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talk-llama : sync llama.cpp

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This commit is contained in:
Georgi Gerganov
2026-08-04 13:37:47 +03:00
parent 81844466e8
commit 6b92fec9e7
50 changed files with 6656 additions and 701 deletions
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examples/talk-llama/CMakeLists.txt
+4 -6
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@@ -2,6 +2,8 @@ if (WHISPER_SDL2)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
file(GLOB SRC_KV_CACHE llama-kv-cache-*.cpp)
file(GLOB SRC_MEMORY llama-memory-*.cpp)
file(GLOB SRC_MODELS models/*.cpp)
set(TARGET whisper-talk-llama)
@@ -19,13 +21,9 @@ if (WHISPER_SDL2)
llama-impl.cpp
llama-io.cpp
llama-kv-cache.cpp
llama-kv-cache-iswa.cpp
llama-kv-cache-dsa.cpp
llama-kv-cache-dsv4.cpp
llama-memory-recurrent.cpp
llama-memory-hybrid.cpp
llama-memory-hybrid-iswa.cpp
${SRC_KV_CACHE}
llama-memory.cpp
${SRC_MEMORY}
llama-mmap.cpp
llama-model-loader.cpp
llama-model-saver.cpp
examples/talk-llama/llama-arch.cpp
+28 -2
View File
@@ -108,11 +108,13 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_DOTS1, "dots1" },
{ LLM_ARCH_ARCEE, "arcee" },
{ LLM_ARCH_AFMOE, "afmoe" },
{ LLM_ARCH_LAGUNA, "laguna" },
{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
{ LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
{ LLM_ARCH_HUNYUAN_DENSE, "hunyuan-dense" },
{ LLM_ARCH_HUNYUAN_VL, "hunyuan_vl" },
{ LLM_ARCH_HY_V3, "hy_v3" },
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
{ LLM_ARCH_LFM2, "lfm2" },
@@ -125,6 +127,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GROVEMOE, "grovemoe" },
{ LLM_ARCH_APERTUS, "apertus" },
{ LLM_ARCH_MINIMAX_M2, "minimax-m2" },
{ LLM_ARCH_MINIMAX_M3, "minimax-m3" },
{ LLM_ARCH_COGVLM, "cogvlm" },
{ LLM_ARCH_RND1, "rnd1" },
{ LLM_ARCH_PANGU_EMBED, "pangu-embedded" },
@@ -140,6 +143,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_KIMI_LINEAR, "kimi-linear" },
{ LLM_ARCH_TALKIE, "talkie" },
{ LLM_ARCH_MELLUM, "mellum" },
{ LLM_ARCH_NANBEIGE, "nanbeige" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -218,6 +222,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" },
{ LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" },
{ LLM_KV_FULL_ATTENTION_INTERVAL, "%s.full_attention_interval" },
{ LLM_KV_NUM_LOOPS, "%s.num_loops" },
{ LLM_KV_SKIP_LOOP_FINAL_NORM, "%s.skip_loop_final_norm" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
@@ -251,6 +257,9 @@ 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_INDEXER_BLOCK_SIZE, "%s.attention.indexer.block_size" },
{ LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, "%s.attention.indexer.local_blocks" },
{ LLM_KV_ATTENTION_INDEXER_TYPES, "%s.attention.indexer.types" },
{ LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, "%s.attention.output_group_count" },
{ LLM_KV_ATTENTION_OUTPUT_LORA_RANK, "%s.attention.output_lora_rank" },
{ LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, "%s.attention.compress_rope_freq_base" },
@@ -308,6 +317,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TARGET_LAYERS, "%s.target_layers" },
{ LLM_KV_TARGET_HIDDEN_SIZE, "%s.target_hidden_size" },
{ LLM_KV_NORM_BEFORE_RESIDUAL, "%s.norm_before_residual" },
{ LLM_KV_NORM_BEFORE_FC, "%s.norm_before_fc" },
{ LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
// sentence-transformers dense modules feature dims
@@ -594,6 +604,9 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_INDEXER_PROJ, "blk.%d.indexer.proj" },
{ LLM_TENSOR_INDEXER_ATTN_K, "blk.%d.indexer.attn_k" },
{ LLM_TENSOR_INDEXER_ATTN_Q_B, "blk.%d.indexer.attn_q_b" },
{ LLM_TENSOR_INDEXER_Q_PROJ, "blk.%d.indexer.q_proj" },
{ LLM_TENSOR_INDEXER_K_PROJ, "blk.%d.indexer.k_proj" },
{ LLM_TENSOR_INDEXER_Q_NORM, "blk.%d.indexer.q_norm" },
{ LLM_TENSOR_INDEXER_COMPRESSOR_WKV, "blk.%d.indexer_compressor_kv" },
{ LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "blk.%d.indexer_compressor_gate" },
{ LLM_TENSOR_INDEXER_COMPRESSOR_APE, "blk.%d.indexer_compressor_ape" },
@@ -603,6 +616,9 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_MASKED_EMBD_ORDERING, "masked_embd_ordering" },
{ LLM_TENSOR_FC, "fc" },
{ LLM_TENSOR_D2T, "d2t" },
{ LLM_TENSOR_DSPARK_MARKOV_W1, "markov_w1" },
{ LLM_TENSOR_DSPARK_MARKOV_W2, "markov_w2" },
{ LLM_TENSOR_DSPARK_CONF_PROJ, "conf_proj" },
};
// declare information about the model weight tensors:
@@ -664,7 +680,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_HC_FFN_SCALE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_ATTN_COMPRESSOR_WKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_ATTN_COMPRESSOR_WGATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_ATTN_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
{LLM_TENSOR_ATTN_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
{LLM_TENSOR_ATTN_COMPRESSOR_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_ATTN_K_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_ATTN_V_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -829,9 +845,12 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_INDEXER_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_Q_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_K_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_INDEXER_COMPRESSOR_WKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_INDEXER_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
{LLM_TENSOR_INDEXER_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
{LLM_TENSOR_INDEXER_COMPRESSOR_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_FFN_GATE_TID2EID, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
{LLM_TENSOR_NEXTN_PROJ_PRE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -854,6 +873,10 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
// eagle3
{LLM_TENSOR_FC, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_D2T, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
// dspark
{LLM_TENSOR_DSPARK_MARKOV_W1, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
{LLM_TENSOR_DSPARK_MARKOV_W2, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_DSPARK_CONF_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
};
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
@@ -945,6 +968,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
case LLM_ARCH_KIMI_LINEAR:
case LLM_ARCH_QWEN35:
case LLM_ARCH_QWEN35MOE:
case LLM_ARCH_DEEPSEEK4:
return true;
default:
return false;
@@ -967,6 +991,7 @@ bool llm_arch_supports_rs_rollback(const llm_arch & arch) {
switch (arch) {
case LLM_ARCH_QWEN35:
case LLM_ARCH_QWEN35MOE:
case LLM_ARCH_DEEPSEEK4:
return true;
default:
return false;
@@ -998,6 +1023,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_MINIMAX_M2:
case LLM_ARCH_MINIMAX_M3:
case LLM_ARCH_MISTRAL4:
case LLM_ARCH_KIMI_LINEAR:
return false;
examples/talk-llama/llama-arch.h
+16
View File
@@ -113,11 +113,13 @@ enum llm_arch {
LLM_ARCH_DOTS1,
LLM_ARCH_ARCEE,
LLM_ARCH_AFMOE,
LLM_ARCH_LAGUNA,
LLM_ARCH_ERNIE4_5,
LLM_ARCH_ERNIE4_5_MOE,
LLM_ARCH_HUNYUAN_MOE,
LLM_ARCH_HUNYUAN_DENSE,
LLM_ARCH_HUNYUAN_VL,
LLM_ARCH_HY_V3,
LLM_ARCH_SMOLLM3,
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_LFM2,
@@ -144,7 +146,9 @@ enum llm_arch {
LLM_ARCH_TALKIE,
LLM_ARCH_MELLUM,
LLM_ARCH_EAGLE3,
LLM_ARCH_MINIMAX_M3,
LLM_ARCH_DFLASH,
LLM_ARCH_NANBEIGE,
LLM_ARCH_UNKNOWN,
};
@@ -223,6 +227,8 @@ enum llm_kv {
LLM_KV_TOKEN_SHIFT_COUNT,
LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
LLM_KV_FULL_ATTENTION_INTERVAL,
LLM_KV_NUM_LOOPS,
LLM_KV_SKIP_LOOP_FINAL_NORM,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -256,6 +262,9 @@ enum llm_kv {
LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
LLM_KV_ATTENTION_INDEXER_TOP_K,
LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE,
LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS,
LLM_KV_ATTENTION_INDEXER_TYPES,
LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,
LLM_KV_ATTENTION_OUTPUT_LORA_RANK,
LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE,
@@ -354,6 +363,7 @@ enum llm_kv {
LLM_KV_TARGET_LAYERS,
LLM_KV_TARGET_HIDDEN_SIZE,
LLM_KV_NORM_BEFORE_RESIDUAL,
LLM_KV_NORM_BEFORE_FC,
LLM_KV_SHORTCONV_L_CACHE,
@@ -594,6 +604,9 @@ enum llm_tensor {
LLM_TENSOR_INDEXER_PROJ,
LLM_TENSOR_INDEXER_ATTN_K,
LLM_TENSOR_INDEXER_ATTN_Q_B,
LLM_TENSOR_INDEXER_Q_PROJ,
LLM_TENSOR_INDEXER_K_PROJ,
LLM_TENSOR_INDEXER_Q_NORM,
LLM_TENSOR_INDEXER_COMPRESSOR_WKV,
LLM_TENSOR_INDEXER_COMPRESSOR_WGATE,
LLM_TENSOR_INDEXER_COMPRESSOR_APE,
@@ -611,6 +624,9 @@ enum llm_tensor {
LLM_TENSOR_MASKED_EMBD_ORDERING,
LLM_TENSOR_FC,
LLM_TENSOR_D2T,
LLM_TENSOR_DSPARK_MARKOV_W1,
LLM_TENSOR_DSPARK_MARKOV_W2,
LLM_TENSOR_DSPARK_CONF_PROJ,
};
examples/talk-llama/llama-context.cpp
+99 -23
View File
@@ -55,6 +55,30 @@ static const llm_fused_op_probe llm_fused_op_gdn_ch_probe = {
/*.n_tokens_per_seq =*/ 16,
};
static const llm_fused_op_probe llm_fused_op_lid_probe = {
/*.op =*/ LLM_FUSED_OP_LIGHTNING_INDEXER,
/*.name =*/ "Lightning Indexer",
/*.n_tokens_per_seq =*/ 1,
};
static const llm_fused_op_probe llm_fused_op_dsv4_hc_pre_probe = {
/*.op =*/ LLM_FUSED_OP_DSV4_HC_PRE,
/*.name =*/ "fused DeepSeek V4 HC pre",
/*.n_tokens_per_seq =*/ 1,
};
static const llm_fused_op_probe llm_fused_op_dsv4_hc_comb_probe = {
/*.op =*/ LLM_FUSED_OP_DSV4_HC_COMB,
/*.name =*/ "fused DeepSeek V4 HC comb",
/*.n_tokens_per_seq =*/ 1,
};
static const llm_fused_op_probe llm_fused_op_dsv4_hc_post_probe = {
/*.op =*/ LLM_FUSED_OP_DSV4_HC_POST,
/*.name =*/ "fused DeepSeek V4 HC post",
/*.n_tokens_per_seq =*/ 1,
};
llama_context::llama_context(
const llama_model & model,
llama_context_params params) :
@@ -96,8 +120,9 @@ llama_context::llama_context(
cparams.no_perf = params.no_perf;
cparams.warmup = false;
cparams.embeddings_layer_inp.resize(hparams.n_layer(), false);
embd_layer_inp.resize(hparams.n_layer());
// +1: id n_layer() taps the output of the last layer ("input" of the head)
cparams.embeddings_layer_inp.resize(hparams.n_layer() + 1, false);
embd_layer_inp.resize(hparams.n_layer() + 1);
cparams.ctx_type = params.ctx_type;
cparams.pooling_type = params.pooling_type;
@@ -226,6 +251,14 @@ llama_context::llama_context(
cparams.fused_gdn_ch = true;
cparams.auto_fgdn = true;
cparams.fused_lid = true;
cparams.auto_flid = true;
cparams.fused_dsv4_hc_pre = true;
cparams.fused_dsv4_hc_comb = true;
cparams.fused_dsv4_hc_post = true;
cparams.auto_fhc = true;
// 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;
@@ -442,6 +475,9 @@ llama_context::llama_context(
}
llama_context::~llama_context() {
// wait for any pending asynchronous copies into the output buffers before they are freed
synchronize();
if (!model.hparams.no_alloc) {
for (size_t i = 0; i < backend_ptrs.size(); ++i) {
ggml_backend_t backend = backend_ptrs[i];
@@ -522,6 +558,20 @@ void llama_context::resolve_fused_ops(const llama_memory_context_i * mctx, uint3
resolve(llm_fused_op_gdn_ch_probe, cparams.fused_gdn_ch);
cparams.auto_fgdn = false;
}
if (cparams.auto_flid) {
LLAMA_LOG_INFO("%s: resolving fused Lightning Indexer support:\n", func);
resolve(llm_fused_op_lid_probe, cparams.fused_lid);
cparams.auto_flid = false;
}
if (cparams.auto_fhc) {
LLAMA_LOG_INFO("%s: resolving fused DeepSeek V4 HC support:\n", func);
resolve(llm_fused_op_dsv4_hc_pre_probe, cparams.fused_dsv4_hc_pre);
resolve(llm_fused_op_dsv4_hc_comb_probe, cparams.fused_dsv4_hc_comb);
resolve(llm_fused_op_dsv4_hc_post_probe, cparams.fused_dsv4_hc_post);
cparams.auto_fhc = false;
}
}
void llama_context::sched_reserve() {
@@ -1115,7 +1165,7 @@ void llama_context::set_embeddings_nextn(bool value, bool masked) {
void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
LLAMA_LOG_DEBUG("%s: lid = %d, enable = %d\n", __func__, lid, enable);
GGML_ASSERT(lid < model.hparams.n_layer());
GGML_ASSERT(lid <= model.hparams.n_layer());
cparams.embeddings_layer_inp[lid] = enable;
@@ -1371,13 +1421,17 @@ int llama_context::encode(const llama_batch & batch_inp) {
// micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
// TODO: this clear of the buffer can easily be forgotten - need something better
// sync first so any in-flight async copies into embd_seq complete before it is freed
if (!embd_seq.empty()) {
synchronize();
}
embd_seq.clear();
if (t_compute_start_us == 0) {
t_compute_start_us = ggml_time_us();
}
// TODO: this clear of the buffer can easily be forgotten - need something better
embd_seq.clear();
sched_reserve();
n_queued_tokens += n_tokens;
@@ -1663,7 +1717,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
const auto & hparams = model.hparams;
const int64_t n_vocab = vocab.n_tokens();
const int64_t n_embd = hparams.n_embd_inp();
const bool mtp_embd = cparams.ctx_type == LLAMA_CONTEXT_TYPE_MTP && batch_inp.embd;
const int64_t n_embd = mtp_embd ? hparams.n_embd_out() : hparams.n_embd_inp();
// when computing embeddings, all tokens are output
const bool output_all = cparams.embeddings;
@@ -1716,13 +1771,18 @@ int llama_context::decode(const llama_batch & batch_inp) {
GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
// TODO: this clear of the buffer can easily be forgotten - need something better
// sync first so any in-flight async copies into embd_seq complete before it is freed
if (!embd_seq.empty()) {
synchronize();
}
embd_seq.clear();
if (t_compute_start_us == 0) {
t_compute_start_us = ggml_time_us();
}
n_queued_tokens += n_tokens_all;
// TODO: this clear of the buffer can easily be forgotten - need something better
embd_seq.clear();
output_swaps.clear();
sched_reserve();
@@ -2217,8 +2277,9 @@ void llama_context::extract_layer_inputs(const llm_graph_result * res, size_t to
}
void llama_context::output_reorder() {
const uint64_t n_vocab = model.vocab.n_tokens();
const uint64_t n_embd = model.hparams.n_embd;
const uint64_t n_vocab = model.vocab.n_tokens();
const uint64_t n_embd = model.hparams.n_embd;
const uint64_t n_embd_out = model.hparams.n_embd_out();
for (size_t s = 0; s < output_swaps.size(); ++s) {
const uint64_t i0 = output_swaps[s].i0;
@@ -2231,14 +2292,14 @@ void llama_context::output_reorder() {
}
if (embd.size > 0) {
for (uint64_t k = 0; k < n_embd; k++) {
std::swap(embd.data[i0*n_embd + k], embd.data[i1*n_embd + k]);
for (uint64_t k = 0; k < n_embd_out; k++) {
std::swap(embd.data[i0*n_embd_out + k], embd.data[i1*n_embd_out + k]);
}
}
if (embd_nextn.size > 0) {
for (uint64_t k = 0; k < n_embd; k++) {
std::swap(embd_nextn.data[i0*n_embd + k], embd_nextn.data[i1*n_embd + k]);
for (uint64_t k = 0; k < n_embd_out; k++) {
std::swap(embd_nextn.data[i0*n_embd_out + k], embd_nextn.data[i1*n_embd_out + k]);
}
}
@@ -2292,7 +2353,10 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
model.arch == LLM_ARCH_KIMI_LINEAR ||
model.arch == LLM_ARCH_QWEN35 ||
model.arch == LLM_ARCH_QWEN35MOE ||
model.arch == LLM_ARCH_DEEPSEEK4) {
model.arch == LLM_ARCH_DEEPSEEK4 ||
(model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
model.arch == LLM_ARCH_NANBEIGE ||
model.arch == LLM_ARCH_MINIMAX_M3) {
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
}
uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
@@ -2426,11 +2490,12 @@ llm_graph_cb llama_context::graph_get_cb() const {
ggml_set_name(cur, name);
}
// norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
// - norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
// - force the last op of the layer on the specified backend to avoid running it on the backend of the next layer due to scheduling
// FIXME: fix in ggml_backend_sched
const bool full_offload = model.n_gpu_layers() > model.hparams.n_layer_all;
if (ubatch.n_tokens < 32 || full_offload) {
if (il != -1 && strcmp(name, "norm") == 0) {
if (il != -1 && (strcmp(name, "norm") == 0 || strcmp(name, "l_last") == 0)) {
const auto & dev_layer = model.dev_layer(il);
for (const auto & backend : backends) {
if (ggml_backend_get_device(backend.get()) == dev_layer) {
@@ -3492,6 +3557,22 @@ llama_context * llama_init_from_model(
}
}
if ((model->hparams.is_mla() || model->arch == LLM_ARCH_DEEPSEEK4) && params.type_k != params.type_v) {
LLAMA_LOG_ERROR("%s: model does not support different K (%s) and V (%s) cache types\n", __func__, ggml_type_name(params.type_k), ggml_type_name(params.type_v));
return nullptr;
}
if (ggml_is_quantized(params.type_v) && params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_ENABLED) {
if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) {
LLAMA_LOG_INFO("%s: enabling flash_attn since it is required for quantized V cache\n", __func__);
params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
}
if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED) {
LLAMA_LOG_ERROR("%s: quantized V cache requires flash_attn to be enabled\n", __func__);
return nullptr;
}
}
if (params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED && ggml_is_quantized(params.type_k)) {
const uint32_t blck_size = ggml_blck_size(params.type_k);
for (uint32_t il = 0; il < model->hparams.n_layer(); ++il) {
@@ -3514,11 +3595,6 @@ llama_context * llama_init_from_model(
}
}
if (ggml_is_quantized(params.type_v) && params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED) {
LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
return nullptr;
}
if (params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED &&
params.pooling_type != model->hparams.pooling_type) {
//user-specified pooling-type is different from the model default
examples/talk-llama/llama-cparams.h
+6
View File
@@ -41,6 +41,12 @@ struct llama_cparams {
bool fused_gdn_ar; // use fused gated delta net (autoregressive)
bool fused_gdn_ch; // use fused gated delta net (chunked)
bool auto_fgdn;
bool fused_lid; // use fused lightning indexer
bool auto_flid;
bool fused_dsv4_hc_pre;
bool fused_dsv4_hc_comb;
bool fused_dsv4_hc_post;
bool auto_fhc;
bool no_perf;
bool warmup; // TODO: remove [TAG_LLAMA_GRAPH_NO_WARMUP]
bool op_offload;
examples/talk-llama/llama-grammar.cpp
+12
View File
@@ -1139,6 +1139,18 @@ struct llama_grammar * llama_grammar_init_impl(
vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
}
// Validate that all rule references point to valid rules
for (size_t i = 0; i < n_rules; i++) {
for (const auto & elem : vec_rules[i]) {
if (elem.type == LLAMA_GRETYPE_RULE_REF) {
if (elem.value >= n_rules || vec_rules[elem.value].empty()) {
LLAMA_LOG_ERROR("invalid grammar: rule %zu references undefined rule %u\n", i, elem.value);
return nullptr;
}
}
}
}
// Check for left recursion
std::vector<bool> rules_visited(n_rules);
std::vector<bool> rules_in_progress(n_rules);
examples/talk-llama/llama-graph.cpp
+247 -7
View File
@@ -8,6 +8,7 @@
#include "llama-kv-cache.h"
#include "llama-kv-cache-iswa.h"
#include "llama-kv-cache-dsa.h"
#include "llama-kv-cache-msa.h"
#include "llama-kv-cache-dsv4.h"
#include "llama-memory-hybrid.h"
#include "llama-memory-hybrid-iswa.h"
@@ -518,6 +519,40 @@ bool llm_graph_input_attn_k::can_reuse(const llm_graph_params & params) {
return res;
}
llm_graph_input_attn_kv_msa::llm_graph_input_attn_kv_msa(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_msa_context * mctx) :
llm_graph_input_attn_kv(hparams, cparams, mctx->get_base()),
mctx_msa(mctx) {
}
void llm_graph_input_attn_kv_msa::set_input(const llama_ubatch * ubatch) {
llm_graph_input_attn_kv::set_input(ubatch);
if (self_k_idxs_idx) {
mctx_msa->get_idx()->set_input_k_idxs(self_k_idxs_idx, ubatch);
}
}
bool llm_graph_input_attn_kv_msa::can_reuse(const llm_graph_params & params) {
mctx_msa = static_cast<const llama_kv_cache_msa_context *>(params.mctx);
// the parent class operates on the base cache context
this->mctx = mctx_msa->get_base();
bool res = true;
res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
if (self_k_idxs_idx) {
res &= self_k_idxs_idx->ne[0] == params.ubatch.n_tokens;
}
res &= can_reuse_kq_mask(self_kq_mask, this->mctx, params.ubatch, params.cparams);
return res;
}
void llm_graph_input_attn_k_dsa::set_input(const llama_ubatch * ubatch) {
mctx->get_mla()->set_input_k_idxs(self_k_idxs_mla, ubatch);
@@ -619,6 +654,63 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
return res;
}
void llm_graph_input_attn_k_iswa::set_input(const llama_ubatch * ubatch) {
// base tensors may not be allocated if there are no non-SWA attention layers
if (self_k_idxs && self_k_idxs->buffer) {
mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
}
// the kq mask guards on its own buffer: shared cells leave idxs unbacked while the mask stays live
if (self_kq_mask && self_kq_mask->buffer) {
mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
// swa tensors may not be allocated if there are no SWA attention layers
if (self_k_idxs_swa && self_k_idxs_swa->buffer) {
mctx->get_swa()->set_input_k_idxs(self_k_idxs_swa, ubatch);
}
if (self_kq_mask_swa && self_kq_mask_swa->buffer) {
mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
}
if (self_k_rot && self_k_rot->buffer) {
mctx->get_base()->set_input_k_rot(self_k_rot);
}
if (self_k_rot_swa && self_k_rot_swa->buffer) {
mctx->get_swa()->set_input_k_rot(self_k_rot_swa);
}
}
bool llm_graph_input_attn_k_iswa::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_kv_cache_iswa_context *>(params.mctx);
this->mctx = mctx;
bool res = true;
// base tensors may not be allocated if there are no non-SWA attention layers
if (self_k_idxs && self_k_idxs->buffer) {
res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
}
if (self_kq_mask && self_kq_mask->buffer) {
res &= can_reuse_kq_mask(self_kq_mask, mctx->get_base(), params.ubatch, params.cparams);
}
// swa tensors may not be allocated if there are no SWA attention layers
if (self_k_idxs_swa && self_k_idxs_swa->buffer) {
res &= self_k_idxs_swa->ne[0] == params.ubatch.n_tokens;
}
if (self_kq_mask_swa && self_kq_mask_swa->buffer) {
res &= can_reuse_kq_mask(self_kq_mask_swa, mctx->get_swa(), params.ubatch, params.cparams);
}
return res;
}
static void dsv4_set_i64(ggml_tensor * dst, const std::vector<int64_t> & src) {
if (!dst || !dst->buffer) {
return;
@@ -754,6 +846,10 @@ static void dsv4_set_comp_inputs(
dsv4_set_i32(inp.state_pos, plan.state_pos);
dsv4_set_i32(inp.state_persist_src_idxs, plan.state_persist_src_idxs);
dsv4_set_i32(inp.state_persist_dst_idxs, plan.state_persist_dst_idxs);
dsv4_set_i32(inp.state_restore_src_idxs, plan.state_restore_src_idxs);
dsv4_set_i32(inp.state_restore_dst_idxs, plan.state_restore_dst_idxs);
dsv4_set_i32(inp.state_snapshot_src_idxs, plan.state_snapshot_src_idxs);
dsv4_set_i32(inp.state_snapshot_dst_idxs, plan.state_snapshot_dst_idxs);
dsv4_set_i32(inp.state_read_idxs, plan.state_read_idxs);
dsv4_set_i64(inp.state_write_idxs, plan.state_write_idxs);
dsv4_set_i32(inp.state_write_pos, plan.state_write_pos);
@@ -798,6 +894,10 @@ static bool dsv4_can_reuse_comp_input(
res &= dsv4_can_reuse_tensor_1d(inp.state_pos, plan.state_pos.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_persist_src_idxs, plan.state_persist_src_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_persist_dst_idxs, plan.state_persist_dst_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_restore_src_idxs, plan.state_restore_src_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_restore_dst_idxs, plan.state_restore_dst_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_snapshot_src_idxs, plan.state_snapshot_src_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_snapshot_dst_idxs, plan.state_snapshot_dst_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_read_idxs, plan.state_read_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_write_idxs, plan.state_write_idxs.size());
res &= dsv4_can_reuse_tensor_1d(inp.state_write_pos, plan.state_write_pos.size());
@@ -832,6 +932,10 @@ static void dsv4_build_comp_inputs(
inp.state_pos = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_pos.size(), std::string("dsv4_") + name + "_state_pos");
inp.state_persist_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_persist_src_idxs.size(), std::string("dsv4_") + name + "_state_persist_src_idxs");
inp.state_persist_dst_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_persist_dst_idxs.size(), std::string("dsv4_") + name + "_state_persist_dst_idxs");
inp.state_restore_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_restore_src_idxs.size(), std::string("dsv4_") + name + "_state_restore_src_idxs");
inp.state_restore_dst_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_restore_dst_idxs.size(), std::string("dsv4_") + name + "_state_restore_dst_idxs");
inp.state_snapshot_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_snapshot_src_idxs.size(), std::string("dsv4_") + name + "_state_snapshot_src_idxs");
inp.state_snapshot_dst_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_snapshot_dst_idxs.size(), std::string("dsv4_") + name + "_state_snapshot_dst_idxs");
inp.state_read_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_read_idxs.size(), std::string("dsv4_") + name + "_state_read_idxs");
inp.state_write_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I64, plan.state_write_idxs.size(), std::string("dsv4_") + name + "_state_write_idxs");
inp.state_write_pos = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_write_pos.size(), std::string("dsv4_") + name + "_state_write_pos");
@@ -842,7 +946,7 @@ static void dsv4_build_comp_inputs(
GGML_ASSERT(n_stream > 0);
GGML_ASSERT(n_tokens%n_stream == 0);
inp.kq_mask = ggml_new_tensor_4d(ctx, cparams.flash_attn && strcmp(name, "lid") != 0 ? GGML_TYPE_F16 : GGML_TYPE_F32, plan.n_kv, n_tokens/n_stream, 1, n_stream);
inp.kq_mask = ggml_new_tensor_4d(ctx, (strcmp(name, "lid") != 0 && cparams.flash_attn) || (strcmp(name, "lid") == 0 && cparams.fused_lid) ? GGML_TYPE_F16 : GGML_TYPE_F32, plan.n_kv, n_tokens/n_stream, 1, n_stream);
ggml_set_input(inp.kq_mask);
ggml_set_name(inp.kq_mask, (std::string("dsv4_") + name + "_kq_mask").c_str());
}
@@ -1195,7 +1299,7 @@ void llm_graph_result::reset() {
t_embd_pooled = nullptr;
t_h_nextn = nullptr;
t_layer_inp.resize(LLAMA_MAX_LAYERS);
t_layer_inp.resize(LLAMA_MAX_LAYERS + 1);
std::fill(t_layer_inp.begin(), t_layer_inp.end(), nullptr);
t_sampled.clear();
@@ -1650,7 +1754,7 @@ ggml_tensor * llm_graph_context::build_ffn(
tmp = ggml_clamp(ctx0, tmp, -limit, limit);
cb(tmp, "ffn_up_clamped", il);
if (arch == LLM_ARCH_DEEPSEEK4) {
if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0)) {
cur = ggml_clamp(ctx0, cur, -INFINITY, limit);
cb(cur, "ffn_gate_clamped", il);
cur = ggml_swiglu_split(ctx0, cur, tmp);
@@ -1709,6 +1813,17 @@ ggml_tensor * llm_graph_context::build_ffn(
cur = ggml_swiglu(ctx0, cur);
cb(cur, "ffn_swiglu", il);
} break;
case LLM_FFN_SWIGLU_OAI_MOE:
if (gate && type_gate == LLM_FFN_PAR) {
// same alpha/limit constants as gpt-oss
const float alpha = 1.702f;
const float limit = 7.0f;
cur = ggml_swiglu_oai(ctx0, cur, tmp, alpha, limit);
cb(cur, "ffn_swiglu_oai", il);
type_gate = LLM_FFN_SEQ;
} else {
GGML_ABORT("LLM_FFN_SWIGLU_OAI_MOE requires a parallel gate");
} break;
case LLM_FFN_GEGLU:
{
cur = ggml_geglu(ctx0, cur);
@@ -2034,7 +2149,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
up = ggml_clamp(ctx0, up, -limit, limit);
cb(up, "ffn_moe_up_clamped", il);
if (arch == LLM_ARCH_DEEPSEEK4) {
if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0)) {
cur = ggml_clamp(ctx0, cur, -INFINITY, limit);
cb(cur, "ffn_moe_gate_clamped", il);
cur = ggml_swiglu_split(ctx0, cur, up);
@@ -2668,7 +2783,7 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
@@ -2951,6 +3066,75 @@ ggml_tensor * llm_graph_context::build_attn(
return cur;
}
ggml_tensor * llm_graph_context::build_attn(
llm_graph_input_attn_k_iswa * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * wo_s,
ggml_tensor * q_cur,
ggml_tensor * k_cur,
ggml_tensor * v_cur,
ggml_tensor * kq_b,
ggml_tensor * sinks,
ggml_tensor * v_mla,
float kq_scale,
int il) const {
const bool is_swa = hparams.is_swa(il);
GGML_UNUSED(v_cur);
auto * k_rot = is_swa ? inp->self_k_rot_swa : inp->self_k_rot;
if (k_rot) {
q_cur = llama_mul_mat_hadamard(ctx0, q_cur, k_rot);
if (k_cur) {
k_cur = llama_mul_mat_hadamard(ctx0, k_cur, k_rot);
}
}
// these nodes are added to the graph together so that they are not reordered
// by doing so, the number of splits in the graph is reduced
ggml_build_forward_expand(gf, q_cur);
if (k_cur) {
ggml_build_forward_expand(gf, k_cur);
}
const auto * mctx_iswa = inp->mctx;
const auto * mctx_cur = is_swa ? mctx_iswa->get_swa() : mctx_iswa->get_base();
// optionally store to KV cache
if (k_cur) {
const auto & k_idxs = is_swa ? inp->get_k_idxs_swa() : inp->get_k_idxs();
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
}
const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
// MLA-style attention: the cached K is used as V
ggml_tensor * q = q_cur;
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = k;
ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
cb(cur, "kqv_out", il);
if (k_rot) {
cur = llama_mul_mat_hadamard(ctx0, cur, k_rot);
}
if (wo) {
cur = build_lora_mm(wo, cur, wo_s);
}
if (wo_b) {
cur = ggml_add(ctx0, cur, wo_b);
}
return cur;
}
llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
auto inp = std::make_unique<llm_graph_input_attn_cross>(cross);
@@ -3025,9 +3209,9 @@ llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
{
inp->self_k_idxs_lid = mctx_cur->get_lid()->build_input_k_idxs(ctx0, ubatch);
// ensure F32 mask
// ensure that mask type matches fused lightning indexer use (requires f16 mask)
auto cparams_copy = cparams;
cparams_copy.flash_attn = false;
cparams_copy.flash_attn = cparams.fused_lid;
inp->self_kq_mask_lid = build_attn_inp_kq_mask(ctx0, mctx_cur->get_lid(), ubatch, cparams_copy);
inp->self_kq_mask_lid_cnv = inp->self_kq_mask_lid;
@@ -3038,6 +3222,34 @@ llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const {
return (llm_graph_input_attn_k_dsa *) res->add_input(std::move(inp));
}
llm_graph_input_attn_kv_msa * llm_graph_context::build_attn_inp_kv_msa(bool msa_enabled) const {
const auto * mctx_cur = static_cast<const llama_kv_cache_msa_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_attn_kv_msa>(hparams, cparams, mctx_cur);
const auto * mctx_base = mctx_cur->get_base();
const auto * mctx_idx = mctx_cur->get_idx();
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_iswa for SWA");
inp->self_k_idxs = mctx_base->build_input_k_idxs(ctx0, ubatch);
inp->self_v_idxs = mctx_base->build_input_v_idxs(ctx0, ubatch);
inp->self_kq_mask = build_attn_inp_kq_mask(ctx0, mctx_base, ubatch, cparams);
inp->self_kq_mask_cnv = inp->self_kq_mask;
}
inp->self_k_rot = mctx_base->build_input_k_rot(ctx0);
inp->self_v_rot = mctx_base->build_input_v_rot(ctx0);
if (msa_enabled) {
inp->self_k_idxs_idx = mctx_idx->build_input_k_idxs(ctx0, ubatch);
}
return (llm_graph_input_attn_kv_msa *) res->add_input(std::move(inp));
}
// TODO: maybe separate the inner implementation into a separate function
// like with the non-sliding window equivalent
// once sliding-window hybrid caches are a thing.
