{ LLM_ARCH_QWEN3NEXT, "qwen3next" },
{ LLM_ARCH_QWEN3VL, "qwen3vl" },
{ LLM_ARCH_QWEN3VLMOE, "qwen3vlmoe" },
+ { LLM_ARCH_QWEN35, "qwen35" },
+ { LLM_ARCH_QWEN35MOE, "qwen35moe" },
{ LLM_ARCH_PHI2, "phi2" },
{ LLM_ARCH_PHI3, "phi3" },
{ LLM_ARCH_PHIMOE, "phimoe" },
{ LLM_ARCH_CHATGLM, "chatglm" },
{ LLM_ARCH_GLM4, "glm4" },
{ LLM_ARCH_GLM4_MOE, "glm4moe" },
+ { LLM_ARCH_GLM_DSA, "glm-dsa" },
{ LLM_ARCH_BITNET, "bitnet" },
{ LLM_ARCH_T5, "t5" },
{ LLM_ARCH_T5ENCODER, "t5encoder" },
{ LLM_KV_EMBEDDING_SCALE, "%s.embedding_scale" },
{ 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_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
{ LLM_KV_ATTENTION_TEMPERATURE_SCALE, "%s.attention.temperature_scale" },
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
+ { 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_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" },
{ LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
{ LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
{ LLM_TENSOR_SSM_BETA_ALPHA, "blk.%d.ssm_ba" },
+ { LLM_TENSOR_SSM_ALPHA, "blk.%d.ssm_alpha" },
{ LLM_TENSOR_SSM_IN, "blk.%d.ssm_in" },
{ LLM_TENSOR_SSM_NORM, "blk.%d.ssm_norm" },
{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
{ LLM_TENSOR_VISEXP_FFN_GATE, "blk.%d.vis_gate" },
{ LLM_TENSOR_VISEXP_FFN_DOWN, "blk.%d.vis_down" },
{ LLM_TENSOR_VISEXP_FFN_UP, "blk.%d.vis_up" },
+ { LLM_TENSOR_INDEXER_K_NORM, "blk.%d.indexer.k_norm" },
+ { 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" },
};
static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_GATE,
- LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS,
LLM_TENSOR_SSM_NORM,
LLM_TENSOR_SSM_OUT,
};
+ case LLM_ARCH_QWEN35:
+ return {
+ LLM_TENSOR_TOKEN_EMBD,
+ LLM_TENSOR_OUTPUT_NORM,
+ LLM_TENSOR_OUTPUT,
+ LLM_TENSOR_ATTN_NORM,
+ LLM_TENSOR_ATTN_POST_NORM,
+ LLM_TENSOR_ATTN_Q,
+ LLM_TENSOR_ATTN_Q_NORM,
+ LLM_TENSOR_ATTN_K,
+ LLM_TENSOR_ATTN_K_NORM,
+ LLM_TENSOR_ATTN_V,
+ LLM_TENSOR_ATTN_OUT,
+ LLM_TENSOR_ATTN_QKV,
+ LLM_TENSOR_ATTN_GATE,
+ LLM_TENSOR_FFN_GATE,
+ LLM_TENSOR_FFN_DOWN,
+ LLM_TENSOR_FFN_UP,
+ LLM_TENSOR_SSM_A_NOSCAN,
+ LLM_TENSOR_SSM_CONV1D,
+ LLM_TENSOR_SSM_DT,
+ LLM_TENSOR_SSM_BETA,
+ LLM_TENSOR_SSM_ALPHA,
+ LLM_TENSOR_SSM_NORM,
+ LLM_TENSOR_SSM_OUT,
+ };
+ case LLM_ARCH_QWEN35MOE:
+ return {
+ LLM_TENSOR_TOKEN_EMBD,
+ LLM_TENSOR_OUTPUT_NORM,
+ LLM_TENSOR_OUTPUT,
+ LLM_TENSOR_ATTN_NORM,
+ LLM_TENSOR_ATTN_POST_NORM,
+ LLM_TENSOR_ATTN_Q,
+ LLM_TENSOR_ATTN_Q_NORM,
+ LLM_TENSOR_ATTN_K,
+ LLM_TENSOR_ATTN_K_NORM,
+ LLM_TENSOR_ATTN_V,
+ LLM_TENSOR_ATTN_OUT,
+ LLM_TENSOR_ATTN_QKV,
+ LLM_TENSOR_ATTN_GATE,
+ LLM_TENSOR_FFN_GATE_INP,
+ LLM_TENSOR_FFN_GATE_EXPS,
+ LLM_TENSOR_FFN_DOWN_EXPS,
+ LLM_TENSOR_FFN_UP_EXPS,
+ LLM_TENSOR_FFN_GATE_INP_SHEXP,
+ LLM_TENSOR_FFN_GATE_SHEXP,
+ LLM_TENSOR_FFN_DOWN_SHEXP,
+ LLM_TENSOR_FFN_UP_SHEXP,
+ LLM_TENSOR_SSM_A_NOSCAN,
+ LLM_TENSOR_SSM_CONV1D,
+ LLM_TENSOR_SSM_DT,
+ LLM_TENSOR_SSM_BETA,
+ LLM_TENSOR_SSM_ALPHA,
+ LLM_TENSOR_SSM_NORM,
+ LLM_TENSOR_SSM_OUT,
+ };
case LLM_ARCH_QWEN3VL:
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_HUNYUAN_DENSE:
LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
};
+ case LLM_ARCH_GLM_DSA:
+ return {
+ LLM_TENSOR_TOKEN_EMBD,
+ LLM_TENSOR_OUTPUT_NORM,
+ LLM_TENSOR_OUTPUT,
+ LLM_TENSOR_ATTN_NORM,
+ LLM_TENSOR_ATTN_Q_A_NORM,
+ LLM_TENSOR_ATTN_KV_A_NORM,
+ LLM_TENSOR_ATTN_Q,
+ LLM_TENSOR_ATTN_Q_A,
+ LLM_TENSOR_ATTN_Q_B,
+ LLM_TENSOR_ATTN_KV_A_MQA,
+ LLM_TENSOR_ATTN_KV_B,
+ LLM_TENSOR_ATTN_K_B,
+ LLM_TENSOR_ATTN_V_B,
+ LLM_TENSOR_ATTN_OUT,
+ LLM_TENSOR_FFN_NORM,
+ LLM_TENSOR_FFN_GATE,
+ LLM_TENSOR_FFN_UP,
+ LLM_TENSOR_FFN_DOWN,
+ LLM_TENSOR_FFN_GATE_INP,
+ LLM_TENSOR_FFN_GATE_EXPS,
+ LLM_TENSOR_FFN_DOWN_EXPS,
+ LLM_TENSOR_FFN_UP_EXPS,
+ LLM_TENSOR_FFN_GATE_INP_SHEXP,
+ LLM_TENSOR_FFN_GATE_SHEXP,
+ LLM_TENSOR_FFN_DOWN_SHEXP,
+ LLM_TENSOR_FFN_UP_SHEXP,
+ LLM_TENSOR_FFN_EXP_PROBS_B,
+ LLM_TENSOR_INDEXER_K_NORM,
+ LLM_TENSOR_INDEXER_PROJ,
+ LLM_TENSOR_INDEXER_ATTN_K,
+ LLM_TENSOR_INDEXER_ATTN_Q_B,
+ LLM_TENSOR_NEXTN_EH_PROJ,
+ LLM_TENSOR_NEXTN_EMBED_TOKENS,
+ LLM_TENSOR_NEXTN_ENORM,
+ LLM_TENSOR_NEXTN_HNORM,
+ LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
+ LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
+ };
case LLM_ARCH_BITNET:
return {
LLM_TENSOR_TOKEN_EMBD,
{LLM_TENSOR_SSM_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_SSM_DT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_SSM_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_SSM_ALPHA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_SSM_BETA_ALPHA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_TIME_MIX_W1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_TIME_MIX_W2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_VISEXP_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_VISEXP_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_VISEXP_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_INDEXER_K_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+ {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}},
// NextN/MTP tensors are currently ignored (reserved for future MTP support)
// These tensors only exist in the last layer(s) and are treated as output tensors
{LLM_TENSOR_NEXTN_EH_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
case LLM_ARCH_NEMOTRON_H_MOE:
case LLM_ARCH_QWEN3NEXT:
case LLM_ARCH_KIMI_LINEAR:
+ case LLM_ARCH_QWEN35:
+ case LLM_ARCH_QWEN35MOE:
return true;
default:
return false;
LLM_ARCH_QWEN3NEXT,
LLM_ARCH_QWEN3VL,
LLM_ARCH_QWEN3VLMOE,
+ LLM_ARCH_QWEN35,
+ LLM_ARCH_QWEN35MOE,
LLM_ARCH_PHI2,
LLM_ARCH_PHI3,
LLM_ARCH_PHIMOE,
LLM_ARCH_CHATGLM,
LLM_ARCH_GLM4,
LLM_ARCH_GLM4_MOE,
+ LLM_ARCH_GLM_DSA,
LLM_ARCH_BITNET,
LLM_ARCH_T5,
LLM_ARCH_T5ENCODER,
LLM_KV_EMBEDDING_SCALE,
LLM_KV_TOKEN_SHIFT_COUNT,
LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
+ LLM_KV_FULL_ATTENTION_INTERVAL,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
LLM_KV_ATTENTION_TEMPERATURE_SCALE,
LLM_KV_ATTENTION_KEY_LENGTH_MLA,
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
+ LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
+ LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
+ LLM_KV_ATTENTION_INDEXER_TOP_K,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_DIMENSION_SECTIONS,
LLM_TENSOR_SSM_NORM,
LLM_TENSOR_SSM_OUT,
LLM_TENSOR_SSM_BETA_ALPHA, // qwen3next
+ LLM_TENSOR_SSM_ALPHA, // qwen3.5
// Kimi Linear KDA (using SSM_ prefix for consistency)
LLM_TENSOR_SSM_CONV1D_Q, // kimi: Q conv1d weight
LLM_TENSOR_SSM_CONV1D_K, // kimi: K conv1d weight
LLM_TENSOR_SSM_CONV1D_V, // kimi: V conv1d weight
LLM_TENSOR_SSM_F_A, // kimi: forget gate projection A
LLM_TENSOR_SSM_F_B, // kimi: forget gate projection B
- LLM_TENSOR_SSM_BETA, // kimi: beta mixing coefficient
+ LLM_TENSOR_SSM_BETA, // kimi: beta mixing coefficient and qwen3.5
LLM_TENSOR_SSM_G_A, // kimi: output gate projection A
LLM_TENSOR_SSM_G_B, // kimi: output gate projection B
LLM_TENSOR_TIME_MIX_W0,
LLM_TENSOR_VISEXP_FFN_GATE,
LLM_TENSOR_VISEXP_FFN_DOWN,
LLM_TENSOR_VISEXP_FFN_UP,
+ LLM_TENSOR_INDEXER_K_NORM,
+ LLM_TENSOR_INDEXER_PROJ,
+ LLM_TENSOR_INDEXER_ATTN_K,
+ LLM_TENSOR_INDEXER_ATTN_Q_B,
LLM_TENSOR_NEXTN_EH_PROJ,
LLM_TENSOR_NEXTN_EMBED_TOKENS,
LLM_TENSOR_NEXTN_ENORM,
float * llama_context::get_logits() {
output_reorder();
- return logits;
+ return logits.data;
}
int64_t llama_context::output_resolve_row(int32_t i) const {
output_reorder();
try {
- if (logits == nullptr) {
+ if (logits.data == nullptr) {
throw std::runtime_error("no logits");
}
throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
}
- return logits + j*model.vocab.n_tokens();
+ return logits.data + j*model.vocab.n_tokens();
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
float * llama_context::get_embeddings() {
output_reorder();
- return embd;
+ return embd.data;
}
llama_token * llama_context::get_sampled_tokens() const{
- return sampling.sampled;
+ return sampling.sampled.data;
}
float * llama_context::get_embeddings_ith(int32_t i) {
output_reorder();
try {
- if (embd == nullptr) {
+ if (embd.data == nullptr) {
throw std::runtime_error("no embeddings");
}
}
const uint32_t n_embd_out = model.hparams.n_embd_out();
- return embd + j*n_embd_out;
+ return embd.data + j*n_embd_out;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
llama_token llama_context::get_sampled_token_ith(int32_t idx) {
output_reorder();
- if (sampling.sampled == nullptr) {
+ if (!sampling.sampled.has_data()) {
return LLAMA_TOKEN_NULL;
}
try {
const int64_t row = output_resolve_row(idx);
- GGML_ASSERT(row < (int64_t) sampling.sampled_size);
- return sampling.sampled[row];
+ GGML_ASSERT(row < (int64_t) sampling.sampled.size);
+ return sampling.sampled.data[row];
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled token id %d, reason: %s\n", __func__, idx, err.what());
return LLAMA_TOKEN_NULL;
float * llama_context::get_sampled_probs_ith(int32_t idx) {
output_reorder();
- if (sampling.probs == nullptr) {
+ if (!sampling.probs.has_data()) {
return nullptr;
}
if ((size_t) row >= sampling.probs_count.size() || sampling.probs_count[row] == 0) {
return nullptr;
}
- return sampling.probs + row*model.vocab.n_tokens();
+ return sampling.probs.data + row*model.vocab.n_tokens();
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled probs id %d, reason: %s\n", __func__, idx, err.what());
return nullptr;
float * llama_context::get_sampled_logits_ith(int32_t idx) {
output_reorder();
- if (sampling.logits == nullptr) {
+ if (!sampling.logits.has_data()) {
return nullptr;
}
if ((size_t) row >= sampling.logits_count.size() || sampling.logits_count[row] == 0) {
return nullptr;
}
- return sampling.logits + row*model.vocab.n_tokens();
+ return sampling.logits.data + row*model.vocab.n_tokens();
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled logits id %d, reason: %s\n", __func__, idx, err.what());
return nullptr;
try {
const int64_t row = output_resolve_row(idx);
- if (sampling.candidates != nullptr &&
+ if (sampling.candidates.has_data() &&
(size_t) row < sampling.candidates_count.size() &&
sampling.candidates_count[row] > 0) {
- return sampling.candidates + row*model.vocab.n_tokens();
+ return sampling.candidates.data + row*model.vocab.n_tokens();
}
} catch (const std::exception & err) {
// fallback to full vocab list
+ GGML_UNUSED(err);
}
return sampling.token_ids_full_vocab.data();
size_t llama_context::get_sampled_candidates_count(int32_t idx) {
output_reorder();
- if (sampling.candidates == nullptr) {
+ if (!sampling.candidates.has_data()) {
return 0;
}
size_t llama_context::get_sampled_logits_count(int32_t idx) {
output_reorder();
- if (sampling.logits == nullptr) {
+ if (!sampling.logits.has_data()) {
return model.vocab.n_tokens();
}
size_t llama_context::get_sampled_probs_count(int32_t idx) {
output_reorder();
- if (sampling.probs == nullptr) {
+ if (!sampling.probs.has_data()) {
return 0;
}
return true;
}
-void llama_context::set_adapter_lora(
- llama_adapter_lora * adapter,
- float scale) {
