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1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 | diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 87a329f26..ebf29b4ff 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1572,6 +1572,8 @@ static void ggml_compute_forward_mul_mat_id(
// row groups
const int n_ids = ids->ne[0]; // n_expert_used
const int n_as = ne02; // n_expert
+ const char * expert_p_env = getenv("KOUKAI_EXPERT_P");
+ const bool koukai_deduplicate = expert_p_env && expert_p_env[0] != '\0';
void * wdata_cur = params->wdata;
@@ -1648,6 +1650,18 @@ static void ggml_compute_forward_mul_mat_id(
assert(i02 >= 0 && i02 < n_as);
+ if (koukai_deduplicate) {
+ bool duplicate = false;
+ for (int prev = 0; prev < id; ++prev) {
+ const int32_t prev_id = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + prev*ids->nb[0]);
+ if (prev_id == i02) {
+ duplicate = true;
+ break;
+ }
+ }
+ if (duplicate) continue;
+ }
+
MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1};
matrix_row_counts[i02] += 1;
}
@@ -1729,6 +1743,30 @@ static void ggml_compute_forward_mul_mat_id(
current_chunk = atomic_fetch_add_explicit(current_chunk_ctr, 1, memory_order_relaxed);
}
}
+
+ if (koukai_deduplicate) {
+ ggml_barrier(params->threadpool);
+ const int64_t n_rows = ids->ne[0] * ids->ne[1];
+ const size_t row_bytes = ggml_row_size(dst->type, dst->ne[0]);
+ for (int64_t row = ith; row < n_rows; row += nth) {
+ const int64_t token = row / ids->ne[0];
+ const int rank = row % ids->ne[0];
+ const int32_t id = *(const int32_t *) ((const char *) ids->data + token*ids->nb[1] + rank*ids->nb[0]);
+ int first_rank = -1;
+ for (int prev = 0; prev < rank; ++prev) {
+ const int32_t prev_id = *(const int32_t *) ((const char *) ids->data + token*ids->nb[1] + prev*ids->nb[0]);
+ if (prev_id == id) {
+ first_rank = prev;
+ break;
+ }
+ }
+ if (first_rank >= 0) {
+ const size_t dst_off = rank*dst->nb[1] + token*dst->nb[2];
+ const size_t src_off = first_rank*dst->nb[1] + token*dst->nb[2];
+ memcpy((char *) dst->data + dst_off, (const char *) dst->data + src_off, row_bytes);
+ }
+ }
+ }
}
/////////////////////////////////
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 5855393ef..85dda9967 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1,4 +1,5 @@
#include "llama-graph.h"
+#include "models/qwen3moe-koukai.h"
#include "llama-impl.h"
#include "llama-model.h"
@@ -18,12 +19,187 @@
#include <cassert>
#include <cmath>
+#include <cstdio>
+#include <cstdlib>
#include <cstring>
#include <numeric>
#include <sstream>
#include <string>
#include <unordered_set>
+static void qwen3moe_select_experts(
+ ggml_tensor * dst, const ggml_tensor * topk, const ggml_tensor * probs,
+ int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+
+ const auto * config = (const qwen3moe_koukai_config *) userdata;
+ const int64_t n_used = topk->ne[0];
+ for (int64_t token = 0; token < topk->ne[1]; ++token) {
+ const auto * ids = (const int32_t *) ((const uint8_t *) topk->data + token * topk->nb[1]);
+ const auto * p = (const float *) ((const uint8_t *) probs->data + token * probs->nb[1]);
+ int64_t n_selected = n_used;
+ double cumulative = 0.0;
+ for (int64_t rank = 0; rank < n_used; ++rank) {
+ cumulative += p[ids[rank]];
+ if (rank + 1 >= config->expert_min && cumulative >= config->expert_p) {
+ n_selected = rank + 1;
+ break;
+ }
+ }
+ for (int64_t rank = 0; rank < n_used; ++rank) {
+ const int32_t id = rank < n_selected ? ids[rank] : ids[0];
+ *(int32_t *) ((uint8_t *) dst->data + token * dst->nb[1] + rank * dst->nb[0]) = id;
+ }
+ }
+}
+
+static void qwen3moe_mask_duplicate_expert_weights(
+ ggml_tensor * dst, const ggml_tensor * weights, const ggml_tensor * ids,
+ int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ GGML_UNUSED(userdata);
+ if (ith != 0) return;
+
+ for (int64_t token = 0; token < ids->ne[1]; ++token) {
+ const auto * token_ids = (const int32_t *) ((const uint8_t *) ids->data + token * ids->nb[1]);
+ const auto * token_weights = (const float *) ((const uint8_t *) weights->data + token * weights->nb[2]);
+ bool padded = false;
+ for (int64_t rank = 0; rank < ids->ne[0]; ++rank) {
+ if (rank > 0 && token_ids[rank] == token_ids[0]) padded = true;
+ const float value = padded ? 0.0f : token_weights[rank * weights->nb[1] / sizeof(float)];
+ *(float *) ((uint8_t *) dst->data + token * dst->nb[2] + rank * dst->nb[1]) = value;
+ }
+ }
+}
+
+static bool koukai_layer_selected(const char * value, int layer) {
+ if (value == nullptr) return false;
+ std::stringstream items(value);
+ std::string item;
+ while (std::getline(items, item, ',')) {
+ char * end = nullptr;
+ const long first = std::strtol(item.c_str(), &end, 10);
+ if (end == item.c_str()) continue;
+ long last = first;
+ if (*end == '-') last = std::strtol(end + 1, nullptr, 10);
+ if (layer >= first && layer <= last) return true;
+ }
+ return false;
+}
+
+static bool koukai_head_selected(const char * value, int layer, int head) {
+ if (value == nullptr) return false;
+ std::string spec(value);
+ const auto colon = spec.find(':');
+ if (colon != std::string::npos) {
+ const std::string layers = spec.substr(0, colon), heads = spec.substr(colon + 1);
+ if (!koukai_layer_selected(layers.c_str(), layer)) return false;
+ char * end = nullptr;
+ const long first = std::strtol(heads.c_str(), &end, 10);
+ if (end == heads.c_str()) return false;
+ const long last = *end == '-' ? std::strtol(end + 1, nullptr, 10) : first;
+ return head >= first && head <= last;
+ }
+ // 旧 env 形式「35-46,8」: 対象層と落とす head 番号。
+ const auto comma = spec.find(',');
+ if (comma == std::string::npos || spec.find(',', comma + 1) != std::string::npos) return false;
+ const std::string layers = spec.substr(0, comma), heads = spec.substr(comma + 1);
+ if (!koukai_layer_selected(layers.c_str(), layer)) return false;
+ char * end = nullptr;
+ const long first = std::strtol(heads.c_str(), &end, 10);
+ if (end == heads.c_str()) return false;
+ const long last = *end == '-' ? std::strtol(end + 1, nullptr, 10) : first;
+ return head >= first && head <= last;
+}
+
+static void koukai_dump_head_magnitudes(ggml_tensor * dst, const ggml_tensor * src, int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+ const int packed = (int) (intptr_t) userdata;
+ const int layer = packed >> 16;
+ const int head_dim = packed & 0xffff;
+ const char * path = std::getenv("KOUKAI_BI_DUMP");
+ if (path == nullptr || head_dim <= 0 || src->type != GGML_TYPE_F32) return;
+ std::memcpy(dst->data, src->data, ggml_nbytes(src));
+ const auto * values = (const float *) src->data;
+ const int64_t n_heads = src->ne[0] / head_dim;