@@ -3073,6 +3285,34 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
return (llm_graph_input_attn_kv_iswa *) res->add_input(std::move(inp));
}
llm_graph_input_attn_k_iswa * llm_graph_context::build_attn_inp_k_iswa() const {
const auto * mctx_cur = static_cast<const llama_kv_cache_iswa_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_attn_k_iswa>(hparams, cparams, mctx_cur);
{
inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch);
inp->self_kq_mask = build_attn_inp_kq_mask(ctx0, mctx_cur->get_base(), ubatch, cparams);
inp->self_kq_mask_cnv = inp->self_kq_mask;
}
{
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache for non-SWA");
inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
inp->self_kq_mask_swa = build_attn_inp_kq_mask(ctx0, mctx_cur->get_swa(), ubatch, cparams);
inp->self_kq_mask_swa_cnv = inp->self_kq_mask_swa;
}
inp->self_k_rot = mctx_cur->get_base()->build_input_k_rot(ctx0);
inp->self_k_rot_swa = mctx_cur->get_swa()->build_input_k_rot(ctx0);
return (llm_graph_input_attn_k_iswa *) res->add_input(std::move(inp));
}
llm_graph_input_dsv4 * llm_graph_context::build_inp_dsv4() const {
const auto * mctx_cur = static_cast<const llama_kv_cache_dsv4_context *>(mctx);
const auto * raw_ctx = mctx_cur->get_raw();
examples/talk-llama/llama-graph.h
+89 -1
View File
@@ -23,6 +23,7 @@ struct llama_memory_context_i;
class llama_kv_cache_context;
class llama_kv_cache_dsa_context;
class llama_kv_cache_msa_context;
class llama_kv_cache_dsv4_raw_context;
class llama_kv_cache_dsv4_context;
class llama_kv_cache_iswa_context;
@@ -42,6 +43,10 @@ enum llm_fused_op {
LLM_FUSED_OP_FLASH_ATTN,
LLM_FUSED_OP_GDN_AR,
LLM_FUSED_OP_GDN_CH,
LLM_FUSED_OP_LIGHTNING_INDEXER,
LLM_FUSED_OP_DSV4_HC_PRE,
LLM_FUSED_OP_DSV4_HC_COMB,
LLM_FUSED_OP_DSV4_HC_POST,
};
enum llm_ffn_op_type : int {
@@ -421,6 +426,26 @@ public:
const llama_kv_cache_dsa_context * mctx;
};
// standard K/V attention input against the base cache, plus destination indices for the indexer key cache
class llm_graph_input_attn_kv_msa : public llm_graph_input_attn_kv {
public:
llm_graph_input_attn_kv_msa(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_msa_context * mctx);
~llm_graph_input_attn_kv_msa() = default;
void set_input(const llama_ubatch * ubatch) override;
bool can_reuse(const llm_graph_params & params) override;
ggml_tensor * get_k_idxs_idx() const { return self_k_idxs_idx; }
ggml_tensor * self_k_idxs_idx = nullptr; // I64 [n_batch]
const llama_kv_cache_msa_context * mctx_msa;
};
class llm_graph_input_attn_kv_iswa : public llm_graph_input_i {
public:
llm_graph_input_attn_kv_iswa(
@@ -467,6 +492,45 @@ public:
const llama_kv_cache_iswa_context * mctx;
};
class llm_graph_input_attn_k_iswa : public llm_graph_input_i {
public:
llm_graph_input_attn_k_iswa(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_iswa_context * mctx) :
hparams(hparams),
cparams(cparams),
mctx(mctx) {
}
~llm_graph_input_attn_k_iswa() = default;
void set_input(const llama_ubatch * ubatch) override;
bool can_reuse(const llm_graph_params & params) override;
ggml_tensor * get_k_idxs() const { return self_k_idxs; }
ggml_tensor * get_k_idxs_swa() const { return self_k_idxs_swa; }
ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
ggml_tensor * get_kq_mask_swa() const { return self_kq_mask_swa_cnv; }
ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch]
ggml_tensor * self_k_idxs_swa = nullptr; // I64 [n_batch]
ggml_tensor * self_kq_mask = nullptr; // F32/F16 [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_kq_mask_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_kq_mask_swa = nullptr; // F32/F16 [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_kq_mask_swa_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream]
ggml_tensor * self_k_rot = nullptr;
ggml_tensor * self_k_rot_swa = nullptr;
const llama_hparams hparams;
const llama_cparams cparams;
const llama_kv_cache_iswa_context * mctx;
};
// DSV4 raw graph inputs are SWA-only, but their mask may be stream-shaped
// so raw K can be concatenated with DSV4 compressed K in one attention op.
class llm_graph_input_dsv4_raw {
@@ -501,6 +565,10 @@ public:
ggml_tensor * state_pos = nullptr; // I32 [n_state]
ggml_tensor * state_persist_src_idxs = nullptr; // I32 [n_state_persist]
ggml_tensor * state_persist_dst_idxs = nullptr; // I32 [n_state_persist]
ggml_tensor * state_restore_src_idxs = nullptr; // I32 [n_state_restore]
ggml_tensor * state_restore_dst_idxs = nullptr; // I32 [n_state_restore]
ggml_tensor * state_snapshot_src_idxs = nullptr; // I32 [n_state_snapshot]
ggml_tensor * state_snapshot_dst_idxs = nullptr; // I32 [n_state_snapshot]
ggml_tensor * state_read_idxs = nullptr; // I32 [ratio*n_state_write]
ggml_tensor * state_write_idxs = nullptr; // I64 [n_state_write]
ggml_tensor * state_write_pos = nullptr; // I32 [n_state_write]
@@ -1064,7 +1132,7 @@ struct llm_graph_context {
ggml_tensor * build_attn_mha(
ggml_tensor * q, // [n_embd_head_q, n_head_q, n_tokens]
ggml_tensor * k, // [n_embd_head_k, n_head_k, n_tokens]
ggml_tensor * v, // [n_embd_head_v, n_head_v, n_tokens] (v_trans == false)
ggml_tensor * v, // [n_embd_head_v, n_head_v, n_tokens] (v_trans = false)
ggml_tensor * kq_b,
ggml_tensor * kq_mask,
ggml_tensor * sinks, // [n_head_q]
@@ -1122,6 +1190,8 @@ struct llm_graph_context {
llm_graph_input_attn_k_dsa * build_attn_inp_k_dsa() const;
llm_graph_input_attn_kv_msa * build_attn_inp_kv_msa(bool msa_enabled) const;
ggml_tensor * build_attn(
llm_graph_input_attn_k_dsa * inp,
ggml_tensor * wo,
@@ -1156,6 +1226,24 @@ struct llm_graph_context {
float kq_scale,
int il) const;
llm_graph_input_attn_k_iswa * build_attn_inp_k_iswa() const;
// note: if k_cur is not provided, it will not be stored in the memory
// note: the K cache is used as V (MLA-style attention)
ggml_tensor * build_attn(
llm_graph_input_attn_k_iswa * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * wo_s,
ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens] optional
ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens] optional
ggml_tensor * kq_b,
ggml_tensor * sinks, // [n_head_q]
ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
float kq_scale,
int il) const;
llm_graph_input_attn_cross * build_attn_inp_cross() const;
ggml_tensor * build_attn(
examples/talk-llama/llama-hparams.cpp
+8
View File
@@ -248,6 +248,14 @@ bool llama_hparams::is_mla() const {
return n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0;
}
bool llama_hparams::is_indexer_full(uint32_t il) const {
if (il < n_layer()) {
return is_indexer_full_impl[il];
}
GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer());
}
uint32_t llama_hparams::n_embd_head_k_mla() const {
return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k();
}
examples/talk-llama/llama-hparams.h
+10
View File
@@ -47,6 +47,7 @@ struct llama_hparams {
bool use_par_res;
bool swin_norm;
bool norm_before_residual = false;
bool norm_before_fc = false;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
@@ -226,6 +227,13 @@ struct llama_hparams {
uint32_t indexer_n_head = 0;
uint32_t indexer_head_size = 0;
uint32_t indexer_top_k = 0;
// MSA
uint32_t indexer_block_size = 0;
uint32_t indexer_local_blocks = 0;
// Indexer is "full" (1) or "shared" (0)
// Shared indexers reuse top-k from previous full layer
std::array<uint32_t, LLAMA_MAX_LAYERS> is_indexer_full_impl;
// DeepSeek-V4
uint32_t dsv4_o_group_count = 0;
@@ -302,6 +310,8 @@ struct llama_hparams {
bool is_swa(uint32_t il) const;
bool is_indexer_full(uint32_t il) const;
void set_recr_pattern(uint32_t n_pattern, bool dense_first = false);
// whether or not the given layer is recurrent (for hybrid models)
examples/talk-llama/llama-kv-cache-dsa.cpp
+4 -3
View File
@@ -23,7 +23,8 @@ llama_kv_cache_dsa::llama_kv_cache_dsa(
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type,
const layer_filter_cb & filter,
const layer_filter_cb & filter_mla,
const layer_filter_cb & filter_lid,
const layer_reuse_cb & reuse) :
hparams_lid(model.hparams), n_stream(unified ? 1 : n_seq_max) {
@@ -32,7 +33,7 @@ llama_kv_cache_dsa::llama_kv_cache_dsa(
kv_mla = std::make_unique<llama_kv_cache>(
model, model.hparams, type_k, type_v,
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
n_swa, swa_type, nullptr, filter, reuse, nullptr);
n_swa, swa_type, nullptr, filter_mla, reuse, nullptr);
// we use llama_kv_cache for caching indexer keys
// by hand-tweaking some hparams we fool it to create
@@ -49,7 +50,7 @@ llama_kv_cache_dsa::llama_kv_cache_dsa(
kv_lid = std::make_unique<llama_kv_cache>(
model, hparams_lid, type_k, type_v,
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
n_swa, swa_type, nullptr, filter, reuse, nullptr);
n_swa, swa_type, nullptr, filter_lid, reuse, nullptr);
}
void llama_kv_cache_dsa::clear(bool data) {
examples/talk-llama/llama-kv-cache-dsa.h
+2 -1
View File
@@ -26,7 +26,8 @@ public:
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type,
const layer_filter_cb & filter,
const layer_filter_cb & filter_mla,
const layer_filter_cb & filter_lid,
const layer_reuse_cb & reuse);
~llama_kv_cache_dsa() = default;
examples/talk-llama/llama-kv-cache-dsv4.cpp
+493 -105
View File
@@ -22,13 +22,32 @@ static constexpr uint32_t DSV4_STATE_MAGIC = 0x34565344; // DSV4
static constexpr uint32_t DSV4_STATE_VERSION = 1;
static constexpr uint32_t DSV4_STATE_MODE_FULL = 0;
static constexpr uint32_t DSV4_STATE_MODE_PARTIAL = 1;
static constexpr uint32_t DSV4_K_CACHE_STATE_VER = 1;
static constexpr uint32_t DSV4_K_CACHE_STATE_VER = 2;
static constexpr uint32_t DSV4_COMP_STATE_VER = 1;
static uint32_t dsv4_comp_size(uint32_t kv_size, uint32_t ratio) {
return std::max<uint32_t>(1, (kv_size + ratio - 1)/ratio);
}
static void dsv4_clear_tensor_stream(ggml_tensor * tensor, uint32_t stream) {
GGML_ASSERT(ggml_is_contiguous(tensor));
GGML_ASSERT(tensor->ne[3] == 1);
GGML_ASSERT(stream < (uint32_t) tensor->ne[2]);
const size_t stream_size = tensor->nb[2];
ggml_backend_tensor_memset(tensor, 0, stream*stream_size, stream_size);
}
static uint32_t dsv4_state_n_used_k_rows(llama_pos pos_max, uint32_t ratio, uint32_t kv_size) {
if (pos_max < 0) {
return 0;
}
const uint64_t n_rows = ((uint64_t) pos_max + 1)/ratio;
return (uint32_t) std::min<uint64_t>(kv_size, n_rows);
}
static int64_t dsv4_stream_offset(uint32_t n_stream, llama_seq_id seq_id, uint32_t size) {
if (n_stream <= 1) {
return 0;
@@ -230,28 +249,49 @@ static void dsv4_state_dst_stream_range(
static void dsv4_state_write_tensor_streams(
llama_io_write_i & io,
ggml_tensor * tensor,
uint32_t tensor_rows,
uint32_t n_rows,
uint32_t s0,
uint32_t ns) {
uint32_t ns,
const std::vector<uint32_t> * stream_ids = nullptr) {
const int32_t type_i = (int32_t) tensor->type;
const uint64_t ne0 = tensor->ne[0];
const uint64_t rows = n_rows;
const uint64_t row_size = ggml_row_size(tensor->type, tensor->ne[0]);
if (n_rows > tensor_rows) {
throw std::runtime_error("DSV4 state tensor row count exceeds storage");
}
io.write(&type_i, sizeof(type_i));
io.write(&ne0, sizeof(ne0));
io.write(&rows, sizeof(rows));
io.write(&row_size, sizeof(row_size));
const size_t offset = (size_t) s0*n_rows*row_size;
const size_t size = (size_t) ns*n_rows*row_size;
const size_t stream_stride = (size_t) tensor_rows*row_size;
const size_t size = (size_t) n_rows*row_size;
if (size == 0) {
return;
}
io.write_tensor(tensor, offset, size);
if (stream_ids && stream_ids->size() != ns) {
throw std::runtime_error("DSV4 state tensor stream map size mismatch");
}
for (uint32_t s = 0; s < ns; ++s) {
const uint32_t stream = stream_ids ? (*stream_ids)[s] : s0 + s;
if ((int64_t) stream >= tensor->ne[2]) {
throw std::runtime_error("DSV4 state tensor stream out of range");
}
const size_t offset = (size_t) stream*stream_stride;
io.write_tensor(tensor, offset, size);
}
}
static void dsv4_state_read_tensor_streams(
llama_io_read_i & io,
ggml_tensor * tensor,
uint32_t tensor_rows,
uint32_t n_rows,
uint32_t s0,
uint32_t ns) {
@@ -273,18 +313,28 @@ static void dsv4_state_read_tensor_streams(
if (type_i != type_i_ref || ne0 != ne0_ref || rows != rows_ref || row_size != row_size_ref) {
throw std::runtime_error("DSV4 state tensor metadata mismatch");
}
if (n_rows > tensor_rows) {
throw std::runtime_error("DSV4 state tensor row count exceeds storage");
}
const size_t offset = (size_t) s0*n_rows*row_size;
const size_t size = (size_t) ns*n_rows*row_size;
const size_t stream_stride = (size_t) tensor_rows*row_size;
const size_t size = (size_t) n_rows*row_size;
if (size == 0) {
return;
}
io.read_tensor(tensor, offset, size);
for (uint32_t s = 0; s < ns; ++s) {
const size_t offset = (size_t) (s0 + s)*stream_stride;
io.read_tensor(tensor, offset, size);
}
}
static void dsv4_state_write_k_cache(
llama_io_write_i & io,
const llama_kv_cache * kv,
llama_seq_id seq_id,
llama_state_seq_flags flags) {
llama_state_seq_flags flags,
uint32_t n_rows) {
GGML_UNUSED(flags);
uint32_t s0;
@@ -296,14 +346,18 @@ static void dsv4_state_write_k_cache(
const auto layer_ids = kv->get_layer_ids();
const uint32_t n_layer = layer_ids.size();
if (n_rows > kv_size) {
throw std::runtime_error("DSV4 K-cache state row count exceeds cache size");
}
io.write(&version, sizeof(version));
io.write(&kv_size, sizeof(kv_size));
io.write(&n_rows, sizeof(n_rows));
io.write(&ns, sizeof(ns));
io.write(&n_layer, sizeof(n_layer));
for (uint32_t il : layer_ids) {
io.write(&il, sizeof(il));
dsv4_state_write_tensor_streams(io, kv->get_k_storage(il), kv_size, s0, ns);
dsv4_state_write_tensor_streams(io, kv->get_k_storage(il), kv_size, n_rows, s0, ns);
}
}
@@ -315,19 +369,26 @@ static void dsv4_state_read_k_cache(
GGML_UNUSED(flags);
uint32_t version;
uint32_t kv_size_ref;
uint32_t n_rows_ref;
uint32_t ns;
uint32_t n_layer_ref;
io.read(&version, sizeof(version));
io.read(&kv_size_ref, sizeof(kv_size_ref));
io.read(&n_rows_ref, sizeof(n_rows_ref));
io.read(&ns, sizeof(ns));
io.read(&n_layer_ref, sizeof(n_layer_ref));
if (version != DSV4_K_CACHE_STATE_VER) {
if (version != 1 && version != DSV4_K_CACHE_STATE_VER) {
throw std::runtime_error("DSV4 K-cache state version mismatch");
}
if (kv_size_ref != kv->get_size()) {
const uint32_t kv_size = kv->get_size();
if (version == 1 && n_rows_ref != kv_size) {
LLAMA_LOG_INFO("kv size ref %d kv %d\n", n_rows_ref, kv_size);
throw std::runtime_error("DSV4 K-cache state size mismatch");
}
if (n_rows_ref > kv_size) {
LLAMA_LOG_INFO("kv rows ref %d kv %d\n", n_rows_ref, kv_size);
throw std::runtime_error("DSV4 K-cache state size mismatch");
}
@@ -346,7 +407,7 @@ static void dsv4_state_read_k_cache(
throw std::runtime_error("DSV4 K-cache layer id mismatch");
}
dsv4_state_read_tensor_streams(io, kv->get_k_storage(il), kv->get_size(), s0, ns);
dsv4_state_read_tensor_streams(io, kv->get_k_storage(il), kv_size, n_rows_ref, s0, ns);
}
}
@@ -369,7 +430,9 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
bool overlap,
uint32_t state_size,
uint32_t kv_size,
uint32_t n_stream) {
uint32_t n_stream,
uint32_t n_rs_seq,
const std::vector<uint32_t> & rs_idx) {
llama_kv_cache_dsv4_context::comp_plan plan;
plan.n_visible.resize(ubatch.n_tokens);
plan.n_stream = dsv4_comp_graph_n_stream(ubatch, n_stream);
@@ -399,6 +462,7 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
std::vector<int32_t> overlap_cur_reads;
std::map<std::pair<llama_seq_id, llama_pos>, int64_t> curr_token_idx_map;
std::map<llama_seq_id, uint32_t> state_write_counts;
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
for (int32_t s = 0; s < ubatch.n_seq_id[i]; ++s) {
@@ -461,6 +525,7 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
plan.state_write_idxs.push_back(cache_off + pos/ratio);
plan.state_write_pos.push_back((int32_t) source_start);
++state_write_counts[seq_id];
if (overlap) {
const llama_pos prev_start = source_start - ratio;
@@ -479,33 +544,57 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
}
}
if (ratio == DSV4_CSA_RATIO && plan.state_write_idxs.empty() && !plan.state_pos.empty()) {
// Non-boundary CSA steps still need a write op so their graph matches
// boundary steps. Use a padded scratch row that is masked from attention.
if (ratio == DSV4_CSA_RATIO && !plan.state_pos.empty()) {
assert(kv_size > 0);
uint32_t i = 0;
while (i < ubatch.n_tokens && ubatch.pos[i] < 0) {
++i;
}
assert(i < ubatch.n_tokens);
// Pad each stream to the reserve plan's block count.
const auto append_dummy_block = [&](llama_seq_id seq_id, uint32_t i) {
const int64_t cache_off = dsv4_stream_offset(n_stream, seq_id, kv_size);
const int32_t source_idx = state_source_idx(seq_id, ubatch.pos[i]);
const llama_pos pos = ubatch.pos[i];
const llama_seq_id seq_id = ubatch.seq_id[i][0];
const int64_t cache_off = dsv4_stream_offset(n_stream, seq_id, kv_size);
const int32_t source_idx = state_source_idx(seq_id, pos);
plan.state_write_idxs.push_back(cache_off + kv_size - 1);
plan.state_write_pos .push_back(0);
plan.state_write_idxs.push_back(cache_off + kv_size - 1);
plan.state_write_pos .push_back(0);
if (overlap) {
for (uint32_t j = 0; j < ratio; ++j) {
overlap_prev_reads.push_back(source_idx);
overlap_cur_reads .push_back(source_idx);
}
} else {
for (uint32_t j = 0; j < ratio; ++j) {
plan.state_read_idxs.push_back(source_idx);
}
}
};
if (overlap) {
for (uint32_t j = 0; j < ratio; ++j) {
overlap_prev_reads.push_back(source_idx);
overlap_cur_reads .push_back(source_idx);
if (dsv4_ubatch_has_coupled(ubatch)) {
if (plan.state_write_idxs.empty()) {
uint32_t i = 0;
while (i < ubatch.n_tokens && ubatch.pos[i] < 0) {
++i;
}
assert(i < ubatch.n_tokens);
append_dummy_block(ubatch.seq_id[i][0], i);
}
} else {
for (uint32_t j = 0; j < ratio; ++j) {
plan.state_read_idxs.push_back(source_idx);
const uint32_t n_blocks = (std::max<uint32_t>(1, ubatch.n_seq_tokens) + ratio - 1)/ratio;
for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
const llama_seq_id seq_id = ubatch.seq_id_unq[s];
const uint32_t n_writes = state_write_counts[seq_id];
if (n_writes >= n_blocks) {
continue;
}
if (n_writes + 1 != n_blocks) {
throw std::runtime_error("DSV4 CSA sequence positions are not contiguous");
}
uint32_t i = 0;
while (i < ubatch.n_tokens && (ubatch.pos[i] < 0 || !dsv4_token_has_seq(ubatch, i, seq_id))) {
++i;
}
assert(i < ubatch.n_tokens);
append_dummy_block(seq_id, i);
}
}
}
@@ -531,6 +620,63 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
plan.state_persist_dst_idxs.push_back(row.dst);
}
if (n_rs_seq > 0) {
for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
const llama_seq_id seq_id = ubatch.seq_id_unq[s];
if (seq_id < 0 || (uint32_t) seq_id >= n_stream) {
continue;
}
const int64_t stream_off = dsv4_stream_offset(n_stream, seq_id, state_size);
const uint32_t rollback = (uint32_t) seq_id < rs_idx.size() ? rs_idx[seq_id] : 0;
// Keep the restore graph fixed-width when no rollback is pending.
const int64_t src_plane = rollback > 0 && rollback <= n_rs_seq ? (int64_t) rollback*state_rows : 0;
for (uint32_t r = 0; r < state_size; ++r) {
plan.state_restore_src_idxs.push_back((int32_t) (src_plane + stream_off + r));
plan.state_restore_dst_idxs.push_back((int32_t) (stream_off + r));
}
std::vector<uint32_t> token_idxs;
token_idxs.reserve(ubatch.n_tokens);
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
if (dsv4_token_has_seq(ubatch, i, seq_id)) {
token_idxs.push_back(i);
}
}
if (token_idxs.empty()) {
continue;
}
const uint32_t n_seq_tokens = (uint32_t) token_idxs.size();
const int64_t scratch_off = (int64_t) state_rows*(1 + n_rs_seq);
for (uint32_t d = 1; d <= n_rs_seq; ++d) {
const int64_t dst_plane = (int64_t) d*state_rows;
for (uint32_t r = 0; r < state_size; ++r) {
int32_t src;
if (d <= n_seq_tokens) {
const uint32_t prefix = n_seq_tokens - d;
src = (int32_t) (stream_off + r);
for (uint32_t j = 0; j < prefix; ++j) {
const uint32_t i_tok = token_idxs[j];
if (ubatch.pos[i_tok] >= 0 && (uint32_t) (ubatch.pos[i_tok]%state_size) == r) {
src = (int32_t) (scratch_off + i_tok);
}
}
} else {
const int64_t src_plane = (int64_t) (d - n_seq_tokens)*state_rows;
src = (int32_t) (src_plane + stream_off + r);
}
plan.state_snapshot_src_idxs.push_back(src);
plan.state_snapshot_dst_idxs.push_back((int32_t) (dst_plane + stream_off + r));
}
}
}
}
static const bool debug = []() {
const char * env = getenv("LLAMA_DSV4_COMPRESS_DEBUG");
return env && atoi(env) > 0;
@@ -552,12 +698,14 @@ static std::vector<llama_kv_cache_dsv4_context::comp_plan> dsv4_build_comp_plans
bool overlap,
uint32_t state_size,
uint32_t kv_size,
uint32_t n_stream) {
uint32_t n_stream,
uint32_t n_rs_seq,
const std::vector<uint32_t> & rs_idx) {
std::vector<llama_kv_cache_dsv4_context::comp_plan> plans;
plans.reserve(ubatches.size());
for (const llama_ubatch & ubatch : ubatches) {
plans.push_back(dsv4_build_comp_plan(ubatch, ratio, overlap, state_size, kv_size, n_stream));
plans.push_back(dsv4_build_comp_plan(ubatch, ratio, overlap, state_size, kv_size, n_stream, n_rs_seq, rs_idx));
}
return plans;
@@ -644,7 +792,8 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_reserve_comp_plan(
bool overlap,
uint32_t state_size,
uint32_t kv_size,
uint32_t n_stream) {
uint32_t n_stream,
uint32_t n_rs_seq) {
llama_kv_cache_dsv4_context::comp_plan plan;
plan.n_visible.resize(ubatch.n_tokens);
plan.n_stream = dsv4_comp_graph_n_stream(ubatch, n_stream);
@@ -662,10 +811,16 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_reserve_comp_plan(
const uint64_t state_rows = (uint64_t) state_size*n_stream;
const size_t n_persist = (size_t) std::min<uint64_t>(ubatch.n_tokens, state_rows);
const size_t n_restore = n_rs_seq > 0 ? (size_t) state_size*std::max<uint32_t>(1, ubatch.n_seqs_unq) : 0;
const size_t n_snapshot = (size_t) n_rs_seq*state_size*std::max<uint32_t>(1, ubatch.n_seqs_unq);
plan.state_pos .resize(ubatch.n_tokens);
plan.state_persist_src_idxs.resize(n_persist);
plan.state_persist_dst_idxs.resize(n_persist);
plan.state_restore_src_idxs.resize(n_restore);
plan.state_restore_dst_idxs.resize(n_restore);
plan.state_snapshot_src_idxs.resize(n_snapshot);
plan.state_snapshot_dst_idxs.resize(n_snapshot);
plan.state_read_idxs .resize((overlap ? 2u : 1u)*ratio*n_blocks);
plan.state_write_idxs.resize(n_blocks);
plan.state_write_pos .resize(n_blocks);
@@ -691,12 +846,14 @@ llama_dsv4_comp_state::llama_dsv4_comp_state(
uint32_t ratio,
uint32_t state_size,
uint32_t n_embd_state,
uint32_t n_rs_seq,
const char * name,
const llama_memory_i::layer_filter_cb & filter) :
ratio(ratio),
state_size(state_size),
n_embd_state(n_embd_state),
n_stream(unified ? 1 : n_seq_max) {
n_stream(unified ? 1 : n_seq_max),
n_rs_seq(n_rs_seq) {
const llama_hparams & hparams = model.hparams;
struct ggml_backend_buft_comparator {
@@ -711,7 +868,7 @@ llama_dsv4_comp_state::llama_dsv4_comp_state(
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
ggml_init_params params = {
/*.mem_size =*/ size_t(2u*hparams.n_layer()*ggml_tensor_overhead()),
/*.mem_size =*/ size_t(2u*(1 + n_stream)*hparams.n_layer()*ggml_tensor_overhead()),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
@@ -752,15 +909,24 @@ llama_dsv4_comp_state::llama_dsv4_comp_state(
throw std::runtime_error("failed to create ggml context for DSV4 compressor state");
}
ggml_tensor * kv = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_stream);
ggml_tensor * score = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_stream);
const uint32_t n_planes = n_stream*(1 + n_rs_seq);
ggml_tensor * kv = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_planes);
ggml_tensor * score = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_planes);
ggml_format_name(kv, "dsv4_%s_state_kv_l%d", name, il);
ggml_format_name(score, "dsv4_%s_state_score_l%d", name, il);
std::vector<ggml_tensor *> kv_stream;
std::vector<ggml_tensor *> score_stream;
for (uint32_t s = 0; s < n_stream; ++s) {
kv_stream.push_back(ggml_view_2d(ctx, kv, n_embd_state, state_size, kv->nb[1], s*kv->nb[2]));
score_stream.push_back(ggml_view_2d(ctx, score, n_embd_state, state_size, score->nb[1], s*score->nb[2]));
}
map_layer_ids[il] = layers.size();
layers.push_back({ il, kv, score });
layers.push_back({ il, kv, score, std::move(kv_stream), std::move(score_stream) });
}
for (auto & [buft, ctx] : ctx_map) {
@@ -777,20 +943,59 @@ llama_dsv4_comp_state::llama_dsv4_comp_state(
ctxs_bufs.emplace_back(std::move(ctx), buf);
}
LLAMA_LOG_INFO("%s: %s ratio = %u, state = %u x %u, streams = %u, layers = %zu, size = %7.2f MiB\n",
__func__, name, ratio, state_size, n_embd_state, n_stream, layers.size(), total_size()/1024.0/1024.0);
LLAMA_LOG_INFO("%s: %s ratio = %u, state = %u x %u, streams = %u, rs_seq = %u, layers = %zu, size = %7.2f MiB\n",
__func__, name, ratio, state_size, n_embd_state, n_stream, n_rs_seq, layers.size(), total_size()/1024.0/1024.0);
}
void llama_dsv4_comp_state::clear(bool data) {
void llama_dsv4_comp_state::clear(llama_seq_id seq_id, bool data) {
if (!data) {
return;
}
if (seq_id >= 0) {
GGML_ASSERT((uint32_t) seq_id < n_stream);
for (const auto & layer : layers) {
for (uint32_t d = 0; d <= n_rs_seq; ++d) {
const uint32_t stream = d*n_stream + (uint32_t) seq_id;
dsv4_clear_tensor_stream(layer.kv, stream);
dsv4_clear_tensor_stream(layer.score, stream);
}
}
return;
}
for (auto & [_, buf] : ctxs_bufs) {
ggml_backend_buffer_clear(buf.get(), 0);
}
}
void llama_dsv4_comp_state::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst) {
GGML_ASSERT(seq_id_src >= 0 && (uint32_t) seq_id_src < n_stream);
GGML_ASSERT(seq_id_dst >= 0 && (uint32_t) seq_id_dst < n_stream);
if (seq_id_src == seq_id_dst) {
return;
}
clear(seq_id_dst, true);
sc_info.ssrc.push_back((uint32_t) seq_id_src);
sc_info.sdst.push_back((uint32_t) seq_id_dst);
}
void llama_dsv4_comp_state::apply_copies(const stream_copy_info & sc_info) const {
for (size_t i = 0; i < sc_info.ssrc.size(); ++i) {
const uint32_t ssrc = sc_info.ssrc[i];
const uint32_t sdst = sc_info.sdst[i];
for (const auto & layer : layers) {
ggml_backend_tensor_copy(layer.kv_stream[ssrc], layer.kv_stream[sdst]);
ggml_backend_tensor_copy(layer.score_stream[ssrc], layer.score_stream[sdst]);
}
}
}
uint32_t llama_dsv4_comp_state::get_ratio() const {
return ratio;
}
@@ -803,6 +1008,14 @@ uint32_t llama_dsv4_comp_state::get_n_stream() const {
return n_stream;
}
uint32_t llama_dsv4_comp_state::get_n_rs_seq() const {
return n_rs_seq;
}
uint32_t llama_dsv4_comp_state::get_n_rows() const {
return state_size*n_stream;
}
std::map<ggml_backend_buffer_type_t, size_t> llama_dsv4_comp_state::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, size_t> ret;
for (const auto & [_, buf] : ctxs_bufs) {
@@ -812,13 +1025,26 @@ std::map<ggml_backend_buffer_type_t, size_t> llama_dsv4_comp_state::memory_break
return ret;
}
void llama_dsv4_comp_state::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
void llama_dsv4_comp_state::state_write(
llama_io_write_i & io,
llama_seq_id seq_id,
llama_state_seq_flags flags,
const std::vector<uint32_t> & rs_idx) const {
GGML_UNUSED(flags);
uint32_t s0;
uint32_t ns;
dsv4_state_src_stream_range(n_stream, seq_id, s0, ns);
std::vector<uint32_t> stream_ids(ns);
for (uint32_t s = 0; s < ns; ++s) {
const uint32_t seq = seq_id >= 0 ? (uint32_t) seq_id : s0 + s;
if (seq >= rs_idx.size() || rs_idx[seq] > n_rs_seq) {
throw std::runtime_error("DSV4 recurrent state rollback index out of range");
}
stream_ids[s] = rs_idx[seq]*n_stream + s0 + s;
}
const uint32_t version = DSV4_COMP_STATE_VER;
const uint32_t n_layer = layers.size();
@@ -832,8 +1058,8 @@ void llama_dsv4_comp_state::state_write(llama_io_write_i & io, llama_seq_id seq_
for (const auto & layer : layers) {
io.write(&layer.il, sizeof(layer.il));
dsv4_state_write_tensor_streams(io, layer.kv, state_size, s0, ns);
dsv4_state_write_tensor_streams(io, layer.score, state_size, s0, ns);
dsv4_state_write_tensor_streams(io, layer.kv, state_size, state_size, s0, ns, &stream_ids);
dsv4_state_write_tensor_streams(io, layer.score, state_size, state_size, s0, ns, &stream_ids);
}
}
@@ -874,33 +1100,45 @@ void llama_dsv4_comp_state::state_read(llama_io_read_i & io, llama_seq_id seq_id
throw std::runtime_error("DSV4 compressor state layer id mismatch");
}
dsv4_state_read_tensor_streams(io, layer.kv, state_size, s0, ns);
dsv4_state_read_tensor_streams(io, layer.score, state_size, s0, ns);
dsv4_state_read_tensor_streams(io, layer.kv, state_size, state_size, s0, ns);
dsv4_state_read_tensor_streams(io, layer.score, state_size, state_size, s0, ns);
}
}
ggml_tensor * llama_dsv4_comp_state::get_kv(ggml_context * ctx, int32_t il) const {
ggml_tensor * llama_dsv4_comp_state::get_kv_all(ggml_context * ctx, int32_t il) const {
const int32_t ids = map_layer_ids.at(il);
ggml_tensor * state = layers[ids].kv;
return ggml_reshape_2d(ctx, state, state->ne[0], state->ne[1]*state->ne[2]);
return ggml_view_2d(ctx, state, state->ne[0], get_n_rows()*(1 + n_rs_seq), state->nb[1], 0);
}
ggml_tensor * llama_dsv4_comp_state::get_score_all(ggml_context * ctx, int32_t il) const {
const int32_t ids = map_layer_ids.at(il);
ggml_tensor * state = layers[ids].score;
return ggml_view_2d(ctx, state, state->ne[0], get_n_rows()*(1 + n_rs_seq), state->nb[1], 0);
}
ggml_tensor * llama_dsv4_comp_state::get_kv(ggml_context * ctx, int32_t il) const {
ggml_tensor * state = get_kv_all(ctx, il);
const size_t row_size = ggml_row_size(state->type, state->ne[0]);
return ggml_view_2d(ctx, state, state->ne[0], get_n_rows(), state->nb[1], 0*row_size);
}
ggml_tensor * llama_dsv4_comp_state::get_score(ggml_context * ctx, int32_t il) const {
const int32_t ids = map_layer_ids.at(il);
ggml_tensor * state = get_score_all(ctx, il);
const size_t row_size = ggml_row_size(state->type, state->ne[0]);
ggml_tensor * state = layers[ids].score;
return ggml_reshape_2d(ctx, state, state->ne[0], state->ne[1]*state->ne[2]);
return ggml_view_2d(ctx, state, state->ne[0], get_n_rows(), state->nb[1], 0*row_size);
}
ggml_tensor * llama_dsv4_comp_state::cpy_kv(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const {
return ggml_set_rows(ctx, get_kv(ctx, il), cur, idxs);
return ggml_set_rows(ctx, get_kv_all(ctx, il), cur, idxs);
}
ggml_tensor * llama_dsv4_comp_state::cpy_score(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const {
return ggml_set_rows(ctx, get_score(ctx, il), cur, idxs);
return ggml_set_rows(ctx, get_score_all(ctx, il), cur, idxs);
}
size_t llama_dsv4_comp_state::total_size() const {
@@ -929,13 +1167,16 @@ llama_kv_cache_dsv4::llama_kv_cache_dsv4(
uint32_t n_seq_max,
uint32_t n_ubatch,
uint32_t n_pad,
uint32_t n_rs_seq,
const layer_filter_cb & filter,
const layer_reuse_cb & reuse) :
hparams_raw(model.hparams),
hparams_csa(model.hparams),
hparams_hca(model.hparams),
hparams_lid(model.hparams),
n_seq_max(n_seq_max) {
n_seq_max(n_seq_max),
n_rs_seq(n_rs_seq),
rs_idx(n_seq_max, 0) {
const layer_filter_cb filter_raw = [&](int32_t il) {
if (filter && !filter(il)) {
@@ -950,6 +1191,11 @@ llama_kv_cache_dsv4::llama_kv_cache_dsv4(
// Keep DSV4 KV/state streams per sequence even when public KV mode is unified.
const bool unified_raw = false;
hparams_raw.n_layer_nextn = 0;
hparams_csa.n_layer_nextn = 0;
hparams_hca.n_layer_nextn = 0;
hparams_lid.n_layer_nextn = 0;
LLAMA_LOG_INFO("%s: creating DSV4 raw KV cache\n", __func__);
dsv4_make_k_only(hparams_raw);
@@ -1016,25 +1262,25 @@ llama_kv_cache_dsv4::llama_kv_cache_dsv4(
csa_state = std::make_unique<llama_dsv4_comp_state>(
model, offload, unified_compressed, n_seq_max, DSV4_CSA_RATIO, 2*DSV4_CSA_RATIO,
2*model.hparams.n_embd_head_k(), "csa", filter_csa);
2*model.hparams.n_embd_head_k(), n_rs_seq, "csa", filter_csa);
LLAMA_LOG_INFO("%s: creating DSV4 HCA compressor state\n", __func__);
hca_state = std::make_unique<llama_dsv4_comp_state>(
model, offload, unified_compressed, n_seq_max, DSV4_HCA_RATIO, DSV4_HCA_RATIO,
model.hparams.n_embd_head_k(), "hca", filter_hca);
model.hparams.n_embd_head_k(), n_rs_seq, "hca", filter_hca);
LLAMA_LOG_INFO("%s: creating DSV4 lightning-indexer compressor state\n", __func__);
lid_state = std::make_unique<llama_dsv4_comp_state>(
model, offload, unified_compressed, n_seq_max, DSV4_CSA_RATIO, 2*DSV4_CSA_RATIO,
2*model.hparams.indexer_head_size, "lid", filter_csa);
2*model.hparams.indexer_head_size, n_rs_seq, "lid", filter_csa);
// DSV4 attention reads compressed-K / compressor-state rows that the current
// graph does not necessarily overwrite; uninitialized buffer contents would
// otherwise leak in (instance-specific garbage) and corrupt recall. Zero all
// compressed buffers up front so reads of un-written rows are deterministic.
clear_compressed(true);
clear_compressed(-1, true);
}
llama_memory_context_ptr llama_kv_cache_dsv4::init_batch(
@@ -1136,7 +1382,13 @@ llama_memory_context_ptr llama_kv_cache_dsv4::init_full() {
}
llama_memory_context_ptr llama_kv_cache_dsv4::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_kv_cache_dsv4_context>(this, lctx, optimize);
return std::make_unique<llama_kv_cache_dsv4_context>(
this,
lctx,
optimize,
std::move(csa_state->sc_info),
std::move(hca_state->sc_info),
std::move(lid_state->sc_info));
}
bool llama_kv_cache_dsv4::get_can_shift() const {
@@ -1147,7 +1399,7 @@ bool llama_kv_cache_dsv4::get_can_shift() const {
void llama_kv_cache_dsv4::clear(bool data) {
kv_raw->clear(data);
clear_compressed(true); // DSV4 compressed buffers must never expose stale/uninit rows
clear_compressed(-1, true); // DSV4 compressed buffers must never expose stale/uninit rows
}
bool llama_kv_cache_dsv4::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
@@ -1156,43 +1408,86 @@ bool llama_kv_cache_dsv4::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1
}
if (p0 > 0) {
// DSV4 compressed cache rows are derived from running compressor state,
// so arbitrary rollback is not reconstructible from the raw cache alone.
// Allow the common prompt-cache cleanup no-op: remove [end, infinity).
if (seq_id >= 0 && p0 > kv_raw->seq_pos_max(seq_id)) {
return true;
if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
return false;
}
return false;
const llama_pos pos_max = kv_raw->seq_pos_max(seq_id);
if (p0 > pos_max) {
bool res = true;
res = res & kv_raw->seq_rm(seq_id, p0, -1);
res = res & kv_csa->seq_rm(seq_id, p0/DSV4_CSA_RATIO, -1);
res = res & kv_hca->seq_rm(seq_id, p0/DSV4_HCA_RATIO, -1);
res = res & kv_lid->seq_rm(seq_id, p0/DSV4_CSA_RATIO, -1);
return res;
}
if (n_rs_seq == 0) {
return false;
}
const llama_pos rollback = pos_max - (p0 - 1);
if (rollback < 1 || rollback > (llama_pos) n_rs_seq) {
return false;
}
const bool res = kv_raw->seq_rm(seq_id, p0, p1);
if (res) {
rs_idx[seq_id] = (uint32_t) rollback;
}
return res;
}
const bool res = kv_raw->seq_rm(seq_id, p0, p1);
if (res) {
clear_compressed(true);
clear_compressed(seq_id, true);
}
return res;
}
void llama_kv_cache_dsv4::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
GGML_ASSERT(p0 <= 0 && p1 < 0 && "DSV4 only supports full sequence copies");
kv_raw->seq_cp(seq_id_src, seq_id_dst, p0, p1);
clear_compressed(true);
kv_csa->seq_cp(seq_id_src, seq_id_dst, -1, -1);
kv_hca->seq_cp(seq_id_src, seq_id_dst, -1, -1);
kv_lid->seq_cp(seq_id_src, seq_id_dst, -1, -1);
csa_state->seq_cp(seq_id_src, seq_id_dst);
hca_state->seq_cp(seq_id_src, seq_id_dst);
lid_state->seq_cp(seq_id_src, seq_id_dst);
if (seq_id_src != seq_id_dst) {
rs_idx[seq_id_dst] = 0;
}
}
void llama_kv_cache_dsv4::seq_keep(llama_seq_id seq_id) {
GGML_ASSERT(seq_id >= 0 && (uint32_t) seq_id < n_seq_max);
kv_raw->seq_keep(seq_id);
clear_compressed(true);
for (llama_seq_id id = 0; id < (llama_seq_id) n_seq_max; ++id) {
if (id == seq_id) {
continue;
}
kv_raw->seq_rm(id, -1, -1);
clear_compressed(id, true);
}
}
void llama_kv_cache_dsv4::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
kv_raw->seq_add(seq_id, p0, p1, shift);
clear_compressed(true);
}
void llama_kv_cache_dsv4::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
kv_raw->seq_div(seq_id, p0, p1, d);
clear_compressed(true);
}
llama_pos llama_kv_cache_dsv4::seq_pos_min(llama_seq_id seq_id) const {
@@ -1251,14 +1546,24 @@ void llama_kv_cache_dsv4::state_write(llama_io_write_i & io, llama_seq_id seq_id
kv_raw->state_write(io, seq_id, flags);
if (!partial_only) {
dsv4_state_write_k_cache(io, kv_csa.get(), seq_id, flags);
dsv4_state_write_k_cache(io, kv_hca.get(), seq_id, flags);
dsv4_state_write_k_cache(io, kv_lid.get(), seq_id, flags);
const llama_pos pos_max = seq_id >= 0 ? kv_raw->seq_pos_max(seq_id) : -1;
//FIXME : note that we conflate token positions with rows, which is not true for multi-modal case.
const uint32_t n_rows_csa = seq_id >= 0 ?
dsv4_state_n_used_k_rows(pos_max, DSV4_CSA_RATIO, kv_csa->get_size()) : kv_csa->get_size();
const uint32_t n_rows_hca = seq_id >= 0 ?
dsv4_state_n_used_k_rows(pos_max, DSV4_HCA_RATIO, kv_hca->get_size()) : kv_hca->get_size();
const uint32_t n_rows_lid = seq_id >= 0 ?
dsv4_state_n_used_k_rows(pos_max, DSV4_CSA_RATIO, kv_lid->get_size()) : kv_lid->get_size();
dsv4_state_write_k_cache(io, kv_csa.get(), seq_id, flags, n_rows_csa);
dsv4_state_write_k_cache(io, kv_hca.get(), seq_id, flags, n_rows_hca);
dsv4_state_write_k_cache(io, kv_lid.get(), seq_id, flags, n_rows_lid);
}
csa_state->state_write(io, seq_id, flags);
hca_state->state_write(io, seq_id, flags);
lid_state->state_write(io, seq_id, flags);
csa_state->state_write(io, seq_id, flags, rs_idx);
hca_state->state_write(io, seq_id, flags, rs_idx);
lid_state->state_write(io, seq_id, flags, rs_idx);
}
void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
@@ -1289,6 +1594,10 @@ void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id,
kv_raw->state_read(io, seq_id, flags);
if (!partial_only) {
kv_csa->clear(true);
kv_hca->clear(true);
kv_lid->clear(true);
dsv4_state_read_k_cache(io, kv_csa.get(), seq_id, flags);
dsv4_state_read_k_cache(io, kv_hca.get(), seq_id, flags);
dsv4_state_read_k_cache(io, kv_lid.get(), seq_id, flags);
@@ -1298,6 +1607,12 @@ void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id,
hca_state->state_read(io, seq_id, flags);
lid_state->state_read(io, seq_id, flags);
if (seq_id >= 0) {
GGML_ASSERT((uint32_t) seq_id < n_seq_max);
rs_idx[seq_id] = 0;
} else {
std::fill(rs_idx.begin(), rs_idx.end(), 0);
}
}
llama_kv_cache_iswa * llama_kv_cache_dsv4::get_raw() const {
@@ -1328,13 +1643,63 @@ llama_dsv4_comp_state * llama_kv_cache_dsv4::get_lid_state() const {
return lid_state.get();
}
void llama_kv_cache_dsv4::clear_compressed(bool data) {
kv_csa->clear(data);
kv_hca->clear(data);
kv_lid->clear(data);
csa_state->clear(data);
hca_state->clear(data);
lid_state->clear(data);
uint32_t llama_kv_cache_dsv4::get_n_rs_seq() const {
return n_rs_seq;
}
const std::vector<uint32_t> & llama_kv_cache_dsv4::get_rs_idx() const {
return rs_idx;
}
void llama_kv_cache_dsv4::reset_rs_idx_for_ubatches(const std::vector<llama_ubatch> & ubatches) {
if (n_rs_seq == 0) {
return;
}
for (const llama_ubatch & ubatch : ubatches) {
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
for (int32_t s = 0; s < ubatch.n_seq_id[i]; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[i][s];
if (seq_id >= 0 && (uint32_t) seq_id < n_seq_max) {
rs_idx[seq_id] = 0;
}
}
}
}
}
void llama_kv_cache_dsv4::clear_compressed(llama_seq_id seq_id, bool data) {
if (seq_id < 0) {
kv_csa->clear(data);
kv_hca->clear(data);
kv_lid->clear(data);
} else {
GGML_ASSERT((uint32_t) seq_id < n_seq_max);
const auto clear_seq = [seq_id, data](llama_kv_cache * kv) {
kv->seq_rm(seq_id, -1, -1);
if (data) {
for (uint32_t il : kv->get_layer_ids()) {
dsv4_clear_tensor_stream(kv->get_k_storage(il), (uint32_t) seq_id);
}
}
};
clear_seq(kv_csa.get());
clear_seq(kv_hca.get());
clear_seq(kv_lid.get());
}
csa_state->clear(seq_id, data);
hca_state->clear(seq_id, data);
lid_state->clear(seq_id, data);
if (seq_id >= 0) {
rs_idx[seq_id] = 0;
} else {
std::fill(rs_idx.begin(), rs_idx.end(), 0);
}
}
//
@@ -1595,20 +1960,26 @@ llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
llama_kv_cache_dsv4 * kv,
llama_context * lctx,
bool optimize) :
bool optimize,
stream_copy_info sc_info_csa,
stream_copy_info sc_info_hca,
stream_copy_info sc_info_lid) :
ctx_raw(std::make_unique<llama_kv_cache_dsv4_raw_context>(kv->get_raw(), lctx, optimize)),
ctx_csa_mem(kv->get_csa()->init_update(lctx, optimize)),
ctx_hca_mem(kv->get_hca()->init_update(lctx, optimize)),
ctx_lid_mem(kv->get_lid()->init_update(lctx, optimize)),
ctx_csa(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_csa())),
ctx_hca(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_hca())),
ctx_lid(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_lid())),
csa_state(kv->get_csa_state()),
hca_state(kv->get_hca_state()),
lid_state(kv->get_lid_state()),
sc_info_csa(std::move(sc_info_csa)),
sc_info_hca(std::move(sc_info_hca)),
sc_info_lid(std::move(sc_info_lid)),
status(llama_memory_status_combine(
llama_memory_status_combine(ctx_raw->get_status(), ctx_csa_mem->get_status()),
llama_memory_status_combine(ctx_hca_mem->get_status(), ctx_lid_mem->get_status()))) {
llama_memory_status_combine(
llama_memory_status_combine(ctx_raw->get_status(), ctx_csa_mem->get_status()),
llama_memory_status_combine(ctx_hca_mem->get_status(), ctx_lid_mem->get_status())),
this->sc_info_csa.empty() && this->sc_info_hca.empty() && this->sc_info_lid.empty() ?