- LLAMA_LOG_DEBUG("%s: adapter = %p, scale = %f\n", __func__, (void *) adapter, scale);
+void llama_context::set_adapters_lora(llama_adapter_lora ** adapters, size_t n_adapters, float * scales) {
+ LLAMA_LOG_DEBUG("%s: adapters = %p\n", __func__, (void *) adapters);
- if (auto it = loras.find(adapter); it != loras.end()) {
- if (it->second == scale) {
- return;
- }
+ if (adapters_lora_are_same(adapters, n_adapters, scales)) {
+ return;
}
- loras[adapter] = scale;
+ loras.clear();
+
+ for (size_t i = 0; i < n_adapters; i ++) {
+ if (scales[i] != 0.0f) {
+ loras[adapters[i]] = scales[i];
+ }
+ }
sched_need_reserve = true;
}
-bool llama_context::rm_adapter_lora(
- llama_adapter_lora * adapter) {
- LLAMA_LOG_DEBUG("%s: adapter = %p\n", __func__, (void *) adapter);
-
- auto it = loras.find(adapter);
- if (it != loras.end()) {
- loras.erase(it);
-
- sched_need_reserve = true;
+bool llama_context::adapters_lora_are_same(llama_adapter_lora ** adapters, size_t n_adapters, float * scales) {
+ LLAMA_LOG_DEBUG("%s: adapters = %p\n", __func__, (void *) adapters);
- return true;
+ if (n_adapters != loras.size()) {
+ return false;
}
- return false;
-}
-
-void llama_context::clear_adapter_lora() {
- LLAMA_LOG_DEBUG("%s: call\n", __func__);
+ for (size_t i = 0; i < n_adapters; i ++) {
+ auto it = loras.find(adapters[i]);
- if (loras.empty()) {
- return;
+ if (it == loras.end() || it->second != scales[i]) {
+ return false;
+ }
}
- loras.clear();
-
- sched_need_reserve = true;
+ return true;
}
-bool llama_context::apply_adapter_cvec(
+bool llama_context::set_adapter_cvec(
const float * data,
size_t len,
int32_t n_embd,
auto * t_embd = res->get_embd_pooled() ? res->get_embd_pooled() : res->get_embd();
// extract logits
- if (logits && t_logits) {
+ if (logits.data && t_logits) {
ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
GGML_ASSERT(backend_res != nullptr);
- GGML_ASSERT(logits != nullptr);
+ GGML_ASSERT(logits.data != nullptr);
- ggml_backend_tensor_get_async(backend_res, t_logits, logits, 0, n_tokens*n_vocab*sizeof(float));
+ ggml_backend_tensor_get_async(backend_res, t_logits, logits.data, 0, n_tokens*n_vocab*sizeof(float));
}
// extract embeddings
- if (embd && t_embd) {
+ if (embd.data && t_embd) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
GGML_ASSERT(backend_embd != nullptr);
case LLAMA_POOLING_TYPE_NONE:
{
// extract token embeddings
- GGML_ASSERT(embd != nullptr);
+ GGML_ASSERT(embd.data != nullptr);
const uint32_t n_embd_out = hparams.n_embd_out();
- GGML_ASSERT(n_tokens*n_embd_out <= (int64_t) embd_size);
- ggml_backend_tensor_get_async(backend_embd, t_embd, embd, 0, n_tokens*n_embd_out*sizeof(float));
+ GGML_ASSERT(n_tokens*n_embd_out <= (int64_t) embd.size);
+ ggml_backend_tensor_get_async(backend_embd, t_embd, embd.data, 0, n_tokens*n_embd_out*sizeof(float));
} break;
case LLAMA_POOLING_TYPE_MEAN:
case LLAMA_POOLING_TYPE_CLS:
cross.n_embd = t_embd->ne[0];
cross.n_enc = t_embd->ne[1];
cross.v_embd.resize(cross.n_embd*cross.n_enc);
- memcpy(cross.v_embd.data(), embd, ggml_nbytes(t_embd));
+ memcpy(cross.v_embd.data(), embd.data, ggml_nbytes(t_embd));
const auto & batch = balloc->get_batch();
static void copy_tensor_async_ints(
const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
- llama_token * sampled,
- size_t sampled_size,
+ const buffer_view<llama_token> & sampled,
const std::map<llama_seq_id, uint32_t> & seq_to_row,
ggml_backend_sched_t sched) {
- if (sampled == nullptr) {
+ if (!sampled.has_data()) {
return;
}
}
const uint32_t row = it->second;
- GGML_ASSERT(row < sampled_size);
+ GGML_ASSERT(row < sampled.size);
GGML_ASSERT(ggml_is_contiguous(tensor) && "sampled tokens tensor must be contiguous for async copy");
ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
- ggml_backend_tensor_get_async(backend, tensor, sampled + row, 0, sizeof(sampled[row]));
+ ggml_backend_tensor_get_async(backend, tensor, sampled.data + row, 0, sizeof(sampled.data[row]));
}
}
static void copy_tensor_async_floats(
const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
- float * dst,
+ const buffer_view<float> & dst,
size_t stride,
std::vector<uint32_t> & counts,
const std::map<llama_seq_id, uint32_t> & seq_to_row,
ggml_backend_sched_t sched) {
- if (dst == nullptr) {
+ if (!dst.has_data()) {
return;
}
GGML_ASSERT(ggml_is_contiguous(tensor) && "logits/probs tensor must be contiguous for async copy");
ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
- float * row_ptr = dst + (size_t) row * stride;
+ float * row_ptr = dst.data + (size_t) row * stride;
ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
// Update the actual number of logits/probabilities that were written for this row.
static void copy_tensor_async_candidates(
const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
- llama_token * dst,
+ const buffer_view<llama_token> & dst,
size_t stride,
std::vector<uint32_t> & counts,
const std::map<llama_seq_id, uint32_t> & seq_to_row,
ggml_backend_sched_t sched) {
- if (dst == nullptr) {
+ if (!dst.has_data()) {
return;
}
GGML_ASSERT(ggml_is_contiguous(tensor) && "candidates tensor must be contiguous for async copy");
ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
- llama_token * row_ptr = dst + (size_t) row * stride;
+ llama_token * row_ptr = dst.data + (size_t) row * stride;
ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
// Update the actual number of candidates that were written.
}
// extract logits
- if (logits && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) {
+ if (logits.data && t_logits && n_outputs > 0 && needs_raw_logits(ubatch, sampling.samplers)) {
ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
GGML_ASSERT(backend_res != nullptr);
- GGML_ASSERT(logits != nullptr);
+ GGML_ASSERT(logits.data != nullptr);
- float * logits_out = logits + n_outputs_prev*n_vocab;
+ float * logits_out = logits.data + n_outputs_prev*n_vocab;
if (n_outputs) {
GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
- GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
+ GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits.size);
ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
}
}
// extract embeddings
- if (embd && t_embd && n_outputs > 0) {
+ if (embd.data && t_embd && n_outputs > 0) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
GGML_ASSERT(backend_embd != nullptr);
case LLAMA_POOLING_TYPE_NONE:
{
// extract token embeddings
- GGML_ASSERT(embd != nullptr);
+ GGML_ASSERT(embd.data != nullptr);
const uint32_t n_embd_out = hparams.n_embd_out();
- float * embd_out = embd + n_outputs_prev*n_embd_out;
+ float * embd_out = embd.data + n_outputs_prev*n_embd_out;
if (n_outputs) {
GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
- GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd_out <= (int64_t) embd_size);
+ GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd_out <= (int64_t) embd.size);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd_out*sizeof(float));
}
} break;
const auto stride = n_vocab;
// async copy the sampling data from the backend to the host
- copy_tensor_async_ints(res->t_sampled, sampling.sampled, sampling.sampled_size, seq_to_output_row, sched.get());
+ copy_tensor_async_ints(res->t_sampled, sampling.sampled, seq_to_output_row, sched.get());
copy_tensor_async_floats (res->t_sampled_logits, sampling.logits, stride, sampling.logits_count, seq_to_output_row, sched.get());
copy_tensor_async_floats (res->t_sampled_probs, sampling.probs, stride, sampling.probs_count, seq_to_output_row, sched.get());
//
uint32_t llama_context::output_reserve(int32_t n_outputs) {
-
const auto & hparams = model.hparams;
const auto & vocab = model.vocab;
size_t backend_float_count = 0;
size_t backend_token_count = 0;
- logits_size = has_logits ? n_vocab*n_outputs_max : 0;
- embd_size = has_embd ? n_embd_out*n_outputs_max : 0;
+ logits.size = has_logits ? n_vocab*n_outputs_max : 0;
+ embd.size = has_embd ? n_embd_out*n_outputs_max : 0;
// Allocate backend sampling output buffers if there are backend samplers configured.
const bool has_sampling = !sampling.samplers.empty();
if (has_sampling) {
- sampling.logits_size = n_vocab*n_outputs_max;
- sampling.probs_size = n_vocab*n_outputs_max;
- sampling.sampled_size = n_outputs_max;
- sampling.candidates_size = n_vocab*n_outputs_max;
-
- backend_float_count = sampling.logits_size + sampling.probs_size;
- backend_token_count = sampling.sampled_size + sampling.candidates_size;
+ backend_float_count = 2 * n_vocab * n_outputs_max; // logits + probs
+ backend_token_count = (1 + n_vocab) * n_outputs_max; // sampled + candidates
}
if (output_ids.empty()) {
const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
const size_t new_size =
- (logits_size + embd_size + backend_float_count) * sizeof(float) +
+ (logits.size + embd.size + backend_float_count) * sizeof(float) +
( backend_token_count) * sizeof(llama_token);
// alloc only when more than the current capacity is required
// TODO: not needed?
buf_output = nullptr;
- logits = nullptr;
- embd = nullptr;
+ logits.data = nullptr;
+ embd.data = nullptr;
}
auto * buft = ggml_backend_cpu_buffer_type();
float * output_base = (float *) ggml_backend_buffer_get_base(buf_output.get());
- logits = nullptr;
- embd = nullptr;
-
size_t offset = 0;
uint8_t * base = (uint8_t *) output_base;
- logits = has_logits ? output_base : nullptr;
- offset += logits_size * sizeof(float);
-
- embd = has_embd ? (float *) (base + offset) : nullptr;
- offset += embd_size * sizeof(float);
+ logits = has_logits ? buffer_view<float>{output_base, logits.size} : buffer_view<float>{nullptr, 0};
+ offset += logits.size * sizeof(float);
- sampling.logits = nullptr;
- sampling.probs = nullptr;
- sampling.sampled = nullptr;
- sampling.candidates = nullptr;
+ embd = has_embd ? buffer_view<float>{(float *) (base + offset), embd.size} : buffer_view<float>{nullptr, 0};
+ offset += embd.size * sizeof(float);
if (has_sampling) {
- sampling.logits = (float *) (base + offset);
- offset += sampling.logits_size * sizeof(float);
+ sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+ offset += sampling.logits.size * sizeof(float);
- sampling.probs = (float *) (base + offset);
- offset += sampling.probs_size * sizeof(float);
+ sampling.probs = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+ offset += sampling.probs.size * sizeof(float);
- sampling.sampled = (llama_token *) (base + offset);
- offset += sampling.sampled_size * sizeof(llama_token);
+ sampling.sampled = {(llama_token *) (base + offset), (size_t)n_outputs_max};
+ offset += sampling.sampled.size * sizeof(llama_token);
- sampling.candidates = (llama_token *) (base + offset);
- offset += sampling.candidates_size * sizeof(llama_token);
+ sampling.candidates = {(llama_token *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
+ offset += sampling.candidates.size * sizeof(llama_token);
// The count vectors keep track of the actual number of logits/probs/candidates
// copied from the backend for each output row.