+ const int64_t n_tokens = src->ne[1] * src->ne[2] * src->ne[3];
+ FILE * file = std::fopen(path, "a");
+ if (file == nullptr) return;
+ for (int64_t h = 0; h < n_heads; ++h) {
+ double sum = 0.0;
+ for (int64_t t = 0; t < n_tokens; ++t) {
+ for (int d = 0; d < head_dim; ++d) {
+ sum += std::abs(values[h * head_dim + d + t * src->ne[0]]);
+ }
+ }
+ const double mean = sum / std::max<int64_t>(1, n_tokens * head_dim);
+ std::fprintf(file, "%d,%lld,%.9g\n", layer, (long long) h, mean);
+ }
+ std::fclose(file);
+}
+
+static void koukai_mask_attention_heads(ggml_tensor * dst, const ggml_tensor * src, int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+ const uint64_t packed = (uint64_t) (uintptr_t) userdata;
+ const int64_t head_dim = (int64_t) (packed >> 32);
+ const uint64_t masked_heads = packed & 0xffffffffu;
+ std::memcpy(dst->data, src->data, ggml_nbytes(src));
+ if (head_dim <= 0 || (src->type != GGML_TYPE_F16 && src->type != GGML_TYPE_F32)) return;
+ for (int64_t i3 = 0; i3 < src->ne[3]; ++i3) for (int64_t i2 = 0; i2 < src->ne[2]; ++i2) {
+ for (int64_t i1 = 0; i1 < src->ne[1]; ++i1) for (int64_t i0 = 0; i0 < src->ne[0]; ++i0) {
+ const int64_t head = i0 / head_dim;
+ if (head >= 32 || !(masked_heads & (uint64_t(1) << head))) continue;
+ const size_t offset = i3 * src->nb[3] + i2 * src->nb[2] + i1 * src->nb[1] + i0 * src->nb[0];
+ if (src->type == GGML_TYPE_F16) {
+ const ggml_fp16_t zero = ggml_fp32_to_fp16(0.0f);
+ std::memcpy((uint8_t *) dst->data + offset, &zero, sizeof(zero));
+ } else {
+ const float zero = 0.0f;
+ std::memcpy((uint8_t *) dst->data + offset, &zero, sizeof(zero));
+ }
+ }
+ }
+}
+
+static void koukai_swa_mask_callback(ggml_tensor * dst, const ggml_tensor * src, int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+ const uint64_t packed = (uint64_t) (uintptr_t) userdata;
+ const int64_t window = (int64_t) (packed >> 32), sink = (int64_t) (packed & 0xffffffffu);
+ std::memcpy(dst->data, src->data, ggml_nbytes(src));
+ if (window <= 0 || (src->type != GGML_TYPE_F16 && src->type != GGML_TYPE_F32)) return;
+ for (int64_t i3 = 0; i3 < src->ne[3]; ++i3) for (int64_t i2 = 0; i2 < src->ne[2]; ++i2) {
+ for (int64_t iq = 0; iq < src->ne[1]; ++iq) {
+ const int64_t min_pos = src->ne[0] - src->ne[1] + iq - window + 1;
+ for (int64_t ik = sink; ik < min_pos; ++ik) {
+ const size_t offset = i3 * src->nb[3] + i2 * src->nb[2] + iq * src->nb[1] + ik * src->nb[0];
+ if (src->type == GGML_TYPE_F16) {
+ const ggml_fp16_t masked = ggml_fp32_to_fp16(-INFINITY);
+ std::memcpy((uint8_t *) dst->data + offset, &masked, sizeof(masked));
+ } else {
+ const float masked = -INFINITY;
+ std::memcpy((uint8_t *) dst->data + offset, &masked, sizeof(masked));
+ }
+ }
+ }
+ }
+}
+
+static ggml_tensor * koukai_apply_swa_mask(ggml_context * ctx, ggml_tensor * mask, int layer) {
+ if (mask == nullptr) return nullptr;
+ if (!koukai_layer_selected(std::getenv("KOUKAI_SWA_LAYERS"), layer)) return mask;
+ const char * window_env = std::getenv("KOUKAI_SWA_WINDOW");
+ const int64_t window = window_env ? std::max<int64_t>(1, std::strtoll(window_env, nullptr, 10)) : 0;
+ if (window == 0) return mask;
+ const char * sink_env = std::getenv("KOUKAI_SWA_SINK");
+ const int64_t sink = sink_env ? std::max<int64_t>(0, std::strtoll(sink_env, nullptr, 10)) : 4;
+ const uint64_t packed = ((uint64_t) window << 32) | (uint32_t) sink;
+ return ggml_map_custom1(ctx, mask, koukai_swa_mask_callback, 1, (void *) (uintptr_t) packed);
+}
+
// dedup helpers
static ggml_tensor * build_attn_inp_kq_mask(
@@ -1622,12 +1760,13 @@ llm_graph_qkv llm_graph_context::build_qkv(
int64_t n_embd_head,
int64_t n_head,
int64_t n_head_kv,
- int il) const {
+ int il,
+ bool build_kv) const {
return build_qkv(layer, cur,
n_embd_head, n_head,
n_embd_head, n_head_kv,
n_embd_head, n_head_kv,
- il);
+ il, true, build_kv);
}
llm_graph_qkv llm_graph_context::build_qkv(
@@ -1640,11 +1779,12 @@ llm_graph_qkv llm_graph_context::build_qkv(
int64_t n_embd_head_v,
int64_t n_head_v,
int il,
- bool reshape) const {
+ bool reshape,
+ bool build_kv) const {
const int64_t n_embd_q = n_embd_head_q * n_head_q;
const int64_t n_embd_k = n_embd_head_k * n_head_k;
- ggml_tensor * Qcur, * Kcur, * Vcur;
+ ggml_tensor * Qcur = nullptr, * Kcur = nullptr, * Vcur = nullptr;
if (layer.wqkv) {
// fused QKV path
@@ -1700,45 +1840,47 @@ llm_graph_qkv llm_graph_context::build_qkv(
Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
cb(Qcur, "Qcur_clamped", il);
}
- Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
- if (reshape) {
- cb(Kcur, "Kcur", il);
- }
- if (layer.wk_b) {
- Kcur = ggml_add(ctx0, Kcur, layer.wk_b);
+ if (build_kv) {
+ Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);
if (reshape) {
cb(Kcur, "Kcur", il);
}
- }
- if (reshape && hparams.f_clamp_kqv > 0.0f) {
- Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
- cb(Kcur, "Kcur_clamped", il);
- }
- Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
- if (reshape) {
- cb(Vcur, "Vcur", il);
- }
- if (layer.wv_b) {
- Vcur = ggml_add(ctx0, Vcur, layer.wv_b);
+ if (layer.wk_b) {
+ Kcur = ggml_add(ctx0, Kcur, layer.wk_b);
+ if (reshape) {
+ cb(Kcur, "Kcur", il);
+ }
+ }
+ if (reshape && hparams.f_clamp_kqv > 0.0f) {
+ Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(Kcur, "Kcur_clamped", il);
+ }
+ Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);
if (reshape) {
cb(Vcur, "Vcur", il);
}
- }
- if (reshape && hparams.f_clamp_kqv > 0.0f) {
- Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
- cb(Vcur, "Vcur_clamped", il);
+ if (layer.wv_b) {
+ Vcur = ggml_add(ctx0, Vcur, layer.wv_b);
+ if (reshape) {
+ cb(Vcur, "Vcur", il);
+ }
+ }
+ if (reshape && hparams.f_clamp_kqv > 0.0f) {
+ Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(Vcur, "Vcur_clamped", il);
+ }
}
if (reshape) {
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_q, n_head_q, n_tokens);
- Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_k, n_tokens);
- Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_v, n_tokens);
+ if (Kcur) Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_k, n_tokens);
+ if (Vcur) Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_v, n_tokens);
}
}
if (reshape) {
cb(Qcur, "Qcur", il);
- cb(Kcur, "Kcur", il);
- cb(Vcur, "Vcur", il);
+ if (Kcur) cb(Kcur, "Kcur", il);
+ if (Vcur) cb(Vcur, "Vcur", il);
}
return { Qcur, Kcur, Vcur };
@@ -1965,7 +2107,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
ggml_tensor * up_exps_s,
ggml_tensor * gate_exps_s,
ggml_tensor * down_exps_s,
- ggml_tensor * selected_experts_in) const {
+ ggml_tensor * selected_experts_in,