LLAMA_MEMORY_STATUS_NO_UPDATE : LLAMA_MEMORY_STATUS_SUCCESS)) {
}
llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
@@ -1620,10 +1991,14 @@ llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
std::vector<llama_ubatch> ubatches_raw) :
ubatches(std::move(ubatches)),
plans_csa(dsv4_build_comp_plans(this->ubatches, DSV4_CSA_RATIO, true,
kv->get_csa_state()->get_state_size(), kv->get_csa()->get_size(), kv->get_csa_state()->get_n_stream())),
kv->get_csa_state()->get_state_size(), kv->get_csa()->get_size(), kv->get_csa_state()->get_n_stream(),
kv->get_n_rs_seq(), kv->get_rs_idx())),
plans_hca(dsv4_build_comp_plans(this->ubatches, DSV4_HCA_RATIO, false,
kv->get_hca_state()->get_state_size(), kv->get_hca()->get_size(), kv->get_hca_state()->get_n_stream())),
plans_lid(plans_csa),
kv->get_hca_state()->get_state_size(), kv->get_hca()->get_size(), kv->get_hca_state()->get_n_stream(),
kv->get_n_rs_seq(), kv->get_rs_idx())),
plans_lid(dsv4_build_comp_plans(this->ubatches, DSV4_CSA_RATIO, true,
kv->get_lid_state()->get_state_size(), kv->get_lid()->get_size(), kv->get_lid_state()->get_n_stream(),
kv->get_n_rs_seq(), kv->get_rs_idx())),
ctx_raw(std::make_unique<llama_kv_cache_dsv4_raw_context>(
kv->get_raw(),
std::move(sinfos_raw_base_write),
@@ -1650,6 +2025,7 @@ llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
hca_state(kv->get_hca_state()),
lid_state(kv->get_lid_state()),
status(ctx_raw->get_status()) {
kv->reset_rs_idx_for_ubatches(this->ubatches);
}
llama_kv_cache_dsv4_context::~llama_kv_cache_dsv4_context() = default;
@@ -1676,6 +2052,18 @@ bool llama_kv_cache_dsv4_context::apply() {
res = res & ctx_raw->apply();
if (ctx_csa_mem) {
res = res & ctx_csa_mem->apply();
res = res & ctx_hca_mem->apply();
res = res & ctx_lid_mem->apply();
}
if (ubatches.empty()) {
csa_state->apply_copies(sc_info_csa);
hca_state->apply_copies(sc_info_hca);
lid_state->apply_copies(sc_info_lid);
}
return res;
}
@@ -1773,7 +2161,7 @@ const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_
reserve_plan_csa = dsv4_build_reserve_comp_plan(
ubatch, DSV4_CSA_RATIO, true,
csa_state->get_state_size(), get_csa()->get_n_kv(), csa_state->get_n_stream());
csa_state->get_state_size(), get_csa()->get_n_kv(), csa_state->get_n_stream(), csa_state->get_n_rs_seq());
return reserve_plan_csa;
}
@@ -1787,7 +2175,7 @@ const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_
reserve_plan_hca = dsv4_build_reserve_comp_plan(
ubatch, DSV4_HCA_RATIO, false,
hca_state->get_state_size(), get_hca()->get_n_kv(), hca_state->get_n_stream());
hca_state->get_state_size(), get_hca()->get_n_kv(), hca_state->get_n_stream(), hca_state->get_n_rs_seq());
return reserve_plan_hca;
}
@@ -1801,7 +2189,7 @@ const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_
reserve_plan_lid = dsv4_build_reserve_comp_plan(
ubatch, DSV4_CSA_RATIO, true,
lid_state->get_state_size(), get_lid()->get_n_kv(), lid_state->get_n_stream());
lid_state->get_state_size(), get_lid()->get_n_kv(), lid_state->get_n_stream(), lid_state->get_n_rs_seq());
return reserve_plan_lid;
}
examples/talk-llama/llama-kv-cache-dsv4.h
+55 -11
View File
@@ -10,6 +10,10 @@
class llama_dsv4_comp_state {
public:
using stream_copy_info = llama_kv_cache::stream_copy_info;
stream_copy_info sc_info;
llama_dsv4_comp_state(
const llama_model & model,
bool offload,
@@ -18,22 +22,29 @@ public:
uint32_t ratio,
uint32_t state_size,
uint32_t n_embd_state,
uint32_t n_rs_seq,
const char * name,
const llama_memory_i::layer_filter_cb & filter);
void clear(bool data);
void clear(llama_seq_id seq_id, bool data);
void seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst);
void apply_copies(const stream_copy_info & sc_info) const;
uint32_t get_ratio() const;
uint32_t get_ratio() const;
uint32_t get_state_size() const;
uint32_t get_n_stream() const;
uint32_t get_n_stream() const;
uint32_t get_n_rs_seq() const;
uint32_t get_n_rows() const;
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const;
void state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const;
void state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags, const std::vector<uint32_t> & rs_idx) const;
void state_read (llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags);
ggml_tensor * get_kv (ggml_context * ctx, int32_t il) const;
ggml_tensor * get_score(ggml_context * ctx, int32_t il) const;
ggml_tensor * get_kv (ggml_context * ctx, int32_t il) const;
ggml_tensor * get_score (ggml_context * ctx, int32_t il) const;
ggml_tensor * get_kv_all (ggml_context * ctx, int32_t il) const;
ggml_tensor * get_score_all(ggml_context * ctx, int32_t il) const;
ggml_tensor * cpy_kv (ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const;
ggml_tensor * cpy_score(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const;
@@ -44,12 +55,16 @@ private:
ggml_tensor * kv;
ggml_tensor * score;
std::vector<ggml_tensor *> kv_stream;
std::vector<ggml_tensor *> score_stream;
};
const uint32_t ratio;
const uint32_t state_size;
const uint32_t n_embd_state;
const uint32_t n_stream;
const uint32_t n_rs_seq;
std::vector<std::pair<ggml_context_ptr, ggml_backend_buffer_ptr>> ctxs_bufs;
@@ -67,6 +82,8 @@ private:
// DSV4 uses a normal raw/SWA token cache plus compressed K-only block caches.
// The compressed caches are storage only; DSV4-specific visibility and block
// planning are handled by llama_kv_cache_dsv4_context / llm_graph_input_dsv4.
// FIXME: currently the cache only supports non-unified mode even if unified flag is passed
// FIXME: we currently conflate token_pos and buffer contents. See https://github.com/ggml-org/llama.cpp/pull/25521#discussion_r3558173819
class llama_kv_cache_dsv4 : public llama_memory_i {
public:
@@ -82,6 +99,7 @@ public:
uint32_t n_seq_max,
uint32_t n_ubatch,
uint32_t n_pad,
uint32_t n_rs_seq,
const layer_filter_cb & filter,
const layer_reuse_cb & reuse);
@@ -130,6 +148,10 @@ public:
llama_dsv4_comp_state * get_hca_state() const;
llama_dsv4_comp_state * get_lid_state() const;
uint32_t get_n_rs_seq() const;
const std::vector<uint32_t> & get_rs_idx() const;
void reset_rs_idx_for_ubatches(const std::vector<llama_ubatch> & ubatches);
private:
llama_hparams hparams_raw;
llama_hparams hparams_csa;
@@ -137,6 +159,9 @@ private:
llama_hparams hparams_lid;
const uint32_t n_seq_max;
const uint32_t n_rs_seq;
std::vector<uint32_t> rs_idx;
std::unique_ptr<llama_kv_cache_iswa> kv_raw;
std::unique_ptr<llama_kv_cache> kv_csa;
@@ -146,7 +171,7 @@ private:
std::unique_ptr<llama_dsv4_comp_state> hca_state;
std::unique_ptr<llama_dsv4_comp_state> lid_state;
void clear_compressed(bool data);
void clear_compressed(llama_seq_id seq_id, bool data);
};
// DSV4 raw attention only uses the SWA half of kv_raw. The base half is kept
@@ -243,6 +268,7 @@ private:
class llama_kv_cache_dsv4_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
using stream_copy_info = llama_kv_cache::stream_copy_info;
struct comp_plan {
// Per-ubatch recipe for updating compressor state, committing completed
@@ -256,6 +282,17 @@ public:
std::vector<int32_t> state_persist_src_idxs;
std::vector<int32_t> state_persist_dst_idxs;
// Device-side rollback restore copies snapshot planes back to the
// current compressor-state plane before the graph reads it.
std::vector<int32_t> state_restore_src_idxs;
std::vector<int32_t> state_restore_dst_idxs;
// Device-side rollback snapshots copy rows from the graph-local
// [persistent_state | current_ubatch_scratch] tensor into rollback
// planes after the graph has computed current-token compressor state.
std::vector<int32_t> state_snapshot_src_idxs;
std::vector<int32_t> state_snapshot_dst_idxs;
// Flattened source row ids used for state-backed commits. Source rows
// index the graph-local [persistent_state | current_ubatch_scratch]
// tensor. For overlapped compression the first half is previous rows
@@ -289,7 +326,10 @@ public:
llama_kv_cache_dsv4_context(
llama_kv_cache_dsv4 * kv,
llama_context * lctx,
bool optimize);
bool optimize,
stream_copy_info sc_info_csa,
stream_copy_info sc_info_hca,
stream_copy_info sc_info_lid);
llama_kv_cache_dsv4_context(
llama_kv_cache_dsv4 * kv,
@@ -349,9 +389,13 @@ private:
const std::unique_ptr<llama_kv_cache_dsv4_comp_context> ctx_hca;
const std::unique_ptr<llama_kv_cache_dsv4_comp_context> ctx_lid;
const llama_dsv4_comp_state * csa_state = nullptr;
const llama_dsv4_comp_state * hca_state = nullptr;
const llama_dsv4_comp_state * lid_state = nullptr;
llama_dsv4_comp_state * csa_state = nullptr;
llama_dsv4_comp_state * hca_state = nullptr;
llama_dsv4_comp_state * lid_state = nullptr;
stream_copy_info sc_info_csa;
stream_copy_info sc_info_hca;
stream_copy_info sc_info_lid;
bool reserve_plans = false;
mutable comp_plan reserve_plan_csa;
examples/talk-llama/llama-kv-cache-msa.cpp
+395
View File
@@ -0,0 +1,395 @@
#include "llama-kv-cache-msa.h"
#include "llama-impl.h"
#include "llama-batch.h"
#include "llama-model.h"
#include <algorithm>
#include <cassert>
#include <cmath>
// llama_kv_cache_msa
llama_kv_cache_msa::llama_kv_cache_msa(
const llama_model & model,
ggml_type type_k,
ggml_type type_v,
bool v_trans,
bool offload,
bool unified,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type,
const layer_filter_cb & filter,
const layer_filter_cb & filter_idx,
const layer_reuse_cb & reuse) :
hparams_idx(model.hparams),
n_stream(unified ? 1 : n_seq_max), n_seq_max(n_seq_max), n_pad(n_pad),
n_swa(n_swa), swa_type(swa_type) {
LLAMA_LOG_INFO("%s: creating main KV cache, size = %u cells\n", __func__, kv_size);
kv_base = std::make_unique<llama_kv_cache>(
model, model.hparams, type_k, type_v,
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
n_swa, swa_type, nullptr, filter, reuse, nullptr);
// the MSA indexer uses a single key head per layer
std::fill(hparams_idx.n_head_kv_arr.begin(), hparams_idx.n_head_kv_arr.end(), 1);
hparams_idx.n_embd_head_k_full = model.hparams.indexer_head_size;
// the rope parameters are kept identical to the main cache
LLAMA_LOG_INFO("%s: creating indexer KV cache, size = %u cells\n", __func__, kv_size);
kv_idx = std::make_unique<llama_kv_cache>(
model, hparams_idx, type_k, type_v,
v_trans, offload, unified, kv_size, n_seq_max, n_pad,
n_swa, swa_type, nullptr, filter_idx, reuse, nullptr);
}
void llama_kv_cache_msa::clear(bool data) {
kv_base->clear(data);
kv_idx ->clear(data);
}
bool llama_kv_cache_msa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
bool res = true;
res = res & kv_base->seq_rm(seq_id, p0, p1);
res = res & kv_idx ->seq_rm(seq_id, p0, p1);
return res;
}
void llama_kv_cache_msa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1);
kv_idx ->seq_cp(seq_id_src, seq_id_dst, p0, p1);
}
void llama_kv_cache_msa::seq_keep(llama_seq_id seq_id) {
kv_base->seq_keep(seq_id);
kv_idx ->seq_keep(seq_id);
}
void llama_kv_cache_msa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
kv_base->seq_add(seq_id, p0, p1, shift);
kv_idx ->seq_add(seq_id, p0, p1, shift);
}
void llama_kv_cache_msa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
kv_base->seq_div(seq_id, p0, p1, d);
kv_idx ->seq_div(seq_id, p0, p1, d);
}
llama_pos llama_kv_cache_msa::seq_pos_min(llama_seq_id seq_id) const {
return kv_base->seq_pos_min(seq_id);
}
llama_pos llama_kv_cache_msa::seq_pos_max(llama_seq_id seq_id) const {
return kv_base->seq_pos_max(seq_id);
}
std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache_msa::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, size_t> mb = kv_base->memory_breakdown();
for (const auto & buft_size : kv_idx->memory_breakdown()) {
mb[buft_size.first] += buft_size.second;
}
return mb;
}
llama_memory_context_ptr llama_kv_cache_msa::init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) {
GGML_UNUSED(embd_all);
do {
balloc.split_reset();
std::vector<llama_ubatch> ubatches;
while (true) {
auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true, 0);
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(std::move(ubatch));
}
if (balloc.get_n_used() < balloc.get_n_tokens()) {
// failed to find a suitable split
break;
}
auto sinfos_base = kv_base->prepare(ubatches);
if (sinfos_base.empty()) {
break;
}
auto sinfos_idx = kv_idx->prepare(ubatches);
if (sinfos_idx.empty()) {
break;
}
assert(sinfos_base.size() == sinfos_idx.size());
return std::make_unique<llama_kv_cache_msa_context>(
this, std::move(sinfos_base), std::move(sinfos_idx), std::move(ubatches));
} while (false);
return std::make_unique<llama_kv_cache_msa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_context_ptr llama_kv_cache_msa::init_full() {
return std::make_unique<llama_kv_cache_msa_context>(this);
}
llama_memory_context_ptr llama_kv_cache_msa::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_kv_cache_msa_context>(this, lctx, optimize);
}
bool llama_kv_cache_msa::get_can_shift() const {
return kv_base->get_can_shift() &&
kv_idx ->get_can_shift() &&
kv_base->get_size() == kv_idx->get_size();
}
void llama_kv_cache_msa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
kv_base->state_write(io, seq_id, flags);
kv_idx ->state_write(io, seq_id, flags);
}
void llama_kv_cache_msa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
kv_base->state_read(io, seq_id, flags);
kv_idx ->state_read(io, seq_id, flags);
}
llama_kv_cache * llama_kv_cache_msa::get_base() const {
return kv_base.get();
}
llama_kv_cache * llama_kv_cache_msa::get_idx() const {
return kv_idx.get();
}
// llama_kv_cache_msa_context
llama_kv_cache_msa_context::llama_kv_cache_msa_context(llama_memory_status status) :
kv(nullptr), status(status) {}
llama_kv_cache_msa_context::llama_kv_cache_msa_context(
llama_kv_cache_msa * kv) :
kv(kv),
ctx_base(kv->get_base()->init_full()),
ctx_idx (kv->get_idx ()->init_full()),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_idx->get_status())) {
}
llama_kv_cache_msa_context::llama_kv_cache_msa_context(
llama_kv_cache_msa * kv,
llama_context * lctx,
bool optimize) :
kv(kv),
ctx_base(kv->get_base()->init_update(lctx, optimize)),
ctx_idx (kv->get_idx ()->init_update(lctx, optimize)),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_idx->get_status())) {
}
llama_kv_cache_msa_context::llama_kv_cache_msa_context(
llama_kv_cache_msa * kv,
slot_info_vec_t sinfos_base,
slot_info_vec_t sinfos_idx,
std::vector<llama_ubatch> ubatches) :
kv(kv),
ubatches(std::move(ubatches)),
// here we copy the ubatches. not sure if this is ideal
ctx_base(new llama_kv_cache_context(kv->get_base(), std::move(sinfos_base), this->ubatches)),
ctx_idx (new llama_kv_cache_context(kv->get_idx (), std::move(sinfos_idx), this->ubatches)),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_idx->get_status())) {
}
llama_kv_cache_msa_context::~llama_kv_cache_msa_context() = default;
bool llama_kv_cache_msa_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
ctx_base->next();
ctx_idx ->next();
if (++i_next >= ubatches.size()) {
return false;
}
return true;
}
bool llama_kv_cache_msa_context::apply() {
assert(!llama_memory_status_is_fail(status));
bool res = true;
res = res & ctx_base->apply();
res = res & ctx_idx ->apply();
return res;
}
llama_memory_status llama_kv_cache_msa_context::get_status() const {
return status;
}
const llama_ubatch & llama_kv_cache_msa_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
const llama_kv_cache_context * llama_kv_cache_msa_context::get_base() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_context *>(ctx_base.get());
}
const llama_kv_cache_context * llama_kv_cache_msa_context::get_idx() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_context *>(ctx_idx.get());
}
uint32_t llama_kv_cache_msa_context::get_n_pos() const {
// pad the value so that the graph remains constant across batches and can be reused
const uint32_t n_pad_cur = std::max(kv->get_n_pad(), 256u);
llama_pos pos_max = -1;
for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) kv->get_n_seq_max(); ++seq_id) {
pos_max = std::max(pos_max, kv->seq_pos_max(seq_id));
}
return std::max(n_pad_cur, GGML_PAD((uint32_t) (pos_max + 1), n_pad_cur));
}
void llama_kv_cache_msa_context::set_input_cell_pos(ggml_tensor * dst, const llama_ubatch * ubatch, int32_t div) const {
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
GGML_ASSERT(dst->type == GGML_TYPE_I32);
GGML_ASSERT(div > 0);
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_kv = dst->ne[0];
const int64_t n_stream_ub = dst->ne[1];
GGML_ASSERT(n_tokens % n_stream_ub == 0);
const int64_t n_tps = n_tokens/n_stream_ub;
int32_t * data = (int32_t *) dst->data;
for (int64_t s = 0; s < n_stream_ub; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s*n_tps][0];
const auto & cells = kv->get_base()->get_cells(seq_id);
for (int64_t j = 0; j < n_kv; ++j) {
// the value for empty or other-sequence cells is irrelevant as consumers mask them
data[s*n_kv + j] =
cells.is_empty(j) || !cells.seq_has(j, seq_id)
? 0
: (int32_t) (cells.pos_get(j)/div);
}
}
}
void llama_kv_cache_msa_context::set_input_pos_slot(ggml_tensor * dst, const llama_ubatch * ubatch) const {
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
GGML_ASSERT(dst->type == GGML_TYPE_I32 || dst->type == GGML_TYPE_F32);
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_pos = dst->ne[0];
const int64_t n_stream_ub = dst->ne[1];
GGML_ASSERT(n_tokens % n_stream_ub == 0);
const int64_t n_tps = n_tokens/n_stream_ub;
for (int64_t s = 0; s < n_stream_ub; ++s) {
const llama_seq_id seq_id = ubatch->seq_id[s*n_tps][0];
const auto & cells = kv->get_base()->get_cells(seq_id);
std::vector<int32_t> map(n_pos, 0);
for (uint32_t j = 0; j < cells.size(); ++j) {
if (cells.is_empty(j) || !cells.seq_has(j, seq_id)) {
continue;
}
const llama_pos p0 = cells.pos_get(j);
if (p0 < 0 || p0 >= n_pos) {
continue;
}
map[p0] = (int32_t) j;
}
if (dst->type == GGML_TYPE_I32) {
int32_t * data = (int32_t *) dst->data + s*n_pos;
std::copy(map.begin(), map.end(), data);
} else {
float * data = (float *) dst->data + s*n_pos;
for (int64_t p = 0; p < n_pos; ++p) {
data[p] = (float) map[p];
}
}
}
}
void llama_kv_cache_msa_context::set_input_pos_mask(ggml_tensor * dst, const llama_ubatch * ubatch) const {
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const int64_t n_tokens = ubatch->n_tokens;
const int64_t n_pos = dst->ne[0];
GGML_ASSERT(dst->ne[1] == n_tokens);
const uint32_t n_swa = kv->get_n_swa();
const llama_swa_type swa_type = kv->get_swa_type();
float * data = (float *) dst->data;
std::fill(data, data + n_pos*n_tokens, -INFINITY);
for (int64_t i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = ubatch->seq_id[i][0];
const auto & cells = kv->get_base()->get_cells(seq_id);
const llama_pos p1 = ubatch->pos[i];
for (uint32_t j = 0; j < cells.size(); ++j) {
if (cells.is_empty(j) || !cells.seq_has(j, seq_id)) {
continue;
}
const llama_pos p0 = cells.pos_get(j);
if (p0 < 0 || p0 >= n_pos) {
continue;
}
// causal mask
if (p0 > p1) {
continue;
}
// apply SWA if any
if (llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) {
continue;
}
data[i*n_pos + p0] = 0.0f;
}
}
}
examples/talk-llama/llama-kv-cache-msa.h
+153
View File
@@ -0,0 +1,153 @@
#pragma once
#include "llama-kv-cache.h"
#include <vector>
// llama_kv_cache_msa
// uses two instances of llama_kv_cache, one for K/V tensors, and one for the MSA indexer tensors
// both receive identical sequence operations and identical ubatches, so their cell layouts stay in synced.
// the context also exposes per-ubatch pos - cell translation maps populated from llama_kv_cells via
// llama_kv_cache::get_cells(), which the model graph uses to run MSA block selection in position space
class llama_kv_cache_msa : public llama_memory_i {
public:
llama_kv_cache_msa(
const llama_model & model,
ggml_type type_k,
ggml_type type_v,
bool v_trans,
bool offload,
bool unified,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type,
const layer_filter_cb & filter,
const layer_filter_cb & filter_idx,
const layer_reuse_cb & reuse);
~llama_kv_cache_msa() = default;
// llama_memory_i
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_context_ptr init_full() override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
void clear(bool data) override;
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
void seq_keep(llama_seq_id seq_id) override;
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
// state write/load
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
// llama_kv_cache_msa specific API
llama_kv_cache * get_base() const;
llama_kv_cache * get_idx () const;
uint32_t get_n_pad() const { return n_pad; }
uint32_t get_n_seq_max() const { return n_seq_max; }
uint32_t get_n_swa() const { return n_swa; }
llama_swa_type get_swa_type() const { return swa_type; }
private:
// keep the indexer KV cache hparams instance here as llama_kv_cache stores only a reference
llama_hparams hparams_idx;
const uint32_t n_stream = 1;
const uint32_t n_seq_max = 1;
const uint32_t n_pad = 1;
const uint32_t n_swa = 0;
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
std::unique_ptr<llama_kv_cache> kv_base;
std::unique_ptr<llama_kv_cache> kv_idx;
};
class llama_kv_cache_msa_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
// used for errors
llama_kv_cache_msa_context(llama_memory_status status);
// used to create a full-cache context
llama_kv_cache_msa_context(
llama_kv_cache_msa * kv);
// used to create an update context
llama_kv_cache_msa_context(
llama_kv_cache_msa * kv,
llama_context * lctx,
bool optimize);
// used to create a batch processing context from a batch
llama_kv_cache_msa_context(
llama_kv_cache_msa * kv,
slot_info_vec_t sinfos_base,
slot_info_vec_t sinfos_idx,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_msa_context();
// llama_memory_context_i
bool next() override;
bool apply() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
// llama_kv_cache_msa_context specific API
const llama_kv_cache_context * get_base() const;
const llama_kv_cache_context * get_idx () const;
// max position currently present in the cache plus one, padded MSA blocks are defined over token positions
// so the block-selection tensors are sized by this value rather than by the number of cells
uint32_t get_n_pos() const;
// position <-> cell translation maps, populated from the base cache cells
// the model graph relates cache contents to token positions only through these per ubatch inputs
// value for empty or other-sequence cells is 0 so consumers must mask them
void set_input_cell_pos(ggml_tensor * dst, const llama_ubatch * ubatch, int32_t div) const;
// positions without a cell map to cell 0, consumers must mask them assumes one sequence per stream
void set_input_pos_slot(ggml_tensor * dst, const llama_ubatch * ubatch) const;
void set_input_pos_mask(ggml_tensor * dst, const llama_ubatch * ubatch) const;
private:
llama_kv_cache_msa * kv;
// the index of the next ubatch to process
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;
const llama_memory_context_ptr ctx_base;
const llama_memory_context_ptr ctx_idx;
const llama_memory_status status;
};
examples/talk-llama/llama-kv-cache.cpp
+13 -2
View File
@@ -323,7 +323,7 @@ llama_kv_cache::llama_kv_cache(
hparams.n_embd_head_k() % 64 == 0;
// always create Hadamard rotation tensors for DeepSeek lightning indexers
if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4) &&
if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 || model.arch == LLM_ARCH_GLM_DSA) &&
hparams.n_embd_head_k_full == hparams.indexer_head_size) {
attn_rot_k = true;
}
@@ -1224,6 +1224,12 @@ ggml_tensor * llama_kv_cache::get_k_storage(int32_t il) const {
return layers[ikv].k;
}
const llama_kv_cells & llama_kv_cache::get_cells(llama_seq_id seq_id) const {
GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size());
return v_cells[seq_to_stream[seq_id]];
}
uint32_t llama_kv_cache::get_n_kv(const slot_info & sinfo) const {
uint32_t result = 0;
@@ -2054,7 +2060,12 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama
bool res = true;
res = res && state_read_meta(io, strm, cell_count, sinfo, seq_id);
res = res && state_read_data(io, strm, cell_count, sinfo);
try {
res = res && state_read_data(io, strm, cell_count, sinfo);
} catch (...) {
res = false;
}
if (!res) {
if (seq_id == -1) {
examples/talk-llama/llama-kv-cache.h
+2
View File
@@ -164,6 +164,8 @@ public:
std::vector<uint32_t> get_layer_ids() const;
ggml_tensor * get_k_storage(int32_t il) const;
const llama_kv_cells & get_cells(llama_seq_id seq_id) const;
//
// graph_build API
//
examples/talk-llama/llama-memory-recurrent.cpp
+6 -1
View File
@@ -819,7 +819,12 @@ void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_i
bool res = true;
res = res && state_read_meta(io, cell_count, seq_id);
res = res && state_read_data(io, cell_count);
try {
res = res && state_read_data(io, cell_count);
} catch (...) {
res = false;
}
if (!res) {
if (seq_id == -1) {
examples/talk-llama/llama-model-loader.cpp
+56 -72
View File
@@ -4,6 +4,7 @@
#include "ggml.h"
#include "gguf.h"
#include "llama-hparams.h"
#include "llama.h"
#include <algorithm>
#include <array>
@@ -522,10 +523,10 @@ llama_model_loader::llama_model_loader(
const std::string & fname,
std::vector<std::string> & splits,
FILE * file,
bool use_mmap,
bool use_direct_io,
llama_load_mode load_mode,
bool check_tensors,
bool no_alloc,
bool load_mtp,
const llama_model_kv_override * param_overrides_p,
const llama_model_tensor_buft_override * param_tensor_buft_overrides_p)
: metadata(meta), set_tensor_data(set_tensor_data), set_tensor_data_ud(set_tensor_data_ud) {
@@ -542,6 +543,9 @@ llama_model_loader::llama_model_loader(
tensor_buft_overrides = param_tensor_buft_overrides_p;
this->use_mmap = load_mode == LLAMA_LOAD_MODE_MMAP || load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK;
this->use_direct_io = load_mode == LLAMA_LOAD_MODE_DIRECT_IO;
if (!fname.empty()) {
// Load the main GGUF
struct ggml_context * ctx = NULL;
@@ -562,20 +566,6 @@ llama_model_loader::llama_model_loader(
files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io));
contexts.emplace_back(ctx);
if (use_mmap && use_direct_io) {
if (files.back()->has_direct_io()) {
LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__);
use_mmap = false;
} else {
LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__);
use_direct_io = false;
// reopen file using std::fopen for mmap
files.pop_back();
files.emplace_back(new llama_file(fname.c_str(), "rb", false));
}
}
// Save tensors data offset of the main file.
// For subsidiary files, `meta` tensor data offset must not be used,
// so we build a unified tensors index for weights.
@@ -816,15 +806,14 @@ llama_model_loader::llama_model_loader(
}
}
if (!llama_mmap::SUPPORTED) {
if (this->use_mmap && !llama_mmap::SUPPORTED) {
LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
use_mmap = false;
this->use_mmap = false;
}
this->use_mmap = use_mmap;
this->use_direct_io = use_direct_io;
this->check_tensors = check_tensors;
this->no_alloc = no_alloc;
this->load_mtp = load_mtp;
}
std::string llama_model_loader::get_arch_name() const {
@@ -868,7 +857,11 @@ struct ggml_tensor * llama_model_loader::require_tensor_meta(const std::string &
return tensor;
}
const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
const struct ggml_tensor * llama_model_loader::check_tensor_dims(
const std::string & name,
const std::vector<int64_t> & ne,
bool required,
bool allow_reshape) const {
const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
if (cur == NULL) {
@@ -878,21 +871,33 @@ const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::stri
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
}
{
bool is_ok = true;
bool is_ok = true;
if (allow_reshape) {
// check total number of elements only
const int64_t ncur = ggml_nelements(cur);
int64_t nexp = 1;
for (size_t i = 0; i < ne.size(); ++i) {
nexp *= ne[i];
}
if (ncur != nexp) {
is_ok = false;
}
} else {
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
is_ok = false;
break;
}
}
if (!is_ok) {
throw std::runtime_error(
format("%s: tensor '%s' has wrong shape; expected %s, got %s",
__func__, name.c_str(),
llama_format_tensor_shape(ne).c_str(),
llama_format_tensor_shape(cur).c_str()));
}
}
if (!is_ok) {
throw std::runtime_error(
format("%s: tensor '%s' has wrong shape; expected %s, got %s",
__func__, name.c_str(),
llama_format_tensor_shape(ne).c_str(),
llama_format_tensor_shape(cur).c_str()));
}
return cur;
@@ -1257,11 +1262,25 @@ struct ggml_tensor * llama_model_loader::create_tensor(
return ret;
}
ggml_tensor * t_meta = get_tensor_meta(tn.str().c_str());
ggml_backend_buffer_type_t buft = buft_for_tensor(t_meta);
if (buft == nullptr) {
return nullptr; // return type is ggml_tensor *
LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str());
const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED), flags & TENSOR_ALLOW_RESHAPE);
if (cur == NULL) {
return NULL;
}
ggml_tensor t_meta = *cur;
if (flags & TENSOR_ALLOW_RESHAPE) {
for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) {
t_meta.ne[dim] = dim < ne.size() ? ne.begin()[dim] : 1;
t_meta.nb[dim] = dim == 0 ? ggml_type_size(t_meta.type) : t_meta.ne[dim-1]*t_meta.nb[dim-1];
}
}
ggml_backend_buffer_type_t buft = buft_for_tensor(&t_meta);
if (buft == nullptr) {
return nullptr;
}
ggml_context * ctx = ctx_for_buft(buft);
// if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
@@ -1272,20 +1291,13 @@ struct ggml_tensor * llama_model_loader::create_tensor(
}
}
LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str());
const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED));
if (cur == NULL) {
return NULL;
}
const bool duplicated = flags & TENSOR_DUPLICATED;
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
ggml_set_name(tensor, ggml_get_name(cur));
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, &t_meta);
ggml_set_name(tensor, ggml_get_name(&t_meta));
if (duplicated) {
size_data += ggml_nbytes(cur);
size_data += ggml_nbytes(&t_meta);
} else {
n_created++;
}
@@ -1293,34 +1305,6 @@ struct ggml_tensor * llama_model_loader::create_tensor(
return tensor;
}
struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required) {
const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
if (cur == NULL) {
return NULL;
}
if (cur->type != base->type) {
throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
}
std::array<int64_t, GGML_MAX_DIMS> dims;
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
dims[i] = i < ne.size() ? ne.begin()[i] : 1;
}
struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
dims[0], dims[1], dims[2], dims[3],
cur->nb[1], cur->nb[2], cur->nb[3],
offset);
ggml_set_name(tensor, name.c_str());
n_created++;
return tensor;
}
void llama_model_loader::done_getting_tensors(bool partial) const {
if (n_created > n_tensors) {
throw std::runtime_error(format("%s: too many tensors created; expected %d, got %d", __func__, n_tensors, n_created));
examples/talk-llama/llama-model-loader.h
+9 -5
View File
@@ -67,6 +67,7 @@ struct llama_model_loader {
static const int TENSOR_DUPLICATED = 1 << 1;
static const int TENSOR_SKIP = 1 << 2;
static const int TENSOR_SKIP_IF_VIRTUAL = 1 << 3;
static const int TENSOR_ALLOW_RESHAPE = 1 << 4;
int n_kv = 0;
int n_tensors = 0;
@@ -79,6 +80,7 @@ struct llama_model_loader {
bool use_direct_io = false;
bool check_tensors;
bool no_alloc;
bool load_mtp;
llama_files files;
llama_ftype ftype;
@@ -126,10 +128,10 @@ struct llama_model_loader {
const std::string & fname,
std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
FILE * file,
bool use_mmap,
bool use_direct_io,
llama_load_mode load_mode,
bool check_tensors,
bool no_alloc,
bool load_mtp,
const llama_model_kv_override * param_overrides_p,
const llama_model_tensor_buft_override * param_tensor_buft_overrides_p);
@@ -176,14 +178,16 @@ struct llama_model_loader {
struct ggml_tensor * require_tensor_meta(const std::string & name) const;
const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const;
const struct ggml_tensor * check_tensor_dims(
const std::string & name,
const std::vector<int64_t> & ne,
bool required,
bool allow_reshape) const;
struct ggml_tensor * create_tensor(
const llama_hparams & hparams, const buft_list_t * buft_list_cpu, const buft_list_t * buft_list_input, const buft_list_t * buft_list_output,
const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);
struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required = true);
void done_getting_tensors(bool partial = false) const;
void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr);
examples/talk-llama/llama-model-saver.cpp
+4
View File
@@ -28,6 +28,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
case LLM_ARCH_MIMO2:
case LLM_ARCH_STEP35:
case LLM_ARCH_MELLUM:
case LLM_ARCH_LAGUNA:
return false;
default:
return true;
@@ -280,6 +281,9 @@ void llama_model_saver::add_kv_from_model() {
add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
add_kv(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, hparams.indexer_block_size);
add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks);
add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, true);
add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, true);
const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
examples/talk-llama/llama-model.cpp
+212 -69
View File
@@ -11,11 +11,13 @@
#include "llama-kv-cache.h"
#include "llama-kv-cache-iswa.h"
#include "llama-kv-cache-dsa.h"
#include "llama-kv-cache-msa.h"
#include "llama-kv-cache-dsv4.h"
#include "llama-memory-hybrid.h"
#include "llama-memory-hybrid-iswa.h"
#include "llama-memory-recurrent.h"
#include "llama.h"
#include "models/models.h"
#include "ggml.h"
@@ -84,6 +86,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_stablelm(params);
case LLM_ARCH_MELLUM:
return new llama_model_mellum(params);
case LLM_ARCH_NANBEIGE:
return new llama_model_nanbeige(params);
case LLM_ARCH_QWEN:
return new llama_model_qwen(params);
case LLM_ARCH_QWEN2:
@@ -250,6 +254,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_arcee(params);
case LLM_ARCH_AFMOE:
return new llama_model_afmoe(params);
case LLM_ARCH_LAGUNA:
return new llama_model_laguna(params);
case LLM_ARCH_ERNIE4_5:
return new llama_model_ernie4_5(params);
case LLM_ARCH_ERNIE4_5_MOE:
@@ -262,6 +268,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_hunyuan_vl(params);
case LLM_ARCH_HUNYUAN_DENSE:
return new llama_model_hunyuan_dense(params);
case LLM_ARCH_HY_V3:
return new llama_model_hy_v3(params);
case LLM_ARCH_SMOLLM3:
return new llama_model_smollm3(params);
case LLM_ARCH_OPENAI_MOE:
@@ -280,6 +288,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_apertus(params);
case LLM_ARCH_MINIMAX_M2:
return new llama_model_minimax_m2(params);
case LLM_ARCH_MINIMAX_M3:
return new llama_model_minimax_m3(params);
case LLM_ARCH_COGVLM:
return new llama_model_cogvlm(params);
case LLM_ARCH_PANGU_EMBED:
@@ -313,8 +323,7 @@ llama_model * llama_model_create(llm_arch arch, const llama_model_params & param
if (model != nullptr) {
model->arch = arch;
auto & devices = model->devices;
if (!devices.empty() && devices[0].is_meta && !llm_arch_supports_sm_tensor(arch)) {
if (params.split_mode == LLAMA_SPLIT_MODE_TENSOR && !llm_arch_supports_sm_tensor(arch)) {
throw std::runtime_error(std::string("LLAMA_SPLIT_MODE_TENSOR not implemented for architecture '") + llm_arch_name(arch) + "'");
}
}
@@ -336,38 +345,40 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
const llama_hparams & hparams = ud->model->hparams;
const std::string tensor_name = tensor->name;
const std::regex pattern_q_weight ("blk\\.\\d*\\.attn_q.weight");
const std::regex pattern_kv_weight ("blk\\.\\d*\\.attn_(k|v).weight");
const std::regex pattern_qkv_weight ("blk\\.\\d*\\.attn_qkv.weight");
const std::regex pattern_q_bias ("blk\\.\\d*\\.attn_q\\.bias");
const std::regex pattern_kv_bias ("blk\\.\\d*\\.attn_(k|v)\\.bias");
const std::regex pattern_qkv_bias ("blk\\.\\d*\\.attn_qkv.bias");
const std::regex pattern_qk_norm ("blk\\.\\d*\\.attn_(q|k)_norm\\.weight");
const std::regex pattern_kv_cache ("cache_(k|v)_l\\d*");
const std::regex pattern_attn_sinks ("blk\\.\\d*\\.attn_sinks.weight");
const std::regex pattern_attn_out_weight ("blk\\.\\d*\\.attn_output.weight");
const std::regex pattern_attn_out_bias ("blk\\.\\d*\\.attn_output.bias");
const std::regex pattern_attn_gate_weight("blk\\.\\d*\\.attn_gate.weight");
static const std::regex pattern_q_weight ("blk\\.\\d*\\.attn_q.weight");
static const std::regex pattern_kv_weight ("blk\\.\\d*\\.attn_(k|v).weight");
static const std::regex pattern_qkv_weight ("blk\\.\\d*\\.attn_qkv.weight");
static const std::regex pattern_q_bias ("blk\\.\\d*\\.attn_q\\.bias");
static const std::regex pattern_kv_bias ("blk\\.\\d*\\.attn_(k|v)\\.bias");
static const std::regex pattern_qkv_bias ("blk\\.\\d*\\.attn_qkv.bias");
static const std::regex pattern_qk_norm ("blk\\.\\d*\\.attn_(q|k)_norm\\.weight");
static const std::regex pattern_kv_cache ("cache_(k|v)_l\\d*");
static const std::regex pattern_attn_sinks ("blk\\.\\d*\\.attn_sinks.weight");
static const std::regex pattern_attn_out_weight ("blk\\.\\d*\\.attn_output.weight");
static const std::regex pattern_attn_out_bias ("blk\\.\\d*\\.attn_output.bias");
static const std::regex pattern_attn_gate_weight("blk\\.\\d*\\.attn_gate.weight");
const std::regex pattern_ssm_dt ("blk\\.\\d*\\.ssm_dt.bias");
const std::regex pattern_ssm_a ("blk\\.\\d*\\.ssm_a");
const std::regex pattern_ssm_alpha ("blk\\.\\d*\\.ssm_alpha.weight");
const std::regex pattern_ssm_beta ("blk\\.\\d*\\.ssm_beta.weight");
const std::regex pattern_ssm_beta_alpha ("blk\\.\\d*\\.ssm_ba.weight");
const std::regex pattern_r_cache ("cache_r_l\\d*");
const std::regex pattern_s_cache ("cache_s_l\\d*");
const std::regex pattern_ssm_conv1d ("blk\\.\\d*\\.ssm_conv1d.weight");
const std::regex pattern_ssm_out_weight ("blk\\.\\d*\\.ssm_out.weight");
static const std::regex pattern_ssm_dt ("blk\\.\\d*\\.ssm_dt.bias");
static const std::regex pattern_ssm_a ("blk\\.\\d*\\.ssm_a");
static const std::regex pattern_ssm_alpha ("blk\\.\\d*\\.ssm_alpha.weight");
static const std::regex pattern_ssm_beta ("blk\\.\\d*\\.ssm_beta.weight");
static const std::regex pattern_ssm_beta_alpha ("blk\\.\\d*\\.ssm_ba.weight");
static const std::regex pattern_r_cache ("cache_r_l\\d*");
static const std::regex pattern_s_cache ("cache_s_l\\d*");
static const std::regex pattern_ssm_conv1d ("blk\\.\\d*\\.ssm_conv1d.weight");
static const std::regex pattern_ssm_out_weight ("blk\\.\\d*\\.ssm_out.weight");
const std::regex pattern_ffn_up_gate_weight("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.weight");
const std::regex pattern_ffn_up_gate_bias ("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.bias");
const std::regex pattern_ffn_gate_up_weight("blk\\.\\d*\\.ffn_gate_up(_exps)?.weight");
const std::regex pattern_ffn_down_weight ("blk\\.\\d*\\.ffn_down(_exps)?.weight");
const std::regex pattern_ffn_down_bias ("blk\\.\\d*\\.ffn_down.bias");
const std::regex pattern_ffn_down_exps_bias("blk\\.\\d*\\.ffn_down_exps.bias");
static const std::regex pattern_ffn_up_weight ("blk\\.\\d*\\.ffn_up(_exps)?.weight");
static const std::regex pattern_ffn_up_bias ("blk\\.\\d*\\.ffn_up(_exps)?.bias");
static const std::regex pattern_ffn_gate_weight ("blk\\.\\d*\\.ffn_gate(_exps)?.weight");
static const std::regex pattern_ffn_gate_bias ("blk\\.\\d*\\.ffn_gate(_exps)?.bias");
static const std::regex pattern_ffn_gate_up_weight("blk\\.\\d*\\.ffn_gate_up(_exps)?.weight");
static const std::regex pattern_ffn_down_weight ("blk\\.\\d*\\.ffn_down(_exps)?.weight");
static const std::regex pattern_ffn_down_bias ("blk\\.\\d*\\.ffn_down.bias");
static const std::regex pattern_ffn_down_exps_bias("blk\\.\\d*\\.ffn_down_exps.bias");
const std::regex pattern_output_weight("output\\.weight");
const std::regex pattern_output_bias ("output\\.bias");
static const std::regex pattern_output_weight("output\\.weight");
static const std::regex pattern_output_bias ("output\\.bias");
struct tensor_config {
ggml_backend_meta_split_axis axis;
@@ -468,10 +479,10 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
}
// FFN
if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight)) {
if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_gate_weight)) {
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "ffn_down.weight", "ffn_down_exps.weight");
}
if (std::regex_match(tensor_name, pattern_ffn_up_gate_bias)) {
if (std::regex_match(tensor_name, pattern_ffn_up_bias) || std::regex_match(tensor_name, pattern_ffn_gate_bias)) {
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "ffn_down.weight", "ffn_down_exps.weight");
}
if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) {
@@ -555,6 +566,14 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
GGML_ASSERT(tensor->ne[axis] == n_embd + 2*n_embd_gqa);
return {{n_embd, 1}, {n_embd_gqa, 2}};
}
if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_up_bias)) {
const int64_t n_ff = hparams.n_ff(il);
// some models such as Phi 3 have fused up + gate tensors named "up" tensors, which need to be segmented
if (tensor->ne[axis] == 2*n_ff) {
return {{n_ff, 2}};
}
return {{tensor->ne[axis], 1}};
}
if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) {
const int64_t n_ff_exp = hparams.n_ff_exp;
GGML_ASSERT(tensor->ne[axis] == 2*n_ff_exp);
@@ -631,7 +650,8 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str
}
// FFN
if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight) || std::regex_match(tensor_name, pattern_ffn_up_gate_bias) ||
if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_up_bias) ||
std::regex_match(tensor_name, pattern_ffn_gate_weight) || std::regex_match(tensor_name, pattern_ffn_gate_bias) ||
std::regex_match(tensor_name, pattern_ffn_gate_up_weight) || std::regex_match(tensor_name, pattern_ffn_down_weight)) {
const int64_t blck_size_perf = std::lcm(blck_size, 128);
GGML_ASSERT(segments.size() == 1);
@@ -799,10 +819,12 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_100B_A6B: return "100B.A6B";
case LLM_TYPE_102B_A12B: return "102B.A12B";
case LLM_TYPE_106B_A12B: return "106B.A12B";
case LLM_TYPE_118B_A8B: return "118B.A8B";
case LLM_TYPE_120B_A12B: return "120B.A12B";
case LLM_TYPE_122B_A10B: return "122B.A10B";
case LLM_TYPE_196B_A11B: return "196B.A11B";
case LLM_TYPE_230B_A10B: return "230B.A10B";
case LLM_TYPE_428B_A23B: return "428B.A23B";
case LLM_TYPE_235B_A22B: return "235B.A22B";
case LLM_TYPE_300B_A47B: return "300B.A47B";
case LLM_TYPE_310B_A15B: return "310B.A15B";
@@ -1068,6 +1090,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer_all);
GGML_ASSERT(hparams.n_layer_all > 0 && hparams.n_layer_all <= LLAMA_MAX_LAYERS);
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups, false);
@@ -1113,6 +1136,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0);
std::fill(hparams.is_swa_impl.begin(), hparams.is_swa_impl.end(), 0);
std::fill(hparams.is_recr_impl.begin(), hparams.is_recr_impl.end(), llm_arch_is_recurrent(ml.get_arch()) ? 1 : 0);
std::fill(hparams.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 0);
std::fill(hparams.xielu_alpha_n.begin(), hparams.xielu_alpha_n.end(), 0.0f);
std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
@@ -1229,7 +1253,7 @@ void llama_model_base::load_vocab(llama_model_loader & ml) {
bool llama_model_base::load_tensors(llama_model_loader & ml) {
const auto & split_mode = params.split_mode;
const auto & use_mlock = params.use_mlock;
const bool use_mlock = params.load_mode == LLAMA_LOAD_MODE_MLOCK || params.load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK;
const auto & tensor_split = params.tensor_split;
const int n_layer_all = hparams.n_layer_all;
@@ -1239,8 +1263,8 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
this->ml = &ml; // to be used by create_tensor() and load_arch_tensors()
LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n",
__func__, ml.use_mmap ? "true" : "false", ml.use_direct_io ? "true" : "false");
LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (load_mode = %s)\n",
__func__, llama_load_mode_name(params.load_mode));
// build a list of buffer types for the CPU and GPU devices
pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
@@ -2048,9 +2072,13 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
{
res = nullptr;
} break;
case LLM_ARCH_DEEPSEEK32:
case LLM_ARCH_MINIMAX_M3:
{
res = new llama_kv_cache_dsa(
// sparse (MSA) layers carry an indexer key cache, but leading dense layers do not
llama_kv_cache::layer_filter_cb filter_idx =
[&](int32_t il) { return (uint32_t) il >= hparams.n_layer_dense_lead; };
res = new llama_kv_cache_msa(
*this,
params.type_k,
params.type_v,
@@ -2063,17 +2091,140 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
hparams.n_swa,
hparams.swa_type,
nullptr,
filter_idx,
nullptr);
} break;
case LLM_ARCH_GLM_DSA:
case LLM_ARCH_DEEPSEEK32:
{
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && hparams.n_layer_nextn > 0) {
// The NextN/MTP draft head runs dense MLA (no DSA indexer), so the
// MTP context uses a plain attention KV cache holding only the
// nextn layer(s) - same pattern as the hybrid Qwen3.5 MTP context.
