.cpp
|
||||
models/gpt2.cpp
|
||||
|
|
|
|||
|
|
@ -56,6 +56,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_GEMMA2, "gemma2" },
|
||||
{ LLM_ARCH_GEMMA3, "gemma3" },
|
||||
{ LLM_ARCH_GEMMA3N, "gemma3n" },
|
||||
{ LLM_ARCH_GEMMA4, "gemma4" },
|
||||
{ LLM_ARCH_GEMMA_EMBEDDING, "gemma-embedding" },
|
||||
{ LLM_ARCH_STARCODER2, "starcoder2" },
|
||||
{ LLM_ARCH_MAMBA, "mamba" },
|
||||
|
|
@ -165,6 +166,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
|||
{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
|
||||
{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
|
||||
{ LLM_KV_EMBEDDING_LENGTH_OUT, "%s.embedding_length_out" },
|
||||
{ LLM_KV_EMBEDDING_LENGTH_PER_LAYER, "%s.embedding_length_per_layer_input" },
|
||||
{ LLM_KV_FEATURES_LENGTH, "%s.features_length" },
|
||||
{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
|
||||
{ LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" },
|
||||
|
|
@ -238,6 +240,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
|||
{ LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, "%s.attention.indexer.head_count" },
|
||||
{ LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, "%s.attention.indexer.key_length" },
|
||||
{ LLM_KV_ATTENTION_INDEXER_TOP_K, "%s.attention.indexer.top_k" },
|
||||
{ LLM_KV_ATTENTION_SHARED_KV_LAYERS, "%s.attention.shared_kv_layers" },
|
||||
|
||||
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
|
||||
{ LLM_KV_ROPE_DIMENSION_COUNT_SWA, "%s.rope.dimension_count_swa" },
|
||||
|
|
@ -364,6 +367,9 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
|
|||
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
|
||||
{ LLM_TENSOR_ATTN_GATE, "blk.%d.attn_gate" },
|
||||
{ LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
|
||||
{ LLM_TENSOR_FFN_POST_NORM_1, "blk.%d.post_ffw_norm_1" },
|
||||
{ LLM_TENSOR_FFN_POST_NORM_2, "blk.%d.post_ffw_norm_2" },
|
||||
{ LLM_TENSOR_FFN_PRE_NORM_2, "blk.%d.pre_ffw_norm_2" },
|
||||
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
|
||||
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
|
||||
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
|
||||
|
|
@ -373,6 +379,7 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
|
|||
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
|
||||
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
||||
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
|
||||
{ LLM_TENSOR_LAYER_OUT_SCALE, "blk.%d.layer_output_scale" },
|
||||
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
|
||||
{ LLM_TENSOR_POS_EMBD, "position_embd" },
|
||||
{ LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" },
|
||||
|
|
@ -1342,6 +1349,38 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
|
|||
LLM_TENSOR_LAUREL_R,
|
||||
LLM_TENSOR_LAUREL_POST_NORM,
|
||||
};
|
||||
case LLM_ARCH_GEMMA4:
|
||||
return {
|
||||
LLM_TENSOR_ROPE_FREQS,
|
||||
LLM_TENSOR_TOKEN_EMBD,
|
||||
LLM_TENSOR_OUTPUT_NORM,
|
||||
LLM_TENSOR_ATTN_NORM,
|
||||
LLM_TENSOR_ATTN_Q,
|
||||
LLM_TENSOR_ATTN_Q_NORM,
|
||||
LLM_TENSOR_ATTN_K,
|
||||
LLM_TENSOR_ATTN_K_NORM,
|
||||
LLM_TENSOR_ATTN_V,
|
||||
LLM_TENSOR_ATTN_OUT,
|
||||
LLM_TENSOR_ATTN_POST_NORM,
|
||||
LLM_TENSOR_FFN_NORM,
|
||||
LLM_TENSOR_FFN_GATE,
|
||||
LLM_TENSOR_FFN_DOWN,
|
||||
LLM_TENSOR_FFN_UP,
|
||||
LLM_TENSOR_FFN_GATE_UP_EXPS,
|
||||
LLM_TENSOR_FFN_DOWN_EXPS,
|
||||
LLM_TENSOR_FFN_GATE_INP,
|
||||
LLM_TENSOR_FFN_POST_NORM,
|
||||
LLM_TENSOR_FFN_POST_NORM_1,
|
||||
LLM_TENSOR_FFN_POST_NORM_2,
|
||||
LLM_TENSOR_FFN_PRE_NORM_2,
|
||||
LLM_TENSOR_LAYER_OUT_SCALE,
|
||||
LLM_TENSOR_PER_LAYER_TOKEN_EMBD,
|
||||
LLM_TENSOR_PER_LAYER_MODEL_PROJ,
|
||||
LLM_TENSOR_PER_LAYER_PROJ_NORM,
|
||||
LLM_TENSOR_PER_LAYER_INP_GATE,
|
||||
LLM_TENSOR_PER_LAYER_PROJ,
|
||||
LLM_TENSOR_PER_LAYER_POST_NORM,
|
||||
};
|
||||
case LLM_ARCH_GEMMA_EMBEDDING:
|
||||
return {
|
||||
LLM_TENSOR_TOKEN_EMBD,
|
||||
|
|