+ const qwen3moe_koukai_config * koukai) const {
return build_moe_ffn(
cur,
gate_inp, /* gate_inp_b */ nullptr,
@@ -1986,7 +2129,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
up_exps_s,
gate_exps_s,
down_exps_s,
- selected_experts_in
+ selected_experts_in,
+ koukai
);
}
@@ -2014,7 +2158,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
ggml_tensor * up_exps_s,
ggml_tensor * gate_exps_s,
ggml_tensor * down_exps_s,
- ggml_tensor * selected_experts_in) const {
+ ggml_tensor * selected_experts_in,
+ const qwen3moe_koukai_config * koukai) const {
const int64_t n_embd = cur->ne[0];
const int64_t n_tokens = cur->ne[1];
const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN
@@ -2107,6 +2252,10 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
ggml_tensor * selected_experts = selected_experts_in;
if (selected_experts == nullptr) {
selected_experts = ggml_argsort_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
+ if (koukai && koukai->expert_p_enabled) {
+ selected_experts = ggml_map_custom2(ctx0, selected_experts, probs,
+ qwen3moe_select_experts, 1, (void *) koukai);
+ }
cb(selected_experts->src[0], "ffn_moe_argsort", il);
}
cb(selected_experts, "ffn_moe_topk", il);
@@ -2121,6 +2270,10 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
}
ggml_tensor * weights = ggml_get_rows(ctx0, probs, selected_experts); // [1, n_expert_used, n_tokens]
+ if (koukai && koukai->expert_p_enabled) {
+ weights = ggml_map_custom2(ctx0, weights, selected_experts,
+ qwen3moe_mask_duplicate_expert_weights, 1, (void *) koukai);
+ }
cb(weights, "ffn_moe_weights", il);
@@ -2599,6 +2752,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
int64_t n_kv_max,
float kq_scale,
int il) const {
+ kq_mask = koukai_apply_swa_mask(ctx0, kq_mask, il);
const bool v_trans = v->nb[1] > v->nb[2];
// split the batch into streams if needed
@@ -2721,6 +2875,25 @@ ggml_tensor * llm_graph_context::build_attn_mha(
}
}
+ const int64_t head_dim = hparams.n_embd_head_v(il);
+ if (std::getenv("KOUKAI_BI_DUMP") != nullptr && head_dim > 0 && head_dim <= 0xffff && il < 0x7fff) {
+ ggml_tensor * dump_src = cur->type == GGML_TYPE_F32 ? cur : ggml_cast(ctx0, cur, GGML_TYPE_F32);
+ const int packed = (il << 16) | (int) head_dim;
+ cur = ggml_map_custom1(ctx0, dump_src, koukai_dump_head_magnitudes, 1, (void *) (intptr_t) packed);
+ }
+ const char * head_mask = std::getenv("KOUKAI_HEAD_MASK");
+ if (head_mask != nullptr && head_dim > 0 && cur->ne[0] % head_dim == 0) {
+ uint64_t masked_heads = 0;
+ const int64_t n_heads = cur->ne[0] / head_dim;
+ for (int64_t head = 0; head < n_heads && head < 32; ++head) {
+ if (koukai_head_selected(head_mask, il, head)) masked_heads |= uint64_t(1) << head;
+ }
+ if (masked_heads != 0) {
+ const uint64_t packed = ((uint64_t) head_dim << 32) | masked_heads;
+ cur = ggml_map_custom1(ctx0, cur, koukai_mask_attention_heads, 1, (void *) (uintptr_t) packed);
+ }
+ }
+
ggml_build_forward_expand(gf, cur);
return cur;
@@ -2846,17 +3018,18 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_tensor * v_cur,
ggml_tensor * kq_b,
ggml_tensor * sinks,
- ggml_tensor * v_mla, // TODO: remove
+ ggml_tensor * v_mla, // TODO: remove
float kq_scale,
- int il) const {
+ int il,
+ bool reuse_kv) const {
GGML_ASSERT(v_mla == nullptr);
if (inp->self_k_rot) {
q_cur = llama_mul_mat_hadamard(ctx0, q_cur, inp->self_k_rot);
- k_cur = llama_mul_mat_hadamard(ctx0, k_cur, inp->self_k_rot);
+ if (!reuse_kv) k_cur = llama_mul_mat_hadamard(ctx0, k_cur, inp->self_k_rot);
}
- if (inp->self_v_rot) {
+ if (inp->self_v_rot && !reuse_kv) {
v_cur = llama_mul_mat_hadamard(ctx0, v_cur, inp->self_v_rot);
}
@@ -2864,13 +3037,15 @@ ggml_tensor * llm_graph_context::build_attn(
// by doing so, the number of splits in the graph is reduced
// expand k later to enable rope fusion which directly writes into k-v cache
ggml_build_forward_expand(gf, q_cur);
- ggml_build_forward_expand(gf, v_cur);
- ggml_build_forward_expand(gf, k_cur);
+ if (!reuse_kv) {
+ ggml_build_forward_expand(gf, v_cur);
+ ggml_build_forward_expand(gf, k_cur);
+ }
const auto * mctx_cur = inp->mctx;
// store to KV cache
- {
+ if (!reuse_kv) {
const auto & k_idxs = inp->get_k_idxs();
const auto & v_idxs = inp->get_v_idxs();
diff --git a/src/llama-graph.h b/src/llama-graph.h
index cc4110639..6e2067651 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -1,5 +1,7 @@
#pragma once
+struct qwen3moe_koukai_config;
+
#include "llama-arch.h"
#include "llama-batch.h"
#include "llama-hparams.h"
@@ -1078,7 +1080,8 @@ struct llm_graph_context {
int64_t n_embd_head,
int64_t n_head,
int64_t n_head_kv,
- int il) const;
+ int il,
+ bool build_kv = true) const;
// Set reshape to false to return contiguous projections before clamp/reshape.
llm_graph_qkv build_qkv(
@@ -1091,7 +1094,8 @@ struct llm_graph_context {
int64_t n_embd_head_v,
int64_t n_head_v,
int il,
- bool reshape = true) const;
+ bool reshape = true,
+ bool build_kv = true) const;
ggml_tensor * build_ffn(
ggml_tensor * cur,
@@ -1129,7 +1133,8 @@ struct llm_graph_context {
ggml_tensor * up_exps_s = nullptr,
ggml_tensor * gate_exps_s = nullptr,
ggml_tensor * down_exps_s = nullptr,
- ggml_tensor * selected_experts_in = nullptr) const;
+ ggml_tensor * selected_experts_in = nullptr,
+ const qwen3moe_koukai_config * koukai = nullptr) const;
ggml_tensor * build_moe_ffn(
ggml_tensor * cur,
@@ -1155,7 +1160,8 @@ struct llm_graph_context {
ggml_tensor * up_exps_s = nullptr,
ggml_tensor * gate_exps_s = nullptr,
ggml_tensor * down_exps_s = nullptr,
- ggml_tensor * selected_experts_in = nullptr) const;
+ ggml_tensor * selected_experts_in = nullptr,
+ const qwen3moe_koukai_config * koukai = nullptr) const;
//
// inputs
@@ -1219,7 +1225,8 @@ struct llm_graph_context {
ggml_tensor * sinks, // [n_head_q]
ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v] // TODO: remove
float kq_scale,
- int il) const;
+ int il,
+ bool reuse_kv = false) const;
llm_graph_input_attn_k * build_attn_inp_k() const;
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index a342ee119..336d2b7a5 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -173,19 +173,33 @@ llama_kv_cache::llama_kv_cache(
continue;
}
- if (share && other) {
+ if (share) {
const int32_t il_share = share(il);
if (il_share >= 0) {
- const auto & layer_share = other->layers[other->map_layer_ids[il_share]];
-
- LLAMA_LOG_WARN("%s: layer %3d: sharing with layer %d. k = %p, v = %p\n", __func__, il, il_share,
- layer_share.k->data, layer_share.v->data);
-
- map_layer_ids[il] = layers.size();
+ if (other) {
+ const auto & layer_share = other->layers[other->map_layer_ids[il_share]];