llama_kv_cache::layer_filter_cb filter =
[&](uint32_t il) { return il >= hparams.n_layer(); };
res = new llama_kv_cache(
*this,
hparams,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
1,
hparams.n_swa,
hparams.swa_type,
nullptr,
filter,
nullptr,
nullptr);
} else {
// Main context: DSA cache for the trunk layers only - the nextn
// layer(s) are never attended by the trunk graph.
llama_kv_cache::layer_filter_cb filter_mla = nullptr;
if (hparams.n_layer_nextn > 0) {
filter_mla = [&](uint32_t il) { return il < hparams.n_layer(); };
}
llama_kv_cache::layer_filter_cb filter_lid = [&](uint32_t il) { return il < hparams.n_layer() && (arch != LLM_ARCH_GLM_DSA || hparams.is_indexer_full(il)); };
res = new llama_kv_cache_dsa(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
1,
hparams.n_swa,
hparams.swa_type,
filter_mla,
filter_lid,
nullptr);
}
} break;
case LLM_ARCH_DEEPSEEK4:
{
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
const llama_memory_i::layer_filter_cb filter_mtp = [&](int32_t il) {
return il >= (int32_t) hparams.n_layer();
};
res = new llama_kv_cache_iswa(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
params.swa_full,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
cparams.n_ubatch,
1,
nullptr,
filter_mtp,
nullptr,
nullptr);
} else {
res = new llama_kv_cache_dsv4(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
params.swa_full,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
cparams.n_ubatch,
1,
cparams.n_rs_seq,
nullptr,
nullptr);
}
} break;
case LLM_ARCH_DFLASH:
{
// DSV4 DSpark stages store a single MLA-style K per position (window = the draft ring)
if (hparams.dsv4_hc_mult > 0) {
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
res = new llama_kv_cache_iswa(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
params.swa_full,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
cparams.n_ubatch,
1,
nullptr,
nullptr,
nullptr,
nullptr);
break;
}
}
[[fallthrough]];
// Models that need standard caching should rely on recurrent/hybrid
// checks
default:
{
// The MTP head is dense-attention only on hybrid Qwen3.5/3.6, so use a plain
// The MTP head is dense-attention only on hybrid Qwen3-Next/3.5/3.6, so use a plain
// attention KV cache for the MTP context instead of the hybrid wrapper.
const bool mtp_on_hybrid_qwen35 =
const bool mtp_on_hybrid_qwen =
params.ctx_type == LLAMA_CONTEXT_TYPE_MTP &&
(arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE);
(arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE);
if (llm_arch_is_recurrent(arch)) {
res = new llama_memory_recurrent(
@@ -2085,7 +2236,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
cparams.n_seq_max,
cparams.n_rs_seq,
nullptr);
} else if (llm_arch_is_hybrid(arch) && !mtp_on_hybrid_qwen35) {
} else if (llm_arch_is_hybrid(arch) && !mtp_on_hybrid_qwen) {
// The main difference between hybrid architectures is the
// layer filters, so pick the right one here
llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
@@ -2100,7 +2251,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
filter_recr = [&](uint32_t il) {
return hparams.is_recr(il) && hparams.n_ff(il) == 0;
};
} else if (arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) {
} else if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) {
filter_attn = [&](uint32_t il) {
return il < hparams.n_layer() && !hparams.is_recr(il);
};
@@ -2166,11 +2317,13 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
};
}
if (mtp_on_hybrid_qwen35) {
if (mtp_on_hybrid_qwen) {
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
}
if (arch == LLM_ARCH_STEP35 && hparams.n_layer_nextn > 0) {
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA ||
arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_DEEPSEEK32) &&
hparams.n_layer_nextn > 0) {
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
} else {
@@ -2178,24 +2331,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
}
}
if (arch == LLM_ARCH_DEEPSEEK4) {
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
res = new llama_kv_cache_dsv4(
*this,
params.type_k,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
params.swa_full,
cparams.kv_unified,
cparams.n_ctx_seq,
cparams.n_seq_max,
cparams.n_ubatch,
1,
filter,
reuse);
} else if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
GGML_ASSERT(hparams.is_swa_any());
if (arch == LLM_ARCH_GEMMA4_ASSISTANT) {
@@ -2304,19 +2440,18 @@ llama_model_params llama_model_default_params() {
/*.tensor_buft_overrides =*/ nullptr,
/*.n_gpu_layers =*/ -1,
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.load_mode =*/ LLAMA_LOAD_MODE_MMAP,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_direct_io =*/ false,
/*.use_mlock =*/ false,
/*.check_tensors =*/ false,
/*.use_extra_bufts =*/ true,
/*.no_host =*/ false,
/*.no_alloc =*/ false,
/*.load_mtp =*/ false,
};
return result;
@@ -2472,6 +2607,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_LLAMA_EMBED:
case LLM_ARCH_MAINCODER:
case LLM_ARCH_GLM_DSA:
case LLM_ARCH_NANBEIGE:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
@@ -2526,6 +2662,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_JAIS2:
case LLM_ARCH_OPENAI_MOE:
case LLM_ARCH_HUNYUAN_DENSE:
case LLM_ARCH_HY_V3:
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_SMALLTHINKER:
@@ -2533,17 +2670,22 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GROVEMOE:
case LLM_ARCH_APERTUS:
case LLM_ARCH_MINIMAX_M2:
case LLM_ARCH_MINIMAX_M3:
case LLM_ARCH_COGVLM:
case LLM_ARCH_PANGU_EMBED:
case LLM_ARCH_AFMOE:
case LLM_ARCH_LAGUNA:
case LLM_ARCH_QWEN3NEXT:
case LLM_ARCH_MIMO2:
case LLM_ARCH_STEP35:
case LLM_ARCH_TALKIE:
case LLM_ARCH_MELLUM:
case LLM_ARCH_DFLASH:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_DFLASH:
// DSV4 DSpark drafters use DeepSeek-V4's normal RoPE; legacy DFlash backbones are NeoX
return model->hparams.dsv4_hc_mult > 0 ? LLAMA_ROPE_TYPE_NORM : LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:
case LLM_ARCH_PADDLEOCR:
return LLAMA_ROPE_TYPE_MROPE;
@@ -2725,7 +2867,8 @@ llama_model_base::llama_model_base(const struct llama_model_params & params) : l
TENSOR_DUPLICATED (llama_model_loader::TENSOR_DUPLICATED),
TENSOR_NOT_REQUIRED (llama_model_loader::TENSOR_NOT_REQUIRED),
TENSOR_SKIP (llama_model_loader::TENSOR_SKIP),
TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL) {}
TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL),
TENSOR_ALLOW_RESHAPE (llama_model_loader::TENSOR_ALLOW_RESHAPE) {}
ggml_tensor * llama_model_base::create_tensor(const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) {
GGML_ASSERT(ml != nullptr);
examples/talk-llama/llama-model.h
+15
View File
@@ -130,10 +130,12 @@ enum llm_type {
LLM_TYPE_100B_A6B,
LLM_TYPE_102B_A12B, // Solar-Open
LLM_TYPE_106B_A12B, // GLM-4.5-Air
LLM_TYPE_118B_A8B, // Laguna-S-2
LLM_TYPE_120B_A12B, // Nemotron 3 Super
LLM_TYPE_122B_A10B, // Qwen3.5
LLM_TYPE_196B_A11B, // Step3.5-Flash
LLM_TYPE_230B_A10B, // Minimax M2
LLM_TYPE_428B_A23B, // Minimax M3
LLM_TYPE_235B_A22B,
LLM_TYPE_300B_A47B, // Ernie MoE big
LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
@@ -515,6 +517,12 @@ 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
// MSA
struct ggml_tensor * index_q_proj = nullptr;
struct ggml_tensor * index_k_proj = nullptr;
struct ggml_tensor * index_q_norm = nullptr;
struct ggml_tensor * index_k_norm = nullptr;
// gemma4 layer output scale, reused for talkie embedding skip scale
struct ggml_tensor * out_scale = nullptr;
@@ -599,6 +607,12 @@ struct llama_model {
struct ggml_tensor * fc = nullptr; // feature fusion layer
struct ggml_tensor * d2t = nullptr; // draft to target vocabulary mapping
// dspark
struct ggml_tensor * dspark_markov_w1 = nullptr;
struct ggml_tensor * dspark_markov_w2 = nullptr;
struct ggml_tensor * dspark_conf_proj = nullptr;
struct ggml_tensor * dspark_conf_proj_b = nullptr;
// unified vector to store target-model extracted layer ids in eagle3, dflash, etc.
std::vector<int32_t> target_layer_ids;
@@ -705,6 +719,7 @@ struct llama_model_base : public llama_model {
const int TENSOR_NOT_REQUIRED;
const int TENSOR_SKIP;
const int TENSOR_SKIP_IF_VIRTUAL;
const int TENSOR_ALLOW_RESHAPE;
explicit llama_model_base(const llama_model_params & params);
virtual ~llama_model_base() = default;
examples/talk-llama/llama-quant.cpp
+18 -5
View File
@@ -2,6 +2,7 @@
#include "llama-model.h"
#include "llama-model-loader.h"
#include "llama-ext.h"
#include "llama.h"
#include <algorithm>
#include <cmath>
@@ -306,6 +307,9 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
// do not quantize the i32 token-id -> expert-id routing table (DeepSeek-V4)
quantize &= name.find("ffn_gate_tid2eid.weight") == std::string::npos;
// these are very small (e.g. 4x4)
quantize &= name.find("altup") == std::string::npos;
quantize &= name.find("laurel") == std::string::npos;
@@ -322,6 +326,10 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param
quantize &= name.find("ssm_conv1d") == std::string::npos;
quantize &= name.find("shortconv.conv.weight") == std::string::npos;
// do not quantize MiniMax's indexer projection weights, they are tiny
quantize &= name.find("indexer.k_proj.weight") == std::string::npos;
quantize &= name.find("indexer.q_proj.weight") == std::string::npos;
// do not quantize RWKV's small yet 2D weights
quantize &= name.find("time_mix_first.weight") == std::string::npos;
quantize &= name.find("time_mix_w0.weight") == std::string::npos;
@@ -351,6 +359,10 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param
quantize &= name.find(".patch_embd") == std::string::npos;
quantize &= name.find(".patch_merger") == std::string::npos;
// audio codebook
quantize &= name.find("a.rvq.codebook") == std::string::npos;
quantize &= name.find("mm.a.code_embd") == std::string::npos;
return quantize;
}
@@ -673,7 +685,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_mod
ggml_type new_type = default_type;
// get more optimal quantization type based on the tensor shape, layer, etc.
if (!params->pure && ggml_is_quantized(default_type)) {
if (ggml_is_quantized(default_type)) {
// if the user provided tensor types - use those
bool manual = false;
if (!qs.tensor_type_patterns.empty()) {
@@ -692,7 +704,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_mod
}
// if not manual - use the standard logic for choosing the quantization type based on the selected mixture
if (!manual) {
if (!manual && !params->pure) {
new_type = llama_tensor_get_type_impl(qs, new_type, tensor, params->ftype, tm.category);
}
@@ -873,15 +885,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// mmap consistently increases speed on Linux, and also increases speed on Windows with
// hot cache. It may cause a slowdown on macOS, possibly related to free memory.
#if defined(__linux__) || defined(_WIN32)
constexpr bool use_mmap = true;
constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_MMAP;
#else
constexpr bool use_mmap = false;
constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_NONE;
#endif
const llama_model_kv_override * kv_overrides = params->kv_overrides;
std::vector<std::string> splits = {};
llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr,
fname_inp, splits, /*file*/ nullptr, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
fname_inp, splits, /*file*/ nullptr, /*load_mode*/ load_mode, /*check_tensors*/ true, /*no_alloc*/ false, /*load_mtp*/ true, kv_overrides, nullptr);
ml.init_mappings(false); // no prefetching
auto mparams = llama_model_default_params();
@@ -1351,6 +1363,7 @@ llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * des
model->hparams.n_embd_head_k_full = desc->n_embd_head_k;
model->hparams.n_embd_head_v_full = desc->n_embd_head_v;
model->hparams.n_layer_all = desc->n_layer;
GGML_ASSERT(desc->n_layer > 0 && desc->n_layer <= LLAMA_MAX_LAYERS);
model->hparams.n_expert = desc->n_expert;
for (uint32_t i = 0; i < desc->n_layer; i++) {
examples/talk-llama/llama-sampler.cpp
+264 -41
View File
@@ -263,6 +263,10 @@ static void llama_log_softmax(float * array, size_t size) {
*/
static void llama_sampler_temp_impl(llama_token_data_array * cur_p, float temp) {
if (cur_p->size == 0) {
return;
}
if (temp <= 0.0f) {
// find the token with the highest logit and set the rest to -inf
size_t max_i = 0;
@@ -989,7 +993,9 @@ static void llama_sampler_greedy_backend_apply(
GGML_UNUSED(gf);
GGML_UNUSED(smpl);
struct ggml_tensor * curl = ggml_argmax(ctx, data->logits);
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
struct ggml_tensor * curl = ggml_argmax(ctx, logits);
ggml_set_name(curl, "greedy_argmax");
data->sampled = curl;
@@ -1154,7 +1160,10 @@ static void llama_sampler_dist_backend_apply(
ggml_set_name (sctx->inp_uniform, "uniform");
ggml_set_input(sctx->inp_uniform);
struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits);
// flatten
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
struct ggml_tensor * probs = ggml_soft_max(ctx, logits);
ggml_set_name(probs, "dist_probs");
struct ggml_tensor * cumsum = ggml_cumsum(ctx, probs);
@@ -1285,22 +1294,22 @@ static void llama_sampler_top_k_backend_apply(
struct llama_sampler_data * data) {
auto * sctx = (llama_sampler_top_k *) smpl->ctx;
struct ggml_tensor * top_k = ggml_top_k(ctx, data->logits, sctx->k);
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
struct ggml_tensor * top_k = ggml_top_k(ctx, logits, sctx->k);
ggml_set_name(top_k, "top_k");
if (data->candidates) {
struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, data->candidates->ne[0]);
data->candidates = ggml_get_rows(ctx, candidates_rows, top_k);
data->candidates = ggml_reshape_1d(ctx, data->candidates, sctx->k);
ggml_set_name(data->candidates, "top_k_candidates");
} else {
data->candidates = top_k;
}
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
data->logits = ggml_reshape_1d(ctx, top_k_rows, sctx->k);
ggml_set_name(top_k_rows, "top_k_rows");
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, logits->ne[0]);
data->logits = ggml_get_rows(ctx, logits_rows, top_k);
ggml_set_name(data->logits, "top_k_rows");
GGML_UNUSED(gf);
}
@@ -1431,21 +1440,25 @@ static void llama_sampler_top_p_backend_apply(
struct llama_sampler_data * data) {
auto * sctx = (llama_sampler_top_p *) smpl->ctx;
// flatten
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
auto ggml_sort = [ctx](struct ggml_tensor * a, struct ggml_tensor * b) {
GGML_ASSERT(ggml_nrows(a) == 1);
struct ggml_tensor * a_reshaped = ggml_reshape_2d(ctx, a, 1, a->ne[0]);
struct ggml_tensor * a_sorted = ggml_get_rows(ctx, a_reshaped, b);
return ggml_reshape_1d(ctx, a_sorted, a->ne[0]);
return a_sorted;
};
// Get the sorted logits in descending order.
struct ggml_tensor * sorted_idx = ggml_argsort(ctx, data->logits, GGML_SORT_ORDER_DESC);
struct ggml_tensor * sorted_idx = ggml_argsort(ctx, logits, GGML_SORT_ORDER_DESC);
ggml_set_name(sorted_idx, "top_p_sorted_idx");
// Do the sorting via reshape + get_rows
struct ggml_tensor * sorted_logits = ggml_sort(data->logits, sorted_idx);
struct ggml_tensor * sorted_logits = ggml_sort(logits, sorted_idx);
ggml_set_name(sorted_logits, "top_p_sorted_logits");
sorted_logits = ggml_reshape_1d(ctx, sorted_logits, ggml_nelements(sorted_logits));
struct ggml_tensor * softmax = ggml_soft_max(ctx, sorted_logits);
ggml_set_name(softmax, "top_p_softmax");
@@ -1622,10 +1635,12 @@ static void llama_sampler_min_p_backend_apply(
struct llama_sampler_data * data) {
auto * sctx = (llama_sampler_min_p *) smpl->ctx;
struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits);
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
struct ggml_tensor * max_idx = ggml_argmax(ctx, logits);
ggml_set_name(max_idx, "max_idx");
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, logits->ne[0]);
ggml_set_name(logits_rows, "logits_rows");
struct ggml_tensor * max_logit = ggml_get_rows(ctx, logits_rows, max_idx);
@@ -1636,7 +1651,7 @@ static void llama_sampler_min_p_backend_apply(
ggml_set_name(threshold, "min_p_threshold");
// Subtract the threshold from logits.
struct ggml_tensor * sub = ggml_sub(ctx, data->logits, threshold);
struct ggml_tensor * sub = ggml_sub(ctx, logits, threshold);
// Create a mask where logits below the threshold are 0 (discard),
// and others are 1 (keep).
@@ -1648,7 +1663,7 @@ static void llama_sampler_min_p_backend_apply(
struct ggml_tensor * min_p_bias = ggml_log(ctx, mask);
ggml_set_name(min_p_bias, "min_p_bias");
data->logits = ggml_add(ctx, data->logits, min_p_bias);
data->logits = ggml_add(ctx, logits, min_p_bias);
ggml_set_name(data->logits, "min_p_logits");
GGML_UNUSED(gf);
@@ -1825,18 +1840,20 @@ static void llama_sampler_backend_temp_sampling(
struct llama_sampler_data * data,
float temp) {
if (temp <= 0.0f) {
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
// Find the most probable token index.
struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits);
struct ggml_tensor * max_idx = ggml_argmax(ctx, logits);
ggml_set_name(max_idx, "temp_max_idx");
if (data->candidates) {
struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, data->candidates->ne[0]);
struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, ggml_nelements(data->candidates));
data->candidates = ggml_get_rows(ctx, candidates_rows, max_idx);
} else {
data->candidates = max_idx;
}
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, ggml_nelements(logits));
data->logits = ggml_get_rows(ctx, logits_rows, max_idx);
return;
@@ -2015,13 +2032,15 @@ static void llama_sampler_temp_ext_backend_apply(
return;
}
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
// Calculate min_temp, max_temp, and max_entropy.
const float min_temp = std::max(0.0f, sctx->temp - sctx->delta);
const float max_temp = sctx->temp + sctx->delta;
const float max_entropy = logf(data->logits->ne[0]);
const float max_entropy = logf(logits->ne[0]);
// Calculate the probabilities.
struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits);
struct ggml_tensor * probs = ggml_soft_max(ctx, logits);
ggml_set_name(probs, "temp_ext_softmax_probs");
// Clamp probabilities to avoid log(0) which would give -inf
@@ -2059,7 +2078,7 @@ static void llama_sampler_temp_ext_backend_apply(
ggml_set_name(dyn_temp, "temp_ext_dyn_temp");
// Scale the logits by the dynamic temperature
struct ggml_tensor * scaled_logits = ggml_div(ctx, data->logits, dyn_temp);
struct ggml_tensor * scaled_logits = ggml_div(ctx, logits, dyn_temp);
ggml_set_name(scaled_logits, "temp_ext_scaled_logits");
data->logits = scaled_logits;
@@ -2619,7 +2638,8 @@ struct llama_sampler * llama_sampler_init_grammar_lazy_patterns(
// penalties
struct llama_sampler_penalties {
struct llama_sampler_penalties : public llama_sampler_backend {
const int32_t n_vocab;
const int32_t penalty_last_n;
const float penalty_repeat;
const float penalty_freq;
@@ -2629,10 +2649,50 @@ struct llama_sampler_penalties {
// a frequency map to count token occurrences
std::unordered_map<llama_token, int> token_count;
// backend graph inputs
ggml_tensor * inp_token_ids = nullptr;
ggml_tensor * inp_counts = nullptr;
// backend helpers
int32_t n_max = 0;
bool has_candidates = false;
std::vector<int32_t> host_token_ids;
std::vector<int32_t> host_counts;
static bool is_disabled(
int32_t penalty_last_n,
float penalty_repeat,
float penalty_freq,
float penalty_present) {
return penalty_last_n == 0 ||
(penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f);
}
bool is_disabled() const {
return is_disabled(penalty_last_n, penalty_repeat, penalty_freq, penalty_present);
}
llama_sampler_penalties(
int32_t n_vocab,
int32_t penalty_last_n,
float penalty_repeat,
float penalty_freq,
float penalty_present)
: llama_sampler_backend("penalties")
, n_vocab (n_vocab)
, penalty_last_n (penalty_last_n)
, penalty_repeat (penalty_repeat)
, penalty_freq (penalty_freq)
, penalty_present (penalty_present)
, prev (penalty_last_n) {
}
};
static const char * llama_sampler_penalties_name(const struct llama_sampler * /*smpl*/) {
return "penalties";
static const char * llama_sampler_penalties_name(const struct llama_sampler * smpl) {
auto * ctx = (llama_sampler_penalties *) smpl->ctx;
return ctx->get_name();
}
static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_token token) {
@@ -2669,8 +2729,7 @@ static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_to
static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
auto * ctx = (llama_sampler_penalties *) smpl->ctx;
if ((ctx->penalty_last_n == 0) ||
(ctx->penalty_repeat == 1.0f && ctx->penalty_freq == 0.0f && ctx->penalty_present == 0.0f)) {
if (ctx->is_disabled()) {
return;
}
@@ -2708,6 +2767,7 @@ static void llama_sampler_penalties_reset(struct llama_sampler * smpl) {
static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_sampler * smpl) {
const auto * ctx = (const llama_sampler_penalties *) smpl->ctx;
auto * result = llama_sampler_init_penalties(
ctx->n_vocab,
ctx->penalty_last_n,
ctx->penalty_repeat,
ctx->penalty_freq,
@@ -2717,7 +2777,8 @@ static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_s
{
auto * result_ctx = (llama_sampler_penalties *) result->ctx;
result_ctx->prev = ctx->prev;
result_ctx->prev = ctx->prev;
result_ctx->token_count = ctx->token_count;
}
return result;
@@ -2727,6 +2788,170 @@ static void llama_sampler_penalties_free(struct llama_sampler * smpl) {
delete (llama_sampler_penalties *) smpl->ctx;
}
static bool llama_sampler_penalties_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
auto * sctx = (llama_sampler_penalties *) smpl->ctx;
const bool res = llama_sampler_backend_support(smpl, buft);
sctx->init(res);
return res;
}
static void llama_sampler_penalties_backend_apply(
struct llama_sampler * smpl,
struct ggml_context * ctx,
struct ggml_cgraph * gf,
struct llama_sampler_data * data) {
GGML_UNUSED(gf);
auto * sctx = (llama_sampler_penalties *) smpl->ctx;
if (sctx->is_disabled()) {
return;
}
GGML_ASSERT(sctx->n_vocab > 0);
sctx->has_candidates = data->candidates != nullptr;
sctx->n_max = std::min(sctx->penalty_last_n, sctx->n_vocab);
sctx->inp_token_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sctx->n_max);
ggml_set_name(sctx->inp_token_ids, "penalties_token_ids");
ggml_set_input(sctx->inp_token_ids);
sctx->inp_counts = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sctx->n_max);
ggml_set_name(sctx->inp_counts, "penalties_counts");
ggml_set_input(sctx->inp_counts);
if ((int32_t) sctx->host_token_ids.size() != sctx->n_max) {
sctx->host_token_ids.assign(sctx->n_max, 0);
sctx->host_counts.assign(sctx->n_max, 0);
}
// flatten
ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
ggml_tensor * gathered = logits;
ggml_tensor * counts_f32 = ggml_cast(ctx, sctx->inp_counts, GGML_TYPE_F32);
if (sctx->has_candidates) {
ggml_tensor * candidates = ggml_reshape_1d(
ctx, data->candidates, ggml_nelements(data->candidates));
const int64_t n_candidates = candidates->ne[0];
GGML_ASSERT(n_candidates == ggml_nelements(logits));
ggml_tensor * counts_rows = ggml_fill(
ctx, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, sctx->n_vocab), 0.0f);
ggml_tensor * scatter_rows = ggml_reshape_2d(ctx, counts_f32, 1, sctx->n_max);
counts_rows = ggml_set_rows(ctx, counts_rows, scatter_rows, sctx->inp_token_ids);
counts_f32 = ggml_get_rows(ctx, counts_rows, candidates);
counts_f32 = ggml_reshape_1d(ctx, counts_f32, n_candidates);
} else {
ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, ggml_nelements(logits));
gathered = ggml_get_rows(ctx, logits_rows, sctx->inp_token_ids);
gathered = ggml_reshape_1d(ctx, gathered, sctx->n_max);
}
ggml_tensor * active_mask = ggml_step(ctx, counts_f32);
ggml_tensor * inactive_mask = ggml_sub(ctx, ggml_fill(ctx, active_mask, 1.0f), active_mask);
ggml_tensor * penalized = gathered;
if (sctx->penalty_repeat != 1.0f) {
ggml_tensor * pos_mask = ggml_step(ctx, penalized);
ggml_tensor * neg_mask = ggml_sub(ctx, ggml_fill(ctx, pos_mask, 1.0f), pos_mask);
ggml_tensor * pos_scale = ggml_scale(ctx, pos_mask, 1.0f/sctx->penalty_repeat);
ggml_tensor * neg_scale = ggml_scale(ctx, neg_mask, sctx->penalty_repeat);
ggml_tensor * repeat_scale = ggml_add(ctx, pos_scale, neg_scale);
// scale inactive entries with 1 to avoid -INF * 0 = NaN for values masked by top-p
repeat_scale = ggml_mul(ctx, repeat_scale, active_mask);
repeat_scale = ggml_add(ctx, repeat_scale, inactive_mask);
penalized = ggml_mul(ctx, gathered, repeat_scale);
}
if (sctx->penalty_freq != 0.0f) {
ggml_tensor * penalty_freq = ggml_scale(ctx, counts_f32, sctx->penalty_freq);
penalized = ggml_sub(ctx, penalized, penalty_freq);
}
if (sctx->penalty_present != 0.0f) {
ggml_tensor * penalty_present = ggml_scale(ctx, active_mask, sctx->penalty_present);
penalized = ggml_sub(ctx, penalized, penalty_present);
}
if (sctx->has_candidates) {
data->logits = penalized;
} else {
ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, ggml_nelements(logits));
ggml_tensor * scatter_rows = ggml_reshape_2d(ctx, penalized, 1, sctx->n_max);
logits_rows = ggml_set_rows(ctx, logits_rows, scatter_rows, sctx->inp_token_ids);
data->logits = ggml_reshape_1d(ctx, logits_rows, ggml_nelements(logits));
}
}
static void llama_sampler_penalties_backend_set_input(struct llama_sampler * smpl) {
auto * sctx = (llama_sampler_penalties *) smpl->ctx;
if (!sctx->inp_token_ids || !sctx->inp_counts || sctx->n_max <= 0 || sctx->n_vocab <= 0) {
return;
}
if (sctx->is_disabled()) {
return;
}
// fill active entries from the map
int32_t n_active = 0;
for (const auto & it : sctx->token_count) {
GGML_ASSERT(n_active < sctx->n_max);
sctx->host_token_ids[n_active] = it.first;
sctx->host_counts [n_active] = it.second;
++n_active;
}
// Sorting is required because backend_apply uses ggml_set_rows (a scatter-back operation)
std::vector<std::pair<int32_t, int32_t>> entries;
entries.reserve(n_active);
for (int32_t i = 0; i < n_active; ++i) {
entries.emplace_back(sctx->host_token_ids[i], sctx->host_counts[i]);
}
std::sort(entries.begin(), entries.end(), [](const auto & a, const auto & b) {
return a.first < b.first;
});
for (int32_t i = 0; i < n_active; ++i) {
sctx->host_token_ids[i] = entries[i].first;
sctx->host_counts [i] = entries[i].second;
}
// Padding: Finds a filler token id that is not present in token_count.
// Use it to do padding for the arrays, it avoids resizing every time.
// The arrays must always have exactly n_max entries (the GPU tensor is a fixed size).
int32_t filler = 0;
if (n_active < sctx->n_max) {
while (sctx->token_count.find(filler) != sctx->token_count.end()) {
++filler;
}
GGML_ASSERT(filler < sctx->n_vocab);
}
// Fill the rest of the arrays with the filler token id and count 0.
// Inactive slots are padded with a unique dummy token ID (count = 0).
// The uniqueness matters because ggml_set_rows with duplicate indices can produce non-deterministic or incorrect results.