@ -2654,11 +2693,15 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
|
|||
{LLM_TENSOR_ATTN_OUT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_ATTN_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_FFN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_FFN_PRE_NORM_2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_FFN_POST_NORM_1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_FFN_POST_NORM_2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_FFN_POST_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_FFN_NORM_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_ATTN_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_ATTN_K_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_LAYER_OUT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_LAYER_OUT_SCALE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_ATTN_Q_A_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_ATTN_KV_A_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
{LLM_TENSOR_ATTN_SUB_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
|
||||
|
|
|
|||
|
|
@ -60,6 +60,7 @@ enum llm_arch {
|
|||
LLM_ARCH_GEMMA2,
|
||||
LLM_ARCH_GEMMA3,
|
||||
LLM_ARCH_GEMMA3N,
|
||||
LLM_ARCH_GEMMA4,
|
||||
LLM_ARCH_GEMMA_EMBEDDING,
|
||||
LLM_ARCH_STARCODER2,
|
||||
LLM_ARCH_MAMBA,
|
||||
|
|
@ -169,6 +170,7 @@ enum llm_kv {
|
|||
LLM_KV_CONTEXT_LENGTH,
|
||||
LLM_KV_EMBEDDING_LENGTH,
|
||||
LLM_KV_EMBEDDING_LENGTH_OUT,
|
||||
LLM_KV_EMBEDDING_LENGTH_PER_LAYER,
|
||||
LLM_KV_FEATURES_LENGTH,
|
||||
LLM_KV_BLOCK_COUNT,
|
||||
LLM_KV_LEADING_DENSE_BLOCK_COUNT,
|
||||
|
|
@ -242,6 +244,7 @@ enum llm_kv {
|
|||
LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
|
||||
LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
|
||||
LLM_KV_ATTENTION_INDEXER_TOP_K,
|
||||
LLM_KV_ATTENTION_SHARED_KV_LAYERS,
|
||||
|
||||
LLM_KV_ROPE_DIMENSION_COUNT,
|
||||
LLM_KV_ROPE_DIMENSION_COUNT_SWA,
|
||||
|
|
@ -369,6 +372,9 @@ enum llm_tensor {
|
|||
LLM_TENSOR_FFN_GATE_INP_SHEXP,
|
||||
LLM_TENSOR_FFN_NORM,
|
||||
LLM_TENSOR_FFN_POST_NORM,
|
||||
LLM_TENSOR_FFN_POST_NORM_1,
|
||||
LLM_TENSOR_FFN_POST_NORM_2,
|
||||
LLM_TENSOR_FFN_PRE_NORM_2,
|
||||
LLM_TENSOR_FFN_GATE,
|
||||
LLM_TENSOR_FFN_DOWN,
|
||||
LLM_TENSOR_FFN_UP,
|
||||
|
|
@ -393,6 +399,7 @@ enum llm_tensor {
|
|||
LLM_TENSOR_ATTN_Q_NORM,
|
||||
LLM_TENSOR_ATTN_K_NORM,
|
||||
LLM_TENSOR_LAYER_OUT_NORM,
|
||||
LLM_TENSOR_LAYER_OUT_SCALE,
|
||||
LLM_TENSOR_POST_ATTN_NORM,
|
||||
LLM_TENSOR_POST_MLP_NORM,
|
||||
LLM_TENSOR_PER_LAYER_TOKEN_EMBD, // gemma3n
|
||||
|
|
|
|||
|
|
@ -209,6 +209,9 @@ struct llama_hparams {
|
|||
// qwen3vl deepstack
|
||||
uint32_t n_deepstack_layers = 0;
|
||||
|
||||
// gemma4 per-layer embedding
|
||||
uint32_t n_embd_per_layer = 0;
|
||||
|
||||
// needed by encoder-decoder models (e.g. T5, FLAN-T5)
|
||||
// ref: https://github.com/ggml-org/llama.cpp/pull/8141
|
||||
llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
|
||||
|
|
|
|||
|
|
@ -1261,6 +1261,31 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
|||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_GEMMA4:
|
||||
{
|
||||
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
|
||||
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
|
||||
|
||||
uint32_t n_kv_shared_layers = 0;
|
||||
ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);
|
||||
|
||||
hparams.n_layer_kv_from_start = hparams.n_layer - (int32_t)n_kv_shared_layers;
|
||||
hparams.f_attention_scale = 1.0f; // Gemma4 uses self.scaling = 1.0 (no pre-attn scaling)
|
||||
|
||||
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
||||
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer);
|
||||
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa);
|
||||
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa);
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 35: type = LLM_TYPE_E2B; break;
|
||||
case 42: type = LLM_TYPE_E4B; break; // to confirm: E4B or E5B?