+ LLAMA_LOG_WARN("%s: layer %3d: sharing with layer %d. k = %p, v = %p\n", __func__, il, il_share,
+ layer_share.k->data, layer_share.v->data);
+
+ map_layer_ids[il] = layers.size();
+ layers.push_back(layer_share);
+ layers.back().il = il;
+ } else {
+ if (il_share >= (int32_t) il || (uint32_t) il_share >= map_layer_ids.size()) {
+ throw std::runtime_error("KV share must reference an earlier layer in the same cache");
+ }
+ const uint32_t shared_id = map_layer_ids[il_share];
+ if (shared_id >= layers.size() || !layers[shared_id].k || !layers[shared_id].v) {
+ throw std::runtime_error("KV share source layer has no allocated KV cache");
+ }
+ const auto & layer_share = layers[shared_id];
+ LLAMA_LOG_WARN("%s: layer %3d: sharing KV with lower layer %d\n", __func__, il, il_share);
- layers.push_back(layer_share);
- layers.back().il = il;
+ map_layer_ids[il] = layers.size();
+ layers.push_back(layer_share);
+ layers.back().il = il;
+ }
continue;
}
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 91bb5e7cc..87df62dc7 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -1111,9 +1111,12 @@ struct ggml_tensor * llama_model_loader::create_tensor(
const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) {
// set below, before buft_for_tensor() runs
bool is_lazy = false;
+ const auto row_selection_it = tensor_row_selections.find(tn.str());
+ const bool has_row_selection = row_selection_it != tensor_row_selections.end();
+ const bool is_compact = has_row_selection;
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
- const ctx_key key { buft, is_lazy };
+ const ctx_key key { buft, is_lazy, is_compact };
auto it = ctx_map.find(key);
if (it == ctx_map.end()) {
@@ -1287,7 +1290,8 @@ struct ggml_tensor * llama_model_loader::create_tensor(
return nullptr;
}
ggml_type type = GGML_TYPE_F32;
- const int64_t tid = gguf_find_tensor(metadata, tn.str().c_str());
+ const std::string tensor_name = has_row_selection ? row_selection_it->second.source_name : tn.str();
+ const int64_t tid = gguf_find_tensor(metadata, tensor_name.c_str());
if (tid != -1) {
type = gguf_get_tensor_type(metadata, tid);
}
@@ -1327,7 +1331,17 @@ struct ggml_tensor * llama_model_loader::create_tensor(
}
LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str());
- const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED), flags & TENSOR_ALLOW_RESHAPE);
+ std::string source_name = tn.str();
+ std::vector<int64_t> source_ne(ne);
+ if (has_row_selection) {
+ const auto & selection = row_selection_it->second;
+ source_name = selection.source_name;
+ if (source_ne.size() != 2 || source_ne[1] != (int64_t) selection.rows.size()) {
+ throw std::runtime_error("row-selected tensor must be a 2D matrix with one selected row per vocabulary id");
+ }
+ source_ne[1] = selection.source_rows;
+ }
+ const struct ggml_tensor * cur = check_tensor_dims(source_name, source_ne, !(flags & TENSOR_NOT_REQUIRED), flags & TENSOR_ALLOW_RESHAPE);
if (cur == NULL) {
return NULL;
}
@@ -1338,7 +1352,13 @@ struct ggml_tensor * llama_model_loader::create_tensor(
}
ggml_tensor t_meta = *cur;
- if (flags & TENSOR_ALLOW_RESHAPE) {
+ if (has_row_selection) {
+ t_meta.ne[1] = (int64_t) row_selection_it->second.rows.size();
+ t_meta.nb[1] = ggml_row_size(t_meta.type, t_meta.ne[0]);
+ for (size_t dim = 2; dim < GGML_MAX_DIMS; dim++) {
+ t_meta.nb[dim] = t_meta.nb[dim - 1] * t_meta.ne[dim - 1];
+ }
+ } else if (flags & TENSOR_ALLOW_RESHAPE) {
for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) {
t_meta.ne[dim] = dim < ne.size() ? ne.begin()[dim] : 1;
if (dim == 0) {
@@ -1351,7 +1371,8 @@ struct ggml_tensor * llama_model_loader::create_tensor(
}
}
- GGML_ASSERT(ggml_nbytes(&t_meta) == ggml_nbytes(cur));
+ ggml_set_name(&t_meta, tn.str().c_str());
+ GGML_ASSERT(has_row_selection || ggml_nbytes(&t_meta) == ggml_nbytes(cur));
ggml_backend_buffer_type_t buft = buft_for_tensor(&t_meta);
if (buft == nullptr) {
@@ -1435,7 +1456,12 @@ void llama_model_loader::init_mappings(bool prefetch, llama_mlocks * mlock_mmaps
// compute the total size of all tensors for progress reporting
for (const auto & it : weights_map) {
- size_data += ggml_nbytes(it.second.tensor);
+ size_t tensor_size = ggml_nbytes(it.second.tensor);
+ const auto selection = tensor_row_selections.find(it.first);
+ if (selection != tensor_row_selections.end() && selection->second.source_name == it.first) {
+ tensor_size = ggml_row_size(it.second.tensor->type, it.second.tensor->ne[0]) * selection->second.rows.size();
+ }
+ size_data += tensor_size;
}
}
@@ -1616,8 +1642,13 @@ bool llama_model_loader::load_all_data(
}
for (struct ggml_tensor * cur : tensors) {
- const auto * weight = get_weight(ggml_get_name(cur));
+ const auto row_selection_it = tensor_row_selections.find(ggml_get_name(cur));
+ const auto * weight = get_weight(row_selection_it == tensor_row_selections.end()
+ ? ggml_get_name(cur) : row_selection_it->second.source_name.c_str());
if (weight == nullptr) {
+ if (row_selection_it != tensor_row_selections.end()) {
+ throw std::runtime_error(format("missing source tensor '%s' for selected rows", row_selection_it->second.source_name.c_str()));
+ }
// this can happen with split experts models
continue;
}
@@ -1630,6 +1661,37 @@ bool llama_model_loader::load_all_data(
size_t n_size = ggml_nbytes(cur);
+ if (row_selection_it != tensor_row_selections.end()) {
+ const auto & selection = row_selection_it->second;
+ const size_t row_size = ggml_row_size(cur->type, cur->ne[0]);
+ if (row_size * selection.rows.size() != n_size) {
+ throw std::runtime_error(format("row selection size mismatch for tensor '%s'", ggml_get_name(cur)));
+ }
+
+ std::vector<uint8_t> selected_rows(n_size);
+ if (use_mmap) {
+ const auto & mapping = mappings.at(weight->idx);
+ const uint8_t * source = (const uint8_t *) mapping->addr() + weight->offs;
+ for (size_t i = 0; i < selection.rows.size(); ++i) {
+ std::memcpy(selected_rows.data() + i * row_size, source + (size_t) selection.rows[i] * row_size, row_size);
+ }
+ } else {
+ const auto & file = files.at(weight->idx);
+ for (size_t i = 0; i < selection.rows.size(); ++i) {
+ file->seek(weight->offs + (size_t) selection.rows[i] * row_size, SEEK_SET);
+ file->read_raw(selected_rows.data() + i * row_size, row_size);
+ }
+ }
+ ggml_backend_tensor_set(cur, selected_rows.data(), 0, n_size);
+ if (check_tensors && !ggml_validate_row_data(cur->type, selected_rows.data(), n_size)) {
+ throw std::runtime_error(format("tensor '%s' has invalid selected row data", ggml_get_name(cur)));
+ }
+ // Separate output tensor is copied into a compact buffer; release its original mmap pages.