// Using a filler token with count 0 that isn't in the active set is safe, because the active_mask step in backend_apply filters them out via ggml_step(counts_f32)
for (int32_t i = n_active; i < sctx->n_max; ++i) {
sctx->host_token_ids[i] = filler;
sctx->host_counts [i] = 0;
}
ggml_backend_tensor_set(sctx->inp_token_ids, sctx->host_token_ids.data(), 0, sctx->n_max * sizeof(int32_t));
ggml_backend_tensor_set(sctx->inp_counts, sctx->host_counts.data(), 0, sctx->n_max * sizeof(int32_t));
}
static struct llama_sampler_i llama_sampler_penalties_i = {
/* .name = */ llama_sampler_penalties_name,
/* .accept = */ llama_sampler_penalties_accept,
@@ -2734,35 +2959,33 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
/* .reset = */ llama_sampler_penalties_reset,
/* .clone = */ llama_sampler_penalties_clone,
/* .free = */ llama_sampler_penalties_free,
/* .backend_init = */ nullptr,
/* .backend_init = */ llama_sampler_penalties_backend_init,
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_apply = */ llama_sampler_penalties_backend_apply,
/* .backend_set_input = */ llama_sampler_penalties_backend_set_input,
};
struct llama_sampler * llama_sampler_init_penalties(
int32_t n_vocab,
int32_t penalty_last_n,
float penalty_repeat,
float penalty_freq,
float penalty_present) {
penalty_last_n = std::max(penalty_last_n, 0);
const bool is_empty = (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f));
if (is_empty) {
if (llama_sampler_penalties::is_disabled(
penalty_last_n, penalty_repeat, penalty_freq, penalty_present)) {
return llama_sampler_init_empty("?penalties");
}
return llama_sampler_init(
/* .iface = */ &llama_sampler_penalties_i,
/* .ctx = */ new llama_sampler_penalties {
/* .penalty_last_n = */ penalty_last_n,
/* .penalty_repeat = */ penalty_repeat,
/* .penalty_freq = */ penalty_freq,
/* .penalty_present = */ penalty_present,
/* .prev = */ ring_buffer<llama_token>(penalty_last_n),
/* .token_count = */ {},
}
/* .ctx = */ new llama_sampler_penalties(
n_vocab,
penalty_last_n,
penalty_repeat,
penalty_freq,
penalty_present)
);
}
examples/talk-llama/llama-vocab.cpp
+49 -2
View File
@@ -496,6 +496,12 @@ struct llm_tokenizer_bpe : llm_tokenizer {
"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\\r\\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_LAGUNA:
regex_exprs = {
"[^\\n]+|[\\n]+",
"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
};
break;
case LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE:
regex_exprs = {
// original regex from tokenizer.json
@@ -1325,6 +1331,9 @@ struct llm_tokenizer_rwkv_session {
token_id = node->value;
token_length = position + 1;
}
if (position + 1 >= text.size()) {
break;
}
node = node->traverse(text[++position]);
}
@@ -2342,6 +2351,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "afmoe") {
pre_type = LLAMA_VOCAB_PRE_TYPE_AFMOE;
clean_spaces = false;
} else if (
tokenizer_pre == "laguna") {
pre_type = LLAMA_VOCAB_PRE_TYPE_LAGUNA;
clean_spaces = false;
} else if (
tokenizer_pre == "minimax-m2") {
pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;
@@ -2519,6 +2532,12 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
const std::string & key = kv(std::get<0>(it));
int32_t & id = std::get<1>(it);
if (id >= 0 && static_cast<size_t>(id) >= id_to_token.size()) {
LLAMA_LOG_WARN("%s: default special token '%s' = %d out of vocab range, disabling\n",
__func__, key.c_str(), id);
id = LLAMA_TOKEN_NULL;
}
uint32_t new_id;
if (!ml.get_key(std::get<0>(it), new_id, false)) {
continue;
@@ -2565,7 +2584,14 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
if (suppress_idx != -1) {
const int n = gguf_get_arr_n(ctx, suppress_idx);
const int32_t * data = (const int32_t *) gguf_get_arr_data(ctx, suppress_idx);
suppress_tokens.assign(data, data + n);
// drop out-of-range ids
suppress_tokens.reserve(n);
for (int i = 0; i < n; ++i) {
const int32_t id = data[i];
if (id >= 0 && id < (int) id_to_token.size()) {
suppress_tokens.push_back(id);
}
}
}
}
@@ -2796,6 +2822,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|| t.first == "<turn|>" // gemma4
|| t.first == "<|tool_response>" // gemma4
|| t.first == "<｜end▁of▁sentence｜>" // deepseek-ocr
|| t.first == "[e~[" // minimax-m2/m3
) {
special_eog_ids.insert(t.second);
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -2855,6 +2882,11 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
LLAMA_LOG_INFO("%s: printing all EOG tokens:\n", __func__);
for (auto tid : special_eog_ids) {
if (tid < 0 || tid >= (llama_token) id_to_token.size()) {
LLAMA_LOG_WARN("%s: EOG token id %d is out of range (vocab size %zu), skipping\n",
__func__, tid, id_to_token.size());
continue;
}
auto & text = id_to_token[tid].text;
LLAMA_LOG_INFO("%s: - %d ('%s')\n", __func__, tid, text.c_str());
@@ -2889,6 +2921,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
llama_token s_id = LLAMA_TOKEN_NULL;
for (auto tid : special_eog_ids) {
if (tid < 0 || tid >= (llama_token) id_to_token.size()) {
continue;
}
const auto & text = id_to_token[tid].text;
if (text == "<|tool_response>") {
has_tool_response = true;
@@ -4018,7 +4053,11 @@ int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string
}
std::vector<std::string> llama_vocab::get_bpe_merges() const {
std::vector<std::string> result(pimpl->bpe_ranks.size());
int max_rank = -1;
for (const auto & pair : pimpl->bpe_ranks) {
max_rank = std::max(max_rank, pair.second);
}
std::vector<std::string> result(max_rank + 1);
for (const auto & pair : pimpl->bpe_ranks) {
result[pair.second] = pair.first.first + " " + pair.first.second;
@@ -4179,6 +4218,14 @@ bool llama_vocab_get_add_sep(const struct llama_vocab * vocab) {
return vocab->get_add_sep();
}
const llama_token * llama_vocab_get_suppress_tokens(const struct llama_vocab * vocab, int32_t * n_suppress_tokens) {
const std::vector<llama_token> & tokens = vocab->get_suppress_tokens();
if (n_suppress_tokens) {
*n_suppress_tokens = (int32_t) tokens.size();
}
return tokens.data();
}
llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab) {
return vocab->token_fim_pre();
}
examples/talk-llama/llama-vocab.h
+1
View File
@@ -64,6 +64,7 @@ enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_WHITESPACE = 53,
LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI = 54,
LLAMA_VOCAB_PRE_TYPE_MELLUM2 = 55,
LLAMA_VOCAB_PRE_TYPE_LAGUNA = 56,
};
struct LLM_KV;
examples/talk-llama/llama.cpp
+28 -3
View File
@@ -46,6 +46,31 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty
GGML_ABORT("fatal error");
}
const char * llama_load_mode_name(enum llama_load_mode load_mode) {
switch (load_mode) {
case LLAMA_LOAD_MODE_NONE:
return "none";
case LLAMA_LOAD_MODE_MMAP:
return "mmap";
case LLAMA_LOAD_MODE_MLOCK:
return "mlock";
case LLAMA_LOAD_MODE_MMAP_MLOCK:
return "mmap+mlock";
case LLAMA_LOAD_MODE_DIRECT_IO:
return "dio";
}
GGML_ABORT("fatal error");
}
enum llama_load_mode llama_load_mode_from_str(const char * str) {
if (std::strcmp(str, "none") == 0) { return LLAMA_LOAD_MODE_NONE; }
if (std::strcmp(str, "mmap") == 0) { return LLAMA_LOAD_MODE_MMAP; }
if (std::strcmp(str, "mlock") == 0) { return LLAMA_LOAD_MODE_MLOCK; }
if (std::strcmp(str, "mmap+mlock") == 0) { return LLAMA_LOAD_MODE_MMAP_MLOCK; }
if (std::strcmp(str, "dio") == 0) { return LLAMA_LOAD_MODE_DIRECT_IO; }
throw std::invalid_argument(std::string("unknown load mode: ") + str);
}
struct llama_sampler_chain_params llama_sampler_chain_default_params() {
struct llama_sampler_chain_params result = {
/*.no_perf =*/ true,
@@ -279,8 +304,8 @@ static bool llama_prepare_model_devices(const llama_model_params & params, llama
static std::pair<int, llama_model *> llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud,
const std::string & fname, std::vector<std::string> & splits, FILE * file, llama_model_params & params) {
try {
llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io,
params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.load_mode,
params.check_tensors, params.no_alloc, params.load_mtp, params.kv_overrides, params.tensor_buft_overrides);
ml.print_info();
std::unique_ptr<llama_model> model_ptr(llama_model_create(ml, params));
@@ -412,7 +437,7 @@ struct llama_model * llama_model_init_from_user(
GGML_ASSERT(metadata != nullptr);
std::string path_model;
std::vector<std::string> splits = {};
params.use_mmap = false;
params.load_mode = LLAMA_LOAD_MODE_NONE;
params.use_extra_bufts = false;
return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, /*file*/ nullptr, params);
}
examples/talk-llama/llama.h
+20 -6
View File
@@ -202,6 +202,17 @@ extern "C" {
LLAMA_SPLIT_MODE_TENSOR = 3,
};
enum llama_load_mode {
LLAMA_LOAD_MODE_NONE = 0, // no special loading mode
LLAMA_LOAD_MODE_MMAP = 1, // memory map the model
LLAMA_LOAD_MODE_MLOCK = 2, // force system to keep model in RAM rather than swapping or compressing
LLAMA_LOAD_MODE_MMAP_MLOCK = 3, // mmap + force system to keep model in RAM rather than swapping or compressing
LLAMA_LOAD_MODE_DIRECT_IO = 4, // use direct I/O if available
};
LLAMA_API const char * llama_load_mode_name(enum llama_load_mode load_mode);
LLAMA_API enum llama_load_mode llama_load_mode_from_str(const char * str);
enum llama_context_type {
LLAMA_CONTEXT_TYPE_DEFAULT = 0,
LLAMA_CONTEXT_TYPE_MTP = 1,
@@ -301,6 +312,7 @@ extern "C" {
int32_t n_gpu_layers; // number of layers to store in VRAM, a negative value means all layers
enum llama_split_mode split_mode; // how to split the model across multiple GPUs
enum llama_load_mode load_mode; // how to load the model
// the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
int32_t main_gpu;
@@ -321,13 +333,11 @@ extern "C" {
// Keep the booleans together to avoid misalignment during copy-by-value.
bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible
bool use_direct_io; // use direct io, takes precedence over use_mmap when supported
bool use_mlock; // force system to keep model in RAM
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
bool load_mtp; // whether to load MTP layers
};
struct llama_sampler_seq_config {
@@ -1093,6 +1103,9 @@ extern "C" {
LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab);
LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab);
// model-specific suppress tokens (gguf key: tokenizer.ggml.suppress_tokens)
LLAMA_API const llama_token * llama_vocab_get_suppress_tokens(const struct llama_vocab * vocab, int32_t * n_suppress_tokens);
LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab);
LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab);
@@ -1411,10 +1424,11 @@ extern "C" {
/// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
int32_t n_vocab,
int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat, // 1.0 = disabled
float penalty_freq, // 0.0 = disabled
float penalty_present); // 0.0 = disabled
float penalty_repeat, // must be > 0.0, 1.0 = disabled
float penalty_freq, // must be finite, 0.0 = disabled
float penalty_present); // must be finite, 0.0 = disabled
/// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
LLAMA_API struct llama_sampler * llama_sampler_init_dry(
examples/talk-llama/models/cohere2moe.cpp
+5 -1
View File
@@ -55,7 +55,11 @@ void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) {
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
examples/talk-llama/models/deepseek2.cpp
+308 -25
View File
@@ -37,6 +37,11 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
hparams.rope_yarn_log_mul /= 0.1f;
}
// NextN/MTP
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
GGML_ASSERT(hparams.n_layer_nextn == 0 ||
hparams.n_layer() + hparams.n_layer_nextn == hparams.n_layer_all);
// (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); // FIXME why not use temperature_length?
@@ -52,10 +57,20 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
}
}
void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
void llama_model_deepseek2::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
const bool is_mla = hparams.is_mla();
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
@@ -81,44 +96,45 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
for (int i = 0; i < n_layer_all; ++i) {
auto & layer = layers[i];
const int flags = i < n_layer ? trunk_flags : mtp_flags;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
if (q_lora_rank > 0) {
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
}
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
if (q_lora_rank > 0) {
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);
} else {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, flags);
}
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, 0);
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
// note: only old legacy GGUF files will have the unsplit wkv_b tensor in
if (is_mla) {
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0);
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);
} else {
layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, 0);
layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, flags);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
if (i < (int) hparams.n_layer_dense_lead) {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
} else {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
if (n_expert == 0) {
throw std::runtime_error("n_expert must be > 0");
@@ -128,21 +144,281 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
}
// MoE branch
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
}
// NextN/MTP tensors
if (i >= n_layer) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, mtp_flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, mtp_flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, mtp_flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_deepseek2::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
llama_model_deepseek2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4 MTP requires n_layer_nextn > 0");
GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4 MTP currently only supports a single MTP block");
GGML_ASSERT(hparams.is_mla() && "GLM4 MTP requires MLA");
GGML_ASSERT(hparams.f_attn_temp_scale == 0.0f && "GLM4 MTP does not support attention temperature scaling");
// The appended MTP block is stored immediately after the main decoder layers.
const int il = hparams.n_layer();
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
GGML_ASSERT((uint32_t) il >= hparams.n_layer_dense_lead && "GLM4 MTP block expected to use MoE FFN");
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
const int64_t kv_lora_rank = hparams.n_lora_kv;
GGML_ASSERT(n_embd_head_qk_nope >= 1);
GGML_ASSERT(hparams.n_lora_q > 0);
GGML_ASSERT(layer.wq_a);
GGML_ASSERT(layer.attn_q_a_norm);
GGML_ASSERT(layer.wq_b);
GGML_ASSERT(layer.wkv_a_mqa);
GGML_ASSERT(layer.attn_kv_a_norm);
GGML_ASSERT(layer.wk_b);
const bool has_split_exps =
layer.ffn_up_exps != nullptr &&
layer.ffn_gate_exps != nullptr;
const bool has_fused_exps = layer.ffn_gate_up_exps != nullptr;
GGML_ASSERT(has_split_exps || has_fused_exps);
GGML_ASSERT(layer.ffn_norm);
GGML_ASSERT(layer.ffn_gate_inp);
GGML_ASSERT(layer.ffn_down_exps);
GGML_ASSERT(layer.ffn_gate_shexp);
GGML_ASSERT(layer.ffn_down_shexp);
GGML_ASSERT(layer.ffn_up_shexp);
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
ggml_set_input(inp->embd);
ggml_tensor * tok_embd;
if (ubatch.token) {
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens
? layer.nextn.embed_tokens
: model.tok_embd;
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
} else {
tok_embd = inp->embd;
}
cb(tok_embd, "mtp_tok_embd", il);
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->h);
ggml_set_name(inp->h, "mtp_h_input");
ggml_tensor * h_embd = inp->h;
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
auto * inp_attn_k = build_attn_inp_k();
ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, 0);
cb(concat, "mtp_concat", il);
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
cb(cur, "mtp_eh_proj", il);
ggml_tensor * inpSA = cur;
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
cb(q, "mtp_q_a", il);
q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
cb(q, "mtp_q_a_norm", il);
q = ggml_mul_mat(ctx0, layer.wq_b, q);
cb(q, "mtp_q_b", il);
ggml_tensor * q_nope =
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
ggml_row_size(q->type, n_embd_head_k_mla),
ggml_row_size(q->type, n_embd_head_k_mla) * n_head, 0);
cb(q_nope, "mtp_q_nope", il);
ggml_tensor * q_pe =
ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
ggml_row_size(q->type, n_embd_head_k_mla),
ggml_row_size(q->type, n_embd_head_k_mla) * n_head,
ggml_row_size(q->type, n_embd_head_qk_nope));
cb(q_pe, "mtp_q_pe", il);
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);
ggml_tensor * kv_cmpr =
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
cb(kv_cmpr, "mtp_kv_cmpr", il);
ggml_tensor * k_pe =
ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
cb(k_pe, "mtp_k_pe", il);
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
cb(kv_cmpr, "mtp_kv_cmpr_norm", il);
GGML_ASSERT(ext_factor >= 0.0f);
const float attn_factor_org =
attn_factor * (1.0f + 0.1f * logf(1.0f / freq_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_mla));
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(q_pe, "mtp_q_pe_rope", il);
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(k_pe, "mtp_k_pe_rope", il);
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope, "mtp_q_nope_perm", il);
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
cb(Qcur, "mtp_Qcur", il);
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, hparams.n_lora_kv, 1, n_tokens);
cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
cb(Kcur, "mtp_Kcur", il);
ggml_tensor * Vcur = kv_cmpr;
cb(Vcur, "mtp_Vcur", il);
cur = build_attn(inp_attn_k,
layer.wo, nullptr, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);
cb(cur, "mtp_attn_out", il);
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "mtp_ffn_inp", il);
cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_ffn_norm", il);
ggml_tensor * moe_out = build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il,
nullptr,
layer.ffn_gate_up_exps);
cb(moe_out, "mtp_ffn_moe_out", il);
ggml_tensor * ffn_shexp = build_ffn(cur,
layer.ffn_up_shexp, nullptr, nullptr,
layer.ffn_gate_shexp, nullptr, nullptr,
layer.ffn_down_shexp, nullptr, nullptr,
nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "mtp_ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "mtp_ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "mtp_post_ffn", il);
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: model.output_norm;
GGML_ASSERT(head_norm_w && "GLM4 MTP: missing both nextn.shared_head_norm and output_norm");
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cb(cur, "mtp_shared_head_norm", -1);
ggml_tensor * head_w = layer.nextn.shared_head_head
? layer.nextn.shared_head_head
: model.output;
ggml_tensor * head_s = layer.nextn.shared_head_head
? layer.nextn.shared_head_head_s
: model.output_s;
GGML_ASSERT(head_w && "GLM4 MTP: missing LM head (nextn.shared_head_head or model.output)");
cur = build_lora_mm(head_w, cur, head_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
// lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
@@ -365,7 +641,7 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
}
}
if (il == n_layer - 1 && inp_out_ids) {
if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
@@ -425,6 +701,13 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cb(cur, "result_norm", -1);
res->t_embd = cur;
examples/talk-llama/models/deepseek32.cpp
+306 -40
View File
@@ -44,13 +44,24 @@ void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) {
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
switch (hparams.n_layer()) {
case 62: type = LLM_TYPE_685B_A37B; break;
case 61: type = LLM_TYPE_685B_A37B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) {
void llama_model_deepseek32::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
const bool is_mla = hparams.is_mla();
if (!is_mla) {
throw std::runtime_error("DEEPSEEK32 architecture requires MLA");
@@ -80,12 +91,7 @@ void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) {
}
for (int i = 0; i < n_layer_all; ++i) {
int flags = 0;
if (i >= n_layer) {
// skip all tensors in the NextN layers
// TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
}
const int flags = (i >= n_layer) ? mtp_flags : trunk_flags;
auto & layer = layers[i];
@@ -138,7 +144,7 @@ void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) {
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
}
// NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
// NextN/MTP tensors - conditionally load for last nextn_predict_layers
if (i >= n_layer) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
@@ -153,6 +159,9 @@ void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) {
}
std::unique_ptr<llm_graph_context> llama_model_deepseek32::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
@@ -301,43 +310,50 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);
indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
// calculate indexer kq
indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
cb(indexer_q, "indexer_q", il);
indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
cb(indexer_k, "indexer_k", il);
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
cb(indexer_kq, "indexer_kq", il);
// ReLU requires contiguous tensors
indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
cb(indexer_kq, "indexer_kq", il);
// apply ReLU
ggml_tensor * indexer_score = ggml_relu(ctx0, indexer_kq);
cb(indexer_score, "indexer_score", il);
// pre-scale weights to avoid scaling operations on huge indexer_score tensor
indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head)));
cb(indexer_weights, "indexer_weights", il);
// multiply scores by indexer weights
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
cb(indexer_score, "indexer_score", il);
ggml_tensor * indexer_score = nullptr;
if (cparams.fused_lid) {
indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn_dsa->get_kq_mask_lid());
cb(indexer_score, "indexer_score", il);
res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il});
} else {
// calculate indexer kq
indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
cb(indexer_q, "indexer_q", il);
indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
cb(indexer_k, "indexer_k", il);
// sum by q n_indexer_head dimension
indexer_score = ggml_sum_rows(ctx0, indexer_score);
cb(indexer_score, "indexer_score", il);
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
cb(indexer_kq, "indexer_kq", il);
// permute result to match KQ mask
indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
cb(indexer_score, "indexer_score", il);
// ReLU requires contiguous tensors
indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
cb(indexer_kq, "indexer_kq", il);
// mask indexer scores
ggml_tensor * indexer_kq_mask = inp_attn_dsa->get_kq_mask_lid();
indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);
cb(indexer_score, "indexer_score", il);
// apply ReLU
indexer_score = ggml_relu(ctx0, indexer_kq);
cb(indexer_score, "indexer_score", il);
// multiply scores by indexer weights
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
cb(indexer_score, "indexer_score", il);
// sum by q n_indexer_head dimension
indexer_score = ggml_sum_rows(ctx0, indexer_score);
cb(indexer_score, "indexer_score", il);
// permute result to match KQ mask
indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
cb(indexer_score, "indexer_score", il);
// mask indexer scores
ggml_tensor * indexer_kq_mask = inp_attn_dsa->get_kq_mask_lid();
indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);
cb(indexer_score, "indexer_score", il);
}
// get indices of top k indexer scores
uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k;
@@ -423,7 +439,9 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
}
}
if (il == n_layer - 1 && inp_out_ids) {
// when unmasked nextn embeddings are requested, t_h_nextn must keep all rows,
// so the early output masking has to be skipped (it is applied after the final norm instead)
if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
@@ -486,6 +504,14 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
// post-norm hidden state feeds the NextN/MTP draft head
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cb(cur, "result_norm", -1);
res->t_embd = cur;
@@ -497,3 +523,243 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
ggml_build_forward_expand(gf, cur);
}
// LLM_GRAPH_TYPE_DECODER_MTP draft head for DeepSeek V3.2 (DEEPSEEK32).
// Semantics mirror the deepseek-family NextN/MTP layer:
// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj ->
// full deepseek32 decoder block (dense MLA attention + sigmoid-gated MoE FFN
// with shared expert, exactly as the trunk deepseek2 graph builds it) ->
// shared_head_norm (fallback output_norm) -> shared LM head.
// The DSA indexer is not used at runtime.
llama_model_deepseek32::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
: llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "DEEPSEEK32 MTP requires n_layer_nextn > 0");
GGML_ASSERT(hparams.n_layer_nextn == 1 && "DEEPSEEK32 MTP currently only supports a single MTP block");
GGML_ASSERT(hparams.is_mla() && "DEEPSEEK32 MTP requires MLA");
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
"nextn_layer_offset out of range [0, n_layer_nextn)");
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
// See the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY.
GGML_ASSERT(ext_factor >= 0.0f);
const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_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));
// TODO: extract in a common llm_graph_context::build_inp_embd_h()
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
ggml_set_input(inp->embd);
ggml_tensor * tok_embd;
if (ubatch.token) {
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
} else {
tok_embd = inp->embd;
}
cb(tok_embd, "mtp_tok_embd", il);
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->h);
ggml_set_name(inp->h, "mtp_h_input");
ggml_tensor * h_embd = inp->h;
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
// MLA with the absorption optimization uses a K-only cache (V is a view of K)
auto * inp_attn = build_attn_inp_k();
ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
cb(concat, "mtp_concat", il);
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
cb(cur, "mtp_eh_proj", il);
ggml_tensor * inpSA = cur;
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
// self-attention: dense MLA, same construction as the deepseek2 trunk graph
{
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
cb(q, "mtp_q", il);
q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
cb(q, "mtp_q", il);
q = ggml_mul_mat(ctx0, layer.wq_b, q);
cb(q, "mtp_q", il);
// split into {n_embd_head_qk_nope, n_head, n_tokens}
ggml_tensor * q_nope =
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
cb(q_nope, "mtp_q_nope", il);
// and {n_embd_head_qk_rope, n_head, n_tokens}
ggml_tensor * q_pe = ggml_view_3d(
ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
cb(q_pe, "mtp_q_pe", il);
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);
// split into {kv_lora_rank, n_tokens}
ggml_tensor * kv_cmpr =
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
cb(kv_cmpr, "mtp_kv_cmpr", il);
// and {n_embd_head_qk_rope, 1, n_tokens}
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
cb(k_pe, "mtp_k_pe", il);
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(q_pe, "mtp_q_pe", il);
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(k_pe, "mtp_k_pe", il);
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
cb(kv_cmpr, "mtp_kv_cmpr", il);
// {n_embd_head_qk_nope, n_tokens, n_head}
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope, "mtp_q_nope_perm", il);
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);
// {kv_lora_rank, n_head, n_tokens}
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
// note: rope must go first for in-place context shifting in build_rope_shift()
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
cb(Qcur, "mtp_Qcur", il);
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
cb(Kcur, "mtp_Kcur", il);
// {kv_lora_rank, 1, n_tokens}
ggml_tensor * Vcur = kv_cmpr;
cb(Vcur, "mtp_Vcur", il);
// note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)
cur = build_attn(inp_attn,
layer.wo, NULL, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);
cb(cur, "mtp_attn_out", il);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "mtp_ffn_inp", il);
cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "mtp_ffn_norm", il);
// MoE FFN with shared expert - same construction as the deepseek2 trunk graph
ggml_tensor * moe_out = build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il,
nullptr,
layer.ffn_gate_up_exps,
layer.ffn_up_exps_s,
layer.ffn_gate_exps_s,
layer.ffn_down_exps_s);
cb(moe_out, "mtp_ffn_moe_out", il);
// FFN shared expert
ggml_tensor * ffn_shexp =
build_ffn(cur,
layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s,
layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s,
layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "mtp_ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "mtp_ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "mtp_post_ffn", il);
// shared_head_norm applied after the decoder block, before the shared LM head.
// The post-norm hidden state seeds the next MTP step.
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: model.output_norm;
GGML_ASSERT(head_norm_w && "DEEPSEEK32 MTP: missing both nextn.shared_head_norm and output_norm");
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
cb(cur, "mtp_shared_head_norm", -1);
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
GGML_ASSERT(head_w && "DEEPSEEK32 MTP: missing LM head (nextn.shared_head_head or model.output)");
cur = build_lora_mm(head_w, cur, head_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/deepseek4.cpp
+496 -115
View File
@@ -16,6 +16,16 @@ static float dsv4_rope_attn_factor(float freq_scale, float ext_factor) {
}
void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
if (hparams.n_layer_nextn > 0 && hparams.n_layer_nextn < hparams.n_layer_all) {
const uint32_t n_layer_main = hparams.n_layer_all - hparams.n_layer_nextn;
const std::string mtp_probe = "blk." + std::to_string(n_layer_main) + ".nextn.eh_proj.weight";
if (ml.get_weight(mtp_probe.c_str()) == nullptr) {
hparams.n_layer_nextn = 0;
}
}
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < block_count");
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
@@ -24,8 +34,8 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm);
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer());
if (!ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer(), 0)) {
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all);
if (!ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer_all, 0)) {
hparams.swiglu_clamp_shexp = hparams.swiglu_clamp_exp;
}
@@ -41,9 +51,11 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps);
ml.get_key(LLM_KV_HASH_LAYER_COUNT, hparams.dsv4_hash_layer_count);
hparams.n_embd_out_impl = hparams.dsv4_hc_mult * hparams.n_embd;
uint32_t n_compress_ratios = 0;
ml.get_arr_n(LLM_KV_ATTENTION_COMPRESS_RATIOS, n_compress_ratios);
if (n_compress_ratios < hparams.n_layer()) {
if (n_compress_ratios < hparams.n_layer_all) {
throw std::runtime_error("DeepSeek-V4 compress_ratios is shorter than block_count");
}
ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios);
@@ -54,6 +66,9 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
}
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(0);
for (uint32_t il = hparams.n_layer(); il < hparams.n_layer_all; ++il) {
hparams.is_swa_impl[il] = true;
}
switch (hparams.n_layer()) {
case 43: type = LLM_TYPE_UNKNOWN; break;
@@ -61,7 +76,7 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
}
}
void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) {
void llama_model_deepseek4::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const int64_t q_lora_rank = hparams.n_lora_q;
@@ -75,6 +90,10 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) {
const int64_t hc_dim = hc_mult * n_embd;
const int64_t hc_mix_dim = (2 + hc_mult) * hc_mult;
const bool mtp_only = (n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
const int mtp_flags = ml.load_mtp ? 0 : TENSOR_SKIP;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
@@ -84,69 +103,84 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) {
hc_head_base = create_tensor(tn(LLM_TENSOR_HC_HEAD_BASE, "weight"), {hc_mult}, 0);
hc_head_scale = create_tensor(tn(LLM_TENSOR_HC_HEAD_SCALE, "weight"), {1}, 0);
for (int i = 0; i < n_layer; ++i) {
for (int i = 0; i < n_layer_all; ++i) {
auto & layer = layers[i];
const int flags = i < n_layer ? trunk_flags : mtp_flags;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head}, 0);
layer.wkv = create_tensor(tn(LLM_TENSOR_ATTN_KV, "weight", i), {n_embd, n_embd_head}, 0);
layer.attn_kv_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM, "weight", i), {n_embd_head}, 0);
layer.wo_a = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A, "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank * o_groups}, 0);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B, "weight", i), {o_groups * o_lora_rank, n_embd}, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, flags);
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head}, flags);
layer.wkv = create_tensor(tn(LLM_TENSOR_ATTN_KV, "weight", i), {n_embd, n_embd_head}, flags);
layer.attn_kv_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM, "weight", i), {n_embd_head}, flags);
// for wo_a, the shape in the file is (n_head * n_embd_head / o_groups, o_lora_rank*o_groups)
// so we reshape here, to avoid reshaping the tensor in the graph
layer.wo_a = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A, "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank, o_groups}, flags | TENSOR_ALLOW_RESHAPE);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B, "weight", i), {o_groups * o_lora_rank, n_embd}, flags);
layer.hc_attn_fn = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0);
layer.hc_attn_base = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE, "weight", i), {hc_mix_dim}, 0);
layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {3}, 0);
layer.hc_ffn_fn = create_tensor(tn(LLM_TENSOR_HC_FFN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0);
layer.hc_ffn_base = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE, "weight", i), {hc_mix_dim}, 0);
layer.hc_ffn_scale = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE, "weight", i), {3}, 0);
layer.hc_attn_fn = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN, "weight", i), {hc_dim, hc_mix_dim}, flags);
layer.hc_attn_base = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE, "weight", i), {hc_mix_dim}, flags);
layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {3}, flags);
layer.hc_ffn_fn = create_tensor(tn(LLM_TENSOR_HC_FFN_FN, "weight", i), {hc_dim, hc_mix_dim}, flags);
layer.hc_ffn_base = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE, "weight", i), {hc_mix_dim}, flags);
layer.hc_ffn_scale = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE, "weight", i), {3}, flags);
const int64_t ratio = hparams.dsv4_compress_ratios[i];
if (ratio != 0) {
const int64_t coff = ratio == 4 ? 2 : 1;
layer.attn_comp_wkv = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WKV, "weight", i), {n_embd, coff * n_embd_head}, 0);
layer.attn_comp_wgate = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WGATE, "weight", i), {n_embd, coff * n_embd_head}, 0);
layer.attn_comp_ape = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_APE, "weight", i), {coff * n_embd_head, ratio}, 0);
layer.attn_comp_norm = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_NORM, "weight", i), {n_embd_head}, 0);
layer.attn_comp_wkv = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WKV, "weight", i), {n_embd, coff * n_embd_head}, flags);
layer.attn_comp_wgate = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WGATE, "weight", i), {n_embd, coff * n_embd_head}, flags);
layer.attn_comp_ape = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_APE, "weight", i), {coff * n_embd_head, ratio}, flags);
layer.attn_comp_norm = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_NORM, "weight", i), {n_embd_head}, flags);
if (ratio == 4) {
const int64_t n_embd_indexer = hparams.indexer_head_size;
layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, 0);
layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * n_embd_indexer}, 0);
layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * n_embd_indexer}, flags);
layer.indexer_comp_wkv = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WKV, "weight", i), {n_embd, 2 * n_embd_indexer}, 0);
layer.indexer_comp_wgate = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "weight", i), {n_embd, 2 * n_embd_indexer}, 0);
layer.indexer_comp_ape = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_APE, "weight", i), {2 * n_embd_indexer, ratio}, 0);
layer.indexer_comp_norm = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_NORM, "weight", i), {n_embd_indexer}, 0);
layer.indexer_comp_wkv = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WKV, "weight", i), {n_embd, 2 * n_embd_indexer}, flags);
layer.indexer_comp_wgate = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "weight", i), {n_embd, 2 * n_embd_indexer}, flags);
layer.indexer_comp_ape = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_APE, "weight", i), {2 * n_embd_indexer, ratio}, flags);
layer.indexer_comp_norm = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_NORM, "weight", i), {n_embd_indexer}, flags);
} else if (ratio != 128) {
throw std::runtime_error("DeepSeek-V4 loader only supports compression ratios 0, 4, and 128");
}
}
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
if ((uint32_t) i < hparams.dsv4_hash_layer_count) {
layer.ffn_gate_tid2eid = create_tensor(tn(LLM_TENSOR_FFN_GATE_TID2EID, "weight", i), {n_expert_used, n_vocab}, 0);
layer.ffn_gate_tid2eid = create_tensor(tn(LLM_TENSOR_FFN_GATE_TID2EID, "weight", i), {n_expert_used, n_vocab}, flags);
} else {
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, flags);
}
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, 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);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd }, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd }, flags);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
if (i >= n_layer) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_deepseek4::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
@@ -175,18 +209,69 @@ static ggml_tensor * dsv4_append_zero_row(ggml_context * ctx, ggml_tensor * t, b
return ggml_concat(ctx, t, row, 1);
}
static ggml_tensor * dsv4_with_zero_dep(ggml_context * ctx, ggml_tensor * t, ggml_tensor * dep) {
if (dep == nullptr) {
return t;
struct dsv4_state_tensors {
ggml_tensor * kv;
ggml_tensor * score;
};
static dsv4_state_tensors dsv4_build_state_restore(
ggml_context * ctx,
const llm_graph_input_dsv4::comp_input & inp,
const llama_dsv4_comp_state * state,
int32_t il) {
dsv4_state_tensors restored = {
state->get_kv_all(ctx, il),
state->get_score_all(ctx, il),
};
if (inp.state_restore_src_idxs == nullptr || inp.state_restore_dst_idxs == nullptr) {
return restored;
}
ggml_tensor * zero = ggml_scale(ctx, ggml_sum(ctx, dep), 0.0f);
return ggml_add(ctx, t, zero);
ggml_tensor * kv_rows = ggml_get_rows(ctx, restored.kv, inp.state_restore_src_idxs);
restored.kv = state->cpy_kv(ctx, kv_rows, inp.state_restore_dst_idxs, il);
ggml_tensor * score_rows = ggml_get_rows(ctx, restored.score, inp.state_restore_src_idxs);
restored.score = state->cpy_score(ctx, score_rows, inp.state_restore_dst_idxs, il);
return restored;
}
static dsv4_state_tensors dsv4_build_state_snapshot(
ggml_context * ctx,
const llm_graph_input_dsv4::comp_input & inp,
const llama_dsv4_comp_state * state,
ggml_tensor * source_kv,
ggml_tensor * source_score,
int32_t il) {
if (inp.state_snapshot_src_idxs == nullptr || inp.state_snapshot_dst_idxs == nullptr ||
source_kv == nullptr || source_score == nullptr) {
return {};
}
ggml_tensor * kv_rows = ggml_get_rows(ctx, source_kv, inp.state_snapshot_src_idxs);
ggml_tensor * kv = state->cpy_kv(ctx, kv_rows, inp.state_snapshot_dst_idxs, il);
ggml_tensor * score_rows = ggml_get_rows(ctx, source_score, inp.state_snapshot_src_idxs);
ggml_tensor * score = state->cpy_score(ctx, score_rows, inp.state_snapshot_dst_idxs, il);
return { kv, score };
}
static constexpr int64_t DSV4_CSA_RATIO = 4;
static constexpr int64_t DSV4_HCA_RATIO = 128;
// mean over the hyper-connection streams: [n_embd, hc, n_tokens] -> [n_embd, n_tokens]
static ggml_tensor * dsv4_hc_mean(ggml_context * ctx, ggml_tensor * x) {
const int64_t hc = x->ne[1];
ggml_tensor * acc = ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], 0);
for (int64_t s = 1; s < hc; ++s) {
acc = ggml_add(ctx, acc, ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], s*x->nb[1]));
}
return ggml_scale(ctx, acc, 1.0f/hc);
}
static ggml_tensor * dsv4_hc_affine(
ggml_context * ctx,
ggml_tensor * x,
@@ -197,22 +282,31 @@ static ggml_tensor * dsv4_hc_affine(
return x;
}
ggml_tensor * llama_model_deepseek4::graph::build_hc_weighted_sum(
ggml_tensor * llama_model_deepseek4::graph::build_hc_pre(
ggml_tensor * x,
ggml_tensor * weights) const {
ggml_tensor * weights,
int il) const {
GGML_ASSERT(x->ne[0] == n_embd);
GGML_ASSERT(x->ne[1] == hparams.dsv4_hc_mult);
const int64_t hc = hparams.dsv4_hc_mult;
const int64_t nt = x->ne[2];
ggml_tensor * acc = nullptr;
if (cparams.fused_dsv4_hc_pre && il >= 0) {
ggml_tensor * result = ggml_dsv4_hc_pre(ctx0, x, weights);
res->add_fused_node({LLM_FUSED_OP_DSV4_HC_PRE, result, il});
return result;
}
ggml_tensor * result = nullptr;
for (int64_t ih = 0; ih < hc; ++ih) {
ggml_tensor * xh = ggml_view_2d(ctx0, x, n_embd, nt, x->nb[2], ih*x->nb[1]);
ggml_tensor * wh = ggml_view_2d(ctx0, weights, 1, nt, weights->nb[1], ih*weights->nb[0]);
ggml_tensor * cur = ggml_mul(ctx0, xh, wh);
acc = acc ? ggml_add(ctx0, acc, cur) : cur;
result = result ? ggml_add(ctx0, result, cur) : cur;
}
return acc;
return result;
}
ggml_tensor * llama_model_deepseek4::graph::build_hc_sinkhorn(
@@ -275,11 +369,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_pre(
ggml_tensor * scale_pre = dsv4_view_1d(ctx0, hc_scale, 1, 0);
ggml_tensor * scale_post = dsv4_view_1d(ctx0, hc_scale, 1, 1);
ggml_tensor * scale_comb = dsv4_view_1d(ctx0, hc_scale, 1, 2);
ggml_tensor * base_pre = dsv4_view_1d(ctx0, hc_base, hc, 0);
ggml_tensor * base_post = dsv4_view_1d(ctx0, hc_base, hc, hc);
ggml_tensor * base_comb = dsv4_view_1d(ctx0, hc_base, hc*hc, 2*hc);
ggml_tensor * pre = dsv4_view_2d(ctx0, mixes, hc, nt, 0);
pre = dsv4_hc_affine(ctx0, pre, scale_pre, base_pre);
@@ -293,13 +385,23 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_pre(
*post = ggml_scale(ctx0, *post, 2.0f);
cb(*post, "hc_post", il);
*comb = dsv4_view_2d(ctx0, mixes, hc*hc, nt, 2*hc);
*comb = dsv4_hc_affine(ctx0, *comb, scale_comb, base_comb);