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_GEMMA_EMBEDDING:
|
||||
{
|
||||
hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
|
||||
|
|
@ -4229,6 +4254,100 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
layer.laurel_post_norm = create_tensor(tn(LLM_TENSOR_LAUREL_POST_NORM, "weight", i), {n_embd}, 0);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_GEMMA4:
|
||||
{
|
||||
const uint32_t n_embd_per_layer = hparams.n_embd_per_layer;
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp;
|
||||
|
||||
if (n_embd_head_k != n_embd_head_v) {
|
||||
throw std::runtime_error("Gemma 4 requires n_embd_head_k == n_embd_head_v");
|
||||
}
|
||||
if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) {
|
||||
throw std::runtime_error("Gemma 4 requires n_embd_head_k_swa == n_embd_head_v_swa");
|
||||
}
|
||||
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
// if output is NULL, init from the input tok embed
|
||||
if (output == NULL) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
if (n_embd_per_layer > 0) {
|
||||
tok_embd_per_layer = create_tensor(tn(LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "weight"), {n_embd_per_layer * n_layer, n_vocab}, 0);
|
||||
per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight"), {n_embd, n_embd_per_layer * n_layer}, 0);
|
||||
per_layer_proj_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM, "weight"), {n_embd_per_layer}, 0);
|
||||
}
|
||||
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
|
||||
int rope_freqs_flag = 0;
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
const int64_t n_head = hparams.n_head(i);
|
||||
const int64_t n_embd_head = hparams.n_embd_head_k(i);
|
||||
const int64_t n_embd_k = hparams.n_embd_k_gqa(i);
|
||||
const int64_t n_embd_v = hparams.n_embd_v_gqa(i);
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
// note: use_alternative_attention (v_proj is optional, if it's not present, use k_proj)
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, TENSOR_NOT_REQUIRED);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
|
||||
|
||||
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0);
|
||||
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head}, 0);
|
||||
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
if (!hparams.is_swa(i)) {
|
||||
// full_attention layers use rope_freqs for proportional rope
|
||||
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_embd_head/2}, rope_freqs_flag);
|
||||
rope_freqs_flag = TENSOR_DUPLICATED;
|
||||
}
|
||||
|
||||
// handle use_double_wide_mlp
|
||||
int64_t n_ff_cur = hparams.n_ff(i);
|
||||
|
||||
// for expert layers, we use normal FFN as shared expert (same as python code)
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff_cur}, 0);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff_cur}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
|
||||
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
// MoE router
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
|
||||
bool has_expert = layer.ffn_gate_inp != nullptr;
|
||||
|
||||
// norm
|
||||
if (has_expert) {
|
||||
layer.ffn_gate_inp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "scale", i), {n_embd}, 0);
|
||||
|
||||
layer.ffn_pre_norm_2 = create_tensor(tn(LLM_TENSOR_FFN_PRE_NORM_2, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_post_norm_1 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_1, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_post_norm_2 = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM_2, "weight", i), {n_embd}, 0);
|
||||
|
||||
// MoE FFN
|
||||
layer.ffn_gate_up_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS, "weight", i), {n_embd, n_ff_exp * 2, n_expert}, 0);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
||||
|
||||
// per-expert scale will be loaded as down_exps_s at the end of the current switch case