+ if (use_mmap && selection.source_name == ggml_get_name(cur)) unmap_weight(*weight);
+ size_done += n_size;
+ continue;
+ }
+
const bool from_mapping = use_mmap || lazy.has(cur);
if (from_mapping) {
diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h
index 9e51d0ce7..2a32338d7 100644
--- a/src/llama-model-loader.h
+++ b/src/llama-model-loader.h
@@ -124,6 +124,12 @@ struct llama_model_loader {
llama_mmaps mappings;
std::map<std::string, llama_tensor_weight, weight_name_comparer> weights_map;
+ struct tensor_row_selection {
+ std::string source_name;
+ int64_t source_rows;
+ std::vector<int32_t> rows;
+ };
+ std::unordered_map<std::string, tensor_row_selection> tensor_row_selections;
std::unordered_map<std::string, llama_model_kv_override> kv_overrides;
const llama_model_tensor_buft_override * tensor_buft_overrides;
@@ -151,6 +157,7 @@ struct llama_model_loader {
struct ctx_key {
ggml_backend_buffer_type_t buft;
bool lazy;
+ bool compact;
};
struct ctx_key_comparator {
@@ -158,6 +165,9 @@ struct llama_model_loader {
if (lhs.lazy != rhs.lazy) {
return lhs.lazy < rhs.lazy;
}
+ if (lhs.compact != rhs.compact) {
+ return lhs.compact < rhs.compact;
+ }
return strcmp(ggml_backend_buft_name(lhs.buft), ggml_backend_buft_name(rhs.buft)) < 0;
}
};
@@ -234,6 +244,21 @@ struct llama_model_loader {
bool required,
bool allow_reshape) const;
+ void set_tensor_row_selection(
+ const std::string & tensor_name,
+ const std::string & source_name,
+ int64_t source_rows,
+ const std::vector<int32_t> & rows) {
+ if (tensor_name.empty() || source_name.empty() || source_rows <= 0 || rows.empty()) {
+ throw std::runtime_error("invalid tensor row selection");
+ }
+ tensor_row_selections[tensor_name] = { source_name, source_rows, rows };
+ }
+
+ bool has_tensor_row_selection() const {
+ return !tensor_row_selections.empty();
+ }
+
struct ggml_tensor * create_tensor(
const llama_hparams & hparams, const buft_list_t * buft_list_cpu, const buft_list_t * buft_list_input, const buft_list_t * buft_list_output,
const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 0adc07449..e395ed21b 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1701,7 +1701,7 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
}
}
- ml.init_mappings(true, use_mlock ? &pimpl->mlock_mmaps : nullptr);
+ ml.init_mappings(!ml.has_tensor_row_selection(), use_mlock ? &pimpl->mlock_mmaps : nullptr);
pimpl->mappings.reserve(ml.mappings.size());
// create the backend buffers
@@ -1743,7 +1743,7 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
// a lazy context is mapped whatever the load mode, but the memory-fit pass maps nothing
const bool is_lazy_mapped = ctx_key.lazy && !ml.no_alloc;
- if ((ml.use_mmap || is_lazy_mapped) && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
+ if ((ml.use_mmap || is_lazy_mapped) && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft && !ctx_key.compact) {
GGML_ASSERT(!ml.no_alloc);
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
// only the mmap region containing the tensors in the model is mapped to the backend buffer
@@ -2651,6 +2651,16 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
}
}
+ if (arch == LLM_ARCH_QWEN3MOE) {
+ const auto & koukai = static_cast<const llama_model_qwen3moe &>(*this).koukai;
+ if (!koukai.skip_attn.empty() || !koukai.skip_layer.empty()) {
+ filter = [&koukai](int32_t il) { return !koukai.skip_attention(il); };
+ }
+ if (!koukai.kv_share.empty()) {
+ share = [&koukai](int32_t il) { return koukai.shares_kv(il) ? il - 1 : -1; };
+ }
+ }
+
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
GGML_ASSERT(hparams.is_swa_any());
diff --git a/src/models/models.h b/src/models/models.h
index 87195fddd..cd8d71afd 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -3,6 +3,7 @@
#include "llama-model.h"
#include "llama-graph.h"
#include "llama-model-loader.h"
+#include "qwen3moe-koukai.h"
// note: almost all graphs require at least sqrtf, so include cmath globally
#include <cmath>
@@ -585,10 +586,12 @@ struct llama_model_qwen3 : public llama_model_base {
struct llama_model_qwen3moe : public llama_model_base {
llama_model_qwen3moe(const struct llama_model_params & params) : llama_model_base(params) {}
+ qwen3moe_koukai_config koukai;
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
struct graph : public llm_graph_context {
+ std::vector<qwen3moe_bi_tap_context> bi_tap_contexts;
graph(const llama_model & model, const llm_graph_params & params);
};
diff --git a/src/models/qwen3moe-koukai.h b/src/models/qwen3moe-koukai.h
new file mode 100644
index 000000000..4fa5c6935
--- /dev/null
+++ b/src/models/qwen3moe-koukai.h
@@ -0,0 +1,148 @@
+#pragma once
+
+#include <cerrno>
+#include <cctype>
+#include <cmath>
+#include <cstdint>
+#include <cstdlib>
+#include <fstream>
+#include <set>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+struct qwen3moe_koukai_config {
+ std::set<int32_t> skip_attn;
+ std::set<int32_t> skip_ffn;
+ std::set<int32_t> skip_layer;
+ std::set<int32_t> kv_share;
+ std::vector<int32_t> vocab_keep;
+ float expert_p = 1.0f;
+ int32_t expert_min = 1;
+ bool expert_p_enabled = false;
+ std::string bi_dump;
+
+ static const char * env(const char * name) {
+ const char * value = std::getenv(name);
+ return value && value[0] != '\0' ? value : nullptr;
+ }
+
+ static int32_t parse_integer(const std::string & value, const char * name) {
+ errno = 0;
+ char * end = nullptr;
+ const long parsed = std::strtol(value.c_str(), &end, 10);
+ while (end && std::isspace((unsigned char) *end)) ++end;
+ if (errno || end == value.c_str() || !end || *end != '\0' || parsed < INT32_MIN || parsed > INT32_MAX) {
+ throw std::runtime_error(std::string("invalid ") + name + " value: " + value);
+ }
+ return (int32_t) parsed;
+ }
+
+ static std::set<int32_t> parse_layers(const char * name, int32_t n_layer) {
+ std::set<int32_t> result;
+ const char * raw = env(name);
+ if (!raw) return result;
+
+ std::string value(raw);
+ size_t begin = 0;
+ while (begin < value.size()) {
+ const size_t end = value.find(',', begin);
+ std::string item = value.substr(begin, end == std::string::npos ? end : end - begin);
+ const size_t first = item.find_first_not_of(" \t\r\n");
+ const size_t last = item.find_last_not_of(" \t\r\n");
+ if (first == std::string::npos) {
+ throw std::runtime_error(std::string("invalid ") + name + " layer list");
+ }
+ item = item.substr(first, last - first + 1);
+
+ const size_t dash = item.find('-');
+ const int32_t lo = parse_integer(item.substr(0, dash), name);
+ const int32_t hi = dash == std::string::npos ? lo : parse_integer(item.substr(dash + 1), name);
+ if (lo < 0 || hi < lo || hi >= n_layer) {
+ throw std::runtime_error(std::string(name) + " layer is outside [0," + std::to_string(n_layer - 1) + "]");
+ }
+ for (int32_t il = lo; il <= hi; ++il) result.insert(il);
+ if (end == std::string::npos) break;
+ begin = end + 1;
+ }
+ return result;
+ }
+
+ static std::vector<int32_t> parse_vocab(const char * path, int64_t n_vocab) {
+ std::vector<int32_t> rows;
+ std::set<int32_t> seen;
+ std::ifstream input(path);
+ if (!input) throw std::runtime_error(std::string("cannot open KOUKAI_VOCAB_KEEP file: ") + path);
+
+ std::string line;
+ while (std::getline(input, line)) {
+ const size_t first = line.find_first_not_of(" \t\r\n");
+ if (first == std::string::npos || line[first] == '#') continue;
+ const size_t last = line.find_last_not_of(" \t\r\n");
+ const int32_t id = parse_integer(line.substr(first, last - first + 1), "KOUKAI_VOCAB_KEEP");
+ if (id < 0 || id >= n_vocab) {
+ throw std::runtime_error("KOUKAI_VOCAB_KEEP token id is outside the model vocabulary");