std::fill(sampling.probs_count.begin(), sampling.probs_count.end(), 0);
std::fill(sampling.candidates_count.begin(), sampling.candidates_count.end(), 0);
- std::fill_n(sampling.sampled, sampling.sampled_size, LLAMA_TOKEN_NULL);
+ std::fill_n(sampling.sampled.data, sampling.sampled.size, LLAMA_TOKEN_NULL);
+ } else {
+ sampling.logits = {nullptr, 0};
+ sampling.probs = {nullptr, 0};
+ sampling.sampled = {nullptr, 0};
+ sampling.candidates = {nullptr, 0};
+
+ sampling.logits_count.clear();
+ sampling.probs_count.clear();
+ sampling.candidates_count.clear();
}
// set all ids as invalid (negative)
const uint64_t i0 = output_swaps[s].i0;
const uint64_t i1 = output_swaps[s].i1;
- if (logits_size > 0) {
+ if (logits.size > 0) {
for (uint64_t k = 0; k < n_vocab; k++) {
- std::swap(logits[i0*n_vocab + k], logits[i1*n_vocab + k]);
+ std::swap(logits.data[i0*n_vocab + k], logits.data[i1*n_vocab + k]);
}
}
- if (embd_size > 0) {
+ if (embd.size > 0) {
for (uint64_t k = 0; k < n_embd; k++) {
- std::swap(embd[i0*n_embd + k], embd[i1*n_embd + k]);
+ std::swap(embd.data[i0*n_embd + k], embd.data[i1*n_embd + k]);
}
}
- if (sampling.logits && sampling.logits_size > 0) {
+ if (!sampling.samplers.empty()) {
+ assert(sampling.logits.size > 0);
+ assert(sampling.probs.size > 0);
+ assert(sampling.candidates.size > 0);
+ assert(sampling.sampled.size > 0);
+ assert(sampling.logits_count.size() > 0);
+ assert(sampling.probs_count.size() > 0);
+ assert(sampling.candidates_count.size() > 0);
+
for (uint64_t k = 0; k < n_vocab; ++k) {
- std::swap(sampling.logits[i0*n_vocab + k], sampling.logits[i1*n_vocab + k]);
+ std::swap(sampling.logits.data[i0*n_vocab + k], sampling.logits.data[i1*n_vocab + k]);
}
- }
- if (sampling.probs && sampling.probs_size > 0) {
for (uint64_t k = 0; k < n_vocab; ++k) {
- std::swap(sampling.probs[i0*n_vocab + k], sampling.probs[i1*n_vocab + k]);
+ std::swap(sampling.probs.data[i0*n_vocab + k], sampling.probs.data[i1*n_vocab + k]);
}
- }
- if (sampling.candidates && sampling.candidates_size > 0) {
for (uint64_t k = 0; k < n_vocab; ++k) {
- std::swap(sampling.candidates[i0*n_vocab + k], sampling.candidates[i1*n_vocab + k]);
+ std::swap(sampling.candidates.data[i0*n_vocab + k], sampling.candidates.data[i1*n_vocab + k]);
}
- }
-
- if (sampling.sampled && sampling.sampled_size > 0) {
- std::swap(sampling.sampled[i0], sampling.sampled[i1]);
- }
-
- if (!sampling.logits_count.empty()) {
- std::swap(sampling.logits_count[i0], sampling.logits_count[i1]);
- }
- if (!sampling.probs_count.empty()) {
- std::swap(sampling.probs_count[i0], sampling.probs_count[i1]);
- }
-
- if (!sampling.candidates_count.empty()) {
+ std::swap(sampling.sampled.data[i0], sampling.sampled.data[i1]);
+ std::swap(sampling.logits_count[i0], sampling.logits_count[i1]);
+ std::swap(sampling.probs_count[i0], sampling.probs_count[i1]);
std::swap(sampling.candidates_count[i0], sampling.candidates_count[i1]);
}
}
//
uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
- if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR) {
+ if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE) {
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
}
uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
{
LLAMA_LOG_DEBUG("%s: - writing logits\n", __func__);
- const uint64_t logits_size = std::min((uint64_t) this->logits_size, (uint64_t) n_outputs * model.vocab.n_tokens());
+ const uint64_t logits_size = std::min((uint64_t) this->logits.size, (uint64_t) n_outputs * model.vocab.n_tokens());
io.write(&logits_size, sizeof(logits_size));
if (logits_size) {
- io.write(logits, logits_size * sizeof(float));
+ io.write(logits.data, logits_size * sizeof(float));
}
}
{
LLAMA_LOG_DEBUG("%s: - writing embeddings\n", __func__);
- const uint64_t embd_size = std::min((uint64_t) this->embd_size, (uint64_t) n_outputs * model.hparams.n_embd);
+ const uint64_t embd_size = std::min((uint64_t) this->embd.size, (uint64_t) n_outputs * model.hparams.n_embd);
io.write(&embd_size, sizeof(embd_size));
if (embd_size) {
- io.write(embd, embd_size * sizeof(float));
+ io.write(embd.data, embd_size * sizeof(float));
}
}
uint64_t logits_size;
io.read_to(&logits_size, sizeof(logits_size));
- if (this->logits_size < logits_size) {
+ if (this->logits.size < logits_size) {
throw std::runtime_error("logits buffer too small");
}
if (logits_size) {
- io.read_to(this->logits, logits_size * sizeof(float));
+ io.read_to(this->logits.data, logits_size * sizeof(float));
}
}
uint64_t embd_size;
io.read_to(&embd_size, sizeof(embd_size));
- if (this->embd_size < embd_size) {
+ if (this->embd.size < embd_size) {
throw std::runtime_error("embeddings buffer too small");
}
if (embd_size) {
- io.read_to(this->embd, embd_size * sizeof(float));
+ io.read_to(this->embd.data, embd_size * sizeof(float));
}
}
// llama adapter API
-int32_t llama_set_adapter_lora(
+int32_t llama_set_adapters_lora(
llama_context * ctx,
- llama_adapter_lora * adapter,
- float scale) {
- ctx->set_adapter_lora(adapter, scale);
-
- return 0;
-}
-
-int32_t llama_rm_adapter_lora(
- llama_context * ctx,
- llama_adapter_lora * adapter) {
- bool res = ctx->rm_adapter_lora(adapter);
+ llama_adapter_lora ** adapters,
+ size_t n_adapters,
+ float * scales) {
+ if (adapters == nullptr || scales == nullptr) {
+ GGML_ASSERT(n_adapters == 0 && "invalid llama_set_adapters_lora call");
+ }
- return res ? 0 : -1;
-}
+ ctx->set_adapters_lora(adapters, n_adapters, scales);
-void llama_clear_adapter_lora(llama_context * ctx) {
- ctx->clear_adapter_lora();
+ return 0;
}
-int32_t llama_apply_adapter_cvec(
+int32_t llama_set_adapter_cvec(
llama_context * ctx,
- const float * data,
- size_t len,
- int32_t n_embd,
- int32_t il_start,
- int32_t il_end) {
- bool res = ctx->apply_adapter_cvec(data, len, n_embd, il_start, il_end);
+ const float * data,
+ size_t len,
+ int32_t n_embd,
+ int32_t il_start,
+ int32_t il_end) {
+ bool res = ctx->set_adapter_cvec(data, len, n_embd, il_start, il_end);
return res ? 0 : -1;
}
#include "llama-cparams.h"
#include "llama-graph.h"
#include "llama-adapter.h"
+#include "llama-impl.h"
#include "ggml-cpp.h"
#include "ggml-opt.h"
void set_causal_attn(bool value);
void set_warmup(bool value);
- void set_adapter_lora(
- llama_adapter_lora * adapter,
- float scale);
+ void set_adapters_lora(llama_adapter_lora ** adapters, size_t n_adapters, float * scales);
- bool rm_adapter_lora(
- llama_adapter_lora * adapter);
+ bool adapters_lora_are_same(llama_adapter_lora ** adapters, size_t n_adapters, float * scales);
- void clear_adapter_lora();
-
- bool apply_adapter_cvec(
+ bool set_adapter_cvec(
const float * data,
size_t len,
int32_t n_embd,
std::unique_ptr<llama_memory_i> memory;
// decode output (2-dimensional array: [n_outputs][n_vocab])
- size_t logits_size = 0; // capacity (of floats) for logits
- float * logits = nullptr;
+ buffer_view<float> logits = {nullptr, 0};
// embeddings output (2-dimensional array: [n_outputs][n_embd])
// populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
- size_t embd_size = 0; // capacity (of floats) for embeddings
- float * embd = nullptr;
+ buffer_view<float> embd = {nullptr, 0};
- // TODO: simplify
struct sampling_info {
+ // !samplers.empty() to check if any samplers are active
std::map<llama_seq_id, llama_sampler *> samplers;
- float * logits = nullptr;
- size_t logits_size = 0;
-
- llama_token * sampled = nullptr;
- size_t sampled_size = 0;
-
- float * probs = nullptr;
- size_t probs_size = 0;
-
- llama_token * candidates = nullptr;
- size_t candidates_size = 0;
+ buffer_view<float> logits = {nullptr, 0};
+ buffer_view<llama_token> sampled = {nullptr, 0};
+ buffer_view<float> probs = {nullptr, 0};
+ buffer_view<llama_token> candidates = {nullptr, 0};
std::vector<uint32_t> logits_count;
std::vector<uint32_t> probs_count;
std::vector<uint32_t> candidates_count;
+ // optimization
std::vector<llama_token> token_ids_full_vocab;
};
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
- uint32_t n_embd_features = 0;
uint32_t n_layer;
int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
uint32_t n_rot;
std::array<float, LLAMA_MAX_LAYERS> xielu_beta;
std::array<float, LLAMA_MAX_LAYERS> xielu_eps;
+ // DSA (deepseek sparse attention)
+ uint32_t indexer_n_head = 0;
+ uint32_t indexer_head_size = 0;
+ uint32_t indexer_top_k = 0;
+
// qwen3vl deepstack
uint32_t n_deepstack_layers = 0;
int64_t & t_acc;
};
+template <typename T>
+struct buffer_view {
+ T * data;
+ size_t size = 0;
+
+ bool has_data() const {
+ return data && size > 0;
+ }
+};
+
void replace_all(std::string & s, const std::string & search, const std::string & replace);
// TODO: rename to llama_format ?
}
}
#elif defined(_WIN32)
+ HANDLE hMapping = nullptr;
+
impl(struct llama_file * file, size_t prefetch, bool numa) {
GGML_UNUSED(numa);
HANDLE hFile = (HANDLE) _get_osfhandle(file->file_id());
- HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
+ hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
if (hMapping == NULL) {
DWORD error = GetLastError();
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
DWORD error = GetLastError();
- CloseHandle(hMapping);
if (addr == NULL) {
+ CloseHandle(hMapping);
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
}
}
~impl() {
- if (!UnmapViewOfFile(addr)) {
- LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
- llama_format_win_err(GetLastError()).c_str());
+ if (hMapping) {
+ if (addr) {
+ if (!UnmapViewOfFile(addr)) {
+ LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
+ llama_format_win_err(GetLastError()).c_str());
+ }
+ }
+ if (!CloseHandle(hMapping)) {
+ LLAMA_LOG_WARN("warning: CloseHandle failed: %s\n",
+ llama_format_win_err(GetLastError()).c_str());
+ }
}
}
#else
case LLM_TYPE_21B_A3B: return "21B.A3B";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_31B_A3_5B: return "31B.A3.5B";
+ case LLM_TYPE_35B_A3B: return "35B.A3B";
case LLM_TYPE_48B_A3B: return "48B.A3B";
case LLM_TYPE_80B_A3B: return "80B.A3B";
case LLM_TYPE_100B_A6B: return "100B.A6B";
case LLM_TYPE_300B_A47B: return "300B.A47B";
case LLM_TYPE_310B_A15B: return "310B.A15B";
case LLM_TYPE_355B_A32B: return "355B.A32B";
+ case LLM_TYPE_744B_A40B: return "744B.A40B";
case LLM_TYPE_E2B: return "E2B";
case LLM_TYPE_E4B: return "E4B";
default: return "?B";
ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used, false);
if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
- ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
+ ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd);
+ ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd_out_impl);
ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
ml.get_key(LLM_KV_POSNET_BLOCK_COUNT, hparams.posnet.n_layer);
default: type = LLM_TYPE_UNKNOWN;
}
} break;
+ case LLM_ARCH_GLM_DSA:
+ {
+ ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
+
+ // MoE parameters
+ ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
+ ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
+ ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+ ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
+ ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+ ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
+
+ // deepseek MLA parameters
+ ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+ ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+ ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl, false);
+ ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, 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);
+
+ // DSA parameters
+ 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);
+
+ // Expert gating function (GLM-4.5 uses sigmoid)
+ ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
+ if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+ hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+ }
+
+ // NextN/MTP parameters
+ ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
+
+ // TODO: when MTP is implemented, this should probably be updated if needed
+ hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
+
+ switch (hparams.n_layer) {
+ case 79: type = LLM_TYPE_744B_A40B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_BITNET:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
// Mark recurrent layers (linear attention layers)
- for (uint32_t i = 0; i < hparams.n_layer; ++i) {
- hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0); // TODO: extract the magic 4 from "full_attention_interval"
+ {
+ uint32_t full_attn_interval = 4;
+ ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+ for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+ hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+ }
}
switch (hparams.n_layer) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
+ case LLM_ARCH_QWEN35:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
+
+ // Load linear attention (gated delta net) parameters
+ ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
+ ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
+ ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
+ 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)
+ {
+ uint32_t full_attn_interval = 4;
+ ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+ for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+ hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+ }
+ }
+
+ switch (hparams.n_layer) {
+ case 24: type = LLM_TYPE_2B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_QWEN35MOE:
+ {
+ ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+ ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
+
+ // Load linear attention (gated delta net) parameters
+ ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
+ ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
+ ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
+ 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)
+ {
+ uint32_t full_attn_interval = 4;
+ ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+ for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+ hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
+ }
+ }
+
+ switch (hparams.n_layer) {
+ case 28: type = LLM_TYPE_35B_A3B; break;
+ case 48: type = LLM_TYPE_80B_A3B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_MISTRAL3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
}
}
break;
+ case LLM_ARCH_GLM_DSA:
+ {
+ const bool is_mla = hparams.is_mla();
+ if (!is_mla) {
+ throw std::runtime_error("GLM_DSA architecture requires 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_mla = hparams.n_embd_head_k_mla();
+ const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_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 q_lora_rank = hparams.n_lora_q;
+ const int64_t kv_lora_rank = hparams.n_lora_kv;
+