|
||||
}
|
||||
|
||||
// per-layer embeddings
|
||||
if (n_embd_per_layer > 0) {
|
||||
layer.per_layer_inp_gate = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE, "weight", i), {n_embd, n_embd_per_layer}, 0);
|
||||
layer.per_layer_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ, "weight", i), {n_embd_per_layer, n_embd}, 0);
|
||||
layer.per_layer_post_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_STARCODER2:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
|
@ -8233,7 +8352,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
|||
} else {
|
||||
llama_memory_i::layer_reuse_cb reuse = nullptr;
|
||||
|
||||
if (arch == LLM_ARCH_GEMMA3N) {
|
||||
if (arch == LLM_ARCH_GEMMA3N || arch == LLM_ARCH_GEMMA4) {
|
||||
reuse = [&](int32_t il) {
|
||||
if (il >= (int32_t) hparams.n_layer_kv_from_start) {
|
||||
return (int32_t) hparams.n_layer_kv_from_start - (hparams.is_swa(il) ? 2 : 1);
|
||||
|
|
@ -8486,6 +8605,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
{
|
||||
llm = std::make_unique<llm_build_gemma3n_iswa>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_GEMMA4:
|
||||
{
|
||||
llm = std::make_unique<llm_build_gemma4_iswa>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_GEMMA_EMBEDDING:
|
||||
{
|
||||
llm = std::make_unique<llm_build_gemma_embedding>(*this, params);
|
||||
|
|
@ -9006,6 +9129,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_GEMMA2:
|
||||
case LLM_ARCH_GEMMA3:
|
||||
case LLM_ARCH_GEMMA3N:
|
||||
case LLM_ARCH_GEMMA4:
|
||||
case LLM_ARCH_GEMMA_EMBEDDING:
|
||||
case LLM_ARCH_STARCODER2:
|
||||
case LLM_ARCH_OPENELM:
|
||||
|
|
|
|||
|
|
@ -270,6 +270,9 @@ struct llama_layer {
|
|||
struct ggml_tensor * ffn_norm = nullptr;
|
||||
struct ggml_tensor * ffn_norm_b = nullptr;
|
||||
struct ggml_tensor * ffn_post_norm = nullptr;
|
||||
struct ggml_tensor * ffn_post_norm_1 = nullptr; // gemma4
|
||||
struct ggml_tensor * ffn_post_norm_2 = nullptr; // gemma4
|
||||
struct ggml_tensor * ffn_pre_norm_2 = nullptr; // gemma4
|
||||
struct ggml_tensor * layer_out_norm = nullptr;
|
||||
struct ggml_tensor * layer_out_norm_b = nullptr;
|
||||
struct ggml_tensor * ffn_norm_exps = nullptr;
|
||||
|
|
@ -285,6 +288,7 @@ struct llama_layer {
|
|||
|
||||
// ff MoE
|
||||
struct ggml_tensor * ffn_gate_inp = nullptr;
|
||||
struct ggml_tensor * ffn_gate_inp_s = nullptr; // gemma4
|
||||
struct ggml_tensor * ffn_gate_exps = nullptr;
|
||||
struct ggml_tensor * ffn_down_exps = nullptr;
|
||||
struct ggml_tensor * ffn_up_exps = nullptr;
|
||||
|
|
@ -483,6 +487,9 @@ struct llama_layer {
|
|||
struct ggml_tensor * indexer_attn_k = nullptr;
|
||||
struct ggml_tensor * indexer_attn_q_b = nullptr; // note: for lora a/b, not bias
|
||||
|
||||
// gemma4 layer output scale
|
||||
struct ggml_tensor * out_scale = nullptr;
|
||||
|
||||
struct llama_layer_posnet posnet;
|
||||
|
||||
struct llama_layer_convnext convnext;
|
||||
|
|
|
|||
|
|
@ -1863,6 +1863,18 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
special_sep_id = LLAMA_TOKEN_NULL;
|
||||
special_pad_id = 3; // <|plamo:pad|>
|
||||
special_mask_id = LLAMA_TOKEN_NULL;
|
||||
} else if (tokenizer_model == "gemma4") {
|
||||
type = LLAMA_VOCAB_TYPE_SPM;
|
||||
|
||||
// default special tokens (to be read from GGUF)
|
||||
special_bos_id = LLAMA_TOKEN_NULL;
|
||||
special_eos_id = LLAMA_TOKEN_NULL;
|
||||
special_unk_id = LLAMA_TOKEN_NULL;
|
||||
special_sep_id = LLAMA_TOKEN_NULL;
|
||||
special_pad_id = LLAMA_TOKEN_NULL;
|
||||
special_mask_id = LLAMA_TOKEN_NULL;
|
||||
|
||||
tokenizer_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
|
||||
} else {
|
||||
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
|
||||
}
|
||||
|
|
@ -2490,6 +2502,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "[EOS]" // Kimi-K2
|
||||
|| t.first == "<|end_of_text|>"
|
||||