*comb = ggml_reshape_3d(ctx0, *comb, hc, hc, nt);
*comb = build_hc_sinkhorn(*comb, il);
if (cparams.fused_dsv4_hc_comb) {
*comb = ggml_dsv4_hc_comb(ctx0, mixes, hc_scale, hc_base, hparams.dsv4_hc_eps,
(int32_t) hparams.dsv4_hc_sinkhorn_iters);
res->add_fused_node({LLM_FUSED_OP_DSV4_HC_COMB, *comb, il});
} else {
ggml_tensor * scale_comb = dsv4_view_1d(ctx0, hc_scale, 1, 2);
ggml_tensor * base_comb = dsv4_view_1d(ctx0, hc_base, hc*hc, 2*hc);
*comb = dsv4_view_2d(ctx0, mixes, hc*hc, nt, 2*hc);
*comb = dsv4_hc_affine(ctx0, *comb, scale_comb, base_comb);
*comb = ggml_reshape_3d(ctx0, *comb, hc, hc, nt);
*comb = build_hc_sinkhorn(*comb, il);
}
cb(*comb, "hc_comb", il);
return build_hc_weighted_sum(x, pre);
ggml_tensor * result = build_hc_pre(x, pre, il);
return result;
}
ggml_tensor * llama_model_deepseek4::graph::build_hc_post(
@@ -308,7 +410,14 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_post(
ggml_tensor * post,
ggml_tensor * comb,
int il) const {
GGML_UNUSED(il);
GGML_ASSERT(x->ne[0] == n_embd);
GGML_ASSERT(residual->ne[1] == hparams.dsv4_hc_mult);
if (cparams.fused_dsv4_hc_post) {
ggml_tensor * result = ggml_dsv4_hc_post(ctx0, x, residual, post, comb);
res->add_fused_node({LLM_FUSED_OP_DSV4_HC_POST, result, il});
return result;
}
const int64_t hc = hparams.dsv4_hc_mult;
const int64_t nt = x->ne[1];
@@ -320,7 +429,8 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_post(
for (int64_t src = 0; src < hc; ++src) {
ggml_tensor * res_src = ggml_view_2d(ctx0, residual, n_embd, nt, residual->nb[2], src*residual->nb[1]);
ggml_tensor * comb_src_dst = ggml_view_2d(ctx0, comb, 1, nt, comb->nb[2], dst*comb->nb[0] + src*comb->nb[1]);
ggml_tensor * comb_src_dst = ggml_view_2d(ctx0, comb, 1, nt, comb->nb[2],
dst*comb->nb[0] + src*comb->nb[1]);
cur = ggml_add(ctx0, cur, ggml_mul(ctx0, res_src, comb_src_dst));
}
@@ -350,7 +460,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_head(
pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps);
cb(pre, "hc_head_pre", -1);
return build_hc_weighted_sum(x, pre);
return build_hc_pre(x, pre, -1);
}
ggml_tensor * llama_model_deepseek4::graph::build_hca_compressed_kv_from_state(
@@ -435,27 +545,29 @@ ggml_tensor * llama_model_deepseek4::graph::build_overlap_compressed_kv_from_sta
kv_state = dsv4_append_zero_row(ctx0, kv_state, false);
score_state = dsv4_append_zero_row(ctx0, score_state, true);
ggml_tensor * prev_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, 0);
ggml_tensor * cur_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, ratio*n_blocks);
const int64_t n_read = ratio*n_blocks;
ggml_tensor * kv_prev = ggml_get_rows(ctx0, kv_state, prev_idxs);
kv_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_prev, n_embd_head, ratio*n_blocks, kv_prev->nb[1], 0));
ggml_tensor * kv_rows = ggml_get_rows(ctx0, kv_state, state_read_idxs);
ggml_tensor * score_rows = ggml_get_rows(ctx0, score_state, state_read_idxs);
ggml_tensor * kv_prev = ggml_cont(ctx0,
ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1], 0));
kv_prev = ggml_reshape_3d(ctx0, kv_prev, n_embd_head, ratio, n_blocks);
cb(kv_prev, name, il);
ggml_tensor * score_prev = ggml_get_rows(ctx0, score_state, prev_idxs);
score_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, score_prev, n_embd_head, ratio*n_blocks, score_prev->nb[1], 0));
ggml_tensor * score_prev = ggml_cont(ctx0,
ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1], 0));
score_prev = ggml_reshape_3d(ctx0, score_prev, n_embd_head, ratio, n_blocks);
cb(score_prev, name, il);
ggml_tensor * kv_cur = ggml_get_rows(ctx0, kv_state, cur_idxs);
kv_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_cur, n_embd_head, ratio*n_blocks, kv_cur->nb[1],
ggml_row_size(kv_cur->type, n_embd_head)));
ggml_tensor * kv_cur = ggml_cont(ctx0,
ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1],
n_read*kv_rows->nb[1] + ggml_row_size(kv_rows->type, n_embd_head)));
kv_cur = ggml_reshape_3d(ctx0, kv_cur, n_embd_head, ratio, n_blocks);
ggml_tensor * score_cur = ggml_get_rows(ctx0, score_state, cur_idxs);
score_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, score_cur, n_embd_head, ratio*n_blocks, score_cur->nb[1],
ggml_row_size(score_cur->type, n_embd_head)));
ggml_tensor * score_cur = ggml_cont(ctx0,
ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1],
n_read*score_rows->nb[1] + ggml_row_size(score_rows->type, n_embd_head)));
score_cur = ggml_reshape_3d(ctx0, score_cur, n_embd_head, ratio, n_blocks);
ggml_tensor * values = ggml_concat(ctx0, kv_prev, kv_cur, 1);
@@ -556,25 +668,32 @@ ggml_tensor * llama_model_deepseek4::graph::build_lid_top_k(
indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream,
indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
cb(indexer_q, "lid_q", il);
indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
cb(indexer_k, "lid_k", il);
ggml_tensor * indexer_score = nullptr;
if (cparams.fused_lid) {
indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_lid.kq_mask);
cb(indexer_score, "lid_score_masked", il);
res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il});
} else {
indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
cb(indexer_q, "lid_q", il);
indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
cb(indexer_k, "lid_k", il);
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
cb(indexer_kq, "lid_kq", il);
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
cb(indexer_kq, "lid_kq", il);
indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
cb(indexer_kq, "lid_kq", il);
indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
cb(indexer_kq, "lid_kq", il);
ggml_tensor * indexer_score = ggml_relu(ctx0, indexer_kq);
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
indexer_score = ggml_sum_rows(ctx0, indexer_score);
indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
cb(indexer_score, "lid_score", il);
indexer_score = ggml_relu(ctx0, indexer_kq);
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
indexer_score = ggml_sum_rows(ctx0, indexer_score);
indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
cb(indexer_score, "lid_score", il);
indexer_score = ggml_add(ctx0, indexer_score, inp_lid.kq_mask);
cb(indexer_score, "lid_score_masked", il);
indexer_score = ggml_add(ctx0, indexer_score, inp_lid.kq_mask);
cb(indexer_score, "lid_score_masked", il);
}
const uint32_t n_top_k = indexer_score->ne[0] < hparams.indexer_top_k ? indexer_score->ne[0] : hparams.indexer_top_k;
ggml_tensor * top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));
@@ -770,8 +889,29 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
ggml_tensor * cur,
ggml_tensor * inp_pos,
int il) const {
return build_attention_impl(model, inp_dsv4, nullptr, cur, inp_pos, il);
}
ggml_tensor * llama_model_deepseek4::graph::build_attention(
const llama_model & model,
llm_graph_input_attn_k_iswa * inp_mtp,
ggml_tensor * cur,
ggml_tensor * inp_pos,
int il) const {
return build_attention_impl(model, nullptr, inp_mtp, cur, inp_pos, il);
}
ggml_tensor * llama_model_deepseek4::graph::build_attention_impl(
const llama_model & model,
llm_graph_input_dsv4 * inp_dsv4,
llm_graph_input_attn_k_iswa * inp_mtp,
ggml_tensor * cur,
ggml_tensor * inp_pos,
int il) const {
GGML_ASSERT((inp_dsv4 == nullptr) != (inp_mtp == nullptr));
const auto & layer = model.layers[il];
llm_graph_input_dsv4_raw * inp_attn = inp_dsv4->get_raw();
llm_graph_input_dsv4_raw * inp_attn = inp_dsv4 ? inp_dsv4->get_raw() : nullptr;
const int64_t n_embd_head = hparams.n_embd_head_k();
const int64_t n_embd_head_rope = hparams.n_rot();
@@ -839,9 +979,12 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
cb(kv, "kv", il);
const int64_t ratio = hparams.dsv4_compress_ratios[il];
GGML_ASSERT(inp_dsv4 || ratio == 0);
ggml_tensor * hca_state_kv = nullptr;
ggml_tensor * hca_state_score = nullptr;
ggml_tensor * hca_source_kv = nullptr;
ggml_tensor * hca_source_score = nullptr;
if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_pos) {
hca_state_kv = build_lora_mm(layer.attn_comp_wkv, cur);
cb(hca_state_kv, "hca_state_kv", il);
@@ -872,10 +1015,16 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
GGML_ASSERT(inp_dsv4->get_csa().state_write_idxs);
ggml_tensor * csa_source_kv = ggml_concat(ctx0,
inp_dsv4->mctx->get_csa_state()->get_kv(ctx0, il), csa_state_kv, 1);
ggml_tensor * csa_source_score = ggml_concat(ctx0,
inp_dsv4->mctx->get_csa_state()->get_score(ctx0, il), csa_state_score, 1);
const auto * csa_state = inp_dsv4->mctx->get_csa_state();
const dsv4_state_tensors csa_restored = dsv4_build_state_restore(
ctx0, inp_dsv4->get_csa(), csa_state, il);
ggml_tensor * csa_base_kv = dsv4_view_2d(
ctx0, csa_restored.kv, csa_restored.kv->ne[0], csa_state->get_n_rows(), 0);
ggml_tensor * csa_base_score = dsv4_view_2d(
ctx0, csa_restored.score, csa_restored.score->ne[0], csa_state->get_n_rows(), 0);
ggml_tensor * csa_source_kv = ggml_concat(ctx0, csa_base_kv, csa_state_kv, 1);
ggml_tensor * csa_source_score = ggml_concat(ctx0, csa_base_score, csa_state_score, 1);
ggml_tensor * kv_comp_csa_state = build_overlap_compressed_kv_from_state(
csa_source_kv,
@@ -896,8 +1045,19 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
ggml_build_forward_expand(gf, inp_dsv4->mctx->get_csa()->cpy_k(ctx0,
kv_comp_csa_state, inp_dsv4->get_csa().state_write_idxs, il));
csa_state_kv = dsv4_with_zero_dep(ctx0, csa_state_kv, kv_comp_csa_state);
csa_state_score = dsv4_with_zero_dep(ctx0, csa_state_score, kv_comp_csa_state);
ggml_tensor * csa_snapshot_source_kv = ggml_concat(ctx0,
csa_restored.kv, csa_state_kv, 1);
ggml_tensor * csa_snapshot_source_score = ggml_concat(ctx0,
csa_restored.score, csa_state_score, 1);
const dsv4_state_tensors csa_snapshot = dsv4_build_state_snapshot(
ctx0, inp_dsv4->get_csa(), csa_state, csa_snapshot_source_kv, csa_snapshot_source_score, il);
if (csa_snapshot.kv != nullptr) {
ggml_build_forward_expand(gf, csa_snapshot.kv);
}
if (csa_snapshot.score != nullptr) {
ggml_build_forward_expand(gf, csa_snapshot.score);
}
ggml_tensor * csa_persist_kv = ggml_get_rows(ctx0, csa_state_kv, inp_dsv4->get_csa().state_persist_src_idxs);
ggml_tensor * csa_persist_score = ggml_get_rows(ctx0, csa_state_score, inp_dsv4->get_csa().state_persist_src_idxs);
@@ -924,10 +1084,16 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
GGML_ASSERT(inp_dsv4->get_lid().state_write_idxs);
ggml_tensor * lid_source_kv = ggml_concat(ctx0,
inp_dsv4->mctx->get_lid_state()->get_kv(ctx0, il), lid_state_kv, 1);
ggml_tensor * lid_source_score = ggml_concat(ctx0,
inp_dsv4->mctx->get_lid_state()->get_score(ctx0, il), lid_state_score, 1);
const auto * lid_state = inp_dsv4->mctx->get_lid_state();
const dsv4_state_tensors lid_restored = dsv4_build_state_restore(
ctx0, inp_dsv4->get_lid(), lid_state, il);
ggml_tensor * lid_base_kv = dsv4_view_2d(
ctx0, lid_restored.kv, lid_restored.kv->ne[0], lid_state->get_n_rows(), 0);
ggml_tensor * lid_base_score = dsv4_view_2d(
ctx0, lid_restored.score, lid_restored.score->ne[0], lid_state->get_n_rows(), 0);
ggml_tensor * lid_source_kv = ggml_concat(ctx0, lid_base_kv, lid_state_kv, 1);
ggml_tensor * lid_source_score = ggml_concat(ctx0, lid_base_score, lid_state_score, 1);
ggml_tensor * kv_comp_lid_state = build_overlap_compressed_kv_from_state(
lid_source_kv,
@@ -948,8 +1114,19 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
ggml_build_forward_expand(gf, inp_dsv4->mctx->get_lid()->cpy_k(ctx0,
kv_comp_lid_state, inp_dsv4->get_lid().state_write_idxs, il));
lid_state_kv = dsv4_with_zero_dep(ctx0, lid_state_kv, kv_comp_lid_state);
lid_state_score = dsv4_with_zero_dep(ctx0, lid_state_score, kv_comp_lid_state);
ggml_tensor * lid_snapshot_source_kv = ggml_concat(ctx0,
lid_restored.kv, lid_state_kv, 1);
ggml_tensor * lid_snapshot_source_score = ggml_concat(ctx0,
lid_restored.score, lid_state_score, 1);
const dsv4_state_tensors lid_snapshot = dsv4_build_state_snapshot(
ctx0, inp_dsv4->get_lid(), lid_state, lid_snapshot_source_kv, lid_snapshot_source_score, il);
if (lid_snapshot.kv != nullptr) {
ggml_build_forward_expand(gf, lid_snapshot.kv);
}
if (lid_snapshot.score != nullptr) {
ggml_build_forward_expand(gf, lid_snapshot.score);
}
ggml_tensor * lid_persist_kv = ggml_get_rows(ctx0, lid_state_kv, inp_dsv4->get_lid().state_persist_src_idxs);
ggml_tensor * lid_persist_score = ggml_get_rows(ctx0, lid_state_score, inp_dsv4->get_lid().state_persist_src_idxs);
@@ -963,15 +1140,21 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
ggml_build_forward_expand(gf, lid_state_score);
}
ggml_tensor * hca_state_dep = nullptr;
const llama_dsv4_comp_state * hca_state = nullptr;
dsv4_state_tensors hca_restored = {};
if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_write_idxs) {
GGML_ASSERT(hca_state_kv);
GGML_ASSERT(hca_state_score);
ggml_tensor * hca_source_kv = ggml_concat(ctx0,
inp_dsv4->mctx->get_hca_state()->get_kv(ctx0, il), hca_state_kv, 1);
ggml_tensor * hca_source_score = ggml_concat(ctx0,
inp_dsv4->mctx->get_hca_state()->get_score(ctx0, il), hca_state_score, 1);
hca_state = inp_dsv4->mctx->get_hca_state();
hca_restored = dsv4_build_state_restore(ctx0, inp_dsv4->get_hca(), hca_state, il);
ggml_tensor * hca_base_kv = dsv4_view_2d(
ctx0, hca_restored.kv, hca_restored.kv->ne[0], hca_state->get_n_rows(), 0);
ggml_tensor * hca_base_score = dsv4_view_2d(
ctx0, hca_restored.score, hca_restored.score->ne[0], hca_state->get_n_rows(), 0);
hca_source_kv = ggml_concat(ctx0, hca_base_kv, hca_state_kv, 1);
hca_source_score = ggml_concat(ctx0, hca_base_score, hca_state_score, 1);
ggml_tensor * kv_comp_hca = build_hca_compressed_kv_from_state(
hca_source_kv,
@@ -990,15 +1173,41 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
ggml_build_forward_expand(gf, inp_dsv4->mctx->get_hca()->cpy_k(ctx0,
kv_comp_hca, inp_dsv4->get_hca().state_write_idxs, il));
hca_state_dep = kv_comp_hca;
}
if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_pos) {
GGML_ASSERT(hca_state_kv);
GGML_ASSERT(hca_state_score);
hca_state_kv = dsv4_with_zero_dep(ctx0, hca_state_kv, hca_state_dep);
hca_state_score = dsv4_with_zero_dep(ctx0, hca_state_score, hca_state_dep);
if (hca_state == nullptr) {
hca_state = inp_dsv4->mctx->get_hca_state();
}
if (hca_restored.kv == nullptr) {
hca_restored = dsv4_build_state_restore(ctx0, inp_dsv4->get_hca(), hca_state, il);
}
if (hca_source_kv == nullptr || hca_source_score == nullptr) {
ggml_tensor * hca_base_kv = dsv4_view_2d(
ctx0, hca_restored.kv, hca_restored.kv->ne[0], hca_state->get_n_rows(), 0);
ggml_tensor * hca_base_score = dsv4_view_2d(
ctx0, hca_restored.score, hca_restored.score->ne[0], hca_state->get_n_rows(), 0);
hca_source_kv = ggml_concat(ctx0, hca_base_kv, hca_state_kv, 1);
hca_source_score = ggml_concat(ctx0, hca_base_score, hca_state_score, 1);
}
ggml_tensor * hca_snapshot_source_kv = ggml_concat(ctx0,
hca_restored.kv, hca_state_kv, 1);
ggml_tensor * hca_snapshot_source_score = ggml_concat(ctx0,
hca_restored.score, hca_state_score, 1);
const dsv4_state_tensors hca_snapshot = dsv4_build_state_snapshot(
ctx0, inp_dsv4->get_hca(), hca_state, hca_snapshot_source_kv, hca_snapshot_source_score, il);
if (hca_snapshot.kv != nullptr) {
ggml_build_forward_expand(gf, hca_snapshot.kv);
}
if (hca_snapshot.score != nullptr) {
ggml_build_forward_expand(gf, hca_snapshot.score);
}
ggml_tensor * hca_persist_kv = ggml_get_rows(ctx0, hca_state_kv, inp_dsv4->get_hca().state_persist_src_idxs);
ggml_tensor * hca_persist_score = ggml_get_rows(ctx0, hca_state_score, inp_dsv4->get_hca().state_persist_src_idxs);
@@ -1013,7 +1222,14 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
}
ggml_tensor * out = nullptr;
if (ratio == DSV4_CSA_RATIO &&
if (inp_mtp) {
out = build_attn(inp_mtp,
nullptr, nullptr, nullptr,
q, kv, nullptr,
nullptr, layer.attn_sinks, nullptr,
1.0f/sqrtf(float(n_embd_head)), il);
cb(out, "attn_raw", il);
} else if (ratio == DSV4_CSA_RATIO &&
inp_dsv4->get_csa().kq_mask &&
inp_dsv4->get_lid().kq_mask &&
inp_dsv4->get_lid().k_rot) {
@@ -1044,7 +1260,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention(
out = ggml_reshape_3d(ctx0, out, o_group_dim, n_groups, nt);
out = ggml_permute(ctx0, out, 0, 2, 1, 3);
ggml_tensor * oa = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, layer.wo_a, layer.wo_a->ne[0], o_lora_rank, n_groups), out);
ggml_tensor * oa = ggml_mul_mat(ctx0, layer.wo_a, out);
cb(oa, "attn_wo_a", il);
oa = ggml_permute(ctx0, oa, 0, 2, 1, 3);
oa = ggml_cont_2d(ctx0, oa, o_lora_rank*n_groups, nt);
@@ -1072,6 +1288,12 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p
cb(inpL, "hc_init", -1);
for (int il = 0; il < n_layer; ++il) {
if ((size_t) il < cparams.embeddings_layer_inp.size() && cparams.embeddings_layer_inp[il]) {
res->t_layer_inp[il] = dsv4_hc_mean(ctx0, inpL);
cb(res->t_layer_inp[il], "layer_inp", il);
ggml_build_forward_expand(gf, res->t_layer_inp[il]);
}
ggml_tensor * residual = inpL;
ggml_tensor * post = nullptr;
ggml_tensor * comb = nullptr;
@@ -1099,6 +1321,10 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p
&post, &comb, il);
cb(cur, "hc_ffn_pre", il);
ggml_build_forward_expand(gf, residual);
ggml_build_forward_expand(gf, post);
ggml_build_forward_expand(gf, comb);
cur = build_norm(cur, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
@@ -1141,13 +1367,26 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p
inpL = build_hc_post(cur, residual, post, comb, il);
inpL = build_cvec(inpL, il);
cb(inpL, "l_out", il);
cb(inpL, "l_last", il);
}
if ((size_t) n_layer < cparams.embeddings_layer_inp.size() && cparams.embeddings_layer_inp[n_layer]) {
res->t_layer_inp[n_layer] = dsv4_hc_mean(ctx0, inpL);
cb(res->t_layer_inp[n_layer], "layer_inp", n_layer);
ggml_build_forward_expand(gf, res->t_layer_inp[n_layer]);
}
ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens);
ggml_tensor * flat_out = inp_out_ids ? ggml_get_rows(ctx0, flat, inp_out_ids) : flat;
if (cparams.embeddings_nextn) {
ggml_tensor * h_nextn = cparams.embeddings_nextn_masked ? flat_out : inpL;
cb(h_nextn, "h_nextn", -1);
res->t_h_nextn = h_nextn;
}
if (inp_out_ids) {
ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens);
flat = ggml_get_rows(ctx0, flat, inp_out_ids);
inpL = ggml_reshape_3d(ctx0, flat, n_embd, hc, n_outputs);
inpL = ggml_reshape_3d(ctx0, flat_out, n_embd, hc, n_outputs);
}
cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base);
@@ -1163,3 +1402,145 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p
ggml_build_forward_expand(gf, cur);
}
llama_model_deepseek4::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) :
graph(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "DEEPSEEK4 MTP requires n_layer_nextn > 0");
GGML_ASSERT(hparams.n_layer_nextn == 1 && "DEEPSEEK4 MTP currently only supports a single MTP block");
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
"nextn_layer_offset out of range [0, n_layer_nextn)");
GGML_ASSERT(ubatch.token && "DEEPSEEK4 MTP requires token input");
const int64_t hc = hparams.dsv4_hc_mult;
GGML_ASSERT(hparams.n_embd_out() == (uint32_t) (n_embd*hc) && "DEEPSEEK4 MTP hidden width mismatch");
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd_out());
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_out(), n_tokens);
ggml_set_input(inp->embd);
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_out(), n_tokens);
ggml_set_input(inp->h);
ggml_set_name(inp->h, "mtp_h_input");
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
cb(tok_embd, "mtp_tok_embd", il);
ggml_tensor * h_state = ggml_reshape_3d(ctx0, inp->h, n_embd, hc, n_tokens);
cb(h_state, "mtp_h_state", il);
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
llm_graph_input_attn_k_iswa * inp_attn = build_attn_inp_k_iswa();
ggml_tensor * h_norm = build_norm(h_state, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
e_norm = ggml_reshape_3d(ctx0, e_norm, n_embd, 1, n_tokens);
e_norm = ggml_repeat_4d(ctx0, e_norm, n_embd, hc, n_tokens, 1);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, 0);
cb(concat, "mtp_concat", il);
ggml_tensor * inpL = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
cb(inpL, "mtp_eh_proj", il);
ggml_tensor * residual = inpL;
ggml_tensor * post = nullptr;
ggml_tensor * comb = nullptr;
ggml_tensor * cur = build_hc_pre(inpL,
layer.hc_attn_fn,
layer.hc_attn_scale,
layer.hc_attn_base,
&post, &comb, il);
cb(cur, "mtp_hc_attn_pre", il);
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
cur = build_attention(model, inp_attn, cur, inp_pos, il);
inpL = build_hc_post(cur, residual, post, comb, il);
cb(inpL, "mtp_hc_attn_post", il);
residual = inpL;
cur = build_hc_pre(inpL,
layer.hc_ffn_fn,
layer.hc_ffn_scale,
layer.hc_ffn_base,
&post, &comb, il);
cb(cur, "mtp_hc_ffn_pre", il);
cur = build_norm(cur, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_ffn_norm", il);
GGML_ASSERT((uint32_t) il >= hparams.dsv4_hash_layer_count && "DEEPSEEK4 MTP does not support hash-routed MTP blocks");
ggml_tensor * moe_out = build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
n_expert, hparams.n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
cb(moe_out, "mtp_ffn_moe_out", il);
ggml_tensor * ffn_shexp = build_ffn(cur,
layer.ffn_up_shexp, nullptr, nullptr,
layer.ffn_gate_shexp, nullptr, nullptr,
layer.ffn_down_shexp, nullptr, nullptr,
nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "mtp_ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "mtp_ffn_out", il);
inpL = build_hc_post(cur, residual, post, comb, il);
inpL = build_cvec(inpL, il);
cb(inpL, "mtp_l_out", il);
ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens);
ggml_tensor * h_nextn = ggml_get_rows(ctx0, flat, inp_out_ids);
cb(h_nextn, "h_nextn", -1);
res->t_h_nextn = h_nextn;
inpL = ggml_reshape_3d(ctx0, h_nextn, n_embd, hc, n_outputs);
cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base);
cb(cur, "mtp_hc_head", -1);
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm ? layer.nextn.shared_head_norm : model.output_norm;
GGML_ASSERT(head_norm_w && "DEEPSEEK4 MTP missing shared head norm");
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
cb(cur, "mtp_shared_head_norm", -1);
res->t_embd = cur;
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
GGML_ASSERT(head_w && "DEEPSEEK4 MTP missing LM head");
cur = ggml_mul_mat(ctx0, head_w, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/dflash.cpp
+399
View File
@@ -1,5 +1,6 @@
#include "models.h"
#include "llama-impl.h"
#include "llama-kv-cache.h"
#include "llama-kv-cache-iswa.h"
@@ -19,6 +20,48 @@ void llama_model_dflash::load_arch_hparams(llama_model_loader & ml) {
}
LLAMA_LOG_INFO("]\n");
// DeepSeek-V4 DSpark backbone: stages are full DSV4 blocks, uniform sliding window (the draft KV ring)
ml.get_key(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult, false);
if (hparams.dsv4_hc_mult > 0) {
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all);
if (!ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer_all, 0)) {
hparams.swiglu_clamp_shexp = hparams.swiglu_clamp_exp;
}
ml.get_key(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, hparams.dsv4_o_group_count);
ml.get_key(LLM_KV_ATTENTION_OUTPUT_LORA_RANK, hparams.dsv4_o_lora_rank);
ml.get_key(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps);
ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios, false);
if (hparams.expert_gating_func != LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS) {
throw std::runtime_error("DSpark DSV4 draft expects sqrtsoftplus MoE scoring");
}
for (uint32_t il = 0; il < hparams.n_layer_all; ++il) {
if (hparams.dsv4_compress_ratios[il] != 0) {
throw std::runtime_error("DSpark DSV4 draft expects uncompressed attention on all stages");
}
}
GGML_ASSERT(hparams.n_swa > 0);
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.set_swa_pattern(0);
for (uint32_t il = 0; il < hparams.n_layer_all; ++il) {
hparams.is_swa_impl[il] = true;
}
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
type = LLM_TYPE_UNKNOWN;
return;
}
// optional interleaved sliding-window attention with per-layer pattern array.
// DFlash has a single rope, so the SWA rope == main rope.
if (ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false) && hparams.n_swa > 0) {
@@ -36,10 +79,77 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
const int64_t n_embd_inp = hparams.n_embd_inp_enc();
// DSpark = DFlash + a semi-autoregressive Markov head and Confidence head
//
// TODO: only Qwen3-style backbones are supported for now; other backbones (e.g. Gemma4)
// need their own conversion path and graph tweaks
const struct ggml_tensor * markov_meta = ml->get_tensor_meta("markov_w1.weight");
if (markov_meta) {
const int64_t dspark_markov_rank = markov_meta->ne[0];
dspark_markov_w1 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W1, "weight"), { dspark_markov_rank, n_vocab }, 0);
dspark_markov_w2 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W2, "weight"), { dspark_markov_rank, n_vocab }, 0);
dspark_conf_proj = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "weight"), { n_embd + dspark_markov_rank, 1 }, 0);
dspark_conf_proj_b = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "bias"), { 1 }, TENSOR_NOT_REQUIRED);
LLAMA_LOG_INFO("%s: DFlash with DSpark markov head (rank = %lld)\n", __func__, (long long) dspark_markov_rank);
}
fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), { n_embd_inp, n_embd }, 0);
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), { n_embd }, 0); // encoder hidden_norm (after fc)
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); // decoder final norm
if (hparams.dsv4_hc_mult > 0) {
const int64_t q_lora_rank = hparams.n_lora_q;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_embd_head = hparams.n_embd_head_k();
const int64_t o_groups = hparams.dsv4_o_group_count;
const int64_t o_lora_rank = hparams.dsv4_o_lora_rank;
const int64_t hc_mult = hparams.dsv4_hc_mult;
const int64_t hc_dim = hc_mult * n_embd;
const int64_t hc_mix_dim = (2 + hc_mult) * hc_mult;
hc_head_fn = create_tensor(tn(LLM_TENSOR_HC_HEAD_FN, "weight"), {hc_dim, hc_mult}, 0);
hc_head_base = create_tensor(tn(LLM_TENSOR_HC_HEAD_BASE, "weight"), {hc_mult}, 0);
hc_head_scale = create_tensor(tn(LLM_TENSOR_HC_HEAD_SCALE, "weight"), {1}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head}, 0);
layer.wkv = create_tensor(tn(LLM_TENSOR_ATTN_KV, "weight", i), {n_embd, n_embd_head}, 0);
layer.attn_kv_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM, "weight", i), {n_embd_head}, 0);
layer.wo_a = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A, "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank * o_groups}, 0);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B, "weight", i), {o_groups * o_lora_rank, n_embd}, 0);
layer.hc_attn_fn = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0);
layer.hc_attn_base = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE, "weight", i), {hc_mix_dim}, 0);
layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {3}, 0);
layer.hc_ffn_fn = create_tensor(tn(LLM_TENSOR_HC_FFN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0);
layer.hc_ffn_base = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE, "weight", i), {hc_mix_dim}, 0);
layer.hc_ffn_scale = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE, "weight", i), {3}, 0);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, 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);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd }, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
}
return;
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
@@ -66,6 +176,9 @@ std::unique_ptr<llm_graph_context> llama_model_dflash::build_arch_graph(const ll
return std::make_unique<graph<true>>(*this, params);
case LLM_GRAPH_TYPE_DEFAULT:
case LLM_GRAPH_TYPE_DECODER:
if (hparams.dsv4_hc_mult > 0) {
return std::make_unique<graph_dsv4>(*this, params);
}
return std::make_unique<graph<false>>(*this, params);
default:
GGML_ABORT("invalid graph type");
@@ -104,6 +217,94 @@ llama_model_dflash::graph<true>::graph(const llama_model & model, const llm_grap
ggml_build_forward_expand(gf, cur);
}
// DSpark (DFlash + Markov & Confidence head): Markov bias on the draft logits, chained per block position
static void build_dspark_markov_head(llm_graph_context & g, const llama_model & model, ggml_tensor * tokens) {
ggml_context * ctx0 = g.ctx0;
auto & res = g.res;
ggml_tensor * w1 = model.dspark_markov_w1;
ggml_tensor * w2 = model.dspark_markov_w2;
GGML_ASSERT(w1 && w2 && model.dspark_conf_proj && "DSpark markov/confidence weights not loaded");
ggml_tensor * base = res->t_logits; // [n_vocab, n_tokens]
const int64_t n_vocab = base->ne[0];
const int64_t n_tok = base->ne[1];
const auto it = model.gguf_kv.find("dflash.block_size");
GGML_ASSERT(it != model.gguf_kv.end() && "DSpark draft requires 'dflash.block_size' in GGUF metadata");
const int64_t block_size = std::stoi(it->second);
GGML_ASSERT(block_size > 0);
const int64_t n_blocks = g.ubatch.n_seqs_unq;
GGML_ASSERT(n_blocks > 0 && n_tok % n_blocks == 0 && "DSpark markov head requires equal-size blocks");
// runtime tokens per block in this ubatch (anchor + drafted positions), bounded by training block_size
const int64_t block_drafts = n_tok / n_blocks;
if (block_drafts > block_size) {
return;
}
// anchor (committed last) token of every block: token 0 of each block, i.e. a strided view
const size_t token_stride = (size_t) block_drafts * tokens->nb[0];
const size_t base_stride = (size_t) block_drafts * base->nb[1];
ggml_tensor * prev = ggml_view_2d(ctx0, tokens, 1, n_blocks, token_stride, 0);
prev = ggml_cont_1d(ctx0, prev, n_blocks);
// confidence head input: predicts per-position acceptance
ggml_tensor * conf_inp = res->t_embd; // [n_embd, n_tok]
ggml_tensor * cat = nullptr;
ggml_tensor * cat_conf = nullptr;
// TODO: the in-graph chain is greedy (argmax); sampling params affect only the final
// token pick, not the Markov conditioning path
for (int64_t i = 0; i < block_drafts; ++i) {
ggml_tensor * w1_prev = ggml_get_rows(ctx0, w1, prev); // [R, n_blocks]
ggml_tensor * bias = ggml_mul_mat(ctx0, w2, w1_prev); // [n_vocab, n_blocks]
// position i of every block: strided view [n_vocab, n_blocks]
ggml_tensor * base_i = ggml_view_2d(ctx0, base, n_vocab, n_blocks, base_stride, i*base->nb[1]);
ggml_tensor * col = ggml_add(ctx0, base_i, bias);
cat = cat ? ggml_concat(ctx0, cat, col, 1) : col;
// conf(i) = sigmoid(conf_proj . [conf_inp(i); markov_w1[prev(i)]] + b) -- [1, n_blocks]
ggml_tensor * conf_inp_i = ggml_view_2d(ctx0, conf_inp, conf_inp->ne[0], n_blocks,
(size_t) block_drafts * conf_inp->nb[1], i*conf_inp->nb[1]);
ggml_tensor * feat = ggml_concat(ctx0, ggml_cont(ctx0, conf_inp_i), w1_prev, 0);
ggml_tensor * conf = ggml_mul_mat(ctx0, model.dspark_conf_proj, feat);
if (model.dspark_conf_proj_b) {
conf = ggml_add(ctx0, conf, model.dspark_conf_proj_b);
}
conf = ggml_sigmoid(ctx0, conf);
cat_conf = cat_conf ? ggml_concat(ctx0, cat_conf, conf, 1) : conf;
if (i + 1 < block_drafts) {
prev = ggml_argmax(ctx0, col);
}
}
// cat is position-major; restore ubatch block-major order
ggml_tensor * out = ggml_reshape_3d(ctx0, cat, n_vocab, n_blocks, block_drafts);
out = ggml_cont(ctx0, ggml_permute(ctx0, out, 0, 2, 1, 3)); // [n_vocab, block_drafts, n_blocks]
out = ggml_reshape_2d(ctx0, out, n_vocab, n_tok);
{
ggml_tensor * conf = ggml_reshape_3d(ctx0, cat_conf, 1, n_blocks, block_drafts);
conf = ggml_cont(ctx0, ggml_permute(ctx0, conf, 0, 2, 1, 3));
conf = ggml_reshape_2d(ctx0, conf, 1, n_tok);
// note: broadcast the [1, n_tok] confidences to n_embd-wide rows to be able to reuse `llama_get_embeddings_nextn`
conf = ggml_repeat(ctx0, conf, res->t_embd);
res->t_h_nextn = conf;
ggml_build_forward_expand(g.gf, conf);
}
res->t_logits = out;
ggml_build_forward_expand(g.gf, out);
}
// DFlash decoder, dual-mode by batch type:
// * embd batch -> fused target features: project + inject K/V into the cache.
// * token batch -> noise-block diffusion: attend over [committed, MASK...] to generate draft tokens
@@ -164,9 +365,25 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra
const auto * kv = is_swa ? inp_attn_iswa->mctx->get_swa() : inp_attn_iswa->mctx->get_base();
ggml_tensor * k_idxs = is_swa ? inp_attn_iswa->get_k_idxs_swa() : inp_attn_iswa->get_k_idxs();
ggml_tensor * v_idxs = is_swa ? inp_attn_iswa->get_v_idxs_swa() : inp_attn_iswa->get_v_idxs();
// rotate K/V into the cache's rotated space
ggml_tensor * k_rot = is_swa ? inp_attn_iswa->self_k_rot_swa : inp_attn_iswa->self_k_rot;
ggml_tensor * v_rot = is_swa ? inp_attn_iswa->self_v_rot_swa : inp_attn_iswa->self_v_rot;
if (k_rot) {
Kcur = llama_mul_mat_hadamard(ctx0, Kcur, k_rot);
}
if (v_rot) {
Vcur = llama_mul_mat_hadamard(ctx0, Vcur, v_rot);
}
ggml_build_forward_expand(gf, kv->cpy_k(ctx0, Kcur, k_idxs, il));
ggml_build_forward_expand(gf, kv->cpy_v(ctx0, Vcur, v_idxs, il));
} else {
// rotate K/V into the cache's rotated space
if (inp_attn->self_k_rot) {
Kcur = llama_mul_mat_hadamard(ctx0, Kcur, inp_attn->self_k_rot);
}
if (inp_attn->self_v_rot) {
Vcur = llama_mul_mat_hadamard(ctx0, Vcur, inp_attn->self_v_rot);
}
ggml_build_forward_expand(gf, inp_attn->mctx->cpy_k(ctx0, Kcur, inp_attn->get_k_idxs(), il));
ggml_build_forward_expand(gf, inp_attn->mctx->cpy_v(ctx0, Vcur, inp_attn->get_v_idxs(), il));
}
@@ -193,6 +410,8 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
ggml_tensor * inp_tokens = inp->tokens;
ggml_tensor * inpL = ggml_get_rows(ctx0, tok_embd, inp->tokens);
cb(inpL, "inp_noise_embd", -1);
@@ -273,4 +492,184 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
// DSpark: bias the draft logits with the Markov head
if (model.dspark_markov_w1) {
build_dspark_markov_head(*this, model, inp_tokens);
}
}
// DSV4 DSpark decoder, dual-mode by batch type (see the DFlash decoder above):
// * embd batch -> project main_x through each stage's wkv and inject K into the ring cache
// * token batch -> noise block through 3 full DSV4 stages (hc + MLA + MoE), markov + confidence heads
llama_model_dflash::graph_dsv4::graph_dsv4(const llama_model & model, const llm_graph_params & params) :
llama_model_deepseek4::graph(params) {
const int64_t n_embd_head = hparams.n_embd_head_k();
const int64_t n_embd_head_rope = hparams.n_rot();
const int64_t n_embd_head_nope = n_embd_head - n_embd_head_rope;
ggml_tensor * inp_pos = build_inp_pos();
llm_graph_input_attn_k_iswa * inp_attn = build_attn_inp_k_iswa();
// KV cache injection: fused target features from the encoder
if (ubatch.embd) {
auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
ggml_set_input(inp->embd);
ggml_tensor * inp_g = inp->embd;
cb(inp_g, "inp_g_embeddings", -1);
res->add_input(std::move(inp));
for (int il = 0; il < n_layer; ++il) {
const auto & layer = model.layers[il];
// main-track KV: kv_norm(wkv(main_x)) with rope on the trailing dims, same
// rope parameters as the uncompressed layers in build_attention_impl
ggml_tensor * kv = build_lora_mm(layer.wkv, inp_g);
kv = build_norm(kv, layer.attn_kv_norm, nullptr, LLM_NORM_RMS, il);
kv = ggml_reshape_3d(ctx0, kv, n_embd_head, 1, n_tokens);
ggml_tensor * kv_nope = ggml_view_3d(ctx0, kv, n_embd_head_nope, 1, n_tokens,
ggml_row_size(kv->type, n_embd_head),
ggml_row_size(kv->type, n_embd_head),
0);
ggml_tensor * kv_pe = ggml_view_3d(ctx0, kv, n_embd_head_rope, 1, n_tokens,
ggml_row_size(kv->type, n_embd_head),
ggml_row_size(kv->type, n_embd_head),
ggml_row_size(kv->type, n_embd_head_nope));
kv_pe = ggml_rope_ext(ctx0, kv_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, 0,
freq_base, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
kv = ggml_concat(ctx0, kv_nope, kv_pe, 0);
cb(kv, "kv_injected", il);
if (inp_attn->self_k_rot_swa) {
kv = llama_mul_mat_hadamard(ctx0, kv, inp_attn->self_k_rot_swa);
}
ggml_build_forward_expand(gf, inp_attn->mctx->get_swa()->cpy_k(ctx0, kv, inp_attn->get_k_idxs_swa(), il));
}
res->t_embd = inp_g;
ggml_build_forward_expand(gf, inp_g);
return;
}
// tok_embd from the target model (shared via ctx_other)
auto * tok_embd = model.tok_embd;
if (tok_embd == nullptr) {
GGML_ASSERT(cparams.ctx_other != nullptr);
const auto * model_other = llama_get_model(cparams.ctx_other);
GGML_ASSERT(model_other->tok_embd != nullptr && "DSpark decoder requires the target model's token embeddings");
tok_embd = model_other->tok_embd;
}
auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
ggml_tensor * inp_tokens = inp->tokens;
ggml_tensor * inpL = ggml_get_rows(ctx0, tok_embd, inp->tokens);
cb(inpL, "inp_noise_embd", -1);
res->add_input(std::move(inp));
const int64_t hc = hparams.dsv4_hc_mult;
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, 1, n_tokens);
inpL = ggml_repeat_4d(ctx0, inpL, n_embd, hc, n_tokens, 1);
cb(inpL, "hc_init", -1);
for (int il = 0; il < n_layer; ++il) {
const auto & layer = model.layers[il];
ggml_tensor * residual = inpL;
ggml_tensor * post = nullptr;
ggml_tensor * comb = nullptr;
ggml_tensor * cur = build_hc_pre(inpL,
layer.hc_attn_fn,
layer.hc_attn_scale,
layer.hc_attn_base,
&post, &comb, il);
cb(cur, "hc_attn_pre", il);
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
cur = build_attention(model, inp_attn, cur, inp_pos, il);
inpL = build_hc_post(cur, residual, post, comb, il);
cb(inpL, "hc_attn_post", il);
residual = inpL;
cur = build_hc_pre(inpL,
layer.hc_ffn_fn,
layer.hc_ffn_scale,
layer.hc_ffn_base,
&post, &comb, il);
cb(cur, "hc_ffn_pre", il);
cur = build_norm(cur, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
ggml_tensor * moe_out = build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
n_expert, hparams.n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
cb(moe_out, "ffn_moe_out", il);
ggml_tensor * ffn_shexp = build_ffn(cur,
layer.ffn_up_shexp, nullptr, nullptr,
layer.ffn_gate_shexp, nullptr, nullptr,
layer.ffn_down_shexp, nullptr, nullptr,
nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
inpL = build_hc_post(cur, residual, post, comb, il);
cb(inpL, "l_out", il);
}
ggml_tensor * cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base);
cb(cur, "hc_head", -1);
// confidence head input: the reference scores the pre-norm collapsed hidden state
res->t_embd = cur;
cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
// lm_head from the target model (shared via ctx_other)
auto * output = model.output;
if (output == nullptr) {
GGML_ASSERT(cparams.ctx_other != nullptr);
const auto * model_other = llama_get_model(cparams.ctx_other);
GGML_ASSERT(model_other->output != nullptr && "DSpark decoder requires the target model's output projection");
output = model_other->output;
}
cur = build_lora_mm(output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
if (model.dspark_markov_w1) {
build_dspark_markov_head(*this, model, inp_tokens);
}
}
examples/talk-llama/models/eagle3.cpp
+15
View File
@@ -28,6 +28,10 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
LLAMA_LOG_INFO("%s: EAGLE3gnorm_before_residual = true\n", __func__);
}
// eagle3 norm_before_fc (optional, default false)
// compatible with eagle3.1 (e.g. nvidia/gpt-oss-120b-Eagle3-v3)
ml.get_key(LLM_KV_NORM_BEFORE_FC, hparams.norm_before_fc, false);
type = LLM_TYPE_UNKNOWN;
}
@@ -53,6 +57,11 @@ void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
// Feature fusion layer: projects 3 target layers to draft hidden size
fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), {n_embd_inp, n_embd}, 0);
// RMSNorm on the fused target features (input to fc), only when norm_before_fc is set.
if (hparams.norm_before_fc) {
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd_inp}, 0);
}
// Output layer (uses draft vocab size)
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_draft_vocab}, TENSOR_NOT_REQUIRED);
@@ -130,6 +139,12 @@ llama_model_eagle3::graph<true>::graph(const llama_model & model, const llm_grap
cur = build_inp_embd_enc();
// RMSNorm on the fused target features before fc
if (hparams.norm_before_fc) {
cur = build_norm(cur, model.output_norm_enc, NULL, LLM_NORM_RMS, -1);
cb(cur, "enc_input_norm", -1);
}
// Feature fusion layer
cur = build_lora_mm(model.fc, cur);
cb(cur, "fc_out", -1);
examples/talk-llama/models/gemma4.cpp
-37
View File
@@ -142,33 +142,6 @@ static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, in
idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
}
// TODO @ngxson : maybe improve this in the future
class llm_graph_input_logits_bias : public llm_graph_input_i {
public:
llm_graph_input_logits_bias(const llama_vocab & vocab) {
arr.resize(vocab.n_tokens(), 0.0f);
for (llama_token id : vocab.get_suppress_tokens()) {
if (0 <= id && id < (int32_t)vocab.n_tokens()) {
arr[id] = -INFINITY;
}
}
}
virtual ~llm_graph_input_logits_bias() = default;
void set_input(const llama_ubatch * /*ubatch*/) override {
const int64_t n_vocab = arr.size();
ggml_backend_tensor_set(logits_bias, arr.data(), 0, n_vocab*ggml_element_size(logits_bias));
}
bool can_reuse(const llm_graph_params & /*params*/) override {
return true;
}
ggml_tensor * logits_bias = nullptr; // F32 [n_vocab]
std::vector<float> arr;
};
llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params),
model(model),
@@ -429,16 +402,6 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
}
// apply logits bias if needed (e.g. for gemma4_unified patch)
// this is to mirror the suppress_tokens patch on transformers, to avoid model from outputing <image|> and <audio|> tokens (which is a known issue related to the checkpoint)
// TODO: maybe handle this inside the sampling system in the future
if (!model.vocab.get_suppress_tokens().empty()) {
auto inp_bias = std::make_unique<llm_graph_input_logits_bias>(model.vocab);
inp_bias->logits_bias = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, inp_bias->arr.size());
cur = ggml_add(ctx0, cur, inp_bias->logits_bias);
res->add_input(std::move(inp_bias));
}
cb(cur, "result_output", -1);
res->t_logits = cur;
examples/talk-llama/models/glm-dsa.cpp
+668 -10
View File
@@ -1,5 +1,31 @@
#include "models.h"
#include "llama-kv-cache-dsa.h"
// https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26
const std::array<uint32_t, LLAMA_MAX_LAYERS> GLM_5_2_DEFAULT_INDEXER_TYPES = {
1, 1,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
1, 0, 0, 0,
};
void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
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);
@@ -34,18 +60,43 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
// NextN/MTP parameters
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl");
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
// BC for GLM 5, 5.1 (full indexers) without indexer_types metadata
const bool is_pre_5_2 = hparams.n_ctx_train < 1048576;
if (is_pre_5_2) {
std::fill(hparams.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 1);
} else {
hparams.is_indexer_full_impl = GLM_5_2_DEFAULT_INDEXER_TYPES;
}
ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);
switch (hparams.n_layer()) {
case 79: type = LLM_TYPE_744B_A40B; break;
case 78: // GGUF with NextN/MTP metadata: n_layer() excludes the nextn layer
case 79:
type = LLM_TYPE_744B_A40B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
void llama_model_glm_dsa::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
// MTP-only: the GGUF carries only the NextN/MTP block(s) (user split target/draft).