+ }
+ if (seen.insert(id).second) rows.push_back(id);
+ }
+ if (input.bad()) throw std::runtime_error(std::string("error reading KOUKAI_VOCAB_KEEP file: ") + path);
+ if (rows.empty()) throw std::runtime_error("KOUKAI_VOCAB_KEEP must contain at least one token id");
+ return rows;
+ }
+
+ static qwen3moe_koukai_config from_env(int32_t n_layer, int64_t n_vocab, int32_t n_expert_used) {
+ qwen3moe_koukai_config result;
+ result.skip_attn = parse_layers("KOUKAI_SKIP_ATTN", n_layer);
+ result.skip_ffn = parse_layers("KOUKAI_SKIP_FFN", n_layer);
+ result.skip_layer = parse_layers("KOUKAI_SKIP_LAYER", n_layer);
+ result.kv_share = parse_layers("KOUKAI_KV_SHARE", n_layer);
+ for (int32_t il : result.kv_share) {
+ if (il == 0) throw std::runtime_error("KOUKAI_KV_SHARE layer 0 has no lower layer");
+ if (result.skip_attention(il - 1) && !result.skip_attention(il)) {
+ throw std::runtime_error("KOUKAI_KV_SHARE requires the lower layer to retain its KV cache");
+ }
+ }
+
+ if (const char * value = env("KOUKAI_EXPERT_P")) {
+ errno = 0;
+ char * end = nullptr;
+ const float parsed = std::strtof(value, &end);
+ if (errno || end == value || !end || *end != '\0' || !std::isfinite(parsed) || parsed < 0.0f || parsed > 1.0f) {
+ throw std::runtime_error("KOUKAI_EXPERT_P must be between 0.0 and 1.0");
+ }
+ result.expert_p = parsed;
+ result.expert_p_enabled = true;
+ result.expert_min = 1;
+ if (const char * minimum = env("KOUKAI_EXPERT_MIN")) {
+ result.expert_min = parse_integer(minimum, "KOUKAI_EXPERT_MIN");
+ }
+ if (result.expert_min < 1 || result.expert_min > n_expert_used) {
+ throw std::runtime_error("KOUKAI_EXPERT_MIN must be between 1 and the model's original expert count");
+ }
+ }
+
+ if (const char * path = env("KOUKAI_VOCAB_KEEP")) {
+ result.vocab_keep = parse_vocab(path, n_vocab);
+ }
+ if (const char * path = env("KOUKAI_BI_DUMP")) result.bi_dump = path;
+ return result;
+ }
+
+ bool skip_attention(int32_t il) const {
+ return skip_attn.count(il) != 0 || skip_layer.count(il) != 0;
+ }
+
+ bool skip_feed_forward(int32_t il) const {
+ return skip_ffn.count(il) != 0 || skip_layer.count(il) != 0;
+ }
+
+ bool shares_kv(int32_t il) const {
+ return kv_share.count(il) != 0;
+ }
+};
+
+struct qwen3moe_bi_tap_context {
+ const qwen3moe_koukai_config * config;
+ int32_t layer;
+ bool capture;
+};
diff --git a/src/models/qwen3moe.cpp b/src/models/qwen3moe.cpp
index a6a3381e5..672067833 100644
--- a/src/models/qwen3moe.cpp
+++ b/src/models/qwen3moe.cpp
@@ -1,5 +1,95 @@
#include "models.h"
+#include <algorithm>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <mutex>
+
+static void qwen3moe_koukai_scatter_logits(ggml_tensor * dst, int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+
+ const auto * config = (const qwen3moe_koukai_config *) userdata;
+ const ggml_tensor * selected = dst->src[0];
+ const int64_t n_vocab = dst->ne[0];
+ for (int64_t token = 0; token < dst->ne[1]; ++token) {
+ auto * output = (float *) ((uint8_t *) dst->data + token * dst->nb[1]);
+ const auto * input = (const float *) ((const uint8_t *) selected->data + token * selected->nb[1]);
+ std::fill(output, output + n_vocab, -INFINITY);
+ for (size_t i = 0; i < config->vocab_keep.size(); ++i) {
+ output[config->vocab_keep[i]] = input[i];
+ }
+ }
+}
+
+static const float * qwen3moe_koukai_row(const ggml_tensor * tensor, int64_t token) {
+ return (const float *) ((const uint8_t *) tensor->data + token * tensor->nb[1]);
+}
+
+static void qwen3moe_koukai_bi_tap(ggml_tensor * dst, int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+
+ const auto * tap = (const qwen3moe_bi_tap_context *) userdata;
+ const ggml_tensor * block_out = dst->src[0];
+ const ggml_tensor * block_in = dst->src[1];
+ const ggml_tensor * attn_add = dst->src[2];
+ const ggml_tensor * attn_res = dst->src[3];
+ const ggml_tensor * ffn_add = dst->src[4];
+ const ggml_tensor * ffn_res = dst->src[5];
+ std::memcpy(dst->data, block_out->data, ggml_nbytes(block_out));
+
+ static std::mutex file_mutex;
+ static std::map<std::string, bool> active_files;
+ std::lock_guard<std::mutex> lock(file_mutex);
+ bool & active = active_files[tap->config->bi_dump];
+ if (!tap->capture) {
+ active = false;
+ return;
+ }
+
+ std::ofstream out(tap->config->bi_dump, active ? std::ios::app : std::ios::trunc);
+ if (!out) {
+ std::fprintf(stderr, "cannot write KOUKAI_BI_DUMP file: %s\n", tap->config->bi_dump.c_str());
+ active = false;
+ return;
+ }
+ if (!active) {
+ out << "layer\ttoken\tcosine\tattn_add_over_residual\tffn_add_over_residual\n";
+ }
+ active = true;
+
+ if (block_out->type != GGML_TYPE_F32 || block_in->type != GGML_TYPE_F32 ||
+ attn_add->type != GGML_TYPE_F32 || attn_res->type != GGML_TYPE_F32 ||
+ ffn_add->type != GGML_TYPE_F32 || ffn_res->type != GGML_TYPE_F32) {
+ return;
+ }
+ const int64_t n_embd = block_in->ne[0];
+ for (int64_t token = 0; token < block_in->ne[1]; ++token) {
+ const float * x = qwen3moe_koukai_row(block_in, token);
+ const float * y = qwen3moe_koukai_row(block_out, token);
+ const float * a = qwen3moe_koukai_row(attn_add, token);
+ const float * ar = qwen3moe_koukai_row(attn_res, token);
+ const float * f = qwen3moe_koukai_row(ffn_add, token);
+ const float * fr = qwen3moe_koukai_row(ffn_res, token);
+ double dot = 0.0, x2 = 0.0, y2 = 0.0, a2 = 0.0, ar2 = 0.0, f2 = 0.0, fr2 = 0.0;
+ for (int64_t i = 0; i < n_embd; ++i) {
+ dot += (double) x[i] * y[i];
+ x2 += (double) x[i] * x[i];
+ y2 += (double) y[i] * y[i];
+ a2 += (double) a[i] * a[i];
+ ar2 += (double) ar[i] * ar[i];
+ f2 += (double) f[i] * f[i];
+ fr2 += (double) fr[i] * fr[i];
+ }
+ const double cosine = dot / std::sqrt(std::max(1e-30, x2 * y2));
+ const double attn_ratio = std::sqrt(a2) / std::max(1e-30, std::sqrt(ar2));
+ const double ffn_ratio = std::sqrt(f2) / std::max(1e-30, std::sqrt(fr2));
+ out << tap->layer << '\t' << token << '\t' << cosine << '\t' << attn_ratio << '\t' << ffn_ratio << '\n';
+ }
+}
+
void llama_model_qwen3moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -11,17 +101,31 @@ void llama_model_qwen3moe::load_arch_hparams(llama_model_loader & ml) {
}
}
-void llama_model_qwen3moe::load_arch_tensors(llama_model_loader &) {
+void llama_model_qwen3moe::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
+ koukai = qwen3moe_koukai_config::from_env(n_layer, n_vocab, n_expert_used);
+
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 auto output_tn = tn(LLM_TENSOR_OUTPUT, "weight");
+ if (!koukai.vocab_keep.empty()) {
+ const auto embd_tn = tn(LLM_TENSOR_TOKEN_EMBD, "weight");
+ const bool has_output_tensor = ml.get_tensor_meta(output_tn.str().c_str()) != nullptr;
+ ml.set_tensor_row_selection(output_tn.str(),
+ has_output_tensor ? output_tn.str() : embd_tn.str(), n_vocab, koukai.vocab_keep);
+ output = create_tensor(output_tn, {n_embd, (int64_t) koukai.vocab_keep.size()}, has_output_tensor ? 0 : TENSOR_DUPLICATED);
+ if (output == nullptr) {
+ throw std::runtime_error("KOUKAI_VOCAB_KEEP could not load the output matrix");
+ }
+ } else {
+ output = create_tensor(output_tn, {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);
+ }
}
for (int i = 0; i < n_layer; ++i) {
@@ -77,64 +181,102 @@ llama_model_qwen3moe::graph::graph(const llama_model & model, const llm_graph_pa
ggml_tensor * inp_out_ids = build_inp_out_ids();
+ const auto & koukai = static_cast<const llama_model_qwen3moe &>(model).koukai;
+ if (!koukai.bi_dump.empty()) {
+ bi_tap_contexts.reserve(n_layer);
+ }
+ auto record_bi = [&](int il, ggml_tensor * block_out, ggml_tensor * block_in,
+ ggml_tensor * attn_add, ggml_tensor * attn_res, ggml_tensor * ffn_add, ggml_tensor * ffn_res) {