|| t.first == "<end_of_utterance>" // smoldocling
|
||||
|| t.first == "<turn|>" // gemma4
|
||||
|| t.first == "<｜end▁of▁sentence｜>" // deepseek-ocr
|
||||
) {
|
||||
special_eog_ids.insert(t.second);
|
||||
|
|
|
|||
311
src/models/gemma4-iswa.cpp
Normal file
311
src/models/gemma4-iswa.cpp
Normal file
|
|
@ -0,0 +1,311 @@
|
|||
#include "models.h"
|
||||
|
||||
llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params),
|
||||
model(model),
|
||||
n_embd_per_layer(model.hparams.n_embd_per_layer) {
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
|
||||
inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
|
||||
cb(inpL, "inp_scaled", -1);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
// TODO: is causal == true correct? might need some changes
|
||||
auto * inp_attn = build_attn_inp_kv_iswa();
|
||||
|
||||
// inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer]
|
||||
ggml_tensor * inp_per_layer = nullptr;
|
||||
if (model.tok_embd_per_layer) {
|
||||
inp_per_layer = project_per_layer_inputs(inpL, get_per_layer_inputs());
|
||||
}
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_k(il);
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_v(il));
|
||||
|
||||
const int64_t n_head = hparams.n_head(il);
|
||||
const int64_t n_head_kv = hparams.n_head_kv(il);
|
||||
|
||||
const float freq_base_l = model.get_rope_freq_base(cparams, il);
|
||||
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
|
||||
const int n_rot_l = hparams.n_rot(il);
|
||||
|
||||
// norm
|
||||
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
ggml_tensor * freq_factors = nullptr;
|
||||
if (!hparams.is_swa(il)) {
|
||||
// full_attention layers use rope_freqs for proportional rope
|
||||
freq_factors = model.layers[il].rope_freqs;
|
||||
}
|
||||
|
||||
// Q projection (shared for both non-KV and KV layers)
|
||||
// this is to mirror Gemma4Attention in pytorch code
|
||||
ggml_tensor * Qcur;
|
||||
{
|
||||
Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_normed", il);
|
||||
|
||||
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(Qcur, "Qcur_pos", il);
|
||||
}
|
||||
|
||||
// self-attention
|
||||
if (hparams.has_kv(il)) {
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
ggml_tensor * Vcur = model.layers[il].wv
|
||||
? build_lora_mm(model.layers[il].wv, cur)
|
||||
: Kcur; // if v_proj is not present, use Kcur as Vcur
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);
|
||||
|
||||
cb(Kcur, "Kcur_normed", il);
|
||||
cb(Vcur, "Vcur_normed", il);
|
||||
|
||||
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
|
||||
cb(Kcur, "Kcur_pos", il);
|
||||
|
||||
cur = build_attn(inp_attn, model.layers[il].wo,
|
||||
nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
|
||||
hparams.f_attention_scale, il);
|
||||
} else {
|
||||
// reuse KV cache of earlier layers
|
||||
cur = build_attn(inp_attn,
|
||||
model.layers[il].wo, nullptr,
|
||||
Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
|
||||
}
|
||||
|
||||
// TODO @ngxson : strip unused token right after the last KV layer to speed up prompt processing
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
||||
}
|
||||
cur = build_norm(cur,
|
||||
model.layers[il].attn_post_norm, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_post_norm", il);
|
||||
|
||||
ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL);
|
||||
cb(attn_out, "attn_out", il);
|
||||
|
||||
// feed-forward network
|
||||
const bool is_moe_layer = model.layers[il].ffn_gate_inp != nullptr;
|
||||
if (is_moe_layer) {
|
||||
// MLP (shared exp)
|
||||
ggml_tensor * cur_mlp = build_norm(attn_out,
|
||||
model.layers[il].ffn_norm, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur_mlp, "ffn_norm_1", il);
|
||||
|
||||
cur_mlp = build_ffn(cur_mlp,
|
||||
model.layers[il].ffn_up, nullptr, nullptr,
|
||||
model.layers[il].ffn_gate, nullptr, nullptr,