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
// Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
// tensors live in a separate file (or were stripped at conversion). Mark
// MTP tensors NOT_REQUIRED so the trunk loads cleanly.
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
const bool is_mla = hparams.is_mla();
if (!is_mla) {
throw std::runtime_error("GLM_DSA architecture requires MLA");
@@ -74,12 +125,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
}
for (int i = 0; i < n_layer_all; ++i) {
int flags = 0;
if (i >= n_layer) {
// skip all tensors in the NextN layers
// TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
}
// NextN/MTP layers (i >= n_layer) are full decoder blocks used by the
// LLM_GRAPH_TYPE_DECODER_MTP draft head; load them like qwen35moe/step35/hy_v3.
const int flags = (i >= n_layer) ? mtp_flags : trunk_flags;
auto & layer = layers[i];
@@ -132,7 +180,7 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
}
// NextN/MTP tensors (preserved but unused) - conditionally load for last n_layer_nextn
// NextN/MTP tensors - the NextN-specific wiring around the extra decoder block
if (i >= n_layer) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
@@ -147,6 +195,616 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
}
std::unique_ptr<llm_graph_context> llama_model_glm_dsa::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
const bool is_mla = hparams.is_mla();
GGML_ASSERT(is_mla);
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_v = hparams.n_embd_head_v_mla();
GGML_UNUSED(n_embd_head_v);
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
const int64_t n_indexer_head = hparams.indexer_n_head;
const int64_t n_embd_indexer_head = hparams.indexer_head_size;
const int64_t n_embd_indexer_head_rope = hparams.n_rot();
const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope;
const uint32_t n_indexer_top_k = hparams.indexer_top_k;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
// See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation.
// 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;
// {n_embd, n_tokens}
inpL = build_inp_embd(model.tok_embd);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
llm_graph_input_attn_k_dsa * inp_attn_dsa = build_attn_inp_k_dsa();
ggml_tensor * inp_out_ids = build_inp_out_ids();
// Difference vs Deepseek 3.2: shared indexer layers reuse the top_k from the previous full indexer layers
// See https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L30
ggml_tensor * prev_top_k = nullptr;
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self_attention
{
ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
cb(qr, "qr", il);
qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
cb(qr, "qr", il);
ggml_tensor * top_k = nullptr;
// lightning indexer
if (hparams.is_indexer_full(il)) {
// "full" layer
ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
cb(indexer_q, "indexer_q", il);
// split into {n_embd_indexer_head_rope, n_indexer_head, n_tokens}
ggml_tensor * indexer_q_pe =
ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, n_tokens,
ggml_row_size(indexer_q->type, n_embd_indexer_head),
ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, 0);
cb(indexer_q_pe, "indexer_q_pe", il);
// and {n_embd_indexer_head_nope, n_indexer_head, n_tokens}
ggml_tensor * indexer_q_nope =
ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, n_tokens,
ggml_row_size(indexer_q->type, n_embd_indexer_head),
ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head,
ggml_row_size(indexer_q->type, n_embd_indexer_head_nope));
cb(indexer_q_nope, "indexer_q_nope", il);
indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_rot,
LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(indexer_q_pe, "indexer_q_pe", il);
// {n_embd_indexer_head_rope + n_embd_indexer_head_nope, n_head, n_tokens}
indexer_q = ggml_concat(ctx0, indexer_q_pe, indexer_q_nope, 0);
cb(indexer_q, "indexer_q", il);
ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);
cb(indexer_k, "indexer_k", il);
indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);
cb(indexer_k, "indexer_k", il);
// split into {n_embd_indexer_head_rope, 1, n_tokens}
ggml_tensor * indexer_k_pe =
ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_rope, 1, n_tokens,
ggml_row_size(indexer_k->type, n_embd_indexer_head),
ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, 0);
cb(indexer_k_pe, "indexer_k_pe", il);
// and {n_embd_indexer_head_nope, 1, n_tokens}
ggml_tensor * indexer_k_nope =
ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_nope, 1, n_tokens,
ggml_row_size(indexer_k->type, n_embd_indexer_head),
ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1,
ggml_row_size(indexer_k->type, n_embd_indexer_head_nope));
cb(indexer_k_nope, "indexer_k_nope", il);
indexer_k_pe = ggml_rope_ext(ctx0, indexer_k_pe, inp_pos, nullptr, n_rot,
LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(indexer_k_pe, "indexer_k_pe", il);
// {n_embd_indexer_head_rope + n_embd_indexer_head_nope, 1, n_tokens}
indexer_k = ggml_concat(ctx0, indexer_k_pe, indexer_k_nope, 0);
cb(indexer_k, "indexer_k", il);
// perform Hadamard transform on indexer q and k
indexer_q = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_q);
cb(indexer_q, "indexer_q", il);
indexer_k = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_k);
cb(indexer_k, "indexer_k", il);
// store indexer keys to KV cache
const auto * mctx_lid = inp_attn_dsa->mctx->get_lid();
const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid();
ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il));
// prepare indexer weights
ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur);
cb(indexer_weights, "indexer_weights", il);
// get cached indexer keys
indexer_k = mctx_lid->get_k(ctx0, il);
// split the batch into streams if needed
const auto n_stream = indexer_k->ne[3];
indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);
indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
// pre-scale weights to avoid scaling operations on huge indexer_score tensor
indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head)));
cb(indexer_weights, "indexer_weights", il);
ggml_tensor * indexer_score = nullptr;
if (cparams.fused_lid) {
indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn_dsa->get_kq_mask_lid());
cb(indexer_score, "indexer_score", il);
res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il});
} else {
// calculate indexer kq
indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
cb(indexer_q, "indexer_q", il);
indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
cb(indexer_k, "indexer_k", il);
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
cb(indexer_kq, "indexer_kq", il);
// ReLU requires contiguous tensors
indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
cb(indexer_kq, "indexer_kq", il);
// apply ReLU
indexer_score = ggml_relu(ctx0, indexer_kq);
cb(indexer_score, "indexer_score", il);
// multiply scores by indexer weights
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
cb(indexer_score, "indexer_score", il);
// sum by q n_indexer_head dimension
indexer_score = ggml_sum_rows(ctx0, indexer_score);
cb(indexer_score, "indexer_score", il);
// permute result to match KQ mask
indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
cb(indexer_score, "indexer_score", il);
// mask indexer scores
ggml_tensor * indexer_kq_mask = inp_attn_dsa->get_kq_mask_lid();
indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);
cb(indexer_score, "indexer_score", il);
}
// get indices of top k indexer scores
uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k;
top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));
prev_top_k = top_k;
cb(top_k, "top_k", il);
} else {
// "shared" indexer layer - reuse top-k from a previous full layer
GGML_ASSERT(prev_top_k != nullptr && "shared indexer layer must follow a previous full indexer layer");
top_k = prev_top_k;
cb(top_k, "top_k", il);
}
ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr);
cb(q, "q", il);
// split into {n_embd_head_qk_nope, n_head, n_tokens}
ggml_tensor * q_nope =
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
cb(q_nope, "q_nope", il);
// and {n_embd_head_qk_rope, n_head, n_tokens}
ggml_tensor * q_pe = ggml_view_3d(
ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
cb(q_pe, "q_pe", il);
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
cb(kv_cmpr_pe, "kv_cmpr_pe", il);
// split into {kv_lora_rank, n_tokens}
ggml_tensor * kv_cmpr =
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
cb(kv_cmpr, "kv_cmpr", il);
// and {n_embd_head_qk_rope, 1, n_tokens}
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
cb(k_pe, "k_pe", il);
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(q_pe, "q_pe", il);
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(k_pe, "k_pe", il);
kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
cb(kv_cmpr, "kv_cmpr", il);
// MLA attention
{
// {n_embd_head_qk_nope, n_tokens, n_head}
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope, "q_nope_perm", il);
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
cb(q_nope_absorbed, "q_nope_absorbed", il);
// {kv_lora_rank, n_head, n_tokens}
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
// note: rope must go first for in-place context shifting in build_rope_shift()
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
cb(Qcur, "Qcur", il);
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
cb(kv_cmpr, "kv_cmpr_reshape", il);
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
cb(Kcur, "Kcur", il);
// {kv_lora_rank, 1, n_tokens}
ggml_tensor * Vcur = kv_cmpr;
cb(Vcur, "Vcur", il);
// note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)
cur = build_attn(inp_attn_dsa,
model.layers[il].wo, NULL, model.layers[il].wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
}
}
// when unmasked nextn embeddings are requested, t_h_nextn must keep all rows,
// so the early output masking has to be skipped (it is applied after the final norm instead)
if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
if ((uint32_t) il < hparams.n_layer_dense_lead) {
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
// MoE branch
ggml_tensor * moe_out = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il,
nullptr,
model.layers[il].ffn_gate_up_exps,
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
model.layers[il].ffn_down_exps_s);
cb(moe_out, "ffn_moe_out", il);
// FFN shared expert
{
ggml_tensor * ffn_shexp =
build_ffn(cur,
model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,
model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
}
cur = ggml_add(ctx0, cur, ffn_inp);
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, NULL, LLM_NORM_RMS, -1);
// post-norm hidden state feeds the NextN/MTP draft head
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
// LLM_GRAPH_TYPE_DECODER_MTP draft head for GLM-5.2 (GLM_DSA).
// Semantics mirror the deepseek-family NextN/MTP layer:
// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj ->
// full glm_dsa decoder block (dense MLA attention + sigmoid-gated MoE FFN
// with shared expert, exactly as the trunk deepseek2 graph builds it) ->
// shared_head_norm (fallback output_norm) -> shared LM head.
// The DSA indexer is not used at runtime (same as the trunk graph).
llama_model_glm_dsa::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
: llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM_DSA MTP requires n_layer_nextn > 0");
GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM_DSA MTP currently only supports a single MTP block");
GGML_ASSERT(hparams.is_mla() && "GLM_DSA MTP requires MLA");
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
"nextn_layer_offset out of range [0, n_layer_nextn)");
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
const uint32_t kv_lora_rank = hparams.n_lora_kv;
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
// See the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY.
GGML_ASSERT(ext_factor >= 0.0f);
const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_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));
// TODO: extract in a common llm_graph_context::build_inp_embd_h()
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
ggml_set_input(inp->embd);
ggml_tensor * tok_embd;
if (ubatch.token) {
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
} else {
tok_embd = inp->embd;
}
cb(tok_embd, "mtp_tok_embd", il);
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->h);
ggml_set_name(inp->h, "mtp_h_input");
ggml_tensor * h_embd = inp->h;
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
// MLA with the absorption optimization uses a K-only cache (V is a view of K)
auto * inp_attn = build_attn_inp_k();
ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
cb(concat, "mtp_concat", il);
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
cb(cur, "mtp_eh_proj", il);
ggml_tensor * inpSA = cur;
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
// self-attention: dense MLA, same construction as the deepseek2 trunk graph
{
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
cb(q, "mtp_q", il);
q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
cb(q, "mtp_q", il);
q = ggml_mul_mat(ctx0, layer.wq_b, q);
cb(q, "mtp_q", il);
// split into {n_embd_head_qk_nope, n_head, n_tokens}
ggml_tensor * q_nope =
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
cb(q_nope, "mtp_q_nope", il);
// and {n_embd_head_qk_rope, n_head, n_tokens}
ggml_tensor * q_pe = ggml_view_3d(
ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
cb(q_pe, "mtp_q_pe", il);
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);
// split into {kv_lora_rank, n_tokens}
ggml_tensor * kv_cmpr =
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
cb(kv_cmpr, "mtp_kv_cmpr", il);
// and {n_embd_head_qk_rope, 1, n_tokens}
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
cb(k_pe, "mtp_k_pe", il);
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(q_pe, "mtp_q_pe", il);
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(k_pe, "mtp_k_pe", il);
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
cb(kv_cmpr, "mtp_kv_cmpr", il);
// {n_embd_head_qk_nope, n_tokens, n_head}
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
cb(q_nope, "mtp_q_nope_perm", il);
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);
// {kv_lora_rank, n_head, n_tokens}
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
// note: rope must go first for in-place context shifting in build_rope_shift()
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
cb(Qcur, "mtp_Qcur", il);
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
cb(Kcur, "mtp_Kcur", il);
// {kv_lora_rank, 1, n_tokens}
ggml_tensor * Vcur = kv_cmpr;
cb(Vcur, "mtp_Vcur", il);
// note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)
cur = build_attn(inp_attn,
layer.wo, NULL, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);
cb(cur, "mtp_attn_out", il);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "mtp_ffn_inp", il);
cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "mtp_ffn_norm", il);
// MoE FFN with shared expert - same construction as the deepseek2 trunk graph
ggml_tensor * moe_out = build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il,
nullptr,
layer.ffn_gate_up_exps,
layer.ffn_up_exps_s,
layer.ffn_gate_exps_s,
layer.ffn_down_exps_s);
cb(moe_out, "mtp_ffn_moe_out", il);
// FFN shared expert
ggml_tensor * ffn_shexp =
build_ffn(cur,
layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s,
layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s,
layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "mtp_ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "mtp_ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "mtp_post_ffn", il);
// shared_head_norm applied after the decoder block, before the shared LM head.
// The post-norm hidden state seeds the next MTP step.
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: model.output_norm;
GGML_ASSERT(head_norm_w && "GLM_DSA MTP: missing both nextn.shared_head_norm and output_norm");
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
cb(cur, "mtp_shared_head_norm", -1);
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
GGML_ASSERT(head_w && "GLM_DSA MTP: missing LM head (nextn.shared_head_head or model.output)");
cur = build_lora_mm(head_w, cur, head_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/hy-v3.cpp
+394
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#include "models.h"
void llama_model_hy_v3::load_arch_hparams(llama_model_loader & ml) {
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_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
// HY V3 uses a sigmoid router with expert selection bias by default
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
}
// NextN/MTP (HY V3): extra decoder block(s) appended beyond the main stack
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
switch (hparams.n_layer()) {
case 48: type = LLM_TYPE_30B_A3B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_hy_v3::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
// Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
// tensors live in a separate file (e.g. user split target/draft). Mark
// MTP tensors NOT_REQUIRED so the trunk loads cleanly.
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
auto load_block = [&](int i, int flags) {
auto & layer = layers[i];
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / (n_expert_used > 0 ? n_expert_used : 1);
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
// dense FFN (leading dense blocks, first_k_dense_replace)
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
// MoE routed experts (sigmoid router + expert selection bias)
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, i), {n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, TENSOR_NOT_REQUIRED);
// shared expert (always active, no gate)
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, TENSOR_NOT_REQUIRED);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, TENSOR_NOT_REQUIRED);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
};
for (int i = 0; i < n_layer; ++i) {
load_block(i, trunk_flags);
}
// NextN/MTP block(s): a full hy_v3 decoder block plus the NextN projections.
for (int i = n_layer; i < n_layer_all; ++i) {
auto & layer = layers[i];
load_block(i, mtp_flags);
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, mtp_flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, mtp_flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, mtp_flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
// hy_v3 stores the MTP block's trailing final_layernorm here (applied
// after the decoder block, before the shared LM head).
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED);
}
}
std::unique_ptr<llm_graph_context> llama_model_hy_v3::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
llama_model_hy_v3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
GGML_ASSERT(n_embd_head == n_rot);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv();
ggml_tensor * inp_out_ids = build_inp_out_ids();
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
// MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors,
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, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cur = build_attn(inp_attn,
model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
if (model.layers[il].ffn_gate_inp == nullptr) {
// dense FFN (leading dense blocks)
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
nullptr,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_dense_out", il);
} else {
// MoE routed experts (sigmoid gating + expert selection bias)
ggml_tensor * moe_out = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU,
hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il,
nullptr, model.layers[il].ffn_gate_up_exps,
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
model.layers[il].ffn_down_exps_s);
cb(moe_out, "ffn_moe_out", il);
// shared expert (always active, no gate)
ggml_tensor * sh_out = build_ffn(cur,
model.layers[il].ffn_up_shexp, nullptr, model.layers[il].ffn_up_shexp_s,
model.layers[il].ffn_gate_shexp, nullptr, model.layers[il].ffn_gate_shexp_s,
model.layers[il].ffn_down_shexp, nullptr, model.layers[il].ffn_down_shexp_s,
nullptr,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(sh_out, "ffn_shared_out", il);
cur = ggml_add(ctx0, moe_out, sh_out);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
inpL = cur;
}
cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1);
// Post-final-norm hidden state: what the MTP draft head's hnorm consumes.
// vLLM feeds the target model's normed output states, and the MTP layer
// itself returns final_layernorm(h), so the chained state is post-norm.
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur, model.output_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
// LLM_GRAPH_TYPE_DECODER_MTP draft head for HY V3 (MoE).
// Semantics mirror vLLM's HYV3MultiTokenPredictorLayer (hy_v3_mtp.py):
// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj ->
// hy_v3 decoder block -> final_layernorm (stored as nextn.shared_head_norm) ->
// shared LM head (the main model's lm_head; the checkpoint has no separate
// MTP head or MTP embeddings).
llama_model_hy_v3::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
: llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "HY_V3 MTP requires n_layer_nextn > 0");
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
GGML_ASSERT(n_embd_head == n_rot);
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
"nextn_layer_offset out of range [0, n_layer_nextn)");
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->embd);
ggml_set_name(inp->embd, "mtp_h_input");
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
ggml_tensor * h_input = inp->embd;
ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
cb(tok_embd, "mtp_tok_embd", il);
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
auto * inp_attn = build_attn_inp_kv();
ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
cb(concat, "mtp_concat", il);
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat);
cb(cur, "mtp_eh_proj", il);
ggml_tensor * inpSA = cur;
// mtp_block: a full hy_v3 decoder layer (mirrors the trunk graph)
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
{
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il);
Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors,
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, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
cur = build_attn(inp_attn,
layer.wo, layer.wo_b, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "mtp_attn_out", il);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "mtp_ffn_inp", il);
cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_ffn_norm", il);
if (layer.ffn_gate_inp == nullptr) {
cur = build_ffn(cur,
layer.ffn_up, layer.ffn_up_b, layer.ffn_up_s,
layer.ffn_gate, layer.ffn_gate_b, layer.ffn_gate_s,
layer.ffn_down, layer.ffn_down_b, layer.ffn_down_s,
nullptr,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "mtp_ffn_dense_out", il);
} else {
ggml_tensor * moe_out = build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU,
hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il,
nullptr, layer.ffn_gate_up_exps,
layer.ffn_up_exps_s,
layer.ffn_gate_exps_s,
layer.ffn_down_exps_s);
cb(moe_out, "mtp_ffn_moe_out", il);
ggml_tensor * sh_out = build_ffn(cur,
layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s,
layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
nullptr,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(sh_out, "mtp_ffn_shared_out", il);
cur = ggml_add(ctx0, moe_out, sh_out);
cb(cur, "mtp_ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "mtp_post_ffn", il);
// final_layernorm applied after the decoder block, before the shared head.
// The post-norm hidden state seeds the next MTP step (matches vLLM, where
// HYV3MultiTokenPredictorLayer returns final_layernorm(h)).
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: model.output_norm;
GGML_ASSERT(head_norm_w && "HY_V3 MTP: missing both nextn.shared_head_norm and output_norm");
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
cb(cur, "mtp_shared_head_norm", -1);
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
GGML_ASSERT(head_w && "HY_V3 MTP: missing LM head (nextn.shared_head_head or model.output)");
cur = build_lora_mm(head_w, cur, head_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/laguna.cpp
+333
View File
@@ -0,0 +1,333 @@
// Laguna (poolside): sigmoid-routed MoE with a score-correction bias, one shared
// expert, a softplus attention output gate, QK-norm, and per-layer-type RoPE
// (YaRN on full-attention layers, plain RoPE on sliding-window layers). XS.2 is
// hybrid full/SWA with a per-head gate; M.1 is full-attention with a per-element
// gate. Shares the MoE/gate structure with afmoe.
#include "models.h"
void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
// Laguna ships one shared expert and stores its size directly (routed and
// shared experts may differ), so read the size from expert_shared_feed_forward_length.
// The count is not in the config; default to 1 but read the key if present.
hparams.n_expert_shared = 1;
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
if (hparams.n_ff_shexp == 0) {
// Weightless fixtures (test-llama-archs) omit this key; derive a nonzero
// size so the shared expert is still built. Real GGUFs always carry the
// exact value (routed and shared FF lengths may differ).
hparams.n_ff_shexp = hparams.n_ff_exp * hparams.n_expert_shared;
}
// Sliding-window attention is OPTIONAL. XS.2 is hybrid (full / SWA / SWA /
// SWA repeating, period 4 starting with full); M.1 has no sliding window
// (all layers full attention). When sliding_window is absent or zero we
// leave swa_type = NONE and skip the SWA-specific per-layer-type RoPE.
hparams.n_swa = 0;
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
if (hparams.n_swa > 0) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
uint32_t swa_period = 4;
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
hparams.set_swa_pattern(swa_period, /*dense_first=*/true); // XS.2: FULL at il%4==0
// Per-layer-type RoPE: full layers use YaRN θ=500000 over 64 dims;
// SWA layers use default RoPE θ=10000 over 128 dims. Base load_hparams
// already reads ROPE_FREQ_BASE and ROPE_DIMENSION_COUNT into the
// non-SWA fields; we explicitly pull the SWA mirrors here.
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = 1.0f; // SWA uses plain RoPE (no YaRN scaling); do NOT inherit full layers 1/factor
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
}
// Default the expert gating function to SIGMOID when the key is absent
// (matches the HF reference).
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
}
switch (hparams.n_layer()) {
case 40: type = LLM_TYPE_30B_A3B; break; // Laguna-XS.2
case 48: type = LLM_TYPE_118B_A8B; break; // Laguna-S.2
case 70: type = LLM_TYPE_230B_A10B; break; // Laguna-M.1
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_laguna::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
if (output == NULL) {
// tied embeddings fallback
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_shexp = hparams.n_ff_shexp;
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
// Per-layer head count — Laguna varies n_head between full and SWA
// layers (48 vs 64 in XS.2). KV head count is uniform.
const int64_t n_head_il = hparams.n_head(i);
const int64_t n_head_kv_il = hparams.n_head_kv(i);
const int64_t n_embd_q_il = n_embd_head_k * n_head_il;
const int64_t n_embd_k_il = n_embd_head_k * n_head_kv_il;
const int64_t n_embd_v_il = n_embd_head_v * n_head_kv_il;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
create_tensor_qkv(layer, i, n_embd, n_embd_q_il, n_embd_k_il, n_embd_v_il, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q_il, n_embd}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
// Attention output gate. XS.2 is per-head (g_proj -> n_head, one scalar
// per head broadcast over head_dim at multiply time); M.1 is per-element
// (g_proj -> n_head*head_dim, like afmoe). Detect from the stored tensor
// shape so a single arch handles both; the graph mirrors this check.
// Gate width selects per-head vs per-element. Real GGUFs always carry the
// gate tensor, so read the width from it and require EXACTLY one of the two
// valid widths -- never guess between them. Weightless fixtures
// (test-llama-archs) have no gate tensor; fall back to the per-head layout so
// the per-head reshape path is still exercised.
const int64_t n_gate_per_head = n_head_il;
const int64_t n_gate_per_elem = n_embd_head_k * n_head_il;
const ggml_tensor * gate_meta = ml.get_tensor_meta(tn(LLM_TENSOR_ATTN_GATE, "weight", i).str().c_str());
int64_t n_gate_out;
if (gate_meta != nullptr) {
n_gate_out = gate_meta->ne[1];
if (n_gate_out != n_gate_per_head && n_gate_out != n_gate_per_elem) {
GGML_ABORT("Laguna: unexpected attention gate width %lld at layer %d "
"(expected %lld per-head or %lld per-element)",
(long long) n_gate_out, i, (long long) n_gate_per_head, (long long) n_gate_per_elem);
}
} else {
n_gate_out = n_gate_per_head;
}
layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_gate_out}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
if ((uint32_t)i >= hparams.n_layer_dense_lead) {
// MoE layer
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, 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);
// Always-on shared expert.
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
} else {
// Dense layer (the leading n_layer_dense_lead layers)
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_laguna::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_laguna::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
// No MuP embedding scale (laguna omits this; afmoe scales by sqrt(hidden)).
ggml_tensor * inp_pos = build_inp_pos();
// XS.2 is hybrid SWA -> interleaved-SWA KV input; M.1 is all-full -> plain
// KV input. Pick the matching input (and build_attn overload) per swa_type.
const bool has_swa = hparams.swa_type != LLAMA_SWA_TYPE_NONE;
llm_graph_input_attn_kv * inp_attn_kv = has_swa ? nullptr : build_attn_inp_kv();
llm_graph_input_attn_kv_iswa * inp_attn_iswa = has_swa ? build_attn_inp_kv_iswa() : nullptr;
ggml_tensor * inp_out_ids = build_inp_out_ids();
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
for (int il = 0; il < n_layer; ++il) {
const bool is_swa_il = hparams.is_swa(il);
const int64_t n_head_il = hparams.n_head(il);
const int64_t n_head_kv_il = hparams.n_head_kv(il);
// Per-layer-type RoPE config. SWA layers run plain rope (no YaRN),
// achieved by zeroing the YaRN ext/beta params for those layers.
const int n_rot_l = is_swa_il ? hparams.n_rot_swa : n_rot;
const float freq_base_l = is_swa_il ? hparams.rope_freq_base_train_swa : freq_base;
const float freq_scale_l = is_swa_il ? hparams.rope_freq_scale_train_swa : freq_scale;
const float ext_factor_l = is_swa_il ? 0.0f : ext_factor;
// YaRN magnitude scaling (mscale) is already handled by the framework:
// llama_context pre-divides cparams.yarn_attn_factor by (1 + 0.1*ln(factor))
// to cancel ggml rope_yarn's internal mscale *= 1 + 0.1*ln(1/freq_scale).
// Pass attn_factor straight through (like every other arch); SWA layers run
// plain RoPE (ext_factor 0, no mscale) so force 1.0 there.
const float attn_factor_l = is_swa_il ? 1.0f : attn_factor;
const float beta_fast_l = is_swa_il ? 0.0f : beta_fast;
const float beta_slow_l = is_swa_il ? 0.0f : beta_slow;
const int n_ctx_orig_l = is_swa_il ? hparams.n_ctx_train : n_ctx_orig;
ggml_tensor * inpSA = inpL;
// Pre-norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// Self-attention
{
ggml_tensor * attn_inp = cur; // saved for the gate projection
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head_il, n_head_kv_il, il);
// g_proj on the *pre-attention* hidden state (matches HF
// reference: gate is computed from the same `hidden_states`
// input as q/k/v, not from the attn output).
ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
cb(gate, "attn_gate_proj", il);
// QK RMSNorm at head_dim level (Qwen3 style)
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
cb(Kcur, "Kcur_normed", il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l,
ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l,
ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
cb(Qcur, "Qcur_rope", il);
cb(Kcur, "Kcur_rope", il);
cur = has_swa
? build_attn(inp_attn_iswa,
NULL, NULL, NULL, // o_proj deferred until after gating
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il)
: build_attn(inp_attn_kv,
NULL, NULL, NULL,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
// Softplus output gate (the unary kernel computes softplus in fp32
// and casts back). Two shapes, distinguished by the g_proj output
// dim (matching the load-time detection):
// XS.2 per-head : gate [n_head_il, n_tokens] -> reshape to
// [1, n_head_il, n_tokens] and broadcast over
// head_dim against cur [head_dim, n_head, T].
// M.1 per-element : gate [n_head_il*head_dim, n_tokens] spans the
// full attention output -> direct ggml_mul.
gate = ggml_softplus(ctx0, gate);
cb(gate, "attn_gate_softplus", il);
const int64_t n_tokens = cur->ne[1];
if (model.layers[il].wqkv_gate->ne[1] == n_head_il) {
cur = ggml_reshape_3d(ctx0, cur, n_embd_head, n_head_il, n_tokens);
gate = ggml_reshape_3d(ctx0, gate, 1, n_head_il, n_tokens);
cur = ggml_mul(ctx0, cur, gate);
cur = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_il, n_tokens);
} else {
cur = ggml_mul(ctx0, cur, gate);
}
cb(cur, "attn_gated", il);
cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
cb(cur, "attn_o_proj", il);
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// Pre-norm only (no post-attn norm)
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
if ((uint32_t)il >= hparams.n_layer_dense_lead) {
// MoE: sigmoid routing + score-correction bias + sum-norm +
// routed_scaling_factor (all handled by build_moe_ffn).
ggml_tensor * moe_out = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU,
hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
cb(moe_out, "ffn_moe_out", il);
// Always-on shared expert, summed in parallel.
ggml_tensor * ffn_shexp = build_ffn(cur,
model.layers[il].ffn_up_shexp, NULL, NULL,
model.layers[il].ffn_gate_shexp, NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
} else {
// Dense FFN for the leading n_layer_dense_lead layers (XS.2: 1, M.1: 3)
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
}
// No post-ffn norm
cur = ggml_add(ctx0, cur, ffn_inp);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/mimo2.cpp
+185 -21
View File
@@ -25,9 +25,17 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
}
}
void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
void llama_model_mimo2::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
@@ -40,41 +48,46 @@ void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
uint32_t n_head = hparams.n_head(i);
// NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support
const bool is_nextn = i >= n_layer;
const int skip = is_nextn ? TENSOR_SKIP : 0;
const int flags = is_nextn ? mtp_flags : 0;
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, skip);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, skip);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, flags);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, skip);
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | skip);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, skip);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
// non-MoE branch
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags);
// MoE branch
int64_t n_ff_exp = hparams.n_ff_exp;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);
if (is_nextn) {
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, skip);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, skip);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, skip);
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, skip);
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_mimo2::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
@@ -89,6 +102,8 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
ggml_tensor * inp_out_ids = build_inp_out_ids();
const float v_scale = hparams.f_attn_value_scale;
const bool emit_h_nextn = cparams.embeddings_nextn;
const bool crop_last_layer = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked);
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
@@ -168,7 +183,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
}
}
if (il == n_layer - 1 && inp_out_ids) {
if (il == n_layer - 1 && crop_last_layer) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
@@ -218,6 +233,15 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
cur = inpL;
if (emit_h_nextn) {
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
}
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
@@ -233,3 +257,143 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
ggml_build_forward_expand(gf, cur);
}
// Mirrors MiMo's appended NextN block: normalize and fuse token and hidden inputs, run the decoder block,
// expose its pre-head-norm state to the next draft step, then apply the shared output norm and LM head.
// Converted checkpoints may store that shared norm as layer_out_norm, so it remains in the fallback chain.
llama_model_mimo2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
: llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "MIMO2 MTP requires n_layer_nextn > 0");
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
"nextn_layer_offset out of range [0, n_layer_nextn)");
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MIMO2 MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MIMO2 MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MIMO2 MTP block missing nextn.hnorm");
GGML_ASSERT(layer.wqkv && "MIMO2 MTP requires fused attn_qkv");
const uint32_t n_head_l = hparams.n_head(il);
const uint32_t n_head_kv_l = 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 float v_scale = hparams.f_attn_value_scale;
auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->embd);
ggml_set_name(inp->embd, "mtp_h_input");
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
ggml_tensor * h_input = inp->embd;
ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
cb(tok_embd, "mtp_tok_embd", il);
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
auto * inp_attn = build_attn_inp_kv_iswa();
ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
cb(concat, "mtp_concat", il);
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
cb(cur, "mtp_eh_proj", il);
ggml_tensor * inpSA = cur;
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * qkv = build_lora_mm(layer.wqkv, cur, layer.wqkv_s);
cb(qkv, "mtp_wqkv", il);
const size_t row_k = ggml_row_size(qkv->type, n_embd_head_k);
const size_t row_v = ggml_row_size(qkv->type, n_embd_head_v);
const size_t row_full = qkv->nb[1];
const size_t k_off = row_k * n_head_l;
const size_t v_off = k_off + row_k * n_head_kv_l;
ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_l, n_tokens, row_k, row_full, 0);
ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv_l, n_tokens, row_k, row_full, k_off);
ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv_l, n_tokens, row_v, row_full, v_off);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
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_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "mtp_Qcur", il);
cb(Kcur, "mtp_Kcur", il);
cb(Vcur, "mtp_Vcur", il);
cur = build_attn(inp_attn,
layer.wo, nullptr, layer.wo_s,
Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, nullptr,
1.0f / sqrtf(float(n_embd_head_k)), il);
cb(cur, "mtp_attn_out", il);
if (v_scale) {
cur = ggml_scale(ctx0, cur, v_scale);
cb(cur, "mtp_attn_out_scaled", il);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "mtp_ffn_inp", il);
cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_ffn_norm", il);
GGML_ASSERT(layer.ffn_gate && layer.ffn_down && layer.ffn_up && "MIMO2 MTP requires dense FFN tensors");
cur = build_ffn(cur,
layer.ffn_up, layer.ffn_up_b, nullptr,
layer.ffn_gate, layer.ffn_gate_b, nullptr,
layer.ffn_down, layer.ffn_down_b, nullptr,
nullptr,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "mtp_ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "mtp_post_ffn", il);
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: (layer.layer_out_norm ? layer.layer_out_norm : model.output_norm);
GGML_ASSERT(head_norm_w && "MIMO2 MTP missing head norm fallback");
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
cb(cur, "mtp_shared_head_norm", -1);
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
GGML_ASSERT(head_w && "MIMO2 MTP missing LM head fallback");
cur = build_lora_mm(head_w, cur, head_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/minimax-m2.cpp
+2
View File
@@ -60,6 +60,8 @@ llama_model_minimax_m2::graph::graph(const llama_model & model, const llm_graph_
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
res->t_layer_inp[il] = inpL;
ggml_tensor * inpSA = inpL;
cur = inpL;
examples/talk-llama/models/minimax-m3.cpp
+601
View File
@@ -0,0 +1,601 @@
#include "models.h"
#include "llama-kv-cache-msa.h"
#include <cmath>
#include <vector>
#include <cstdint>
// MiniMax-M3: MiniMax-M2 style GQA (per-head QK-norm, partial rotary) with
// DeepSeek-V3 leading-dense + routed/shared experts (sigmoid gating, routed scaling),
// swigluoai activation, and MiniMax Sparse Attention (MSA). MTP is not in released model weights.
// MSA blocks are defined over token positions. The graph translates between position space (block
// selection) and cell space (K/V/indexer storage) via per-ubatch pos<->cell maps populated from llama_kv_cells
void llama_model_minimax_m3::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
ml.get_key(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, hparams.indexer_block_size);
ml.get_key(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks);
msa_p = { (int) hparams.indexer_block_size, (int) hparams.indexer_top_k, (int) hparams.indexer_local_blocks };
switch (hparams.n_layer()) {
case 60: type = LLM_TYPE_428B_A23B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_minimax_m3::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_ff_exp = hparams.n_ff_exp;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
// per-head QK-norm: a single head_dim vector applied to every head
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
if (i < (int) hparams.n_layer_dense_lead) {
// leading dense layers
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
} else {
// routed experts
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, 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);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
// shared expert
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
// indexer
layer.index_q_proj = create_tensor(tn(LLM_TENSOR_INDEXER_Q_PROJ, "weight", i), {n_embd, hparams.indexer_n_head * hparams.indexer_head_size}, 0);
layer.index_k_proj = create_tensor(tn(LLM_TENSOR_INDEXER_K_PROJ, "weight", i), {n_embd, hparams.indexer_head_size}, 0);
layer.index_q_norm = create_tensor(tn(LLM_TENSOR_INDEXER_Q_NORM, "weight", i), {hparams.indexer_head_size}, 0);
layer.index_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, 0);
}
}
}
std::unique_ptr<llm_graph_context> llama_model_minimax_m3::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
class llm_graph_input_msa : public llm_graph_input_i {
public:
llm_graph_input_msa(const llama_kv_cache_msa_context * mctx, int blk, int local) :
mctx(mctx), blk(blk), local(local) {}
void set_input(const llama_ubatch * ubatch) override {
if (pos_slot_i) { mctx->set_input_pos_slot(pos_slot_i, ubatch); }
if (pos_slot_f) { mctx->set_input_pos_slot(pos_slot_f, ubatch); }
if (cell_blk) { mctx->set_input_cell_pos(cell_blk, ubatch, blk); }
if (pos_mask) { mctx->set_input_pos_mask(pos_mask, ubatch); }
// local-force bias over position blocks
if (bias && ubatch->pos) {
const int64_t n_tokens = ubatch->n_tokens;
const int64_t nblk = bias->ne[0];
std::vector<float> data((size_t) nblk * n_tokens, 0.0f);
for (int64_t i = 0; i < n_tokens; ++i) {
const int64_t L = ubatch->pos[i] / blk;
for (int l = 0; l < local && L - l >= 0; ++l) {
if (L - l < nblk) {
data[(size_t) i * nblk + (L - l)] = 1e30f;
}
}
}
ggml_backend_tensor_set(bias, data.data(), 0, data.size() * sizeof(float));
}
}
// valid as long as the tensor dims still match the new ubatch/cache window and the
// ubatch is in the same regime (decode graphs have pos_slot_f, batch graphs cell_blk)
bool can_reuse(const llm_graph_params & params) override {
const auto * mctx_new = static_cast<const llama_kv_cache_msa_context *>(params.mctx);
this->mctx = mctx_new;
const int64_t n_ps = GGML_PAD((int64_t) mctx_new->get_n_pos(), blk);
const int64_t ns = params.cparams.kv_unified ? 1 : params.ubatch.n_seqs_unq;
const bool decode = params.ubatch.n_tokens == ns; // one token per stream
bool res = true;
res &= bias->ne[0] * blk == n_ps;
res &= bias->ne[1] == params.ubatch.n_tokens;
res &= pos_mask->ne[0] == n_ps;
res &= pos_mask->ne[1] == params.ubatch.n_tokens;
res &= pos_slot_i->ne[0] == n_ps;
res &= pos_slot_i->ne[1] == ns;
res &= decode == (pos_slot_f != nullptr);
res &= decode == (cell_blk == nullptr);
if (pos_slot_f) {
res &= pos_slot_f->ne[0] == n_ps;
res &= pos_slot_f->ne[1] == ns;
}
if (cell_blk) {
res &= cell_blk->ne[0] == (int64_t) mctx_new->get_base()->get_n_kv();
res &= cell_blk->ne[1] == ns;
}
return res;
}
ggml_tensor * bias = nullptr; // F32 [nblk, n_tokens] local-force bias (position blocks)
ggml_tensor * pos_mask = nullptr; // F32 [n_ps, n_tokens] 0/-inf visibility, by position
ggml_tensor * pos_slot_i = nullptr; // I32 [n_ps, ns] pos -> cell (get_rows index)
ggml_tensor * pos_slot_f = nullptr; // F32 [n_ps, ns] pos -> cell (gatherable values, decode)
ggml_tensor * cell_blk = nullptr; // I32 [n_kv, ns] cell -> position block (batch)
const llama_kv_cache_msa_context * mctx;
int blk;
int local;
};
// One FA call for all GQA groups (and at multi-stream decode, all streams) by mapping them onto the FA sequence dim (ne[3])
ggml_tensor * llama_model_minimax_m3::graph::build_attn_msa_fa(
ggml_tensor * q_cur, // [D, HQ, T]
ggml_tensor * k, // [D, n_keys, 1, C]
ggml_tensor * v, // [D, n_keys, 1, C]
ggml_tensor * mask, // [n_keys, R, 1, C] f16, contiguous
int64_t Gp, float kq_scale, int il) const {
const int64_t D = q_cur->ne[0];
const int64_t HQ = q_cur->ne[1];
const int64_t T = q_cur->ne[2];
const int64_t C = k->ne[3];
const int64_t R = HQ*T/(Gp*C);
GGML_ASSERT(Gp*C*R == HQ*T);
GGML_ASSERT(mask->type == GGML_TYPE_F16);
// [D, HQ, T] -> [D, Gp, C, R] -> [D, R, Gp, C]
// batch (C=HKV, R=T): channel = group
// decode (C=HKV*ns, R=1): channel = (group, stream), group innermost
ggml_tensor * q = ggml_reshape_4d(ctx0, q_cur, D, Gp, C, R);
q = ggml_permute(ctx0, q, 0, 2, 3, 1);
ggml_tensor * o = ggml_flash_attn_ext(ctx0, q, k, v, mask, kq_scale,
hparams.f_max_alibi_bias, 0.0f);
ggml_flash_attn_ext_set_prec(o, GGML_PREC_F32);
cb(o, "msa_fattn", il);
// [D, Gp, R, C] -> [D, Gp, C, R] -> [n_embd, T]
o = ggml_permute(ctx0, o, 0, 1, 3, 2);
if (!ggml_is_contiguous(o)) {
o = ggml_cont(ctx0, o); // no-op layout at decode (R == 1), copy at batch
}
return ggml_reshape_2d(ctx0, o, D*HQ, T);
}
llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
const auto & mm = static_cast<const llama_model_minimax_m3 &>(model);
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
// partial rotary: head_dim != n_rot, so don't assert n_embd_head == n_rot
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
// ==========================================
// TODO: avoid such kind of complexity in the model graphs
// MSA calls ggml_flash_attn_ext directly and assumes the non-transposed V layout that
// llama.cpp only provides when flash attention is enabled. Block selection is anchored
// to absolute KV cache slots, which equal positions only for append-only per-stream
// caches either a single sequence, or multiple sequences with kv_unified == false (each
// stream then has its own slot space). A unified cache with multiple sequences
// interleaves slots and would silently break block anchoring so it falls back to dense.
const bool fa_on = cparams.flash_attn;
const bool streams_ok = cparams.n_seq_max == 1 || !cparams.kv_unified;
const bool msa_enabled = fa_on && streams_ok;
auto * inp_attn = build_attn_inp_kv_msa(msa_enabled);
static bool warned_no_fa = false;
if (!fa_on && !warned_no_fa) {
LLAMA_LOG_WARN("%s: flash attention disabled; MSA requires it -> running DENSE attention "
"(output may be degraded). Enable flash attention for MSA.\n", __func__);
warned_no_fa = true;
}
static bool warned_unified = false;
if (fa_on && !streams_ok && !warned_unified) {
LLAMA_LOG_WARN("%s: unified KV cache with n_seq_max > 1; MSA needs per-sequence streams "
"-> running DENSE attention. Output may be degraded. Drop --kv-unified to enable MSA.\n", __func__);
warned_unified = true;
}
// ==========================================
// hoisted per-graph MSA state (shared by every sparse layer)
llm_graph_input_msa * msa = nullptr;
ggml_tensor * msa_kqm = nullptr;
ggml_tensor * msa_mf = nullptr; // F32 copy of the FA mask for the final mask add
int64_t n_kv = 0, n_ps = 0, nblk = 0, ns = 1, n_tps = 0;
bool msa_decode = false; // gather (1 token per stream) vs mask
const int blk = mm.msa_p.blk;
const int64_t Hd = hparams.indexer_n_head; // one indexer head per GQA group
if (msa_enabled) {
const auto * mctx_msa = static_cast<const llama_kv_cache_msa_context *>(mctx);
msa_kqm = inp_attn->get_kq_mask();
n_kv = msa_kqm->ne[0];
n_tps = msa_kqm->ne[1]; // tokens per stream
ns = msa_kqm->ne[3]; // streams in this ubatch
GGML_ASSERT(msa_kqm->type == GGML_TYPE_F16 && "MSA requires the FA (f16) mask");
GGML_ASSERT(n_tps*ns == n_tokens);
// the position axis covers every position currently in the cache and is padded to whole blocks
n_ps = GGML_PAD((int64_t) mctx_msa->get_n_pos(), blk);
nblk = n_ps / blk;
msa_decode = n_tps == 1;
auto inp = std::make_unique<llm_graph_input_msa>(mctx_msa, blk, mm.msa_p.local);
inp->bias = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, nblk, n_tokens); // stream-grouped tokens
ggml_set_input(inp->bias);
inp->pos_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_ps, n_tokens);
ggml_set_input(inp->pos_mask);
inp->pos_slot_i = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_ps, ns);
ggml_set_input(inp->pos_slot_i);
if (msa_decode) {
inp->pos_slot_f = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_ps, ns);
ggml_set_input(inp->pos_slot_f);
} else {
inp->cell_blk = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, ns);
ggml_set_input(inp->cell_blk);
msa_mf = ggml_cast(ctx0, msa_kqm, GGML_TYPE_F32);
}
msa = (llm_graph_input_msa *) res->add_input(std::move(inp));
}
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// self-attention
{
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head, n_head_kv, il);
// per-head QK RMSNorm (weights already include Gemma's +1)
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
// partial rotary: only the first n_rot dims are rotated
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);
cb(Vcur, "Vcur", il);
const bool is_sparse = msa_enabled && il >= (int) hparams.n_layer_dense_lead;
if (!is_sparse) {
cur = build_attn(inp_attn, model.layers[il].wo, NULL, model.layers[il].wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
1.0f/sqrtf(float(n_embd_head)), il);
} else {
const int64_t n_idx_dim = hparams.indexer_head_size; // 128
// Index Branch, project, norm, partial RoPE, cache
ggml_tensor * iq = build_lora_mm(model.layers[il].index_q_proj, cur);
ggml_tensor * ik = build_lora_mm(model.layers[il].index_k_proj, cur);
iq = ggml_reshape_3d(ctx0, iq, n_idx_dim, Hd, n_tokens);
ik = ggml_reshape_3d(ctx0, ik, n_idx_dim, 1, n_tokens);
iq = build_norm(iq, model.layers[il].index_q_norm, NULL, LLM_NORM_RMS, il); // +1 baked
ik = build_norm(ik, model.layers[il].index_k_norm, NULL, LLM_NORM_RMS, il);
iq = ggml_rope_ext(ctx0, iq, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
ik = ggml_rope_ext(ctx0, ik, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
const auto * mctx_msa_l = static_cast<const llama_kv_cache_msa_context *>(mctx);
const auto * mctx_cur = mctx_msa_l->get_base();
const auto * mctx_idx = mctx_msa_l->get_idx();
ggml_build_forward_expand(gf, mctx_idx->cpy_k(ctx0, ik, inp_attn->get_k_idxs_idx(), il));
ggml_tensor * ik_kv = mctx_idx->get_k(ctx0, il);
if (inp_attn->self_k_rot) {
Qcur = llama_mul_mat_hadamard(ctx0, Qcur, inp_attn->self_k_rot);
Kcur = llama_mul_mat_hadamard(ctx0, Kcur, inp_attn->self_k_rot);
}
if (inp_attn->self_v_rot) {
Vcur = llama_mul_mat_hadamard(ctx0, Vcur, inp_attn->self_v_rot);
}
// Main branch: store K/V, take cache views
ggml_build_forward_expand(gf, Qcur);
ggml_build_forward_expand(gf, Kcur);
ggml_build_forward_expand(gf, Vcur);
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, Kcur, inp_attn->get_k_idxs(), il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, Vcur, inp_attn->get_v_idxs(), il));
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
GGML_ASSERT(!(v->nb[1] > v->nb[2]) && "MSA assumes v_trans=false (FA on)");
const int64_t D = k->ne[0];
const int64_t HKV = k->ne[1];
const int64_t Gp = n_head/HKV;
GGML_ASSERT(HKV == Hd && "MSA: one indexer head per GQA group");
GGML_ASSERT(k->ne[3] == ns);
const int K = mm.msa_p.topk_blocks < (int) nblk ? mm.msa_p.topk_blocks : (int) nblk;
const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
if (msa_decode) {
// decode: batched over streams top-k + gather, one grouped FA
// gather the indexer keys through the pos -> cell map
ggml_tensor * ik3 = ggml_view_3d(ctx0, ik_kv, n_idx_dim, n_kv, ns,
ik_kv->nb[2], ik_kv->nb[3], 0);
ggml_tensor * ikp = ggml_get_rows(ctx0, ik3, msa->pos_slot_i); // [n_idx_dim, n_ps, ns]
ggml_tensor * iq4 = ggml_reshape_4d(ctx0, iq, n_idx_dim, Hd, 1, ns);
ggml_tensor * sc = ggml_mul_mat(ctx0,
ggml_reshape_4d(ctx0, ikp, n_idx_dim, n_ps, 1, ns), iq4);
ggml_mul_mat_set_prec(sc, GGML_PREC_F32);
// unmapped positions come out -inf, so they can never rank into the top-k
sc = ggml_add_inplace(ctx0, sc,
ggml_reshape_4d(ctx0, msa->pos_mask, n_ps, 1, 1, ns));
ggml_tensor * bs = ggml_pool_2d(ctx0, sc, GGML_OP_POOL_MAX, blk, 1, blk, 1, 0, 0);
cb(bs, "msa_bs", il);
ggml_tensor * bsf = ggml_add(ctx0, bs,
ggml_reshape_4d(ctx0, msa->bias, nblk, 1, 1, ns));
ggml_tensor * idx = ggml_top_k(ctx0, bsf, K); // position blocks
// pos idx: tj[t,k,h,s] = blk*idx[k,h,s] + t (positions - mask gather)
// cell idx: cs[t,k,h,s] = pos_slot[tj] (pos -> cell translation)
// row idx: tr[t,k,h,s] = cs*HKV + h (per-stream K/V gather)
ggml_tensor * a = ggml_scale(ctx0, ggml_cast(ctx0, idx, GGML_TYPE_F32), (float) blk);
a = ggml_reshape_4d(ctx0, a, 1, K, Hd, ns);
ggml_tensor * tj = ggml_add(ctx0,
ggml_repeat_4d(ctx0, a, blk, K, Hd, ns),
ggml_reshape_3d(ctx0, ggml_arange(ctx0, 0.0f, (float) blk, 1.0f), blk, 1, 1));
ggml_tensor * tokj = ggml_cast(ctx0, ggml_reshape_2d(ctx0, tj, (int64_t) blk*K*Hd, ns), GGML_TYPE_I32);
ggml_tensor * cs = ggml_get_rows(ctx0,
ggml_reshape_3d(ctx0, msa->pos_slot_f, 1, n_ps, ns), tokj); // [1, blk*K*Hd, ns]
cs = ggml_reshape_4d(ctx0, cs, blk, K, Hd, ns);
ggml_tensor * tr = ggml_add(ctx0,
ggml_scale(ctx0, cs, (float) HKV),
ggml_reshape_3d(ctx0, ggml_arange(ctx0, 0.0f, (float) HKV, 1.0f), 1, 1, Hd));
ggml_tensor * tokr = ggml_cast(ctx0, ggml_reshape_2d(ctx0, tr, (int64_t) blk*K*Hd, ns), GGML_TYPE_I32);
ggml_tensor * k3 = ggml_view_3d(ctx0, k, D, HKV*n_kv, ns, k->nb[1], k->nb[3], 0);
ggml_tensor * v3 = ggml_view_3d(ctx0, v, D, HKV*n_kv, ns, v->nb[1], v->nb[3], 0);
ggml_tensor * mp = ggml_reshape_3d(ctx0, msa->pos_mask, 1, n_ps, ns);
ggml_tensor * kg = ggml_get_rows(ctx0, k3, tokr);
ggml_tensor * vg = ggml_get_rows(ctx0, v3, tokr);
ggml_tensor * mg = ggml_get_rows(ctx0, mp, tokj);
// fold (group, stream) onto the FA channel dim
const ggml_type kt = ggml_is_quantized(k->type) ? GGML_TYPE_F16 : k->type;
const ggml_type vt = ggml_is_quantized(v->type) ? GGML_TYPE_F16 : v->type;
ggml_tensor * kfa = ggml_reshape_4d(ctx0, kg, D, (int64_t) blk*K, 1, Hd*ns);
ggml_tensor * vfa = ggml_reshape_4d(ctx0, vg, D, (int64_t) blk*K, 1, Hd*ns);
if (kfa->type != kt) { kfa = ggml_cast(ctx0, kfa, kt); }
if (vfa->type != vt) { vfa = ggml_cast(ctx0, vfa, vt); }
// the FA mask must be F16
ggml_tensor * mfa = ggml_cast(ctx0, ggml_reshape_4d(ctx0, mg, (int64_t) blk*K, 1, 1, Hd*ns), GGML_TYPE_F16);
cur = build_attn_msa_fa(Qcur, kfa, vfa, mfa, Gp, kq_scale, il);
} else {
// batch: per-stream loop
std::vector<ggml_tensor *> outs(ns);
for (int64_t st = 0; st < ns; ++st) {
ggml_tensor * iq_s = ggml_view_3d(ctx0, iq, n_idx_dim, Hd, n_tps,
iq->nb[1], iq->nb[2], st*n_tps*iq->nb[2]);
ggml_tensor * ik_s = ggml_view_2d(ctx0, ik_kv, n_idx_dim, n_kv,
ik_kv->nb[2], st*ik_kv->nb[3]);
ggml_tensor * psl_s = ggml_view_1d(ctx0, msa->pos_slot_i, n_ps,
st*msa->pos_slot_i->nb[1]);
ggml_tensor * pm_s = ggml_view_3d(ctx0, msa->pos_mask, n_ps, 1, n_tps,
msa->pos_mask->nb[1], msa->pos_mask->nb[1], st*n_tps*msa->pos_mask->nb[1]);
ggml_tensor * cb_s = ggml_view_1d(ctx0, msa->cell_blk, n_kv,
st*msa->cell_blk->nb[1]);
ggml_tensor * mf_s = ggml_view_3d(ctx0, msa_mf, n_kv, n_tps, 1,
msa_mf->nb[1], msa_mf->nb[3], st*msa_mf->nb[3]);
ggml_tensor * bias_s = ggml_view_3d(ctx0, msa->bias, nblk, 1, n_tps,
msa->bias->nb[1], msa->bias->nb[1], st*n_tps*msa->bias->nb[1]);
ggml_tensor * q_s = ggml_view_3d(ctx0, Qcur, D, n_head, n_tps,
Qcur->nb[1], Qcur->nb[2], st*n_tps*Qcur->nb[2]);
ggml_tensor * k_s = ggml_view_4d(ctx0, k, D, HKV, n_kv, 1,
k->nb[1], k->nb[2], k->nb[3], st*k->nb[3]);
ggml_tensor * v_s = ggml_view_4d(ctx0, v, D, HKV, n_kv, 1,
v->nb[1], v->nb[2], v->nb[3], st*v->nb[3]);
// block scores: the indexer keys are gathered through the pos -> cell map first
// scores are unscaled, only the top-k ordering matters
ggml_tensor * ikp = ggml_get_rows(ctx0, ik_s, psl_s); // [n_idx_dim, n_ps]
ggml_tensor * sc = ggml_mul_mat(ctx0, ikp,
ggml_reshape_2d(ctx0, iq_s, n_idx_dim, Hd*n_tps));
// indexer scores run in F32
ggml_mul_mat_set_prec(sc, GGML_PREC_F32);
sc = ggml_reshape_3d(ctx0, sc, n_ps, Hd, n_tps);
// unmapped positions (holes, padding, empty cells) come out -inf
sc = ggml_add_inplace(ctx0, sc, pm_s);
ggml_tensor * bs = ggml_pool_2d(ctx0, sc, GGML_OP_POOL_MAX, blk, 1, blk, 1, 0, 0);
cb(bs, "msa_bs", il);
// bias the scores so locally-forced blocks always rank first
ggml_tensor * bsf = ggml_add(ctx0, bs, bias_s); // [nblk, Hd, n_tps]
cb(bsf, "msa_bsf", il);
ggml_tensor * idx = ggml_top_k(ctx0, bsf, K); // [K, Hd, n_tps] i32
ggml_tensor * ninf = ggml_cast(ctx0,
ggml_scale_bias(ctx0, bias_s, 0.0f, -1e30f),
GGML_TYPE_F16); // [nblk, 1, n_tps]
ninf = ggml_repeat_4d(ctx0, ninf, nblk, Hd, n_tps, 1);
ggml_tensor * zero = ggml_scale(ctx0,
ggml_cast(ctx0, idx, GGML_TYPE_F32), 0.0f);
ggml_tensor * bm = ggml_set_rows(ctx0,
ggml_reshape_3d(ctx0, ninf, 1, nblk, Hd*n_tps),
ggml_reshape_3d(ctx0, zero, 1, K, Hd*n_tps),
ggml_reshape_2d(ctx0, idx, K, Hd*n_tps));
bm = ggml_reshape_3d(ctx0, bm, nblk, Hd, n_tps);
bm = ggml_cont(ctx0, ggml_permute(ctx0, bm, 0, 2, 1, 3)); // [nblk, n_tps, Hd]
cb(bm, "msa_block_mask", il);
// expand block -> cell granularity through the cell -> position block
// map, then combine with the causal mask. empty cells are masked by the causal mask.
ggml_tensor * bm2 = ggml_cont(ctx0, ggml_transpose(ctx0,
ggml_reshape_2d(ctx0, bm, nblk, n_tps*Hd))); // [n_tps*Hd, nblk]
ggml_tensor * bmc = ggml_get_rows(ctx0, bm2, cb_s); // [n_tps*Hd, n_kv] F32
ggml_tensor * bmx = ggml_cont(ctx0, ggml_transpose(ctx0, bmc));
bmx = ggml_reshape_3d(ctx0, bmx, n_kv, n_tps, Hd);
ggml_tensor * mask4 = ggml_add_inplace(ctx0, bmx, mf_s);
mask4 = ggml_cast(ctx0,
ggml_reshape_4d(ctx0, mask4, n_kv, n_tps, 1, Hd), GGML_TYPE_F16);
cb(mask4, "msa_mask4", il);
// cache views with groups on ne[3];
ggml_tensor * kfa = ggml_permute(ctx0, k_s, 0, 3, 1, 2);
ggml_tensor * vfa = ggml_permute(ctx0, v_s, 0, 3, 1, 2);
outs[st] = build_attn_msa_fa(q_s, kfa, vfa, mask4, Gp, kq_scale, il);
}
cur = outs[0];
for (int64_t st = 1; st < ns; ++st) {
cur = ggml_concat(ctx0, cur, outs[st], 1);
}
}
if (inp_attn->self_v_rot) {
cur = llama_mul_mat_hadamard(ctx0, cur, inp_attn->self_v_rot);
}
cb(cur, "kqv_out", il);
if (model.layers[il].wo) {
cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
}
}
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
if ((uint32_t) il < hparams.n_layer_dense_lead) {
// leading dense FFN (swigluoai)
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SWIGLU_OAI_MOE, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
// routed experts (swigluoai MoE)
ggml_tensor * moe_out = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SWIGLU_OAI_MOE, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
cb(moe_out, "ffn_moe_out", il);
// shared expert (swigluoai)
ggml_tensor * ffn_shexp = build_ffn(cur,
model.layers[il].ffn_up_shexp, NULL, NULL,
model.layers[il].ffn_gate_shexp, NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
NULL,
LLM_FFN_SWIGLU_OAI_MOE, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
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, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur, model.output_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/models.h
+118 -3
View File
@@ -424,6 +424,22 @@ struct llama_model_mellum : public llama_model_base {
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_nanbeige : public llama_model_base {
llama_model_nanbeige(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
int n_loops = 1;
int n_layer_phys = 0;
bool skip_loop_final_norm = false;
struct graph : public llm_graph_context {
graph(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_qwen : public llama_model_base {
llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
@@ -1068,6 +1084,10 @@ struct llama_model_deepseek2 : public llama_model_base {
graph(const llama_model & model, const llm_graph_params & params);
};
struct graph_mtp : public llm_graph_context {
graph_mtp(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
@@ -1081,6 +1101,10 @@ struct llama_model_deepseek32 : public llama_model_base {
graph(const llama_model & model, const llm_graph_params & params);
};
struct graph_mtp : public llm_graph_context {
graph_mtp(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
@@ -1091,6 +1115,7 @@ struct llama_model_deepseek4 : public llama_model_base {
void load_arch_tensors(llama_model_loader & ml) override;
struct graph : public llm_graph_context {
graph(const llm_graph_params & params) : llm_graph_context(params) {}
graph(const llama_model & model, const llm_graph_params & params);
ggml_tensor * build_hc_pre(
@@ -1122,6 +1147,21 @@ struct llama_model_deepseek4 : public llama_model_base {
ggml_tensor * inp_pos,
int il) const;
ggml_tensor * build_attention(
const llama_model & model,
llm_graph_input_attn_k_iswa * inp_mtp,
ggml_tensor * cur,
ggml_tensor * inp_pos,
int il) const;
ggml_tensor * build_attention_impl(
const llama_model & model,
llm_graph_input_dsv4 * inp_dsv4,
llm_graph_input_attn_k_iswa * inp_mtp,
ggml_tensor * cur,
ggml_tensor * inp_pos,
int il) const;
ggml_tensor * build_hca_compressed_kv_from_state(
ggml_tensor * kv_state,
ggml_tensor * score_state,
@@ -1187,15 +1227,20 @@ struct llama_model_deepseek4 : public llama_model_base {
float kq_scale,
int il) const;
ggml_tensor * build_hc_weighted_sum(
ggml_tensor * build_hc_pre(
ggml_tensor * x,
ggml_tensor * weights) const;
ggml_tensor * weights,
int il) const;
ggml_tensor * build_hc_sinkhorn(
ggml_tensor * comb,
int il) const;
};
struct graph_mtp : public graph {
graph_mtp(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
@@ -1216,7 +1261,13 @@ struct llama_model_glm_dsa : public llama_model_base {
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
using graph = llama_model_deepseek2::graph;
struct graph : public llm_graph_context {
graph(const llama_model & model, const llm_graph_params & params);
};
struct graph_mtp : public llm_graph_context {
graph_mtp(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
@@ -1249,6 +1300,10 @@ struct llama_model_dflash : public llama_model_base {
ggml_tensor * build_inp_embd_enc() const;
};
struct graph_dsv4 : public llama_model_deepseek4::graph {
graph_dsv4(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
@@ -1680,6 +1735,19 @@ struct llama_model_afmoe : public llama_model_base {
};
struct llama_model_laguna : public llama_model_base {
llama_model_laguna(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
struct graph : public llm_graph_context {
graph(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_ernie4_5 : public llama_model_base {
llama_model_ernie4_5(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
@@ -1729,6 +1797,22 @@ struct llama_model_hunyuan_moe : public llama_model_base {
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_hy_v3 : public llama_model_base {
llama_model_hy_v3(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
struct graph : public llm_graph_context {
graph(const llama_model & model, const llm_graph_params & params);
};
struct graph_mtp : public llm_graph_context {
graph_mtp(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_hunyuan_vl : public llama_model_base {
llama_model_hunyuan_vl(const struct llama_model_params & params) : llama_model_base(params) {}
@@ -1870,6 +1954,29 @@ struct llama_model_minimax_m2 : public llama_model_base {
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct msa_params {
int blk;
int topk_blocks;
int local;
};
struct llama_model_minimax_m3 : public llama_model_base {
llama_model_minimax_m3(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
msa_params msa_p;
struct graph : public llm_graph_context {
graph(const llama_model & model, const llm_graph_params & params);
ggml_tensor * build_attn_msa_fa(
ggml_tensor * q_cur, // [D, HQ, S] f32
ggml_tensor * k, // [D, n_keys, 1, C] C = HKV or HKV*n_stream
ggml_tensor * v, // [D, n_keys, 1, C]
ggml_tensor * mask, // [n_keys, R, 1, C] f16, R = HQ*T/(Gp*C)
int64_t Gp, float kq_scale, int il) const;
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_cogvlm : public llama_model_base {
llama_model_cogvlm(const struct llama_model_params & params) : llama_model_base(params) {}
@@ -1934,6 +2041,10 @@ struct llama_model_qwen3next : public llama_model_base {
const llama_model & model;
};
struct graph_mtp : public llm_graph_context {
graph_mtp(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
@@ -2052,6 +2163,10 @@ struct llama_model_mimo2 : public llama_model_base {
graph(const llama_model & model, const llm_graph_params & params);
};
struct graph_mtp : public llm_graph_context {
graph_mtp(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
examples/talk-llama/models/nanbeige.cpp
+184
View File
@@ -0,0 +1,184 @@
#include "models.h"
void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
uint32_t n_loops_u = 1;
ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false);
GGML_ASSERT(n_loops_u >= 1);
skip_loop_final_norm = false;
ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false);
n_layer_phys = (int) hparams.n_layer();
// Bound-check before casting: signed int mul can overflow and bypass the guard.
GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS);
n_loops = (int) n_loops_u;
// Expand logical layer count before load_tensors() allocates layers / KV.
if (n_loops > 1) {
for (int j = 1; j < n_loops; ++j) {
for (int i = 0; i < n_layer_phys; ++i) {
const int dst = i + j * n_layer_phys;
hparams.n_head_arr[dst] = hparams.n_head_arr[i];
hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i];
hparams.n_ff_arr[dst] = hparams.n_ff_arr[i];
hparams.is_swa_impl[dst] = hparams.is_swa_impl[i];
hparams.is_recr_impl[dst] = hparams.is_recr_impl[i];
}
}
hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops);
}
type = LLM_TYPE_UNKNOWN;
}
void llama_model_nanbeige::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer;
for (int i = 0; i < n_phys; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2},
TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
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}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
// Share physical weights across loops; each slot still has its own KV index.
if (n_loops > 1) {
for (int j = 1; j < n_loops; ++j) {
for (int i = 0; i < n_phys; ++i) {
layers[i + j * n_phys] = layers[i];
}
}
}
}
std::unique_ptr<llm_graph_context> llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params) {
const auto & nb = static_cast<const llama_model_nanbeige &>(model);
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
const int n_phys = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer;
const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1;
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv();
const float kq_scale = hparams.f_attention_scale == 0.0f
? 1.0f / sqrtf(float(n_embd_head))
: hparams.f_attention_scale;
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
{
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head, n_head_kv, il);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, rope_factors,
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, rope_factors,
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);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn,
model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
inpL = cur;
if (n_loops > 1 &&
((il + 1) % n_phys) == 0 &&
(il + 1) < n_layer &&
!nb.skip_loop_final_norm) {
cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "loop_norm", il);
inpL = cur;
}
}
cur = inpL;
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur, model.output_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/openai-moe.cpp
+7 -1
View File
@@ -116,7 +116,7 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_
cb(cur, "attn_out", il);
}
if (il == n_layer - 1) {
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
// skip computing output for unused tokens
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
@@ -154,6 +154,12 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_
}
cur = inpL;
res->t_h_nextn = cur;
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
examples/talk-llama/models/qwen35.cpp
+16 -15
View File
@@ -39,6 +39,7 @@ void llama_model_qwen35::load_arch_tensors(llama_model_loader & ml) {
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
@@ -97,25 +98,25 @@ void llama_model_qwen35::load_arch_tensors(llama_model_loader & ml) {
auto & layer = layers[il];
// MTP block looks like a full-attention Qwen3.5 decoder block.
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, 0);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, mtp_flags);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, mtp_flags);
create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, 0);
create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, mtp_flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, mtp_flags);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, mtp_flags);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, mtp_flags);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", il), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", il), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", il), {n_embd, n_ff}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", il), {n_embd, n_ff}, mtp_flags);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", il), { n_ff, n_embd}, mtp_flags);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", il), {n_embd, n_ff}, mtp_flags);
// NextN-specific tensors that define the MTP block.
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, 0);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, 0);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, 0);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, TENSOR_NOT_REQUIRED);
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, mtp_flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, mtp_flags|TENSOR_NOT_REQUIRED);
};
for (int i = 0; i < n_layer; ++i) {
examples/talk-llama/models/qwen35moe.cpp
+20 -19
View File
@@ -42,6 +42,7 @@ void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
@@ -113,32 +114,32 @@ void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
// MTP block looks like a full-attention Qwen3.5 decoder block with MoE FFN.
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, 0);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, mtp_flags);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, mtp_flags);
create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, 0);
create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, mtp_flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, mtp_flags);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, mtp_flags);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, mtp_flags);
// Routed experts
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, n_expert }, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, 0);
create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, 0);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, n_expert }, mtp_flags);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, mtp_flags);
create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, mtp_flags);
// Shared experts
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, 0);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, 0);
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, mtp_flags);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, mtp_flags);
// NextN-specific tensors that define the MTP block.
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, 0);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, 0);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, 0);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, TENSOR_NOT_REQUIRED);
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, mtp_flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, mtp_flags|TENSOR_NOT_REQUIRED);
};
for (int i = 0; i < n_layer; ++i) {
examples/talk-llama/models/qwen3next.cpp
+276 -48
View File
@@ -13,7 +13,11 @@ void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
// Mark recurrent layers (linear attention layers)
// NextN/MTP: extra decoder block appended beyond the main stack
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
// Mark recurrent layers (linear attention layers).
if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
uint32_t full_attn_interval = 4;
ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
@@ -28,13 +32,17 @@ void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) {
}
}
void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) {
void llama_model_qwen3next::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
if (n_expert == 0) {
throw std::runtime_error(arch_name() + " model cannot have zero experts");
}
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
// output
@@ -61,49 +69,73 @@ void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) {
const int64_t qkvz_dim = key_dim * 2 + value_dim * 2;
const int64_t ba_dim = n_v_heads * 2;
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i);
auto load_block_trunk = [&](int il, int flags) {
auto & layer = layers[il];
const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(il);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, flags);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, flags);
if (!hparams.is_recr(i)) {
if (!hparams.is_recr(il)) {
// Attention layers
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, flags);
// Q/K normalization for attention layers
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, flags);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, flags);
} else {
// Linear attention (gated delta net) specific tensors
// Create tensors with calculated dimensions
// note: ssm_in is used by legacy GGUF
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, qkvz_dim }, TENSOR_NOT_REQUIRED);
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);
layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0);
layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0);
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", il), { n_embd, qkvz_dim }, TENSOR_NOT_REQUIRED | flags);
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", il), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED | flags);
layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", il), { n_embd, value_dim }, TENSOR_NOT_REQUIRED | flags);
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", il), { hparams.ssm_d_conv, conv_dim }, flags);
layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", il), { hparams.ssm_dt_rank }, flags);
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, il), { hparams.ssm_dt_rank }, flags);
layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", il), { n_embd, ba_dim }, flags);
layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", il), { head_v_dim }, flags);
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", il), { value_dim, n_embd }, flags);
}
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, 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);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, n_expert }, flags);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, flags);
create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, flags);
// Shared experts
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, 0);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, 0);
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, flags);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, flags);
};
auto load_block_mtp = [&](int il) {
// MTP head is identical to the trunk block (full attention + FFN)
load_block_trunk(il, mtp_flags);
auto & layer = layers[il];
// NextN-specific tensors that define the MTP block.
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, mtp_flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, mtp_flags | TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, mtp_flags | TENSOR_NOT_REQUIRED);
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, mtp_flags | TENSOR_NOT_REQUIRED);
};
for (int i = 0; i < n_layer; i++) {
load_block_trunk(i, trunk_flags);
}
for (int i = n_layer; i < n_layer_all; i++) {
load_block_mtp(i);
}
}
std::unique_ptr<llm_graph_context> llama_model_qwen3next::build_arch_graph(const llm_graph_params & params) const {
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
return std::make_unique<graph_mtp>(*this, params);
}
return std::make_unique<graph>(*this, params);
}
@@ -120,6 +152,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
// MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
for (int il = 0; il < n_layer; ++il) {
res->t_layer_inp[il] = inpL;
@@ -139,7 +172,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
}
if (il == n_layer - 1 && inp_out_ids) {
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
@@ -171,9 +204,16 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
}
cur = inpL;
// Final norm
// post-norm hidden state is input to both the LM head and the MTP head
cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cb(cur, "result_norm", -1);
res->t_embd = cur;
@@ -186,14 +226,6 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
ggml_build_forward_expand(gf, cur);
}
// utility to get one slice from the third dimension
// input dim: [x, y, c, b]
// output dim: [x, y, 1, b]
static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
}
ggml_tensor * llama_model_qwen3next::graph::build_norm_gated(
ggml_tensor * input,
ggml_tensor * weights,
@@ -216,7 +248,7 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
// Qwen3Next uses a single Q projection that outputs query + gate
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur);
ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
cb(Qcur_full, "Qcur_full", il);
Qcur_full = ggml_reshape_4d(ctx0, Qcur_full, n_embd_head * 2, n_head, n_tokens, 1);
@@ -232,10 +264,10 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], n_embd_head * ggml_element_size(Qcur_full));
cb(gate, "gate", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
@@ -274,8 +306,6 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn(
gate = ggml_sigmoid(ctx0, gate);
cb(gate, "gate_sigmoid", il);
gate = ggml_reshape_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
cur = ggml_mul(ctx0, cur, gate);
cb(cur, "attn_gated", il);
@@ -550,16 +580,19 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_ffn(ggml_tensor * cur, c
LLM_FFN_SILU, true,
hparams.expert_weights_scale,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
nullptr, model.layers[il].ffn_gate_up_exps);
nullptr, model.layers[il].ffn_gate_up_exps,
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
model.layers[il].ffn_down_exps_s);
cb(moe_out, "ffn_moe_out", il);
// Add shared experts if present - following Qwen3Next reference implementation
if (model.layers[il].ffn_up_shexp != nullptr) {
ggml_tensor * ffn_shexp =
build_ffn(cur,
model.layers[il].ffn_up_shexp, NULL, NULL,
model.layers[il].ffn_gate_shexp, NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,
model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
@@ -593,3 +626,198 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_ffn(ggml_tensor * cur, c
}
return cur;
}
// LLM_GRAPH_TYPE_DECODER_MTP draft head for Qwen3-Next
llama_model_qwen3next::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
: llm_graph_context(params) {
GGML_ASSERT(hparams.n_layer_nextn > 0 && "QWEN3NEXT MTP requires n_layer_nextn > 0");
GGML_ASSERT(hparams.n_layer_nextn == 1 && "QWEN3NEXT MTP currently only supports a single MTP block");
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
const int il = hparams.n_layer();
const auto & layer = model.layers[il];
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
// TODO: extract in a common llm_graph_context::build_inp_embd_h()
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
ggml_set_input(inp->tokens);
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
ggml_set_input(inp->embd);
// TODO: make static using `ggml_build_forward_select()`
// see llm_graph_context::build_inp_embd() for reference
ggml_tensor * tok_embd;
if (ubatch.token) {
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
} else {
tok_embd = inp->embd;
}
cb(tok_embd, "mtp_tok_embd", il);
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
ggml_set_input(inp->h);
ggml_set_name(inp->h, "mtp_h_input");
ggml_tensor * h_embd = inp->h;
res->add_input(std::move(inp));
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
auto * inp_attn = build_attn_inp_kv();
ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
cb(h_norm, "mtp_hnorm", il);
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
cb(e_norm, "mtp_enorm", il);
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
cb(concat, "mtp_concat", il);
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
cb(cur, "mtp_eh_proj", il);
ggml_tensor * inpSA = cur;
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_norm", il);
ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);
cb(Qcur_full, "mtp_Qcur_full", il);
ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,
n_embd_head, n_head, n_tokens,
ggml_element_size(Qcur_full) * n_embd_head * 2,
ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
0);
Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "mtp_Qcur_normed", il);
ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "mtp_Kcur_normed", il);
ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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, "mtp_Qcur", il);
cb(Kcur, "mtp_Kcur", il);
cb(Vcur, "mtp_Vcur", il);
const float kq_scale = hparams.f_attention_scale == 0.0f
? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
cur = build_attn(inp_attn,
nullptr, nullptr, nullptr,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "mtp_attn_pregate", il);
ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full,
n_embd_head, n_head, n_tokens,
ggml_element_size(Qcur_full) * n_embd_head * 2,
ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
ggml_element_size(Qcur_full) * n_embd_head);
// TODO: CUDA is missing non-contiguous unary ops. when implemented: remove this cont
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
cb(gate, "mtp_gate", il);
cur = ggml_mul(ctx0, cur, ggml_sigmoid(ctx0, gate));
cur = build_lora_mm(layer.wo, cur, layer.wo_s);
cb(cur, "mtp_attn_out", il);
if (inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
cur = ggml_add(ctx0, cur, inpSA);
cb(cur, "mtp_attn_residual", il);
ggml_tensor * ffn_residual = cur;
cur = build_norm(cur, layer.attn_post_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "mtp_attn_post_norm", il);
// MoE FFN — routed experts plus gated shared expert (mirrors the trunk).
ggml_tensor * moe_out =
build_moe_ffn(cur,
layer.ffn_gate_inp,
layer.ffn_up_exps,
layer.ffn_gate_exps,
layer.ffn_down_exps,
nullptr,
n_expert, n_expert_used,
LLM_FFN_SILU, true,
hparams.expert_weights_scale,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
nullptr, layer.ffn_gate_up_exps,
layer.ffn_up_exps_s,
layer.ffn_gate_exps_s,
layer.ffn_down_exps_s);
cb(moe_out, "mtp_ffn_moe_out", il);
if (layer.ffn_up_shexp != nullptr) {
ggml_tensor * ffn_shexp =
build_ffn(cur,
layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s,
layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
nullptr,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "mtp_ffn_shexp", il);
ggml_tensor * shared_gate = build_lora_mm(layer.ffn_gate_inp_shexp, cur);
shared_gate = ggml_sigmoid(ctx0, shared_gate);
cb(shared_gate, "mtp_shared_expert_gate_sigmoid", il);
ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
cb(ffn_shexp, "mtp_ffn_shexp_gated", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
} else {
cur = moe_out;
}
cb(cur, "mtp_ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_residual);
cb(cur, "mtp_post_ffn", il);
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
? layer.nextn.shared_head_norm
: model.output_norm;
GGML_ASSERT(head_norm_w && "QWEN3NEXT MTP: missing both nextn.shared_head_norm and output_norm");
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
cb(cur, "h_nextn", -1);
res->t_h_nextn = cur;
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
GGML_ASSERT(head_w && "QWEN3NEXT MTP: missing LM head (nextn.shared_head_head or model.output)");
cur = build_lora_mm(head_w, cur, head_s);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
examples/talk-llama/models/step35.cpp
+5 -1
View File
@@ -48,7 +48,11 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
if (!ml.load_mtp) {
mtp_flags |= TENSOR_SKIP;
}
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);