+ if (koukai.bi_dump.empty()) return block_out;
+ bi_tap_contexts.push_back({ &koukai, il, n_tokens > 1 });
+ ggml_tensor * args[] = { block_out, block_in, attn_add, attn_res, ffn_add, ffn_res };
+ return ggml_custom_4d(ctx0, block_out->type,
+ block_out->ne[0], block_out->ne[1], block_out->ne[2], block_out->ne[3],
+ args, 6, qwen3moe_koukai_bi_tap, 1, &bi_tap_contexts.back());
+ };
+
for (int il = 0; il < n_layer; ++il) {
res->t_layer_inp[il] = inpL;
- ggml_tensor * inpSA = inpL;
+ if (koukai.skip_layer.count(il)) {
+ if (!koukai.bi_dump.empty()) {
+ ggml_tensor * zero = ggml_scale(ctx0, inpL, 0.0f);
+ inpL = record_bi(il, inpL, inpL, zero, inpL, zero, inpL);
+ }
+ continue;
+ }
- // norm
- cur = build_norm(inpL,
- model.layers[il].attn_norm, NULL,
- LLM_NORM_RMS, il);
- cb(cur, "attn_norm", il);
-
- // self_attention
- {
- // compute Q and K and RoPE them
- auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
- n_embd_head, n_head, n_head_kv, il);
-
- Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
- cb(Qcur, "Qcur_normed", il);
-
- Qcur = ggml_rope_ext(
- ctx0, Qcur, inp_pos, nullptr,
- n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow
- );
-
- Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
- cb(Kcur, "Kcur_normed", il);
-
- Kcur = ggml_rope_ext(
- ctx0, Kcur, inp_pos, nullptr,
- n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- ext_factor, attn_factor, beta_fast, beta_slow
- );
-
- cb(Qcur, "Qcur", il);
- cb(Kcur, "Kcur", il);
- cb(Vcur, "Vcur", il);
-
- cur = build_attn(inp_attn,
- model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
- Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+ ggml_tensor * inpSA = inpL;
+ ggml_tensor * attn_add = nullptr;
+
+ if (koukai.skip_attention(il)) {
+ cur = ggml_scale(ctx0, inpSA, 0.0f);
+ attn_add = cur;
+ } else {
+ // norm
+ cur = build_norm(inpL,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self_attention
+ {
+ const bool share_kv = koukai.shares_kv(il);
+ auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+ n_embd_head, n_head, n_head_kv, il, !share_kv);
+
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_normed", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ if (!share_kv) {
+ Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+ cb(Kcur, "Kcur_normed", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+ }
+
+ cb(Qcur, "Qcur", il);
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il, share_kv);
+ }
+ attn_add = cur;
}
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);
+ if (!koukai.bi_dump.empty()) {
+ attn_add = ggml_get_rows(ctx0, attn_add, inp_out_ids);
+ }
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
- // MoE branch
- cur = build_norm(ffn_inp,
- model.layers[il].ffn_norm, NULL,
- LLM_NORM_RMS, il);
- cb(cur, "ffn_norm", il);
-
- ggml_tensor * moe_out =
- build_moe_ffn(cur,
+ ggml_tensor * moe_out;
+ if (koukai.skip_feed_forward(il)) {
+ moe_out = ggml_scale(ctx0, ffn_inp, 0.0f);
+ cur = ffn_inp;
+ } else {
+ // MoE branch
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ moe_out = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
@@ -148,18 +290,23 @@ llama_model_qwen3moe::graph::graph(const llama_model & model, const llm_graph_pa
nullptr, nullptr,
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
- model.layers[il].ffn_down_exps_s);
- cb(moe_out, "ffn_moe_out", il);
- cur = moe_out;
-
- cur = ggml_add(ctx0, cur, ffn_inp);
+ model.layers[il].ffn_down_exps_s,
+ nullptr, &koukai);
+ cb(moe_out, "ffn_moe_out", il);
+ cur = ggml_add(ctx0, moe_out, ffn_inp);
+ }
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
+ cur = record_bi(il, cur, inpSA, attn_add, inpSA, moe_out, ffn_inp);
+
// input for next layer
inpL = cur;
}
+ if (inp_out_ids && koukai.skip_layer.count(n_layer - 1)) {
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
cur = inpL;
cur = build_norm(cur,
@@ -170,7 +317,14 @@ llama_model_qwen3moe::graph::graph(const llama_model & model, const llm_graph_pa
res->t_embd = cur;
// lm_head
- cur = build_lora_mm(model.output, cur, model.output_s);
+ if (!koukai.vocab_keep.empty()) {
+ cur = build_lora_mm(model.output, cur, model.output_s);
+ ggml_tensor * args[] = { cur };
+ cur = ggml_custom_4d(ctx0, GGML_TYPE_F32, model.vocab.n_tokens(), cur->ne[1], 1, 1,
+ args, 1, qwen3moe_koukai_scatter_logits, 1, (void *) &koukai);
+ } else {
+ cur = build_lora_mm(model.output, cur, model.output_s);
+ }
cb(cur, "result_output", -1);
res->t_logits = cur;
diff --git a/src/models/models.h b/src/models/models.h
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -2301,6 +2301,7 @@
struct llama_model_qwen35 : public llama_model_base {
llama_model_qwen35(const struct llama_model_params & params) : llama_model_base(params) {}
+ std::vector<int32_t> vocab_keep;
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp
--- a/src/models/qwen35.cpp
+++ b/src/models/qwen35.cpp
@@ -1,5 +1,22 @@
#include "models.h"
#include "llama-memory-recurrent.h"
+#include "qwen3moe-koukai.h"
+
+#include <algorithm>
+#include <cmath>
+
+static void qwen35_koukai_scatter_logits(ggml_tensor * dst, int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+ const auto & rows = *static_cast<const std::vector<int32_t> *>(userdata);
+ const ggml_tensor * selected = dst->src[0];
+ for (int64_t token = 0; token < dst->ne[1]; ++token) {
+ auto * output = (float *) ((uint8_t *) dst->data + token * dst->nb[1]);
+ const auto * input = (const float *) ((const uint8_t *) selected->data + token * selected->nb[1]);
+ std::fill(output, output + dst->ne[0], -INFINITY);
+ for (size_t i = 0; i < rows.size(); ++i) output[rows[i]] = input[i];
+ }
+}
void llama_model_qwen35::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -37,15 +54,29 @@
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
+ if (const char * path = qwen3moe_koukai_config::env("KOUKAI_VOCAB_KEEP")) {
+ vocab_keep = qwen3moe_koukai_config::parse_vocab(path, n_vocab);
+ }
+
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);
+ if (!vocab_keep.empty()) {
+ const auto output_tn = tn(LLM_TENSOR_OUTPUT, "weight");
+ const auto embd_tn = tn(LLM_TENSOR_TOKEN_EMBD, "weight");
+ const bool has_output = ml.get_tensor_meta(output_tn.str().c_str()) != nullptr;
+ ml.set_tensor_row_selection(output_tn.str(),
+ has_output ? output_tn.str() : embd_tn.str(), n_vocab, vocab_keep);
+ output = create_tensor(output_tn, { n_embd, (int64_t) vocab_keep.size() },
+ has_output ? 0 : TENSOR_DUPLICATED);
+ if (!output) throw std::runtime_error("KOUKAI_VOCAB_KEEP could not load the output matrix");
+ } else {
+ 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);
+ }
}
auto load_block_trunk = [&](int il, int flags) {
@@ -215,8 +246,14 @@
cb(cur, "result_norm", -1);
res->t_embd = cur;
- // LM head
+ // LM head: 小さい行列で計算し、token ID の元の位置へ戻す。
cur = build_lora_mm(model.output, cur, model.output_s);
+ const auto & rows = static_cast<const llama_model_qwen35 &>(model).vocab_keep;
+ if (!rows.empty()) {
+ ggml_tensor * args[] = { cur };
+ cur = ggml_custom_4d(ctx0, GGML_TYPE_F32, model.vocab.n_tokens(), cur->ne[1], 1, 1,
+ args, 1, qwen35_koukai_scatter_logits, 1, (void *) &rows);
+ }
cb(cur, "result_output", -1);
res->t_logits = cur;
diff --git a/src/models/models.h b/src/models/models.h
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -2452,6 +2452,7 @@
struct llama_model_qwen35moe : public llama_model_base {
llama_model_qwen35moe(const struct llama_model_params & params) : llama_model_base(params) {}
+ qwen3moe_koukai_config koukai;
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp
--- a/src/models/qwen35moe.cpp