|
||||
model.layers[il].ffn_down, nullptr, nullptr,
|
||||
nullptr,
|
||||
LLM_FFN_GELU, LLM_FFN_PAR, il);
|
||||
cur_mlp = build_norm(cur_mlp,
|
||||
model.layers[il].ffn_post_norm_1, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur_mlp, "ffn_mlp", il);
|
||||
|
||||
// Expert FFN
|
||||
ggml_tensor * cur_moe = build_norm(attn_out,
|
||||
model.layers[il].ffn_pre_norm_2, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur_moe, "ffn_norm_2", il);
|
||||
|
||||
// custom MoE logits calculation (router operates on attn_out, not cur)
|
||||
ggml_tensor * tmp = ggml_rms_norm(ctx0, attn_out, hparams.f_norm_rms_eps);
|
||||
tmp = ggml_scale(ctx0, tmp, 1.0f / sqrtf((float) n_embd));
|
||||
tmp = ggml_mul(ctx0, tmp, model.layers[il].ffn_gate_inp_s);
|
||||
ggml_tensor * logits = build_lora_mm(model.layers[il].ffn_gate_inp, tmp); // [n_expert, n_tokens]
|
||||
cb(logits, "ffn_moe_logits", il);
|
||||
|
||||
cur_moe = build_moe_ffn(cur_moe,
|
||||
nullptr, // gate_inp
|
||||
nullptr, // up_exps
|
||||
nullptr, // gate_exps
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr, // exp_probs_b (not used for gemma4)
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_GELU, true,
|
||||
1.0f,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
il, logits,
|
||||
model.layers[il].ffn_gate_up_exps,
|
||||
nullptr, // up_exps_s
|
||||
nullptr, // gate_exps_s
|
||||
model.layers[il].ffn_down_exps_s);
|
||||
cur_moe = build_norm(cur_moe,
|
||||
model.layers[il].ffn_post_norm_2, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur_moe, "ffn_moe", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur_mlp, cur_moe);
|
||||
cb(cur, "ffn_moe_combined", il);
|
||||
} else {
|
||||
cur = build_norm(attn_out,
|
||||
model.layers[il].ffn_norm, nullptr,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, nullptr, nullptr,
|
||||
model.layers[il].ffn_gate, nullptr, nullptr,
|
||||
model.layers[il].ffn_down, nullptr, nullptr,
|
||||
nullptr,
|
||||
LLM_FFN_GELU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
cur = build_norm(cur,
|
||||
model.layers[il].ffn_post_norm, nullptr,
|
||||
LLM_NORM_RMS, -1);
|
||||
cb(cur, "ffn_post_norm", il);
|
||||
|
||||
// residual connection
|
||||
cur = ggml_add(ctx0, cur, attn_out);
|
||||
|
||||
// per-layer embedding
|
||||
if (inp_per_layer) {
|
||||
ggml_tensor * pe_in = cur;
|
||||
cb(cur, "pe_in", il);
|
||||
|
||||
cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens]
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
ggml_tensor * inp_this_layer = view_2d_slice(inp_per_layer, il); // [n_embd_per_layer, n_tokens]
|
||||
|
||||
// TODO @ngxson : improve this
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids);
|
||||
}
|
||||
|
||||
cur = ggml_mul(ctx0, cur, inp_this_layer);
|
||||
cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens]
|
||||
cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "per_layer_embd_out", il);
|
||||
|
||||
// residual connection
|
||||
cur = ggml_add(ctx0, pe_in, cur);
|
||||
}
|
||||
|
||||
// layer_scalar
|
||||
if (model.layers[il].out_scale) {
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);
|
||||
cb(cur, "out_scaled", il);
|
||||
}
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur,
|
||||
model.output_norm, nullptr,
|
||||
LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// lm_head
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
|
||||
if (hparams.f_final_logit_softcapping) {
|
||||
cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
|
||||
cur = ggml_tanh(ctx0, cur);
|
||||
cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
|
||||
}
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
// get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
|
||||
ggml_tensor * llm_build_gemma4_iswa::view_2d_slice(ggml_tensor * x, int idx) {
|
||||
GGML_ASSERT(idx < (int) x->ne[2]);