+ const int64_t n_ff_exp = hparams.n_ff_exp;
+ const int64_t n_expert_shared = hparams.n_expert_shared;
+
+ 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);
+ // try to load output.weight, if not found, use token_embd (tied embeddings)
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+ if (!output) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ int flags = 0;
+ if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+ // 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;
+ }
+
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
+ 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}, flags);
+
+ 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);
+
+ 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
+ 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);
+
+ 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}, flags);
+
+ // DSA indexer
+ layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags);
+ layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags);
+ layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
+ layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags);
+ layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, 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}, 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}, flags);
+ layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+ if (n_expert == 0) {
+ throw std::runtime_error("n_expert must be > 0");
+ }
+ if (n_expert_used == 0) {
+ throw std::runtime_error("n_expert_used must be > 0");
+ }
+
+ // MoE branch
+ 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);
+
+ // 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}, 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 (preserved but unused) - conditionally load for last nextn_predict_layers
+ if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
+ 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);
+
+ // Optional tensors
+ layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+ layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+ layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
+ }
+ }
+ } break;
case LLM_ARCH_NEMOTRON:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
} break;
case LLM_ARCH_WAVTOKENIZER_DEC:
{
- tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd, n_vocab}, 0);
- conv1d = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
+ conv1d = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd, hparams.posnet.n_embd}, 0);
conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"), {1, hparams.posnet.n_embd}, 0);
// posnet
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
}
- output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
- output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, hparams.n_embd_out()}, 0);
+ output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {hparams.n_embd_out()}, 0);
} break;
case LLM_ARCH_BAILINGMOE:
{
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
}
} break;
+ case LLM_ARCH_QWEN35MOE:
+ {
+ 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 }, TENSOR_NOT_REQUIRED);
+
+ // if output is NULL, init from the input tok embed
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+ }
+
+ const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+ // Calculate dimensions from hyperparameters
+ const int64_t head_k_dim = hparams.ssm_d_state;
+ const int64_t head_v_dim = hparams.ssm_d_state;
+ const int64_t n_k_heads = hparams.ssm_n_group;
+ const int64_t n_v_heads = hparams.ssm_dt_rank;
+ const int64_t key_dim = head_k_dim * n_k_heads;
+ const int64_t value_dim = head_v_dim * n_v_heads;
+ const int64_t conv_dim = key_dim * 2 + value_dim;
+
+ 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_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+ if (!hparams.is_recurrent(i)) {
+ // Attention layers
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, 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);
+
+ // 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);
+ } else {
+ // Linear attention (gated delta net) specific tensors
+ // Create tensors with calculated dimensions
+ 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 = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), { n_embd, n_v_heads }, 0);
+ layer.ssm_alpha = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "weight", i), { n_embd, n_v_heads }, 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.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, 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 experts
+ const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+ 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);
+ }
+ } break;
+ case LLM_ARCH_QWEN35:
+ {
+ 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 }, TENSOR_NOT_REQUIRED);
+
+ // if output is NULL, init from the input tok embed
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+ }
+
+ // Calculate dimensions from hyperparameters
+ const int64_t head_k_dim = hparams.ssm_d_state;
+ const int64_t head_v_dim = hparams.ssm_d_state;
+ const int64_t n_k_heads = hparams.ssm_n_group;
+ const int64_t n_v_heads = hparams.ssm_dt_rank;
+ const int64_t key_dim = head_k_dim * n_k_heads;
+ const int64_t value_dim = head_v_dim * n_v_heads;
+ const int64_t conv_dim = key_dim * 2 + value_dim;
+
+ 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_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+ if (!hparams.is_recurrent(i)) {
+ // Attention layers
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, 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);
+
+ // 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);
+ } else {
+ // Linear attention (gated delta net) specific tensors
+ // Create tensors with calculated dimensions
+ 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 = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), { n_embd, n_v_heads }, 0);
+ layer.ssm_alpha = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "weight", i), { n_embd, n_v_heads }, 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.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);
+ }
+ } break;
case LLM_ARCH_MIMO2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
arch == LLM_ARCH_PLAMO2 ||
arch == LLM_ARCH_GRANITE_HYBRID ||
arch == LLM_ARCH_QWEN3NEXT ||
+ arch == LLM_ARCH_QWEN35 ||
+ arch == LLM_ARCH_QWEN35MOE ||
arch == LLM_ARCH_NEMOTRON_H ||
arch == LLM_ARCH_NEMOTRON_H_MOE) {
LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
}
- if (arch == LLM_ARCH_DEEPSEEK2) {
+ if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
cparams.n_seq_max,
nullptr);
} else if (llm_arch_is_hybrid(arch)) {
-
// 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;
/* attn_type_v */ params.type_v,
/* attn_v_trans */ !cparams.flash_attn,
/* attn_swa_full */ params.swa_full,
- /* attn_kv_size */ cparams.n_ctx,
+ /* attn_kv_size */ cparams.n_ctx_seq,
/* attn_n_ubatch */ cparams.n_ubatch,
/* attn_n_pad */ 1,
/* recurrent_type_r */ GGML_TYPE_F32,
/* attn_type_k */ params.type_k,
/* attn_type_v */ params.type_v,
/* attn_v_trans */ !cparams.flash_attn,
- /* attn_kv_size */ cparams.n_ctx,
+ /* attn_kv_size */ cparams.n_ctx_seq,
/* attn_n_pad */ 1,
/* attn_n_swa */ hparams.n_swa,
/* attn_swa_type */ hparams.swa_type,
llm = std::make_unique<llm_build_deepseek>(*this, params);
} break;
case LLM_ARCH_DEEPSEEK2:
+ case LLM_ARCH_GLM_DSA:
{
llm = std::make_unique<llm_build_deepseek2>(*this, params);
} break;
{
llm = std::make_unique<llm_build_qwen3next>(*this, params);
} break;
+ case LLM_ARCH_QWEN35:
+ {
+ llm = std::make_unique<llm_build_qwen35>(*this, params);
+ } break;
+ case LLM_ARCH_QWEN35MOE:
+ {
+ llm = std::make_unique<llm_build_qwen35moe>(*this, params);
+ } break;
case LLM_ARCH_MISTRAL3:
{
llm = std::make_unique<llm_build_mistral3>(*this, params);
case LLM_ARCH_MISTRAL3:
case LLM_ARCH_LLAMA_EMBED:
case LLM_ARCH_MAINCODER:
+ case LLM_ARCH_GLM_DSA:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
return LLAMA_ROPE_TYPE_MROPE;
case LLM_ARCH_QWEN3VL:
case LLM_ARCH_QWEN3VLMOE:
+ case LLM_ARCH_QWEN35:
+ case LLM_ARCH_QWEN35MOE:
return LLAMA_ROPE_TYPE_IMROPE;
case LLM_ARCH_GLM4:
LLM_TYPE_21B_A3B, // Ernie MoE small
LLM_TYPE_30B_A3B,
LLM_TYPE_31B_A3_5B,
+ LLM_TYPE_35B_A3B, // Qwen3.5
LLM_TYPE_48B_A3B, // Kimi Linear
LLM_TYPE_80B_A3B, // Qwen3 Next
LLM_TYPE_100B_A6B,
LLM_TYPE_300B_A47B, // Ernie MoE big
LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
LLM_TYPE_355B_A32B, // GLM-4.5
+ LLM_TYPE_744B_A40B, // GLM-5
LLM_TYPE_E2B,
LLM_TYPE_E4B,
};
// qwen3next
struct ggml_tensor * ssm_beta_alpha = nullptr;
+ // qwen3.5
+ struct ggml_tensor * ssm_alpha = nullptr;
+
// rwkv
struct ggml_tensor * time_mix_w1 = nullptr;
struct ggml_tensor * time_mix_w2 = nullptr;
struct ggml_tensor * ssm_g_b = nullptr;
struct ggml_tensor * ssm_o_norm = nullptr;
+ // DSA (deepseek sparse attention)
+ struct ggml_tensor * indexer_k_norm = nullptr;
+ struct ggml_tensor * indexer_k_norm_b = nullptr;
+ struct ggml_tensor * indexer_proj = nullptr;
+ struct ggml_tensor * indexer_attn_k = nullptr;
+ struct ggml_tensor * indexer_attn_q_b = nullptr; // note: for lora a/b, not bias
+
struct llama_layer_posnet posnet;
struct llama_layer_convnext convnext;
"(?:'[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_QWEN35:
+ regex_exprs = {
+ // original regex from tokenizer.json
+ // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+ "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+ };
+ break;
case LLAMA_VOCAB_PRE_TYPE_PORO:
case LLAMA_VOCAB_PRE_TYPE_BLOOM:
case LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH:
tokenizer_pre == "kormo") {
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
clean_spaces = false;
+ } else if (
+ tokenizer_pre == "qwen35") {
+ pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN35;
+ clean_spaces = false;
} else if (
tokenizer_pre == "stablelm2") {
pre_type = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN = 43,
LLAMA_VOCAB_PRE_TYPE_YOUTU = 44,
LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE = 45,
+ LLAMA_VOCAB_PRE_TYPE_QWEN35 = 46,
};
struct LLM_KV;
enum llama_params_fit_status {
LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, // found allocations that are projected to fit
LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, // could not find allocations that are projected to fit
- LLAMA_PARAMS_FIT_STATUS_ERROR = 2, // a hard error occured, e.g. because no model could be found at the specified path
+ LLAMA_PARAMS_FIT_STATUS_ERROR = 2, // a hard error occurred, e.g. because no model could be found at the specified path
};
// fits mparams and cparams to free device memory (assumes system memory is unlimited)
// The following functions operate on a llama_context, hence the naming: llama_verb_...
- // Add a loaded LoRA adapter to given context
- // This will not modify model's weight
- LLAMA_API int32_t llama_set_adapter_lora(
+ // Set LoRa adapters on the context. Will only modify if the adapters currently in context are different.
+ LLAMA_API int32_t llama_set_adapters_lora(
struct llama_context * ctx,
- struct llama_adapter_lora * adapter,
- float scale);
-
- // Remove a specific LoRA adapter from given context
- // Return -1 if the adapter is not present in the context
- LLAMA_API int32_t llama_rm_adapter_lora(
- struct llama_context * ctx,
- struct llama_adapter_lora * adapter);
-
- // Remove all LoRA adapters from given context
- LLAMA_API void llama_clear_adapter_lora(struct llama_context * ctx);
+ struct llama_adapter_lora ** adapters,
+ size_t n_adapters,
+ float * scales);
// Apply a loaded control vector to a llama_context, or if data is NULL, clear
// the currently loaded vector.
// to an n_embd x n_layers buffer starting from layer 1.
// il_start and il_end are the layer range the vector should apply to (both inclusive)
// See llama_control_vector_load in common to load a control vector.
- LLAMA_API int32_t llama_apply_adapter_cvec(
+ LLAMA_API int32_t llama_set_adapter_cvec(
struct llama_context * ctx,
const float * data,
size_t len,
//
/// Apply chat template. Inspired by hf apply_chat_template() on python.
- /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
+ ///
/// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
- /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
+ /// @param tmpl A Jinja template to use for this chat.
/// @param chat Pointer to a list of multiple llama_chat_message
/// @param n_msg Number of llama_chat_message in this chat
/// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
ggml_tensor * inp_out_ids = build_inp_out_ids();
- for (int il = 0; il < n_layer; ++il) {
+ int effective_n_layers = hparams.n_layer - hparams.nextn_predict_layers;
+ for (int il = 0; il < effective_n_layers; ++il) {
ggml_tensor * inpSA = inpL;
// norm
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
}
}
- if (il == n_layer - 1 && inp_out_ids) {
+ if (il == effective_n_layers - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]);
ggml_build_forward_expand(gf,
ggml_cpy(ctx0, last_conv_x,
- ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs,
- (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all))));
+ ggml_view_3d(ctx0, conv_states_all,
+ d_conv - 1, d_inner, n_seqs,
+ (d_conv - 1) * ggml_element_size(conv_states_all), // nb1: contiguous within one channel's conv taps
+ n_embd_r_total * ggml_element_size(conv_states_all), // nb2: stride between sequences (skip over K,V states)
+ (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all)))); // offset to first seq's Q/K/V state
// Reshape conv weight: GGUF [d_conv, 1, d_inner, 1] -> ggml_ssm_conv expects [d_conv, d_inner]
// GGUF stores as [d_conv, 1, d_inner, 1] with memory layout w[conv_step + channel * d_conv]
// vLLM stores as [d_inner, d_conv] with memory layout w[channel * d_conv + conv_step]
struct llm_build_qwen3vlmoe : public llm_graph_context {
llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
};
+
struct llm_build_qwen3next : public llm_graph_context_mamba {
llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * inp_pos,
int il);
+ ggml_tensor * build_layer_attn_linear(
+ llm_graph_input_rs * inp,
+ ggml_tensor * cur,
+ int il);
+
+ ggml_tensor * build_layer_ffn(
+ ggml_tensor * cur,
+ int il);
+
+ // returns pair of output and new state
+ std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ int il);
+
+ // returns pair of output and new state
+ std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ int il);
+
+ ggml_tensor * build_norm_gated(
+ ggml_tensor * input,
+ ggml_tensor * weights,
+ ggml_tensor * gate,
+ int layer);
+
+ // returns pair of qkv, z
+ std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+ ggml_tensor * input,
+ int il);
+
+ const llama_model & model;
+};
+
+struct llm_build_qwen35 : public llm_graph_context_mamba {
+ llm_build_qwen35(const llama_model & model, const llm_graph_params & params);
+private:
+ ggml_tensor * build_layer_attn(
+ llm_graph_input_attn_kv * inp_attn,
+ ggml_tensor * cur,
+ ggml_tensor * inp_pos,
+ int * sections,
+ int il);
+
+ ggml_tensor * build_layer_attn_linear(
+ llm_graph_input_rs * inp,
+ ggml_tensor * cur,
+ ggml_tensor * causal_mask,
+ ggml_tensor * identity,
+ ggml_tensor * diag_mask,
+ int il);
+
+ ggml_tensor * build_layer_ffn(
+ ggml_tensor * cur,
+ int il);
+
+ // returns pair of output and new state
+ std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ ggml_tensor * causal_mask,
+ ggml_tensor * identity,
+ ggml_tensor * diag_mask,
+ int il);
+
+ // returns pair of output and new state
+ std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ int il);
+
+ ggml_tensor * build_norm_gated(
+ ggml_tensor * input,
+ ggml_tensor * weights,
+ ggml_tensor * gate,
+ int layer);
+
+ // returns pair of qkv, z
+ std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+ ggml_tensor * input,
+ int il);
+
+ const llama_model & model;
+};
+
+struct llm_build_qwen35moe : public llm_graph_context_mamba {
+ llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params);
+private:
+ ggml_tensor * build_layer_attn(
+ llm_graph_input_attn_kv * inp_attn,
+ ggml_tensor * cur,
+ ggml_tensor * inp_pos,
+ int * sections,
+ int il);
+
ggml_tensor * build_layer_attn_linear(
llm_graph_input_rs * inp,
ggml_tensor * cur,
--- /dev/null
+#include "ggml.h"
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) :
+ llm_graph_context_mamba(params), model(model) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ int sections[4];
+ std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ cb(inpL, "model.input_embed", -1);
+
+ auto * inp = build_inp_mem_hybrid();
+
+ ggml_tensor * inp_pos = build_inp_pos();
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ ggml_tensor * causal_mask =
+ ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+ GGML_TRI_TYPE_LOWER);
+
+ ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+ ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+ ggml_build_forward_expand(gf, causal_mask);
+ ggml_build_forward_expand(gf, identity);
+ ggml_build_forward_expand(gf, diag_mask);
+
+ 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);
+
+ // Determine layer type and build appropriate attention mechanism
+ if (hparams.is_recurrent(il)) {
+ // Linear attention layer (gated delta net)
+ cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+ } else {
+ // Full attention layer
+ cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, 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);
+ }
+
+ // Residual connection
+ cur = ggml_add(ctx0, cur, inpSA);
+ cb(cur, "attn_residual", il);
+
+ // Save the tensor before post-attention norm for residual connection
+ ggml_tensor * ffn_residual = cur;
+
+ // Post-attention norm
+ ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+ cb(attn_post_norm, "attn_post_norm", il);
+
+ // Dense FFN layer - without residual connection
+ cur = build_layer_ffn(attn_post_norm, il);
+ cb(cur, "ffn_out", il);
+
+ // Residual connection for FFN - add to the tensor from before post_attention_layernorm
+ cur = ggml_add(ctx0, cur, ffn_residual);
+ cb(cur, "post_ffn", il);
+
+ // Input for next layer
+ inpL = cur;
+ }
+ cur = inpL;
+
+ // Final norm
+ cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ // LM head
+ cur = build_lora_mm(model.output, cur);
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ 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);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_delta_net_chunking(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ ggml_tensor * causal_mask,
+ ggml_tensor * identity,
+ ggml_tensor * diag_mask,
+ int il) {
+ const int64_t S_k = q->ne[0];
+ const int64_t H_k = q->ne[1];
+ const int64_t n_tokens = q->ne[2];
+ const int64_t n_seqs = q->ne[3];
+
+ const int64_t S_v = v->ne[0];
+ const int64_t H_v = v->ne[1];
+
+ GGML_ASSERT(v->ne[2] == n_tokens);
+ GGML_ASSERT(k->ne[2] == n_tokens);
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+ GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+ GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+ GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
+
+ const float eps_norm = hparams.f_norm_rms_eps;
+
+ q = ggml_l2_norm(ctx0, q, eps_norm);
+ k = ggml_l2_norm(ctx0, k, eps_norm);
+
+ const float scale = 1.0f / sqrtf(S_v);
+
+ q = ggml_scale(ctx0, q, scale);
+
+ beta = ggml_sigmoid(ctx0, beta);
+
+ cb(q, "q_in", il);
+ cb(k, "k_in", il);
+ cb(v, "v_in", il);
+ cb(beta, "beta_in", il);
+ cb(g, "g_in", il);
+
+ q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+ beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+ state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+ cb(q, "q_perm", il);
+ cb(k, "k_perm", il);
+ cb(v, "v_perm", il);
+ cb(beta, "beta_perm", il);
+ cb(g, "g_perm", il);
+ cb(state, "state_in", il);
+
+ GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+ GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+ GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+ // Do padding
+ const int64_t chunk_size = CHUNK_SIZE;
+
+ const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+ const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+ q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+ k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+ v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+ g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+ beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+ cb(q, "q_pad", il);
+ cb(k, "k_pad", il);
+ cb(v, "v_pad", il);
+ cb(beta, "beta_pad", il);
+ cb(g, "g_pad", il);
+
+ ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+ ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+ cb(v_beta, "v_beta", il);
+ cb(k_beta, "k_beta", il);
+
+ q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
+ v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+ g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+ beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+ ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+ cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+ ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+ ggml_tensor * gcs_j_broadcast =
+ ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+ ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+ cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+ decay_mask = ggml_exp(ctx0, decay_mask);
+ decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+ ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+ ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+ ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+ cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+ ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+ ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+ attn = ggml_mul(ctx0, lin_solve, causal_mask);
+ attn = ggml_add(ctx0, attn, identity);
+ cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+ ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+ ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
+
+ ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+ cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * k_cumdecay =
+ ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+ cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+ attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+ attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+ cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+ // vectorized calculation of key_gdiff
+ // improved from the chunked version:
+ // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+ // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+ // key_gdiff = key * g_diff.unsqueeze(-1)
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+ // get last element in g_cumsum along chunk_size dimension (ne0)
+ // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+ ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+ g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+ (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+ g_last = ggml_cont(ctx0, g_last);
+ cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+ cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+ cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+ ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+ 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+ ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+ cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+ cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+ // state to be updated per chunk
+ ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+ cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+ // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+ ggml_tensor * core_attn_out = nullptr;
+
+ for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+ // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+ ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+ // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+ ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+ // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+ ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+ // shape: (chunk_size, 1, H_v * n_seqs)
+ ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+ // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+ // replaced by precomputed attn_kq
+ ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+ cb(attn_chunk, "attn_chunk", il);
+
+ ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+ // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+ ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+ cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+ // v_new = v_i - v_prime
+ ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+ ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+ cb(v_new, "v_new_chunk", il);
+
+ // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+ ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
+ ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+ cb(attn_inter, "attn_inter_chunk", il);
+
+ // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+ ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+ cb(v_attn, "v_attn_chunk", il);
+
+ ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+ cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+ core_attn_out = core_attn_out == nullptr
+ ? core_attn_out_chunk
+ : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+ ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+ //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+ ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+ ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+ new_state = ggml_add(ctx0,
+ ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+ ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+ }
+
+ // truncate padded tokens
+ ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+ S_v, n_tokens, H_v, n_seqs,
+ ggml_row_size(core_attn_out->type, S_v),
+ ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+ ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+ output_tokens = ggml_cont(ctx0, output_tokens);
+ cb(output_tokens, "output_tokens", il);
+
+ // permute back to (S_v, H_v, n_tokens, n_seqs)
+ output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+ output_tokens = ggml_cont(ctx0, output_tokens);
+
+ return {output_tokens, new_state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_delta_net_autoregressive(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ int il) {
+ const int64_t S_k = q->ne[0];
+ const int64_t H_k = q->ne[1];
+ const int64_t n_tokens = q->ne[2];
+ const int64_t n_seqs = q->ne[3];
+
+ const int64_t S_v = v->ne[0];
+ const int64_t H_v = v->ne[1];
+
+ GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing
+ GGML_ASSERT(v->ne[2] == n_tokens);
+ GGML_ASSERT(k->ne[2] == n_tokens);
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+ GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+ GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+ GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
+
+ const float eps_norm = hparams.f_norm_rms_eps;
+
+ q = ggml_l2_norm(ctx0, q, eps_norm);
+ k = ggml_l2_norm(ctx0, k, eps_norm);
+
+ const float scale = 1.0f / sqrtf(S_v);
+
+ q = ggml_scale(ctx0, q, scale);
+ beta = ggml_sigmoid(ctx0, beta);
+
+ cb(q, "q_in", il);
+ cb(k, "k_in", il);
+ cb(v, "v_in", il);
+ cb(beta, "beta_in", il);
+ cb(g, "g_in", il);
+
+ state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+ ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+ ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+ // Apply exponential to g_t
+ g_t = ggml_exp(ctx0, g_t);
+
+ // Apply the gated delta rule for the single timestep
+ // last_recurrent_state = last_recurrent_state * g_t
+ state = ggml_mul(ctx0, state, g_t);
+
+ // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+ ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+ ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed);
+ // we need to sum over dim=-2, so we transpose, sum, then transpose again
+ kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+ // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+ ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+ // delta = (v_t - kv_mem) * beta_t
+ ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs]
+ ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t);
+
+ // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+ ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+ state = ggml_add(ctx0, state, k_t_delta);
+
+ // Compute the attention output
+ // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+ ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t
+ ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed);
+ // again, since it's over dim = -2, transpose, sum, transpose back
+ ggml_tensor * core_attn_out =
+ ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+ // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+ cb(core_attn_out, "output_tokens", il);
+ cb(state, "new_state", il);
+
+ return {core_attn_out, state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35::build_qkvz(
+ ggml_tensor * input,
+ int il) {
+ const int64_t n_seqs = ubatch.n_seqs;
+ const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+ qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+ cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+ ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+ cb(z, "z", il);
+
+ return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen35::build_norm_gated(
+ ggml_tensor * input,
+ ggml_tensor * weights,
+ ggml_tensor * gate,
+ int layer) {
+ ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+ ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+ return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_attn(
+ llm_graph_input_attn_kv * inp,
+ ggml_tensor * cur,
+ ggml_tensor * inp_pos,
+ int * sections,
+ int il) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ // 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); // [ (n_embd_head * 2) * n_head, n_tokens ]
+ cb(Qcur_full, "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);
+ cb(Qcur, "Qcur_reshaped", il);
+
+ // Apply Q normalization
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_normed", il);
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ // Apply K normalization
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Kcur, "Kcur_normed", 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);
+ gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+ cb(gate, "gate_reshaped", il);
+
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ // Apply MRoPE
+ Qcur = ggml_rope_multi(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ Kcur = ggml_rope_multi(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ // Attention computation
+ 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,
+ nullptr, nullptr,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+ cb(cur, "attn_pregate", il);
+
+ ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+ cb(gate_sigmoid, "gate_sigmoid", il);
+
+ cur = ggml_mul(ctx0, cur, gate_sigmoid);
+ cb(cur, "attn_gated", il);
+
+ cur = build_lora_mm(model.layers[il].wo, cur);
+ cb(cur, "attn_output", il);
+
+ return cur;
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_attn_linear(
+ llm_graph_input_rs * inp,
+ ggml_tensor * cur,
+ ggml_tensor * causal_mask,
+ ggml_tensor * identity,
+ ggml_tensor * diag_mask,
+ int il) {
+ const auto * mctx_cur = inp->mctx;
+
+ const int64_t d_inner = hparams.ssm_d_inner;
+ const int64_t n_seqs = ubatch.n_seqs;
+ const int64_t head_k_dim = hparams.ssm_d_state;
+ const int64_t num_k_heads = hparams.ssm_n_group;
+ const int64_t num_v_heads = hparams.ssm_dt_rank;
+ const int64_t head_v_dim = d_inner / num_v_heads;
+ const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ const auto kv_head = mctx_cur->get_head();
+
+ GGML_ASSERT(n_seqs != 0);
+ GGML_ASSERT(ubatch.equal_seqs());
+ GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+ // Input projections
+ auto qkvz = build_qkvz(cur, il);
+ ggml_tensor * qkv_mixed = qkvz.first;
+ ggml_tensor * z = qkvz.second;
+
+ ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
+ beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
+ cb(beta, "beta", il);
+ ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
+ alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
+ cb(alpha, "alpha", il);
+
+ ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+ ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+ cb(alpha_softplus, "a_softplus", il);
+ ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus
+ cb(gate, "gate", il);
+
+ // Get convolution states from cache
+ ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+ ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
+
+ // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
+
+ // Build the convolution states tensor
+ ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+ cb(conv_states, "conv_states", il);
+
+ // Calculate convolution kernel size
+ ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
+ const int64_t conv_kernel_size = conv_kernel->ne[0];
+ const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+ conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+ cb(conv_states, "conv_states_reshaped", il);
+
+ qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+ cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+ ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+ cb(conv_input, "conv_input", il);
+
+ // Update convolution state cache
+ // Extract the last (conv_kernel_size - 1) states from conv_input
+ ggml_tensor * last_conv_states =
+ ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+ conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+ cb(last_conv_states, "last_conv_states", il);
+
+ ggml_tensor * state_update_target =
+ ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+ kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+ cb(state_update_target, "state_update_target", il);
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+ cb(conv_states_all, "conv_states_updated", il);
+
+ // Apply SSM convolution
+ ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+ cb(conv_output_proper, "conv_output_raw", il);
+
+ ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+ cb(conv_output_silu, "conv_output_silu", il);
+
+ ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+ // Calculate the total conv dimension
+ int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+ int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+ // Extract the convolved Q, K, V from conv_output
+ ggml_tensor * q_conv =
+ ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+ cb(q_conv, "q_conv", il);
+ ggml_tensor * k_conv =
+ ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+ head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+ cb(k_conv, "k_conv", il);
+ ggml_tensor * v_conv =
+ ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+ 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+ cb(v_conv, "v_conv", il);
+
+ // Unsqueeze them
+ q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+ ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+ state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+ cb(state, "state_predelta", il);
+
+ // if head keys and value keys are different, repeat Q/K to match V's head count
+ // V heads are in tiled order (from conversion), so simple tiled repeat works
+ if (num_k_heads != num_v_heads) {
+ GGML_ASSERT(num_v_heads % num_k_heads == 0);
+ q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+ k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+ }
+
+ cb(q_conv, "q_conv_predelta", il);
+ cb(k_conv, "k_conv_predelta", il);
+ cb(v_conv, "v_conv_predelta", il);
+
+ // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+ std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
+ if (n_seq_tokens == 1) {
+ attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+ } else {
+ attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+ }
+ ggml_tensor * output = attn_out.first;
+ ggml_tensor * new_state = attn_out.second;
+ cb(output, "attn_output", il);
+ cb(new_state, "new_state", il);
+
+ // Update the recurrent states
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0, new_state,
+ ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+ kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+ // Reshape both attn_out_final and z to 2D tensors for normalization
+ // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+ ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+ // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+ ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+ // Apply gated normalization: self.norm(core_attn_out, z)
+ ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+ // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
+ ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+ cb(final_output, "final_output", il);
+
+ // Output projection
+ cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+ cb(cur, "linear_attn_out", il);
+
+ // Reshape back to original dimensions
+ cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+ return cur;
+}
+
+ggml_tensor * llm_build_qwen35::build_layer_ffn(ggml_tensor * cur, const int il) {
+ // Qwen3.5 does not use MoE FFN
+ GGML_ASSERT(model.layers[il].ffn_gate_inp == nullptr);
+
+ 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);
+
+ return cur;
+}
--- /dev/null
+#include "ggml.h"
+#include "models.h"
+
+#define CHUNK_SIZE 64
+
+llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) :
+ llm_graph_context_mamba(params), model(model) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ int sections[4];
+ std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ cb(inpL, "model.input_embed", -1);
+
+ auto * inp = build_inp_mem_hybrid();
+
+ ggml_tensor * inp_pos = build_inp_pos();
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ ggml_tensor * causal_mask =
+ ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
+ GGML_TRI_TYPE_LOWER);
+
+ ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+ ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
+
+ ggml_build_forward_expand(gf, causal_mask);
+ ggml_build_forward_expand(gf, identity);
+ ggml_build_forward_expand(gf, diag_mask);
+
+ 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);
+
+ // Determine layer type and build appropriate attention mechanism
+ if (hparams.is_recurrent(il)) {
+ // Linear attention layer (gated delta net)
+ cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+ } else {
+ // Full attention layer
+ cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, 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);
+ }
+
+ // Residual connection
+ cur = ggml_add(ctx0, cur, inpSA);
+ cb(cur, "attn_residual", il);
+
+ // Save the tensor before post-attention norm for residual connection
+ ggml_tensor * ffn_residual = cur;
+
+ // Post-attention norm
+ ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
+ cb(attn_post_norm, "attn_post_norm", il);
+
+ // MOE FFN layer
+ cur = build_layer_ffn(attn_post_norm, il);
+ cb(cur, "ffn_out", il);
+
+ // Residual connection for FFN - add to the tensor from before post_attention_layernorm
+ cur = ggml_add(ctx0, cur, ffn_residual);
+ cb(cur, "post_moe", il);
+
+ // Input for next layer
+ inpL = cur;
+ }
+ cur = inpL;
+
+ // Final norm
+ cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ // LM head
+ cur = build_lora_mm(model.output, cur);
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ 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);
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_delta_net_chunking(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ ggml_tensor * causal_mask,
+ ggml_tensor * identity,
+ ggml_tensor * diag_mask,
+ int il) {
+ const int64_t S_k = q->ne[0];
+ const int64_t H_k = q->ne[1];
+ const int64_t n_tokens = q->ne[2];
+ const int64_t n_seqs = q->ne[3];
+
+ const int64_t S_v = v->ne[0];
+ const int64_t H_v = v->ne[1];
+
+ GGML_ASSERT(v->ne[2] == n_tokens);
+ GGML_ASSERT(k->ne[2] == n_tokens);
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+ GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+ GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+ GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
+
+ const float eps_norm = hparams.f_norm_rms_eps;
+
+ q = ggml_l2_norm(ctx0, q, eps_norm);
+ k = ggml_l2_norm(ctx0, k, eps_norm);
+
+ const float scale = 1.0f / sqrtf(S_v);
+
+ q = ggml_scale(ctx0, q, scale);
+
+ beta = ggml_sigmoid(ctx0, beta);
+
+ cb(q, "q_in", il);
+ cb(k, "k_in", il);
+ cb(v, "v_in", il);
+ cb(beta, "beta_in", il);
+ cb(g, "g_in", il);
+
+ q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
+ g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
+
+ beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
+ state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+ cb(q, "q_perm", il);
+ cb(k, "k_perm", il);
+ cb(v, "v_perm", il);
+ cb(beta, "beta_perm", il);
+ cb(g, "g_perm", il);
+ cb(state, "state_in", il);
+
+ GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
+ GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
+ GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+
+ // Do padding
+ const int64_t chunk_size = CHUNK_SIZE;
+
+ const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
+ const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+
+ q = ggml_pad(ctx0, q, 0, pad, 0, 0);
+ k = ggml_pad(ctx0, k, 0, pad, 0, 0);
+ v = ggml_pad(ctx0, v, 0, pad, 0, 0);
+ g = ggml_pad(ctx0, g, pad, 0, 0, 0);
+ beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+
+ cb(q, "q_pad", il);
+ cb(k, "k_pad", il);
+ cb(v, "v_pad", il);
+ cb(beta, "beta_pad", il);
+ cb(g, "g_pad", il);
+
+ ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
+ ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+
+ cb(v_beta, "v_beta", il);
+ cb(k_beta, "k_beta", il);
+
+ q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+ v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
+ v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+
+ g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+ beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+
+ ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+ cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+ ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+
+ ggml_tensor * gcs_j_broadcast =
+ ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+
+ ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
+ cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+ decay_mask = ggml_exp(ctx0, decay_mask);
+ decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+
+ ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+
+ ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
+ ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
+ cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+ ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+
+ ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+ attn = ggml_mul(ctx0, lin_solve, causal_mask);
+ attn = ggml_add(ctx0, attn, identity);
+ cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+
+ ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
+ ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
+
+ ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
+ cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * k_cumdecay =
+ ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
+ cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
+ attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
+ attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
+ cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+
+
+ // vectorized calculation of key_gdiff
+ // improved from the chunked version:
+ // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
+ // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
+ // key_gdiff = key * g_diff.unsqueeze(-1)
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+
+ // get last element in g_cumsum along chunk_size dimension (ne0)
+ // example: [[x, y, z, ..., last], ...] -> [[last], ...]
+ ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
+ g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
+ (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+ g_last = ggml_cont(ctx0, g_last);
+ cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
+ cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
+ cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+ ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
+ 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+
+ ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
+ cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+
+ ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
+ cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+
+
+ // state to be updated per chunk
+ ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
+ cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+
+ // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
+ ggml_tensor * core_attn_out = nullptr;
+
+ for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
+ // shape: (S_k, chunk_size, 1, H_k * n_seqs)
+ ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+
+ // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+ ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+
+ // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+ ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+
+ // shape: (chunk_size, 1, H_v * n_seqs)
+ ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+
+ // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+ // replaced by precomputed attn_kq
+ ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
+ cb(attn_chunk, "attn_chunk", il);
+
+ ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+
+ // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+ ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
+ cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
+
+ // v_new = v_i - v_prime
+ ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
+ ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
+ cb(v_new, "v_new_chunk", il);
+
+ // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+ ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
+ ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
+ cb(attn_inter, "attn_inter_chunk", il);
+
+ // core_attn_out[:, :, i] = attn_inter + attn @ v_new
+ ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
+ cb(v_attn, "v_attn_chunk", il);
+
+ ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
+ cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
+
+ core_attn_out = core_attn_out == nullptr
+ ? core_attn_out_chunk
+ : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
+ ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
+ //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
+ ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
+ ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
+ new_state = ggml_add(ctx0,
+ ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
+ ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+ }
+
+ // truncate padded tokens
+ ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+ S_v, n_tokens, H_v, n_seqs,
+ ggml_row_size(core_attn_out->type, S_v),
+ ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
+ ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
+ output_tokens = ggml_cont(ctx0, output_tokens);
+ cb(output_tokens, "output_tokens", il);
+
+ // permute back to (S_v, H_v, n_tokens, n_seqs)
+ output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
+ output_tokens = ggml_cont(ctx0, output_tokens);
+
+ return {output_tokens, new_state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_delta_net_autoregressive(
+ ggml_tensor * q,
+ ggml_tensor * k,
+ ggml_tensor * v,
+ ggml_tensor * g,
+ ggml_tensor * beta,
+ ggml_tensor * state,
+ int il) {
+ const int64_t S_k = q->ne[0];
+ const int64_t H_k = q->ne[1];
+ const int64_t n_tokens = q->ne[2];
+ const int64_t n_seqs = q->ne[3];
+
+ const int64_t S_v = v->ne[0];
+ const int64_t H_v = v->ne[1];
+
+ GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing
+ GGML_ASSERT(v->ne[2] == n_tokens);
+ GGML_ASSERT(k->ne[2] == n_tokens);
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
+ GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+
+ GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
+ GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+
+ GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
+
+ const float eps_norm = hparams.f_norm_rms_eps;
+
+ q = ggml_l2_norm(ctx0, q, eps_norm);
+ k = ggml_l2_norm(ctx0, k, eps_norm);
+
+ const float scale = 1.0f / sqrtf(S_v);
+
+ q = ggml_scale(ctx0, q, scale);
+ beta = ggml_sigmoid(ctx0, beta);
+
+ cb(q, "q_in", il);
+ cb(k, "k_in", il);
+ cb(v, "v_in", il);
+ cb(beta, "beta_in", il);
+ cb(g, "g_in", il);
+
+ state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+
+ ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+ ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+
+ // Apply exponential to g_t
+ g_t = ggml_exp(ctx0, g_t);
+
+ // Apply the gated delta rule for the single timestep
+ // last_recurrent_state = last_recurrent_state * g_t
+ state = ggml_mul(ctx0, state, g_t);
+
+ // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+ ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+ ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed);
+ // we need to sum over dim=-2, so we transpose, sum, then transpose again
+ kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+
+ // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+ ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+ // delta = (v_t - kv_mem) * beta_t
+ ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs]
+ ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t);
+
+ // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+ ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+ state = ggml_add(ctx0, state, k_t_delta);
+
+ // Compute the attention output
+ // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+ ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t
+ ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed);
+ // again, since it's over dim = -2, transpose, sum, transpose back
+ ggml_tensor * core_attn_out =
+ ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+
+ // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+ cb(core_attn_out, "output_tokens", il);
+ cb(state, "new_state", il);
+
+ return {core_attn_out, state};
+}
+
+std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_qkvz(
+ ggml_tensor * input,
+ int il) {
+ const int64_t n_seqs = ubatch.n_seqs;
+ const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
+ qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
+ cb(qkv_mixed, "linear_attn_qkv_mixed", il);
+
+ ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
+ cb(z, "z", il);
+
+ return { qkv_mixed, z };
+}
+
+ggml_tensor * llm_build_qwen35moe::build_norm_gated(
+ ggml_tensor * input,
+ ggml_tensor * weights,
+ ggml_tensor * gate,
+ int layer) {
+ ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
+ ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
+
+ return ggml_mul(ctx0, normalized, gated_silu);
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_attn(
+ llm_graph_input_attn_kv * inp,
+ ggml_tensor * cur,
+ ggml_tensor * inp_pos,
+ int * sections,
+ int il) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ // 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); // [ (n_embd_head * 2) * n_head, n_tokens ]
+ cb(Qcur_full, "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);
+ cb(Qcur, "Qcur_reshaped", il);
+
+ // Apply Q normalization
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_normed", il);
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ // Apply K normalization
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Kcur, "Kcur_normed", 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);
+ gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
+ cb(gate, "gate_reshaped", il);
+
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ // Apply IMRoPE
+ Qcur = ggml_rope_multi(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ Kcur = ggml_rope_multi(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ // Attention computation
+ 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,
+ nullptr, nullptr,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+ cb(cur, "attn_pregate", il);
+
+ ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
+ cb(gate_sigmoid, "gate_sigmoid", il);
+
+ cur = ggml_mul(ctx0, cur, gate_sigmoid);
+ cb(cur, "attn_gated", il);
+
+ cur = build_lora_mm(model.layers[il].wo, cur);
+ cb(cur, "attn_output", il);
+
+ return cur;
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
+ llm_graph_input_rs * inp,
+ ggml_tensor * cur,
+ ggml_tensor * causal_mask,
+ ggml_tensor * identity,
+ ggml_tensor * diag_mask,
+ int il) {
+ const auto * mctx_cur = inp->mctx;
+
+ const int64_t d_inner = hparams.ssm_d_inner;
+ const int64_t n_seqs = ubatch.n_seqs;
+ const int64_t head_k_dim = hparams.ssm_d_state;
+ const int64_t num_k_heads = hparams.ssm_n_group;
+ const int64_t num_v_heads = hparams.ssm_dt_rank;
+ const int64_t head_v_dim = d_inner / num_v_heads;
+ const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ const auto kv_head = mctx_cur->get_head();
+
+ GGML_ASSERT(n_seqs != 0);
+ GGML_ASSERT(ubatch.equal_seqs());
+ GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+ // Input projections
+ auto qkvz = build_qkvz(cur, il);
+ ggml_tensor * qkv_mixed = qkvz.first;
+ ggml_tensor * z = qkvz.second;
+
+ ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
+ beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
+ cb(beta, "beta", il);
+ ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
+ alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
+ cb(alpha, "alpha", il);
+
+ ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
+ ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
+ cb(alpha_softplus, "a_softplus", il);
+ ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus
+ cb(gate, "gate", il);
+
+ // Get convolution states from cache
+ ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+ ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
+
+ // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
+
+ // Build the convolution states tensor
+ ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+ cb(conv_states, "conv_states", il);
+
+ // Calculate convolution kernel size
+ ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
+ const int64_t conv_kernel_size = conv_kernel->ne[0];
+ const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
+ conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+ cb(conv_states, "conv_states_reshaped", il);
+
+ qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
+ cb(qkv_mixed, "qkv_mixed_permuted", il);
+
+ ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
+ cb(conv_input, "conv_input", il);
+
+ // Update convolution state cache
+ // Extract the last (conv_kernel_size - 1) states from conv_input
+ ggml_tensor * last_conv_states =
+ ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
+ conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
+ cb(last_conv_states, "last_conv_states", il);
+
+ ggml_tensor * state_update_target =
+ ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
+ kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
+ cb(state_update_target, "state_update_target", il);
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
+ cb(conv_states_all, "conv_states_updated", il);
+
+ // Apply SSM convolution
+ ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
+ cb(conv_output_proper, "conv_output_raw", il);
+
+ ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
+ cb(conv_output_silu, "conv_output_silu", il);
+
+ ggml_tensor * conv_qkv_mix = conv_output_silu;
+
+ // Calculate the total conv dimension
+ int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
+ int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
+
+ // Extract the convolved Q, K, V from conv_output
+ ggml_tensor * q_conv =
+ ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+ cb(q_conv, "q_conv", il);
+ ggml_tensor * k_conv =
+ ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
+ head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+ cb(k_conv, "k_conv", il);
+ ggml_tensor * v_conv =
+ ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
+ 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+ cb(v_conv, "v_conv", il);
+
+ // Unsqueeze them
+ q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+
+ ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+ state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
+ cb(state, "state_predelta", il);
+
+ // if head keys and value keys are different, repeat Q/K to match V's head count
+ // V heads are in tiled order (from conversion), so simple tiled repeat works
+ if (num_k_heads != num_v_heads) {
+ GGML_ASSERT(num_v_heads % num_k_heads == 0);
+ q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+ k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
+ }
+
+ cb(q_conv, "q_conv_predelta", il);
+ cb(k_conv, "k_conv_predelta", il);
+ cb(v_conv, "v_conv_predelta", il);
+
+ // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+ std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
+ if (n_seq_tokens == 1) {
+ attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+ } else {
+ attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+ }
+ ggml_tensor * output = attn_out.first;
+ ggml_tensor * new_state = attn_out.second;
+ cb(output, "attn_output", il);
+ cb(new_state, "new_state", il);
+
+ // Update the recurrent states
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0, new_state,
+ ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+ kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
+
+ // Reshape both attn_out_final and z to 2D tensors for normalization
+ // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+ ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+ // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
+ ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+
+ // Apply gated normalization: self.norm(core_attn_out, z)
+ ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+
+ // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
+ ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+ cb(final_output, "final_output", il);
+
+ // Output projection
+ cur = build_lora_mm(model.layers[il].ssm_out, final_output);
+ cb(cur, "linear_attn_out", il);
+
+ // Reshape back to original dimensions
+ cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+ return cur;
+}
+
+ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int il) {
+ // Check if this is an MoE layer
+ GGML_ASSERT(model.layers[il].ffn_gate_inp != nullptr);
+
+ 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,
+ nullptr,
+ n_expert, n_expert_used, LLM_FFN_SILU,
+ true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+ 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,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(ffn_shexp, "ffn_shexp", il);
+
+ // Apply shared expert gating as in the reference implementation
+ // The shared expert has its own gate that is sigmoided
+ // Note: ffn_gate_inp_shexp is the shared expert gate (outputs 1 value per token)
+ ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
+ cb(shared_gate, "shared_expert_gate", il);
+
+ // Apply sigmoid to the gate
+ shared_gate = ggml_sigmoid(ctx0, shared_gate);
+ cb(shared_gate, "shared_expert_gate_sigmoid", il);
+
+
+ // Apply the gate to the shared expert output
+ ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
+ cb(ffn_shexp, "ffn_shexp_gated", il);
+
+ cur = ggml_add(ctx0, moe_out, ffn_shexp);
+ cb(cur, "ffn_out", il);
+ } else {
+ cur = moe_out;
+ }
+
+ return cur;
+}
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
- ggml_tensor * causal_mask =
- ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
- GGML_TRI_TYPE_LOWER);
-
- ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
- ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
-
- ggml_build_forward_expand(gf, causal_mask);
- ggml_build_forward_expand(gf, identity);
- ggml_build_forward_expand(gf, diag_mask);
-
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// Determine layer type and build appropriate attention mechanism
if (hparams.is_recurrent(il)) {
// Linear attention layer (gated delta net)
- cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
+ cur = build_layer_attn_linear(inp->get_recr(), cur, il);
} else {
// Full attention layer
cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
+ ggml_tensor * b,
+ ggml_tensor * s,
int il) {
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t S_v = v->ne[0];
const int64_t H_v = v->ne[1];
- GGML_ASSERT(v->ne[2] == n_tokens);
- GGML_ASSERT(k->ne[2] == n_tokens);
- GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
- GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
- GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+ GGML_ASSERT(S_k == S_v);
+ GGML_ASSERT(H_v % H_k == 0);
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+ GGML_ASSERT(v->ne[0] == S_v && v->ne[1] == H_v && v->ne[2] == n_tokens && v->ne[3] == n_seqs);
- GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
-
- const float eps_norm = hparams.f_norm_rms_eps;
-
- q = ggml_l2_norm(ctx0, q, eps_norm);
- k = ggml_l2_norm(ctx0, k, eps_norm);
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(b->ne[0] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
+ GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs);
- const float scale = 1.0f / sqrtf(S_v);
+ const float scale = 1.0f / sqrtf(S_k);
q = ggml_scale(ctx0, q, scale);
- beta = ggml_sigmoid(ctx0, beta);
-
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
- cb(beta, "beta_in", il);
+ cb(b, "b_in", il);
cb(g, "g_in", il);
- q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
- k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
- v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
- g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-
- beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
- state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
-
- cb(q, "q_perm", il);
- cb(k, "k_perm", il);
- cb(v, "v_perm", il);
- cb(beta, "beta_perm", il);
- cb(g, "g_perm", il);
- cb(state, "state_in", il);
-
- GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
- GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
- GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
- GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+ q = ggml_permute(ctx0, q, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
+ k = ggml_permute(ctx0, k, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
+ v = ggml_permute(ctx0, v, 0, 2, 1, 3); // [S_v, n_tokens, H_v, n_seqs]
+ g = ggml_permute(ctx0, g, 2, 1, 3, 0); // [ 1, n_tokens, H_v, n_seqs]
+ b = ggml_permute(ctx0, b, 2, 0, 1, 3); // [ 1, n_tokens, H_v, n_seqs]
- // Do padding
- const int64_t chunk_size = CHUNK_SIZE;
+ const int CS = CHUNK_SIZE;
- const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
- const int64_t n_chunks = (n_tokens + pad) / chunk_size;
+ const int pad = (CS - n_tokens % CS) % CS;
+ const int n_chunks = (n_tokens + pad) / CS;
q = ggml_pad(ctx0, q, 0, pad, 0, 0);
k = ggml_pad(ctx0, k, 0, pad, 0, 0);
v = ggml_pad(ctx0, v, 0, pad, 0, 0);
- g = ggml_pad(ctx0, g, pad, 0, 0, 0);
- beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
+ g = ggml_pad(ctx0, g, 0, pad, 0, 0);
+ b = ggml_pad(ctx0, b, 0, pad, 0, 0);
- cb(q, "q_pad", il);
- cb(k, "k_pad", il);
- cb(v, "v_pad", il);
- cb(beta, "beta_pad", il);
- cb(g, "g_pad", il);
+ ggml_tensor * v_b = ggml_mul(ctx0, v, b);
+ ggml_tensor * k_b = ggml_mul(ctx0, k, b);
- ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
- ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+ cb(v_b, "v_b", il);
+ cb(k_b, "k_b", il);
- cb(v_beta, "v_beta", il);
- cb(k_beta, "k_beta", il);
+ q = ggml_reshape_4d(ctx0, q, S_k, CS, n_chunks, H_k * n_seqs);
+ k = ggml_reshape_4d(ctx0, k, S_k, CS, n_chunks, H_k * n_seqs);
+ k_b = ggml_reshape_4d(ctx0, k_b, S_k, CS, n_chunks, H_v * n_seqs);
+ v = ggml_reshape_4d(ctx0, v, S_v, CS, n_chunks, H_v * n_seqs);
+ v_b = ggml_reshape_4d(ctx0, v_b, S_v, CS, n_chunks, H_v * n_seqs);
- q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
- k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
- k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
- v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
- v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
+ g = ggml_reshape_4d(ctx0, g, CS, 1, n_chunks, H_v * n_seqs);
+ b = ggml_reshape_4d(ctx0, b, 1, CS, n_chunks, H_v * n_seqs);
- g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
- beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+ // [CS, 1, n_chunks, H_v * n_seqs]
+ ggml_tensor * g_cs = ggml_cumsum(ctx0, g);
+ cb(g_cs, "g_cs", il);
- ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
- cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+ ggml_tensor * g_cs_i = g_cs;
+ ggml_tensor * g_cs_j = ggml_reshape_4d(ctx0, g_cs, 1, CS, n_chunks, H_v * n_seqs);
- ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
- ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+ g_cs_j = ggml_repeat_4d(ctx0, g_cs_j, CS, CS, n_chunks, H_v * n_seqs);
- ggml_tensor * gcs_j_broadcast =
- ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
+ // [CS, CS, n_chunks, H_v * n_seqs]
+ ggml_tensor * decay_mask;
+ decay_mask = ggml_sub(ctx0, g_cs_j, g_cs_i);
+ decay_mask = ggml_tri(ctx0, decay_mask, GGML_TRI_TYPE_LOWER_DIAG);
+ decay_mask = ggml_exp(ctx0, decay_mask);
+ cb(decay_mask, "decay_mask", il);
- ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
- cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+ // [CS, CS, n_chunks, H_k * n_seqs]
+ ggml_tensor * kb;
+ kb = ggml_mul_mat(ctx0, k, k_b);
+ kb = ggml_mul (ctx0, kb, decay_mask);
- decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
- decay_mask = ggml_exp(ctx0, decay_mask);
- decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
+ // [CS, CS, n_chunks, H_k * n_seqs]
+ ggml_tensor * attn;
+ attn = ggml_tri(ctx0, kb, GGML_TRI_TYPE_LOWER);
- ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
+ ggml_tensor * identity;
+ identity = ggml_view_1d(ctx0, attn, CS, 0);
+ identity = ggml_fill (ctx0, identity, 1.0f);
+ identity = ggml_diag (ctx0, identity);
- ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
- ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
- cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+ ggml_tensor * lhs = ggml_add(ctx0, attn, identity);
+ cb(lhs, "dnet_add_ch_lhs", il);
- ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
- ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
+ attn = ggml_neg(ctx0, attn);
- ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
- attn = ggml_mul(ctx0, lin_solve, causal_mask);
- attn = ggml_add(ctx0, attn, identity);
- cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+ ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
+ attn = ggml_add(ctx0, lin_solve, identity);
+ cb(attn, "dnet_add_ch_attn_solved", il); // [CS, CS, n_chunks, H_k * n_seqs]
- v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
+ // [S_v, CS, n_chunks, H_v * n_seqs]
+ v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_b)), attn);
- ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
- ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
+ // [CS, 1, n_chunks, H_v * n_seqs]
+ ggml_tensor * g_exp = ggml_exp(ctx0, g_cs);
- ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
- cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+ k_b = ggml_cont(ctx0, ggml_transpose(ctx0, k_b));
- ggml_tensor * k_cumdecay =
- ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
- cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+ // [CS, S_k, n_chunks, H_k * n_seqs]
+ ggml_tensor * kbg = ggml_mul(ctx0, k_b, g_exp);
+ cb(kbg, "k_beta_g_exp", il);
- ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
- attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
- attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
- cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
+ // [S_k, CS, n_chunks, H_k * n_seqs]
+ ggml_tensor * k_cd = ggml_mul_mat(ctx0, kbg, attn);
+ cb(k_cd, "k_cumdecay", il);
+ // [S_k, CS, n_chunks, H_k * n_seqs]
+ ggml_tensor * g_exp_t = ggml_transpose(ctx0, g_exp);
+ ggml_tensor * q_g_exp = ggml_mul(ctx0, q, g_exp_t);
+
+ // [CS, CS, n_chunks, H_k * n_seqs]
+ ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+ kq = ggml_mul(ctx0, kq, decay_mask);
+ kq = ggml_tri(ctx0, kq, GGML_TRI_TYPE_LOWER_DIAG);
+ cb(kq, "kq", il);
// vectorized calculation of key_gdiff
// improved from the chunked version:
// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
- // get last element in g_cumsum along chunk_size dimension (ne0)
+ // get last element in g_cumsum along CS dimension (ne0)
// example: [[x, y, z, ..., last], ...] -> [[last], ...]
- ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
- g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
- (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
+ // [1, 1, n_chunks, H_v * n_seqs]
+ ggml_tensor * g_last = ggml_view_4d(ctx0, g_cs, 1, 1, g_cs->ne[2], g_cs->ne[3],
+ g_cs->nb[1],
+ g_cs->nb[2],
+ g_cs->nb[3],
+ ggml_row_size(g_cs->type, g_cs->ne[0] - 1));
+ cb(g_last, "g_last", il);
+
+ // TODO: remove this cont when CUDA supports non-cont unary ops
g_last = ggml_cont(ctx0, g_last);
- cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
+ // [1, 1, n_chunks, H_v * n_seqs]
ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
- cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
-
- ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
- cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
+ cb(g_last_exp, "g_last_exp", il);
- ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
- ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp,
- 1, chunk_size, n_chunks, g_diff_exp->ne[3]);
+ // [CS, 1, n_chunks, H_v * n_seqs]
+ ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cs, g_last));
+ cb(g_diff, "g_diff", il);
- ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t);
- cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
+ ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
+ ggml_tensor * g_diff_exp_t = ggml_transpose(ctx0, g_diff_exp);
- ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff));
- cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs)
+ // [S_k, CS, n_chunks, H_v * n_seqs]
+ ggml_tensor * kg = ggml_mul(ctx0, k, g_diff_exp_t);
+ cb(kg, "key_gdiff", il);
+ // [CS, S_k, n_chunks, H_v * n_seqs]
+ ggml_tensor * kg_t = ggml_cont(ctx0, ggml_transpose(ctx0, kg));
+ cb(kg_t, "key_gdiff_t", il);
- // state to be updated per chunk
- ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
- cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
+ ggml_tensor * s_t = ggml_transpose(ctx0, s);
+ s_t = ggml_cont_4d(ctx0, s_t, S_v, S_v, 1, H_v * n_seqs);
+ cb(s_t, "dnet_add_ch_state", il);
- // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
- ggml_tensor * core_attn_out = nullptr;
+ // [CS, S_v, n_chunks, H_v * n_seqs]
+ ggml_tensor * v_t = ggml_cont(ctx0, ggml_transpose(ctx0, v));
for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
- // shape: (S_k, chunk_size, 1, H_k * n_seqs)
- ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
+ ggml_tensor * ch_k_cd = get_slice_2d(ctx0, k_cd, chunk); // [S_k, CS, 1, H_k * n_seqs]
+ ggml_tensor * ch_v_t = get_slice_2d(ctx0, v_t, chunk); // [ CS, S_v, 1, H_v * n_seqs]
+ ggml_tensor * ch_kq = get_slice_2d(ctx0, kq, chunk); // [ CS, CS, 1, H_k * n_seqs]
+ ggml_tensor * ch_q_g_exp = get_slice_2d(ctx0, q_g_exp, chunk); // [S_k, CS, 1, H_k * n_seqs]
+ ggml_tensor * ch_kg_t = get_slice_2d(ctx0, kg_t, chunk); // [ CS, S_k, 1, H_v * n_seqs]
- // shape: (S_v, chunk_size, 1, H_v * n_seqs)
- ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
+ // [CS, S_v, 1, H_v * n_seqs]
+ ggml_tensor * v_t_p = ggml_mul_mat(ctx0, ch_k_cd, s_t);
+ cb(v_t_p, "v_prime", il);
- // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
- ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
+ // [CS, S_v, 1, H_v * n_seqs]
+ ggml_tensor * v_t_new = ggml_sub(ctx0, ch_v_t, v_t_p);
+ cb(v_t_new, "v_t_new", il);
- // shape: (chunk_size, 1, H_v * n_seqs)
- ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
+ // [S_v, CS, 1, H_v * n_seqs]
+ ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_t_new, ch_kq);
+ cb(v_attn, "v_attn", il);
- // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
- // replaced by precomputed attn_kq
- ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
- cb(attn_chunk, "attn_chunk", il);
+ // [S_v, CS, 1, H_v * n_seqs]
+ ggml_tensor * attn_inter = ggml_mul_mat(ctx0, s_t, ch_q_g_exp);
+ cb(attn_inter, "attn_inter", il);
- ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
+ // [S_v, CS, 1, H_v * n_seqs]
+ ggml_tensor * o_ch = ggml_add(ctx0, attn_inter, v_attn);
+ cb(o_ch, "dnet_add_ch_attn_out", il);
- // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
- ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
- cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
-
- // v_new = v_i - v_prime
- ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
- ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
- cb(v_new, "v_new_chunk", il);
-
- // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
- ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
- ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
- cb(attn_inter, "attn_inter_chunk", il);
-
- // core_attn_out[:, :, i] = attn_inter + attn @ v_new
- ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
- cb(v_attn, "v_attn_chunk", il);
-
- ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
- cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
-
- core_attn_out = core_attn_out == nullptr
- ? core_attn_out_chunk
- : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
+ v = ggml_set_inplace(ctx0, v, o_ch, v->nb[1], v->nb[2], v->nb[3], chunk * v->nb[2]);
// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
- ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk);
- //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
- ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t);
+ // TODO: head broadcast might not work here - probably will need a transpose
+ ggml_tensor * kgv = ggml_mul_mat(ctx0, ch_kg_t, v_t_new); // [S_k, S_v, 1, H_k * n_seqs]
// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
- ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk));
- new_state = ggml_add(ctx0,
- ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)),
- ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
+ ggml_tensor * ch_g_last_exp = get_slice_2d(ctx0, g_last_exp, chunk);
+ s_t = ggml_mul(ctx0, s_t, ch_g_last_exp);
+ s_t = ggml_add(ctx0, s_t, kgv);
+ cb(s_t, "dnet_add_ch_state", il);
}
+ s_t = ggml_reshape_4d(ctx0, s_t, S_v, S_v, H_v, n_seqs);
+
// truncate padded tokens
- ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out,
+ ggml_tensor * o = ggml_view_4d(ctx0, v,
S_v, n_tokens, H_v, n_seqs,
- ggml_row_size(core_attn_out->type, S_v),
- ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks),
- ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0);
- output_tokens = ggml_cont(ctx0, output_tokens);
- cb(output_tokens, "output_tokens", il);
+ ggml_row_size(v->type, S_v),
+ ggml_row_size(v->type, S_v * CS * n_chunks),
+ ggml_row_size(v->type, S_v * CS * n_chunks * H_v), 0);
- // permute back to (S_v, H_v, n_tokens, n_seqs)
- output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3);
- output_tokens = ggml_cont(ctx0, output_tokens);
+ o = ggml_permute (ctx0, o, 0, 2, 1, 3); // [S_v, H_v, n_tokens, n_seqs]
+ s = ggml_transpose(ctx0, s_t); // [S_v, S_v, H_v, n_seqs]
- return {output_tokens, new_state};
+ return {o, s};
}
std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
- ggml_tensor * beta,
- ggml_tensor * state,
+ ggml_tensor * b, // beta
+ ggml_tensor * s, // state
int il) {
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t S_v = v->ne[0];
const int64_t H_v = v->ne[1];
- GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing
- GGML_ASSERT(v->ne[2] == n_tokens);
- GGML_ASSERT(k->ne[2] == n_tokens);
- GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
- GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
- GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
+ GGML_ASSERT(n_tokens == 1);
+
+ GGML_ASSERT(S_k == S_v);
+ GGML_ASSERT(H_v % H_k == 0);
GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
+ GGML_ASSERT(v->ne[0] == S_v && v->ne[1] == H_v && v->ne[2] == n_tokens && v->ne[3] == n_seqs);
- GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
-
- const float eps_norm = hparams.f_norm_rms_eps;
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
+ GGML_ASSERT(b->ne[0] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
+ GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs);
- q = ggml_l2_norm(ctx0, q, eps_norm);
- k = ggml_l2_norm(ctx0, k, eps_norm);
+ const float scale = 1.0f / sqrtf(S_k);
- const float scale = 1.0f / sqrtf(S_v);
+ q = ggml_scale(ctx0, q, scale);
- q = ggml_scale(ctx0, q, scale);
- beta = ggml_sigmoid(ctx0, beta);
+ q = ggml_permute(ctx0, q, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
+ k = ggml_permute(ctx0, k, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
+ v = ggml_permute(ctx0, v, 0, 2, 1, 3); // [S_v, n_tokens, H_v, n_seqs]
cb(q, "q_in", il);
cb(k, "k_in", il);
cb(v, "v_in", il);
- cb(beta, "beta_in", il);
+ cb(b, "b_in", il);
cb(g, "g_in", il);
- state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
+ g = ggml_reshape_4d(ctx0, g, 1, 1, H_v, n_seqs);
+ b = ggml_reshape_4d(ctx0, b, 1, 1, H_v, n_seqs);
- ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
- ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
+ // [S_v, S_v, H_v, n_seqs]
+ g = ggml_exp(ctx0, g);
+ s = ggml_mul(ctx0, s, g);
- // Apply exponential to g_t
- g_t = ggml_exp(ctx0, g_t);
+ ggml_tensor * s_t = ggml_cont(ctx0, ggml_transpose(ctx0, s));
- // Apply the gated delta rule for the single timestep
- // last_recurrent_state = last_recurrent_state * g_t
- state = ggml_mul(ctx0, state, g_t);
+ // [1, S_v, H_v, n_seqs]
+ ggml_tensor * sk;
+ sk = ggml_mul (ctx0, s_t, k);
+ sk = ggml_sum_rows(ctx0, sk);
- // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
- ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
- ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed);
- // we need to sum over dim=-2, so we transpose, sum, then transpose again
- kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
+ // [S_v, 1, H_v, n_seqs]
+ ggml_tensor * d;
+ d = ggml_sub(ctx0, v, ggml_transpose(ctx0, sk));
+ d = ggml_mul(ctx0, d, b);
- // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
- ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
- // delta = (v_t - kv_mem) * beta_t
- ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs]
- ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t);
+ // [1, S_v, H_v, n_seqs]
+ ggml_tensor * d_t;
+ d_t = ggml_transpose(ctx0, d);
- // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
- ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
- state = ggml_add(ctx0, state, k_t_delta);
+ // [S_v, S_v, H_v, n_seqs]
+ ggml_tensor * kd;
+ k = ggml_repeat(ctx0, k, s);
+ kd = ggml_mul (ctx0, k, d_t);
- // Compute the attention output
- // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
- ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t
- ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed);
- // again, since it's over dim = -2, transpose, sum, transpose back
- ggml_tensor * core_attn_out =
- ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
+ s_t = ggml_add(ctx0, s_t, kd);
- // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
- cb(core_attn_out, "output_tokens", il);
- cb(state, "new_state", il);
+ cb(s_t, "dnet_add_ar_state", il);
- return {core_attn_out, state};
+ ggml_tensor * s_q = ggml_mul (ctx0, s_t, q);
+ ggml_tensor * o = ggml_sum_rows(ctx0, s_q);
+
+ o = ggml_permute (ctx0, o, 2, 0, 1, 3); // [S_v, H_v, n_tokens, n_seqs]
+ s = ggml_transpose(ctx0, s_t); // [S_v, S_v, H_v, n_seqs]
+
+ return {o, s};
}
ggml_tensor * llm_build_qwen3next::build_norm_gated(
// Split Q projection into query and gate
// The split should be along dimension 0 (the feature dimension)
ggml_tensor * Qcur = ggml_view_4d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, 1,
- Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], 0);
+ Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], 0);
+ cb(Qcur, "Qcur_view", il);
+
ggml_tensor * gate =
ggml_view_4d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, 1,
Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], n_embd_head * ggml_element_size(Qcur_full));
- cb(Qcur, "Qcur", il);
cb(gate, "gate", il);
- // Now reshape Qcur to [n_embd_head, n_head, n_tokens] for multi-head attention
- Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
- cb(Qcur, "Qcur_reshaped", il);
-
- // Apply Q normalization
- Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
- cb(Qcur, "Qcur_normed", il);
-
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
- // Apply K normalization
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
- Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
- cb(Kcur, "Kcur_normed", il);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
- // Reshape gate to [n_embd, n_tokens] for the sigmoid gating (flatten the heads)
- gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
- cb(gate, "gate_reshaped", il);
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_normed", il);
- Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+ Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Kcur, "Kcur_normed", il);
- // Apply RoPE
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
- // Attention computation
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,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_pregate", il);
- ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
- cb(gate_sigmoid, "gate_sigmoid", il);
+ // 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);
+
+ 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_sigmoid);
+ cur = ggml_mul(ctx0, cur, gate);
cb(cur, "attn_gated", il);
cur = build_lora_mm(model.layers[il].wo, cur);
cb(z, "z", il);
return { qkv_mixed, z };
-
} else {
// legacy (slower) path
ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
llm_graph_input_rs * inp,
ggml_tensor * cur,
- ggml_tensor * causal_mask,
- ggml_tensor * identity,
- ggml_tensor * diag_mask,
int il) {
const auto * mctx_cur = inp->mctx;
split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
cb(a, "a", il);
- ggml_tensor * beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
+ // TODO: CUDA is missing non-contiguous unary ops. when implemented: remove this cont
+ b = ggml_cont(ctx0, b);
+
+ ggml_tensor * beta = ggml_sigmoid(ctx0, b);
+
+ beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs);
// Reshape a to merge head dimensions: [batch, seq_len, num_k_heads, num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads]
ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
cb(alpha_softplus, "a_softplus", il);
+
ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus
cb(gate, "gate", il);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
- // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state();
-
// Build the convolution states tensor
ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
cb(conv_states, "conv_states", il);
ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
const int64_t conv_kernel_size = conv_kernel->ne[0];
const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
- conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
+
+ conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
cb(conv_states, "conv_states_reshaped", il);
- qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
- cb(qkv_mixed, "qkv_mixed_permuted", il);
+ qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
+ cb(qkv_mixed, "qkv_mixed_transposed", il);
ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
cb(conv_input, "conv_input", il);
ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
cb(conv_states_all, "conv_states_updated", il);
- // Apply SSM convolution
+ ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
+ state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
+ cb(state, "state_predelta", il);
+
ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
cb(conv_output_proper, "conv_output_raw", il);
int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
// Extract the convolved Q, K, V from conv_output
- ggml_tensor * q_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
+ ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_k_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ 0);
+
+ ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_k_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
+
+ ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs,
+ ggml_row_size(conv_qkv_mix->type, head_v_dim),
+ nb1_qkv,
+ nb1_qkv * n_seq_tokens,
+ ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads));
+
cb(q_conv, "q_conv", il);
- ggml_tensor * k_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
- head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
cb(k_conv, "k_conv", il);
- ggml_tensor * v_conv =
- ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
- 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
cb(v_conv, "v_conv", il);
- // Unsqueeze them
- q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
- v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
+ const float eps_norm = hparams.f_norm_rms_eps;
- ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
- state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs);
- cb(state, "state_predelta", il);
+ q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm);
+ k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm);
+
+ //q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ //k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
+ //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
// if head keys and value keys are different, repeat to force tensors into matching shapes
if (num_k_heads != num_v_heads) {
if (n_seq_tokens == 1) {
attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
} else {
- attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+ attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il);
}
ggml_tensor * output = attn_out.first;
ggml_tensor * new_state = attn_out.second;
// Update the recurrent states
ggml_build_forward_expand(gf,
- ggml_cpy(ctx0, new_state,
- ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
- kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
-
- // Reshape both attn_out_final and z to 2D tensors for normalization
- // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
- ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+ ggml_cpy(ctx0, new_state,
+ ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
+ kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
// z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
- ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
+ ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
// Apply gated normalization: self.norm(core_attn_out, z)
- ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
+ ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
// Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
cb(cur, "linear_attn_out", il);
// Reshape back to original dimensions
- cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+ cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
+
return cur;
}
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_up_shexp, NULL, NULL,
model.layers[il].ffn_gate_shexp, NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
NULL,
ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
cb(shared_gate, "shared_expert_gate", il);
- // Apply sigmoid to the gate
shared_gate = ggml_sigmoid(ctx0, shared_gate);
cb(shared_gate, "shared_expert_gate_sigmoid", il);
- // Apply the gate to the shared expert output
ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
cb(ffn_shexp, "ffn_shexp_gated", il);
-#if defined(_MSC_VER)
-#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
-#endif
-
#include "unicode.h"
#include "unicode-data.h"
#include <algorithm>
#include <cassert>
-#include <codecvt>
#include <cstddef>
#include <cstdint>
-#include <locale>
#include <map>
#include <regex>
#include <stdexcept>
return map;
}
-static inline std::wstring unicode_wstring_from_utf8(const std::string & s) {
-#if defined(__clang__)
- // disable C++17 deprecation warning for std::codecvt_utf8
-# pragma clang diagnostic push
-# pragma clang diagnostic ignored "-Wdeprecated-declarations"
-#elif defined(__GNUC__)
-# pragma GCC diagnostic push
-# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#endif
-
- std::wstring_convert<std::codecvt_utf8<wchar_t>> conv;
-
-#if defined(__clang__)
-# pragma clang diagnostic pop
-#elif defined(__GNUC__)
-# pragma GCC diagnostic pop
-#endif
-
- return conv.from_bytes(s);
-}
-
static std::vector<std::string> unicode_byte_encoding_process(const std::vector<std::string> & bpe_words) {
std::vector<std::string> bpe_encoded_words;
for (const auto & word : bpe_words) {
break;
}
}
+ const auto cpts_regex = unicode_cpts_from_utf8(regex_expr);
if (use_collapsed) {
// sanity-check that the original regex does not contain any non-ASCII characters
- const auto cpts_regex = unicode_cpts_from_utf8(regex_expr);
for (size_t i = 0; i < cpts_regex.size(); ++i) {
if (cpts_regex[i] >= 128) {
throw std::runtime_error("Regex includes both unicode categories and non-ASCII characters - not supported");
bpe_offsets = unicode_regex_split_stl(text_collapsed, regex_expr_collapsed, bpe_offsets);
} else {
// no unicode category used, we can use std::wregex directly
- const std::wstring wregex_expr = unicode_wstring_from_utf8(regex_expr);
+ std::wstring wregex_expr(cpts_regex.begin(), cpts_regex.end());
// std::wregex \s does not mach non-ASCII whitespaces, using 0x0B as fallback
std::wstring wtext(cpts.begin(), cpts.end());
overview / pkg / ggml / sources / whisper.cpp / commitdiff