+++ b/src/models/qwen35moe.cpp
@@ -1,9 +1,36 @@
#include "models.h"
#include "llama-memory-recurrent.h"
+
+static void qwen35moe_koukai_scatter_logits(ggml_tensor * dst, int ith, int nth, void * userdata) {
+ GGML_UNUSED(nth);
+ if (ith != 0) return;
+ const auto & rows = *static_cast<const std::vector<int32_t> *>(userdata);
+ const ggml_tensor * selected = dst->src[0];
+ for (int64_t token = 0; token < dst->ne[1]; ++token) {
+ auto * output = (float *) ((uint8_t *) dst->data + token * dst->nb[1]);
+ const auto * input = (const float *) ((const uint8_t *) selected->data + token * selected->nb[1]);
+ std::fill(output, output + dst->ne[0], -INFINITY);
+ for (size_t i = 0; i < rows.size(); ++i) output[rows[i]] = input[i];
+ }
+}
void llama_model_qwen35moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all, false);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+ ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
+ if (hparams.n_expert_shared && !hparams.n_ff_shexp) {
+ throw std::runtime_error("qwen35moe shared experts require GGUF expert_shared_feed_forward_length");
+ }
+ for (const char * name : { "KOUKAI_SKIP_ATTN", "KOUKAI_SKIP_FFN", "KOUKAI_SKIP_LAYER",
+ "KOUKAI_KV_SHARE", "KOUKAI_BI_DUMP", "KOUKAI_HEAD_MASK",
+ "KOUKAI_SWA_WINDOW", "KOUKAI_SWA_LAYERS", "KOUKAI_SWA_SINK" }) {
+ if (qwen3moe_koukai_config::env(name)) {
+ throw std::runtime_error(std::string(name) + " is not supported by qwen35moe");
+ }
+ }
+ if (qwen3moe_koukai_config::env("KOUKAI_EXPERT_MIN") && !qwen3moe_koukai_config::env("KOUKAI_EXPERT_P")) {
+ throw std::runtime_error("KOUKAI_EXPERT_MIN requires KOUKAI_EXPERT_P");
+ }
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);
@@ -36,6 +63,13 @@
void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
LLAMA_LOAD_LOCALS;
+ koukai = qwen3moe_koukai_config::from_env(n_layer, n_vocab, n_expert_used);
+ for (uint32_t il = 0; il < hparams.n_layer_all; ++il) {
+ if (koukai.expert_p_enabled && koukai.expert_min > (int32_t) hparams.n_expert_used(il)) {
+ throw std::runtime_error("KOUKAI_EXPERT_MIN exceeds the layer's GGUF expert_used_count");
+ }
+ }
+
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0;
@@ -44,18 +78,28 @@
// 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);
+ if (!koukai.vocab_keep.empty()) {
+ const auto output_tn = tn(LLM_TENSOR_OUTPUT, "weight");
+ const auto embd_tn = tn(LLM_TENSOR_TOKEN_EMBD, "weight");
+ const bool has_output = ml.get_tensor_meta(output_tn.str().c_str()) != nullptr;
+ ml.set_tensor_row_selection(output_tn.str(),
+ has_output ? output_tn.str() : embd_tn.str(), n_vocab, koukai.vocab_keep);
+ output = create_tensor(output_tn, { n_embd, (int64_t) koukai.vocab_keep.size() },
+ has_output ? 0 : TENSOR_DUPLICATED);
+ if (!output) throw std::runtime_error("KOUKAI_VOCAB_KEEP could not load the output matrix");
+ } else {
+ 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);
+ }
}
auto load_block_trunk = [&](int il, int flags) {
auto & layer = layers[il];
const int64_t n_ff_exp = hparams.n_ff_exp() ? hparams.n_ff_exp() : n_ff / n_expert_used;
- const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+ const int64_t n_ff_shexp = hparams.n_ff_shexp;
// Calculate dimensions from hyperparameters
const int64_t head_k_dim = hparams.ssm_d_state;
@@ -96,18 +140,20 @@
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, flags);
create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, flags);
- // Shared experts
- layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, flags);
- layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags);
- layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags);
- layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, flags);
+ // No shared experts when GGUF has no shared FF length; do not assume one.
+ if (n_ff_shexp > 0) {
+ layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, flags);
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, flags);
+ }
};
auto load_block_mtp = [&](int il) {
auto & layer = layers[il];
const int64_t n_ff_exp = hparams.n_ff_exp() ? hparams.n_ff_exp() : n_ff / n_expert_used;
- const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+ const int64_t n_ff_shexp = hparams.n_ff_shexp;
// MTP block looks like a full-attention Qwen3.5 decoder block with MoE FFN.
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, mtp_flags);
@@ -123,18 +169,26 @@
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, mtp_flags);
create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, mtp_flags);
- // Shared experts
- layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, mtp_flags);
- layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags);
- layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags);
- layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, mtp_flags);
+ // Shared experts, only when declared by GGUF.
+ if (n_ff_shexp > 0) {
+ layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, mtp_flags);
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, mtp_flags);
+ }
// NextN-specific tensors that define the MTP block.
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, mtp_flags);
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, mtp_flags);
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED);
- layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED);
+ const auto head_tn = tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il);
+ if (ml.load_mtp && !koukai.vocab_keep.empty() && ml.get_tensor_meta(head_tn.str().c_str())) {
+ ml.set_tensor_row_selection(head_tn.str(), head_tn.str(), n_vocab, koukai.vocab_keep);
+ layer.nextn.shared_head_head = create_tensor(head_tn, { n_embd, (int64_t) koukai.vocab_keep.size() }, mtp_flags);
+ } else {
+ layer.nextn.shared_head_head = create_tensor(head_tn, { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED);
+ }
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, mtp_flags|TENSOR_NOT_REQUIRED);
};
@@ -241,6 +295,12 @@
// LM head
cur = build_lora_mm(model.output, cur, model.output_s);
+ const auto & rows = static_cast<const llama_model_qwen35moe &>(model).koukai.vocab_keep;
+ if (!rows.empty()) {
+ ggml_tensor * args[] = { cur };
+ cur = ggml_custom_4d(ctx0, GGML_TYPE_F32, model.vocab.n_tokens(), cur->ne[1], 1, 1,
+ args, 1, qwen35moe_koukai_scatter_logits, 1, (void *) &rows);
+ }
cb(cur, "result_output", -1);
res->t_logits = cur;
@@ -509,7 +569,8 @@
nullptr, model.layers[il].ffn_gate_up_exps,
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
- model.layers[il].ffn_down_exps_s);
+ model.layers[il].ffn_down_exps_s,
+ nullptr, &static_cast<const llama_model_qwen35moe &>(model).koukai);
cb(moe_out, "ffn_moe_out", il);
// Add shared experts if present - following Qwen3Next reference implementation
@@ -689,7 +750,8 @@
nullptr, layer.ffn_gate_up_exps,
layer.ffn_up_exps_s,
layer.ffn_gate_exps_s,
- layer.ffn_down_exps_s);
+ layer.ffn_down_exps_s,
+ nullptr, &static_cast<const llama_model_qwen35moe &>(model).koukai);
cb(moe_out, "mtp_ffn_moe_out", il);
if (layer.ffn_up_shexp != nullptr) {
@@ -734,6 +796,12 @@
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
GGML_ASSERT(head_w && "QWEN35MOE MTP: missing LM head (nextn.shared_head_head or model.output)");
cur = build_lora_mm(head_w, cur, head_s);
+ const auto & rows = static_cast<const llama_model_qwen35moe &>(model).koukai.vocab_keep;
+ if (!rows.empty()) {
+ ggml_tensor * args[] = { cur };
+ cur = ggml_custom_4d(ctx0, GGML_TYPE_F32, model.vocab.n_tokens(), cur->ne[1], 1, 1,
+ args, 1, qwen35moe_koukai_scatter_logits, 1, (void *) &rows);
+ }
cb(cur, "result_output", -1);
res->t_logits = cur;
|