|
||||
return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),
|
||||
idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
|
||||
}
|
||||
|
||||
// equivalent to get_per_layer_inputs() in python code
|
||||
// output shape: [n_embd_per_layer, n_layer, n_tokens]
|
||||
ggml_tensor * llm_build_gemma4_iswa::get_per_layer_inputs() {
|
||||
auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
|
||||
ggml_tensor * inp_per_layer;
|
||||
if (ubatch.token) {
|
||||
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
|
||||
ggml_set_input(inp->tokens);
|
||||
res->t_inp_tokens = inp->tokens;
|
||||
inp_per_layer = ggml_get_rows(ctx0, model.tok_embd_per_layer, inp->tokens);
|
||||
inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, n_tokens);
|
||||
inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float) n_embd_per_layer));
|
||||
cb(inp_per_layer, "inp_per_layer_selected", -1);
|
||||
res->add_input(std::move(inp));
|
||||
} else {
|
||||
// Vision embedding path: use padding token (ID=0) embedding
|
||||
// TODO: verify if this is the correct behavior in transformers implementation
|
||||
const int64_t embd_size = model.tok_embd_per_layer->ne[0]; // n_embd_per_layer * n_layer
|
||||
|
||||
// Extract and dequantize padding token embedding (row 0)
|
||||
ggml_tensor * padding = ggml_view_1d(ctx0, model.tok_embd_per_layer, embd_size, 0);
|
||||
inp_per_layer = ggml_cast(ctx0, padding, GGML_TYPE_F32);
|
||||
|
||||
// Reshape to [n_embd_per_layer, n_layer, 1]
|
||||
inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, 1);
|
||||
cb(inp_per_layer, "inp_per_layer_vision", -1);
|
||||
}
|
||||
return inp_per_layer;
|
||||
}
|
||||
|
||||
// equivalent to project_per_layer_inputs() in python code
|
||||
// this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
|
||||
// inputs_embeds shape: [n_embd, n_tokens]
|
||||
// inp_per_layer shape: [n_embd_per_layer, n_layer, n_tokens] (from get_per_layer_inputs)
|
||||
// output shape: [n_embd_per_layer, n_tokens, n_layer]
|
||||
ggml_tensor * llm_build_gemma4_iswa::project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer) {
|
||||
const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);
|
||||
const float per_layer_input_scale = 1.0f / sqrtf(2.0f);
|
||||
|
||||
ggml_tensor * per_layer_proj = ggml_mul_mat(ctx0, model.per_layer_model_proj, inputs_embeds);
|
||||
per_layer_proj = ggml_scale(ctx0, per_layer_proj, per_layer_projection_scale);
|
||||
per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_per_layer, n_layer, n_tokens);
|
||||
per_layer_proj = build_norm(per_layer_proj, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS,
|
||||
-1); // [n_embd_per_layer, n_layer, n_tokens]
|
||||
cb(per_layer_proj, "per_layer_proj", -1);
|
||||
|
||||
inp_per_layer = ggml_add(ctx0, per_layer_proj, inp_per_layer);
|
||||
inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
|
||||
cb(inp_per_layer, "inp_per_layer", -1);
|
||||
|
||||
// permute to shape: [n_embd_per_layer, n_tokens, n_layer]
|
||||
inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));
|
||||
return inp_per_layer;
|
||||
}
|
||||
|
|
@ -266,6 +266,17 @@ struct llm_build_gemma3n_iswa : public llm_graph_context {
|
|||
ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il);
|
||||
};
|
||||
|
||||
struct llm_build_gemma4_iswa : public llm_graph_context {
|
||||
const llama_model & model;
|
||||
|
||||
const int64_t n_embd_per_layer;
|
||||
|
||||
llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params);
|
||||
ggml_tensor * view_2d_slice(ggml_tensor * x, int idx);
|
||||
ggml_tensor * get_per_layer_inputs();
|
||||
ggml_tensor * project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer);
|
||||
};
|
||||
|
||||
struct llm_build_gemma_embedding : public llm_graph_context {
|
||||
llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue